fumiyau/python_comments_free_500000 · Datasets at Hugging Face

f70f6c2e837dc663e4225ba197c388dee678dd48

624

py

Python

Algorithms/0091_Decode_Ways/Python/Decode_Ways_Solution_1.py

lht19900714/Leetcode_Solutions

dac7a038329a5c1f8a78e86cc6f49116b963f1fb

[ "MIT" ]

null

Algorithms/0091_Decode_Ways/Python/Decode_Ways_Solution_1.py

lht19900714/Leetcode_Solutions

dac7a038329a5c1f8a78e86cc6f49116b963f1fb

[ "MIT" ]

null

Algorithms/0091_Decode_Ways/Python/Decode_Ways_Solution_1.py

lht19900714/Leetcode_Solutions

dac7a038329a5c1f8a78e86cc6f49116b963f1fb

[ "MIT" ]

null

# Space: O(n) # Time: O(n) class Solution: def numDecodings(self, s: str) -> int: if len(s) == 0: return 0 self.cache = {} self.cache[''] = 1 def recursive(string): if string in self.cache: return self.cache[string] if string[0] == '0': return 0 if len(string) == 1: return 1 temp_res = recursive(string[1:]) prefix = int(string[:2]) if 0 < prefix <= 26: temp_res += recursive(string[2:]) self.cache[string] = temp_res return temp_res return recursive(s)

19.5

62

0.491987

class Solution: def numDecodings(self, s: str) -> int: if len(s) == 0: return 0 self.cache = {} self.cache[''] = 1 def recursive(string): if string in self.cache: return self.cache[string] if string[0] == '0': return 0 if len(string) == 1: return 1 temp_res = recursive(string[1:]) prefix = int(string[:2]) if 0 < prefix <= 26: temp_res += recursive(string[2:]) self.cache[string] = temp_res return temp_res return recursive(s)

true

f70f6d36aba38dbe1415ec7683f3960500689480

968

py

Python

tempest/api/compute/security_groups/base.py

KiranPawar72/tempest

1fef3dd92b083055793065dd0693454735ec2c01

[ "Apache-2.0" ]

3

2016-07-15T12:27:23.000Z

2021-04-23T04:41:10.000Z

tempest/api/compute/security_groups/base.py

LIS/lis-tempest

8e6403b2d6de81c5d18ed867b4977385c8278b75

[ "Apache-2.0" ]

null

tempest/api/compute/security_groups/base.py

LIS/lis-tempest

8e6403b2d6de81c5d18ed867b4977385c8278b75

[ "Apache-2.0" ]

12

2016-07-14T18:13:05.000Z

2017-07-08T18:45:42.000Z

# Copyright 2012 OpenStack Foundation # All Rights Reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); you may # not use this file except in compliance with the License. You may obtain # a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, WITHOUT # WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the # License for the specific language governing permissions and limitations # under the License. from tempest.api.compute import base class BaseSecurityGroupsTest(base.BaseV2ComputeTest): @classmethod def setup_credentials(cls): # A network and a subnet will be created for these tests cls.set_network_resources(network=True, subnet=True) super(BaseSecurityGroupsTest, cls).setup_credentials()

37.230769

78

0.737603

from tempest.api.compute import base class BaseSecurityGroupsTest(base.BaseV2ComputeTest): @classmethod def setup_credentials(cls): cls.set_network_resources(network=True, subnet=True) super(BaseSecurityGroupsTest, cls).setup_credentials()

true

f70f6ea82999764663604c0defd9f8fd956a3e54

43

py

Python

embedvideos/__init__.py

vkaracic/EmbedVideosXBlock

e0cd04b41d655d8e8e69c6c9c4c0a41c22600965

[ "MIT" ]

null

embedvideos/__init__.py

vkaracic/EmbedVideosXBlock

e0cd04b41d655d8e8e69c6c9c4c0a41c22600965

[ "MIT" ]

null

embedvideos/__init__.py

vkaracic/EmbedVideosXBlock

e0cd04b41d655d8e8e69c6c9c4c0a41c22600965

[ "MIT" ]

null

from .embedvideos import EmbedVideosXBlock

21.5

42

0.883721

from .embedvideos import EmbedVideosXBlock

true

f70f70495df42bcff3545d60f74aba312be6b44a

228

py

Python

vininfo/__init__.py

ghilesmeddour/vininfo

63cbf7dcdd9d106fb9c9a56d5c4f11c3dd794b1d

[ "BSD-3-Clause" ]

60

2018-07-28T14:53:57.000Z

2022-02-22T12:11:24.000Z

vininfo/__init__.py

ghilesmeddour/vininfo

63cbf7dcdd9d106fb9c9a56d5c4f11c3dd794b1d

[ "BSD-3-Clause" ]

16

2018-07-30T08:57:08.000Z

2021-12-25T09:20:03.000Z

vininfo/__init__.py

ghilesmeddour/vininfo

63cbf7dcdd9d106fb9c9a56d5c4f11c3dd794b1d

[ "BSD-3-Clause" ]

29

2018-07-30T08:36:07.000Z

2022-03-09T12:02:06.000Z

from .toolbox import Vin from .exceptions import ValidationError, VininfoException VERSION = (1, 6, 0) """Application version number tuple.""" VERSION_STR = '.'.join(map(str, VERSION)) """Application version number string."""

25.333333

57

0.736842

from .toolbox import Vin from .exceptions import ValidationError, VininfoException VERSION = (1, 6, 0) VERSION_STR = '.'.join(map(str, VERSION))

true

f70f70dc9554cc5a86433d7d1c74bb4e02d3ad76

46,498

py

Python

gbxml/gbxml.py

building-energy/gbxml

039edf6e33cccbb76dcda5fbb871aeb950ad0a87

[ "MIT" ]

5

2020-04-24T15:59:45.000Z

2022-02-23T14:40:14.000Z

gbxml/gbxml.py

building-energy/gbxml

039edf6e33cccbb76dcda5fbb871aeb950ad0a87

[ "MIT" ]

2

2021-07-05T12:09:09.000Z

2022-02-05T07:05:59.000Z

gbxml/gbxml.py

building-energy/gbxml

039edf6e33cccbb76dcda5fbb871aeb950ad0a87

[ "MIT" ]

1

2020-04-24T15:59:48.000Z

# -*- coding: utf-8 -*- from lxml import etree import pkgutil from io import BytesIO from . import xml_functions, construction_functions, layer_functions from . import surface_functions, space_functions, building_functions from . import opening_functions, zone_functions class Gbxml(): "A class that represents a gbXML file and the gbXML schema" def __init__(self, gbxml_fp=None, gbxsd_fp=None): """Initialises a new Gbxml instance Arguments: gbxml_fp (str): filepath to a gbXML file. This is read in as an lxml._ElementTree object. If not supplied then a new lxml._ElementTree object with only a root element is created. gbxsd_fp (str): filepath to a gbXML schema file. If not supplied then a default gbXMl schema file is used. """ if gbxml_fp: self._ElementTree=etree.parse(gbxml_fp) else: st = pkgutil.get_data(__package__, 'blank.xml') self._ElementTree=etree.parse(BytesIO(st)) if gbxsd_fp: self._ElementTree_gbxsd=etree.parse(gbxml_fp) else: st = pkgutil.get_data(__package__, 'GreenBuildingXML_Ver6.01.xsd') self._ElementTree_gbxsd=etree.parse(BytesIO(st)) self.ns={'gbxml':'http://www.gbxml.org/schema'} # general query methods def get_ids(self, tag=None): """Returns the id attributes of elements :param tag: an element tag to filter on :type tag: str, optional :return: a list of element ids :rtype: list """ if tag is None: tag='*' element=self._ElementTree.getroot() return xml_functions.get_ids(element,tag) def get_xmlstring(self,id=None): """Returns a string of an xml element :param id: an element id to filter on :type id: str, optional :return: a string of xml contents :rtype: str """ element=self._ElementTree.getroot() if not id is None: st='//gbxml:*[@id="%s"]' % id element=element.xpath(st,namespaces=self.ns)[0] return xml_functions.get_xmlstring(element) def get_attributes(self,id): """Returns the attributes of an element :param id: an element id :type id: str :return: the attributes of the element :rtype: dict """ st='//gbxml:*[@id="%s"]' % id element=self._ElementTree.getroot().xpath(st,namespaces=self.ns)[0] return xml_functions.get_attributes(element) def get_child_tags(self,id): """Returns the child tags of an element :param id: an element id :type id: str :return: a list of the tags of the child elements :rtype: list """ st='//gbxml:*[@id="%s"]' % id element=self._ElementTree.getroot().xpath(st,namespaces=self.ns)[0] return xml_functions.get_child_tags(element) def get_child_tag_text(self,id,child_tag): """Returns the text of child elements :param id: an element id :type id: str :param child_tag: a tag of a child element :type child_tag: str :return: a list of the text of child elements with the child_tag tag :rtype: list """ st='//gbxml:*[@id="%s"]' % id element=self._ElementTree.getroot().xpath(st,namespaces=self.ns)[0] return xml_functions.get_child_tag_text(element,child_tag) def get_child_tag_attributes(self,id,child_tag): """Returns the attributes of child elements :param id: an element id :type id: str :param child_tag: a tag of a child element :type child_tag: str :return: a list of the attributes of each child element with the child_tag tag :rtype: list """ st='//gbxml:*[@id="%s"]' % id element=self._ElementTree.getroot().xpath(st,namespaces=self.ns)[0] return xml_functions.get_child_tag_attributes(element,child_tag) def get_children_list(self,id): """Returns a list of dicts representing each child element :param id: an element id :type id: str :return: a list of dicts {'tag':(str),'text':(str),'attributes':(dict)} :rtype: list """ st='//gbxml:*[@id="%s"]' % id element=self._ElementTree.getroot().xpath(st,namespaces=self.ns)[0] return xml_functions.get_children_list(element) # campus query methods def get_campus_location_tags(self,id): """Returns the child tags of the Location element of a campus :param id: a Campus element id :type id: str :return: a list of the tags of the Location element :rtype: list """ st='./gbxml:Campus[@id="%s"]/gbxml:Location' % id element=self._ElementTree.getroot().xpath(st,namespaces=self.ns)[0] return xml_functions.get_child_tags(element) def get_campus_location_tag_text(self,id,child_tag): """Returns the text of Location child elements of a campus :param id: a Campus element id :type id: str :param child_tag: a tag of a child element of the Location element :type child_tag: str :return: a list of the text of child elements of the Location element with the child_tag tag :rtype: list """ st='./gbxml:Campus[@id="%s"]/gbxml:Location' % id element=self._ElementTree.getroot().xpath(st,namespaces=self.ns)[0] return xml_functions.get_child_tag_text(element,child_tag) # building query methods def get_building_space_ids(self,id): """Returns the ids of all spaces in a building :param id: a Building element id :type id: str :return: a list of Space ids :rtype: list """ # get element from id st='./gbxml:Campus/gbxml:Building[@id="%s"]' % id element=self._ElementTree.getroot().xpath(st,namespaces=self.ns)[0] # get space ids return building_functions.get_space_ids(element) def get_building_surface_ids(self,id): """Returns the ids of all surfaces in a building :param id: a Building element id :type id: str :return: a list of Surface ids :rtype: list """ # get element from id st='./gbxml:Campus/gbxml:Building[@id="%s"]' % id element=self._ElementTree.getroot().xpath(st,namespaces=self.ns)[0] # get surface ids return building_functions.get_surface_ids(element) # space query methods def get_space_surface_ids(self,id): """Returns the ids of all surfaces adjacent to a space :param id: a Space element id :type id: str :return: a list of surface ids :rtype: list """ # get element from id st='./gbxml:Campus/gbxml:Building/gbxml:Space[@id="%s"]' % id element=self._ElementTree.getroot().xpath(st,namespaces=self.ns)[0] # get surface ids return space_functions.get_surface_ids(element) # construction query methods def get_construction_layer_ids(self,id): """Returns the layer ids of a construction :param id: a Construction element id :type id: str :return: a list of layer ids :rtype: list """ # get element from id st='./gbxml:Construction[@id="%s"]' % id element=self._ElementTree.getroot().xpath(st,namespaces=self.ns)[0] # get layer ids return construction_functions.get_layer_ids(element) def get_construction_material_ids(self,id): """Returns the material ids of a construction :param id: a Construction element id :type id: str :return: a list of material ids :rtype: list """ # get element from id st='./gbxml:Construction[@id="%s"]' % id element=self._ElementTree.getroot().xpath(st,namespaces=self.ns)[0] # get material ids return construction_functions.get_material_ids(element) # layer query methods def get_layer_material_ids(self,id): """Returns the material ids of a construction :param id: a Layer element id :type id: str :return: a list of material ids :rtype: list """ # get element from id st='./gbxml:Layer[@id="%s"]' % id element=self._ElementTree.getroot().xpath(st,namespaces=self.ns)[0] # get layer ids return layer_functions.get_material_ids(element) # surface query methods def get_surface_inner_space_id(self,id): """Returns the inner space id of a surface :param id: a Surface element id :type id: str :return: the inner Space id :rtype: str or None """ # get element from id st='./gbxml:Campus/gbxml:Surface[@id="%s"]' % id element=self._ElementTree.getroot().xpath(st,namespaces=self.ns)[0] # get inner space id return surface_functions.get_inner_space_id(element) def get_surface_outer_space_id(self,id): """Returns the outer space id of a surface :param id: a Surface element id :type id: str :return: the outer Space id :rtype: str or None """ # get element from id st='./gbxml:Campus/gbxml:Surface[@id="%s"]' % id element=self._ElementTree.getroot().xpath(st,namespaces=self.ns)[0] # get outer space id return surface_functions.get_outer_space_id(element) def get_surface_azimuth(self,id): """Returns the azimuth of a surface :param id: a Surface element id :type id: str :return: the azimuth value :rtype: float or None """ # get element from id st='./gbxml:Campus/gbxml:Surface[@id="%s"]' % id element=self._ElementTree.getroot().xpath(st,namespaces=self.ns)[0] # get azimuth return surface_functions.get_azimuth(element) def get_surface_tilt(self,id): """Returns the tilt of a surface :param id: a Surface element id :type id: str :return: the tilt value :rtype: float or None """ # get element from id st='./gbxml:Campus/gbxml:Surface[@id="%s"]' % id element=self._ElementTree.getroot().xpath(st,namespaces=self.ns)[0] # get tilt return surface_functions.get_tilt(element) def get_surface_coordinates(self,id): """Returns the coordinates of a surface :param id: a Surface element id :type id: str :return: a list of coordinate tuples (x,y,z) :rtype: list (of tuples) """ # get element from id st='./gbxml:Campus/gbxml:Surface[@id="%s"]' % id element=self._ElementTree.getroot().xpath(st,namespaces=self.ns)[0] # get coordinates return surface_functions.get_coordinates(element) def get_surface_area(self,id): """Returns the area of a surface This is calculated using the surface coordiantes and includes the area of any openings. :param id: a Surface element id :type id: str :return: the area value :rtype: float or None """ # get element from id st='./gbxml:Campus/gbxml:Surface[@id="%s"]' % id element=self._ElementTree.getroot().xpath(st,namespaces=self.ns)[0] # get area return surface_functions.get_area(element) def get_surface_opening_ids(self,id): """Returns the opening ids of a surface :param id: a Surface element id :type id: str :return: a list of Opening ids :rtype: list """ # get element from id st='./gbxml:Campus/gbxml:Surface[@id="%s"]' % id element=self._ElementTree.getroot().xpath(st,namespaces=self.ns)[0] # get opening ids return surface_functions.get_opening_ids(element) # opening query methods def get_opening_surface_id(self,id): """Returns the parent surface id of an opening :param id: a Opening element id :type id: str :return: a Surface id :rtype: str """ # get element from id st='./gbxml:Campus/gbxml:Surface/gbxml:Opening[@id="%s"]' % id element=self._ElementTree.getroot().xpath(st,namespaces=self.ns)[0] # get surface id return opening_functions.get_surface_id(element) def get_opening_coordinates(self,id): """Returns the coordinates of an opening :param id: a Opening element id :type id: str :return: a list of coordinate tuples (x,y,z) :rtype: list (of tuples) """ # get element from id st='./gbxml:Campus/gbxml:Surface/gbxml:Opening[@id="%s"]' % id element=self._ElementTree.getroot().xpath(st,namespaces=self.ns)[0] # get coordinates return opening_functions.get_coordinates(element) def get_opening_area(self,id): """Returns the area of an opening :param id: a Opening element id :type id: str :return: the area value :rtype: float or None """ # get element from id st='./gbxml:Campus/gbxml:Surface/gbxml:Opening[@id="%s"]' % id element=self._ElementTree.getroot().xpath(st,namespaces=self.ns)[0] # get area return opening_functions.get_area(element) # zone query methods def get_zone_space_ids(self,id): """Returns the ids of all spaces in a zone :param id: a Zone element id :type id: str :return: a list of Space ids :rtype: list """ # get element from id st='./gbxml:Zone[@id="%s"]' % id element=self._ElementTree.getroot().xpath(st,namespaces=self.ns)[0] # get space ids return zone_functions.get_space_ids(element) ## OUTPUT # # # def __xmlstring(self,element=None): # """Returns a string of an xml element # # Arguments: # - element (lxml.etree._Element): default is root node # # """ # if element is None: element=self.root() # return etree.tostring(element,pretty_print=True).decode() # # # def xpath(self,element,st_xpath): # """Returns the result of an xpath operation on the gbXML file # # Arguments # - st_xpath (str): the xpath string # - element (lxml.etree._Element): the element for the xpath operation. The # default is the root element # # """ # return element.xpath(st_xpath,namespaces=self.ns) # # # def write(self,fp): # """Writes the gbXML file to disc # # Arguments: # fp (str): the filepath # """ # st=etree.tostring(self.root(),xml_declaration=True) # with open(fp,'wb') as f: # f.write(st) # ## VALIDATION # # def validate(self): # """Validates the gbXMl file using the schema # # Returns True if the gbXML file is valid, otherwise False # # """ # xmlschema = etree.XMLSchema(self.gbxsd._ElementTree) # result=xmlschema.validate(self._ElementTree) # return result # ## EDITING # # def add_element(self,parent_element,label,text=None,**kwargs): # """Adds an element to the gbXML # # Returns the newly created element # # Arguments: # - parent_element (lxml._Element or str): the parent element that the # new element is added to. This can be either a lxml._Element object # or a string with the element id. # - label (str): the label or tag of the new element # - text (str): the text of the new element # - **kwargs (keywords): the attributes of the new element # # """ # if isinstance(parent_element,str): # parent_element=self.element(parent_element) # e=etree.SubElement(parent_element,'{%s}%s' % (self.ns['gbxml'],label)) # if text: e.text=text # if kwargs: # for k,v in kwargs.items(): # e.set(k,v) # return e # # def set_attribute(self,element,key,value): # """Sets the attribute of an element # # Returns the modified element # # Arguments: # - element (lxml._Element or str): This a lxml._Element object # or a string with the element id. # - key (str): the name of the attribute # - value (str): the value of the attribute # # """ # if isinstance(element,str): # element=self.element(element) # element.set(key,value) # return element # # # def set_element_id(self,element,new_id): # """Sets a new id attribute for an element and updates all links # # Arguments: # - element (lxml._Element or str): This a lxml._Element object # or a string with the element id. # - new_id (str): # # Return value: # - new_id (str) # # """ # #check if new_id already exists # l=self.elements() # ids=[x.get('id') for x in l if x.get('id')] # if new_id in ids: # raise ValueError('new_id %s already exists' % new_id) # # #get element # if isinstance(element,str): # element=self.element(element) # # #get old id # old_id=element.get('id') # # #set new id # element.set('id',new_id) # # #find all elements with attribute labelRefId=old_id # label=self.label(element) # prefix=label[0].lower()+label[1:] # st='.//gbxml:*[@%sIdRef="%s"]' % (prefix,old_id) # l=self.xpath(self.root(),st) # # #update with id # for e in l: # e.set('%sIdRef' % prefix,new_id) # #return new id # return new_id # # # def set_text(self,element,text): # """Sets the text of an element # # Returns the modified element # # Arguments: # - element (lxml._Element or str): This a lxml._Element object # or a string with the element id. # - text (str): the text # # """ # if isinstance(element,str): # element=self.element(element) # element.text=text # return element # # # def remove_element(self,element): # """Removes an element # # Arguments: # - element (lxml._Element or str): This a lxml._Element object # or a string with the element id. # # """ # if isinstance(element,str): # element=self.element(element) # # #remove links to element # id=element.get('id') # label=self.label(element) # prefix=label[0].lower()+label[1:] # st='.//gbxml:*[@%sIdRef="%s"]' % (prefix,id) # l=self.xpath(self.root(),st) # for x in l: # self.remove_attribute(x,'%sIdRef' % prefix) # # #remove element # parent=element.getparent() # parent.remove(element) # # # def remove_attribute(self,element,key): # """Removes an element # # Arguments: # - element (lxml._Element or str): This a lxml._Element object # or a string with the element id. # - key (str): The name of the attribute to delete # # """ # if isinstance(element,str): # element=self.element(element) # element.attrib.pop(key) # # # def remove_text(self,element): # pass # # # ## QUERYING # # def elements(self,label='*'): # """Returns the elements of the gbXML file # # Arguments: # - label (str): the label of the elements # # """ # st='//gbxml:%s' % label # return self.xpath(self.root(),st) # # # def root(self): # "Returns the root element" # return self._ElementTree.getroot() # # # def element(self,id,label='*'): # """Returns an element from the gbXML file # # Arguments: # - id (str): the id of the element # - label (str): the label of the element # # """ # st='//gbxml:%s[@id="%s"]' % (label,id) # try: # return self.xpath(self.root(),st)[0] # except IndexError: # raise KeyError('there is no element with an id of %s' % id) # # # def label(self,element): # """Returns the label of an element # # Arguments: # - element (lxml._Element or str): This a lxml._Element object # or a string with the element id. # """ # if isinstance(element,str): # element=self.element(element) # return element.tag.split('}')[1] # # # def attributes(self,element): # """Returns the attributes of an element # # Return value is a dictionary # # Arguments: # - element (lxml._Element or str): This a lxml._Element object # or a string with the element id. # """ # if isinstance(element,str): # element=self.element(element) # return dict(element.attrib) # # # def text(self,element): # """Returns the text of an element, or None # # Return value is a string # # Arguments: # - element (lxml._Element or str): This a lxml._Element object # or a string with the element id. # """ # if isinstance(element,str): # element=self.element(element) # return element.text # # # def text_value(self,element): # """Returns the text value of an element, i.e the text converted # according to its schema data type # # Return value is an object with data type dependent on the schema # # Arguments: # - element (lxml._Element or str): This a lxml._Element object # or a string with the element id. # """ # # #JUST RETURNS STRINGS AT PRESENT - TO DO # # if isinstance(element,str): # element=self.element(element) # text=element.text # return text # # # def child_elements(self,element,label='*'): # """Returns the child elements of an element # # Return value is a list of elements # # Arguments: # - element (lxml._Element or str): This a lxml._Element object # or a string with the element id. # - label (str): the label of the element # """ # if isinstance(element,str): # element=self.element(element) # st='./gbxml:%s' % label # return self.xpath(element,st) # # # def descendent_elements(self,element,label='*'): # """Returns the descendent elements of an element # # Return value is a list of elements # # Arguments: # - element (lxml._Element or str): This a lxml._Element object # or a string with the element id. # - label (str): the label of the element # """ # if isinstance(element,str): # element=self.element(element) # st='.//gbxml:%s' % label # return self.xpath(element,st) # # ## CONSTRUCTION FUNCTIONS # # def construction_layers(self,construction_element): # "Returns the layer elements of a construction" # if isinstance(construction_element,str): # construction_element=self.element(construction_element,label='Construction') # layerId_elements=self.child_elements(construction_element,'LayerId') # layer_elements=[self.element(layerId_element.get('layerIdRef'),'Layer') # for layerId_element in layerId_elements] # return layer_elements # # def construction_materials(self,construction_element): # "Returns the layer elements of a construction" # if isinstance(construction_element,str): # construction_element=self.element(construction_element,label='Construction') # layer_elements=self.construction_layers(construction_element) # material_elements=[] # for layer_element in layer_elements: # material_elements+=self.layer_materials(layer_element) # return material_elements # # ## LAYER FUNCTIONS # # def layer_materials(self,layer_element): # "Returns the layer elements of a construction" # if isinstance(layer_element,str): # layer_element=self.element(layer_element,label='Layer') # materialId_elements=self.child_elements(layer_element,'MaterialId') # material_elements=[self.element(materialId_element.get('materialIdRef'),'Material') # for materialId_element in materialId_elements] # return material_elements # # # ## OPENING FUNCTIONS # # def opening_coordinates(self,opening_element): # """Returns a list of coordinate tuples # # Arguments: # - opening_element (lxml._Element or str): This a lxml._Element object # or a string with the element id. # # Return value: # - coordinates (list): a list where each item is a tuple of (x,y,z) coordinates. # i.e. [(x1,y1,z1),(x2,y2,z2),(x3,y3,z3),...] # or None # # """ # if isinstance(opening_element,str): # opening_element=self.element(opening_element,label='Opening') # l=[] # st='./gbxml:PlanarGeometry/gbxml:PolyLoop/gbxml:CartesianPoint' # cartesian_points=self.xpath(opening_element,st) # for cartesian_point in cartesian_points: # st='./gbxml:Coordinate' # coordinates=self.xpath(cartesian_point,st) # t=(float(self.text_value(coordinates[0])), # float(self.text_value(coordinates[1])), # float(self.text_value(coordinates[2]))) # l.append(t) # return l # ## SURFACE FUNCTIONS # # def surface_azimuth(self,surface_element): # """Returns the azimuth of a surface # # Arguments: # - surface_element (lxml._Element or str): This a lxml._Element object # or a string with the element id. # # Return value: # - azimuth (float) or None # # """ # if isinstance(surface_element,str): # surface_element=self.element(surface_element,label='Surface') # l=self.xpath(surface_element,'./gbxml:RectangularGeometry/gbxml:Azimuth') # if len(l)>0: # azimuth=l[0] # return float(self.text_value(azimuth)) # # # def surface_coordinates(self,surface_element): # """Returns a list of coordinate tuples # # Arguments: # - surface_element (lxml._Element or str): This a lxml._Element object # or a string with the element id. # # Return value: # - coordinates (list): a list where each item is a tuple of (x,y,z) coordinates. # i.e. [(x1,y1,z1),(x2,y2,z2),(x3,y3,z3),...] # or None # # """ # if isinstance(surface_element,str): # surface_element=self.element(surface_element,label='Surface') # l=[] # st='./gbxml:PlanarGeometry/gbxml:PolyLoop/gbxml:CartesianPoint' # cartesian_points=self.xpath(surface_element,st) # for cartesian_point in cartesian_points: # st='./gbxml:Coordinate' # coordinates=self.xpath(cartesian_point,st) # t=(float(self.text_value(coordinates[0])), # float(self.text_value(coordinates[1])), # float(self.text_value(coordinates[2]))) # l.append(t) # return l # # # def surface_inner_space(self,surface_element): # """Returns the inner Space element of a Surface, or None # # Arguments: # - surface_element (lxml._Element or str): This a lxml._Element object # or a string with the element id. # # Return value: # - space (lxml._Element) or None # # """ # if isinstance(surface_element,str): # surface_element=self.element(surface_element,label='Surface') # adjacentSpaceIds=self.child_elements(surface_element,label='AdjacentSpaceId') # if len(adjacentSpaceIds)>0: # adjacentSpaceId=adjacentSpaceIds[0] # spaceIdRef=adjacentSpaceId.get('spaceIdRef') # return self.element(spaceIdRef) # # # def surface_outer_space(self,surface_element): # """Returns the outer Space element of a Surface, or None # # Arguments: # - surface_element (lxml._Element or str): This a lxml._Element object # or a string with the element id. # # Return value: # - space (lxml._Element) or None # # """ # if isinstance(surface_element,str): # surface_element=self.element(surface_element,label='Surface') # adjacentSpaceIds=self.child_elements(surface_element,label='AdjacentSpaceId') # if len(adjacentSpaceIds)>1: # adjacentSpaceId=adjacentSpaceIds[1] # spaceIdRef=adjacentSpaceId.get('spaceIdRef') # return self.element(spaceIdRef) # # # def surface_tilt(self,surface_element): # """Returns the tilt of a surface # # Arguments: # - surface_element (lxml._Element or str): This a lxml._Element object # or a string with the element id. # # Return value: # - tilt (float) or None # # """ # if isinstance(surface_element,str): # surface_element=self.element(surface_element,label='Surface') # l=self.xpath(surface_element,'./gbxml:RectangularGeometry/gbxml:Tilt') # if len(l)>0: # tilt=l[0] # return float(self.text_value(tilt)) # # def surface_construction(self,surface_element): # "Returns the construction element of a surface" # if isinstance(surface_element,str): # surface_element=self.element(surface_element,label='Surface') # construction_id=surface_element.get('constructionIdRef') # construction_element=self.element(construction_id,'Construction') # return construction_element # # def surface_layers(self,surface_element): # "Returns the layer elements of a surface" # if isinstance(surface_element,str): # surface_element=self.element(surface_element,label='Surface') # construction_element=self.surface_construction(surface_element) # layer_elements=self.construction_layers(construction_element) # return layer_elements # # def surface_materials(self,surface_element): # "Returns the layer elements of a surface" # if isinstance(surface_element,str): # surface_element=self.element(surface_element,label='Surface') # construction_element=self.surface_construction(surface_element) # material_elements=self.construction_materials(construction_element) # return material_elements # # # # # # # # # # ### SPACE FUNCTIONS ## ## def set_space_id(self,space_element,id): ## """Sets a new id attribute for a Space element and updates all links ## ## ## """ ## if isinstance(space_element,str): ## space_element=self.element(space_element) ## #get old id ## old_id=space_element.get('id') ## #set new id ## space_element.set('id',id) ## #find all elements with attribute spaceRefId=old_id ## st='.//gbxml:*[@spaceIdRef="%s"]' % old_id ## l=self.xpath(self.root(),st) ## #update with id ## for e in l: ## e.set('spaceIdRef',id) ## #return new id ## return id # # ## WINDOWTYPE FUNCTIONS # # def windowType_materials(self,windowType_element): # """Returns the Glaze and Gap elements of a windowType in order # # Arguments: # - windowType_element (lxml._Element or str): This a lxml._Element object # or a string with the element id. # # Return value: # - glaze_and_gap_elements (list) # # """ # l=[] # if isinstance(windowType_element,str): # windowType_element=self.element(windowType_element,label='WindowType') # l=self.child_elements(windowType_element) # return [x for x in l if self.label(x) in ['Glaze','Gap']] # # ## ZONE FUNCTIONS # # def add_zone(self,zone_id,space_ids): # """Adds a zone element and the IdRef links to it. # # Arguments: # - zone_id (str): the id of the new zone # - space_ids (str or list): the ids of the spaces that link to the zone # """ # #adds element # parent=self.root() # e=self.add_element(parent,'Zone') # self.set_attribute(e,'id',zone_id) # #adds links # if isinstance(space_ids,str): # space_ids=[space_ids] # for space_id in space_ids: # space=self.element(space_id,'Space') # self.set_attribute(space,'zoneIdRef',zone_id) # #returns the new zone element # return e # # # def remove_zone(self,zone_element): # """Removes a Zone element and all IdRef links to the zone. # # Arguments: # - zone_element (lxml._Element or str): This a lxml._Element object # or a string with the element id. # """ # #find id # if isinstance(zone_element,str): # id=zone_element # else: # id=zone_element.get('id') # #find all elements with attribute zoneRefId=id # st='.//gbxml:*[@zoneIdRef="%s"]' % id # l=self.xpath(self.root(),st) # #removes all attributes zoneRefId=id # for x in l: # self.remove_attribute(x,'zoneIdRef') # #remove node # self.remove_element(zone_element) # # # # # # LAYERS # # # ## OUTPUT # #def xpath(element,st_xpath): # """Returns the result of an xpath operation on the gbXML file # # Arguments # - st_xpath (str): the xpath string # - element (lxml.etree._Element): the element for the xpath operation. The # default is the root element # # """ # return element.xpath(st_xpath,namespaces=ns) # ## QUERYING # #def get_child(element,id=None,label='*'): # """Returns the child of an element # # Arguments: # - id (str): the id of the element # - label (str): the label of the element # # """ # if id is None: # return get_children(element,label)[0] # else: # st='./gbxml:%s[@id="%s"]' % (label,id) # return xpath(element,st)[0] # # #def get_child_text(element,label='*',dtype=None): # "Returns the first child text value, or None" # children=get_children(element,label) # if children: # if dtype is None: # return children[0].text # else: # return dtype(children[0].text) # else: # return None # #def get_children(element,label='*'): # """Returns the child elements of an element # # Return value is a list of elements # # Arguments: # - element (lxml._Element or str): This a lxml._Element object # or a string with the element id. # - label (str): the label of the element # """ # st='./gbxml:%s' % label # return xpath(element,st) # #def get_descendents(element,label='*'): # """Returns the descendent elements of an element # # Return value is a list of elements # # Arguments: # - element (lxml._Element): This a lxml._Element object # - label (str): the label of the element # """ # st='.//gbxml:%s' % label # return xpath(element,st) # #def get_element(element,id,label='*'): # """Returns an element from the gbXML file # """ # st='//gbxml:%s[@id="%s"]' % (label,id) # return xpath(element.getroottree(),st)[0] # # ## CONSTRUCTION FUNCTIONS # #def construction_layers(construction_element): # "Returns the layer elements of a construction" # layerId_elements=get_children(construction_element,'LayerId') # layer_elements=[get_layer(layerId_element, # layerId_element.get('layerIdRef')) # for layerId_element in layerId_elements] # return layer_elements # #def construction_materials(construction_element): # "Returns the layer elements of a construction" # layer_elements=construction_layers(construction_element) # material_elements=[] # for layer_element in layer_elements: # material_elements+=layer_materials(layer_element) # return material_elements # # ## LAYER FUNCTIONS # #def get_layer(element,id): # root=element.getroottree() # result=xpath(root,'./gbxml:Layer[@id="%s"]' % id) # return result[0] # #def layer_materials(layer_element): # "Returns the layer elements of a construction" # materialId_elements=get_children(layer_element,'MaterialId') # material_elements=[get_element(materialId_element, # materialId_element.get('materialIdRef'), # 'Material') # for materialId_element in materialId_elements] # return material_elements # ## MATERIAL FUNCTIONS # #def get_material(element,id): # root=element.getroottree() # result=xpath(root,'./gbxml:Material[@id="%s"]' % id) # return result[0] # # ## SURFACE FUNCTION # #def get_surface_coordinates(surface_element): # """Returns a list of coordinate tuples # # Arguments: # - surface_element (lxml._Element or str): This a lxml._Element object # # Return value: # - coordinates (list): a list where each item is a tuple of (x,y,z) coordinates. # i.e. [(x1,y1,z1),(x2,y2,z2),(x3,y3,z3),...] # or None # # """ # l=[] # st='./gbxml:PlanarGeometry/gbxml:PolyLoop/gbxml:CartesianPoint' # cartesian_points=xpath(surface_element,st) # for cartesian_point in cartesian_points: # st='./gbxml:Coordinate' # coordinates=xpath(cartesian_point,st) # t=(float(coordinates[0].text), # float(coordinates[1].text), # float(coordinates[2].text)) # l.append(t) # return l # #def get_surface_inner_space(surface_element): # """Returns the inner Space element of a Surface, or None # """ # adjacentSpaceIds=get_children(surface_element,label='AdjacentSpaceId') # if len(adjacentSpaceIds)>0: # adjacentSpaceId=adjacentSpaceIds[0] # spaceIdRef=adjacentSpaceId.get('spaceIdRef') # return get_element(surface_element,spaceIdRef) # #def get_surface_outer_space(surface_element): # """Returns the outer Space element of a Surface, or None # """ # adjacentSpaceIds=get_children(surface_element,label='AdjacentSpaceId') # if len(adjacentSpaceIds)>1: # adjacentSpaceId=adjacentSpaceIds[1] # spaceIdRef=adjacentSpaceId.get('spaceIdRef') # return get_element(surface_element,spaceIdRef) # # # # # # # # ## def child_node_text(self,id,label='*'): ## """Returns a dictionary listing any child nodes which have text ## ## Return values is {tag:text} ## ## """ ## e=self._element(id,label) ## d={} ## for e1 in e: ## if e1.text: ## label=e1.tag.split('}')[1] ## d[label]=e1.text ## return d ## ## ## def child_node_values(self,id,label='*'): ## """Returns a dictionary listing any child nodes which have text ## ## Node text values are converted from strings into their datatype ## i.e. the text from an 'Area' node is converted into a float ## ## Return values is {label:value} ## ## """ ## d=self.xml.child_node_text(id=id,label=label) ## d1={} ## for k,v in d.items(): ## xml_type=self.xsd.element_type(k) ## #print(xml_type) ## if xml_type=='xsd:string': ## value=v ## elif xml_type=='xsd:decimal': ## value=float(v) ## else: ## raise Exception(xml_type) ## d1[k]=value ## return d1 ## ## ## ## def node_attributes(self,id,label='*'): ## "Returns the attribute dict of node with id 'id'" ## e=self._element(id,label) ## return dict(e.attrib) ## ## ## def node_ids(self,label='*'): ## """Returns the ids of all nodes ## ## Arguments: ## label (str): the node tag to filter on ## ## """ ## #filter by label ## st='//a:%s' % (label) ## l=self._ElementTree.getroot().xpath(st,namespaces=self.ns) ## return [x.get('id') for x in l] ## ## ## def parent_object(self,id,label='*'): ## """Returns the parent of an element ## ## Return value is a dictionary {'id':value,'label':value} ## ## """ ## e=self._element(id,label) ## parent=e.getparent() ## return {'id':self._id(parent), ## 'label':self._label(parent)} ## ## ## ## ## ## def surface_adjacent_objects(self,id): ## """Returns the objects adjacent to the surface ## ## Return value is a 2 item list of dictionaries [{'id':value,'label':value}] ## ## """ ## label='Surface' ## e=self._element(id,label) ## st='./a:AdjacentSpaceId/@spaceIdRef' ## l=e.xpath(st,namespaces=self.ns) ## l=l+[None]*(2-len(l)) ## surfaceType=e.get('surfaceType') ## d=\ ## {'InteriorWall':None, ## 'ExteriorWall':{'id':'Climate1','label':'Climate'}, ## 'Roof':{'id':'Climate1','label':'Climate'}, ## 'InteriorFloor':None, ## 'ExposedFloor':{'id':'Climate1','label':'Climate'}, ## 'Shade':{'id':'Climate1','label':'Climate'}, ## 'UndergroundWall':{'id':'Ground1','label':'Ground'}, ## 'UndergroundSlab':{'id':'Ground1','label':'Ground'}, ## 'Ceiling':None, ## 'Air':None, ## 'UndergroundCeiling':{'id':'Ground1','label':'Ground'}, ## 'RaisedFloor':{'id':'Climate1','label':'Climate'}, ## 'SlabOnGrade':{'id':'Ground1','label':'Ground'}, ## 'FreestandingColumn':None, ## 'EmbeddedColumn':None ## } ## l1=[] ## for x in l: ## if not x is None: ## l1.append({'id':x,'label':'Space'}) ## else: ## l1.append(d[surfaceType]) ## return l1 ## ## ## def surface_building_ids(self,id): ## """Returns a list of building ids that the surface belongs to ## """ ## l=self.surface_adjacent_objects(id) ## l=[self.parent_object(x['id'])['id'] for x in l if x['label']=='Space'] ## return l ## ## ## # ## def elements(xml, tag='*'): ## """Returns a list of lxml elements, filtered by tag ## ## Arguments: ## xml (lxml.etree._ElementTree): the gbXML instance ## tag (str): the tag name, not including the namespace ## ## """ ## st='//a:%s' % (tag) ## #print(st) ## return xml.getroot().xpath(st,namespaces=ns) # #

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from lxml import etree import pkgutil from io import BytesIO from . import xml_functions, construction_functions, layer_functions from . import surface_functions, space_functions, building_functions from . import opening_functions, zone_functions class Gbxml(): def __init__(self, gbxml_fp=None, gbxsd_fp=None): if gbxml_fp: self._ElementTree=etree.parse(gbxml_fp) else: st = pkgutil.get_data(__package__, 'blank.xml') self._ElementTree=etree.parse(BytesIO(st)) if gbxsd_fp: self._ElementTree_gbxsd=etree.parse(gbxml_fp) else: st = pkgutil.get_data(__package__, 'GreenBuildingXML_Ver6.01.xsd') self._ElementTree_gbxsd=etree.parse(BytesIO(st)) self.ns={'gbxml':'http://www.gbxml.org/schema'} def get_ids(self, tag=None): if tag is None: tag='*' element=self._ElementTree.getroot() return xml_functions.get_ids(element,tag) def get_xmlstring(self,id=None): element=self._ElementTree.getroot() if not id is None: st='//gbxml:*[@id="%s"]' % id element=element.xpath(st,namespaces=self.ns)[0] return xml_functions.get_xmlstring(element) def get_attributes(self,id): st='//gbxml:*[@id="%s"]' % id element=self._ElementTree.getroot().xpath(st,namespaces=self.ns)[0] return xml_functions.get_attributes(element) def get_child_tags(self,id): st='//gbxml:*[@id="%s"]' % id element=self._ElementTree.getroot().xpath(st,namespaces=self.ns)[0] return xml_functions.get_child_tags(element) def get_child_tag_text(self,id,child_tag): st='//gbxml:*[@id="%s"]' % id element=self._ElementTree.getroot().xpath(st,namespaces=self.ns)[0] return xml_functions.get_child_tag_text(element,child_tag) def get_child_tag_attributes(self,id,child_tag): st='//gbxml:*[@id="%s"]' % id element=self._ElementTree.getroot().xpath(st,namespaces=self.ns)[0] return xml_functions.get_child_tag_attributes(element,child_tag) def get_children_list(self,id): st='//gbxml:*[@id="%s"]' % id element=self._ElementTree.getroot().xpath(st,namespaces=self.ns)[0] return xml_functions.get_children_list(element) def get_campus_location_tags(self,id): st='./gbxml:Campus[@id="%s"]/gbxml:Location' % id element=self._ElementTree.getroot().xpath(st,namespaces=self.ns)[0] return xml_functions.get_child_tags(element) def get_campus_location_tag_text(self,id,child_tag): st='./gbxml:Campus[@id="%s"]/gbxml:Location' % id element=self._ElementTree.getroot().xpath(st,namespaces=self.ns)[0] return xml_functions.get_child_tag_text(element,child_tag) def get_building_space_ids(self,id): st='./gbxml:Campus/gbxml:Building[@id="%s"]' % id element=self._ElementTree.getroot().xpath(st,namespaces=self.ns)[0] return building_functions.get_space_ids(element) def get_building_surface_ids(self,id): st='./gbxml:Campus/gbxml:Building[@id="%s"]' % id element=self._ElementTree.getroot().xpath(st,namespaces=self.ns)[0] return building_functions.get_surface_ids(element) def get_space_surface_ids(self,id): st='./gbxml:Campus/gbxml:Building/gbxml:Space[@id="%s"]' % id element=self._ElementTree.getroot().xpath(st,namespaces=self.ns)[0] return space_functions.get_surface_ids(element) def get_construction_layer_ids(self,id): st='./gbxml:Construction[@id="%s"]' % id element=self._ElementTree.getroot().xpath(st,namespaces=self.ns)[0] return construction_functions.get_layer_ids(element) def get_construction_material_ids(self,id): st='./gbxml:Construction[@id="%s"]' % id element=self._ElementTree.getroot().xpath(st,namespaces=self.ns)[0] return construction_functions.get_material_ids(element) def get_layer_material_ids(self,id): st='./gbxml:Layer[@id="%s"]' % id element=self._ElementTree.getroot().xpath(st,namespaces=self.ns)[0] return layer_functions.get_material_ids(element) def get_surface_inner_space_id(self,id): st='./gbxml:Campus/gbxml:Surface[@id="%s"]' % id element=self._ElementTree.getroot().xpath(st,namespaces=self.ns)[0] return surface_functions.get_inner_space_id(element) def get_surface_outer_space_id(self,id): st='./gbxml:Campus/gbxml:Surface[@id="%s"]' % id element=self._ElementTree.getroot().xpath(st,namespaces=self.ns)[0] return surface_functions.get_outer_space_id(element) def get_surface_azimuth(self,id): st='./gbxml:Campus/gbxml:Surface[@id="%s"]' % id element=self._ElementTree.getroot().xpath(st,namespaces=self.ns)[0] return surface_functions.get_azimuth(element) def get_surface_tilt(self,id): st='./gbxml:Campus/gbxml:Surface[@id="%s"]' % id element=self._ElementTree.getroot().xpath(st,namespaces=self.ns)[0] return surface_functions.get_tilt(element) def get_surface_coordinates(self,id): st='./gbxml:Campus/gbxml:Surface[@id="%s"]' % id element=self._ElementTree.getroot().xpath(st,namespaces=self.ns)[0] return surface_functions.get_coordinates(element) def get_surface_area(self,id): st='./gbxml:Campus/gbxml:Surface[@id="%s"]' % id element=self._ElementTree.getroot().xpath(st,namespaces=self.ns)[0] return surface_functions.get_area(element) def get_surface_opening_ids(self,id): st='./gbxml:Campus/gbxml:Surface[@id="%s"]' % id element=self._ElementTree.getroot().xpath(st,namespaces=self.ns)[0] return surface_functions.get_opening_ids(element) def get_opening_surface_id(self,id): st='./gbxml:Campus/gbxml:Surface/gbxml:Opening[@id="%s"]' % id element=self._ElementTree.getroot().xpath(st,namespaces=self.ns)[0] return opening_functions.get_surface_id(element) def get_opening_coordinates(self,id): st='./gbxml:Campus/gbxml:Surface/gbxml:Opening[@id="%s"]' % id element=self._ElementTree.getroot().xpath(st,namespaces=self.ns)[0] return opening_functions.get_coordinates(element) def get_opening_area(self,id): st='./gbxml:Campus/gbxml:Surface/gbxml:Opening[@id="%s"]' % id element=self._ElementTree.getroot().xpath(st,namespaces=self.ns)[0] return opening_functions.get_area(element) def get_zone_space_ids(self,id): st='./gbxml:Zone[@id="%s"]' % id element=self._ElementTree.getroot().xpath(st,namespaces=self.ns)[0] return zone_functions.get_space_ids(element) # Arguments: # - element (lxml.etree._Element): default is root node # # """ # # Arguments # - st_xpath (str): the xpath string # - element (lxml.etree._Element): the element for the xpath operation. The # default is the root element # # """ # # Arguments: # fp (str): the filepath # """ # Returns True if the gbXML file is valid, otherwise False # # """ # Returns the newly created element # # Arguments: # - parent_element (lxml._Element or str): the parent element that the # new element is added to. This can be either a lxml._Element object # or a string with the element id. # - label (str): the label or tag of the new element # - text (str): the text of the new element # - **kwargs (keywords): the attributes of the new element # # """ # # Returns the modified element # # Arguments: # - element (lxml._Element or str): This a lxml._Element object # or a string with the element id. # - key (str): the name of the attribute # - value (str): the value of the attribute # # """ # # Arguments: # - element (lxml._Element or str): This a lxml._Element object # or a string with the element id. # - new_id (str): # # Return value: # - new_id (str) # # """ or str): This a lxml._Element object # or a string with the element id. # - text (str): the text # # """ # # Arguments: # - element (lxml._Element or str): This a lxml._Element object # or a string with the element id. # # """ ments: # - element (lxml._Element or str): This a lxml._Element object # or a string with the element id. # - key (str): The name of the attribute to delete # # """ # Arguments: # - label (str): the label of the elements # # """ # # Arguments: # - id (str): the id of the element # - label (str): the label of the element # # """ # # Arguments: # - element (lxml._Element or str): This a lxml._Element object # or a string with the element id. # """ # # Return value is a dictionary # # Arguments: # - element (lxml._Element or str): This a lxml._Element object # or a string with the element id. # """ # # Return value is a string # # Arguments: # - element (lxml._Element or str): This a lxml._Element object # or a string with the element id. # """ # according to its schema data type # # Return value is an object with data type dependent on the schema # # Arguments: # - element (lxml._Element or str): This a lxml._Element object # or a string with the element id. # """ e is a list of elements # # Arguments: # - element (lxml._Element or str): This a lxml._Element object # or a string with the element id. # - label (str): the label of the element # """ # # Return value is a list of elements # # Arguments: # - element (lxml._Element or str): This a lxml._Element object # or a string with the element id. # - label (str): the label of the element # """ guments: # - opening_element (lxml._Element or str): This a lxml._Element object # or a string with the element id. # # Return value: # - coordinates (list): a list where each item is a tuple of (x,y,z) coordinates. # i.e. [(x1,y1,z1),(x2,y2,z2),(x3,y3,z3),...] # or None # # """ Arguments: # - surface_element (lxml._Element or str): This a lxml._Element object # or a string with the element id. # # Return value: # - azimuth (float) or None # # """ # # Arguments: # - surface_element (lxml._Element or str): This a lxml._Element object # or a string with the element id. # # Return value: # - coordinates (list): a list where each item is a tuple of (x,y,z) coordinates. # i.e. [(x1,y1,z1),(x2,y2,z2),(x3,y3,z3),...] # or None # # """ # # Arguments: # - surface_element (lxml._Element or str): This a lxml._Element object # or a string with the element id. # # Return value: # - space (lxml._Element) or None # # """ # # Arguments: # - surface_element (lxml._Element or str): This a lxml._Element object # or a string with the element id. # # Return value: # - space (lxml._Element) or None # # """ # # Arguments: # - surface_element (lxml._Element or str): This a lxml._Element object # or a string with the element id. # # Return value: # - tilt (float) or None # # """ # Arguments: # - id (str): the id of the element # - label (str): the label of the element # # """ # # Return value is a list of elements # # Arguments: # - element (lxml._Element or str): This a lxml._Element object # or a string with the element id. # - label (str): the label of the element # """ # # Return value is a list of elements # # Arguments: # - element (lxml._Element): This a lxml._Element object # - label (str): the label of the element # """ # """ face_element (lxml._Element or str): This a lxml._Element object # # Return value: # - coordinates (list): a list where each item is a tuple of (x,y,z) coordinates. # i.e. [(x1,y1,z1),(x2,y2,z2),(x3,y3,z3),...] # or None # # """ # """ # """

true

f70f71544831b4d1ffff7c6948b00d3bdd751afe

38,151

py

Python

google/net/proto2/python/internal/python_message.py

vladushakov987/appengine_python3

0dd481c73e2537a50ee10f1b79cd65938087e555

[ "Apache-2.0" ]

null

google/net/proto2/python/internal/python_message.py

vladushakov987/appengine_python3

0dd481c73e2537a50ee10f1b79cd65938087e555

[ "Apache-2.0" ]

null

google/net/proto2/python/internal/python_message.py

vladushakov987/appengine_python3

0dd481c73e2537a50ee10f1b79cd65938087e555

[ "Apache-2.0" ]

null

#!/usr/bin/env python # # Copyright 2007 Google Inc. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. # """Contains a metaclass and helper functions used to create protocol message classes from Descriptor objects at runtime. Recall that a metaclass is the "type" of a class. (A class is to a metaclass what an instance is to a class.) In this case, we use the GeneratedProtocolMessageType metaclass to inject all the useful functionality into the classes output by the protocol compiler at compile-time. The upshot of all this is that the real implementation details for ALL pure-Python protocol buffers are *here in this file*. """ from __future__ import absolute_import from future import standard_library standard_library.install_aliases() from builtins import str from builtins import range from builtins import object import sys import six from six.moves import range if sys.version_info[0] < 3: try: from io import StringIO as BytesIO except ImportError: from io import StringIO as BytesIO import six.moves.copyreg as copyreg else: from io import BytesIO import copyreg import struct import weakref from google.net.proto2.python.internal import containers from google.net.proto2.python.internal import decoder from google.net.proto2.python.internal import encoder from google.net.proto2.python.internal import enum_type_wrapper from google.net.proto2.python.internal import message_listener as message_listener_mod from google.net.proto2.python.internal import type_checkers from google.net.proto2.python.internal import wire_format from google.net.proto2.python.public import descriptor as descriptor_mod from google.net.proto2.python.public import message as message_mod from google.net.proto2.python.public import text_format _FieldDescriptor = descriptor_mod.FieldDescriptor def NewMessage(bases, descriptor, dictionary): _AddClassAttributesForNestedExtensions(descriptor, dictionary) _AddSlots(descriptor, dictionary) return bases def InitMessage(descriptor, cls): cls._decoders_by_tag = {} cls._extensions_by_name = {} cls._extensions_by_number = {} if (descriptor.has_options and descriptor.GetOptions().message_set_wire_format): cls._decoders_by_tag[decoder.MESSAGE_SET_ITEM_TAG] = ( decoder.MessageSetItemDecoder(cls._extensions_by_number), None) for field in descriptor.fields: _AttachFieldHelpers(cls, field) _AddEnumValues(descriptor, cls) _AddInitMethod(descriptor, cls) _AddPropertiesForFields(descriptor, cls) _AddPropertiesForExtensions(descriptor, cls) _AddStaticMethods(cls) _AddMessageMethods(descriptor, cls) _AddPrivateHelperMethods(descriptor, cls) copyreg.pickle(cls, lambda obj: (cls, (), obj.__getstate__())) def _PropertyName(proto_field_name): """Returns the name of the public property attribute which clients can use to get and (in some cases) set the value of a protocol message field. Args: proto_field_name: The protocol message field name, exactly as it appears (or would appear) in a .proto file. """ return proto_field_name def _VerifyExtensionHandle(message, extension_handle): """Verify that the given extension handle is valid.""" if not isinstance(extension_handle, _FieldDescriptor): raise KeyError('HasExtension() expects an extension handle, got: %s' % extension_handle) if not extension_handle.is_extension: raise KeyError('"%s" is not an extension.' % extension_handle.full_name) if not extension_handle.containing_type: raise KeyError('"%s" is missing a containing_type.' % extension_handle.full_name) if extension_handle.containing_type is not message.DESCRIPTOR: raise KeyError('Extension "%s" extends message type "%s", but this ' 'message is of type "%s".' % (extension_handle.full_name, extension_handle.containing_type.full_name, message.DESCRIPTOR.full_name)) def _AddSlots(message_descriptor, dictionary): """Adds a __slots__ entry to dictionary, containing the names of all valid attributes for this message type. Args: message_descriptor: A Descriptor instance describing this message type. dictionary: Class dictionary to which we'll add a '__slots__' entry. """ dictionary['__slots__'] = ['_cached_byte_size', '_cached_byte_size_dirty', '_fields', '_unknown_fields', '_is_present_in_parent', '_listener', '_listener_for_children', '__weakref__', '_oneofs'] def _IsMessageSetExtension(field): return (field.is_extension and field.containing_type.has_options and field.containing_type.GetOptions().message_set_wire_format and field.type == _FieldDescriptor.TYPE_MESSAGE and field.message_type == field.extension_scope and field.label == _FieldDescriptor.LABEL_OPTIONAL) def _AttachFieldHelpers(cls, field_descriptor): is_repeated = (field_descriptor.label == _FieldDescriptor.LABEL_REPEATED) is_packed = (field_descriptor.has_options and field_descriptor.GetOptions().packed) if _IsMessageSetExtension(field_descriptor): field_encoder = encoder.MessageSetItemEncoder(field_descriptor.number) sizer = encoder.MessageSetItemSizer(field_descriptor.number) else: field_encoder = type_checkers.TYPE_TO_ENCODER[field_descriptor.type]( field_descriptor.number, is_repeated, is_packed) sizer = type_checkers.TYPE_TO_SIZER[field_descriptor.type]( field_descriptor.number, is_repeated, is_packed) field_descriptor._encoder = field_encoder field_descriptor._sizer = sizer field_descriptor._default_constructor = _DefaultValueConstructorForField( field_descriptor) def AddDecoder(wiretype, is_packed): tag_bytes = encoder.TagBytes(field_descriptor.number, wiretype) cls._decoders_by_tag[tag_bytes] = ( type_checkers.TYPE_TO_DECODER[field_descriptor.type]( field_descriptor.number, is_repeated, is_packed, field_descriptor, field_descriptor._default_constructor), field_descriptor if field_descriptor.containing_oneof is not None else None) AddDecoder(type_checkers.FIELD_TYPE_TO_WIRE_TYPE[field_descriptor.type], False) if is_repeated and wire_format.IsTypePackable(field_descriptor.type): AddDecoder(wire_format.WIRETYPE_LENGTH_DELIMITED, True) def _AddClassAttributesForNestedExtensions(descriptor, dictionary): extension_dict = descriptor.extensions_by_name for extension_name, extension_field in six.iteritems(extension_dict): assert extension_name not in dictionary dictionary[extension_name] = extension_field def _AddEnumValues(descriptor, cls): """Sets class-level attributes for all enum fields defined in this message. Also exporting a class-level object that can name enum values. Args: descriptor: Descriptor object for this message type. cls: Class we're constructing for this message type. """ for enum_type in descriptor.enum_types: setattr(cls, enum_type.name, enum_type_wrapper.EnumTypeWrapper(enum_type)) for enum_value in enum_type.values: setattr(cls, enum_value.name, enum_value.number) def _DefaultValueConstructorForField(field): """Returns a function which returns a default value for a field. Args: field: FieldDescriptor object for this field. The returned function has one argument: message: Message instance containing this field, or a weakref proxy of same. That function in turn returns a default value for this field. The default value may refer back to |message| via a weak reference. """ if field.label == _FieldDescriptor.LABEL_REPEATED: if field.has_default_value and field.default_value != []: raise ValueError('Repeated field default value not empty list: %s' % ( field.default_value)) if field.cpp_type == _FieldDescriptor.CPPTYPE_MESSAGE: message_type = field.message_type def MakeRepeatedMessageDefault(message): return containers.RepeatedCompositeFieldContainer( message._listener_for_children, field.message_type) return MakeRepeatedMessageDefault else: type_checker = type_checkers.GetTypeChecker(field) def MakeRepeatedScalarDefault(message): return containers.RepeatedScalarFieldContainer( message._listener_for_children, type_checker) return MakeRepeatedScalarDefault if field.cpp_type == _FieldDescriptor.CPPTYPE_MESSAGE: message_type = field.message_type def MakeSubMessageDefault(message): result = message_type._concrete_class() result._SetListener(message._listener_for_children) return result return MakeSubMessageDefault def MakeScalarDefault(message): return field.default_value return MakeScalarDefault def _ReraiseTypeErrorWithFieldName(message_name, field_name): """Re-raise the currently-handled TypeError with the field name added.""" exc = sys.exc_info()[1] if len(exc.args) == 1 and type(exc) is TypeError: exc = TypeError('%s for field %s.%s' % (str(exc), message_name, field_name)) six.reraise(type(exc), exc, sys.exc_info()[2]) def _AddInitMethod(message_descriptor, cls): """Adds an __init__ method to cls.""" fields = message_descriptor.fields def init(self, **kwargs): self._cached_byte_size = 0 self._cached_byte_size_dirty = len(kwargs) > 0 self._fields = {} self._oneofs = {} self._unknown_fields = () self._is_present_in_parent = False self._listener = message_listener_mod.NullMessageListener() self._listener_for_children = _Listener(self) for field_name, field_value in six.iteritems(kwargs): field = _GetFieldByName(message_descriptor, field_name) if field is None: raise TypeError("%s() got an unexpected keyword argument '%s'" % (message_descriptor.name, field_name)) if field.label == _FieldDescriptor.LABEL_REPEATED: copy = field._default_constructor(self) if field.cpp_type == _FieldDescriptor.CPPTYPE_MESSAGE: for val in field_value: copy.add().MergeFrom(val) else: copy.extend(field_value) self._fields[field] = copy elif field.cpp_type == _FieldDescriptor.CPPTYPE_MESSAGE: copy = field._default_constructor(self) try: copy.MergeFrom(field_value) except TypeError: _ReraiseTypeErrorWithFieldName(message_descriptor.name, field_name) self._fields[field] = copy else: try: setattr(self, field_name, field_value) except TypeError: _ReraiseTypeErrorWithFieldName(message_descriptor.name, field_name) init.__module__ = None init.__doc__ = None cls.__init__ = init def _GetFieldByName(message_descriptor, field_name): """Returns a field descriptor by field name. Args: message_descriptor: A Descriptor describing all fields in message. field_name: The name of the field to retrieve. Returns: The field descriptor associated with the field name. """ try: return message_descriptor.fields_by_name[field_name] except KeyError: raise ValueError('Protocol message has no "%s" field.' % field_name) def _AddPropertiesForFields(descriptor, cls): """Adds properties for all fields in this protocol message type.""" for field in descriptor.fields: _AddPropertiesForField(field, cls) if descriptor.is_extendable: cls.Extensions = property(lambda self: _ExtensionDict(self)) def _AddPropertiesForField(field, cls): """Adds a public property for a protocol message field. Clients can use this property to get and (in the case of non-repeated scalar fields) directly set the value of a protocol message field. Args: field: A FieldDescriptor for this field. cls: The class we're constructing. """ assert _FieldDescriptor.MAX_CPPTYPE == 10 constant_name = field.name.upper() + "_FIELD_NUMBER" setattr(cls, constant_name, field.number) if field.label == _FieldDescriptor.LABEL_REPEATED: _AddPropertiesForRepeatedField(field, cls) elif field.cpp_type == _FieldDescriptor.CPPTYPE_MESSAGE: _AddPropertiesForNonRepeatedCompositeField(field, cls) else: _AddPropertiesForNonRepeatedScalarField(field, cls) def _AddPropertiesForRepeatedField(field, cls): """Adds a public property for a "repeated" protocol message field. Clients can use this property to get the value of the field, which will be either a _RepeatedScalarFieldContainer or _RepeatedCompositeFieldContainer (see below). Note that when clients add values to these containers, we perform type-checking in the case of repeated scalar fields, and we also set any necessary "has" bits as a side-effect. Args: field: A FieldDescriptor for this field. cls: The class we're constructing. """ proto_field_name = field.name property_name = _PropertyName(proto_field_name) def getter(self): field_value = self._fields.get(field) if field_value is None: field_value = field._default_constructor(self) field_value = self._fields.setdefault(field, field_value) return field_value getter.__module__ = None getter.__doc__ = 'Getter for %s.' % proto_field_name def setter(self, new_value): raise AttributeError('Assignment not allowed to repeated field ' '"%s" in protocol message object.' % proto_field_name) doc = 'Magic attribute generated for "%s" proto field.' % proto_field_name setattr(cls, property_name, property(getter, setter, doc=doc)) def _AddPropertiesForNonRepeatedScalarField(field, cls): """Adds a public property for a nonrepeated, scalar protocol message field. Clients can use this property to get and directly set the value of the field. Note that when the client sets the value of a field by using this property, all necessary "has" bits are set as a side-effect, and we also perform type-checking. Args: field: A FieldDescriptor for this field. cls: The class we're constructing. """ proto_field_name = field.name property_name = _PropertyName(proto_field_name) type_checker = type_checkers.GetTypeChecker(field) default_value = field.default_value valid_values = set() def getter(self): return self._fields.get(field, default_value) getter.__module__ = None getter.__doc__ = 'Getter for %s.' % proto_field_name def field_setter(self, new_value): self._fields[field] = type_checker.CheckValue(new_value) if not self._cached_byte_size_dirty: self._Modified() if field.containing_oneof is not None: def setter(self, new_value): field_setter(self, new_value) self._UpdateOneofState(field) else: setter = field_setter setter.__module__ = None setter.__doc__ = 'Setter for %s.' % proto_field_name doc = 'Magic attribute generated for "%s" proto field.' % proto_field_name setattr(cls, property_name, property(getter, setter, doc=doc)) def _AddPropertiesForNonRepeatedCompositeField(field, cls): """Adds a public property for a nonrepeated, composite protocol message field. A composite field is a "group" or "message" field. Clients can use this property to get the value of the field, but cannot assign to the property directly. Args: field: A FieldDescriptor for this field. cls: The class we're constructing. """ proto_field_name = field.name property_name = _PropertyName(proto_field_name) message_type = field.message_type def getter(self): field_value = self._fields.get(field) if field_value is None: field_value = message_type._concrete_class() field_value._SetListener( _OneofListener(self, field) if field.containing_oneof is not None else self._listener_for_children) field_value = self._fields.setdefault(field, field_value) return field_value getter.__module__ = None getter.__doc__ = 'Getter for %s.' % proto_field_name def setter(self, new_value): raise AttributeError('Assignment not allowed to composite field ' '"%s" in protocol message object.' % proto_field_name) doc = 'Magic attribute generated for "%s" proto field.' % proto_field_name setattr(cls, property_name, property(getter, setter, doc=doc)) def _AddPropertiesForExtensions(descriptor, cls): """Adds properties for all fields in this protocol message type.""" extension_dict = descriptor.extensions_by_name for extension_name, extension_field in six.iteritems(extension_dict): constant_name = extension_name.upper() + "_FIELD_NUMBER" setattr(cls, constant_name, extension_field.number) def _AddStaticMethods(cls): def RegisterExtension(extension_handle): extension_handle.containing_type = cls.DESCRIPTOR _AttachFieldHelpers(cls, extension_handle) actual_handle = cls._extensions_by_number.setdefault( extension_handle.number, extension_handle) if actual_handle is not extension_handle: raise AssertionError( 'Extensions "%s" and "%s" both try to extend message type "%s" with ' 'field number %d.' % (extension_handle.full_name, actual_handle.full_name, cls.DESCRIPTOR.full_name, extension_handle.number)) cls._extensions_by_name[extension_handle.full_name] = extension_handle handle = extension_handle if _IsMessageSetExtension(handle): cls._extensions_by_name[ extension_handle.message_type.full_name] = extension_handle cls.RegisterExtension = staticmethod(RegisterExtension) def FromString(s): message = cls() message.MergeFromString(s) return message cls.FromString = staticmethod(FromString) def _IsPresent(item): """Given a (FieldDescriptor, value) tuple from _fields, return true if the value should be included in the list returned by ListFields().""" if item[0].label == _FieldDescriptor.LABEL_REPEATED: return bool(item[1]) elif item[0].cpp_type == _FieldDescriptor.CPPTYPE_MESSAGE: return item[1]._is_present_in_parent else: return True def _AddListFieldsMethod(message_descriptor, cls): """Helper for _AddMessageMethods().""" def ListFields(self): all_fields = [item for item in six.iteritems(self._fields) if _IsPresent(item)] all_fields.sort(key = lambda item: item[0].number) return all_fields cls.ListFields = ListFields def _AddHasFieldMethod(message_descriptor, cls): """Helper for _AddMessageMethods().""" singular_fields = {} for field in message_descriptor.fields: if field.label != _FieldDescriptor.LABEL_REPEATED: singular_fields[field.name] = field for field in message_descriptor.oneofs: singular_fields[field.name] = field def HasField(self, field_name): try: field = singular_fields[field_name] except KeyError: raise ValueError( 'Protocol message has no singular "%s" field.' % field_name) if isinstance(field, descriptor_mod.OneofDescriptor): try: return HasField(self, self._oneofs[field].name) except KeyError: return False else: if field.cpp_type == _FieldDescriptor.CPPTYPE_MESSAGE: value = self._fields.get(field) return value is not None and value._is_present_in_parent else: return field in self._fields cls.HasField = HasField def _AddClearFieldMethod(message_descriptor, cls): """Helper for _AddMessageMethods().""" def ClearField(self, field_name): try: field = message_descriptor.fields_by_name[field_name] except KeyError: try: field = message_descriptor.oneofs_by_name[field_name] if field in self._oneofs: field = self._oneofs[field] else: return except KeyError: raise ValueError('Protocol message has no "%s" field.' % field_name) if field in self._fields: del self._fields[field] if self._oneofs.get(field.containing_oneof, None) is field: del self._oneofs[field.containing_oneof] self._Modified() cls.ClearField = ClearField def _AddClearExtensionMethod(cls): """Helper for _AddMessageMethods().""" def ClearExtension(self, extension_handle): _VerifyExtensionHandle(self, extension_handle) if extension_handle in self._fields: del self._fields[extension_handle] self._Modified() cls.ClearExtension = ClearExtension def _AddClearMethod(message_descriptor, cls): """Helper for _AddMessageMethods().""" def Clear(self): self._fields = {} self._unknown_fields = () self._oneofs = {} self._Modified() cls.Clear = Clear def _AddHasExtensionMethod(cls): """Helper for _AddMessageMethods().""" def HasExtension(self, extension_handle): _VerifyExtensionHandle(self, extension_handle) if extension_handle.label == _FieldDescriptor.LABEL_REPEATED: raise KeyError('"%s" is repeated.' % extension_handle.full_name) if extension_handle.cpp_type == _FieldDescriptor.CPPTYPE_MESSAGE: value = self._fields.get(extension_handle) return value is not None and value._is_present_in_parent else: return extension_handle in self._fields cls.HasExtension = HasExtension def _AddEqualsMethod(message_descriptor, cls): """Helper for _AddMessageMethods().""" def __eq__(self, other): if (not isinstance(other, message_mod.Message) or other.DESCRIPTOR != self.DESCRIPTOR): return False if self is other: return True if not self.ListFields() == other.ListFields(): return False unknown_fields = list(self._unknown_fields) unknown_fields.sort() other_unknown_fields = list(other._unknown_fields) other_unknown_fields.sort() return unknown_fields == other_unknown_fields cls.__eq__ = __eq__ def _AddStrMethod(message_descriptor, cls): """Helper for _AddMessageMethods().""" def __str__(self): return text_format.MessageToString(self) cls.__str__ = __str__ def _AddUnicodeMethod(unused_message_descriptor, cls): """Helper for _AddMessageMethods().""" def __unicode__(self): return text_format.MessageToString(self, as_utf8=True).decode('utf-8') cls.__unicode__ = __unicode__ def _AddSetListenerMethod(cls): """Helper for _AddMessageMethods().""" def SetListener(self, listener): if listener is None: self._listener = message_listener_mod.NullMessageListener() else: self._listener = listener cls._SetListener = SetListener def _BytesForNonRepeatedElement(value, field_number, field_type): """Returns the number of bytes needed to serialize a non-repeated element. The returned byte count includes space for tag information and any other additional space associated with serializing value. Args: value: Value we're serializing. field_number: Field number of this value. (Since the field number is stored as part of a varint-encoded tag, this has an impact on the total bytes required to serialize the value). field_type: The type of the field. One of the TYPE_* constants within FieldDescriptor. """ try: fn = type_checkers.TYPE_TO_BYTE_SIZE_FN[field_type] return fn(field_number, value) except KeyError: raise message_mod.EncodeError('Unrecognized field type: %d' % field_type) def _AddByteSizeMethod(message_descriptor, cls): """Helper for _AddMessageMethods().""" def ByteSize(self): if not self._cached_byte_size_dirty: return self._cached_byte_size size = 0 for field_descriptor, field_value in self.ListFields(): size += field_descriptor._sizer(field_value) for tag_bytes, value_bytes in self._unknown_fields: size += len(tag_bytes) + len(value_bytes) self._cached_byte_size = size self._cached_byte_size_dirty = False self._listener_for_children.dirty = False return size cls.ByteSize = ByteSize def _AddSerializeToStringMethod(message_descriptor, cls): """Helper for _AddMessageMethods().""" def SerializeToString(self): errors = [] if not self.IsInitialized(): raise message_mod.EncodeError( 'Message %s is missing required fields: %s' % ( self.DESCRIPTOR.full_name, ','.join(self.FindInitializationErrors()))) return self.SerializePartialToString() cls.SerializeToString = SerializeToString def _AddSerializePartialToStringMethod(message_descriptor, cls): """Helper for _AddMessageMethods().""" def SerializePartialToString(self): out = BytesIO() self._InternalSerialize(out.write) return out.getvalue() cls.SerializePartialToString = SerializePartialToString def InternalSerialize(self, write_bytes): for field_descriptor, field_value in self.ListFields(): field_descriptor._encoder(write_bytes, field_value) for tag_bytes, value_bytes in self._unknown_fields: write_bytes(tag_bytes) write_bytes(value_bytes) cls._InternalSerialize = InternalSerialize def _AddMergeFromStringMethod(message_descriptor, cls): """Helper for _AddMessageMethods().""" def MergeFromString(self, serialized): length = len(serialized) try: if self._InternalParse(serialized, 0, length) != length: raise message_mod.DecodeError('Unexpected end-group tag.') except (IndexError, TypeError): raise message_mod.DecodeError('Truncated message.') except struct.error as e: raise message_mod.DecodeError(e) return length cls.MergeFromString = MergeFromString local_ReadTag = decoder.ReadTag local_SkipField = decoder.SkipField decoders_by_tag = cls._decoders_by_tag def InternalParse(self, buffer, pos, end): self._Modified() field_dict = self._fields unknown_field_list = self._unknown_fields while pos != end: (tag_bytes, new_pos) = local_ReadTag(buffer, pos) field_decoder, field_desc = decoders_by_tag.get(tag_bytes, (None, None)) if field_decoder is None: value_start_pos = new_pos new_pos = local_SkipField(buffer, new_pos, end, tag_bytes) if new_pos == -1: return pos if not unknown_field_list: unknown_field_list = self._unknown_fields = [] unknown_field_list.append((tag_bytes, buffer[value_start_pos:new_pos])) pos = new_pos else: pos = field_decoder(buffer, new_pos, end, self, field_dict) if field_desc: self._UpdateOneofState(field_desc) return pos cls._InternalParse = InternalParse def _AddIsInitializedMethod(message_descriptor, cls): """Adds the IsInitialized and FindInitializationError methods to the protocol message class.""" required_fields = [field for field in message_descriptor.fields if field.label == _FieldDescriptor.LABEL_REQUIRED] def IsInitialized(self, errors=None): """Checks if all required fields of a message are set. Args: errors: A list which, if provided, will be populated with the field paths of all missing required fields. Returns: True iff the specified message has all required fields set. """ for field in required_fields: if (field not in self._fields or (field.cpp_type == _FieldDescriptor.CPPTYPE_MESSAGE and not self._fields[field]._is_present_in_parent)): if errors is not None: errors.extend(self.FindInitializationErrors()) return False for field, value in list(self._fields.items()): if field.cpp_type == _FieldDescriptor.CPPTYPE_MESSAGE: if field.label == _FieldDescriptor.LABEL_REPEATED: for element in value: if not element.IsInitialized(): if errors is not None: errors.extend(self.FindInitializationErrors()) return False elif value._is_present_in_parent and not value.IsInitialized(): if errors is not None: errors.extend(self.FindInitializationErrors()) return False return True cls.IsInitialized = IsInitialized def FindInitializationErrors(self): """Finds required fields which are not initialized. Returns: A list of strings. Each string is a path to an uninitialized field from the top-level message, e.g. "foo.bar[5].baz". """ errors = [] for field in required_fields: if not self.HasField(field.name): errors.append(field.name) for field, value in self.ListFields(): if field.cpp_type == _FieldDescriptor.CPPTYPE_MESSAGE: if field.is_extension: name = "(%s)" % field.full_name else: name = field.name if field.label == _FieldDescriptor.LABEL_REPEATED: for i in range(len(value)): element = value[i] prefix = "%s[%d]." % (name, i) sub_errors = element.FindInitializationErrors() errors += [ prefix + error for error in sub_errors ] else: prefix = name + "." sub_errors = value.FindInitializationErrors() errors += [ prefix + error for error in sub_errors ] return errors cls.FindInitializationErrors = FindInitializationErrors def _AddMergeFromMethod(cls): LABEL_REPEATED = _FieldDescriptor.LABEL_REPEATED CPPTYPE_MESSAGE = _FieldDescriptor.CPPTYPE_MESSAGE def MergeFrom(self, msg): if not isinstance(msg, cls): raise TypeError( "Parameter to MergeFrom() must be instance of same class: " "expected %s got %s." % (cls.__name__, type(msg).__name__)) assert msg is not self self._Modified() fields = self._fields for field, value in six.iteritems(msg._fields): if field.label == LABEL_REPEATED: field_value = fields.get(field) if field_value is None: field_value = field._default_constructor(self) fields[field] = field_value field_value.MergeFrom(value) elif field.cpp_type == CPPTYPE_MESSAGE: if value._is_present_in_parent: field_value = fields.get(field) if field_value is None: field_value = field._default_constructor(self) fields[field] = field_value field_value.MergeFrom(value) else: self._fields[field] = value if msg._unknown_fields: if not self._unknown_fields: self._unknown_fields = [] self._unknown_fields.extend(msg._unknown_fields) cls.MergeFrom = MergeFrom def _AddWhichOneofMethod(message_descriptor, cls): def WhichOneof(self, oneof_name): """Returns the name of the currently set field inside a oneof, or None.""" try: field = message_descriptor.oneofs_by_name[oneof_name] except KeyError: raise ValueError( 'Protocol message has no oneof "%s" field.' % oneof_name) nested_field = self._oneofs.get(field, None) if nested_field is not None and self.HasField(nested_field.name): return nested_field.name else: return None cls.WhichOneof = WhichOneof def _AddMessageMethods(message_descriptor, cls): """Adds implementations of all Message methods to cls.""" _AddListFieldsMethod(message_descriptor, cls) _AddHasFieldMethod(message_descriptor, cls) _AddClearFieldMethod(message_descriptor, cls) if message_descriptor.is_extendable: _AddClearExtensionMethod(cls) _AddHasExtensionMethod(cls) _AddClearMethod(message_descriptor, cls) _AddEqualsMethod(message_descriptor, cls) _AddStrMethod(message_descriptor, cls) _AddUnicodeMethod(message_descriptor, cls) _AddSetListenerMethod(cls) _AddByteSizeMethod(message_descriptor, cls) _AddSerializeToStringMethod(message_descriptor, cls) _AddSerializePartialToStringMethod(message_descriptor, cls) _AddMergeFromStringMethod(message_descriptor, cls) _AddIsInitializedMethod(message_descriptor, cls) _AddMergeFromMethod(cls) _AddWhichOneofMethod(message_descriptor, cls) def _AddPrivateHelperMethods(message_descriptor, cls): """Adds implementation of private helper methods to cls.""" def Modified(self): """Sets the _cached_byte_size_dirty bit to true, and propagates this to our listener iff this was a state change. """ if not self._cached_byte_size_dirty: self._cached_byte_size_dirty = True self._listener_for_children.dirty = True self._is_present_in_parent = True self._listener.Modified() def _UpdateOneofState(self, field): """Sets field as the active field in its containing oneof. Will also delete currently active field in the oneof, if it is different from the argument. Does not mark the message as modified. """ other_field = self._oneofs.setdefault(field.containing_oneof, field) if other_field is not field: del self._fields[other_field] self._oneofs[field.containing_oneof] = field cls._Modified = Modified cls.SetInParent = Modified cls._UpdateOneofState = _UpdateOneofState class _Listener(object): """MessageListener implementation that a parent message registers with its child message. In order to support semantics like: foo.bar.baz.qux = 23 assert foo.HasField('bar') ...child objects must have back references to their parents. This helper class is at the heart of this support. """ def __init__(self, parent_message): """Args: parent_message: The message whose _Modified() method we should call when we receive Modified() messages. """ if isinstance(parent_message, weakref.ProxyType): self._parent_message_weakref = parent_message else: self._parent_message_weakref = weakref.proxy(parent_message) self.dirty = False def Modified(self): if self.dirty: return try: self._parent_message_weakref._Modified() except ReferenceError: pass class _OneofListener(_Listener): """Special listener implementation for setting composite oneof fields.""" def __init__(self, parent_message, field): """Args: parent_message: The message whose _Modified() method we should call when we receive Modified() messages. field: The descriptor of the field being set in the parent message. """ super(_OneofListener, self).__init__(parent_message) self._field = field def Modified(self): """Also updates the state of the containing oneof in the parent message.""" try: self._parent_message_weakref._UpdateOneofState(self._field) super(_OneofListener, self).Modified() except ReferenceError: pass class _ExtensionDict(object): """Dict-like container for supporting an indexable "Extensions" field on proto instances. Note that in all cases we expect extension handles to be FieldDescriptors. """ def __init__(self, extended_message): """extended_message: Message instance for which we are the Extensions dict. """ self._extended_message = extended_message def __getitem__(self, extension_handle): """Returns the current value of the given extension handle.""" _VerifyExtensionHandle(self._extended_message, extension_handle) result = self._extended_message._fields.get(extension_handle) if result is not None: return result if extension_handle.label == _FieldDescriptor.LABEL_REPEATED: result = extension_handle._default_constructor(self._extended_message) elif extension_handle.cpp_type == _FieldDescriptor.CPPTYPE_MESSAGE: result = extension_handle.message_type._concrete_class() try: result._SetListener(self._extended_message._listener_for_children) except ReferenceError: pass else: return extension_handle.default_value result = self._extended_message._fields.setdefault( extension_handle, result) return result def __eq__(self, other): if not isinstance(other, self.__class__): return False my_fields = self._extended_message.ListFields() other_fields = other._extended_message.ListFields() my_fields = [ field for field in my_fields if field.is_extension ] other_fields = [ field for field in other_fields if field.is_extension ] return my_fields == other_fields def __ne__(self, other): return not self == other def __hash__(self): raise TypeError('unhashable object') def __setitem__(self, extension_handle, value): """If extension_handle specifies a non-repeated, scalar extension field, sets the value of that field. """ _VerifyExtensionHandle(self._extended_message, extension_handle) if (extension_handle.label == _FieldDescriptor.LABEL_REPEATED or extension_handle.cpp_type == _FieldDescriptor.CPPTYPE_MESSAGE): raise TypeError( 'Cannot assign to extension "%s" because it is a repeated or ' 'composite type.' % extension_handle.full_name) type_checker = type_checkers.GetTypeChecker( extension_handle) self._extended_message._fields[extension_handle] = ( type_checker.CheckValue(value)) self._extended_message._Modified() def _FindExtensionByName(self, name): """Tries to find a known extension with the specified name. Args: name: Extension full name. Returns: Extension field descriptor. """ return self._extended_message._extensions_by_name.get(name, None)

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from __future__ import absolute_import from future import standard_library standard_library.install_aliases() from builtins import str from builtins import range from builtins import object import sys import six from six.moves import range if sys.version_info[0] < 3: try: from io import StringIO as BytesIO except ImportError: from io import StringIO as BytesIO import six.moves.copyreg as copyreg else: from io import BytesIO import copyreg import struct import weakref from google.net.proto2.python.internal import containers from google.net.proto2.python.internal import decoder from google.net.proto2.python.internal import encoder from google.net.proto2.python.internal import enum_type_wrapper from google.net.proto2.python.internal import message_listener as message_listener_mod from google.net.proto2.python.internal import type_checkers from google.net.proto2.python.internal import wire_format from google.net.proto2.python.public import descriptor as descriptor_mod from google.net.proto2.python.public import message as message_mod from google.net.proto2.python.public import text_format _FieldDescriptor = descriptor_mod.FieldDescriptor def NewMessage(bases, descriptor, dictionary): _AddClassAttributesForNestedExtensions(descriptor, dictionary) _AddSlots(descriptor, dictionary) return bases def InitMessage(descriptor, cls): cls._decoders_by_tag = {} cls._extensions_by_name = {} cls._extensions_by_number = {} if (descriptor.has_options and descriptor.GetOptions().message_set_wire_format): cls._decoders_by_tag[decoder.MESSAGE_SET_ITEM_TAG] = ( decoder.MessageSetItemDecoder(cls._extensions_by_number), None) for field in descriptor.fields: _AttachFieldHelpers(cls, field) _AddEnumValues(descriptor, cls) _AddInitMethod(descriptor, cls) _AddPropertiesForFields(descriptor, cls) _AddPropertiesForExtensions(descriptor, cls) _AddStaticMethods(cls) _AddMessageMethods(descriptor, cls) _AddPrivateHelperMethods(descriptor, cls) copyreg.pickle(cls, lambda obj: (cls, (), obj.__getstate__())) def _PropertyName(proto_field_name): return proto_field_name def _VerifyExtensionHandle(message, extension_handle): if not isinstance(extension_handle, _FieldDescriptor): raise KeyError('HasExtension() expects an extension handle, got: %s' % extension_handle) if not extension_handle.is_extension: raise KeyError('"%s" is not an extension.' % extension_handle.full_name) if not extension_handle.containing_type: raise KeyError('"%s" is missing a containing_type.' % extension_handle.full_name) if extension_handle.containing_type is not message.DESCRIPTOR: raise KeyError('Extension "%s" extends message type "%s", but this ' 'message is of type "%s".' % (extension_handle.full_name, extension_handle.containing_type.full_name, message.DESCRIPTOR.full_name)) def _AddSlots(message_descriptor, dictionary): dictionary['__slots__'] = ['_cached_byte_size', '_cached_byte_size_dirty', '_fields', '_unknown_fields', '_is_present_in_parent', '_listener', '_listener_for_children', '__weakref__', '_oneofs'] def _IsMessageSetExtension(field): return (field.is_extension and field.containing_type.has_options and field.containing_type.GetOptions().message_set_wire_format and field.type == _FieldDescriptor.TYPE_MESSAGE and field.message_type == field.extension_scope and field.label == _FieldDescriptor.LABEL_OPTIONAL) def _AttachFieldHelpers(cls, field_descriptor): is_repeated = (field_descriptor.label == _FieldDescriptor.LABEL_REPEATED) is_packed = (field_descriptor.has_options and field_descriptor.GetOptions().packed) if _IsMessageSetExtension(field_descriptor): field_encoder = encoder.MessageSetItemEncoder(field_descriptor.number) sizer = encoder.MessageSetItemSizer(field_descriptor.number) else: field_encoder = type_checkers.TYPE_TO_ENCODER[field_descriptor.type]( field_descriptor.number, is_repeated, is_packed) sizer = type_checkers.TYPE_TO_SIZER[field_descriptor.type]( field_descriptor.number, is_repeated, is_packed) field_descriptor._encoder = field_encoder field_descriptor._sizer = sizer field_descriptor._default_constructor = _DefaultValueConstructorForField( field_descriptor) def AddDecoder(wiretype, is_packed): tag_bytes = encoder.TagBytes(field_descriptor.number, wiretype) cls._decoders_by_tag[tag_bytes] = ( type_checkers.TYPE_TO_DECODER[field_descriptor.type]( field_descriptor.number, is_repeated, is_packed, field_descriptor, field_descriptor._default_constructor), field_descriptor if field_descriptor.containing_oneof is not None else None) AddDecoder(type_checkers.FIELD_TYPE_TO_WIRE_TYPE[field_descriptor.type], False) if is_repeated and wire_format.IsTypePackable(field_descriptor.type): AddDecoder(wire_format.WIRETYPE_LENGTH_DELIMITED, True) def _AddClassAttributesForNestedExtensions(descriptor, dictionary): extension_dict = descriptor.extensions_by_name for extension_name, extension_field in six.iteritems(extension_dict): assert extension_name not in dictionary dictionary[extension_name] = extension_field def _AddEnumValues(descriptor, cls): for enum_type in descriptor.enum_types: setattr(cls, enum_type.name, enum_type_wrapper.EnumTypeWrapper(enum_type)) for enum_value in enum_type.values: setattr(cls, enum_value.name, enum_value.number) def _DefaultValueConstructorForField(field): if field.label == _FieldDescriptor.LABEL_REPEATED: if field.has_default_value and field.default_value != []: raise ValueError('Repeated field default value not empty list: %s' % ( field.default_value)) if field.cpp_type == _FieldDescriptor.CPPTYPE_MESSAGE: message_type = field.message_type def MakeRepeatedMessageDefault(message): return containers.RepeatedCompositeFieldContainer( message._listener_for_children, field.message_type) return MakeRepeatedMessageDefault else: type_checker = type_checkers.GetTypeChecker(field) def MakeRepeatedScalarDefault(message): return containers.RepeatedScalarFieldContainer( message._listener_for_children, type_checker) return MakeRepeatedScalarDefault if field.cpp_type == _FieldDescriptor.CPPTYPE_MESSAGE: message_type = field.message_type def MakeSubMessageDefault(message): result = message_type._concrete_class() result._SetListener(message._listener_for_children) return result return MakeSubMessageDefault def MakeScalarDefault(message): return field.default_value return MakeScalarDefault def _ReraiseTypeErrorWithFieldName(message_name, field_name): exc = sys.exc_info()[1] if len(exc.args) == 1 and type(exc) is TypeError: exc = TypeError('%s for field %s.%s' % (str(exc), message_name, field_name)) six.reraise(type(exc), exc, sys.exc_info()[2]) def _AddInitMethod(message_descriptor, cls): fields = message_descriptor.fields def init(self, **kwargs): self._cached_byte_size = 0 self._cached_byte_size_dirty = len(kwargs) > 0 self._fields = {} self._oneofs = {} self._unknown_fields = () self._is_present_in_parent = False self._listener = message_listener_mod.NullMessageListener() self._listener_for_children = _Listener(self) for field_name, field_value in six.iteritems(kwargs): field = _GetFieldByName(message_descriptor, field_name) if field is None: raise TypeError("%s() got an unexpected keyword argument '%s'" % (message_descriptor.name, field_name)) if field.label == _FieldDescriptor.LABEL_REPEATED: copy = field._default_constructor(self) if field.cpp_type == _FieldDescriptor.CPPTYPE_MESSAGE: for val in field_value: copy.add().MergeFrom(val) else: copy.extend(field_value) self._fields[field] = copy elif field.cpp_type == _FieldDescriptor.CPPTYPE_MESSAGE: copy = field._default_constructor(self) try: copy.MergeFrom(field_value) except TypeError: _ReraiseTypeErrorWithFieldName(message_descriptor.name, field_name) self._fields[field] = copy else: try: setattr(self, field_name, field_value) except TypeError: _ReraiseTypeErrorWithFieldName(message_descriptor.name, field_name) init.__module__ = None init.__doc__ = None cls.__init__ = init def _GetFieldByName(message_descriptor, field_name): try: return message_descriptor.fields_by_name[field_name] except KeyError: raise ValueError('Protocol message has no "%s" field.' % field_name) def _AddPropertiesForFields(descriptor, cls): for field in descriptor.fields: _AddPropertiesForField(field, cls) if descriptor.is_extendable: cls.Extensions = property(lambda self: _ExtensionDict(self)) def _AddPropertiesForField(field, cls): assert _FieldDescriptor.MAX_CPPTYPE == 10 constant_name = field.name.upper() + "_FIELD_NUMBER" setattr(cls, constant_name, field.number) if field.label == _FieldDescriptor.LABEL_REPEATED: _AddPropertiesForRepeatedField(field, cls) elif field.cpp_type == _FieldDescriptor.CPPTYPE_MESSAGE: _AddPropertiesForNonRepeatedCompositeField(field, cls) else: _AddPropertiesForNonRepeatedScalarField(field, cls) def _AddPropertiesForRepeatedField(field, cls): proto_field_name = field.name property_name = _PropertyName(proto_field_name) def getter(self): field_value = self._fields.get(field) if field_value is None: field_value = field._default_constructor(self) field_value = self._fields.setdefault(field, field_value) return field_value getter.__module__ = None getter.__doc__ = 'Getter for %s.' % proto_field_name def setter(self, new_value): raise AttributeError('Assignment not allowed to repeated field ' '"%s" in protocol message object.' % proto_field_name) doc = 'Magic attribute generated for "%s" proto field.' % proto_field_name setattr(cls, property_name, property(getter, setter, doc=doc)) def _AddPropertiesForNonRepeatedScalarField(field, cls): proto_field_name = field.name property_name = _PropertyName(proto_field_name) type_checker = type_checkers.GetTypeChecker(field) default_value = field.default_value valid_values = set() def getter(self): return self._fields.get(field, default_value) getter.__module__ = None getter.__doc__ = 'Getter for %s.' % proto_field_name def field_setter(self, new_value): self._fields[field] = type_checker.CheckValue(new_value) if not self._cached_byte_size_dirty: self._Modified() if field.containing_oneof is not None: def setter(self, new_value): field_setter(self, new_value) self._UpdateOneofState(field) else: setter = field_setter setter.__module__ = None setter.__doc__ = 'Setter for %s.' % proto_field_name doc = 'Magic attribute generated for "%s" proto field.' % proto_field_name setattr(cls, property_name, property(getter, setter, doc=doc)) def _AddPropertiesForNonRepeatedCompositeField(field, cls): proto_field_name = field.name property_name = _PropertyName(proto_field_name) message_type = field.message_type def getter(self): field_value = self._fields.get(field) if field_value is None: field_value = message_type._concrete_class() field_value._SetListener( _OneofListener(self, field) if field.containing_oneof is not None else self._listener_for_children) field_value = self._fields.setdefault(field, field_value) return field_value getter.__module__ = None getter.__doc__ = 'Getter for %s.' % proto_field_name def setter(self, new_value): raise AttributeError('Assignment not allowed to composite field ' '"%s" in protocol message object.' % proto_field_name) doc = 'Magic attribute generated for "%s" proto field.' % proto_field_name setattr(cls, property_name, property(getter, setter, doc=doc)) def _AddPropertiesForExtensions(descriptor, cls): extension_dict = descriptor.extensions_by_name for extension_name, extension_field in six.iteritems(extension_dict): constant_name = extension_name.upper() + "_FIELD_NUMBER" setattr(cls, constant_name, extension_field.number) def _AddStaticMethods(cls): def RegisterExtension(extension_handle): extension_handle.containing_type = cls.DESCRIPTOR _AttachFieldHelpers(cls, extension_handle) actual_handle = cls._extensions_by_number.setdefault( extension_handle.number, extension_handle) if actual_handle is not extension_handle: raise AssertionError( 'Extensions "%s" and "%s" both try to extend message type "%s" with ' 'field number %d.' % (extension_handle.full_name, actual_handle.full_name, cls.DESCRIPTOR.full_name, extension_handle.number)) cls._extensions_by_name[extension_handle.full_name] = extension_handle handle = extension_handle if _IsMessageSetExtension(handle): cls._extensions_by_name[ extension_handle.message_type.full_name] = extension_handle cls.RegisterExtension = staticmethod(RegisterExtension) def FromString(s): message = cls() message.MergeFromString(s) return message cls.FromString = staticmethod(FromString) def _IsPresent(item): if item[0].label == _FieldDescriptor.LABEL_REPEATED: return bool(item[1]) elif item[0].cpp_type == _FieldDescriptor.CPPTYPE_MESSAGE: return item[1]._is_present_in_parent else: return True def _AddListFieldsMethod(message_descriptor, cls): def ListFields(self): all_fields = [item for item in six.iteritems(self._fields) if _IsPresent(item)] all_fields.sort(key = lambda item: item[0].number) return all_fields cls.ListFields = ListFields def _AddHasFieldMethod(message_descriptor, cls): singular_fields = {} for field in message_descriptor.fields: if field.label != _FieldDescriptor.LABEL_REPEATED: singular_fields[field.name] = field for field in message_descriptor.oneofs: singular_fields[field.name] = field def HasField(self, field_name): try: field = singular_fields[field_name] except KeyError: raise ValueError( 'Protocol message has no singular "%s" field.' % field_name) if isinstance(field, descriptor_mod.OneofDescriptor): try: return HasField(self, self._oneofs[field].name) except KeyError: return False else: if field.cpp_type == _FieldDescriptor.CPPTYPE_MESSAGE: value = self._fields.get(field) return value is not None and value._is_present_in_parent else: return field in self._fields cls.HasField = HasField def _AddClearFieldMethod(message_descriptor, cls): def ClearField(self, field_name): try: field = message_descriptor.fields_by_name[field_name] except KeyError: try: field = message_descriptor.oneofs_by_name[field_name] if field in self._oneofs: field = self._oneofs[field] else: return except KeyError: raise ValueError('Protocol message has no "%s" field.' % field_name) if field in self._fields: del self._fields[field] if self._oneofs.get(field.containing_oneof, None) is field: del self._oneofs[field.containing_oneof] self._Modified() cls.ClearField = ClearField def _AddClearExtensionMethod(cls): def ClearExtension(self, extension_handle): _VerifyExtensionHandle(self, extension_handle) if extension_handle in self._fields: del self._fields[extension_handle] self._Modified() cls.ClearExtension = ClearExtension def _AddClearMethod(message_descriptor, cls): def Clear(self): self._fields = {} self._unknown_fields = () self._oneofs = {} self._Modified() cls.Clear = Clear def _AddHasExtensionMethod(cls): def HasExtension(self, extension_handle): _VerifyExtensionHandle(self, extension_handle) if extension_handle.label == _FieldDescriptor.LABEL_REPEATED: raise KeyError('"%s" is repeated.' % extension_handle.full_name) if extension_handle.cpp_type == _FieldDescriptor.CPPTYPE_MESSAGE: value = self._fields.get(extension_handle) return value is not None and value._is_present_in_parent else: return extension_handle in self._fields cls.HasExtension = HasExtension def _AddEqualsMethod(message_descriptor, cls): def __eq__(self, other): if (not isinstance(other, message_mod.Message) or other.DESCRIPTOR != self.DESCRIPTOR): return False if self is other: return True if not self.ListFields() == other.ListFields(): return False unknown_fields = list(self._unknown_fields) unknown_fields.sort() other_unknown_fields = list(other._unknown_fields) other_unknown_fields.sort() return unknown_fields == other_unknown_fields cls.__eq__ = __eq__ def _AddStrMethod(message_descriptor, cls): def __str__(self): return text_format.MessageToString(self) cls.__str__ = __str__ def _AddUnicodeMethod(unused_message_descriptor, cls): def __unicode__(self): return text_format.MessageToString(self, as_utf8=True).decode('utf-8') cls.__unicode__ = __unicode__ def _AddSetListenerMethod(cls): def SetListener(self, listener): if listener is None: self._listener = message_listener_mod.NullMessageListener() else: self._listener = listener cls._SetListener = SetListener def _BytesForNonRepeatedElement(value, field_number, field_type): try: fn = type_checkers.TYPE_TO_BYTE_SIZE_FN[field_type] return fn(field_number, value) except KeyError: raise message_mod.EncodeError('Unrecognized field type: %d' % field_type) def _AddByteSizeMethod(message_descriptor, cls): def ByteSize(self): if not self._cached_byte_size_dirty: return self._cached_byte_size size = 0 for field_descriptor, field_value in self.ListFields(): size += field_descriptor._sizer(field_value) for tag_bytes, value_bytes in self._unknown_fields: size += len(tag_bytes) + len(value_bytes) self._cached_byte_size = size self._cached_byte_size_dirty = False self._listener_for_children.dirty = False return size cls.ByteSize = ByteSize def _AddSerializeToStringMethod(message_descriptor, cls): def SerializeToString(self): errors = [] if not self.IsInitialized(): raise message_mod.EncodeError( 'Message %s is missing required fields: %s' % ( self.DESCRIPTOR.full_name, ','.join(self.FindInitializationErrors()))) return self.SerializePartialToString() cls.SerializeToString = SerializeToString def _AddSerializePartialToStringMethod(message_descriptor, cls): def SerializePartialToString(self): out = BytesIO() self._InternalSerialize(out.write) return out.getvalue() cls.SerializePartialToString = SerializePartialToString def InternalSerialize(self, write_bytes): for field_descriptor, field_value in self.ListFields(): field_descriptor._encoder(write_bytes, field_value) for tag_bytes, value_bytes in self._unknown_fields: write_bytes(tag_bytes) write_bytes(value_bytes) cls._InternalSerialize = InternalSerialize def _AddMergeFromStringMethod(message_descriptor, cls): def MergeFromString(self, serialized): length = len(serialized) try: if self._InternalParse(serialized, 0, length) != length: raise message_mod.DecodeError('Unexpected end-group tag.') except (IndexError, TypeError): raise message_mod.DecodeError('Truncated message.') except struct.error as e: raise message_mod.DecodeError(e) return length cls.MergeFromString = MergeFromString local_ReadTag = decoder.ReadTag local_SkipField = decoder.SkipField decoders_by_tag = cls._decoders_by_tag def InternalParse(self, buffer, pos, end): self._Modified() field_dict = self._fields unknown_field_list = self._unknown_fields while pos != end: (tag_bytes, new_pos) = local_ReadTag(buffer, pos) field_decoder, field_desc = decoders_by_tag.get(tag_bytes, (None, None)) if field_decoder is None: value_start_pos = new_pos new_pos = local_SkipField(buffer, new_pos, end, tag_bytes) if new_pos == -1: return pos if not unknown_field_list: unknown_field_list = self._unknown_fields = [] unknown_field_list.append((tag_bytes, buffer[value_start_pos:new_pos])) pos = new_pos else: pos = field_decoder(buffer, new_pos, end, self, field_dict) if field_desc: self._UpdateOneofState(field_desc) return pos cls._InternalParse = InternalParse def _AddIsInitializedMethod(message_descriptor, cls): required_fields = [field for field in message_descriptor.fields if field.label == _FieldDescriptor.LABEL_REQUIRED] def IsInitialized(self, errors=None): for field in required_fields: if (field not in self._fields or (field.cpp_type == _FieldDescriptor.CPPTYPE_MESSAGE and not self._fields[field]._is_present_in_parent)): if errors is not None: errors.extend(self.FindInitializationErrors()) return False for field, value in list(self._fields.items()): if field.cpp_type == _FieldDescriptor.CPPTYPE_MESSAGE: if field.label == _FieldDescriptor.LABEL_REPEATED: for element in value: if not element.IsInitialized(): if errors is not None: errors.extend(self.FindInitializationErrors()) return False elif value._is_present_in_parent and not value.IsInitialized(): if errors is not None: errors.extend(self.FindInitializationErrors()) return False return True cls.IsInitialized = IsInitialized def FindInitializationErrors(self): errors = [] for field in required_fields: if not self.HasField(field.name): errors.append(field.name) for field, value in self.ListFields(): if field.cpp_type == _FieldDescriptor.CPPTYPE_MESSAGE: if field.is_extension: name = "(%s)" % field.full_name else: name = field.name if field.label == _FieldDescriptor.LABEL_REPEATED: for i in range(len(value)): element = value[i] prefix = "%s[%d]." % (name, i) sub_errors = element.FindInitializationErrors() errors += [ prefix + error for error in sub_errors ] else: prefix = name + "." sub_errors = value.FindInitializationErrors() errors += [ prefix + error for error in sub_errors ] return errors cls.FindInitializationErrors = FindInitializationErrors def _AddMergeFromMethod(cls): LABEL_REPEATED = _FieldDescriptor.LABEL_REPEATED CPPTYPE_MESSAGE = _FieldDescriptor.CPPTYPE_MESSAGE def MergeFrom(self, msg): if not isinstance(msg, cls): raise TypeError( "Parameter to MergeFrom() must be instance of same class: " "expected %s got %s." % (cls.__name__, type(msg).__name__)) assert msg is not self self._Modified() fields = self._fields for field, value in six.iteritems(msg._fields): if field.label == LABEL_REPEATED: field_value = fields.get(field) if field_value is None: field_value = field._default_constructor(self) fields[field] = field_value field_value.MergeFrom(value) elif field.cpp_type == CPPTYPE_MESSAGE: if value._is_present_in_parent: field_value = fields.get(field) if field_value is None: field_value = field._default_constructor(self) fields[field] = field_value field_value.MergeFrom(value) else: self._fields[field] = value if msg._unknown_fields: if not self._unknown_fields: self._unknown_fields = [] self._unknown_fields.extend(msg._unknown_fields) cls.MergeFrom = MergeFrom def _AddWhichOneofMethod(message_descriptor, cls): def WhichOneof(self, oneof_name): try: field = message_descriptor.oneofs_by_name[oneof_name] except KeyError: raise ValueError( 'Protocol message has no oneof "%s" field.' % oneof_name) nested_field = self._oneofs.get(field, None) if nested_field is not None and self.HasField(nested_field.name): return nested_field.name else: return None cls.WhichOneof = WhichOneof def _AddMessageMethods(message_descriptor, cls): _AddListFieldsMethod(message_descriptor, cls) _AddHasFieldMethod(message_descriptor, cls) _AddClearFieldMethod(message_descriptor, cls) if message_descriptor.is_extendable: _AddClearExtensionMethod(cls) _AddHasExtensionMethod(cls) _AddClearMethod(message_descriptor, cls) _AddEqualsMethod(message_descriptor, cls) _AddStrMethod(message_descriptor, cls) _AddUnicodeMethod(message_descriptor, cls) _AddSetListenerMethod(cls) _AddByteSizeMethod(message_descriptor, cls) _AddSerializeToStringMethod(message_descriptor, cls) _AddSerializePartialToStringMethod(message_descriptor, cls) _AddMergeFromStringMethod(message_descriptor, cls) _AddIsInitializedMethod(message_descriptor, cls) _AddMergeFromMethod(cls) _AddWhichOneofMethod(message_descriptor, cls) def _AddPrivateHelperMethods(message_descriptor, cls): def Modified(self): if not self._cached_byte_size_dirty: self._cached_byte_size_dirty = True self._listener_for_children.dirty = True self._is_present_in_parent = True self._listener.Modified() def _UpdateOneofState(self, field): other_field = self._oneofs.setdefault(field.containing_oneof, field) if other_field is not field: del self._fields[other_field] self._oneofs[field.containing_oneof] = field cls._Modified = Modified cls.SetInParent = Modified cls._UpdateOneofState = _UpdateOneofState class _Listener(object): def __init__(self, parent_message): if isinstance(parent_message, weakref.ProxyType): self._parent_message_weakref = parent_message else: self._parent_message_weakref = weakref.proxy(parent_message) self.dirty = False def Modified(self): if self.dirty: return try: self._parent_message_weakref._Modified() except ReferenceError: pass class _OneofListener(_Listener): def __init__(self, parent_message, field): super(_OneofListener, self).__init__(parent_message) self._field = field def Modified(self): try: self._parent_message_weakref._UpdateOneofState(self._field) super(_OneofListener, self).Modified() except ReferenceError: pass class _ExtensionDict(object): def __init__(self, extended_message): self._extended_message = extended_message def __getitem__(self, extension_handle): _VerifyExtensionHandle(self._extended_message, extension_handle) result = self._extended_message._fields.get(extension_handle) if result is not None: return result if extension_handle.label == _FieldDescriptor.LABEL_REPEATED: result = extension_handle._default_constructor(self._extended_message) elif extension_handle.cpp_type == _FieldDescriptor.CPPTYPE_MESSAGE: result = extension_handle.message_type._concrete_class() try: result._SetListener(self._extended_message._listener_for_children) except ReferenceError: pass else: return extension_handle.default_value result = self._extended_message._fields.setdefault( extension_handle, result) return result def __eq__(self, other): if not isinstance(other, self.__class__): return False my_fields = self._extended_message.ListFields() other_fields = other._extended_message.ListFields() my_fields = [ field for field in my_fields if field.is_extension ] other_fields = [ field for field in other_fields if field.is_extension ] return my_fields == other_fields def __ne__(self, other): return not self == other def __hash__(self): raise TypeError('unhashable object') def __setitem__(self, extension_handle, value): _VerifyExtensionHandle(self._extended_message, extension_handle) if (extension_handle.label == _FieldDescriptor.LABEL_REPEATED or extension_handle.cpp_type == _FieldDescriptor.CPPTYPE_MESSAGE): raise TypeError( 'Cannot assign to extension "%s" because it is a repeated or ' 'composite type.' % extension_handle.full_name) type_checker = type_checkers.GetTypeChecker( extension_handle) self._extended_message._fields[extension_handle] = ( type_checker.CheckValue(value)) self._extended_message._Modified() def _FindExtensionByName(self, name): return self._extended_message._extensions_by_name.get(name, None)

true

f70f7266246a72a1f47bb872e52660dc84524048

167

py

Python

REL/ner/__init__.py

theblackcat102/REL

9daaf924d3b7ee75ba0738fd218ddbaeab989bd8

[ "MIT" ]

210

2020-02-27T14:10:57.000Z

2022-03-30T01:32:52.000Z

REL/ner/__init__.py

theblackcat102/REL

9daaf924d3b7ee75ba0738fd218ddbaeab989bd8

[ "MIT" ]

69

2020-03-06T09:58:43.000Z

2022-03-31T16:24:35.000Z

REL/ner/__init__.py

cnnlabs/REL

7e680a13fb26cb23d9ba9ea45efd01cb4c6c7871

[ "MIT" ]

57

2020-02-28T15:52:33.000Z

2022-03-16T11:28:19.000Z

#!/usr/bin/env python3 # -*- coding: utf-8 -*- from REL.ner.base import NERBase, Span from REL.ner.flair_wrapper import load_flair_ner from REL.ner.ngram import Cmns

23.857143

48

0.748503

from REL.ner.base import NERBase, Span from REL.ner.flair_wrapper import load_flair_ner from REL.ner.ngram import Cmns

true

f70f729a2c7bd23c7413efdf4accb9acf8f8503f

411

py

Python

main.py

deadaf/tickets-bot

1f72668a6843cb67daa77e911057775f9d9a37f1

[ "MIT" ]

5

2021-02-05T05:50:29.000Z

2021-08-17T16:09:59.000Z

main.py

deadaf/tickets-bot

1f72668a6843cb67daa77e911057775f9d9a37f1

[ "MIT" ]

null

main.py

deadaf/tickets-bot

1f72668a6843cb67daa77e911057775f9d9a37f1

[ "MIT" ]

3

2021-03-20T13:01:16.000Z

2022-03-05T12:38:24.000Z

import discord from discord.ext import commands from discord.ext.commands import has_permissions, MissingPermissions import json import asyncio bot = commands.Bot(command_prefix=".") bot.remove_command("help") @bot.event async def on_ready(): print("Bot running with:") print("Username: ", bot.user.name) print("User ID: ", bot.user.id) bot.load_extension('cogs.tickets') bot.run("TOKEN")

19.571429

68

0.729927

import discord from discord.ext import commands from discord.ext.commands import has_permissions, MissingPermissions import json import asyncio bot = commands.Bot(command_prefix=".") bot.remove_command("help") @bot.event async def on_ready(): print("Bot running with:") print("Username: ", bot.user.name) print("User ID: ", bot.user.id) bot.load_extension('cogs.tickets') bot.run("TOKEN")

true

f70f73cdbebb8824480c01d15f16886fcde78be7

914

py

Python

corehq/motech/dhis2/migrations/0006_sqldhis2connection.py

dimagilg/commcare-hq

ea1786238eae556bb7f1cbd8d2460171af1b619c

[ "BSD-3-Clause" ]

471

2015-01-10T02:55:01.000Z

2022-03-29T18:07:18.000Z

corehq/motech/dhis2/migrations/0006_sqldhis2connection.py

dimagilg/commcare-hq

ea1786238eae556bb7f1cbd8d2460171af1b619c

[ "BSD-3-Clause" ]

14,354

2015-01-01T07:38:23.000Z

2022-03-31T20:55:14.000Z

corehq/motech/dhis2/migrations/0006_sqldhis2connection.py

dimagilg/commcare-hq

ea1786238eae556bb7f1cbd8d2460171af1b619c

[ "BSD-3-Clause" ]

175

2015-01-06T07:16:47.000Z

2022-03-29T13:27:01.000Z

# -*- coding: utf-8 -*- # Generated by Django 1.11.26 on 2020-01-14 21:25 from __future__ import unicode_literals from django.db import migrations, models class Migration(migrations.Migration): initial = True dependencies = [ ('dhis2', '0005_delete_jsonapilog'), ] operations = [ migrations.CreateModel( name='SQLDhis2Connection', fields=[ ('id', models.AutoField(auto_created=True, primary_key=True, serialize=False, verbose_name='ID')), ('domain', models.CharField(max_length=255, unique=True)), ('server_url', models.CharField(max_length=255, null=True)), ('username', models.CharField(max_length=255)), ('password', models.CharField(max_length=255, null=True)), ('skip_cert_verify', models.BooleanField(default=False)), ], ), ]

31.517241

114

0.601751

from __future__ import unicode_literals from django.db import migrations, models class Migration(migrations.Migration): initial = True dependencies = [ ('dhis2', '0005_delete_jsonapilog'), ] operations = [ migrations.CreateModel( name='SQLDhis2Connection', fields=[ ('id', models.AutoField(auto_created=True, primary_key=True, serialize=False, verbose_name='ID')), ('domain', models.CharField(max_length=255, unique=True)), ('server_url', models.CharField(max_length=255, null=True)), ('username', models.CharField(max_length=255)), ('password', models.CharField(max_length=255, null=True)), ('skip_cert_verify', models.BooleanField(default=False)), ], ), ]

true

f70f74b17c66f2afe1b4d538e6f2c4b8da58986d

37,822

py

Python

src/pip/_internal/index/package_finder.py

Shivansh-007/pip

0c284520c6d068cb25ac89d9dbee0456c2eba23a

[ "MIT" ]

1

2022-03-14T20:15:00.000Z

src/pip/_internal/index/package_finder.py

Shivansh-007/pip

0c284520c6d068cb25ac89d9dbee0456c2eba23a

[ "MIT" ]

1

2022-01-27T19:09:25.000Z

src/pip/_internal/index/package_finder.py

Shivansh-007/pip

0c284520c6d068cb25ac89d9dbee0456c2eba23a

[ "MIT" ]

1

2021-09-27T11:14:58.000Z

"""Routines related to PyPI, indexes""" # The following comment should be removed at some point in the future. # mypy: strict-optional=False import enum import functools import itertools import logging import re from typing import FrozenSet, Iterable, List, Optional, Set, Tuple, Union from pip._vendor.packaging import specifiers from pip._vendor.packaging.tags import Tag from pip._vendor.packaging.utils import canonicalize_name from pip._vendor.packaging.version import _BaseVersion from pip._vendor.packaging.version import parse as parse_version from pip._internal.exceptions import ( BestVersionAlreadyInstalled, DistributionNotFound, InvalidWheelFilename, UnsupportedWheel, ) from pip._internal.index.collector import LinkCollector, parse_links from pip._internal.models.candidate import InstallationCandidate from pip._internal.models.format_control import FormatControl from pip._internal.models.link import Link from pip._internal.models.search_scope import SearchScope from pip._internal.models.selection_prefs import SelectionPreferences from pip._internal.models.target_python import TargetPython from pip._internal.models.wheel import Wheel from pip._internal.req import InstallRequirement from pip._internal.utils._log import getLogger from pip._internal.utils.filetypes import WHEEL_EXTENSION from pip._internal.utils.hashes import Hashes from pip._internal.utils.logging import indent_log from pip._internal.utils.misc import build_netloc from pip._internal.utils.packaging import check_requires_python from pip._internal.utils.unpacking import SUPPORTED_EXTENSIONS __all__ = ["FormatControl", "BestCandidateResult", "PackageFinder"] logger = getLogger(__name__) BuildTag = Union[Tuple[()], Tuple[int, str]] CandidateSortingKey = Tuple[int, int, int, _BaseVersion, Optional[int], BuildTag] def _check_link_requires_python( link: Link, version_info: Tuple[int, int, int], ignore_requires_python: bool = False, ) -> bool: """ Return whether the given Python version is compatible with a link's "Requires-Python" value. :param version_info: A 3-tuple of ints representing the Python major-minor-micro version to check. :param ignore_requires_python: Whether to ignore the "Requires-Python" value if the given Python version isn't compatible. """ try: is_compatible = check_requires_python( link.requires_python, version_info=version_info, ) except specifiers.InvalidSpecifier: logger.debug( "Ignoring invalid Requires-Python (%r) for link: %s", link.requires_python, link, ) else: if not is_compatible: version = ".".join(map(str, version_info)) if not ignore_requires_python: logger.verbose( "Link requires a different Python (%s not in: %r): %s", version, link.requires_python, link, ) return False logger.debug( "Ignoring failed Requires-Python check (%s not in: %r) for link: %s", version, link.requires_python, link, ) return True class LinkType(enum.Enum): candidate = enum.auto() different_project = enum.auto() yanked = enum.auto() format_unsupported = enum.auto() format_invalid = enum.auto() platform_mismatch = enum.auto() requires_python_mismatch = enum.auto() class LinkEvaluator: """ Responsible for evaluating links for a particular project. """ _py_version_re = re.compile(r"-py([123]\.?[0-9]?)$") # Don't include an allow_yanked default value to make sure each call # site considers whether yanked releases are allowed. This also causes # that decision to be made explicit in the calling code, which helps # people when reading the code. def __init__( self, project_name: str, canonical_name: str, formats: FrozenSet[str], target_python: TargetPython, allow_yanked: bool, ignore_requires_python: Optional[bool] = None, ) -> None: """ :param project_name: The user supplied package name. :param canonical_name: The canonical package name. :param formats: The formats allowed for this package. Should be a set with 'binary' or 'source' or both in it. :param target_python: The target Python interpreter to use when evaluating link compatibility. This is used, for example, to check wheel compatibility, as well as when checking the Python version, e.g. the Python version embedded in a link filename (or egg fragment) and against an HTML link's optional PEP 503 "data-requires-python" attribute. :param allow_yanked: Whether files marked as yanked (in the sense of PEP 592) are permitted to be candidates for install. :param ignore_requires_python: Whether to ignore incompatible PEP 503 "data-requires-python" values in HTML links. Defaults to False. """ if ignore_requires_python is None: ignore_requires_python = False self._allow_yanked = allow_yanked self._canonical_name = canonical_name self._ignore_requires_python = ignore_requires_python self._formats = formats self._target_python = target_python self.project_name = project_name def evaluate_link(self, link: Link) -> Tuple[LinkType, str]: """ Determine whether a link is a candidate for installation. :return: A tuple (result, detail), where *result* is an enum representing whether the evaluation found a candidate, or the reason why one is not found. If a candidate is found, *detail* will be the candidate's version string; if one is not found, it contains the reason the link fails to qualify. """ version = None if link.is_yanked and not self._allow_yanked: reason = link.yanked_reason or "<none given>" return (LinkType.yanked, f"yanked for reason: {reason}") if link.egg_fragment: egg_info = link.egg_fragment ext = link.ext else: egg_info, ext = link.splitext() if not ext: return (LinkType.format_unsupported, "not a file") if ext not in SUPPORTED_EXTENSIONS: return ( LinkType.format_unsupported, f"unsupported archive format: {ext}", ) if "binary" not in self._formats and ext == WHEEL_EXTENSION: reason = f"No binaries permitted for {self.project_name}" return (LinkType.format_unsupported, reason) if "macosx10" in link.path and ext == ".zip": return (LinkType.format_unsupported, "macosx10 one") if ext == WHEEL_EXTENSION: try: wheel = Wheel(link.filename) except InvalidWheelFilename: return ( LinkType.format_invalid, "invalid wheel filename", ) if canonicalize_name(wheel.name) != self._canonical_name: reason = f"wrong project name (not {self.project_name})" return (LinkType.different_project, reason) supported_tags = self._target_python.get_tags() if not wheel.supported(supported_tags): # Include the wheel's tags in the reason string to # simplify troubleshooting compatibility issues. file_tags = ", ".join(wheel.get_formatted_file_tags()) reason = ( f"none of the wheel's tags ({file_tags}) are compatible " f"(run pip debug --verbose to show compatible tags)" ) return (LinkType.platform_mismatch, reason) version = wheel.version # This should be up by the self.ok_binary check, but see issue 2700. if "source" not in self._formats and ext != WHEEL_EXTENSION: reason = f"No sources permitted for {self.project_name}" return (LinkType.format_unsupported, reason) if not version: version = _extract_version_from_fragment( egg_info, self._canonical_name, ) if not version: reason = f"Missing project version for {self.project_name}" return (LinkType.format_invalid, reason) match = self._py_version_re.search(version) if match: version = version[: match.start()] py_version = match.group(1) if py_version != self._target_python.py_version: return ( LinkType.platform_mismatch, "Python version is incorrect", ) supports_python = _check_link_requires_python( link, version_info=self._target_python.py_version_info, ignore_requires_python=self._ignore_requires_python, ) if not supports_python: reason = f"{version} Requires-Python {link.requires_python}" return (LinkType.requires_python_mismatch, reason) logger.debug("Found link %s, version: %s", link, version) return (LinkType.candidate, version) def filter_unallowed_hashes( candidates: List[InstallationCandidate], hashes: Hashes, project_name: str, ) -> List[InstallationCandidate]: """ Filter out candidates whose hashes aren't allowed, and return a new list of candidates. If at least one candidate has an allowed hash, then all candidates with either an allowed hash or no hash specified are returned. Otherwise, the given candidates are returned. Including the candidates with no hash specified when there is a match allows a warning to be logged if there is a more preferred candidate with no hash specified. Returning all candidates in the case of no matches lets pip report the hash of the candidate that would otherwise have been installed (e.g. permitting the user to more easily update their requirements file with the desired hash). """ if not hashes: logger.debug( "Given no hashes to check %s links for project %r: " "discarding no candidates", len(candidates), project_name, ) # Make sure we're not returning back the given value. return list(candidates) matches_or_no_digest = [] # Collect the non-matches for logging purposes. non_matches = [] match_count = 0 for candidate in candidates: link = candidate.link if not link.has_hash: pass elif link.is_hash_allowed(hashes=hashes): match_count += 1 else: non_matches.append(candidate) continue matches_or_no_digest.append(candidate) if match_count: filtered = matches_or_no_digest else: # Make sure we're not returning back the given value. filtered = list(candidates) if len(filtered) == len(candidates): discard_message = "discarding no candidates" else: discard_message = "discarding {} non-matches:\n {}".format( len(non_matches), "\n ".join(str(candidate.link) for candidate in non_matches), ) logger.debug( "Checked %s links for project %r against %s hashes " "(%s matches, %s no digest): %s", len(candidates), project_name, hashes.digest_count, match_count, len(matches_or_no_digest) - match_count, discard_message, ) return filtered class CandidatePreferences: """ Encapsulates some of the preferences for filtering and sorting InstallationCandidate objects. """ def __init__( self, prefer_binary: bool = False, allow_all_prereleases: bool = False, ) -> None: """ :param allow_all_prereleases: Whether to allow all pre-releases. """ self.allow_all_prereleases = allow_all_prereleases self.prefer_binary = prefer_binary class BestCandidateResult: """A collection of candidates, returned by `PackageFinder.find_best_candidate`. This class is only intended to be instantiated by CandidateEvaluator's `compute_best_candidate()` method. """ def __init__( self, candidates: List[InstallationCandidate], applicable_candidates: List[InstallationCandidate], best_candidate: Optional[InstallationCandidate], ) -> None: """ :param candidates: A sequence of all available candidates found. :param applicable_candidates: The applicable candidates. :param best_candidate: The most preferred candidate found, or None if no applicable candidates were found. """ assert set(applicable_candidates) <= set(candidates) if best_candidate is None: assert not applicable_candidates else: assert best_candidate in applicable_candidates self._applicable_candidates = applicable_candidates self._candidates = candidates self.best_candidate = best_candidate def iter_all(self) -> Iterable[InstallationCandidate]: """Iterate through all candidates.""" return iter(self._candidates) def iter_applicable(self) -> Iterable[InstallationCandidate]: """Iterate through the applicable candidates.""" return iter(self._applicable_candidates) class CandidateEvaluator: """ Responsible for filtering and sorting candidates for installation based on what tags are valid. """ @classmethod def create( cls, project_name: str, target_python: Optional[TargetPython] = None, prefer_binary: bool = False, allow_all_prereleases: bool = False, specifier: Optional[specifiers.BaseSpecifier] = None, hashes: Optional[Hashes] = None, ) -> "CandidateEvaluator": """Create a CandidateEvaluator object. :param target_python: The target Python interpreter to use when checking compatibility. If None (the default), a TargetPython object will be constructed from the running Python. :param specifier: An optional object implementing `filter` (e.g. `packaging.specifiers.SpecifierSet`) to filter applicable versions. :param hashes: An optional collection of allowed hashes. """ if target_python is None: target_python = TargetPython() if specifier is None: specifier = specifiers.SpecifierSet() supported_tags = target_python.get_tags() return cls( project_name=project_name, supported_tags=supported_tags, specifier=specifier, prefer_binary=prefer_binary, allow_all_prereleases=allow_all_prereleases, hashes=hashes, ) def __init__( self, project_name: str, supported_tags: List[Tag], specifier: specifiers.BaseSpecifier, prefer_binary: bool = False, allow_all_prereleases: bool = False, hashes: Optional[Hashes] = None, ) -> None: """ :param supported_tags: The PEP 425 tags supported by the target Python in order of preference (most preferred first). """ self._allow_all_prereleases = allow_all_prereleases self._hashes = hashes self._prefer_binary = prefer_binary self._project_name = project_name self._specifier = specifier self._supported_tags = supported_tags # Since the index of the tag in the _supported_tags list is used # as a priority, precompute a map from tag to index/priority to be # used in wheel.find_most_preferred_tag. self._wheel_tag_preferences = { tag: idx for idx, tag in enumerate(supported_tags) } def get_applicable_candidates( self, candidates: List[InstallationCandidate], ) -> List[InstallationCandidate]: """ Return the applicable candidates from a list of candidates. """ # Using None infers from the specifier instead. allow_prereleases = self._allow_all_prereleases or None specifier = self._specifier versions = { str(v) for v in specifier.filter( # We turn the version object into a str here because otherwise # when we're debundled but setuptools isn't, Python will see # packaging.version.Version and # pkg_resources._vendor.packaging.version.Version as different # types. This way we'll use a str as a common data interchange # format. If we stop using the pkg_resources provided specifier # and start using our own, we can drop the cast to str(). (str(c.version) for c in candidates), prereleases=allow_prereleases, ) } # Again, converting version to str to deal with debundling. applicable_candidates = [c for c in candidates if str(c.version) in versions] filtered_applicable_candidates = filter_unallowed_hashes( candidates=applicable_candidates, hashes=self._hashes, project_name=self._project_name, ) return sorted(filtered_applicable_candidates, key=self._sort_key) def _sort_key(self, candidate: InstallationCandidate) -> CandidateSortingKey: """ Function to pass as the `key` argument to a call to sorted() to sort InstallationCandidates by preference. Returns a tuple such that tuples sorting as greater using Python's default comparison operator are more preferred. The preference is as follows: First and foremost, candidates with allowed (matching) hashes are always preferred over candidates without matching hashes. This is because e.g. if the only candidate with an allowed hash is yanked, we still want to use that candidate. Second, excepting hash considerations, candidates that have been yanked (in the sense of PEP 592) are always less preferred than candidates that haven't been yanked. Then: If not finding wheels, they are sorted by version only. If finding wheels, then the sort order is by version, then: 1. existing installs 2. wheels ordered via Wheel.support_index_min(self._supported_tags) 3. source archives If prefer_binary was set, then all wheels are sorted above sources. Note: it was considered to embed this logic into the Link comparison operators, but then different sdist links with the same version, would have to be considered equal """ valid_tags = self._supported_tags support_num = len(valid_tags) build_tag: BuildTag = () binary_preference = 0 link = candidate.link if link.is_wheel: # can raise InvalidWheelFilename wheel = Wheel(link.filename) try: pri = -( wheel.find_most_preferred_tag( valid_tags, self._wheel_tag_preferences ) ) except ValueError: raise UnsupportedWheel( "{} is not a supported wheel for this platform. It " "can't be sorted.".format(wheel.filename) ) if self._prefer_binary: binary_preference = 1 if wheel.build_tag is not None: match = re.match(r"^(\d+)(.*)$", wheel.build_tag) build_tag_groups = match.groups() build_tag = (int(build_tag_groups[0]), build_tag_groups[1]) else: # sdist pri = -(support_num) has_allowed_hash = int(link.is_hash_allowed(self._hashes)) yank_value = -1 * int(link.is_yanked) # -1 for yanked. return ( has_allowed_hash, yank_value, binary_preference, candidate.version, pri, build_tag, ) def sort_best_candidate( self, candidates: List[InstallationCandidate], ) -> Optional[InstallationCandidate]: """ Return the best candidate per the instance's sort order, or None if no candidate is acceptable. """ if not candidates: return None best_candidate = max(candidates, key=self._sort_key) return best_candidate def compute_best_candidate( self, candidates: List[InstallationCandidate], ) -> BestCandidateResult: """ Compute and return a `BestCandidateResult` instance. """ applicable_candidates = self.get_applicable_candidates(candidates) best_candidate = self.sort_best_candidate(applicable_candidates) return BestCandidateResult( candidates, applicable_candidates=applicable_candidates, best_candidate=best_candidate, ) class PackageFinder: """This finds packages. This is meant to match easy_install's technique for looking for packages, by reading pages and looking for appropriate links. """ def __init__( self, link_collector: LinkCollector, target_python: TargetPython, allow_yanked: bool, use_deprecated_html5lib: bool, format_control: Optional[FormatControl] = None, candidate_prefs: Optional[CandidatePreferences] = None, ignore_requires_python: Optional[bool] = None, ) -> None: """ This constructor is primarily meant to be used by the create() class method and from tests. :param format_control: A FormatControl object, used to control the selection of source packages / binary packages when consulting the index and links. :param candidate_prefs: Options to use when creating a CandidateEvaluator object. """ if candidate_prefs is None: candidate_prefs = CandidatePreferences() format_control = format_control or FormatControl(set(), set()) self._allow_yanked = allow_yanked self._candidate_prefs = candidate_prefs self._ignore_requires_python = ignore_requires_python self._link_collector = link_collector self._target_python = target_python self._use_deprecated_html5lib = use_deprecated_html5lib self.format_control = format_control # These are boring links that have already been logged somehow. self._logged_links: Set[Tuple[Link, LinkType, str]] = set() # Don't include an allow_yanked default value to make sure each call # site considers whether yanked releases are allowed. This also causes # that decision to be made explicit in the calling code, which helps # people when reading the code. @classmethod def create( cls, link_collector: LinkCollector, selection_prefs: SelectionPreferences, target_python: Optional[TargetPython] = None, *, use_deprecated_html5lib: bool, ) -> "PackageFinder": """Create a PackageFinder. :param selection_prefs: The candidate selection preferences, as a SelectionPreferences object. :param target_python: The target Python interpreter to use when checking compatibility. If None (the default), a TargetPython object will be constructed from the running Python. """ if target_python is None: target_python = TargetPython() candidate_prefs = CandidatePreferences( prefer_binary=selection_prefs.prefer_binary, allow_all_prereleases=selection_prefs.allow_all_prereleases, ) return cls( candidate_prefs=candidate_prefs, link_collector=link_collector, target_python=target_python, allow_yanked=selection_prefs.allow_yanked, format_control=selection_prefs.format_control, ignore_requires_python=selection_prefs.ignore_requires_python, use_deprecated_html5lib=use_deprecated_html5lib, ) @property def target_python(self) -> TargetPython: return self._target_python @property def search_scope(self) -> SearchScope: return self._link_collector.search_scope @search_scope.setter def search_scope(self, search_scope: SearchScope) -> None: self._link_collector.search_scope = search_scope @property def find_links(self) -> List[str]: return self._link_collector.find_links @property def index_urls(self) -> List[str]: return self.search_scope.index_urls @property def trusted_hosts(self) -> Iterable[str]: for host_port in self._link_collector.session.pip_trusted_origins: yield build_netloc(*host_port) @property def allow_all_prereleases(self) -> bool: return self._candidate_prefs.allow_all_prereleases def set_allow_all_prereleases(self) -> None: self._candidate_prefs.allow_all_prereleases = True @property def prefer_binary(self) -> bool: return self._candidate_prefs.prefer_binary def set_prefer_binary(self) -> None: self._candidate_prefs.prefer_binary = True def requires_python_skipped_reasons(self) -> List[str]: reasons = { detail for _, result, detail in self._logged_links if result == LinkType.requires_python_mismatch } return sorted(reasons) def make_link_evaluator(self, project_name: str) -> LinkEvaluator: canonical_name = canonicalize_name(project_name) formats = self.format_control.get_allowed_formats(canonical_name) return LinkEvaluator( project_name=project_name, canonical_name=canonical_name, formats=formats, target_python=self._target_python, allow_yanked=self._allow_yanked, ignore_requires_python=self._ignore_requires_python, ) def _sort_links(self, links: Iterable[Link]) -> List[Link]: """ Returns elements of links in order, non-egg links first, egg links second, while eliminating duplicates """ eggs, no_eggs = [], [] seen: Set[Link] = set() for link in links: if link not in seen: seen.add(link) if link.egg_fragment: eggs.append(link) else: no_eggs.append(link) return no_eggs + eggs def _log_skipped_link(self, link: Link, result: LinkType, detail: str) -> None: entry = (link, result, detail) if entry not in self._logged_links: # Put the link at the end so the reason is more visible and because # the link string is usually very long. logger.debug("Skipping link: %s: %s", detail, link) self._logged_links.add(entry) def get_install_candidate( self, link_evaluator: LinkEvaluator, link: Link ) -> Optional[InstallationCandidate]: """ If the link is a candidate for install, convert it to an InstallationCandidate and return it. Otherwise, return None. """ result, detail = link_evaluator.evaluate_link(link) if result != LinkType.candidate: self._log_skipped_link(link, result, detail) return None return InstallationCandidate( name=link_evaluator.project_name, link=link, version=detail, ) def evaluate_links( self, link_evaluator: LinkEvaluator, links: Iterable[Link] ) -> List[InstallationCandidate]: """ Convert links that are candidates to InstallationCandidate objects. """ candidates = [] for link in self._sort_links(links): candidate = self.get_install_candidate(link_evaluator, link) if candidate is not None: candidates.append(candidate) return candidates def process_project_url( self, project_url: Link, link_evaluator: LinkEvaluator ) -> List[InstallationCandidate]: logger.debug( "Fetching project page and analyzing links: %s", project_url, ) html_page = self._link_collector.fetch_page(project_url) if html_page is None: return [] page_links = list(parse_links(html_page, self._use_deprecated_html5lib)) with indent_log(): package_links = self.evaluate_links( link_evaluator, links=page_links, ) return package_links @functools.lru_cache(maxsize=None) def find_all_candidates(self, project_name: str) -> List[InstallationCandidate]: """Find all available InstallationCandidate for project_name This checks index_urls and find_links. All versions found are returned as an InstallationCandidate list. See LinkEvaluator.evaluate_link() for details on which files are accepted. """ link_evaluator = self.make_link_evaluator(project_name) collected_sources = self._link_collector.collect_sources( project_name=project_name, candidates_from_page=functools.partial( self.process_project_url, link_evaluator=link_evaluator, ), ) page_candidates_it = itertools.chain.from_iterable( source.page_candidates() for sources in collected_sources for source in sources if source is not None ) page_candidates = list(page_candidates_it) file_links_it = itertools.chain.from_iterable( source.file_links() for sources in collected_sources for source in sources if source is not None ) file_candidates = self.evaluate_links( link_evaluator, sorted(file_links_it, reverse=True), ) if logger.isEnabledFor(logging.DEBUG) and file_candidates: paths = [] for candidate in file_candidates: assert candidate.link.url # we need to have a URL try: paths.append(candidate.link.file_path) except Exception: paths.append(candidate.link.url) # it's not a local file logger.debug("Local files found: %s", ", ".join(paths)) # This is an intentional priority ordering return file_candidates + page_candidates def make_candidate_evaluator( self, project_name: str, specifier: Optional[specifiers.BaseSpecifier] = None, hashes: Optional[Hashes] = None, ) -> CandidateEvaluator: """Create a CandidateEvaluator object to use.""" candidate_prefs = self._candidate_prefs return CandidateEvaluator.create( project_name=project_name, target_python=self._target_python, prefer_binary=candidate_prefs.prefer_binary, allow_all_prereleases=candidate_prefs.allow_all_prereleases, specifier=specifier, hashes=hashes, ) @functools.lru_cache(maxsize=None) def find_best_candidate( self, project_name: str, specifier: Optional[specifiers.BaseSpecifier] = None, hashes: Optional[Hashes] = None, ) -> BestCandidateResult: """Find matches for the given project and specifier. :param specifier: An optional object implementing `filter` (e.g. `packaging.specifiers.SpecifierSet`) to filter applicable versions. :return: A `BestCandidateResult` instance. """ candidates = self.find_all_candidates(project_name) candidate_evaluator = self.make_candidate_evaluator( project_name=project_name, specifier=specifier, hashes=hashes, ) return candidate_evaluator.compute_best_candidate(candidates) def find_requirement( self, req: InstallRequirement, upgrade: bool ) -> Optional[InstallationCandidate]: """Try to find a Link matching req Expects req, an InstallRequirement and upgrade, a boolean Returns a InstallationCandidate if found, Raises DistributionNotFound or BestVersionAlreadyInstalled otherwise """ hashes = req.hashes(trust_internet=False) best_candidate_result = self.find_best_candidate( req.name, specifier=req.specifier, hashes=hashes, ) best_candidate = best_candidate_result.best_candidate installed_version: Optional[_BaseVersion] = None if req.satisfied_by is not None: installed_version = req.satisfied_by.version def _format_versions(cand_iter: Iterable[InstallationCandidate]) -> str: # This repeated parse_version and str() conversion is needed to # handle different vendoring sources from pip and pkg_resources. # If we stop using the pkg_resources provided specifier and start # using our own, we can drop the cast to str(). return ( ", ".join( sorted( {str(c.version) for c in cand_iter}, key=parse_version, ) ) or "none" ) if installed_version is None and best_candidate is None: logger.critical( "Could not find a version that satisfies the requirement %s " "(from versions: %s)", req, _format_versions(best_candidate_result.iter_all()), ) raise DistributionNotFound( "No matching distribution found for {}".format(req) ) best_installed = False if installed_version and ( best_candidate is None or best_candidate.version <= installed_version ): best_installed = True if not upgrade and installed_version is not None: if best_installed: logger.debug( "Existing installed version (%s) is most up-to-date and " "satisfies requirement", installed_version, ) else: logger.debug( "Existing installed version (%s) satisfies requirement " "(most up-to-date version is %s)", installed_version, best_candidate.version, ) return None if best_installed: # We have an existing version, and its the best version logger.debug( "Installed version (%s) is most up-to-date (past versions: %s)", installed_version, _format_versions(best_candidate_result.iter_applicable()), ) raise BestVersionAlreadyInstalled logger.debug( "Using version %s (newest of versions: %s)", best_candidate.version, _format_versions(best_candidate_result.iter_applicable()), ) return best_candidate def _find_name_version_sep(fragment: str, canonical_name: str) -> int: """Find the separator's index based on the package's canonical name. :param fragment: A <package>+<version> filename "fragment" (stem) or egg fragment. :param canonical_name: The package's canonical name. This function is needed since the canonicalized name does not necessarily have the same length as the egg info's name part. An example:: >>> fragment = 'foo__bar-1.0' >>> canonical_name = 'foo-bar' >>> _find_name_version_sep(fragment, canonical_name) 8 """ # Project name and version must be separated by one single dash. Find all # occurrences of dashes; if the string in front of it matches the canonical # name, this is the one separating the name and version parts. for i, c in enumerate(fragment): if c != "-": continue if canonicalize_name(fragment[:i]) == canonical_name: return i raise ValueError(f"{fragment} does not match {canonical_name}") def _extract_version_from_fragment(fragment: str, canonical_name: str) -> Optional[str]: """Parse the version string from a <package>+<version> filename "fragment" (stem) or egg fragment. :param fragment: The string to parse. E.g. foo-2.1 :param canonical_name: The canonicalized name of the package this belongs to. """ try: version_start = _find_name_version_sep(fragment, canonical_name) + 1 except ValueError: return None version = fragment[version_start:] if not version: return None return version

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import enum import functools import itertools import logging import re from typing import FrozenSet, Iterable, List, Optional, Set, Tuple, Union from pip._vendor.packaging import specifiers from pip._vendor.packaging.tags import Tag from pip._vendor.packaging.utils import canonicalize_name from pip._vendor.packaging.version import _BaseVersion from pip._vendor.packaging.version import parse as parse_version from pip._internal.exceptions import ( BestVersionAlreadyInstalled, DistributionNotFound, InvalidWheelFilename, UnsupportedWheel, ) from pip._internal.index.collector import LinkCollector, parse_links from pip._internal.models.candidate import InstallationCandidate from pip._internal.models.format_control import FormatControl from pip._internal.models.link import Link from pip._internal.models.search_scope import SearchScope from pip._internal.models.selection_prefs import SelectionPreferences from pip._internal.models.target_python import TargetPython from pip._internal.models.wheel import Wheel from pip._internal.req import InstallRequirement from pip._internal.utils._log import getLogger from pip._internal.utils.filetypes import WHEEL_EXTENSION from pip._internal.utils.hashes import Hashes from pip._internal.utils.logging import indent_log from pip._internal.utils.misc import build_netloc from pip._internal.utils.packaging import check_requires_python from pip._internal.utils.unpacking import SUPPORTED_EXTENSIONS __all__ = ["FormatControl", "BestCandidateResult", "PackageFinder"] logger = getLogger(__name__) BuildTag = Union[Tuple[()], Tuple[int, str]] CandidateSortingKey = Tuple[int, int, int, _BaseVersion, Optional[int], BuildTag] def _check_link_requires_python( link: Link, version_info: Tuple[int, int, int], ignore_requires_python: bool = False, ) -> bool: try: is_compatible = check_requires_python( link.requires_python, version_info=version_info, ) except specifiers.InvalidSpecifier: logger.debug( "Ignoring invalid Requires-Python (%r) for link: %s", link.requires_python, link, ) else: if not is_compatible: version = ".".join(map(str, version_info)) if not ignore_requires_python: logger.verbose( "Link requires a different Python (%s not in: %r): %s", version, link.requires_python, link, ) return False logger.debug( "Ignoring failed Requires-Python check (%s not in: %r) for link: %s", version, link.requires_python, link, ) return True class LinkType(enum.Enum): candidate = enum.auto() different_project = enum.auto() yanked = enum.auto() format_unsupported = enum.auto() format_invalid = enum.auto() platform_mismatch = enum.auto() requires_python_mismatch = enum.auto() class LinkEvaluator: _py_version_re = re.compile(r"-py([123]\.?[0-9]?)$") # site considers whether yanked releases are allowed. This also causes # that decision to be made explicit in the calling code, which helps # people when reading the code. def __init__( self, project_name: str, canonical_name: str, formats: FrozenSet[str], target_python: TargetPython, allow_yanked: bool, ignore_requires_python: Optional[bool] = None, ) -> None: if ignore_requires_python is None: ignore_requires_python = False self._allow_yanked = allow_yanked self._canonical_name = canonical_name self._ignore_requires_python = ignore_requires_python self._formats = formats self._target_python = target_python self.project_name = project_name def evaluate_link(self, link: Link) -> Tuple[LinkType, str]: version = None if link.is_yanked and not self._allow_yanked: reason = link.yanked_reason or "<none given>" return (LinkType.yanked, f"yanked for reason: {reason}") if link.egg_fragment: egg_info = link.egg_fragment ext = link.ext else: egg_info, ext = link.splitext() if not ext: return (LinkType.format_unsupported, "not a file") if ext not in SUPPORTED_EXTENSIONS: return ( LinkType.format_unsupported, f"unsupported archive format: {ext}", ) if "binary" not in self._formats and ext == WHEEL_EXTENSION: reason = f"No binaries permitted for {self.project_name}" return (LinkType.format_unsupported, reason) if "macosx10" in link.path and ext == ".zip": return (LinkType.format_unsupported, "macosx10 one") if ext == WHEEL_EXTENSION: try: wheel = Wheel(link.filename) except InvalidWheelFilename: return ( LinkType.format_invalid, "invalid wheel filename", ) if canonicalize_name(wheel.name) != self._canonical_name: reason = f"wrong project name (not {self.project_name})" return (LinkType.different_project, reason) supported_tags = self._target_python.get_tags() if not wheel.supported(supported_tags): # Include the wheel's tags in the reason string to file_tags = ", ".join(wheel.get_formatted_file_tags()) reason = ( f"none of the wheel's tags ({file_tags}) are compatible " f"(run pip debug --verbose to show compatible tags)" ) return (LinkType.platform_mismatch, reason) version = wheel.version # This should be up by the self.ok_binary check, but see issue 2700. if "source" not in self._formats and ext != WHEEL_EXTENSION: reason = f"No sources permitted for {self.project_name}" return (LinkType.format_unsupported, reason) if not version: version = _extract_version_from_fragment( egg_info, self._canonical_name, ) if not version: reason = f"Missing project version for {self.project_name}" return (LinkType.format_invalid, reason) match = self._py_version_re.search(version) if match: version = version[: match.start()] py_version = match.group(1) if py_version != self._target_python.py_version: return ( LinkType.platform_mismatch, "Python version is incorrect", ) supports_python = _check_link_requires_python( link, version_info=self._target_python.py_version_info, ignore_requires_python=self._ignore_requires_python, ) if not supports_python: reason = f"{version} Requires-Python {link.requires_python}" return (LinkType.requires_python_mismatch, reason) logger.debug("Found link %s, version: %s", link, version) return (LinkType.candidate, version) def filter_unallowed_hashes( candidates: List[InstallationCandidate], hashes: Hashes, project_name: str, ) -> List[InstallationCandidate]: if not hashes: logger.debug( "Given no hashes to check %s links for project %r: " "discarding no candidates", len(candidates), project_name, ) # Make sure we're not returning back the given value. return list(candidates) matches_or_no_digest = [] non_matches = [] match_count = 0 for candidate in candidates: link = candidate.link if not link.has_hash: pass elif link.is_hash_allowed(hashes=hashes): match_count += 1 else: non_matches.append(candidate) continue matches_or_no_digest.append(candidate) if match_count: filtered = matches_or_no_digest else: filtered = list(candidates) if len(filtered) == len(candidates): discard_message = "discarding no candidates" else: discard_message = "discarding {} non-matches:\n {}".format( len(non_matches), "\n ".join(str(candidate.link) for candidate in non_matches), ) logger.debug( "Checked %s links for project %r against %s hashes " "(%s matches, %s no digest): %s", len(candidates), project_name, hashes.digest_count, match_count, len(matches_or_no_digest) - match_count, discard_message, ) return filtered class CandidatePreferences: def __init__( self, prefer_binary: bool = False, allow_all_prereleases: bool = False, ) -> None: self.allow_all_prereleases = allow_all_prereleases self.prefer_binary = prefer_binary class BestCandidateResult: def __init__( self, candidates: List[InstallationCandidate], applicable_candidates: List[InstallationCandidate], best_candidate: Optional[InstallationCandidate], ) -> None: assert set(applicable_candidates) <= set(candidates) if best_candidate is None: assert not applicable_candidates else: assert best_candidate in applicable_candidates self._applicable_candidates = applicable_candidates self._candidates = candidates self.best_candidate = best_candidate def iter_all(self) -> Iterable[InstallationCandidate]: return iter(self._candidates) def iter_applicable(self) -> Iterable[InstallationCandidate]: return iter(self._applicable_candidates) class CandidateEvaluator: @classmethod def create( cls, project_name: str, target_python: Optional[TargetPython] = None, prefer_binary: bool = False, allow_all_prereleases: bool = False, specifier: Optional[specifiers.BaseSpecifier] = None, hashes: Optional[Hashes] = None, ) -> "CandidateEvaluator": if target_python is None: target_python = TargetPython() if specifier is None: specifier = specifiers.SpecifierSet() supported_tags = target_python.get_tags() return cls( project_name=project_name, supported_tags=supported_tags, specifier=specifier, prefer_binary=prefer_binary, allow_all_prereleases=allow_all_prereleases, hashes=hashes, ) def __init__( self, project_name: str, supported_tags: List[Tag], specifier: specifiers.BaseSpecifier, prefer_binary: bool = False, allow_all_prereleases: bool = False, hashes: Optional[Hashes] = None, ) -> None: self._allow_all_prereleases = allow_all_prereleases self._hashes = hashes self._prefer_binary = prefer_binary self._project_name = project_name self._specifier = specifier self._supported_tags = supported_tags # Since the index of the tag in the _supported_tags list is used # as a priority, precompute a map from tag to index/priority to be # used in wheel.find_most_preferred_tag. self._wheel_tag_preferences = { tag: idx for idx, tag in enumerate(supported_tags) } def get_applicable_candidates( self, candidates: List[InstallationCandidate], ) -> List[InstallationCandidate]: # Using None infers from the specifier instead. allow_prereleases = self._allow_all_prereleases or None specifier = self._specifier versions = { str(v) for v in specifier.filter( # We turn the version object into a str here because otherwise # when we're debundled but setuptools isn't, Python will see # packaging.version.Version and # pkg_resources._vendor.packaging.version.Version as different # types. This way we'll use a str as a common data interchange (str(c.version) for c in candidates), prereleases=allow_prereleases, ) } applicable_candidates = [c for c in candidates if str(c.version) in versions] filtered_applicable_candidates = filter_unallowed_hashes( candidates=applicable_candidates, hashes=self._hashes, project_name=self._project_name, ) return sorted(filtered_applicable_candidates, key=self._sort_key) def _sort_key(self, candidate: InstallationCandidate) -> CandidateSortingKey: valid_tags = self._supported_tags support_num = len(valid_tags) build_tag: BuildTag = () binary_preference = 0 link = candidate.link if link.is_wheel: wheel = Wheel(link.filename) try: pri = -( wheel.find_most_preferred_tag( valid_tags, self._wheel_tag_preferences ) ) except ValueError: raise UnsupportedWheel( "{} is not a supported wheel for this platform. It " "can't be sorted.".format(wheel.filename) ) if self._prefer_binary: binary_preference = 1 if wheel.build_tag is not None: match = re.match(r"^(\d+)(.*)$", wheel.build_tag) build_tag_groups = match.groups() build_tag = (int(build_tag_groups[0]), build_tag_groups[1]) else: # sdist pri = -(support_num) has_allowed_hash = int(link.is_hash_allowed(self._hashes)) yank_value = -1 * int(link.is_yanked) # -1 for yanked. return ( has_allowed_hash, yank_value, binary_preference, candidate.version, pri, build_tag, ) def sort_best_candidate( self, candidates: List[InstallationCandidate], ) -> Optional[InstallationCandidate]: if not candidates: return None best_candidate = max(candidates, key=self._sort_key) return best_candidate def compute_best_candidate( self, candidates: List[InstallationCandidate], ) -> BestCandidateResult: applicable_candidates = self.get_applicable_candidates(candidates) best_candidate = self.sort_best_candidate(applicable_candidates) return BestCandidateResult( candidates, applicable_candidates=applicable_candidates, best_candidate=best_candidate, ) class PackageFinder: def __init__( self, link_collector: LinkCollector, target_python: TargetPython, allow_yanked: bool, use_deprecated_html5lib: bool, format_control: Optional[FormatControl] = None, candidate_prefs: Optional[CandidatePreferences] = None, ignore_requires_python: Optional[bool] = None, ) -> None: if candidate_prefs is None: candidate_prefs = CandidatePreferences() format_control = format_control or FormatControl(set(), set()) self._allow_yanked = allow_yanked self._candidate_prefs = candidate_prefs self._ignore_requires_python = ignore_requires_python self._link_collector = link_collector self._target_python = target_python self._use_deprecated_html5lib = use_deprecated_html5lib self.format_control = format_control # These are boring links that have already been logged somehow. self._logged_links: Set[Tuple[Link, LinkType, str]] = set() # Don't include an allow_yanked default value to make sure each call @classmethod def create( cls, link_collector: LinkCollector, selection_prefs: SelectionPreferences, target_python: Optional[TargetPython] = None, *, use_deprecated_html5lib: bool, ) -> "PackageFinder": if target_python is None: target_python = TargetPython() candidate_prefs = CandidatePreferences( prefer_binary=selection_prefs.prefer_binary, allow_all_prereleases=selection_prefs.allow_all_prereleases, ) return cls( candidate_prefs=candidate_prefs, link_collector=link_collector, target_python=target_python, allow_yanked=selection_prefs.allow_yanked, format_control=selection_prefs.format_control, ignore_requires_python=selection_prefs.ignore_requires_python, use_deprecated_html5lib=use_deprecated_html5lib, ) @property def target_python(self) -> TargetPython: return self._target_python @property def search_scope(self) -> SearchScope: return self._link_collector.search_scope @search_scope.setter def search_scope(self, search_scope: SearchScope) -> None: self._link_collector.search_scope = search_scope @property def find_links(self) -> List[str]: return self._link_collector.find_links @property def index_urls(self) -> List[str]: return self.search_scope.index_urls @property def trusted_hosts(self) -> Iterable[str]: for host_port in self._link_collector.session.pip_trusted_origins: yield build_netloc(*host_port) @property def allow_all_prereleases(self) -> bool: return self._candidate_prefs.allow_all_prereleases def set_allow_all_prereleases(self) -> None: self._candidate_prefs.allow_all_prereleases = True @property def prefer_binary(self) -> bool: return self._candidate_prefs.prefer_binary def set_prefer_binary(self) -> None: self._candidate_prefs.prefer_binary = True def requires_python_skipped_reasons(self) -> List[str]: reasons = { detail for _, result, detail in self._logged_links if result == LinkType.requires_python_mismatch } return sorted(reasons) def make_link_evaluator(self, project_name: str) -> LinkEvaluator: canonical_name = canonicalize_name(project_name) formats = self.format_control.get_allowed_formats(canonical_name) return LinkEvaluator( project_name=project_name, canonical_name=canonical_name, formats=formats, target_python=self._target_python, allow_yanked=self._allow_yanked, ignore_requires_python=self._ignore_requires_python, ) def _sort_links(self, links: Iterable[Link]) -> List[Link]: eggs, no_eggs = [], [] seen: Set[Link] = set() for link in links: if link not in seen: seen.add(link) if link.egg_fragment: eggs.append(link) else: no_eggs.append(link) return no_eggs + eggs def _log_skipped_link(self, link: Link, result: LinkType, detail: str) -> None: entry = (link, result, detail) if entry not in self._logged_links: logger.debug("Skipping link: %s: %s", detail, link) self._logged_links.add(entry) def get_install_candidate( self, link_evaluator: LinkEvaluator, link: Link ) -> Optional[InstallationCandidate]: result, detail = link_evaluator.evaluate_link(link) if result != LinkType.candidate: self._log_skipped_link(link, result, detail) return None return InstallationCandidate( name=link_evaluator.project_name, link=link, version=detail, ) def evaluate_links( self, link_evaluator: LinkEvaluator, links: Iterable[Link] ) -> List[InstallationCandidate]: candidates = [] for link in self._sort_links(links): candidate = self.get_install_candidate(link_evaluator, link) if candidate is not None: candidates.append(candidate) return candidates def process_project_url( self, project_url: Link, link_evaluator: LinkEvaluator ) -> List[InstallationCandidate]: logger.debug( "Fetching project page and analyzing links: %s", project_url, ) html_page = self._link_collector.fetch_page(project_url) if html_page is None: return [] page_links = list(parse_links(html_page, self._use_deprecated_html5lib)) with indent_log(): package_links = self.evaluate_links( link_evaluator, links=page_links, ) return package_links @functools.lru_cache(maxsize=None) def find_all_candidates(self, project_name: str) -> List[InstallationCandidate]: link_evaluator = self.make_link_evaluator(project_name) collected_sources = self._link_collector.collect_sources( project_name=project_name, candidates_from_page=functools.partial( self.process_project_url, link_evaluator=link_evaluator, ), ) page_candidates_it = itertools.chain.from_iterable( source.page_candidates() for sources in collected_sources for source in sources if source is not None ) page_candidates = list(page_candidates_it) file_links_it = itertools.chain.from_iterable( source.file_links() for sources in collected_sources for source in sources if source is not None ) file_candidates = self.evaluate_links( link_evaluator, sorted(file_links_it, reverse=True), ) if logger.isEnabledFor(logging.DEBUG) and file_candidates: paths = [] for candidate in file_candidates: assert candidate.link.url try: paths.append(candidate.link.file_path) except Exception: paths.append(candidate.link.url) logger.debug("Local files found: %s", ", ".join(paths)) # This is an intentional priority ordering return file_candidates + page_candidates def make_candidate_evaluator( self, project_name: str, specifier: Optional[specifiers.BaseSpecifier] = None, hashes: Optional[Hashes] = None, ) -> CandidateEvaluator: candidate_prefs = self._candidate_prefs return CandidateEvaluator.create( project_name=project_name, target_python=self._target_python, prefer_binary=candidate_prefs.prefer_binary, allow_all_prereleases=candidate_prefs.allow_all_prereleases, specifier=specifier, hashes=hashes, ) @functools.lru_cache(maxsize=None) def find_best_candidate( self, project_name: str, specifier: Optional[specifiers.BaseSpecifier] = None, hashes: Optional[Hashes] = None, ) -> BestCandidateResult: candidates = self.find_all_candidates(project_name) candidate_evaluator = self.make_candidate_evaluator( project_name=project_name, specifier=specifier, hashes=hashes, ) return candidate_evaluator.compute_best_candidate(candidates) def find_requirement( self, req: InstallRequirement, upgrade: bool ) -> Optional[InstallationCandidate]: hashes = req.hashes(trust_internet=False) best_candidate_result = self.find_best_candidate( req.name, specifier=req.specifier, hashes=hashes, ) best_candidate = best_candidate_result.best_candidate installed_version: Optional[_BaseVersion] = None if req.satisfied_by is not None: installed_version = req.satisfied_by.version def _format_versions(cand_iter: Iterable[InstallationCandidate]) -> str: # This repeated parse_version and str() conversion is needed to # handle different vendoring sources from pip and pkg_resources. # If we stop using the pkg_resources provided specifier and start # using our own, we can drop the cast to str(). return ( ", ".join( sorted( {str(c.version) for c in cand_iter}, key=parse_version, ) ) or "none" ) if installed_version is None and best_candidate is None: logger.critical( "Could not find a version that satisfies the requirement %s " "(from versions: %s)", req, _format_versions(best_candidate_result.iter_all()), ) raise DistributionNotFound( "No matching distribution found for {}".format(req) ) best_installed = False if installed_version and ( best_candidate is None or best_candidate.version <= installed_version ): best_installed = True if not upgrade and installed_version is not None: if best_installed: logger.debug( "Existing installed version (%s) is most up-to-date and " "satisfies requirement", installed_version, ) else: logger.debug( "Existing installed version (%s) satisfies requirement " "(most up-to-date version is %s)", installed_version, best_candidate.version, ) return None if best_installed: # We have an existing version, and its the best version logger.debug( "Installed version (%s) is most up-to-date (past versions: %s)", installed_version, _format_versions(best_candidate_result.iter_applicable()), ) raise BestVersionAlreadyInstalled logger.debug( "Using version %s (newest of versions: %s)", best_candidate.version, _format_versions(best_candidate_result.iter_applicable()), ) return best_candidate def _find_name_version_sep(fragment: str, canonical_name: str) -> int: # Project name and version must be separated by one single dash. Find all # occurrences of dashes; if the string in front of it matches the canonical # name, this is the one separating the name and version parts. for i, c in enumerate(fragment): if c != "-": continue if canonicalize_name(fragment[:i]) == canonical_name: return i raise ValueError(f"{fragment} does not match {canonical_name}") def _extract_version_from_fragment(fragment: str, canonical_name: str) -> Optional[str]: try: version_start = _find_name_version_sep(fragment, canonical_name) + 1 except ValueError: return None version = fragment[version_start:] if not version: return None return version

true

f70f74c49693a3c8825166a36842545400ad789b

2,992

py

Python

algorithms/anchor_detector.py

songheony/AAA-multi

80a988d8d312664d8ca19dee82c844183cf4f55d

[ "MIT" ]

null

algorithms/anchor_detector.py

songheony/AAA-multi

80a988d8d312664d8ca19dee82c844183cf4f55d

[ "MIT" ]

null

algorithms/anchor_detector.py

songheony/AAA-multi

80a988d8d312664d8ca19dee82c844183cf4f55d

[ "MIT" ]

1

2021-03-01T06:58:15.000Z

from .aaa_util import eval_results, get_summary, convert_df class AnchorDetector: def __init__(self, offline): self.offline = offline def initialize(self, seq_info): self.seq_info = seq_info self.previous_offline = None def fixed_detect(self, frame_idx, duration): feedback_length = duration if (frame_idx + 1) % duration == 0: is_anchor, feedback = ( True, self._get_feedback(frame_idx - duration + 1, frame_idx), ) else: is_anchor, feedback = False, None return is_anchor, feedback, feedback_length def stable_detect(self, seq_info, frame_idx, duration, threshold): if frame_idx + 1 > duration: current_offline = self._get_feedback(frame_idx - duration + 1, frame_idx) if self.previous_offline is not None and current_offline is not None: overlap_previous = self.previous_offline[ self.previous_offline[:, 0] > 1 ] overlap_previous[:, 0] -= 1 overlap_previous = convert_df(overlap_previous, is_offline=True) overlap_current = current_offline[current_offline[:, 0] < duration] overlap_current = convert_df(overlap_current, is_offline=True) feedback_length = duration else: current_offline = self._get_feedback(0, frame_idx) if self.previous_offline is not None and current_offline is not None: overlap_previous = convert_df(self.previous_offline, is_offline=True) overlap_current = current_offline[current_offline[:, 0] <= frame_idx] overlap_current = convert_df(overlap_current, is_offline=True) feedback_length = frame_idx + 1 if self.previous_offline is not None and current_offline is not None: prev_acc, prev_ana, _ = eval_results( seq_info, overlap_previous, overlap_current ) prev_sum = get_summary(prev_acc, prev_ana) curr_acc, curr_ana, _ = eval_results( seq_info, overlap_current, overlap_previous ) curr_sum = get_summary(curr_acc, curr_ana) mean_mota = (prev_sum[3] + curr_sum[3]) / 2 if mean_mota >= threshold: is_anchor = True feedback = current_offline else: is_anchor = False feedback = None # print(f"Frame {frame_idx}, MOTA {mean_mota}") else: is_anchor = False feedback = None self.previous_offline = current_offline return is_anchor, feedback, feedback_length def _get_feedback(self, start_frame, end_frame): try: feedback = self.offline.track(start_frame, end_frame) except (RuntimeError, ValueError): feedback = None return feedback

35.2

85

0.60127

from .aaa_util import eval_results, get_summary, convert_df class AnchorDetector: def __init__(self, offline): self.offline = offline def initialize(self, seq_info): self.seq_info = seq_info self.previous_offline = None def fixed_detect(self, frame_idx, duration): feedback_length = duration if (frame_idx + 1) % duration == 0: is_anchor, feedback = ( True, self._get_feedback(frame_idx - duration + 1, frame_idx), ) else: is_anchor, feedback = False, None return is_anchor, feedback, feedback_length def stable_detect(self, seq_info, frame_idx, duration, threshold): if frame_idx + 1 > duration: current_offline = self._get_feedback(frame_idx - duration + 1, frame_idx) if self.previous_offline is not None and current_offline is not None: overlap_previous = self.previous_offline[ self.previous_offline[:, 0] > 1 ] overlap_previous[:, 0] -= 1 overlap_previous = convert_df(overlap_previous, is_offline=True) overlap_current = current_offline[current_offline[:, 0] < duration] overlap_current = convert_df(overlap_current, is_offline=True) feedback_length = duration else: current_offline = self._get_feedback(0, frame_idx) if self.previous_offline is not None and current_offline is not None: overlap_previous = convert_df(self.previous_offline, is_offline=True) overlap_current = current_offline[current_offline[:, 0] <= frame_idx] overlap_current = convert_df(overlap_current, is_offline=True) feedback_length = frame_idx + 1 if self.previous_offline is not None and current_offline is not None: prev_acc, prev_ana, _ = eval_results( seq_info, overlap_previous, overlap_current ) prev_sum = get_summary(prev_acc, prev_ana) curr_acc, curr_ana, _ = eval_results( seq_info, overlap_current, overlap_previous ) curr_sum = get_summary(curr_acc, curr_ana) mean_mota = (prev_sum[3] + curr_sum[3]) / 2 if mean_mota >= threshold: is_anchor = True feedback = current_offline else: is_anchor = False feedback = None else: is_anchor = False feedback = None self.previous_offline = current_offline return is_anchor, feedback, feedback_length def _get_feedback(self, start_frame, end_frame): try: feedback = self.offline.track(start_frame, end_frame) except (RuntimeError, ValueError): feedback = None return feedback

true

f70f74dd4d6743c0e5a61696e8f4284ad3a589ae

300

py

Python

maxsmi/tests/test_maxsmi.py

t-kimber/maxsmi

d7d52a9ba95efb6b4219928425bb5de965c4b3b5

[ "MIT" ]

1

2021-01-22T17:56:54.000Z

maxsmi/tests/test_maxsmi.py

t-kimber/maxsmi

d7d52a9ba95efb6b4219928425bb5de965c4b3b5

[ "MIT" ]

12

2020-10-16T10:13:56.000Z

2021-04-14T07:25:05.000Z

maxsmi/tests/test_maxsmi.py

t-kimber/maxsmi

d7d52a9ba95efb6b4219928425bb5de965c4b3b5

[ "MIT" ]

null

""" Unit and regression test for the maxsmi package. """ # Import package, test suite, and other packages as needed # import maxsmi # import pytest import sys def test_maxsmi_imported(): """Sample test, will always pass so long as import statement worked""" assert "maxsmi" in sys.modules

21.428571

74

0.73

import sys def test_maxsmi_imported(): assert "maxsmi" in sys.modules

true

f70f75b56fefe5b1ddf643a702b9df5d8dde9dd9

1,130

py

Python

catalyst/assets/__init__.py

guilhermeprokisch/catalyst

21e096b261912d9e905584178d6ee626072c23cb

[ "Apache-2.0" ]

null

catalyst/assets/__init__.py

guilhermeprokisch/catalyst

21e096b261912d9e905584178d6ee626072c23cb

[ "Apache-2.0" ]

null

catalyst/assets/__init__.py

guilhermeprokisch/catalyst

21e096b261912d9e905584178d6ee626072c23cb

[ "Apache-2.0" ]

null

# # Copyright 2015 Quantopian, Inc. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. from ._assets import ( Asset, Equity, Future, make_asset_array, CACHE_FILE_TEMPLATE ) from .assets import ( AssetFinder, AssetConvertible, PricingDataAssociable, ) from .asset_db_schema import ASSET_DB_VERSION from .asset_writer import AssetDBWriter __all__ = [ 'ASSET_DB_VERSION', 'Asset', 'AssetDBWriter', 'Equity', 'Future', 'AssetFinder', 'AssetConvertible', 'PricingDataAssociable', 'make_asset_array', 'CACHE_FILE_TEMPLATE' ]

26.27907

75

0.69646

from ._assets import ( Asset, Equity, Future, make_asset_array, CACHE_FILE_TEMPLATE ) from .assets import ( AssetFinder, AssetConvertible, PricingDataAssociable, ) from .asset_db_schema import ASSET_DB_VERSION from .asset_writer import AssetDBWriter __all__ = [ 'ASSET_DB_VERSION', 'Asset', 'AssetDBWriter', 'Equity', 'Future', 'AssetFinder', 'AssetConvertible', 'PricingDataAssociable', 'make_asset_array', 'CACHE_FILE_TEMPLATE' ]

true

f70f76797352e63f38e9029343013e191ad89605

3,744

py

Python

ansible/plugins/action/sros.py

EnjoyLifeFund/macHighSierra-py36-pkgs

5668b5785296b314ea1321057420bcd077dba9ea

[ "BSD-3-Clause", "BSD-2-Clause", "MIT" ]

1

2022-01-25T22:52:58.000Z

ansible/plugins/action/sros.py

EnjoyLifeFund/Debian_py36_packages

1985d4c73fabd5f08f54b922e73a9306e09c77a5

[ "BSD-3-Clause", "BSD-2-Clause", "MIT" ]

null

ansible/plugins/action/sros.py

EnjoyLifeFund/Debian_py36_packages

1985d4c73fabd5f08f54b922e73a9306e09c77a5

[ "BSD-3-Clause", "BSD-2-Clause", "MIT" ]

null

# # (c) 2016 Red Hat Inc. # # This file is part of Ansible # # Ansible is free software: you can redistribute it and/or modify # it under the terms of the GNU General Public License as published by # the Free Software Foundation, either version 3 of the License, or # (at your option) any later version. # # Ansible is distributed in the hope that it will be useful, # but WITHOUT ANY WARRANTY; without even the implied warranty of # MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the # GNU General Public License for more details. # # You should have received a copy of the GNU General Public License # along with Ansible. If not, see <http://www.gnu.org/licenses/>. # from __future__ import (absolute_import, division, print_function) __metaclass__ = type import sys import copy from ansible import constants as C from ansible.plugins.action.normal import ActionModule as _ActionModule from ansible.module_utils.sros import sros_argument_spec from ansible.module_utils.basic import AnsibleFallbackNotFound from ansible.module_utils.six import iteritems try: from __main__ import display except ImportError: from ansible.utils.display import Display display = Display() class ActionModule(_ActionModule): def run(self, tmp=None, task_vars=None): if self._play_context.connection != 'local': return dict( failed=True, msg='invalid connection specified, expected connection=local, ' 'got %s' % self._play_context.connection ) provider = self.load_provider() pc = copy.deepcopy(self._play_context) pc.connection = 'network_cli' pc.network_os = 'sros' pc.remote_addr = provider['host'] or self._play_context.remote_addr pc.port = int(provider['port'] or self._play_context.port or 22) pc.remote_user = provider['username'] or self._play_context.connection_user pc.password = provider['password'] or self._play_context.password pc.private_key_file = provider['ssh_keyfile'] or self._play_context.private_key_file pc.timeout = int(provider['timeout'] or C.PERSISTENT_COMMAND_TIMEOUT) display.vvv('using connection plugin %s' % pc.connection, pc.remote_addr) connection = self._shared_loader_obj.connection_loader.get('persistent', pc, sys.stdin) socket_path = connection.run() display.vvvv('socket_path: %s' % socket_path, pc.remote_addr) if not socket_path: return {'failed': True, 'msg': 'unable to open shell. Please see: ' + 'https://docs.ansible.com/ansible/network_debug_troubleshooting.html#unable-to-open-shell'} task_vars['ansible_socket'] = socket_path result = super(ActionModule, self).run(tmp, task_vars) return result def load_provider(self): provider = self._task.args.get('provider', {}) for key, value in iteritems(sros_argument_spec): if key != 'provider' and key not in provider: if key in self._task.args: provider[key] = self._task.args[key] elif 'fallback' in value: provider[key] = self._fallback(value['fallback']) elif key not in provider: provider[key] = None return provider def _fallback(self, fallback): strategy = fallback[0] args = [] kwargs = {} for item in fallback[1:]: if isinstance(item, dict): kwargs = item else: args = item try: return strategy(*args, **kwargs) except AnsibleFallbackNotFound: pass

36.705882

118

0.654647

from __future__ import (absolute_import, division, print_function) __metaclass__ = type import sys import copy from ansible import constants as C from ansible.plugins.action.normal import ActionModule as _ActionModule from ansible.module_utils.sros import sros_argument_spec from ansible.module_utils.basic import AnsibleFallbackNotFound from ansible.module_utils.six import iteritems try: from __main__ import display except ImportError: from ansible.utils.display import Display display = Display() class ActionModule(_ActionModule): def run(self, tmp=None, task_vars=None): if self._play_context.connection != 'local': return dict( failed=True, msg='invalid connection specified, expected connection=local, ' 'got %s' % self._play_context.connection ) provider = self.load_provider() pc = copy.deepcopy(self._play_context) pc.connection = 'network_cli' pc.network_os = 'sros' pc.remote_addr = provider['host'] or self._play_context.remote_addr pc.port = int(provider['port'] or self._play_context.port or 22) pc.remote_user = provider['username'] or self._play_context.connection_user pc.password = provider['password'] or self._play_context.password pc.private_key_file = provider['ssh_keyfile'] or self._play_context.private_key_file pc.timeout = int(provider['timeout'] or C.PERSISTENT_COMMAND_TIMEOUT) display.vvv('using connection plugin %s' % pc.connection, pc.remote_addr) connection = self._shared_loader_obj.connection_loader.get('persistent', pc, sys.stdin) socket_path = connection.run() display.vvvv('socket_path: %s' % socket_path, pc.remote_addr) if not socket_path: return {'failed': True, 'msg': 'unable to open shell. Please see: ' + 'https://docs.ansible.com/ansible/network_debug_troubleshooting.html#unable-to-open-shell'} task_vars['ansible_socket'] = socket_path result = super(ActionModule, self).run(tmp, task_vars) return result def load_provider(self): provider = self._task.args.get('provider', {}) for key, value in iteritems(sros_argument_spec): if key != 'provider' and key not in provider: if key in self._task.args: provider[key] = self._task.args[key] elif 'fallback' in value: provider[key] = self._fallback(value['fallback']) elif key not in provider: provider[key] = None return provider def _fallback(self, fallback): strategy = fallback[0] args = [] kwargs = {} for item in fallback[1:]: if isinstance(item, dict): kwargs = item else: args = item try: return strategy(*args, **kwargs) except AnsibleFallbackNotFound: pass

true

f70f78983d59b550f32fb5bc0b61b997923e1baf

11,459

py

Python

graphsense/model/entity_tags.py

iknaio/graphsense-python

b61c66b6ec0bb9720036ae61777e90ce63a971cc

[ "MIT" ]

null

graphsense/model/entity_tags.py

iknaio/graphsense-python

b61c66b6ec0bb9720036ae61777e90ce63a971cc

[ "MIT" ]

1

2022-02-24T11:21:49.000Z

graphsense/model/entity_tags.py

INTERPOL-Innovation-Centre/GraphSense-Maltego-transform

2a9b352289ab64903a7012c5d84cb4c6d8172ade

[ "MIT" ]

null

""" GraphSense API GraphSense API # noqa: E501 The version of the OpenAPI document: 0.5.1 Generated by: https://openapi-generator.tech """ import re # noqa: F401 import sys # noqa: F401 from graphsense.model_utils import ( # noqa: F401 ApiTypeError, ModelComposed, ModelNormal, ModelSimple, cached_property, change_keys_js_to_python, convert_js_args_to_python_args, date, datetime, file_type, none_type, validate_get_composed_info, ) from ..model_utils import OpenApiModel from graphsense.exceptions import ApiAttributeError def lazy_import(): from graphsense.model.entity_tag import EntityTag globals()['EntityTag'] = EntityTag class EntityTags(ModelNormal): """NOTE: This class is auto generated by OpenAPI Generator. Ref: https://openapi-generator.tech Do not edit the class manually. Attributes: allowed_values (dict): The key is the tuple path to the attribute and the for var_name this is (var_name,). The value is a dict with a capitalized key describing the allowed value and an allowed value. These dicts store the allowed enum values. attribute_map (dict): The key is attribute name and the value is json key in definition. discriminator_value_class_map (dict): A dict to go from the discriminator variable value to the discriminator class name. validations (dict): The key is the tuple path to the attribute and the for var_name this is (var_name,). The value is a dict that stores validations for max_length, min_length, max_items, min_items, exclusive_maximum, inclusive_maximum, exclusive_minimum, inclusive_minimum, and regex. additional_properties_type (tuple): A tuple of classes accepted as additional properties values. """ allowed_values = { } validations = { } @cached_property def additional_properties_type(): """ This must be a method because a model may have properties that are of type self, this must run after the class is loaded """ lazy_import() return (bool, date, datetime, dict, float, int, list, str, none_type,) # noqa: E501 _nullable = False @cached_property def openapi_types(): """ This must be a method because a model may have properties that are of type self, this must run after the class is loaded Returns openapi_types (dict): The key is attribute name and the value is attribute type. """ lazy_import() return { 'entity_tags': ([EntityTag],), # noqa: E501 'next_page': (str,), # noqa: E501 } @cached_property def discriminator(): return None attribute_map = { 'entity_tags': 'entity_tags', # noqa: E501 'next_page': 'next_page', # noqa: E501 } read_only_vars = { } _composed_schemas = {} @classmethod @convert_js_args_to_python_args def _from_openapi_data(cls, entity_tags, *args, **kwargs): # noqa: E501 """EntityTags - a model defined in OpenAPI Args: entity_tags ([EntityTag]): Keyword Args: _check_type (bool): if True, values for parameters in openapi_types will be type checked and a TypeError will be raised if the wrong type is input. Defaults to True _path_to_item (tuple/list): This is a list of keys or values to drill down to the model in received_data when deserializing a response _spec_property_naming (bool): True if the variable names in the input data are serialized names, as specified in the OpenAPI document. False if the variable names in the input data are pythonic names, e.g. snake case (default) _configuration (Configuration): the instance to use when deserializing a file_type parameter. If passed, type conversion is attempted If omitted no type conversion is done. _visited_composed_classes (tuple): This stores a tuple of classes that we have traveled through so that if we see that class again we will not use its discriminator again. When traveling through a discriminator, the composed schema that is is traveled through is added to this set. For example if Animal has a discriminator petType and we pass in "Dog", and the class Dog allOf includes Animal, we move through Animal once using the discriminator, and pick Dog. Then in Dog, we will make an instance of the Animal class but this time we won't travel through its discriminator because we passed in _visited_composed_classes = (Animal,) next_page (str): [optional] # noqa: E501 """ _check_type = kwargs.pop('_check_type', True) _spec_property_naming = kwargs.pop('_spec_property_naming', False) _path_to_item = kwargs.pop('_path_to_item', ()) _configuration = kwargs.pop('_configuration', None) _visited_composed_classes = kwargs.pop('_visited_composed_classes', ()) self = super(OpenApiModel, cls).__new__(cls) if args: raise ApiTypeError( "Invalid positional arguments=%s passed to %s. Remove those invalid positional arguments." % ( args, self.__class__.__name__, ), path_to_item=_path_to_item, valid_classes=(self.__class__,), ) self._data_store = {} self._check_type = _check_type self._spec_property_naming = _spec_property_naming self._path_to_item = _path_to_item self._configuration = _configuration self._visited_composed_classes = _visited_composed_classes + (self.__class__,) self.entity_tags = entity_tags for var_name, var_value in kwargs.items(): if var_name not in self.attribute_map and \ self._configuration is not None and \ self._configuration.discard_unknown_keys and \ self.additional_properties_type is None: # discard variable. continue setattr(self, var_name, var_value) return self required_properties = set([ '_data_store', '_check_type', '_spec_property_naming', '_path_to_item', '_configuration', '_visited_composed_classes', ]) @convert_js_args_to_python_args def __init__(self, entity_tags, *args, **kwargs): # noqa: E501 """EntityTags - a model defined in OpenAPI Args: entity_tags ([EntityTag]): Keyword Args: _check_type (bool): if True, values for parameters in openapi_types will be type checked and a TypeError will be raised if the wrong type is input. Defaults to True _path_to_item (tuple/list): This is a list of keys or values to drill down to the model in received_data when deserializing a response _spec_property_naming (bool): True if the variable names in the input data are serialized names, as specified in the OpenAPI document. False if the variable names in the input data are pythonic names, e.g. snake case (default) _configuration (Configuration): the instance to use when deserializing a file_type parameter. If passed, type conversion is attempted If omitted no type conversion is done. _visited_composed_classes (tuple): This stores a tuple of classes that we have traveled through so that if we see that class again we will not use its discriminator again. When traveling through a discriminator, the composed schema that is is traveled through is added to this set. For example if Animal has a discriminator petType and we pass in "Dog", and the class Dog allOf includes Animal, we move through Animal once using the discriminator, and pick Dog. Then in Dog, we will make an instance of the Animal class but this time we won't travel through its discriminator because we passed in _visited_composed_classes = (Animal,) next_page (str): [optional] # noqa: E501 """ _check_type = kwargs.pop('_check_type', True) _spec_property_naming = kwargs.pop('_spec_property_naming', False) _path_to_item = kwargs.pop('_path_to_item', ()) _configuration = kwargs.pop('_configuration', None) _visited_composed_classes = kwargs.pop('_visited_composed_classes', ()) if args: raise ApiTypeError( "Invalid positional arguments=%s passed to %s. Remove those invalid positional arguments." % ( args, self.__class__.__name__, ), path_to_item=_path_to_item, valid_classes=(self.__class__,), ) self._data_store = {} self._check_type = _check_type self._spec_property_naming = _spec_property_naming self._path_to_item = _path_to_item self._configuration = _configuration self._visited_composed_classes = _visited_composed_classes + (self.__class__,) self.entity_tags = entity_tags for var_name, var_value in kwargs.items(): if var_name not in self.attribute_map and \ self._configuration is not None and \ self._configuration.discard_unknown_keys and \ self.additional_properties_type is None: # discard variable. continue setattr(self, var_name, var_value) if var_name in self.read_only_vars: raise ApiAttributeError(f"`{var_name}` is a read-only attribute. Use `from_openapi_data` to instantiate " f"class with read only attributes.")

42.128676

121

0.569421

import re import sys from graphsense.model_utils import ( ApiTypeError, ModelComposed, ModelNormal, ModelSimple, cached_property, change_keys_js_to_python, convert_js_args_to_python_args, date, datetime, file_type, none_type, validate_get_composed_info, ) from ..model_utils import OpenApiModel from graphsense.exceptions import ApiAttributeError def lazy_import(): from graphsense.model.entity_tag import EntityTag globals()['EntityTag'] = EntityTag class EntityTags(ModelNormal): allowed_values = { } validations = { } @cached_property def additional_properties_type(): lazy_import() return (bool, date, datetime, dict, float, int, list, str, none_type,) _nullable = False @cached_property def openapi_types(): lazy_import() return { 'entity_tags': ([EntityTag],), 'next_page': (str,), } @cached_property def discriminator(): return None attribute_map = { 'entity_tags': 'entity_tags', 'next_page': 'next_page', } read_only_vars = { } _composed_schemas = {} @classmethod @convert_js_args_to_python_args def _from_openapi_data(cls, entity_tags, *args, **kwargs): _check_type = kwargs.pop('_check_type', True) _spec_property_naming = kwargs.pop('_spec_property_naming', False) _path_to_item = kwargs.pop('_path_to_item', ()) _configuration = kwargs.pop('_configuration', None) _visited_composed_classes = kwargs.pop('_visited_composed_classes', ()) self = super(OpenApiModel, cls).__new__(cls) if args: raise ApiTypeError( "Invalid positional arguments=%s passed to %s. Remove those invalid positional arguments." % ( args, self.__class__.__name__, ), path_to_item=_path_to_item, valid_classes=(self.__class__,), ) self._data_store = {} self._check_type = _check_type self._spec_property_naming = _spec_property_naming self._path_to_item = _path_to_item self._configuration = _configuration self._visited_composed_classes = _visited_composed_classes + (self.__class__,) self.entity_tags = entity_tags for var_name, var_value in kwargs.items(): if var_name not in self.attribute_map and \ self._configuration is not None and \ self._configuration.discard_unknown_keys and \ self.additional_properties_type is None: continue setattr(self, var_name, var_value) return self required_properties = set([ '_data_store', '_check_type', '_spec_property_naming', '_path_to_item', '_configuration', '_visited_composed_classes', ]) @convert_js_args_to_python_args def __init__(self, entity_tags, *args, **kwargs): _check_type = kwargs.pop('_check_type', True) _spec_property_naming = kwargs.pop('_spec_property_naming', False) _path_to_item = kwargs.pop('_path_to_item', ()) _configuration = kwargs.pop('_configuration', None) _visited_composed_classes = kwargs.pop('_visited_composed_classes', ()) if args: raise ApiTypeError( "Invalid positional arguments=%s passed to %s. Remove those invalid positional arguments." % ( args, self.__class__.__name__, ), path_to_item=_path_to_item, valid_classes=(self.__class__,), ) self._data_store = {} self._check_type = _check_type self._spec_property_naming = _spec_property_naming self._path_to_item = _path_to_item self._configuration = _configuration self._visited_composed_classes = _visited_composed_classes + (self.__class__,) self.entity_tags = entity_tags for var_name, var_value in kwargs.items(): if var_name not in self.attribute_map and \ self._configuration is not None and \ self._configuration.discard_unknown_keys and \ self.additional_properties_type is None: continue setattr(self, var_name, var_value) if var_name in self.read_only_vars: raise ApiAttributeError(f"`{var_name}` is a read-only attribute. Use `from_openapi_data` to instantiate " f"class with read only attributes.")

true

f70f79489ce5d6799a41c07cf1757179521b1ce4

2,632

py

Python

the_mechanic_backend/apps/stock/migrations/0003_sparecustomer_spareorder_sparesold.py

muthukumar4999/the-mechanic-backend

1e31affddf60d2de72445a85dd2055bdeba6f670

[ "MIT" ]

null

the_mechanic_backend/apps/stock/migrations/0003_sparecustomer_spareorder_sparesold.py

muthukumar4999/the-mechanic-backend

1e31affddf60d2de72445a85dd2055bdeba6f670

[ "MIT" ]

5

2020-06-05T22:30:20.000Z

2021-09-08T01:12:27.000Z

the_mechanic_backend/apps/stock/migrations/0003_sparecustomer_spareorder_sparesold.py

muthukumar4999/the-mechanic-backend

1e31affddf60d2de72445a85dd2055bdeba6f670

[ "MIT" ]

null

# Generated by Django 2.1.5 on 2019-03-31 18:24 from django.conf import settings from django.db import migrations, models import django.db.models.deletion class Migration(migrations.Migration): dependencies = [ ('accounts', '0001_initial'), migrations.swappable_dependency(settings.AUTH_USER_MODEL), ('stock', '0002_spare_store'), ] operations = [ migrations.CreateModel( name='SpareCustomer', fields=[ ('id', models.AutoField(auto_created=True, primary_key=True, serialize=False, verbose_name='ID')), ('name', models.CharField(max_length=100)), ('email', models.CharField(max_length=100)), ('phone_number', models.CharField(max_length=10)), ('address', models.TextField()), ], ), migrations.CreateModel( name='SpareOrder', fields=[ ('id', models.AutoField(auto_created=True, primary_key=True, serialize=False, verbose_name='ID')), ('order_id', models.CharField(max_length=20)), ('order_type', models.CharField(choices=[('IN_SOURCE', 'IN_SOURCE'), ('OUT_SOURCE', 'OUT_SOURCE')], max_length=20)), ('total', models.DecimalField(decimal_places=2, max_digits=10)), ('order_date', models.DateTimeField(auto_now=True)), ('customer', models.ForeignKey(on_delete=django.db.models.deletion.CASCADE, to='stock.SpareCustomer')), ('sold_by', models.ForeignKey(blank=True, null=True, on_delete=django.db.models.deletion.SET_NULL, to=settings.AUTH_USER_MODEL)), ('store', models.ForeignKey(on_delete=django.db.models.deletion.CASCADE, to='accounts.Store')), ], ), migrations.CreateModel( name='SpareSold', fields=[ ('id', models.AutoField(auto_created=True, primary_key=True, serialize=False, verbose_name='ID')), ('spare_count', models.IntegerField()), ('spare_name', models.CharField(max_length=100)), ('spare_price', models.DecimalField(decimal_places=2, max_digits=10)), ('spare_price_type', models.CharField(choices=[('MRP', 'MRP'), ('MECHANIC', 'MECHANIC'), ('WHOLESALER', 'WHOLESALER'), ('CUSTOMER', 'CUSTOMER')], max_length=20)), ('order', models.ForeignKey(on_delete=django.db.models.deletion.CASCADE, to='stock.SpareOrder')), ('spare', models.ForeignKey(on_delete=models.SET('deleted'), to='stock.Spare')), ], ), ]

49.660377

178

0.602204

from django.conf import settings from django.db import migrations, models import django.db.models.deletion class Migration(migrations.Migration): dependencies = [ ('accounts', '0001_initial'), migrations.swappable_dependency(settings.AUTH_USER_MODEL), ('stock', '0002_spare_store'), ] operations = [ migrations.CreateModel( name='SpareCustomer', fields=[ ('id', models.AutoField(auto_created=True, primary_key=True, serialize=False, verbose_name='ID')), ('name', models.CharField(max_length=100)), ('email', models.CharField(max_length=100)), ('phone_number', models.CharField(max_length=10)), ('address', models.TextField()), ], ), migrations.CreateModel( name='SpareOrder', fields=[ ('id', models.AutoField(auto_created=True, primary_key=True, serialize=False, verbose_name='ID')), ('order_id', models.CharField(max_length=20)), ('order_type', models.CharField(choices=[('IN_SOURCE', 'IN_SOURCE'), ('OUT_SOURCE', 'OUT_SOURCE')], max_length=20)), ('total', models.DecimalField(decimal_places=2, max_digits=10)), ('order_date', models.DateTimeField(auto_now=True)), ('customer', models.ForeignKey(on_delete=django.db.models.deletion.CASCADE, to='stock.SpareCustomer')), ('sold_by', models.ForeignKey(blank=True, null=True, on_delete=django.db.models.deletion.SET_NULL, to=settings.AUTH_USER_MODEL)), ('store', models.ForeignKey(on_delete=django.db.models.deletion.CASCADE, to='accounts.Store')), ], ), migrations.CreateModel( name='SpareSold', fields=[ ('id', models.AutoField(auto_created=True, primary_key=True, serialize=False, verbose_name='ID')), ('spare_count', models.IntegerField()), ('spare_name', models.CharField(max_length=100)), ('spare_price', models.DecimalField(decimal_places=2, max_digits=10)), ('spare_price_type', models.CharField(choices=[('MRP', 'MRP'), ('MECHANIC', 'MECHANIC'), ('WHOLESALER', 'WHOLESALER'), ('CUSTOMER', 'CUSTOMER')], max_length=20)), ('order', models.ForeignKey(on_delete=django.db.models.deletion.CASCADE, to='stock.SpareOrder')), ('spare', models.ForeignKey(on_delete=models.SET('deleted'), to='stock.Spare')), ], ), ]

true

f70f797573bfd52476fcff70d64ce151275711dc

3,122

py

Python

meteors.py

Stafferson/YandexLycP2

f5c50cc89ca6716612f0b91f2e22315c414d5541

[ "MIT" ]

null

meteors.py

Stafferson/YandexLycP2

f5c50cc89ca6716612f0b91f2e22315c414d5541

[ "MIT" ]

null

meteors.py

Stafferson/YandexLycP2

f5c50cc89ca6716612f0b91f2e22315c414d5541

[ "MIT" ]

null

import os import sys import random import pygame def load_image(name, colorkey=None): # not sure if this method is needed fullname = os.path.join('data', name) # если файл не существует, то выходим if not os.path.isfile(fullname): print(f"Файл с изображением '{fullname}' не найден") sys.exit() image = pygame.image.load(fullname) # we can just use this one, cuz we know that pics are ok return image enemies = pygame.sprite.Group() bullets = pygame.sprite.Group() class Meteor(pygame.sprite.Sprite): def __init__(self): super().__init__() self.frames = [] self.cut_sheet(load_image("meteors1.png"), 5, 1) self.cur_frame = 0 self.image = self.frames[self.cur_frame] self.count = 0 self.mask = pygame.mask.from_surface(self.image) self.rect.x = random.randrange(width) self.rect.y = -1 * self.image.get_height() while pygame.sprite.spritecollideany(self, enemies, pygame.sprite.collide_mask) or\ self.rect.x < 0 or self.rect.right > width: self.rect.x = random.randrange(width) self.life = 1 def cut_sheet(self, sheet, columns, rows): self.rect = pygame.Rect(0, 0, sheet.get_width() // columns, sheet.get_height() // rows) for j in range(rows): for i in range(columns): frame_location = (self.rect.w * i, self.rect.h * j) self.frames.append(sheet.subsurface(pygame.Rect( frame_location, self.rect.size))) def update(self): if pygame.sprite.spritecollideany(self, bullets, pygame.sprite.collide_mask): self.life -= 1 if self.life > 0 and self.rect.y <= height: self.rect = self.rect.move(0, 1) self.count += 1 if self.count % 7 == 0: self.cur_frame = (self.cur_frame + 1) % len(self.frames) self.image = self.frames[self.cur_frame] else: self.kill() def except_hook(cls, exception, traceback): sys.__excepthook__(cls, exception, traceback) if __name__ == '__main__': pygame.init() size = width, height = 500, 700 # other parameters may be set in the main game screen = pygame.display.set_mode(size) clock = pygame.time.Clock() fps = 60 MYEVENTTYPE = pygame.USEREVENT + 1 pygame.time.set_timer(MYEVENTTYPE, 3000) for _ in range(random.randrange(1, 4)): enemies.add(Meteor()) running = True while running: for event in pygame.event.get(): if event.type == pygame.QUIT: running = False if event.type == MYEVENTTYPE: # every 3000 frames new enemies are created for _ in range(random.randrange(1, 4)): enemies.add(Meteor()) screen.fill(pygame.Color('blue')) # in the main game, there will be a background(animated?) enemies.draw(screen) enemies.update() clock.tick(fps) pygame.display.flip() pygame.quit() sys.excepthook = except_hook

35.078652

100

0.601217

import os import sys import random import pygame def load_image(name, colorkey=None): fullname = os.path.join('data', name) if not os.path.isfile(fullname): print(f"Файл с изображением '{fullname}' не найден") sys.exit() image = pygame.image.load(fullname) return image enemies = pygame.sprite.Group() bullets = pygame.sprite.Group() class Meteor(pygame.sprite.Sprite): def __init__(self): super().__init__() self.frames = [] self.cut_sheet(load_image("meteors1.png"), 5, 1) self.cur_frame = 0 self.image = self.frames[self.cur_frame] self.count = 0 self.mask = pygame.mask.from_surface(self.image) self.rect.x = random.randrange(width) self.rect.y = -1 * self.image.get_height() while pygame.sprite.spritecollideany(self, enemies, pygame.sprite.collide_mask) or\ self.rect.x < 0 or self.rect.right > width: self.rect.x = random.randrange(width) self.life = 1 def cut_sheet(self, sheet, columns, rows): self.rect = pygame.Rect(0, 0, sheet.get_width() // columns, sheet.get_height() // rows) for j in range(rows): for i in range(columns): frame_location = (self.rect.w * i, self.rect.h * j) self.frames.append(sheet.subsurface(pygame.Rect( frame_location, self.rect.size))) def update(self): if pygame.sprite.spritecollideany(self, bullets, pygame.sprite.collide_mask): self.life -= 1 if self.life > 0 and self.rect.y <= height: self.rect = self.rect.move(0, 1) self.count += 1 if self.count % 7 == 0: self.cur_frame = (self.cur_frame + 1) % len(self.frames) self.image = self.frames[self.cur_frame] else: self.kill() def except_hook(cls, exception, traceback): sys.__excepthook__(cls, exception, traceback) if __name__ == '__main__': pygame.init() size = width, height = 500, 700 screen = pygame.display.set_mode(size) clock = pygame.time.Clock() fps = 60 MYEVENTTYPE = pygame.USEREVENT + 1 pygame.time.set_timer(MYEVENTTYPE, 3000) for _ in range(random.randrange(1, 4)): enemies.add(Meteor()) running = True while running: for event in pygame.event.get(): if event.type == pygame.QUIT: running = False if event.type == MYEVENTTYPE: for _ in range(random.randrange(1, 4)): enemies.add(Meteor()) screen.fill(pygame.Color('blue')) enemies.draw(screen) enemies.update() clock.tick(fps) pygame.display.flip() pygame.quit() sys.excepthook = except_hook

true

f70f79ef125a14fdd24574d066c3d6d527e43b4f

330

py

Python

flask_app/__init__.py

maawoo/S1GRASS-Webapp

b34335f2aaa64dff075b955b98ad01f062ba9891

[ "Unlicense" ]

null

flask_app/__init__.py

maawoo/S1GRASS-Webapp

b34335f2aaa64dff075b955b98ad01f062ba9891

[ "Unlicense" ]

1

2020-09-10T12:18:37.000Z

flask_app/__init__.py

maawoo/S1GRASS-Webapp

b34335f2aaa64dff075b955b98ad01f062ba9891

[ "Unlicense" ]

2

2020-09-09T13:37:45.000Z

2021-04-23T18:57:24.000Z

from config import Config from flask import Flask from flask_sqlalchemy import SQLAlchemy from flask_migrate import Migrate from flask_bootstrap import Bootstrap app = Flask(__name__) app.config.from_object(Config) db = SQLAlchemy(app) migrate = Migrate(app, db) bootstrap = Bootstrap(app) from flask_app import routes, models

22

39

0.818182

from config import Config from flask import Flask from flask_sqlalchemy import SQLAlchemy from flask_migrate import Migrate from flask_bootstrap import Bootstrap app = Flask(__name__) app.config.from_object(Config) db = SQLAlchemy(app) migrate = Migrate(app, db) bootstrap = Bootstrap(app) from flask_app import routes, models

true

f70f7a2b99b2903b8866d778aaaf850f6f9f1fa1

6,688

py

Python

gammapy/utils/testing.py

Rishank2610/gammapy

3cd64fdb2c53c8e5c697a9b85ef8d0486bff0b76

[ "BSD-3-Clause" ]

1

2017-11-22T17:07:56.000Z

gammapy/utils/testing.py

Rishank2610/gammapy

3cd64fdb2c53c8e5c697a9b85ef8d0486bff0b76

[ "BSD-3-Clause" ]

null

gammapy/utils/testing.py

Rishank2610/gammapy

3cd64fdb2c53c8e5c697a9b85ef8d0486bff0b76

[ "BSD-3-Clause" ]

1

2019-09-04T14:03:33.000Z

# Licensed under a 3-clause BSD style license - see LICENSE.rst """Utilities for testing""" import os import sys from numpy.testing import assert_allclose import astropy.units as u from astropy.coordinates import SkyCoord from astropy.time import Time __all__ = [ "requires_dependency", "requires_data", "mpl_plot_check", "assert_quantity_allclose", "assert_skycoord_allclose", "assert_time_allclose", "Checker", ] # Cache for `requires_dependency` _requires_dependency_cache = {} def requires_dependency(name): """Decorator to declare required dependencies for tests. Examples -------- :: from gammapy.utils.testing import requires_dependency @requires_dependency('scipy') def test_using_scipy(): import scipy ... """ import pytest if name in _requires_dependency_cache: skip_it = _requires_dependency_cache[name] else: try: __import__(name) skip_it = False except ImportError: skip_it = True _requires_dependency_cache[name] = skip_it reason = f"Missing dependency: {name}" return pytest.mark.skipif(skip_it, reason=reason) def has_data(name): """Is a certain set of data available?""" if name == "gammapy-extra": return "GAMMAPY_EXTRA" in os.environ elif name == "gammapy-data": return "GAMMAPY_DATA" in os.environ elif name == "gamma-cat": return "GAMMA_CAT" in os.environ elif name == "fermi-lat": return "GAMMAPY_FERMI_LAT_DATA" in os.environ else: raise ValueError(f"Invalid name: {name}") def requires_data(name="gammapy-data"): """Decorator to declare required data for tests. Examples -------- :: from gammapy.utils.testing import requires_data @requires_data() def test_using_data_files(): filename = "$GAMMAPY_DATA/..." ... """ import pytest if not isinstance(name, str): raise TypeError( "You must call @requires_data with a name (str). " "Usually this: @requires_data()" ) skip_it = not has_data(name) reason = f"Missing data: {name}" return pytest.mark.skipif(skip_it, reason=reason) def run_cli(cli, args, exit_code=0): """Run Click command line tool. Thin wrapper around `click.testing.CliRunner` that prints info to stderr if the command fails. Parameters ---------- cli : click.Command Click command args : list of str Argument list exit_code : int Expected exit code of the command Returns ------- result : `click.testing.Result` Result """ from click.testing import CliRunner result = CliRunner().invoke(cli, args, catch_exceptions=False) if result.exit_code != exit_code: sys.stderr.write("Exit code mismatch!\n") sys.stderr.write("Output:\n") sys.stderr.write(result.output) return result def assert_skycoord_allclose(actual, desired): """Assert all-close for `astropy.coordinates.SkyCoord` objects. - Frames can be different, aren't checked at the moment. """ assert isinstance(actual, SkyCoord) assert isinstance(desired, SkyCoord) assert_allclose(actual.data.lon.deg, desired.data.lon.deg) assert_allclose(actual.data.lat.deg, desired.data.lat.deg) def assert_time_allclose(actual, desired, atol=1e-3): """Assert all-close for `astropy.time.Time` objects. atol is absolute tolerance in seconds. """ assert isinstance(actual, Time) assert isinstance(desired, Time) assert actual.scale == desired.scale assert actual.format == desired.format dt = actual - desired assert_allclose(dt.sec, 0, rtol=0, atol=atol) def assert_quantity_allclose(actual, desired, rtol=1.0e-7, atol=None, **kwargs): """Assert all-close for `astropy.units.Quantity` objects. Requires that ``unit`` is identical, not just that quantities are allclose taking different units into account. We prefer this kind of assert for testing, since units should only change on purpose, so this tests more behaviour. """ # TODO: change this later to explicitly check units are the same! # assert actual.unit == desired.unit args = _unquantify_allclose_arguments(actual, desired, rtol, atol) assert_allclose(*args, **kwargs) def _unquantify_allclose_arguments(actual, desired, rtol, atol): actual = u.Quantity(actual, subok=True, copy=False) desired = u.Quantity(desired, subok=True, copy=False) try: desired = desired.to(actual.unit) except u.UnitsError: raise u.UnitsError( "Units for 'desired' ({}) and 'actual' ({}) " "are not convertible".format(desired.unit, actual.unit) ) if atol is None: # by default, we assume an absolute tolerance of 0 atol = u.Quantity(0) else: atol = u.Quantity(atol, subok=True, copy=False) try: atol = atol.to(actual.unit) except u.UnitsError: raise u.UnitsError( "Units for 'atol' ({}) and 'actual' ({}) " "are not convertible".format(atol.unit, actual.unit) ) rtol = u.Quantity(rtol, subok=True, copy=False) try: rtol = rtol.to(u.dimensionless_unscaled) except Exception: raise u.UnitsError("`rtol` should be dimensionless") return actual.value, desired.value, rtol.value, atol.value def mpl_plot_check(): """Matplotlib plotting test context manager. It create a new figure on __enter__ and calls savefig for the current figure in __exit__. This will trigger a render of the Figure, which can sometimes raise errors if there is a problem. This is writing to an in-memory byte buffer, i.e. is faster than writing to disk. """ from io import BytesIO import matplotlib.pyplot as plt class MPLPlotCheck: def __enter__(self): plt.figure() def __exit__(self, type, value, traceback): plt.savefig(BytesIO(), format="png") plt.close() return MPLPlotCheck() class Checker: """Base class for checker classes in Gammapy.""" def run(self, checks="all"): if checks == "all": checks = self.CHECKS.keys() unknown_checks = sorted(set(checks).difference(self.CHECKS.keys())) if unknown_checks: raise ValueError(f"Unknown checks: {unknown_checks!r}") for check in checks: method = getattr(self, self.CHECKS[check]) yield from method()

27.866667

80

0.643541

import os import sys from numpy.testing import assert_allclose import astropy.units as u from astropy.coordinates import SkyCoord from astropy.time import Time __all__ = [ "requires_dependency", "requires_data", "mpl_plot_check", "assert_quantity_allclose", "assert_skycoord_allclose", "assert_time_allclose", "Checker", ] _requires_dependency_cache = {} def requires_dependency(name): import pytest if name in _requires_dependency_cache: skip_it = _requires_dependency_cache[name] else: try: __import__(name) skip_it = False except ImportError: skip_it = True _requires_dependency_cache[name] = skip_it reason = f"Missing dependency: {name}" return pytest.mark.skipif(skip_it, reason=reason) def has_data(name): if name == "gammapy-extra": return "GAMMAPY_EXTRA" in os.environ elif name == "gammapy-data": return "GAMMAPY_DATA" in os.environ elif name == "gamma-cat": return "GAMMA_CAT" in os.environ elif name == "fermi-lat": return "GAMMAPY_FERMI_LAT_DATA" in os.environ else: raise ValueError(f"Invalid name: {name}") def requires_data(name="gammapy-data"): import pytest if not isinstance(name, str): raise TypeError( "You must call @requires_data with a name (str). " "Usually this: @requires_data()" ) skip_it = not has_data(name) reason = f"Missing data: {name}" return pytest.mark.skipif(skip_it, reason=reason) def run_cli(cli, args, exit_code=0): from click.testing import CliRunner result = CliRunner().invoke(cli, args, catch_exceptions=False) if result.exit_code != exit_code: sys.stderr.write("Exit code mismatch!\n") sys.stderr.write("Output:\n") sys.stderr.write(result.output) return result def assert_skycoord_allclose(actual, desired): assert isinstance(actual, SkyCoord) assert isinstance(desired, SkyCoord) assert_allclose(actual.data.lon.deg, desired.data.lon.deg) assert_allclose(actual.data.lat.deg, desired.data.lat.deg) def assert_time_allclose(actual, desired, atol=1e-3): assert isinstance(actual, Time) assert isinstance(desired, Time) assert actual.scale == desired.scale assert actual.format == desired.format dt = actual - desired assert_allclose(dt.sec, 0, rtol=0, atol=atol) def assert_quantity_allclose(actual, desired, rtol=1.0e-7, atol=None, **kwargs): args = _unquantify_allclose_arguments(actual, desired, rtol, atol) assert_allclose(*args, **kwargs) def _unquantify_allclose_arguments(actual, desired, rtol, atol): actual = u.Quantity(actual, subok=True, copy=False) desired = u.Quantity(desired, subok=True, copy=False) try: desired = desired.to(actual.unit) except u.UnitsError: raise u.UnitsError( "Units for 'desired' ({}) and 'actual' ({}) " "are not convertible".format(desired.unit, actual.unit) ) if atol is None: atol = u.Quantity(0) else: atol = u.Quantity(atol, subok=True, copy=False) try: atol = atol.to(actual.unit) except u.UnitsError: raise u.UnitsError( "Units for 'atol' ({}) and 'actual' ({}) " "are not convertible".format(atol.unit, actual.unit) ) rtol = u.Quantity(rtol, subok=True, copy=False) try: rtol = rtol.to(u.dimensionless_unscaled) except Exception: raise u.UnitsError("`rtol` should be dimensionless") return actual.value, desired.value, rtol.value, atol.value def mpl_plot_check(): from io import BytesIO import matplotlib.pyplot as plt class MPLPlotCheck: def __enter__(self): plt.figure() def __exit__(self, type, value, traceback): plt.savefig(BytesIO(), format="png") plt.close() return MPLPlotCheck() class Checker: def run(self, checks="all"): if checks == "all": checks = self.CHECKS.keys() unknown_checks = sorted(set(checks).difference(self.CHECKS.keys())) if unknown_checks: raise ValueError(f"Unknown checks: {unknown_checks!r}") for check in checks: method = getattr(self, self.CHECKS[check]) yield from method()

true

f70f7a739c8358bfeaf9b03fd0832dae1581d974

1,088

py

Python

src/snlayers/snconv1d.py

Zihang97/PAGAN

9233fc54ecf49d6a82bb0794333d61f707439a68

[ "MIT" ]

29

2019-11-04T12:46:17.000Z

2022-02-19T10:06:16.000Z

src/snlayers/snconv1d.py

Zihang97/PAGAN

9233fc54ecf49d6a82bb0794333d61f707439a68

[ "MIT" ]

2

2020-07-05T04:15:57.000Z

2021-04-10T03:45:09.000Z

src/snlayers/snconv1d.py

Zihang97/PAGAN

9233fc54ecf49d6a82bb0794333d61f707439a68

[ "MIT" ]

9

2020-05-04T01:23:37.000Z

2021-07-13T06:47:02.000Z

# coding=utf-8 import torch import torch.nn as nn import torch.nn.functional as F from torch.nn.modules import conv from torch.nn.modules.utils import _single from ..functions.max_sv import max_singular_value class SNConv1d(conv._ConvNd): def __init__(self, in_channels, out_channels, kernel_size, stride=1, padding=0, dilation=1, groups=1, bias=True): kernel_size = _single(kernel_size) stride = _single(stride) padding = _single(padding) dilation = _single(dilation) super(SNConv1d, self).__init__( in_channels, out_channels, kernel_size, stride, padding, dilation, False, _single(0), groups, bias) self.register_buffer('u', torch.Tensor(1, out_channels).normal_()) @property def W_(self): w_mat = self.weight.view(self.weight.size(0), -1) sigma, _u = max_singular_value(w_mat, self.u) self.u.copy_(_u) return self.weight / sigma def forward(self, input): return F.conv1d(input, self.W_, self.bias, self.stride, self.padding, self.dilation, self.groups)

36.266667

117

0.682904

import torch import torch.nn as nn import torch.nn.functional as F from torch.nn.modules import conv from torch.nn.modules.utils import _single from ..functions.max_sv import max_singular_value class SNConv1d(conv._ConvNd): def __init__(self, in_channels, out_channels, kernel_size, stride=1, padding=0, dilation=1, groups=1, bias=True): kernel_size = _single(kernel_size) stride = _single(stride) padding = _single(padding) dilation = _single(dilation) super(SNConv1d, self).__init__( in_channels, out_channels, kernel_size, stride, padding, dilation, False, _single(0), groups, bias) self.register_buffer('u', torch.Tensor(1, out_channels).normal_()) @property def W_(self): w_mat = self.weight.view(self.weight.size(0), -1) sigma, _u = max_singular_value(w_mat, self.u) self.u.copy_(_u) return self.weight / sigma def forward(self, input): return F.conv1d(input, self.W_, self.bias, self.stride, self.padding, self.dilation, self.groups)

true

f70f7a9a71e9c09452054da1066ca9dc4f363773

678

py

Python

tf_agents/agents/sac/__init__.py

FlorisHoogenboom/agents

2cd5a61e1838b52012271f1fb8617c29a55279a9

[ "Apache-2.0" ]

16

2020-09-23T06:21:49.000Z

2022-03-28T05:45:04.000Z

tf_agents/agents/sac/__init__.py

FlorisHoogenboom/agents

2cd5a61e1838b52012271f1fb8617c29a55279a9

[ "Apache-2.0" ]

13

2019-06-18T03:36:39.000Z

2019-08-28T18:30:29.000Z

tf_agents/agents/sac/__init__.py

FlorisHoogenboom/agents

2cd5a61e1838b52012271f1fb8617c29a55279a9

[ "Apache-2.0" ]

6

2020-10-09T06:33:23.000Z

2022-02-03T16:16:36.000Z

# coding=utf-8 # Copyright 2018 The TF-Agents Authors. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. """A Soft Actor Critic agent.""" from tf_agents.agents.sac import sac_agent

37.666667

74

0.759587

from tf_agents.agents.sac import sac_agent

true

f70f7ac9a07e731a63b7367bb5601c7b352c07cb

6,766

py

Python

src/classifiers.py

samirsahoo007/Naive-Bayes-and-Decision-Tree-Classifiers

619c5c0b17438d1014f7ca7e4ce13cc44c45de3c

[ "MIT" ]

1

2020-11-17T16:09:13.000Z

src/classifiers.py

samirsahoo007/Naive-Bayes-and-Decision-Tree-Classifiers

619c5c0b17438d1014f7ca7e4ce13cc44c45de3c

[ "MIT" ]

null

src/classifiers.py

samirsahoo007/Naive-Bayes-and-Decision-Tree-Classifiers

619c5c0b17438d1014f7ca7e4ce13cc44c45de3c

[ "MIT" ]

4

2019-07-05T02:03:02.000Z

2022-01-21T22:12:16.000Z

# -*- coding: utf-8 -*- # """*********************************************************************************************""" # FileName [ classifiers.py ] # Synopsis [ 'Naive Bayes' and 'Decision Tree' training, testing, and tunning functions ] # Author [ Ting-Wei Liu (Andi611) ] # Copyright [ Copyleft(c), NTUEE, NTU, Taiwan ] """*********************************************************************************************""" ############### # IMPORTATION # ############### import numpy as np from sklearn.naive_bayes import GaussianNB from sklearn.naive_bayes import MultinomialNB from sklearn.naive_bayes import ComplementNB from sklearn.naive_bayes import BernoulliNB from sklearn.model_selection import cross_val_score from sklearn import metrics from sklearn import tree ############ # CONSTANT # ############ N_FOLD = 10 DEPTHS = np.arange(1, 64) ALPHAS = np.arange(0.001, 1.0, 0.001) ALPHAS_MUSHROOM = np.arange(0.0001, 1.0, 0.0001) BEST_DISTRIBUTION = 'Multinominal' ############### # NAIVE BAYES # ############### class naive_bayes_runner(object): def __init__(self, MODEL, train_x, train_y, test_x, test_y): #---data---# self.train_x = train_x self.train_y = train_y self.test_x = test_x self.test_y = test_y #---model---# self.cross_validate = False self.MODEL = MODEL if self.MODEL == 'NEWS': self.models = { 'Guassian' : GaussianNB(), 'Multinominal' : MultinomialNB(alpha=0.065), 'Complement' : ComplementNB(alpha=0.136), 'Bernoulli' : BernoulliNB(alpha=0.002) } if self.MODEL == 'MUSHROOM': ALPHAS = ALPHAS_MUSHROOM self.models = { 'Guassian' : GaussianNB(), 'Multinominal' : MultinomialNB(alpha=0.0001), 'Complement' : ComplementNB(alpha=0.0001), 'Bernoulli' : BernoulliNB(alpha=0.0001) } if self.MODEL == 'INCOME': self.cross_validate = True self.models = { 'Guassian' : GaussianNB(), 'Multinominal' : MultinomialNB(alpha=0.959), 'Complement' : ComplementNB(alpha=0.16), 'Bernoulli' : BernoulliNB(alpha=0.001) } def _fit_and_evaluate(self, model): model_fit = model.fit(self.train_x, self.train_y) pred_y = model_fit.predict(self.test_x) acc = metrics.accuracy_score(self.test_y, pred_y) return acc, pred_y def search_alpha(self): try: from tqdm import tqdm except: raise ImportError('Failed to import tqdm, use the following command to install: pip3 install tqdm') for distribution, model in self.models.items(): best_acc = 0.0 best_alpha = 0.001 if distribution != 'Guassian': print('>> [Naive Bayes Runner] Searching for best alpha value, distribution:', distribution) for alpha in tqdm(ALPHAS): model.set_params(alpha=alpha) if self.cross_validate: scores = cross_val_score(model, self.train_x, self.train_y, cv=N_FOLD, scoring='accuracy') acc = scores.mean() else: acc, _ = self._fit_and_evaluate(model) if acc > best_acc: best_acc = acc best_alpha = alpha print('>> [Naive Bayes Runner] '+ distribution + ' - Best Alpha Value:', best_alpha) def run_best_all(self): for distribution, model in self.models.items(): if self.cross_validate: scores = cross_val_score(model, self.train_x, self.train_y, cv=N_FOLD, scoring='accuracy') acc = scores.mean() else: acc, _ = self._fit_and_evaluate(model) print('>> [Naive Bayes Runner] '+ distribution + ' - Accuracy:', acc) def run_best(self): if self.cross_validate: scores = cross_val_score(self.models[BEST_DISTRIBUTION], self.train_x, self.train_y, cv=N_FOLD, scoring='accuracy') acc = scores.mean() model_fit = self.models[BEST_DISTRIBUTION].fit(self.train_x, self.train_y) pred_y = model_fit.predict(self.test_x) else: acc, pred_y = self._fit_and_evaluate(self.models[BEST_DISTRIBUTION]) print('>> [Naive Bayes Runner] '+ BEST_DISTRIBUTION + ' - Accuracy:', acc) return pred_y ################# # DECISION TREE # ################# class decision_tree_runner(object): def __init__(self, MODEL, train_x, train_y, test_x, test_y): #---data---# self.train_x = train_x self.train_y = train_y self.test_x = test_x self.test_y = test_y #---model---# self.cross_validate = False self.MODEL = MODEL if self.MODEL == 'NEWS': self.model = tree.DecisionTreeClassifier(criterion='gini', splitter='random', max_depth=47, random_state=1337) elif self.MODEL == 'MUSHROOM': self.model = tree.DecisionTreeClassifier(criterion='gini', splitter='random', max_depth=7, random_state=1337) elif self.MODEL == 'INCOME': self.cross_validate = True self.model = tree.DecisionTreeClassifier(criterion='entropy', min_impurity_decrease=2e-4, max_depth=15, random_state=1337) def _fit_and_evaluate(self): model_fit = self.model.fit(self.train_x, self.train_y) pred_y = model_fit.predict(self.test_x) acc = metrics.accuracy_score(self.test_y, pred_y) return acc, pred_y def search_max_depth(self): try: from tqdm import tqdm except: raise ImportError('Failed to import tqdm, use the following command to install: $ pip3 install tqdm') best_acc = 0.0 best_depth = 1 print('>> [Naive Bayes Runner] Searching for best max depth value...') for depth in tqdm(DEPTHS): self.model.set_params(max_depth=depth) if self.cross_validate: scores = cross_val_score(self.model, self.train_x, self.train_y, cv=N_FOLD, scoring='accuracy') acc = scores.mean() else: acc, _ = self._fit_and_evaluate() if acc > best_acc: best_acc = acc best_depth = depth print('>> [Decision Tree Runner] - Best Dpeth Value:', best_depth) def visualize(self): try: import graphviz except: raise ImportError('Failed to import graphviz, use the following command to install: $ pip3 install graphviz, and $ sudo apt-get install graphviz') model_fit = self.model.fit(self.train_x, self.train_y) dot_data = tree.export_graphviz(model_fit, out_file=None, filled=True, rounded=True, special_characters=True) graph = graphviz.Source(dot_data) graph.format = 'png' graph.render('../image/TREE_' + self.MODEL) print('>> [Decision Tree Runner] - Tree visualization complete.') def run_best(self): if self.cross_validate: scores = cross_val_score(self.model, self.train_x, self.train_y, cv=N_FOLD, scoring='accuracy') acc = scores.mean() model_fit = self.model.fit(self.train_x, self.train_y) pred_y = model_fit.predict(self.test_x) else: acc, pred_y = self._fit_and_evaluate() print('>> [Decision Tree Runner] - Accuracy:', acc) return pred_y

31.915094

149

0.649571

n.model_selection import cross_val_score from sklearn import metrics from sklearn import tree DISTRIBUTION = 'Multinominal' cross_validate = False self.MODEL = MODEL if self.MODEL == 'NEWS': self.models = { 'Guassian' : GaussianNB(), 'Multinominal' : MultinomialNB(alpha=0.065), 'Complement' : ComplementNB(alpha=0.136), 'Bernoulli' : BernoulliNB(alpha=0.002) } if self.MODEL == 'MUSHROOM': ALPHAS = ALPHAS_MUSHROOM self.models = { 'Guassian' : GaussianNB(), 'Multinominal' : MultinomialNB(alpha=0.0001), 'Complement' : ComplementNB(alpha=0.0001), 'Bernoulli' : BernoulliNB(alpha=0.0001) } if self.MODEL == 'INCOME': self.cross_validate = True self.models = { 'Guassian' : GaussianNB(), 'Multinominal' : MultinomialNB(alpha=0.959), 'Complement' : ComplementNB(alpha=0.16), 'Bernoulli' : BernoulliNB(alpha=0.001) } def _fit_and_evaluate(self, model): model_fit = model.fit(self.train_x, self.train_y) pred_y = model_fit.predict(self.test_x) acc = metrics.accuracy_score(self.test_y, pred_y) return acc, pred_y def search_alpha(self): try: from tqdm import tqdm except: raise ImportError('Failed to import tqdm, use the following command to install: pip3 install tqdm') for distribution, model in self.models.items(): best_acc = 0.0 best_alpha = 0.001 if distribution != 'Guassian': print('>> [Naive Bayes Runner] Searching for best alpha value, distribution:', distribution) for alpha in tqdm(ALPHAS): model.set_params(alpha=alpha) if self.cross_validate: scores = cross_val_score(model, self.train_x, self.train_y, cv=N_FOLD, scoring='accuracy') acc = scores.mean() else: acc, _ = self._fit_and_evaluate(model) if acc > best_acc: best_acc = acc best_alpha = alpha print('>> [Naive Bayes Runner] '+ distribution + ' - Best Alpha Value:', best_alpha) def run_best_all(self): for distribution, model in self.models.items(): if self.cross_validate: scores = cross_val_score(model, self.train_x, self.train_y, cv=N_FOLD, scoring='accuracy') acc = scores.mean() else: acc, _ = self._fit_and_evaluate(model) print('>> [Naive Bayes Runner] '+ distribution + ' - Accuracy:', acc) def run_best(self): if self.cross_validate: scores = cross_val_score(self.models[BEST_DISTRIBUTION], self.train_x, self.train_y, cv=N_FOLD, scoring='accuracy') acc = scores.mean() model_fit = self.models[BEST_DISTRIBUTION].fit(self.train_x, self.train_y) pred_y = model_fit.predict(self.test_x) else: acc, pred_y = self._fit_and_evaluate(self.models[BEST_DISTRIBUTION]) print('>> [Naive Bayes Runner] '+ BEST_DISTRIBUTION + ' - Accuracy:', acc) return pred_y L == 'NEWS': self.model = tree.DecisionTreeClassifier(criterion='gini', splitter='random', max_depth=47, random_state=1337) elif self.MODEL == 'MUSHROOM': self.model = tree.DecisionTreeClassifier(criterion='gini', splitter='random', max_depth=7, random_state=1337) elif self.MODEL == 'INCOME': self.cross_validate = True self.model = tree.DecisionTreeClassifier(criterion='entropy', min_impurity_decrease=2e-4, max_depth=15, random_state=1337) def _fit_and_evaluate(self): model_fit = self.model.fit(self.train_x, self.train_y) pred_y = model_fit.predict(self.test_x) acc = metrics.accuracy_score(self.test_y, pred_y) return acc, pred_y def search_max_depth(self): try: from tqdm import tqdm except: raise ImportError('Failed to import tqdm, use the following command to install: $ pip3 install tqdm') best_acc = 0.0 best_depth = 1 print('>> [Naive Bayes Runner] Searching for best max depth value...') for depth in tqdm(DEPTHS): self.model.set_params(max_depth=depth) if self.cross_validate: scores = cross_val_score(self.model, self.train_x, self.train_y, cv=N_FOLD, scoring='accuracy') acc = scores.mean() else: acc, _ = self._fit_and_evaluate() if acc > best_acc: best_acc = acc best_depth = depth print('>> [Decision Tree Runner] - Best Dpeth Value:', best_depth) def visualize(self): try: import graphviz except: raise ImportError('Failed to import graphviz, use the following command to install: $ pip3 install graphviz, and $ sudo apt-get install graphviz') model_fit = self.model.fit(self.train_x, self.train_y) dot_data = tree.export_graphviz(model_fit, out_file=None, filled=True, rounded=True, special_characters=True) graph = graphviz.Source(dot_data) graph.format = 'png' graph.render('../image/TREE_' + self.MODEL) print('>> [Decision Tree Runner] - Tree visualization complete.') def run_best(self): if self.cross_validate: scores = cross_val_score(self.model, self.train_x, self.train_y, cv=N_FOLD, scoring='accuracy') acc = scores.mean() model_fit = self.model.fit(self.train_x, self.train_y) pred_y = model_fit.predict(self.test_x) else: acc, pred_y = self._fit_and_evaluate() print('>> [Decision Tree Runner] - Accuracy:', acc) return pred_y

true

f70f7cdaa34aef41cecf2fa32f6c3bc75d3c6636

19

py

Python

xontrib/avox_poetry/__init__.py

jnoortheen/xontrib-avox-poetry

aef6fd087108ec66c53e473d9492ae99c357a00e

[ "MIT" ]

3

2021-02-21T05:46:52.000Z

2021-12-01T16:07:31.000Z

xontrib/avox_poetry/__init__.py

jnoortheen/xontrib-avox-poetry

aef6fd087108ec66c53e473d9492ae99c357a00e

[ "MIT" ]

3

2021-03-03T22:49:35.000Z

2022-03-17T15:40:19.000Z

xontrib/avox_poetry/__init__.py

jnoortheen/xontrib-avox-poetry

aef6fd087108ec66c53e473d9492ae99c357a00e

[ "MIT" ]

1

2022-03-20T18:20:54.000Z

from . import venv

9.5

18

0.736842

from . import venv

true

f70f7e6d8c477abc39ccd009b82ebc37062afbec

561

py

Python

Curve/Parms.py

olesmith/SmtC

dfae5097f02192b60aae05b9d02404fcfe893be3

[ "CC0-1.0" ]

null

Curve/Parms.py

olesmith/SmtC

dfae5097f02192b60aae05b9d02404fcfe893be3

[ "CC0-1.0" ]

null

Curve/Parms.py

olesmith/SmtC

dfae5097f02192b60aae05b9d02404fcfe893be3

[ "CC0-1.0" ]

null

class Curve_Parms(): def Curve_Parms_Paths(self): return [str(self.a),str(self.b),str(self.c),str(self.NFrames)] def Curve_Parms_Path(self): return "/".join( self.Curve_Parms_Paths() ) def Curve_Parms_FileName(self,cname,fname,ext="svg"): fnames=self.Curve_Parms_Paths() n=fnames.pop() paths=[self.BasePath,self.Name] fnames=[ fname,]+fnames+[ n+"."+ext ] fname="-".join(fnames) paths.append( "-".join(fnames) ) return "/".join(paths)

22.44

71

0.561497

class Curve_Parms(): def Curve_Parms_Paths(self): return [str(self.a),str(self.b),str(self.c),str(self.NFrames)] def Curve_Parms_Path(self): return "/".join( self.Curve_Parms_Paths() ) def Curve_Parms_FileName(self,cname,fname,ext="svg"): fnames=self.Curve_Parms_Paths() n=fnames.pop() paths=[self.BasePath,self.Name] fnames=[ fname,]+fnames+[ n+"."+ext ] fname="-".join(fnames) paths.append( "-".join(fnames) ) return "/".join(paths)

true

f70f7e730fa4c4fa5d6d670b19ebb19549c82ecc

79,847

py

Python

src/opserver/test/test_analytics_uve.py

codilime/contrail-controller-arch

e87a974950fc1bbdc2b834212dbdfee5e94008de

[ "Apache-2.0" ]

null

src/opserver/test/test_analytics_uve.py

codilime/contrail-controller-arch

e87a974950fc1bbdc2b834212dbdfee5e94008de

[ "Apache-2.0" ]

null

src/opserver/test/test_analytics_uve.py

codilime/contrail-controller-arch

e87a974950fc1bbdc2b834212dbdfee5e94008de

[ "Apache-2.0" ]

1

2020-07-04T12:08:02.000Z

#!/usr/bin/env python # # Copyright (c) 2013 Juniper Networks, Inc. All rights reserved. # # # analytics_uvetest.py # # UVE and Alarm tests # import os import sys import threading threading._DummyThread._Thread__stop = lambda x: 42 import signal import gevent from gevent import monkey monkey.patch_all() import unittest import testtools import fixtures import socket from utils.util import obj_to_dict, find_buildroot from utils.analytics_fixture import AnalyticsFixture from utils.generator_fixture import GeneratorFixture from mockredis import mockredis from mockzoo import mockzoo import logging import time from opserver.sandesh.viz.constants import * from opserver.sandesh.viz.constants import _OBJECT_TABLES from sandesh_common.vns.ttypes import Module from sandesh_common.vns.constants import ModuleNames import platform logging.basicConfig(level=logging.INFO, format='%(asctime)s %(levelname)s %(message)s') builddir = find_buildroot(os.getcwd()) class AnalyticsUveTest(testtools.TestCase, fixtures.TestWithFixtures): @classmethod def setUpClass(cls): if (os.getenv('LD_LIBRARY_PATH', '').find('build/lib') < 0): if (os.getenv('DYLD_LIBRARY_PATH', '').find('build/lib') < 0): assert(False) cls.redis_port = AnalyticsUveTest.get_free_port() mockredis.start_redis(cls.redis_port) @classmethod def tearDownClass(cls): mockredis.stop_redis(cls.redis_port) #@unittest.skip('Skipping non-cassandra test with vizd') def test_00_nocassandra(self): ''' This test starts redis,vizd,opserver and qed Then it checks that the collector UVE (via redis) can be accessed from opserver. ''' logging.info("%%% test_00_nocassandra %%%") vizd_obj = self.useFixture( AnalyticsFixture(logging, builddir, self.__class__.redis_port, 0)) assert vizd_obj.verify_on_setup() return True # end test_00_nocassandra #@unittest.skip('Skipping VM UVE test') def test_01_vm_uve(self): ''' This test starts redis, vizd, opserver, qed, and a python generator that simulates vrouter and sends UveVirtualMachineAgentTrace messages. Then it checks that the VM UVE (via redis) can be accessed from opserver. ''' logging.info("%%% test_01_vm_uve %%%") vizd_obj = self.useFixture( AnalyticsFixture(logging, builddir, self.__class__.redis_port, 0)) assert vizd_obj.verify_on_setup() collectors = [vizd_obj.get_collector()] generator_obj = self.useFixture( GeneratorFixture("contrail-vrouter-agent", collectors, logging, vizd_obj.get_opserver_port())) assert generator_obj.verify_on_setup() generator_obj.send_vm_uve(vm_id='abcd', num_vm_ifs=5, msg_count=5) assert generator_obj.verify_vm_uve(vm_id='abcd', num_vm_ifs=5, msg_count=5) # Delete the VM UVE and verify that the deleted flag is set # in the UVE cache generator_obj.delete_vm_uve('abcd') assert generator_obj.verify_vm_uve_cache(vm_id='abcd', delete=True) # Add the VM UVE with the same vm_id and verify that the deleted flag # is cleared in the UVE cache generator_obj.send_vm_uve(vm_id='abcd', num_vm_ifs=5, msg_count=5) assert generator_obj.verify_vm_uve_cache(vm_id='abcd') assert generator_obj.verify_vm_uve(vm_id='abcd', num_vm_ifs=5, msg_count=5) # Generate VM with vm_id containing XML control character generator_obj.send_vm_uve(vm_id='<abcd&>', num_vm_ifs=2, msg_count=2) assert generator_obj.verify_vm_uve(vm_id='<abcd&>', num_vm_ifs=2, msg_count=2) return True # end test_01_vm_uve #@unittest.skip('Skipping VM UVE test') def test_02_vm_uve_with_password(self): ''' This test starts redis, vizd, opserver, qed, and a python generator that simulates vrouter and sends UveVirtualMachineAgentTrace messages. Then it checks that the VM UVE (via redis) can be accessed from opserver. ''' logging.info("%%% test_02_vm_uve_with_password %%%") vizd_obj = self.useFixture( AnalyticsFixture(logging, builddir, -1, 0, redis_password='contrail')) assert vizd_obj.verify_on_setup() collectors = [vizd_obj.get_collector()] generator_obj = self.useFixture( GeneratorFixture("contrail-vrouter-agent", collectors, logging, vizd_obj.get_opserver_port())) assert generator_obj.verify_on_setup() generator_obj.send_vm_uve(vm_id='abcd', num_vm_ifs=5, msg_count=5) assert generator_obj.verify_vm_uve(vm_id='abcd', num_vm_ifs=5, msg_count=5) return True # end test_02_vm_uve_with_password #@unittest.skip('verify redis-uve restart') def test_03_redis_uve_restart(self): logging.info('%%% test_03_redis_uve_restart %%%') vizd_obj = self.useFixture( AnalyticsFixture(logging, builddir, -1, 0, start_kafka = True)) assert vizd_obj.verify_on_setup() collectors = [vizd_obj.get_collector()] alarm_gen1 = self.useFixture( GeneratorFixture('vrouter-agent', collectors, logging, None, hostname=socket.gethostname())) alarm_gen1.verify_on_setup() # send vrouter UVE without build_info !!! # check for PartialSysinfo alarm alarm_gen1.send_vrouterinfo("myvrouter1") assert(vizd_obj.verify_uvetable_alarm("ObjectVRouter", "ObjectVRouter:myvrouter1", "partial-sysinfo-compute")) self.verify_uve_resync(vizd_obj) # Alarm should return after redis restart assert(vizd_obj.verify_uvetable_alarm("ObjectVRouter", "ObjectVRouter:myvrouter1", "partial-sysinfo-compute")) # should there be a return True here? # end test_03_redis_uve_restart #@unittest.skip('verify redis-uve restart') def test_04_redis_uve_restart_with_password(self): logging.info('%%% test_03_redis_uve_restart_with_password %%%') vizd_obj = self.useFixture( AnalyticsFixture(logging, builddir, -1, 0, redis_password='contrail')) self.verify_uve_resync(vizd_obj) return True # end test_04_redis_uve_restart def verify_uve_resync(self, vizd_obj): assert vizd_obj.verify_on_setup() assert vizd_obj.verify_collector_redis_uve_connection( vizd_obj.collectors[0]) assert vizd_obj.verify_opserver_redis_uve_connection( vizd_obj.opserver) # verify redis-uve list host = socket.gethostname() gen_list = [host+':Analytics:contrail-collector:0', host+':Analytics:contrail-query-engine:0', host+':Analytics:contrail-analytics-api:0'] assert vizd_obj.verify_generator_uve_list(gen_list) # stop redis-uve vizd_obj.redis_uves[0].stop() assert vizd_obj.verify_collector_redis_uve_connection( vizd_obj.collectors[0], False) assert vizd_obj.verify_opserver_redis_uve_connection( vizd_obj.opserver, False) # start redis-uve and verify that contrail-collector and Opserver are # connected to the redis-uve vizd_obj.redis_uves[0].start() assert vizd_obj.verify_collector_redis_uve_connection( vizd_obj.collectors[0]) assert vizd_obj.verify_opserver_redis_uve_connection( vizd_obj.opserver) # verify that UVEs are resynced with redis-uve assert vizd_obj.verify_generator_uve_list(gen_list) #@unittest.skip('Skipping contrail-collector HA test') def test_05_collector_ha(self): logging.info('%%% test_05_collector_ha %%%') vizd_obj = self.useFixture( AnalyticsFixture(logging, builddir, -1, 0, collector_ha_test=True)) assert vizd_obj.verify_on_setup() # OpServer, AlarmGen and QE are started with collectors[0] as # primary and collectors[1] as secondary exp_genlist = ['contrail-collector', 'contrail-analytics-api', 'contrail-query-engine'] assert vizd_obj.verify_generator_list(vizd_obj.collectors[0], exp_genlist) # start the contrail-vrouter-agent with collectors[1] as primary and # collectors[0] as secondary collectors = [vizd_obj.collectors[1].get_addr(), vizd_obj.collectors[0].get_addr()] vr_agent = self.useFixture( GeneratorFixture("contrail-vrouter-agent", collectors, logging, vizd_obj.get_opserver_port())) assert vr_agent.verify_on_setup() exp_genlist = ['contrail-collector', 'contrail-vrouter-agent'] assert vizd_obj.verify_generator_list(vizd_obj.collectors[1], exp_genlist) # stop collectors[0] and verify that OpServer, AlarmGen and QE switch # from primary to secondary collector vizd_obj.collectors[0].stop() exp_genlist = ['contrail-collector', 'contrail-vrouter-agent', 'contrail-analytics-api', 'contrail-query-engine'] assert vizd_obj.verify_generator_list(vizd_obj.collectors[1], exp_genlist) # start collectors[0] vizd_obj.collectors[0].start() exp_genlist = ['contrail-collector'] assert vizd_obj.verify_generator_list(vizd_obj.collectors[0], exp_genlist) # verify that the old UVEs are flushed from redis when collector restarts exp_genlist = [vizd_obj.collectors[0].get_generator_id()] assert vizd_obj.verify_generator_list_in_redis(\ vizd_obj.collectors[0].get_redis_uve(), exp_genlist) # stop collectors[1] and verify that OpServer, AlarmGen and QE switch # from secondary to primary and contrail-vrouter-agent from primary to # secondary vizd_obj.collectors[1].stop() exp_genlist = ['contrail-collector', 'contrail-vrouter-agent', 'contrail-analytics-api', 'contrail-query-engine'] assert vizd_obj.verify_generator_list(vizd_obj.collectors[0], exp_genlist) # verify the generator list in redis exp_genlist = [vizd_obj.collectors[0].get_generator_id(), vr_agent.get_generator_id(), vizd_obj.opserver.get_generator_id(), vizd_obj.query_engine.get_generator_id()] assert vizd_obj.verify_generator_list_in_redis(\ vizd_obj.collectors[0].get_redis_uve(), exp_genlist) # stop QE vizd_obj.query_engine.stop() exp_genlist = ['contrail-collector', 'contrail-vrouter-agent', 'contrail-analytics-api'] assert vizd_obj.verify_generator_list(vizd_obj.collectors[0], exp_genlist) # verify the generator list in redis exp_genlist = [vizd_obj.collectors[0].get_generator_id(), vizd_obj.opserver.get_generator_id(), vr_agent.get_generator_id()] assert vizd_obj.verify_generator_list_in_redis(\ vizd_obj.collectors[0].get_redis_uve(), exp_genlist) # start a python generator and QE with collectors[1] as the primary and # collectors[0] as the secondary. On generator startup, verify # that they connect to the secondary collector, if the # connection to the primary fails vr2_collectors = [vizd_obj.collectors[1].get_addr(), vizd_obj.collectors[0].get_addr()] vr2_agent = self.useFixture( GeneratorFixture("contrail-snmp-collector", collectors, logging, vizd_obj.get_opserver_port())) assert vr2_agent.verify_on_setup() vizd_obj.query_engine.set_primary_collector( vizd_obj.collectors[1].get_addr()) vizd_obj.query_engine.set_secondary_collector( vizd_obj.collectors[0].get_addr()) vizd_obj.query_engine.start() exp_genlist = ['contrail-collector', 'contrail-vrouter-agent', 'contrail-analytics-api', 'contrail-snmp-collector', 'contrail-query-engine'] assert vizd_obj.verify_generator_list(vizd_obj.collectors[0], exp_genlist) # stop the collectors[0] - both collectors[0] and collectors[1] are down # send the VM UVE and verify that the VM UVE is synced after connection # to the collector vizd_obj.collectors[0].stop() # Make sure the connection to the collector is teared down before # sending the VM UVE while True: if vr_agent.verify_on_setup() is False: break vr_agent.send_vm_uve(vm_id='abcd-1234-efgh-5678', num_vm_ifs=5, msg_count=5) vizd_obj.collectors[1].start() exp_genlist = ['contrail-collector', 'contrail-vrouter-agent', 'contrail-analytics-api', 'contrail-snmp-collector', 'contrail-query-engine'] assert vizd_obj.verify_generator_list(vizd_obj.collectors[1], exp_genlist) assert vr_agent.verify_vm_uve(vm_id='abcd-1234-efgh-5678', num_vm_ifs=5, msg_count=5) # end test_05_collector_ha #@unittest.skip('Skipping AlarmGen basic test') def test_06_alarmgen_basic(self): ''' This test starts the analytics processes. It enables partition 0 on alarmgen, and confirms that it got enabled ''' logging.info("%%% test_06_alarmgen_basic %%%") if AnalyticsUveTest._check_skip_kafka() is True: return True vizd_obj = self.useFixture( AnalyticsFixture(logging, builddir, self.__class__.redis_port, 0, start_kafka = True)) assert vizd_obj.verify_on_setup() assert(vizd_obj.verify_uvetable_alarm("ObjectCollectorInfo", "ObjectCollectorInfo:" + socket.gethostname(), "process-status")) # setup generator for sending Vrouter build_info collector = vizd_obj.collectors[0].get_addr() alarm_gen1 = self.useFixture( GeneratorFixture('vrouter-agent', [collector], logging, None, hostname=socket.gethostname())) alarm_gen1.verify_on_setup() # send vrouter UVE without build_info !!! # check for PartialSysinfo alarm alarm_gen1.send_vrouterinfo("myvrouter1") assert(vizd_obj.verify_uvetable_alarm("ObjectVRouter", "ObjectVRouter:myvrouter1", "partial-sysinfo-compute", rules=[{"and_list": [{ "condition": { "operation": "==", "operand1": "ObjectVRouter.build_info", "operand2": { "json_value": "null" } }, "match": [{"json_operand1_value": "null"}] }]}] )) # Now try to clear the alarm by sending build_info alarm_gen1.send_vrouterinfo("myvrouter1", b_info = True) assert(vizd_obj.verify_uvetable_alarm("ObjectVRouter", "ObjectVRouter:myvrouter1", "partial-sysinfo-compute", is_set = False)) # send vrouter UVE without build_info !!! # check for PartialSysinfo alarm alarm_gen1.send_vrouterinfo("myvrouter1", deleted = True) alarm_gen1.send_vrouterinfo("myvrouter1") assert(vizd_obj.verify_uvetable_alarm("ObjectVRouter", "ObjectVRouter:myvrouter1", "partial-sysinfo-compute")) # Now try to clear the alarm by deleting the UVE alarm_gen1.send_vrouterinfo("myvrouter1", deleted = True) assert(vizd_obj.verify_uvetable_alarm("ObjectVRouter", "ObjectVRouter:myvrouter1", "partial-sysinfo-compute", is_set = False)) alarm_gen2 = self.useFixture( GeneratorFixture('vrouter-agent', [collector], logging, None, hostname=socket.gethostname(), inst = "1")) alarm_gen2.verify_on_setup() # send vrouter UVE without build_info !!! # check for PartialSysinfo alarm alarm_gen2.send_vrouterinfo("myvrouter2") assert(vizd_obj.verify_uvetable_alarm("ObjectVRouter", "ObjectVRouter:myvrouter2", "partial-sysinfo-compute")) # Now try to clear the alarm by disconnecting the generator alarm_gen2._sandesh_instance._client._connection.set_admin_state(\ down=True) assert(vizd_obj.verify_uvetable_alarm("ObjectVRouter", "ObjectVRouter:myvrouter2", "partial-sysinfo-compute", is_set = False)) # send vrouter UVE of myvrouter without build_info again !!! # check for PartialSysinfo alarm alarm_gen1.send_vrouterinfo("myvrouter1") assert(vizd_obj.verify_uvetable_alarm("ObjectVRouter", "ObjectVRouter:myvrouter1", "partial-sysinfo-compute")) # Verify that we can give up partition ownership assert(vizd_obj.set_alarmgen_partition(0,0) == 'true') assert(vizd_obj.verify_alarmgen_partition(0,'false')) # Give up the other partitions assert(vizd_obj.set_alarmgen_partition(1,0) == 'true') assert(vizd_obj.set_alarmgen_partition(2,0) == 'true') assert(vizd_obj.set_alarmgen_partition(3,0) == 'true') # Confirm that alarms are all gone assert(vizd_obj.verify_uvetable_alarm("ObjectVRouter", None, None)) # Get the partitions again assert(vizd_obj.set_alarmgen_partition(0,1) == 'true') assert(vizd_obj.set_alarmgen_partition(1,1) == 'true') assert(vizd_obj.set_alarmgen_partition(2,1) == 'true') assert(vizd_obj.set_alarmgen_partition(3,1) == 'true') assert(vizd_obj.verify_alarmgen_partition(0,'true')) # The PartialSysinfo alarm om myvrouter should return assert(vizd_obj.verify_uvetable_alarm("ObjectVRouter", "ObjectVRouter:myvrouter1", "partial-sysinfo-compute")) return True # end test_06_alarmgen_basic #@unittest.skip('Skipping Alarm test') def test_07_alarm(self): ''' This test starts redis, collectors, analytics-api and python generators that simulates alarm generator. This test sends alarms from alarm generators and verifies the retrieval of alarms from analytics-api. ''' logging.info('%%% test_07_alarm %%%') if AnalyticsUveTest._check_skip_kafka() is True: return True # collector_ha_test flag is set to True, because we wanna test # retrieval of alarms across multiple redis servers. vizd_obj = self.useFixture( AnalyticsFixture(logging, builddir, -1, 0, collector_ha_test=True, start_kafka = True)) assert vizd_obj.verify_on_setup() # create alarm-generator and attach it to the first collector. collectors = [vizd_obj.collectors[0].get_addr(), vizd_obj.collectors[1].get_addr()] alarm_gen1 = self.useFixture( GeneratorFixture('contrail-alarm-gen', [collectors[0]], logging, None, hostname=socket.gethostname()+'_1')) alarm_gen1.verify_on_setup() # send process state alarm for analytics-node alarms = alarm_gen1.create_process_state_alarm( 'contrail-query-engine') alarm_gen1.send_alarm(socket.gethostname()+'_1', alarms, COLLECTOR_INFO_TABLE) analytics_tbl = _OBJECT_TABLES[COLLECTOR_INFO_TABLE].log_query_name # send proces state alarm for control-node alarms = alarm_gen1.create_process_state_alarm('contrail-dns') alarm_gen1.send_alarm('<&'+socket.gethostname()+'_1>', alarms, BGP_ROUTER_TABLE) control_tbl = _OBJECT_TABLES[BGP_ROUTER_TABLE].log_query_name # create another alarm-generator and attach it to the second collector. alarm_gen2 = self.useFixture( GeneratorFixture('contrail-alarm-gen', [collectors[1]], logging, None, hostname=socket.gethostname()+'_2')) alarm_gen2.verify_on_setup() # send process state alarm for analytics-node alarms = alarm_gen2.create_process_state_alarm( 'contrail-topology') alarm_gen2.send_alarm(socket.gethostname()+'_2', alarms, COLLECTOR_INFO_TABLE) keys = [socket.gethostname()+'_1', socket.gethostname()+'_2'] assert(vizd_obj.verify_alarm_list_include(analytics_tbl, expected_alarms=keys)) assert(vizd_obj.verify_alarm(analytics_tbl, keys[0], obj_to_dict( alarm_gen1.alarms[COLLECTOR_INFO_TABLE][keys[0]].data))) assert(vizd_obj.verify_alarm(analytics_tbl, keys[1], obj_to_dict( alarm_gen2.alarms[COLLECTOR_INFO_TABLE][keys[1]].data))) keys = ['<&'+socket.gethostname()+'_1>'] assert(vizd_obj.verify_alarm_list_include(control_tbl, expected_alarms=keys)) assert(vizd_obj.verify_alarm(control_tbl, keys[0], obj_to_dict( alarm_gen1.alarms[BGP_ROUTER_TABLE][keys[0]].data))) # delete analytics-node alarm generated by alarm_gen2 alarm_gen2.delete_alarm(socket.gethostname()+'_2', COLLECTOR_INFO_TABLE) # verify analytics-node alarms keys = [socket.gethostname()+'_1'] assert(vizd_obj.verify_alarm_list_include(analytics_tbl, expected_alarms=keys)) ukeys = [socket.gethostname()+'_2'] assert(vizd_obj.verify_alarm_list_exclude(analytics_tbl, unexpected_alms=ukeys)) assert(vizd_obj.verify_alarm(analytics_tbl, keys[0], obj_to_dict( alarm_gen1.alarms[COLLECTOR_INFO_TABLE][keys[0]].data))) assert(vizd_obj.verify_alarm(analytics_tbl, ukeys[0], {})) # Disconnect alarm_gen1 from Collector and verify that all # alarms generated by alarm_gen1 is removed by the Collector. alarm_gen1.disconnect_from_collector() ukeys = [socket.gethostname()+'_1'] assert(vizd_obj.verify_alarm_list_exclude(analytics_tbl, unexpected_alms=ukeys)) assert(vizd_obj.verify_alarm(analytics_tbl, ukeys[0], {})) ukeys = ['<&'+socket.gethostname()+'_1'] assert(vizd_obj.verify_alarm_list_exclude(control_tbl, unexpected_alms=ukeys)) assert(vizd_obj.verify_alarm(control_tbl, ukeys[0], {})) # update analytics-node alarm in disconnect state alarms = alarm_gen1.create_process_state_alarm( 'contrail-snmp-collector') alarm_gen1.send_alarm(socket.gethostname()+'_1', alarms, COLLECTOR_INFO_TABLE) # Connect alarm_gen1 to Collector and verify that all # alarms generated by alarm_gen1 is synced with Collector. alarm_gen1.connect_to_collector() keys = [socket.gethostname()+'_1'] assert(vizd_obj.verify_alarm_list_include(analytics_tbl, expected_alarms=keys)) assert(vizd_obj.verify_alarm(analytics_tbl, keys[0], obj_to_dict( alarm_gen1.alarms[COLLECTOR_INFO_TABLE][keys[0]].data))) keys = ['<&'+socket.gethostname()+'_1>'] assert(vizd_obj.verify_alarm_list_include(control_tbl, expected_alarms=keys)) assert(vizd_obj.verify_alarm(control_tbl, keys[0], obj_to_dict( alarm_gen1.alarms[BGP_ROUTER_TABLE][keys[0]].data))) # end test_07_alarm #@unittest.skip('Skipping UVE/Alarm Filter test') def test_08_uve_alarm_filter(self): ''' This test verifies the filter options kfilt, sfilt, mfilt and cfilt in the UVE/Alarm GET and POST methods. ''' logging.info('%%% test_08_uve_alarm_filter %%%') if AnalyticsUveTest._check_skip_kafka() is True: return True vizd_obj = self.useFixture( AnalyticsFixture(logging, builddir, -1, 0, collector_ha_test=True, start_kafka = True)) assert vizd_obj.verify_on_setup() collectors = [vizd_obj.collectors[0].get_addr(), vizd_obj.collectors[1].get_addr()] api_server_name = socket.gethostname()+'_1' api_server = self.useFixture( GeneratorFixture('contrail-api', [collectors[0]], logging, None, node_type='Config', hostname=api_server_name)) vr_agent_name = socket.gethostname()+'_2' vr_agent = self.useFixture( GeneratorFixture('contrail-vrouter-agent', [collectors[1]], logging, None, node_type='Compute', hostname=vr_agent_name)) alarm_gen1_name = socket.gethostname()+'_1' alarm_gen1 = self.useFixture( GeneratorFixture('contrail-alarm-gen', [collectors[0]], logging, None, node_type='Analytics', hostname=alarm_gen1_name)) alarm_gen2_name = socket.gethostname()+'_3' alarm_gen2 = self.useFixture( GeneratorFixture('contrail-alarm-gen', [collectors[1]], logging, None, node_type='Analytics', hostname=alarm_gen2_name)) api_server.verify_on_setup() vr_agent.verify_on_setup() alarm_gen1.verify_on_setup() alarm_gen2.verify_on_setup() vn_list = ['default-domain:project1:vn1', 'default-domain:project1:vn2', 'default-domain:project2:vn1', 'default-domain:project2:vn1&'] # generate UVEs for the filter test api_server.send_vn_config_uve(name=vn_list[0], partial_conn_nw=[vn_list[1]], num_acl_rules=2) api_server.send_vn_config_uve(name=vn_list[1], num_acl_rules=3) vr_agent.send_vn_agent_uve(name=vn_list[1], num_acl_rules=3, ipkts=2, ibytes=1024) vr_agent.send_vn_agent_uve(name=vn_list[2], ipkts=4, ibytes=128) vr_agent.send_vn_agent_uve(name=vn_list[3], ipkts=8, ibytes=256) # generate Alarms for the filter test alarms = alarm_gen1.create_alarm('InPktsThreshold') alarms += alarm_gen1.create_alarm('InBytesThreshold', ack=True) alarm_gen1.send_alarm(vn_list[1], alarms, VN_TABLE) alarms = alarm_gen2.create_alarm('ConfigNotPresent', ack=False) alarm_gen2.send_alarm(vn_list[2], alarms, VN_TABLE) alarms = alarm_gen2.create_alarm('ConfigNotPresent', ack=False) alarm_gen2.send_alarm(vn_list[3], alarms, VN_TABLE) filt_test = [ # no filter { 'uve_list_get': [ 'default-domain:project1:vn1', 'default-domain:project1:vn2', 'default-domain:project2:vn1', 'default-domain:project2:vn1&' ], 'get_alarms': { 'virtual-network': [ { 'name' : 'default-domain:project1:vn2', 'value' : { 'UVEAlarms': { 'alarms': [ { 'type': 'InPktsThreshold', }, { 'type': 'InBytesThreshold', 'ack': True } ] } } }, { 'name' : 'default-domain:project2:vn1', 'value' : { 'UVEAlarms': { 'alarms': [ { 'type': 'ConfigNotPresent', 'ack': False } ] } } }, { 'name' : 'default-domain:project2:vn1&', 'value' : { 'UVEAlarms': { 'alarms': [ { 'type': 'ConfigNotPresent', 'ack': False } ] } } }, ] }, 'uve_get_post': { 'value': [ { 'name': 'default-domain:project1:vn1', 'value': { 'UveVirtualNetworkConfig': { 'partially_connected_networks': [ 'default-domain:project1:vn2' ], 'total_acl_rules': 2 } } }, { 'name': 'default-domain:project1:vn2', 'value': { 'UveVirtualNetworkAgent': { 'in_tpkts': 2, 'in_bytes': 1024, 'total_acl_rules': 3 }, 'UveVirtualNetworkConfig': { 'total_acl_rules': 3 }, 'UVEAlarms': { 'alarms': [ { 'type': 'InPktsThreshold', }, { 'type': 'InBytesThreshold', 'ack': True } ] } } }, { 'name': 'default-domain:project2:vn1', 'value': { 'UveVirtualNetworkAgent': { 'in_tpkts': 4, 'in_bytes': 128 }, 'UVEAlarms': { 'alarms': [ { 'type': 'ConfigNotPresent', 'ack': False } ] } } }, { 'name': 'default-domain:project2:vn1&', 'value': { 'UveVirtualNetworkAgent': { 'in_tpkts': 8, 'in_bytes': 256 }, 'UVEAlarms': { 'alarms': [ { 'type': 'ConfigNotPresent', 'ack': False } ] } } } ] }, }, # kfilt { 'kfilt': ['*'], 'uve_list_get': [ 'default-domain:project1:vn1', 'default-domain:project1:vn2', 'default-domain:project2:vn1', 'default-domain:project2:vn1&' ], 'uve_get_post': { 'value': [ { 'name': 'default-domain:project1:vn1', 'value': { 'UveVirtualNetworkConfig': { 'partially_connected_networks': [ 'default-domain:project1:vn2' ], 'total_acl_rules': 2 } } }, { 'name': 'default-domain:project1:vn2', 'value': { 'UveVirtualNetworkAgent': { 'in_tpkts': 2, 'in_bytes': 1024, 'total_acl_rules': 3 }, 'UveVirtualNetworkConfig': { 'total_acl_rules': 3 }, 'UVEAlarms': { 'alarms': [ { 'type': 'InPktsThreshold', }, { 'type': 'InBytesThreshold', 'ack': True } ] } } }, { 'name': 'default-domain:project2:vn1', 'value': { 'UveVirtualNetworkAgent': { 'in_tpkts': 4, 'in_bytes': 128 }, 'UVEAlarms': { 'alarms': [ { 'type': 'ConfigNotPresent', 'ack': False } ] } } }, { 'name': 'default-domain:project2:vn1&', 'value': { 'UveVirtualNetworkAgent': { 'in_tpkts': 8, 'in_bytes': 256 }, 'UVEAlarms': { 'alarms': [ { 'type': 'ConfigNotPresent', 'ack': False } ] } } } ] }, }, { 'kfilt': ['default-domain:project1:*', 'default-domain:project2:*'], 'uve_list_get': [ 'default-domain:project1:vn1', 'default-domain:project1:vn2', 'default-domain:project2:vn1', 'default-domain:project2:vn1&' ], 'uve_get_post': { 'value': [ { 'name': 'default-domain:project1:vn1', 'value': { 'UveVirtualNetworkConfig': { 'partially_connected_networks': [ 'default-domain:project1:vn2' ], 'total_acl_rules': 2 } } }, { 'name': 'default-domain:project1:vn2', 'value': { 'UveVirtualNetworkAgent': { 'in_tpkts': 2, 'in_bytes': 1024, 'total_acl_rules': 3 }, 'UveVirtualNetworkConfig': { 'total_acl_rules': 3 }, 'UVEAlarms': { 'alarms': [ { 'type': 'InPktsThreshold', }, { 'type': 'InBytesThreshold', 'ack': True } ] } } }, { 'name': 'default-domain:project2:vn1', 'value': { 'UveVirtualNetworkAgent': { 'in_tpkts': 4, 'in_bytes': 128 }, 'UVEAlarms': { 'alarms': [ { 'type': 'ConfigNotPresent', 'ack': False } ] } } }, { 'name': 'default-domain:project2:vn1&', 'value': { 'UveVirtualNetworkAgent': { 'in_tpkts': 8, 'in_bytes': 256 }, 'UVEAlarms': { 'alarms': [ { 'type': 'ConfigNotPresent', 'ack': False } ] } } } ] }, }, { 'kfilt': ['default-domain:project1:vn1', 'default-domain:project2:*'], 'uve_list_get': [ 'default-domain:project1:vn1', 'default-domain:project2:vn1', 'default-domain:project2:vn1&' ], 'uve_get_post': { 'value': [ { 'name': 'default-domain:project1:vn1', 'value': { 'UveVirtualNetworkConfig': { 'partially_connected_networks': [ 'default-domain:project1:vn2' ], 'total_acl_rules': 2 } } }, { 'name': 'default-domain:project2:vn1', 'value': { 'UveVirtualNetworkAgent': { 'in_tpkts': 4, 'in_bytes': 128 }, 'UVEAlarms': { 'alarms': [ { 'type': 'ConfigNotPresent', 'ack': False } ] } } }, { 'name': 'default-domain:project2:vn1&', 'value': { 'UveVirtualNetworkAgent': { 'in_tpkts': 8, 'in_bytes': 256 }, 'UVEAlarms': { 'alarms': [ { 'type': 'ConfigNotPresent', 'ack': False } ] } } } ] }, }, { 'kfilt': [ 'default-domain:project2:*', 'invalid-vn:*' ], 'get_alarms': { 'virtual-network': [ { 'name' : 'default-domain:project2:vn1', 'value' : { 'UVEAlarms': { 'alarms': [ { 'type': 'ConfigNotPresent', 'ack': False } ] } } }, { 'name' : 'default-domain:project2:vn1&', 'value' : { 'UVEAlarms': { 'alarms': [ { 'type': 'ConfigNotPresent', 'ack': False } ] } } }, ] }, 'uve_list_get': [ 'default-domain:project2:vn1', 'default-domain:project2:vn1&' ], 'uve_get_post': { 'value': [ { 'name': 'default-domain:project2:vn1', 'value': { 'UveVirtualNetworkAgent': { 'in_tpkts': 4, 'in_bytes': 128 }, 'UVEAlarms': { 'alarms': [ { 'type': 'ConfigNotPresent', 'ack': False } ] } } }, { 'name': 'default-domain:project2:vn1&', 'value': { 'UveVirtualNetworkAgent': { 'in_tpkts': 8, 'in_bytes': 256 }, 'UVEAlarms': { 'alarms': [ { 'type': 'ConfigNotPresent', 'ack': False } ] } } } ] }, }, { 'kfilt': [ 'default-domain:project1:vn2', 'default-domain:project2:vn1&', 'invalid-vn' ], 'uve_list_get': [ 'default-domain:project1:vn2', 'default-domain:project2:vn1&' ], 'uve_get_post': { 'value': [ { 'name': 'default-domain:project1:vn2', 'value': { 'UveVirtualNetworkAgent': { 'in_tpkts': 2, 'in_bytes': 1024, 'total_acl_rules': 3 }, 'UveVirtualNetworkConfig': { 'total_acl_rules': 3 }, 'UVEAlarms': { 'alarms': [ { 'type': 'InPktsThreshold', }, { 'type': 'InBytesThreshold', 'ack': True } ] } } }, { 'name': 'default-domain:project2:vn1&', 'value': { 'UveVirtualNetworkAgent': { 'in_tpkts': 8, 'in_bytes': 256 }, 'UVEAlarms': { 'alarms': [ { 'type': 'ConfigNotPresent', 'ack': False } ] } } } ] }, }, { 'kfilt': ['invalid-vn'], 'uve_list_get': [], 'uve_get_post': {'value': []}, }, # sfilt { 'sfilt': socket.gethostname()+'_1', 'uve_list_get': [ 'default-domain:project1:vn1', 'default-domain:project1:vn2' ], 'uve_get_post': { 'value': [ { 'name': 'default-domain:project1:vn1', 'value': { 'UveVirtualNetworkConfig': { 'partially_connected_networks': [ 'default-domain:project1:vn2' ], 'total_acl_rules': 2 } } }, { 'name': 'default-domain:project1:vn2', 'value': { 'UveVirtualNetworkConfig': { 'total_acl_rules': 3 }, 'UVEAlarms': { 'alarms': [ { 'type': 'InPktsThreshold', }, { 'type': 'InBytesThreshold', 'ack': True } ] } } } ] }, }, { 'sfilt': socket.gethostname()+'_3', 'uve_list_get': [ 'default-domain:project2:vn1', 'default-domain:project2:vn1&' ], 'uve_get_post': { 'value': [ { 'name': 'default-domain:project2:vn1', 'value': { 'UVEAlarms': { 'alarms': [ { 'type': 'ConfigNotPresent', 'ack': False } ] } } }, { 'name': 'default-domain:project2:vn1&', 'value': { 'UVEAlarms': { 'alarms': [ { 'type': 'ConfigNotPresent', 'ack': False } ] } } } ] }, }, { 'sfilt': 'invalid_source', 'uve_list_get': [], 'uve_get_post': {'value': []}, }, # mfilt { 'mfilt': 'Config:contrail-api:0', 'uve_list_get': [ 'default-domain:project1:vn1', 'default-domain:project1:vn2' ], 'uve_get_post': { 'value': [ { 'name': 'default-domain:project1:vn1', 'value': { 'UveVirtualNetworkConfig': { 'partially_connected_networks': [ 'default-domain:project1:vn2' ], 'total_acl_rules': 2 } } }, { 'name': 'default-domain:project1:vn2', 'value': { 'UveVirtualNetworkConfig': { 'total_acl_rules': 3 } } } ] }, }, { 'mfilt': 'Analytics:contrail-alarm-gen:0', 'uve_list_get': [ 'default-domain:project1:vn2', 'default-domain:project2:vn1', 'default-domain:project2:vn1&' ], 'uve_get_post': { 'value': [ { 'name': 'default-domain:project1:vn2', 'value': { 'UVEAlarms': { 'alarms': [ { 'type': 'InPktsThreshold', }, { 'type': 'InBytesThreshold', 'ack': True } ] } } }, { 'name': 'default-domain:project2:vn1', 'value': { 'UVEAlarms': { 'alarms': [ { 'type': 'ConfigNotPresent', 'ack': False } ] } } }, { 'name': 'default-domain:project2:vn1&', 'value': { 'UVEAlarms': { 'alarms': [ { 'type': 'ConfigNotPresent', 'ack': False } ] } } } ] }, }, { 'mfilt': 'Analytics:contrail-invalid:0', 'uve_list_get': [], 'uve_get_post': {'value': []}, }, # cfilt { 'cfilt': ['UveVirtualNetworkAgent'], 'uve_list_get': [ 'default-domain:project1:vn2', 'default-domain:project2:vn1', 'default-domain:project2:vn1&' ], 'uve_get_post': { 'value': [ { 'name': 'default-domain:project1:vn2', 'value': { 'UveVirtualNetworkAgent': { 'in_tpkts': 2, 'in_bytes': 1024, 'total_acl_rules': 3 } } }, { 'name': 'default-domain:project2:vn1', 'value': { 'UveVirtualNetworkAgent': { 'in_tpkts': 4, 'in_bytes': 128 } } }, { 'name': 'default-domain:project2:vn1&', 'value': { 'UveVirtualNetworkAgent': { 'in_tpkts': 8, 'in_bytes': 256 } } } ] }, }, { 'cfilt': [ 'UveVirtualNetworkAgent:total_acl_rules', 'UveVirtualNetworkConfig:partially_connected_networks' ], 'uve_list_get': [ 'default-domain:project1:vn1', 'default-domain:project1:vn2' ], 'uve_get_post': { 'value': [ { 'name': 'default-domain:project1:vn1', 'value': { 'UveVirtualNetworkConfig': { 'partially_connected_networks': [ 'default-domain:project1:vn2' ] } } }, { 'name': 'default-domain:project1:vn2', 'value': { 'UveVirtualNetworkAgent': { 'total_acl_rules': 3 } } } ] }, }, { 'cfilt': [ 'UveVirtualNetworkConfig:invalid', 'UveVirtualNetworkAgent:in_tpkts', 'UVEAlarms:alarms' ], 'uve_list_get': [ 'default-domain:project1:vn2', 'default-domain:project2:vn1', 'default-domain:project2:vn1&' ], 'uve_get_post': { 'value': [ { 'name': 'default-domain:project1:vn2', 'value': { 'UveVirtualNetworkAgent': { 'in_tpkts': 2, }, 'UVEAlarms': { 'alarms': [ { 'type': 'InPktsThreshold', }, { 'type': 'InBytesThreshold', 'ack': True } ] } } }, { 'name': 'default-domain:project2:vn1', 'value': { 'UveVirtualNetworkAgent': { 'in_tpkts': 4, }, 'UVEAlarms': { 'alarms': [ { 'type': 'ConfigNotPresent', 'ack': False } ] } } }, { 'name': 'default-domain:project2:vn1&', 'value': { 'UveVirtualNetworkAgent': { 'in_tpkts': 8, }, 'UVEAlarms': { 'alarms': [ { 'type': 'ConfigNotPresent', 'ack': False } ] } } } ] }, }, { 'cfilt': [ 'UveVirtualNetworkAgent:invalid', 'UVEAlarms:invalid_alarms', 'invalid' ], 'uve_list_get': [], 'uve_get_post': {'value': []}, }, # ackfilt { 'ackfilt': True, 'uve_list_get': [ 'default-domain:project1:vn1', 'default-domain:project1:vn2', 'default-domain:project2:vn1', 'default-domain:project2:vn1&' ], 'uve_get_post': { 'value': [ { 'name': 'default-domain:project1:vn1', 'value': { 'UveVirtualNetworkConfig': { 'partially_connected_networks': [ 'default-domain:project1:vn2' ], 'total_acl_rules': 2 } } }, { 'name': 'default-domain:project1:vn2', 'value': { 'UveVirtualNetworkAgent': { 'in_tpkts': 2, 'in_bytes': 1024, 'total_acl_rules': 3 }, 'UveVirtualNetworkConfig': { 'total_acl_rules': 3 }, 'UVEAlarms': { 'alarms': [ { 'type': 'InBytesThreshold', 'ack': True } ] } } }, { 'name': 'default-domain:project2:vn1', 'value': { 'UveVirtualNetworkAgent': { 'in_tpkts': 4, 'in_bytes': 128 }, } }, { 'name': 'default-domain:project2:vn1&', 'value': { 'UveVirtualNetworkAgent': { 'in_tpkts': 8, 'in_bytes': 256 }, } } ] }, }, { 'ackfilt': False, 'uve_list_get': [ 'default-domain:project1:vn1', 'default-domain:project1:vn2', 'default-domain:project2:vn1', 'default-domain:project2:vn1&' ], 'get_alarms': { 'virtual-network': [ { 'name' : 'default-domain:project1:vn2', 'value' : { 'UVEAlarms': { 'alarms': [ { 'type': 'InPktsThreshold', }, ] } } }, { 'name' : 'default-domain:project2:vn1', 'value' : { 'UVEAlarms': { 'alarms': [ { 'type': 'ConfigNotPresent', 'ack': False } ] } } }, { 'name' : 'default-domain:project2:vn1&', 'value' : { 'UVEAlarms': { 'alarms': [ { 'type': 'ConfigNotPresent', 'ack': False } ] } } }, ] }, 'uve_get_post': { 'value': [ { 'name': 'default-domain:project1:vn1', 'value': { 'UveVirtualNetworkConfig': { 'partially_connected_networks': [ 'default-domain:project1:vn2' ], 'total_acl_rules': 2 } } }, { 'name': 'default-domain:project1:vn2', 'value': { 'UveVirtualNetworkAgent': { 'in_tpkts': 2, 'in_bytes': 1024, 'total_acl_rules': 3 }, 'UveVirtualNetworkConfig': { 'total_acl_rules': 3 }, 'UVEAlarms': { 'alarms': [ { 'type': 'InPktsThreshold', } ] } } }, { 'name': 'default-domain:project2:vn1', 'value': { 'UveVirtualNetworkAgent': { 'in_tpkts': 4, 'in_bytes': 128 }, 'UVEAlarms': { 'alarms': [ { 'type': 'ConfigNotPresent', 'ack': False } ] } } }, { 'name': 'default-domain:project2:vn1&', 'value': { 'UveVirtualNetworkAgent': { 'in_tpkts': 8, 'in_bytes': 256 }, 'UVEAlarms': { 'alarms': [ { 'type': 'ConfigNotPresent', 'ack': False } ] } } } ] }, }, # kfilt + sfilt { 'kfilt': [ 'default-domain:project1:*', 'default-domain:project2:vn1', 'default-domain:invalid' ], 'sfilt': socket.gethostname()+'_2', 'uve_list_get': [ 'default-domain:project1:vn2', 'default-domain:project2:vn1' ], 'uve_get_post': { 'value': [ { 'name': 'default-domain:project1:vn2', 'value': { 'UveVirtualNetworkAgent': { 'in_tpkts': 2, 'in_bytes': 1024, 'total_acl_rules': 3 } } }, { 'name': 'default-domain:project2:vn1', 'value': { 'UveVirtualNetworkAgent': { 'in_tpkts': 4, 'in_bytes': 128 } } } ] }, }, # kfilt + sfilt + ackfilt { 'kfilt': [ 'default-domain:project1:vn1', 'default-domain:project2:*', 'default-domain:invalid' ], 'sfilt': socket.gethostname()+'_2', 'ackfilt': True, 'uve_list_get': [ 'default-domain:project2:vn1', 'default-domain:project2:vn1&' ], 'uve_get_post': { 'value': [ { 'name': 'default-domain:project2:vn1', 'value': { 'UveVirtualNetworkAgent': { 'in_tpkts': 4, 'in_bytes': 128 } } }, { 'name': 'default-domain:project2:vn1&', 'value': { 'UveVirtualNetworkAgent': { 'in_tpkts': 8, 'in_bytes': 256 } } } ] }, }, # kfilt + sfilt + cfilt { 'kfilt': [ 'default-domain:project1:vn1', 'default-domain:project1:vn2', 'default-domain:project2:vn1' ], 'sfilt': socket.gethostname()+'_1', 'cfilt': [ 'UveVirtualNetworkAgent', 'UVEAlarms', 'UveVirtualNetworkConfig:Invalid' ], 'uve_list_get': [ 'default-domain:project1:vn2' ], 'uve_get_post': { 'value': [ { 'name': 'default-domain:project1:vn2', 'value': { 'UVEAlarms': { 'alarms': [ { 'type': 'InPktsThreshold', }, { 'type': 'InBytesThreshold', 'ack': True } ] } } } ] }, }, # kfilt + mfilt + cfilt { 'kfilt': ['*'], 'mfilt': 'Config:contrail-api:0', 'cfilt': [ 'UveVirtualNetworkAgent', 'UVEAlarms:alarms' ], 'uve_list_get': [], 'uve_get_post': {'value': []}, }, # kfilt + sfilt + mfilt + cfilt { 'kfilt': [ 'default-domain:project1:vn1', 'default-domain:project1:vn2', 'default-domain:project2:*' ], 'sfilt': socket.gethostname()+'_1', 'mfilt': 'Config:contrail-api:0', 'cfilt': [ 'UveVirtualNetworkConfig:partially_connected_networks', 'UveVirtualNetworkConfig:total_acl_rules', 'UVEAlarms' ], 'uve_list_get': [ 'default-domain:project1:vn1', 'default-domain:project1:vn2' ], 'uve_get_post': { 'value': [ { 'name': 'default-domain:project1:vn1', 'value': { 'UveVirtualNetworkConfig': { 'partially_connected_networks': [ 'default-domain:project1:vn2' ], 'total_acl_rules': 2 } } }, { 'name': 'default-domain:project1:vn2', 'value': { 'UveVirtualNetworkConfig': { 'total_acl_rules': 3 }, } } ] }, }, { 'kfilt': [ 'default-domain:project1:*', 'default-domain:project2:vn1', 'default-domain:project2:invalid' ], 'sfilt': socket.gethostname()+'_3', 'mfilt': 'Analytics:contrail-alarm-gen:0', 'cfilt': [ 'UveVirtualNetworkConfig', 'UVEAlarms:alarms', 'UveVirtualNetworkAgent' ], 'uve_list_get': [ 'default-domain:project2:vn1' ], 'uve_get_post': { 'value': [ { 'name': 'default-domain:project2:vn1', 'value': { 'UVEAlarms': { 'alarms': [ { 'type': 'ConfigNotPresent', 'ack': False } ] } } } ] }, }, # kfilt + sfilt + mfilt + cfilt + ackfilt { 'kfilt': [ 'default-domain:project1:*', 'default-domain:project2:vn1&', 'default-domain:project2:invalid' ], 'sfilt': socket.gethostname()+'_3', 'mfilt': 'Analytics:contrail-alarm-gen:0', 'cfilt': [ 'UveVirtualNetworkConfig', 'UVEAlarms:alarms', 'UveVirtualNetworkAgent' ], 'ackfilt': True, 'uve_list_get': [ 'default-domain:project2:vn1&' ], 'uve_get_post': {'value': []}, } ] vn_table = _OBJECT_TABLES[VN_TABLE].log_query_name for i in range(len(filt_test)): filters = dict(kfilt=filt_test[i].get('kfilt'), sfilt=filt_test[i].get('sfilt'), mfilt=filt_test[i].get('mfilt'), cfilt=filt_test[i].get('cfilt'), ackfilt=filt_test[i].get('ackfilt')) assert(vizd_obj.verify_uve_list(vn_table, filts=filters, exp_uve_list=filt_test[i]['uve_list_get'])) assert(vizd_obj.verify_multi_uve_get(vn_table, filts=filters, exp_uves=filt_test[i]['uve_get_post'])) assert(vizd_obj.verify_uve_post(vn_table, filts=filters, exp_uves=filt_test[i]['uve_get_post'])) if 'get_alarms' in filt_test[i]: filters['tablefilt'] = 'virtual-network' assert(vizd_obj.verify_get_alarms(vn_table, filts=filters, exp_uves=filt_test[i]['get_alarms'])) # end test_08_uve_alarm_filter @staticmethod def get_free_port(): cs = socket.socket(socket.AF_INET, socket.SOCK_STREAM) cs.bind(("", 0)) cport = cs.getsockname()[1] cs.close() return cport @staticmethod def _check_skip_kafka(): (PLATFORM, VERSION, EXTRA) = platform.linux_distribution() if PLATFORM.lower() == 'ubuntu': if VERSION.find('12.') == 0: return True if PLATFORM.lower() == 'centos': if VERSION.find('6.') == 0: return True return False def _term_handler(*_): raise IntSignal() if __name__ == '__main__': gevent.signal(signal.SIGINT,_term_handler) unittest.main(catchbreak=True)

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import os import sys import threading threading._DummyThread._Thread__stop = lambda x: 42 import signal import gevent from gevent import monkey monkey.patch_all() import unittest import testtools import fixtures import socket from utils.util import obj_to_dict, find_buildroot from utils.analytics_fixture import AnalyticsFixture from utils.generator_fixture import GeneratorFixture from mockredis import mockredis from mockzoo import mockzoo import logging import time from opserver.sandesh.viz.constants import * from opserver.sandesh.viz.constants import _OBJECT_TABLES from sandesh_common.vns.ttypes import Module from sandesh_common.vns.constants import ModuleNames import platform logging.basicConfig(level=logging.INFO, format='%(asctime)s %(levelname)s %(message)s') builddir = find_buildroot(os.getcwd()) class AnalyticsUveTest(testtools.TestCase, fixtures.TestWithFixtures): @classmethod def setUpClass(cls): if (os.getenv('LD_LIBRARY_PATH', '').find('build/lib') < 0): if (os.getenv('DYLD_LIBRARY_PATH', '').find('build/lib') < 0): assert(False) cls.redis_port = AnalyticsUveTest.get_free_port() mockredis.start_redis(cls.redis_port) @classmethod def tearDownClass(cls): mockredis.stop_redis(cls.redis_port) def test_00_nocassandra(self): logging.info("%%% test_00_nocassandra %%%") vizd_obj = self.useFixture( AnalyticsFixture(logging, builddir, self.__class__.redis_port, 0)) assert vizd_obj.verify_on_setup() return True def test_01_vm_uve(self): logging.info("%%% test_01_vm_uve %%%") vizd_obj = self.useFixture( AnalyticsFixture(logging, builddir, self.__class__.redis_port, 0)) assert vizd_obj.verify_on_setup() collectors = [vizd_obj.get_collector()] generator_obj = self.useFixture( GeneratorFixture("contrail-vrouter-agent", collectors, logging, vizd_obj.get_opserver_port())) assert generator_obj.verify_on_setup() generator_obj.send_vm_uve(vm_id='abcd', num_vm_ifs=5, msg_count=5) assert generator_obj.verify_vm_uve(vm_id='abcd', num_vm_ifs=5, msg_count=5) generator_obj.delete_vm_uve('abcd') assert generator_obj.verify_vm_uve_cache(vm_id='abcd', delete=True) generator_obj.send_vm_uve(vm_id='abcd', num_vm_ifs=5, msg_count=5) assert generator_obj.verify_vm_uve_cache(vm_id='abcd') assert generator_obj.verify_vm_uve(vm_id='abcd', num_vm_ifs=5, msg_count=5) generator_obj.send_vm_uve(vm_id='<abcd&>', num_vm_ifs=2, msg_count=2) assert generator_obj.verify_vm_uve(vm_id='<abcd&>', num_vm_ifs=2, msg_count=2) return True def test_02_vm_uve_with_password(self): logging.info("%%% test_02_vm_uve_with_password %%%") vizd_obj = self.useFixture( AnalyticsFixture(logging, builddir, -1, 0, redis_password='contrail')) assert vizd_obj.verify_on_setup() collectors = [vizd_obj.get_collector()] generator_obj = self.useFixture( GeneratorFixture("contrail-vrouter-agent", collectors, logging, vizd_obj.get_opserver_port())) assert generator_obj.verify_on_setup() generator_obj.send_vm_uve(vm_id='abcd', num_vm_ifs=5, msg_count=5) assert generator_obj.verify_vm_uve(vm_id='abcd', num_vm_ifs=5, msg_count=5) return True def test_03_redis_uve_restart(self): logging.info('%%% test_03_redis_uve_restart %%%') vizd_obj = self.useFixture( AnalyticsFixture(logging, builddir, -1, 0, start_kafka = True)) assert vizd_obj.verify_on_setup() collectors = [vizd_obj.get_collector()] alarm_gen1 = self.useFixture( GeneratorFixture('vrouter-agent', collectors, logging, None, hostname=socket.gethostname())) alarm_gen1.verify_on_setup() alarm_gen1.send_vrouterinfo("myvrouter1") assert(vizd_obj.verify_uvetable_alarm("ObjectVRouter", "ObjectVRouter:myvrouter1", "partial-sysinfo-compute")) self.verify_uve_resync(vizd_obj) assert(vizd_obj.verify_uvetable_alarm("ObjectVRouter", "ObjectVRouter:myvrouter1", "partial-sysinfo-compute")) def test_04_redis_uve_restart_with_password(self): logging.info('%%% test_03_redis_uve_restart_with_password %%%') vizd_obj = self.useFixture( AnalyticsFixture(logging, builddir, -1, 0, redis_password='contrail')) self.verify_uve_resync(vizd_obj) return True def verify_uve_resync(self, vizd_obj): assert vizd_obj.verify_on_setup() assert vizd_obj.verify_collector_redis_uve_connection( vizd_obj.collectors[0]) assert vizd_obj.verify_opserver_redis_uve_connection( vizd_obj.opserver) host = socket.gethostname() gen_list = [host+':Analytics:contrail-collector:0', host+':Analytics:contrail-query-engine:0', host+':Analytics:contrail-analytics-api:0'] assert vizd_obj.verify_generator_uve_list(gen_list) vizd_obj.redis_uves[0].stop() assert vizd_obj.verify_collector_redis_uve_connection( vizd_obj.collectors[0], False) assert vizd_obj.verify_opserver_redis_uve_connection( vizd_obj.opserver, False) vizd_obj.redis_uves[0].start() assert vizd_obj.verify_collector_redis_uve_connection( vizd_obj.collectors[0]) assert vizd_obj.verify_opserver_redis_uve_connection( vizd_obj.opserver) assert vizd_obj.verify_generator_uve_list(gen_list) def test_05_collector_ha(self): logging.info('%%% test_05_collector_ha %%%') vizd_obj = self.useFixture( AnalyticsFixture(logging, builddir, -1, 0, collector_ha_test=True)) assert vizd_obj.verify_on_setup() exp_genlist = ['contrail-collector', 'contrail-analytics-api', 'contrail-query-engine'] assert vizd_obj.verify_generator_list(vizd_obj.collectors[0], exp_genlist) collectors = [vizd_obj.collectors[1].get_addr(), vizd_obj.collectors[0].get_addr()] vr_agent = self.useFixture( GeneratorFixture("contrail-vrouter-agent", collectors, logging, vizd_obj.get_opserver_port())) assert vr_agent.verify_on_setup() exp_genlist = ['contrail-collector', 'contrail-vrouter-agent'] assert vizd_obj.verify_generator_list(vizd_obj.collectors[1], exp_genlist) vizd_obj.collectors[0].stop() exp_genlist = ['contrail-collector', 'contrail-vrouter-agent', 'contrail-analytics-api', 'contrail-query-engine'] assert vizd_obj.verify_generator_list(vizd_obj.collectors[1], exp_genlist) vizd_obj.collectors[0].start() exp_genlist = ['contrail-collector'] assert vizd_obj.verify_generator_list(vizd_obj.collectors[0], exp_genlist) exp_genlist = [vizd_obj.collectors[0].get_generator_id()] assert vizd_obj.verify_generator_list_in_redis(\ vizd_obj.collectors[0].get_redis_uve(), exp_genlist) vizd_obj.collectors[1].stop() exp_genlist = ['contrail-collector', 'contrail-vrouter-agent', 'contrail-analytics-api', 'contrail-query-engine'] assert vizd_obj.verify_generator_list(vizd_obj.collectors[0], exp_genlist) exp_genlist = [vizd_obj.collectors[0].get_generator_id(), vr_agent.get_generator_id(), vizd_obj.opserver.get_generator_id(), vizd_obj.query_engine.get_generator_id()] assert vizd_obj.verify_generator_list_in_redis(\ vizd_obj.collectors[0].get_redis_uve(), exp_genlist) vizd_obj.query_engine.stop() exp_genlist = ['contrail-collector', 'contrail-vrouter-agent', 'contrail-analytics-api'] assert vizd_obj.verify_generator_list(vizd_obj.collectors[0], exp_genlist) exp_genlist = [vizd_obj.collectors[0].get_generator_id(), vizd_obj.opserver.get_generator_id(), vr_agent.get_generator_id()] assert vizd_obj.verify_generator_list_in_redis(\ vizd_obj.collectors[0].get_redis_uve(), exp_genlist) vr2_collectors = [vizd_obj.collectors[1].get_addr(), vizd_obj.collectors[0].get_addr()] vr2_agent = self.useFixture( GeneratorFixture("contrail-snmp-collector", collectors, logging, vizd_obj.get_opserver_port())) assert vr2_agent.verify_on_setup() vizd_obj.query_engine.set_primary_collector( vizd_obj.collectors[1].get_addr()) vizd_obj.query_engine.set_secondary_collector( vizd_obj.collectors[0].get_addr()) vizd_obj.query_engine.start() exp_genlist = ['contrail-collector', 'contrail-vrouter-agent', 'contrail-analytics-api', 'contrail-snmp-collector', 'contrail-query-engine'] assert vizd_obj.verify_generator_list(vizd_obj.collectors[0], exp_genlist) vizd_obj.collectors[0].stop() while True: if vr_agent.verify_on_setup() is False: break vr_agent.send_vm_uve(vm_id='abcd-1234-efgh-5678', num_vm_ifs=5, msg_count=5) vizd_obj.collectors[1].start() exp_genlist = ['contrail-collector', 'contrail-vrouter-agent', 'contrail-analytics-api', 'contrail-snmp-collector', 'contrail-query-engine'] assert vizd_obj.verify_generator_list(vizd_obj.collectors[1], exp_genlist) assert vr_agent.verify_vm_uve(vm_id='abcd-1234-efgh-5678', num_vm_ifs=5, msg_count=5) def test_06_alarmgen_basic(self): logging.info("%%% test_06_alarmgen_basic %%%") if AnalyticsUveTest._check_skip_kafka() is True: return True vizd_obj = self.useFixture( AnalyticsFixture(logging, builddir, self.__class__.redis_port, 0, start_kafka = True)) assert vizd_obj.verify_on_setup() assert(vizd_obj.verify_uvetable_alarm("ObjectCollectorInfo", "ObjectCollectorInfo:" + socket.gethostname(), "process-status")) collector = vizd_obj.collectors[0].get_addr() alarm_gen1 = self.useFixture( GeneratorFixture('vrouter-agent', [collector], logging, None, hostname=socket.gethostname())) alarm_gen1.verify_on_setup() alarm_gen1.send_vrouterinfo("myvrouter1") assert(vizd_obj.verify_uvetable_alarm("ObjectVRouter", "ObjectVRouter:myvrouter1", "partial-sysinfo-compute", rules=[{"and_list": [{ "condition": { "operation": "==", "operand1": "ObjectVRouter.build_info", "operand2": { "json_value": "null" } }, "match": [{"json_operand1_value": "null"}] }]}] )) alarm_gen1.send_vrouterinfo("myvrouter1", b_info = True) assert(vizd_obj.verify_uvetable_alarm("ObjectVRouter", "ObjectVRouter:myvrouter1", "partial-sysinfo-compute", is_set = False)) alarm_gen1.send_vrouterinfo("myvrouter1", deleted = True) alarm_gen1.send_vrouterinfo("myvrouter1") assert(vizd_obj.verify_uvetable_alarm("ObjectVRouter", "ObjectVRouter:myvrouter1", "partial-sysinfo-compute")) alarm_gen1.send_vrouterinfo("myvrouter1", deleted = True) assert(vizd_obj.verify_uvetable_alarm("ObjectVRouter", "ObjectVRouter:myvrouter1", "partial-sysinfo-compute", is_set = False)) alarm_gen2 = self.useFixture( GeneratorFixture('vrouter-agent', [collector], logging, None, hostname=socket.gethostname(), inst = "1")) alarm_gen2.verify_on_setup() alarm_gen2.send_vrouterinfo("myvrouter2") assert(vizd_obj.verify_uvetable_alarm("ObjectVRouter", "ObjectVRouter:myvrouter2", "partial-sysinfo-compute")) alarm_gen2._sandesh_instance._client._connection.set_admin_state(\ down=True) assert(vizd_obj.verify_uvetable_alarm("ObjectVRouter", "ObjectVRouter:myvrouter2", "partial-sysinfo-compute", is_set = False)) alarm_gen1.send_vrouterinfo("myvrouter1") assert(vizd_obj.verify_uvetable_alarm("ObjectVRouter", "ObjectVRouter:myvrouter1", "partial-sysinfo-compute")) assert(vizd_obj.set_alarmgen_partition(0,0) == 'true') assert(vizd_obj.verify_alarmgen_partition(0,'false')) assert(vizd_obj.set_alarmgen_partition(1,0) == 'true') assert(vizd_obj.set_alarmgen_partition(2,0) == 'true') assert(vizd_obj.set_alarmgen_partition(3,0) == 'true') assert(vizd_obj.verify_uvetable_alarm("ObjectVRouter", None, None)) assert(vizd_obj.set_alarmgen_partition(0,1) == 'true') assert(vizd_obj.set_alarmgen_partition(1,1) == 'true') assert(vizd_obj.set_alarmgen_partition(2,1) == 'true') assert(vizd_obj.set_alarmgen_partition(3,1) == 'true') assert(vizd_obj.verify_alarmgen_partition(0,'true')) assert(vizd_obj.verify_uvetable_alarm("ObjectVRouter", "ObjectVRouter:myvrouter1", "partial-sysinfo-compute")) return True def test_07_alarm(self): logging.info('%%% test_07_alarm %%%') if AnalyticsUveTest._check_skip_kafka() is True: return True vizd_obj = self.useFixture( AnalyticsFixture(logging, builddir, -1, 0, collector_ha_test=True, start_kafka = True)) assert vizd_obj.verify_on_setup() collectors = [vizd_obj.collectors[0].get_addr(), vizd_obj.collectors[1].get_addr()] alarm_gen1 = self.useFixture( GeneratorFixture('contrail-alarm-gen', [collectors[0]], logging, None, hostname=socket.gethostname()+'_1')) alarm_gen1.verify_on_setup() alarms = alarm_gen1.create_process_state_alarm( 'contrail-query-engine') alarm_gen1.send_alarm(socket.gethostname()+'_1', alarms, COLLECTOR_INFO_TABLE) analytics_tbl = _OBJECT_TABLES[COLLECTOR_INFO_TABLE].log_query_name alarms = alarm_gen1.create_process_state_alarm('contrail-dns') alarm_gen1.send_alarm('<&'+socket.gethostname()+'_1>', alarms, BGP_ROUTER_TABLE) control_tbl = _OBJECT_TABLES[BGP_ROUTER_TABLE].log_query_name alarm_gen2 = self.useFixture( GeneratorFixture('contrail-alarm-gen', [collectors[1]], logging, None, hostname=socket.gethostname()+'_2')) alarm_gen2.verify_on_setup() alarms = alarm_gen2.create_process_state_alarm( 'contrail-topology') alarm_gen2.send_alarm(socket.gethostname()+'_2', alarms, COLLECTOR_INFO_TABLE) keys = [socket.gethostname()+'_1', socket.gethostname()+'_2'] assert(vizd_obj.verify_alarm_list_include(analytics_tbl, expected_alarms=keys)) assert(vizd_obj.verify_alarm(analytics_tbl, keys[0], obj_to_dict( alarm_gen1.alarms[COLLECTOR_INFO_TABLE][keys[0]].data))) assert(vizd_obj.verify_alarm(analytics_tbl, keys[1], obj_to_dict( alarm_gen2.alarms[COLLECTOR_INFO_TABLE][keys[1]].data))) keys = ['<&'+socket.gethostname()+'_1>'] assert(vizd_obj.verify_alarm_list_include(control_tbl, expected_alarms=keys)) assert(vizd_obj.verify_alarm(control_tbl, keys[0], obj_to_dict( alarm_gen1.alarms[BGP_ROUTER_TABLE][keys[0]].data))) alarm_gen2.delete_alarm(socket.gethostname()+'_2', COLLECTOR_INFO_TABLE) keys = [socket.gethostname()+'_1'] assert(vizd_obj.verify_alarm_list_include(analytics_tbl, expected_alarms=keys)) ukeys = [socket.gethostname()+'_2'] assert(vizd_obj.verify_alarm_list_exclude(analytics_tbl, unexpected_alms=ukeys)) assert(vizd_obj.verify_alarm(analytics_tbl, keys[0], obj_to_dict( alarm_gen1.alarms[COLLECTOR_INFO_TABLE][keys[0]].data))) assert(vizd_obj.verify_alarm(analytics_tbl, ukeys[0], {})) alarm_gen1.disconnect_from_collector() ukeys = [socket.gethostname()+'_1'] assert(vizd_obj.verify_alarm_list_exclude(analytics_tbl, unexpected_alms=ukeys)) assert(vizd_obj.verify_alarm(analytics_tbl, ukeys[0], {})) ukeys = ['<&'+socket.gethostname()+'_1'] assert(vizd_obj.verify_alarm_list_exclude(control_tbl, unexpected_alms=ukeys)) assert(vizd_obj.verify_alarm(control_tbl, ukeys[0], {})) alarms = alarm_gen1.create_process_state_alarm( 'contrail-snmp-collector') alarm_gen1.send_alarm(socket.gethostname()+'_1', alarms, COLLECTOR_INFO_TABLE) alarm_gen1.connect_to_collector() keys = [socket.gethostname()+'_1'] assert(vizd_obj.verify_alarm_list_include(analytics_tbl, expected_alarms=keys)) assert(vizd_obj.verify_alarm(analytics_tbl, keys[0], obj_to_dict( alarm_gen1.alarms[COLLECTOR_INFO_TABLE][keys[0]].data))) keys = ['<&'+socket.gethostname()+'_1>'] assert(vizd_obj.verify_alarm_list_include(control_tbl, expected_alarms=keys)) assert(vizd_obj.verify_alarm(control_tbl, keys[0], obj_to_dict( alarm_gen1.alarms[BGP_ROUTER_TABLE][keys[0]].data))) def test_08_uve_alarm_filter(self): logging.info('%%% test_08_uve_alarm_filter %%%') if AnalyticsUveTest._check_skip_kafka() is True: return True vizd_obj = self.useFixture( AnalyticsFixture(logging, builddir, -1, 0, collector_ha_test=True, start_kafka = True)) assert vizd_obj.verify_on_setup() collectors = [vizd_obj.collectors[0].get_addr(), vizd_obj.collectors[1].get_addr()] api_server_name = socket.gethostname()+'_1' api_server = self.useFixture( GeneratorFixture('contrail-api', [collectors[0]], logging, None, node_type='Config', hostname=api_server_name)) vr_agent_name = socket.gethostname()+'_2' vr_agent = self.useFixture( GeneratorFixture('contrail-vrouter-agent', [collectors[1]], logging, None, node_type='Compute', hostname=vr_agent_name)) alarm_gen1_name = socket.gethostname()+'_1' alarm_gen1 = self.useFixture( GeneratorFixture('contrail-alarm-gen', [collectors[0]], logging, None, node_type='Analytics', hostname=alarm_gen1_name)) alarm_gen2_name = socket.gethostname()+'_3' alarm_gen2 = self.useFixture( GeneratorFixture('contrail-alarm-gen', [collectors[1]], logging, None, node_type='Analytics', hostname=alarm_gen2_name)) api_server.verify_on_setup() vr_agent.verify_on_setup() alarm_gen1.verify_on_setup() alarm_gen2.verify_on_setup() vn_list = ['default-domain:project1:vn1', 'default-domain:project1:vn2', 'default-domain:project2:vn1', 'default-domain:project2:vn1&'] api_server.send_vn_config_uve(name=vn_list[0], partial_conn_nw=[vn_list[1]], num_acl_rules=2) api_server.send_vn_config_uve(name=vn_list[1], num_acl_rules=3) vr_agent.send_vn_agent_uve(name=vn_list[1], num_acl_rules=3, ipkts=2, ibytes=1024) vr_agent.send_vn_agent_uve(name=vn_list[2], ipkts=4, ibytes=128) vr_agent.send_vn_agent_uve(name=vn_list[3], ipkts=8, ibytes=256) alarms = alarm_gen1.create_alarm('InPktsThreshold') alarms += alarm_gen1.create_alarm('InBytesThreshold', ack=True) alarm_gen1.send_alarm(vn_list[1], alarms, VN_TABLE) alarms = alarm_gen2.create_alarm('ConfigNotPresent', ack=False) alarm_gen2.send_alarm(vn_list[2], alarms, VN_TABLE) alarms = alarm_gen2.create_alarm('ConfigNotPresent', ack=False) alarm_gen2.send_alarm(vn_list[3], alarms, VN_TABLE) filt_test = [ { 'uve_list_get': [ 'default-domain:project1:vn1', 'default-domain:project1:vn2', 'default-domain:project2:vn1', 'default-domain:project2:vn1&' ], 'get_alarms': { 'virtual-network': [ { 'name' : 'default-domain:project1:vn2', 'value' : { 'UVEAlarms': { 'alarms': [ { 'type': 'InPktsThreshold', }, { 'type': 'InBytesThreshold', 'ack': True } ] } } }, { 'name' : 'default-domain:project2:vn1', 'value' : { 'UVEAlarms': { 'alarms': [ { 'type': 'ConfigNotPresent', 'ack': False } ] } } }, { 'name' : 'default-domain:project2:vn1&', 'value' : { 'UVEAlarms': { 'alarms': [ { 'type': 'ConfigNotPresent', 'ack': False } ] } } }, ] }, 'uve_get_post': { 'value': [ { 'name': 'default-domain:project1:vn1', 'value': { 'UveVirtualNetworkConfig': { 'partially_connected_networks': [ 'default-domain:project1:vn2' ], 'total_acl_rules': 2 } } }, { 'name': 'default-domain:project1:vn2', 'value': { 'UveVirtualNetworkAgent': { 'in_tpkts': 2, 'in_bytes': 1024, 'total_acl_rules': 3 }, 'UveVirtualNetworkConfig': { 'total_acl_rules': 3 }, 'UVEAlarms': { 'alarms': [ { 'type': 'InPktsThreshold', }, { 'type': 'InBytesThreshold', 'ack': True } ] } } }, { 'name': 'default-domain:project2:vn1', 'value': { 'UveVirtualNetworkAgent': { 'in_tpkts': 4, 'in_bytes': 128 }, 'UVEAlarms': { 'alarms': [ { 'type': 'ConfigNotPresent', 'ack': False } ] } } }, { 'name': 'default-domain:project2:vn1&', 'value': { 'UveVirtualNetworkAgent': { 'in_tpkts': 8, 'in_bytes': 256 }, 'UVEAlarms': { 'alarms': [ { 'type': 'ConfigNotPresent', 'ack': False } ] } } } ] }, }, { 'kfilt': ['*'], 'uve_list_get': [ 'default-domain:project1:vn1', 'default-domain:project1:vn2', 'default-domain:project2:vn1', 'default-domain:project2:vn1&' ], 'uve_get_post': { 'value': [ { 'name': 'default-domain:project1:vn1', 'value': { 'UveVirtualNetworkConfig': { 'partially_connected_networks': [ 'default-domain:project1:vn2' ], 'total_acl_rules': 2 } } }, { 'name': 'default-domain:project1:vn2', 'value': { 'UveVirtualNetworkAgent': { 'in_tpkts': 2, 'in_bytes': 1024, 'total_acl_rules': 3 }, 'UveVirtualNetworkConfig': { 'total_acl_rules': 3 }, 'UVEAlarms': { 'alarms': [ { 'type': 'InPktsThreshold', }, { 'type': 'InBytesThreshold', 'ack': True } ] } } }, { 'name': 'default-domain:project2:vn1', 'value': { 'UveVirtualNetworkAgent': { 'in_tpkts': 4, 'in_bytes': 128 }, 'UVEAlarms': { 'alarms': [ { 'type': 'ConfigNotPresent', 'ack': False } ] } } }, { 'name': 'default-domain:project2:vn1&', 'value': { 'UveVirtualNetworkAgent': { 'in_tpkts': 8, 'in_bytes': 256 }, 'UVEAlarms': { 'alarms': [ { 'type': 'ConfigNotPresent', 'ack': False } ] } } } ] }, }, { 'kfilt': ['default-domain:project1:*', 'default-domain:project2:*'], 'uve_list_get': [ 'default-domain:project1:vn1', 'default-domain:project1:vn2', 'default-domain:project2:vn1', 'default-domain:project2:vn1&' ], 'uve_get_post': { 'value': [ { 'name': 'default-domain:project1:vn1', 'value': { 'UveVirtualNetworkConfig': { 'partially_connected_networks': [ 'default-domain:project1:vn2' ], 'total_acl_rules': 2 } } }, { 'name': 'default-domain:project1:vn2', 'value': { 'UveVirtualNetworkAgent': { 'in_tpkts': 2, 'in_bytes': 1024, 'total_acl_rules': 3 }, 'UveVirtualNetworkConfig': { 'total_acl_rules': 3 }, 'UVEAlarms': { 'alarms': [ { 'type': 'InPktsThreshold', }, { 'type': 'InBytesThreshold', 'ack': True } ] } } }, { 'name': 'default-domain:project2:vn1', 'value': { 'UveVirtualNetworkAgent': { 'in_tpkts': 4, 'in_bytes': 128 }, 'UVEAlarms': { 'alarms': [ { 'type': 'ConfigNotPresent', 'ack': False } ] } } }, { 'name': 'default-domain:project2:vn1&', 'value': { 'UveVirtualNetworkAgent': { 'in_tpkts': 8, 'in_bytes': 256 }, 'UVEAlarms': { 'alarms': [ { 'type': 'ConfigNotPresent', 'ack': False } ] } } } ] }, }, { 'kfilt': ['default-domain:project1:vn1', 'default-domain:project2:*'], 'uve_list_get': [ 'default-domain:project1:vn1', 'default-domain:project2:vn1', 'default-domain:project2:vn1&' ], 'uve_get_post': { 'value': [ { 'name': 'default-domain:project1:vn1', 'value': { 'UveVirtualNetworkConfig': { 'partially_connected_networks': [ 'default-domain:project1:vn2' ], 'total_acl_rules': 2 } } }, { 'name': 'default-domain:project2:vn1', 'value': { 'UveVirtualNetworkAgent': { 'in_tpkts': 4, 'in_bytes': 128 }, 'UVEAlarms': { 'alarms': [ { 'type': 'ConfigNotPresent', 'ack': False } ] } } }, { 'name': 'default-domain:project2:vn1&', 'value': { 'UveVirtualNetworkAgent': { 'in_tpkts': 8, 'in_bytes': 256 }, 'UVEAlarms': { 'alarms': [ { 'type': 'ConfigNotPresent', 'ack': False } ] } } } ] }, }, { 'kfilt': [ 'default-domain:project2:*', 'invalid-vn:*' ], 'get_alarms': { 'virtual-network': [ { 'name' : 'default-domain:project2:vn1', 'value' : { 'UVEAlarms': { 'alarms': [ { 'type': 'ConfigNotPresent', 'ack': False } ] } } }, { 'name' : 'default-domain:project2:vn1&', 'value' : { 'UVEAlarms': { 'alarms': [ { 'type': 'ConfigNotPresent', 'ack': False } ] } } }, ] }, 'uve_list_get': [ 'default-domain:project2:vn1', 'default-domain:project2:vn1&' ], 'uve_get_post': { 'value': [ { 'name': 'default-domain:project2:vn1', 'value': { 'UveVirtualNetworkAgent': { 'in_tpkts': 4, 'in_bytes': 128 }, 'UVEAlarms': { 'alarms': [ { 'type': 'ConfigNotPresent', 'ack': False } ] } } }, { 'name': 'default-domain:project2:vn1&', 'value': { 'UveVirtualNetworkAgent': { 'in_tpkts': 8, 'in_bytes': 256 }, 'UVEAlarms': { 'alarms': [ { 'type': 'ConfigNotPresent', 'ack': False } ] } } } ] }, }, { 'kfilt': [ 'default-domain:project1:vn2', 'default-domain:project2:vn1&', 'invalid-vn' ], 'uve_list_get': [ 'default-domain:project1:vn2', 'default-domain:project2:vn1&' ], 'uve_get_post': { 'value': [ { 'name': 'default-domain:project1:vn2', 'value': { 'UveVirtualNetworkAgent': { 'in_tpkts': 2, 'in_bytes': 1024, 'total_acl_rules': 3 }, 'UveVirtualNetworkConfig': { 'total_acl_rules': 3 }, 'UVEAlarms': { 'alarms': [ { 'type': 'InPktsThreshold', }, { 'type': 'InBytesThreshold', 'ack': True } ] } } }, { 'name': 'default-domain:project2:vn1&', 'value': { 'UveVirtualNetworkAgent': { 'in_tpkts': 8, 'in_bytes': 256 }, 'UVEAlarms': { 'alarms': [ { 'type': 'ConfigNotPresent', 'ack': False } ] } } } ] }, }, { 'kfilt': ['invalid-vn'], 'uve_list_get': [], 'uve_get_post': {'value': []}, }, { 'sfilt': socket.gethostname()+'_1', 'uve_list_get': [ 'default-domain:project1:vn1', 'default-domain:project1:vn2' ], 'uve_get_post': { 'value': [ { 'name': 'default-domain:project1:vn1', 'value': { 'UveVirtualNetworkConfig': { 'partially_connected_networks': [ 'default-domain:project1:vn2' ], 'total_acl_rules': 2 } } }, { 'name': 'default-domain:project1:vn2', 'value': { 'UveVirtualNetworkConfig': { 'total_acl_rules': 3 }, 'UVEAlarms': { 'alarms': [ { 'type': 'InPktsThreshold', }, { 'type': 'InBytesThreshold', 'ack': True } ] } } } ] }, }, { 'sfilt': socket.gethostname()+'_3', 'uve_list_get': [ 'default-domain:project2:vn1', 'default-domain:project2:vn1&' ], 'uve_get_post': { 'value': [ { 'name': 'default-domain:project2:vn1', 'value': { 'UVEAlarms': { 'alarms': [ { 'type': 'ConfigNotPresent', 'ack': False } ] } } }, { 'name': 'default-domain:project2:vn1&', 'value': { 'UVEAlarms': { 'alarms': [ { 'type': 'ConfigNotPresent', 'ack': False } ] } } } ] }, }, { 'sfilt': 'invalid_source', 'uve_list_get': [], 'uve_get_post': {'value': []}, }, { 'mfilt': 'Config:contrail-api:0', 'uve_list_get': [ 'default-domain:project1:vn1', 'default-domain:project1:vn2' ], 'uve_get_post': { 'value': [ { 'name': 'default-domain:project1:vn1', 'value': { 'UveVirtualNetworkConfig': { 'partially_connected_networks': [ 'default-domain:project1:vn2' ], 'total_acl_rules': 2 } } }, { 'name': 'default-domain:project1:vn2', 'value': { 'UveVirtualNetworkConfig': { 'total_acl_rules': 3 } } } ] }, }, { 'mfilt': 'Analytics:contrail-alarm-gen:0', 'uve_list_get': [ 'default-domain:project1:vn2', 'default-domain:project2:vn1', 'default-domain:project2:vn1&' ], 'uve_get_post': { 'value': [ { 'name': 'default-domain:project1:vn2', 'value': { 'UVEAlarms': { 'alarms': [ { 'type': 'InPktsThreshold', }, { 'type': 'InBytesThreshold', 'ack': True } ] } } }, { 'name': 'default-domain:project2:vn1', 'value': { 'UVEAlarms': { 'alarms': [ { 'type': 'ConfigNotPresent', 'ack': False } ] } } }, { 'name': 'default-domain:project2:vn1&', 'value': { 'UVEAlarms': { 'alarms': [ { 'type': 'ConfigNotPresent', 'ack': False } ] } } } ] }, }, { 'mfilt': 'Analytics:contrail-invalid:0', 'uve_list_get': [], 'uve_get_post': {'value': []}, }, { 'cfilt': ['UveVirtualNetworkAgent'], 'uve_list_get': [ 'default-domain:project1:vn2', 'default-domain:project2:vn1', 'default-domain:project2:vn1&' ], 'uve_get_post': { 'value': [ { 'name': 'default-domain:project1:vn2', 'value': { 'UveVirtualNetworkAgent': { 'in_tpkts': 2, 'in_bytes': 1024, 'total_acl_rules': 3 } } }, { 'name': 'default-domain:project2:vn1', 'value': { 'UveVirtualNetworkAgent': { 'in_tpkts': 4, 'in_bytes': 128 } } }, { 'name': 'default-domain:project2:vn1&', 'value': { 'UveVirtualNetworkAgent': { 'in_tpkts': 8, 'in_bytes': 256 } } } ] }, }, { 'cfilt': [ 'UveVirtualNetworkAgent:total_acl_rules', 'UveVirtualNetworkConfig:partially_connected_networks' ], 'uve_list_get': [ 'default-domain:project1:vn1', 'default-domain:project1:vn2' ], 'uve_get_post': { 'value': [ { 'name': 'default-domain:project1:vn1', 'value': { 'UveVirtualNetworkConfig': { 'partially_connected_networks': [ 'default-domain:project1:vn2' ] } } }, { 'name': 'default-domain:project1:vn2', 'value': { 'UveVirtualNetworkAgent': { 'total_acl_rules': 3 } } } ] }, }, { 'cfilt': [ 'UveVirtualNetworkConfig:invalid', 'UveVirtualNetworkAgent:in_tpkts', 'UVEAlarms:alarms' ], 'uve_list_get': [ 'default-domain:project1:vn2', 'default-domain:project2:vn1', 'default-domain:project2:vn1&' ], 'uve_get_post': { 'value': [ { 'name': 'default-domain:project1:vn2', 'value': { 'UveVirtualNetworkAgent': { 'in_tpkts': 2, }, 'UVEAlarms': { 'alarms': [ { 'type': 'InPktsThreshold', }, { 'type': 'InBytesThreshold', 'ack': True } ] } } }, { 'name': 'default-domain:project2:vn1', 'value': { 'UveVirtualNetworkAgent': { 'in_tpkts': 4, }, 'UVEAlarms': { 'alarms': [ { 'type': 'ConfigNotPresent', 'ack': False } ] } } }, { 'name': 'default-domain:project2:vn1&', 'value': { 'UveVirtualNetworkAgent': { 'in_tpkts': 8, }, 'UVEAlarms': { 'alarms': [ { 'type': 'ConfigNotPresent', 'ack': False } ] } } } ] }, }, { 'cfilt': [ 'UveVirtualNetworkAgent:invalid', 'UVEAlarms:invalid_alarms', 'invalid' ], 'uve_list_get': [], 'uve_get_post': {'value': []}, }, { 'ackfilt': True, 'uve_list_get': [ 'default-domain:project1:vn1', 'default-domain:project1:vn2', 'default-domain:project2:vn1', 'default-domain:project2:vn1&' ], 'uve_get_post': { 'value': [ { 'name': 'default-domain:project1:vn1', 'value': { 'UveVirtualNetworkConfig': { 'partially_connected_networks': [ 'default-domain:project1:vn2' ], 'total_acl_rules': 2 } } }, { 'name': 'default-domain:project1:vn2', 'value': { 'UveVirtualNetworkAgent': { 'in_tpkts': 2, 'in_bytes': 1024, 'total_acl_rules': 3 }, 'UveVirtualNetworkConfig': { 'total_acl_rules': 3 }, 'UVEAlarms': { 'alarms': [ { 'type': 'InBytesThreshold', 'ack': True } ] } } }, { 'name': 'default-domain:project2:vn1', 'value': { 'UveVirtualNetworkAgent': { 'in_tpkts': 4, 'in_bytes': 128 }, } }, { 'name': 'default-domain:project2:vn1&', 'value': { 'UveVirtualNetworkAgent': { 'in_tpkts': 8, 'in_bytes': 256 }, } } ] }, }, { 'ackfilt': False, 'uve_list_get': [ 'default-domain:project1:vn1', 'default-domain:project1:vn2', 'default-domain:project2:vn1', 'default-domain:project2:vn1&' ], 'get_alarms': { 'virtual-network': [ { 'name' : 'default-domain:project1:vn2', 'value' : { 'UVEAlarms': { 'alarms': [ { 'type': 'InPktsThreshold', }, ] } } }, { 'name' : 'default-domain:project2:vn1', 'value' : { 'UVEAlarms': { 'alarms': [ { 'type': 'ConfigNotPresent', 'ack': False } ] } } }, { 'name' : 'default-domain:project2:vn1&', 'value' : { 'UVEAlarms': { 'alarms': [ { 'type': 'ConfigNotPresent', 'ack': False } ] } } }, ] }, 'uve_get_post': { 'value': [ { 'name': 'default-domain:project1:vn1', 'value': { 'UveVirtualNetworkConfig': { 'partially_connected_networks': [ 'default-domain:project1:vn2' ], 'total_acl_rules': 2 } } }, { 'name': 'default-domain:project1:vn2', 'value': { 'UveVirtualNetworkAgent': { 'in_tpkts': 2, 'in_bytes': 1024, 'total_acl_rules': 3 }, 'UveVirtualNetworkConfig': { 'total_acl_rules': 3 }, 'UVEAlarms': { 'alarms': [ { 'type': 'InPktsThreshold', } ] } } }, { 'name': 'default-domain:project2:vn1', 'value': { 'UveVirtualNetworkAgent': { 'in_tpkts': 4, 'in_bytes': 128 }, 'UVEAlarms': { 'alarms': [ { 'type': 'ConfigNotPresent', 'ack': False } ] } } }, { 'name': 'default-domain:project2:vn1&', 'value': { 'UveVirtualNetworkAgent': { 'in_tpkts': 8, 'in_bytes': 256 }, 'UVEAlarms': { 'alarms': [ { 'type': 'ConfigNotPresent', 'ack': False } ] } } } ] }, }, { 'kfilt': [ 'default-domain:project1:*', 'default-domain:project2:vn1', 'default-domain:invalid' ], 'sfilt': socket.gethostname()+'_2', 'uve_list_get': [ 'default-domain:project1:vn2', 'default-domain:project2:vn1' ], 'uve_get_post': { 'value': [ { 'name': 'default-domain:project1:vn2', 'value': { 'UveVirtualNetworkAgent': { 'in_tpkts': 2, 'in_bytes': 1024, 'total_acl_rules': 3 } } }, { 'name': 'default-domain:project2:vn1', 'value': { 'UveVirtualNetworkAgent': { 'in_tpkts': 4, 'in_bytes': 128 } } } ] }, }, { 'kfilt': [ 'default-domain:project1:vn1', 'default-domain:project2:*', 'default-domain:invalid' ], 'sfilt': socket.gethostname()+'_2', 'ackfilt': True, 'uve_list_get': [ 'default-domain:project2:vn1', 'default-domain:project2:vn1&' ], 'uve_get_post': { 'value': [ { 'name': 'default-domain:project2:vn1', 'value': { 'UveVirtualNetworkAgent': { 'in_tpkts': 4, 'in_bytes': 128 } } }, { 'name': 'default-domain:project2:vn1&', 'value': { 'UveVirtualNetworkAgent': { 'in_tpkts': 8, 'in_bytes': 256 } } } ] }, }, { 'kfilt': [ 'default-domain:project1:vn1', 'default-domain:project1:vn2', 'default-domain:project2:vn1' ], 'sfilt': socket.gethostname()+'_1', 'cfilt': [ 'UveVirtualNetworkAgent', 'UVEAlarms', 'UveVirtualNetworkConfig:Invalid' ], 'uve_list_get': [ 'default-domain:project1:vn2' ], 'uve_get_post': { 'value': [ { 'name': 'default-domain:project1:vn2', 'value': { 'UVEAlarms': { 'alarms': [ { 'type': 'InPktsThreshold', }, { 'type': 'InBytesThreshold', 'ack': True } ] } } } ] }, }, { 'kfilt': ['*'], 'mfilt': 'Config:contrail-api:0', 'cfilt': [ 'UveVirtualNetworkAgent', 'UVEAlarms:alarms' ], 'uve_list_get': [], 'uve_get_post': {'value': []}, }, { 'kfilt': [ 'default-domain:project1:vn1', 'default-domain:project1:vn2', 'default-domain:project2:*' ], 'sfilt': socket.gethostname()+'_1', 'mfilt': 'Config:contrail-api:0', 'cfilt': [ 'UveVirtualNetworkConfig:partially_connected_networks', 'UveVirtualNetworkConfig:total_acl_rules', 'UVEAlarms' ], 'uve_list_get': [ 'default-domain:project1:vn1', 'default-domain:project1:vn2' ], 'uve_get_post': { 'value': [ { 'name': 'default-domain:project1:vn1', 'value': { 'UveVirtualNetworkConfig': { 'partially_connected_networks': [ 'default-domain:project1:vn2' ], 'total_acl_rules': 2 } } }, { 'name': 'default-domain:project1:vn2', 'value': { 'UveVirtualNetworkConfig': { 'total_acl_rules': 3 }, } } ] }, }, { 'kfilt': [ 'default-domain:project1:*', 'default-domain:project2:vn1', 'default-domain:project2:invalid' ], 'sfilt': socket.gethostname()+'_3', 'mfilt': 'Analytics:contrail-alarm-gen:0', 'cfilt': [ 'UveVirtualNetworkConfig', 'UVEAlarms:alarms', 'UveVirtualNetworkAgent' ], 'uve_list_get': [ 'default-domain:project2:vn1' ], 'uve_get_post': { 'value': [ { 'name': 'default-domain:project2:vn1', 'value': { 'UVEAlarms': { 'alarms': [ { 'type': 'ConfigNotPresent', 'ack': False } ] } } } ] }, }, { 'kfilt': [ 'default-domain:project1:*', 'default-domain:project2:vn1&', 'default-domain:project2:invalid' ], 'sfilt': socket.gethostname()+'_3', 'mfilt': 'Analytics:contrail-alarm-gen:0', 'cfilt': [ 'UveVirtualNetworkConfig', 'UVEAlarms:alarms', 'UveVirtualNetworkAgent' ], 'ackfilt': True, 'uve_list_get': [ 'default-domain:project2:vn1&' ], 'uve_get_post': {'value': []}, } ] vn_table = _OBJECT_TABLES[VN_TABLE].log_query_name for i in range(len(filt_test)): filters = dict(kfilt=filt_test[i].get('kfilt'), sfilt=filt_test[i].get('sfilt'), mfilt=filt_test[i].get('mfilt'), cfilt=filt_test[i].get('cfilt'), ackfilt=filt_test[i].get('ackfilt')) assert(vizd_obj.verify_uve_list(vn_table, filts=filters, exp_uve_list=filt_test[i]['uve_list_get'])) assert(vizd_obj.verify_multi_uve_get(vn_table, filts=filters, exp_uves=filt_test[i]['uve_get_post'])) assert(vizd_obj.verify_uve_post(vn_table, filts=filters, exp_uves=filt_test[i]['uve_get_post'])) if 'get_alarms' in filt_test[i]: filters['tablefilt'] = 'virtual-network' assert(vizd_obj.verify_get_alarms(vn_table, filts=filters, exp_uves=filt_test[i]['get_alarms'])) @staticmethod def get_free_port(): cs = socket.socket(socket.AF_INET, socket.SOCK_STREAM) cs.bind(("", 0)) cport = cs.getsockname()[1] cs.close() return cport @staticmethod def _check_skip_kafka(): (PLATFORM, VERSION, EXTRA) = platform.linux_distribution() if PLATFORM.lower() == 'ubuntu': if VERSION.find('12.') == 0: return True if PLATFORM.lower() == 'centos': if VERSION.find('6.') == 0: return True return False def _term_handler(*_): raise IntSignal() if __name__ == '__main__': gevent.signal(signal.SIGINT,_term_handler) unittest.main(catchbreak=True)

true

f70f7f844b3f8ee5ade345d734bba14d2d862c60

8,736

py

Python

PyPowerDNS/api.py

TheDJVG/PyPowerDNS

2e0e47c3bb7a7b20c08ddfa6f0cd93e663d02dc7

[ "MIT" ]

1

2021-04-05T21:40:34.000Z

PyPowerDNS/api.py

TheDJVG/PyPowerDNS

2e0e47c3bb7a7b20c08ddfa6f0cd93e663d02dc7

[ "MIT" ]

1

2020-09-21T15:00:44.000Z

2020-09-22T00:38:15.000Z

PyPowerDNS/api.py

TheDJVG/PyPowerDNS

2e0e47c3bb7a7b20c08ddfa6f0cd93e663d02dc7

[ "MIT" ]

null

from .objects import Server, Zone, RRSet, Record, Comment, Cryptokey, Metadata, SearchResult, StatisticItem, \ MapStatisticItem, RingStatisticItem, SimpleStatisticItem, CacheFlushResult from .exceptions import PDNSApiException, PDNSApiNotFound import json from functools import partial import requests import logging logger = logging.getLogger(__name__) # TODO: # - Logging # - TSIGKeys class APIClient: def __init__(self, api_host, api_key, tls_verify=True, request_timeout=None): self._api_url = api_host if 'api/v1' in api_host else f"{api_host}/api/v1" self._api_key = api_key self._tls_verify = tls_verify self._request_timeout = request_timeout if not self._tls_verify: logger.warning("Disabling TLS certificate validation.") import urllib3 urllib3.disable_warnings(urllib3.exceptions.InsecureRequestWarning) self.request_headers = {'X-API-Key': self._api_key} self.get = partial(self.request, method='GET') self.post = partial(self.request, method='POST') self.put = partial(self.request, method='PUT') self.patch = partial(self.request, method='PATCH') self.delete = partial(self.request, method='DELETE') self.servers = self._set_servers() self.current_server = self.servers[0] self.zones = self._set_zones() def request(self, path: str, method: str, data=None, **kwargs): url = f"{self._api_url}/{path.lstrip('/')}" if data is None: data = {} response = requests.request(method, url, json=data, headers=self.request_headers, timeout=self._request_timeout, verify=self._tls_verify, **kwargs ) try: response.raise_for_status() return response.json() except requests.exceptions.HTTPError as e: if response.status_code == 404: raise (PDNSApiNotFound(e)) from None try: status_message = response.json() status_message = status_message.get('error', status_message.get('errors', 'Unknown error')) except: status_message = response.text raise PDNSApiException(response.status_code, status_message) from None except json.decoder.JSONDecodeError: return response.text def _set_servers(self): new_servers = list() for server in self.get('servers'): new_servers.append(Server(**server)) return new_servers def _set_zones(self): new_zones = list() for zone in self.get(f'servers/{self.current_server.id}/zones'): new_zones.append(Zone(**zone)) return new_zones def create_zone(self, zone: Zone): path = f'servers/{self.current_server.id}/zones' return Zone(**self.post(path, data=zone)) # Zones def get_zone(self, zone_name): path = f'servers/{self.current_server.id}/zones/{zone_name}' zone = Zone(**self.get(path)) new_rrsets = [] for rrset in zone.rrsets: new_comments = [] new_records = [] rrset = RRSet(**rrset) for comment in rrset.comments: new_comments.append(Comment(**comment)) for record in rrset.records: new_records.append(Record(**record)) rrset.comments = new_comments rrset.records = new_records new_rrsets.append(rrset) zone.rrsets = new_rrsets return zone def delete_zone(self, zone_name): path = f'servers/{self.current_server.id}/zones/{zone_name}' self.delete(path) def update_zone_metadata(self, zone: Zone): path = f'servers/{self.current_server.id}/zones/{zone.name}' self.put(path, data=zone) return self.get_zone(zone.name) def patch_rrsets(self, zone: Zone): path = f'servers/{self.current_server.id}/zones/{zone.name}' self.patch(path, data={'rrsets': zone.rrsets}) return self.get_zone(zone.name) def create_records(self, zone: Zone, rrsets: list): for rrset in rrsets: rrset.changetype = 'REPLACE' zone = Zone(name=zone.name, kind=zone.kind, rrsets=rrsets) return self.patch_rrsets(zone) def delete_records(self, zone: Zone, rrsets: list): for rrset in rrsets: rrset.changetype = 'DELETE' zone = Zone(name=zone.name, kind=zone.kind, rrsets=rrsets) return self.patch_rrsets(zone) # Cryptokeys def get_zone_cryptokeys(self, zone: Zone): path = f'servers/{self.current_server.id}/zones/{zone.name}/cryptokeys' cryptkeys_new = [] for cryptokey in self.get(path): cryptkeys_new.append(Cryptokey(**cryptokey)) return cryptkeys_new def create_cryptokey(self, zone: Zone, cryptokey: Cryptokey): path = f'servers/{self.current_server.id}/zones/{zone.name}/cryptokeys' return self.post(path, data=cryptokey) def get_cryptokey(self, zone: Zone, key_id): path = f'servers/{self.current_server.id}/zones/{zone.name}/cryptokeys/{key_id}' return Cryptokey(**self.get(path)) def put_cryptokey(self, zone: Zone, cryptokey: Cryptokey): path = f'servers/{self.current_server.id}/zones/{zone.name}/cryptokeys/{cryptokey.id}' self.put(path, data=cryptokey) # Metadata def get_zone_metadata(self, zone: Zone): path = f'servers/{self.current_server.id}/zones/{zone.name}/metadata' metadata_new = [] for metadata in self.get(path): metadata_new.append(Metadata(**metadata)) return metadata_new def create_metadata(self, zone: Zone, metadata: Metadata): path = f'servers/{self.current_server.id}/zones/{zone.name}/metadata' self.post(path, data=metadata) return self.get_zone_metadata(zone) def get_metadata(self, zone: Zone, metadata_kind): path = f'servers/{self.current_server.id}/zones/{zone.name}/metadata/{metadata_kind}' return Metadata(**self.get(path)) def put_metadata(self, zone: Zone, metadata: Metadata): path = f'servers/{self.current_server.id}/zones/{zone.name}/metadata/{metadata.kind}' return Metadata(**self.put(path, data=metadata)) def delete_metadata(self, zone: Zone, metadata: Metadata): path = f'servers/{self.current_server.id}/zones/{zone.name}/metadata/{metadata.kind}' self.delete(path) # TSIGKeys # FIXME TBW # Searching def search(self, query: str, max_results: int, object_type: str): path = f'servers/{self.current_server.id}/search-data' object_types = ['all', 'zone', 'record', 'comment'] if object_type not in object_types: raise TypeError(f"object_type must be one of {', '.join(object_types)}") if not isinstance(max_results, int): raise TypeError("max_results needs to be an integer.") payload = {'q': query, 'max': max_results, 'object_type': object_type} new_results = [] for result in self.get(path, params=payload): new_results.append(SearchResult(**result)) return new_results # Statistics def statistics(self, statistic=None, includerings=True): path = f'servers/{self.current_server.id}/statistics' payload = {'statistic': statistic, 'includerings': includerings} type_map = { 'StatisticItem': StatisticItem, 'MapStatisticItem': MapStatisticItem, 'RingStatisticItem': RingStatisticItem } new_statistics = [] for item in self.get(path, params=payload): if item.get('type') in type_map.keys(): new_statistic = type_map[item.get('type')](**item) if isinstance(new_statistic.value, list): new_values = [] for value in new_statistic.value: new_values.append(SimpleStatisticItem(**value)) new_statistic.value = new_values if statistic is not None: return new_statistic new_statistics.append(new_statistic) return new_statistics # Cache def flush_cache(self, domain: str): path = f'servers/{self.current_server.id}/cache/flush' payload = {'domain': domain} return CacheFlushResult(**self.put(path, params=payload))

38.484581

110

0.615156

from .objects import Server, Zone, RRSet, Record, Comment, Cryptokey, Metadata, SearchResult, StatisticItem, \ MapStatisticItem, RingStatisticItem, SimpleStatisticItem, CacheFlushResult from .exceptions import PDNSApiException, PDNSApiNotFound import json from functools import partial import requests import logging logger = logging.getLogger(__name__) class APIClient: def __init__(self, api_host, api_key, tls_verify=True, request_timeout=None): self._api_url = api_host if 'api/v1' in api_host else f"{api_host}/api/v1" self._api_key = api_key self._tls_verify = tls_verify self._request_timeout = request_timeout if not self._tls_verify: logger.warning("Disabling TLS certificate validation.") import urllib3 urllib3.disable_warnings(urllib3.exceptions.InsecureRequestWarning) self.request_headers = {'X-API-Key': self._api_key} self.get = partial(self.request, method='GET') self.post = partial(self.request, method='POST') self.put = partial(self.request, method='PUT') self.patch = partial(self.request, method='PATCH') self.delete = partial(self.request, method='DELETE') self.servers = self._set_servers() self.current_server = self.servers[0] self.zones = self._set_zones() def request(self, path: str, method: str, data=None, **kwargs): url = f"{self._api_url}/{path.lstrip('/')}" if data is None: data = {} response = requests.request(method, url, json=data, headers=self.request_headers, timeout=self._request_timeout, verify=self._tls_verify, **kwargs ) try: response.raise_for_status() return response.json() except requests.exceptions.HTTPError as e: if response.status_code == 404: raise (PDNSApiNotFound(e)) from None try: status_message = response.json() status_message = status_message.get('error', status_message.get('errors', 'Unknown error')) except: status_message = response.text raise PDNSApiException(response.status_code, status_message) from None except json.decoder.JSONDecodeError: return response.text def _set_servers(self): new_servers = list() for server in self.get('servers'): new_servers.append(Server(**server)) return new_servers def _set_zones(self): new_zones = list() for zone in self.get(f'servers/{self.current_server.id}/zones'): new_zones.append(Zone(**zone)) return new_zones def create_zone(self, zone: Zone): path = f'servers/{self.current_server.id}/zones' return Zone(**self.post(path, data=zone)) def get_zone(self, zone_name): path = f'servers/{self.current_server.id}/zones/{zone_name}' zone = Zone(**self.get(path)) new_rrsets = [] for rrset in zone.rrsets: new_comments = [] new_records = [] rrset = RRSet(**rrset) for comment in rrset.comments: new_comments.append(Comment(**comment)) for record in rrset.records: new_records.append(Record(**record)) rrset.comments = new_comments rrset.records = new_records new_rrsets.append(rrset) zone.rrsets = new_rrsets return zone def delete_zone(self, zone_name): path = f'servers/{self.current_server.id}/zones/{zone_name}' self.delete(path) def update_zone_metadata(self, zone: Zone): path = f'servers/{self.current_server.id}/zones/{zone.name}' self.put(path, data=zone) return self.get_zone(zone.name) def patch_rrsets(self, zone: Zone): path = f'servers/{self.current_server.id}/zones/{zone.name}' self.patch(path, data={'rrsets': zone.rrsets}) return self.get_zone(zone.name) def create_records(self, zone: Zone, rrsets: list): for rrset in rrsets: rrset.changetype = 'REPLACE' zone = Zone(name=zone.name, kind=zone.kind, rrsets=rrsets) return self.patch_rrsets(zone) def delete_records(self, zone: Zone, rrsets: list): for rrset in rrsets: rrset.changetype = 'DELETE' zone = Zone(name=zone.name, kind=zone.kind, rrsets=rrsets) return self.patch_rrsets(zone) def get_zone_cryptokeys(self, zone: Zone): path = f'servers/{self.current_server.id}/zones/{zone.name}/cryptokeys' cryptkeys_new = [] for cryptokey in self.get(path): cryptkeys_new.append(Cryptokey(**cryptokey)) return cryptkeys_new def create_cryptokey(self, zone: Zone, cryptokey: Cryptokey): path = f'servers/{self.current_server.id}/zones/{zone.name}/cryptokeys' return self.post(path, data=cryptokey) def get_cryptokey(self, zone: Zone, key_id): path = f'servers/{self.current_server.id}/zones/{zone.name}/cryptokeys/{key_id}' return Cryptokey(**self.get(path)) def put_cryptokey(self, zone: Zone, cryptokey: Cryptokey): path = f'servers/{self.current_server.id}/zones/{zone.name}/cryptokeys/{cryptokey.id}' self.put(path, data=cryptokey) def get_zone_metadata(self, zone: Zone): path = f'servers/{self.current_server.id}/zones/{zone.name}/metadata' metadata_new = [] for metadata in self.get(path): metadata_new.append(Metadata(**metadata)) return metadata_new def create_metadata(self, zone: Zone, metadata: Metadata): path = f'servers/{self.current_server.id}/zones/{zone.name}/metadata' self.post(path, data=metadata) return self.get_zone_metadata(zone) def get_metadata(self, zone: Zone, metadata_kind): path = f'servers/{self.current_server.id}/zones/{zone.name}/metadata/{metadata_kind}' return Metadata(**self.get(path)) def put_metadata(self, zone: Zone, metadata: Metadata): path = f'servers/{self.current_server.id}/zones/{zone.name}/metadata/{metadata.kind}' return Metadata(**self.put(path, data=metadata)) def delete_metadata(self, zone: Zone, metadata: Metadata): path = f'servers/{self.current_server.id}/zones/{zone.name}/metadata/{metadata.kind}' self.delete(path) def search(self, query: str, max_results: int, object_type: str): path = f'servers/{self.current_server.id}/search-data' object_types = ['all', 'zone', 'record', 'comment'] if object_type not in object_types: raise TypeError(f"object_type must be one of {', '.join(object_types)}") if not isinstance(max_results, int): raise TypeError("max_results needs to be an integer.") payload = {'q': query, 'max': max_results, 'object_type': object_type} new_results = [] for result in self.get(path, params=payload): new_results.append(SearchResult(**result)) return new_results def statistics(self, statistic=None, includerings=True): path = f'servers/{self.current_server.id}/statistics' payload = {'statistic': statistic, 'includerings': includerings} type_map = { 'StatisticItem': StatisticItem, 'MapStatisticItem': MapStatisticItem, 'RingStatisticItem': RingStatisticItem } new_statistics = [] for item in self.get(path, params=payload): if item.get('type') in type_map.keys(): new_statistic = type_map[item.get('type')](**item) if isinstance(new_statistic.value, list): new_values = [] for value in new_statistic.value: new_values.append(SimpleStatisticItem(**value)) new_statistic.value = new_values if statistic is not None: return new_statistic new_statistics.append(new_statistic) return new_statistics def flush_cache(self, domain: str): path = f'servers/{self.current_server.id}/cache/flush' payload = {'domain': domain} return CacheFlushResult(**self.put(path, params=payload))

true

f70f80addbf2038f17208bc47e55b1fabb3e74e7

5,534

py

Python

ezcoach/ezcoach/agent.py

Pawel-M/EZ-Coach

ee078b8ab7409730e99cb38653d03aa574ab914b

[ "MIT" ]

1

2021-09-14T13:17:33.000Z

ezcoach/ezcoach/agent.py

Pawel-M/EZ-Coach

ee078b8ab7409730e99cb38653d03aa574ab914b

[ "MIT" ]

null

ezcoach/ezcoach/agent.py

Pawel-M/EZ-Coach

ee078b8ab7409730e99cb38653d03aa574ab914b

[ "MIT" ]

null

""" The agent module contains three abstract classes that are subclassed in order to create algorithms. The classes are: * Player - for an algorithm that cannot learn and can only play * Learner - for a learning algorithm controlling a single agent * MultiLearner - for a learning algorithm of controlling a number of agents """ import abc from typing import List, Iterable from ezcoach.enviroment import Manifest class Player(abc.ABC): """ The abstract class representing a playing agent. It can be initialized with the manifest of the game and can react to states by selecting actions. Both methods are empty and must be implemented in the concrete class. A class that inherits from the Player class can be used with the Runner's test procedure. """ @abc.abstractmethod def initialize(self, manifest: Manifest): """ Initializes the object with the manifest that describe the game. :param manifest: a Manifest class obtained from the environment. """ @abc.abstractmethod def act(self, state): """ Selects an action to be performed in the given state. :param state: a state received from the environment :return: an action compliant with the manifest provided in initialize method """ @classmethod def __subclasshook__(cls, obj): if cls is Player: methods = ('initialize', 'act') if all(any(method in superclass.__dict__ for superclass in obj.__mro__) for method in methods): return True return NotImplemented class Learner(Player): """ The abstract class representing an agent that is capable of learning. It inherits from the Player class and thus it is capable of playing. Only do_start_episode method must be implemented. Other methods can be left unimplemented and consequently empty. Rewards are received on the step basis in receive_reward method and on episode basis with episode_ended method. Methods that ensure persistence are added for convenience. An agent derived from Learner can be used in both training and testing procedures. """ @abc.abstractmethod def do_start_episode(self, episode: int) -> bool: """ Decides if next episode should be started. :param episode: the number of an episode to be started (starting from 1) :return: the decision if the next episode should be started """ def episode_started(self, episode: int): """ Informs the algorithm that the episode was started. :param episode: the number of the started episode (starting from 1) """ def receive_reward(self, previous_state, action, reward: float, accumulated_reward: float, next_state): """ Receives a reward from an environment. :param previous_state: the state that precedes the reward :param action: the action that precedes the reward :param reward: the numerical reward signal :param accumulated_reward: the reward accumulated during the current episode :param next_state: the state that follow the reward """ def episode_ended(self, terminal_state, accumulated_reward): """ Receives the accumulated reward for an episode. If a discount is used this value should be ignored and the actual reward should be calculated using receive_reward method during the episode. :param terminal_state: the last state of the episode :param accumulated_reward: the accumulated reward assuming no discount """ @classmethod def __subclasshook__(cls, obj): if cls is Learner: methods = ('initialize', 'act', 'do_start_episode', 'episode_started', 'receive_reward', 'episode_ended') if all(any(method in superclass.__dict__ for superclass in obj.__mro__) for method in methods): return True return NotImplemented class MultiLearner(Learner): """ The class representing a learning algorithm capable of controlling a number of agents. It inherits from Learner class. The list of player numbers is provided in set_players method before each episode. The number identifying currently acting player is set in set_acting_player method which is invoked before act and receive_reward methods during an episode and before episode_ended method at the end of an episode. """ @abc.abstractmethod def set_players(self, players: Iterable[int]): """ Informs the learner about the players that it will control. :param players: an iterable of numbers identifying players """ @abc.abstractmethod def set_acting_player(self, player): """ Sets the current player that will act, receive reward and end episode. :param player: a number identifying the acting player """ @classmethod def __subclasshook__(cls, obj): if cls is MultiLearner: methods = ('initialize', 'act', 'do_start_episode', 'episode_started', 'receive_reward', 'episode_ended', 'set_players', 'set_acting_player') if all(any(method in superclass.__dict__ for superclass in obj.__mro__) for method in methods): return True return NotImplemented

36.893333

117

0.669859

import abc from typing import List, Iterable from ezcoach.enviroment import Manifest class Player(abc.ABC): @abc.abstractmethod def initialize(self, manifest: Manifest): @abc.abstractmethod def act(self, state): @classmethod def __subclasshook__(cls, obj): if cls is Player: methods = ('initialize', 'act') if all(any(method in superclass.__dict__ for superclass in obj.__mro__) for method in methods): return True return NotImplemented class Learner(Player): @abc.abstractmethod def do_start_episode(self, episode: int) -> bool: def episode_started(self, episode: int): def receive_reward(self, previous_state, action, reward: float, accumulated_reward: float, next_state): def episode_ended(self, terminal_state, accumulated_reward): @classmethod def __subclasshook__(cls, obj): if cls is Learner: methods = ('initialize', 'act', 'do_start_episode', 'episode_started', 'receive_reward', 'episode_ended') if all(any(method in superclass.__dict__ for superclass in obj.__mro__) for method in methods): return True return NotImplemented class MultiLearner(Learner): @abc.abstractmethod def set_players(self, players: Iterable[int]): @abc.abstractmethod def set_acting_player(self, player): @classmethod def __subclasshook__(cls, obj): if cls is MultiLearner: methods = ('initialize', 'act', 'do_start_episode', 'episode_started', 'receive_reward', 'episode_ended', 'set_players', 'set_acting_player') if all(any(method in superclass.__dict__ for superclass in obj.__mro__) for method in methods): return True return NotImplemented

true

f70f81401c08d654abb1e9dcd49531c69b6cbd11

8,969

py

Python

ceilometer/tests/alarm/test_notifier.py

NeCTAR-RC/ceilometer

25cb8740b83bfbf5c526be816fa3ae10f936bff5

[ "Apache-2.0" ]

1

2015-02-26T03:23:09.000Z

ceilometer/tests/alarm/test_notifier.py

NeCTAR-RC/ceilometer

25cb8740b83bfbf5c526be816fa3ae10f936bff5

[ "Apache-2.0" ]

null

ceilometer/tests/alarm/test_notifier.py

NeCTAR-RC/ceilometer

25cb8740b83bfbf5c526be816fa3ae10f936bff5

[ "Apache-2.0" ]

null

# -*- encoding: utf-8 -*- # # Copyright © 2013 eNovance # # Author: Julien Danjou <julien@danjou.info> # # Licensed under the Apache License, Version 2.0 (the "License"); you may # not use this file except in compliance with the License. You may obtain # a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, WITHOUT # WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the # License for the specific language governing permissions and limitations # under the License. import six.moves.urllib.parse as urlparse import mock import requests from ceilometer.alarm import service from ceilometer.openstack.common import context from ceilometer.openstack.common.fixture import config from ceilometer.openstack.common import test DATA_JSON = ('{"current": "ALARM", "alarm_id": "foobar",' ' "reason": "what ?", "reason_data": {"test": "test"},' ' "previous": "OK"}') NOTIFICATION = dict(alarm_id='foobar', condition=dict(threshold=42), reason='what ?', reason_data={'test': 'test'}, previous='OK', current='ALARM') class TestAlarmNotifier(test.BaseTestCase): def setUp(self): super(TestAlarmNotifier, self).setUp() self.CONF = self.useFixture(config.Config()).conf self.service = service.AlarmNotifierService('somehost', 'sometopic') @mock.patch('ceilometer.pipeline.setup_pipeline', mock.MagicMock()) def test_init_host(self): # If we try to create a real RPC connection, init_host() never # returns. Mock it out so we can establish the service # configuration. with mock.patch('ceilometer.openstack.common.rpc.create_connection'): self.service.start() def test_notify_alarm(self): data = { 'actions': ['test://'], 'alarm_id': 'foobar', 'previous': 'OK', 'current': 'ALARM', 'reason': 'Everything is on fire', 'reason_data': {'fire': 'everywhere'} } self.service.notify_alarm(context.get_admin_context(), data) notifications = self.service.notifiers['test'].obj.notifications self.assertEqual(1, len(notifications)) self.assertEqual((urlparse.urlsplit(data['actions'][0]), data['alarm_id'], data['previous'], data['current'], data['reason'], data['reason_data']), notifications[0]) def test_notify_alarm_no_action(self): self.service.notify_alarm(context.get_admin_context(), {}) def test_notify_alarm_log_action(self): self.service.notify_alarm(context.get_admin_context(), { 'actions': ['log://'], 'alarm_id': 'foobar', 'condition': {'threshold': 42}, }) @staticmethod def _fake_spawn_n(func, *args, **kwargs): func(*args, **kwargs) @staticmethod def _notification(action): notification = {} notification.update(NOTIFICATION) notification['actions'] = [action] return notification HTTP_HEADERS = {'content-type': 'application/json'} def test_notify_alarm_rest_action_ok(self): action = 'http://host/action' with mock.patch('eventlet.spawn_n', self._fake_spawn_n): with mock.patch.object(requests, 'post') as poster: self.service.notify_alarm(context.get_admin_context(), self._notification(action)) poster.assert_called_with(action, data=DATA_JSON, headers=self.HTTP_HEADERS) def test_notify_alarm_rest_action_with_ssl_client_cert(self): action = 'https://host/action' certificate = "/etc/ssl/cert/whatever.pem" self.CONF.set_override("rest_notifier_certificate_file", certificate, group='alarm') with mock.patch('eventlet.spawn_n', self._fake_spawn_n): with mock.patch.object(requests, 'post') as poster: self.service.notify_alarm(context.get_admin_context(), self._notification(action)) poster.assert_called_with(action, data=DATA_JSON, headers=self.HTTP_HEADERS, cert=certificate, verify=True) def test_notify_alarm_rest_action_with_ssl_client_cert_and_key(self): action = 'https://host/action' certificate = "/etc/ssl/cert/whatever.pem" key = "/etc/ssl/cert/whatever.key" self.CONF.set_override("rest_notifier_certificate_file", certificate, group='alarm') self.CONF.set_override("rest_notifier_certificate_key", key, group='alarm') with mock.patch('eventlet.spawn_n', self._fake_spawn_n): with mock.patch.object(requests, 'post') as poster: self.service.notify_alarm(context.get_admin_context(), self._notification(action)) poster.assert_called_with(action, data=DATA_JSON, headers=self.HTTP_HEADERS, cert=(certificate, key), verify=True) def test_notify_alarm_rest_action_with_ssl_verify_disable_by_cfg(self): action = 'https://host/action' self.CONF.set_override("rest_notifier_ssl_verify", False, group='alarm') with mock.patch('eventlet.spawn_n', self._fake_spawn_n): with mock.patch.object(requests, 'post') as poster: self.service.notify_alarm(context.get_admin_context(), self._notification(action)) poster.assert_called_with(action, data=DATA_JSON, headers=self.HTTP_HEADERS, verify=False) def test_notify_alarm_rest_action_with_ssl_verify_disable(self): action = 'https://host/action?ceilometer-alarm-ssl-verify=0' with mock.patch('eventlet.spawn_n', self._fake_spawn_n): with mock.patch.object(requests, 'post') as poster: self.service.notify_alarm(context.get_admin_context(), self._notification(action)) poster.assert_called_with(action, data=DATA_JSON, headers=self.HTTP_HEADERS, verify=False) def test_notify_alarm_rest_action_with_ssl_verify_enable_by_user(self): action = 'https://host/action?ceilometer-alarm-ssl-verify=1' self.CONF.set_override("rest_notifier_ssl_verify", False, group='alarm') with mock.patch('eventlet.spawn_n', self._fake_spawn_n): with mock.patch.object(requests, 'post') as poster: self.service.notify_alarm(context.get_admin_context(), self._notification(action)) poster.assert_called_with(action, data=DATA_JSON, headers=self.HTTP_HEADERS, verify=True) @staticmethod def _fake_urlsplit(*args, **kwargs): raise Exception("Evil urlsplit!") def test_notify_alarm_invalid_url(self): with mock.patch('ceilometer.openstack.common.network_utils.urlsplit', self._fake_urlsplit): LOG = mock.MagicMock() with mock.patch('ceilometer.alarm.service.LOG', LOG): self.service.notify_alarm( context.get_admin_context(), { 'actions': ['no-such-action-i-am-sure'], 'alarm_id': 'foobar', 'condition': {'threshold': 42}, }) self.assertTrue(LOG.error.called) def test_notify_alarm_invalid_action(self): LOG = mock.MagicMock() with mock.patch('ceilometer.alarm.service.LOG', LOG): self.service.notify_alarm( context.get_admin_context(), { 'actions': ['no-such-action-i-am-sure://'], 'alarm_id': 'foobar', 'condition': {'threshold': 42}, }) self.assertTrue(LOG.error.called)

42.709524

79

0.563051

import six.moves.urllib.parse as urlparse import mock import requests from ceilometer.alarm import service from ceilometer.openstack.common import context from ceilometer.openstack.common.fixture import config from ceilometer.openstack.common import test DATA_JSON = ('{"current": "ALARM", "alarm_id": "foobar",' ' "reason": "what ?", "reason_data": {"test": "test"},' ' "previous": "OK"}') NOTIFICATION = dict(alarm_id='foobar', condition=dict(threshold=42), reason='what ?', reason_data={'test': 'test'}, previous='OK', current='ALARM') class TestAlarmNotifier(test.BaseTestCase): def setUp(self): super(TestAlarmNotifier, self).setUp() self.CONF = self.useFixture(config.Config()).conf self.service = service.AlarmNotifierService('somehost', 'sometopic') @mock.patch('ceilometer.pipeline.setup_pipeline', mock.MagicMock()) def test_init_host(self): with mock.patch('ceilometer.openstack.common.rpc.create_connection'): self.service.start() def test_notify_alarm(self): data = { 'actions': ['test://'], 'alarm_id': 'foobar', 'previous': 'OK', 'current': 'ALARM', 'reason': 'Everything is on fire', 'reason_data': {'fire': 'everywhere'} } self.service.notify_alarm(context.get_admin_context(), data) notifications = self.service.notifiers['test'].obj.notifications self.assertEqual(1, len(notifications)) self.assertEqual((urlparse.urlsplit(data['actions'][0]), data['alarm_id'], data['previous'], data['current'], data['reason'], data['reason_data']), notifications[0]) def test_notify_alarm_no_action(self): self.service.notify_alarm(context.get_admin_context(), {}) def test_notify_alarm_log_action(self): self.service.notify_alarm(context.get_admin_context(), { 'actions': ['log://'], 'alarm_id': 'foobar', 'condition': {'threshold': 42}, }) @staticmethod def _fake_spawn_n(func, *args, **kwargs): func(*args, **kwargs) @staticmethod def _notification(action): notification = {} notification.update(NOTIFICATION) notification['actions'] = [action] return notification HTTP_HEADERS = {'content-type': 'application/json'} def test_notify_alarm_rest_action_ok(self): action = 'http://host/action' with mock.patch('eventlet.spawn_n', self._fake_spawn_n): with mock.patch.object(requests, 'post') as poster: self.service.notify_alarm(context.get_admin_context(), self._notification(action)) poster.assert_called_with(action, data=DATA_JSON, headers=self.HTTP_HEADERS) def test_notify_alarm_rest_action_with_ssl_client_cert(self): action = 'https://host/action' certificate = "/etc/ssl/cert/whatever.pem" self.CONF.set_override("rest_notifier_certificate_file", certificate, group='alarm') with mock.patch('eventlet.spawn_n', self._fake_spawn_n): with mock.patch.object(requests, 'post') as poster: self.service.notify_alarm(context.get_admin_context(), self._notification(action)) poster.assert_called_with(action, data=DATA_JSON, headers=self.HTTP_HEADERS, cert=certificate, verify=True) def test_notify_alarm_rest_action_with_ssl_client_cert_and_key(self): action = 'https://host/action' certificate = "/etc/ssl/cert/whatever.pem" key = "/etc/ssl/cert/whatever.key" self.CONF.set_override("rest_notifier_certificate_file", certificate, group='alarm') self.CONF.set_override("rest_notifier_certificate_key", key, group='alarm') with mock.patch('eventlet.spawn_n', self._fake_spawn_n): with mock.patch.object(requests, 'post') as poster: self.service.notify_alarm(context.get_admin_context(), self._notification(action)) poster.assert_called_with(action, data=DATA_JSON, headers=self.HTTP_HEADERS, cert=(certificate, key), verify=True) def test_notify_alarm_rest_action_with_ssl_verify_disable_by_cfg(self): action = 'https://host/action' self.CONF.set_override("rest_notifier_ssl_verify", False, group='alarm') with mock.patch('eventlet.spawn_n', self._fake_spawn_n): with mock.patch.object(requests, 'post') as poster: self.service.notify_alarm(context.get_admin_context(), self._notification(action)) poster.assert_called_with(action, data=DATA_JSON, headers=self.HTTP_HEADERS, verify=False) def test_notify_alarm_rest_action_with_ssl_verify_disable(self): action = 'https://host/action?ceilometer-alarm-ssl-verify=0' with mock.patch('eventlet.spawn_n', self._fake_spawn_n): with mock.patch.object(requests, 'post') as poster: self.service.notify_alarm(context.get_admin_context(), self._notification(action)) poster.assert_called_with(action, data=DATA_JSON, headers=self.HTTP_HEADERS, verify=False) def test_notify_alarm_rest_action_with_ssl_verify_enable_by_user(self): action = 'https://host/action?ceilometer-alarm-ssl-verify=1' self.CONF.set_override("rest_notifier_ssl_verify", False, group='alarm') with mock.patch('eventlet.spawn_n', self._fake_spawn_n): with mock.patch.object(requests, 'post') as poster: self.service.notify_alarm(context.get_admin_context(), self._notification(action)) poster.assert_called_with(action, data=DATA_JSON, headers=self.HTTP_HEADERS, verify=True) @staticmethod def _fake_urlsplit(*args, **kwargs): raise Exception("Evil urlsplit!") def test_notify_alarm_invalid_url(self): with mock.patch('ceilometer.openstack.common.network_utils.urlsplit', self._fake_urlsplit): LOG = mock.MagicMock() with mock.patch('ceilometer.alarm.service.LOG', LOG): self.service.notify_alarm( context.get_admin_context(), { 'actions': ['no-such-action-i-am-sure'], 'alarm_id': 'foobar', 'condition': {'threshold': 42}, }) self.assertTrue(LOG.error.called) def test_notify_alarm_invalid_action(self): LOG = mock.MagicMock() with mock.patch('ceilometer.alarm.service.LOG', LOG): self.service.notify_alarm( context.get_admin_context(), { 'actions': ['no-such-action-i-am-sure://'], 'alarm_id': 'foobar', 'condition': {'threshold': 42}, }) self.assertTrue(LOG.error.called)

true

f70f82382935e2fef97fc4bfb9b6127666b2db6e

1,793

py

Python

app.py

ds19991999/CKUser

c66ebda6ef5068a79b816de2c57a443b25d7096d

[ "MIT" ]

null

app.py

ds19991999/CKUser

c66ebda6ef5068a79b816de2c57a443b25d7096d

[ "MIT" ]

null

app.py

ds19991999/CKUser

c66ebda6ef5068a79b816de2c57a443b25d7096d

[ "MIT" ]

null

#!/usr/bin/python3 # -*- coding:utf-8 -*- from ckuser import client,server import os def print_client_menu(): print("用户菜单:") print("-"*25) print("0"+"-"*10+"显示用户菜单"+"-"*10) print("1"+"-"*10+"显示服务菜单"+"-"*10) print("2"+"-"*10+"用户登录系统"+"-"*10) print("3"+"-"*10+"用户修改信息"+"-"*10) print("4"+"-"*10+"用户注册信息"+"-"*10) print("6"+"-"*10+"退出系统") def print_server_menu(): print("服务菜单:") print("-"*25) print("0"+"-"*10+"显示用户菜单"+"-"*10) print("1"+"-"*10+"显示服务菜单"+"-"*10) print("2"+"-"*10+"添加用户帐号"+"-"*10) print("3"+"-"*10+"删除用户帐号"+"-"*10) print("4"+"-"*10+"修改用户帐号"+"-"*10) print("5"+"-"*10+"查找用户帐号"+"-"*10) print("6"+"-"*10+"退出系统") def server_oper(): print_server_menu() while True: try: i = int(input("请输入操作符：")) if i == 0: os.system("clear") break elif i == 1: os.system("clear") print_server_menu() elif i == 2: server.user_add() elif i == 3: server.user_del() elif i == 4: server.user_update() elif i == 5: server.user_find() elif i == 6: os.system("clear") os.system(exit()) except Exception as msg: os.system("clear") print_server_menu() print("输入错误!") client_oper() def client_oper(): print_client_menu() while True: try: i = int(input("请输入操作符：")) if i == 0: os.system("clear") print_client_menu() elif i == 1: os.system("clear") break elif i == 2: client.login() elif i == 3: client.update() elif i == 4: client.register() elif i == 6: os.system("clear") os.system(exit()) else: os.system("clear") print_client_menu() print("输入错误!") except Exception: os.system("clear") print_client_menu() print("输入错误!") server_oper() def main(): # server.user_update() client_oper() if __name__ == '__main__': main()

19.703297

34

0.557167

from ckuser import client,server import os def print_client_menu(): print("用户菜单:") print("-"*25) print("0"+"-"*10+"显示用户菜单"+"-"*10) print("1"+"-"*10+"显示服务菜单"+"-"*10) print("2"+"-"*10+"用户登录系统"+"-"*10) print("3"+"-"*10+"用户修改信息"+"-"*10) print("4"+"-"*10+"用户注册信息"+"-"*10) print("6"+"-"*10+"退出系统") def print_server_menu(): print("服务菜单:") print("-"*25) print("0"+"-"*10+"显示用户菜单"+"-"*10) print("1"+"-"*10+"显示服务菜单"+"-"*10) print("2"+"-"*10+"添加用户帐号"+"-"*10) print("3"+"-"*10+"删除用户帐号"+"-"*10) print("4"+"-"*10+"修改用户帐号"+"-"*10) print("5"+"-"*10+"查找用户帐号"+"-"*10) print("6"+"-"*10+"退出系统") def server_oper(): print_server_menu() while True: try: i = int(input("请输入操作符：")) if i == 0: os.system("clear") break elif i == 1: os.system("clear") print_server_menu() elif i == 2: server.user_add() elif i == 3: server.user_del() elif i == 4: server.user_update() elif i == 5: server.user_find() elif i == 6: os.system("clear") os.system(exit()) except Exception as msg: os.system("clear") print_server_menu() print("输入错误!") client_oper() def client_oper(): print_client_menu() while True: try: i = int(input("请输入操作符：")) if i == 0: os.system("clear") print_client_menu() elif i == 1: os.system("clear") break elif i == 2: client.login() elif i == 3: client.update() elif i == 4: client.register() elif i == 6: os.system("clear") os.system(exit()) else: os.system("clear") print_client_menu() print("输入错误!") except Exception: os.system("clear") print_client_menu() print("输入错误!") server_oper() def main(): client_oper() if __name__ == '__main__': main()

true

f70f84ea78513abd82f40253fd127865d7f56a02

2,181

py

Python

sound_factory/sound_factory.py

jphacks/C_2008

65d7a1d3a90045b149397cdd1e038ab648bb842e

[ "MIT" ]

2

2020-11-28T05:10:48.000Z

2020-11-29T01:23:53.000Z

sound_factory/sound_factory.py

jphacks/C_2008

65d7a1d3a90045b149397cdd1e038ab648bb842e

[ "MIT" ]

5

2020-11-01T06:34:02.000Z

2020-11-01T06:37:46.000Z

sound_factory/sound_factory.py

jphacks/C_2008

65d7a1d3a90045b149397cdd1e038ab648bb842e

[ "MIT" ]

null

import os import re import argparse import numpy as np import tensorflow as tf from tensorflow import keras from tensorflow.keras.preprocessing.image import load_img, img_to_array IMAGE_SHAPE = [(224, 224), (240, 240), (260, 260), (300, 300), (380, 380), (456, 456), (528, 528), (600, 600)] def main(paths : list, model_name : str): try: model = tf.keras.models.load_model(os.path.join(os.path.abspath(os.path.dirname(__file__)), 'model', model_name)) except Exception: print('そのようなモデルはありません') exit() model_index = int(re.search('\d', model_name).group(0)) with open(os.path.join(os.path.abspath(os.path.dirname(__file__)), 'model', model_name, 'labels.txt'), mode='r', encoding='utf-8') as f1: labels = [s.strip() for s in f1.readlines()] with open('manga_sound_labels.csv', mode='w', encoding='utf-8') as f2: for path in paths: if os.path.isfile(path): try: img = np.expand_dims(img_to_array(load_img(path,target_size=IMAGE_SHAPE[model_index])) / 255, axis=0) except Exception: continue pridict = labels[np.argmax(model.predict(img)[0])] f2.write(path + ',' + pridict + '\n') else: for filename in os.listdir(path): try: img = np.expand_dims(img_to_array(load_img(os.path.join(path, filename),target_size=IMAGE_SHAPE[model_index])) / 255, axis=0) except Exception: continue pridict = labels[np.argmax(model.predict(img)[0])] f2.write(os.path.join(path, filename) + ',' + pridict + '\n') if __name__ == '__main__': parser = argparse.ArgumentParser(description='コマの画像から背景音を予測します') parser.add_argument('path',nargs='*', help='解析するファイル名かディレクトリ名') parser.add_argument('--model', default=os.path.join('best','b0'), help='クラス分けに使用するモデル名') args = parser.parse_args() if 'manga_sound_labels.csv' in os.listdir(os.getcwd()): print('manga_sound_labels.csvがすでにあるので終了します') exit() main(args.path, args.model)

41.942308

149

0.604768

import os import re import argparse import numpy as np import tensorflow as tf from tensorflow import keras from tensorflow.keras.preprocessing.image import load_img, img_to_array IMAGE_SHAPE = [(224, 224), (240, 240), (260, 260), (300, 300), (380, 380), (456, 456), (528, 528), (600, 600)] def main(paths : list, model_name : str): try: model = tf.keras.models.load_model(os.path.join(os.path.abspath(os.path.dirname(__file__)), 'model', model_name)) except Exception: print('そのようなモデルはありません') exit() model_index = int(re.search('\d', model_name).group(0)) with open(os.path.join(os.path.abspath(os.path.dirname(__file__)), 'model', model_name, 'labels.txt'), mode='r', encoding='utf-8') as f1: labels = [s.strip() for s in f1.readlines()] with open('manga_sound_labels.csv', mode='w', encoding='utf-8') as f2: for path in paths: if os.path.isfile(path): try: img = np.expand_dims(img_to_array(load_img(path,target_size=IMAGE_SHAPE[model_index])) / 255, axis=0) except Exception: continue pridict = labels[np.argmax(model.predict(img)[0])] f2.write(path + ',' + pridict + '\n') else: for filename in os.listdir(path): try: img = np.expand_dims(img_to_array(load_img(os.path.join(path, filename),target_size=IMAGE_SHAPE[model_index])) / 255, axis=0) except Exception: continue pridict = labels[np.argmax(model.predict(img)[0])] f2.write(os.path.join(path, filename) + ',' + pridict + '\n') if __name__ == '__main__': parser = argparse.ArgumentParser(description='コマの画像から背景音を予測します') parser.add_argument('path',nargs='*', help='解析するファイル名かディレクトリ名') parser.add_argument('--model', default=os.path.join('best','b0'), help='クラス分けに使用するモデル名') args = parser.parse_args() if 'manga_sound_labels.csv' in os.listdir(os.getcwd()): print('manga_sound_labels.csvがすでにあるので終了します') exit() main(args.path, args.model)

true

f70f862db871c216db3b2e5ea714abdc5fdf04bd

3,238

py

Python

scripts/load_file_into_mod.py

strategineer/crusader_kings_3_mods

e290c3e8e542875c0ced2d1b7a013eb85b2037fb

[ "MIT" ]

null

scripts/load_file_into_mod.py

strategineer/crusader_kings_3_mods

e290c3e8e542875c0ced2d1b7a013eb85b2037fb

[ "MIT" ]

null

scripts/load_file_into_mod.py

strategineer/crusader_kings_3_mods

e290c3e8e542875c0ced2d1b7a013eb85b2037fb

[ "MIT" ]

null

#!/usr/bin/python3 import os import sys from shutil import copyfile import argparse from pathlib import Path import logging logging.basicConfig(level=logging.INFO) NUMBERED_FILENAME_SPLIT_CHARACTER = "_" parser = argparse.ArgumentParser(description='') parser.add_argument('filepath', help='') parser.add_argument('--force', '-f', action="store_true", help='Override any existing files') parser.add_argument('--increment', '-i', action="store_true", help='Increment the version number on the file so 00_X.txt will be copied as 01_X.txt') args = parser.parse_args() CRUSADER_KINGS_3_CURRENT_MOD_NAME = "CRUSADER_KINGS_3_CURRENT_MOD_NAME" CRUSADER_KINGS_3_MAIN_DIR = "CRUSADER_KINGS_3_MAIN_DIR" CRUSADER_KINGS_3_MOD_DIR = "CRUSADER_KINGS_3_MOD_DIR" mod_name = os.environ.get(CRUSADER_KINGS_3_CURRENT_MOD_NAME, '') main_directory_str = os.environ.get(CRUSADER_KINGS_3_MAIN_DIR, '').replace(" ", "\\ ") base_mod_directory_str = os.environ.get(CRUSADER_KINGS_3_MOD_DIR, '').replace(" ", "\\ ") if not mod_name: logging.error(f"The {CRUSADER_KINGS_3_CURRENT_MOD_NAME} environment variable must be set") sys.exit(1) if not main_directory_str: logging.error(f"The {CRUSADER_KINGS_3_MAIN_DIR} environment variable must be set") sys.exit(1) if not base_mod_directory_str: logging.error(f"The {CRUSADER_KINGS_3_MOD_DIR} environment variable must be set") sys.exit(1) main_path = Path(main_directory_str) if not main_path.exists() or not main_path.is_dir(): logging.error(f"Please ensure that {main_directory_str} points to a valid directory") sys.exit(1) base_mod_path = Path(base_mod_directory_str) if not base_mod_path.exists() or not base_mod_path.is_dir(): logging.error(f"Please ensure that {base_mod_directory_str} points to a valid directory") sys.exit(1) mod_directory_str = f"{base_mod_directory_str}/{mod_name}" mod_path = Path(mod_directory_str) if not mod_path.exists() or not mod_path.is_dir(): logging.error(f"Please ensure that {mod_directory_str} points to a valid directory") sys.exit(1) filepath_str = f"{main_directory_str}/{args.filepath}" filepath_path = Path(filepath_str) if not filepath_path.exists() or not filepath_path.is_file(): logging.error(f"Please ensure that {filepath_str} points to an existing file") sys.exit(1) destination_filepath = args.filepath if args.increment: filepath = Path(args.filepath) if NUMBERED_FILENAME_SPLIT_CHARACTER in filepath.name: (n, tail) = filepath.name.split(NUMBERED_FILENAME_SPLIT_CHARACTER, 1) n = str(int(n) + 1).zfill(len(n)) destination_filepath = str(filepath.parents[0]) + f"/{n}_{tail}" destination_filepath_str = f"{mod_directory_str}/{destination_filepath}" destination_filepath_path = Path(destination_filepath_str) if destination_filepath_path.exists() and not args.force: logging.error(f"File exists at {destination_filepath_str} already, please use the --force/-f parameter if you want to write over it") sys.exit(1) destination_filepath_path.parents[0].mkdir(parents=True, exist_ok=True) destination_filepath_path.touch(exist_ok=True) destination_filepath_path.write_text(filepath_path.read_text()) logging.info(f"Created at {destination_filepath_path}")

39.012048

149

0.774552

import os import sys from shutil import copyfile import argparse from pathlib import Path import logging logging.basicConfig(level=logging.INFO) NUMBERED_FILENAME_SPLIT_CHARACTER = "_" parser = argparse.ArgumentParser(description='') parser.add_argument('filepath', help='') parser.add_argument('--force', '-f', action="store_true", help='Override any existing files') parser.add_argument('--increment', '-i', action="store_true", help='Increment the version number on the file so 00_X.txt will be copied as 01_X.txt') args = parser.parse_args() CRUSADER_KINGS_3_CURRENT_MOD_NAME = "CRUSADER_KINGS_3_CURRENT_MOD_NAME" CRUSADER_KINGS_3_MAIN_DIR = "CRUSADER_KINGS_3_MAIN_DIR" CRUSADER_KINGS_3_MOD_DIR = "CRUSADER_KINGS_3_MOD_DIR" mod_name = os.environ.get(CRUSADER_KINGS_3_CURRENT_MOD_NAME, '') main_directory_str = os.environ.get(CRUSADER_KINGS_3_MAIN_DIR, '').replace(" ", "\\ ") base_mod_directory_str = os.environ.get(CRUSADER_KINGS_3_MOD_DIR, '').replace(" ", "\\ ") if not mod_name: logging.error(f"The {CRUSADER_KINGS_3_CURRENT_MOD_NAME} environment variable must be set") sys.exit(1) if not main_directory_str: logging.error(f"The {CRUSADER_KINGS_3_MAIN_DIR} environment variable must be set") sys.exit(1) if not base_mod_directory_str: logging.error(f"The {CRUSADER_KINGS_3_MOD_DIR} environment variable must be set") sys.exit(1) main_path = Path(main_directory_str) if not main_path.exists() or not main_path.is_dir(): logging.error(f"Please ensure that {main_directory_str} points to a valid directory") sys.exit(1) base_mod_path = Path(base_mod_directory_str) if not base_mod_path.exists() or not base_mod_path.is_dir(): logging.error(f"Please ensure that {base_mod_directory_str} points to a valid directory") sys.exit(1) mod_directory_str = f"{base_mod_directory_str}/{mod_name}" mod_path = Path(mod_directory_str) if not mod_path.exists() or not mod_path.is_dir(): logging.error(f"Please ensure that {mod_directory_str} points to a valid directory") sys.exit(1) filepath_str = f"{main_directory_str}/{args.filepath}" filepath_path = Path(filepath_str) if not filepath_path.exists() or not filepath_path.is_file(): logging.error(f"Please ensure that {filepath_str} points to an existing file") sys.exit(1) destination_filepath = args.filepath if args.increment: filepath = Path(args.filepath) if NUMBERED_FILENAME_SPLIT_CHARACTER in filepath.name: (n, tail) = filepath.name.split(NUMBERED_FILENAME_SPLIT_CHARACTER, 1) n = str(int(n) + 1).zfill(len(n)) destination_filepath = str(filepath.parents[0]) + f"/{n}_{tail}" destination_filepath_str = f"{mod_directory_str}/{destination_filepath}" destination_filepath_path = Path(destination_filepath_str) if destination_filepath_path.exists() and not args.force: logging.error(f"File exists at {destination_filepath_str} already, please use the --force/-f parameter if you want to write over it") sys.exit(1) destination_filepath_path.parents[0].mkdir(parents=True, exist_ok=True) destination_filepath_path.touch(exist_ok=True) destination_filepath_path.write_text(filepath_path.read_text()) logging.info(f"Created at {destination_filepath_path}")

true

f70f86fe1ee1ad646b17cffe4377c00e9ea1c90b

13,474

py

Python

chapter05/blackjack.py

bhomaidan1990/reinforcement-learning-an-introduction

fbf020d9da2daec3194a17f968ef29d12ebde6f6

[ "MIT" ]

12,197

2016-10-04T03:34:49.000Z

2022-03-31T12:55:36.000Z

chapter05/blackjack.py

bhomaidan1990/reinforcement-learning-an-introduction

fbf020d9da2daec3194a17f968ef29d12ebde6f6

[ "MIT" ]

134

2016-11-01T06:06:51.000Z

2022-02-07T00:12:01.000Z

chapter05/blackjack.py

bhomaidan1990/reinforcement-learning-an-introduction

fbf020d9da2daec3194a17f968ef29d12ebde6f6

[ "MIT" ]

4,738

2016-09-27T07:38:23.000Z

2022-03-31T10:09:14.000Z

####################################################################### # Copyright (C) # # 2016-2018 Shangtong Zhang(zhangshangtong.cpp@gmail.com) # # 2016 Kenta Shimada(hyperkentakun@gmail.com) # # 2017 Nicky van Foreest(vanforeest@gmail.com) # # Permission given to modify the code as long as you keep this # # declaration at the top # ####################################################################### import numpy as np import matplotlib matplotlib.use('Agg') import matplotlib.pyplot as plt import seaborn as sns from tqdm import tqdm # actions: hit or stand ACTION_HIT = 0 ACTION_STAND = 1 # "strike" in the book ACTIONS = [ACTION_HIT, ACTION_STAND] # policy for player POLICY_PLAYER = np.zeros(22, dtype=np.int) for i in range(12, 20): POLICY_PLAYER[i] = ACTION_HIT POLICY_PLAYER[20] = ACTION_STAND POLICY_PLAYER[21] = ACTION_STAND # function form of target policy of player def target_policy_player(usable_ace_player, player_sum, dealer_card): return POLICY_PLAYER[player_sum] # function form of behavior policy of player def behavior_policy_player(usable_ace_player, player_sum, dealer_card): if np.random.binomial(1, 0.5) == 1: return ACTION_STAND return ACTION_HIT # policy for dealer POLICY_DEALER = np.zeros(22) for i in range(12, 17): POLICY_DEALER[i] = ACTION_HIT for i in range(17, 22): POLICY_DEALER[i] = ACTION_STAND # get a new card def get_card(): card = np.random.randint(1, 14) card = min(card, 10) return card # get the value of a card (11 for ace). def card_value(card_id): return 11 if card_id == 1 else card_id # play a game # @policy_player: specify policy for player # @initial_state: [whether player has a usable Ace, sum of player's cards, one card of dealer] # @initial_action: the initial action def play(policy_player, initial_state=None, initial_action=None): # player status # sum of player player_sum = 0 # trajectory of player player_trajectory = [] # whether player uses Ace as 11 usable_ace_player = False # dealer status dealer_card1 = 0 dealer_card2 = 0 usable_ace_dealer = False if initial_state is None: # generate a random initial state while player_sum < 12: # if sum of player is less than 12, always hit card = get_card() player_sum += card_value(card) # If the player's sum is larger than 21, he may hold one or two aces. if player_sum > 21: assert player_sum == 22 # last card must be ace player_sum -= 10 else: usable_ace_player |= (1 == card) # initialize cards of dealer, suppose dealer will show the first card he gets dealer_card1 = get_card() dealer_card2 = get_card() else: # use specified initial state usable_ace_player, player_sum, dealer_card1 = initial_state dealer_card2 = get_card() # initial state of the game state = [usable_ace_player, player_sum, dealer_card1] # initialize dealer's sum dealer_sum = card_value(dealer_card1) + card_value(dealer_card2) usable_ace_dealer = 1 in (dealer_card1, dealer_card2) # if the dealer's sum is larger than 21, he must hold two aces. if dealer_sum > 21: assert dealer_sum == 22 # use one Ace as 1 rather than 11 dealer_sum -= 10 assert dealer_sum <= 21 assert player_sum <= 21 # game starts! # player's turn while True: if initial_action is not None: action = initial_action initial_action = None else: # get action based on current sum action = policy_player(usable_ace_player, player_sum, dealer_card1) # track player's trajectory for importance sampling player_trajectory.append([(usable_ace_player, player_sum, dealer_card1), action]) if action == ACTION_STAND: break # if hit, get new card card = get_card() # Keep track of the ace count. the usable_ace_player flag is insufficient alone as it cannot # distinguish between having one ace or two. ace_count = int(usable_ace_player) if card == 1: ace_count += 1 player_sum += card_value(card) # If the player has a usable ace, use it as 1 to avoid busting and continue. while player_sum > 21 and ace_count: player_sum -= 10 ace_count -= 1 # player busts if player_sum > 21: return state, -1, player_trajectory assert player_sum <= 21 usable_ace_player = (ace_count == 1) # dealer's turn while True: # get action based on current sum action = POLICY_DEALER[dealer_sum] if action == ACTION_STAND: break # if hit, get a new card new_card = get_card() ace_count = int(usable_ace_dealer) if new_card == 1: ace_count += 1 dealer_sum += card_value(new_card) # If the dealer has a usable ace, use it as 1 to avoid busting and continue. while dealer_sum > 21 and ace_count: dealer_sum -= 10 ace_count -= 1 # dealer busts if dealer_sum > 21: return state, 1, player_trajectory usable_ace_dealer = (ace_count == 1) # compare the sum between player and dealer assert player_sum <= 21 and dealer_sum <= 21 if player_sum > dealer_sum: return state, 1, player_trajectory elif player_sum == dealer_sum: return state, 0, player_trajectory else: return state, -1, player_trajectory # Monte Carlo Sample with On-Policy def monte_carlo_on_policy(episodes): states_usable_ace = np.zeros((10, 10)) # initialze counts to 1 to avoid 0 being divided states_usable_ace_count = np.ones((10, 10)) states_no_usable_ace = np.zeros((10, 10)) # initialze counts to 1 to avoid 0 being divided states_no_usable_ace_count = np.ones((10, 10)) for i in tqdm(range(0, episodes)): _, reward, player_trajectory = play(target_policy_player) for (usable_ace, player_sum, dealer_card), _ in player_trajectory: player_sum -= 12 dealer_card -= 1 if usable_ace: states_usable_ace_count[player_sum, dealer_card] += 1 states_usable_ace[player_sum, dealer_card] += reward else: states_no_usable_ace_count[player_sum, dealer_card] += 1 states_no_usable_ace[player_sum, dealer_card] += reward return states_usable_ace / states_usable_ace_count, states_no_usable_ace / states_no_usable_ace_count # Monte Carlo with Exploring Starts def monte_carlo_es(episodes): # (playerSum, dealerCard, usableAce, action) state_action_values = np.zeros((10, 10, 2, 2)) # initialze counts to 1 to avoid division by 0 state_action_pair_count = np.ones((10, 10, 2, 2)) # behavior policy is greedy def behavior_policy(usable_ace, player_sum, dealer_card): usable_ace = int(usable_ace) player_sum -= 12 dealer_card -= 1 # get argmax of the average returns(s, a) values_ = state_action_values[player_sum, dealer_card, usable_ace, :] / \ state_action_pair_count[player_sum, dealer_card, usable_ace, :] return np.random.choice([action_ for action_, value_ in enumerate(values_) if value_ == np.max(values_)]) # play for several episodes for episode in tqdm(range(episodes)): # for each episode, use a randomly initialized state and action initial_state = [bool(np.random.choice([0, 1])), np.random.choice(range(12, 22)), np.random.choice(range(1, 11))] initial_action = np.random.choice(ACTIONS) current_policy = behavior_policy if episode else target_policy_player _, reward, trajectory = play(current_policy, initial_state, initial_action) first_visit_check = set() for (usable_ace, player_sum, dealer_card), action in trajectory: usable_ace = int(usable_ace) player_sum -= 12 dealer_card -= 1 state_action = (usable_ace, player_sum, dealer_card, action) if state_action in first_visit_check: continue first_visit_check.add(state_action) # update values of state-action pairs state_action_values[player_sum, dealer_card, usable_ace, action] += reward state_action_pair_count[player_sum, dealer_card, usable_ace, action] += 1 return state_action_values / state_action_pair_count # Monte Carlo Sample with Off-Policy def monte_carlo_off_policy(episodes): initial_state = [True, 13, 2] rhos = [] returns = [] for i in range(0, episodes): _, reward, player_trajectory = play(behavior_policy_player, initial_state=initial_state) # get the importance ratio numerator = 1.0 denominator = 1.0 for (usable_ace, player_sum, dealer_card), action in player_trajectory: if action == target_policy_player(usable_ace, player_sum, dealer_card): denominator *= 0.5 else: numerator = 0.0 break rho = numerator / denominator rhos.append(rho) returns.append(reward) rhos = np.asarray(rhos) returns = np.asarray(returns) weighted_returns = rhos * returns weighted_returns = np.add.accumulate(weighted_returns) rhos = np.add.accumulate(rhos) ordinary_sampling = weighted_returns / np.arange(1, episodes + 1) with np.errstate(divide='ignore',invalid='ignore'): weighted_sampling = np.where(rhos != 0, weighted_returns / rhos, 0) return ordinary_sampling, weighted_sampling def figure_5_1(): states_usable_ace_1, states_no_usable_ace_1 = monte_carlo_on_policy(10000) states_usable_ace_2, states_no_usable_ace_2 = monte_carlo_on_policy(500000) states = [states_usable_ace_1, states_usable_ace_2, states_no_usable_ace_1, states_no_usable_ace_2] titles = ['Usable Ace, 10000 Episodes', 'Usable Ace, 500000 Episodes', 'No Usable Ace, 10000 Episodes', 'No Usable Ace, 500000 Episodes'] _, axes = plt.subplots(2, 2, figsize=(40, 30)) plt.subplots_adjust(wspace=0.1, hspace=0.2) axes = axes.flatten() for state, title, axis in zip(states, titles, axes): fig = sns.heatmap(np.flipud(state), cmap="YlGnBu", ax=axis, xticklabels=range(1, 11), yticklabels=list(reversed(range(12, 22)))) fig.set_ylabel('player sum', fontsize=30) fig.set_xlabel('dealer showing', fontsize=30) fig.set_title(title, fontsize=30) plt.savefig('../images/figure_5_1.png') plt.close() def figure_5_2(): state_action_values = monte_carlo_es(500000) state_value_no_usable_ace = np.max(state_action_values[:, :, 0, :], axis=-1) state_value_usable_ace = np.max(state_action_values[:, :, 1, :], axis=-1) # get the optimal policy action_no_usable_ace = np.argmax(state_action_values[:, :, 0, :], axis=-1) action_usable_ace = np.argmax(state_action_values[:, :, 1, :], axis=-1) images = [action_usable_ace, state_value_usable_ace, action_no_usable_ace, state_value_no_usable_ace] titles = ['Optimal policy with usable Ace', 'Optimal value with usable Ace', 'Optimal policy without usable Ace', 'Optimal value without usable Ace'] _, axes = plt.subplots(2, 2, figsize=(40, 30)) plt.subplots_adjust(wspace=0.1, hspace=0.2) axes = axes.flatten() for image, title, axis in zip(images, titles, axes): fig = sns.heatmap(np.flipud(image), cmap="YlGnBu", ax=axis, xticklabels=range(1, 11), yticklabels=list(reversed(range(12, 22)))) fig.set_ylabel('player sum', fontsize=30) fig.set_xlabel('dealer showing', fontsize=30) fig.set_title(title, fontsize=30) plt.savefig('../images/figure_5_2.png') plt.close() def figure_5_3(): true_value = -0.27726 episodes = 10000 runs = 100 error_ordinary = np.zeros(episodes) error_weighted = np.zeros(episodes) for i in tqdm(range(0, runs)): ordinary_sampling_, weighted_sampling_ = monte_carlo_off_policy(episodes) # get the squared error error_ordinary += np.power(ordinary_sampling_ - true_value, 2) error_weighted += np.power(weighted_sampling_ - true_value, 2) error_ordinary /= runs error_weighted /= runs plt.plot(np.arange(1, episodes + 1), error_ordinary, color='green', label='Ordinary Importance Sampling') plt.plot(np.arange(1, episodes + 1), error_weighted, color='red', label='Weighted Importance Sampling') plt.ylim(-0.1, 5) plt.xlabel('Episodes (log scale)') plt.ylabel(f'Mean square error\n(average over {runs} runs)') plt.xscale('log') plt.legend() plt.savefig('../images/figure_5_3.png') plt.close() if __name__ == '__main__': figure_5_1() figure_5_2() figure_5_3()

36.318059

113

0.635149

_sum -= 12 dealer_card -= 1 if usable_ace: states_usable_ace_count[player_sum, dealer_card] += 1 states_usable_ace[player_sum, dealer_card] += reward else: states_no_usable_ace_count[player_sum, dealer_card] += 1 states_no_usable_ace[player_sum, dealer_card] += reward return states_usable_ace / states_usable_ace_count, states_no_usable_ace / states_no_usable_ace_count # Monte Carlo with Exploring Starts def monte_carlo_es(episodes): # (playerSum, dealerCard, usableAce, action) state_action_values = np.zeros((10, 10, 2, 2)) # initialze counts to 1 to avoid division by 0 state_action_pair_count = np.ones((10, 10, 2, 2)) # behavior policy is greedy def behavior_policy(usable_ace, player_sum, dealer_card): usable_ace = int(usable_ace) player_sum -= 12 dealer_card -= 1 # get argmax of the average returns(s, a) values_ = state_action_values[player_sum, dealer_card, usable_ace, :] / \ state_action_pair_count[player_sum, dealer_card, usable_ace, :] return np.random.choice([action_ for action_, value_ in enumerate(values_) if value_ == np.max(values_)]) # play for several episodes for episode in tqdm(range(episodes)): # for each episode, use a randomly initialized state and action initial_state = [bool(np.random.choice([0, 1])), np.random.choice(range(12, 22)), np.random.choice(range(1, 11))] initial_action = np.random.choice(ACTIONS) current_policy = behavior_policy if episode else target_policy_player _, reward, trajectory = play(current_policy, initial_state, initial_action) first_visit_check = set() for (usable_ace, player_sum, dealer_card), action in trajectory: usable_ace = int(usable_ace) player_sum -= 12 dealer_card -= 1 state_action = (usable_ace, player_sum, dealer_card, action) if state_action in first_visit_check: continue first_visit_check.add(state_action) # update values of state-action pairs state_action_values[player_sum, dealer_card, usable_ace, action] += reward state_action_pair_count[player_sum, dealer_card, usable_ace, action] += 1 return state_action_values / state_action_pair_count # Monte Carlo Sample with Off-Policy def monte_carlo_off_policy(episodes): initial_state = [True, 13, 2] rhos = [] returns = [] for i in range(0, episodes): _, reward, player_trajectory = play(behavior_policy_player, initial_state=initial_state) # get the importance ratio numerator = 1.0 denominator = 1.0 for (usable_ace, player_sum, dealer_card), action in player_trajectory: if action == target_policy_player(usable_ace, player_sum, dealer_card): denominator *= 0.5 else: numerator = 0.0 break rho = numerator / denominator rhos.append(rho) returns.append(reward) rhos = np.asarray(rhos) returns = np.asarray(returns) weighted_returns = rhos * returns weighted_returns = np.add.accumulate(weighted_returns) rhos = np.add.accumulate(rhos) ordinary_sampling = weighted_returns / np.arange(1, episodes + 1) with np.errstate(divide='ignore',invalid='ignore'): weighted_sampling = np.where(rhos != 0, weighted_returns / rhos, 0) return ordinary_sampling, weighted_sampling def figure_5_1(): states_usable_ace_1, states_no_usable_ace_1 = monte_carlo_on_policy(10000) states_usable_ace_2, states_no_usable_ace_2 = monte_carlo_on_policy(500000) states = [states_usable_ace_1, states_usable_ace_2, states_no_usable_ace_1, states_no_usable_ace_2] titles = ['Usable Ace, 10000 Episodes', 'Usable Ace, 500000 Episodes', 'No Usable Ace, 10000 Episodes', 'No Usable Ace, 500000 Episodes'] _, axes = plt.subplots(2, 2, figsize=(40, 30)) plt.subplots_adjust(wspace=0.1, hspace=0.2) axes = axes.flatten() for state, title, axis in zip(states, titles, axes): fig = sns.heatmap(np.flipud(state), cmap="YlGnBu", ax=axis, xticklabels=range(1, 11), yticklabels=list(reversed(range(12, 22)))) fig.set_ylabel('player sum', fontsize=30) fig.set_xlabel('dealer showing', fontsize=30) fig.set_title(title, fontsize=30) plt.savefig('../images/figure_5_1.png') plt.close() def figure_5_2(): state_action_values = monte_carlo_es(500000) state_value_no_usable_ace = np.max(state_action_values[:, :, 0, :], axis=-1) state_value_usable_ace = np.max(state_action_values[:, :, 1, :], axis=-1) # get the optimal policy action_no_usable_ace = np.argmax(state_action_values[:, :, 0, :], axis=-1) action_usable_ace = np.argmax(state_action_values[:, :, 1, :], axis=-1) images = [action_usable_ace, state_value_usable_ace, action_no_usable_ace, state_value_no_usable_ace] titles = ['Optimal policy with usable Ace', 'Optimal value with usable Ace', 'Optimal policy without usable Ace', 'Optimal value without usable Ace'] _, axes = plt.subplots(2, 2, figsize=(40, 30)) plt.subplots_adjust(wspace=0.1, hspace=0.2) axes = axes.flatten() for image, title, axis in zip(images, titles, axes): fig = sns.heatmap(np.flipud(image), cmap="YlGnBu", ax=axis, xticklabels=range(1, 11), yticklabels=list(reversed(range(12, 22)))) fig.set_ylabel('player sum', fontsize=30) fig.set_xlabel('dealer showing', fontsize=30) fig.set_title(title, fontsize=30) plt.savefig('../images/figure_5_2.png') plt.close() def figure_5_3(): true_value = -0.27726 episodes = 10000 runs = 100 error_ordinary = np.zeros(episodes) error_weighted = np.zeros(episodes) for i in tqdm(range(0, runs)): ordinary_sampling_, weighted_sampling_ = monte_carlo_off_policy(episodes) # get the squared error error_ordinary += np.power(ordinary_sampling_ - true_value, 2) error_weighted += np.power(weighted_sampling_ - true_value, 2) error_ordinary /= runs error_weighted /= runs plt.plot(np.arange(1, episodes + 1), error_ordinary, color='green', label='Ordinary Importance Sampling') plt.plot(np.arange(1, episodes + 1), error_weighted, color='red', label='Weighted Importance Sampling') plt.ylim(-0.1, 5) plt.xlabel('Episodes (log scale)') plt.ylabel(f'Mean square error\n(average over {runs} runs)') plt.xscale('log') plt.legend() plt.savefig('../images/figure_5_3.png') plt.close() if __name__ == '__main__': figure_5_1() figure_5_2() figure_5_3()

true

f70f874944a8646dbb751573e99b3ab43f3e5dcf

1,826

py

Python

image_preprocessing.py

kpullak/PlantPhenotyping

a0b5bd68787b0850ad1d4d56cab7767cc4f2dc61

[ "Apache-2.0" ]

null

image_preprocessing.py

kpullak/PlantPhenotyping

a0b5bd68787b0850ad1d4d56cab7767cc4f2dc61

[ "Apache-2.0" ]

null

image_preprocessing.py

kpullak/PlantPhenotyping

a0b5bd68787b0850ad1d4d56cab7767cc4f2dc61

[ "Apache-2.0" ]

null

import os import cv2 source_path = './test_images/' def processImage(filename, mImage): if '2019' in filename: # ---------------------------------- # Remove noise - by applying guassian blur on src image mImage = cv2.GaussianBlur(mImage, (5, 5), cv2.BORDER_DEFAULT) # pink rgb values - 255, 153, 255 # white rgb values - 255, 255, 255 # ghost white values - 248, 248, 255 # mImage = mImage[np.where((mImage == [255, 255, 255]).all(axis=2))] = [255, 153, 255] # working (best performing, descending) - gtruth 55 - 200 (58), 220 (86), 180 (33), 150 (0) mImage[mImage >= 128] = 200 mImage[mImage < 128] = 0 ''' hsvImg = cv2.cvtColor(mImage,cv2.COLOR_BGR2HSV) value = 5 # changeable vValue = hsvImg[..., 2] hsvImg[..., 2] = np.where((255-vValue) < value, 255, vValue + value) ''' # save the processed image with a new file name new_name = source_path + os.path.splitext(filename)[0] + '_processed.jpg' cv2.imwrite(new_name, mImage) else: pass for filename in os.listdir(source_path): if filename.lower().endswith(('.bmp', '.jpeg', '.jpg', '.png', '.tif', '.tiff')): # read the image img = cv2.imread(os.path.join(source_path, filename)) if img is not None: processImage(filename, img) for filename in os.listdir(source_path): if filename.lower().endswith(('.bmp', '.jpeg', '.jpg', '.png', '.tif', '.tiff')): if '_processed' in filename: to_remove = filename.replace('_processed', '') to_remove_file = os.path.join(source_path, to_remove) os.remove(to_remove_file) for filename in os.listdir(source_path): if filename.lower().endswith(('.bmp', '.jpeg', '.jpg', '.png', '.tif', '.tiff')): if '_processed' in filename: new_name = filename.replace('_processed', '') os.rename(os.path.join(source_path, filename), os.path.join(source_path, new_name))

33.814815

93

0.646769

import os import cv2 source_path = './test_images/' def processImage(filename, mImage): if '2019' in filename: mImage = cv2.GaussianBlur(mImage, (5, 5), cv2.BORDER_DEFAULT) mImage[mImage >= 128] = 200 mImage[mImage < 128] = 0 new_name = source_path + os.path.splitext(filename)[0] + '_processed.jpg' cv2.imwrite(new_name, mImage) else: pass for filename in os.listdir(source_path): if filename.lower().endswith(('.bmp', '.jpeg', '.jpg', '.png', '.tif', '.tiff')): img = cv2.imread(os.path.join(source_path, filename)) if img is not None: processImage(filename, img) for filename in os.listdir(source_path): if filename.lower().endswith(('.bmp', '.jpeg', '.jpg', '.png', '.tif', '.tiff')): if '_processed' in filename: to_remove = filename.replace('_processed', '') to_remove_file = os.path.join(source_path, to_remove) os.remove(to_remove_file) for filename in os.listdir(source_path): if filename.lower().endswith(('.bmp', '.jpeg', '.jpg', '.png', '.tif', '.tiff')): if '_processed' in filename: new_name = filename.replace('_processed', '') os.rename(os.path.join(source_path, filename), os.path.join(source_path, new_name))

true

f70f87745a9dddf3fca3ea25b23a3a50d07f934b

765

py

Python

kur/engine/__init__.py

greedyuser/kur

ba6588ebfa5dec66d1e462c180618cc115fd38ef

[ "Apache-2.0" ]

867

2016-12-05T20:24:23.000Z

2022-02-18T09:07:14.000Z

kur/engine/__init__.py

greedyuser/kur

ba6588ebfa5dec66d1e462c180618cc115fd38ef

[ "Apache-2.0" ]

90

2017-01-14T22:46:23.000Z

2021-02-09T13:32:27.000Z

kur/engine/__init__.py

greedyuser/kur

ba6588ebfa5dec66d1e462c180618cc115fd38ef

[ "Apache-2.0" ]

135

2017-01-18T19:21:20.000Z

2022-01-24T16:57:59.000Z

""" Copyright 2016 Deepgram Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with the License. You may obtain a copy of the License at http://www.apache.org/licenses/LICENSE-2.0 Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the specific language governing permissions and limitations under the License. """ from .engine import Engine, ScopeStack from .passthrough_engine import PassthroughEngine from .jinja_engine import JinjaEngine ### EOF.EOF.EOF.EOF.EOF.EOF.EOF.EOF.EOF.EOF.EOF.EOF.EOF.EOF.EOF.EOF.EOF.EOF.EOF

34.772727

79

0.789542

from .engine import Engine, ScopeStack from .passthrough_engine import PassthroughEngine from .jinja_engine import JinjaEngine

true

f70f8807470cc0430f7ec49eed3f61f2abc42cb2

14,684

py

Python

sphinx_ext/design_choice.py

friendly-traceback/docs

413a1d6980b605e2305d5b0ab5757f098a1700c1

[ "CC0-1.0" ]

null

sphinx_ext/design_choice.py

friendly-traceback/docs

413a1d6980b605e2305d5b0ab5757f098a1700c1

[ "CC0-1.0" ]

3

2021-07-17T17:19:47.000Z

2022-02-01T13:39:12.000Z

sphinx_ext/design_choice.py

friendly-traceback/docs

413a1d6980b605e2305d5b0ab5757f098a1700c1

[ "CC0-1.0" ]

1

2021-07-11T12:59:46.000Z

""" design_choice ~~~~~~~~~~~~~~ IMPORTANT: This is a straightforward adaptation of sphinx's todo extension done by search/replace. Allow design_choices to be inserted into your documentation. Inclusion of design_choices can be switched of by a configuration variable. The design_choice_list directive collects all design_choices of your project and lists them along with a backlink to the original location. :copyright: Copyright 2007-2021 by the Sphinx team, see AUTHORS. :license: BSD, see LICENSE for details. """ import warnings from typing import Any, Dict, Iterable, List, Tuple, cast from docutils import nodes from docutils.nodes import Element, Node from docutils.parsers.rst import directives from docutils.parsers.rst.directives.admonitions import BaseAdmonition import sphinx from sphinx import addnodes from sphinx.application import Sphinx # from sphinx.deprecation import RemovedInSphinx40Warning from sphinx.domains import Domain from sphinx.environment import BuildEnvironment from sphinx.errors import NoUri from sphinx.locale import _, __ from sphinx.util import logging, texescape from sphinx.util.docutils import SphinxDirective, new_document from sphinx.util.nodes import make_refnode from sphinx.writers.html import HTMLTranslator from sphinx.writers.latex import LaTeXTranslator logger = logging.getLogger(__name__) class design_choice_node(nodes.Admonition, nodes.Element): pass class design_choice_list(nodes.General, nodes.Element): pass class DesignChoice(BaseAdmonition, SphinxDirective): """ A design_choice entry, displayed (if configured) in the form of an admonition. """ node_class = design_choice_node has_content = True required_arguments = 0 optional_arguments = 0 final_argument_whitespace = False option_spec = { "class": directives.class_option, "name": directives.unchanged, "title": directives.unchanged, "prefix": directives.unchanged, } def run(self) -> List[Node]: if not self.options.get("class"): self.options["class"] = ["admonition-design_choice"] (design_choice,) = super().run() # type: Tuple[Node] if isinstance(design_choice, nodes.system_message): return [design_choice] elif isinstance(design_choice, design_choice_node): prefix = '' if "prefix" in self.options: prefix = self.options["prefix"] + " " design_choice.insert( 0, nodes.title(text=prefix + _("Design Choice: ") + self.options["title"]) ) design_choice["docname"] = self.env.docname self.add_name(design_choice) self.set_source_info(design_choice) self.state.document.note_explicit_target(design_choice) return [design_choice] else: raise RuntimeError # never reached here class DesignChoiceDomain(Domain): name = "design_choice" label = "design_choice" @property def design_choices(self) -> Dict[str, List[design_choice_node]]: return self.data.setdefault("design_choices", {}) def clear_doc(self, docname: str) -> None: self.design_choices.pop(docname, None) def merge_domaindata(self, docnames: List[str], otherdata: Dict) -> None: for docname in docnames: self.design_choices[docname] = otherdata["design_choices"][docname] def process_doc( self, env: BuildEnvironment, docname: str, document: nodes.document ) -> None: design_choices = self.design_choices.setdefault(docname, []) for design_choice in document.traverse(design_choice_node): env.app.emit("design_choice-defined", design_choice) design_choices.append(design_choice) if env.config.design_choice_emit_warnings: logger.warning( __("TODO entry found: %s"), design_choice[1].astext(), location=design_choice, ) def process_design_choices(app: Sphinx, doctree: nodes.document) -> None: # warnings.warn( # "process_design_choices() is deprecated.", # RemovedInSphinx40Warning, # stacklevel=2, # ) # collect all design_choices in the environment # this is not done in the directive itself because it some transformations # must have already been run, e.g. substitutions env = app.builder.env if not hasattr(env, "design_choice_all_design_choices"): env.design_choice_all_design_choices = [] # type: ignore for node in doctree.traverse(design_choice_node): app.events.emit("design_choice-defined", node) newnode = node.deepcopy() newnode["ids"] = [] env.design_choice_all_design_choices.append( { # type: ignore "docname": env.docname, "source": node.source or env.doc2path(env.docname), "lineno": node.line, "design_choice": newnode, "target": node["ids"][0], } ) if env.config.design_choice_emit_warnings: label = cast(nodes.Element, node[1]) logger.warning(__("TODO entry found: %s"), label.astext(), location=node) class DesignChoiceList(SphinxDirective): """ A list of all design_choice entries. """ has_content = False required_arguments = 0 optional_arguments = 0 final_argument_whitespace = False option_spec = {} # type: Dict def run(self) -> List[Node]: # Simply insert an empty design_choice_list node which will be replaced later # when process_design_choice_nodes is called return [design_choice_list("")] class DesignChoiceListProcessor: def __init__(self, app: Sphinx, doctree: nodes.document, docname: str) -> None: self.builder = app.builder self.config = app.config self.env = app.env self.domain = cast(DesignChoiceDomain, app.env.get_domain("design_choice")) self.document = new_document("") self.process(doctree, docname) def process(self, doctree: nodes.document, docname: str) -> None: design_choices = sum( self.domain.design_choices.values(), [] ) # type: List[design_choice_node] for node in doctree.traverse(design_choice_list): if not self.config.design_choice_include_design_choices: node.parent.remove(node) continue if node.get("ids"): content = [nodes.target()] # type: List[Element] else: content = [] for design_choice in design_choices: # Create a copy of the design_choice node new_design_choice = design_choice.deepcopy() new_design_choice["ids"].clear() self.resolve_reference(new_design_choice, docname) content.append(new_design_choice) design_choice_ref = self.create_design_choice_reference( design_choice, docname ) content.append(design_choice_ref) node.replace_self(content) def create_design_choice_reference( self, design_choice: design_choice_node, docname: str ) -> nodes.paragraph: if self.config.design_choice_link_only: description = _("<<original entry>>") else: description = _("(The <<original entry>> is located in %s, line %d.)") % ( design_choice.source, design_choice.line, ) prefix = description[: description.find("<<")] suffix = description[description.find(">>") + 2 :] para = nodes.paragraph(classes=["design_choice-source"]) para += nodes.Text(prefix, prefix) # Create a reference linktext = nodes.emphasis(_("original entry"), _("original entry")) reference = nodes.reference("", "", linktext, internal=True) try: reference["refuri"] = self.builder.get_relative_uri( docname, design_choice["docname"] ) reference["refuri"] += "#" + design_choice["ids"][0] except NoUri: # ignore if no URI can be determined, e.g. for LaTeX output pass para += reference para += nodes.Text(suffix, suffix) return para def resolve_reference( self, design_choice: design_choice_node, docname: str ) -> None: """Resolve references in the design_choice content.""" for node in design_choice.traverse(addnodes.pending_xref): if "refdoc" in node: node["refdoc"] = docname # Note: To resolve references, it is needed to wrap it with document node self.document += design_choice self.env.resolve_references(self.document, docname, self.builder) self.document.remove(design_choice) def process_design_choice_nodes( app: Sphinx, doctree: nodes.document, fromdocname: str ) -> None: """Replace all design_choice_list nodes with a list of the collected design_choices. Augment each design_choice with a backlink to the original location. """ # warnings.warn( # "process_design_choice_nodes() is deprecated.", # RemovedInSphinx40Warning, # stacklevel=2, # ) domain = cast(DesignChoiceDomain, app.env.get_domain("design_choice")) design_choices = sum( domain.design_choices.values(), [] ) # type: List[design_choice_node] for node in doctree.traverse(design_choice_list): if node.get("ids"): content = [nodes.target()] # type: List[Element] else: content = [] if not app.config["design_choice_include_design_choices"]: node.replace_self(content) continue for design_choice_info in design_choices: para = nodes.paragraph(classes=["design_choice-source"]) if app.config["design_choice_link_only"]: description = _("<<original entry>>") else: description = _( "(The <<original entry>> is located in %s, line %d.)" ) % (design_choice_info.source, design_choice_info.line) desc1 = description[: description.find("<<")] desc2 = description[description.find(">>") + 2 :] para += nodes.Text(desc1, desc1) # Create a reference innernode = nodes.emphasis(_("original entry"), _("original entry")) try: para += make_refnode( app.builder, fromdocname, design_choice_info["docname"], design_choice_info["ids"][0], innernode, ) except NoUri: # ignore if no URI can be determined, e.g. for LaTeX output pass para += nodes.Text(desc2, desc2) design_choice_entry = design_choice_info.deepcopy() design_choice_entry["ids"].clear() # (Recursively) resolve references in the design_choice content app.env.resolve_references(design_choice_entry, design_choice_info["docname"], app.builder) # type: ignore # NOQA # Insert into the design_choice_list content.append(design_choice_entry) content.append(para) node.replace_self(content) def purge_design_choices(app: Sphinx, env: BuildEnvironment, docname: str) -> None: # warnings.warn( # "purge_design_choices() is deprecated.", RemovedInSphinx40Warning, stacklevel=2 # ) if not hasattr(env, "design_choice_all_design_choices"): return env.design_choice_all_design_choices = [ design_choice for design_choice in env.design_choice_all_design_choices # type: ignore if design_choice["docname"] != docname ] def merge_info( app: Sphinx, env: BuildEnvironment, docnames: Iterable[str], other: BuildEnvironment ) -> None: # warnings.warn("merge_info() is deprecated.", RemovedInSphinx40Warning, stacklevel=2) if not hasattr(other, "design_choice_all_design_choices"): return if not hasattr(env, "design_choice_all_design_choices"): env.design_choice_all_design_choices = [] # type: ignore env.design_choice_all_design_choices.extend(other.design_choice_all_design_choices) # type: ignore def visit_design_choice_node(self: HTMLTranslator, node: design_choice_node) -> None: if self.config.design_choice_include_design_choices: self.visit_admonition(node) else: raise nodes.SkipNode def depart_design_choice_node(self: HTMLTranslator, node: design_choice_node) -> None: self.depart_admonition(node) def latex_visit_design_choice_node( self: LaTeXTranslator, node: design_choice_node ) -> None: if self.config.design_choice_include_design_choices: self.body.append("\n\\begin{sphinxadmonition}{note}{") self.body.append(self.hypertarget_to(node)) title_node = cast(nodes.title, node[0]) title = texescape.escape(title_node.astext(), self.config.latex_engine) self.body.append("%s:}" % title) node.pop(0) else: raise nodes.SkipNode def latex_depart_design_choice_node( self: LaTeXTranslator, node: design_choice_node ) -> None: self.body.append("\\end{sphinxadmonition}\n") def setup(app: Sphinx) -> Dict[str, Any]: app.add_event("design_choice-defined") app.add_config_value("design_choice_include_design_choices", False, "html") app.add_config_value("design_choice_link_only", False, "html") app.add_config_value("design_choice_emit_warnings", False, "html") app.add_node(design_choice_list) app.add_node( design_choice_node, html=(visit_design_choice_node, depart_design_choice_node), latex=(latex_visit_design_choice_node, latex_depart_design_choice_node), text=(visit_design_choice_node, depart_design_choice_node), man=(visit_design_choice_node, depart_design_choice_node), texinfo=(visit_design_choice_node, depart_design_choice_node), ) app.add_directive("design_choice", DesignChoice) app.add_directive("design_choice_list", DesignChoiceList) app.add_domain(DesignChoiceDomain) app.connect("doctree-resolved", DesignChoiceListProcessor) return { "version": sphinx.__display_version__, "env_version": 2, "parallel_read_safe": True, }

36.078624

127

0.649346

import warnings from typing import Any, Dict, Iterable, List, Tuple, cast from docutils import nodes from docutils.nodes import Element, Node from docutils.parsers.rst import directives from docutils.parsers.rst.directives.admonitions import BaseAdmonition import sphinx from sphinx import addnodes from sphinx.application import Sphinx from sphinx.domains import Domain from sphinx.environment import BuildEnvironment from sphinx.errors import NoUri from sphinx.locale import _, __ from sphinx.util import logging, texescape from sphinx.util.docutils import SphinxDirective, new_document from sphinx.util.nodes import make_refnode from sphinx.writers.html import HTMLTranslator from sphinx.writers.latex import LaTeXTranslator logger = logging.getLogger(__name__) class design_choice_node(nodes.Admonition, nodes.Element): pass class design_choice_list(nodes.General, nodes.Element): pass class DesignChoice(BaseAdmonition, SphinxDirective): node_class = design_choice_node has_content = True required_arguments = 0 optional_arguments = 0 final_argument_whitespace = False option_spec = { "class": directives.class_option, "name": directives.unchanged, "title": directives.unchanged, "prefix": directives.unchanged, } def run(self) -> List[Node]: if not self.options.get("class"): self.options["class"] = ["admonition-design_choice"] (design_choice,) = super().run() if isinstance(design_choice, nodes.system_message): return [design_choice] elif isinstance(design_choice, design_choice_node): prefix = '' if "prefix" in self.options: prefix = self.options["prefix"] + " " design_choice.insert( 0, nodes.title(text=prefix + _("Design Choice: ") + self.options["title"]) ) design_choice["docname"] = self.env.docname self.add_name(design_choice) self.set_source_info(design_choice) self.state.document.note_explicit_target(design_choice) return [design_choice] else: raise RuntimeError class DesignChoiceDomain(Domain): name = "design_choice" label = "design_choice" @property def design_choices(self) -> Dict[str, List[design_choice_node]]: return self.data.setdefault("design_choices", {}) def clear_doc(self, docname: str) -> None: self.design_choices.pop(docname, None) def merge_domaindata(self, docnames: List[str], otherdata: Dict) -> None: for docname in docnames: self.design_choices[docname] = otherdata["design_choices"][docname] def process_doc( self, env: BuildEnvironment, docname: str, document: nodes.document ) -> None: design_choices = self.design_choices.setdefault(docname, []) for design_choice in document.traverse(design_choice_node): env.app.emit("design_choice-defined", design_choice) design_choices.append(design_choice) if env.config.design_choice_emit_warnings: logger.warning( __("TODO entry found: %s"), design_choice[1].astext(), location=design_choice, ) def process_design_choices(app: Sphinx, doctree: nodes.document) -> None: env = app.builder.env if not hasattr(env, "design_choice_all_design_choices"): env.design_choice_all_design_choices = [] for node in doctree.traverse(design_choice_node): app.events.emit("design_choice-defined", node) newnode = node.deepcopy() newnode["ids"] = [] env.design_choice_all_design_choices.append( { "docname": env.docname, "source": node.source or env.doc2path(env.docname), "lineno": node.line, "design_choice": newnode, "target": node["ids"][0], } ) if env.config.design_choice_emit_warnings: label = cast(nodes.Element, node[1]) logger.warning(__("TODO entry found: %s"), label.astext(), location=node) class DesignChoiceList(SphinxDirective): has_content = False required_arguments = 0 optional_arguments = 0 final_argument_whitespace = False option_spec = {} def run(self) -> List[Node]: return [design_choice_list("")] class DesignChoiceListProcessor: def __init__(self, app: Sphinx, doctree: nodes.document, docname: str) -> None: self.builder = app.builder self.config = app.config self.env = app.env self.domain = cast(DesignChoiceDomain, app.env.get_domain("design_choice")) self.document = new_document("") self.process(doctree, docname) def process(self, doctree: nodes.document, docname: str) -> None: design_choices = sum( self.domain.design_choices.values(), [] ) for node in doctree.traverse(design_choice_list): if not self.config.design_choice_include_design_choices: node.parent.remove(node) continue if node.get("ids"): content = [nodes.target()] else: content = [] for design_choice in design_choices: new_design_choice = design_choice.deepcopy() new_design_choice["ids"].clear() self.resolve_reference(new_design_choice, docname) content.append(new_design_choice) design_choice_ref = self.create_design_choice_reference( design_choice, docname ) content.append(design_choice_ref) node.replace_self(content) def create_design_choice_reference( self, design_choice: design_choice_node, docname: str ) -> nodes.paragraph: if self.config.design_choice_link_only: description = _("<<original entry>>") else: description = _("(The <<original entry>> is located in %s, line %d.)") % ( design_choice.source, design_choice.line, ) prefix = description[: description.find("<<")] suffix = description[description.find(">>") + 2 :] para = nodes.paragraph(classes=["design_choice-source"]) para += nodes.Text(prefix, prefix) linktext = nodes.emphasis(_("original entry"), _("original entry")) reference = nodes.reference("", "", linktext, internal=True) try: reference["refuri"] = self.builder.get_relative_uri( docname, design_choice["docname"] ) reference["refuri"] += "#" + design_choice["ids"][0] except NoUri: pass para += reference para += nodes.Text(suffix, suffix) return para def resolve_reference( self, design_choice: design_choice_node, docname: str ) -> None: for node in design_choice.traverse(addnodes.pending_xref): if "refdoc" in node: node["refdoc"] = docname self.document += design_choice self.env.resolve_references(self.document, docname, self.builder) self.document.remove(design_choice) def process_design_choice_nodes( app: Sphinx, doctree: nodes.document, fromdocname: str ) -> None: domain = cast(DesignChoiceDomain, app.env.get_domain("design_choice")) design_choices = sum( domain.design_choices.values(), [] ) for node in doctree.traverse(design_choice_list): if node.get("ids"): content = [nodes.target()] else: content = [] if not app.config["design_choice_include_design_choices"]: node.replace_self(content) continue for design_choice_info in design_choices: para = nodes.paragraph(classes=["design_choice-source"]) if app.config["design_choice_link_only"]: description = _("<<original entry>>") else: description = _( "(The <<original entry>> is located in %s, line %d.)" ) % (design_choice_info.source, design_choice_info.line) desc1 = description[: description.find("<<")] desc2 = description[description.find(">>") + 2 :] para += nodes.Text(desc1, desc1) innernode = nodes.emphasis(_("original entry"), _("original entry")) try: para += make_refnode( app.builder, fromdocname, design_choice_info["docname"], design_choice_info["ids"][0], innernode, ) except NoUri: pass para += nodes.Text(desc2, desc2) design_choice_entry = design_choice_info.deepcopy() design_choice_entry["ids"].clear() app.env.resolve_references(design_choice_entry, design_choice_info["docname"], app.builder) content.append(design_choice_entry) content.append(para) node.replace_self(content) def purge_design_choices(app: Sphinx, env: BuildEnvironment, docname: str) -> None: if not hasattr(env, "design_choice_all_design_choices"): return env.design_choice_all_design_choices = [ design_choice for design_choice in env.design_choice_all_design_choices if design_choice["docname"] != docname ] def merge_info( app: Sphinx, env: BuildEnvironment, docnames: Iterable[str], other: BuildEnvironment ) -> None: if not hasattr(other, "design_choice_all_design_choices"): return if not hasattr(env, "design_choice_all_design_choices"): env.design_choice_all_design_choices = [] env.design_choice_all_design_choices.extend(other.design_choice_all_design_choices) def visit_design_choice_node(self: HTMLTranslator, node: design_choice_node) -> None: if self.config.design_choice_include_design_choices: self.visit_admonition(node) else: raise nodes.SkipNode def depart_design_choice_node(self: HTMLTranslator, node: design_choice_node) -> None: self.depart_admonition(node) def latex_visit_design_choice_node( self: LaTeXTranslator, node: design_choice_node ) -> None: if self.config.design_choice_include_design_choices: self.body.append("\n\\begin{sphinxadmonition}{note}{") self.body.append(self.hypertarget_to(node)) title_node = cast(nodes.title, node[0]) title = texescape.escape(title_node.astext(), self.config.latex_engine) self.body.append("%s:}" % title) node.pop(0) else: raise nodes.SkipNode def latex_depart_design_choice_node( self: LaTeXTranslator, node: design_choice_node ) -> None: self.body.append("\\end{sphinxadmonition}\n") def setup(app: Sphinx) -> Dict[str, Any]: app.add_event("design_choice-defined") app.add_config_value("design_choice_include_design_choices", False, "html") app.add_config_value("design_choice_link_only", False, "html") app.add_config_value("design_choice_emit_warnings", False, "html") app.add_node(design_choice_list) app.add_node( design_choice_node, html=(visit_design_choice_node, depart_design_choice_node), latex=(latex_visit_design_choice_node, latex_depart_design_choice_node), text=(visit_design_choice_node, depart_design_choice_node), man=(visit_design_choice_node, depart_design_choice_node), texinfo=(visit_design_choice_node, depart_design_choice_node), ) app.add_directive("design_choice", DesignChoice) app.add_directive("design_choice_list", DesignChoiceList) app.add_domain(DesignChoiceDomain) app.connect("doctree-resolved", DesignChoiceListProcessor) return { "version": sphinx.__display_version__, "env_version": 2, "parallel_read_safe": True, }

true

f70f8831567b456197e476ae0d16b0a4367f7af6

4,769

py

Python

src/alias/azext_alias/_validators.py

PoisonousJohn/azure-cli-extensions

cf0d7b6c031ba844dd5e43cc4e07533b85ef1269

[ "MIT" ]

1

2018-09-22T14:53:04.000Z

src/alias/azext_alias/_validators.py

PoisonousJohn/azure-cli-extensions

cf0d7b6c031ba844dd5e43cc4e07533b85ef1269

[ "MIT" ]

null

src/alias/azext_alias/_validators.py

PoisonousJohn/azure-cli-extensions

cf0d7b6c031ba844dd5e43cc4e07533b85ef1269

[ "MIT" ]

null

# -------------------------------------------------------------------------------------------- # Copyright (c) Microsoft Corporation. All rights reserved. # Licensed under the MIT License. See License.txt in the project root for license information. # -------------------------------------------------------------------------------------------- import re import shlex from knack.util import CLIError import azext_alias from azext_alias.argument import get_placeholders from azext_alias._const import ( COLLISION_CHECK_LEVEL_DEPTH, INVALID_ALIAS_COMMAND_ERROR, EMPTY_ALIAS_ERROR, INVALID_STARTING_CHAR_ERROR, INCONSISTENT_ARG_ERROR, COMMAND_LVL_ERROR ) from azext_alias.alias import AliasManager def process_alias_create_namespace(namespace): """ Validate input arguments when the user invokes 'az alias create'. Args: namespace: argparse namespace object. """ _validate_alias_name(namespace.alias_name) _validate_alias_command(namespace.alias_command) _validate_alias_command_level(namespace.alias_name, namespace.alias_command) _validate_pos_args_syntax(namespace.alias_name, namespace.alias_command) def _validate_alias_name(alias_name): """ Check if the alias name is valid. Args: alias_name: The name of the alias to validate. """ if not alias_name: raise CLIError(EMPTY_ALIAS_ERROR) if not re.match('^[a-zA-Z]', alias_name): raise CLIError(INVALID_STARTING_CHAR_ERROR.format(alias_name[0])) def _validate_alias_command(alias_command): """ Check if the alias command is valid. Args: alias_command: The command to validate. """ if not alias_command: raise CLIError(EMPTY_ALIAS_ERROR) # Boundary index is the index at which named argument or positional argument starts split_command = shlex.split(alias_command) boundary_index = len(split_command) for i, subcommand in enumerate(split_command): if not re.match('^[a-z]', subcommand.lower()) or i > COLLISION_CHECK_LEVEL_DEPTH: boundary_index = i break # Extract possible CLI commands and validate command_to_validate = ' '.join(split_command[:boundary_index]).lower() for command in azext_alias.cached_reserved_commands: if re.match(r'([a-z\-]*\s)*{}($|\s)'.format(command_to_validate), command): return raise CLIError(INVALID_ALIAS_COMMAND_ERROR.format(command_to_validate if command_to_validate else alias_command)) def _validate_pos_args_syntax(alias_name, alias_command): """ Check if the positional argument syntax is valid in alias name and alias command. Args: alias_name: The name of the alias to validate. alias_command: The command to validate. """ pos_args_from_alias = get_placeholders(alias_name) # Split by '|' to extract positional argument name from Jinja filter (e.g. {{ arg_name | upper }}) # Split by '.' to extract positional argument name from function call (e.g. {{ arg_name.split()[0] }}) pos_args_from_command = [x.split('|')[0].split('.')[0].strip() for x in get_placeholders(alias_command)] if set(pos_args_from_alias) != set(pos_args_from_command): arg_diff = set(pos_args_from_alias) ^ set(pos_args_from_command) raise CLIError(INCONSISTENT_ARG_ERROR.format('' if len(arg_diff) == 1 else 's', arg_diff, 'is' if len(arg_diff) == 1 else 'are')) def _validate_alias_command_level(alias, command): """ Make sure that if the alias is a reserved command, the command that the alias points to in the command tree does not conflict in levels. e.g. 'dns' -> 'network dns' is valid because dns is a level 2 command and network dns starts at level 1. However, 'list' -> 'show' is not valid because list and show are both reserved commands at level 2. Args: alias: The name of the alias. command: The command that the alias points to. """ alias_collision_table = AliasManager.build_collision_table([alias], azext_alias.cached_reserved_commands) # Alias is not a reserved command, so it can point to any command if not alias_collision_table: return command_collision_table = AliasManager.build_collision_table([command], azext_alias.cached_reserved_commands) alias_collision_levels = alias_collision_table.get(alias.split()[0], []) command_collision_levels = command_collision_table.get(command.split()[0], []) # Check if there is a command level conflict if set(alias_collision_levels) & set(command_collision_levels): raise CLIError(COMMAND_LVL_ERROR.format(alias, command))

38.772358

117

0.681275

import re import shlex from knack.util import CLIError import azext_alias from azext_alias.argument import get_placeholders from azext_alias._const import ( COLLISION_CHECK_LEVEL_DEPTH, INVALID_ALIAS_COMMAND_ERROR, EMPTY_ALIAS_ERROR, INVALID_STARTING_CHAR_ERROR, INCONSISTENT_ARG_ERROR, COMMAND_LVL_ERROR ) from azext_alias.alias import AliasManager def process_alias_create_namespace(namespace): _validate_alias_name(namespace.alias_name) _validate_alias_command(namespace.alias_command) _validate_alias_command_level(namespace.alias_name, namespace.alias_command) _validate_pos_args_syntax(namespace.alias_name, namespace.alias_command) def _validate_alias_name(alias_name): if not alias_name: raise CLIError(EMPTY_ALIAS_ERROR) if not re.match('^[a-zA-Z]', alias_name): raise CLIError(INVALID_STARTING_CHAR_ERROR.format(alias_name[0])) def _validate_alias_command(alias_command): if not alias_command: raise CLIError(EMPTY_ALIAS_ERROR) split_command = shlex.split(alias_command) boundary_index = len(split_command) for i, subcommand in enumerate(split_command): if not re.match('^[a-z]', subcommand.lower()) or i > COLLISION_CHECK_LEVEL_DEPTH: boundary_index = i break command_to_validate = ' '.join(split_command[:boundary_index]).lower() for command in azext_alias.cached_reserved_commands: if re.match(r'([a-z\-]*\s)*{}($|\s)'.format(command_to_validate), command): return raise CLIError(INVALID_ALIAS_COMMAND_ERROR.format(command_to_validate if command_to_validate else alias_command)) def _validate_pos_args_syntax(alias_name, alias_command): pos_args_from_alias = get_placeholders(alias_name) pos_args_from_command = [x.split('|')[0].split('.')[0].strip() for x in get_placeholders(alias_command)] if set(pos_args_from_alias) != set(pos_args_from_command): arg_diff = set(pos_args_from_alias) ^ set(pos_args_from_command) raise CLIError(INCONSISTENT_ARG_ERROR.format('' if len(arg_diff) == 1 else 's', arg_diff, 'is' if len(arg_diff) == 1 else 'are')) def _validate_alias_command_level(alias, command): alias_collision_table = AliasManager.build_collision_table([alias], azext_alias.cached_reserved_commands) if not alias_collision_table: return command_collision_table = AliasManager.build_collision_table([command], azext_alias.cached_reserved_commands) alias_collision_levels = alias_collision_table.get(alias.split()[0], []) command_collision_levels = command_collision_table.get(command.split()[0], []) if set(alias_collision_levels) & set(command_collision_levels): raise CLIError(COMMAND_LVL_ERROR.format(alias, command))

true

f70f8a2f41f8fdb18d9b0a96c1d23e7abc0f59f7

1,752

py

Python

python/lvmieb/exceptions.py

sdss/OsuActor

bf3d92448e07cefc4c1346db04b1eb9b7e00dd41

[ "BSD-3-Clause" ]

2

2021-07-30T04:38:30.000Z

2021-08-13T13:34:04.000Z

python/lvmieb/exceptions.py

sdss/OsuActor

bf3d92448e07cefc4c1346db04b1eb9b7e00dd41

[ "BSD-3-Clause" ]

4

2021-06-03T12:01:00.000Z

2021-08-14T09:34:12.000Z

python/lvmieb/exceptions.py

sdss/osuactor

bf3d92448e07cefc4c1346db04b1eb9b7e00dd41

[ "BSD-3-Clause" ]

2

2021-05-04T06:19:39.000Z

2021-05-11T08:35:02.000Z

# -*- coding: utf-8 -*- # # Licensed under a 3-clause BSD license. # # @Author: Changgon Kim, Mingeyong Yang, Taeeun Kim # @Date: 2021-04-26 17:14 # @Last modified by: Changgon Kim from __future__ import absolute_import, division, print_function class LvmIebError(Exception): """A custom core LvmIeb exception""" def __init__(self, message=None): message = "There has been an error" if not message else message super(LvmIebError, self).__init__(message) class LvmIebNotImplemented(LvmIebError): """A custom exception for not yet implemented features.""" def __init__(self, message=None): message = "This feature is not implemented yet." if not message else message super(LvmIebNotImplemented, self).__init__(message) class LvmIebAPIError(LvmIebError): """A custom exception for API errors""" def __init__(self, message=None): if not message: message = "Error with Http Response from LvmIeb API" else: message = "Http response error from LvmIeb API. {0}".format(message) super(LvmIebAPIError, self).__init__(message) class LvmIebApiAuthError(LvmIebAPIError): """A custom exception for API authentication errors""" pass class LvmIebMissingDependency(LvmIebError): """A custom exception for missing dependencies.""" pass class LvmIebWarning(Warning): """Base warning for LvmIeb.""" class LvmIebUserWarning(UserWarning, LvmIebWarning): """The primary warning class.""" pass class LvmIebSkippedTestWarning(LvmIebUserWarning): """A warning for when a test is skipped.""" pass class LvmIebDeprecationWarning(LvmIebUserWarning): """A warning for deprecated features.""" pass

23.052632

84

0.695205

from __future__ import absolute_import, division, print_function class LvmIebError(Exception): def __init__(self, message=None): message = "There has been an error" if not message else message super(LvmIebError, self).__init__(message) class LvmIebNotImplemented(LvmIebError): def __init__(self, message=None): message = "This feature is not implemented yet." if not message else message super(LvmIebNotImplemented, self).__init__(message) class LvmIebAPIError(LvmIebError): def __init__(self, message=None): if not message: message = "Error with Http Response from LvmIeb API" else: message = "Http response error from LvmIeb API. {0}".format(message) super(LvmIebAPIError, self).__init__(message) class LvmIebApiAuthError(LvmIebAPIError): pass class LvmIebMissingDependency(LvmIebError): pass class LvmIebWarning(Warning): class LvmIebUserWarning(UserWarning, LvmIebWarning): pass class LvmIebSkippedTestWarning(LvmIebUserWarning): pass class LvmIebDeprecationWarning(LvmIebUserWarning): pass

true

f70f8a6fdb098ef333f9170579450717e8ff2c68

12,261

py

Python

common/manager.py

hxwork/OMNet

be88a734e7327def365e1875bbc7cd2fea1539b0

[ "MIT" ]

null

common/manager.py

hxwork/OMNet

be88a734e7327def365e1875bbc7cd2fea1539b0

[ "MIT" ]

null

common/manager.py

hxwork/OMNet

be88a734e7327def365e1875bbc7cd2fea1539b0

[ "MIT" ]

1

2021-11-14T12:56:40.000Z

import os from collections import defaultdict import numpy as np import torch from termcolor import colored from torch.utils.tensorboard import SummaryWriter from common import utils class Manager(): def __init__(self, model, optimizer, scheduler, params, dataloaders, logger): # params status self.params = params self.model = model self.optimizer = optimizer self.scheduler = scheduler self.dataloaders = dataloaders self.logger = logger self.epoch = 0 self.step = 0 self.best_val_score = np.inf self.cur_val_score = np.inf self.best_test_score = np.inf self.cur_test_score = np.inf # train status self.train_status = defaultdict(utils.AverageMeter) # val status self.val_status = defaultdict(utils.AverageMeter) # test status self.test_status = defaultdict(utils.AverageMeter) # model status self.loss_status = defaultdict(utils.AverageMeter) # init local tensorboard and html self.init_tb_and_html() def init_tb_and_html(self): # tensorboard loss local_tb_dir = os.path.join(self.params.model_dir, "summary/loss") os.makedirs(local_tb_dir, exist_ok=True) self.local_loss_writter = SummaryWriter(log_dir=local_tb_dir) # tensorboard metric local_tb_dir = os.path.join(self.params.model_dir, "summary/metric") os.makedirs(local_tb_dir, exist_ok=True) self.local_metric_writter = SummaryWriter(log_dir=local_tb_dir) # html local_html_dir = os.path.join(self.params.model_dir, "summary/html") os.makedirs(local_html_dir, exist_ok=True) self.local_html_dir = local_html_dir def update_step(self): self.step += 1 def update_epoch(self): self.epoch += 1 def update_loss_status(self, loss, batch_size): for k, v in loss.items(): self.loss_status[k].update(val=v.item(), num=batch_size) def update_metric_status(self, metrics, split, batch_size): if split == "val": for k, v in metrics.items(): self.val_status[k].update(val=v.item(), num=batch_size) self.cur_val_score = self.val_status[self.params.major_metric].avg elif split == "test": for k, v in metrics.items(): self.test_status[k].update(val=v.item(), num=batch_size) self.cur_test_score = self.test_status[self.params.major_metric].avg else: raise ValueError("Wrong eval type: {}".format(split)) def summarize_metric_status(self, metrics, split): if split == "val": for k in metrics: if k.endswith('MSE'): self.val_status[k[:-3] + 'RMSE'].set(val=np.sqrt(self.val_status[k].avg)) else: continue elif split == "test": for k in metrics: if k.endswith('MSE'): self.test_status[k[:-3] + 'RMSE'].set(val=np.sqrt(self.test_status[k].avg)) else: continue else: raise ValueError("Wrong eval type: {}".format(split)) def reset_loss_status(self): for k, v in self.loss_status.items(): self.loss_status[k].reset() def reset_metric_status(self, split): if split == "val": for k, v in self.val_status.items(): self.val_status[k].reset() elif split == "test": for k, v in self.test_status.items(): self.test_status[k].reset() else: raise ValueError("Wrong split string: {}".format(split)) def print_train_info(self): exp_name = self.params.model_dir.split('/')[-1] print_str = "{} Epoch: {:4d}, lr={:.4f} ".format(exp_name, self.epoch, self.scheduler.get_last_lr()[0]) print_str += "total loss: %.4f(%.4f)" % (self.loss_status['total'].val, self.loss_status['total'].avg) return print_str def print_metrics(self, split, title="Eval", color="red", only_best=False): if split == "val": metric_status = self.val_status is_best = self.cur_val_score < self.best_val_score elif split == "test": metric_status = self.test_status is_best = self.cur_test_score < self.best_test_score else: raise ValueError("Wrong split string: {}".format(split)) print_str = " | ".join("{}: {:4g}".format(k, v.avg) for k, v in metric_status.items()) if only_best: if is_best: self.logger.info(colored("Best Epoch: {}, {} Results: {}".format(self.epoch, title, print_str), color, attrs=["bold"])) else: self.logger.info(colored("Epoch: {}, {} Results: {}".format(self.epoch, title, print_str), color, attrs=["bold"])) def write_loss_to_tb(self, split): for k, v in self.loss_status.items(): if split == "train": self.local_loss_writter.add_scalar("train_Loss/{}".format(k), v.val, self.step) elif split == "val": self.local_loss_writter.add_scalar("val_Loss/{}".format(k), v.val, self.step) elif split == "test": self.local_loss_writter.add_scalar("test_Loss/{}".format(k), v.val, self.step) else: raise ValueError("Wrong split string: {}".format(split)) def write_metric_to_tb(self, split): if split == "val": for k, v in self.val_status.items(): self.local_metric_writter.add_scalar("val_Metric/{}".format(k), v.avg, self.epoch) elif split == "test": for k, v in self.test_status.items(): self.local_metric_writter.add_scalar("test_Metric/{}".format(k), v.avg, self.epoch) else: raise ValueError("Wrong split string: {}".format(split)) def check_best_save_last_checkpoints(self, save_latest_freq=5, save_best_after=50): state = { "state_dict": self.model.state_dict(), "optimizer": self.optimizer.state_dict(), "scheduler": self.scheduler.state_dict(), "step": self.step, "epoch": self.epoch, } if self.dataloaders["val"] is not None: state["best_val_score"] = self.best_val_score if self.dataloaders["test"] is not None: state["best_test_score"] = self.best_test_score # save latest checkpoint if self.epoch % save_latest_freq == 0: latest_ckpt_name = os.path.join(self.params.model_dir, "model_latest.pth") torch.save(state, latest_ckpt_name) self.logger.info("Saved latest checkpoint to: {}".format(latest_ckpt_name)) # save val latest metrics, and check if val is best checkpoints if self.dataloaders["val"] is not None: val_latest_metrics_name = os.path.join(self.params.model_dir, "val_metrics_latest.json") utils.save_dict_to_json(self.val_status, val_latest_metrics_name) is_best = self.cur_val_score < self.best_val_score if is_best: # save metrics self.best_val_score = self.cur_val_score best_metrics_name = os.path.join(self.params.model_dir, "val_metrics_best.json") utils.save_dict_to_json(self.val_status, best_metrics_name) self.logger.info("Current is val best, score={:.7f}".format(self.best_val_score)) # save checkpoint if self.epoch > save_best_after: best_ckpt_name = os.path.join(self.params.model_dir, "val_model_best.pth") torch.save(state, best_ckpt_name) self.logger.info("Saved val best checkpoint to: {}".format(best_ckpt_name)) # save test latest metrics, and check if test is best checkpoints if self.dataloaders["test"] is not None: test_latest_metrics_name = os.path.join(self.params.model_dir, "test_metrics_latest.json") utils.save_dict_to_json(self.test_status, test_latest_metrics_name) is_best = self.cur_test_score < self.best_test_score if is_best: # save metrics self.best_test_score = self.cur_test_score best_metrics_name = os.path.join(self.params.model_dir, "test_metrics_best.json") utils.save_dict_to_json(self.test_status, best_metrics_name) self.logger.info("Current is test best, score={:.7f}".format(self.best_test_score)) # save checkpoint if self.epoch > save_best_after: best_ckpt_name = os.path.join(self.params.model_dir, "test_model_best.pth") torch.save(state, best_ckpt_name) self.logger.info("Saved test best checkpoint to: {}".format(best_ckpt_name)) def load_checkpoints(self): state = torch.load(self.params.restore_file) ckpt_component = [] if "state_dict" in state and self.model is not None: try: self.model.load_state_dict(state["state_dict"]) except RuntimeError: print("Using custom loading net") net_dict = self.model.state_dict() if "module" not in list(state["state_dict"].keys())[0]: state_dict = {"module." + k: v for k, v in state["state_dict"].items() if "module." + k in net_dict.keys()} else: state_dict = {k: v for k, v in state["state_dict"].items() if k in net_dict.keys()} net_dict.update(state_dict) self.model.load_state_dict(net_dict, strict=False) ckpt_component.append("net") if not self.params.only_weights: if "optimizer" in state and self.optimizer is not None: try: self.optimizer.load_state_dict(state["optimizer"]) except RuntimeError: print("Using custom loading optimizer") optimizer_dict = self.optimizer.state_dict() state_dict = {k: v for k, v in state["optimizer"].items() if k in optimizer_dict.keys()} optimizer_dict.update(state_dict) self.optimizer.load_state_dict(optimizer_dict) ckpt_component.append("opt") if "scheduler" in state and self.train_status["scheduler"] is not None: try: self.scheduler.load_state_dict(state["scheduler"]) except RuntimeError: print("Using custom loading scheduler") scheduler_dict = self.scheduler.state_dict() state_dict = {k: v for k, v in state["scheduler"].items() if k in scheduler_dict.keys()} scheduler_dict.update(state_dict) self.scheduler.load_state_dict(scheduler_dict) ckpt_component.append("sch") if "step" in state: self.step = state["step"] + 1 ckpt_component.append("step") if "epoch" in state: self.epoch = state["epoch"] + 1 ckpt_component.append("epoch") if "best_val_score" in state: self.best_val_score = state["best_val_score"] ckpt_component.append("best val score: {:.3g}".format(self.best_val_score)) if "best_test_score" in state: self.best_test_score = state["best_test_score"] ckpt_component.append("best test score: {:.3g}".format(self.best_test_score)) ckpt_component = ", ".join(i for i in ckpt_component) self.logger.info("Loaded models from: {}".format(self.params.restore_file)) self.logger.info("Ckpt load: {}".format(ckpt_component))

45.077206

136

0.581437

import os from collections import defaultdict import numpy as np import torch from termcolor import colored from torch.utils.tensorboard import SummaryWriter from common import utils class Manager(): def __init__(self, model, optimizer, scheduler, params, dataloaders, logger): self.params = params self.model = model self.optimizer = optimizer self.scheduler = scheduler self.dataloaders = dataloaders self.logger = logger self.epoch = 0 self.step = 0 self.best_val_score = np.inf self.cur_val_score = np.inf self.best_test_score = np.inf self.cur_test_score = np.inf self.train_status = defaultdict(utils.AverageMeter) self.val_status = defaultdict(utils.AverageMeter) self.test_status = defaultdict(utils.AverageMeter) self.loss_status = defaultdict(utils.AverageMeter) self.init_tb_and_html() def init_tb_and_html(self): local_tb_dir = os.path.join(self.params.model_dir, "summary/loss") os.makedirs(local_tb_dir, exist_ok=True) self.local_loss_writter = SummaryWriter(log_dir=local_tb_dir) local_tb_dir = os.path.join(self.params.model_dir, "summary/metric") os.makedirs(local_tb_dir, exist_ok=True) self.local_metric_writter = SummaryWriter(log_dir=local_tb_dir) local_html_dir = os.path.join(self.params.model_dir, "summary/html") os.makedirs(local_html_dir, exist_ok=True) self.local_html_dir = local_html_dir def update_step(self): self.step += 1 def update_epoch(self): self.epoch += 1 def update_loss_status(self, loss, batch_size): for k, v in loss.items(): self.loss_status[k].update(val=v.item(), num=batch_size) def update_metric_status(self, metrics, split, batch_size): if split == "val": for k, v in metrics.items(): self.val_status[k].update(val=v.item(), num=batch_size) self.cur_val_score = self.val_status[self.params.major_metric].avg elif split == "test": for k, v in metrics.items(): self.test_status[k].update(val=v.item(), num=batch_size) self.cur_test_score = self.test_status[self.params.major_metric].avg else: raise ValueError("Wrong eval type: {}".format(split)) def summarize_metric_status(self, metrics, split): if split == "val": for k in metrics: if k.endswith('MSE'): self.val_status[k[:-3] + 'RMSE'].set(val=np.sqrt(self.val_status[k].avg)) else: continue elif split == "test": for k in metrics: if k.endswith('MSE'): self.test_status[k[:-3] + 'RMSE'].set(val=np.sqrt(self.test_status[k].avg)) else: continue else: raise ValueError("Wrong eval type: {}".format(split)) def reset_loss_status(self): for k, v in self.loss_status.items(): self.loss_status[k].reset() def reset_metric_status(self, split): if split == "val": for k, v in self.val_status.items(): self.val_status[k].reset() elif split == "test": for k, v in self.test_status.items(): self.test_status[k].reset() else: raise ValueError("Wrong split string: {}".format(split)) def print_train_info(self): exp_name = self.params.model_dir.split('/')[-1] print_str = "{} Epoch: {:4d}, lr={:.4f} ".format(exp_name, self.epoch, self.scheduler.get_last_lr()[0]) print_str += "total loss: %.4f(%.4f)" % (self.loss_status['total'].val, self.loss_status['total'].avg) return print_str def print_metrics(self, split, title="Eval", color="red", only_best=False): if split == "val": metric_status = self.val_status is_best = self.cur_val_score < self.best_val_score elif split == "test": metric_status = self.test_status is_best = self.cur_test_score < self.best_test_score else: raise ValueError("Wrong split string: {}".format(split)) print_str = " | ".join("{}: {:4g}".format(k, v.avg) for k, v in metric_status.items()) if only_best: if is_best: self.logger.info(colored("Best Epoch: {}, {} Results: {}".format(self.epoch, title, print_str), color, attrs=["bold"])) else: self.logger.info(colored("Epoch: {}, {} Results: {}".format(self.epoch, title, print_str), color, attrs=["bold"])) def write_loss_to_tb(self, split): for k, v in self.loss_status.items(): if split == "train": self.local_loss_writter.add_scalar("train_Loss/{}".format(k), v.val, self.step) elif split == "val": self.local_loss_writter.add_scalar("val_Loss/{}".format(k), v.val, self.step) elif split == "test": self.local_loss_writter.add_scalar("test_Loss/{}".format(k), v.val, self.step) else: raise ValueError("Wrong split string: {}".format(split)) def write_metric_to_tb(self, split): if split == "val": for k, v in self.val_status.items(): self.local_metric_writter.add_scalar("val_Metric/{}".format(k), v.avg, self.epoch) elif split == "test": for k, v in self.test_status.items(): self.local_metric_writter.add_scalar("test_Metric/{}".format(k), v.avg, self.epoch) else: raise ValueError("Wrong split string: {}".format(split)) def check_best_save_last_checkpoints(self, save_latest_freq=5, save_best_after=50): state = { "state_dict": self.model.state_dict(), "optimizer": self.optimizer.state_dict(), "scheduler": self.scheduler.state_dict(), "step": self.step, "epoch": self.epoch, } if self.dataloaders["val"] is not None: state["best_val_score"] = self.best_val_score if self.dataloaders["test"] is not None: state["best_test_score"] = self.best_test_score if self.epoch % save_latest_freq == 0: latest_ckpt_name = os.path.join(self.params.model_dir, "model_latest.pth") torch.save(state, latest_ckpt_name) self.logger.info("Saved latest checkpoint to: {}".format(latest_ckpt_name)) if self.dataloaders["val"] is not None: val_latest_metrics_name = os.path.join(self.params.model_dir, "val_metrics_latest.json") utils.save_dict_to_json(self.val_status, val_latest_metrics_name) is_best = self.cur_val_score < self.best_val_score if is_best: self.best_val_score = self.cur_val_score best_metrics_name = os.path.join(self.params.model_dir, "val_metrics_best.json") utils.save_dict_to_json(self.val_status, best_metrics_name) self.logger.info("Current is val best, score={:.7f}".format(self.best_val_score)) if self.epoch > save_best_after: best_ckpt_name = os.path.join(self.params.model_dir, "val_model_best.pth") torch.save(state, best_ckpt_name) self.logger.info("Saved val best checkpoint to: {}".format(best_ckpt_name)) if self.dataloaders["test"] is not None: test_latest_metrics_name = os.path.join(self.params.model_dir, "test_metrics_latest.json") utils.save_dict_to_json(self.test_status, test_latest_metrics_name) is_best = self.cur_test_score < self.best_test_score if is_best: self.best_test_score = self.cur_test_score best_metrics_name = os.path.join(self.params.model_dir, "test_metrics_best.json") utils.save_dict_to_json(self.test_status, best_metrics_name) self.logger.info("Current is test best, score={:.7f}".format(self.best_test_score)) if self.epoch > save_best_after: best_ckpt_name = os.path.join(self.params.model_dir, "test_model_best.pth") torch.save(state, best_ckpt_name) self.logger.info("Saved test best checkpoint to: {}".format(best_ckpt_name)) def load_checkpoints(self): state = torch.load(self.params.restore_file) ckpt_component = [] if "state_dict" in state and self.model is not None: try: self.model.load_state_dict(state["state_dict"]) except RuntimeError: print("Using custom loading net") net_dict = self.model.state_dict() if "module" not in list(state["state_dict"].keys())[0]: state_dict = {"module." + k: v for k, v in state["state_dict"].items() if "module." + k in net_dict.keys()} else: state_dict = {k: v for k, v in state["state_dict"].items() if k in net_dict.keys()} net_dict.update(state_dict) self.model.load_state_dict(net_dict, strict=False) ckpt_component.append("net") if not self.params.only_weights: if "optimizer" in state and self.optimizer is not None: try: self.optimizer.load_state_dict(state["optimizer"]) except RuntimeError: print("Using custom loading optimizer") optimizer_dict = self.optimizer.state_dict() state_dict = {k: v for k, v in state["optimizer"].items() if k in optimizer_dict.keys()} optimizer_dict.update(state_dict) self.optimizer.load_state_dict(optimizer_dict) ckpt_component.append("opt") if "scheduler" in state and self.train_status["scheduler"] is not None: try: self.scheduler.load_state_dict(state["scheduler"]) except RuntimeError: print("Using custom loading scheduler") scheduler_dict = self.scheduler.state_dict() state_dict = {k: v for k, v in state["scheduler"].items() if k in scheduler_dict.keys()} scheduler_dict.update(state_dict) self.scheduler.load_state_dict(scheduler_dict) ckpt_component.append("sch") if "step" in state: self.step = state["step"] + 1 ckpt_component.append("step") if "epoch" in state: self.epoch = state["epoch"] + 1 ckpt_component.append("epoch") if "best_val_score" in state: self.best_val_score = state["best_val_score"] ckpt_component.append("best val score: {:.3g}".format(self.best_val_score)) if "best_test_score" in state: self.best_test_score = state["best_test_score"] ckpt_component.append("best test score: {:.3g}".format(self.best_test_score)) ckpt_component = ", ".join(i for i in ckpt_component) self.logger.info("Loaded models from: {}".format(self.params.restore_file)) self.logger.info("Ckpt load: {}".format(ckpt_component))

true

f70f8c67ca5c8f4c4bc0844a6821081bdf4ce0d1

33,589

py

Python

Cogs/Strike.py

camielverdult/CorpBot.py

56cf3ee736625525d05f9f447b31e34baf93596d

[ "MIT" ]

null

Cogs/Strike.py

camielverdult/CorpBot.py

56cf3ee736625525d05f9f447b31e34baf93596d

[ "MIT" ]

null

Cogs/Strike.py

camielverdult/CorpBot.py

56cf3ee736625525d05f9f447b31e34baf93596d

[ "MIT" ]

null

import asyncio import discord import time import parsedatetime from datetime import datetime from operator import itemgetter from discord.ext import commands from Cogs import ReadableTime from Cogs import DisplayName from Cogs import Nullify def setup(bot): # Add the bot and deps settings = bot.get_cog("Settings") mute = bot.get_cog("Mute") bot.add_cog(Strike(bot, settings, mute)) # This is the Strike module. It keeps track of warnings and kicks/bans accordingly # Strikes = [ time until drops off ] # StrikeOut = 3 (3 strikes and you're out) # StrikeLevel (a list similar to xproles) # Standard strike roles: # 0 = Not been punished already # 1 = Muted for x amount of time # 2 = Already been kicked (id in kick list) # 3 = Already been banned (auto-mute) class Strike(commands.Cog): # Init with the bot reference, and a reference to the settings var def __init__(self, bot, settings, mute): self.bot = bot self.settings = settings self.mute = mute self.loop_list = [] global Utils, DisplayName Utils = self.bot.get_cog("Utils") DisplayName = self.bot.get_cog("DisplayName") async def onjoin(self, member, server): # Check id against the kick and ban list and react accordingly kickList = self.settings.getServerStat(server, "KickList") if str(member.id) in kickList: # The user has been kicked before - set their strikeLevel to 2 self.settings.setUserStat(member, server, "StrikeLevel", 2) banList = self.settings.getServerStat(server, "BanList") if str(member.id) in banList: # The user has been kicked before - set their strikeLevel to 3 # Also mute them self.settings.setUserStat(member, server, "StrikeLevel", 3) self.settings.setUserStat(member, server, "Muted", True) self.settings.setUserStat(member, server, "Cooldown", None) await self.mute._mute(member, server) # Proof of concept stuff for reloading cog/extension def _is_submodule(self, parent, child): return parent == child or child.startswith(parent + ".") @commands.Cog.listener() async def on_unloaded_extension(self, ext): # Called to shut things down if not self._is_submodule(ext.__name__, self.__module__): return for task in self.loop_list: task.cancel() @commands.Cog.listener() async def on_loaded_extension(self, ext): # See if we were loaded if not self._is_submodule(ext.__name__, self.__module__): return self.bot.loop.create_task(self.start_loading()) async def start_loading(self): await self.bot.wait_until_ready() await self.bot.loop.run_in_executor(None, self.check_strikes) def check_strikes(self): # Check all strikes - and start timers print("Checking strikes...") t = time.time() for server in self.bot.guilds: for member in server.members: strikes = self.settings.getUserStat(member, server, "Strikes") if strikes == None: continue if len(strikes): # We have a list for strike in strikes: # Make sure it's a strike that *can* roll off if not strike['Time'] == -1: self.loop_list.append(self.bot.loop.create_task( self.checkStrike(member, strike))) print("Strikes checked - took {} seconds.".format(time.time() - t)) async def checkStrike(self, member, strike): # Start our countdown countDown = int(strike['Time'])-int(time.time()) if countDown > 0: # We have a positive countdown - let's wait await asyncio.sleep(countDown) strikes = self.settings.getUserStat(member, member.guild, "Strikes") # Verify strike is still valid if not strike in strikes: return strikes.remove(strike) self.settings.setUserStat(member, member.guild, "Strikes", strikes) @commands.command(pass_context=True) async def strike(self, ctx, member: discord.Member = None, days=None, *, message: str = None): """Give a user a strike (bot-admin only).""" isAdmin = ctx.message.author.permissions_in( ctx.message.channel).administrator if not isAdmin: checkAdmin = self.settings.getServerStat( ctx.message.guild, "AdminArray") for role in ctx.message.author.roles: for aRole in checkAdmin: # Get the role that corresponds to the id if str(aRole['ID']) == str(role.id): isAdmin = True # Only allow admins to change server stats if not isAdmin: await ctx.channel.send('You do not have sufficient privileges to access this command.') return if member == None: msg = 'Usage: `{}strike [member] [strike timeout (in days) - 0 = forever] [message (optional)]`'.format( ctx.prefix) await ctx.channel.send(msg) return # Check if we're striking ourselves if member.id == ctx.message.author.id: # We're giving ourselves a strike? await ctx.channel.send('You can\'t give yourself a strike, silly.') return # Check if the bot is getting the strike if member.id == self.bot.user.id: await ctx.channel.send('I can\'t do that, *{}*.'.format(DisplayName.name(ctx.message.author))) return # Check if we're striking another admin/bot-admin isAdmin = member.permissions_in(ctx.message.channel).administrator if not isAdmin: checkAdmin = self.settings.getServerStat( ctx.message.guild, "AdminArray") for role in member.roles: for aRole in checkAdmin: # Get the role that corresponds to the id if str(aRole['ID']) == str(role.id): isAdmin = True if isAdmin: await ctx.channel.send('You can\'t give other admins/bot-admins strikes, bub.') return # Check if days is an int - otherwise assume it's part of the message try: days = int(days) except Exception: if not days == None: if message == None: message = days else: message = days + ' ' + message days = 0 # If it's not at least a day, it's forever if days < 1: days = -1 currentTime = int(time.time()) # Build our Strike strike = {} if days == -1: strike['Time'] = -1 else: strike['Time'] = currentTime+(86400*days) self.loop_list.append(self.bot.loop.create_task( self.checkStrike(member, strike))) strike['Message'] = message strike['GivenBy'] = ctx.message.author.id strikes = self.settings.getUserStat( member, ctx.message.guild, "Strikes") strikeout = int(self.settings.getServerStat( ctx.message.guild, "StrikeOut")) strikeLevel = int(self.settings.getUserStat( member, ctx.message.guild, "StrikeLevel")) strikes.append(strike) self.settings.setUserStat( member, ctx.message.guild, "Strikes", strikes) strikeNum = len(strikes) # Set up consequences if strikeLevel == 0: consequence = '**muted for a day**.' elif strikeLevel == 1: consequence = '**kicked**.' else: consequence = '**banned**.' # Check if we've struck out if strikeNum < strikeout: # We haven't struck out yet msg = '*{}* has just received *strike {}*. *{}* more and they will be {}'.format( DisplayName.name(member), strikeNum, strikeout-strikeNum, consequence) else: # We struck out - let's evaluate if strikeLevel == 0: cooldownFinal = currentTime+86400 checkRead = ReadableTime.getReadableTimeBetween( currentTime, cooldownFinal) if message: mutemessage = 'You have been muted in *{}*.\nThe Reason:\n{}'.format( Nullify.escape_all(ctx.guild.name), message) else: mutemessage = 'You have been muted in *{}*.'.format( Nullify.escape_all(ctx.guild.name)) # Check if already muted alreadyMuted = self.settings.getUserStat( member, ctx.message.guild, "Muted") if alreadyMuted: # Find out for how long muteTime = self.settings.getUserStat( member, ctx.message.guild, "Cooldown") if not muteTime == None: if muteTime < cooldownFinal: self.settings.setUserStat( member, ctx.message.guild, "Cooldown", cooldownFinal) timeRemains = ReadableTime.getReadableTimeBetween( currentTime, cooldownFinal) if message: mutemessage = 'Your muted time in *{}* has been extended to *{}*.\nThe Reason:\n{}'.format( Nullify.escape_all(ctx.guild.name), timeRemains, message) else: mutemessage = 'You muted time in *{}* has been extended to *{}*.'.format( Nullify.escape_all(ctx.guild.name), timeRemains) else: self.settings.setUserStat( member, ctx.message.guild, "Muted", True) self.settings.setUserStat( member, ctx.message.guild, "Cooldown", cooldownFinal) await self.mute._mute(member, ctx.message.guild, cooldownFinal) await member.send(mutemessage) elif strikeLevel == 1: kickList = self.settings.getServerStat( ctx.message.guild, "KickList") if not str(member.id) in kickList: kickList.append(str(member.id)) self.settings.setServerStat( ctx.message.guild, "KickList", kickList) if message: kickmessage = 'You have been kicked from *{}*.\nThe Reason:\n{}'.format( Nullify.escape_all(ctx.guild.name), message) else: kickmessage = 'You have been kicked from *{}*.'.format( Nullify.escape_all(ctx.guild.name)) await member.send(kickmessage) await ctx.guild.kick(member) else: banList = self.settings.getServerStat( ctx.message.guild, "BanList") if not str(member.id) in banList: banList.append(str(member.id)) self.settings.setServerStat( ctx.message.guild, "BanList", banList) if message: banmessage = 'You have been banned from *{}*.\nThe Reason:\n{}'.format( Nullify.escape_all(ctx.guild.name), message) else: banmessage = 'You have been banned from *{}*.'.format( Nullify.escape_all(ctx.guild.name)) await member.send(banmessage) await ctx.guild.ban(member) self.settings.incrementStat( member, ctx.message.guild, "StrikeLevel", 1) self.settings.setUserStat(member, ctx.message.guild, "Strikes", []) msg = '*{}* has just received *strike {}*. They have been {}'.format( DisplayName.name(member), strikeNum, consequence) await ctx.channel.send(msg) @strike.error async def strike_error(self, ctx, error): # do stuff msg = 'strike Error: {}'.format(error) await ctx.channel.send(msg) @commands.command(pass_context=True) async def strikes(self, ctx, *, member=None): """Check a your own, or another user's total strikes (bot-admin needed to check other users).""" isAdmin = ctx.message.author.permissions_in( ctx.message.channel).administrator if not isAdmin: checkAdmin = self.settings.getServerStat( ctx.message.guild, "AdminArray") for role in ctx.message.author.roles: for aRole in checkAdmin: # Get the role that corresponds to the id if str(aRole['ID']) == str(role.id): isAdmin = True if member == None: member = ctx.message.author if type(member) is str: memberName = member member = DisplayName.memberForName(memberName, ctx.message.guild) if not member: msg = 'I couldn\'t find *{}*...'.format( Nullify.escape_all(memberName)) await ctx.channel.send(msg) return # Only allow admins to check others' strikes if not isAdmin: if member: if not member.id == ctx.message.author.id: await ctx.channel.send('You are not a bot-admin. You can only see your own strikes.') member = ctx.message.author # Create blank embed stat_embed = discord.Embed(color=member.color) strikes = self.settings.getUserStat( member, ctx.message.guild, "Strikes") strikeout = int(self.settings.getServerStat( ctx.message.guild, "StrikeOut")) strikeLevel = int(self.settings.getUserStat( member, ctx.message.guild, "StrikeLevel")) # Add strikes, and strike level stat_embed.add_field(name="Strikes", value=len(strikes), inline=True) stat_embed.add_field(name="Strike Level", value=strikeLevel, inline=True) # Get member's avatar url avURL = member.avatar_url if not len(avURL): avURL = member.default_avatar_url if member.nick: # We have a nickname msg = "__***{},*** **who currently goes by** ***{}:***__\n\n".format( member.name, member.nick) # Add to embed stat_embed.set_author(name='{}, who currently goes by {}'.format( member.name, member.nick), icon_url=avURL) else: msg = "__***{}:***__\n\n".format(member.name) # Add to embed stat_embed.set_author(name='{}'.format( member.name), icon_url=avURL) # Get messages - and cooldowns currentTime = int(time.time()) if not len(strikes): # no strikes messages = "None." cooldowns = "None." givenBy = "None." else: messages = '' cooldowns = '' givenBy = '' for i in range(0, len(strikes)): if strikes[i]['Message']: messages += '{}. {}\n'.format(i+1, strikes[i]['Message']) else: messages += '{}. No message\n'.format(i+1) timeLeft = strikes[i]['Time'] if timeLeft == -1: cooldowns += '{}. Never rolls off\n'.format(i+1) else: timeRemains = ReadableTime.getReadableTimeBetween( currentTime, timeLeft) cooldowns += '{}. {}\n'.format(i+1, timeRemains) given = strikes[i]['GivenBy'] givenBy += '{}. {}\n'.format(i+1, DisplayName.name( DisplayName.memberForID(given, ctx.message.guild))) # Add messages and cooldowns stat_embed.add_field(name="Messages", value=messages, inline=True) stat_embed.add_field(name="Time Left", value=cooldowns, inline=True) stat_embed.add_field(name="Given By", value=givenBy, inline=True) # Strikes remaining stat_embed.add_field(name="Strikes Remaining", value=strikeout-len(strikes), inline=True) await ctx.channel.send(embed=stat_embed) @commands.command(pass_context=True) async def removestrike(self, ctx, *, member=None): """Removes a strike given to a member (bot-admin only).""" isAdmin = ctx.message.author.permissions_in( ctx.message.channel).administrator if not isAdmin: checkAdmin = self.settings.getServerStat( ctx.message.guild, "AdminArray") for role in ctx.message.author.roles: for aRole in checkAdmin: # Get the role that corresponds to the id if str(aRole['ID']) == str(role.id): isAdmin = True # Only allow admins to change server stats if not isAdmin: await ctx.channel.send('You do not have sufficient privileges to access this command.') return if member == None: msg = 'Usage: `{}removestrike [member]`'.format(ctx.prefix) await ctx.channel.send(msg) return if type(member) is str: memberName = member member = DisplayName.memberForName(memberName, ctx.message.guild) if not member: msg = 'I couldn\'t find *{}*...'.format( Nullify.escape_all(memberName)) await ctx.channel.send(msg) return # We have what we need - get the list strikes = self.settings.getUserStat( member, ctx.message.guild, "Strikes") # Return if no strikes to take if not len(strikes): await ctx.channel.send('*{}* has no strikes to remove.'.format(DisplayName.name(member))) return # We have some - naughty naughty! strikes = sorted(strikes, key=lambda x: int(x['Time'])) for strike in strikes: # Check if we've got one that's not -1 if not strike['Time'] == -1: # First item that isn't forever - kill it strikes.remove(strike) self.settings.setUserStat( member, ctx.message.guild, "Strikes", strikes) await ctx.channel.send('*{}* has one less strike. They are down to *{}*.'.format(DisplayName.name(member), len(strikes))) return # If we're here - we just remove one del strikes[0] self.settings.setUserStat( member, ctx.message.guild, "Strikes", strikes) await ctx.channel.send('*{}* has one less strike. They are down to *{}*.'.format(DisplayName.name(member), len(strikes))) return @commands.command(pass_context=True) async def setstrikelevel(self, ctx, *, member=None, strikelevel: int = None): """Sets the strike level of the passed user (bot-admin only).""" isAdmin = ctx.message.author.permissions_in( ctx.message.channel).administrator if not isAdmin: checkAdmin = self.settings.getServerStat( ctx.message.guild, "AdminArray") for role in ctx.message.author.roles: for aRole in checkAdmin: # Get the role that corresponds to the id if str(aRole['ID']) == str(role.id): isAdmin = True # Only allow admins to change server stats if not isAdmin: await ctx.channel.send('You do not have sufficient privileges to access this command.') return author = ctx.message.author server = ctx.message.guild channel = ctx.message.channel usage = 'Usage: `{}setstrikelevel [member] [strikelevel]`'.format( ctx.prefix) if member == None: await ctx.channel.send(usage) return # Check for formatting issues if strikelevel == None: # Either strike level wasn't set - or it's the last section if type(member) is str: # It' a string - the hope continues nameCheck = DisplayName.checkNameForInt(member, server) if not nameCheck: await ctx.channel.send(usage) return if not nameCheck["Member"]: msg = 'I couldn\'t find *{}* on the server.'.format( Nullify.escape_all(member)) await ctx.channel.send(msg) return member = nameCheck["Member"] strikelevel = nameCheck["Int"] if strikelevel == None: # Still no strike level await ctx.channel.send(usage) return self.settings.setUserStat( member, ctx.message.guild, "StrikeLevel", strikelevel) msg = '*{}\'s* strike level has been set to *{}!*'.format( DisplayName.name(member), strikelevel) await ctx.channel.send(msg) @commands.command(pass_context=True) async def addkick(self, ctx, *, member=None): """Adds the passed user to the kick list (bot-admin only).""" isAdmin = ctx.message.author.permissions_in( ctx.message.channel).administrator if not isAdmin: checkAdmin = self.settings.getServerStat( ctx.message.guild, "AdminArray") for role in ctx.message.author.roles: for aRole in checkAdmin: # Get the role that corresponds to the id if str(aRole['ID']) == str(role.id): isAdmin = True # Only allow admins to change server stats if not isAdmin: await ctx.channel.send('You do not have sufficient privileges to access this command.') return if member == None: msg = 'Usage: `{}addkick [member]`'.format(ctx.prefix) await ctx.channel.send(msg) return if type(member) is str: memberName = member member = DisplayName.memberForName(memberName, ctx.message.guild) if not member: msg = 'I couldn\'t find *{}*...'.format( Nullify.escape_all(memberName)) await ctx.channel.send(msg) return msg = '' kickList = self.settings.getServerStat(ctx.message.guild, "KickList") if not str(member.id) in kickList: kickList.append(str(member.id)) self.settings.setServerStat( ctx.message.guild, "KickList", kickList) msg = '*{}* was added to the kick list.'.format( DisplayName.name(member)) else: msg = '*{}* is already in the kick list.'.format( DisplayName.name(member)) await ctx.channel.send(msg) @commands.command(pass_context=True) async def removekick(self, ctx, *, member=None): """Removes the passed user from the kick list (bot-admin only).""" isAdmin = ctx.message.author.permissions_in( ctx.message.channel).administrator if not isAdmin: checkAdmin = self.settings.getServerStat( ctx.message.guild, "AdminArray") for role in ctx.message.author.roles: for aRole in checkAdmin: # Get the role that corresponds to the id if str(aRole['ID']) == str(role.id): isAdmin = True # Only allow admins to change server stats if not isAdmin: await ctx.channel.send('You do not have sufficient privileges to access this command.') return if member == None: msg = 'Usage: `{}removekick [member]`'.format(ctx.prefix) await ctx.channel.send(msg) return if type(member) is str: memberName = member member = DisplayName.memberForName(memberName, ctx.message.guild) if not member: msg = 'I couldn\'t find *{}*...'.format( Nullify.escape_all(memberName)) await ctx.channel.send(msg) return msg = '' kickList = self.settings.getServerStat(ctx.message.guild, "KickList") if str(member.id) in kickList: kickList.remove(str(member.id)) self.settings.setServerStat( ctx.message.guild, "KickList", kickList) msg = '*{}* was removed from the kick list.'.format( DisplayName.name(member)) else: msg = '*{}* was not found in the kick list.'.format( DisplayName.name(member)) await ctx.channel.send(msg) @commands.command(pass_context=True) async def addban(self, ctx, *, member=None): """Adds the passed user to the ban list (bot-admin only).""" isAdmin = ctx.message.author.permissions_in( ctx.message.channel).administrator if not isAdmin: checkAdmin = self.settings.getServerStat( ctx.message.guild, "AdminArray") for role in ctx.message.author.roles: for aRole in checkAdmin: # Get the role that corresponds to the id if str(aRole['ID']) == str(role.id): isAdmin = True # Only allow admins to change server stats if not isAdmin: await ctx.channel.send('You do not have sufficient privileges to access this command.') return if member == None: msg = 'Usage: `{}addban [member]`'.format(ctx.prefix) await ctx.channel.send(msg) return if type(member) is str: memberName = member member = DisplayName.memberForName(memberName, ctx.message.guild) if not member: msg = 'I couldn\'t find *{}*...'.format( Nullify.escape_all(memberName)) await ctx.channel.send(msg) return msg = '' banList = self.settings.getServerStat(ctx.message.guild, "BanList") if not str(member.id) in banList: banList.append(str(member.id)) self.settings.setServerStat(ctx.message.guild, "BanList", banList) msg = '*{}* was added to the ban list.'.format( DisplayName.name(member)) else: msg = '*{}* is already in the ban list.'.format( DisplayName.name(member)) await ctx.channel.send(msg) @commands.command(pass_context=True) async def removeban(self, ctx, *, member=None): """Removes the passed user from the ban list (bot-admin only).""" isAdmin = ctx.message.author.permissions_in( ctx.message.channel).administrator if not isAdmin: checkAdmin = self.settings.getServerStat( ctx.message.guild, "AdminArray") for role in ctx.message.author.roles: for aRole in checkAdmin: # Get the role that corresponds to the id if str(aRole['ID']) == str(role.id): isAdmin = True # Only allow admins to change server stats if not isAdmin: await ctx.channel.send('You do not have sufficient privileges to access this command.') return if member == None: msg = 'Usage: `{}removeban [member]`'.format(ctx.prefix) await ctx.channel.send(msg) return if type(member) is str: memberName = member member = DisplayName.memberForName(memberName, ctx.message.guild) if not member: msg = 'I couldn\'t find *{}*...'.format( Nullify.escape_all(memberName)) await ctx.channel.send(msg) return msg = '' banList = self.settings.getServerStat(ctx.message.guild, "BanList") if str(member.id) in banList: banList.remove(str(member.id)) self.settings.setServerStat(ctx.message.guild, "BanList", banList) msg = '*{}* was removed from the ban list.'.format( DisplayName.name(member)) else: msg = '*{}* was not found in the ban list.'.format( DisplayName.name(member)) await ctx.channel.send(msg) @commands.command(pass_context=True) async def iskicked(self, ctx, *, member=None): """Lists whether the user is in the kick list.""" if member == None: member = ctx.message.author if type(member) is str: memberName = member member = DisplayName.memberForName(memberName, ctx.message.guild) if not member: msg = 'I couldn\'t find *{}*...'.format( Nullify.escape_all(memberName)) await ctx.channel.send(msg) return kickList = self.settings.getServerStat(ctx.message.guild, "KickList") if str(member.id) in kickList: msg = '*{}* is in the kick list.'.format(DisplayName.name(member)) else: msg = '*{}* is **not** in the kick list.'.format( DisplayName.name(member)) await ctx.channel.send(msg) @commands.command(pass_context=True) async def isbanned(self, ctx, *, member=None): """Lists whether the user is in the ban list.""" if member == None: member = ctx.message.author if type(member) is str: memberName = member member = DisplayName.memberForName(memberName, ctx.message.guild) if not member: msg = 'I couldn\'t find *{}*...'.format( Nullify.escape_all(memberName)) await ctx.channel.send(msg) return banList = self.settings.getServerStat(ctx.message.guild, "BanList") if str(member.id) in banList: msg = '*{}* is in the ban list.'.format(DisplayName.name(member)) else: msg = '*{}* is **not** in the ban list.'.format( DisplayName.name(member)) await ctx.channel.send(msg) @commands.command(pass_context=True) async def strikelimit(self, ctx): """Lists the number of strikes before advancing to the next consequence.""" strikeout = int(self.settings.getServerStat( ctx.message.guild, "StrikeOut")) msg = '*{}* strikes are required to strike out.'.format(strikeout) await ctx.channel.send(msg) @commands.command(pass_context=True) async def setstrikelimit(self, ctx, limit=None): """Sets the number of strikes before advancing to the next consequence (bot-admin only).""" isAdmin = ctx.message.author.permissions_in( ctx.message.channel).administrator if not isAdmin: checkAdmin = self.settings.getServerStat( ctx.message.guild, "AdminArray") for role in ctx.message.author.roles: for aRole in checkAdmin: # Get the role that corresponds to the id if str(aRole['ID']) == str(role.id): isAdmin = True # Only allow admins to change server stats if not isAdmin: await ctx.channel.send('You do not have sufficient privileges to access this command.') return if not limit: await ctx.channel.send('Strike limit must be *at least* one.') return try: limit = int(limit) except Exception: await ctx.channel.send('Strike limit must be an integer.') return self.settings.setServerStat(ctx.message.guild, "StrikeOut", limit) msg = '*{}* strikes are now required to strike out.'.format(limit) await ctx.channel.send(msg) @setstrikelimit.error async def setstrikelimit_error(self, ctx, error): # do stuff msg = 'setstrikelimit Error: {}'.format(ctx) await error.channel.send(msg)

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import asyncio import discord import time import parsedatetime from datetime import datetime from operator import itemgetter from discord.ext import commands from Cogs import ReadableTime from Cogs import DisplayName from Cogs import Nullify def setup(bot): settings = bot.get_cog("Settings") mute = bot.get_cog("Mute") bot.add_cog(Strike(bot, settings, mute)) # StrikeLevel (a list similar to xproles) # Standard strike roles: # 0 = Not been punished already # 1 = Muted for x amount of time # 2 = Already been kicked (id in kick list) # 3 = Already been banned (auto-mute) class Strike(commands.Cog): # Init with the bot reference, and a reference to the settings var def __init__(self, bot, settings, mute): self.bot = bot self.settings = settings self.mute = mute self.loop_list = [] global Utils, DisplayName Utils = self.bot.get_cog("Utils") DisplayName = self.bot.get_cog("DisplayName") async def onjoin(self, member, server): # Check id against the kick and ban list and react accordingly kickList = self.settings.getServerStat(server, "KickList") if str(member.id) in kickList: # The user has been kicked before - set their strikeLevel to 2 self.settings.setUserStat(member, server, "StrikeLevel", 2) banList = self.settings.getServerStat(server, "BanList") if str(member.id) in banList: # The user has been kicked before - set their strikeLevel to 3 # Also mute them self.settings.setUserStat(member, server, "StrikeLevel", 3) self.settings.setUserStat(member, server, "Muted", True) self.settings.setUserStat(member, server, "Cooldown", None) await self.mute._mute(member, server) # Proof of concept stuff for reloading cog/extension def _is_submodule(self, parent, child): return parent == child or child.startswith(parent + ".") @commands.Cog.listener() async def on_unloaded_extension(self, ext): # Called to shut things down if not self._is_submodule(ext.__name__, self.__module__): return for task in self.loop_list: task.cancel() @commands.Cog.listener() async def on_loaded_extension(self, ext): # See if we were loaded if not self._is_submodule(ext.__name__, self.__module__): return self.bot.loop.create_task(self.start_loading()) async def start_loading(self): await self.bot.wait_until_ready() await self.bot.loop.run_in_executor(None, self.check_strikes) def check_strikes(self): # Check all strikes - and start timers print("Checking strikes...") t = time.time() for server in self.bot.guilds: for member in server.members: strikes = self.settings.getUserStat(member, server, "Strikes") if strikes == None: continue if len(strikes): # We have a list for strike in strikes: # Make sure it's a strike that *can* roll off if not strike['Time'] == -1: self.loop_list.append(self.bot.loop.create_task( self.checkStrike(member, strike))) print("Strikes checked - took {} seconds.".format(time.time() - t)) async def checkStrike(self, member, strike): countDown = int(strike['Time'])-int(time.time()) if countDown > 0: await asyncio.sleep(countDown) strikes = self.settings.getUserStat(member, member.guild, "Strikes") # Verify strike is still valid if not strike in strikes: return strikes.remove(strike) self.settings.setUserStat(member, member.guild, "Strikes", strikes) @commands.command(pass_context=True) async def strike(self, ctx, member: discord.Member = None, days=None, *, message: str = None): isAdmin = ctx.message.author.permissions_in( ctx.message.channel).administrator if not isAdmin: checkAdmin = self.settings.getServerStat( ctx.message.guild, "AdminArray") for role in ctx.message.author.roles: for aRole in checkAdmin: # Get the role that corresponds to the id if str(aRole['ID']) == str(role.id): isAdmin = True # Only allow admins to change server stats if not isAdmin: await ctx.channel.send('You do not have sufficient privileges to access this command.') return if member == None: msg = 'Usage: `{}strike [member] [strike timeout (in days) - 0 = forever] [message (optional)]`'.format( ctx.prefix) await ctx.channel.send(msg) return # Check if we're striking ourselves if member.id == ctx.message.author.id: await ctx.channel.send('You can\'t give yourself a strike, silly.') return if member.id == self.bot.user.id: await ctx.channel.send('I can\'t do that, *{}*.'.format(DisplayName.name(ctx.message.author))) return # Check if we're striking another admin/bot-admin isAdmin = member.permissions_in(ctx.message.channel).administrator if not isAdmin: checkAdmin = self.settings.getServerStat( ctx.message.guild, "AdminArray") for role in member.roles: for aRole in checkAdmin: if str(aRole['ID']) == str(role.id): isAdmin = True if isAdmin: await ctx.channel.send('You can\'t give other admins/bot-admins strikes, bub.') return # Check if days is an int - otherwise assume it's part of the message try: days = int(days) except Exception: if not days == None: if message == None: message = days else: message = days + ' ' + message days = 0 if days < 1: days = -1 currentTime = int(time.time()) strike = {} if days == -1: strike['Time'] = -1 else: strike['Time'] = currentTime+(86400*days) self.loop_list.append(self.bot.loop.create_task( self.checkStrike(member, strike))) strike['Message'] = message strike['GivenBy'] = ctx.message.author.id strikes = self.settings.getUserStat( member, ctx.message.guild, "Strikes") strikeout = int(self.settings.getServerStat( ctx.message.guild, "StrikeOut")) strikeLevel = int(self.settings.getUserStat( member, ctx.message.guild, "StrikeLevel")) strikes.append(strike) self.settings.setUserStat( member, ctx.message.guild, "Strikes", strikes) strikeNum = len(strikes) if strikeLevel == 0: consequence = '**muted for a day**.' elif strikeLevel == 1: consequence = '**kicked**.' else: consequence = '**banned**.' if strikeNum < strikeout: # We haven't struck out yet msg = '*{}* has just received *strike {}*. *{}* more and they will be {}'.format( DisplayName.name(member), strikeNum, strikeout-strikeNum, consequence) else: if strikeLevel == 0: cooldownFinal = currentTime+86400 checkRead = ReadableTime.getReadableTimeBetween( currentTime, cooldownFinal) if message: mutemessage = 'You have been muted in *{}*.\nThe Reason:\n{}'.format( Nullify.escape_all(ctx.guild.name), message) else: mutemessage = 'You have been muted in *{}*.'.format( Nullify.escape_all(ctx.guild.name)) # Check if already muted alreadyMuted = self.settings.getUserStat( member, ctx.message.guild, "Muted") if alreadyMuted: # Find out for how long muteTime = self.settings.getUserStat( member, ctx.message.guild, "Cooldown") if not muteTime == None: if muteTime < cooldownFinal: self.settings.setUserStat( member, ctx.message.guild, "Cooldown", cooldownFinal) timeRemains = ReadableTime.getReadableTimeBetween( currentTime, cooldownFinal) if message: mutemessage = 'Your muted time in *{}* has been extended to *{}*.\nThe Reason:\n{}'.format( Nullify.escape_all(ctx.guild.name), timeRemains, message) else: mutemessage = 'You muted time in *{}* has been extended to *{}*.'.format( Nullify.escape_all(ctx.guild.name), timeRemains) else: self.settings.setUserStat( member, ctx.message.guild, "Muted", True) self.settings.setUserStat( member, ctx.message.guild, "Cooldown", cooldownFinal) await self.mute._mute(member, ctx.message.guild, cooldownFinal) await member.send(mutemessage) elif strikeLevel == 1: kickList = self.settings.getServerStat( ctx.message.guild, "KickList") if not str(member.id) in kickList: kickList.append(str(member.id)) self.settings.setServerStat( ctx.message.guild, "KickList", kickList) if message: kickmessage = 'You have been kicked from *{}*.\nThe Reason:\n{}'.format( Nullify.escape_all(ctx.guild.name), message) else: kickmessage = 'You have been kicked from *{}*.'.format( Nullify.escape_all(ctx.guild.name)) await member.send(kickmessage) await ctx.guild.kick(member) else: banList = self.settings.getServerStat( ctx.message.guild, "BanList") if not str(member.id) in banList: banList.append(str(member.id)) self.settings.setServerStat( ctx.message.guild, "BanList", banList) if message: banmessage = 'You have been banned from *{}*.\nThe Reason:\n{}'.format( Nullify.escape_all(ctx.guild.name), message) else: banmessage = 'You have been banned from *{}*.'.format( Nullify.escape_all(ctx.guild.name)) await member.send(banmessage) await ctx.guild.ban(member) self.settings.incrementStat( member, ctx.message.guild, "StrikeLevel", 1) self.settings.setUserStat(member, ctx.message.guild, "Strikes", []) msg = '*{}* has just received *strike {}*. They have been {}'.format( DisplayName.name(member), strikeNum, consequence) await ctx.channel.send(msg) @strike.error async def strike_error(self, ctx, error): # do stuff msg = 'strike Error: {}'.format(error) await ctx.channel.send(msg) @commands.command(pass_context=True) async def strikes(self, ctx, *, member=None): isAdmin = ctx.message.author.permissions_in( ctx.message.channel).administrator if not isAdmin: checkAdmin = self.settings.getServerStat( ctx.message.guild, "AdminArray") for role in ctx.message.author.roles: for aRole in checkAdmin: # Get the role that corresponds to the id if str(aRole['ID']) == str(role.id): isAdmin = True if member == None: member = ctx.message.author if type(member) is str: memberName = member member = DisplayName.memberForName(memberName, ctx.message.guild) if not member: msg = 'I couldn\'t find *{}*...'.format( Nullify.escape_all(memberName)) await ctx.channel.send(msg) return if not isAdmin: if member: if not member.id == ctx.message.author.id: await ctx.channel.send('You are not a bot-admin. You can only see your own strikes.') member = ctx.message.author # Create blank embed stat_embed = discord.Embed(color=member.color) strikes = self.settings.getUserStat( member, ctx.message.guild, "Strikes") strikeout = int(self.settings.getServerStat( ctx.message.guild, "StrikeOut")) strikeLevel = int(self.settings.getUserStat( member, ctx.message.guild, "StrikeLevel")) # Add strikes, and strike level stat_embed.add_field(name="Strikes", value=len(strikes), inline=True) stat_embed.add_field(name="Strike Level", value=strikeLevel, inline=True) # Get member's avatar url avURL = member.avatar_url if not len(avURL): avURL = member.default_avatar_url if member.nick: msg = "__***{},*** **who currently goes by** ***{}:***__\n\n".format( member.name, member.nick) stat_embed.set_author(name='{}, who currently goes by {}'.format( member.name, member.nick), icon_url=avURL) else: msg = "__***{}:***__\n\n".format(member.name) stat_embed.set_author(name='{}'.format( member.name), icon_url=avURL) currentTime = int(time.time()) if not len(strikes): messages = "None." cooldowns = "None." givenBy = "None." else: messages = '' cooldowns = '' givenBy = '' for i in range(0, len(strikes)): if strikes[i]['Message']: messages += '{}. {}\n'.format(i+1, strikes[i]['Message']) else: messages += '{}. No message\n'.format(i+1) timeLeft = strikes[i]['Time'] if timeLeft == -1: cooldowns += '{}. Never rolls off\n'.format(i+1) else: timeRemains = ReadableTime.getReadableTimeBetween( currentTime, timeLeft) cooldowns += '{}. {}\n'.format(i+1, timeRemains) given = strikes[i]['GivenBy'] givenBy += '{}. {}\n'.format(i+1, DisplayName.name( DisplayName.memberForID(given, ctx.message.guild))) stat_embed.add_field(name="Messages", value=messages, inline=True) stat_embed.add_field(name="Time Left", value=cooldowns, inline=True) stat_embed.add_field(name="Given By", value=givenBy, inline=True) stat_embed.add_field(name="Strikes Remaining", value=strikeout-len(strikes), inline=True) await ctx.channel.send(embed=stat_embed) @commands.command(pass_context=True) async def removestrike(self, ctx, *, member=None): isAdmin = ctx.message.author.permissions_in( ctx.message.channel).administrator if not isAdmin: checkAdmin = self.settings.getServerStat( ctx.message.guild, "AdminArray") for role in ctx.message.author.roles: for aRole in checkAdmin: if str(aRole['ID']) == str(role.id): isAdmin = True if not isAdmin: await ctx.channel.send('You do not have sufficient privileges to access this command.') return if member == None: msg = 'Usage: `{}removestrike [member]`'.format(ctx.prefix) await ctx.channel.send(msg) return if type(member) is str: memberName = member member = DisplayName.memberForName(memberName, ctx.message.guild) if not member: msg = 'I couldn\'t find *{}*...'.format( Nullify.escape_all(memberName)) await ctx.channel.send(msg) return # We have what we need - get the list strikes = self.settings.getUserStat( member, ctx.message.guild, "Strikes") # Return if no strikes to take if not len(strikes): await ctx.channel.send('*{}* has no strikes to remove.'.format(DisplayName.name(member))) return # We have some - naughty naughty! strikes = sorted(strikes, key=lambda x: int(x['Time'])) for strike in strikes: # Check if we've got one that's not -1 if not strike['Time'] == -1: # First item that isn't forever - kill it strikes.remove(strike) self.settings.setUserStat( member, ctx.message.guild, "Strikes", strikes) await ctx.channel.send('*{}* has one less strike. They are down to *{}*.'.format(DisplayName.name(member), len(strikes))) return del strikes[0] self.settings.setUserStat( member, ctx.message.guild, "Strikes", strikes) await ctx.channel.send('*{}* has one less strike. They are down to *{}*.'.format(DisplayName.name(member), len(strikes))) return @commands.command(pass_context=True) async def setstrikelevel(self, ctx, *, member=None, strikelevel: int = None): isAdmin = ctx.message.author.permissions_in( ctx.message.channel).administrator if not isAdmin: checkAdmin = self.settings.getServerStat( ctx.message.guild, "AdminArray") for role in ctx.message.author.roles: for aRole in checkAdmin: # Get the role that corresponds to the id if str(aRole['ID']) == str(role.id): isAdmin = True # Only allow admins to change server stats if not isAdmin: await ctx.channel.send('You do not have sufficient privileges to access this command.') return author = ctx.message.author server = ctx.message.guild channel = ctx.message.channel usage = 'Usage: `{}setstrikelevel [member] [strikelevel]`'.format( ctx.prefix) if member == None: await ctx.channel.send(usage) return # Check for formatting issues if strikelevel == None: # Either strike level wasn't set - or it's the last section if type(member) is str: # It' a string - the hope continues nameCheck = DisplayName.checkNameForInt(member, server) if not nameCheck: await ctx.channel.send(usage) return if not nameCheck["Member"]: msg = 'I couldn\'t find *{}* on the server.'.format( Nullify.escape_all(member)) await ctx.channel.send(msg) return member = nameCheck["Member"] strikelevel = nameCheck["Int"] if strikelevel == None: # Still no strike level await ctx.channel.send(usage) return self.settings.setUserStat( member, ctx.message.guild, "StrikeLevel", strikelevel) msg = '*{}\'s* strike level has been set to *{}!*'.format( DisplayName.name(member), strikelevel) await ctx.channel.send(msg) @commands.command(pass_context=True) async def addkick(self, ctx, *, member=None): isAdmin = ctx.message.author.permissions_in( ctx.message.channel).administrator if not isAdmin: checkAdmin = self.settings.getServerStat( ctx.message.guild, "AdminArray") for role in ctx.message.author.roles: for aRole in checkAdmin: if str(aRole['ID']) == str(role.id): isAdmin = True if not isAdmin: await ctx.channel.send('You do not have sufficient privileges to access this command.') return if member == None: msg = 'Usage: `{}addkick [member]`'.format(ctx.prefix) await ctx.channel.send(msg) return if type(member) is str: memberName = member member = DisplayName.memberForName(memberName, ctx.message.guild) if not member: msg = 'I couldn\'t find *{}*...'.format( Nullify.escape_all(memberName)) await ctx.channel.send(msg) return msg = '' kickList = self.settings.getServerStat(ctx.message.guild, "KickList") if not str(member.id) in kickList: kickList.append(str(member.id)) self.settings.setServerStat( ctx.message.guild, "KickList", kickList) msg = '*{}* was added to the kick list.'.format( DisplayName.name(member)) else: msg = '*{}* is already in the kick list.'.format( DisplayName.name(member)) await ctx.channel.send(msg) @commands.command(pass_context=True) async def removekick(self, ctx, *, member=None): isAdmin = ctx.message.author.permissions_in( ctx.message.channel).administrator if not isAdmin: checkAdmin = self.settings.getServerStat( ctx.message.guild, "AdminArray") for role in ctx.message.author.roles: for aRole in checkAdmin: # Get the role that corresponds to the id if str(aRole['ID']) == str(role.id): isAdmin = True # Only allow admins to change server stats if not isAdmin: await ctx.channel.send('You do not have sufficient privileges to access this command.') return if member == None: msg = 'Usage: `{}removekick [member]`'.format(ctx.prefix) await ctx.channel.send(msg) return if type(member) is str: memberName = member member = DisplayName.memberForName(memberName, ctx.message.guild) if not member: msg = 'I couldn\'t find *{}*...'.format( Nullify.escape_all(memberName)) await ctx.channel.send(msg) return msg = '' kickList = self.settings.getServerStat(ctx.message.guild, "KickList") if str(member.id) in kickList: kickList.remove(str(member.id)) self.settings.setServerStat( ctx.message.guild, "KickList", kickList) msg = '*{}* was removed from the kick list.'.format( DisplayName.name(member)) else: msg = '*{}* was not found in the kick list.'.format( DisplayName.name(member)) await ctx.channel.send(msg) @commands.command(pass_context=True) async def addban(self, ctx, *, member=None): isAdmin = ctx.message.author.permissions_in( ctx.message.channel).administrator if not isAdmin: checkAdmin = self.settings.getServerStat( ctx.message.guild, "AdminArray") for role in ctx.message.author.roles: for aRole in checkAdmin: if str(aRole['ID']) == str(role.id): isAdmin = True if not isAdmin: await ctx.channel.send('You do not have sufficient privileges to access this command.') return if member == None: msg = 'Usage: `{}addban [member]`'.format(ctx.prefix) await ctx.channel.send(msg) return if type(member) is str: memberName = member member = DisplayName.memberForName(memberName, ctx.message.guild) if not member: msg = 'I couldn\'t find *{}*...'.format( Nullify.escape_all(memberName)) await ctx.channel.send(msg) return msg = '' banList = self.settings.getServerStat(ctx.message.guild, "BanList") if not str(member.id) in banList: banList.append(str(member.id)) self.settings.setServerStat(ctx.message.guild, "BanList", banList) msg = '*{}* was added to the ban list.'.format( DisplayName.name(member)) else: msg = '*{}* is already in the ban list.'.format( DisplayName.name(member)) await ctx.channel.send(msg) @commands.command(pass_context=True) async def removeban(self, ctx, *, member=None): isAdmin = ctx.message.author.permissions_in( ctx.message.channel).administrator if not isAdmin: checkAdmin = self.settings.getServerStat( ctx.message.guild, "AdminArray") for role in ctx.message.author.roles: for aRole in checkAdmin: # Get the role that corresponds to the id if str(aRole['ID']) == str(role.id): isAdmin = True # Only allow admins to change server stats if not isAdmin: await ctx.channel.send('You do not have sufficient privileges to access this command.') return if member == None: msg = 'Usage: `{}removeban [member]`'.format(ctx.prefix) await ctx.channel.send(msg) return if type(member) is str: memberName = member member = DisplayName.memberForName(memberName, ctx.message.guild) if not member: msg = 'I couldn\'t find *{}*...'.format( Nullify.escape_all(memberName)) await ctx.channel.send(msg) return msg = '' banList = self.settings.getServerStat(ctx.message.guild, "BanList") if str(member.id) in banList: banList.remove(str(member.id)) self.settings.setServerStat(ctx.message.guild, "BanList", banList) msg = '*{}* was removed from the ban list.'.format( DisplayName.name(member)) else: msg = '*{}* was not found in the ban list.'.format( DisplayName.name(member)) await ctx.channel.send(msg) @commands.command(pass_context=True) async def iskicked(self, ctx, *, member=None): if member == None: member = ctx.message.author if type(member) is str: memberName = member member = DisplayName.memberForName(memberName, ctx.message.guild) if not member: msg = 'I couldn\'t find *{}*...'.format( Nullify.escape_all(memberName)) await ctx.channel.send(msg) return kickList = self.settings.getServerStat(ctx.message.guild, "KickList") if str(member.id) in kickList: msg = '*{}* is in the kick list.'.format(DisplayName.name(member)) else: msg = '*{}* is **not** in the kick list.'.format( DisplayName.name(member)) await ctx.channel.send(msg) @commands.command(pass_context=True) async def isbanned(self, ctx, *, member=None): if member == None: member = ctx.message.author if type(member) is str: memberName = member member = DisplayName.memberForName(memberName, ctx.message.guild) if not member: msg = 'I couldn\'t find *{}*...'.format( Nullify.escape_all(memberName)) await ctx.channel.send(msg) return banList = self.settings.getServerStat(ctx.message.guild, "BanList") if str(member.id) in banList: msg = '*{}* is in the ban list.'.format(DisplayName.name(member)) else: msg = '*{}* is **not** in the ban list.'.format( DisplayName.name(member)) await ctx.channel.send(msg) @commands.command(pass_context=True) async def strikelimit(self, ctx): strikeout = int(self.settings.getServerStat( ctx.message.guild, "StrikeOut")) msg = '*{}* strikes are required to strike out.'.format(strikeout) await ctx.channel.send(msg) @commands.command(pass_context=True) async def setstrikelimit(self, ctx, limit=None): isAdmin = ctx.message.author.permissions_in( ctx.message.channel).administrator if not isAdmin: checkAdmin = self.settings.getServerStat( ctx.message.guild, "AdminArray") for role in ctx.message.author.roles: for aRole in checkAdmin: if str(aRole['ID']) == str(role.id): isAdmin = True if not isAdmin: await ctx.channel.send('You do not have sufficient privileges to access this command.') return if not limit: await ctx.channel.send('Strike limit must be *at least* one.') return try: limit = int(limit) except Exception: await ctx.channel.send('Strike limit must be an integer.') return self.settings.setServerStat(ctx.message.guild, "StrikeOut", limit) msg = '*{}* strikes are now required to strike out.'.format(limit) await ctx.channel.send(msg) @setstrikelimit.error async def setstrikelimit_error(self, ctx, error): msg = 'setstrikelimit Error: {}'.format(ctx) await error.channel.send(msg)

true

f70f8da0066e2601d4d7d9e6e313a05b5a080127

19,727

py

Python

src/watchdog/observers/inotify_c.py

rec/watchdog

0224e4424daea365a41125c6691c3477ee1bf86f

[ "ECL-2.0", "Apache-2.0" ]

null

src/watchdog/observers/inotify_c.py

rec/watchdog

0224e4424daea365a41125c6691c3477ee1bf86f

[ "ECL-2.0", "Apache-2.0" ]

null

src/watchdog/observers/inotify_c.py

rec/watchdog

0224e4424daea365a41125c6691c3477ee1bf86f

[ "ECL-2.0", "Apache-2.0" ]

null

# -*- coding: utf-8 -*- # # Copyright 2011 Yesudeep Mangalapilly <yesudeep@gmail.com> # Copyright 2012 Google, Inc. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. from __future__ import with_statement import os import errno import struct import threading import ctypes import ctypes.util from functools import reduce from ctypes import c_int, c_char_p, c_uint32 from watchdog.utils import has_attribute from watchdog.utils import UnsupportedLibc from watchdog.utils.unicode_paths import decode def _load_libc(): libc_path = None try: libc_path = ctypes.util.find_library('c') except (OSError, IOError, RuntimeError): # Note: find_library will on some platforms raise these undocumented # errors, e.g.on android IOError "No usable temporary directory found" # will be raised. pass if libc_path is not None: return ctypes.CDLL(libc_path) # Fallbacks try: return ctypes.CDLL('libc.so') except (OSError, IOError): pass try: return ctypes.CDLL('libc.so.6') except (OSError, IOError): pass # uClibc try: return ctypes.CDLL('libc.so.0') except (OSError, IOError) as err: raise err libc = _load_libc() if not has_attribute(libc, 'inotify_init') or \ not has_attribute(libc, 'inotify_add_watch') or \ not has_attribute(libc, 'inotify_rm_watch'): raise UnsupportedLibc("Unsupported libc version found: %s" % libc._name) inotify_add_watch = ctypes.CFUNCTYPE(c_int, c_int, c_char_p, c_uint32, use_errno=True)( ("inotify_add_watch", libc)) inotify_rm_watch = ctypes.CFUNCTYPE(c_int, c_int, c_uint32, use_errno=True)( ("inotify_rm_watch", libc)) inotify_init = ctypes.CFUNCTYPE(c_int, use_errno=True)( ("inotify_init", libc)) class InotifyConstants(object): # User-space events IN_ACCESS = 0x00000001 # File was accessed. IN_MODIFY = 0x00000002 # File was modified. IN_ATTRIB = 0x00000004 # Meta-data changed. IN_CLOSE_WRITE = 0x00000008 # Writable file was closed. IN_CLOSE_NOWRITE = 0x00000010 # Unwritable file closed. IN_OPEN = 0x00000020 # File was opened. IN_MOVED_FROM = 0x00000040 # File was moved from X. IN_MOVED_TO = 0x00000080 # File was moved to Y. IN_CREATE = 0x00000100 # Subfile was created. IN_DELETE = 0x00000200 # Subfile was deleted. IN_DELETE_SELF = 0x00000400 # Self was deleted. IN_MOVE_SELF = 0x00000800 # Self was moved. # Helper user-space events. IN_CLOSE = IN_CLOSE_WRITE | IN_CLOSE_NOWRITE # Close. IN_MOVE = IN_MOVED_FROM | IN_MOVED_TO # Moves. # Events sent by the kernel to a watch. IN_UNMOUNT = 0x00002000 # Backing file system was unmounted. IN_Q_OVERFLOW = 0x00004000 # Event queued overflowed. IN_IGNORED = 0x00008000 # File was ignored. # Special flags. IN_ONLYDIR = 0x01000000 # Only watch the path if it's a directory. IN_DONT_FOLLOW = 0x02000000 # Do not follow a symbolic link. IN_EXCL_UNLINK = 0x04000000 # Exclude events on unlinked objects IN_MASK_ADD = 0x20000000 # Add to the mask of an existing watch. IN_ISDIR = 0x40000000 # Event occurred against directory. IN_ONESHOT = 0x80000000 # Only send event once. # All user-space events. IN_ALL_EVENTS = reduce( lambda x, y: x | y, [ IN_ACCESS, IN_MODIFY, IN_ATTRIB, IN_CLOSE_WRITE, IN_CLOSE_NOWRITE, IN_OPEN, IN_MOVED_FROM, IN_MOVED_TO, IN_DELETE, IN_CREATE, IN_DELETE_SELF, IN_MOVE_SELF, ]) # Flags for ``inotify_init1`` IN_CLOEXEC = 0x02000000 IN_NONBLOCK = 0x00004000 # Watchdog's API cares only about these events. WATCHDOG_ALL_EVENTS = reduce( lambda x, y: x | y, [ InotifyConstants.IN_MODIFY, InotifyConstants.IN_ATTRIB, InotifyConstants.IN_MOVED_FROM, InotifyConstants.IN_MOVED_TO, InotifyConstants.IN_CREATE, InotifyConstants.IN_DELETE, InotifyConstants.IN_DELETE_SELF, InotifyConstants.IN_DONT_FOLLOW, ]) class inotify_event_struct(ctypes.Structure): """ Structure representation of the inotify_event structure (used in buffer size calculations):: struct inotify_event { __s32 wd; /* watch descriptor */ __u32 mask; /* watch mask */ __u32 cookie; /* cookie to synchronize two events */ __u32 len; /* length (including nulls) of name */ char name[0]; /* stub for possible name */ }; """ _fields_ = [('wd', c_int), ('mask', c_uint32), ('cookie', c_uint32), ('len', c_uint32), ('name', c_char_p)] EVENT_SIZE = ctypes.sizeof(inotify_event_struct) DEFAULT_NUM_EVENTS = 2048 DEFAULT_EVENT_BUFFER_SIZE = DEFAULT_NUM_EVENTS * (EVENT_SIZE + 16) class Inotify(object): """ Linux inotify(7) API wrapper class. :param path: The directory path for which we want an inotify object. :type path: :class:`bytes` :param recursive: ``True`` if subdirectories should be monitored; ``False`` otherwise. """ def __init__(self, path, recursive=False, event_mask=WATCHDOG_ALL_EVENTS): # The file descriptor associated with the inotify instance. inotify_fd = inotify_init() if inotify_fd == -1: Inotify._raise_error() self._inotify_fd = inotify_fd self._lock = threading.Lock() # Stores the watch descriptor for a given path. self._wd_for_path = dict() self._path_for_wd = dict() self._path = path self._event_mask = event_mask self._is_recursive = recursive if os.path.isdir(path): self._add_dir_watch(path, recursive, event_mask) else: self._add_watch(path, event_mask) self._moved_from_events = dict() @property def event_mask(self): """The event mask for this inotify instance.""" return self._event_mask @property def path(self): """The path associated with the inotify instance.""" return self._path @property def is_recursive(self): """Whether we are watching directories recursively.""" return self._is_recursive @property def fd(self): """The file descriptor associated with the inotify instance.""" return self._inotify_fd def clear_move_records(self): """Clear cached records of MOVED_FROM events""" self._moved_from_events = dict() def source_for_move(self, destination_event): """ The source path corresponding to the given MOVED_TO event. If the source path is outside the monitored directories, None is returned instead. """ if destination_event.cookie in self._moved_from_events: return self._moved_from_events[destination_event.cookie].src_path else: return None def remember_move_from_event(self, event): """ Save this event as the source event for future MOVED_TO events to reference. """ self._moved_from_events[event.cookie] = event def add_watch(self, path): """ Adds a watch for the given path. :param path: Path to begin monitoring. """ with self._lock: self._add_watch(path, self._event_mask) def remove_watch(self, path): """ Removes a watch for the given path. :param path: Path string for which the watch will be removed. """ with self._lock: wd = self._wd_for_path.pop(path) del self._path_for_wd[wd] if inotify_rm_watch(self._inotify_fd, wd) == -1: Inotify._raise_error() def close(self): """ Closes the inotify instance and removes all associated watches. """ with self._lock: if self._path in self._wd_for_path: wd = self._wd_for_path[self._path] inotify_rm_watch(self._inotify_fd, wd) os.close(self._inotify_fd) def read_events(self, event_buffer_size=DEFAULT_EVENT_BUFFER_SIZE): """ Reads events from inotify and yields them. """ # HACK: We need to traverse the directory path # recursively and simulate events for newly # created subdirectories/files. This will handle # mkdir -p foobar/blah/bar; touch foobar/afile def _recursive_simulate(src_path): events = [] for root, dirnames, filenames in os.walk(src_path): for dirname in dirnames: try: full_path = os.path.join(root, dirname) wd_dir = self._add_watch(full_path, self._event_mask) e = InotifyEvent( wd_dir, InotifyConstants.IN_CREATE | InotifyConstants.IN_ISDIR, 0, dirname, full_path) events.append(e) except OSError: pass for filename in filenames: full_path = os.path.join(root, filename) wd_parent_dir = self._wd_for_path[os.path.dirname(full_path)] e = InotifyEvent( wd_parent_dir, InotifyConstants.IN_CREATE, 0, filename, full_path) events.append(e) return events event_buffer = None while True: try: event_buffer = os.read(self._inotify_fd, event_buffer_size) except OSError as e: if e.errno == errno.EINTR: continue break with self._lock: event_list = [] for wd, mask, cookie, name in Inotify._parse_event_buffer(event_buffer): if wd == -1: continue wd_path = self._path_for_wd[wd] src_path = os.path.join(wd_path, name) if name else wd_path # avoid trailing slash inotify_event = InotifyEvent(wd, mask, cookie, name, src_path) if inotify_event.is_moved_from: self.remember_move_from_event(inotify_event) elif inotify_event.is_moved_to: move_src_path = self.source_for_move(inotify_event) if move_src_path in self._wd_for_path: moved_wd = self._wd_for_path[move_src_path] del self._wd_for_path[move_src_path] self._wd_for_path[inotify_event.src_path] = moved_wd self._path_for_wd[moved_wd] = inotify_event.src_path if self.is_recursive: for _path, _wd in self._wd_for_path.copy().items(): if _path.startswith(move_src_path + os.path.sep.encode()): moved_wd = self._wd_for_path.pop(_path) _move_to_path = _path.replace(move_src_path, inotify_event.src_path) self._wd_for_path[_move_to_path] = moved_wd self._path_for_wd[moved_wd] = _move_to_path src_path = os.path.join(wd_path, name) inotify_event = InotifyEvent(wd, mask, cookie, name, src_path) if inotify_event.is_ignored: # Clean up book-keeping for deleted watches. path = self._path_for_wd.pop(wd) if self._wd_for_path[path] == wd: del self._wd_for_path[path] continue event_list.append(inotify_event) if (self.is_recursive and inotify_event.is_directory and inotify_event.is_create): # TODO: When a directory from another part of the # filesystem is moved into a watched directory, this # will not generate events for the directory tree. # We need to coalesce IN_MOVED_TO events and those # IN_MOVED_TO events which don't pair up with # IN_MOVED_FROM events should be marked IN_CREATE # instead relative to this directory. try: self._add_watch(src_path, self._event_mask) except OSError: continue event_list.extend(_recursive_simulate(src_path)) return event_list # Non-synchronized methods. def _add_dir_watch(self, path, recursive, mask): """ Adds a watch (optionally recursively) for the given directory path to monitor events specified by the mask. :param path: Path to monitor :param recursive: ``True`` to monitor recursively. :param mask: Event bit mask. """ if not os.path.isdir(path): raise OSError(errno.ENOTDIR, os.strerror(errno.ENOTDIR), path) self._add_watch(path, mask) if recursive: for root, dirnames, _ in os.walk(path): for dirname in dirnames: full_path = os.path.join(root, dirname) if os.path.islink(full_path): continue self._add_watch(full_path, mask) def _add_watch(self, path, mask): """ Adds a watch for the given path to monitor events specified by the mask. :param path: Path to monitor :param mask: Event bit mask. """ wd = inotify_add_watch(self._inotify_fd, path, mask) if wd == -1: Inotify._raise_error() self._wd_for_path[path] = wd self._path_for_wd[wd] = path return wd @staticmethod def _raise_error(): """ Raises errors for inotify failures. """ err = ctypes.get_errno() if err == errno.ENOSPC: raise OSError(errno.ENOSPC, "inotify watch limit reached") elif err == errno.EMFILE: raise OSError(errno.EMFILE, "inotify instance limit reached") else: raise OSError(err, os.strerror(err)) @staticmethod def _parse_event_buffer(event_buffer): """ Parses an event buffer of ``inotify_event`` structs returned by inotify:: struct inotify_event { __s32 wd; /* watch descriptor */ __u32 mask; /* watch mask */ __u32 cookie; /* cookie to synchronize two events */ __u32 len; /* length (including nulls) of name */ char name[0]; /* stub for possible name */ }; The ``cookie`` member of this struct is used to pair two related events, for example, it pairs an IN_MOVED_FROM event with an IN_MOVED_TO event. """ i = 0 while i + 16 <= len(event_buffer): wd, mask, cookie, length = struct.unpack_from('iIII', event_buffer, i) name = event_buffer[i + 16:i + 16 + length].rstrip(b'\0') i += 16 + length yield wd, mask, cookie, name class InotifyEvent(object): """ Inotify event struct wrapper. :param wd: Watch descriptor :param mask: Event mask :param cookie: Event cookie :param name: Base name of the event source path. :param src_path: Full event source path. """ def __init__(self, wd, mask, cookie, name, src_path): self._wd = wd self._mask = mask self._cookie = cookie self._name = name self._src_path = src_path @property def src_path(self): return self._src_path @property def wd(self): return self._wd @property def mask(self): return self._mask @property def cookie(self): return self._cookie @property def name(self): return self._name @property def is_modify(self): return self._mask & InotifyConstants.IN_MODIFY > 0 @property def is_close_write(self): return self._mask & InotifyConstants.IN_CLOSE_WRITE > 0 @property def is_close_nowrite(self): return self._mask & InotifyConstants.IN_CLOSE_NOWRITE > 0 @property def is_access(self): return self._mask & InotifyConstants.IN_ACCESS > 0 @property def is_delete(self): return self._mask & InotifyConstants.IN_DELETE > 0 @property def is_delete_self(self): return self._mask & InotifyConstants.IN_DELETE_SELF > 0 @property def is_create(self): return self._mask & InotifyConstants.IN_CREATE > 0 @property def is_moved_from(self): return self._mask & InotifyConstants.IN_MOVED_FROM > 0 @property def is_moved_to(self): return self._mask & InotifyConstants.IN_MOVED_TO > 0 @property def is_move(self): return self._mask & InotifyConstants.IN_MOVE > 0 @property def is_move_self(self): return self._mask & InotifyConstants.IN_MOVE_SELF > 0 @property def is_attrib(self): return self._mask & InotifyConstants.IN_ATTRIB > 0 @property def is_ignored(self): return self._mask & InotifyConstants.IN_IGNORED > 0 @property def is_directory(self): # It looks like the kernel does not provide this information for # IN_DELETE_SELF and IN_MOVE_SELF. In this case, assume it's a dir. # See also: https://github.com/seb-m/pyinotify/blob/2c7e8f8/python2/pyinotify.py#L897 return (self.is_delete_self or self.is_move_self or self._mask & InotifyConstants.IN_ISDIR > 0) @property def key(self): return self._src_path, self._wd, self._mask, self._cookie, self._name def __eq__(self, inotify_event): return self.key == inotify_event.key def __ne__(self, inotify_event): return self.key == inotify_event.key def __hash__(self): return hash(self.key) @staticmethod def _get_mask_string(mask): masks = [] for c in dir(InotifyConstants): if c.startswith('IN_') and c not in ['IN_ALL_EVENTS', 'IN_CLOSE', 'IN_MOVE']: c_val = getattr(InotifyConstants, c) if mask & c_val: masks.append(c) mask_string = '|'.join(masks) return mask_string def __repr__(self): mask_string = self._get_mask_string(self.mask) s = '<%s: src_path=%r, wd=%d, mask=%s, cookie=%d, name=%s>' return s % (type(self).__name__, self.src_path, self.wd, mask_string, self.cookie, decode(self.name))

33.54932

114

0.596441

from __future__ import with_statement import os import errno import struct import threading import ctypes import ctypes.util from functools import reduce from ctypes import c_int, c_char_p, c_uint32 from watchdog.utils import has_attribute from watchdog.utils import UnsupportedLibc from watchdog.utils.unicode_paths import decode def _load_libc(): libc_path = None try: libc_path = ctypes.util.find_library('c') except (OSError, IOError, RuntimeError): pass if libc_path is not None: return ctypes.CDLL(libc_path) try: return ctypes.CDLL('libc.so') except (OSError, IOError): pass try: return ctypes.CDLL('libc.so.6') except (OSError, IOError): pass try: return ctypes.CDLL('libc.so.0') except (OSError, IOError) as err: raise err libc = _load_libc() if not has_attribute(libc, 'inotify_init') or \ not has_attribute(libc, 'inotify_add_watch') or \ not has_attribute(libc, 'inotify_rm_watch'): raise UnsupportedLibc("Unsupported libc version found: %s" % libc._name) inotify_add_watch = ctypes.CFUNCTYPE(c_int, c_int, c_char_p, c_uint32, use_errno=True)( ("inotify_add_watch", libc)) inotify_rm_watch = ctypes.CFUNCTYPE(c_int, c_int, c_uint32, use_errno=True)( ("inotify_rm_watch", libc)) inotify_init = ctypes.CFUNCTYPE(c_int, use_errno=True)( ("inotify_init", libc)) class InotifyConstants(object): IN_ACCESS = 0x00000001 IN_MODIFY = 0x00000002 IN_ATTRIB = 0x00000004 IN_CLOSE_WRITE = 0x00000008 IN_CLOSE_NOWRITE = 0x00000010 IN_OPEN = 0x00000020 IN_MOVED_FROM = 0x00000040 IN_MOVED_TO = 0x00000080 IN_CREATE = 0x00000100 IN_DELETE = 0x00000200 IN_DELETE_SELF = 0x00000400 IN_MOVE_SELF = 0x00000800 IN_CLOSE = IN_CLOSE_WRITE | IN_CLOSE_NOWRITE IN_MOVE = IN_MOVED_FROM | IN_MOVED_TO IN_UNMOUNT = 0x00002000 IN_Q_OVERFLOW = 0x00004000 IN_IGNORED = 0x00008000 IN_ONLYDIR = 0x01000000 IN_DONT_FOLLOW = 0x02000000 # Do not follow a symbolic link. IN_EXCL_UNLINK = 0x04000000 # Exclude events on unlinked objects IN_MASK_ADD = 0x20000000 # Add to the mask of an existing watch. IN_ISDIR = 0x40000000 # Event occurred against directory. IN_ONESHOT = 0x80000000 # Only send event once. # All user-space events. IN_ALL_EVENTS = reduce( lambda x, y: x | y, [ IN_ACCESS, IN_MODIFY, IN_ATTRIB, IN_CLOSE_WRITE, IN_CLOSE_NOWRITE, IN_OPEN, IN_MOVED_FROM, IN_MOVED_TO, IN_DELETE, IN_CREATE, IN_DELETE_SELF, IN_MOVE_SELF, ]) # Flags for ``inotify_init1`` IN_CLOEXEC = 0x02000000 IN_NONBLOCK = 0x00004000 # Watchdog's API cares only about these events. WATCHDOG_ALL_EVENTS = reduce( lambda x, y: x | y, [ InotifyConstants.IN_MODIFY, InotifyConstants.IN_ATTRIB, InotifyConstants.IN_MOVED_FROM, InotifyConstants.IN_MOVED_TO, InotifyConstants.IN_CREATE, InotifyConstants.IN_DELETE, InotifyConstants.IN_DELETE_SELF, InotifyConstants.IN_DONT_FOLLOW, ]) class inotify_event_struct(ctypes.Structure): _fields_ = [('wd', c_int), ('mask', c_uint32), ('cookie', c_uint32), ('len', c_uint32), ('name', c_char_p)] EVENT_SIZE = ctypes.sizeof(inotify_event_struct) DEFAULT_NUM_EVENTS = 2048 DEFAULT_EVENT_BUFFER_SIZE = DEFAULT_NUM_EVENTS * (EVENT_SIZE + 16) class Inotify(object): def __init__(self, path, recursive=False, event_mask=WATCHDOG_ALL_EVENTS): inotify_fd = inotify_init() if inotify_fd == -1: Inotify._raise_error() self._inotify_fd = inotify_fd self._lock = threading.Lock() self._wd_for_path = dict() self._path_for_wd = dict() self._path = path self._event_mask = event_mask self._is_recursive = recursive if os.path.isdir(path): self._add_dir_watch(path, recursive, event_mask) else: self._add_watch(path, event_mask) self._moved_from_events = dict() @property def event_mask(self): return self._event_mask @property def path(self): return self._path @property def is_recursive(self): return self._is_recursive @property def fd(self): return self._inotify_fd def clear_move_records(self): self._moved_from_events = dict() def source_for_move(self, destination_event): if destination_event.cookie in self._moved_from_events: return self._moved_from_events[destination_event.cookie].src_path else: return None def remember_move_from_event(self, event): self._moved_from_events[event.cookie] = event def add_watch(self, path): with self._lock: self._add_watch(path, self._event_mask) def remove_watch(self, path): with self._lock: wd = self._wd_for_path.pop(path) del self._path_for_wd[wd] if inotify_rm_watch(self._inotify_fd, wd) == -1: Inotify._raise_error() def close(self): with self._lock: if self._path in self._wd_for_path: wd = self._wd_for_path[self._path] inotify_rm_watch(self._inotify_fd, wd) os.close(self._inotify_fd) def read_events(self, event_buffer_size=DEFAULT_EVENT_BUFFER_SIZE): def _recursive_simulate(src_path): events = [] for root, dirnames, filenames in os.walk(src_path): for dirname in dirnames: try: full_path = os.path.join(root, dirname) wd_dir = self._add_watch(full_path, self._event_mask) e = InotifyEvent( wd_dir, InotifyConstants.IN_CREATE | InotifyConstants.IN_ISDIR, 0, dirname, full_path) events.append(e) except OSError: pass for filename in filenames: full_path = os.path.join(root, filename) wd_parent_dir = self._wd_for_path[os.path.dirname(full_path)] e = InotifyEvent( wd_parent_dir, InotifyConstants.IN_CREATE, 0, filename, full_path) events.append(e) return events event_buffer = None while True: try: event_buffer = os.read(self._inotify_fd, event_buffer_size) except OSError as e: if e.errno == errno.EINTR: continue break with self._lock: event_list = [] for wd, mask, cookie, name in Inotify._parse_event_buffer(event_buffer): if wd == -1: continue wd_path = self._path_for_wd[wd] src_path = os.path.join(wd_path, name) if name else wd_path inotify_event = InotifyEvent(wd, mask, cookie, name, src_path) if inotify_event.is_moved_from: self.remember_move_from_event(inotify_event) elif inotify_event.is_moved_to: move_src_path = self.source_for_move(inotify_event) if move_src_path in self._wd_for_path: moved_wd = self._wd_for_path[move_src_path] del self._wd_for_path[move_src_path] self._wd_for_path[inotify_event.src_path] = moved_wd self._path_for_wd[moved_wd] = inotify_event.src_path if self.is_recursive: for _path, _wd in self._wd_for_path.copy().items(): if _path.startswith(move_src_path + os.path.sep.encode()): moved_wd = self._wd_for_path.pop(_path) _move_to_path = _path.replace(move_src_path, inotify_event.src_path) self._wd_for_path[_move_to_path] = moved_wd self._path_for_wd[moved_wd] = _move_to_path src_path = os.path.join(wd_path, name) inotify_event = InotifyEvent(wd, mask, cookie, name, src_path) if inotify_event.is_ignored: path = self._path_for_wd.pop(wd) if self._wd_for_path[path] == wd: del self._wd_for_path[path] continue event_list.append(inotify_event) if (self.is_recursive and inotify_event.is_directory and inotify_event.is_create): # IN_MOVED_FROM events should be marked IN_CREATE # instead relative to this directory. try: self._add_watch(src_path, self._event_mask) except OSError: continue event_list.extend(_recursive_simulate(src_path)) return event_list # Non-synchronized methods. def _add_dir_watch(self, path, recursive, mask): if not os.path.isdir(path): raise OSError(errno.ENOTDIR, os.strerror(errno.ENOTDIR), path) self._add_watch(path, mask) if recursive: for root, dirnames, _ in os.walk(path): for dirname in dirnames: full_path = os.path.join(root, dirname) if os.path.islink(full_path): continue self._add_watch(full_path, mask) def _add_watch(self, path, mask): wd = inotify_add_watch(self._inotify_fd, path, mask) if wd == -1: Inotify._raise_error() self._wd_for_path[path] = wd self._path_for_wd[wd] = path return wd @staticmethod def _raise_error(): err = ctypes.get_errno() if err == errno.ENOSPC: raise OSError(errno.ENOSPC, "inotify watch limit reached") elif err == errno.EMFILE: raise OSError(errno.EMFILE, "inotify instance limit reached") else: raise OSError(err, os.strerror(err)) @staticmethod def _parse_event_buffer(event_buffer): i = 0 while i + 16 <= len(event_buffer): wd, mask, cookie, length = struct.unpack_from('iIII', event_buffer, i) name = event_buffer[i + 16:i + 16 + length].rstrip(b'\0') i += 16 + length yield wd, mask, cookie, name class InotifyEvent(object): def __init__(self, wd, mask, cookie, name, src_path): self._wd = wd self._mask = mask self._cookie = cookie self._name = name self._src_path = src_path @property def src_path(self): return self._src_path @property def wd(self): return self._wd @property def mask(self): return self._mask @property def cookie(self): return self._cookie @property def name(self): return self._name @property def is_modify(self): return self._mask & InotifyConstants.IN_MODIFY > 0 @property def is_close_write(self): return self._mask & InotifyConstants.IN_CLOSE_WRITE > 0 @property def is_close_nowrite(self): return self._mask & InotifyConstants.IN_CLOSE_NOWRITE > 0 @property def is_access(self): return self._mask & InotifyConstants.IN_ACCESS > 0 @property def is_delete(self): return self._mask & InotifyConstants.IN_DELETE > 0 @property def is_delete_self(self): return self._mask & InotifyConstants.IN_DELETE_SELF > 0 @property def is_create(self): return self._mask & InotifyConstants.IN_CREATE > 0 @property def is_moved_from(self): return self._mask & InotifyConstants.IN_MOVED_FROM > 0 @property def is_moved_to(self): return self._mask & InotifyConstants.IN_MOVED_TO > 0 @property def is_move(self): return self._mask & InotifyConstants.IN_MOVE > 0 @property def is_move_self(self): return self._mask & InotifyConstants.IN_MOVE_SELF > 0 @property def is_attrib(self): return self._mask & InotifyConstants.IN_ATTRIB > 0 @property def is_ignored(self): return self._mask & InotifyConstants.IN_IGNORED > 0 @property def is_directory(self): # It looks like the kernel does not provide this information for # IN_DELETE_SELF and IN_MOVE_SELF. In this case, assume it's a dir. return (self.is_delete_self or self.is_move_self or self._mask & InotifyConstants.IN_ISDIR > 0) @property def key(self): return self._src_path, self._wd, self._mask, self._cookie, self._name def __eq__(self, inotify_event): return self.key == inotify_event.key def __ne__(self, inotify_event): return self.key == inotify_event.key def __hash__(self): return hash(self.key) @staticmethod def _get_mask_string(mask): masks = [] for c in dir(InotifyConstants): if c.startswith('IN_') and c not in ['IN_ALL_EVENTS', 'IN_CLOSE', 'IN_MOVE']: c_val = getattr(InotifyConstants, c) if mask & c_val: masks.append(c) mask_string = '|'.join(masks) return mask_string def __repr__(self): mask_string = self._get_mask_string(self.mask) s = '<%s: src_path=%r, wd=%d, mask=%s, cookie=%d, name=%s>' return s % (type(self).__name__, self.src_path, self.wd, mask_string, self.cookie, decode(self.name))

true

f70f8e73132e3e8bee5eadba32f1938ebaba2aa7

319

py

Python

example/routes.py

fitahol/aiohttprest

b9f1a386b22ad03e53f2f0e74ed3b29da5bcc220

[ "Apache-2.0" ]

1

2017-03-14T23:39:55.000Z

example/routes.py

fitahol/aiohttprest

b9f1a386b22ad03e53f2f0e74ed3b29da5bcc220

[ "Apache-2.0" ]

null

example/routes.py

fitahol/aiohttprest

b9f1a386b22ad03e53f2f0e74ed3b29da5bcc220

[ "Apache-2.0" ]

null

#!/usr/bin/env python # coding=utf-8 """ __created__ = '06/01/2017' __author__ = 'deling.ma' """ from aio_rest.routes import RouteCollector, Route from example.views import publish, IndexView routes = RouteCollector(prefix='/app', routes=[ Route('/', IndexView), Route('/publish', publish, method='GET'), ])

19.9375

49

0.689655

from aio_rest.routes import RouteCollector, Route from example.views import publish, IndexView routes = RouteCollector(prefix='/app', routes=[ Route('/', IndexView), Route('/publish', publish, method='GET'), ])

true

f70f8ec2299360b366ee181be4fd170341ad6326

869

py

Python

Inflearn_SungKim/1.LinearRegression/LinearRegression(placeholders).py

shinhaha/tensorflow

4647017a727985d64c5b0addee92f0ec516952c1

[ "MIT" ]

null

Inflearn_SungKim/1.LinearRegression/LinearRegression(placeholders).py

shinhaha/tensorflow

4647017a727985d64c5b0addee92f0ec516952c1

[ "MIT" ]

null

Inflearn_SungKim/1.LinearRegression/LinearRegression(placeholders).py

shinhaha/tensorflow

4647017a727985d64c5b0addee92f0ec516952c1

[ "MIT" ]

null

import tensorflow as tf #placeholder variable(scalar) X=tf.placeholder(tf.float32,shape=[None]) Y=tf.placeholder(tf.float32,shape=[None]) W=tf.Variable(tf.random_normal([1]),name='weight') b=tf.Variable(tf.random_normal([1]),name='bias') hypothesis=X*W+b #average cost=tf.reduce_mean(tf.square(hypothesis-Y)) optimizer=tf.train.GradientDescentOptimizer(learning_rate=0.01) #minimize cost train=optimizer.minimize(cost) sess=tf.Session() #initialize var sess.run(tf.global_variables_initializer()) #learning for step in range(2001): cost_val,W_val,b_val,_=sess.run([cost,W,b,train], feed_dict={X:[1,2,3,4,5],Y:[2.1,3.1,4.1,5.1,6.1]}) if step%20==0: print(step,cost_val,W_val,b_val) #evlauation print(sess.run(hypothesis,feed_dict={X:[5]})) print(sess.run(hypothesis,feed_dict={X:[2.5]})) print(sess.run(hypothesis,feed_dict={X:[1.5,3.5]}))

27.15625

63

0.727273

import tensorflow as tf X=tf.placeholder(tf.float32,shape=[None]) Y=tf.placeholder(tf.float32,shape=[None]) W=tf.Variable(tf.random_normal([1]),name='weight') b=tf.Variable(tf.random_normal([1]),name='bias') hypothesis=X*W+b cost=tf.reduce_mean(tf.square(hypothesis-Y)) optimizer=tf.train.GradientDescentOptimizer(learning_rate=0.01) train=optimizer.minimize(cost) sess=tf.Session() sess.run(tf.global_variables_initializer()) for step in range(2001): cost_val,W_val,b_val,_=sess.run([cost,W,b,train], feed_dict={X:[1,2,3,4,5],Y:[2.1,3.1,4.1,5.1,6.1]}) if step%20==0: print(step,cost_val,W_val,b_val) print(sess.run(hypothesis,feed_dict={X:[5]})) print(sess.run(hypothesis,feed_dict={X:[2.5]})) print(sess.run(hypothesis,feed_dict={X:[1.5,3.5]}))

true

f70f8f560c5f80a9cb4578a691c38108667a8423

1,211

py

Python

deel/model/lstm.py

ghelia/deel

6ff67d7246daf12d1884357010dd82842fbc31d1

[ "MIT" ]

null

deel/model/lstm.py

ghelia/deel

6ff67d7246daf12d1884357010dd82842fbc31d1

[ "MIT" ]

null

deel/model/lstm.py

ghelia/deel

6ff67d7246daf12d1884357010dd82842fbc31d1

[ "MIT" ]

null

import chainer import chainer.functions as F import chainer.links as L """ Based on chainer official example https://github.com/pfnet/chainer/tree/master/examples/ptb Modified by shi3z March 28,2016 """ class RNNLM(chainer.Chain): """Recurrent neural net languabe model for penn tree bank corpus. This is an example of deep LSTM network for infinite length input. """ def __init__(self, n_input_units=1000,n_vocab=100, n_units=100, train=True): super(RNNLM, self).__init__( inputVector= L.Linear(n_input_units, n_units), embed=L.EmbedID(n_vocab, n_units), l1=L.LSTM(n_units, n_units), l2=L.LSTM(n_units, n_units), l3=L.Linear(n_units, n_vocab), ) self.train = train def reset_state(self): self.l1.reset_state() self.l2.reset_state() self.l3.reset_state() def __call__(self, x,mode=0): if mode == 1: h0 = self.inputVector(x) else: h0 = self.embed(x) h1 = self.l1(F.dropout(h0, train=self.train)) h2 = self.l2(F.dropout(h1, train=self.train)) y = self.l3(F.dropout(h2, train=self.train)) return y

28.162791

80

0.618497

import chainer import chainer.functions as F import chainer.links as L class RNNLM(chainer.Chain): def __init__(self, n_input_units=1000,n_vocab=100, n_units=100, train=True): super(RNNLM, self).__init__( inputVector= L.Linear(n_input_units, n_units), embed=L.EmbedID(n_vocab, n_units), l1=L.LSTM(n_units, n_units), l2=L.LSTM(n_units, n_units), l3=L.Linear(n_units, n_vocab), ) self.train = train def reset_state(self): self.l1.reset_state() self.l2.reset_state() self.l3.reset_state() def __call__(self, x,mode=0): if mode == 1: h0 = self.inputVector(x) else: h0 = self.embed(x) h1 = self.l1(F.dropout(h0, train=self.train)) h2 = self.l2(F.dropout(h1, train=self.train)) y = self.l3(F.dropout(h2, train=self.train)) return y

true

f70f910ce1791081915a783482feb7b0db02c894

5,716

py

Python

src/films/tests/test_hdrfilm.py

tizian/layer-laboratory

008cc94b76127e9eb74227fcd3d0145da8ddec30

[ "CNRI-Python" ]

7

2020-07-24T03:19:59.000Z

2022-03-30T10:56:12.000Z

src/films/tests/test_hdrfilm.py

tizian/layer-laboratory

008cc94b76127e9eb74227fcd3d0145da8ddec30

[ "CNRI-Python" ]

1

2021-04-07T22:30:23.000Z

2021-04-08T00:55:36.000Z

src/films/tests/test_hdrfilm.py

tizian/layer-laboratory

008cc94b76127e9eb74227fcd3d0145da8ddec30

[ "CNRI-Python" ]

2

2020-06-08T08:25:09.000Z

2021-04-05T22:13:08.000Z

import mitsuba import pytest import os import enoki as ek def test01_construct(variant_scalar_rgb): from mitsuba.core.xml import load_string # With default reconstruction filter film = load_string("""<film version="2.0.0" type="hdrfilm"></film>""") assert film is not None assert film.reconstruction_filter() is not None # With a provided reconstruction filter film = load_string("""<film version="2.0.0" type="hdrfilm"> <rfilter type="gaussian"> <float name="stddev" value="18.5"/> </rfilter> </film>""") assert film is not None assert film.reconstruction_filter().radius() == (4 * 18.5) # Certain parameter values are not allowed with pytest.raises(RuntimeError): load_string("""<film version="2.0.0" type="hdrfilm"> <string name="component_format" value="uint8"/> </film>""") with pytest.raises(RuntimeError): load_string("""<film version="2.0.0" type="hdrfilm"> <string name="pixel_format" value="brga"/> </film>""") def test02_crops(variant_scalar_rgb): from mitsuba.core.xml import load_string film = load_string("""<film version="2.0.0" type="hdrfilm"> <integer name="width" value="32"/> <integer name="height" value="21"/> <integer name="crop_width" value="11"/> <integer name="crop_height" value="5"/> <integer name="crop_offset_x" value="2"/> <integer name="crop_offset_y" value="3"/> <boolean name="high_quality_edges" value="true"/> <string name="pixel_format" value="rgba"/> </film>""") assert film is not None assert ek.all(film.size() == [32, 21]) assert ek.all(film.crop_size() == [11, 5]) assert ek.all(film.crop_offset() == [2, 3]) assert film.has_high_quality_edges() # Crop size doesn't adjust its size, so an error should be raised if the # resulting crop window goes out of bounds. incomplete = """<film version="2.0.0" type="hdrfilm"> <integer name="width" value="32"/> <integer name="height" value="21"/> <integer name="crop_offset_x" value="30"/> <integer name="crop_offset_y" value="20"/>""" with pytest.raises(RuntimeError): film = load_string(incomplete + "</film>") film = load_string(incomplete + """ <integer name="crop_width" value="2"/> <integer name="crop_height" value="1"/> </film>""") assert film is not None assert ek.all(film.size() == [32, 21]) assert ek.all(film.crop_size() == [2, 1]) assert ek.all(film.crop_offset() == [30, 20]) @pytest.mark.parametrize('file_format', ['exr', 'rgbe', 'pfm']) def test03_develop(variant_scalar_rgb, file_format, tmpdir): from mitsuba.core.xml import load_string from mitsuba.core import Bitmap, Struct, ReconstructionFilter, float_dtype from mitsuba.render import ImageBlock import numpy as np """Create a test image. Develop it to a few file format, each time reading it back and checking that contents are unchanged.""" np.random.seed(12345 + ord(file_format[0])) # Note: depending on the file format, the alpha channel may be automatically removed. film = load_string("""<film version="2.0.0" type="hdrfilm"> <integer name="width" value="41"/> <integer name="height" value="37"/> <string name="file_format" value="{}"/> <string name="pixel_format" value="rgba"/> <string name="component_format" value="float32"/> <rfilter type="box"/> </film>""".format(file_format)) # Regardless of the output file format, values are stored as XYZAW (5 channels). contents = np.random.uniform(size=(film.size()[1], film.size()[0], 5)) # RGBE and will only reconstruct well images that have similar scales on # all channel (because exponent is shared between channels). if file_format is "rgbe": contents = 1 + 0.1 * contents # Use unit weights. contents[:, :, 4] = 1.0 block = ImageBlock(film.size(), 5, film.reconstruction_filter()) block.clear() for x in range(film.size()[1]): for y in range(film.size()[0]): block.put([y+0.5, x+0.5], contents[x, y, :]) film.prepare(['X', 'Y', 'Z', 'A', 'W']) film.put(block) with pytest.raises(RuntimeError): # Should raise when the destination file hasn't been specified. film.develop() filename = str(tmpdir.join('test_image.' + file_format)) film.set_destination_file(filename) film.develop() # Read back and check contents other = Bitmap(filename).convert(Bitmap.PixelFormat.XYZAW, Struct.Type.Float32, srgb_gamma=False) img = np.array(other, copy=False) if False: import matplotlib.pyplot as plt plt.figure() plt.subplot(1, 3, 1) plt.imshow(contents[:, :, :3]) plt.subplot(1, 3, 2) plt.imshow(img[:, :, :3]) plt.subplot(1, 3, 3) plt.imshow(ek.sum(ek.abs(img[:, :, :3] - contents[:, :, :3]), axis=2), cmap='coolwarm') plt.colorbar() plt.show() if file_format == "exr": assert ek.allclose(img, contents, atol=1e-5) else: if file_format == "rgbe": assert ek.allclose(img[:, :, :3], contents[:, :, :3], atol=1e-2), \ '\n{}\nvs\n{}\n'.format(img[:4, :4, :3], contents[:4, :4, :3]) else: assert ek.allclose(img[:, :, :3], contents[:, :, :3], atol=1e-5) # Alpha channel was ignored, alpha and weights should default to 1.0. assert ek.allclose(img[:, :, 3:5], 1.0, atol=1e-6)

39.42069

101

0.603569

import mitsuba import pytest import os import enoki as ek def test01_construct(variant_scalar_rgb): from mitsuba.core.xml import load_string film = load_string("""<film version="2.0.0" type="hdrfilm"></film>""") assert film is not None assert film.reconstruction_filter() is not None film = load_string("""<film version="2.0.0" type="hdrfilm"> <rfilter type="gaussian"> <float name="stddev" value="18.5"/> </rfilter> </film>""") assert film is not None assert film.reconstruction_filter().radius() == (4 * 18.5) with pytest.raises(RuntimeError): load_string("""<film version="2.0.0" type="hdrfilm"> <string name="component_format" value="uint8"/> </film>""") with pytest.raises(RuntimeError): load_string("""<film version="2.0.0" type="hdrfilm"> <string name="pixel_format" value="brga"/> </film>""") def test02_crops(variant_scalar_rgb): from mitsuba.core.xml import load_string film = load_string("""<film version="2.0.0" type="hdrfilm"> <integer name="width" value="32"/> <integer name="height" value="21"/> <integer name="crop_width" value="11"/> <integer name="crop_height" value="5"/> <integer name="crop_offset_x" value="2"/> <integer name="crop_offset_y" value="3"/> <boolean name="high_quality_edges" value="true"/> <string name="pixel_format" value="rgba"/> </film>""") assert film is not None assert ek.all(film.size() == [32, 21]) assert ek.all(film.crop_size() == [11, 5]) assert ek.all(film.crop_offset() == [2, 3]) assert film.has_high_quality_edges() # resulting crop window goes out of bounds. incomplete = """<film version="2.0.0" type="hdrfilm"> <integer name="width" value="32"/> <integer name="height" value="21"/> <integer name="crop_offset_x" value="30"/> <integer name="crop_offset_y" value="20"/>""" with pytest.raises(RuntimeError): film = load_string(incomplete + "</film>") film = load_string(incomplete + """ <integer name="crop_width" value="2"/> <integer name="crop_height" value="1"/> </film>""") assert film is not None assert ek.all(film.size() == [32, 21]) assert ek.all(film.crop_size() == [2, 1]) assert ek.all(film.crop_offset() == [30, 20]) @pytest.mark.parametrize('file_format', ['exr', 'rgbe', 'pfm']) def test03_develop(variant_scalar_rgb, file_format, tmpdir): from mitsuba.core.xml import load_string from mitsuba.core import Bitmap, Struct, ReconstructionFilter, float_dtype from mitsuba.render import ImageBlock import numpy as np np.random.seed(12345 + ord(file_format[0])) # Note: depending on the file format, the alpha channel may be automatically removed. film = load_string("""<film version="2.0.0" type="hdrfilm"> <integer name="width" value="41"/> <integer name="height" value="37"/> <string name="file_format" value="{}"/> <string name="pixel_format" value="rgba"/> <string name="component_format" value="float32"/> <rfilter type="box"/> </film>""".format(file_format)) # Regardless of the output file format, values are stored as XYZAW (5 channels). contents = np.random.uniform(size=(film.size()[1], film.size()[0], 5)) # RGBE and will only reconstruct well images that have similar scales on # all channel (because exponent is shared between channels). if file_format is "rgbe": contents = 1 + 0.1 * contents # Use unit weights. contents[:, :, 4] = 1.0 block = ImageBlock(film.size(), 5, film.reconstruction_filter()) block.clear() for x in range(film.size()[1]): for y in range(film.size()[0]): block.put([y+0.5, x+0.5], contents[x, y, :]) film.prepare(['X', 'Y', 'Z', 'A', 'W']) film.put(block) with pytest.raises(RuntimeError): # Should raise when the destination file hasn't been specified. film.develop() filename = str(tmpdir.join('test_image.' + file_format)) film.set_destination_file(filename) film.develop() other = Bitmap(filename).convert(Bitmap.PixelFormat.XYZAW, Struct.Type.Float32, srgb_gamma=False) img = np.array(other, copy=False) if False: import matplotlib.pyplot as plt plt.figure() plt.subplot(1, 3, 1) plt.imshow(contents[:, :, :3]) plt.subplot(1, 3, 2) plt.imshow(img[:, :, :3]) plt.subplot(1, 3, 3) plt.imshow(ek.sum(ek.abs(img[:, :, :3] - contents[:, :, :3]), axis=2), cmap='coolwarm') plt.colorbar() plt.show() if file_format == "exr": assert ek.allclose(img, contents, atol=1e-5) else: if file_format == "rgbe": assert ek.allclose(img[:, :, :3], contents[:, :, :3], atol=1e-2), \ '\n{}\nvs\n{}\n'.format(img[:4, :4, :3], contents[:4, :4, :3]) else: assert ek.allclose(img[:, :, :3], contents[:, :, :3], atol=1e-5) assert ek.allclose(img[:, :, 3:5], 1.0, atol=1e-6)

true

f70f925d50a5f908572efb96b2f9609c818088a6

2,959

py

Python

tests/test_inline_functions/test_query.py

pyansys/pyansys

adf51893be746c632f40a9dc8c9247dbee138dda

[ "MIT" ]

1

2021-08-19T14:08:34.000Z

tests/test_inline_functions/test_query.py

pyansys/pyansys

adf51893be746c632f40a9dc8c9247dbee138dda

[ "MIT" ]

null

tests/test_inline_functions/test_query.py

pyansys/pyansys

adf51893be746c632f40a9dc8c9247dbee138dda

[ "MIT" ]

null

import pytest class TestParseParameter: @pytest.mark.parametrize( "values", [ ("PARAMETER test = 4", 4.0), ("PARAMETER=4", 4.0), ("PARAMETER WARNING = 4", 4.0), ("PARAMETER = _=4", 4.0), ("WARNING = PARAMETER = 4", 4.0), ("PARAMETER = .4", 0.4), ], ) def test_parse_float(self, values, query): input_, output = values assert query._parse_parameter_float_response(input_) == output @pytest.mark.parametrize( "values", [ ("PARAMETER test = 4", 4), ("PARAMETER=4", 4), ("PARAMETER WARNING = 4", 4), ("PARAMETER = _=4", 4), ("WARNING = PARAMETER = 4", 4), ("PARAMETER = .4", 0), ], ) def test_parse_int(self, values, query): input_, output = values assert query._parse_parameter_integer_response(input_) == output def test_parse_float_type_warning(self, query): input_ = "WARNING PARAMETER = 4" with pytest.warns(UserWarning): query._parse_parameter_float_response(input_) def test_parse_int_type_warning(self, query): input_ = "WARNING PARAMETER = 4" with pytest.warns(UserWarning): query._parse_parameter_integer_response(input_) @pytest.mark.parametrize( "value", ["parameter test = 4", "PARAMETER 4", "WARNING = 4", ""] ) def test_parse_float_type_error(self, value, query): input_ = value with pytest.raises(TypeError): query._parse_parameter_float_response(input_) @pytest.mark.parametrize( "value", ["parameter test = 4", "PARAMETER 4", "WARNING = 4", ""] ) def test_parse_int_type_error(self, value, query): input_ = value with pytest.raises(TypeError): query._parse_parameter_integer_response(input_) class TestRunQuery: @pytest.mark.parametrize('command', [('KX(1)', float), ('KP(1,1,1)', int)]) def test_run_query_returned_type(self, line_geometry, command): q, kps, l0 = line_geometry cmd, type_ = command integer = False if type_ == float else True v = q._run_query(cmd, integer=integer) assert isinstance(v, type_) def test_interactive_mode_error(self, mapdl, line_geometry): q, kps, l0 = line_geometry with mapdl.non_interactive: with pytest.raises(RuntimeError): v = q.kx(1) @pytest.mark.skip_grpc # only works in gRPC mode def test_nopr_mode(self, mapdl, line_geometry): try: # enter no printout mode mapdl._run('/NOPR', mute=True) assert mapdl.prep7() is None # verify that queries still work q, kps, l0 = line_geometry assert q.kx(2) == 1.0 finally: # always return printing mapdl._run('/GOPR', mute=True)

32.516484

79

0.57756

import pytest class TestParseParameter: @pytest.mark.parametrize( "values", [ ("PARAMETER test = 4", 4.0), ("PARAMETER=4", 4.0), ("PARAMETER WARNING = 4", 4.0), ("PARAMETER = _=4", 4.0), ("WARNING = PARAMETER = 4", 4.0), ("PARAMETER = .4", 0.4), ], ) def test_parse_float(self, values, query): input_, output = values assert query._parse_parameter_float_response(input_) == output @pytest.mark.parametrize( "values", [ ("PARAMETER test = 4", 4), ("PARAMETER=4", 4), ("PARAMETER WARNING = 4", 4), ("PARAMETER = _=4", 4), ("WARNING = PARAMETER = 4", 4), ("PARAMETER = .4", 0), ], ) def test_parse_int(self, values, query): input_, output = values assert query._parse_parameter_integer_response(input_) == output def test_parse_float_type_warning(self, query): input_ = "WARNING PARAMETER = 4" with pytest.warns(UserWarning): query._parse_parameter_float_response(input_) def test_parse_int_type_warning(self, query): input_ = "WARNING PARAMETER = 4" with pytest.warns(UserWarning): query._parse_parameter_integer_response(input_) @pytest.mark.parametrize( "value", ["parameter test = 4", "PARAMETER 4", "WARNING = 4", ""] ) def test_parse_float_type_error(self, value, query): input_ = value with pytest.raises(TypeError): query._parse_parameter_float_response(input_) @pytest.mark.parametrize( "value", ["parameter test = 4", "PARAMETER 4", "WARNING = 4", ""] ) def test_parse_int_type_error(self, value, query): input_ = value with pytest.raises(TypeError): query._parse_parameter_integer_response(input_) class TestRunQuery: @pytest.mark.parametrize('command', [('KX(1)', float), ('KP(1,1,1)', int)]) def test_run_query_returned_type(self, line_geometry, command): q, kps, l0 = line_geometry cmd, type_ = command integer = False if type_ == float else True v = q._run_query(cmd, integer=integer) assert isinstance(v, type_) def test_interactive_mode_error(self, mapdl, line_geometry): q, kps, l0 = line_geometry with mapdl.non_interactive: with pytest.raises(RuntimeError): v = q.kx(1) @pytest.mark.skip_grpc def test_nopr_mode(self, mapdl, line_geometry): try: mapdl._run('/NOPR', mute=True) assert mapdl.prep7() is None q, kps, l0 = line_geometry assert q.kx(2) == 1.0 finally: mapdl._run('/GOPR', mute=True)

true

f70f94b36a95bfd5abb974ec2563582a58d0197b

169

py

Python

app/src/imports/requests.py

jkulak/spotify-grabtrack

e6cd16709195ca6d2e186a3b8cc7ce1419b6aace

[ "MIT" ]

null

app/src/imports/requests.py

jkulak/spotify-grabtrack

e6cd16709195ca6d2e186a3b8cc7ce1419b6aace

[ "MIT" ]

13

2022-02-10T20:07:49.000Z

2022-03-27T20:07:21.000Z

app/src/imports/requests.py

jkulak/spotify-grabtrack

e6cd16709195ca6d2e186a3b8cc7ce1419b6aace

[ "MIT" ]

null

import requests_cache from requests_cache import SQLiteCache requests_cache.install_cache( "grabtrack_sqlite_cache", SQLiteCache("spotify_api_cache", timeout=30) )

24.142857

74

0.83432

import requests_cache from requests_cache import SQLiteCache requests_cache.install_cache( "grabtrack_sqlite_cache", SQLiteCache("spotify_api_cache", timeout=30) )

true

f70f95230083e964837c7ccf662a8d3e815a9abf

3,469

py

Python

gui/mon/views.py

erigones/esdc-ce

2e39211a8f5132d66e574d3a657906c7d3c406fe

[ "Apache-2.0" ]

97

2016-11-15T14:44:23.000Z

2022-03-13T18:09:15.000Z

gui/mon/views.py

erigones/esdc-ce

2e39211a8f5132d66e574d3a657906c7d3c406fe

[ "Apache-2.0" ]

334

2016-11-17T19:56:57.000Z

2022-03-18T10:45:53.000Z

gui/mon/views.py

erigones/esdc-ce

2e39211a8f5132d66e574d3a657906c7d3c406fe

[ "Apache-2.0" ]

33

2017-01-02T16:04:13.000Z

2022-02-07T19:20:24.000Z

import json from re import match from django.contrib.auth.decorators import login_required from django.views.decorators.http import require_POST from django.shortcuts import redirect, render from gui.mon.forms import BaseAlertFilterForm from gui.utils import collect_view_data from gui.decorators import ajax_required, profile_required, admin_required from api.decorators import setting_required from api.utils.views import call_api_view from api.mon.alerting.views import mon_alert_list @login_required @admin_required @profile_required @setting_required('MON_ZABBIX_ENABLED') def mon_server_redirect(request): """ Monitoring management. """ if match("^http", request.dc.settings.MON_ZABBIX_SERVER_EXTERNAL_URL): return redirect(request.dc.settings.MON_ZABBIX_SERVER_EXTERNAL_URL) else: return redirect(request.dc.settings.MON_ZABBIX_SERVER) @login_required @admin_required @ajax_required @require_POST def alert_list_table(request): context = collect_view_data(request, 'mon_alert_list') try: api_data = json.loads(request.POST.get('alert_filter', None)) except (ValueError, TypeError): context['error'] = 'Unexpected error: could not parse alert filter.' else: context['alert_filter'] = api_data res = call_api_view(request, 'GET', mon_alert_list, data=api_data) if res.status_code == 200: context['alerts'] = res.data['result'] elif res.status_code == 201: context['error'] = 'Unexpected error: got into an API loop.' else: context['error'] = res.data.get('result', {}).get('error', res.data) return render(request, 'gui/mon/alert_table.html', context) @login_required @admin_required @profile_required @setting_required('MON_ZABBIX_ENABLED') def alert_list(request): context = collect_view_data(request, 'mon_alert_list') data = request.GET.copy() data.pop('_', None) if not data and request.user.is_staff and request.dc.is_default(): data['show_nodes'] = True context['filters'] = form = BaseAlertFilterForm(request, data) context['init'] = True if form.is_valid() and form.api_data is not None: # new visit, or form submission context['alert_filter'] = form.api_data context['alert_filter_ok'] = True else: context['alert_filter_ok'] = False # Do not run javascript API TASKs! return render(request, 'gui/mon/alert_list.html', context) @login_required @admin_required @profile_required @setting_required('MON_ZABBIX_ENABLED') def hostgroup_list(request): context = collect_view_data(request, 'mon_hostgroup_list') return render(request, 'gui/mon/hostgroup_list.html', context) @login_required @admin_required @profile_required @setting_required('MON_ZABBIX_ENABLED') def template_list(request): context = collect_view_data(request, 'mon_template_list') return render(request, 'gui/mon/template_list.html', context) @login_required @admin_required @profile_required @setting_required('MON_ZABBIX_ENABLED') def action_list(request): context = collect_view_data(request, 'mon_action_list') return render(request, 'gui/mon/action_list.html', context) @login_required @admin_required @profile_required @setting_required('MON_ZABBIX_ENABLED') def webcheck_list(request): context = collect_view_data(request, 'mon_webcheck_list') return render(request, 'gui/mon/webcheck_list.html', context)

29.649573

86

0.742289

import json from re import match from django.contrib.auth.decorators import login_required from django.views.decorators.http import require_POST from django.shortcuts import redirect, render from gui.mon.forms import BaseAlertFilterForm from gui.utils import collect_view_data from gui.decorators import ajax_required, profile_required, admin_required from api.decorators import setting_required from api.utils.views import call_api_view from api.mon.alerting.views import mon_alert_list @login_required @admin_required @profile_required @setting_required('MON_ZABBIX_ENABLED') def mon_server_redirect(request): if match("^http", request.dc.settings.MON_ZABBIX_SERVER_EXTERNAL_URL): return redirect(request.dc.settings.MON_ZABBIX_SERVER_EXTERNAL_URL) else: return redirect(request.dc.settings.MON_ZABBIX_SERVER) @login_required @admin_required @ajax_required @require_POST def alert_list_table(request): context = collect_view_data(request, 'mon_alert_list') try: api_data = json.loads(request.POST.get('alert_filter', None)) except (ValueError, TypeError): context['error'] = 'Unexpected error: could not parse alert filter.' else: context['alert_filter'] = api_data res = call_api_view(request, 'GET', mon_alert_list, data=api_data) if res.status_code == 200: context['alerts'] = res.data['result'] elif res.status_code == 201: context['error'] = 'Unexpected error: got into an API loop.' else: context['error'] = res.data.get('result', {}).get('error', res.data) return render(request, 'gui/mon/alert_table.html', context) @login_required @admin_required @profile_required @setting_required('MON_ZABBIX_ENABLED') def alert_list(request): context = collect_view_data(request, 'mon_alert_list') data = request.GET.copy() data.pop('_', None) if not data and request.user.is_staff and request.dc.is_default(): data['show_nodes'] = True context['filters'] = form = BaseAlertFilterForm(request, data) context['init'] = True if form.is_valid() and form.api_data is not None: context['alert_filter'] = form.api_data context['alert_filter_ok'] = True else: context['alert_filter_ok'] = False return render(request, 'gui/mon/alert_list.html', context) @login_required @admin_required @profile_required @setting_required('MON_ZABBIX_ENABLED') def hostgroup_list(request): context = collect_view_data(request, 'mon_hostgroup_list') return render(request, 'gui/mon/hostgroup_list.html', context) @login_required @admin_required @profile_required @setting_required('MON_ZABBIX_ENABLED') def template_list(request): context = collect_view_data(request, 'mon_template_list') return render(request, 'gui/mon/template_list.html', context) @login_required @admin_required @profile_required @setting_required('MON_ZABBIX_ENABLED') def action_list(request): context = collect_view_data(request, 'mon_action_list') return render(request, 'gui/mon/action_list.html', context) @login_required @admin_required @profile_required @setting_required('MON_ZABBIX_ENABLED') def webcheck_list(request): context = collect_view_data(request, 'mon_webcheck_list') return render(request, 'gui/mon/webcheck_list.html', context)

true

f70f9523490f01a422da6c46e57bf7055ca504f2

515

py

Python

ucp_intro/07_mad_lib_game.py

matiasmasca/python

7631583820d51e3132bdb793fed28cc83f4877a2

[ "MIT" ]

null

ucp_intro/07_mad_lib_game.py

matiasmasca/python

7631583820d51e3132bdb793fed28cc83f4877a2

[ "MIT" ]

null

ucp_intro/07_mad_lib_game.py

matiasmasca/python

7631583820d51e3132bdb793fed28cc83f4877a2

[ "MIT" ]

null

# Primer juego... print("Mi poesia:") print("Las rosas son Rojas") print("Las violetas son Azules") print("Y yo te amo a ti") # Mad Libs # ingresar palabras random, adjetivos, verbos, sustantivos. print("Ahora te toca a vos") print("") color = input("Ingrese un color: ") sustantivo_plular = input("Ingrese un sustantivo en plural: ") celebridad = input("Ingrese el nombre de una celebridad: ") print("Las rosas son " + color) print( sustantivo_plular + " son Azules") print("Y yo te amo a ti " + celebridad )

24.52381

62

0.702913

print("Mi poesia:") print("Las rosas son Rojas") print("Las violetas son Azules") print("Y yo te amo a ti") print("Ahora te toca a vos") print("") color = input("Ingrese un color: ") sustantivo_plular = input("Ingrese un sustantivo en plural: ") celebridad = input("Ingrese el nombre de una celebridad: ") print("Las rosas son " + color) print( sustantivo_plular + " son Azules") print("Y yo te amo a ti " + celebridad )

true

f70f96b662e9909e240adb12a588cfe7baf1df63

14,338

py

Python

src/pytorch_metric_learning/utils/logging_presets.py

kvzhao/pytorch-metric-learning

9c8a94bd1a906317d5834f26d8a94e59d578b825

[ "MIT" ]

2

2020-08-11T03:42:15.000Z

2022-01-11T07:25:30.000Z

src/pytorch_metric_learning/utils/logging_presets.py

FadouaKhm/pytorch-metric-learning

9eb792bcfc1616b599e6ee457514e3cb3a7235dd

[ "MIT" ]

null

src/pytorch_metric_learning/utils/logging_presets.py

FadouaKhm/pytorch-metric-learning

9eb792bcfc1616b599e6ee457514e3cb3a7235dd

[ "MIT" ]

1

2021-03-15T04:24:52.000Z

import logging from . import common_functions as c_f import os import torch from collections import defaultdict import sqlite3 # You can write your own hooks for logging. # But if you'd like something that just works, then use this HookContainer. # You'll need to install record-keeper and tensorboard. # pip install record-keeper tensorboard class HookContainer: def __init__(self, record_keeper, record_group_name_prefix=None, primary_metric="mean_average_precision_at_r", validation_split_name="val"): self.record_keeper = record_keeper self.record_group_name_prefix = record_group_name_prefix self.saveable_trainer_objects = ["models", "optimizers", "lr_schedulers", "loss_funcs", "mining_funcs"] self.primary_metric = primary_metric self.validation_split_name = validation_split_name ############################################ ############################################ ################## HOOKS ################# ############################################ ############################################ ### Define the end_of_iteration hook. This will be executed at the end of every iteration. ### def end_of_iteration_hook(self, trainer): record_these = [[trainer.loss_tracker.losses, {"input_group_name_for_non_objects": "loss_histories"}], [trainer.loss_tracker.loss_weights, {"input_group_name_for_non_objects": "loss_weights"}], [trainer.loss_funcs, {"recursive_types": [torch.nn.Module]}], [trainer.mining_funcs, {}], [trainer.models, {}], [trainer.optimizers, {"custom_attr_func": self.optimizer_custom_attr_func}]] for record, kwargs in record_these: self.record_keeper.update_records(record, trainer.get_global_iteration(), **kwargs) # This hook will be passed into the trainer and will be executed at the end of every epoch. def end_of_epoch_hook(self, tester, dataset_dict, model_folder, test_interval=1, patience=None, test_collate_fn=None): if not self.primary_metric in tester.accuracy_calculator.get_curr_metrics(): raise ValueError("HookContainer `primary_metric` must be one of: {}".format(tester.accuracy_calculator.get_curr_metrics())) if not os.path.exists(model_folder): os.makedirs(model_folder) def actual_hook(trainer): continue_training = True if trainer.epoch % test_interval == 0: best_epoch = self.save_models_and_eval(trainer, dataset_dict, model_folder, test_interval, tester, test_collate_fn) continue_training = self.patience_remaining(trainer.epoch, best_epoch, patience) return continue_training return actual_hook def end_of_testing_hook(self, tester): for split_name, accuracies in tester.all_accuracies.items(): epoch = accuracies["epoch"] self.record_keeper.update_records(accuracies, epoch, input_group_name_for_non_objects=self.record_group_name(tester, split_name)) _, _, best_epoch, best_accuracy = self.is_new_best_accuracy(tester, split_name, epoch) best = {"best_epoch":best_epoch, "best_accuracy": best_accuracy} self.record_keeper.update_records(best, epoch, input_group_name_for_non_objects=self.record_group_name(tester, split_name)) for split_name, u in tester.dim_reduced_embeddings.items(): for k, (dim_reduced, labels) in u.items(): tag = '%s/%s'%(self.record_group_name(tester, split_name), k) self.record_keeper.add_embedding_plot(dim_reduced, labels, tag, epoch) ############################################ ############################################ ######### MODEL LOADING AND SAVING ######### ############################################ ############################################ def load_latest_saved_models(self, trainer, model_folder, device=None, best=False): if device is None: device = torch.device("cuda" if torch.cuda.is_available() else "cpu") resume_epoch, model_suffix = c_f.latest_version(model_folder, "trunk_*.pth", best=best) if resume_epoch > 0: for obj_dict in [getattr(trainer, x, {}) for x in self.saveable_trainer_objects]: c_f.load_dict_of_models(obj_dict, model_suffix, model_folder, device, log_if_successful=True) return resume_epoch + 1 def save_models(self, trainer, model_folder, curr_suffix, prev_suffix=None): for obj_dict in [getattr(trainer, x, {}) for x in self.saveable_trainer_objects]: c_f.save_dict_of_models(obj_dict, curr_suffix, model_folder) if prev_suffix is not None: c_f.delete_dict_of_models(obj_dict, prev_suffix, model_folder) def save_models_and_eval(self, trainer, dataset_dict, model_folder, test_interval, tester, collate_fn): epoch = trainer.epoch tester.test(dataset_dict, epoch, trainer.models["trunk"], trainer.models["embedder"], list(dataset_dict.keys()), collate_fn) prev_best_epoch, _ = self.get_best_epoch_and_accuracy(tester, self.validation_split_name) is_new_best, curr_accuracy, best_epoch, best_accuracy = self.is_new_best_accuracy(tester, self.validation_split_name, epoch) self.record_keeper.save_records() trainer.step_lr_plateau_schedulers(curr_accuracy) self.save_models(trainer, model_folder, epoch, epoch-test_interval) # save latest model if is_new_best: logging.info("New best accuracy! {}".format(curr_accuracy)) curr_suffix = "best%d"%best_epoch prev_suffix = "best%d"%prev_best_epoch if prev_best_epoch is not None else None self.save_models(trainer, model_folder, curr_suffix, prev_suffix) # save best model return best_epoch def is_new_best_accuracy(self, tester, split_name, epoch): curr_accuracy = self.get_curr_primary_metric(tester, split_name) best_epoch, best_accuracy = self.get_best_epoch_and_accuracy(tester, split_name) is_new_best = False if (curr_accuracy > best_accuracy) or (best_epoch is None): best_epoch, best_accuracy = epoch, curr_accuracy is_new_best = True return is_new_best, curr_accuracy, best_epoch, best_accuracy ############################################ ############################################ ##### BEST EPOCH AND ACCURACY TRACKING ##### ############################################ ############################################ def get_loss_history(self, loss_names=()): columns = "*" if len(loss_names) == 0 else ", ".join(loss_names) table_name = "loss_histories" if not self.record_keeper.table_exists(table_name): return {} output = self.record_keeper.query("SELECT {} FROM {}".format(columns, table_name), return_dict=True) output.pop("id", None) return output def get_accuracy_history(self, tester, split_name, return_all_metrics=False, metrics=()): table_name = self.record_group_name(tester, split_name) if not self.record_keeper.table_exists(table_name): return {} def get_accuracies(keys): keys = "*" if return_all_metrics else "epoch, %s"%keys query = "SELECT {} FROM {}".format(keys, table_name) return self.record_keeper.query(query, return_dict=True) keys = metrics if len(metrics) > 0 else [self.primary_metric] output = self.try_keys(keys, tester, get_accuracies) output.pop("id", None) return output def get_curr_primary_metric(self, tester, split_name): def get_curr(key): return tester.all_accuracies[split_name][key] return self.try_primary_metric(tester, get_curr) def try_keys(self, input_keys, tester, input_func): for average in [True, False]: keys = ", ".join([tester.accuracies_keyname(k, average=average, label_hierarchy_level=tester.label_hierarchy_level) for k in input_keys]) try: return input_func(keys) except (KeyError, sqlite3.OperationalError): pass raise KeyError def try_primary_metric(self, tester, input_func): return self.try_keys([self.primary_metric], tester, input_func) # returns accuracies of a specified epoch def get_accuracies_of_epoch(self, tester, split_name, epoch, select_all=True): table_name = self.record_group_name(tester, split_name) if not self.record_keeper.table_exists(table_name): return [] def get_accuracies(key): columns = "*" if select_all else "epoch, %s"%key query = "SELECT %s FROM %s WHERE epoch=?"%(columns, table_name) return self.record_keeper.query(query, (epoch, )) return self.try_primary_metric(tester, get_accuracies) # returns accuracies of best epoch and the metric name used to determine best acuracy def get_accuracies_of_best_epoch(self, tester, split_name, select_all=True, ignore_epoch=(-1,)): table_name = self.record_group_name(tester, split_name) if not self.record_keeper.table_exists(table_name): return [], None def get_accuracies(key): columns = "*" if select_all else "epoch, %s"%key params = ", ".join(["?"]*len(ignore_epoch)) query = """SELECT {0} FROM {1} WHERE {2}= (SELECT max({2}) FROM {1} WHERE epoch NOT IN ({3})) AND epoch NOT IN ({3})""".format(columns, table_name, key, params) output = self.record_keeper.query(query, ignore_epoch+ignore_epoch) return output, key return self.try_primary_metric(tester, get_accuracies) def get_best_epoch_and_accuracy(self, tester, split_name, ignore_epoch=(-1,)): accuracies, key = self.get_accuracies_of_best_epoch(tester, split_name, select_all=False, ignore_epoch=ignore_epoch) if len(accuracies) > 0: return accuracies[0]["epoch"], accuracies[0][key] return None, 0 def patience_remaining(self, epoch, best_epoch, patience): if patience is not None and best_epoch is not None: if epoch - best_epoch > patience: logging.info("Validation accuracy has plateaued. Exiting.") return False return True def run_tester_separately(self, tester, dataset_dict, epoch, trunk, embedder, splits_to_eval=None, collate_fn=None, skip_eval_if_already_done=True): if skip_eval_if_already_done: splits_to_eval = self.get_splits_to_eval(tester, dataset_dict, epoch, splits_to_eval) if len(splits_to_eval) == 0: logging.info("Already evaluated") return False tester.test(dataset_dict, epoch, trunk, embedder, splits_to_eval, collate_fn) return True def get_splits_to_eval(self, tester, dataset_dict, epoch, input_splits_to_eval): input_splits_to_eval = list(dataset_dict.keys()) if input_splits_to_eval is None else input_splits_to_eval splits_to_eval = [] for split in input_splits_to_eval: if len(self.get_accuracies_of_epoch(tester, split, epoch)) == 0: splits_to_eval.append(split) return splits_to_eval def base_record_group_name(self, tester): base_record_group_name = "%s_"%self.record_group_name_prefix if self.record_group_name_prefix else '' base_record_group_name += tester.description_suffixes("accuracies") return base_record_group_name def record_group_name(self, tester, split_name): base_record_group_name = self.base_record_group_name(tester) return "%s_%s"%(base_record_group_name, split_name.upper()) def optimizer_custom_attr_func(self, optimizer): return {"lr": optimizer.param_groups[0]["lr"]} class EmptyContainer: def end_of_epoch_hook(self, *args): return None end_of_iteration_hook = None end_of_testing_hook = None def get_record_keeper(csv_folder, tensorboard_folder, global_db_path=None, experiment_name=None, is_new_experiment=True, save_figures=False, save_lists=False): try: import record_keeper as record_keeper_package from torch.utils.tensorboard import SummaryWriter record_writer = record_keeper_package.RecordWriter(folder = csv_folder, global_db_path = global_db_path, experiment_name = experiment_name, is_new_experiment = is_new_experiment, save_lists = save_lists) tensorboard_writer = SummaryWriter(log_dir=tensorboard_folder) record_keeper = record_keeper_package.RecordKeeper(tensorboard_writer = tensorboard_writer, record_writer = record_writer, attributes_to_search_for = c_f.list_of_recordable_attributes_list_names(), save_figures=save_figures) return record_keeper, record_writer, tensorboard_writer except ModuleNotFoundError as e: logging.warn(e) logging.warn("There won't be any logging or model saving.") logging.warn("To fix this, pip install record-keeper tensorboard") return None, None, None def get_hook_container(record_keeper, **kwargs): if record_keeper: return HookContainer(record_keeper, **kwargs) else: logging.warn("No record_keeper, so no preset hooks are being returned.") return EmptyContainer()

52.328467

160

0.625122

import logging from . import common_functions as c_f import os import torch from collections import defaultdict import sqlite3 # You'll need to install record-keeper and tensorboard. class HookContainer: def __init__(self, record_keeper, record_group_name_prefix=None, primary_metric="mean_average_precision_at_r", validation_split_name="val"): self.record_keeper = record_keeper self.record_group_name_prefix = record_group_name_prefix self.saveable_trainer_objects = ["models", "optimizers", "lr_schedulers", "loss_funcs", "mining_funcs"] self.primary_metric = primary_metric self.validation_split_name = validation_split_name

true

f70f96f0024b1699103a63385d86facba9fae422

9,937

py

Python

xfer/contrib/xfer_leap/synthetic_data.py

apaleyes/xfer

99cd83424bc7e76a2c2def9d5b1dacd06f6e9eb5

[ "Apache-2.0" ]

null

xfer/contrib/xfer_leap/synthetic_data.py

apaleyes/xfer

99cd83424bc7e76a2c2def9d5b1dacd06f6e9eb5

[ "Apache-2.0" ]

null

xfer/contrib/xfer_leap/synthetic_data.py

apaleyes/xfer

99cd83424bc7e76a2c2def9d5b1dacd06f6e9eb5

[ "Apache-2.0" ]

null

# Copyright 2018 Amazon.com, Inc. or its affiliates. All Rights Reserved. # # Licensed under the Apache License, Version 2.0 (the "License"). # You may not use this file except in compliance with the License. # A copy of the License is located at # # http://www.apache.org/licenses/LICENSE-2.0 # # or in the "license" file accompanying this file. This file is distributed # on an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either # express or implied. See the License for the specific language governing # permissions and limitations under the License. # ============================================================================== import os import random import numpy as np import matplotlib.pyplot as plt from mxnet.gluon.data import ArrayDataset import mxnet from .data import MetaTaskDataContainer, TaskDataContainer from .config import DEFAULT_CONFIG_SYNTHETIC class MetaTaskSynthetic(MetaTaskDataContainer): def __init__(self, config=None, weights=None, bias=None, seed=1, context=None): """ :param config: If None, DEFAULT_CONFIG_SYNTHETIC is loaded. :param weights: Tasks' weights matrix. Row k corresponds to the weight parameters of task k. If None, w is sampled from a N(0,1). :param bias: Tasks' biases vector. Row k corresponds to the bias parameters of task k. If None, w is sampled from a N(0,1). :param seed: Seed for random generator. """ if config is None: config = DEFAULT_CONFIG_SYNTHETIC self.config = config self.weights = weights self.bias = bias if context is None: context = mxnet.cpu() self.context = context self.seed = seed random.seed(self.seed) num_tasks_train = config["num_tasks_train"] num_tasks_test = config["num_tasks_test"] num_tasks_val = config["num_tasks_val"] num_tasks = num_tasks_train + num_tasks_test + num_tasks_val self.num_tasks = num_tasks self._generate_parameters() self._validate_parameters() num_examples = config["num_examples_per_task"] std_x = config["std_x"] hold_out = config["hold_out"] noise = config["std_noise"] # Generate the training/test/val dataset. # Each dataset is a list of TaskSynthetic objects (one per task) data_train = [TaskSynthetic(self.weights[t, :], self.bias[t], num_examples, std_x, noise, hold_out, context=context) for t in np.arange(0, num_tasks_train)] data_test = [TaskSynthetic(self.weights[t, :], self.bias[t], num_examples, std_x, noise, hold_out, context=context) for t in np.arange(num_tasks_train, num_tasks_train + num_tasks_test)] data_val = [TaskSynthetic(self.weights[t, :], self.bias[t], num_examples, std_x, noise, hold_out, context=context) for t in np.arange(num_tasks_train + num_tasks_test, num_tasks)] super().__init__(data_train, data_test, data_val, context=context) def plot_sample(self, root="./sample_synth"): """Plot N images from each alphabet and store the images in root.""" if self.weights.shape[1] != 2: raise ValueError("Only 2D datasets can be plot.") if not os.path.exists(root): os.makedirs(root) fig_train = self._plot([dd._train_dataset for dd in self.train_tasks], "Training Samples for Training Tasks") fig_train.savefig(os.path.join(root, "sample_train_train_tasks.png")) del fig_train fig_test = self._plot([dd._train_dataset for dd in self.test_tasks], "Training Samples for Test Tasks") fig_test.savefig(os.path.join(root, "sample_train_test_tasks.png")) del fig_test fig_val = self._plot([dd._train_dataset for dd in self.val_tasks], "Training Samples for Validation Tasks") fig_val.savefig(os.path.join(root, "sample_train_val_tasks.png")) del fig_val if self.config["hold_out"] > 0: fig_train = self._plot([dd._val_dataset for dd in self.train_tasks], "Validation Samples for Training Tasks") fig_train.savefig(os.path.join(root, "sample_val_train_tasks.png")) del fig_train fig_test = self._plot([dd._val_dataset for dd in self.test_tasks], "Validation Samples for Test Tasks") fig_test.savefig(os.path.join(root, "sample_val_test_tasks.png")) del fig_test fig_val = self._plot([dd._val_dataset for dd in self.val_tasks], "Validation Samples for Validation Tasks") fig_val.savefig(os.path.join(root, "sample_val_val_tasks.png")) del fig_val def _plot(self, data, title): """Helper function for plotting.""" num_tasks = len(data) fig, ax = plt.subplots(1, num_tasks, figsize=(num_tasks*5, 5)) fig.subplots_adjust(left=None, bottom=None, right=None, top=None, wspace=0.5, hspace=0.5) for mm in range(num_tasks): X, y = data[mm][:] X = X.asnumpy() y = y.asnumpy() ax[mm].scatter(X[:, 0], X[:, 1], c=y.flatten()) fig.suptitle(title, size=18) return fig def _validate_parameters(self): if self.weights.shape[0] != self.num_tasks: raise ValueError("Number of rows in w must be equal to the total number of tasks") if len(self.bias) != self.num_tasks: raise ValueError("Length of b must be equal to the total number of tasks") def _generate_parameters(self): if self.weights is None: dim = self.config["dim"] self.weights = self.config["global_bias"] + mxnet.nd.random_normal(shape=(self.num_tasks, dim), ctx=self.context) if self.bias is None: if self.config["task_bias"]: self.bias = mxnet.nd.random_normal(shape=self.num_tasks, ctx=self.context) else: self.bias = mxnet.nd.zeros(num_tasks, ctx=self.context) class TaskSynthetic(TaskDataContainer): """ Synthetic Task Container: Linear Regression. """ def __init__(self, w, b, num_examples, std_x, noise, hold_out=None, seed=None, context=None): """ :param w: Task's weights vector. :param b: Task's bias. :param num_examples: Total number of examples per task. :param std_x: The covariates are sampled from a zero mean normal distribution with standard deviation equal to std_x. :param hold_out: Number of examples to hold out for validation :param seed: seed for the random generator """ self.w = w self.b = b self.num_examples = num_examples self.seed = seed if context is None: context = mxnet.cpu() self.context = context if seed: random.seed(seed) if hold_out and hold_out < num_examples: Xtr, Ytr = self._real_fn(std_x * mxnet.nd.random_normal(shape=(num_examples - hold_out, len(w)), ctx=context), noise) train_dataset = ArrayDataset(Xtr, Ytr) Xval, Yval = self._real_fn(std_x * mxnet.nd.random_normal(shape=(hold_out, len(w)), ctx=context), noise) val_dataset = ArrayDataset(Xval, Yval) else: Xtr, Ytr = self._real_fn(std_x * mxnet.nd.random_normal(shape=(num_examples, len(w)), ctx=context), noise) train_dataset = ArrayDataset(Xtr, Ytr) val_dataset = None super().__init__(train_dataset, val_dataset, context=context) def _real_fn(self, X, noise): y = mxnet.nd.dot(X, mxnet.nd.expand_dims(self.w, axis=1)) + self.b if noise > 0.0: y += mxnet.nd.expand_dims(noise * mxnet.nd.random_normal(shape=(X.shape[0],)), axis=1) return X, y if __name__ == '__main__': s1 = MetaTaskSynthetic() s1.plot_sample() batch_size = 20 train_tasks = s1.train_tasks assert len(s1.train_tasks) == 3 for task in train_tasks: tr_iterator = task.get_train_iterator(batch_size) for data in tr_iterator: assert (data[0].shape == (batch_size, 2)) assert (data[1].shape == (batch_size, 1)) assert (data[1].asnumpy().dtype == np.float32) break val_iterator = task.get_val_iterator(batch_size) for data in val_iterator: assert (data[0].shape == (batch_size, 2)) assert (data[1].shape == (batch_size, 1)) assert (data[1].asnumpy().dtype == np.float32) break dim = 2 num_tasks = 15 w = mxnet.nd.random_normal(shape=(num_tasks, dim)) b = mxnet.nd.random_normal(shape=num_tasks) s2 = MetaTaskSynthetic(weights=w, bias=b) s2.plot_sample(root="./sample_synth_w_b_given") batch_size = 20 train_tasks = s2.train_tasks assert len(train_tasks) == 3 for task in train_tasks: tr_iterator = task.get_train_iterator(batch_size) for data in tr_iterator: assert (data[0].shape == (batch_size, 2)) assert (data[1].shape == (batch_size, 1)) assert (data[1].asnumpy().dtype == np.float32) break val_iterator = task.get_val_iterator(batch_size) for data in val_iterator: assert (data[0].shape == (batch_size, 2)) assert (data[1].shape == (batch_size, 1)) assert (data[1].asnumpy().dtype == np.float32) break

39.748

118

0.603301

import os import random import numpy as np import matplotlib.pyplot as plt from mxnet.gluon.data import ArrayDataset import mxnet from .data import MetaTaskDataContainer, TaskDataContainer from .config import DEFAULT_CONFIG_SYNTHETIC class MetaTaskSynthetic(MetaTaskDataContainer): def __init__(self, config=None, weights=None, bias=None, seed=1, context=None): if config is None: config = DEFAULT_CONFIG_SYNTHETIC self.config = config self.weights = weights self.bias = bias if context is None: context = mxnet.cpu() self.context = context self.seed = seed random.seed(self.seed) num_tasks_train = config["num_tasks_train"] num_tasks_test = config["num_tasks_test"] num_tasks_val = config["num_tasks_val"] num_tasks = num_tasks_train + num_tasks_test + num_tasks_val self.num_tasks = num_tasks self._generate_parameters() self._validate_parameters() num_examples = config["num_examples_per_task"] std_x = config["std_x"] hold_out = config["hold_out"] noise = config["std_noise"] data_train = [TaskSynthetic(self.weights[t, :], self.bias[t], num_examples, std_x, noise, hold_out, context=context) for t in np.arange(0, num_tasks_train)] data_test = [TaskSynthetic(self.weights[t, :], self.bias[t], num_examples, std_x, noise, hold_out, context=context) for t in np.arange(num_tasks_train, num_tasks_train + num_tasks_test)] data_val = [TaskSynthetic(self.weights[t, :], self.bias[t], num_examples, std_x, noise, hold_out, context=context) for t in np.arange(num_tasks_train + num_tasks_test, num_tasks)] super().__init__(data_train, data_test, data_val, context=context) def plot_sample(self, root="./sample_synth"): if self.weights.shape[1] != 2: raise ValueError("Only 2D datasets can be plot.") if not os.path.exists(root): os.makedirs(root) fig_train = self._plot([dd._train_dataset for dd in self.train_tasks], "Training Samples for Training Tasks") fig_train.savefig(os.path.join(root, "sample_train_train_tasks.png")) del fig_train fig_test = self._plot([dd._train_dataset for dd in self.test_tasks], "Training Samples for Test Tasks") fig_test.savefig(os.path.join(root, "sample_train_test_tasks.png")) del fig_test fig_val = self._plot([dd._train_dataset for dd in self.val_tasks], "Training Samples for Validation Tasks") fig_val.savefig(os.path.join(root, "sample_train_val_tasks.png")) del fig_val if self.config["hold_out"] > 0: fig_train = self._plot([dd._val_dataset for dd in self.train_tasks], "Validation Samples for Training Tasks") fig_train.savefig(os.path.join(root, "sample_val_train_tasks.png")) del fig_train fig_test = self._plot([dd._val_dataset for dd in self.test_tasks], "Validation Samples for Test Tasks") fig_test.savefig(os.path.join(root, "sample_val_test_tasks.png")) del fig_test fig_val = self._plot([dd._val_dataset for dd in self.val_tasks], "Validation Samples for Validation Tasks") fig_val.savefig(os.path.join(root, "sample_val_val_tasks.png")) del fig_val def _plot(self, data, title): num_tasks = len(data) fig, ax = plt.subplots(1, num_tasks, figsize=(num_tasks*5, 5)) fig.subplots_adjust(left=None, bottom=None, right=None, top=None, wspace=0.5, hspace=0.5) for mm in range(num_tasks): X, y = data[mm][:] X = X.asnumpy() y = y.asnumpy() ax[mm].scatter(X[:, 0], X[:, 1], c=y.flatten()) fig.suptitle(title, size=18) return fig def _validate_parameters(self): if self.weights.shape[0] != self.num_tasks: raise ValueError("Number of rows in w must be equal to the total number of tasks") if len(self.bias) != self.num_tasks: raise ValueError("Length of b must be equal to the total number of tasks") def _generate_parameters(self): if self.weights is None: dim = self.config["dim"] self.weights = self.config["global_bias"] + mxnet.nd.random_normal(shape=(self.num_tasks, dim), ctx=self.context) if self.bias is None: if self.config["task_bias"]: self.bias = mxnet.nd.random_normal(shape=self.num_tasks, ctx=self.context) else: self.bias = mxnet.nd.zeros(num_tasks, ctx=self.context) class TaskSynthetic(TaskDataContainer): def __init__(self, w, b, num_examples, std_x, noise, hold_out=None, seed=None, context=None): self.w = w self.b = b self.num_examples = num_examples self.seed = seed if context is None: context = mxnet.cpu() self.context = context if seed: random.seed(seed) if hold_out and hold_out < num_examples: Xtr, Ytr = self._real_fn(std_x * mxnet.nd.random_normal(shape=(num_examples - hold_out, len(w)), ctx=context), noise) train_dataset = ArrayDataset(Xtr, Ytr) Xval, Yval = self._real_fn(std_x * mxnet.nd.random_normal(shape=(hold_out, len(w)), ctx=context), noise) val_dataset = ArrayDataset(Xval, Yval) else: Xtr, Ytr = self._real_fn(std_x * mxnet.nd.random_normal(shape=(num_examples, len(w)), ctx=context), noise) train_dataset = ArrayDataset(Xtr, Ytr) val_dataset = None super().__init__(train_dataset, val_dataset, context=context) def _real_fn(self, X, noise): y = mxnet.nd.dot(X, mxnet.nd.expand_dims(self.w, axis=1)) + self.b if noise > 0.0: y += mxnet.nd.expand_dims(noise * mxnet.nd.random_normal(shape=(X.shape[0],)), axis=1) return X, y if __name__ == '__main__': s1 = MetaTaskSynthetic() s1.plot_sample() batch_size = 20 train_tasks = s1.train_tasks assert len(s1.train_tasks) == 3 for task in train_tasks: tr_iterator = task.get_train_iterator(batch_size) for data in tr_iterator: assert (data[0].shape == (batch_size, 2)) assert (data[1].shape == (batch_size, 1)) assert (data[1].asnumpy().dtype == np.float32) break val_iterator = task.get_val_iterator(batch_size) for data in val_iterator: assert (data[0].shape == (batch_size, 2)) assert (data[1].shape == (batch_size, 1)) assert (data[1].asnumpy().dtype == np.float32) break dim = 2 num_tasks = 15 w = mxnet.nd.random_normal(shape=(num_tasks, dim)) b = mxnet.nd.random_normal(shape=num_tasks) s2 = MetaTaskSynthetic(weights=w, bias=b) s2.plot_sample(root="./sample_synth_w_b_given") batch_size = 20 train_tasks = s2.train_tasks assert len(train_tasks) == 3 for task in train_tasks: tr_iterator = task.get_train_iterator(batch_size) for data in tr_iterator: assert (data[0].shape == (batch_size, 2)) assert (data[1].shape == (batch_size, 1)) assert (data[1].asnumpy().dtype == np.float32) break val_iterator = task.get_val_iterator(batch_size) for data in val_iterator: assert (data[0].shape == (batch_size, 2)) assert (data[1].shape == (batch_size, 1)) assert (data[1].asnumpy().dtype == np.float32) break

true

f70f977c652da15ff9173d2b3951c11786d65299

6,282

py

Python

solution/10. regular-expression-match.py

sundaycat/Leetcode-Practice

65c3ab0f967331a095fd8a6eb2f3d7765cbf7d5a

[ "MIT" ]

null

solution/10. regular-expression-match.py

sundaycat/Leetcode-Practice

65c3ab0f967331a095fd8a6eb2f3d7765cbf7d5a

[ "MIT" ]

null

solution/10. regular-expression-match.py

sundaycat/Leetcode-Practice

65c3ab0f967331a095fd8a6eb2f3d7765cbf7d5a

[ "MIT" ]

null

''' A: suffix solution 1. subproblems: define dp(i, j) = is_match(s[i:], p[j:]), suffix 2. guess, 2.1 the current char in p is a '*' - use '*', repeat the char before it - do not use '*', skip to next char after '*' 2.2 current char in s and p are match, s[i] == p[j] or p[j] == '.' 3. relate subproblems: dp(i, j) = match(s[i:], s[j:]) dp(i, j) = a. if j + 1 is in bound and p[j + 1] == '*', then dp(i, j + 2) or (s[i] = p[j] or p[j] = '.' and dp(i + 1, j)) b. if s[i] == p[j] or p[j] == '.', then dp(i + 1, j + 1) c. esle false B: prefix solution 1. subproblems: define dp(i, j) = is_match(s[:i], p[:j]), prefix 2. guess, 2.1 current char in s and p are match, s[i] == p[j] or p[j] == '.' 2.2 the current char in p is a '*' - use '*', repeat the char before it - do not use '*', skip to next char after '*' 3. relate subproblems: dp(i, j) = match(s[:i], s[:j]) dp(i, j) = a. if s[i] == p[j] or p[j] == '.', then dp(i - 1, j - 1) b. if p[j] == '*', then dp(i, j - 2) or (s[i] = p[j - 1] or p[j - 1] = '.' and dp(i - 1, j)) c. else false reference: 1. https://www.youtube.com/watch?v=HAA8mgxlov8 (use * or no use) 2. https://www.youtube.com/watch?v=l3hda49XcDE (dp solution) ''' class Solution: def isMatch(self, s: str, p: str) -> bool: # Somtimes there still matches even s is out of bound, but p is still in bound(s:a, p: a*b*). # But if p is out of bound, then we must return false # return self.dfs_td(s, p, 0, 0, {}) # return self.dfs_prefix(s, p, len(s) - 1, len(p) - 1) # return self.dp_bottome_up_prefix(s, p) return self.dp_bottom_up_suffix(s, p) # top down, dfs + memorization, suffix def dfs_suffix(self, s, p, i, j, memo): # base case # if both i and j are out of boud, then we found our solution if (i, j) in memo: return memo[(i, j)] if i >= len(s) and j >= len(p): return True # if i is in bound, but j is out of bound, return false. if j >= len(p): return False # 注意括号的顺序, 在i没有超出数组下标的范围的情况下, 判断是否有匹配 match = i < len(s) and (s[i] == p[j] or p[j] == '.') # if the next character in p is a star(need to prevent the j + 1 go byond the bounday) if j + 1 < len(p) and p[j + 1] == '*': # either repeating the current character in p and move to the next character in s # or no repeating in p and skip to next character in p memo[(i, j)] = (match and self.dfs_td(s, p, i + 1, j, memo)) or self.dfs_td(s, p, i, j + 2, memo) return memo[(i, j)] # if it is not a star but a match found in the current index of s and p if match: memo[(i, j)] = self.dfs_td(s, p, i + 1, j + 1, memo) return memo[(i, j)] # if no a match and next character is not star memo[(i, j)] = False return False # bottom up solution, suffix. def dp_bottom_up_suffix(self, s, p): s_len = len(s) p_len = len(p) dp = [[False for col in range(p_len + 1)] for row in range(s_len + 1)] dp[s_len][p_len] = True # deal with the case like a*b*c* for the last row for j in range(p_len - 2, -1, -1): if p[j + 1] == '*': dp[s_len][j] = dp[s_len][j + 2] for i in range(s_len - 1, -1, -1): for j in range(p_len - 1, -1, -1): # for suffix, checking '*' goes first. if j <= p_len - 2 and p[j + 1] == '*': if s[i] == p[j] or p[j] == '.': dp[i][j] = dp[i + 1][j] dp[i][j] = (dp[i][j] or dp[i][j + 2]) continue if s[i] == p[j] or p[j] == '.': dp[i][j] = dp[i + 1][j + 1] for i in dp: print(i) print() return dp[0][0] # top down solution, start at (n, n) def dfs_prefix(self, s, p, i, j): # base case if i < 0 and j < 0: return True # if i is in bound, but j is out of bound, return false. if j < 0: return False # if the current char is a star if j >= 0 and p[j] == '*': # check if there is a match of the current char in s and previous char in p(before *) match = (i >= 0) and (s[i] == p[j - 1] or p[j - 1] == '.') # if current charts match, then go dp(i-1, j), if no match, go check dp(i, j-2) return (match and self.dfs_prefix(s, p, i - 1, j)) or self.dfs_prefix(s, p, i, j - 2) # if there is a match of the current char in s and p if i >= 0 and (s[i] == p[j] or p[j] == '.'): return self.dfs_prefix(s, p, i - 1, j - 1) return False # bottom up algorithm, start from dp(0,0) -> dp(n, n) def dp_bottome_up_prefix(self, s, p): s_len, p_len = len(s), len(p) dp = [[False for col in range(p_len + 1)] for row in range(s_len + 1)] dp[0][0] = True # handle the pattern like a*, a*b* or a*b*c* for the 0th row for j in range(1, p_len + 1): if p[j - 1] == '*': dp[0][j] = dp[0][j - 2] for i in range(1, s_len + 1): for j in range(1, p_len + 1): if s[i - 1] == p[j - 1] or p[j - 1] == '.': dp[i][j] = dp[i - 1][j - 1] continue if p[j - 1] == '*': if s[i - 1] == p[j - 2] or p[j - 2] == '.': dp[i][j] = dp[i - 1][j] dp[i][j] = (dp[i][j] or dp[i][j - 2]) for i in dp: print(i) print() return dp[s_len][p_len] s = 'aab' p = 'c*a*b' # s = 'aaa' # p = 'aaaa' # s = "a" # p = ".*..a*" s = 'aa' p = 'a*' sol = Solution() print(sol.isMatch(s, p)) x = 'abc' print(x[1:1])

32.381443

109

0.446832

class Solution: def isMatch(self, s: str, p: str) -> bool: return self.dp_bottom_up_suffix(s, p) def dfs_suffix(self, s, p, i, j, memo): if (i, j) in memo: return memo[(i, j)] if i >= len(s) and j >= len(p): return True if j >= len(p): return False match = i < len(s) and (s[i] == p[j] or p[j] == '.') if j + 1 < len(p) and p[j + 1] == '*': memo[(i, j)] = (match and self.dfs_td(s, p, i + 1, j, memo)) or self.dfs_td(s, p, i, j + 2, memo) return memo[(i, j)] if match: memo[(i, j)] = self.dfs_td(s, p, i + 1, j + 1, memo) return memo[(i, j)] memo[(i, j)] = False return False def dp_bottom_up_suffix(self, s, p): s_len = len(s) p_len = len(p) dp = [[False for col in range(p_len + 1)] for row in range(s_len + 1)] dp[s_len][p_len] = True for j in range(p_len - 2, -1, -1): if p[j + 1] == '*': dp[s_len][j] = dp[s_len][j + 2] for i in range(s_len - 1, -1, -1): for j in range(p_len - 1, -1, -1): if j <= p_len - 2 and p[j + 1] == '*': if s[i] == p[j] or p[j] == '.': dp[i][j] = dp[i + 1][j] dp[i][j] = (dp[i][j] or dp[i][j + 2]) continue if s[i] == p[j] or p[j] == '.': dp[i][j] = dp[i + 1][j + 1] for i in dp: print(i) print() return dp[0][0] def dfs_prefix(self, s, p, i, j): if i < 0 and j < 0: return True if j < 0: return False if j >= 0 and p[j] == '*': match = (i >= 0) and (s[i] == p[j - 1] or p[j - 1] == '.') return (match and self.dfs_prefix(s, p, i - 1, j)) or self.dfs_prefix(s, p, i, j - 2) if i >= 0 and (s[i] == p[j] or p[j] == '.'): return self.dfs_prefix(s, p, i - 1, j - 1) return False def dp_bottome_up_prefix(self, s, p): s_len, p_len = len(s), len(p) dp = [[False for col in range(p_len + 1)] for row in range(s_len + 1)] dp[0][0] = True for j in range(1, p_len + 1): if p[j - 1] == '*': dp[0][j] = dp[0][j - 2] for i in range(1, s_len + 1): for j in range(1, p_len + 1): if s[i - 1] == p[j - 1] or p[j - 1] == '.': dp[i][j] = dp[i - 1][j - 1] continue if p[j - 1] == '*': if s[i - 1] == p[j - 2] or p[j - 2] == '.': dp[i][j] = dp[i - 1][j] dp[i][j] = (dp[i][j] or dp[i][j - 2]) for i in dp: print(i) print() return dp[s_len][p_len] s = 'aab' p = 'c*a*b' s = 'aa' p = 'a*' sol = Solution() print(sol.isMatch(s, p)) x = 'abc' print(x[1:1])

true

f70f988e2189afa8f9091db3302eee4536752431

5,172

py

Python

opentamp/src/policy_hooks/vae/run_training.py

Algorithmic-Alignment-Lab/openTAMP-legacy

3b7c3be164cc968ad77a928286d6460cd70a670e

[ "MIT" ]

2

2022-03-09T19:48:20.000Z

2022-03-26T17:31:07.000Z

opentamp/src/policy_hooks/vae/run_training.py

Algorithmic-Alignment-Lab/OpenTAMP

eecb950bd273da8cbed4394487630e8453f2c242

[ "MIT" ]

null

opentamp/src/policy_hooks/vae/run_training.py

Algorithmic-Alignment-Lab/OpenTAMP

eecb950bd273da8cbed4394487630e8453f2c242

[ "MIT" ]

null

import argparse import imp import importlib import random from opentamp.src.policy_hooks.vae.vae_main import MultiProcessMain def load_config(args, reload_module=None): config_file = args.config if config_file != '': if reload_module is not None: config_module = reload_module imp.reload(config_module) else: config_module = importlib.import_module('policy_hooks.'+config_file) config = config_module.config else: config_module = None config = {} config['use_local'] = not args.remote config['num_conds'] = args.nconds if args.nconds > 0 else config['num_conds'] if 'num_conds' in config else 1 if 'common' in config: config['common']['num_conds'] = config['num_conds'] config['num_objs'] = args.nobjs if args.nobjs > 0 else config['num_objs'] if 'num_objs' in config else 1 config['weight_dir'] = config['base_weight_dir'] + str(config['num_objs']) if 'base_weight_dir' in config else args.weight_dir config['log_timing'] = args.timing config['hl_timeout'] = 0 config['rollout_server'] = args.rollout_server or args.all_servers config['vae_server'] = args.vae_server or args.all_servers config['viewer'] = args.viewer config['server_id'] = args.server_id if args.server_id != '' else str(random.randint(0,2**32)) config['n_rollout_servers'] = args.n_rollout_servers config['no_child_process'] = args.no_child_process config['rollout_len'] = args.rollout_len config['train_vae'] = args.train_vae config['unconditional'] = args.unconditional config['train_reward'] = args.train_reward config['load_step'] = args.load_step config['train_params'] = { 'use_recurrent_dynamics': args.use_recurrent_dynamics, 'use_overshooting': args.use_overshooting, 'data_limit': args.train_samples if args.train_samples > 0 else None, 'beta': args.beta, 'overshoot_beta': args.overshoot_beta, 'dist_constraint': args.dist_constraint, } return config, config_module def load_env(args, reload_module=None): env_path = args.environment_path if reload_module is not None: module = reload_module imp.reload(module) else: module = importlib.import_module(env_path) env = args.environment return getattr(module, env) def main(): parser = argparse.ArgumentParser() parser.add_argument('-c', '--config', type=str, default='') parser.add_argument('-wd', '--weight_dir', type=str, default='') parser.add_argument('-nf', '--nofull', action='store_true', default=False) parser.add_argument('-n', '--nconds', type=int, default=0) parser.add_argument('-o', '--nobjs', type=int, default=0) # parser.add_argument('-ptt', '--pretrain_timeout', type=int, default=300) parser.add_argument('-hlt', '--hl_timeout', type=int, default=0) parser.add_argument('-k', '--killall', action='store_true', default=True) parser.add_argument('-r', '--remote', action='store_true', default=False) parser.add_argument('-t', '--timing', action='store_true', default=False) parser.add_argument('-vae', '--vae_server', action='store_true', default=False) parser.add_argument('-sim', '--rollout_server', action='store_true', default=False) parser.add_argument('-all', '--all_servers', action='store_true', default=False) parser.add_argument('-v', '--viewer', action='store_true', default=False) parser.add_argument('-id', '--server_id', type=str, default='') parser.add_argument('-env_path', '--environment_path', type=str, default='') parser.add_argument('-env', '--environment', type=str, default='') parser.add_argument('-tamp', '--use_tamp', type=str, default='') parser.add_argument('-nrs', '--n_rollout_servers', type=int, default=1) parser.add_argument('-ncp', '--no_child_process', action='store_true', default=False) parser.add_argument('-rl', '--rollout_len', type=int, default=0) parser.add_argument('-tv', '--train_vae', action='store_true', default=False) parser.add_argument('-uncond', '--unconditional', action='store_true', default=False) parser.add_argument('-tr', '--train_reward', action='store_true', default=False) parser.add_argument('-loadstep', '--load_step', type=int, default=-1) parser.add_argument('-beta', '--beta', type=int, default=1) parser.add_argument('-beta_d', '--overshoot_beta', type=int, default=1) parser.add_argument('-nts', '--train_samples', type=int, default=-1) parser.add_argument('-rnn', '--use_recurrent_dynamics', action='store_true', default=False) parser.add_argument('-over', '--use_overshooting', action='store_true', default=False) parser.add_argument('-dist', '--dist_constraint', action='store_true', default=False) args = parser.parse_args() config, config_module = load_config(args) if args.config != '': main = MultiProcessMain(config) else: env_cls = load_env(args) main = MultiProcessMain.no_config_load(env_cls, args.environment, config) main.start(kill_all=args.killall) if __name__ == '__main__': main()

45.769912

130

0.683875

import argparse import imp import importlib import random from opentamp.src.policy_hooks.vae.vae_main import MultiProcessMain def load_config(args, reload_module=None): config_file = args.config if config_file != '': if reload_module is not None: config_module = reload_module imp.reload(config_module) else: config_module = importlib.import_module('policy_hooks.'+config_file) config = config_module.config else: config_module = None config = {} config['use_local'] = not args.remote config['num_conds'] = args.nconds if args.nconds > 0 else config['num_conds'] if 'num_conds' in config else 1 if 'common' in config: config['common']['num_conds'] = config['num_conds'] config['num_objs'] = args.nobjs if args.nobjs > 0 else config['num_objs'] if 'num_objs' in config else 1 config['weight_dir'] = config['base_weight_dir'] + str(config['num_objs']) if 'base_weight_dir' in config else args.weight_dir config['log_timing'] = args.timing config['hl_timeout'] = 0 config['rollout_server'] = args.rollout_server or args.all_servers config['vae_server'] = args.vae_server or args.all_servers config['viewer'] = args.viewer config['server_id'] = args.server_id if args.server_id != '' else str(random.randint(0,2**32)) config['n_rollout_servers'] = args.n_rollout_servers config['no_child_process'] = args.no_child_process config['rollout_len'] = args.rollout_len config['train_vae'] = args.train_vae config['unconditional'] = args.unconditional config['train_reward'] = args.train_reward config['load_step'] = args.load_step config['train_params'] = { 'use_recurrent_dynamics': args.use_recurrent_dynamics, 'use_overshooting': args.use_overshooting, 'data_limit': args.train_samples if args.train_samples > 0 else None, 'beta': args.beta, 'overshoot_beta': args.overshoot_beta, 'dist_constraint': args.dist_constraint, } return config, config_module def load_env(args, reload_module=None): env_path = args.environment_path if reload_module is not None: module = reload_module imp.reload(module) else: module = importlib.import_module(env_path) env = args.environment return getattr(module, env) def main(): parser = argparse.ArgumentParser() parser.add_argument('-c', '--config', type=str, default='') parser.add_argument('-wd', '--weight_dir', type=str, default='') parser.add_argument('-nf', '--nofull', action='store_true', default=False) parser.add_argument('-n', '--nconds', type=int, default=0) parser.add_argument('-o', '--nobjs', type=int, default=0) parser.add_argument('-hlt', '--hl_timeout', type=int, default=0) parser.add_argument('-k', '--killall', action='store_true', default=True) parser.add_argument('-r', '--remote', action='store_true', default=False) parser.add_argument('-t', '--timing', action='store_true', default=False) parser.add_argument('-vae', '--vae_server', action='store_true', default=False) parser.add_argument('-sim', '--rollout_server', action='store_true', default=False) parser.add_argument('-all', '--all_servers', action='store_true', default=False) parser.add_argument('-v', '--viewer', action='store_true', default=False) parser.add_argument('-id', '--server_id', type=str, default='') parser.add_argument('-env_path', '--environment_path', type=str, default='') parser.add_argument('-env', '--environment', type=str, default='') parser.add_argument('-tamp', '--use_tamp', type=str, default='') parser.add_argument('-nrs', '--n_rollout_servers', type=int, default=1) parser.add_argument('-ncp', '--no_child_process', action='store_true', default=False) parser.add_argument('-rl', '--rollout_len', type=int, default=0) parser.add_argument('-tv', '--train_vae', action='store_true', default=False) parser.add_argument('-uncond', '--unconditional', action='store_true', default=False) parser.add_argument('-tr', '--train_reward', action='store_true', default=False) parser.add_argument('-loadstep', '--load_step', type=int, default=-1) parser.add_argument('-beta', '--beta', type=int, default=1) parser.add_argument('-beta_d', '--overshoot_beta', type=int, default=1) parser.add_argument('-nts', '--train_samples', type=int, default=-1) parser.add_argument('-rnn', '--use_recurrent_dynamics', action='store_true', default=False) parser.add_argument('-over', '--use_overshooting', action='store_true', default=False) parser.add_argument('-dist', '--dist_constraint', action='store_true', default=False) args = parser.parse_args() config, config_module = load_config(args) if args.config != '': main = MultiProcessMain(config) else: env_cls = load_env(args) main = MultiProcessMain.no_config_load(env_cls, args.environment, config) main.start(kill_all=args.killall) if __name__ == '__main__': main()

true

f70f9b444405e0c9fb473ff45e8dc0b8422e10c7

2,325

py

Python

tests/test_action_tag_category_create.py

lingfish/stackstorm-vsphere

49199f5ebdc05b70b7504962e104642b0c30ba30

[ "Apache-2.0" ]

null

tests/test_action_tag_category_create.py

lingfish/stackstorm-vsphere

49199f5ebdc05b70b7504962e104642b0c30ba30

[ "Apache-2.0" ]

2

2019-03-25T18:03:02.000Z

2019-03-26T13:13:59.000Z

tests/test_action_tag_category_create.py

lingfish/stackstorm-vsphere

49199f5ebdc05b70b7504962e104642b0c30ba30

[ "Apache-2.0" ]

1

2021-03-05T10:12:21.000Z

# Licensed to the StackStorm, Inc ('StackStorm') under one or more # contributor license agreements. See the NOTICE file distributed with # this work for additional information regarding copyright ownership. # The ASF licenses this file to You under the Apache License, Version 2.0 # (the "License"); you may not use this file except in compliance with # the License. You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and import mock from tag_category_create import CategoryCreate from vsphere_base_action_test_case import VsphereBaseActionTestCase __all__ = [ 'CategoryCreateTestCase' ] class CategoryCreateTestCase(VsphereBaseActionTestCase): __test__ = True action_cls = CategoryCreate @mock.patch("vmwarelib.actions.BaseAction.connect_rest") def test_run(self, mock_connect): action = self.get_action_instance(self.new_config) # mock expected_result = "result" action.tagging = mock.Mock() action.tagging.category_create.return_value = expected_result # define test variables category_name = "name" category_description = "test description" category_cardinality = "SINGLE" category_types = [] vsphere = "default" test_kwargs = { "category_name": category_name, "category_description": category_description, "category_cardinality": category_cardinality, "category_types": category_types, "vsphere": vsphere } # invoke action with valid parameters result = action.run(**test_kwargs) self.assertEqual(result, expected_result) action.tagging.category_create.assert_called_with(category_name, category_description, category_cardinality, category_types) mock_connect.assert_called_with(vsphere)

38.114754

79

0.669247

import mock from tag_category_create import CategoryCreate from vsphere_base_action_test_case import VsphereBaseActionTestCase __all__ = [ 'CategoryCreateTestCase' ] class CategoryCreateTestCase(VsphereBaseActionTestCase): __test__ = True action_cls = CategoryCreate @mock.patch("vmwarelib.actions.BaseAction.connect_rest") def test_run(self, mock_connect): action = self.get_action_instance(self.new_config) expected_result = "result" action.tagging = mock.Mock() action.tagging.category_create.return_value = expected_result category_name = "name" category_description = "test description" category_cardinality = "SINGLE" category_types = [] vsphere = "default" test_kwargs = { "category_name": category_name, "category_description": category_description, "category_cardinality": category_cardinality, "category_types": category_types, "vsphere": vsphere } result = action.run(**test_kwargs) self.assertEqual(result, expected_result) action.tagging.category_create.assert_called_with(category_name, category_description, category_cardinality, category_types) mock_connect.assert_called_with(vsphere)

true

f70f9c881df168564cbf2431bbc2ebdf7e7f7ded

18,480

py

Python

tensorflow/contrib/data/python/ops/readers.py

idharmateja/tensorflow

4e108ef30d7cd7ae5e1c550ec5ae27e79b8c6e39

[ "Apache-2.0" ]

13

2018-07-23T18:53:35.000Z

2021-11-18T19:56:45.000Z

tensorflow/contrib/data/python/ops/readers.py

DandelionCN/tensorflow

1712002ad02f044f7569224bf465e0ea00e6a6c4

[ "Apache-2.0" ]

null

tensorflow/contrib/data/python/ops/readers.py

DandelionCN/tensorflow

1712002ad02f044f7569224bf465e0ea00e6a6c4

[ "Apache-2.0" ]

13

2018-09-07T13:28:38.000Z

2020-07-17T15:06:24.000Z

# Copyright 2017 The TensorFlow Authors. All Rights Reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. # ============================================================================== """Python wrappers for reader Datasets.""" from __future__ import absolute_import from __future__ import division from __future__ import print_function from tensorflow.contrib.data.python.ops import interleave_ops from tensorflow.contrib.data.python.ops import shuffle_ops from tensorflow.python.data.ops import dataset_ops from tensorflow.python.data.ops import readers as core_readers from tensorflow.python.data.util import nest from tensorflow.python.framework import constant_op from tensorflow.python.framework import dtypes from tensorflow.python.framework import ops from tensorflow.python.framework import tensor_shape from tensorflow.python.ops import gen_dataset_ops from tensorflow.python.ops import parsing_ops from tensorflow.python.platform import gfile from tensorflow.python.util import deprecation _ACCEPTABLE_CSV_TYPES = (dtypes.float32, dtypes.float64, dtypes.int32, dtypes.int64, dtypes.string) def make_csv_dataset( file_pattern, batch_size, column_keys, column_defaults, label_key=None, field_delim=",", use_quote_delim=True, skip=0, filter_fn=None, num_epochs=None, shuffle=True, shuffle_buffer_size=10000, shuffle_seed=None, prefetch_buffer_size=1, ): """Reads CSV files into a dataset. Reads CSV files into a dataset, where each element is a (features, labels) tuple that corresponds to a batch of CSV rows. The features dictionary maps feature column names to `Tensor`s containing the corresponding feature data, and labels is a `Tensor` containing the batch's label data. Args: file_pattern: List of files or patterns of file paths containing CSV records. See @{tf.gfile.Glob} for pattern rules. batch_size: An int representing the number of consecutive elements of this dataset to combine in a single batch. column_keys: A list of strings that corresponds to the CSV columns, in order. One per column of the input record. column_defaults: A list of default values for the CSV fields. One item per column of the input record. Each item in the list is either one of the following dtypes: float32, float64, int32, int64, or string, or a `Tensor` with one of the aforementioned types. One item per column of the input record, with either scalar default value for that column if it is required, or, if the column is required, an empty `Tensor` or a dtype. label_key: A optional string corresponding to the label column. If provided, the data for this column is returned as a separate `Tensor` from the features dictionary, so that the dataset complies with the format expected by a `tf.Estimator.train` or `tf.Estimator.evaluate` input function. field_delim: An optional `string`. Defaults to `","`. Char delimiter to separate fields in a record. use_quote_delim: An optional bool. Defaults to `True`. If false, treats double quotation marks as regular characters inside of the string fields. skip: An integer that corresponds to the number of lines to skip at the head of each CSV file. Defaults to 0. filter_fn: A callable function that takes in a CSV string and returns a boolean that corresponds to whether the record should be included. If None, does not filter records. num_epochs: An int specifying the number of times this dataset is repeated. If None, cycles through the dataset forever. shuffle: A bool that indicates whether the input should be shuffled. shuffle_buffer_size: Buffer size to use for shuffling. A large buffer size ensures better shuffling, but would increase memory usage and startup time. shuffle_seed: Randomization seed to use for shuffling. prefetch_buffer_size: An int specifying the number of feature batches to prefetch for performance improvement. Recommended value is the number of batches consumed per training step. Returns: A dataset, where each element is a (features, labels) tuple that corresponds to a batch of `batch_size` CSV rows. The features dictionary maps feature column names to `Tensor`s containing the corresponding column data, and labels is a `Tensor` containing the column data for the label column specified by `label_key`. """ filenames = _get_file_names(file_pattern, False) column_defaults = [ constant_op.constant([], dtype=x) if x in _ACCEPTABLE_CSV_TYPES else x for x in column_defaults ] dataset = dataset_ops.Dataset.from_tensor_slices(filenames) if label_key is not None: assert label_key in column_keys def filename_to_dataset(filename): ds = core_readers.TextLineDataset(filename) if skip > 0: ds = ds.skip(skip) if filter_fn is not None: ds = ds.filter(filter_fn) return ds def decode_csv(line): """Decodes csv line into features. Args: line: String tensor corresponding to one csv record. Returns: A dictionary of feature names to values for that particular record. If label_key is provided, extracts the label feature to be returned as the second element of the tuple. """ columns = parsing_ops.decode_csv( line, column_defaults, field_delim=field_delim, use_quote_delim=use_quote_delim) features = dict(zip(column_keys, columns)) if label_key is not None: label = features.pop(label_key) return features, label return features # TODO(rachelim): interleave records from files for better shuffling dataset = dataset.flat_map(filename_to_dataset) # TODO(rachelim): use fused shuffle_and_repeat for perf if shuffle: dataset = dataset.shuffle(shuffle_buffer_size, shuffle_seed) if num_epochs != 1: dataset = dataset.repeat(num_epochs) dataset = dataset.batch(batch_size) dataset = dataset.map(decode_csv) dataset = dataset.prefetch(prefetch_buffer_size) return dataset def make_batched_features_dataset(file_pattern, batch_size, features, reader=core_readers.TFRecordDataset, reader_args=None, num_epochs=None, shuffle=True, shuffle_buffer_size=10000, shuffle_seed=None, prefetch_buffer_size=1, reader_num_threads=1, parser_num_threads=2, sloppy_ordering=False): """Returns a `Dataset` of feature dictionaries from `Example` protos. Example: ``` serialized_examples = [ features { feature { key: "age" value { int64_list { value: [ 0 ] } } } feature { key: "gender" value { bytes_list { value: [ "f" ] } } } feature { key: "kws" value { bytes_list { value: [ "code", "art" ] } } } }, features { feature { key: "age" value { int64_list { value: [] } } } feature { key: "gender" value { bytes_list { value: [ "f" ] } } } feature { key: "kws" value { bytes_list { value: [ "sports" ] } } } } ] ``` We can use arguments: ``` features: { "age": FixedLenFeature([], dtype=tf.int64, default_value=-1), "gender": FixedLenFeature([], dtype=tf.string), "kws": VarLenFeature(dtype=tf.string), } ``` And the expected output is: ```python { "age": [[0], [-1]], "gender": [["f"], ["f"]], "kws": SparseTensor( indices=[[0, 0], [0, 1], [1, 0]], values=["code", "art", "sports"] dense_shape=[2, 2]), } ``` Args: file_pattern: List of files or patterns of file paths containing `Example` records. See `tf.gfile.Glob` for pattern rules. batch_size: An int representing the number of consecutive elements of this dataset to combine in a single batch. features: A `dict` mapping feature keys to `FixedLenFeature` or `VarLenFeature` values. See `tf.parse_example`. reader: A function or class that can be called with a `filenames` tensor and (optional) `reader_args` and returns a `Dataset` of `Example` tensors. Defaults to `tf.data.TFRecordDataset`. reader_args: Additional arguments to pass to the reader class. num_epochs: Integer specifying the number of times to read through the dataset. If None, cycles through the dataset forever. Defaults to `None`. shuffle: A boolean, indicates whether the input should be shuffled. Defaults to `True`. shuffle_buffer_size: Buffer size of the ShuffleDataset. A large capacity ensures better shuffling but would increase memory usage and startup time. shuffle_seed: Randomization seed to use for shuffling. prefetch_buffer_size: Number of feature batches to prefetch in order to improve performance. Recommended value is the number of batches consumed per training step (default is 1). reader_num_threads: Number of threads used to read `Example` records. If >1, the results will be interleaved. parser_num_threads: Number of threads to use for parsing `Example` tensors into a dictionary of `Feature` tensors. sloppy_ordering: If `True`, reading performance will be improved at the cost of non-deterministic ordering. If `False`, the order of elements produced is deterministic prior to shuffling (elements are still randomized if `shuffle=True`. Note that if the seed is set, then order of elements after shuffling is deterministic). Defaults to `False`. Returns: A dataset of `dict` elements. Each `dict` maps feature keys to `Tensor` or `SparseTensor` objects. """ # Create dataset of all matching filenames if shuffle: dataset = dataset_ops.Dataset.list_files(file_pattern, shuffle=True) else: # TODO(b/73959787): Use Dataset.list_files() once ordering is deterministic. filenames = _get_file_names(file_pattern, shuffle) dataset = dataset_ops.Dataset.from_tensor_slices(filenames) # Read `Example` records from files as tensor objects. if reader_args is None: reader_args = [] # Read files sequentially (if reader_num_threads=1) or in parallel dataset = dataset.apply( interleave_ops.parallel_interleave( lambda filename: reader(filename, *reader_args), cycle_length=reader_num_threads, sloppy=sloppy_ordering)) # Extract values if the `Example` tensors are stored as key-value tuples. if dataset.output_types == (dtypes.string, dtypes.string): dataset = dataset.map(lambda _, v: v) # Apply dataset repeat and shuffle transformations. repeat_dataset = (num_epochs != 1) if repeat_dataset and shuffle: # Used fused shuffle_and_repeat operation for better performance dataset = dataset.apply( shuffle_ops.shuffle_and_repeat(shuffle_buffer_size, num_epochs, shuffle_seed)) elif repeat_dataset: dataset = dataset.repeat(num_epochs) elif shuffle: dataset = dataset.shuffle(shuffle_buffer_size, shuffle_seed) dataset = dataset.batch(batch_size) # Parse `Example` tensors to a dictionary of `Feature` tensors. dataset = dataset.map( lambda x: parsing_ops.parse_example(x, features), num_parallel_calls=parser_num_threads) # TODO(rachelim): Add an optional label_key argument for extracting the label # from the features dictionary, to comply with the type expected by the # input_fn to a `tf.Estimator.train` or `tf.Estimator.evaluate` function. dataset = dataset.prefetch(prefetch_buffer_size) return dataset @deprecation.deprecated(None, "Use `tf.contrib.data.make_batched_features_dataset`") def read_batch_features(file_pattern, batch_size, features, reader=core_readers.TFRecordDataset, reader_args=None, randomize_input=True, num_epochs=None, capacity=10000): """Reads batches of Examples. Example: ``` serialized_examples = [ features { feature { key: "age" value { int64_list { value: [ 0 ] } } } feature { key: "gender" value { bytes_list { value: [ "f" ] } } } feature { key: "kws" value { bytes_list { value: [ "code", "art" ] } } } }, features { feature { key: "age" value { int64_list { value: [] } } } feature { key: "gender" value { bytes_list { value: [ "f" ] } } } feature { key: "kws" value { bytes_list { value: [ "sports" ] } } } } ] ``` We can use arguments: ``` features: { "age": FixedLenFeature([], dtype=tf.int64, default_value=-1), "gender": FixedLenFeature([], dtype=tf.string), "kws": VarLenFeature(dtype=tf.string), } ``` And the expected output is: ```python { "age": [[0], [-1]], "gender": [["f"], ["f"]], "kws": SparseTensor( indices=[[0, 0], [0, 1], [1, 0]], values=["code", "art", "sports"] dense_shape=[2, 2]), } ``` Args: file_pattern: List of files or patterns of file paths containing `Example` records. See `tf.gfile.Glob` for pattern rules. batch_size: An int representing the number of consecutive elements of this dataset to combine in a single batch. features: A `dict` mapping feature keys to `FixedLenFeature` or `VarLenFeature` values. See `tf.parse_example`. reader: A function or class that can be called with a `filenames` tensor and (optional) `reader_args` and returns a `Dataset` of `Example` tensors. Defaults to `tf.data.TFRecordDataset`. reader_args: Additional arguments to pass to the reader class. randomize_input: Whether the input should be randomized. num_epochs: Integer specifying the number of times to read through the dataset. If None, cycles through the dataset forever. capacity: Buffer size of the ShuffleDataset. A large capacity ensures better shuffling but would increase memory usage and startup time. Returns: A dict from keys in features to `Tensor` or `SparseTensor` objects. """ dataset = make_batched_features_dataset( file_pattern, batch_size, features, reader=reader, reader_args=reader_args, shuffle=randomize_input, num_epochs=num_epochs, shuffle_buffer_size=capacity) iterator = dataset.make_one_shot_iterator() outputs = iterator.get_next() return outputs def _get_file_names(file_pattern, shuffle): """Parse list of file names from pattern, optionally shuffled. Args: file_pattern: File glob pattern, or list of glob patterns. shuffle: Whether to shuffle the order of file names. Returns: List of file names matching `file_pattern`. Raises: ValueError: If `file_pattern` is empty, or pattern matches no files. """ if isinstance(file_pattern, list): if not file_pattern: raise ValueError("File pattern is empty.") file_names = [] for entry in file_pattern: file_names.extend(gfile.Glob(entry)) else: file_names = list(gfile.Glob(file_pattern)) if not file_names: raise ValueError("No files match %s." % file_pattern) # Sort files so it will be deterministic for unit tests. if not shuffle: file_names = sorted(file_names) return file_names class SqlDataset(dataset_ops.Dataset): """A `Dataset` consisting of the results from a SQL query.""" def __init__(self, driver_name, data_source_name, query, output_types): """Creates a `SqlDataset`. `SqlDataset` allows a user to read data from the result set of a SQL query. For example: ```python dataset = tf.contrib.data.SqlDataset("sqlite", "/foo/bar.sqlite3", "SELECT name, age FROM people", (tf.string, tf.int32)) iterator = dataset.make_one_shot_iterator() next_element = iterator.get_next() # Prints the rows of the result set of the above query. while True: try: print(sess.run(next_element)) except tf.errors.OutOfRangeError: break ``` Args: driver_name: A 0-D `tf.string` tensor containing the database type. Currently, the only supported value is 'sqlite'. data_source_name: A 0-D `tf.string` tensor containing a connection string to connect to the database. query: A 0-D `tf.string` tensor containing the SQL query to execute. output_types: A tuple of `tf.DType` objects representing the types of the columns returned by `query`. """ super(SqlDataset, self).__init__() self._driver_name = ops.convert_to_tensor( driver_name, dtype=dtypes.string, name="driver_name") self._data_source_name = ops.convert_to_tensor( data_source_name, dtype=dtypes.string, name="data_source_name") self._query = ops.convert_to_tensor( query, dtype=dtypes.string, name="query") self._output_types = output_types def _as_variant_tensor(self): return gen_dataset_ops.sql_dataset(self._driver_name, self._data_source_name, self._query, nest.flatten(self.output_types), nest.flatten(self.output_shapes)) @property def output_classes(self): return nest.map_structure(lambda _: ops.Tensor, self._output_types) @property def output_shapes(self): return nest.map_structure(lambda _: tensor_shape.TensorShape([]), self._output_types) @property def output_types(self): return self._output_types

39.152542

80

0.676082

from __future__ import absolute_import from __future__ import division from __future__ import print_function from tensorflow.contrib.data.python.ops import interleave_ops from tensorflow.contrib.data.python.ops import shuffle_ops from tensorflow.python.data.ops import dataset_ops from tensorflow.python.data.ops import readers as core_readers from tensorflow.python.data.util import nest from tensorflow.python.framework import constant_op from tensorflow.python.framework import dtypes from tensorflow.python.framework import ops from tensorflow.python.framework import tensor_shape from tensorflow.python.ops import gen_dataset_ops from tensorflow.python.ops import parsing_ops from tensorflow.python.platform import gfile from tensorflow.python.util import deprecation _ACCEPTABLE_CSV_TYPES = (dtypes.float32, dtypes.float64, dtypes.int32, dtypes.int64, dtypes.string) def make_csv_dataset( file_pattern, batch_size, column_keys, column_defaults, label_key=None, field_delim=",", use_quote_delim=True, skip=0, filter_fn=None, num_epochs=None, shuffle=True, shuffle_buffer_size=10000, shuffle_seed=None, prefetch_buffer_size=1, ): filenames = _get_file_names(file_pattern, False) column_defaults = [ constant_op.constant([], dtype=x) if x in _ACCEPTABLE_CSV_TYPES else x for x in column_defaults ] dataset = dataset_ops.Dataset.from_tensor_slices(filenames) if label_key is not None: assert label_key in column_keys def filename_to_dataset(filename): ds = core_readers.TextLineDataset(filename) if skip > 0: ds = ds.skip(skip) if filter_fn is not None: ds = ds.filter(filter_fn) return ds def decode_csv(line): columns = parsing_ops.decode_csv( line, column_defaults, field_delim=field_delim, use_quote_delim=use_quote_delim) features = dict(zip(column_keys, columns)) if label_key is not None: label = features.pop(label_key) return features, label return features dataset = dataset.flat_map(filename_to_dataset) if shuffle: dataset = dataset.shuffle(shuffle_buffer_size, shuffle_seed) if num_epochs != 1: dataset = dataset.repeat(num_epochs) dataset = dataset.batch(batch_size) dataset = dataset.map(decode_csv) dataset = dataset.prefetch(prefetch_buffer_size) return dataset def make_batched_features_dataset(file_pattern, batch_size, features, reader=core_readers.TFRecordDataset, reader_args=None, num_epochs=None, shuffle=True, shuffle_buffer_size=10000, shuffle_seed=None, prefetch_buffer_size=1, reader_num_threads=1, parser_num_threads=2, sloppy_ordering=False): if shuffle: dataset = dataset_ops.Dataset.list_files(file_pattern, shuffle=True) else: filenames = _get_file_names(file_pattern, shuffle) dataset = dataset_ops.Dataset.from_tensor_slices(filenames) if reader_args is None: reader_args = [] dataset = dataset.apply( interleave_ops.parallel_interleave( lambda filename: reader(filename, *reader_args), cycle_length=reader_num_threads, sloppy=sloppy_ordering)) if dataset.output_types == (dtypes.string, dtypes.string): dataset = dataset.map(lambda _, v: v) repeat_dataset = (num_epochs != 1) if repeat_dataset and shuffle: dataset = dataset.apply( shuffle_ops.shuffle_and_repeat(shuffle_buffer_size, num_epochs, shuffle_seed)) elif repeat_dataset: dataset = dataset.repeat(num_epochs) elif shuffle: dataset = dataset.shuffle(shuffle_buffer_size, shuffle_seed) dataset = dataset.batch(batch_size) dataset = dataset.map( lambda x: parsing_ops.parse_example(x, features), num_parallel_calls=parser_num_threads) dataset = dataset.prefetch(prefetch_buffer_size) return dataset @deprecation.deprecated(None, "Use `tf.contrib.data.make_batched_features_dataset`") def read_batch_features(file_pattern, batch_size, features, reader=core_readers.TFRecordDataset, reader_args=None, randomize_input=True, num_epochs=None, capacity=10000): dataset = make_batched_features_dataset( file_pattern, batch_size, features, reader=reader, reader_args=reader_args, shuffle=randomize_input, num_epochs=num_epochs, shuffle_buffer_size=capacity) iterator = dataset.make_one_shot_iterator() outputs = iterator.get_next() return outputs def _get_file_names(file_pattern, shuffle): if isinstance(file_pattern, list): if not file_pattern: raise ValueError("File pattern is empty.") file_names = [] for entry in file_pattern: file_names.extend(gfile.Glob(entry)) else: file_names = list(gfile.Glob(file_pattern)) if not file_names: raise ValueError("No files match %s." % file_pattern) if not shuffle: file_names = sorted(file_names) return file_names class SqlDataset(dataset_ops.Dataset): def __init__(self, driver_name, data_source_name, query, output_types): super(SqlDataset, self).__init__() self._driver_name = ops.convert_to_tensor( driver_name, dtype=dtypes.string, name="driver_name") self._data_source_name = ops.convert_to_tensor( data_source_name, dtype=dtypes.string, name="data_source_name") self._query = ops.convert_to_tensor( query, dtype=dtypes.string, name="query") self._output_types = output_types def _as_variant_tensor(self): return gen_dataset_ops.sql_dataset(self._driver_name, self._data_source_name, self._query, nest.flatten(self.output_types), nest.flatten(self.output_shapes)) @property def output_classes(self): return nest.map_structure(lambda _: ops.Tensor, self._output_types) @property def output_shapes(self): return nest.map_structure(lambda _: tensor_shape.TensorShape([]), self._output_types) @property def output_types(self): return self._output_types

true

f70f9fbd792e49e3bb17519f8daf955fc3b614b8

3,041

py

Python

projects/api/main.py

lborro/projects

c971c2fc65cdb09310d0b3782cd7119203aa4db3

[ "Apache-2.0" ]

null

projects/api/main.py

lborro/projects

c971c2fc65cdb09310d0b3782cd7119203aa4db3

[ "Apache-2.0" ]

null

projects/api/main.py

lborro/projects

c971c2fc65cdb09310d0b3782cd7119203aa4db3

[ "Apache-2.0" ]

null

# -*- coding: utf-8 -*- """WSGI server.""" import argparse import sys from flask import Flask, jsonify from flask_cors import CORS from werkzeug.exceptions import BadRequest, NotFound, MethodNotAllowed, \ Forbidden, InternalServerError from projects.api.compare_results import bp as compare_results_blueprint from projects.api.experiments import bp as experiments_blueprint from projects.api.json_encoder import CustomJSONEncoder from projects.api.operators import bp as operators_blueprint from projects.api.parameters import bp as parameters_blueprint from projects.api.projects import bp as projects_blueprint from projects.api.tasks import bp as tasks_blueprint from projects.api.templates import bp as templates_blueprint from projects.database import db_session, init_db from projects.samples import init_tasks app = Flask(__name__) app.json_encoder = CustomJSONEncoder app.register_blueprint(projects_blueprint, url_prefix="/projects") app.register_blueprint(compare_results_blueprint, url_prefix="/projects/<project_id>/comparisons") app.register_blueprint(experiments_blueprint, url_prefix="/projects/<project_id>/experiments") app.register_blueprint(tasks_blueprint, url_prefix="/tasks") app.register_blueprint(parameters_blueprint, url_prefix="/tasks/<task_id>/parameters") app.register_blueprint(operators_blueprint, url_prefix="/projects/<project_id>/experiments/<experiment_id>/operators") app.register_blueprint(templates_blueprint, url_prefix="/templates") @app.teardown_appcontext def shutdown_session(exception=None): db_session.remove() @app.route("/", methods=["GET"]) def ping(): """Handles GET requests to /.""" return "pong" @app.errorhandler(BadRequest) @app.errorhandler(NotFound) @app.errorhandler(MethodNotAllowed) @app.errorhandler(Forbidden) @app.errorhandler(InternalServerError) def handle_errors(e): """Handles exceptions raised by the API.""" return jsonify({"message": e.description}), e.code def parse_args(args): """Takes argv and parses API options.""" parser = argparse.ArgumentParser( description="Projects API" ) parser.add_argument( "--port", type=int, default=8080, help="Port for HTTP server (default: 8080)" ) parser.add_argument("--enable-cors", action="count") parser.add_argument( "--debug", action="count", help="Enable debug" ) parser.add_argument( "--init-db", action="count", help="Create database and tables before the HTTP server starts" ) parser.add_argument( "--samples-config", help="Path to sample tasks config file." ) return parser.parse_args(args) if __name__ == "__main__": args = parse_args(sys.argv[1:]) # Enable CORS if required if args.enable_cors: CORS(app) # Initializes DB if required if args.init_db: init_db() # Install sample tasks if required if args.samples_config: init_tasks(args.samples_config) app.run(host="0.0.0.0", port=args.port, debug=args.debug)

33.054348

100

0.74515

import argparse import sys from flask import Flask, jsonify from flask_cors import CORS from werkzeug.exceptions import BadRequest, NotFound, MethodNotAllowed, \ Forbidden, InternalServerError from projects.api.compare_results import bp as compare_results_blueprint from projects.api.experiments import bp as experiments_blueprint from projects.api.json_encoder import CustomJSONEncoder from projects.api.operators import bp as operators_blueprint from projects.api.parameters import bp as parameters_blueprint from projects.api.projects import bp as projects_blueprint from projects.api.tasks import bp as tasks_blueprint from projects.api.templates import bp as templates_blueprint from projects.database import db_session, init_db from projects.samples import init_tasks app = Flask(__name__) app.json_encoder = CustomJSONEncoder app.register_blueprint(projects_blueprint, url_prefix="/projects") app.register_blueprint(compare_results_blueprint, url_prefix="/projects/<project_id>/comparisons") app.register_blueprint(experiments_blueprint, url_prefix="/projects/<project_id>/experiments") app.register_blueprint(tasks_blueprint, url_prefix="/tasks") app.register_blueprint(parameters_blueprint, url_prefix="/tasks/<task_id>/parameters") app.register_blueprint(operators_blueprint, url_prefix="/projects/<project_id>/experiments/<experiment_id>/operators") app.register_blueprint(templates_blueprint, url_prefix="/templates") @app.teardown_appcontext def shutdown_session(exception=None): db_session.remove() @app.route("/", methods=["GET"]) def ping(): return "pong" @app.errorhandler(BadRequest) @app.errorhandler(NotFound) @app.errorhandler(MethodNotAllowed) @app.errorhandler(Forbidden) @app.errorhandler(InternalServerError) def handle_errors(e): return jsonify({"message": e.description}), e.code def parse_args(args): parser = argparse.ArgumentParser( description="Projects API" ) parser.add_argument( "--port", type=int, default=8080, help="Port for HTTP server (default: 8080)" ) parser.add_argument("--enable-cors", action="count") parser.add_argument( "--debug", action="count", help="Enable debug" ) parser.add_argument( "--init-db", action="count", help="Create database and tables before the HTTP server starts" ) parser.add_argument( "--samples-config", help="Path to sample tasks config file." ) return parser.parse_args(args) if __name__ == "__main__": args = parse_args(sys.argv[1:]) if args.enable_cors: CORS(app) if args.init_db: init_db() if args.samples_config: init_tasks(args.samples_config) app.run(host="0.0.0.0", port=args.port, debug=args.debug)

true

f70fa0d1d8aad41a969517b69bc42c6dd87cbd53

2,411

py

Python

codes/preprocess/collect_noise.py

yicrane/Real-SR

a6e380b791129b80fe58bf282089c0cfd9159b36

[ "Apache-2.0" ]

null

codes/preprocess/collect_noise.py

yicrane/Real-SR

a6e380b791129b80fe58bf282089c0cfd9159b36

[ "Apache-2.0" ]

null

codes/preprocess/collect_noise.py

yicrane/Real-SR

a6e380b791129b80fe58bf282089c0cfd9159b36

[ "Apache-2.0" ]

1

2021-07-07T13:56:30.000Z

from PIL import Image import numpy as np import os.path as osp import glob import os import argparse import yaml parser = argparse.ArgumentParser(description='create a dataset') parser.add_argument('--dataset', default='df2k', type=str, help='selecting different datasets') parser.add_argument('--artifacts', default='', type=str, help='selecting different artifacts type') parser.add_argument('--cleanup_factor', default=2, type=int, help='downscaling factor for image cleanup') parser.add_argument('--upscale_factor', default=4, type=int, choices=[4], help='super resolution upscale factor') opt = parser.parse_args() # define input and target directories with open('./preprocess/paths.yml', 'r') as stream: PATHS = yaml.load(stream) def noise_patch(rgb_img, sp, max_var, min_mean): img = rgb_img.convert('L') rgb_img = np.array(rgb_img) img = np.array(img) w, h = img.shape collect_patchs = [] for i in range(0, w - sp, sp): for j in range(0, h - sp, sp): patch = img[i:i + sp, j:j + sp] var_global = np.var(patch) mean_global = np.mean(patch) if var_global < max_var and mean_global > min_mean: rgb_patch = rgb_img[i:i + sp, j:j + sp, :] collect_patchs.append(rgb_patch) return collect_patchs if __name__ == '__main__': if opt.dataset == 'df2k': img_dir = PATHS[opt.dataset][opt.artifacts]['source'] noise_dir = PATHS['datasets']['df2k'] + '/Corrupted_noise' sp = 256 max_var = 20 min_mean = 0 else: img_dir = PATHS[opt.dataset][opt.artifacts]['hr']['train'] noise_dir = PATHS['datasets']['dped'] + '/DPEDiphone_noise_sp32v20m50' sp = 256 max_var = 20 min_mean = 50 assert not os.path.exists(noise_dir) os.mkdir(noise_dir) img_paths = sorted(glob.glob(osp.join(img_dir, '*.png'))) cnt = 0 for path in img_paths: img_name = osp.splitext(osp.basename(path))[0] print('**********', img_name, '**********') img = Image.open(path).convert('RGB') patchs = noise_patch(img, sp, max_var, min_mean) for idx, patch in enumerate(patchs): save_path = osp.join(noise_dir, '{}_{:03}.png'.format(img_name, idx)) cnt += 1 print('collect:', cnt, save_path) Image.fromarray(patch).save(save_path)

33.957746

113

0.623393

from PIL import Image import numpy as np import os.path as osp import glob import os import argparse import yaml parser = argparse.ArgumentParser(description='create a dataset') parser.add_argument('--dataset', default='df2k', type=str, help='selecting different datasets') parser.add_argument('--artifacts', default='', type=str, help='selecting different artifacts type') parser.add_argument('--cleanup_factor', default=2, type=int, help='downscaling factor for image cleanup') parser.add_argument('--upscale_factor', default=4, type=int, choices=[4], help='super resolution upscale factor') opt = parser.parse_args() with open('./preprocess/paths.yml', 'r') as stream: PATHS = yaml.load(stream) def noise_patch(rgb_img, sp, max_var, min_mean): img = rgb_img.convert('L') rgb_img = np.array(rgb_img) img = np.array(img) w, h = img.shape collect_patchs = [] for i in range(0, w - sp, sp): for j in range(0, h - sp, sp): patch = img[i:i + sp, j:j + sp] var_global = np.var(patch) mean_global = np.mean(patch) if var_global < max_var and mean_global > min_mean: rgb_patch = rgb_img[i:i + sp, j:j + sp, :] collect_patchs.append(rgb_patch) return collect_patchs if __name__ == '__main__': if opt.dataset == 'df2k': img_dir = PATHS[opt.dataset][opt.artifacts]['source'] noise_dir = PATHS['datasets']['df2k'] + '/Corrupted_noise' sp = 256 max_var = 20 min_mean = 0 else: img_dir = PATHS[opt.dataset][opt.artifacts]['hr']['train'] noise_dir = PATHS['datasets']['dped'] + '/DPEDiphone_noise_sp32v20m50' sp = 256 max_var = 20 min_mean = 50 assert not os.path.exists(noise_dir) os.mkdir(noise_dir) img_paths = sorted(glob.glob(osp.join(img_dir, '*.png'))) cnt = 0 for path in img_paths: img_name = osp.splitext(osp.basename(path))[0] print('**********', img_name, '**********') img = Image.open(path).convert('RGB') patchs = noise_patch(img, sp, max_var, min_mean) for idx, patch in enumerate(patchs): save_path = osp.join(noise_dir, '{}_{:03}.png'.format(img_name, idx)) cnt += 1 print('collect:', cnt, save_path) Image.fromarray(patch).save(save_path)

true

f70fa0e5384e444c718b501e35ff39db46d5b99a

6,872

py

Python

pennylane/transforms/__init__.py

XanaduAI/pennylane

0620b8a8bb56ff55bfc2130619fa0a5a1af2b2a4

[ "Apache-2.0" ]

539

2018-11-13T08:45:42.000Z

2020-07-27T18:17:16.000Z

pennylane/transforms/__init__.py

XanaduAI/pennylane

0620b8a8bb56ff55bfc2130619fa0a5a1af2b2a4

[ "Apache-2.0" ]

588

2018-11-14T10:21:47.000Z

2020-07-28T06:27:14.000Z

pennylane/transforms/__init__.py

XanaduAI/pennylane

0620b8a8bb56ff55bfc2130619fa0a5a1af2b2a4

[ "Apache-2.0" ]

165

2018-11-13T18:58:56.000Z

2020-07-27T17:18:17.000Z

# Copyright 2018-2021 Xanadu Quantum Technologies Inc. # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # http://www.apache.org/licenses/LICENSE-2.0 # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. """ This subpackage contains QNode, quantum function, device, and tape transforms. .. currentmodule:: pennylane Transforms ---------- Transforms that act on QNodes ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ These transforms accept QNodes, and return new transformed functions that compute the desired quantity. .. autosummary:: :toctree: api ~transforms.classical_jacobian ~batch_params ~batch_input ~metric_tensor ~adjoint_metric_tensor ~specs ~transforms.mitigate_with_zne ~transforms.split_non_commuting Transforms that act on quantum functions ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ These transforms accept quantum functions (Python functions containing quantum operations) that are used to construct QNodes. .. autosummary:: :toctree: api ~adjoint ~ctrl ~transforms.cond ~defer_measurements ~apply_controlled_Q ~quantum_monte_carlo ~transforms.insert Transforms for circuit compilation ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ This set of transforms accept quantum functions, and perform basic circuit compilation tasks. .. autosummary:: :toctree: api ~compile ~transforms.cancel_inverses ~transforms.commute_controlled ~transforms.merge_rotations ~transforms.single_qubit_fusion ~transforms.unitary_to_rot ~transforms.merge_amplitude_embedding ~transforms.remove_barrier ~transforms.undo_swaps ~transforms.pattern_matching_optimization ~transforms.transpile There are also utility functions and decompositions available that assist with both transforms, and decompositions within the larger PennyLane codebase. .. autosummary:: :toctree: api ~transforms.zyz_decomposition ~transforms.two_qubit_decomposition ~transforms.set_decomposition ~transforms.simplify ~transforms.pattern_matching There are also utility functions that take a circuit and return a DAG. .. autosummary:: :toctree: api ~transforms.commutation_dag ~transforms.CommutationDAG ~transforms.CommutationDAGNode Transform for circuit cutting ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ The :func:`~.cut_circuit` transform accepts a QNode and returns a new function that cuts the original circuit, allowing larger circuits to be split into smaller circuits that are compatible with devices that have a restricted number of qubits. .. autosummary:: :toctree: api ~cut_circuit The :func:`~.cut_circuit_mc` transform is designed to be used for cutting circuits which contain :func:`~.sample` measurements and is implemented using a Monte Carlo method. Similarly to the :func:`~.cut_circuit` transform, this transform accepts a QNode and returns a new function that cuts the original circuit. This transform can also accept an optional classical processing function to calculate an expectation value. .. autosummary:: :toctree: api ~cut_circuit_mc There are also low-level functions that can be used to build up the circuit cutting functionalities: .. autosummary:: :toctree: api ~transforms.qcut.tape_to_graph ~transforms.qcut.replace_wire_cut_nodes ~transforms.qcut.fragment_graph ~transforms.qcut.graph_to_tape ~transforms.qcut.remap_tape_wires ~transforms.qcut.expand_fragment_tape ~transforms.qcut.expand_fragment_tapes_mc ~transforms.qcut.qcut_processing_fn ~transforms.qcut.qcut_processing_fn_sample ~transforms.qcut.qcut_processing_fn_mc ~transforms.qcut.CutStrategy ~transforms.qcut.kahypar_cut ~transforms.qcut.place_wire_cuts ~transforms.qcut.find_and_place_cuts Transforms that act on tapes ~~~~~~~~~~~~~~~~~~~~~~~~~~~~ These transforms accept quantum tapes, and return one or more tapes as well as a classical processing function. .. autosummary:: :toctree: api ~transforms.measurement_grouping ~transforms.hamiltonian_expand Decorators and utility functions -------------------------------- The following decorators and convenience functions are provided to help build custom QNode, quantum function, and tape transforms: .. autosummary:: :toctree: api ~single_tape_transform ~batch_transform ~qfunc_transform ~op_transform ~transforms.make_tape ~transforms.map_batch_transform ~transforms.create_expand_fn ~transforms.create_decomp_expand_fn ~transforms.expand_invalid_trainable ~transforms.expand_multipar ~transforms.expand_trainable_multipar ~transforms.expand_nonunitary_gen """ # Import the decorators first to prevent circular imports when used in other transforms from .batch_transform import batch_transform, map_batch_transform from .qfunc_transforms import make_tape, single_tape_transform, qfunc_transform from .op_transforms import op_transform from .adjoint import adjoint from .batch_params import batch_params from .batch_input import batch_input from .classical_jacobian import classical_jacobian from .condition import cond, Conditional from .compile import compile from .control import ControlledOperation, ctrl from .decompositions import zyz_decomposition, two_qubit_decomposition from .defer_measurements import defer_measurements from .hamiltonian_expand import hamiltonian_expand from .split_non_commuting import split_non_commuting from .measurement_grouping import measurement_grouping from .metric_tensor import metric_tensor from .adjoint_metric_tensor import adjoint_metric_tensor from .insert_ops import insert from .mitigate import mitigate_with_zne from .optimization import ( cancel_inverses, commute_controlled, merge_rotations, single_qubit_fusion, merge_amplitude_embedding, remove_barrier, undo_swaps, pattern_matching, pattern_matching_optimization, ) from .specs import specs from .qmc import apply_controlled_Q, quantum_monte_carlo from .unitary_to_rot import unitary_to_rot from .commutation_dag import ( commutation_dag, is_commuting, CommutationDAG, CommutationDAGNode, simplify, ) from .tape_expand import ( expand_invalid_trainable, expand_multipar, expand_nonunitary_gen, expand_trainable_multipar, create_expand_fn, create_decomp_expand_fn, set_decomposition, ) from .transpile import transpile from . import qcut from .qcut import cut_circuit, cut_circuit_mc

30.008734

113

0.766589

from .batch_transform import batch_transform, map_batch_transform from .qfunc_transforms import make_tape, single_tape_transform, qfunc_transform from .op_transforms import op_transform from .adjoint import adjoint from .batch_params import batch_params from .batch_input import batch_input from .classical_jacobian import classical_jacobian from .condition import cond, Conditional from .compile import compile from .control import ControlledOperation, ctrl from .decompositions import zyz_decomposition, two_qubit_decomposition from .defer_measurements import defer_measurements from .hamiltonian_expand import hamiltonian_expand from .split_non_commuting import split_non_commuting from .measurement_grouping import measurement_grouping from .metric_tensor import metric_tensor from .adjoint_metric_tensor import adjoint_metric_tensor from .insert_ops import insert from .mitigate import mitigate_with_zne from .optimization import ( cancel_inverses, commute_controlled, merge_rotations, single_qubit_fusion, merge_amplitude_embedding, remove_barrier, undo_swaps, pattern_matching, pattern_matching_optimization, ) from .specs import specs from .qmc import apply_controlled_Q, quantum_monte_carlo from .unitary_to_rot import unitary_to_rot from .commutation_dag import ( commutation_dag, is_commuting, CommutationDAG, CommutationDAGNode, simplify, ) from .tape_expand import ( expand_invalid_trainable, expand_multipar, expand_nonunitary_gen, expand_trainable_multipar, create_expand_fn, create_decomp_expand_fn, set_decomposition, ) from .transpile import transpile from . import qcut from .qcut import cut_circuit, cut_circuit_mc

true

f70fa191b2427b7e00ffd7084a2710be0b35e10c

10,735

py

Python

src/m3_calling_functions_returning_values.py

DavidMutchler/03-AccumulatorsAndFunctionsWithParameters

548b9a527357e4a18c6ab3e0cc84c907c6e33d87

[ "MIT" ]

null

src/m3_calling_functions_returning_values.py

DavidMutchler/03-AccumulatorsAndFunctionsWithParameters

548b9a527357e4a18c6ab3e0cc84c907c6e33d87

[ "MIT" ]

null

src/m3_calling_functions_returning_values.py

DavidMutchler/03-AccumulatorsAndFunctionsWithParameters

548b9a527357e4a18c6ab3e0cc84c907c6e33d87

[ "MIT" ]

66

2018-03-08T12:57:23.000Z

2020-11-09T18:59:08.000Z

""" This module demonstrates and practices: -- using ARGUMENTs in function CALLs, -- having PARAMETERs in function DEFINITIONs, and -- RETURNING a value from a function, possibly CAPTURING the RETURNED VALUE in a VARIABLE. -- UNIT TESTING. Authors: David Mutchler, Valerie Galluzzi, Mark Hays, Amanda Stouder, their colleagues and PUT_YOUR_NAME_HERE. """ # TODO: 1. PUT YOUR NAME IN THE ABOVE LINE. import m3t_tester def main(): """ Calls the TEST functions in this module. """ run_test_sum_of_digits() run_test_digits_in_cube() run_test_digits_in_power() run_test_fancy_sums_of_digits() # ------------------------------------------------------------------ # TODO: 9. DO THIS LAST! # -- Uncomment the line of code below to run the main function # in m3t_tester.py (do not make changes to it). # It runs OUR tests on your code. # -- Check to see whether all test cases indicate they # "COMPLETED SUCCESSFULLY!" # -- If your code fails any of OUR tests but passes YOUR tests, # then you are likely not TESTING the methods correctly. # ** Ask a TA or your professor for help in that case. ** # ------------------------------------------------------------------ # m3t_tester.main() def run_test_sum_of_digits(): """ Tests the sum_of_digits function. """ # ------------------------------------------------------------------ # TODO: 2. Implement this TEST function, as follows: # # Step 1: This TEST function tests the sum_of_digits function. # So read the doc-string of the sum_of_digits function # defined below. Be sure that you understand from the # doc-string what the sum_of_digits function SHOULD return. # # Step 2: Pick a test case: a number that you could send as # an actual argument to the sum_of_digits function. # - For example, you could pick the test case 826. # # Step 3: Figure out the CORRECT (EXPECTED) answer for your # test case. In the example of 826 the correct answer # for the sum of its digits is 8 + 2 + 6, which is 16. # # Step 4: Write code that prints both the EXPECTED answer # and the ACTUAL answer returned when you call the function. # See the example below. # ------------------------------------------------------------------ print() print('--------------------------------------------------') print('Testing the sum_of_digits function:') print('--------------------------------------------------') # Test 1: expected = 16 answer = sum_of_digits(826) print('Test 1 expected:', expected) print(' actual: ', answer) # ------------------------------------------------------------------ # TO DO: 2 (continued). # Below this comment, add 3 more test cases of your own choosing. # ------------------------------------------------------------------ def sum_of_digits(number): """ What comes in: An integer. What goes out: The sum of the digits in the given integer. Side effects: None. Example: If the integer is 83135, this function returns (8 + 3 + 1 + 3 + 5), which is 20. """ # ------------------------------------------------------------------ # Students: # Do NOT touch this function - it has no TO DO in it. # Do NOT copy code from this function. # # Instead, ** CALL ** this function as needed in the other problems. # # Ask for help if you are unsure what it means to CALL a function. # The ONLY part of this function that you need to understand is # the doc-string above. Treat this function as a black box. # ------------------------------------------------------------------ if number < 0: number = -number digit_sum = 0 while True: if number == 0: break digit_sum = digit_sum + (number % 10) number = number // 10 return digit_sum def run_test_digits_in_cube(): """ Tests the digits_in_cube function. """ # ------------------------------------------------------------------ # TODO: 3. Implement this function. # It TESTS the digits_in_cube function defined below. # Include at least ** 3 ** tests. # # To implement this TEST function, use the same 4 steps as above: # # Step 1: Read the doc-string of digits_in_cube below. # Understand what that function SHOULD return. # # Step 2: Pick a test case: a number(s) that you could send as # actual argument(s) to the digits_in_cube function. # # Step 3: Figure out the CORRECT (EXPECTED) answer for your test case. # # Step 4: Write code that prints both the EXPECTED answer # and the ACTUAL answer returned when you call the function. # Follow the same form as in previous examples. # # Include at least ** 3 ** tests. # ------------------------------------------------------------------ print() print('-----------------------------------------------------') print('Testing the digits_in_cube function:') print('-----------------------------------------------------') def digits_in_cube(n): """ What comes in: A positive integer. What goes out: The sum of the digits in the CUBE of the integer. Side effects: None. Example: If the integer (n) is 5 (so n cubed is 125), this function returns (1 + 2 + 5), which is 8. """ # ------------------------------------------------------------------ # TODO: 4. Implement and test this function. # Note that you should write its TEST function first (above). # That is called TEST-DRIVEN DEVELOPMENT (TDD). # #################################################################### # IMPORTANT: CALL, as many times as needed, # the sum_of_digits function that is DEFINED ABOVE. #################################################################### # ------------------------------------------------------------------ def run_test_digits_in_power(): """ Tests the digits_in_power function. """ # ------------------------------------------------------------------ # TODO: 5. Implement this function. # It TESTS the digits_in_power function defined below. # Include at least ** 3 ** tests. # # Use the same 4-step process as in implementing previous TEST functions. # ------------------------------------------------------------------ print() print('--------------------------------------------------') print('Testing the digits_in_power function:') print('--------------------------------------------------') def digits_in_power(n, k): """ What comes in: Two positive integers, n and k. What goes out: The sum of the digits in x, where x is n raised to the kth power. Side effects: None. Example: If the arguments are 12 and 3, respectively, this function returns 18 since 12 to the 3rd power is 1728 (whose digits sum to 18). """ # ------------------------------------------------------------------ # TODO: 6. Implement and test this function. # #################################################################### # IMPORTANT: CALL, as many times as needed, # the sum_of_digits function that is DEFINED ABOVE. #################################################################### # ------------------------------------------------------------------ def run_test_fancy_sums_of_digits(): """ Tests the fancy_sums_of_digits function. """ # ------------------------------------------------------------------ # TODO: 7. Implement this function. # It TESTS the fancy_sums_of_digits function defined below. # Include at least ** 3 ** tests. # # Use the same 4-step process as in implementing the previous # TEST functions. # ------------------------------------------------------------------ print() print('--------------------------------------------------') print('Testing the fancy_sums_of_digits function:') print('--------------------------------------------------') # ------------------------------------------------------------------ # HINT: For your 1st test, consider n=10. Figure out BY HAND # the correct (expected) answer for that test case. (It's easy.) # The doc-string below gives test cases you can use for # your 2nd and 3rd tests but READ THOSE TEST CASES CAREFULLY # in the doc-string to be sure that you understand the specification. # ------------------------------------------------------------------ def fancy_sums_of_digits(n): """ What comes in: A positive integer n. What goes out: -- Let X denote the sum of the digits in (n ** 1000). -- Let Y denote the sum of the digits in (n ** 999). This function RETURNs the sum of the digits in (X ** Y). Side effects: None. Examples: -- If n is 2, then: -- the sum of the digits in n ** 1000 is 1366 (trust me!). -- the sum of the digits in n ** 999 is 1367 (trust me!). -- so X ** Y is VERY LARGE in this case (don't try to print it!) -- the sum of the digits in (X ** Y) is 19084 (trust me!) -- so this function returns 19084. -- If n is 35, then: -- the sum of the digits in n ** 1000 is 7021 (trust me!). -- the sum of the digits in n ** 999 is 7145 (trust me!). -- so X ** Y is VERY LARGE in this case (don't try to print it!) -- the sum of the digits in (X ** Y) is 124309 (trust me!) -- so this function returns 124309. """ # ------------------------------------------------------------------ # TODO: 8. Implement and test this function. # #################################################################### # IMPORTANT: CALL, as many times as needed, # the sum_of_digits function that is DEFINED ABOVE. #################################################################### # ------------------------------------------------------------------ # ---------------------------------------------------------------------- # Calls main to start the ball rolling. # This unusual form is necessary for the special testing we provided. # ---------------------------------------------------------------------- if __name__ == '__main__': main()

41.447876

77

0.469213

import m3t_tester def main(): run_test_sum_of_digits() run_test_digits_in_cube() run_test_digits_in_power() run_test_fancy_sums_of_digits() def run_test_sum_of_digits(): print() print('--------------------------------------------------') print('Testing the sum_of_digits function:') print('--------------------------------------------------') expected = 16 answer = sum_of_digits(826) print('Test 1 expected:', expected) print(' actual: ', answer) def sum_of_digits(number): if number < 0: number = -number digit_sum = 0 while True: if number == 0: break digit_sum = digit_sum + (number % 10) number = number // 10 return digit_sum def run_test_digits_in_cube(): print() print('-----------------------------------------------------') print('Testing the digits_in_cube function:') print('-----------------------------------------------------') def digits_in_cube(n):

true

f70fa2944e0e3fcde401f3381a70b6633f36db93

5,384

py

Python

indieauth/views.py

wivn/feed-reader

1b4524fcdfc79391a5cf982ce9c5681e600f4303

[ "MIT" ]

null

indieauth/views.py

wivn/feed-reader

1b4524fcdfc79391a5cf982ce9c5681e600f4303

[ "MIT" ]

null

indieauth/views.py

wivn/feed-reader

1b4524fcdfc79391a5cf982ce9c5681e600f4303

[ "MIT" ]

null

from django.shortcuts import render, HttpResponseRedirect from django.urls import reverse from urllib.parse import urlencode, unquote import requests from bs4 import BeautifulSoup from django.utils.crypto import get_random_string from django.contrib import messages from urllib.parse import urlparse, urljoin from django.contrib.auth import get_user_model from django.contrib.auth import login as login_auth def redirect_logged_in_users(function): def _function(request,*args, **kwargs): if request.user.is_authenticated: return HttpResponseRedirect(reverse("feed:index")) return function(request, *args, **kwargs) return _function @redirect_logged_in_users def index(request): cleanup(request) return render(request, 'indieauth/index.html', {}) def login(request): try: if request.method == 'POST': site = request.POST.get("site", None) url_data = urlparse(site) if site and url_data.netloc != '' and (url_data.scheme == 'http' or url_data.scheme == 'https'): if url_data.path == '': site = site + '/' print(site) r = requests.get(site) soup = BeautifulSoup(r.text, 'html.parser') unique_id = get_random_string(length=32) for link in soup.find_all('link'): if link.get('rel')[0] == "authorization_endpoint": authorization_endpoint = link.get('href') # if relative URL, this will attach it to the end of the redirected url authorization_endpoint = urljoin(r.url, authorization_endpoint) if r.headers.get('Link', None): links = r.headers['Link'] print(links) for link in links.split(","): possible_url = link.split(";")[0].strip() possible_url = possible_url[1:len(possible_url)-1] possible_rel = link.split(";")[1].strip() if possible_rel == "rel=authorization_endpoint": authorization_endpoint = urljoin(r.url, possible_url) # after redirects, the final URL will be contained in the response site = r.url print(r.history) searchHistory = True i = -1 # ensure that if there's temp redirects that the "me" url is always the last permanent redirect while searchHistory and (i*-1) <= len(r.history): history_piece = r.history[i] if history_piece.status_code == 301: site = history_piece.url i -= 1 # If ALL of them are temporary redirects than use the initial value if all(i.status_code == 302 for i in r.history): site = request.POST.get("site", None) if authorization_endpoint: request.session['authorization_endpoint']=authorization_endpoint request.session['client_id'] = site request.session['state'] = unique_id payload = {'me': site, 'redirect_uri': request.build_absolute_uri(reverse('indieauth:redirect')), 'client_id': f'{request.scheme}://{ request.get_host() }/indieauth/application_info', 'state': unique_id, 'response_type': 'id'} redirect_site = authorization_endpoint + "?" + urlencode(payload) return HttpResponseRedirect(redirect_site) else: cleanup(request) messages.error(request, 'No authorization_endpoint found.') return HttpResponseRedirect(reverse('indieauth:index')) except Exception as e: print(e) messages.error(request, 'Error in retrieving url.') return HttpResponseRedirect(reverse('indieauth:index')) messages.error(request, 'No site submitted or the URL submitted was not valid.') return HttpResponseRedirect(reverse('indieauth:index')) def redirect(request): if request.GET.get('state', None) == request.session.get('state', None) and request.session.get('state', None) != None: client_id = request.session['client_id'] authorization_endpoint = request.session['authorization_endpoint'] redirect_uri = request.build_absolute_uri(reverse('indieauth:redirect')) code = request.GET.get('code') r = requests.post(authorization_endpoint, data = {'code':code, 'client_id':client_id, 'redirect_uri': redirect_uri}) if r.headers['content-type'] == "application/x-www-form-urlencoded": user_site = unquote(r.text)[3:] elif r.headers['content-type'] == "application/json": user_site = r.text['me'] else: user_site = None user_site_matches_domain = urlparse(client_id).netloc == urlparse(user_site).netloc print(urlparse(client_id).netloc, urlparse(user_site).netloc) if r.status_code == 200 and user_site and user_site_matches_domain: messages.success(request, 'Your URL is: ' + user_site) user_model = get_user_model() user = user_model.objects.filter(site=user_site) if user: login_auth(request, user[0]) else: user = user_model.objects.create_user(username=user_site, site=user_site) user.set_unusable_password() login_auth(request, user) cleanup(request) return HttpResponseRedirect(reverse('feed:index')) else: messages.error(request, 'Error in URL. Please try again.') cleanup(request) return HttpResponseRedirect(reverse('indieauth:index')) else: messages.error(request, 'Major error. Likely timeout. Please try again.') cleanup(request) return HttpResponseRedirect(reverse('indieauth:index')) def cleanup(request): try: del request.session['authorization_endpoint'] del request.session['state'] del request.session['client_id'] except KeyError: pass def application_info(request): return render(request, "indieauth/application_info.html")

40.481203

120

0.717125

from django.shortcuts import render, HttpResponseRedirect from django.urls import reverse from urllib.parse import urlencode, unquote import requests from bs4 import BeautifulSoup from django.utils.crypto import get_random_string from django.contrib import messages from urllib.parse import urlparse, urljoin from django.contrib.auth import get_user_model from django.contrib.auth import login as login_auth def redirect_logged_in_users(function): def _function(request,*args, **kwargs): if request.user.is_authenticated: return HttpResponseRedirect(reverse("feed:index")) return function(request, *args, **kwargs) return _function @redirect_logged_in_users def index(request): cleanup(request) return render(request, 'indieauth/index.html', {}) def login(request): try: if request.method == 'POST': site = request.POST.get("site", None) url_data = urlparse(site) if site and url_data.netloc != '' and (url_data.scheme == 'http' or url_data.scheme == 'https'): if url_data.path == '': site = site + '/' print(site) r = requests.get(site) soup = BeautifulSoup(r.text, 'html.parser') unique_id = get_random_string(length=32) for link in soup.find_all('link'): if link.get('rel')[0] == "authorization_endpoint": authorization_endpoint = link.get('href') authorization_endpoint = urljoin(r.url, authorization_endpoint) if r.headers.get('Link', None): links = r.headers['Link'] print(links) for link in links.split(","): possible_url = link.split(";")[0].strip() possible_url = possible_url[1:len(possible_url)-1] possible_rel = link.split(";")[1].strip() if possible_rel == "rel=authorization_endpoint": authorization_endpoint = urljoin(r.url, possible_url) site = r.url print(r.history) searchHistory = True i = -1 while searchHistory and (i*-1) <= len(r.history): history_piece = r.history[i] if history_piece.status_code == 301: site = history_piece.url i -= 1 # If ALL of them are temporary redirects than use the initial value if all(i.status_code == 302 for i in r.history): site = request.POST.get("site", None) if authorization_endpoint: request.session['authorization_endpoint']=authorization_endpoint request.session['client_id'] = site request.session['state'] = unique_id payload = {'me': site, 'redirect_uri': request.build_absolute_uri(reverse('indieauth:redirect')), 'client_id': f'{request.scheme}://{ request.get_host() }/indieauth/application_info', 'state': unique_id, 'response_type': 'id'} redirect_site = authorization_endpoint + "?" + urlencode(payload) return HttpResponseRedirect(redirect_site) else: cleanup(request) messages.error(request, 'No authorization_endpoint found.') return HttpResponseRedirect(reverse('indieauth:index')) except Exception as e: print(e) messages.error(request, 'Error in retrieving url.') return HttpResponseRedirect(reverse('indieauth:index')) messages.error(request, 'No site submitted or the URL submitted was not valid.') return HttpResponseRedirect(reverse('indieauth:index')) def redirect(request): if request.GET.get('state', None) == request.session.get('state', None) and request.session.get('state', None) != None: client_id = request.session['client_id'] authorization_endpoint = request.session['authorization_endpoint'] redirect_uri = request.build_absolute_uri(reverse('indieauth:redirect')) code = request.GET.get('code') r = requests.post(authorization_endpoint, data = {'code':code, 'client_id':client_id, 'redirect_uri': redirect_uri}) if r.headers['content-type'] == "application/x-www-form-urlencoded": user_site = unquote(r.text)[3:] elif r.headers['content-type'] == "application/json": user_site = r.text['me'] else: user_site = None user_site_matches_domain = urlparse(client_id).netloc == urlparse(user_site).netloc print(urlparse(client_id).netloc, urlparse(user_site).netloc) if r.status_code == 200 and user_site and user_site_matches_domain: messages.success(request, 'Your URL is: ' + user_site) user_model = get_user_model() user = user_model.objects.filter(site=user_site) if user: login_auth(request, user[0]) else: user = user_model.objects.create_user(username=user_site, site=user_site) user.set_unusable_password() login_auth(request, user) cleanup(request) return HttpResponseRedirect(reverse('feed:index')) else: messages.error(request, 'Error in URL. Please try again.') cleanup(request) return HttpResponseRedirect(reverse('indieauth:index')) else: messages.error(request, 'Major error. Likely timeout. Please try again.') cleanup(request) return HttpResponseRedirect(reverse('indieauth:index')) def cleanup(request): try: del request.session['authorization_endpoint'] del request.session['state'] del request.session['client_id'] except KeyError: pass def application_info(request): return render(request, "indieauth/application_info.html")

true

f70fa2ec4e8ddf2e3938910c093b9fd6af4215c2

8,957

py

Python

mpf/devices/switch.py

cloudjor/mpf

1cf6bf18b0d81120383b0b128b0ebbfa1c62717c

[ "MIT" ]

null

mpf/devices/switch.py

cloudjor/mpf

1cf6bf18b0d81120383b0b128b0ebbfa1c62717c

[ "MIT" ]

null

mpf/devices/switch.py

cloudjor/mpf

1cf6bf18b0d81120383b0b128b0ebbfa1c62717c

[ "MIT" ]

null

"""Contains the Switch parent class.""" import asyncio from functools import partial from mpf.core.device_monitor import DeviceMonitor from mpf.core.machine import MachineController from mpf.core.system_wide_device import SystemWideDevice from mpf.core.utility_functions import Util from mpf.core.platform import SwitchConfig from mpf.devices.device_mixins import DevicePositionMixin MYPY = False if MYPY: # pragma: no cover from mpf.platforms.interfaces.switch_platform_interface import SwitchPlatformInterface from mpf.core.platform import SwitchPlatform @DeviceMonitor("state", "recycle_jitter_count") class Switch(SystemWideDevice, DevicePositionMixin): """A switch in a pinball machine.""" config_section = 'switches' collection = 'switches' class_label = 'switch' __slots__ = ["hw_switch", "platform", "state", "hw_state", "invert", "recycle_secs", "recycle_clear_time", "recycle_jitter_count", "_events_to_post", "last_change"] def __init__(self, machine: MachineController, name: str) -> None: """Initialise switch.""" self.hw_switch = None # type: SwitchPlatformInterface self.platform = None # type: SwitchPlatform super().__init__(machine, name) self.state = 0 """ The logical state of a switch. 1 = active, 0 = inactive. This takes into consideration the NC or NO settings for the switch.""" self.hw_state = 0 """ The physical hardware state of the switch. 1 = active, 0 = inactive. This is what the actual hardware is reporting and does not consider whether a switch is NC or NO.""" self.invert = 0 self.recycle_secs = 0 self.recycle_clear_time = None self.recycle_jitter_count = 0 self._events_to_post = {0: [], 1: []} self.last_change = -100000 # register switch so other devices can add handlers to it self.machine.switch_controller.register_switch(self) @classmethod def device_class_init(cls, machine: MachineController): """Register handler for duplicate switch number checks.""" machine.events.add_handler("init_phase_4", cls._check_duplicate_switch_numbers, machine=machine) @staticmethod def _check_duplicate_switch_numbers(machine, **kwargs): del kwargs check_set = set() for switch in machine.switches: key = (switch.platform, switch.hw_switch.number) if key in check_set: raise AssertionError( "Duplicate switch number {} for switch {}".format( switch.hw_switch.number, switch)) check_set.add(key) def validate_and_parse_config(self, config, is_mode_config, debug_prefix: str = None): """Validate switch config.""" config = super().validate_and_parse_config(config, is_mode_config, debug_prefix) platform = self.machine.get_platform_sections( 'switches', getattr(config, "platform", None)) config['platform_settings'] = platform.validate_switch_section( self, config.get('platform_settings', None)) self._configure_device_logging(config) return config def _create_activation_event(self, event_str: str, state: int): if "|" in event_str: event, ev_time = event_str.split("|") ms = Util.string_to_ms(ev_time) self.machine.switch_controller.add_switch_handler( switch_name=self.name, state=state, callback=partial(self.machine.events.post, event=event), ms=ms ) else: self._events_to_post[state].append(event_str) def _recycle_passed(self, state): self.recycle_clear_time = None # only post event if the switch toggled if self.state != state: self._post_events(self.state) def _post_events_with_recycle(self, state): # if recycle is ongoing do nothing if not self.recycle_clear_time: # calculate clear time self.recycle_clear_time = self.machine.clock.get_time() + self.recycle_secs self.machine.clock.loop.call_at(self.recycle_clear_time, partial(self._recycle_passed, state)) # post event self._post_events(state) def _post_events(self, state): for event in self._events_to_post[state]: if self.machine.events.does_event_exist(event): self.machine.events.post(event) @asyncio.coroutine def _initialize(self): yield from super()._initialize() self.platform = self.machine.get_platform_sections( 'switches', self.config['platform']) if self.config['type'].upper() == 'NC': self.invert = 1 self.recycle_secs = self.config['ignore_window_ms'] / 1000.0 config = SwitchConfig(invert=self.invert, debounce=self.config['debounce']) try: self.hw_switch = self.platform.configure_switch( self.config['number'], config, self.config['platform_settings']) except AssertionError as e: raise AssertionError("Failed to configure switch {} in platform. See error above".format(self.name)) from e if self.recycle_secs: self.add_handler(state=1, callback=self._post_events_with_recycle, callback_kwargs={"state": 1}) self.add_handler(state=0, callback=self._post_events_with_recycle, callback_kwargs={"state": 0}) else: self.add_handler(state=1, callback=self._post_events, callback_kwargs={"state": 1}) self.add_handler(state=0, callback=self._post_events, callback_kwargs={"state": 0}) if self.machine.config['mpf']['auto_create_switch_events']: self._create_activation_event( self.machine.config['mpf']['switch_event_active'].replace( '%', self.name), 1) self._create_activation_event( self.machine.config['mpf']['switch_event_inactive'].replace( '%', self.name), 0) for tag in self.tags: self._create_activation_event( self.machine.config['mpf']['switch_tag_event'].replace( '%', tag), 1) self._create_activation_event( self.machine.config['mpf']['switch_tag_event'].replace( '%', tag) + "_active", 1) self._create_activation_event( self.machine.config['mpf']['switch_tag_event'].replace( '%', tag) + "_inactive", 0) for event in Util.string_to_lowercase_list( self.config['events_when_activated']): self._create_activation_event(event, 1) for event in Util.string_to_lowercase_list( self.config['events_when_deactivated']): self._create_activation_event(event, 0) # pylint: disable-msg=too-many-arguments def add_handler(self, callback, state=1, ms=0, return_info=False, callback_kwargs=None): """Add switch handler (callback) for this switch which is called when this switch state changes. Note that this method just calls the :doc:`Switch Controller's <self.machine.switch_controller>` ``add_switch_handler()`` method behind the scenes. Args: callback: A callable method that will be called when the switch state changes. state: The state that the switch which change into which triggers the callback to be called. Values are 0 or 1, with 0 meaning the switch changed to inactive, and 1 meaning the switch changed to an active state. ms: How many milliseconds the switch needs to be in the new state before the callback is called. Default is 0 which means that the callback will be called immediately. You can use this setting as a form of software debounce, as the switch needs to be in the state consistently before the callback is called. return_info: If True, the switch controller will pass the parameters of the switch handler as arguments to the callback, including switch_name, state, and ms. callback_kwargs: Additional kwargs that will be passed with the callback. """ return self.machine.switch_controller.add_switch_handler( self.name, callback, state, ms, return_info, callback_kwargs) def remove_handler(self, callback, state=1, ms=0): """Remove switch handler for this switch.""" return self.machine.switch_controller.remove_switch_handler( self.name, callback, state, ms)

43.692683

119

0.635927

import asyncio from functools import partial from mpf.core.device_monitor import DeviceMonitor from mpf.core.machine import MachineController from mpf.core.system_wide_device import SystemWideDevice from mpf.core.utility_functions import Util from mpf.core.platform import SwitchConfig from mpf.devices.device_mixins import DevicePositionMixin MYPY = False if MYPY: from mpf.platforms.interfaces.switch_platform_interface import SwitchPlatformInterface from mpf.core.platform import SwitchPlatform @DeviceMonitor("state", "recycle_jitter_count") class Switch(SystemWideDevice, DevicePositionMixin): config_section = 'switches' collection = 'switches' class_label = 'switch' __slots__ = ["hw_switch", "platform", "state", "hw_state", "invert", "recycle_secs", "recycle_clear_time", "recycle_jitter_count", "_events_to_post", "last_change"] def __init__(self, machine: MachineController, name: str) -> None: self.hw_switch = None self.platform = None super().__init__(machine, name) self.state = 0 self.hw_state = 0 self.invert = 0 self.recycle_secs = 0 self.recycle_clear_time = None self.recycle_jitter_count = 0 self._events_to_post = {0: [], 1: []} self.last_change = -100000 self.machine.switch_controller.register_switch(self) @classmethod def device_class_init(cls, machine: MachineController): machine.events.add_handler("init_phase_4", cls._check_duplicate_switch_numbers, machine=machine) @staticmethod def _check_duplicate_switch_numbers(machine, **kwargs): del kwargs check_set = set() for switch in machine.switches: key = (switch.platform, switch.hw_switch.number) if key in check_set: raise AssertionError( "Duplicate switch number {} for switch {}".format( switch.hw_switch.number, switch)) check_set.add(key) def validate_and_parse_config(self, config, is_mode_config, debug_prefix: str = None): config = super().validate_and_parse_config(config, is_mode_config, debug_prefix) platform = self.machine.get_platform_sections( 'switches', getattr(config, "platform", None)) config['platform_settings'] = platform.validate_switch_section( self, config.get('platform_settings', None)) self._configure_device_logging(config) return config def _create_activation_event(self, event_str: str, state: int): if "|" in event_str: event, ev_time = event_str.split("|") ms = Util.string_to_ms(ev_time) self.machine.switch_controller.add_switch_handler( switch_name=self.name, state=state, callback=partial(self.machine.events.post, event=event), ms=ms ) else: self._events_to_post[state].append(event_str) def _recycle_passed(self, state): self.recycle_clear_time = None if self.state != state: self._post_events(self.state) def _post_events_with_recycle(self, state): if not self.recycle_clear_time: self.recycle_clear_time = self.machine.clock.get_time() + self.recycle_secs self.machine.clock.loop.call_at(self.recycle_clear_time, partial(self._recycle_passed, state)) self._post_events(state) def _post_events(self, state): for event in self._events_to_post[state]: if self.machine.events.does_event_exist(event): self.machine.events.post(event) @asyncio.coroutine def _initialize(self): yield from super()._initialize() self.platform = self.machine.get_platform_sections( 'switches', self.config['platform']) if self.config['type'].upper() == 'NC': self.invert = 1 self.recycle_secs = self.config['ignore_window_ms'] / 1000.0 config = SwitchConfig(invert=self.invert, debounce=self.config['debounce']) try: self.hw_switch = self.platform.configure_switch( self.config['number'], config, self.config['platform_settings']) except AssertionError as e: raise AssertionError("Failed to configure switch {} in platform. See error above".format(self.name)) from e if self.recycle_secs: self.add_handler(state=1, callback=self._post_events_with_recycle, callback_kwargs={"state": 1}) self.add_handler(state=0, callback=self._post_events_with_recycle, callback_kwargs={"state": 0}) else: self.add_handler(state=1, callback=self._post_events, callback_kwargs={"state": 1}) self.add_handler(state=0, callback=self._post_events, callback_kwargs={"state": 0}) if self.machine.config['mpf']['auto_create_switch_events']: self._create_activation_event( self.machine.config['mpf']['switch_event_active'].replace( '%', self.name), 1) self._create_activation_event( self.machine.config['mpf']['switch_event_inactive'].replace( '%', self.name), 0) for tag in self.tags: self._create_activation_event( self.machine.config['mpf']['switch_tag_event'].replace( '%', tag), 1) self._create_activation_event( self.machine.config['mpf']['switch_tag_event'].replace( '%', tag) + "_active", 1) self._create_activation_event( self.machine.config['mpf']['switch_tag_event'].replace( '%', tag) + "_inactive", 0) for event in Util.string_to_lowercase_list( self.config['events_when_activated']): self._create_activation_event(event, 1) for event in Util.string_to_lowercase_list( self.config['events_when_deactivated']): self._create_activation_event(event, 0) def add_handler(self, callback, state=1, ms=0, return_info=False, callback_kwargs=None): return self.machine.switch_controller.add_switch_handler( self.name, callback, state, ms, return_info, callback_kwargs) def remove_handler(self, callback, state=1, ms=0): return self.machine.switch_controller.remove_switch_handler( self.name, callback, state, ms)

true

f70fa4716a8ad9de69464eb03d1b8acc667bcf25

8,629

py

Python

lucida/speechrecognition/kaldi_gstreamer_asr/kaldigstserver/decoder2.py

extremenelson/sirius

0bad428bb763fe404d01db5d9e08ee33a8f3776c

[ "BSD-3-Clause" ]

1,808

2015-12-23T09:38:57.000Z

2022-03-24T05:55:03.000Z

lucida/speechrecognition/kaldi_gstreamer_asr/kaldigstserver/decoder2.py

extremenelson/sirius

0bad428bb763fe404d01db5d9e08ee33a8f3776c

[ "BSD-3-Clause" ]

164

2015-12-22T17:32:16.000Z

2022-01-30T16:19:28.000Z

lucida/speechrecognition/kaldi_gstreamer_asr/kaldigstserver/decoder2.py

mrinformatics/lucida1604

f17fba20be9765c3464437f40e97278bba29b9d5

[ "BSD-3-Clause" ]

554

2015-12-23T11:29:34.000Z

2022-02-08T05:31:49.000Z

""" Created on May 17, 2013 @author: tanel """ import gi gi.require_version('Gst', '1.0') from gi.repository import GObject, Gst GObject.threads_init() Gst.init(None) import logging import thread import os logger = logging.getLogger(__name__) import pdb class DecoderPipeline2(object): def __init__(self, conf={}): logger.info("Creating decoder using conf: %s" % conf) self.create_pipeline(conf) self.outdir = conf.get("out-dir", None) if not os.path.exists(self.outdir): os.makedirs(self.outdir) elif not os.path.isdir(self.outdir): raise Exception("Output directory %s already exists as a file" % self.outdir) self.result_handler = None self.full_result_handler = None self.eos_handler = None self.error_handler = None self.request_id = "<undefined>" def create_pipeline(self, conf): self.appsrc = Gst.ElementFactory.make("appsrc", "appsrc") self.decodebin = Gst.ElementFactory.make("decodebin", "decodebin") self.audioconvert = Gst.ElementFactory.make("audioconvert", "audioconvert") self.audioresample = Gst.ElementFactory.make("audioresample", "audioresample") self.tee = Gst.ElementFactory.make("tee", "tee") self.queue1 = Gst.ElementFactory.make("queue", "queue1") self.filesink = Gst.ElementFactory.make("filesink", "filesink") self.queue2 = Gst.ElementFactory.make("queue", "queue2") self.asr = Gst.ElementFactory.make("kaldinnet2onlinedecoder", "asr") self.fakesink = Gst.ElementFactory.make("fakesink", "fakesink") # This needs to be set first if "use-threaded-decoder" in conf["decoder"]: self.asr.set_property("use-threaded-decoder", conf["decoder"]["use-threaded-decoder"]) for (key, val) in conf.get("decoder", {}).iteritems(): if key != "use-threaded-decoder": logger.info("Setting decoder property: %s = %s" % (key, val)) self.asr.set_property(key, val) self.appsrc.set_property("is-live", True) self.filesink.set_property("location", "/dev/null") logger.info('Created GStreamer elements') self.pipeline = Gst.Pipeline() for element in [self.appsrc, self.decodebin, self.audioconvert, self.audioresample, self.tee, self.queue1, self.filesink, self.queue2, self.asr, self.fakesink]: logger.debug("Adding %s to the pipeline" % element) self.pipeline.add(element) logger.info('Linking GStreamer elements') self.appsrc.link(self.decodebin) #self.appsrc.link(self.audioconvert) self.decodebin.connect('pad-added', self._connect_decoder) self.audioconvert.link(self.audioresample) self.audioresample.link(self.tee) self.tee.link(self.queue1) self.queue1.link(self.filesink) self.tee.link(self.queue2) self.queue2.link(self.asr) self.asr.link(self.fakesink) # Create bus and connect several handlers self.bus = self.pipeline.get_bus() self.bus.add_signal_watch() self.bus.enable_sync_message_emission() self.bus.connect('message::eos', self._on_eos) self.bus.connect('message::error', self._on_error) #self.bus.connect('message::cutter', self._on_cutter) self.asr.connect('partial-result', self._on_partial_result) self.asr.connect('final-result', self._on_final_result) self.asr.connect('full-final-result', self._on_full_final_result) logger.info("Setting pipeline to READY") self.pipeline.set_state(Gst.State.READY) logger.info("Set pipeline to READY") def _connect_decoder(self, element, pad): logger.info("%s: Connecting audio decoder" % self.request_id) pad.link(self.audioconvert.get_static_pad("sink")) logger.info("%s: Connected audio decoder" % self.request_id) def _on_partial_result(self, asr, hyp): logger.info("%s: Got partial result: %s" % (self.request_id, hyp.decode('utf8'))) if self.result_handler: self.result_handler(hyp, False) def _on_final_result(self, asr, hyp): logger.info("%s: Got final result: %s" % (self.request_id, hyp.decode('utf8'))) if self.result_handler: self.result_handler(hyp, True) def _on_full_final_result(self, asr, result_json): logger.info("%s: Got full final result: %s" % (self.request_id, result_json.decode('utf8'))) if self.full_result_handler: self.full_result_handler(result_json) def _on_error(self, bus, msg): self.error = msg.parse_error() logger.error(self.error) self.finish_request() if self.error_handler: self.error_handler(self.error[0].message) def _on_eos(self, bus, msg): logger.info('%s: Pipeline received eos signal' % self.request_id) #self.decodebin.unlink(self.audioconvert) self.finish_request() if self.eos_handler: self.eos_handler[0](self.eos_handler[1]) def get_adaptation_state(self): return self.asr.get_property("adaptation-state") def set_adaptation_state(self, adaptation_state): """Sets the adaptation state to a certian value, previously retrieved using get_adaptation_state() Should be called after init_request(..) """ return self.asr.set_property("adaptation-state", adaptation_state) def finish_request(self): logger.info("%s: Resetting decoder state" % self.request_id) if self.outdir: self.filesink.set_state(Gst.State.NULL) self.filesink.set_property('location', "/dev/null") self.filesink.set_state(Gst.State.PLAYING) self.pipeline.set_state(Gst.State.NULL) self.request_id = "<undefined>" def init_request(self, id, caps_str): self.request_id = id logger.info("%s: Initializing request" % (self.request_id)) if caps_str and len(caps_str) > 0: logger.info("%s: Setting caps to %s" % (self.request_id, caps_str)) caps = Gst.caps_from_string(caps_str) self.appsrc.set_property("caps", caps) else: #caps = Gst.caps_from_string("") self.appsrc.set_property("caps", None) #self.pipeline.set_state(Gst.State.READY) pass #self.appsrc.set_state(Gst.State.PAUSED) if self.outdir: self.pipeline.set_state(Gst.State.PAUSED) self.filesink.set_state(Gst.State.NULL) self.filesink.set_property('location', "%s/%s.raw" % (self.outdir, id)) self.filesink.set_state(Gst.State.PLAYING) #self.filesink.set_state(Gst.State.PLAYING) #self.decodebin.set_state(Gst.State.PLAYING) self.pipeline.set_state(Gst.State.PLAYING) self.filesink.set_state(Gst.State.PLAYING) # push empty buffer (to avoid hang on client diconnect) #buf = Gst.Buffer.new_allocate(None, 0, None) #self.appsrc.emit("push-buffer", buf) # reset adaptation state self.set_adaptation_state("") def process_data(self, data): logger.debug('%s: Pushing buffer of size %d to pipeline' % (self.request_id, len(data))) buf = Gst.Buffer.new_allocate(None, len(data), None) buf.fill(0, data) self.appsrc.emit("push-buffer", buf) logger.debug('%s: Pushing buffer done' % self.request_id) def end_request(self): logger.info("%s: Pushing EOS to pipeline" % self.request_id) self.appsrc.emit("end-of-stream") def set_result_handler(self, handler): self.result_handler = handler def set_full_result_handler(self, handler): self.full_result_handler = handler def set_eos_handler(self, handler, user_data=None): self.eos_handler = (handler, user_data) def set_error_handler(self, handler): self.error_handler = handler def cancel(self): logger.info("%s: Sending EOS to pipeline in order to cancel processing" % self.request_id) self.appsrc.emit("end-of-stream") #self.asr.set_property("silent", True) #self.pipeline.set_state(Gst.State.NULL) #if (self.pipeline.get_state() == Gst.State.PLAYING): #logger.debug("Sending EOS to pipeline") #self.pipeline.send_event(Gst.Event.new_eos()) #self.pipeline.set_state(Gst.State.READY) logger.info("%s: Cancelled pipeline" % self.request_id)

38.013216

106

0.644918

import gi gi.require_version('Gst', '1.0') from gi.repository import GObject, Gst GObject.threads_init() Gst.init(None) import logging import thread import os logger = logging.getLogger(__name__) import pdb class DecoderPipeline2(object): def __init__(self, conf={}): logger.info("Creating decoder using conf: %s" % conf) self.create_pipeline(conf) self.outdir = conf.get("out-dir", None) if not os.path.exists(self.outdir): os.makedirs(self.outdir) elif not os.path.isdir(self.outdir): raise Exception("Output directory %s already exists as a file" % self.outdir) self.result_handler = None self.full_result_handler = None self.eos_handler = None self.error_handler = None self.request_id = "<undefined>" def create_pipeline(self, conf): self.appsrc = Gst.ElementFactory.make("appsrc", "appsrc") self.decodebin = Gst.ElementFactory.make("decodebin", "decodebin") self.audioconvert = Gst.ElementFactory.make("audioconvert", "audioconvert") self.audioresample = Gst.ElementFactory.make("audioresample", "audioresample") self.tee = Gst.ElementFactory.make("tee", "tee") self.queue1 = Gst.ElementFactory.make("queue", "queue1") self.filesink = Gst.ElementFactory.make("filesink", "filesink") self.queue2 = Gst.ElementFactory.make("queue", "queue2") self.asr = Gst.ElementFactory.make("kaldinnet2onlinedecoder", "asr") self.fakesink = Gst.ElementFactory.make("fakesink", "fakesink") if "use-threaded-decoder" in conf["decoder"]: self.asr.set_property("use-threaded-decoder", conf["decoder"]["use-threaded-decoder"]) for (key, val) in conf.get("decoder", {}).iteritems(): if key != "use-threaded-decoder": logger.info("Setting decoder property: %s = %s" % (key, val)) self.asr.set_property(key, val) self.appsrc.set_property("is-live", True) self.filesink.set_property("location", "/dev/null") logger.info('Created GStreamer elements') self.pipeline = Gst.Pipeline() for element in [self.appsrc, self.decodebin, self.audioconvert, self.audioresample, self.tee, self.queue1, self.filesink, self.queue2, self.asr, self.fakesink]: logger.debug("Adding %s to the pipeline" % element) self.pipeline.add(element) logger.info('Linking GStreamer elements') self.appsrc.link(self.decodebin) self.decodebin.connect('pad-added', self._connect_decoder) self.audioconvert.link(self.audioresample) self.audioresample.link(self.tee) self.tee.link(self.queue1) self.queue1.link(self.filesink) self.tee.link(self.queue2) self.queue2.link(self.asr) self.asr.link(self.fakesink) self.bus = self.pipeline.get_bus() self.bus.add_signal_watch() self.bus.enable_sync_message_emission() self.bus.connect('message::eos', self._on_eos) self.bus.connect('message::error', self._on_error) self.asr.connect('partial-result', self._on_partial_result) self.asr.connect('final-result', self._on_final_result) self.asr.connect('full-final-result', self._on_full_final_result) logger.info("Setting pipeline to READY") self.pipeline.set_state(Gst.State.READY) logger.info("Set pipeline to READY") def _connect_decoder(self, element, pad): logger.info("%s: Connecting audio decoder" % self.request_id) pad.link(self.audioconvert.get_static_pad("sink")) logger.info("%s: Connected audio decoder" % self.request_id) def _on_partial_result(self, asr, hyp): logger.info("%s: Got partial result: %s" % (self.request_id, hyp.decode('utf8'))) if self.result_handler: self.result_handler(hyp, False) def _on_final_result(self, asr, hyp): logger.info("%s: Got final result: %s" % (self.request_id, hyp.decode('utf8'))) if self.result_handler: self.result_handler(hyp, True) def _on_full_final_result(self, asr, result_json): logger.info("%s: Got full final result: %s" % (self.request_id, result_json.decode('utf8'))) if self.full_result_handler: self.full_result_handler(result_json) def _on_error(self, bus, msg): self.error = msg.parse_error() logger.error(self.error) self.finish_request() if self.error_handler: self.error_handler(self.error[0].message) def _on_eos(self, bus, msg): logger.info('%s: Pipeline received eos signal' % self.request_id) self.finish_request() if self.eos_handler: self.eos_handler[0](self.eos_handler[1]) def get_adaptation_state(self): return self.asr.get_property("adaptation-state") def set_adaptation_state(self, adaptation_state): return self.asr.set_property("adaptation-state", adaptation_state) def finish_request(self): logger.info("%s: Resetting decoder state" % self.request_id) if self.outdir: self.filesink.set_state(Gst.State.NULL) self.filesink.set_property('location', "/dev/null") self.filesink.set_state(Gst.State.PLAYING) self.pipeline.set_state(Gst.State.NULL) self.request_id = "<undefined>" def init_request(self, id, caps_str): self.request_id = id logger.info("%s: Initializing request" % (self.request_id)) if caps_str and len(caps_str) > 0: logger.info("%s: Setting caps to %s" % (self.request_id, caps_str)) caps = Gst.caps_from_string(caps_str) self.appsrc.set_property("caps", caps) else: self.appsrc.set_property("caps", None) pass if self.outdir: self.pipeline.set_state(Gst.State.PAUSED) self.filesink.set_state(Gst.State.NULL) self.filesink.set_property('location', "%s/%s.raw" % (self.outdir, id)) self.filesink.set_state(Gst.State.PLAYING) self.pipeline.set_state(Gst.State.PLAYING) self.filesink.set_state(Gst.State.PLAYING) self.set_adaptation_state("") def process_data(self, data): logger.debug('%s: Pushing buffer of size %d to pipeline' % (self.request_id, len(data))) buf = Gst.Buffer.new_allocate(None, len(data), None) buf.fill(0, data) self.appsrc.emit("push-buffer", buf) logger.debug('%s: Pushing buffer done' % self.request_id) def end_request(self): logger.info("%s: Pushing EOS to pipeline" % self.request_id) self.appsrc.emit("end-of-stream") def set_result_handler(self, handler): self.result_handler = handler def set_full_result_handler(self, handler): self.full_result_handler = handler def set_eos_handler(self, handler, user_data=None): self.eos_handler = (handler, user_data) def set_error_handler(self, handler): self.error_handler = handler def cancel(self): logger.info("%s: Sending EOS to pipeline in order to cancel processing" % self.request_id) self.appsrc.emit("end-of-stream") logger.info("%s: Cancelled pipeline" % self.request_id)

true

f70fa47cf19e266644db800bdfe629f660dc9d2e

419

py

Python

Mundo 2 - Exercicios/61Exercicio.py

andrezzadede/Curso_Python_Guanabara_Mundo_2

e4ebf171f74809f8a65e846c59978db95c5d3b1b

[ "MIT" ]

null

Mundo 2 - Exercicios/61Exercicio.py

andrezzadede/Curso_Python_Guanabara_Mundo_2

e4ebf171f74809f8a65e846c59978db95c5d3b1b

[ "MIT" ]

null

Mundo 2 - Exercicios/61Exercicio.py

andrezzadede/Curso_Python_Guanabara_Mundo_2

e4ebf171f74809f8a65e846c59978db95c5d3b1b

[ "MIT" ]

null

print('---------- Bem vindo ao exercicio 61 ------') print('\033[32m Reçaca o desafio 51. Lendo o primeiro termo e a razao de uma PA. Mostrando os 10 primeiros termos da progressa usando a estrutura while\033[m') primeiro = int(input('Primeiro termo: ')) razao = int(input('Razão: ')) termo = primeiro c = 1 while c <= 10: print('{} -> '.format(termo), end='') termo += razao c += 1 print('Fim')

18.217391

159

0.618138

print('---------- Bem vindo ao exercicio 61 ------') print('\033[32m Reçaca o desafio 51. Lendo o primeiro termo e a razao de uma PA. Mostrando os 10 primeiros termos da progressa usando a estrutura while\033[m') primeiro = int(input('Primeiro termo: ')) razao = int(input('Razão: ')) termo = primeiro c = 1 while c <= 10: print('{} -> '.format(termo), end='') termo += razao c += 1 print('Fim')

true

f70fa4df361d80f9ecacef1088f93b318fd49fe6

2,056

py

Python

docs/conf.py

Moody-Tunes/spotify-client

496c72d915d92c29795a31a18cc9af26a9015b4b

[ "MIT" ]

1

2020-12-21T02:35:18.000Z

docs/conf.py

Moody-Tunes/spotify-client

496c72d915d92c29795a31a18cc9af26a9015b4b

[ "MIT" ]

9

2020-09-04T15:35:23.000Z

2021-04-24T02:10:56.000Z

docs/conf.py

Moody-Tunes/spotify-client

496c72d915d92c29795a31a18cc9af26a9015b4b

[ "MIT" ]

2

2020-12-21T02:35:24.000Z

2020-12-29T07:38:16.000Z

# Configuration file for the Sphinx documentation builder. # # This file only contains a selection of the most common options. For a full # list see the documentation: # https://www.sphinx-doc.org/en/master/usage/configuration.html # -- Path setup -------------------------------------------------------------- # If extensions (or modules to document with autodoc) are in another directory, # add these directories to sys.path here. If the directory is relative to the # documentation root, use os.path.abspath to make it absolute, like shown here. # import os import sys sys.path.insert(0, os.path.abspath('..')) # -- Project information ----------------------------------------------------- project = 'spotify-client' copyright = '2020, MoodyTunes' author = 'MoodyTunes' # The full version, including alpha/beta/rc tags with open("../VERSION", "r") as version_file: version = version_file.read().strip() release = version # -- General configuration --------------------------------------------------- # Add any Sphinx extension module names here, as strings. They can be # extensions coming with Sphinx (named 'sphinx.ext.*') or your custom # ones. extensions = ['sphinx.ext.autodoc'] pygments_style = 'sphinx' # Add any paths that contain templates here, relative to this directory. templates_path = ['_templates'] # List of patterns, relative to source directory, that match files and # directories to ignore when looking for source files. # This pattern also affects html_static_path and html_extra_path. exclude_patterns = ['_build', 'Thumbs.db', '.DS_Store'] # -- Options for HTML output ------------------------------------------------- # The theme to use for HTML and HTML Help pages. See the documentation for # a list of builtin themes. # html_theme = 'alabaster' # Add any paths that contain custom static files (such as style sheets) here, # relative to this directory. They are copied after the builtin static files, # so a file named "default.css" will overwrite the builtin "default.css". html_static_path = ['_static']

35.448276

79

0.669747

import os import sys sys.path.insert(0, os.path.abspath('..')) project = 'spotify-client' copyright = '2020, MoodyTunes' author = 'MoodyTunes' with open("../VERSION", "r") as version_file: version = version_file.read().strip() release = version extensions = ['sphinx.ext.autodoc'] pygments_style = 'sphinx' templates_path = ['_templates'] exclude_patterns = ['_build', 'Thumbs.db', '.DS_Store'] html_theme = 'alabaster' html_static_path = ['_static']

true

f70fa5c21d44574a534aa9a438155cd0428d003d

946

py

Python

decorator/send_commands_netmiko.py

levs72/pyneng-examples

d6288292dcf9d1ebc5a9db4a0d620bd11b4a2df9

[ "MIT" ]

11

2021-04-05T09:30:23.000Z

2022-03-09T13:27:56.000Z

decorator/send_commands_netmiko.py

levs72/pyneng-examples

d6288292dcf9d1ebc5a9db4a0d620bd11b4a2df9

[ "MIT" ]

null

decorator/send_commands_netmiko.py

levs72/pyneng-examples

d6288292dcf9d1ebc5a9db4a0d620bd11b4a2df9

[ "MIT" ]

11

2021-04-06T03:44:35.000Z

2022-03-04T21:20:40.000Z

from netmiko import ConnectHandler import yaml from pprint import pprint def send_show_command(device, show_command): with ConnectHandler(**device) as ssh: ssh.enable() result = ssh.send_command(show_command) return result def send_config_commands(device, config_commands): with ConnectHandler(**device) as ssh: ssh.enable() result = ssh.send_config_set(config_commands) return result def send_commands(device, config=None, show=None): if show: return send_show_command(device_list, show) elif config: return send_config_commands(device_list, config) if __name__ == "__main__": commands = ["logging 10.255.255.1", "logging buffered 20010", "no logging console"] show_command = "sh ip int br" with open("devices.yaml") as f: dev_list = yaml.safe_load(f) send_commands(dev_list, config=commands) send_commands(dev_list, show=show_command)

27.028571

87

0.707188

from netmiko import ConnectHandler import yaml from pprint import pprint def send_show_command(device, show_command): with ConnectHandler(**device) as ssh: ssh.enable() result = ssh.send_command(show_command) return result def send_config_commands(device, config_commands): with ConnectHandler(**device) as ssh: ssh.enable() result = ssh.send_config_set(config_commands) return result def send_commands(device, config=None, show=None): if show: return send_show_command(device_list, show) elif config: return send_config_commands(device_list, config) if __name__ == "__main__": commands = ["logging 10.255.255.1", "logging buffered 20010", "no logging console"] show_command = "sh ip int br" with open("devices.yaml") as f: dev_list = yaml.safe_load(f) send_commands(dev_list, config=commands) send_commands(dev_list, show=show_command)

true

f70fa6b243087d007e8f52414d27fa83ded92b20

2,335

py

Python

venv/Lib/site-packages/IPython/html/widgets/widget_container.py

Tyranicangel/dtrans

a5e23d200a310701bb357bff09e35a5629a3f7a3

[ "BSD-3-Clause" ]

8

2021-12-14T21:30:01.000Z

2022-02-14T11:30:03.000Z

IPython/html/widgets/widget_container.py

khinsen/ipython

dfd5cb1d3e34048593ba537dacdbef08fe766624

[ "BSD-3-Clause-Clear" ]

1

2021-09-11T14:30:32.000Z

IPython/html/widgets/widget_container.py

khinsen/ipython

dfd5cb1d3e34048593ba537dacdbef08fe766624

[ "BSD-3-Clause-Clear" ]

2

2016-12-19T02:27:46.000Z

2019-07-29T02:53:54.000Z

"""ContainerWidget class. Represents a container that can be used to group other widgets. """ #----------------------------------------------------------------------------- # Copyright (c) 2013, the IPython Development Team. # # Distributed under the terms of the Modified BSD License. # # The full license is in the file COPYING.txt, distributed with this software. #----------------------------------------------------------------------------- #----------------------------------------------------------------------------- # Imports #----------------------------------------------------------------------------- from .widget import DOMWidget from IPython.utils.traitlets import Unicode, Tuple, TraitError #----------------------------------------------------------------------------- # Classes #----------------------------------------------------------------------------- class ContainerWidget(DOMWidget): _view_name = Unicode('ContainerView', sync=True) # Child widgets in the container. # Using a tuple here to force reassignment to update the list. # When a proper notifying-list trait exists, that is what should be used here. children = Tuple() _children = Tuple(sync=True) def __init__(self, **kwargs): super(ContainerWidget, self).__init__(**kwargs) self.on_displayed(ContainerWidget._fire_children_displayed) def _fire_children_displayed(self): for child in self._children: child._handle_displayed() def _children_changed(self, name, old, new): """Validate children list. Makes sure only one instance of any given model can exist in the children list. An excellent post on uniqifiers is available at http://www.peterbe.com/plog/uniqifiers-benchmark which provides the inspiration for using this implementation. Below I've implemented the `f5` algorithm using Python comprehensions.""" if new is not None: seen = {} def add_item(i): seen[i.model_id] = True return i self._children = [add_item(i) for i in new if not i.model_id in seen] class PopupWidget(ContainerWidget): _view_name = Unicode('PopupView', sync=True) description = Unicode(sync=True) button_text = Unicode(sync=True)

37.063492

82

0.544325

from .widget import DOMWidget from IPython.utils.traitlets import Unicode, Tuple, TraitError class ContainerWidget(DOMWidget): _view_name = Unicode('ContainerView', sync=True) children = Tuple() _children = Tuple(sync=True) def __init__(self, **kwargs): super(ContainerWidget, self).__init__(**kwargs) self.on_displayed(ContainerWidget._fire_children_displayed) def _fire_children_displayed(self): for child in self._children: child._handle_displayed() def _children_changed(self, name, old, new): if new is not None: seen = {} def add_item(i): seen[i.model_id] = True return i self._children = [add_item(i) for i in new if not i.model_id in seen] class PopupWidget(ContainerWidget): _view_name = Unicode('PopupView', sync=True) description = Unicode(sync=True) button_text = Unicode(sync=True)

true

f70fa6ee938b2345b273950c3d615a8cb65e74f1

3,036

py

Python

inst/CnaAnnotator.py

jalavery/gnomeR

b0031bd5eb1c8c5636910d0b779a8808947245f5

[ "MIT" ]

15

2020-02-20T18:20:15.000Z

2021-12-23T08:49:09.000Z

inst/CnaAnnotator.py

jalavery/gnomeR

b0031bd5eb1c8c5636910d0b779a8808947245f5

[ "MIT" ]

36

2020-02-21T20:23:41.000Z

2022-03-04T21:12:44.000Z

inst/CnaAnnotator.py

MSKCC-Epi-Bio/gnomeR

4f165774eb3c5f442881a915ee70e18a5f33b387

[ "MIT" ]

9

2020-02-17T23:43:35.000Z

2022-03-21T12:01:36.000Z

import argparse # from AnnotatorCore import * import sys import csv import requests import os.path import logging import re import matplotlib matplotlib.use('Agg') import matplotlib.pyplot as plt from datetime import date import logging logging.basicConfig(level=logging.INFO) log = logging.getLogger('CnaAnnotator') def main(argv): if argv.help: log.info('\n' 'CnaAnnotator.py -i <input CNA file> -o <output CNA file> [-p previous results] [-c <input clinical file>] [-s sample list filter] [-t <default tumor type>] [-u oncokb-base-url] [-b oncokb_api_bear_token] [-z annotate_gain_loss]\n' ' Input CNA file should follow the GISTIC output (https://docs.cbioportal.org/5.1-data-loading/data-loading/file-formats#data-file-1)\n' ' Essential clinical columns:\n' ' SAMPLE_ID: sample ID\n' ' Cancer type will be assigned based on the following priority:\n' ' 1) ONCOTREE_CODE in clinical data file\n' ' 2) ONCOTREE_CODE exist in MAF\n' ' 3) default tumor type (-t)\n' ' We do not annotate Gain and Loss by default, add -z to include the analysis. See https://github.com/oncokb/oncokb-annotator/issues/51 for more information.\n' ' Default OncoKB base url is https://www.oncokb.org') sys.exit() if argv.input_file == '' or argv.output_file == '' or argv.oncokb_api_bearer_token == '': log.info('for help: python CnaAnnotator.py -h') sys.exit(2) if argv.sample_ids_filter: setsampleidsfileterfile(argv.sample_ids_filter) if argv.oncokb_api_url: setoncokbbaseurl(argv.oncokb_api_url) setoncokbapitoken(argv.oncokb_api_bearer_token) cancertypemap = {} if argv.input_clinical_file: readCancerTypes(argv.input_clinical_file, cancertypemap) log.info('annotating %s ...' % argv.input_file) processcnagisticdata(argv.input_file, argv.output_file, argv.previous_result_file, argv.default_cancer_type, cancertypemap, argv.annotate_gain_loss) log.info('done!') if __name__ == "__main__": parser = argparse.ArgumentParser(add_help=False) parser.add_argument('-h', dest='help', action="store_true", default=False) parser.add_argument('-i', dest='input_file', default='', type=str) parser.add_argument('-o', dest='output_file', default='', type=str) parser.add_argument('-p', dest='previous_result_file', default='', type=str) parser.add_argument('-c', dest='input_clinical_file', default='', type=str) parser.add_argument('-s', dest='sample_ids_filter', default='', type=str) parser.add_argument('-t', dest='default_cancer_type', default='', type=str) parser.add_argument('-u', dest='oncokb_api_url', default='', type=str) parser.add_argument('-b', dest='oncokb_api_bearer_token', default='', type=str) parser.add_argument('-z', dest='annotate_gain_loss', action="store_true", default=False) parser.set_defaults(func=main) args = parser.parse_args() args.func(args)

44.647059

239

0.693676

import argparse import sys import csv import requests import os.path import logging import re import matplotlib matplotlib.use('Agg') import matplotlib.pyplot as plt from datetime import date import logging logging.basicConfig(level=logging.INFO) log = logging.getLogger('CnaAnnotator') def main(argv): if argv.help: log.info('\n' 'CnaAnnotator.py -i <input CNA file> -o <output CNA file> [-p previous results] [-c <input clinical file>] [-s sample list filter] [-t <default tumor type>] [-u oncokb-base-url] [-b oncokb_api_bear_token] [-z annotate_gain_loss]\n' ' Input CNA file should follow the GISTIC output (https://docs.cbioportal.org/5.1-data-loading/data-loading/file-formats#data-file-1)\n' ' Essential clinical columns:\n' ' SAMPLE_ID: sample ID\n' ' Cancer type will be assigned based on the following priority:\n' ' 1) ONCOTREE_CODE in clinical data file\n' ' 2) ONCOTREE_CODE exist in MAF\n' ' 3) default tumor type (-t)\n' ' We do not annotate Gain and Loss by default, add -z to include the analysis. See https://github.com/oncokb/oncokb-annotator/issues/51 for more information.\n' ' Default OncoKB base url is https://www.oncokb.org') sys.exit() if argv.input_file == '' or argv.output_file == '' or argv.oncokb_api_bearer_token == '': log.info('for help: python CnaAnnotator.py -h') sys.exit(2) if argv.sample_ids_filter: setsampleidsfileterfile(argv.sample_ids_filter) if argv.oncokb_api_url: setoncokbbaseurl(argv.oncokb_api_url) setoncokbapitoken(argv.oncokb_api_bearer_token) cancertypemap = {} if argv.input_clinical_file: readCancerTypes(argv.input_clinical_file, cancertypemap) log.info('annotating %s ...' % argv.input_file) processcnagisticdata(argv.input_file, argv.output_file, argv.previous_result_file, argv.default_cancer_type, cancertypemap, argv.annotate_gain_loss) log.info('done!') if __name__ == "__main__": parser = argparse.ArgumentParser(add_help=False) parser.add_argument('-h', dest='help', action="store_true", default=False) parser.add_argument('-i', dest='input_file', default='', type=str) parser.add_argument('-o', dest='output_file', default='', type=str) parser.add_argument('-p', dest='previous_result_file', default='', type=str) parser.add_argument('-c', dest='input_clinical_file', default='', type=str) parser.add_argument('-s', dest='sample_ids_filter', default='', type=str) parser.add_argument('-t', dest='default_cancer_type', default='', type=str) parser.add_argument('-u', dest='oncokb_api_url', default='', type=str) parser.add_argument('-b', dest='oncokb_api_bearer_token', default='', type=str) parser.add_argument('-z', dest='annotate_gain_loss', action="store_true", default=False) parser.set_defaults(func=main) args = parser.parse_args() args.func(args)

true

f70fa803280ab9f216b7ca2a38b01efb67cd1f5e

3,693

py

Python

tmot/matching.py

JunweiLiang/Object_Detection_Tracking

f86caaec97669a6da56f1b402cca4e179a85d2f0

[ "MIT" ]

328

2019-05-27T03:09:02.000Z

2022-03-31T05:12:04.000Z

tmot/matching.py

AnjaliPC/Object_Detection_Tracking

f86caaec97669a6da56f1b402cca4e179a85d2f0

[ "MIT" ]

43

2019-06-05T14:04:09.000Z

2022-01-25T03:16:39.000Z

tmot/matching.py

AnjaliPC/Object_Detection_Tracking

f86caaec97669a6da56f1b402cca4e179a85d2f0

[ "MIT" ]

107

2019-05-27T06:26:38.000Z

2022-03-25T03:32:58.000Z

import numpy as np import scipy from scipy.spatial.distance import cdist import lap # 0.4.0 from cython_bbox import bbox_overlaps as bbox_ious from . import kalman_filter def merge_matches(m1, m2, shape): O,P,Q = shape m1 = np.asarray(m1) m2 = np.asarray(m2) M1 = scipy.sparse.coo_matrix((np.ones(len(m1)), (m1[:, 0], m1[:, 1])), shape=(O, P)) M2 = scipy.sparse.coo_matrix((np.ones(len(m2)), (m2[:, 0], m2[:, 1])), shape=(P, Q)) mask = M1*M2 match = mask.nonzero() match = list(zip(match[0], match[1])) unmatched_O = tuple(set(range(O)) - set([i for i, j in match])) unmatched_Q = tuple(set(range(Q)) - set([j for i, j in match])) return match, unmatched_O, unmatched_Q def linear_assignment(cost_matrix, thresh): if cost_matrix.size == 0: return np.empty((0, 2), dtype=int), tuple(range(cost_matrix.shape[0])), tuple(range(cost_matrix.shape[1])) matches, unmatched_a, unmatched_b = [], [], [] cost, x, y = lap.lapjv(cost_matrix, extend_cost=True, cost_limit=thresh) for ix, mx in enumerate(x): if mx >= 0: matches.append([ix, mx]) unmatched_a = np.where(x < 0)[0] unmatched_b = np.where(y < 0)[0] matches = np.asarray(matches) return matches, unmatched_a, unmatched_b def ious(atlbrs, btlbrs): """ Compute cost based on IoU :type atlbrs: list[tlbr] | np.ndarray :type atlbrs: list[tlbr] | np.ndarray :rtype ious np.ndarray """ ious = np.zeros((len(atlbrs), len(btlbrs)), dtype=np.float) if ious.size == 0: return ious ious = bbox_ious( np.ascontiguousarray(atlbrs, dtype=np.float), np.ascontiguousarray(btlbrs, dtype=np.float) ) return ious def iou_distance(atracks, btracks): """ Compute cost based on IoU :type atracks: list[STrack] :type btracks: list[STrack] :rtype cost_matrix np.ndarray """ if (len(atracks)>0 and isinstance(atracks[0], np.ndarray)) or (len(btracks) > 0 and isinstance(btracks[0], np.ndarray)): atlbrs = atracks btlbrs = btracks else: atlbrs = [track.tlbr for track in atracks] btlbrs = [track.tlbr for track in btracks] _ious = ious(atlbrs, btlbrs) cost_matrix = 1 - _ious return cost_matrix def embedding_distance(tracks, detections, metric='cosine'): """ :param tracks: list[STrack] :param detections: list[BaseTrack] :param metric: :return: cost_matrix np.ndarray """ cost_matrix = np.zeros((len(tracks), len(detections)), dtype=np.float) if cost_matrix.size == 0: return cost_matrix det_features = np.asarray([track.curr_feat for track in detections], dtype=np.float) track_features = np.asarray([track.smooth_feat for track in tracks], dtype=np.float) cost_matrix = np.maximum(0.0, cdist(track_features, det_features)) # Nomalized features return cost_matrix def fuse_motion(kf, cost_matrix, tracks, detections, only_position=False, lambda_=0.98): if cost_matrix.size == 0: return cost_matrix gating_dim = 2 if only_position else 4 gating_threshold = kalman_filter.chi2inv95[gating_dim] measurements = np.asarray([det.to_xyah() for det in detections]) for row, track in enumerate(tracks): gating_distance = kf.gating_distance( track.mean, track.covariance, measurements, only_position, metric='maha') cost_matrix[row, gating_distance > gating_threshold] = np.inf cost_matrix[row] = lambda_ * cost_matrix[row] + (1-lambda_)* gating_distance return cost_matrix

33.572727

125

0.638776

import numpy as np import scipy from scipy.spatial.distance import cdist import lap from cython_bbox import bbox_overlaps as bbox_ious from . import kalman_filter def merge_matches(m1, m2, shape): O,P,Q = shape m1 = np.asarray(m1) m2 = np.asarray(m2) M1 = scipy.sparse.coo_matrix((np.ones(len(m1)), (m1[:, 0], m1[:, 1])), shape=(O, P)) M2 = scipy.sparse.coo_matrix((np.ones(len(m2)), (m2[:, 0], m2[:, 1])), shape=(P, Q)) mask = M1*M2 match = mask.nonzero() match = list(zip(match[0], match[1])) unmatched_O = tuple(set(range(O)) - set([i for i, j in match])) unmatched_Q = tuple(set(range(Q)) - set([j for i, j in match])) return match, unmatched_O, unmatched_Q def linear_assignment(cost_matrix, thresh): if cost_matrix.size == 0: return np.empty((0, 2), dtype=int), tuple(range(cost_matrix.shape[0])), tuple(range(cost_matrix.shape[1])) matches, unmatched_a, unmatched_b = [], [], [] cost, x, y = lap.lapjv(cost_matrix, extend_cost=True, cost_limit=thresh) for ix, mx in enumerate(x): if mx >= 0: matches.append([ix, mx]) unmatched_a = np.where(x < 0)[0] unmatched_b = np.where(y < 0)[0] matches = np.asarray(matches) return matches, unmatched_a, unmatched_b def ious(atlbrs, btlbrs): ious = np.zeros((len(atlbrs), len(btlbrs)), dtype=np.float) if ious.size == 0: return ious ious = bbox_ious( np.ascontiguousarray(atlbrs, dtype=np.float), np.ascontiguousarray(btlbrs, dtype=np.float) ) return ious def iou_distance(atracks, btracks): if (len(atracks)>0 and isinstance(atracks[0], np.ndarray)) or (len(btracks) > 0 and isinstance(btracks[0], np.ndarray)): atlbrs = atracks btlbrs = btracks else: atlbrs = [track.tlbr for track in atracks] btlbrs = [track.tlbr for track in btracks] _ious = ious(atlbrs, btlbrs) cost_matrix = 1 - _ious return cost_matrix def embedding_distance(tracks, detections, metric='cosine'): cost_matrix = np.zeros((len(tracks), len(detections)), dtype=np.float) if cost_matrix.size == 0: return cost_matrix det_features = np.asarray([track.curr_feat for track in detections], dtype=np.float) track_features = np.asarray([track.smooth_feat for track in tracks], dtype=np.float) cost_matrix = np.maximum(0.0, cdist(track_features, det_features)) return cost_matrix def fuse_motion(kf, cost_matrix, tracks, detections, only_position=False, lambda_=0.98): if cost_matrix.size == 0: return cost_matrix gating_dim = 2 if only_position else 4 gating_threshold = kalman_filter.chi2inv95[gating_dim] measurements = np.asarray([det.to_xyah() for det in detections]) for row, track in enumerate(tracks): gating_distance = kf.gating_distance( track.mean, track.covariance, measurements, only_position, metric='maha') cost_matrix[row, gating_distance > gating_threshold] = np.inf cost_matrix[row] = lambda_ * cost_matrix[row] + (1-lambda_)* gating_distance return cost_matrix

true

f70fa88268e7f2a8c55619f7c4dd3c7747d1770a

149

py

Python

tardis/io/setup_package.py

chvogl/tardis

e444ffeebef92811165ec982a5c23785932a7f8e

[ "BSD-3-Clause" ]

1

2016-03-24T13:14:25.000Z

tardis/io/setup_package.py

chvogl/tardis

e444ffeebef92811165ec982a5c23785932a7f8e

[ "BSD-3-Clause" ]

6

2015-03-16T10:31:40.000Z

2019-02-21T17:56:55.000Z

tardis/io/setup_package.py

chvogl/tardis

e444ffeebef92811165ec982a5c23785932a7f8e

[ "BSD-3-Clause" ]

5

2015-03-17T18:56:20.000Z

2019-02-12T12:53:15.000Z

# Licensed under a 3-clause BSD style license - see LICENSE.rst def get_package_data(): return {'tardis.io.tests':['data/*.dat', 'data/*.yml']}

29.8

63

0.677852

def get_package_data(): return {'tardis.io.tests':['data/*.dat', 'data/*.yml']}

true

f70fa8b722e119309bc970211e06b4c03d657183

1,320

py

Python

tests/test_stat.py

cailab-tamu/scTenifoldXct

d25ded8dfb7f2951217a30ab71eccd6b060178f6

[ "MIT" ]

null

tests/test_stat.py

cailab-tamu/scTenifoldXct

d25ded8dfb7f2951217a30ab71eccd6b060178f6

[ "MIT" ]

null

tests/test_stat.py

cailab-tamu/scTenifoldXct

d25ded8dfb7f2951217a30ab71eccd6b060178f6

[ "MIT" ]

null

import pytest import itertools import pandas as pd import numpy as np from scTenifoldXct.core import null_test def generate_fake_df_nn(n_ligand=3000, n_receptors=3000, n_cands=200): gene_names = [f"GENE{i}" for i in range(max(n_ligand, n_receptors))] iteration = itertools.product(gene_names, gene_names) inds, ligands, receptors = [], [], [] for i, j in iteration: inds.append(f"{i}_{j}") ligands.append(i) receptors.append(j) df = pd.DataFrame({"ligand": ligands, "receptor": receptors, "dist": np.random.chisquare(1, (n_ligand * n_receptors,)), "correspondence": np.random.lognormal(0, 4, size=(n_ligand * n_receptors,))}, index=inds) return df, np.random.choice(df.index, size=(n_cands,), replace=False) @pytest.mark.parametrize("df_nn,candidates", [ generate_fake_df_nn(3000, 3000, 200), generate_fake_df_nn(1000, 1000, 200), ]) @pytest.mark.parametrize("filter_zeros", [True]) def test_null_test(df_nn, candidates, filter_zeros): null_test(df_nn=df_nn, candidates=candidates, filter_zeros=filter_zeros) def test_chi2_test(xct_skin): xct_skin.train_nn(n_steps= 1000, lr = 0.001) xct_skin.chi2_test(dof=3, pval=0.05, cal_FDR=True, plot_result=True)

34.736842

100

0.666667

import pytest import itertools import pandas as pd import numpy as np from scTenifoldXct.core import null_test def generate_fake_df_nn(n_ligand=3000, n_receptors=3000, n_cands=200): gene_names = [f"GENE{i}" for i in range(max(n_ligand, n_receptors))] iteration = itertools.product(gene_names, gene_names) inds, ligands, receptors = [], [], [] for i, j in iteration: inds.append(f"{i}_{j}") ligands.append(i) receptors.append(j) df = pd.DataFrame({"ligand": ligands, "receptor": receptors, "dist": np.random.chisquare(1, (n_ligand * n_receptors,)), "correspondence": np.random.lognormal(0, 4, size=(n_ligand * n_receptors,))}, index=inds) return df, np.random.choice(df.index, size=(n_cands,), replace=False) @pytest.mark.parametrize("df_nn,candidates", [ generate_fake_df_nn(3000, 3000, 200), generate_fake_df_nn(1000, 1000, 200), ]) @pytest.mark.parametrize("filter_zeros", [True]) def test_null_test(df_nn, candidates, filter_zeros): null_test(df_nn=df_nn, candidates=candidates, filter_zeros=filter_zeros) def test_chi2_test(xct_skin): xct_skin.train_nn(n_steps= 1000, lr = 0.001) xct_skin.chi2_test(dof=3, pval=0.05, cal_FDR=True, plot_result=True)

true

f70fa8cb9d7f1e49ef9b56b7615433e16e26661d

852

py

Python

dataExtractor.py

bdburak/AmazonReviewSentimentAnalysis

8e68d27f5ecd6c5e1b0c153f79c8b3ea1767ea50

[ "MIT" ]

null

dataExtractor.py

bdburak/AmazonReviewSentimentAnalysis

8e68d27f5ecd6c5e1b0c153f79c8b3ea1767ea50

[ "MIT" ]

1

2021-04-28T18:26:41.000Z

dataExtractor.py

bdburak/AmazonReviewSentimentAnalysis

8e68d27f5ecd6c5e1b0c153f79c8b3ea1767ea50

[ "MIT" ]

null

#Review Seperator def reviewToList(strDataLocation): #reviewToList(str_DataLocation) file = open(strDataLocation) listFile=(file.readlines()) firstReviewItem=0 lastReviewItem=0 listReviews = [] reviewText ="" for item in range(len(listFile)): if('<review_text>\n'==listFile[item]): firstReviewItem = item+1 if('</review_text>\n'==listFile[item]): ReviewItemRange = item - firstReviewItem for i in range(ReviewItemRange): reviewText = reviewText + (listFile[firstReviewItem]) firstReviewItem = firstReviewItem + 1 reviewText = reviewText.rstrip('\n') listReviews.append(reviewText) reviewText ="" return listReviews

25.818182

84

0.564554

def reviewToList(strDataLocation): file = open(strDataLocation) listFile=(file.readlines()) firstReviewItem=0 lastReviewItem=0 listReviews = [] reviewText ="" for item in range(len(listFile)): if('<review_text>\n'==listFile[item]): firstReviewItem = item+1 if('</review_text>\n'==listFile[item]): ReviewItemRange = item - firstReviewItem for i in range(ReviewItemRange): reviewText = reviewText + (listFile[firstReviewItem]) firstReviewItem = firstReviewItem + 1 reviewText = reviewText.rstrip('\n') listReviews.append(reviewText) reviewText ="" return listReviews

true

f70fa8ec2ca8e6448f8e67c80257b049471e9c70

6,343

py

Python

lab_4/main.py

SoullessDark/2020-2-level-labs

9555eb5a86a0f330b2f99e991928b0337f519b7a

[ "MIT" ]

null

lab_4/main.py

SoullessDark/2020-2-level-labs

9555eb5a86a0f330b2f99e991928b0337f519b7a

[ "MIT" ]

null

lab_4/main.py

SoullessDark/2020-2-level-labs

9555eb5a86a0f330b2f99e991928b0337f519b7a

[ "MIT" ]

null

""" Lab 4 """ import re from ngrams.ngram_trie import NGramTrie def tokenize_by_sentence(text: str) -> tuple: if not isinstance(text, str): raise ValueError sents = re.split(r'[.?!]', text) tokenized_sent = [] for sent in sents: tokens = re.sub(r'[^a-z \n]', '', sent.lower()).split() if tokens: tokenized_sent += tokens + ['<END>'] return tuple(tokenized_sent) class WordStorage: def __init__(self): self.storage = {} def _put_word(self, word: str): if not isinstance(word, str) or not word: raise ValueError if word not in self.storage: self.storage[word] = len(self.storage) + 1 return self.storage[word] def get_id(self, word: str) -> int: if not isinstance(word, str) or not word: raise ValueError if word not in self.storage: raise KeyError return self.storage[word] def get_word(self, word_id: int) -> str: if not isinstance(word_id, int): raise ValueError for key, value in self.storage.items(): if value == word_id: return key raise KeyError def update(self, corpus: tuple): if not isinstance(corpus, tuple): raise ValueError for word in corpus: self._put_word(word) def encode_text(storage: WordStorage, text: tuple) -> tuple: if not isinstance(storage, WordStorage) or not isinstance(text, tuple): raise ValueError encoded_text = [storage.get_id(word) for word in text] return tuple(encoded_text) class NGramTextGenerator: def __init__(self, word_storage: WordStorage, n_gram_trie: NGramTrie): self._word_storage = word_storage self._n_gram_trie = n_gram_trie def _generate_next_word(self, context: tuple) -> int: if not isinstance(context, tuple) or len(context) + 1 != self._n_gram_trie.size: raise ValueError top_word = '' word_freq = 0 for n_gram, n_gram_freq in self._n_gram_trie.n_gram_frequencies.items(): if context == n_gram[:-1] and n_gram_freq > word_freq: top_word = n_gram[-1] word_freq = n_gram_freq if not top_word: top_word = max(self._n_gram_trie.uni_grams, key=self._n_gram_trie.uni_grams.get)[0] return top_word def _generate_sentence(self, context: tuple) -> tuple: if not isinstance(context, tuple): raise ValueError sent = self.sent_is(context) for _ in range(20): sent.append(self._generate_next_word(context)) context = tuple(list(context) + sent)[-len(context):] if sent[-1] == self._word_storage.get_id('<END>'): return tuple(sent) sent.append(self._word_storage.get_id('<END>')) return tuple(sent) def sent_is(self, context): if context[-1] == self._word_storage.get_id('<END>'): sent = [] else: sent = list(context) return sent def generate_text(self, context: tuple, number_of_sentences: int) -> tuple: if not isinstance(context, tuple) or not isinstance(number_of_sentences, int) \ or isinstance(number_of_sentences, bool): raise ValueError text = [] for _ in range(number_of_sentences): sentence = self._generate_sentence(context) text.extend(sentence) context = tuple(text[-len(context):]) return tuple(text) class LikelihoodBasedTextGenerator(NGramTextGenerator): def _calculate_maximum_likelihood(self, word: int, context: tuple) -> float: type_check = [isinstance(word, int), isinstance(context, tuple)] if not all(type_check) or word not in self._word_storage.storage.values() or \ len([wrd for wrd in context if wrd in self._word_storage.storage.values()]) != len(context): raise ValueError wrd_freq = 0 avrg_freq = 0 length = self._n_gram_trie.size - 1 for n_gram in self._n_gram_trie.n_grams: if context == n_gram[:length]: avrg_freq += 1 if word == n_gram[-1]: wrd_freq += 1 try: likelihood = wrd_freq / avrg_freq except ZeroDivisionError: likelihood = 0.0 return likelihood def _generate_next_word(self, context: tuple) -> int: if not isinstance(context, tuple) or \ len([w for w in context if w in self._word_storage.storage.values()]) != len(context): raise ValueError next_wrd = 0 word_freq = 0.0 for word in self._word_storage.storage.values(): frequency = self._calculate_maximum_likelihood(word, context) if frequency > word_freq: word_freq = frequency next_wrd = word next_word = self.if_not_freq(next_wrd, word_freq) return next_word def if_not_freq(self, next_wrd, word_freq): if not word_freq: next_wrd = max(self._n_gram_trie.uni_grams, key=self._n_gram_trie.uni_grams.get)[0] return next_wrd class BackOffGenerator(NGramTextGenerator): def __init__(self, word_storage: WordStorage, n_gram_trie: NGramTrie, *args): super().__init__(word_storage, n_gram_trie) def _generate_next_word(self, context: tuple) -> int: pass def decode_text(storage: WordStorage, encoded_text: tuple) -> tuple: if not isinstance(storage, WordStorage) or not isinstance(encoded_text, tuple) or not encoded_text: raise ValueError decoded_text = [[]] for encoded_word in encoded_text: decoded_word = storage.get_word(encoded_word) if decoded_word == '<END>': decoded_text.append([]) else: decoded_text[-1].append(decoded_word) decoded_text = [sentence[0][0].upper() + sentence[0][1:] + ' ' + ' '.join(sentence[1:]) for sentence in decoded_text if sentence] return tuple(decoded_text) def save_model(model: NGramTextGenerator, path_to_saved_model: str): pass def load_model(path_to_saved_model: str) -> NGramTextGenerator: pass

29.365741

108

0.612329

import re from ngrams.ngram_trie import NGramTrie def tokenize_by_sentence(text: str) -> tuple: if not isinstance(text, str): raise ValueError sents = re.split(r'[.?!]', text) tokenized_sent = [] for sent in sents: tokens = re.sub(r'[^a-z \n]', '', sent.lower()).split() if tokens: tokenized_sent += tokens + ['<END>'] return tuple(tokenized_sent) class WordStorage: def __init__(self): self.storage = {} def _put_word(self, word: str): if not isinstance(word, str) or not word: raise ValueError if word not in self.storage: self.storage[word] = len(self.storage) + 1 return self.storage[word] def get_id(self, word: str) -> int: if not isinstance(word, str) or not word: raise ValueError if word not in self.storage: raise KeyError return self.storage[word] def get_word(self, word_id: int) -> str: if not isinstance(word_id, int): raise ValueError for key, value in self.storage.items(): if value == word_id: return key raise KeyError def update(self, corpus: tuple): if not isinstance(corpus, tuple): raise ValueError for word in corpus: self._put_word(word) def encode_text(storage: WordStorage, text: tuple) -> tuple: if not isinstance(storage, WordStorage) or not isinstance(text, tuple): raise ValueError encoded_text = [storage.get_id(word) for word in text] return tuple(encoded_text) class NGramTextGenerator: def __init__(self, word_storage: WordStorage, n_gram_trie: NGramTrie): self._word_storage = word_storage self._n_gram_trie = n_gram_trie def _generate_next_word(self, context: tuple) -> int: if not isinstance(context, tuple) or len(context) + 1 != self._n_gram_trie.size: raise ValueError top_word = '' word_freq = 0 for n_gram, n_gram_freq in self._n_gram_trie.n_gram_frequencies.items(): if context == n_gram[:-1] and n_gram_freq > word_freq: top_word = n_gram[-1] word_freq = n_gram_freq if not top_word: top_word = max(self._n_gram_trie.uni_grams, key=self._n_gram_trie.uni_grams.get)[0] return top_word def _generate_sentence(self, context: tuple) -> tuple: if not isinstance(context, tuple): raise ValueError sent = self.sent_is(context) for _ in range(20): sent.append(self._generate_next_word(context)) context = tuple(list(context) + sent)[-len(context):] if sent[-1] == self._word_storage.get_id('<END>'): return tuple(sent) sent.append(self._word_storage.get_id('<END>')) return tuple(sent) def sent_is(self, context): if context[-1] == self._word_storage.get_id('<END>'): sent = [] else: sent = list(context) return sent def generate_text(self, context: tuple, number_of_sentences: int) -> tuple: if not isinstance(context, tuple) or not isinstance(number_of_sentences, int) \ or isinstance(number_of_sentences, bool): raise ValueError text = [] for _ in range(number_of_sentences): sentence = self._generate_sentence(context) text.extend(sentence) context = tuple(text[-len(context):]) return tuple(text) class LikelihoodBasedTextGenerator(NGramTextGenerator): def _calculate_maximum_likelihood(self, word: int, context: tuple) -> float: type_check = [isinstance(word, int), isinstance(context, tuple)] if not all(type_check) or word not in self._word_storage.storage.values() or \ len([wrd for wrd in context if wrd in self._word_storage.storage.values()]) != len(context): raise ValueError wrd_freq = 0 avrg_freq = 0 length = self._n_gram_trie.size - 1 for n_gram in self._n_gram_trie.n_grams: if context == n_gram[:length]: avrg_freq += 1 if word == n_gram[-1]: wrd_freq += 1 try: likelihood = wrd_freq / avrg_freq except ZeroDivisionError: likelihood = 0.0 return likelihood def _generate_next_word(self, context: tuple) -> int: if not isinstance(context, tuple) or \ len([w for w in context if w in self._word_storage.storage.values()]) != len(context): raise ValueError next_wrd = 0 word_freq = 0.0 for word in self._word_storage.storage.values(): frequency = self._calculate_maximum_likelihood(word, context) if frequency > word_freq: word_freq = frequency next_wrd = word next_word = self.if_not_freq(next_wrd, word_freq) return next_word def if_not_freq(self, next_wrd, word_freq): if not word_freq: next_wrd = max(self._n_gram_trie.uni_grams, key=self._n_gram_trie.uni_grams.get)[0] return next_wrd class BackOffGenerator(NGramTextGenerator): def __init__(self, word_storage: WordStorage, n_gram_trie: NGramTrie, *args): super().__init__(word_storage, n_gram_trie) def _generate_next_word(self, context: tuple) -> int: pass def decode_text(storage: WordStorage, encoded_text: tuple) -> tuple: if not isinstance(storage, WordStorage) or not isinstance(encoded_text, tuple) or not encoded_text: raise ValueError decoded_text = [[]] for encoded_word in encoded_text: decoded_word = storage.get_word(encoded_word) if decoded_word == '<END>': decoded_text.append([]) else: decoded_text[-1].append(decoded_word) decoded_text = [sentence[0][0].upper() + sentence[0][1:] + ' ' + ' '.join(sentence[1:]) for sentence in decoded_text if sentence] return tuple(decoded_text) def save_model(model: NGramTextGenerator, path_to_saved_model: str): pass def load_model(path_to_saved_model: str) -> NGramTextGenerator: pass

true

f70fa93743ec576b39568159579d16544248a1ec

83

py

Python

conf/apps.py

invernoa/Conferences

9e821948311dc9c28323ede8a26764899fc05255

[ "MIT" ]

41

2019-01-02T09:36:54.000Z

2022-02-20T13:13:05.000Z

conf/apps.py

invernoa/Conferences

9e821948311dc9c28323ede8a26764899fc05255

[ "MIT" ]

15

2019-09-30T05:40:20.000Z

2022-02-17T19:28:41.000Z

conf/apps.py

invernoa/Conferences

9e821948311dc9c28323ede8a26764899fc05255

[ "MIT" ]

23

2019-02-18T10:50:10.000Z

2022-01-06T07:53:18.000Z

from django.apps import AppConfig class ConfConfig(AppConfig): name = 'conf'

13.833333

33

0.73494

from django.apps import AppConfig class ConfConfig(AppConfig): name = 'conf'

true

f70facb47d95871efcad46d686499cc18bc105f0

937

py

Python

Rendering/Core/Testing/Python/TestCgShader.py

collects/VTK

004944f0d54df673c38b3d4016a4bee74fa7d813

[ "BSD-3-Clause" ]

null

Rendering/Core/Testing/Python/TestCgShader.py

collects/VTK

004944f0d54df673c38b3d4016a4bee74fa7d813

[ "BSD-3-Clause" ]

null

Rendering/Core/Testing/Python/TestCgShader.py

collects/VTK

004944f0d54df673c38b3d4016a4bee74fa7d813

[ "BSD-3-Clause" ]

2

2019-09-09T22:42:12.000Z

2020-10-22T07:10:08.000Z

#!/usr/bin/env python renWin = vtk.vtkRenderWindow() iren = vtk.vtkRenderWindowInteractor() iren.SetRenderWindow(renWin) renderer = vtk.vtkRenderer() renWin.AddRenderer(renderer) src1 = vtk.vtkSphereSource() src1.SetRadius(5) src1.SetPhiResolution(20) src1.SetThetaResolution(20) mapper = vtk.vtkPolyDataMapper() mapper.SetInputConnection(src1.GetOutputPort()) actor = vtk.vtkActor() actor.SetMapper(mapper) # Load the material. Here, we are loading a material # defined in the Vtk Library. One can also specify # a filename to a material description xml. actor.GetProperty().LoadMaterial("CgTwisted") # Turn shading on. Otherwise, shaders are not used. actor.GetProperty().ShadingOn() # Pass a shader variable need by CgTwisted. actor.GetProperty().AddShaderVariable("Rate",1.0) renderer.AddActor(actor) renWin.Render() renderer.GetActiveCamera().Azimuth(-50) renderer.GetActiveCamera().Roll(70) renWin.Render() # --- end of script --

31.233333

52

0.781217

renWin = vtk.vtkRenderWindow() iren = vtk.vtkRenderWindowInteractor() iren.SetRenderWindow(renWin) renderer = vtk.vtkRenderer() renWin.AddRenderer(renderer) src1 = vtk.vtkSphereSource() src1.SetRadius(5) src1.SetPhiResolution(20) src1.SetThetaResolution(20) mapper = vtk.vtkPolyDataMapper() mapper.SetInputConnection(src1.GetOutputPort()) actor = vtk.vtkActor() actor.SetMapper(mapper) actor.GetProperty().LoadMaterial("CgTwisted") actor.GetProperty().ShadingOn() actor.GetProperty().AddShaderVariable("Rate",1.0) renderer.AddActor(actor) renWin.Render() renderer.GetActiveCamera().Azimuth(-50) renderer.GetActiveCamera().Roll(70) renWin.Render()

true

f70fad1a401b33614f837e3bfd9c10fa8a5570aa

1,451

py

Python

tests/lib/utils.py

booneng/mobly

539788309c7631c20fa5381937e10f9cd997e2d0

[ "Apache-2.0" ]

532

2016-11-07T22:01:00.000Z

2022-03-30T17:11:40.000Z

tests/lib/utils.py

booneng/mobly

539788309c7631c20fa5381937e10f9cd997e2d0

[ "Apache-2.0" ]

528

2016-11-22T01:42:19.000Z

2022-03-24T02:27:15.000Z

tests/lib/utils.py

booneng/mobly

539788309c7631c20fa5381937e10f9cd997e2d0

[ "Apache-2.0" ]

169

2016-11-18T15:12:26.000Z

2022-03-24T01:22:08.000Z

# Copyright 2017 Google Inc. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. # This module holds util functions that are used in more than one test module. from mobly import records def validate_test_result(result): """Validate basic properties of a test result. The records in each bucket of the test result should have the corresponding result enum. Args: result: The `records.TestResult` object to validate. """ buckets = [ (result.passed, records.TestResultEnums.TEST_RESULT_PASS), (result.failed, records.TestResultEnums.TEST_RESULT_FAIL), (result.error, records.TestResultEnums.TEST_RESULT_ERROR), (result.skipped, records.TestResultEnums.TEST_RESULT_SKIP), ] for bucket_list, expected_enum in buckets: for record in bucket_list: if record.result != expected_enum: raise AssertionError('Expected result %s, got %s.' % (expected_enum, record.result))

36.275

78

0.731909

from mobly import records def validate_test_result(result): buckets = [ (result.passed, records.TestResultEnums.TEST_RESULT_PASS), (result.failed, records.TestResultEnums.TEST_RESULT_FAIL), (result.error, records.TestResultEnums.TEST_RESULT_ERROR), (result.skipped, records.TestResultEnums.TEST_RESULT_SKIP), ] for bucket_list, expected_enum in buckets: for record in bucket_list: if record.result != expected_enum: raise AssertionError('Expected result %s, got %s.' % (expected_enum, record.result))

true

f70fad449b902120499a1dec1a4d6c495074a31f

607

py

Python

venv/Lib/site-packages/tensorflow/python/keras/api/_v2/keras/applications/resnet/__init__.py

rexliu3/StockTradingBotCloud

46b732b9c05f73bc0e856a3c4a16854b6d12e18e

[ "MIT" ]

null

venv/Lib/site-packages/tensorflow/python/keras/api/_v2/keras/applications/resnet/__init__.py

rexliu3/StockTradingBotCloud

46b732b9c05f73bc0e856a3c4a16854b6d12e18e

[ "MIT" ]

null

venv/Lib/site-packages/tensorflow/python/keras/api/_v2/keras/applications/resnet/__init__.py

rexliu3/StockTradingBotCloud

46b732b9c05f73bc0e856a3c4a16854b6d12e18e

[ "MIT" ]

1

2020-06-28T11:47:47.000Z

# This file is MACHINE GENERATED! Do not edit. # Generated by: tensorflow/python/tools/api/generator/create_python_api.py script. """ResNet models for Keras. """ from __future__ import print_function as _print_function import sys as _sys from tensorflow.python.keras.applications.resnet import ResNet101 from tensorflow.python.keras.applications.resnet import ResNet152 from tensorflow.python.keras.applications.resnet import ResNet50 from tensorflow.python.keras.applications.resnet import decode_predictions from tensorflow.python.keras.applications.resnet import preprocess_input del _print_function

35.705882

82

0.84514

from __future__ import print_function as _print_function import sys as _sys from tensorflow.python.keras.applications.resnet import ResNet101 from tensorflow.python.keras.applications.resnet import ResNet152 from tensorflow.python.keras.applications.resnet import ResNet50 from tensorflow.python.keras.applications.resnet import decode_predictions from tensorflow.python.keras.applications.resnet import preprocess_input del _print_function

true

f70faf3f1a8e372280b0a707e88d882149db4909

3,167

py

Python

batch_netmeta.py

gesiscss/Homophilic_Directed_ScaleFree_Networks

a0d27b44eaafdda46b6d3379859fa428398ef476

[ "Apache-2.0" ]

1

2022-03-23T15:34:38.000Z

batch_netmeta.py

gesiscss/Homophilic_Directed_ScaleFree_Networks

a0d27b44eaafdda46b6d3379859fa428398ef476

[ "Apache-2.0" ]

2

2019-02-02T13:54:53.000Z

2019-02-04T09:15:51.000Z

batch_netmeta.py

gesiscss/Homophilic_Directed_ScaleFree_Networks

a0d27b44eaafdda46b6d3379859fa428398ef476

[ "Apache-2.0" ]

null

################################################################ # System's dependencies ################################################################ import os import sys import time import argparse ################################################################ # Local dependencies ################################################################ from org.gesis.lib import io from org.gesis.lib import graph from org.gesis.lib import homophily ################################################################ # Constants ################################################################ DATASETS = ['aps','hate','blogs','wikipedia'] ################################################################ # Main ################################################################ def run(datapath, dataset, steps, njobs, output): if dataset not in DATASETS: raise Exception("dataset " + dataset +" does not exist.") print(dataset, steps, njobs) g = graph.get_graph(datapath, dataset) N, fm, d, plo_M, plo_m, pli_M, pli_m, EMM, EMm, EmM, Emm, hMM, hmm, _N, _d, _mindiff = homophily.get_metadata(g, steps, njobs=njobs, verbose=True, seed=None) print("N:{}".format(N)) print("fm:{}".format(fm)) print("d:{}".format(d)) print("plo_M:{}".format(plo_M)) print("plo_m:{}".format(plo_m)) print("pli_M:{}".format(pli_M)) print("pli_m:{}".format(pli_m)) print("EMM:{}".format(EMM)) print("EMm:{}".format(EMm)) print("EmM:{}".format(EmM)) print("Emm:{}".format(Emm)) print("hMM:{}".format(hMM)) print("hmm:{}".format(hmm)) print("_N:{}".format(_N)) print("_d:{}".format(_d)) print("_mindiff:{}".format(_mindiff)) ### Storing metadata info into .csv file t1 = "dataset,N,fm,d,plo_M,plo_m,pli_M,pli_m,EMM,EMm,EmM,Emm,hMM,hmm,_N,_d,_mindiff" t2 = ",".join([dataset, str(N), str(fm), str(d), str(plo_M), str(plo_m), str(pli_M), str(pli_m), str(EMM), str(EMm), str(EmM), str(Emm), str(hMM), str(hmm), str(_N), str(_d), str(_mindiff)]) path = os.path.join(output,dataset,"network_metadata.csv") io.save_text("{}\n{}".format(t1,t2), path) ################################################################ # Main ################################################################ if __name__ == "__main__": parser = argparse.ArgumentParser() parser.add_argument("--dataset", help=",".join(DATASETS), type=str, required=True) parser.add_argument("--steps", help="decimals (eg. 0.01, 0.05) to compute homophily", type=float, required=True) parser.add_argument("--njobs", help="parallel jobs", type=int, default=1) parser.add_argument("--datapath", help="path/folder where the .gpickle files are.", type=str, required=True) parser.add_argument("--output", help="path/folder where to store csv file", type=str, default='.') args = parser.parse_args() start_time = time.time() run(args.datapath, args.dataset, args.steps, args.njobs, args.output) print("--- %s seconds ---" % (time.time() - start_time))

43.383562

128

0.487843

MM), str(hmm), str(_N), str(_d), str(_mindiff)]) path = os.path.join(output,dataset,"network_metadata.csv") io.save_text("{}\n{}".format(t1,t2), path) ################################################################ # Main ################################################################ if __name__ == "__main__": parser = argparse.ArgumentParser() parser.add_argument("--dataset", help=",".join(DATASETS), type=str, required=True) parser.add_argument("--steps", help="decimals (eg. 0.01, 0.05) to compute homophily", type=float, required=True) parser.add_argument("--njobs", help="parallel jobs", type=int, default=1) parser.add_argument("--datapath", help="path/folder where the .gpickle files are.", type=str, required=True) parser.add_argument("--output", help="path/folder where to store csv file", type=str, default='.') args = parser.parse_args() start_time = time.time() run(args.datapath, args.dataset, args.steps, args.njobs, args.output) print("--- %s seconds ---" % (time.time() - start_time))

true

f70fb01bb8bfa089b4844ff0143d97c5770f6f22

2,502

py

Python

scripts/rpc/__init__.py

kacperg/spdk

4906323d47b1bf5290152e85b9a6fac1970cdfed

[ "BSD-3-Clause" ]

null

scripts/rpc/__init__.py

kacperg/spdk

4906323d47b1bf5290152e85b9a6fac1970cdfed

[ "BSD-3-Clause" ]

null

scripts/rpc/__init__.py

kacperg/spdk

4906323d47b1bf5290152e85b9a6fac1970cdfed

[ "BSD-3-Clause" ]

null

import json import sys from . import app from . import bdev from . import iscsi from . import log from . import lvol from . import nbd from . import net from . import nvmf from . import pmem from . import subsystem from . import vhost def start_subsystem_init(client): return client.call('start_subsystem_init') def get_rpc_methods(client, args): params = {} if args.current: params['current'] = args.current return client.call('get_rpc_methods', params) def save_config(client, args): config = { 'subsystems': [] } for elem in client.call('get_subsystems'): cfg = { 'subsystem': elem['subsystem'], 'config': client.call('get_subsystem_config', {"name": elem['subsystem']}) } config['subsystems'].append(cfg) indent = args.indent if args.filename is None: if indent is None: indent = 2 elif indent < 0: indent = None json.dump(config, sys.stdout, indent=indent) sys.stdout.write('\n') else: if indent is None or indent < 0: indent = None with open(args.filename, 'w') as file: json.dump(config, file, indent=indent) file.write('\n') def load_config(client, args): if not args.filename or args.filename == '-': json_config = json.load(sys.stdin) else: with open(args.filename, 'r') as file: json_config = json.load(file) subsystems = json_config['subsystems'] while subsystems: allowed_methods = client.call('get_rpc_methods', {'current': True}) allowed_found = False for subsystem in list(subsystems): if not subsystem['config']: subsystems.remove(subsystem) continue config = subsystem['config'] for elem in list(config): if not elem or 'method' not in elem or elem['method'] not in allowed_methods: continue client.call(elem['method'], elem['params']) config.remove(elem) allowed_found = True if not config: subsystems.remove(subsystem) if 'start_subsystem_init' in allowed_methods: client.call('start_subsystem_init') allowed_found = True if subsystems and not allowed_found: raise JSONRPCException("Some config left but did not found any allowed method to execute")

26.903226

102

0.593525

import json import sys from . import app from . import bdev from . import iscsi from . import log from . import lvol from . import nbd from . import net from . import nvmf from . import pmem from . import subsystem from . import vhost def start_subsystem_init(client): return client.call('start_subsystem_init') def get_rpc_methods(client, args): params = {} if args.current: params['current'] = args.current return client.call('get_rpc_methods', params) def save_config(client, args): config = { 'subsystems': [] } for elem in client.call('get_subsystems'): cfg = { 'subsystem': elem['subsystem'], 'config': client.call('get_subsystem_config', {"name": elem['subsystem']}) } config['subsystems'].append(cfg) indent = args.indent if args.filename is None: if indent is None: indent = 2 elif indent < 0: indent = None json.dump(config, sys.stdout, indent=indent) sys.stdout.write('\n') else: if indent is None or indent < 0: indent = None with open(args.filename, 'w') as file: json.dump(config, file, indent=indent) file.write('\n') def load_config(client, args): if not args.filename or args.filename == '-': json_config = json.load(sys.stdin) else: with open(args.filename, 'r') as file: json_config = json.load(file) subsystems = json_config['subsystems'] while subsystems: allowed_methods = client.call('get_rpc_methods', {'current': True}) allowed_found = False for subsystem in list(subsystems): if not subsystem['config']: subsystems.remove(subsystem) continue config = subsystem['config'] for elem in list(config): if not elem or 'method' not in elem or elem['method'] not in allowed_methods: continue client.call(elem['method'], elem['params']) config.remove(elem) allowed_found = True if not config: subsystems.remove(subsystem) if 'start_subsystem_init' in allowed_methods: client.call('start_subsystem_init') allowed_found = True if subsystems and not allowed_found: raise JSONRPCException("Some config left but did not found any allowed method to execute")

true

f70fb0261784177b0d3669369a858adf97f9b9a9

5,250

py

Python

practices/week6/assignment_exercise_3.py

andreyyec/Texas_Tech_AI

e4e8e41c65b41a1a684f1f65d21cf5427abdb046

[ "MIT" ]

null

practices/week6/assignment_exercise_3.py

andreyyec/Texas_Tech_AI

e4e8e41c65b41a1a684f1f65d21cf5427abdb046

[ "MIT" ]

5

2020-01-28T22:57:31.000Z

2022-02-10T00:37:58.000Z

practices/week6/assignment_exercise_3.py

andreyyec/Texas_Tech_AI

e4e8e41c65b41a1a684f1f65d21cf5427abdb046

[ "MIT" ]

null

import numpy as np import pandas as pd import pickle import tensorflow as tf import sklearn.metrics import matplotlib.pyplot as plt # Load the training and test data from the Pickle file with open("../datasets/credit_card_default_dataset.pickle", "rb") as f: train_data, train_labels, test_data, test_labels = pickle.load(f) # Get some lengths n_inputs = train_data.shape[1] nsamples = train_data.shape[0] # Training constants n_nodes_l1 = 5 batch_size = 32 learning_rate = .001 # Initial rate for Adam n_epochs = 1000 eval_step = 5 n_batches = int(np.ceil(nsamples / batch_size)) # Print the configuration print("Batch size: {} Num batches: {} Num epochs: {} Learning rate: {}".format(batch_size, n_batches, n_epochs, learning_rate)) print("Num nodes in L1: {} Activation function: ELU".format(n_nodes_l1)) # TensorFlow constants # Input vector placeholders. Length is unspecified. X = tf.placeholder(tf.float32, shape=(None, n_inputs), name="X") Y = tf.placeholder(tf.float32, shape=(None, 1), name="Y") # Hidden layer 1: # Inputs: n_inputs # Outputs: n_nodes_l1 # Activation: ELU W_L1 = tf.Variable(tf.truncated_normal([n_inputs, n_nodes_l1], stddev=2/np.sqrt(n_inputs))) b_L1 = tf.Variable(tf.zeros(n_nodes_l1)) Y_L1 = tf.nn.elu(tf.add(tf.matmul(X, W_L1), b_L1)) #Y_L1 = tf.nn.relu(tf.add(tf.matmul(X, W_L1), b_L1)) # Output layer: # Inputs: n_nodes_l1 # Outputs: 1 # Activation: logistic W_L2 = tf.Variable(tf.truncated_normal([n_nodes_l1, 1], stddev=1/np.sqrt(n_nodes_l1))) b_L2 = tf.Variable(tf.zeros(1)) Y_L2_linear = tf.add(tf.matmul(Y_L1, W_L2), b_L2) # Cost function, plus the sigmoid part of the prediction cost = tf.reduce_mean(tf.nn.sigmoid_cross_entropy_with_logits( logits = Y_L2_linear, labels = Y)) # Optimize cost through gradient descent #optimizer = tf.train.GradientDescentOptimizer(learning_rate) optimizer = tf.train.AdamOptimizer(learning_rate) update_op = optimizer.minimize(cost) # Prediction probability values Y_pred_proba_calc = tf.nn.sigmoid(Y_L2_linear) # Create TensorFlow session and initialize it sess = tf.Session() init = tf.global_variables_initializer() sess.run(init) # Initialize lists to hold the history of metrics per epoch trn_cost_hist = [] test_cost_hist = [] trn_auroc_hist = [] test_auroc_hist = [] epoch = 0 while epoch < n_epochs: batch = 0 # Save a vector of cost values per batch cost_vals = np.zeros(n_batches) while batch < n_batches: # Select the data for the next batch dataidx = batch * batch_size X_batch = train_data[dataidx:(dataidx+batch_size)] Y_batch = train_labels[dataidx:(dataidx+batch_size)].values.reshape(-1,1) feed_dict = {X: X_batch, Y: Y_batch} # Run one iteration of the computation session to update coefficients _, cost_vals[batch] = sess.run([update_op, cost], feed_dict=feed_dict) batch += 1 # Evaluate and print the results so far if (epoch % eval_step == 0): # Compute the average cost for all mini-batches in this epoch trn_cost_avg = np.mean(cost_vals) # Compute the ROC AUC against the full training data feed_dict = {X: train_data, Y: train_labels.values.reshape(-1,1)} Y_pred_proba_train = sess.run(Y_pred_proba_calc, feed_dict=feed_dict) train_auroc = sklearn.metrics.roc_auc_score(train_labels, Y_pred_proba_train) # Compute the cost and ROC AUC against the test data feed_dict = {X: test_data, Y: test_labels.values.reshape(-1,1)} Y_pred_proba_test = sess.run(Y_pred_proba_calc, feed_dict=feed_dict) test_cost = sess.run(cost, feed_dict=feed_dict) test_auroc = sklearn.metrics.roc_auc_score(test_labels, Y_pred_proba_test) print("Epoch: {:4d} trn_cost: {:.5f} test_cost: {:.5f} trn_auroc: {:.4f} test_auroc: {:.4f}".\ format(epoch, trn_cost_avg, test_cost, train_auroc, test_auroc)) # Save the metrics to the history trn_cost_hist.append(trn_cost_avg) test_cost_hist.append(test_cost) trn_auroc_hist.append(train_auroc) test_auroc_hist.append(test_auroc) epoch += 1 # Print the best results (as if we had done early stopping) epoch_hist = [i for i in range(0, n_epochs, eval_step)] best_idx = test_auroc_hist.index(max(test_auroc_hist)) print("Max test ROC AUC: {:.4f} at epoch: {}".format(test_auroc_hist[best_idx], epoch_hist[best_idx])) best_idx = trn_auroc_hist.index(max(trn_auroc_hist)) print("Max train ROC AUC: {:.4f} at epoch: {}".format(trn_auroc_hist[best_idx], epoch_hist[best_idx])) best_idx = test_cost_hist.index(min(test_cost_hist)) print("Min test cost: {:.5f} at epoch: {}".format(test_cost_hist[best_idx], epoch_hist[best_idx])) best_idx = trn_cost_hist.index(min(trn_cost_hist)) print("Min train cost: {:.5f} at epoch: {}".format(trn_cost_hist[best_idx], epoch_hist[best_idx])) # Plot the metrics history plt.plot(epoch_hist, trn_cost_hist, "b") plt.plot(epoch_hist, test_cost_hist, "r") plt.xlabel("epoch") plt.ylabel("cost") plt.title("Cost vs. epoch") plt.figure() plt.plot(epoch_hist, trn_auroc_hist, "b") plt.plot(epoch_hist, test_auroc_hist, "r") plt.xlabel("epoch") plt.ylabel("ROC AUC") plt.title("ROC AUC vs. epoch") plt.show()

35

127

0.719238

import numpy as np import pandas as pd import pickle import tensorflow as tf import sklearn.metrics import matplotlib.pyplot as plt with open("../datasets/credit_card_default_dataset.pickle", "rb") as f: train_data, train_labels, test_data, test_labels = pickle.load(f) n_inputs = train_data.shape[1] nsamples = train_data.shape[0] n_nodes_l1 = 5 batch_size = 32 learning_rate = .001 n_epochs = 1000 eval_step = 5 n_batches = int(np.ceil(nsamples / batch_size)) print("Batch size: {} Num batches: {} Num epochs: {} Learning rate: {}".format(batch_size, n_batches, n_epochs, learning_rate)) print("Num nodes in L1: {} Activation function: ELU".format(n_nodes_l1)) X = tf.placeholder(tf.float32, shape=(None, n_inputs), name="X") Y = tf.placeholder(tf.float32, shape=(None, 1), name="Y") W_L1 = tf.Variable(tf.truncated_normal([n_inputs, n_nodes_l1], stddev=2/np.sqrt(n_inputs))) b_L1 = tf.Variable(tf.zeros(n_nodes_l1)) Y_L1 = tf.nn.elu(tf.add(tf.matmul(X, W_L1), b_L1)) W_L2 = tf.Variable(tf.truncated_normal([n_nodes_l1, 1], stddev=1/np.sqrt(n_nodes_l1))) b_L2 = tf.Variable(tf.zeros(1)) Y_L2_linear = tf.add(tf.matmul(Y_L1, W_L2), b_L2) cost = tf.reduce_mean(tf.nn.sigmoid_cross_entropy_with_logits( logits = Y_L2_linear, labels = Y)) optimizer = tf.train.AdamOptimizer(learning_rate) update_op = optimizer.minimize(cost) Y_pred_proba_calc = tf.nn.sigmoid(Y_L2_linear) sess = tf.Session() init = tf.global_variables_initializer() sess.run(init) trn_cost_hist = [] test_cost_hist = [] trn_auroc_hist = [] test_auroc_hist = [] epoch = 0 while epoch < n_epochs: batch = 0 cost_vals = np.zeros(n_batches) while batch < n_batches: dataidx = batch * batch_size X_batch = train_data[dataidx:(dataidx+batch_size)] Y_batch = train_labels[dataidx:(dataidx+batch_size)].values.reshape(-1,1) feed_dict = {X: X_batch, Y: Y_batch} _, cost_vals[batch] = sess.run([update_op, cost], feed_dict=feed_dict) batch += 1 if (epoch % eval_step == 0): trn_cost_avg = np.mean(cost_vals) feed_dict = {X: train_data, Y: train_labels.values.reshape(-1,1)} Y_pred_proba_train = sess.run(Y_pred_proba_calc, feed_dict=feed_dict) train_auroc = sklearn.metrics.roc_auc_score(train_labels, Y_pred_proba_train) feed_dict = {X: test_data, Y: test_labels.values.reshape(-1,1)} Y_pred_proba_test = sess.run(Y_pred_proba_calc, feed_dict=feed_dict) test_cost = sess.run(cost, feed_dict=feed_dict) test_auroc = sklearn.metrics.roc_auc_score(test_labels, Y_pred_proba_test) print("Epoch: {:4d} trn_cost: {:.5f} test_cost: {:.5f} trn_auroc: {:.4f} test_auroc: {:.4f}".\ format(epoch, trn_cost_avg, test_cost, train_auroc, test_auroc)) trn_cost_hist.append(trn_cost_avg) test_cost_hist.append(test_cost) trn_auroc_hist.append(train_auroc) test_auroc_hist.append(test_auroc) epoch += 1 epoch_hist = [i for i in range(0, n_epochs, eval_step)] best_idx = test_auroc_hist.index(max(test_auroc_hist)) print("Max test ROC AUC: {:.4f} at epoch: {}".format(test_auroc_hist[best_idx], epoch_hist[best_idx])) best_idx = trn_auroc_hist.index(max(trn_auroc_hist)) print("Max train ROC AUC: {:.4f} at epoch: {}".format(trn_auroc_hist[best_idx], epoch_hist[best_idx])) best_idx = test_cost_hist.index(min(test_cost_hist)) print("Min test cost: {:.5f} at epoch: {}".format(test_cost_hist[best_idx], epoch_hist[best_idx])) best_idx = trn_cost_hist.index(min(trn_cost_hist)) print("Min train cost: {:.5f} at epoch: {}".format(trn_cost_hist[best_idx], epoch_hist[best_idx])) plt.plot(epoch_hist, trn_cost_hist, "b") plt.plot(epoch_hist, test_cost_hist, "r") plt.xlabel("epoch") plt.ylabel("cost") plt.title("Cost vs. epoch") plt.figure() plt.plot(epoch_hist, trn_auroc_hist, "b") plt.plot(epoch_hist, test_auroc_hist, "r") plt.xlabel("epoch") plt.ylabel("ROC AUC") plt.title("ROC AUC vs. epoch") plt.show()

true

f70fb188e60fc3f2ee8fa90f4379d87b3fd93cae

169

py

Python

src/user/views/__init__.py

ResearchHub/ResearchHub-Backend-Open

d36dca33afae2d442690694bb2ab17180d84bcd3

[ "MIT" ]

18

2021-05-20T13:20:16.000Z

2022-02-11T02:40:18.000Z

src/user/views/__init__.py

ResearchHub/ResearchHub-Backend-Open

d36dca33afae2d442690694bb2ab17180d84bcd3

[ "MIT" ]

109

2021-05-21T20:14:23.000Z

2022-03-31T20:56:10.000Z

src/user/views/__init__.py

ResearchHub/ResearchHub-Backend-Open

d36dca33afae2d442690694bb2ab17180d84bcd3

[ "MIT" ]

4

2021-05-17T13:47:53.000Z

2022-02-12T10:48:21.000Z

# flake8: noqa from user.views.user_views import * from user.views.gatekeeper_view import GatekeeperViewSet from user.views.organization_view import OrganizationViewSet

33.8

60

0.857988

from user.views.user_views import * from user.views.gatekeeper_view import GatekeeperViewSet from user.views.organization_view import OrganizationViewSet

true

f70fb191003034cfa869b208b1b4a32aa36da6d7

4,198

py

Python

python_modules/dagster/dagster_tests/core_tests/config_types_tests/test_config_spec.py

JPeer264/dagster-fork

32cc87a36134be7c442fa85d6867eb1d3301aea0

[ "Apache-2.0" ]

1

2020-09-19T16:35:59.000Z

python_modules/dagster/dagster_tests/core_tests/config_types_tests/test_config_spec.py

JPeer264/dagster-fork

32cc87a36134be7c442fa85d6867eb1d3301aea0

[ "Apache-2.0" ]

null

python_modules/dagster/dagster_tests/core_tests/config_types_tests/test_config_spec.py

JPeer264/dagster-fork

32cc87a36134be7c442fa85d6867eb1d3301aea0

[ "Apache-2.0" ]

null

import pytest from dagster import DagsterInvalidConfigDefinitionError, Noneable, Selector, execute_solid, solid def test_kitchen_sink(): @solid( config_schema={ 'str_field': str, 'int_field': int, 'list_int': [int], 'list_list_int': [[int]], 'dict_field': {'a_string': str}, 'list_dict_field': [{'an_int': int}], 'selector_of_things': Selector( {'select_list_dict_field': [{'an_int': int}], 'select_int': int} ), # this is a good argument to use () instead of [] for type parameterization in # the config system 'optional_list_of_optional_string': Noneable([Noneable(str)]), } ) def kitchen_sink(context): return context.solid_config solid_config_one = { 'str_field': 'kjf', 'int_field': 2, 'list_int': [3], 'list_list_int': [[1], [2, 3]], 'dict_field': {'a_string': 'kdjfkd'}, 'list_dict_field': [{'an_int': 2}, {'an_int': 4}], 'selector_of_things': {'select_int': 3}, 'optional_list_of_optional_string': ['foo', None], } assert ( execute_solid( kitchen_sink, run_config={'solids': {'kitchen_sink': {'config': solid_config_one}}}, ).output_value() == solid_config_one ) solid_config_two = { 'str_field': 'kjf', 'int_field': 2, 'list_int': [3], 'list_list_int': [[1], [2, 3]], 'dict_field': {'a_string': 'kdjfkd'}, 'list_dict_field': [{'an_int': 2}, {'an_int': 4}], 'selector_of_things': {'select_list_dict_field': [{'an_int': 5}]}, 'optional_list_of_optional_string': None, } assert ( execute_solid( kitchen_sink, run_config={'solids': {'kitchen_sink': {'config': solid_config_two}}}, ).output_value() == solid_config_two ) def test_bad_solid_config_argument(): with pytest.raises(DagsterInvalidConfigDefinitionError) as exc_info: @solid(config='dkjfkd') def _bad_config(_): pass assert str(exc_info.value).startswith( "Error defining config. Original value passed: 'dkjfkd'. 'dkjfkd' cannot be resolved." ) def test_bad_solid_config_argument_nested(): with pytest.raises(DagsterInvalidConfigDefinitionError) as exc_info: @solid(config={'field': 'kdjkfjd'}) def _bad_config(_): pass assert str(exc_info.value).startswith( "Error defining config. Original value passed: {'field': 'kdjkfjd'}. " "Error at stack path :field. 'kdjkfjd' cannot be resolved." ) def test_bad_solid_config_argument_list_wrong_length(): with pytest.raises(DagsterInvalidConfigDefinitionError) as exc_info: @solid(config={'bad_list': []}) def _bad_list_config(_): pass assert str(exc_info.value).startswith( "Error defining config. Original value passed: {'bad_list': []}. " "Error at stack path :bad_list. [] cannot be resolved. " "Reason: List must be of length 1." ) def test_bad_solid_config_argument_list_bad_item(): with pytest.raises(DagsterInvalidConfigDefinitionError) as exc_info: @solid(config={'bad_list': ['kdjfkd']}) def _bad_list_config(_): pass assert str(exc_info.value).startswith( "Error defining config. Original value passed: {'bad_list': ['kdjfkd']}. " "Error at stack path :bad_list. ['kdjfkd'] cannot be resolved. " "Reason: List have a single item and contain a valid type i.e. [int]. " "Got item 'kdjfkd'." ) def test_bad_solid_config_argument_list_bad_nested_item(): with pytest.raises(DagsterInvalidConfigDefinitionError) as exc_info: @solid(config={'bad_nested_list': [{'bad_field': 'kjdkfd'}]}) def _bad_list_config(_): pass assert str(exc_info.value).startswith( "Error defining config. Original value passed: {'bad_nested_list': " "[{'bad_field': 'kjdkfd'}]}. Error at stack path " ":bad_nested_list:bad_field. 'kjdkfd' cannot be resolved." )

32.045802

97

0.610052

import pytest from dagster import DagsterInvalidConfigDefinitionError, Noneable, Selector, execute_solid, solid def test_kitchen_sink(): @solid( config_schema={ 'str_field': str, 'int_field': int, 'list_int': [int], 'list_list_int': [[int]], 'dict_field': {'a_string': str}, 'list_dict_field': [{'an_int': int}], 'selector_of_things': Selector( {'select_list_dict_field': [{'an_int': int}], 'select_int': int} ), 'optional_list_of_optional_string': Noneable([Noneable(str)]), } ) def kitchen_sink(context): return context.solid_config solid_config_one = { 'str_field': 'kjf', 'int_field': 2, 'list_int': [3], 'list_list_int': [[1], [2, 3]], 'dict_field': {'a_string': 'kdjfkd'}, 'list_dict_field': [{'an_int': 2}, {'an_int': 4}], 'selector_of_things': {'select_int': 3}, 'optional_list_of_optional_string': ['foo', None], } assert ( execute_solid( kitchen_sink, run_config={'solids': {'kitchen_sink': {'config': solid_config_one}}}, ).output_value() == solid_config_one ) solid_config_two = { 'str_field': 'kjf', 'int_field': 2, 'list_int': [3], 'list_list_int': [[1], [2, 3]], 'dict_field': {'a_string': 'kdjfkd'}, 'list_dict_field': [{'an_int': 2}, {'an_int': 4}], 'selector_of_things': {'select_list_dict_field': [{'an_int': 5}]}, 'optional_list_of_optional_string': None, } assert ( execute_solid( kitchen_sink, run_config={'solids': {'kitchen_sink': {'config': solid_config_two}}}, ).output_value() == solid_config_two ) def test_bad_solid_config_argument(): with pytest.raises(DagsterInvalidConfigDefinitionError) as exc_info: @solid(config='dkjfkd') def _bad_config(_): pass assert str(exc_info.value).startswith( "Error defining config. Original value passed: 'dkjfkd'. 'dkjfkd' cannot be resolved." ) def test_bad_solid_config_argument_nested(): with pytest.raises(DagsterInvalidConfigDefinitionError) as exc_info: @solid(config={'field': 'kdjkfjd'}) def _bad_config(_): pass assert str(exc_info.value).startswith( "Error defining config. Original value passed: {'field': 'kdjkfjd'}. " "Error at stack path :field. 'kdjkfjd' cannot be resolved." ) def test_bad_solid_config_argument_list_wrong_length(): with pytest.raises(DagsterInvalidConfigDefinitionError) as exc_info: @solid(config={'bad_list': []}) def _bad_list_config(_): pass assert str(exc_info.value).startswith( "Error defining config. Original value passed: {'bad_list': []}. " "Error at stack path :bad_list. [] cannot be resolved. " "Reason: List must be of length 1." ) def test_bad_solid_config_argument_list_bad_item(): with pytest.raises(DagsterInvalidConfigDefinitionError) as exc_info: @solid(config={'bad_list': ['kdjfkd']}) def _bad_list_config(_): pass assert str(exc_info.value).startswith( "Error defining config. Original value passed: {'bad_list': ['kdjfkd']}. " "Error at stack path :bad_list. ['kdjfkd'] cannot be resolved. " "Reason: List have a single item and contain a valid type i.e. [int]. " "Got item 'kdjfkd'." ) def test_bad_solid_config_argument_list_bad_nested_item(): with pytest.raises(DagsterInvalidConfigDefinitionError) as exc_info: @solid(config={'bad_nested_list': [{'bad_field': 'kjdkfd'}]}) def _bad_list_config(_): pass assert str(exc_info.value).startswith( "Error defining config. Original value passed: {'bad_nested_list': " "[{'bad_field': 'kjdkfd'}]}. Error at stack path " ":bad_nested_list:bad_field. 'kjdkfd' cannot be resolved." )

true

f70fb27cfab9dd0b2e6b5adae0010670d24a0187

1,579

py

Python

test_Calculator/testing/test_cal_plus.py

XuXuClassMate/My_Test_PyProject

5822455af47f5855d1db4c388c2c973c440a4d3f

[ "Apache-2.0" ]

null

test_Calculator/testing/test_cal_plus.py

XuXuClassMate/My_Test_PyProject

5822455af47f5855d1db4c388c2c973c440a4d3f

[ "Apache-2.0" ]

null

test_Calculator/testing/test_cal_plus.py

XuXuClassMate/My_Test_PyProject

5822455af47f5855d1db4c388c2c973c440a4d3f

[ "Apache-2.0" ]

null

""" 1、case顺序：加-除-减-乘 2、fixture方法在case前打印【开始计算】，结束后打印【计算结束】 3、fixture方法存在在conftest.py，设置scope=module 4、控制case只执行顺序为：加-减-乘-除 5、结合allure生成本地测试报告 """ import allure import pytest import yaml from test_Calculator.src.calculator import Calculator def get_data(): with open('./data.yml') as data_x: data = yaml.safe_load(data_x) data_data = data['datas'] data_name = data['ids'] return [data_data, data_name] data = get_data() get_cal = Calculator() @pytest.mark.feature("测试方法") class Test_Calculator: @pytest.mark.story('加法测试') @pytest.mark.run(order=0) @pytest.mark.usefixtures("prints") @pytest.mark.parametrize("a, b, result", data[0]['data_add'], ids=data[1]['ids_add']) def test_add(self, a, b, result): assert get_cal.add(a, b) == result @pytest.mark.story('除法测试') @pytest.mark.run(order=3) @pytest.mark.parametrize("a, b, result", data[0]['data_div'], ids=data[1]['ids_div']) def test_div(self, a, b, result): assert get_cal.div(a, b) == result @pytest.mark.story('减法测试') @pytest.mark.run(order=1) @pytest.mark.parametrize("a, b, result", data[0]['data_sub'], ids=data[1]['ids_sub']) def test_sub(self, a, b, result): assert get_cal.sub(a, b) == result @pytest.mark.story('乘法测试') @pytest.mark.run(order=2) @pytest.mark.parametrize("a, b, result", data[0]['data_mul'], ids=data[1]['ids_mul']) def test_mul(self, a, b, result): assert get_cal.mul(a, b) == result if __name__ == '__main__': pytest.main('test_cal_plus.py', '-vs')

27.701754

89

0.645345

import allure import pytest import yaml from test_Calculator.src.calculator import Calculator def get_data(): with open('./data.yml') as data_x: data = yaml.safe_load(data_x) data_data = data['datas'] data_name = data['ids'] return [data_data, data_name] data = get_data() get_cal = Calculator() @pytest.mark.feature("测试方法") class Test_Calculator: @pytest.mark.story('加法测试') @pytest.mark.run(order=0) @pytest.mark.usefixtures("prints") @pytest.mark.parametrize("a, b, result", data[0]['data_add'], ids=data[1]['ids_add']) def test_add(self, a, b, result): assert get_cal.add(a, b) == result @pytest.mark.story('除法测试') @pytest.mark.run(order=3) @pytest.mark.parametrize("a, b, result", data[0]['data_div'], ids=data[1]['ids_div']) def test_div(self, a, b, result): assert get_cal.div(a, b) == result @pytest.mark.story('减法测试') @pytest.mark.run(order=1) @pytest.mark.parametrize("a, b, result", data[0]['data_sub'], ids=data[1]['ids_sub']) def test_sub(self, a, b, result): assert get_cal.sub(a, b) == result @pytest.mark.story('乘法测试') @pytest.mark.run(order=2) @pytest.mark.parametrize("a, b, result", data[0]['data_mul'], ids=data[1]['ids_mul']) def test_mul(self, a, b, result): assert get_cal.mul(a, b) == result if __name__ == '__main__': pytest.main('test_cal_plus.py', '-vs')

true

f70fb2c5d6c94d72c11d58d67c3da8ca3e2648c3

2,641

py

Python

siamesenetwork/siamesePreTrainedEmbeddings.py

pengfei99/openfood

2b65af02ce34bf8193d357ef3661da749d2d9671

[ "MIT" ]

2

2021-09-13T14:46:24.000Z

2021-09-13T14:46:35.000Z

siamesenetwork/siamesePreTrainedEmbeddings.py

pengfei99/openfood

2b65af02ce34bf8193d357ef3661da749d2d9671

[ "MIT" ]

null

siamesenetwork/siamesePreTrainedEmbeddings.py

pengfei99/openfood

2b65af02ce34bf8193d357ef3661da749d2d9671

[ "MIT" ]

null

# !/usr/bin/env python3 # -*- coding: utf-8 -*- """ Define the siamese network for one-shot learning, for french short labels 02/06/2021 @author: milena-git, from jeremylhour courtesy """ import torch import torch.nn as nn def _createEmbeddingLayer(weights_matrix, non_trainable=False): """ _createEmbeddingLayer: create a layer from pre-trained embeddings @param weights_matrix (np.array): @param non_trainable (bool): """ weights_matrix = torch.tensor(weights_matrix) num_embeddings, embedding_dim = weights_matrix.size() emb_layer = nn.Embedding(num_embeddings, embedding_dim) emb_layer.load_state_dict({'weight': weights_matrix}) if non_trainable: emb_layer.weight.requires_grad = False return emb_layer, num_embeddings, embedding_dim class SiamesePreTrainedQuadruplet(nn.Module): def __init__(self, weights_matrix, length, dim=100): """ Initialize the siamese network with pre-trained embeddings @param weights_matrix (torch.tensor): @param length (int): longueur des inputs @param dim (int): dimension of the output embedding space """ super(SiamesePreTrainedQuadruplet, self).__init__() self.dim = dim self.length = length self.embedding = nn.Embedding.from_pretrained(weights_matrix, padding_idx=0) self.fc1 = nn.Sequential( nn.Linear(self.length * weights_matrix.size()[1], 1000), nn.ReLU(inplace=True), nn.Linear(1000, 800), nn.Dropout(0.2), nn.Linear(800, 500), nn.Dropout(0.2), nn.Linear(500, self.dim) ) def forward_once(self, x): """ Run one of the network on a single image @param x (): img output from SiameseNetworkDataset """ embedded = self.embedding(x) embedded = torch.reshape(embedded, (embedded.size()[0], embedded.size()[1] * embedded.size()[2])) output = self.fc1(embedded) return output def forward(self, anchor, positive, negative1, negative2): """ Run the model forward, by applying forward_once to each inputs Main forward that is used during train, wraps forward_once(). @param anchor, positive, negative1, negative2 (): output from SiameseNetworkDataset """ anchor_o, positive_o, negative1_o, negative2_o = self.forward_once(anchor), self.forward_once( positive), self.forward_once(negative1), self.forward_once(negative2) return anchor_o, positive_o, negative1_o, negative2_o if __name__ == '__main__': pass

33.43038

105

0.658463

import torch import torch.nn as nn def _createEmbeddingLayer(weights_matrix, non_trainable=False): weights_matrix = torch.tensor(weights_matrix) num_embeddings, embedding_dim = weights_matrix.size() emb_layer = nn.Embedding(num_embeddings, embedding_dim) emb_layer.load_state_dict({'weight': weights_matrix}) if non_trainable: emb_layer.weight.requires_grad = False return emb_layer, num_embeddings, embedding_dim class SiamesePreTrainedQuadruplet(nn.Module): def __init__(self, weights_matrix, length, dim=100): super(SiamesePreTrainedQuadruplet, self).__init__() self.dim = dim self.length = length self.embedding = nn.Embedding.from_pretrained(weights_matrix, padding_idx=0) self.fc1 = nn.Sequential( nn.Linear(self.length * weights_matrix.size()[1], 1000), nn.ReLU(inplace=True), nn.Linear(1000, 800), nn.Dropout(0.2), nn.Linear(800, 500), nn.Dropout(0.2), nn.Linear(500, self.dim) ) def forward_once(self, x): embedded = self.embedding(x) embedded = torch.reshape(embedded, (embedded.size()[0], embedded.size()[1] * embedded.size()[2])) output = self.fc1(embedded) return output def forward(self, anchor, positive, negative1, negative2): anchor_o, positive_o, negative1_o, negative2_o = self.forward_once(anchor), self.forward_once( positive), self.forward_once(negative1), self.forward_once(negative2) return anchor_o, positive_o, negative1_o, negative2_o if __name__ == '__main__': pass

true

f70fb3476d36e16a6599d538e8c7c982416ef57c

2,165

py

Python

test/PR_test/unit_test/op/numpyop/univariate/test_autocontrast.py

DwijayDS/fastestimator

9b288cb2bd870f971ec4cee09d0b3205e1316a94

[ "Apache-2.0" ]

57

2019-05-21T21:29:26.000Z

2022-02-23T05:55:21.000Z

test/PR_test/unit_test/op/numpyop/univariate/test_autocontrast.py

vbvg2008/fastestimator

6061a4fbbeb62a2194ef82ba8017f651710d0c65

[ "Apache-2.0" ]

93

2019-05-23T18:36:07.000Z

2022-03-23T17:15:55.000Z

test/PR_test/unit_test/op/numpyop/univariate/test_autocontrast.py

vbvg2008/fastestimator

6061a4fbbeb62a2194ef82ba8017f651710d0c65

[ "Apache-2.0" ]

47

2019-05-09T15:41:37.000Z

2022-03-26T17:00:08.000Z

# Copyright 2021 The FastEstimator Authors. All Rights Reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. # ============================================================================== import unittest import numpy as np from fastestimator.op.numpyop.univariate import AutoContrast class TestAutoContrast(unittest.TestCase): @classmethod def setUpClass(cls): cls.single_input = [np.random.randint(0, 256, size=(28, 28, 3)).astype(np.uint8)] cls.single_output_shape = (28, 28, 3) cls.multi_input = [ np.random.randint(0, 256, size=(28, 28, 3)).astype(np.uint8), np.random.randint(0, 256, size=(28, 28, 3)).astype(np.uint8) ] cls.multi_output_shape = (28, 28, 3) def test_single_input(self): autocontrast = AutoContrast(inputs='x', outputs='x') output = autocontrast.forward(data=self.single_input, state={}) with self.subTest('Check output type'): self.assertEqual(type(output), list) with self.subTest('Check output image shape'): self.assertEqual(output[0].shape, self.single_output_shape) def test_multi_input(self): autocontrast = AutoContrast(inputs='x', outputs='x') output = autocontrast.forward(data=self.multi_input, state={}) with self.subTest('Check output type'): self.assertEqual(type(output), list) with self.subTest('Check output list length'): self.assertEqual(len(output), 2) for img_output in output: with self.subTest('Check output image shape'): self.assertEqual(img_output.shape, self.multi_output_shape)

42.45098

89

0.655427

import unittest import numpy as np from fastestimator.op.numpyop.univariate import AutoContrast class TestAutoContrast(unittest.TestCase): @classmethod def setUpClass(cls): cls.single_input = [np.random.randint(0, 256, size=(28, 28, 3)).astype(np.uint8)] cls.single_output_shape = (28, 28, 3) cls.multi_input = [ np.random.randint(0, 256, size=(28, 28, 3)).astype(np.uint8), np.random.randint(0, 256, size=(28, 28, 3)).astype(np.uint8) ] cls.multi_output_shape = (28, 28, 3) def test_single_input(self): autocontrast = AutoContrast(inputs='x', outputs='x') output = autocontrast.forward(data=self.single_input, state={}) with self.subTest('Check output type'): self.assertEqual(type(output), list) with self.subTest('Check output image shape'): self.assertEqual(output[0].shape, self.single_output_shape) def test_multi_input(self): autocontrast = AutoContrast(inputs='x', outputs='x') output = autocontrast.forward(data=self.multi_input, state={}) with self.subTest('Check output type'): self.assertEqual(type(output), list) with self.subTest('Check output list length'): self.assertEqual(len(output), 2) for img_output in output: with self.subTest('Check output image shape'): self.assertEqual(img_output.shape, self.multi_output_shape)

true

f70fb44b8b726f2718970f214122633936106d39

3,774

py

Python

pythonsdk/face/face_pb2_grpc.py

jjrobotcn/andy4

4a0cb57aa5f318a3099fbfe6198620555b3a45af

[ "MIT" ]

null

pythonsdk/face/face_pb2_grpc.py

jjrobotcn/andy4

4a0cb57aa5f318a3099fbfe6198620555b3a45af

[ "MIT" ]

null

pythonsdk/face/face_pb2_grpc.py

jjrobotcn/andy4

4a0cb57aa5f318a3099fbfe6198620555b3a45af

[ "MIT" ]

null

# Generated by the gRPC Python protocol compiler plugin. DO NOT EDIT! import grpc from . import face_pb2 as face__pb2 class FaceServiceStub(object): """faceRecognition.FaceService 人脸服务 """ def __init__(self, channel): """Constructor. Args: channel: A grpc.Channel. """ self.Compare = channel.unary_unary( '/faceRecognition.FaceService/Compare', request_serializer=face__pb2.CompareRequest.SerializeToString, response_deserializer=face__pb2.CompareResponse.FromString, ) self.Search = channel.unary_unary( '/faceRecognition.FaceService/Search', request_serializer=face__pb2.SearchRequest.SerializeToString, response_deserializer=face__pb2.SearchResponse.FromString, ) class FaceServiceServicer(object): """faceRecognition.FaceService 人脸服务 """ def Compare(self, request, context): """Compare 实现两张人脸图片对比识别，返回两张人脸图片对比的可信度开发管理平台功能参考: http://10.10.10.2/face/compare """ context.set_code(grpc.StatusCode.UNIMPLEMENTED) context.set_details('Method not implemented!') raise NotImplementedError('Method not implemented!') def Search(self, request, context): """Search 从FaceSet中搜索近似人脸数据若存在匹配数据时返回一个FaceDetail及可信度开发管理平台功能参考: http://10.10.10.2/face/compare """ context.set_code(grpc.StatusCode.UNIMPLEMENTED) context.set_details('Method not implemented!') raise NotImplementedError('Method not implemented!') def add_FaceServiceServicer_to_server(servicer, server): rpc_method_handlers = { 'Compare': grpc.unary_unary_rpc_method_handler( servicer.Compare, request_deserializer=face__pb2.CompareRequest.FromString, response_serializer=face__pb2.CompareResponse.SerializeToString, ), 'Search': grpc.unary_unary_rpc_method_handler( servicer.Search, request_deserializer=face__pb2.SearchRequest.FromString, response_serializer=face__pb2.SearchResponse.SerializeToString, ), } generic_handler = grpc.method_handlers_generic_handler( 'faceRecognition.FaceService', rpc_method_handlers) server.add_generic_rpc_handlers((generic_handler,)) # This class is part of an EXPERIMENTAL API. class FaceService(object): """faceRecognition.FaceService 人脸服务 """ @staticmethod def Compare(request, target, options=(), channel_credentials=None, call_credentials=None, compression=None, wait_for_ready=None, timeout=None, metadata=None): return grpc.experimental.unary_unary(request, target, '/faceRecognition.FaceService/Compare', face__pb2.CompareRequest.SerializeToString, face__pb2.CompareResponse.FromString, options, channel_credentials, call_credentials, compression, wait_for_ready, timeout, metadata) @staticmethod def Search(request, target, options=(), channel_credentials=None, call_credentials=None, compression=None, wait_for_ready=None, timeout=None, metadata=None): return grpc.experimental.unary_unary(request, target, '/faceRecognition.FaceService/Search', face__pb2.SearchRequest.SerializeToString, face__pb2.SearchResponse.FromString, options, channel_credentials, call_credentials, compression, wait_for_ready, timeout, metadata)

35.271028

101

0.645999

import grpc from . import face_pb2 as face__pb2 class FaceServiceStub(object): def __init__(self, channel): self.Compare = channel.unary_unary( '/faceRecognition.FaceService/Compare', request_serializer=face__pb2.CompareRequest.SerializeToString, response_deserializer=face__pb2.CompareResponse.FromString, ) self.Search = channel.unary_unary( '/faceRecognition.FaceService/Search', request_serializer=face__pb2.SearchRequest.SerializeToString, response_deserializer=face__pb2.SearchResponse.FromString, ) class FaceServiceServicer(object): def Compare(self, request, context): context.set_code(grpc.StatusCode.UNIMPLEMENTED) context.set_details('Method not implemented!') raise NotImplementedError('Method not implemented!') def Search(self, request, context): context.set_code(grpc.StatusCode.UNIMPLEMENTED) context.set_details('Method not implemented!') raise NotImplementedError('Method not implemented!') def add_FaceServiceServicer_to_server(servicer, server): rpc_method_handlers = { 'Compare': grpc.unary_unary_rpc_method_handler( servicer.Compare, request_deserializer=face__pb2.CompareRequest.FromString, response_serializer=face__pb2.CompareResponse.SerializeToString, ), 'Search': grpc.unary_unary_rpc_method_handler( servicer.Search, request_deserializer=face__pb2.SearchRequest.FromString, response_serializer=face__pb2.SearchResponse.SerializeToString, ), } generic_handler = grpc.method_handlers_generic_handler( 'faceRecognition.FaceService', rpc_method_handlers) server.add_generic_rpc_handlers((generic_handler,)) class FaceService(object): @staticmethod def Compare(request, target, options=(), channel_credentials=None, call_credentials=None, compression=None, wait_for_ready=None, timeout=None, metadata=None): return grpc.experimental.unary_unary(request, target, '/faceRecognition.FaceService/Compare', face__pb2.CompareRequest.SerializeToString, face__pb2.CompareResponse.FromString, options, channel_credentials, call_credentials, compression, wait_for_ready, timeout, metadata) @staticmethod def Search(request, target, options=(), channel_credentials=None, call_credentials=None, compression=None, wait_for_ready=None, timeout=None, metadata=None): return grpc.experimental.unary_unary(request, target, '/faceRecognition.FaceService/Search', face__pb2.SearchRequest.SerializeToString, face__pb2.SearchResponse.FromString, options, channel_credentials, call_credentials, compression, wait_for_ready, timeout, metadata)

true

f70fb47f91fd9a5618daafe068d611e8a5784530

1,876

py

Python

setup.py

wbogen/cardio

b8826295b7e27168441e2595e9592aff77cf7722

[ "Apache-2.0" ]

250

2017-11-22T14:41:57.000Z

2022-02-02T22:41:28.000Z

setup.py

supertime1/cardio

58087b21295ebe18fb5a5dfbb68479b39ddb4971

[ "Apache-2.0" ]

34

2017-11-23T18:27:20.000Z

2020-09-10T11:55:16.000Z

setup.py

supertime1/cardio

58087b21295ebe18fb5a5dfbb68479b39ddb4971

[ "Apache-2.0" ]

85

2017-11-23T13:07:31.000Z

2021-11-24T08:34:07.000Z

""" CardIO is a library that works with electrocardiograms. Documentation - https://analysiscenter.github.io/cardio/ """ from setuptools import setup, find_packages import re with open('cardio/__init__.py', 'r') as f: version = re.search(r'^__version__\s*=\s*[\'"]([^\'"]*)[\'"]', f.read(), re.MULTILINE).group(1) with open('docs/index.rst', 'r') as f: long_description = f.read() setup( name='cardio', packages=find_packages(exclude=['tutorials', 'examples', 'docs']), version=version, url='https://github.com/analysiscenter/cardio', license='Apache License 2.0', author='Data Analysis Center team', author_email='cardio@analysiscenter.ru', description='A framework for deep research of electrocardiograms', long_description=long_description, zip_safe=False, platforms='any', install_requires=[ 'numpy>=1.13.1', 'scipy>=0.19.1', 'pandas>=0.21.1', 'scikit-learn==0.19.1', 'numba>=0.35.0', 'pywavelets>=0.5.2', 'matplotlib>=2.1.0', 'dill>=0.2.7.1', 'pydicom>=0.9.9', 'pyedflib>=0.1.11', 'wfdb==2.2.1', 'pint>=0.8.1', ], extras_require={ 'tensorflow': ['tensorflow>=1.4'], 'tensorflow-gpu': ['tensorflow-gpu>=1.4'], 'keras': ['keras>=2.0.0'], 'hmmlearn': ['hmmlearn==0.2.0'] }, classifiers=[ 'Development Status :: 4 - Beta', 'Intended Audience :: Developers', 'Intended Audience :: Science/Research', 'License :: OSI Approved :: Apache Software License', 'Operating System :: OS Independent', 'Programming Language :: Python', 'Programming Language :: Python :: 3', 'Programming Language :: Python :: 3.5', 'Programming Language :: Python :: 3.6', 'Topic :: Scientific/Engineering' ], )

30.258065

99

0.584755

from setuptools import setup, find_packages import re with open('cardio/__init__.py', 'r') as f: version = re.search(r'^__version__\s*=\s*[\'"]([^\'"]*)[\'"]', f.read(), re.MULTILINE).group(1) with open('docs/index.rst', 'r') as f: long_description = f.read() setup( name='cardio', packages=find_packages(exclude=['tutorials', 'examples', 'docs']), version=version, url='https://github.com/analysiscenter/cardio', license='Apache License 2.0', author='Data Analysis Center team', author_email='cardio@analysiscenter.ru', description='A framework for deep research of electrocardiograms', long_description=long_description, zip_safe=False, platforms='any', install_requires=[ 'numpy>=1.13.1', 'scipy>=0.19.1', 'pandas>=0.21.1', 'scikit-learn==0.19.1', 'numba>=0.35.0', 'pywavelets>=0.5.2', 'matplotlib>=2.1.0', 'dill>=0.2.7.1', 'pydicom>=0.9.9', 'pyedflib>=0.1.11', 'wfdb==2.2.1', 'pint>=0.8.1', ], extras_require={ 'tensorflow': ['tensorflow>=1.4'], 'tensorflow-gpu': ['tensorflow-gpu>=1.4'], 'keras': ['keras>=2.0.0'], 'hmmlearn': ['hmmlearn==0.2.0'] }, classifiers=[ 'Development Status :: 4 - Beta', 'Intended Audience :: Developers', 'Intended Audience :: Science/Research', 'License :: OSI Approved :: Apache Software License', 'Operating System :: OS Independent', 'Programming Language :: Python', 'Programming Language :: Python :: 3', 'Programming Language :: Python :: 3.5', 'Programming Language :: Python :: 3.6', 'Topic :: Scientific/Engineering' ], )

true

f70fb4b707686268baecdf75a4c2e0bd818d3206

15,820

py

Python

Password-Locker/run.py

HASSAN1A/Password-Locker

bea88438936fa9ffa174d6f9c7941046d713092b

[ "MIT" ]

2

2021-05-19T12:58:21.000Z

2021-05-28T14:03:02.000Z

Password-Locker/run.py

HASSAN1A/Password-Locker

bea88438936fa9ffa174d6f9c7941046d713092b

[ "MIT" ]

null

Password-Locker/run.py

HASSAN1A/Password-Locker

bea88438936fa9ffa174d6f9c7941046d713092b

[ "MIT" ]

null

#!/usr/bin/env python3.8 from account import Account from credential import Credential from termcolor import colored, cprint import os import time import pickle # Functions that implement the behaviours in account class. def create_account(username, fname, lname, p_word): ''' Function to create new account ''' new_account = Account(username, fname, lname, p_word) return new_account def save_account(account): ''' Function to save account ''' account.save_account() def delete_account(account): ''' Function to delete an account ''' account.delete_account() def check_account_exists(username): ''' Function that check if an account with that username already exists and return a Boolean ''' return Account.account_exists(username) def auth_user(username, password): ''' Function to authenicate user during login ''' return Account.auth_user(username, password) # Functions that implement the behaviours in credential class. def create_credential(page, username, password): ''' Function to create credentials ''' new_credential = Credential(page, username, password) return new_credential def save_credential(credential): ''' Function to save credential ''' credential.save_credential() def delete_credential(credential): ''' Function to delete credential ''' credential.delete_credential() def find_cred_by_pagename(pagename): """ Function that finds a credential by pagename and returns the credentials """ return Credential.find_by_pagename(pagename) def copy_cred_pass(pagename): ''' Function to copy credential password ''' return Credential.copy_cred_password(pagename) def check_credential_exists(pagename): ''' Function that check if a credential exists with that pagename and return a Boolean ''' return Credential.credential_exists(pagename) def display_credentials(): ''' Function that returns all the saved credentials ''' return Credential.display_credentials() def generate_password(length): ''' Function that generte a random password ''' return Credential.generate_password(length) def main(): login = False # Set initial login value to false sign_name = '' # Name of user currently logged in logged = True def load_pickles(): try: file_object = open('accounts.pydata', 'rb') Account.accounts_list = pickle.load(file_object) file_object.close() print("\nLOADED PICKLES ACCOUNTS") except: print("\nCLDN'T LOAD PICKLES ACCOUNTS") Account.accounts_list = [] try: file_objectt = open('credentials.pydata', 'rb') Credential.credentials_list = pickle.load(file_objectt) file_object.close() print("\nLOADED PICKLES CREDENTIALS") except: print("\nCLDN'T LOAD PICKLES CREDENTIALS") Credential.credentials_list = [] def pickle_save(): try: file_object = open('accounts.pydata', 'wb') pickle.dump(Account.accounts_list, file_object) file_object.close() print("\nSAVED ACCOUNTS TO PICKLE") except Exception as e: print(e) print("\nCOULDN'T ACCOUNTS SAVE TO PICKLES.") try: file_objectt = open('credentials.pydata', 'wb') pickle.dump(display_credentials(), file_objectt) file_objectt.close() print("\nSAVED CREDENTIALS TO PICKLE") except Exception as e: print(e) print("\nCOULDN'T CREDENTIALS SAVE TO PICKLES.") def display_title(): os.system('clear') ''' Function to display app title bar ''' cprint(""" \n\t\t\t\t********************************************** \t\t************************************************************************** \t******************************************************************************************* \n \t\t\t\t \t\t\t\t \t\t\t\t |\ /| \t\t\t\t | \ / | \t\t\t\t | \/ | \n\t\t\t\t*** WELCOME TO PASSWORD LOCKER *** \n`\t\t\t****************************************************************** """, "magenta") while logged: display_title() load_pickles() while login == False: cprint(""" Use the following short codes to manage your password locker account 'ln' - Login 'xx' - Close app """, "blue") s_code = input( colored('\tWhat would you like to do? >> ', 'cyan')).lower() if s_code == 'ln': acc_code = input( colored('\tDo you have an account? Y/N >> ', 'cyan')).upper() if acc_code == 'Y': cprint( '\tEnter your username and password to login >>>\n', 'pink') login_user_name = input( colored('\tEnter username >> ', 'cyan')) login_password = input( colored('\tEnter password >> ', 'cyan')) print("\n\t\tSigning in...") time.sleep(1.5) if auth_user(login_user_name, login_password): cprint('\n\t\tLOGIN SUCCESSFUL', 'green', attrs=['bold']) sign_name = login_user_name login = True else: cprint('\n\t\tSORRY COULD NOT VERIFY', 'red', attrs=['bold']) elif acc_code == 'N': cprint( '\tEnter your username,firstname,lastname and password to register account >>>\n', 'blue') reg_user_name = input( colored('\tEnter username >> ', 'cyan')) reg_f_name = input( colored('\tEnter firstname >> ', 'cyan')) reg_l_name = input(colored('\tEnter lastname >> ', 'cyan')) reg_password = input( colored('\tEnter password >> ', 'cyan')) print("\n\t\tRegistering ...") time.sleep(1.5) if check_account_exists(reg_user_name): cprint( f"\n\t\tACCOUNT WITH, {reg_user_name.upper()} USERNAME ALREADY CREATED", "red", attrs=['bold']) else: new_acc = create_account( reg_user_name, reg_f_name, reg_l_name, reg_password) save_account(new_acc) cprint( "\n\t\tCONGRATULATIONS, YOUR ACCOUNT HAS BEEN CREATED", "green", attrs=['bold']) cprint("\n\tSign into your new account", "blue") sign_username = input( colored('\n\tEnter username >> ', 'cyan')) sign_password = input( colored('\n\tEnter password >> ', 'cyan')) print("\n\t\tSigning in ...") time.sleep(1.5) if auth_user(sign_username, sign_password): cprint("\n\t\tLOGIN SUCCESSFUL", "green", attrs=['bold']) sign_name = sign_username login = True else: cprint('\n\t\tSORRY COULD NOT VERIFY USER', 'red', attrs=['bold']) else: cprint('\n\t\tPLEASE USE THE GIVEN SHORT CODES', 'red', attrs=['bold']) elif s_code == 'xx': cprint(f"""\n\t\tTHANK YOU FOR USING PASSWORD LOCKER \t\tBye... \t\t\t\t\tClosing App >>>>> """, "red", attrs=['bold']) pickle_save() time.sleep(1.5) logged = False break else: cprint('\n\t\tPLEASE USE THE GIVEN SHORT CODES', 'red', attrs=['bold']) while login == True: time.sleep(1.5) cprint(f""" {sign_name.upper()}, WELCOME TO YOUR PASSWORD LOCKER: Use the following commands to navigate the application: 'sc' >> Save existing page credentials 'cc' >> Create new page credentials 'dc' >> Display all credentials saved 'fc' >> Find credential saved by page name 'cp' >> Copy pagename credential password to clipboard 'dl' >> Delete page credential 'lgo' >> Log out 'ex' >> Close App """, "blue") app_code = input( colored('\tWhat would you like to do? >> ', 'cyan')).lower() if app_code == 'sc': cprint( '\tEnter pagename,username and password to save credentials >>>\n', 'blue') page_name = input( colored('\n\tEnter pagename >> ', 'cyan')).lower() user_name = input( colored('\n\tEnter page username >> ', 'cyan')) pass_word = input( colored('\n\tEnter page password >> ', 'cyan')) print("\n\t\tSaving credentials ...") time.sleep(1.5) if check_credential_exists(page_name): cprint('\n\t\tCREDENTIALS FOR '+page_name.upper() + ' ALREADY EXISTS', 'red', attrs=['bold']) else: new_credential = create_credential( page_name, user_name, pass_word) save_credential(new_credential) cprint("\n\t\t"+page_name.upper() + ", CREDENTIALS SAVED", "green", attrs=['bold']) elif app_code == 'cc': cprint( '\tEnter pagename,username and password to create and save new page credentials >>>\n', 'blue') page_name = input( colored('\n\tEnter pagename >> ', 'cyan')).lower() user_name = input( colored('\n\tEnter page username >> ', 'cyan')) gen_pass_code = input(colored( '\tWould you like to generate a random password? Y/N >> ', 'cyan')).upper() pass_word = '' if gen_pass_code == 'Y': pass_len = int(input(colored( '\tHow long would you like your password? Provide numbers only >> ', 'cyan'))) pass_word = generate_password(pass_len) else: pass_word = input( colored('\n\tEnter page password >> ', 'cyan')) print("\n\t\tCreating and Saving credentials ...") time.sleep(1.5) if check_credential_exists(page_name): cprint('\n\t\tCREDENTIALS FOR '+page_name.upper() + ' ALREADY EXISTS', 'red', attrs=['bold']) else: new_credential = create_credential( page_name, user_name, pass_word) save_credential(new_credential) cprint("\n\t\t"+page_name.upper() + ", CREDENTIALS SAVED", "green", attrs=['bold']) elif app_code == 'dc': if len(display_credentials()) > 0: cprint("\n\t\t"+sign_name.upper() + ", CREDENTIALS", "green", attrs=['bold']) for credential in display_credentials(): cprint(f''' ------------------------------------------------------- Page Name >>>> {credential.page_name.upper()} Page Username >>>> {credential.user_name} Page Password >>>> {credential.pass_word} ------------------------------------------------------- ''', 'green') else: cprint("\n\t\t"+sign_name.upper() + ",HAS NO CREDENTIALS SAVED", "green", attrs=['bold']) elif app_code == 'fc': search_page = input( colored('\n\tEnter page name to search credentials >> ', 'cyan')).lower() print("\n\t\tLoading ...") time.sleep(1.5) if check_credential_exists(search_page): found_credential = find_cred_by_pagename(search_page) cprint(f''' ------------------------------------------------------- Page Name >>>> {found_credential.page_name.upper()} Page Username >>>> {found_credential.user_name} Page Password >>>> {found_credential.pass_word} ------------------------------------------------------- ''', 'green') else: cprint( f'\n\t\t{search_page.upper()} DOES NOT EXISTS', 'red', attrs=['bold']) elif app_code == 'cp': search_page = input(colored( '\n\tEnter page name to copy password to clipboard >> ', 'cyan')).lower() print("\n\t\tSearching ...") time.sleep(1.5) if check_credential_exists(search_page): copy_cred_pass(search_page) cprint("\n\t\t"+search_page.upper() + ", PASSWORD COPIED TO CLIPBOARD", "green", attrs=['bold']) else: cprint( f'\n\t\t{search_page.upper()} DOES NOT EXISTS', 'red', attrs=['bold']) elif app_code == 'dl': del_page = input( colored('\n\tEnter page name you want to delete >> ', 'cyan')).lower() print("\n\t\tDeleting ...") time.sleep(1.5) if check_credential_exists(del_page): found_page = find_cred_by_pagename(del_page) found_page.delete_credential() cprint("\n\t\t"+del_page.upper() + ", CREDENTIALS DELETED", "green", attrs=['bold']) else: cprint( f'\n\t\t{del_page.upper()} DOES NOT EXISTS', 'red', attrs=['bold']) elif app_code == 'lgo': cprint(f"""\n\t\t{sign_name.upper()}, THANK YOU FOR USING PASSWORD LOCKER \t\tBye... \t\t\t\t\tLogin out >>>>> """, "green", attrs=['bold']) time.sleep(1.5) login = False elif app_code == 'ex': cprint(f"""\n\t\t{sign_name.upper()}, THANK YOU FOR USING PASSWORD LOCKER \t\tBye... \t\t\t\t\tClosing App >>>>> """, "red", attrs=['bold']) pickle_save() time.sleep(1.5) login = False logged = False else: cprint('\n\t\tPLEASE USE THE GIVEN SHORT CODES', 'red', attrs=['bold']) if __name__ == '__main__': main()

38.77451

123

0.468078

from account import Account from credential import Credential from termcolor import colored, cprint import os import time import pickle def create_account(username, fname, lname, p_word): new_account = Account(username, fname, lname, p_word) return new_account def save_account(account): account.save_account() def delete_account(account): account.delete_account() def check_account_exists(username): return Account.account_exists(username) def auth_user(username, password): return Account.auth_user(username, password) def create_credential(page, username, password): new_credential = Credential(page, username, password) return new_credential def save_credential(credential): credential.save_credential() def delete_credential(credential): credential.delete_credential() def find_cred_by_pagename(pagename): return Credential.find_by_pagename(pagename) def copy_cred_pass(pagename): return Credential.copy_cred_password(pagename) def check_credential_exists(pagename): return Credential.credential_exists(pagename) def display_credentials(): return Credential.display_credentials() def generate_password(length): return Credential.generate_password(length) def main(): login = False sign_name = '' logged = True def load_pickles(): try: file_object = open('accounts.pydata', 'rb') Account.accounts_list = pickle.load(file_object) file_object.close() print("\nLOADED PICKLES ACCOUNTS") except: print("\nCLDN'T LOAD PICKLES ACCOUNTS") Account.accounts_list = [] try: file_objectt = open('credentials.pydata', 'rb') Credential.credentials_list = pickle.load(file_objectt) file_object.close() print("\nLOADED PICKLES CREDENTIALS") except: print("\nCLDN'T LOAD PICKLES CREDENTIALS") Credential.credentials_list = [] def pickle_save(): try: file_object = open('accounts.pydata', 'wb') pickle.dump(Account.accounts_list, file_object) file_object.close() print("\nSAVED ACCOUNTS TO PICKLE") except Exception as e: print(e) print("\nCOULDN'T ACCOUNTS SAVE TO PICKLES.") try: file_objectt = open('credentials.pydata', 'wb') pickle.dump(display_credentials(), file_objectt) file_objectt.close() print("\nSAVED CREDENTIALS TO PICKLE") except Exception as e: print(e) print("\nCOULDN'T CREDENTIALS SAVE TO PICKLES.") def display_title(): os.system('clear') cprint(""" \n\t\t\t\t********************************************** \t\t************************************************************************** \t******************************************************************************************* \n \t\t\t\t \t\t\t\t \t\t\t\t |\ /| \t\t\t\t | \ / | \t\t\t\t | \/ | \n\t\t\t\t*** WELCOME TO PASSWORD LOCKER *** \n`\t\t\t****************************************************************** """, "magenta") while logged: display_title() load_pickles() while login == False: cprint(""" Use the following short codes to manage your password locker account 'ln' - Login 'xx' - Close app """, "blue") s_code = input( colored('\tWhat would you like to do? >> ', 'cyan')).lower() if s_code == 'ln': acc_code = input( colored('\tDo you have an account? Y/N >> ', 'cyan')).upper() if acc_code == 'Y': cprint( '\tEnter your username and password to login >>>\n', 'pink') login_user_name = input( colored('\tEnter username >> ', 'cyan')) login_password = input( colored('\tEnter password >> ', 'cyan')) print("\n\t\tSigning in...") time.sleep(1.5) if auth_user(login_user_name, login_password): cprint('\n\t\tLOGIN SUCCESSFUL', 'green', attrs=['bold']) sign_name = login_user_name login = True else: cprint('\n\t\tSORRY COULD NOT VERIFY', 'red', attrs=['bold']) elif acc_code == 'N': cprint( '\tEnter your username,firstname,lastname and password to register account >>>\n', 'blue') reg_user_name = input( colored('\tEnter username >> ', 'cyan')) reg_f_name = input( colored('\tEnter firstname >> ', 'cyan')) reg_l_name = input(colored('\tEnter lastname >> ', 'cyan')) reg_password = input( colored('\tEnter password >> ', 'cyan')) print("\n\t\tRegistering ...") time.sleep(1.5) if check_account_exists(reg_user_name): cprint( f"\n\t\tACCOUNT WITH, {reg_user_name.upper()} USERNAME ALREADY CREATED", "red", attrs=['bold']) else: new_acc = create_account( reg_user_name, reg_f_name, reg_l_name, reg_password) save_account(new_acc) cprint( "\n\t\tCONGRATULATIONS, YOUR ACCOUNT HAS BEEN CREATED", "green", attrs=['bold']) cprint("\n\tSign into your new account", "blue") sign_username = input( colored('\n\tEnter username >> ', 'cyan')) sign_password = input( colored('\n\tEnter password >> ', 'cyan')) print("\n\t\tSigning in ...") time.sleep(1.5) if auth_user(sign_username, sign_password): cprint("\n\t\tLOGIN SUCCESSFUL", "green", attrs=['bold']) sign_name = sign_username login = True else: cprint('\n\t\tSORRY COULD NOT VERIFY USER', 'red', attrs=['bold']) else: cprint('\n\t\tPLEASE USE THE GIVEN SHORT CODES', 'red', attrs=['bold']) elif s_code == 'xx': cprint(f"""\n\t\tTHANK YOU FOR USING PASSWORD LOCKER \t\tBye... \t\t\t\t\tClosing App >>>>> """, "red", attrs=['bold']) pickle_save() time.sleep(1.5) logged = False break else: cprint('\n\t\tPLEASE USE THE GIVEN SHORT CODES', 'red', attrs=['bold']) while login == True: time.sleep(1.5) cprint(f""" {sign_name.upper()}, WELCOME TO YOUR PASSWORD LOCKER: Use the following commands to navigate the application: 'sc' >> Save existing page credentials 'cc' >> Create new page credentials 'dc' >> Display all credentials saved 'fc' >> Find credential saved by page name 'cp' >> Copy pagename credential password to clipboard 'dl' >> Delete page credential 'lgo' >> Log out 'ex' >> Close App """, "blue") app_code = input( colored('\tWhat would you like to do? >> ', 'cyan')).lower() if app_code == 'sc': cprint( '\tEnter pagename,username and password to save credentials >>>\n', 'blue') page_name = input( colored('\n\tEnter pagename >> ', 'cyan')).lower() user_name = input( colored('\n\tEnter page username >> ', 'cyan')) pass_word = input( colored('\n\tEnter page password >> ', 'cyan')) print("\n\t\tSaving credentials ...") time.sleep(1.5) if check_credential_exists(page_name): cprint('\n\t\tCREDENTIALS FOR '+page_name.upper() + ' ALREADY EXISTS', 'red', attrs=['bold']) else: new_credential = create_credential( page_name, user_name, pass_word) save_credential(new_credential) cprint("\n\t\t"+page_name.upper() + ", CREDENTIALS SAVED", "green", attrs=['bold']) elif app_code == 'cc': cprint( '\tEnter pagename,username and password to create and save new page credentials >>>\n', 'blue') page_name = input( colored('\n\tEnter pagename >> ', 'cyan')).lower() user_name = input( colored('\n\tEnter page username >> ', 'cyan')) gen_pass_code = input(colored( '\tWould you like to generate a random password? Y/N >> ', 'cyan')).upper() pass_word = '' if gen_pass_code == 'Y': pass_len = int(input(colored( '\tHow long would you like your password? Provide numbers only >> ', 'cyan'))) pass_word = generate_password(pass_len) else: pass_word = input( colored('\n\tEnter page password >> ', 'cyan')) print("\n\t\tCreating and Saving credentials ...") time.sleep(1.5) if check_credential_exists(page_name): cprint('\n\t\tCREDENTIALS FOR '+page_name.upper() + ' ALREADY EXISTS', 'red', attrs=['bold']) else: new_credential = create_credential( page_name, user_name, pass_word) save_credential(new_credential) cprint("\n\t\t"+page_name.upper() + ", CREDENTIALS SAVED", "green", attrs=['bold']) elif app_code == 'dc': if len(display_credentials()) > 0: cprint("\n\t\t"+sign_name.upper() + ", CREDENTIALS", "green", attrs=['bold']) for credential in display_credentials(): cprint(f''' ------------------------------------------------------- Page Name >>>> {credential.page_name.upper()} Page Username >>>> {credential.user_name} Page Password >>>> {credential.pass_word} ------------------------------------------------------- ''', 'green') else: cprint("\n\t\t"+sign_name.upper() + ",HAS NO CREDENTIALS SAVED", "green", attrs=['bold']) elif app_code == 'fc': search_page = input( colored('\n\tEnter page name to search credentials >> ', 'cyan')).lower() print("\n\t\tLoading ...") time.sleep(1.5) if check_credential_exists(search_page): found_credential = find_cred_by_pagename(search_page) cprint(f''' ------------------------------------------------------- Page Name >>>> {found_credential.page_name.upper()} Page Username >>>> {found_credential.user_name} Page Password >>>> {found_credential.pass_word} ------------------------------------------------------- ''', 'green') else: cprint( f'\n\t\t{search_page.upper()} DOES NOT EXISTS', 'red', attrs=['bold']) elif app_code == 'cp': search_page = input(colored( '\n\tEnter page name to copy password to clipboard >> ', 'cyan')).lower() print("\n\t\tSearching ...") time.sleep(1.5) if check_credential_exists(search_page): copy_cred_pass(search_page) cprint("\n\t\t"+search_page.upper() + ", PASSWORD COPIED TO CLIPBOARD", "green", attrs=['bold']) else: cprint( f'\n\t\t{search_page.upper()} DOES NOT EXISTS', 'red', attrs=['bold']) elif app_code == 'dl': del_page = input( colored('\n\tEnter page name you want to delete >> ', 'cyan')).lower() print("\n\t\tDeleting ...") time.sleep(1.5) if check_credential_exists(del_page): found_page = find_cred_by_pagename(del_page) found_page.delete_credential() cprint("\n\t\t"+del_page.upper() + ", CREDENTIALS DELETED", "green", attrs=['bold']) else: cprint( f'\n\t\t{del_page.upper()} DOES NOT EXISTS', 'red', attrs=['bold']) elif app_code == 'lgo': cprint(f"""\n\t\t{sign_name.upper()}, THANK YOU FOR USING PASSWORD LOCKER \t\tBye... \t\t\t\t\tLogin out >>>>> """, "green", attrs=['bold']) time.sleep(1.5) login = False elif app_code == 'ex': cprint(f"""\n\t\t{sign_name.upper()}, THANK YOU FOR USING PASSWORD LOCKER \t\tBye... \t\t\t\t\tClosing App >>>>> """, "red", attrs=['bold']) pickle_save() time.sleep(1.5) login = False logged = False else: cprint('\n\t\tPLEASE USE THE GIVEN SHORT CODES', 'red', attrs=['bold']) if __name__ == '__main__': main()

true

f70fb5030be554ad24d7c738e890f3d047427e38

4,209

py

Python

config/jobs/kubernetes/kops/build-pipeline.py

celestehorgan/test-infra

3a4d5a94f214381ecca8146aef354bba29b0ac67

[ "Apache-2.0" ]

null

config/jobs/kubernetes/kops/build-pipeline.py

celestehorgan/test-infra

3a4d5a94f214381ecca8146aef354bba29b0ac67

[ "Apache-2.0" ]

null

config/jobs/kubernetes/kops/build-pipeline.py

celestehorgan/test-infra

3a4d5a94f214381ecca8146aef354bba29b0ac67

[ "Apache-2.0" ]

null

# Copyright 2020 The Kubernetes Authors. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. import json template = """ # Verify the latest-ci version from the {{branch}} branch of kops # Runs a small subset of the e2e tests. # Publishes the version to latest-ci-updown-green on success. - interval: 60m name: {{name}} decorate: true decoration_config: timeout: 45m labels: preset-service-account: "true" preset-aws-ssh: "true" preset-aws-credential: "true" spec: containers: - image: {{e2e_image}} command: - runner.sh - kubetest args: # Generic e2e test args - --up - --test - --down - --dump=$(ARTIFACTS) - --timeout=45m - --gcp-service-account=$(E2E_GOOGLE_APPLICATION_CREDENTIALS) # kops-specific test args - --deployment=kops - --provider=aws - --cluster={{name}}.test-cncf-aws.k8s.io - --kops-ssh-user={{ssh_user}} - --kops-nodes=4 - --extract={{extract}} - --kops-state=s3://k8s-kops-prow/ - --kops-ssh-key=$(AWS_SSH_PRIVATE_KEY_FILE) - --kops-ssh-public-key=$(AWS_SSH_PUBLIC_KEY_FILE) - --kops-publish=gs://k8s-staging-kops/kops/releases/markers/{{branch}}/latest-ci-updown-green.txt - --kops-version=https://storage.googleapis.com/k8s-staging-kops/kops/releases/markers/{{branch}}/latest-ci.txt #- --kops-kubernetes-version should be inferred by kubetest from --extract #- --kops-zone should be randomized by kubetest # Specific test args - --test_args=--ginkgo.focus=\\[k8s.io\\]\\sNetworking.*\\[Conformance\\] --ginkgo.skip=\\[Slow\\]|\\[Serial\\] - --ginkgo-parallel annotations: testgrid-dashboards: sig-cluster-lifecycle-kops, google-aws, kops-misc, kops-k8s-{{k8s_version}} testgrid-tab-name: {{tab}} """ def build_tests(branch, k8s_version, ssh_user): def expand(s): subs = {} if k8s_version: subs['k8s_version'] = k8s_version if branch: subs['branch'] = branch return s.format(**subs) if branch == 'master': extract = "release/latest-1.19" e2e_image = "gcr.io/k8s-testimages/kubekins-e2e:v20200713-e9b3d9d-1.19" else: extract = expand("release/stable-{k8s_version}") # Hack to stop the autobumper getting confused e2e_image = "gcr.io/k8s-testimages/kubekins-e2e:v20200713-e9b3d9d-1.18" e2e_image = e2e_image[:-4] + k8s_version tab = expand('kops-pipeline-updown-{branch}') # Names must be valid pod and DNS names name = expand('e2e-kops-pipeline-updown-kops{branch}') name = name.replace('.', '') y = template y = y.replace('{{extract}}', extract) y = y.replace('{{e2e_image}}', e2e_image) y = y.replace('{{k8s_version}}', k8s_version) y = y.replace('{{name}}', name) y = y.replace('{{ssh_user}}', ssh_user) y = y.replace('{{tab}}', tab) if branch == 'master': y = y.replace('{{branch}}', "master") else: y = y.replace('{{branch}}', "release-" + branch) spec = { 'branch': branch, 'k8s_version': k8s_version, } jsonspec = json.dumps(spec, sort_keys=True) print("") print("# " + jsonspec) print(y.strip()) branches = [ "master", "1.16", "1.17", "1.18", ] def generate(): print("# Test scenarios generated by build-pipeline.py (do not manually edit)") print("periodics:") for branch in branches: k8s_version = "1.19" if branch == "master" else branch ssh_user = "admin" if branch in ("1.16", "1.17") else "ubuntu" build_tests(branch=branch, k8s_version=k8s_version, ssh_user=ssh_user) generate()

32.376923

117

0.628415

import json template = """ # Verify the latest-ci version from the {{branch}} branch of kops # Runs a small subset of the e2e tests. # Publishes the version to latest-ci-updown-green on success. - interval: 60m name: {{name}} decorate: true decoration_config: timeout: 45m labels: preset-service-account: "true" preset-aws-ssh: "true" preset-aws-credential: "true" spec: containers: - image: {{e2e_image}} command: - runner.sh - kubetest args: # Generic e2e test args - --up - --test - --down - --dump=$(ARTIFACTS) - --timeout=45m - --gcp-service-account=$(E2E_GOOGLE_APPLICATION_CREDENTIALS) # kops-specific test args - --deployment=kops - --provider=aws - --cluster={{name}}.test-cncf-aws.k8s.io - --kops-ssh-user={{ssh_user}} - --kops-nodes=4 - --extract={{extract}} - --kops-state=s3://k8s-kops-prow/ - --kops-ssh-key=$(AWS_SSH_PRIVATE_KEY_FILE) - --kops-ssh-public-key=$(AWS_SSH_PUBLIC_KEY_FILE) - --kops-publish=gs://k8s-staging-kops/kops/releases/markers/{{branch}}/latest-ci-updown-green.txt - --kops-version=https://storage.googleapis.com/k8s-staging-kops/kops/releases/markers/{{branch}}/latest-ci.txt #- --kops-kubernetes-version should be inferred by kubetest from --extract #- --kops-zone should be randomized by kubetest # Specific test args - --test_args=--ginkgo.focus=\\[k8s.io\\]\\sNetworking.*\\[Conformance\\] --ginkgo.skip=\\[Slow\\]|\\[Serial\\] - --ginkgo-parallel annotations: testgrid-dashboards: sig-cluster-lifecycle-kops, google-aws, kops-misc, kops-k8s-{{k8s_version}} testgrid-tab-name: {{tab}} """ def build_tests(branch, k8s_version, ssh_user): def expand(s): subs = {} if k8s_version: subs['k8s_version'] = k8s_version if branch: subs['branch'] = branch return s.format(**subs) if branch == 'master': extract = "release/latest-1.19" e2e_image = "gcr.io/k8s-testimages/kubekins-e2e:v20200713-e9b3d9d-1.19" else: extract = expand("release/stable-{k8s_version}") e2e_image = "gcr.io/k8s-testimages/kubekins-e2e:v20200713-e9b3d9d-1.18" e2e_image = e2e_image[:-4] + k8s_version tab = expand('kops-pipeline-updown-{branch}') name = expand('e2e-kops-pipeline-updown-kops{branch}') name = name.replace('.', '') y = template y = y.replace('{{extract}}', extract) y = y.replace('{{e2e_image}}', e2e_image) y = y.replace('{{k8s_version}}', k8s_version) y = y.replace('{{name}}', name) y = y.replace('{{ssh_user}}', ssh_user) y = y.replace('{{tab}}', tab) if branch == 'master': y = y.replace('{{branch}}', "master") else: y = y.replace('{{branch}}', "release-" + branch) spec = { 'branch': branch, 'k8s_version': k8s_version, } jsonspec = json.dumps(spec, sort_keys=True) print("") print("# " + jsonspec) print(y.strip()) branches = [ "master", "1.16", "1.17", "1.18", ] def generate(): print("# Test scenarios generated by build-pipeline.py (do not manually edit)") print("periodics:") for branch in branches: k8s_version = "1.19" if branch == "master" else branch ssh_user = "admin" if branch in ("1.16", "1.17") else "ubuntu" build_tests(branch=branch, k8s_version=k8s_version, ssh_user=ssh_user) generate()

true

f70fb6a161b5ea492c9c47948d6ae0810e774828

828

py

Python

src/utils/api/permissions.py

jsmesami/naovoce

235c6e05ef37be23d3b9bd0b76d80080c58617a0

[ "BSD-3-Clause" ]

18

2016-02-23T15:34:58.000Z

2022-02-28T08:15:30.000Z

src/utils/api/permissions.py

jsmesami/naovoce

235c6e05ef37be23d3b9bd0b76d80080c58617a0

[ "BSD-3-Clause" ]

66

2016-03-15T19:59:09.000Z

2022-03-11T23:25:41.000Z

src/utils/api/permissions.py

jsmesami/naovoce

235c6e05ef37be23d3b9bd0b76d80080c58617a0

[ "BSD-3-Clause" ]

7

2016-03-24T09:13:07.000Z

2018-09-16T17:04:50.000Z

from rest_framework import permissions class IsAuthenticated(permissions.BasePermission): def has_permission(self, request, view): return ( request.user and request.user.is_authenticated and request.user.is_email_verified ) class IsAuthenticatedOrReadOnly(permissions.BasePermission): def has_permission(self, request, view): return ( request.method in permissions.SAFE_METHODS or request.user and request.user.is_authenticated and request.user.is_email_verified ) class IsOwnerOrReadOnly(permissions.BasePermission): def has_object_permission(self, request, view, obj): if request.method in permissions.SAFE_METHODS: return True return obj.is_owner(request.user)

27.6

60

0.678744

from rest_framework import permissions class IsAuthenticated(permissions.BasePermission): def has_permission(self, request, view): return ( request.user and request.user.is_authenticated and request.user.is_email_verified ) class IsAuthenticatedOrReadOnly(permissions.BasePermission): def has_permission(self, request, view): return ( request.method in permissions.SAFE_METHODS or request.user and request.user.is_authenticated and request.user.is_email_verified ) class IsOwnerOrReadOnly(permissions.BasePermission): def has_object_permission(self, request, view, obj): if request.method in permissions.SAFE_METHODS: return True return obj.is_owner(request.user)

true

f70fb71ce3042a4cc28deea40be606cfe703d92d

11,029

py

Python

ncappzoo/tensorflow/topcoder_andresduque/supporting/inferences.py

yockgen/movidius

cc32f1951a4d00d2250bb0d2b9000c5f2435b41a

[ "MIT" ]

1

2018-11-23T01:48:59.000Z

ncappzoo/tensorflow/topcoder_andresduque/supporting/inferences.py

yockgen/movidius

cc32f1951a4d00d2250bb0d2b9000c5f2435b41a

[ "MIT" ]

null

ncappzoo/tensorflow/topcoder_andresduque/supporting/inferences.py

yockgen/movidius

cc32f1951a4d00d2250bb0d2b9000c5f2435b41a

[ "MIT" ]

1

2020-10-01T15:38:04.000Z

#!/usr/bin/env python3 # -*- coding: utf-8 -*- # #~ The MIT License (MIT) #~ Copyright 2018 Â©klo86min #~ Permission is hereby granted, free of charge, to any person obtaining a copy #~ of this software and associated documentation files (the "Software"), to deal #~ in the Software without restriction, including without limitation the rights #~ to use, copy, modify, merge, publish, distribute, sublicense, and/or sell #~ copies of the Software, and to permit persons to whom the Software is #~ furnished to do so, subject to the following conditions: #~ The above copyright notice and this permission notice shall be included in #~ all copies or substantial portions of the Software. #~ THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR #~ IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, #~ FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE #~ AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER #~ LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, #~ OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE #~ SOFTWARE. import argparse import csv import cv2 import mvnc.mvncapi as mvnc import numpy as np import os.path # image settings IMAGE_DIM = 299 ############################################################################### # # Modified code from https://github.com/ashwinvijayakumar/ncappzoo/apps/ # rapid-image-classifier/rapid-image-classifier.py # also under the MIT License # ############################################################################### # ---- Step 1: Open the enumerated device and get a handle to it ------------- def open_ncs_device(verbose=False): if verbose: mvnc.SetGlobalOption(mvnc.GlobalOption.LOG_LEVEL, 2) # Look for enumerated NCS device(s); quit program if none found. devices = mvnc.EnumerateDevices() if len( devices ) == 0: print( 'No devices found' ) quit() # Get a handle to the first enumerated device and open it device = mvnc.Device( devices[0] ) device.OpenDevice() return device # ---- Step 2: Load a graph file onto the NCS device ------------------------- def load_graph( device, graph_file): # Read the graph file into a buffer with open( graph_file, mode='rb' ) as f: blob = f.read() # Load the graph buffer into the NCS graph = device.AllocateGraph( blob ) return graph # ---- Step 5: Unload the graph and close the device ------------------------- def close_ncs_device( device, graph ): graph.DeallocateGraph() device.CloseDevice() ##################### End of ncappzoo code ################################ class MovidiusImage(object): """Image metadata and loader for Movidius NCS Args: name (str): image reference name as used in CSV files path (str): image path class_index (int): 1-based class label index Attributes: top_k (list): list of predicted (class_index, proba) inference_time (float): computation time in ms """ def __init__(self, name, path, class_index = None): self.name = name self.path = path self.class_index = class_index self.top_k = None self.inference_time = None def load_BGR(self, dim, dtype=np.float16): """Return image data in BGR order Args: dim (tuple): image dimensions dtype (numpy.dtype): new type for the BGR blob Returns: numpy.ndarray: the transformed BGR blob """ mean = 128 std = 1/128 img = cv2.imread(self.path).astype(np.float32) dx,dy,dz= img.shape delta=float(abs(dy-dx)) if dx > dy: #crop the x dimension img=img[int(0.5*delta):dx-int(0.5*delta),0:dy] else: img=img[0:dx,int(0.5*delta):dy-int(0.5*delta)] img = cv2.resize(img, (dim, dim)) img=cv2.cvtColor(img,cv2.COLOR_BGR2RGB) for i in range(3): img[:,:,i] = (img[:,:,i] - mean) * std img = img.astype(dtype) return img def save_top_k(self, predictions, labels, k=5): """Save the top_k predicted probabilities Args: predictions (numpy.ndarray): the probabilities for each class k (int): Number of top_k probas """ order_k = predictions.argsort()[::-1][:k] # class_index is 1-based self.top_k = [(labels[pos], np.float(predictions[pos])) for pos in order_k] def result_string(self): """ Return image results with the following fields: [name, top1, proba1, ... top5, proba5, time] Returns: str: formatted CSV string """ res = [ self.name, ] for k, prob in self.top_k: res += [k, prob] res += [self.inference_time] pattern = "%s," + "%d,%.9f," * len(self.top_k) + "%.9f" return pattern % tuple(res) def init_images(data_dir, images_file): """Parse image_file CSV and create one MovidiusImage per row. Args: data_dir (str): path of the folder containing images image_file (str): CSV file (one image path per row) Returns: list: list of MovidiusImage instances """ images_dir = {} images = [] for file in sorted(os.listdir(data_dir)): if file.endswith(".jpg"): image = MovidiusImage(file, os.path.realpath(data_dir) + "/" + "/" + file, -1) images_dir[file] = image images.append(image) if os.path.isfile(images_file): images = [] with open(images_file, 'r') as csvfile: reader = csv.reader(csvfile, delimiter=',') # skip header next(reader) for row_pos, row in enumerate(reader): name = row[0] truth = int(row[1]) img = images_dir[name] img.class_index = truth images.append(img) return images def write_inferences_csv(output_path, images): """ For each image, retrieve and write results. Args: output_path (str): path for the CSV output images (list): list of processed MovidiusImage instances """ with open(output_path, 'w') as output_file: for image in images: output_file.write(image.result_string() + '\n') def score_inferences(images, min_proba = 1e-15, mult = 100, n_classes=200, log_loss_max=15.0, time_limit=1000.0): """ Compute the logLoss and reference computation time Args: images (list): list of processed MovidiusImage instances min_proba (float): minimum probability to be used in logLoss mult (int): number of images used for the reference time n_classes (int): total number of classes log_loss_limit (float): minimum log_loss requirement time_limit (float): maximum time per image (in ms) Returns: tuple: LogLoss and reference_time float values """ min_proba = np.float(min_proba) max_proba = 1.0 - min_proba n_images = len(images) probas = np.zeros(n_images, dtype=np.float) image_time = 0.0 top_1_accuracy = 0.0 top_k_accuracy = 0.0 for i, image in enumerate(images): class_probas = dict(image.top_k) if image.class_index == image.top_k[0][0]: top_1_accuracy += 1.0 if image.class_index in class_probas: top_k_accuracy += 1.0 probas[i] = class_probas[image.class_index] if probas[i] > 0: sum_probas = sum(class_probas.values()) probas[i] /= sum_probas probas[i] = max(min_proba, min(max_proba, probas[i])) image_time += image.inference_time log_loss = np.mean(-np.log(probas)) top_1_accuracy /= n_images top_k_accuracy /= n_images image_time /= n_images t = mult * image_time print("top_1_accuracy = %.9f" % top_1_accuracy) print("top_k_accuracy = %.9f" % top_k_accuracy ) print("log_loss = %.9f" % log_loss) print("image_time = %.9f" % image_time) if image_time > time_limit or log_loss > log_loss_max: score = 0.0 else: t_max = mult * time_limit score = 1e6 * (1.0 - log_loss * np.log(t) / (log_loss_max * np.log(t_max))) print("score = %.2f" % score) return score def main(args): parser = argparse.ArgumentParser(description='TopCoder Movidius MM') parser.add_argument( "-images-dir", dest="images_dir", help="""Folder containing images to classify""" ) parser.add_argument( "-output-file", dest="output_file", default="", help="""Output CSV file to save inference results""" ) parser.add_argument( "-graph-file", dest="graph_file", default="", help="""Movidius graph file path""" ) parser.add_argument( "-labels-map-file", dest="labels_map_file", default="", help="""Labels map file""" ) parser.add_argument( "-images-file", dest="images_file", default="", help="""CSV file containing list of images filenames to classify in images-dir folder, only filenames listed here will be processed""" ) args = parser.parse_args() if not os.path.isdir(args.images_dir): print("data is not a directory: %s" % args.images_dir) print("Please use the right path as argument, and/or change the Makefile MOVIDIUSDIR variable") return 0 print("IMAGE_DIM", IMAGE_DIM) # start NCS device = open_ncs_device() graph = load_graph(device, args.graph_file) # prepare images images = init_images(args.images_dir, args.images_file) n_images = len(images) info_frequency = 100 print("n_images = %d" % n_images) # load labels map file labelsLines = [line.rstrip('\n') for line in open(args.labels_map_file)] labels = {} for label in labelsLines: split = label.split(":") labels[int(split[0])] = int(split[1]) # process images for i, image in enumerate(images): if (i+1) % info_frequency == 0: print("progess %d/%d ..." % (i+1, n_images), flush=True) bgr_blob = image.load_BGR(IMAGE_DIM) graph.LoadTensor(bgr_blob, 'user object') output, userobj = graph.GetResult() #print(output) image.inference_time = np.sum( graph.GetGraphOption( mvnc.GraphOption.TIME_TAKEN ) ) image.save_top_k(output, labels, 5) # stop NCS close_ncs_device(device, graph) # process results write_inferences_csv(args.output_file, images) if os.path.isfile(args.images_file): score_inferences(images) return 0 if __name__ == '__main__': import sys sys.exit(main(sys.argv))

33.625

140

0.602684

import argparse import csv import cv2 import mvnc.mvncapi as mvnc import numpy as np import os.path IMAGE_DIM = 299

true

f70fb7c77d935e9a4bdb46140f8bc3e6d53a17ee

2,501

py

Python

radio_bridge/tests/unit/test_dtmf_decoder.py

Kami/raspberry-pi-ham-radio

7ff9180e3a4d645b92e07ce92cbcbf73c7a0a628

[ "Apache-2.0" ]

2

2020-10-26T06:16:52.000Z

2021-11-15T11:05:29.000Z

radio_bridge/tests/unit/test_dtmf_decoder.py

Kami/raspberry-pi-ham-radio

7ff9180e3a4d645b92e07ce92cbcbf73c7a0a628

[ "Apache-2.0" ]

null

radio_bridge/tests/unit/test_dtmf_decoder.py

Kami/raspberry-pi-ham-radio

7ff9180e3a4d645b92e07ce92cbcbf73c7a0a628

[ "Apache-2.0" ]

null

# -*- coding: utf-8 -*- # Copyright 2020 Tomaz Muraus # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. import os import unittest from radio_bridge.dtmf import FFTDTMFDecoderImplementation __all__ = ["TestFFTDTMFDecoder"] BASE_DIR = os.path.dirname(os.path.abspath(__file__)) FIXTURES_DIR = os.path.abspath(os.path.join(BASE_DIR, "../fixtures/dtmf")) class TestFFTDTMFDecoder(unittest.TestCase): def test_decode_anytone_578_dtmf_data(self): values = [ ("1.wav", "1"), ("2.wav", "2"), ("3.wav", "3"), ("4.wav", "4"), ("5.wav", "5"), ("6.wav", "6"), ("7.wav", "7"), ("8.wav", "8"), ("9.wav", "9"), ("*.wav", "*"), ("0.wav", "0"), ("#.wav", "#"), ] for file_path, expected_code in values: file_path = os.path.join(FIXTURES_DIR, "anytone_578/", file_path) decoder = FFTDTMFDecoderImplementation(file_path=file_path) self.assertEqual(decoder.decode(), expected_code) def test_decode_audio_check_tone_generator_data(self): values = [ ("audiocheck.net_dtmf_1.wav", "1"), ("audiocheck.net_dtmf_2.wav", "2"), ("audiocheck.net_dtmf_3.wav", "3"), ("audiocheck.net_dtmf_4.wav", "4"), ("audiocheck.net_dtmf_5.wav", "5"), ("audiocheck.net_dtmf_6.wav", "6"), ("audiocheck.net_dtmf_7.wav", "7"), ("audiocheck.net_dtmf_8.wav", "8"), ("audiocheck.net_dtmf_9.wav", "9"), ("audiocheck.net_dtmf_*.wav", "*"), ("audiocheck.net_dtmf_0.wav", "0"), ("audiocheck.net_dtmf_#.wav", "#"), ] for file_path, expected_code in values: file_path = os.path.join(FIXTURES_DIR, "audiochecknet/", file_path) decoder = FFTDTMFDecoderImplementation(file_path=file_path) self.assertEqual(decoder.decode(), expected_code)

36.246377

79

0.594562

import os import unittest from radio_bridge.dtmf import FFTDTMFDecoderImplementation __all__ = ["TestFFTDTMFDecoder"] BASE_DIR = os.path.dirname(os.path.abspath(__file__)) FIXTURES_DIR = os.path.abspath(os.path.join(BASE_DIR, "../fixtures/dtmf")) class TestFFTDTMFDecoder(unittest.TestCase): def test_decode_anytone_578_dtmf_data(self): values = [ ("1.wav", "1"), ("2.wav", "2"), ("3.wav", "3"), ("4.wav", "4"), ("5.wav", "5"), ("6.wav", "6"), ("7.wav", "7"), ("8.wav", "8"), ("9.wav", "9"), ("*.wav", "*"), ("0.wav", "0"), ("#.wav", "#"), ] for file_path, expected_code in values: file_path = os.path.join(FIXTURES_DIR, "anytone_578/", file_path) decoder = FFTDTMFDecoderImplementation(file_path=file_path) self.assertEqual(decoder.decode(), expected_code) def test_decode_audio_check_tone_generator_data(self): values = [ ("audiocheck.net_dtmf_1.wav", "1"), ("audiocheck.net_dtmf_2.wav", "2"), ("audiocheck.net_dtmf_3.wav", "3"), ("audiocheck.net_dtmf_4.wav", "4"), ("audiocheck.net_dtmf_5.wav", "5"), ("audiocheck.net_dtmf_6.wav", "6"), ("audiocheck.net_dtmf_7.wav", "7"), ("audiocheck.net_dtmf_8.wav", "8"), ("audiocheck.net_dtmf_9.wav", "9"), ("audiocheck.net_dtmf_*.wav", "*"), ("audiocheck.net_dtmf_0.wav", "0"), ("audiocheck.net_dtmf_#.wav", "#"), ] for file_path, expected_code in values: file_path = os.path.join(FIXTURES_DIR, "audiochecknet/", file_path) decoder = FFTDTMFDecoderImplementation(file_path=file_path) self.assertEqual(decoder.decode(), expected_code)

true

f70fb837d095c7afa802c8994d156aba293785e2

4,084

py

Python

sharinator/settings.py

Technikradio/sharinator

3aa72d01d3829520c9627320d044af14fda913b8

[ "BSD-3-Clause" ]

null

sharinator/settings.py

Technikradio/sharinator

3aa72d01d3829520c9627320d044af14fda913b8

[ "BSD-3-Clause" ]

9

2020-05-31T16:24:49.000Z

2020-06-19T17:42:56.000Z

sharinator/settings.py

Technikradio/sharinator

3aa72d01d3829520c9627320d044af14fda913b8

[ "BSD-3-Clause" ]

null

""" Django settings for sharinator project. Generated by 'django-admin startproject' using Django 2.2.4. For more information on this file, see https://docs.djangoproject.com/en/2.2/topics/settings/ For the full list of settings and their values, see https://docs.djangoproject.com/en/2.2/ref/settings/ """ import os from django.contrib.messages import constants as messages # Build paths inside the project like this: os.path.join(BASE_DIR, ...) BASE_DIR = os.path.dirname(os.path.dirname(os.path.abspath(__file__))) # Quick-start development settings - unsuitable for production # See https://docs.djangoproject.com/en/2.2/howto/deployment/checklist/ # SECURITY WARNING: keep the secret key used in production secret! SECRET_KEY = 'ofj2gu)@$2xahppvk%25217+y!-1d4#@1-*#)c6zssk%&s87ai' # SECURITY WARNING: don't run with debug turned on in production! DEBUG = True ALLOWED_HOSTS = [] # Application definition INSTALLED_APPS = [ 'django.contrib.admin', 'django.contrib.auth', 'django.contrib.contenttypes', 'django.contrib.sessions', 'django.contrib.messages', 'django.contrib.staticfiles', 'django.contrib.humanize', 'sharinator.administration', 'sharinator.dashboard', 'sharinator.equipment', 'sharinator.peers', 'sharinator.shares', 'crispy_forms', ] MIDDLEWARE = [ 'django.middleware.security.SecurityMiddleware', 'django.contrib.sessions.middleware.SessionMiddleware', 'django.middleware.common.CommonMiddleware', 'django.middleware.csrf.CsrfViewMiddleware', 'django.contrib.auth.middleware.AuthenticationMiddleware', 'django.contrib.messages.middleware.MessageMiddleware', 'django.middleware.clickjacking.XFrameOptionsMiddleware', #'sharinator.administration.middleware.ForceLogoutMiddleware', ] ROOT_URLCONF = 'sharinator.urls' TEMPLATES = [ { 'BACKEND': 'django.template.backends.django.DjangoTemplates', 'DIRS': [], 'APP_DIRS': True, 'OPTIONS': { 'context_processors': [ 'django.template.context_processors.debug', 'django.template.context_processors.request', 'django.contrib.auth.context_processors.auth', 'django.contrib.messages.context_processors.messages', ], }, }, ] WSGI_APPLICATION = 'sharinator.wsgi.application' # Database # https://docs.djangoproject.com/en/2.2/ref/settings/#databases DATABASES = { 'default': { 'ENGINE': 'django.db.backends.sqlite3', 'NAME': os.path.join(BASE_DIR, 'db.sqlite3'), } } CRISPY_TEMPLATE_PACK = 'bootstrap4' # Password validation # https://docs.djangoproject.com/en/2.2/ref/settings/#auth-password-validators AUTH_PASSWORD_VALIDATORS = [ { 'NAME': 'django.contrib.auth.password_validation.UserAttributeSimilarityValidator', }, { 'NAME': 'django.contrib.auth.password_validation.MinimumLengthValidator', }, { 'NAME': 'django.contrib.auth.password_validation.CommonPasswordValidator', }, { 'NAME': 'django.contrib.auth.password_validation.NumericPasswordValidator', }, ] MESSAGE_TAGS = { messages.DEBUG: 'alert alert-dark', messages.INFO: 'alert alert-info', messages.SUCCESS: 'alert alert-success', messages.WARNING: 'alert alert-warning', messages.ERROR: 'alert alert-danger', } # Internationalization # https://docs.djangoproject.com/en/2.2/topics/i18n/ LANGUAGE_CODE = 'en-us' TIME_ZONE = 'UTC' USE_I18N = True USE_L10N = True USE_TZ = True # Static files (CSS, JavaScript, Images) # https://docs.djangoproject.com/en/2.2/howto/static-files/ STATIC_URL = '/static/' STATIC_ROOT = os.path.join(os.path.dirname(__file__), 'static.dist') MEDIA_ROOT = os.path.join(BASE_DIR, "media") MEDIA_URL = "/media/" STATICFILES_DIRS = [ os.path.join(BASE_DIR, "sharinator", "static"), ] LOGIN_URL = "/admin/dbadmin/login" TEST_RUNNER = 'django_nose.NoseTestSuiteRunner' NOSE_ARGS = [ '--with-coverage', '--cover-package=sharinator', '--logging-level=WARN' ]

26.012739

91

0.698335

import os from django.contrib.messages import constants as messages BASE_DIR = os.path.dirname(os.path.dirname(os.path.abspath(__file__))) SECRET_KEY = 'ofj2gu)@$2xahppvk%25217+y!-1d4#@1-*#)c6zssk%&s87ai' DEBUG = True ALLOWED_HOSTS = [] # Application definition INSTALLED_APPS = [ 'django.contrib.admin', 'django.contrib.auth', 'django.contrib.contenttypes', 'django.contrib.sessions', 'django.contrib.messages', 'django.contrib.staticfiles', 'django.contrib.humanize', 'sharinator.administration', 'sharinator.dashboard', 'sharinator.equipment', 'sharinator.peers', 'sharinator.shares', 'crispy_forms', ] MIDDLEWARE = [ 'django.middleware.security.SecurityMiddleware', 'django.contrib.sessions.middleware.SessionMiddleware', 'django.middleware.common.CommonMiddleware', 'django.middleware.csrf.CsrfViewMiddleware', 'django.contrib.auth.middleware.AuthenticationMiddleware', 'django.contrib.messages.middleware.MessageMiddleware', 'django.middleware.clickjacking.XFrameOptionsMiddleware', #'sharinator.administration.middleware.ForceLogoutMiddleware', ] ROOT_URLCONF = 'sharinator.urls' TEMPLATES = [ { 'BACKEND': 'django.template.backends.django.DjangoTemplates', 'DIRS': [], 'APP_DIRS': True, 'OPTIONS': { 'context_processors': [ 'django.template.context_processors.debug', 'django.template.context_processors.request', 'django.contrib.auth.context_processors.auth', 'django.contrib.messages.context_processors.messages', ], }, }, ] WSGI_APPLICATION = 'sharinator.wsgi.application' # Database # https://docs.djangoproject.com/en/2.2/ref/settings/#databases DATABASES = { 'default': { 'ENGINE': 'django.db.backends.sqlite3', 'NAME': os.path.join(BASE_DIR, 'db.sqlite3'), } } CRISPY_TEMPLATE_PACK = 'bootstrap4' # Password validation # https://docs.djangoproject.com/en/2.2/ref/settings/#auth-password-validators AUTH_PASSWORD_VALIDATORS = [ { 'NAME': 'django.contrib.auth.password_validation.UserAttributeSimilarityValidator', }, { 'NAME': 'django.contrib.auth.password_validation.MinimumLengthValidator', }, { 'NAME': 'django.contrib.auth.password_validation.CommonPasswordValidator', }, { 'NAME': 'django.contrib.auth.password_validation.NumericPasswordValidator', }, ] MESSAGE_TAGS = { messages.DEBUG: 'alert alert-dark', messages.INFO: 'alert alert-info', messages.SUCCESS: 'alert alert-success', messages.WARNING: 'alert alert-warning', messages.ERROR: 'alert alert-danger', } # Internationalization # https://docs.djangoproject.com/en/2.2/topics/i18n/ LANGUAGE_CODE = 'en-us' TIME_ZONE = 'UTC' USE_I18N = True USE_L10N = True USE_TZ = True # Static files (CSS, JavaScript, Images) # https://docs.djangoproject.com/en/2.2/howto/static-files/ STATIC_URL = '/static/' STATIC_ROOT = os.path.join(os.path.dirname(__file__), 'static.dist') MEDIA_ROOT = os.path.join(BASE_DIR, "media") MEDIA_URL = "/media/" STATICFILES_DIRS = [ os.path.join(BASE_DIR, "sharinator", "static"), ] LOGIN_URL = "/admin/dbadmin/login" TEST_RUNNER = 'django_nose.NoseTestSuiteRunner' NOSE_ARGS = [ '--with-coverage', '--cover-package=sharinator', '--logging-level=WARN' ]

true

f70fb89aca0288cdc68dc3bcc49697d7f2d22348

2,530

py

Python

tests/py3/test_property_map.py

agarwalrounak/qmt

6fb8ee55fb9d544b72f6dc0c275000914e03af06

[ "MIT" ]

1

2018-09-30T00:45:53.000Z

tests/py3/test_property_map.py

imagineagents/qmt

5e8a7001cc020979636e492448abcfd894396038

[ "MIT" ]

null

tests/py3/test_property_map.py

imagineagents/qmt

5e8a7001cc020979636e492448abcfd894396038

[ "MIT" ]

null

import numpy as np from qmt.geometry import PropertyMap, MaterialPropertyMap from qmt.materials import Materials class DummyPartMap: def __init__(self, part_ids): assert len(part_ids) == 2 self.partIds = part_ids def __call__(self, x): assert np.ndim(x) >= 1 x = np.asanyarray(x) if np.ndim(x) == 1: return self.partIds[x[0] > 0] else: return np.where(x[..., 0] > 0, self.partIds[1], self.partIds[0]) def test_property_map(): int_map = DummyPartMap([0, 1]) str_map = DummyPartMap(['part1', 'part2']) prop_map1 = PropertyMap(int_map, np.vectorize(lambda p: 'yes' if p > 0 else 'no')) assert prop_map1.get_part((1., 2.)) == 1 assert np.all(prop_map1.get_part(-np.ones((2, 3))) == 0) assert prop_map1((1., 2.)) == 'yes' assert np.all(prop_map1(-np.ones((2, 3))) == 'no') props = {'part1': 'yes', 'part2': 'no'} prop_map2 = PropertyMap(str_map, np.vectorize(lambda p: props[p])) assert prop_map2.get_part((1., 2.)) == 'part2' assert np.all(prop_map2.get_part(-np.ones((2, 3))) == 'part1') assert prop_map1((1., 2.)) == 'yes' assert np.all(prop_map1(-np.ones((2, 3))) == 'no') def test_materials_property_map(): int_map = DummyPartMap([0, 1]) str_map = DummyPartMap(['part1', 'part2']) part_materials1 = {0: 'InAs', 1: 'GaSb'} part_materials2 = {'part1': 'InAs', 'part2': 'Al'} mat_lib = Materials(matDict={}) mat_lib.add_material('InAs', 'semi', electronMass=0.026, directBandGap=417., valenceBandOffset=-590.) mat_lib.add_material('GaSb', 'semi', electronMass=.039, directBandGap=812., valenceBandOffset=-30.) mat_lib.add_material('Al', 'metal', workFunction=4280.) prop_map1 = MaterialPropertyMap(int_map, part_materials1, mat_lib, 'electronMass') assert prop_map1.get_part((1., 2.)) == 1 assert np.all(prop_map1.get_part(-np.ones((2, 3))) == 0) assert prop_map1((1., 2.)) == mat_lib['GaSb']['electronMass'] assert np.all(prop_map1(-np.ones((2, 3))) == mat_lib['InAs']['electronMass']) prop_map2 = MaterialPropertyMap(str_map, part_materials2, mat_lib, 'directBandGap', eunit='eV', fill_value=0.) assert prop_map2.get_part((1., 2.)) == 'part2' assert np.all(prop_map2.get_part(-np.ones((2, 3))) == 'part1') assert prop_map2((1., 2.)) == 0. assert np.all(prop_map2(-np.ones((2, 3))) == mat_lib.find('InAs', 'eV')['directBandGap'])

40.15873

99

0.608696

import numpy as np from qmt.geometry import PropertyMap, MaterialPropertyMap from qmt.materials import Materials class DummyPartMap: def __init__(self, part_ids): assert len(part_ids) == 2 self.partIds = part_ids def __call__(self, x): assert np.ndim(x) >= 1 x = np.asanyarray(x) if np.ndim(x) == 1: return self.partIds[x[0] > 0] else: return np.where(x[..., 0] > 0, self.partIds[1], self.partIds[0]) def test_property_map(): int_map = DummyPartMap([0, 1]) str_map = DummyPartMap(['part1', 'part2']) prop_map1 = PropertyMap(int_map, np.vectorize(lambda p: 'yes' if p > 0 else 'no')) assert prop_map1.get_part((1., 2.)) == 1 assert np.all(prop_map1.get_part(-np.ones((2, 3))) == 0) assert prop_map1((1., 2.)) == 'yes' assert np.all(prop_map1(-np.ones((2, 3))) == 'no') props = {'part1': 'yes', 'part2': 'no'} prop_map2 = PropertyMap(str_map, np.vectorize(lambda p: props[p])) assert prop_map2.get_part((1., 2.)) == 'part2' assert np.all(prop_map2.get_part(-np.ones((2, 3))) == 'part1') assert prop_map1((1., 2.)) == 'yes' assert np.all(prop_map1(-np.ones((2, 3))) == 'no') def test_materials_property_map(): int_map = DummyPartMap([0, 1]) str_map = DummyPartMap(['part1', 'part2']) part_materials1 = {0: 'InAs', 1: 'GaSb'} part_materials2 = {'part1': 'InAs', 'part2': 'Al'} mat_lib = Materials(matDict={}) mat_lib.add_material('InAs', 'semi', electronMass=0.026, directBandGap=417., valenceBandOffset=-590.) mat_lib.add_material('GaSb', 'semi', electronMass=.039, directBandGap=812., valenceBandOffset=-30.) mat_lib.add_material('Al', 'metal', workFunction=4280.) prop_map1 = MaterialPropertyMap(int_map, part_materials1, mat_lib, 'electronMass') assert prop_map1.get_part((1., 2.)) == 1 assert np.all(prop_map1.get_part(-np.ones((2, 3))) == 0) assert prop_map1((1., 2.)) == mat_lib['GaSb']['electronMass'] assert np.all(prop_map1(-np.ones((2, 3))) == mat_lib['InAs']['electronMass']) prop_map2 = MaterialPropertyMap(str_map, part_materials2, mat_lib, 'directBandGap', eunit='eV', fill_value=0.) assert prop_map2.get_part((1., 2.)) == 'part2' assert np.all(prop_map2.get_part(-np.ones((2, 3))) == 'part1') assert prop_map2((1., 2.)) == 0. assert np.all(prop_map2(-np.ones((2, 3))) == mat_lib.find('InAs', 'eV')['directBandGap'])

true

f70fb93da9d51c1f9838f67977dbbd4aef65562e

4,576

py

Python

tensorflow/python/kernel_tests/batch_scatter_ops_test.py

abhaikollara/tensorflow

4f96df3659696990cb34d0ad07dc67843c4225a9

[ "Apache-2.0" ]

848

2019-12-03T00:16:17.000Z

2022-03-31T22:53:17.000Z

tensorflow/python/kernel_tests/batch_scatter_ops_test.py

abhaikollara/tensorflow

4f96df3659696990cb34d0ad07dc67843c4225a9

[ "Apache-2.0" ]

656

2019-12-03T00:48:46.000Z

2022-03-31T18:41:54.000Z

tensorflow/python/kernel_tests/batch_scatter_ops_test.py

abhaikollara/tensorflow

4f96df3659696990cb34d0ad07dc67843c4225a9

[ "Apache-2.0" ]

506

2019-12-03T00:46:26.000Z

2022-03-30T10:34:56.000Z

# Copyright 2015 The TensorFlow Authors. All Rights Reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. # ============================================================================== """Tests for tensorflow.ops.tf.scatter.""" from __future__ import absolute_import from __future__ import division from __future__ import print_function import numpy as np from tensorflow.python.framework import constant_op from tensorflow.python.framework import dtypes from tensorflow.python.framework import ops from tensorflow.python.framework import test_util from tensorflow.python.ops import state_ops from tensorflow.python.ops import variables from tensorflow.python.platform import test def _AsType(v, vtype): return v.astype(vtype) if isinstance(v, np.ndarray) else vtype(v) def _NumpyUpdate(ref, indices, updates): for i, indx in np.ndenumerate(indices): indx = i[:-1] + (indx,) ref[indx] = updates[i] _TF_OPS_TO_NUMPY = { state_ops.batch_scatter_update: _NumpyUpdate, } class ScatterTest(test.TestCase): def _VariableRankTest(self, tf_scatter, vtype, itype, repeat_indices=False, updates_are_scalar=False, method=False): np.random.seed(8) with self.cached_session(use_gpu=False): for indices_shape in (2,), (3, 7), (3, 4, 7): for extra_shape in (), (5,), (5, 9): # Generate random indices with no duplicates for easy numpy comparison sparse_dim = len(indices_shape) - 1 indices = np.random.randint( indices_shape[sparse_dim], size=indices_shape, dtype=itype) updates = _AsType( np.random.randn(*(indices_shape + extra_shape)), vtype) old = _AsType(np.random.randn(*(indices_shape + extra_shape)), vtype) # Scatter via numpy new = old.copy() np_scatter = _TF_OPS_TO_NUMPY[tf_scatter] np_scatter(new, indices, updates) # Scatter via tensorflow ref = variables.Variable(old) ref.initializer.run() if method: ref.batch_scatter_update(ops.IndexedSlices(indices, updates)) else: tf_scatter(ref, indices, updates).eval() self.assertAllClose(ref.eval(), new) @test_util.run_deprecated_v1 def testVariableRankUpdate(self): vtypes = [np.float32, np.float64] for vtype in vtypes: for itype in (np.int32, np.int64): self._VariableRankTest( state_ops.batch_scatter_update, vtype, itype) @test_util.run_deprecated_v1 def testBooleanScatterUpdate(self): with self.session(use_gpu=False) as session: var = variables.Variable([True, False]) update0 = state_ops.batch_scatter_update(var, [1], [True]) update1 = state_ops.batch_scatter_update( var, constant_op.constant( [0], dtype=dtypes.int64), [False]) var.initializer.run() session.run([update0, update1]) self.assertAllEqual([False, True], self.evaluate(var)) @test_util.run_deprecated_v1 def testScatterOutOfRange(self): params = np.array([1, 2, 3, 4, 5, 6]).astype(np.float32) updates = np.array([-3, -4, -5]).astype(np.float32) with self.session(use_gpu=False): ref = variables.Variable(params) ref.initializer.run() # Indices all in range, no problem. indices = np.array([2, 0, 5]) state_ops.batch_scatter_update(ref, indices, updates).eval() # Test some out of range errors. indices = np.array([-1, 0, 5]) with self.assertRaisesOpError( r'indices\[0\] = \[-1\] does not index into shape \[6\]'): state_ops.batch_scatter_update(ref, indices, updates).eval() indices = np.array([2, 0, 6]) with self.assertRaisesOpError(r'indices\[2\] = \[6\] does not index into ' r'shape \[6\]'): state_ops.batch_scatter_update(ref, indices, updates).eval() if __name__ == '__main__': test.main()

35.472868

80

0.644231

from __future__ import absolute_import from __future__ import division from __future__ import print_function import numpy as np from tensorflow.python.framework import constant_op from tensorflow.python.framework import dtypes from tensorflow.python.framework import ops from tensorflow.python.framework import test_util from tensorflow.python.ops import state_ops from tensorflow.python.ops import variables from tensorflow.python.platform import test def _AsType(v, vtype): return v.astype(vtype) if isinstance(v, np.ndarray) else vtype(v) def _NumpyUpdate(ref, indices, updates): for i, indx in np.ndenumerate(indices): indx = i[:-1] + (indx,) ref[indx] = updates[i] _TF_OPS_TO_NUMPY = { state_ops.batch_scatter_update: _NumpyUpdate, } class ScatterTest(test.TestCase): def _VariableRankTest(self, tf_scatter, vtype, itype, repeat_indices=False, updates_are_scalar=False, method=False): np.random.seed(8) with self.cached_session(use_gpu=False): for indices_shape in (2,), (3, 7), (3, 4, 7): for extra_shape in (), (5,), (5, 9): sparse_dim = len(indices_shape) - 1 indices = np.random.randint( indices_shape[sparse_dim], size=indices_shape, dtype=itype) updates = _AsType( np.random.randn(*(indices_shape + extra_shape)), vtype) old = _AsType(np.random.randn(*(indices_shape + extra_shape)), vtype) new = old.copy() np_scatter = _TF_OPS_TO_NUMPY[tf_scatter] np_scatter(new, indices, updates) ref = variables.Variable(old) ref.initializer.run() if method: ref.batch_scatter_update(ops.IndexedSlices(indices, updates)) else: tf_scatter(ref, indices, updates).eval() self.assertAllClose(ref.eval(), new) @test_util.run_deprecated_v1 def testVariableRankUpdate(self): vtypes = [np.float32, np.float64] for vtype in vtypes: for itype in (np.int32, np.int64): self._VariableRankTest( state_ops.batch_scatter_update, vtype, itype) @test_util.run_deprecated_v1 def testBooleanScatterUpdate(self): with self.session(use_gpu=False) as session: var = variables.Variable([True, False]) update0 = state_ops.batch_scatter_update(var, [1], [True]) update1 = state_ops.batch_scatter_update( var, constant_op.constant( [0], dtype=dtypes.int64), [False]) var.initializer.run() session.run([update0, update1]) self.assertAllEqual([False, True], self.evaluate(var)) @test_util.run_deprecated_v1 def testScatterOutOfRange(self): params = np.array([1, 2, 3, 4, 5, 6]).astype(np.float32) updates = np.array([-3, -4, -5]).astype(np.float32) with self.session(use_gpu=False): ref = variables.Variable(params) ref.initializer.run() indices = np.array([2, 0, 5]) state_ops.batch_scatter_update(ref, indices, updates).eval() indices = np.array([-1, 0, 5]) with self.assertRaisesOpError( r'indices\[0\] = \[-1\] does not index into shape \[6\]'): state_ops.batch_scatter_update(ref, indices, updates).eval() indices = np.array([2, 0, 6]) with self.assertRaisesOpError(r'indices\[2\] = \[6\] does not index into ' r'shape \[6\]'): state_ops.batch_scatter_update(ref, indices, updates).eval() if __name__ == '__main__': test.main()

true

f70fb9ac8a380f47eb700eb0aa4f64af7b5fd5bd

2,144

py

Python

nova/scheduler/filters/retry_filter.py

teresa-ho/stx-nova

1f82323439da2449edbbaed2fe1c8414a550c86f

[ "Apache-2.0" ]

null

nova/scheduler/filters/retry_filter.py

teresa-ho/stx-nova

1f82323439da2449edbbaed2fe1c8414a550c86f

[ "Apache-2.0" ]

null

nova/scheduler/filters/retry_filter.py

teresa-ho/stx-nova

1f82323439da2449edbbaed2fe1c8414a550c86f

[ "Apache-2.0" ]

null

# Copyright (c) 2012 OpenStack Foundation # All Rights Reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); you may # not use this file except in compliance with the License. You may obtain # a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, WITHOUT # WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the # License for the specific language governing permissions and limitations # under the License. # # Copyright (c) 2013-2017 Wind River Systems, Inc. # from oslo_log import log as logging from nova.i18n import _LI from nova.scheduler import filters LOG = logging.getLogger(__name__) class RetryFilter(filters.BaseHostFilter): """Filter out nodes that have already been attempted for scheduling purposes """ # NOTE(danms): This does not affect _where_ an instance lands, so not # related to rebuild. RUN_ON_REBUILD = False def host_passes(self, host_state, spec_obj): """Skip nodes that have already been attempted.""" retry = spec_obj.retry if not retry: # Re-scheduling is disabled LOG.debug("Re-scheduling is disabled") return True # TODO(sbauza): Once the HostState is actually a ComputeNode, we could # easily get this one... host = [host_state.host, host_state.nodename] # TODO(sbauza)... and we wouldn't need to primitive the hosts into # lists hosts = [[cn.host, cn.hypervisor_hostname] for cn in retry.hosts] passes = host not in hosts if not passes: LOG.info(_LI("Host %(host)s fails. Previously tried hosts: " "%(hosts)s"), {'host': host, 'hosts': hosts}) msg = ('Previously tried: %(hosts)s' % {'hosts': hosts}) self.filter_reject(host_state, spec_obj, msg, append=True) # Host passes if it's not in the list of previously attempted hosts: return passes

35.147541

78

0.661381

from oslo_log import log as logging from nova.i18n import _LI from nova.scheduler import filters LOG = logging.getLogger(__name__) class RetryFilter(filters.BaseHostFilter): RUN_ON_REBUILD = False def host_passes(self, host_state, spec_obj): retry = spec_obj.retry if not retry: LOG.debug("Re-scheduling is disabled") return True host = [host_state.host, host_state.nodename] # lists hosts = [[cn.host, cn.hypervisor_hostname] for cn in retry.hosts] passes = host not in hosts if not passes: LOG.info(_LI("Host %(host)s fails. Previously tried hosts: " "%(hosts)s"), {'host': host, 'hosts': hosts}) msg = ('Previously tried: %(hosts)s' % {'hosts': hosts}) self.filter_reject(host_state, spec_obj, msg, append=True) # Host passes if it's not in the list of previously attempted hosts: return passes

true

f70fb9ca8e2cc531992c308148f77617cc1fff51

10,495

py

Python

server/.vim/bundle/YouCompleteMe/third_party/ycmd/ycmd/tests/identifier_completer_test.py

hkdb/sysconf

99d334f7309657647059c4b37f25e33dffc81fc3

[ "MIT" ]

10

2020-07-21T21:59:54.000Z

2021-07-19T11:01:47.000Z

server/.vim/bundle/YouCompleteMe/third_party/ycmd/ycmd/tests/identifier_completer_test.py

hkdb/sysconf

99d334f7309657647059c4b37f25e33dffc81fc3

[ "MIT" ]

null

server/.vim/bundle/YouCompleteMe/third_party/ycmd/ycmd/tests/identifier_completer_test.py

hkdb/sysconf

99d334f7309657647059c4b37f25e33dffc81fc3

[ "MIT" ]

1

2021-01-30T18:17:01.000Z

# Copyright (C) 2020 ycmd contributors # # This file is part of ycmd. # # ycmd is free software: you can redistribute it and/or modify # it under the terms of the GNU General Public License as published by # the Free Software Foundation, either version 3 of the License, or # (at your option) any later version. # # ycmd is distributed in the hope that it will be useful, # but WITHOUT ANY WARRANTY; without even the implied warranty of # MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the # GNU General Public License for more details. # # You should have received a copy of the GNU General Public License # along with ycmd. If not, see <http://www.gnu.org/licenses/>. import os from hamcrest import assert_that, empty, equal_to, contains_exactly from ycmd.user_options_store import DefaultOptions from ycmd.completers.all import identifier_completer as ic from ycmd.completers.all.identifier_completer import IdentifierCompleter from ycmd.request_wrap import RequestWrap from ycmd.tests import PathToTestFile from ycmd.tests.test_utils import BuildRequest def BuildRequestWrap( contents, column_num, line_num = 1 ): return RequestWrap( BuildRequest( column_num = column_num, line_num = line_num, contents = contents ) ) def GetCursorIdentifier_StartOfLine_test(): assert_that( 'foo', equal_to( ic._GetCursorIdentifier( False, BuildRequestWrap( 'foo', 1 ) ) ) ) assert_that( 'fooBar', equal_to( ic._GetCursorIdentifier( False, BuildRequestWrap( 'fooBar', 1 ) ) ) ) def GetCursorIdentifier_EndOfLine_test(): assert_that( 'foo', equal_to( ic._GetCursorIdentifier( False, BuildRequestWrap( 'foo', 3 ) ) ) ) def GetCursorIdentifier_PastEndOfLine_test(): assert_that( '', equal_to( ic._GetCursorIdentifier( False, BuildRequestWrap( 'foo', 11 ) ) ) ) def GetCursorIdentifier_NegativeColumn_test(): assert_that( 'foo', equal_to( ic._GetCursorIdentifier( False, BuildRequestWrap( 'foo', -10 ) ) ) ) def GetCursorIdentifier_StartOfLine_StopsAtNonIdentifierChar_test(): assert_that( 'foo', equal_to( ic._GetCursorIdentifier( False, BuildRequestWrap( 'foo(goo)', 1 ) ) ) ) def GetCursorIdentifier_AtNonIdentifier_test(): assert_that( 'goo', equal_to( ic._GetCursorIdentifier( False, BuildRequestWrap( 'foo(goo)', 4 ) ) ) ) def GetCursorIdentifier_WalksForwardForIdentifier_test(): assert_that( 'foo', equal_to( ic._GetCursorIdentifier( False, BuildRequestWrap( ' foo', 1 ) ) ) ) def GetCursorIdentifier_FindsNothingForward_test(): assert_that( '', equal_to( ic._GetCursorIdentifier( False, BuildRequestWrap( 'foo ()***()', 5 ) ) ) ) def GetCursorIdentifier_SingleCharIdentifier_test(): assert_that( 'f', equal_to( ic._GetCursorIdentifier( False, BuildRequestWrap( ' f ', 1 ) ) ) ) def GetCursorIdentifier_StartsInMiddleOfIdentifier_test(): assert_that( 'foobar', equal_to( ic._GetCursorIdentifier( False, BuildRequestWrap( 'foobar', 4 ) ) ) ) def GetCursorIdentifier_LineEmpty_test(): assert_that( '', equal_to( ic._GetCursorIdentifier( False, BuildRequestWrap( '', 12 ) ) ) ) def GetCursorIdentifier_IgnoreIdentifierFromCommentsAndStrings_test(): assert_that( '', equal_to( ic._GetCursorIdentifier( False, BuildRequestWrap( '"foobar"', 4 ) ) ) ) assert_that( '', equal_to( ic._GetCursorIdentifier( False, BuildRequestWrap( '/*\n' ' * foobar\n' ' */', 5, 2 ) ) ) ) def GetCursorIdentifier_CollectIdentifierFromCommentsAndStrings_test(): assert_that( 'foobar', equal_to( ic._GetCursorIdentifier( True, BuildRequestWrap( '"foobar"', 4 ) ) ) ) assert_that( 'foobar', equal_to( ic._GetCursorIdentifier( True, BuildRequestWrap( '/*\n' ' * foobar\n' ' */', 5, 2 ) ) ) ) def PreviousIdentifier_Simple_test(): assert_that( 'foo', equal_to( ic._PreviousIdentifier( 2, False, BuildRequestWrap( 'foo', 4 ) ) ) ) def PreviousIdentifier_WholeIdentShouldBeBeforeColumn_test(): assert_that( '', equal_to( ic._PreviousIdentifier( 2, False, BuildRequestWrap( 'foobar', column_num = 4 ) ) ) ) def PreviousIdentifier_DoNotWrap_test(): assert_that( '', equal_to( ic._PreviousIdentifier( 2, False, BuildRequestWrap( 'foobar\n bar', column_num = 4 ) ) ) ) def PreviousIdentifier_IgnoreForwardIdents_test(): assert_that( 'foo', equal_to( ic._PreviousIdentifier( 2, False, BuildRequestWrap( 'foo bar zoo', 4 ) ) ) ) def PreviousIdentifier_IgnoreTooSmallIdent_test(): assert_that( '', equal_to( ic._PreviousIdentifier( 4, False, BuildRequestWrap( 'foo', 4 ) ) ) ) def PreviousIdentifier_IgnoreTooSmallIdent_DontContinueLooking_test(): assert_that( '', equal_to( ic._PreviousIdentifier( 4, False, BuildRequestWrap( 'abcde foo', 10 ) ) ) ) def PreviousIdentifier_WhitespaceAfterIdent_test(): assert_that( 'foo', equal_to( ic._PreviousIdentifier( 2, False, BuildRequestWrap( 'foo ', 6 ) ) ) ) def PreviousIdentifier_JunkAfterIdent_test(): assert_that( 'foo', equal_to( ic._PreviousIdentifier( 2, False, BuildRequestWrap( 'foo ;;()** ', 13 ) ) ) ) def PreviousIdentifier_IdentInMiddleOfJunk_test(): assert_that( 'aa', equal_to( ic._PreviousIdentifier( 2, False, BuildRequestWrap( 'foo ;;(aa)** ', 13 ) ) ) ) def PreviousIdentifier_IdentOnPreviousLine_test(): assert_that( 'foo', equal_to( ic._PreviousIdentifier( 2, False, BuildRequestWrap( 'foo\n ', column_num = 3, line_num = 2 ) ) ) ) assert_that( 'foo', equal_to( ic._PreviousIdentifier( 2, False, BuildRequestWrap( 'foo\n', column_num = 1, line_num = 2 ) ) ) ) def PreviousIdentifier_IdentOnPreviousLine_JunkAfterIdent_test(): assert_that( 'foo', equal_to( ic._PreviousIdentifier( 2, False, BuildRequestWrap( 'foo **;()\n ', column_num = 3, line_num = 2 ) ) ) ) def PreviousIdentifier_NoGoodIdentFound_test(): assert_that( '', equal_to( ic._PreviousIdentifier( 5, False, BuildRequestWrap( 'foo\n ', column_num = 2, line_num = 2 ) ) ) ) def PreviousIdentifier_IgnoreIdentifierFromCommentsAndStrings_test(): assert_that( '', equal_to( ic._PreviousIdentifier( 2, False, BuildRequestWrap( '"foo"\n', column_num = 1, line_num = 2 ) ) ) ) assert_that( '', equal_to( ic._PreviousIdentifier( 2, False, BuildRequestWrap( '/*\n' ' * foo\n' ' */', column_num = 2, line_num = 3 ) ) ) ) def PreviousIdentifier_CollectIdentifierFromCommentsAndStrings_test(): assert_that( 'foo', equal_to( ic._PreviousIdentifier( 2, True, BuildRequestWrap( '"foo"\n', column_num = 1, line_num = 2 ) ) ) ) assert_that( 'foo', equal_to( ic._PreviousIdentifier( 2, True, BuildRequestWrap( '/*\n' ' * foo\n' ' */', column_num = 2, line_num = 3 ) ) ) ) def FilterUnchangedTagFiles_NoFiles_test(): ident_completer = IdentifierCompleter( DefaultOptions() ) assert_that( list( ident_completer._FilterUnchangedTagFiles( [] ) ), empty() ) def FilterUnchangedTagFiles_SkipBadFiles_test(): ident_completer = IdentifierCompleter( DefaultOptions() ) assert_that( list( ident_completer._FilterUnchangedTagFiles( [ '/some/tags' ] ) ), empty() ) def FilterUnchangedTagFiles_KeepGoodFiles_test(): ident_completer = IdentifierCompleter( DefaultOptions() ) tag_file = PathToTestFile( 'basic.tags' ) assert_that( ident_completer._FilterUnchangedTagFiles( [ tag_file ] ), contains_exactly( tag_file ) ) def FilterUnchangedTagFiles_SkipUnchangesFiles_test(): ident_completer = IdentifierCompleter( DefaultOptions() ) # simulate an already open tags file that didn't change in the meantime. tag_file = PathToTestFile( 'basic.tags' ) ident_completer._tags_file_last_mtime[ tag_file ] = os.path.getmtime( tag_file ) assert_that( list( ident_completer._FilterUnchangedTagFiles( [ tag_file ] ) ), empty() )

34.409836

80

0.536541

import os from hamcrest import assert_that, empty, equal_to, contains_exactly from ycmd.user_options_store import DefaultOptions from ycmd.completers.all import identifier_completer as ic from ycmd.completers.all.identifier_completer import IdentifierCompleter from ycmd.request_wrap import RequestWrap from ycmd.tests import PathToTestFile from ycmd.tests.test_utils import BuildRequest def BuildRequestWrap( contents, column_num, line_num = 1 ): return RequestWrap( BuildRequest( column_num = column_num, line_num = line_num, contents = contents ) ) def GetCursorIdentifier_StartOfLine_test(): assert_that( 'foo', equal_to( ic._GetCursorIdentifier( False, BuildRequestWrap( 'foo', 1 ) ) ) ) assert_that( 'fooBar', equal_to( ic._GetCursorIdentifier( False, BuildRequestWrap( 'fooBar', 1 ) ) ) ) def GetCursorIdentifier_EndOfLine_test(): assert_that( 'foo', equal_to( ic._GetCursorIdentifier( False, BuildRequestWrap( 'foo', 3 ) ) ) ) def GetCursorIdentifier_PastEndOfLine_test(): assert_that( '', equal_to( ic._GetCursorIdentifier( False, BuildRequestWrap( 'foo', 11 ) ) ) ) def GetCursorIdentifier_NegativeColumn_test(): assert_that( 'foo', equal_to( ic._GetCursorIdentifier( False, BuildRequestWrap( 'foo', -10 ) ) ) ) def GetCursorIdentifier_StartOfLine_StopsAtNonIdentifierChar_test(): assert_that( 'foo', equal_to( ic._GetCursorIdentifier( False, BuildRequestWrap( 'foo(goo)', 1 ) ) ) ) def GetCursorIdentifier_AtNonIdentifier_test(): assert_that( 'goo', equal_to( ic._GetCursorIdentifier( False, BuildRequestWrap( 'foo(goo)', 4 ) ) ) ) def GetCursorIdentifier_WalksForwardForIdentifier_test(): assert_that( 'foo', equal_to( ic._GetCursorIdentifier( False, BuildRequestWrap( ' foo', 1 ) ) ) ) def GetCursorIdentifier_FindsNothingForward_test(): assert_that( '', equal_to( ic._GetCursorIdentifier( False, BuildRequestWrap( 'foo ()***()', 5 ) ) ) ) def GetCursorIdentifier_SingleCharIdentifier_test(): assert_that( 'f', equal_to( ic._GetCursorIdentifier( False, BuildRequestWrap( ' f ', 1 ) ) ) ) def GetCursorIdentifier_StartsInMiddleOfIdentifier_test(): assert_that( 'foobar', equal_to( ic._GetCursorIdentifier( False, BuildRequestWrap( 'foobar', 4 ) ) ) ) def GetCursorIdentifier_LineEmpty_test(): assert_that( '', equal_to( ic._GetCursorIdentifier( False, BuildRequestWrap( '', 12 ) ) ) ) def GetCursorIdentifier_IgnoreIdentifierFromCommentsAndStrings_test(): assert_that( '', equal_to( ic._GetCursorIdentifier( False, BuildRequestWrap( '"foobar"', 4 ) ) ) ) assert_that( '', equal_to( ic._GetCursorIdentifier( False, BuildRequestWrap( '/*\n' ' * foobar\n' ' */', 5, 2 ) ) ) ) def GetCursorIdentifier_CollectIdentifierFromCommentsAndStrings_test(): assert_that( 'foobar', equal_to( ic._GetCursorIdentifier( True, BuildRequestWrap( '"foobar"', 4 ) ) ) ) assert_that( 'foobar', equal_to( ic._GetCursorIdentifier( True, BuildRequestWrap( '/*\n' ' * foobar\n' ' */', 5, 2 ) ) ) ) def PreviousIdentifier_Simple_test(): assert_that( 'foo', equal_to( ic._PreviousIdentifier( 2, False, BuildRequestWrap( 'foo', 4 ) ) ) ) def PreviousIdentifier_WholeIdentShouldBeBeforeColumn_test(): assert_that( '', equal_to( ic._PreviousIdentifier( 2, False, BuildRequestWrap( 'foobar', column_num = 4 ) ) ) ) def PreviousIdentifier_DoNotWrap_test(): assert_that( '', equal_to( ic._PreviousIdentifier( 2, False, BuildRequestWrap( 'foobar\n bar', column_num = 4 ) ) ) ) def PreviousIdentifier_IgnoreForwardIdents_test(): assert_that( 'foo', equal_to( ic._PreviousIdentifier( 2, False, BuildRequestWrap( 'foo bar zoo', 4 ) ) ) ) def PreviousIdentifier_IgnoreTooSmallIdent_test(): assert_that( '', equal_to( ic._PreviousIdentifier( 4, False, BuildRequestWrap( 'foo', 4 ) ) ) ) def PreviousIdentifier_IgnoreTooSmallIdent_DontContinueLooking_test(): assert_that( '', equal_to( ic._PreviousIdentifier( 4, False, BuildRequestWrap( 'abcde foo', 10 ) ) ) ) def PreviousIdentifier_WhitespaceAfterIdent_test(): assert_that( 'foo', equal_to( ic._PreviousIdentifier( 2, False, BuildRequestWrap( 'foo ', 6 ) ) ) ) def PreviousIdentifier_JunkAfterIdent_test(): assert_that( 'foo', equal_to( ic._PreviousIdentifier( 2, False, BuildRequestWrap( 'foo ;;()** ', 13 ) ) ) ) def PreviousIdentifier_IdentInMiddleOfJunk_test(): assert_that( 'aa', equal_to( ic._PreviousIdentifier( 2, False, BuildRequestWrap( 'foo ;;(aa)** ', 13 ) ) ) ) def PreviousIdentifier_IdentOnPreviousLine_test(): assert_that( 'foo', equal_to( ic._PreviousIdentifier( 2, False, BuildRequestWrap( 'foo\n ', column_num = 3, line_num = 2 ) ) ) ) assert_that( 'foo', equal_to( ic._PreviousIdentifier( 2, False, BuildRequestWrap( 'foo\n', column_num = 1, line_num = 2 ) ) ) ) def PreviousIdentifier_IdentOnPreviousLine_JunkAfterIdent_test(): assert_that( 'foo', equal_to( ic._PreviousIdentifier( 2, False, BuildRequestWrap( 'foo **;()\n ', column_num = 3, line_num = 2 ) ) ) ) def PreviousIdentifier_NoGoodIdentFound_test(): assert_that( '', equal_to( ic._PreviousIdentifier( 5, False, BuildRequestWrap( 'foo\n ', column_num = 2, line_num = 2 ) ) ) ) def PreviousIdentifier_IgnoreIdentifierFromCommentsAndStrings_test(): assert_that( '', equal_to( ic._PreviousIdentifier( 2, False, BuildRequestWrap( '"foo"\n', column_num = 1, line_num = 2 ) ) ) ) assert_that( '', equal_to( ic._PreviousIdentifier( 2, False, BuildRequestWrap( '/*\n' ' * foo\n' ' */', column_num = 2, line_num = 3 ) ) ) ) def PreviousIdentifier_CollectIdentifierFromCommentsAndStrings_test(): assert_that( 'foo', equal_to( ic._PreviousIdentifier( 2, True, BuildRequestWrap( '"foo"\n', column_num = 1, line_num = 2 ) ) ) ) assert_that( 'foo', equal_to( ic._PreviousIdentifier( 2, True, BuildRequestWrap( '/*\n' ' * foo\n' ' */', column_num = 2, line_num = 3 ) ) ) ) def FilterUnchangedTagFiles_NoFiles_test(): ident_completer = IdentifierCompleter( DefaultOptions() ) assert_that( list( ident_completer._FilterUnchangedTagFiles( [] ) ), empty() ) def FilterUnchangedTagFiles_SkipBadFiles_test(): ident_completer = IdentifierCompleter( DefaultOptions() ) assert_that( list( ident_completer._FilterUnchangedTagFiles( [ '/some/tags' ] ) ), empty() ) def FilterUnchangedTagFiles_KeepGoodFiles_test(): ident_completer = IdentifierCompleter( DefaultOptions() ) tag_file = PathToTestFile( 'basic.tags' ) assert_that( ident_completer._FilterUnchangedTagFiles( [ tag_file ] ), contains_exactly( tag_file ) ) def FilterUnchangedTagFiles_SkipUnchangesFiles_test(): ident_completer = IdentifierCompleter( DefaultOptions() ) tag_file = PathToTestFile( 'basic.tags' ) ident_completer._tags_file_last_mtime[ tag_file ] = os.path.getmtime( tag_file ) assert_that( list( ident_completer._FilterUnchangedTagFiles( [ tag_file ] ) ), empty() )

true

f70fba585f35db352b62ef695402806501bee0cf

5,147

py

Python

models/eweights.py

RemiBe/Crack

334df92c5598f2a3249cae022e570ab32068ba79

[ "Apache-2.0" ]

1

2021-09-16T01:13:51.000Z

models/eweights.py

RemiBe/crack

334df92c5598f2a3249cae022e570ab32068ba79

[ "Apache-2.0" ]

null

models/eweights.py

RemiBe/crack

334df92c5598f2a3249cae022e570ab32068ba79

[ "Apache-2.0" ]

null

"""Edge weights. """ __author__ = "Rémi Barat" __version__ = "1.0" import math import random from crack.models.weights import condition_models, format_crit ##################################################### ### Format the models for init_EWeights functions ### ##################################################### def _init_EWeights(init_fct): """Decorator that prepares the [models] to the [init_fct]. """ def wrapper(models, records, crit=0, key_out="eweights", **kwargs): condition_models(init_fct, models, records, crit, key_out, "eweights", **kwargs) return wrapper ###################### ### Initialization ### ###################### def init_EWeights_from_args(models, records, wgts, key_in=None, key_out="eweights"): if isinstance(key_in, str): key_in = [key_in] nbr_n = len(wgts) nbr_c = len(wgts[0]) models[key_out] = { "entity" : "eweights", "nbr_n" : nbr_n, "nbr_c" : nbr_c, "weights": wgts, "totals" : [sum(w[c] for w in wgts) for c in range(nbr_c)], "keys" : key_in, } @_init_EWeights def init_EWeights_from_HWeights(models, records, key_out="eweights", key_graph="graph", key_hypergraph="hypergraph", key_hweights="hweights", f=None, f_args="sum_centers"): # Arguments # nbr_e = models[key_graph]["nbr_e"] edges = models[key_graph]["edges"] hwgts = models[key_hweights]["weights"] hedges = models[key_hypergraph]["edges"] if f is None: def f(*hwgts): return sum(hwgt[0] for hwgt in hwgts) ############# if f_args == "sum_centers": wgts = [f(hwgts[i], hwgts[j]) for i, j in edges] else: crack_error( ValueError, "init_EWeights_from_HWeights", "Unknown 'f_args'. Possible values are: 'sum_centers'." ) return wgts @_init_EWeights def init_EWeights_from_NWeights( models, records, key_out="eweights", key_in="graph", key_nweights="nweights", nweights_crit=0, f=None, f_args="all_ends", ): """Returns Weights based on the weights of the nodes for a given criterion. """ nbr_e = models[key_in]["nbr_e"] edges = models[key_in]["edges"] nwgts = models[key_nweights]["weights"] crit = format_crit(nweights_crit) if f is None: def f(*nwgts): return sum(nwgt[c] for c in crit for nwgt in nwgts) if f_args == "all_ends": wgts = [f(*[nwgts[i] for i in edges[e]]) for e in range(nbr_e)] else: crack_error( ValueError, "init_EWeights_from_NWeights", "Unknown 'f_args'. Possible values are: 'all_ends'." ) return wgts @_init_EWeights def init_EWeights_random(models, records, key_in=None, nbr_e=None, inf=1, sup=100, **kwargs): """Generates (uniformly) random eweights. """ if nbr_e is None: nbr_e = models[key_in]["nbr_e"] return [random.randint(inf, sup) for e in range(nbr_e)] @_init_EWeights def init_EWeights_unit(models, records, key_in=None, nbr_e=None): """Give a unit weight to every element. Options: key_in: str: Key of the entity the weights will correspond to. """ if nbr_e is None: nbr_e = models[key_in]["nbr_e"] return [1] * nbr_e @_init_EWeights def init_EWeights_topologic_mountains(structs, inf=1, sup=100, npeaks=2): """Some Edges are picked randomly to serve as peaks. The more an Edge is close to a peak, the higher is its weight. """ # TODO pass ############### ### Coarsen ### ############### def coarsen_EWeights(models, records, c_models, key_eweights, aggregation): """Add the coarsen edge weights to [c_models], under [key_weights]. """ nbr_c = models[key_eweights]["nbr_c"] ewgts = models[key_eweights]["weights"] key_in = models[key_eweights]["keys"] key_topo = key_in[0] edges = models[key_topo]["edges"] nbr_e_ = c_models[key_topo]["nbr_e"] edges_ = c_models[key_topo]["edges"] nodes_ = c_models[key_topo]["nodes"] ewgts_ = [[0] * nbr_c for _ in range(nbr_e_)] tots_ = [0] * nbr_c for e, edge in enumerate(edges): i = aggregation[edge[0]] j = aggregation[edge[1]] if i != j: e_ = nodes_[i][1][ next(f for f, j_ in enumerate(nodes_[i][0]) if j_ == j) ] for c in range(nbr_c): ewgts_[e_][c] += ewgts[e][c] tots_[c] += ewgts[e][c] c_models[key_eweights] = { "entity" : "eweights", "nbr_n" : nbr_e_, "nbr_c" : nbr_c, "weights": ewgts_, "totals" : models[key_eweights]["totals"], "keys" : models[key_eweights]["keys"], } #################### ### Function IDs ### #################### INIT_EWGT_FCTS = { "init_EWeights_from_HWeights" : init_EWeights_from_HWeights, "init_EWeights_from_NWeights" : init_EWeights_from_NWeights, "init_EWeights_topologic_mountains": init_EWeights_topologic_mountains, "init_EWeights_random" : init_EWeights_random, "init_EWeights_unit" : init_EWeights_unit, }

30.099415

172

0.592967

__author__ = "Rémi Barat" __version__ = "1.0" import math import random from crack.models.weights import condition_models, format_crit

true

f70fbb21c94acb9d07d8e2e1ca75454e92d0eaf5

28,076

py

Python

game_client.py

wenlianglaw/Tetris-in-Python

d4f0a22c4827e7eeb44c55def3f024e0c6932ebe

[ "MIT" ]

1

2021-06-25T20:43:19.000Z

game_client.py

wenlianglaw/Tetris-in-Python

d4f0a22c4827e7eeb44c55def3f024e0c6932ebe

[ "MIT" ]

null

game_client.py

wenlianglaw/Tetris-in-Python

d4f0a22c4827e7eeb44c55def3f024e0c6932ebe

[ "MIT" ]

null

# This file defines the back end of the Tetris game # # GameState is the base class of GameClient. # # GameClient.Run() will start two threads: # - _ProcessActions: Process the action list every x seconds # - _AutoDrop: Auto drops the current piece. # # GameClient: # - current piece # - held piece # - piece list # - color_map: game board # - InputActions(...): Inputs a list of actions. # - ProcessActions(...): Lets the game client process a list of actions # directly # - ProcessAction(...): Lets the game client process one actions directly # - PutPiece(...): Puts the current piece if the position is valid. # - GetState(...): Gets game state, useful to AI # - CheckValidity(...): Checks if a move is valid # - SpawnPiece(...): Sets the current piece. # - Restart(...): Restarts the game. # - Rotate(...): Alternatively, callers can directly call Rotate to rotate # current_piece # - Move(...): Alternatively, callers can directly call Move to move the # current_piece # import copy import queue import threading import time from threading import Lock from typing import Tuple, List import numpy as np import actions import shape # Some global settings DEFAULT_LENGTH = 20 DEFAULT_WIDTH = 10 MAP_PADDING_SIZE = 4 # When there are less than threshold pieces, spawn a new bag. REFILL_THRESHOLD = 5 # Disable the auto drop in next few seconds MAXIMUM_LOCK_TIME = 4 INCREMENTAL_LOCK_TIME = 1 # Scores SINGLE = 5 DOUBLE = 10 TSS = 20 TRIPLE = 40 QUAD = 50 TSD = 60 TST = 80 PC = 120 # ATTACKS ATTACK_DOUBLE = 1 ATTACK_TSS = 2 ATTACK_TRIPLE = 2 ATTACK_QUAD = 4 ATTACK_TSD = 4 ATTACK_TST = 6 ATTACK_PC = 10 class InternalError(Exception): """Any internal errors.""" class GameState: def __init__(self): self.height = 0 self.width = 0 self.color_map = np.array([]) self.current_piece = None self.held_piece = None self.score = 0 self.piece_list = [] self.is_gameover = False self.can_swap = True self.accumulated_lines_eliminated = 0 self.piece_dropped = 0 self.blevel_increase = False self.level = 0 self.line_sent = 0 self.line_received = 0 def __deepcopy__(self, memodict=None): if memodict is None: memodict = dict() another = copy.copy(self) another.color_map = self.color_map.copy() if self.current_piece is not None: another.current_piece = self.current_piece.copy() if self.held_piece is not None: another.held_piece = self.held_piece.copy() another.piece_list = copy.deepcopy(self.piece_list.copy()) return another def copy(self): return self.__deepcopy__() def __str__(self): ret = "" ret += f"""height: {self.height} width: {self.width} color_map: {self.color_map} current_piece: {self.current_piece} held_piece: {self.held_piece} score: {self.score} piece_list: {self.piece_list} is_gameover: {self.is_gameover} can_swap: {self.can_swap} piece_dropped: {self.piece_dropped} level: {self.level} """ class GameClient(GameState): def __init__(self, height: int = DEFAULT_LENGTH, width: int = DEFAULT_WIDTH, map_height_padding=MAP_PADDING_SIZE, map_side_padding=MAP_PADDING_SIZE): super().__init__() self.height = height self.width = width self.map_height_padding = map_height_padding self.map_side_padding = map_side_padding self.dtype = np.uint8 self.dtype_length = 8 if self.width + 2 * map_side_padding > 8: self.dtype = np.uint16 self.dtype_length = 16 if self.width + 2 * map_side_padding > 16: self.dtype = np.uint32 self.dtype_length = 32 if self.width + 2 * map_side_padding > 32: self.dtype = np.uint64 self.dtype_length = 64 if self.width + 2 * map_side_padding > 64: self.dtype = np.uint128 self.dtype_length = 128 if self.width + 2 * map_side_padding > 128: raise InternalError( "width too long to support bit map. Consider chaning it to a smaller value.") # Lock time settings # When the lock is enabled, count the lock time. # When the accumulated lock time is greater than the current maximum lock time, # force to perform the auto drop. Otherwise autodop is disabled for this turn. # When current locktime is reached but an refresh lock time request is genertaed. # increase the current maximum lock time by incremental lock time. self.maximum_lock_time = MAXIMUM_LOCK_TIME self.current_maximum_lock_time = 0 self.incremental_lock_time = INCREMENTAL_LOCK_TIME self.accumulate_lock_time = 0 # Only when move or rotate at bottom locks the auto drop self._enable_lock_time = False # Color map marks the color for each cell. self.color_map = np.array([[]], dtype=self.dtype) # Bit map for a better performance in some calculation. self.bit_map = np.array([], dtype=self.dtype) # Lock for current_piece self.mutex_current_piece = Lock() self.last_put_piece = None # List of actions to process self.action_list = queue.Queue() self._init_spawn_interval = 500 # 500 ms at level 0 self._current_spawn_interval = 500 # actions.Action self.last_action = None self.disable_autodrop = False self.line_tobesent = 0 # Used when calculate the auto drop interval decrease based on current level. # Generated from the sigmoid function # x = np.linspace(0, 40, 40) # interval_decrease = 110 / (1 + np.exp(0.16 * x)) # interval_decrease = np.cumsum(interval_decrease) # print(repr(np.cumsum(interval_decrease))) self.interval_decrease = np.array( [55., 100.49727968, 150.55179446, 190.28030383, 230.85041422, 260.47244367, 290.38990828, 320.86947489, 345.19115272, 350.63934095, 380.49515164, 400.03022699, 410.5020957, 420.15098155, 430.19789113, 440.8437644, 450.26946046, 455.63636342, 461.08741849, 465.74844074, 469.72957119, 473.12678557, 476.02338748, 478.4914391, 480.59310001, 482.38185737, 483.90364044, 485.19781892, 486.29808909, 487.23325451, 488.02790975, 488.70303602, 489.27651798, 489.76359062, 490.17722443, 490.52845671, 490.82667585, 491.07986489, 491.2948099, 491.47727802]) self._RefillPieces() self._TakePieceFromList() self.accumulated_lines_eliminated = 0 # When soft-dropping, temporarily disable auto-drop self.soft_drop = False self.piece_dropped = 0 # Must be put after the initializations above self._InitMap() def _InitMap(self): side_padding = (1 << self.map_side_padding) - 1 init_row = (side_padding << (self.map_side_padding + self.width)) | side_padding bottom_padding = (1 << (self.width + 2 * self.map_side_padding)) - 1 self.bit_map = np.concatenate(( np.array((self.map_height_padding + self.height) * [init_row], dtype=self.dtype), np.array(self.map_height_padding * [bottom_padding], dtype=self.dtype)), dtype=self.dtype) self.color_map = np.array([[0 for i in range(self.width)] for x in range(self.height + self.map_height_padding)], dtype=self.dtype) def Restart(self): self._InitMap() self.piece_list = [] self.held_piece = None self.current_piece = None # Lock of the game state self.mutex_current_piece = Lock() self.is_gameover = False self.last_put_piece = None # List of actions to process self.action_list = queue.Queue() self._init_spawn_interval = 500.0 self._current_spawn_interval = 500.0 # actions.Action self.last_action = [] self.can_swap = True self.score = 0 self.accumulate_lock_time = 0 self.accumulated_lines_eliminated = 0 self.soft_drop = False self.piece_dropped = 0 self.line_sent = 0 self.line_received = 0 self.line_tobesent = 0 self._enable_lock_time = False self._RefillPieces() self._TakePieceFromList() def Run(self): auto_drop_th = threading.Thread(target=self.AutoDrop, name="auto_drop", daemon=True) process_input_th = threading.Thread(target=self._ProcessActionsThread, daemon=True) if not self.disable_autodrop: auto_drop_th.start() process_input_th.start() if not self.disable_autodrop: auto_drop_th.join() process_input_th.join() print("game ends") def GetState(self) -> GameState: """Gets game state. Returns the objects ref instead of copy For better performance. """ return copy.deepcopy(super()) def GetCell(self, i: int, j: int) -> int: """Gets cell at [i,j]. Notes: This function doesn't check the index out of boundary error. """ return self.color_map[i, j] def GetMap(self): """Gets whole color_map.""" return self.color_map def GetMapArea(self, corner: Tuple[int, int], size: Tuple[int, int]) -> np.array: """Gets an area of :param top_left: :param bottom_right: :return: The area of the color_map. """ size = (np.min([size[0], self.color_map.shape[0] - corner[0]]), np.min([size[1], self.color_map.shape[1] - corner[1]])) return self.color_map[corner[0]: corner[0] + size[0], corner[1]: corner[1] + size[1]] def SetMap(self, pos: Tuple[int, int], v: int, map: np.array = None): """Sets the cell at [i,j] to value v.""" (i, j) = pos bit_map = self.bit_map.copy() if map is None or map is self.color_map: map = self.color_map bit_map = self.bit_map map[i, j] = v # Set a bit to value: Clear to bit to 0 and then set to value bit_v = 0 if v == 0 else 1 bit_j_pos = self.width + self.map_side_padding - 1 - j bit_map[i] = (bit_map[i] & ~(1 << bit_j_pos)) | (bit_v << bit_j_pos) def SetWholeMap(self, map: np.array): if map.shape != self.color_map.shape: raise InternalError( f"Map shape {map.shape}" f" must match the color_map shape: {self.color_map.shape}") self.color_map = map # Convert the map to Bollean map bit_color_map = map != 0 # Revert the order and padding, then call the packbits(..., order="little") fn bit_color_map = bit_color_map[:, ::-1] bit_color_map = np.pad( bit_color_map, ((0, 0), (self.map_side_padding, self.map_side_padding)), "constant", constant_values=(1,)) padding0_len = self.dtype_length - bit_color_map.shape[1] bit_color_map = np.pad(bit_color_map, ((0, 0), (0, padding0_len)), "constant", constant_values=(0,)) int_color_map = np.packbits(bit_color_map, bitorder="little").view(self.dtype) self.bit_map[0:self.map_height_padding + self.height] = int_color_map print(int_color_map) print(self.bit_map) def copy(self): another = copy.copy(self) another.last_action = copy.copy(self.last_action) if self.last_put_piece is not None: another.last_put_piece = self.last_put_piece.copy() another.color_map = np.copy(self.color_map) another.bit_map = np.copy(self.bit_map) another.action_list = copy.copy(self.action_list) another.piece_list = self.piece_list.copy() another.current_piece = self.current_piece.copy() if self.held_piece is None: another.held_piece = None else: another.held_piece = self.held_piece.copy() return another def AutoDrop(self): while True: if self.soft_drop: # If it is soft dropping, we don't perform auto drop. self.soft_drop = False else: if self.CheckValidity(self.current_piece, offset=(1, 0)): self.Move(actions.Action(down=True, source_user_or_ai=False)) else: if (not self._enable_lock_time or self.accumulate_lock_time >= self.current_maximum_lock_time): self.PutPiece() else: self.accumulate_lock_time += self._current_spawn_interval / 1000 time.sleep(self._current_spawn_interval / 1000) def InputActions(self, acts: List[actions.Action]): if self.is_gameover: return if len(acts) > 30: print("len:", len(acts)) acts = acts[-30:] for act in acts: if self.action_list.qsize() > 50: break self.action_list.put(act) def ProcessActions(self, actions: List[actions.Action], post_processing=True): for a in actions: self.ProcessAction(a, post_processing=post_processing) def ProcessAction(self, action: actions.Action, post_processing=True): if self.is_gameover: return # print(f"Processed action: {action.direction}, {action.rotation}, {action.swap}") # self.test += 1 # print(self.test) if action.swap: self.Swap() self.Rotate(action.rotation) self.Move(action, post_processing=post_processing) def _ProcessActionsThread(self): while True: while not self.action_list.empty(): act = self.action_list.get() self.ProcessAction(act) self.action_list.task_done() time.sleep(0.001) def SetLevel(self, level: int = 0): """Let the front end set!""" self.level = level i = min(len(self.interval_decrease), self.level) self._current_spawn_interval = max( 10, self._init_spawn_interval - self.interval_decrease[i]) def IncreaseLevel(self, inc: int = 1): """Let the front end decide!""" self.level += inc self.SetLevel(self.level) def Move(self, action: actions.Action, post_processing=True) -> bool: """Moves the current piece. :param direction: Direction to move :param post_processing: if True, put the piece to color_map and apply line eliminate. Otherwise just update the current_piece's states. :return True if moved; False otherwise """ if (action.direction == actions.NONE and not action.down): return False moved = False if action.down: try: self.mutex_current_piece.acquire() if self.CheckValidity(self.current_piece, (1, 0)): self.current_piece.x += 1 moved = True self.soft_drop = True finally: self.mutex_current_piece.release() if action.direction == actions.LEFT: try: self.mutex_current_piece.acquire() if self.CheckValidity(self.current_piece, (0, -1)): self.current_piece.y += -1 moved = True finally: self.mutex_current_piece.release() if action.direction == actions.RIGHT: try: self.mutex_current_piece.acquire() if self.CheckValidity(self.current_piece, (0, 1)): self.current_piece.y += 1 moved = True finally: self.mutex_current_piece.release() if action.direction == actions.HARD_DROP or action.direction == actions.SOFT_DROP: try: self.mutex_current_piece.acquire() while self.CheckValidity(self.current_piece, (1, 0)): self.current_piece.x += 1 moved = True finally: self.mutex_current_piece.release() if post_processing and action.direction == actions.HARD_DROP: self.PutPiece() if moved: self.last_action = action at_bottom = not self.CheckValidity(self.current_piece, (1, 0)) if (at_bottom and action.direction != actions.HARD_DROP and action.source_user): self._RefreshLockTime() return moved def _RefreshLockTime(self): self._enable_lock_time = True if self.accumulate_lock_time >= self.current_maximum_lock_time: self.current_maximum_lock_time = min( self.current_maximum_lock_time + self.incremental_lock_time, self.maximum_lock_time) def _ResetLockTime(self): self._enable_lock_time = False self.accumulate_lock_time = 0 self.current_maximum_lock_time = 0 def Swap(self): """Swaps the held piece and the current if its swappable""" if not self.can_swap: return try: self.mutex_current_piece.acquire() t = self.held_piece self.held_piece = self.current_piece self.current_piece = t if not self.current_piece: self._TakePieceFromList() self.current_piece.Init() self.held_piece.Init() self.can_swap = False finally: self.mutex_current_piece.release() def CheckGameOver(self): self.is_gameover = np.any( self.GetMapArea((0, 0), (self.map_height_padding, self.width)) != 0) return self.is_gameover def _AnalyzeElimination(self, n_eliminate: int) -> int: ret = 0 is_last_put_t = isinstance(self.last_put_piece, shape.T) if n_eliminate == 1: if (is_last_put_t and self.last_action and self.last_action.rotation != 0): print("TSS") ret += TSS self.line_tobesent += ATTACK_TSS else: ret += SINGLE if n_eliminate == 2: # TSD if (is_last_put_t and self.last_action and self.last_action.rotation != 0): print("TSD") ret += TSD self.line_tobesent += ATTACK_TSD # Normal Double else: ret += DOUBLE self.line_tobesent += ATTACK_DOUBLE if n_eliminate == 3: # TST if (is_last_put_t and self.last_action and self.last_action.rotation != 0): print("TST") ret += TST self.line_tobesent += ATTACK_TST else: ret += TRIPLE self.line_tobesent += ATTACK_TRIPLE if n_eliminate == 4: ret += QUAD self.line_tobesent += ATTACK_QUAD # Checks for PC if np.all(self.color_map == 0): print("PC") ret += PC self.line_tobesent += ATTACK_PC return ret * (self.level + 3) def _LineClear(self): elimated_lines = [] elimated_cnt = 0 # Checks the 4 lines... This is not adapt to shape with higher than 4 lines # but that's not a part of this game. I don't have plan to support custom # shapes. for row in range(4): if not (self.last_put_piece.x + row >= 0 and self.last_put_piece.x + row < self.height + self.map_height_padding): continue if np.all(self.color_map[self.last_put_piece.x + row, :] != 0): elimated_lines.append(row + self.last_put_piece.x) elimated_cnt += 1 self.color_map = np.vstack((np.zeros((elimated_cnt, self.width), dtype=self.dtype), np.delete(self.color_map, elimated_lines, axis=0))) # Updates the bit_map side_padding = (1 << self.map_side_padding) - 1 init_row = (side_padding << (self.map_side_padding + self.width)) | side_padding self.bit_map = np.concatenate((elimated_cnt * [init_row], np.delete(self.bit_map, elimated_lines))).astype(self.dtype) self.accumulated_lines_eliminated += elimated_cnt self.score += self._AnalyzeElimination(n_eliminate=elimated_cnt) def _SendAttack(self): """Send attack to target.""" # This feature has not been implemented yet. self.line_sent += self.line_tobesent self.line_tobesent = 0 def PutPiece(self, piece: shape.Shape = None): """ Puts a piece to color_map if it is a valid placement then execute the post processing. :param piece: The piece to put, if None, put the self.current_piece :param color_map: The color_map where the piece puts, if None, self.color_map will be used. :returns: True if the piece has been put. False otherwise. """ if self._PrePutPiece(piece): self._PostPutPiece(piece) return True else: return False def _PrePutPiece(self, piece: shape.Shape = None, map: np.array = None): """ Puts a piece to color_map if it is a valid placement. Post put processing such as self._LineClear will not be executed :param piece: The piece to put, if None, put the self.current_piece :param map: The color_map where the piece puts, if None, self.color_map will be used. :returns: True if the piece has been put. False otherwise. """ try: if not piece: self.mutex_current_piece.acquire() piece = self.current_piece if map is None: map = self.color_map if not self.CheckValidity(piece): return False for (i, j) in piece.GetShape(): self.SetMap((piece.x + i, piece.y + j), piece.id, map) return True finally: if self.mutex_current_piece.locked(): self.mutex_current_piece.release() def _PostPutPiece(self, piece: shape.Shape = None): if piece is not None: self.last_put_piece = piece else: self.last_put_piece = self.current_piece # LineClear should be called prior to SendAttack self._LineClear() if piece is None: self._TakePieceFromList() self.CheckGameOver() self._ResetLockTime() self._SendAttack() self.can_swap = True self.piece_dropped += 1 def TextDraw(self): preview_map = self.color_map.copy() self._PrePutPiece(self.current_piece, preview_map) for i in preview_map: print(i) print() def SpawnPiece(self, piece: shape.Shape = None) -> bool: if not piece: self._TakePieceFromList() else: self.current_piece = piece.copy() return self.CheckValidity(self.current_piece) def _FindFittedPiece(self, piece: shape.Shape = None, num_90rotations: int = 0): """Finds a location that fits this piece with n 90rotations. Ref: https://tetris.fandom.com/wiki/SRS :param piece: The piece to be put in the color_map. If none, it will be set to the current_piece :param num_90rotations: How many 90 rotations :return: piece - shape.Shape: the piece with rotations that fits the color_map. """ if not piece: piece = self.current_piece def _IsJLSTZ(piece: shape.Shape): jlstz = [shape.J, shape.L, shape.S, shape.T, shape.Z] for s in jlstz: if isinstance(piece, s): return True return False # The 180 rotation wall kick table is copied from # https://tetris.fandom.com/wiki/SRS#180.C2.B0_rotation # which is origined from # https://github.com/JoshuaWebb/nullpomino/blob/master/src/mu/nu/nullpo/game/subsystem/wallkick/StandardWallkick.java offset_map_jlstz = [ # state 0 ([(0, 0), (0, -1), (-1, -1), (2, 0), (2, -1)], # 0>>1 # 0>>2, 180 rotation # [(0,0), (1, 0), (2, 0), (1, 1), (2, 1), (-1, 0), (-2, 0), (-1, 1), (-2, 1), (0, -1), (3, 0), (-3, 0)], [(0, 0)], [(0, 0), (0, 1), (-1, 1), (2, 0), (2, 1)]), # 0>>3 # state 1 ([(0, 0), (0, 1), (1, 1), (-2, 0), (-2, 1)], # 1>>2 # l>>3, 180 rotation # [(0,0), (0, 1), (0, 2), (-1, 1), (-1, 2), (0, -1), (0, -2), (-1, -1), (-1, -2), (1, 0), (0, 3), (0, -3)], [(0, 0)], [(0, 0), (0, 1), (1, 1), (-2, 0), (-2, 1)]), # 1>>0 # state 2 ([(0, 0), (0, 1), (-1, 1), (2, 0), (2, 1)], # 2>>3 # [(0,0), (-1, 0), (-2, 0), (-1, -1), (-2, -1), (1, 0), (2, 0), (1, -1), (2, -1), (0, 1), (-3, 0), (3, 0)], # 2>>0, [(0, 0)], [(0, 0), (0, -1), (-1, -1), (2, 0), (2, -1)]), # 2>>1 # state 3 ([(0, 0), (0, -1), (1, -1), (2, 0), (-2, -1)], # 3>>0 # 3>>1, 180 rotation # [(0,0), (0, 1), (0, 2), (1, 1), (1, 2), (0, -1), (0, -2), (1, -1), (1, -2), (-1, 0), (0, 3), (0, -3)], [(0, 0)], [(0, 0), (0, -1), (1, -1), (2, 0), (-2, -1)]), # 3>>2 ] offset_map_i = [ # state 0 [[(0, 0), (0, -2), (0, 1), (1, -2), (-2, 1), ], # 0>>1 # [(0,0), (-1, 0), (-2, 0), (1, 0), (2, 0), (0, 1)], # 0>>2, 180 rotation [(0, 0)], [(0, 0), (0, -1), (0, 2), (-2, -1), (1, 2)]], # 0>>3 # state 1 [[(0, 0), (0, -1), (0, 2), (-2, -1), (1, 2)], # 1>>2 # [(0,0), (0, 1), (0, 2), (0, -1), (0, -2), (-1, 0)], # 1>>3, 180 rotation, [(0, 0)], [(0, 0), (0, 2), (0, -1), (-1, 2), (2, -1)]], # 1>>0 # state 2 [[(0, 0), (0, 2), (0, -1), (-1, 2), (2, -1)], # 2>>3 # [(0, 0), (1, 0), (2, 0), (-1, 0), (-2, 0), (0, -1)], # 2>>0, 180 rotation [(0, 0)], [(0, 0), (0, 1), (0, -2), (2, 1), (-1, -2)]], # 2>>1 # state 3 [[(0, 0), (0, 1), (0, -2), (2, 1), (-1, -2)], # 3>>0 # [(0, 0), (0, 1), (0, 2), (0, -1), (0, -2), (1, 0)], # 3>>1, 180 rotation [(0, 0)], [(0, 0), (0, -2), (0, 1), (1, -2), (2, 1)]], # 3>>2 ] state = piece.state num_90rotations %= 4 offset_piece = piece.copy() ori_x = offset_piece.x ori_y = offset_piece.y for _ in range(num_90rotations): offset_piece.Rotate90() if num_90rotations == 0: if self.CheckValidity(offset_piece): return offset_piece num_90rotations -= 1 if _IsJLSTZ(piece): for (offset_x, offset_y) in offset_map_jlstz[state][num_90rotations]: offset_piece.x = ori_x + offset_x offset_piece.y = ori_y + offset_y if (offset_piece.y >= self.width or offset_piece.x >= self.height + self.map_height_padding): continue if self.CheckValidity(offset_piece): return offset_piece else: for (offset_x, offset_y) in offset_map_i[state][num_90rotations]: offset_piece.x = ori_x + offset_x offset_piece.y = ori_y + offset_y if (offset_piece.y >= self.width or offset_piece.x >= self.height + self.map_height_padding): continue if self.CheckValidity(offset_piece): return offset_piece return None def Rotate(self, n: int) -> bool: """Rotates the current piece. :param n: rotations, in range [0,4) :return: True if the current piece can be rotated. False otherwise. """ n %= 4 if n == 0: return False fitted_piece = self._FindFittedPiece(num_90rotations=n) if fitted_piece: self.current_piece = fitted_piece self.last_action = actions.Action(dir=0, rotation=n) if not self.CheckValidity(self.current_piece, (1, 0)): self._RefreshLockTime() return fitted_piece is not None def CheckValidity(self, piece: shape.Shape, offset: Tuple[int, int] = (0, 0)): """Checks if the piece with offset can be put in the color_map :param piece: The piece to be put. :param offset: The inital offset to the piece :return: True if the current state can fit into the color_map. False otherwise. """ (ox, oy, os) = (piece.x, piece.y, piece.state) piece.x += offset[0] piece.y += offset[1] a = self.bit_map[piece.x: piece.x + 4] b = self.width - piece.y c = piece.GetBitMap().astype(self.dtype) d = c << b e = a & d check_rst = e == 0 (piece.x, piece.y, piece.state) = (ox, oy, os) return np.all(check_rst) def _GetNextBag(self): start_y = int((self.width - 3) / 2) assert start_y >= 0 bag = [shape.I(start_y=start_y), shape.J(start_y=start_y), shape.L(start_y=start_y), shape.O(start_y=start_y), shape.S(start_y=start_y), shape.T(start_y=start_y), shape.Z(start_y=start_y)] np.random.shuffle(bag) return bag def _RefillPieces(self): """ When there are less than REFILL_THRESHOLD pieces in the list, refill it with a new bag. """ if len(self.piece_list) <= REFILL_THRESHOLD: self.piece_list.extend(self._GetNextBag()) def _TakePieceFromList(self): self._RefillPieces() self.current_piece = self.piece_list[0].copy() self.piece_list = self.piece_list[1:] def CreateGameFromState(state: GameState) -> GameClient: game = GameClient(height=state.height, width=state.width) game.color_map = np.copy(state.color_map) game.current_piece = state.current_piece.copy() if state.held_piece is not None: game.held_piece = state.held_piece.copy() else: game.held_piece = None game.score = state.score game.piece_list = state.piece_list.copy() game.can_swap = state.can_swap game.is_gameover = state.is_gameover game.accumulated_lines_eliminated = state.accumulated_lines_eliminated game.piece_dropped = state.piece_dropped game.line_sent = state.line_sent game.line_received = state.line_received return game

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0.63036

import copy import queue import threading import time from threading import Lock from typing import Tuple, List import numpy as np import actions import shape DEFAULT_LENGTH = 20 DEFAULT_WIDTH = 10 MAP_PADDING_SIZE = 4 REFILL_THRESHOLD = 5 MAXIMUM_LOCK_TIME = 4 INCREMENTAL_LOCK_TIME = 1 SINGLE = 5 DOUBLE = 10 TSS = 20 TRIPLE = 40 QUAD = 50 TSD = 60 TST = 80 PC = 120 ATTACK_DOUBLE = 1 ATTACK_TSS = 2 ATTACK_TRIPLE = 2 ATTACK_QUAD = 4 ATTACK_TSD = 4 ATTACK_TST = 6 ATTACK_PC = 10 class InternalError(Exception): class GameState: def __init__(self): self.height = 0 self.width = 0 self.color_map = np.array([]) self.current_piece = None self.held_piece = None self.score = 0 self.piece_list = [] self.is_gameover = False self.can_swap = True self.accumulated_lines_eliminated = 0 self.piece_dropped = 0 self.blevel_increase = False self.level = 0 self.line_sent = 0 self.line_received = 0 def __deepcopy__(self, memodict=None): if memodict is None: memodict = dict() another = copy.copy(self) another.color_map = self.color_map.copy() if self.current_piece is not None: another.current_piece = self.current_piece.copy() if self.held_piece is not None: another.held_piece = self.held_piece.copy() another.piece_list = copy.deepcopy(self.piece_list.copy()) return another def copy(self): return self.__deepcopy__() def __str__(self): ret = "" ret += f"""height: {self.height} width: {self.width} color_map: {self.color_map} current_piece: {self.current_piece} held_piece: {self.held_piece} score: {self.score} piece_list: {self.piece_list} is_gameover: {self.is_gameover} can_swap: {self.can_swap} piece_dropped: {self.piece_dropped} level: {self.level} """ class GameClient(GameState): def __init__(self, height: int = DEFAULT_LENGTH, width: int = DEFAULT_WIDTH, map_height_padding=MAP_PADDING_SIZE, map_side_padding=MAP_PADDING_SIZE): super().__init__() self.height = height self.width = width self.map_height_padding = map_height_padding self.map_side_padding = map_side_padding self.dtype = np.uint8 self.dtype_length = 8 if self.width + 2 * map_side_padding > 8: self.dtype = np.uint16 self.dtype_length = 16 if self.width + 2 * map_side_padding > 16: self.dtype = np.uint32 self.dtype_length = 32 if self.width + 2 * map_side_padding > 32: self.dtype = np.uint64 self.dtype_length = 64 if self.width + 2 * map_side_padding > 64: self.dtype = np.uint128 self.dtype_length = 128 if self.width + 2 * map_side_padding > 128: raise InternalError( "width too long to support bit map. Consider chaning it to a smaller value.") self.maximum_lock_time = MAXIMUM_LOCK_TIME self.current_maximum_lock_time = 0 self.incremental_lock_time = INCREMENTAL_LOCK_TIME self.accumulate_lock_time = 0 self._enable_lock_time = False self.color_map = np.array([[]], dtype=self.dtype) self.bit_map = np.array([], dtype=self.dtype) self.mutex_current_piece = Lock() self.last_put_piece = None self.action_list = queue.Queue() self._init_spawn_interval = 500 self._current_spawn_interval = 500 self.last_action = None self.disable_autodrop = False self.line_tobesent = 0 self.interval_decrease = np.array( [55., 100.49727968, 150.55179446, 190.28030383, 230.85041422, 260.47244367, 290.38990828, 320.86947489, 345.19115272, 350.63934095, 380.49515164, 400.03022699, 410.5020957, 420.15098155, 430.19789113, 440.8437644, 450.26946046, 455.63636342, 461.08741849, 465.74844074, 469.72957119, 473.12678557, 476.02338748, 478.4914391, 480.59310001, 482.38185737, 483.90364044, 485.19781892, 486.29808909, 487.23325451, 488.02790975, 488.70303602, 489.27651798, 489.76359062, 490.17722443, 490.52845671, 490.82667585, 491.07986489, 491.2948099, 491.47727802]) self._RefillPieces() self._TakePieceFromList() self.accumulated_lines_eliminated = 0 self.soft_drop = False self.piece_dropped = 0 self._InitMap() def _InitMap(self): side_padding = (1 << self.map_side_padding) - 1 init_row = (side_padding << (self.map_side_padding + self.width)) | side_padding bottom_padding = (1 << (self.width + 2 * self.map_side_padding)) - 1 self.bit_map = np.concatenate(( np.array((self.map_height_padding + self.height) * [init_row], dtype=self.dtype), np.array(self.map_height_padding * [bottom_padding], dtype=self.dtype)), dtype=self.dtype) self.color_map = np.array([[0 for i in range(self.width)] for x in range(self.height + self.map_height_padding)], dtype=self.dtype) def Restart(self): self._InitMap() self.piece_list = [] self.held_piece = None self.current_piece = None self.mutex_current_piece = Lock() self.is_gameover = False self.last_put_piece = None self.action_list = queue.Queue() self._init_spawn_interval = 500.0 self._current_spawn_interval = 500.0 self.last_action = [] self.can_swap = True self.score = 0 self.accumulate_lock_time = 0 self.accumulated_lines_eliminated = 0 self.soft_drop = False self.piece_dropped = 0 self.line_sent = 0 self.line_received = 0 self.line_tobesent = 0 self._enable_lock_time = False self._RefillPieces() self._TakePieceFromList() def Run(self): auto_drop_th = threading.Thread(target=self.AutoDrop, name="auto_drop", daemon=True) process_input_th = threading.Thread(target=self._ProcessActionsThread, daemon=True) if not self.disable_autodrop: auto_drop_th.start() process_input_th.start() if not self.disable_autodrop: auto_drop_th.join() process_input_th.join() print("game ends") def GetState(self) -> GameState: return copy.deepcopy(super()) def GetCell(self, i: int, j: int) -> int: return self.color_map[i, j] def GetMap(self): return self.color_map def GetMapArea(self, corner: Tuple[int, int], size: Tuple[int, int]) -> np.array: size = (np.min([size[0], self.color_map.shape[0] - corner[0]]), np.min([size[1], self.color_map.shape[1] - corner[1]])) return self.color_map[corner[0]: corner[0] + size[0], corner[1]: corner[1] + size[1]] def SetMap(self, pos: Tuple[int, int], v: int, map: np.array = None): (i, j) = pos bit_map = self.bit_map.copy() if map is None or map is self.color_map: map = self.color_map bit_map = self.bit_map map[i, j] = v bit_v = 0 if v == 0 else 1 bit_j_pos = self.width + self.map_side_padding - 1 - j bit_map[i] = (bit_map[i] & ~(1 << bit_j_pos)) | (bit_v << bit_j_pos) def SetWholeMap(self, map: np.array): if map.shape != self.color_map.shape: raise InternalError( f"Map shape {map.shape}" f" must match the color_map shape: {self.color_map.shape}") self.color_map = map bit_color_map = map != 0 bit_color_map = bit_color_map[:, ::-1] bit_color_map = np.pad( bit_color_map, ((0, 0), (self.map_side_padding, self.map_side_padding)), "constant", constant_values=(1,)) padding0_len = self.dtype_length - bit_color_map.shape[1] bit_color_map = np.pad(bit_color_map, ((0, 0), (0, padding0_len)), "constant", constant_values=(0,)) int_color_map = np.packbits(bit_color_map, bitorder="little").view(self.dtype) self.bit_map[0:self.map_height_padding + self.height] = int_color_map print(int_color_map) print(self.bit_map) def copy(self): another = copy.copy(self) another.last_action = copy.copy(self.last_action) if self.last_put_piece is not None: another.last_put_piece = self.last_put_piece.copy() another.color_map = np.copy(self.color_map) another.bit_map = np.copy(self.bit_map) another.action_list = copy.copy(self.action_list) another.piece_list = self.piece_list.copy() another.current_piece = self.current_piece.copy() if self.held_piece is None: another.held_piece = None else: another.held_piece = self.held_piece.copy() return another def AutoDrop(self): while True: if self.soft_drop: self.soft_drop = False else: if self.CheckValidity(self.current_piece, offset=(1, 0)): self.Move(actions.Action(down=True, source_user_or_ai=False)) else: if (not self._enable_lock_time or self.accumulate_lock_time >= self.current_maximum_lock_time): self.PutPiece() else: self.accumulate_lock_time += self._current_spawn_interval / 1000 time.sleep(self._current_spawn_interval / 1000) def InputActions(self, acts: List[actions.Action]): if self.is_gameover: return if len(acts) > 30: print("len:", len(acts)) acts = acts[-30:] for act in acts: if self.action_list.qsize() > 50: break self.action_list.put(act) def ProcessActions(self, actions: List[actions.Action], post_processing=True): for a in actions: self.ProcessAction(a, post_processing=post_processing) def ProcessAction(self, action: actions.Action, post_processing=True): if self.is_gameover: return # print(f"Processed action: {action.direction}, {action.rotation}, {action.swap}") # self.test += 1 # print(self.test) if action.swap: self.Swap() self.Rotate(action.rotation) self.Move(action, post_processing=post_processing) def _ProcessActionsThread(self): while True: while not self.action_list.empty(): act = self.action_list.get() self.ProcessAction(act) self.action_list.task_done() time.sleep(0.001) def SetLevel(self, level: int = 0): self.level = level i = min(len(self.interval_decrease), self.level) self._current_spawn_interval = max( 10, self._init_spawn_interval - self.interval_decrease[i]) def IncreaseLevel(self, inc: int = 1): self.level += inc self.SetLevel(self.level) def Move(self, action: actions.Action, post_processing=True) -> bool: if (action.direction == actions.NONE and not action.down): return False moved = False if action.down: try: self.mutex_current_piece.acquire() if self.CheckValidity(self.current_piece, (1, 0)): self.current_piece.x += 1 moved = True self.soft_drop = True finally: self.mutex_current_piece.release() if action.direction == actions.LEFT: try: self.mutex_current_piece.acquire() if self.CheckValidity(self.current_piece, (0, -1)): self.current_piece.y += -1 moved = True finally: self.mutex_current_piece.release() if action.direction == actions.RIGHT: try: self.mutex_current_piece.acquire() if self.CheckValidity(self.current_piece, (0, 1)): self.current_piece.y += 1 moved = True finally: self.mutex_current_piece.release() if action.direction == actions.HARD_DROP or action.direction == actions.SOFT_DROP: try: self.mutex_current_piece.acquire() while self.CheckValidity(self.current_piece, (1, 0)): self.current_piece.x += 1 moved = True finally: self.mutex_current_piece.release() if post_processing and action.direction == actions.HARD_DROP: self.PutPiece() if moved: self.last_action = action at_bottom = not self.CheckValidity(self.current_piece, (1, 0)) if (at_bottom and action.direction != actions.HARD_DROP and action.source_user): self._RefreshLockTime() return moved def _RefreshLockTime(self): self._enable_lock_time = True if self.accumulate_lock_time >= self.current_maximum_lock_time: self.current_maximum_lock_time = min( self.current_maximum_lock_time + self.incremental_lock_time, self.maximum_lock_time) def _ResetLockTime(self): self._enable_lock_time = False self.accumulate_lock_time = 0 self.current_maximum_lock_time = 0 def Swap(self): if not self.can_swap: return try: self.mutex_current_piece.acquire() t = self.held_piece self.held_piece = self.current_piece self.current_piece = t if not self.current_piece: self._TakePieceFromList() self.current_piece.Init() self.held_piece.Init() self.can_swap = False finally: self.mutex_current_piece.release() def CheckGameOver(self): self.is_gameover = np.any( self.GetMapArea((0, 0), (self.map_height_padding, self.width)) != 0) return self.is_gameover def _AnalyzeElimination(self, n_eliminate: int) -> int: ret = 0 is_last_put_t = isinstance(self.last_put_piece, shape.T) if n_eliminate == 1: if (is_last_put_t and self.last_action and self.last_action.rotation != 0): print("TSS") ret += TSS self.line_tobesent += ATTACK_TSS else: ret += SINGLE if n_eliminate == 2: # TSD if (is_last_put_t and self.last_action and self.last_action.rotation != 0): print("TSD") ret += TSD self.line_tobesent += ATTACK_TSD # Normal Double else: ret += DOUBLE self.line_tobesent += ATTACK_DOUBLE if n_eliminate == 3: # TST if (is_last_put_t and self.last_action and self.last_action.rotation != 0): print("TST") ret += TST self.line_tobesent += ATTACK_TST else: ret += TRIPLE self.line_tobesent += ATTACK_TRIPLE if n_eliminate == 4: ret += QUAD self.line_tobesent += ATTACK_QUAD # Checks for PC if np.all(self.color_map == 0): print("PC") ret += PC self.line_tobesent += ATTACK_PC return ret * (self.level + 3) def _LineClear(self): elimated_lines = [] elimated_cnt = 0 # Checks the 4 lines... This is not adapt to shape with higher than 4 lines # but that's not a part of this game. I don't have plan to support custom # shapes. for row in range(4): if not (self.last_put_piece.x + row >= 0 and self.last_put_piece.x + row < self.height + self.map_height_padding): continue if np.all(self.color_map[self.last_put_piece.x + row, :] != 0): elimated_lines.append(row + self.last_put_piece.x) elimated_cnt += 1 self.color_map = np.vstack((np.zeros((elimated_cnt, self.width), dtype=self.dtype), np.delete(self.color_map, elimated_lines, axis=0))) # Updates the bit_map side_padding = (1 << self.map_side_padding) - 1 init_row = (side_padding << (self.map_side_padding + self.width)) | side_padding self.bit_map = np.concatenate((elimated_cnt * [init_row], np.delete(self.bit_map, elimated_lines))).astype(self.dtype) self.accumulated_lines_eliminated += elimated_cnt self.score += self._AnalyzeElimination(n_eliminate=elimated_cnt) def _SendAttack(self): # This feature has not been implemented yet. self.line_sent += self.line_tobesent self.line_tobesent = 0 def PutPiece(self, piece: shape.Shape = None): if self._PrePutPiece(piece): self._PostPutPiece(piece) return True else: return False def _PrePutPiece(self, piece: shape.Shape = None, map: np.array = None): try: if not piece: self.mutex_current_piece.acquire() piece = self.current_piece if map is None: map = self.color_map if not self.CheckValidity(piece): return False for (i, j) in piece.GetShape(): self.SetMap((piece.x + i, piece.y + j), piece.id, map) return True finally: if self.mutex_current_piece.locked(): self.mutex_current_piece.release() def _PostPutPiece(self, piece: shape.Shape = None): if piece is not None: self.last_put_piece = piece else: self.last_put_piece = self.current_piece # LineClear should be called prior to SendAttack self._LineClear() if piece is None: self._TakePieceFromList() self.CheckGameOver() self._ResetLockTime() self._SendAttack() self.can_swap = True self.piece_dropped += 1 def TextDraw(self): preview_map = self.color_map.copy() self._PrePutPiece(self.current_piece, preview_map) for i in preview_map: print(i) print() def SpawnPiece(self, piece: shape.Shape = None) -> bool: if not piece: self._TakePieceFromList() else: self.current_piece = piece.copy() return self.CheckValidity(self.current_piece) def _FindFittedPiece(self, piece: shape.Shape = None, num_90rotations: int = 0): if not piece: piece = self.current_piece def _IsJLSTZ(piece: shape.Shape): jlstz = [shape.J, shape.L, shape.S, shape.T, shape.Z] for s in jlstz: if isinstance(piece, s): return True return False # The 180 rotation wall kick table is copied from # https://tetris.fandom.com/wiki/SRS#180.C2.B0_rotation # which is origined from # https://github.com/JoshuaWebb/nullpomino/blob/master/src/mu/nu/nullpo/game/subsystem/wallkick/StandardWallkick.java offset_map_jlstz = [ # state 0 ([(0, 0), (0, -1), (-1, -1), (2, 0), (2, -1)], # 0>>1 # 0>>2, 180 rotation # [(0,0), (1, 0), (2, 0), (1, 1), (2, 1), (-1, 0), (-2, 0), (-1, 1), (-2, 1), (0, -1), (3, 0), (-3, 0)], [(0, 0)], [(0, 0), (0, 1), (-1, 1), (2, 0), (2, 1)]), # 0>>3 # state 1 ([(0, 0), (0, 1), (1, 1), (-2, 0), (-2, 1)], # 1>>2 # l>>3, 180 rotation # [(0,0), (0, 1), (0, 2), (-1, 1), (-1, 2), (0, -1), (0, -2), (-1, -1), (-1, -2), (1, 0), (0, 3), (0, -3)], [(0, 0)], [(0, 0), (0, 1), (1, 1), (-2, 0), (-2, 1)]), # 1>>0 # state 2 ([(0, 0), (0, 1), (-1, 1), (2, 0), (2, 1)], # 2>>3 # [(0,0), (-1, 0), (-2, 0), (-1, -1), (-2, -1), (1, 0), (2, 0), (1, -1), (2, -1), (0, 1), (-3, 0), (3, 0)], # 2>>0, [(0, 0)], [(0, 0), (0, -1), (-1, -1), (2, 0), (2, -1)]), # 2>>1 # state 3 ([(0, 0), (0, -1), (1, -1), (2, 0), (-2, -1)], # 3>>0 # 3>>1, 180 rotation # [(0,0), (0, 1), (0, 2), (1, 1), (1, 2), (0, -1), (0, -2), (1, -1), (1, -2), (-1, 0), (0, 3), (0, -3)], [(0, 0)], [(0, 0), (0, -1), (1, -1), (2, 0), (-2, -1)]), # 3>>2 ] offset_map_i = [ # state 0 [[(0, 0), (0, -2), (0, 1), (1, -2), (-2, 1), ], # 0>>1 # [(0,0), (-1, 0), (-2, 0), (1, 0), (2, 0), (0, 1)], # 0>>2, 180 rotation [(0, 0)], [(0, 0), (0, -1), (0, 2), (-2, -1), (1, 2)]], # 0>>3 # state 1 [[(0, 0), (0, -1), (0, 2), (-2, -1), (1, 2)], # 1>>2 # [(0,0), (0, 1), (0, 2), (0, -1), (0, -2), (-1, 0)], # 1>>3, 180 rotation, [(0, 0)], [(0, 0), (0, 2), (0, -1), (-1, 2), (2, -1)]], # 1>>0 # state 2 [[(0, 0), (0, 2), (0, -1), (-1, 2), (2, -1)], # 2>>3 # [(0, 0), (1, 0), (2, 0), (-1, 0), (-2, 0), (0, -1)], # 2>>0, 180 rotation [(0, 0)], [(0, 0), (0, 1), (0, -2), (2, 1), (-1, -2)]], # 2>>1 # state 3 [[(0, 0), (0, 1), (0, -2), (2, 1), (-1, -2)], # 3>>0 # [(0, 0), (0, 1), (0, 2), (0, -1), (0, -2), (1, 0)], # 3>>1, 180 rotation [(0, 0)], [(0, 0), (0, -2), (0, 1), (1, -2), (2, 1)]], # 3>>2 ] state = piece.state num_90rotations %= 4 offset_piece = piece.copy() ori_x = offset_piece.x ori_y = offset_piece.y for _ in range(num_90rotations): offset_piece.Rotate90() if num_90rotations == 0: if self.CheckValidity(offset_piece): return offset_piece num_90rotations -= 1 if _IsJLSTZ(piece): for (offset_x, offset_y) in offset_map_jlstz[state][num_90rotations]: offset_piece.x = ori_x + offset_x offset_piece.y = ori_y + offset_y if (offset_piece.y >= self.width or offset_piece.x >= self.height + self.map_height_padding): continue if self.CheckValidity(offset_piece): return offset_piece else: for (offset_x, offset_y) in offset_map_i[state][num_90rotations]: offset_piece.x = ori_x + offset_x offset_piece.y = ori_y + offset_y if (offset_piece.y >= self.width or offset_piece.x >= self.height + self.map_height_padding): continue if self.CheckValidity(offset_piece): return offset_piece return None def Rotate(self, n: int) -> bool: n %= 4 if n == 0: return False fitted_piece = self._FindFittedPiece(num_90rotations=n) if fitted_piece: self.current_piece = fitted_piece self.last_action = actions.Action(dir=0, rotation=n) if not self.CheckValidity(self.current_piece, (1, 0)): self._RefreshLockTime() return fitted_piece is not None def CheckValidity(self, piece: shape.Shape, offset: Tuple[int, int] = (0, 0)): (ox, oy, os) = (piece.x, piece.y, piece.state) piece.x += offset[0] piece.y += offset[1] a = self.bit_map[piece.x: piece.x + 4] b = self.width - piece.y c = piece.GetBitMap().astype(self.dtype) d = c << b e = a & d check_rst = e == 0 (piece.x, piece.y, piece.state) = (ox, oy, os) return np.all(check_rst) def _GetNextBag(self): start_y = int((self.width - 3) / 2) assert start_y >= 0 bag = [shape.I(start_y=start_y), shape.J(start_y=start_y), shape.L(start_y=start_y), shape.O(start_y=start_y), shape.S(start_y=start_y), shape.T(start_y=start_y), shape.Z(start_y=start_y)] np.random.shuffle(bag) return bag def _RefillPieces(self): if len(self.piece_list) <= REFILL_THRESHOLD: self.piece_list.extend(self._GetNextBag()) def _TakePieceFromList(self): self._RefillPieces() self.current_piece = self.piece_list[0].copy() self.piece_list = self.piece_list[1:] def CreateGameFromState(state: GameState) -> GameClient: game = GameClient(height=state.height, width=state.width) game.color_map = np.copy(state.color_map) game.current_piece = state.current_piece.copy() if state.held_piece is not None: game.held_piece = state.held_piece.copy() else: game.held_piece = None game.score = state.score game.piece_list = state.piece_list.copy() game.can_swap = state.can_swap game.is_gameover = state.is_gameover game.accumulated_lines_eliminated = state.accumulated_lines_eliminated game.piece_dropped = state.piece_dropped game.line_sent = state.line_sent game.line_received = state.line_received return game

true

f70fbcc4d201ff1d88c227e27e1344dec0d9a084

5,208

py

Python

homeassistant/components/upnp/__init__.py

Juggler00/home-assistant

3f87d413813de84935ea67b5212c55348524447f

[ "Apache-2.0" ]

1

2021-04-08T06:02:25.000Z

homeassistant/components/upnp/__init__.py

ayonix/home-assistant

8fda70537736db9a73c0a863800d6bb4df67f5fc

[ "Apache-2.0" ]

null

homeassistant/components/upnp/__init__.py

ayonix/home-assistant

8fda70537736db9a73c0a863800d6bb4df67f5fc

[ "Apache-2.0" ]

null

""" Will open a port in your router for Home Assistant and provide statistics. For more details about this component, please refer to the documentation at https://home-assistant.io/components/upnp/ """ import asyncio from ipaddress import ip_address import aiohttp import voluptuous as vol from homeassistant.config_entries import ConfigEntry from homeassistant.const import EVENT_HOMEASSISTANT_STOP from homeassistant.helpers import config_validation as cv from homeassistant.helpers import dispatcher from homeassistant.helpers.typing import ConfigType from homeassistant.helpers.typing import HomeAssistantType from homeassistant.components.discovery import DOMAIN as DISCOVERY_DOMAIN from .const import ( CONF_ENABLE_PORT_MAPPING, CONF_ENABLE_SENSORS, CONF_HASS, CONF_LOCAL_IP, CONF_PORTS, CONF_UDN, CONF_SSDP_DESCRIPTION, SIGNAL_REMOVE_SENSOR, ) from .const import DOMAIN from .const import LOGGER as _LOGGER from .config_flow import ensure_domain_data from .device import Device REQUIREMENTS = ['async-upnp-client==0.12.4'] DEPENDENCIES = ['http'] NOTIFICATION_ID = 'upnp_notification' NOTIFICATION_TITLE = 'UPnP/IGD Setup' CONFIG_SCHEMA = vol.Schema({ DOMAIN: vol.Schema({ vol.Optional(CONF_ENABLE_PORT_MAPPING, default=False): cv.boolean, vol.Optional(CONF_ENABLE_SENSORS, default=True): cv.boolean, vol.Optional(CONF_LOCAL_IP): vol.All(ip_address, cv.string), vol.Optional(CONF_PORTS): vol.Schema({ vol.Any(CONF_HASS, cv.positive_int): vol.Any(CONF_HASS, cv.positive_int) }) }), }, extra=vol.ALLOW_EXTRA) def _substitute_hass_ports(ports, hass_port): """Substitute 'hass' for the hass_port.""" ports = ports.copy() # substitute 'hass' for hass_port, both keys and values if CONF_HASS in ports: ports[hass_port] = ports[CONF_HASS] del ports[CONF_HASS] for port in ports: if ports[port] == CONF_HASS: ports[port] = hass_port return ports # config async def async_setup(hass: HomeAssistantType, config: ConfigType): """Register a port mapping for Home Assistant via UPnP.""" ensure_domain_data(hass) # ensure sane config if DOMAIN not in config: return True if DISCOVERY_DOMAIN not in config: _LOGGER.warning('UPNP needs discovery, please enable it') return False # overridden local ip upnp_config = config[DOMAIN] if CONF_LOCAL_IP in upnp_config: hass.data[DOMAIN]['local_ip'] = upnp_config[CONF_LOCAL_IP] # determine ports ports = {CONF_HASS: CONF_HASS} # default, port_mapping disabled by default if CONF_PORTS in upnp_config: # copy from config ports = upnp_config[CONF_PORTS] hass.data[DOMAIN]['auto_config'] = { 'active': True, 'enable_sensors': upnp_config[CONF_ENABLE_SENSORS], 'enable_port_mapping': upnp_config[CONF_ENABLE_PORT_MAPPING], 'ports': ports, } return True # config flow async def async_setup_entry(hass: HomeAssistantType, config_entry: ConfigEntry): """Set up UPnP/IGD-device from a config entry.""" ensure_domain_data(hass) data = config_entry.data # build UPnP/IGD device ssdp_description = data[CONF_SSDP_DESCRIPTION] try: device = await Device.async_create_device(hass, ssdp_description) except (asyncio.TimeoutError, aiohttp.ClientError): _LOGGER.error('Unable to create upnp-device') return False hass.data[DOMAIN]['devices'][device.udn] = device # port mapping if data.get(CONF_ENABLE_PORT_MAPPING): local_ip = hass.data[DOMAIN].get('local_ip') ports = hass.data[DOMAIN]['auto_config']['ports'] _LOGGER.debug('Enabling port mappings: %s', ports) hass_port = hass.http.server_port ports = _substitute_hass_ports(ports, hass_port) await device.async_add_port_mappings(ports, local_ip=local_ip) # sensors if data.get(CONF_ENABLE_SENSORS): _LOGGER.debug('Enabling sensors') # register sensor setup handlers hass.async_create_task(hass.config_entries.async_forward_entry_setup( config_entry, 'sensor')) async def unload_entry(event): """Unload entry on quit.""" await async_unload_entry(hass, config_entry) hass.bus.async_listen_once(EVENT_HOMEASSISTANT_STOP, unload_entry) return True async def async_unload_entry(hass: HomeAssistantType, config_entry: ConfigEntry): """Unload a config entry.""" data = config_entry.data udn = data[CONF_UDN] if udn not in hass.data[DOMAIN]['devices']: return True device = hass.data[DOMAIN]['devices'][udn] # port mapping if data.get(CONF_ENABLE_PORT_MAPPING): _LOGGER.debug('Deleting port mappings') await device.async_delete_port_mappings() # sensors if data.get(CONF_ENABLE_SENSORS): _LOGGER.debug('Deleting sensors') dispatcher.async_dispatcher_send(hass, SIGNAL_REMOVE_SENSOR, device) # clear stored device del hass.data[DOMAIN]['devices'][udn] return True

30.635294

79

0.699117

import asyncio from ipaddress import ip_address import aiohttp import voluptuous as vol from homeassistant.config_entries import ConfigEntry from homeassistant.const import EVENT_HOMEASSISTANT_STOP from homeassistant.helpers import config_validation as cv from homeassistant.helpers import dispatcher from homeassistant.helpers.typing import ConfigType from homeassistant.helpers.typing import HomeAssistantType from homeassistant.components.discovery import DOMAIN as DISCOVERY_DOMAIN from .const import ( CONF_ENABLE_PORT_MAPPING, CONF_ENABLE_SENSORS, CONF_HASS, CONF_LOCAL_IP, CONF_PORTS, CONF_UDN, CONF_SSDP_DESCRIPTION, SIGNAL_REMOVE_SENSOR, ) from .const import DOMAIN from .const import LOGGER as _LOGGER from .config_flow import ensure_domain_data from .device import Device REQUIREMENTS = ['async-upnp-client==0.12.4'] DEPENDENCIES = ['http'] NOTIFICATION_ID = 'upnp_notification' NOTIFICATION_TITLE = 'UPnP/IGD Setup' CONFIG_SCHEMA = vol.Schema({ DOMAIN: vol.Schema({ vol.Optional(CONF_ENABLE_PORT_MAPPING, default=False): cv.boolean, vol.Optional(CONF_ENABLE_SENSORS, default=True): cv.boolean, vol.Optional(CONF_LOCAL_IP): vol.All(ip_address, cv.string), vol.Optional(CONF_PORTS): vol.Schema({ vol.Any(CONF_HASS, cv.positive_int): vol.Any(CONF_HASS, cv.positive_int) }) }), }, extra=vol.ALLOW_EXTRA) def _substitute_hass_ports(ports, hass_port): ports = ports.copy() if CONF_HASS in ports: ports[hass_port] = ports[CONF_HASS] del ports[CONF_HASS] for port in ports: if ports[port] == CONF_HASS: ports[port] = hass_port return ports async def async_setup(hass: HomeAssistantType, config: ConfigType): ensure_domain_data(hass) if DOMAIN not in config: return True if DISCOVERY_DOMAIN not in config: _LOGGER.warning('UPNP needs discovery, please enable it') return False upnp_config = config[DOMAIN] if CONF_LOCAL_IP in upnp_config: hass.data[DOMAIN]['local_ip'] = upnp_config[CONF_LOCAL_IP] ports = {CONF_HASS: CONF_HASS} if CONF_PORTS in upnp_config: ports = upnp_config[CONF_PORTS] hass.data[DOMAIN]['auto_config'] = { 'active': True, 'enable_sensors': upnp_config[CONF_ENABLE_SENSORS], 'enable_port_mapping': upnp_config[CONF_ENABLE_PORT_MAPPING], 'ports': ports, } return True async def async_setup_entry(hass: HomeAssistantType, config_entry: ConfigEntry): ensure_domain_data(hass) data = config_entry.data ssdp_description = data[CONF_SSDP_DESCRIPTION] try: device = await Device.async_create_device(hass, ssdp_description) except (asyncio.TimeoutError, aiohttp.ClientError): _LOGGER.error('Unable to create upnp-device') return False hass.data[DOMAIN]['devices'][device.udn] = device if data.get(CONF_ENABLE_PORT_MAPPING): local_ip = hass.data[DOMAIN].get('local_ip') ports = hass.data[DOMAIN]['auto_config']['ports'] _LOGGER.debug('Enabling port mappings: %s', ports) hass_port = hass.http.server_port ports = _substitute_hass_ports(ports, hass_port) await device.async_add_port_mappings(ports, local_ip=local_ip) if data.get(CONF_ENABLE_SENSORS): _LOGGER.debug('Enabling sensors') hass.async_create_task(hass.config_entries.async_forward_entry_setup( config_entry, 'sensor')) async def unload_entry(event): await async_unload_entry(hass, config_entry) hass.bus.async_listen_once(EVENT_HOMEASSISTANT_STOP, unload_entry) return True async def async_unload_entry(hass: HomeAssistantType, config_entry: ConfigEntry): data = config_entry.data udn = data[CONF_UDN] if udn not in hass.data[DOMAIN]['devices']: return True device = hass.data[DOMAIN]['devices'][udn] if data.get(CONF_ENABLE_PORT_MAPPING): _LOGGER.debug('Deleting port mappings') await device.async_delete_port_mappings() if data.get(CONF_ENABLE_SENSORS): _LOGGER.debug('Deleting sensors') dispatcher.async_dispatcher_send(hass, SIGNAL_REMOVE_SENSOR, device) del hass.data[DOMAIN]['devices'][udn] return True

true

f70fbcdfcc1d45f5ca92376a915b19a073966d04

8,295

py

Python

components/diagnostics/diagnose_me/component.py

areshytko/pipelines

9e818e9d13569614b7188a7bff47770ae449827c

[ "Apache-2.0" ]

null

components/diagnostics/diagnose_me/component.py

areshytko/pipelines

9e818e9d13569614b7188a7bff47770ae449827c

[ "Apache-2.0" ]

null

components/diagnostics/diagnose_me/component.py

areshytko/pipelines

9e818e9d13569614b7188a7bff47770ae449827c

[ "Apache-2.0" ]

null

# Copyright 2020 Google Inc. All Rights Reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. from typing import Any, List, NamedTuple, Optional def run_diagnose_me( bucket: str, execution_mode: str, project_id: str, target_apis: str, quota_check: Optional[List[Any]] = None, ) -> NamedTuple('Outputs', [('bucket', str), ('project_id', str)]): """ Performs environment verification specific to this pipeline. args: bucket: string name of the bucket to be checked. Must be of the format gs://bucket_root/any/path/here/is/ignored where any path beyond root is ignored. execution_mode: If set to HALT_ON_ERROR will case any error to raise an exception. This is intended to stop the data processing of a pipeline. Can set to False to only report Errors/Warnings. project_id: GCP project ID which is assumed to be the project under which current pod is executing. target_apis: String consisting of a comma separated list of apis to be verified. quota_check: List of entries describing how much quota is required. Each entry has three fields: region, metric and quota_needed. All string-typed. Raises: RuntimeError: If configuration is not setup properly and HALT_ON_ERROR flag is set. """ # Installing pip3 and kfp, since the base image 'google/cloud-sdk:279.0.0' # does not come with pip3 pre-installed. import subprocess subprocess.run([ 'curl', 'https://bootstrap.pypa.io/get-pip.py', '-o', 'get-pip.py' ], capture_output=True) subprocess.run(['apt-get', 'install', 'python3-distutils', '--yes'], capture_output=True) subprocess.run(['python3', 'get-pip.py'], capture_output=True) subprocess.run(['python3', '-m', 'pip', 'install', 'kfp>=0.1.31', '--quiet'], capture_output=True) import sys from kfp.cli.diagnose_me import gcp config_error_observed = False quota_list = gcp.get_gcp_configuration( gcp.Commands.GET_QUOTAS, human_readable=False ) if quota_list.has_error: print('Failed to retrieve project quota with error %s\n' % (quota_list.stderr)) config_error_observed = True else: # Check quota. quota_dict = {} # Mapping from region to dict[metric, available] for region_quota in quota_list: quota_dict[region_quota['name']] = {} for quota in region_quota['quotas']: quota_dict[region_quota['name']][quota['metric'] ] = quota['limit'] - quota['usage'] quota_check = [] or quota_check for single_check in quota_check: if single_check['region'] not in quota_dict: print( 'Regional quota for %s does not exist in current project.\n' % (single_check['region']) ) config_error_observed = True else: if quota_dict[single_check['region']][single_check['metric'] ] < single_check['quota_needed']: print( 'Insufficient quota observed for %s at %s: %s is needed but only %s is available.\n' % ( single_check['metric'], single_check['region'], str(single_check['quota_needed'] ), str(quota_dict[single_check['region']][single_check['metric']]) ) ) config_error_observed = True # Get the project ID # from project configuration project_config = gcp.get_gcp_configuration( gcp.Commands.GET_GCLOUD_DEFAULT, human_readable=False ) if not project_config.has_error: auth_project_id = project_config.parsed_output['core']['project'] print( 'GCP credentials are configured with access to project: %s ...\n' % (project_id) ) print('Following account(s) are active under this pipeline:\n') subprocess.run(['gcloud', 'auth', 'list', '--format', 'json']) print('\n') else: print( 'Project configuration is not accessible with error %s\n' % (project_config.stderr), file=sys.stderr ) config_error_observed = True if auth_project_id != project_id: print( 'User provided project ID %s does not match the configuration %s\n' % (project_id, auth_project_id), file=sys.stderr ) config_error_observed = True # Get project buckets get_project_bucket_results = gcp.get_gcp_configuration( gcp.Commands.GET_STORAGE_BUCKETS, human_readable=False ) if get_project_bucket_results.has_error: print( 'could not retrieve project buckets with error: %s' % (get_project_bucket_results.stderr), file=sys.stderr ) config_error_observed = True # Get the root of the user provided bucket i.e. gs://root. bucket_root = '/'.join(bucket.split('/')[0:3]) print( 'Checking to see if the provided GCS bucket\n %s\nis accessible ...\n' % (bucket) ) if bucket_root in get_project_bucket_results.json_output: print( 'Provided bucket \n %s\nis accessible within the project\n %s\n' % (bucket, project_id) ) else: print( 'Could not find the bucket %s in project %s' % (bucket, project_id) + 'Please verify that you have provided the correct GCS bucket name.\n' + 'Only the following buckets are visible in this project:\n%s' % (get_project_bucket_results.parsed_output), file=sys.stderr ) config_error_observed = True # Verify APIs that are required are enabled api_config_results = gcp.get_gcp_configuration(gcp.Commands.GET_APIS) api_status = {} if api_config_results.has_error: print( 'could not retrieve API status with error: %s' % (api_config_results.stderr), file=sys.stderr ) config_error_observed = True print('Checking APIs status ...') for item in api_config_results.parsed_output: api_status[item['config']['name']] = item['state'] # printing the results in stdout for logging purposes print('%s %s' % (item['config']['name'], item['state'])) # Check if target apis are enabled api_check_results = True for api in target_apis.replace(' ', '').split(','): if 'ENABLED' != api_status.get(api, 'DISABLED'): api_check_results = False print( 'API \"%s\" is not accessible or not enabled. To enable this api go to ' % (api) + 'https://console.cloud.google.com/apis/library/%s?project=%s' % (api, project_id), file=sys.stderr ) config_error_observed = True if 'HALT_ON_ERROR' in execution_mode and config_error_observed: raise RuntimeError( 'There was an error in your environment configuration.\n' + 'Note that resolving such issues generally require a deep knowledge of Kubernetes.\n' + '\n' + 'We highly recommend that you recreate the cluster and check "Allow access ..." \n' + 'checkbox during cluster creation to have the cluster configured automatically.\n' + 'For more information on this and other troubleshooting instructions refer to\n' + 'our troubleshooting guide.\n' + '\n' + 'If you have intentionally modified the cluster configuration, you may\n' + 'bypass this error by removing the execution_mode HALT_ON_ERROR flag.\n' ) return (project_id, bucket) if __name__ == '__main__': import kfp.components as comp comp.func_to_container_op( run_diagnose_me, base_image='google/cloud-sdk:279.0.0', output_component_file='component.yaml', )

35.75431

98

0.642917

from typing import Any, List, NamedTuple, Optional def run_diagnose_me( bucket: str, execution_mode: str, project_id: str, target_apis: str, quota_check: Optional[List[Any]] = None, ) -> NamedTuple('Outputs', [('bucket', str), ('project_id', str)]): import subprocess subprocess.run([ 'curl', 'https://bootstrap.pypa.io/get-pip.py', '-o', 'get-pip.py' ], capture_output=True) subprocess.run(['apt-get', 'install', 'python3-distutils', '--yes'], capture_output=True) subprocess.run(['python3', 'get-pip.py'], capture_output=True) subprocess.run(['python3', '-m', 'pip', 'install', 'kfp>=0.1.31', '--quiet'], capture_output=True) import sys from kfp.cli.diagnose_me import gcp config_error_observed = False quota_list = gcp.get_gcp_configuration( gcp.Commands.GET_QUOTAS, human_readable=False ) if quota_list.has_error: print('Failed to retrieve project quota with error %s\n' % (quota_list.stderr)) config_error_observed = True else: quota_dict = {} for region_quota in quota_list: quota_dict[region_quota['name']] = {} for quota in region_quota['quotas']: quota_dict[region_quota['name']][quota['metric'] ] = quota['limit'] - quota['usage'] quota_check = [] or quota_check for single_check in quota_check: if single_check['region'] not in quota_dict: print( 'Regional quota for %s does not exist in current project.\n' % (single_check['region']) ) config_error_observed = True else: if quota_dict[single_check['region']][single_check['metric'] ] < single_check['quota_needed']: print( 'Insufficient quota observed for %s at %s: %s is needed but only %s is available.\n' % ( single_check['metric'], single_check['region'], str(single_check['quota_needed'] ), str(quota_dict[single_check['region']][single_check['metric']]) ) ) config_error_observed = True project_config = gcp.get_gcp_configuration( gcp.Commands.GET_GCLOUD_DEFAULT, human_readable=False ) if not project_config.has_error: auth_project_id = project_config.parsed_output['core']['project'] print( 'GCP credentials are configured with access to project: %s ...\n' % (project_id) ) print('Following account(s) are active under this pipeline:\n') subprocess.run(['gcloud', 'auth', 'list', '--format', 'json']) print('\n') else: print( 'Project configuration is not accessible with error %s\n' % (project_config.stderr), file=sys.stderr ) config_error_observed = True if auth_project_id != project_id: print( 'User provided project ID %s does not match the configuration %s\n' % (project_id, auth_project_id), file=sys.stderr ) config_error_observed = True get_project_bucket_results = gcp.get_gcp_configuration( gcp.Commands.GET_STORAGE_BUCKETS, human_readable=False ) if get_project_bucket_results.has_error: print( 'could not retrieve project buckets with error: %s' % (get_project_bucket_results.stderr), file=sys.stderr ) config_error_observed = True bucket_root = '/'.join(bucket.split('/')[0:3]) print( 'Checking to see if the provided GCS bucket\n %s\nis accessible ...\n' % (bucket) ) if bucket_root in get_project_bucket_results.json_output: print( 'Provided bucket \n %s\nis accessible within the project\n %s\n' % (bucket, project_id) ) else: print( 'Could not find the bucket %s in project %s' % (bucket, project_id) + 'Please verify that you have provided the correct GCS bucket name.\n' + 'Only the following buckets are visible in this project:\n%s' % (get_project_bucket_results.parsed_output), file=sys.stderr ) config_error_observed = True api_config_results = gcp.get_gcp_configuration(gcp.Commands.GET_APIS) api_status = {} if api_config_results.has_error: print( 'could not retrieve API status with error: %s' % (api_config_results.stderr), file=sys.stderr ) config_error_observed = True print('Checking APIs status ...') for item in api_config_results.parsed_output: api_status[item['config']['name']] = item['state'] print('%s %s' % (item['config']['name'], item['state'])) api_check_results = True for api in target_apis.replace(' ', '').split(','): if 'ENABLED' != api_status.get(api, 'DISABLED'): api_check_results = False print( 'API \"%s\" is not accessible or not enabled. To enable this api go to ' % (api) + 'https://console.cloud.google.com/apis/library/%s?project=%s' % (api, project_id), file=sys.stderr ) config_error_observed = True if 'HALT_ON_ERROR' in execution_mode and config_error_observed: raise RuntimeError( 'There was an error in your environment configuration.\n' + 'Note that resolving such issues generally require a deep knowledge of Kubernetes.\n' + '\n' + 'We highly recommend that you recreate the cluster and check "Allow access ..." \n' + 'checkbox during cluster creation to have the cluster configured automatically.\n' + 'For more information on this and other troubleshooting instructions refer to\n' + 'our troubleshooting guide.\n' + '\n' + 'If you have intentionally modified the cluster configuration, you may\n' + 'bypass this error by removing the execution_mode HALT_ON_ERROR flag.\n' ) return (project_id, bucket) if __name__ == '__main__': import kfp.components as comp comp.func_to_container_op( run_diagnose_me, base_image='google/cloud-sdk:279.0.0', output_component_file='component.yaml', )

true

f70fbd594ac7cc796d70fbf753d9788864624349

135,700

py

Python

test/test_binary_ufuncs.py

ZackPashkin/pytorch

5b1f5c8f17ec4067dc9f9df98bbcc6757ab24444

[ "Intel" ]

1

2022-01-25T15:48:31.000Z

test/test_binary_ufuncs.py

ZackPashkin/pytorch

5b1f5c8f17ec4067dc9f9df98bbcc6757ab24444

[ "Intel" ]

null

test/test_binary_ufuncs.py

ZackPashkin/pytorch

5b1f5c8f17ec4067dc9f9df98bbcc6757ab24444

[ "Intel" ]

null

import torch import numpy as np import itertools from itertools import product import math import random import unittest import warnings import operator from functools import partial from torch._six import inf, nan from torch.testing._internal.common_utils import ( TestCase, iter_indices, TEST_WITH_ASAN, run_tests, torch_to_numpy_dtype_dict, make_tensor, TEST_SCIPY, set_default_dtype) from torch.testing._internal.common_device_type import ( instantiate_device_type_tests, onlyCUDA, onlyCPU, dtypes, dtypesIfCUDA, dtypesIfCPU, deviceCountAtLeast, precisionOverride, onlyOnCPUAndCUDA, skipCUDAIfRocm, skipIf) from torch.testing import all_types_and_complex_and if TEST_SCIPY: import scipy.special # TODO: remove this def _generate_input(shape, dtype, device, with_extremal): if shape == (): x = torch.tensor((), dtype=dtype, device=device) else: if dtype.is_floating_point or dtype.is_complex: # work around torch.randn not being implemented for bfloat16 if dtype == torch.bfloat16: x = torch.randn(*shape, device=device) * random.randint(30, 100) x = x.to(torch.bfloat16) else: x = torch.randn(*shape, dtype=dtype, device=device) * random.randint(30, 100) x[torch.randn(*shape) > 0.5] = 0 if with_extremal and dtype.is_floating_point: # Use extremal values x[torch.randn(*shape) > 0.5] = float('nan') x[torch.randn(*shape) > 0.5] = float('inf') x[torch.randn(*shape) > 0.5] = float('-inf') elif with_extremal and dtype.is_complex: x[torch.randn(*shape) > 0.5] = complex('nan') x[torch.randn(*shape) > 0.5] = complex('inf') x[torch.randn(*shape) > 0.5] = complex('-inf') elif dtype == torch.bool: x = torch.zeros(shape, dtype=dtype, device=device) x[torch.randn(*shape) > 0.5] = True else: x = torch.randint(15, 100, shape, dtype=dtype, device=device) return x # TODO: refactor this out # Converts half/bfloat16 dtype to float when device is cpu def _convert_t(dtype, device): if device == 'cpu' and dtype in {torch.half, torch.bfloat16}: return torch.float return dtype # TODO: revise the tests to use make_tensor in common_utils.py instead # Returns a tensor of the requested shape, dtype, and device # Requesting a half CPU tensor returns a float CPU tensor with # values representable by a half. # Initialization uses randint for non-float types and randn for float types. def _make_tensor(shape, dtype, device, fill_ones=False) -> torch.Tensor: # Returns a tensor filled with ones if fill_ones: return torch.ones(*shape, dtype=_convert_t(dtype, device), device=device) # Returns a tensor with random integer values if not (dtype.is_floating_point or dtype.is_complex): t = torch.randint(0, 10, shape, device=device) if dtype != torch.uint8: t = t - 5 # generate negative values also return t.to(_convert_t(dtype, device)) # Populates the CPU tensor with floats representable as half/bfloat16 if dtype == torch.half and device == 'cpu': return torch.randn(*shape, dtype=torch.float, device=device).half().float() if dtype == torch.bfloat16 and device == 'cpu': return torch.randn(*shape, dtype=torch.float, device=device).bfloat16().float() # Default: returns a tensor with random float values return torch.randn(shape, dtype=dtype, device=device).to(dtype=dtype) # TODO: update to use opinfos consistently class TestBinaryUfuncs(TestCase): def test_add_broadcast_empty(self, device): # empty + empty self.assertRaises(RuntimeError, lambda: torch.randn(5, 0, device=device) + torch.randn(0, 5, device=device)) self.assertEqual(torch.randn(5, 0, device=device), torch.randn(0, device=device) + torch.randn(5, 0, device=device)) self.assertEqual(torch.randn(5, 0, 0, device=device), torch.randn(0, device=device) + torch.randn(5, 0, 1, device=device)) # scalar + empty self.assertEqual(torch.randn(5, 0, 6, device=device), torch.randn((), device=device) + torch.randn(5, 0, 6, device=device)) # non-empty, empty self.assertEqual(torch.randn(0, device=device), torch.randn(0, device=device) + torch.randn(1, device=device)) self.assertEqual(torch.randn(0, 7, 0, 6, 5, 0, 7, device=device), torch.randn(0, 7, 0, 6, 5, 0, 1, device=device) + torch.randn(1, 1, 5, 1, 7, device=device)) self.assertRaises(RuntimeError, lambda: torch.randn(7, 0, device=device) + torch.randn(2, 1, device=device)) def test_addcmul_scalars_as_floats(self, device): # zero-dim variables that don't require grad should bind to scalar arguments x = torch.tensor(2.) y = torch.tensor(3., device=device) # 3 + (3 * 3) * 2 self.assertEqual(y.addcmul(y, y, value=x), 21) x = torch.tensor(2., requires_grad=True) self.assertRaises(Exception, lambda: y.addcmul(y, y, value=x)) # TODO: update to work on CUDA, too @onlyCPU def test_comparison_ops(self, device): x = torch.randn(5, 5) y = torch.randn(5, 5) eq = x == y for idx in iter_indices(x): self.assertEqual(x[idx] == y[idx], eq[idx] == 1) ne = x != y for idx in iter_indices(x): self.assertEqual(x[idx] != y[idx], ne[idx] == 1) lt = x < y for idx in iter_indices(x): self.assertEqual(x[idx] < y[idx], lt[idx] == 1) le = x <= y for idx in iter_indices(x): self.assertEqual(x[idx] <= y[idx], le[idx] == 1) gt = x > y for idx in iter_indices(x): self.assertEqual(x[idx] > y[idx], gt[idx] == 1) ge = x >= y for idx in iter_indices(x): self.assertEqual(x[idx] >= y[idx], ge[idx] == 1) # TODO: update to work on CUDA, too @onlyCPU def test_comparison_ops_must_take_bool_output(self, device): for op in [torch.lt, torch.le, torch.gt, torch.ge, torch.eq, torch.ne, torch.logical_and, torch.logical_or, torch.logical_xor]: self.assertEqual(op(torch.tensor([True]), torch.tensor([False])).dtype, torch.bool) # TODO: update to work on CUDA, too @onlyCPU def test_inplace_comparison_ops_require_inputs_have_same_dtype(self, device): with self.assertRaisesRegex(RuntimeError, 'Expected object of scalar type'): for op in ['lt_', 'le_', 'gt_', 'ge_', 'eq_', 'ne_', 'logical_xor_', 'logical_and_', 'logical_or_']: x = torch.tensor([1], dtype=torch.int) y = torch.tensor([2], dtype=torch.long) in_place_method = getattr(x, op) in_place_method(y) # TODO: update to work on CUDA, too @onlyCPU def test_comparison_ops_check_for_scalar_overflow(self, device): s = 1 << 20 t = torch.tensor([1 << 5], dtype=torch.uint8) with self.assertRaisesRegex(RuntimeError, 'value cannot be converted to type'): self.assertTrue(t < s) with self.assertRaisesRegex(RuntimeError, 'value cannot be converted to type'): self.assertTrue(s < t) with self.assertRaisesRegex(RuntimeError, 'value cannot be converted to type'): self.assertTrue(t <= s) with self.assertRaisesRegex(RuntimeError, 'value cannot be converted to type'): self.assertTrue(s <= t) with self.assertRaisesRegex(RuntimeError, 'value cannot be converted to type'): self.assertTrue(t > s) with self.assertRaisesRegex(RuntimeError, 'value cannot be converted to type'): self.assertTrue(s > t) with self.assertRaisesRegex(RuntimeError, 'value cannot be converted to type'): self.assertTrue(t >= s) with self.assertRaisesRegex(RuntimeError, 'value cannot be converted to type'): self.assertTrue(s >= t) with self.assertRaisesRegex(RuntimeError, 'value cannot be converted to type'): self.assertTrue(t == s) with self.assertRaisesRegex(RuntimeError, 'value cannot be converted to type'): self.assertTrue(s == t) with self.assertRaisesRegex(RuntimeError, 'value cannot be converted to type'): self.assertTrue(t != s) with self.assertRaisesRegex(RuntimeError, 'value cannot be converted to type'): self.assertTrue(s != t) # TODO: update to work on CUDA, too @onlyCPU def test_comparison_ops_check_for_zerodim_tensor_overflow(self, device): t1 = torch.tensor([1 << 5], dtype=torch.uint8) t2 = torch.tensor([1 << 30], dtype=torch.int32) ts1 = torch.tensor(1 << 20, dtype=torch.int32) ts2 = torch.tensor(1 << 40, dtype=torch.int64) with self.assertRaisesRegex(RuntimeError, 'value cannot be converted to type'): self.assertTrue(t1 < ts1) with self.assertRaisesRegex(RuntimeError, 'value cannot be converted to type'): self.assertTrue(ts2 < t2) with self.assertRaisesRegex(RuntimeError, 'value cannot be converted to type'): self.assertTrue(t1 <= ts1) with self.assertRaisesRegex(RuntimeError, 'value cannot be converted to type'): self.assertTrue(ts2 <= t2) with self.assertRaisesRegex(RuntimeError, 'value cannot be converted to type'): self.assertTrue(t1 > ts1) with self.assertRaisesRegex(RuntimeError, 'value cannot be converted to type'): self.assertTrue(ts2 > t2) with self.assertRaisesRegex(RuntimeError, 'value cannot be converted to type'): self.assertTrue(t1 >= ts1) with self.assertRaisesRegex(RuntimeError, 'value cannot be converted to type'): self.assertTrue(ts2 >= t2) with self.assertRaisesRegex(RuntimeError, 'value cannot be converted to type'): self.assertTrue(t1 == ts1) with self.assertRaisesRegex(RuntimeError, 'value cannot be converted to type'): self.assertTrue(ts2 == t2) with self.assertRaisesRegex(RuntimeError, 'value cannot be converted to type'): self.assertTrue(t1 != ts1) with self.assertRaisesRegex(RuntimeError, 'value cannot be converted to type'): self.assertTrue(ts2 != t2) # TODO: update to work on CUDA, too @onlyCPU def test_bitwise_ops(self, device): x = torch.randn(5, 5).gt(0) y = torch.randn(5, 5).gt(0) and_result = x & y for idx in iter_indices(x): if and_result[idx]: self.assertTrue(x[idx] and y[idx]) else: self.assertFalse(x[idx] and y[idx]) or_result = x | y for idx in iter_indices(x): if or_result[idx]: self.assertTrue(x[idx] or y[idx]) else: self.assertFalse(x[idx] or y[idx]) xor_result = x ^ y for idx in iter_indices(x): if xor_result[idx]: self.assertTrue(x[idx] ^ y[idx]) else: self.assertFalse(x[idx] ^ y[idx]) x_clone = x.clone() x_clone &= y self.assertEqual(x_clone, and_result) x_clone = x.clone() x_clone |= y self.assertEqual(x_clone, or_result) x_clone = x.clone() x_clone ^= y self.assertEqual(x_clone, xor_result) def test_inplace_division(self, device): t = torch.rand(5, 5, device=device) id_before = id(t) t /= 2 id_after = id(t) self.assertEqual(id_before, id_after) @dtypes(*torch.testing.get_all_dtypes(include_bool=False, include_complex=False)) def test_div_rounding_modes(self, device, dtype): if dtype.is_floating_point: low, high = -10.0, 10.0 else: info = torch.iinfo(dtype) low, high = info.min, info.max a = make_tensor((100,), device, dtype, low=low, high=high) b = make_tensor((100,), device, dtype, low=low, high=high) # Avoid division by zero so we can test (a / b) * b == a if dtype.is_floating_point: eps = 0.1 b[(-eps < b) & (b < eps)] = eps else: b[b == 0] = 1 if not dtype.is_floating_point: # floor(a / b) * b can be < a, so fixup slightly to avoid underflow a = torch.where(a < 0, a + b, a) d_true = torch.divide(a, b, rounding_mode=None) self.assertTrue(d_true.is_floating_point()) self.assertEqual(d_true * b, a.to(d_true.dtype)) d_floor = torch.divide(a, b, rounding_mode='floor') if dtype not in (torch.bfloat16, torch.half): self.assertEqual(d_floor * b + torch.remainder(a, b), a) else: self.assertEqual(d_floor * b + torch.remainder(a.float(), b.float()), a, exact_dtype=False) d_trunc = torch.divide(a, b, rounding_mode='trunc') rounding_unsupported = ( dtype == torch.half and device != 'cuda' or dtype == torch.bfloat16 and device != 'cpu') d_ref = d_true.float() if rounding_unsupported else d_true self.assertEqual(d_trunc, d_ref.trunc().to(dtype)) @dtypes(torch.bfloat16, torch.half, torch.float32, torch.float64) def test_div_rounding_nonfinite(self, device, dtype): # Compare division of special floating point values against NumPy num = torch.tensor([1.0, -1.0, 0, 0.1, -0.1, np.pi, -np.pi, np.inf, -np.inf, np.nan], dtype=dtype) # Divide by zero is tested seperately denom = num[num != 0] a, b = num[None, :].clone(), denom[:, None].clone() # Compare bfloat16 against NumPy float exact_dtype = dtype != torch.bfloat16 if exact_dtype: an, bn = a.cpu().numpy(), b.cpu().numpy() else: an, bn = a.float().cpu().numpy(), b.float().cpu().numpy() for mode, np_ref in ((None, np.true_divide), ("floor", np.floor_divide)): with np.errstate(all='ignore'): expect = np_ref(an, bn) kwargs = dict(rounding_mode=mode) if mode is not None else {} with set_default_dtype(torch.double): actual = torch.divide(a, b, **kwargs) self.assertEqual(actual, torch.from_numpy(expect), exact_device=False, exact_dtype=exact_dtype) # Compare contiguous (likely vectorized) against non-contiguous (not vectorized) a_noncontig = torch.empty([2 * i for i in a.shape], dtype=dtype, device=device)[::2, ::2] a_noncontig[:] = a b_noncontig = torch.empty([2 * i for i in b.shape], dtype=dtype, device=device)[::2, ::2] b_noncontig[:] = b for rounding_mode in (None, "trunc", "floor"): expect = torch.divide(a_noncontig, b_noncontig, rounding_mode=rounding_mode) actual = torch.divide(a, b, rounding_mode=rounding_mode) self.assertEqual(actual, expect) @dtypes(torch.bfloat16, torch.half, torch.float32, torch.float64) def test_divide_by_zero_rounding(self, device, dtype): a = torch.tensor([1.0, -1.0, 0, 0.1, -0.1, np.pi, -np.pi, np.inf, -np.inf, np.nan], dtype=dtype) exact_dtype = (dtype != torch.bfloat16) if exact_dtype: an = a.cpu().numpy() else: an = a.float().cpu().numpy() zero = torch.zeros_like(a) # NOTE: NumPy's floor_divide rounding changed in 1.20.0 to be consistent with divide expect = np.divide(an, 0) for rounding_mode in (None, 'floor'): # CPU scalar actual = torch.divide(a, 0, rounding_mode=rounding_mode) self.assertEqual(actual, expect, exact_dtype=exact_dtype) # Device tensor actual = torch.divide(a, zero, rounding_mode=rounding_mode) self.assertEqual(actual, expect, exact_dtype=exact_dtype) @dtypes(*torch.testing.get_all_dtypes( include_bool=False, include_complex=False, include_bfloat16=False)) def test_div_rounding_numpy(self, device, dtype): info = (torch.finfo(dtype) if dtype.is_floating_point else torch.iinfo(dtype)) low, high = info.min, info.max # Compare division of random values against NumPy a = make_tensor((4096,), device, dtype, low=low, high=high) b = make_tensor((4096,), device, dtype, low=low, high=high) # Avoid division by zero which raises for integers and, for floats, # NumPy 1.20 changed floor_divide to follow IEEE rules for inf/nan # after dividing by zero. b[b == 0] = 1 # Compare bfloat16 against NumPy float exact_dtype = dtype != torch.bfloat16 if exact_dtype: an, bn = a.cpu().numpy(), b.cpu().numpy() else: an, bn = a.float().cpu().numpy(), b.float().cpu().numpy() for mode, np_ref in ( (None, np.true_divide), ("floor", np.floor_divide), ("trunc", lambda a, b: np.trunc(np.true_divide(a, b)).astype(a.dtype)) ): with np.errstate(all='ignore'): expect = torch.from_numpy(np_ref(an, bn)) kwargs = dict(rounding_mode=mode) if mode is not None else {} # Contiguous (likely vectorized) with set_default_dtype(torch.double): actual = torch.divide(a, b, **kwargs) self.assertEqual(actual, expect, exact_device=False, exact_dtype=exact_dtype) # Non-contiguous (not vectorized) expect = expect[::2] with set_default_dtype(torch.double): actual = torch.divide(a[::2], b[::2], **kwargs) self.assertEqual(actual, expect, exact_device=False, exact_dtype=exact_dtype) # Tests that trying to add, inplace, a CUDA tensor to a CPU tensor # throws the correct error message @onlyCUDA def test_cross_device_inplace_error_msg(self, device): a = torch.tensor(2.) b = torch.tensor(2., device=device) with self.assertRaisesRegex(RuntimeError, "Expected all tensors to be on the same device"): a += b # TODO: refactor this test into a more generic one, it's parked here currently @onlyOnCPUAndCUDA def test_out_resize_warning(self, device): a = torch.tensor((1, 2, 3), device=device, dtype=torch.float32) b = torch.tensor((4, 5, 6), device=device, dtype=torch.float32) unary_inputs = (a,) binary_inputs = (a, b) unary_ops = (torch.ceil, torch.exp) binary_ops = (torch.add, torch.sub) for op in (unary_ops + binary_ops): with warnings.catch_warnings(record=True) as w: warnings.simplefilter("always") inputs = unary_inputs if op in unary_ops else binary_inputs # No warnings op(*inputs, out=torch.empty(3, device=device)) op(*inputs, out=torch.empty(0, device=device)) self.assertEqual(len(w), 0) # Cases that throw warnings op(*inputs, out=torch.empty(2, device=device)) self.assertEqual(len(w), 1) # Verifies that the inplace dunders (like idiv) actually are in place @onlyOnCPUAndCUDA def test_inplace_dunders(self, device): t = torch.randn((1,), device=device) expected = t.data_ptr() t += 1 t -= 1 t *= 1 t /= 1 with self.assertWarnsOnceRegex(UserWarning, 'floor_divide'): t //= 1 t %= 1 self.assertEqual(expected, t.data_ptr()) def check_internal_mem_overlap(self, inplace_op, num_inputs, dtype, device, expected_failure=False): if isinstance(inplace_op, str): inplace_op = getattr(torch.Tensor, inplace_op) input = torch.randn(1, dtype=dtype, device=device).expand(3, 3) inputs = [input] + [torch.randn_like(input) for i in range(num_inputs - 1)] if not expected_failure: with self.assertRaisesRegex(RuntimeError, 'single memory location'): inplace_op(*inputs) else: with self.assertRaises(AssertionError): with self.assertRaisesRegex(RuntimeError, 'single memory location'): inplace_op(*inputs) def unary_check_input_output_mem_overlap(self, data, sz, op, expected_failure=False): def _test(op, output, input): output_exp = torch.empty_like(output) op(input, out=output_exp) self.assertEqual(op(input, out=output), output_exp, msg=op.__name__) # output is identical to input: _test(op, output=data[0:sz], input=data[0:sz]) # output and input are independent: _test(op, output=data[0:sz], input=data[sz:2 * sz]) # output partially overlaps with input: if not expected_failure: with self.assertRaisesRegex(RuntimeError, 'unsupported operation'): _test(op, data[0:sz], data[1:sz + 1]) else: with self.assertRaises(AssertionError): with self.assertRaisesRegex(RuntimeError, 'unsupported operation'): _test(op, data[0:sz], data[1:sz + 1]) def binary_check_input_output_mem_overlap(self, op, device, expected_failure=False): sz = 3 data = torch.randn(2 * sz, device=device) other = torch.randn(sz, device=device) self.unary_check_input_output_mem_overlap( data, sz, lambda input, out: op(other, input, out=out), expected_failure=expected_failure) self.unary_check_input_output_mem_overlap( data, sz, lambda input, out: op(input, other, out=out), expected_failure=expected_failure) @dtypes(torch.double) def test_binary_op_mem_overlap(self, device, dtype): ops = [ ("add", True, True, 'cpu'), ("add", True, True, 'cuda'), ("mul", True, True, 'cpu'), ("mul", True, True, 'cuda'), ("sub", True, True, 'cpu'), ("sub", True, True, 'cuda'), ("div", True, True, 'cpu'), ("div", True, True, 'cuda'), ("pow", True, True, 'cpu'), ("pow", True, True, 'cuda'), ("fmod", True, True, 'cpu'), ("fmod", True, True, 'cuda'), ("atan2", True, True, 'cpu'), ("atan2", True, True, 'cuda'), ("hypot", True, True, 'cpu'), ("hypot", True, True, 'cuda'), ("igamma", True, True, 'cpu'), ("igamma", True, True, 'cuda'), ("igammac", True, True, 'cpu'), ("igammac", True, True, 'cuda'), ("nextafter", True, True, 'cpu'), ("nextafter", True, True, 'cuda'), ("le", True, True, 'cpu'), ("le", True, True, 'cuda'), ("lt", True, True, 'cpu'), ("lt", True, True, 'cuda'), ("ge", True, True, 'cpu'), ("ge", True, True, 'cuda'), ("gt", True, True, 'cpu'), ("gt", True, True, 'cuda'), ("eq", True, True, 'cpu'), ("eq", True, True, 'cuda'), ("ne", True, True, 'cpu'), ("ne", True, True, 'cuda'), ("logical_and", True, True, 'cpu'), ("logical_and", True, True, 'cuda'), ("logical_or", True, True, 'cpu'), ("logical_or", True, True, 'cuda'), ("logical_xor", True, True, 'cpu'), ("logical_xor", True, True, 'cuda'), ] for (fn, has_input_output_mem_overlap_check, has_internal_mem_overlap_check, dev) in ops: if dev != device: continue out_op = getattr(torch, fn) inplace_op = getattr(torch.Tensor, fn + '_') self.check_internal_mem_overlap( inplace_op, 2, dtype, device, expected_failure=not has_internal_mem_overlap_check) self.binary_check_input_output_mem_overlap(out_op, device, expected_failure=not has_input_output_mem_overlap_check) def _do_pow_for_exponents(self, m1, exponents, pow_fn, atol): for num in exponents: if isinstance(num, int) and num < 0 and not m1.is_floating_point() and not m1.is_complex(): with self.assertRaisesRegex(RuntimeError, r'Integers to negative integer powers are not allowed\.'): torch.pow(m1[4], num) else: # base - tensor, exponent - number # contiguous res1 = torch.pow(m1[4], num) res2 = res1.clone().zero_() # `math.pow` has issues with complex exponentiation so we need to resort to normal `pow`. for i in range(res2.size(0)): res2[i] = pow_fn(m1[4][i], num) rtol = 0 if atol is not None else None self.assertEqual(res1, res2, atol=atol, rtol=rtol) # non-contiguous res1 = torch.pow(m1[:, 4], num) res2 = res1.clone().zero_() for i in range(res2.size(0)): res2[i] = pow_fn(m1[i, 4], num) self.assertEqual(res1, res2, atol=atol, rtol=rtol) # scalar ** tensor to enforce correct handling of dtypes for __rpow__(). expected_dtype = torch.result_type(num, m1) res1 = num ** m1[4] res2 = torch.tensor(num, dtype=expected_dtype, device=m1.device) ** m1[4] self.assertEqual(res1, res2) self.assertEqual(res1.dtype, expected_dtype) @dtypes(*all_types_and_complex_and(torch.half, torch.bfloat16)) def test_pow(self, device, dtype): m1 = torch.empty(0, dtype=dtype, device=device) if m1.is_floating_point() or m1.is_complex(): m1 = make_tensor((100, 100), low=0, high=1, dtype=dtype, device=device) + 0.5 else: # math.pow will overflow and throw exceptions for large integers range_high = 4 if dtype in (torch.int8, torch.uint8) else 10 m1 = make_tensor((100, 100), low=1, high=range_high, dtype=dtype, device=device) exponents = [-2.8, -2, -1, -0.5, 0, 0.5, 1, 2, 3, 4, 3.3] complex_exponents = [-2.5j, -1.0j, 0j, 1.0j, 2.5j, 1.0 + 1.0j, -1.0 - 1.5j, 3.3j] if m1.is_complex(): self._do_pow_for_exponents(m1, exponents + complex_exponents, pow, 10e-4) else: self._do_pow_for_exponents(m1, exponents, math.pow, None) self._do_pow_for_exponents(m1, complex_exponents, pow, 10e-4) # base - number, exponent - tensor # contiguous res1 = torch.pow(3, m1[4]) res2 = res1.clone().zero_() for i in range(res2.size(0)): res2[i] = pow(3, m1[4, i]) self.assertEqual(res1, res2) # non-contiguous res1 = torch.pow(3, m1[:, 4]) res2 = res1.clone().zero_() for i in range(res2.size(0)): res2[i] = pow(3, m1[i][4]) self.assertEqual(res1, res2) # TODO: refactor all these tests using opinfos properly def _test_pow(self, base, exponent, np_exponent=None): if np_exponent is None: np_exponent = exponent def to_np(value): if isinstance(value, torch.Tensor): return value.cpu().numpy() return value try: np_res = np.power(to_np(base), to_np(np_exponent)) expected = torch.from_numpy(np_res) if isinstance(np_res, np.ndarray) else torch.tensor(np_res, dtype=base.dtype) except ValueError as e: err_msg = "Integers to negative integer powers are not allowed." self.assertEqual(str(e), err_msg) out = torch.empty_like(base) test_cases = [ lambda: base.pow(exponent), lambda: base.pow_(exponent), lambda: torch.pow(base, exponent), lambda: torch.pow(base, exponent, out=out) ] for test_case in test_cases: self.assertRaisesRegex(RuntimeError, err_msg, test_case) else: if isinstance(base, torch.Tensor): actual = base.pow(exponent) self.assertEqual(actual, expected.to(actual)) actual = base.clone() # When base is a 0-dim cpu tensor and exp is a cuda tensor, we exp `pow` to work but `pow_` to fail, since # `pow` will try to create the output tensor on a cuda device, but `pow_` needs to use the cpu tensor as the output if (isinstance(exponent, torch.Tensor) and base.dim() == 0 and base.device.type == 'cpu' and exponent.device.type == 'cuda'): regex = 'Expected all tensors to be on the same device, but found at least two devices, cuda.* and cpu!' self.assertRaisesRegex(RuntimeError, regex, base.pow_, exponent) elif torch.can_cast(torch.result_type(base, exponent), base.dtype): actual2 = actual.pow_(exponent) self.assertEqual(actual, expected) self.assertEqual(actual2, expected) else: self.assertRaisesRegex(RuntimeError, "Found dtype \\w+ but expected \\w+", lambda: actual.pow_(exponent)) actual = torch.pow(base, exponent) self.assertEqual(actual, expected.to(actual)) actual2 = torch.pow(base, exponent, out=actual) self.assertEqual(actual, expected.to(actual)) self.assertEqual(actual2, expected.to(actual)) # Tests pow() for integral, floating-type tensors, with integral, floating-type # exponents (tensor or scalar), respectively. noncontiguous tensors are also tested. def test_int_and_float_pow(self, device): def _test_int_and_float_pow(dt, low, high, dev): test_cases = ( ((4, 4), 0, (4, 1)), ((3, 1), 4, (3, 1)), ((2,), 4, (1,)), ((1,), 2, ()), ((513, 513), 4, (513,)), ((5, 5, 5), 5, (5,)), ((), 2, ()), ) for base_shape, exp_scalar, exp_shape in test_cases: base_tensor = make_tensor(base_shape, dtype=dt, device=dev, low=low, high=high) # int tensors don't take negative exponents if dt in [torch.uint8, torch.int8, torch.int16, torch.int32, torch.int64]: exp_tensor = make_tensor(exp_shape, dtype=dt, device=dev, low=0, high=high) else: exp_tensor = make_tensor(exp_shape, dtype=dt, device=dev, low=low, high=high) self._test_pow(base_tensor, exp_scalar) self._test_pow(base_tensor, exp_tensor) # test non-contiguous tensors as well base_tensor = make_tensor(base_shape, dtype=dt, device=dev, low=low, high=high, noncontiguous=True) if dt in [torch.uint8, torch.int8, torch.int16, torch.int32, torch.int64]: exp_tensor = make_tensor(exp_shape, dtype=dt, device=dev, low=0, high=high, noncontiguous=True) else: exp_tensor = make_tensor(exp_shape, dtype=dt, device=dev, low=low, high=high, noncontiguous=True) self._test_pow(base_tensor, exp_scalar) self._test_pow(base_tensor, exp_tensor) _test_int_and_float_pow(torch.int8, -2, 2, device) _test_int_and_float_pow(torch.uint8, 0, 3, device) _test_int_and_float_pow(torch.int16, -5, 5, device) _test_int_and_float_pow(torch.int64, -10, 10, device) _test_int_and_float_pow(torch.int32, -10, 10, device) _test_int_and_float_pow(torch.float16, 0., 5., device) _test_int_and_float_pow(torch.float32, 0., 10., device) _test_int_and_float_pow(torch.float64, 0., 10., device) # pow's output would have some NaNs as well _test_int_and_float_pow(torch.float32, -10., 10., device) _test_int_and_float_pow(torch.float64, -10., 10., device) # Tests that a Runtime error occurs when a base tensor cannot be resized # by pow's inplace variant due to PyTorch's broadcasting semantics. def test_pow_inplace_resizing_exception(self, device): test_cases = ( ((), (3,)), ((2,), (2, 1)), ((2, 1), (2, 2)), ((2, 2), (2, 1, 1)), ) test_inputs = list((make_tensor(base_size, dtype=torch.float64, device=device, high=10., low=0.), make_tensor(exp_size, dtype=torch.float64, device=device, high=10., low=0.)) for base_size, exp_size in test_cases) for base, exponent in test_inputs: regex = "doesn't match the broadcast shape" self.assertRaisesRegex(RuntimeError, regex, base.pow_, exponent) def test_int_tensor_pow_neg_ints(self, device): ints = [torch.iinfo(torch.int32).min, -3, -2, -1, 0, 1, 2, 3, torch.iinfo(torch.int32).max] neg_ints = [torch.iinfo(torch.int32).min, -3, -2, -1] tensor = torch.tensor(ints, dtype=torch.int32, device=device) for pow in neg_ints: self._test_pow(tensor, pow) def test_long_tensor_pow_floats(self, device): ints = [0, 1, 23, 4567] floats = [0.0, 1 / 3, 1 / 2, 1.0, 3 / 2, 2.0] tensor = torch.tensor(ints, dtype=torch.int64, device=device) for pow in floats: self._test_pow(tensor, pow) @dtypes(*[torch.float32, torch.float64]) def test_float_scalar_pow_float_tensor(self, device, dtype): floats = [2.0, -3 / 2, -1.0, -1 / 2, -1 / 3, 0.0, 1 / 3, 1 / 2, 1.0, 3 / 2, 2.0] exponent_shapes = ( (1,), (2, 2), (2, 1), (2, 2, 2), ) tensors = list(make_tensor(shape, dtype=dtype, device=device, low=0) for shape in exponent_shapes) floats_tensor = torch.tensor(floats, dtype=dtype, device=device) for base in floats: self._test_pow(base, floats_tensor) for tensor in tensors: self._test_pow(base, tensor) @onlyCUDA def test_cuda_tensor_pow_scalar_tensor(self, device): cuda_tensors = [torch.randn((3, 3), device=device), torch.tensor(3.0, device=device)] scalar_tensors = [torch.tensor(5.0, device='cpu'), torch.tensor(-3), torch.tensor(1)] for base, exp in product(cuda_tensors, scalar_tensors): self._test_pow(base, exp) @onlyCUDA def test_cpu_tensor_pow_cuda_scalar_tensor(self, device): cuda_tensors = [torch.tensor(5.0, device='cuda'), torch.tensor(-3, device='cuda')] for exp in cuda_tensors: base = torch.randn((3, 3), device='cpu') regex = 'Expected all tensors to be on the same device, but found at least two devices, cuda.* and cpu!' self.assertRaisesRegex(RuntimeError, regex, torch.pow, base, exp) for exp in cuda_tensors: # Binary ops with a cpu + cuda tensor are allowed if the cpu tensor has 0 dimension base = torch.tensor(3.0, device='cpu') self._test_pow(base, exp) @onlyCUDA @dtypes(torch.complex64, torch.complex128) def test_pow_cuda_complex_extremal_failing(self, device, dtype): t = torch.tensor(complex(-1., float('inf')), dtype=dtype, device=device) with self.assertRaises(AssertionError): cuda_out = t.pow(2) cpu_out = t.cpu().pow(2) self.assertEqual(cpu_out, cuda_out) @onlyOnCPUAndCUDA @dtypes(*(torch.testing.get_all_dtypes(include_bool=False, include_bfloat16=False))) def test_complex_scalar_pow_tensor(self, device, dtype): complexes = [0.5j, 1. + 1.j, -1.5j, 2.2 - 1.6j, 1 + 0j] first_exp = make_tensor((100,), device, dtype, low=-2, high=2) second_exp = make_tensor((100,), device, dtype, low=-2, high=2, noncontiguous=True) first_exp[0] = first_exp[10] = first_exp[20] = 0 second_exp[0] = second_exp[10] = second_exp[20] = 0 for base in complexes: self._test_pow(base, first_exp) self._test_pow(base, second_exp) @onlyOnCPUAndCUDA def test_pow_scalar_type_promotion(self, device): # Test against a scalar and non-scalar input inputs = [17, [17]] for input in inputs: # We expect the computation to be performed in uint8 (overflowing to 0), and then cast to int64 input_tensor_uint8 = torch.tensor(input, dtype=torch.uint8, device=device) out_uint8_computation = torch.pow(2, input_tensor_uint8, out=torch.tensor(0, dtype=torch.int64, device=device)) # Computation should run in int64, and not overflow input_tensor_int64 = torch.tensor(input, dtype=torch.int64, device=device) out_int64_computation = torch.pow(2, input_tensor_int64, out=torch.tensor(0, dtype=torch.int64, device=device)) self.assertNotEqual(out_uint8_computation, out_int64_computation) self.assertEqual(out_uint8_computation.to(dtype=torch.uint8), out_int64_computation.to(dtype=torch.uint8)) def test_tensor_pow_tensor(self, dev): def rotate(l, n): return l[-n:] + l[:-n] def test_tensor_pow_tensor(values, torch_type, numpy_type): vals_tensor = torch.tensor(values, dtype=torch_type, device=dev) for i in range(len(values)): pows = rotate(values, i) pows_tensor = torch.tensor(pows, dtype=torch_type, device=dev) self._test_pow(vals_tensor, pows_tensor) ints = [0, 1, 2, 3] test_tensor_pow_tensor(ints, torch.uint8, np.uint8) test_tensor_pow_tensor(ints, torch.int8, np.int8) test_tensor_pow_tensor(ints, torch.int16, np.int16) test_tensor_pow_tensor(ints, torch.int32, np.int32) test_tensor_pow_tensor(ints, torch.int64, np.int64) floats = [-3.0, -2.0, -1.0, -1 / 2, -1 / 3, 0.0, 1 / 3, 1 / 2, 1.0, 2.0, 3.0] test_tensor_pow_tensor(floats, torch.float16, np.float16) test_tensor_pow_tensor(floats, torch.float32, np.float32) test_tensor_pow_tensor(floats, torch.float64, np.float64) def test_logical_xor_with_nontrivial_alignment(self, device): # test tensor that is not aligned to multiple of 16 bytes size = 128 a = (torch.randn(size, device=device) > 0) b = (torch.randn(size, device=device) > 0) c = (torch.randn(size, device=device) > 0) non_trivial_alignment = [1, 2, 4, 8, 15] for i in non_trivial_alignment: for j in non_trivial_alignment: for k in non_trivial_alignment: a_ = a[i: 100 + i] b_ = b[j: 100 + j] c_ = c[k: 100 + k] torch.logical_xor(a_, b_, out=c_) for x, y, z in zip(a_.tolist(), b_.tolist(), c_.tolist()): self.assertEqual(x ^ y, z) @dtypes(torch.float) def test_add_with_tail(self, device, dtype): # test tensor where there is a tail which is not a multiple # of GPU warp size for tail_size in [1, 63, 67, 130]: size = 4096 + tail_size a = torch.randn(size, device=device, dtype=dtype) b = torch.randn(size, device=device, dtype=dtype) c = a + b for x, y, z in zip(a.tolist(), b.tolist(), c.tolist()): self.assertEqual(x + y, z) # Tests that CUDA tensors on different devices cannot be used in the same # binary operation, and that CUDA "scalars" cannot be used in the same # binary operation as non-scalar CPU tensors. @deviceCountAtLeast(2) @onlyCUDA def test_cross_device_binary_ops(self, devices): vals = (1., (2.,)) cpu_tensor = torch.randn(2, 2) def do_test(op, a, b): with self.assertRaisesRegex(RuntimeError, "Expected all tensors.+"): op(a, b) with self.assertRaisesRegex(RuntimeError, "Expected all tensors.+"): op(b, a) with self.assertRaisesRegex(RuntimeError, "Expected all tensors.+"): op(a, cpu_tensor) with self.assertRaisesRegex(RuntimeError, "Expected all tensors.+"): op(cpu_tensor, a) for op in (operator.add, torch.add, operator.sub, torch.sub, operator.mul, torch.mul, operator.truediv, torch.true_divide, operator.floordiv, torch.floor_divide): for a, b in product(vals, vals): a = torch.tensor(a, device=devices[0]) b = torch.tensor(b, device=devices[1]) do_test(op, a, b) # This test ensures that a scalar Tensor can be safely used # in a binary operation in conjunction with a Tensor on all # available CUDA devices @deviceCountAtLeast(2) @onlyCUDA def test_binary_op_scalar_device_unspecified(self, devices): scalar_val = torch.tensor(1.) for default_device in devices: with torch.cuda.device(default_device): for device in devices: device_obj = torch.device(device) x = torch.rand(3, device=device) y0 = x * scalar_val self.assertEqual(y0.device, device_obj) y1 = scalar_val * x self.assertEqual(y1.device, device_obj) self.assertEqual(y0, y1) def test_div_and_floordiv_vs_python(self, device): # Tests torch division ops which can handle both arguments being # scalars. # NOTE: torch.floor_divide currently truncates instead of flooring. # the quotient. See https://github.com/pytorch/pytorch/issues/43874. def _scalar_helper(python_op, torch_op): for a, b in product(range(-10, 10), range(-10, 10)): for op in (lambda x: x * .5, lambda x: math.floor(x)): a = op(a) b = op(b) # Skips zero divisors if b == 0: continue expected = python_op(a, b) for op in (operator.truediv, torch.true_divide): actual_scalar = torch_op(a, b) a_t = torch.tensor(a, device=device) b_t = torch.tensor(b, device=device) actual_tensor = torch_op(a_t, b_t) actual_first_tensor = torch_op(a_t, b) actual_second_tensor = torch_op(a, b_t) self.assertEqual(actual_scalar, expected_div) self.assertEqual(actual_tensor.item(), expected_div) self.assertEqual(actual_first_tensor, actual_tensor) self.assertEqual(actual_second_tensor, actual_tensor) _scalar_helper(operator.truediv, operator.truediv) _scalar_helper(operator.truediv, torch.true_divide) with self.assertWarnsOnceRegex(UserWarning, 'floor_divide'): _scalar_helper(lambda a, b: math.trunc(a / b), operator.floordiv) _scalar_helper(lambda a, b: math.trunc(a / b), torch.floor_divide) # NOTE: torch.floor_divide currently truncates instead of flooring. # See https://github.com/pytorch/pytorch/issues/43874. @onlyOnCPUAndCUDA def test_div_and_floordiv_script_vs_python(self, device): # Creates jitted functions of two tensors def _wrapped_div(a, b): return a / b def _wrapped_floordiv(a, b): return a // b scripted_div = torch.jit.script(_wrapped_div) scripted_floordiv = torch.jit.script(_wrapped_floordiv) for a, b in product(range(-10, 10), range(-10, 10)): for op in (lambda x: x * .5, lambda x: math.floor(x)): a = op(a) b = op(b) # Skips zero divisors if b == 0: continue expected_div = a / b expected_truncdiv = math.trunc(a / b) a_t = torch.tensor(a, device=device) b_t = torch.tensor(b, device=device) self.assertEqual(scripted_div(a_t, b_t), expected_div) with self.assertWarnsOnceRegex(UserWarning, 'floor_divide'): self.assertEqual(scripted_floordiv(a_t, b_t), expected_truncdiv) # Creates jitted functions of one tensor def _wrapped_div_scalar(a): return a / 5 # NOTE: the JIT implements division as torch.reciprocal(a) * 5 def _wrapped_rdiv_scalar(a): return 5 / a def _wrapped_floordiv_scalar(a): return a // 5 # NOTE: this fails if the input is not an integer tensor # See https://github.com/pytorch/pytorch/issues/45199 def _wrapped_rfloordiv_scalar(a): return 5 // a scripted_div_scalar = torch.jit.script(_wrapped_div_scalar) scripted_rdiv_scalar = torch.jit.script(_wrapped_rdiv_scalar) scripted_floordiv_scalar = torch.jit.script(_wrapped_floordiv_scalar) scripted_rfloordiv_scalar = torch.jit.script(_wrapped_rfloordiv_scalar) for a in range(-10, 10): for op in (lambda x: x * .5, lambda x: math.floor(x)): a = op(a) a_t = torch.tensor(a, device=device) self.assertEqual(a / 5, scripted_div_scalar(a_t)) with self.assertWarnsOnceRegex(UserWarning, 'floor_divide'): self.assertEqual(math.trunc(a / 5), scripted_floordiv_scalar(a_t)) # Skips zero divisors if a == 0: continue self.assertEqual(5 / a, scripted_rdiv_scalar(a_t)) # Handles Issue 45199 (see comment above) if a_t.is_floating_point(): with self.assertRaises(RuntimeError): scripted_rfloordiv_scalar(a_t) else: # This should emit a UserWarning, why doesn't it? # See issue gh-52387 self.assertEqual(5 // a, scripted_rfloordiv_scalar(a_t)) # NOTE: torch.floor_divide currently truncates instead of flooring # the quotient. See https://github.com/pytorch/pytorch/issues/43874. @onlyOnCPUAndCUDA def test_idiv_and_ifloordiv_vs_python(self, device): def _wrapped_idiv_tensor(a, b): a /= b return a def _wrapped_idiv_scalar(a): a /= 5 return a def _wrapped_true_divide__tensor(a, b): a.true_divide_(b) return a def _wrapped_true_divide__scalar(a): a.true_divide_(5) return a def _wrapped_floor_divide__tensor(a, b): a.floor_divide_(b) return a def _wrapped_floor_divide__scalar(a): a.floor_divide_(5) return a # The following functions are unsupported by the JIT def _wrapped_ifloordiv_tensor(a, b): a //= b return a def _wrapped_ifloordiv_scalar(a): a //= 5 return a with self.assertRaises(torch.jit.frontend.NotSupportedError): scripted_ifloordiv_tensor = torch.jit.script(_wrapped_ifloordiv_tensor) with self.assertRaises(torch.jit.frontend.NotSupportedError): scripted_ifloordiv_scalar = torch.jit.script(_wrapped_ifloordiv_scalar) scripted_idiv_tensor = torch.jit.script(_wrapped_idiv_tensor) scripted_idiv_scalar = torch.jit.script(_wrapped_idiv_scalar) scripted_true_divide__tensor = torch.jit.script(_wrapped_true_divide__tensor) scripted_true_divide__scalar = torch.jit.script(_wrapped_true_divide__scalar) scripted_floor_divide__tensor = torch.jit.script(_wrapped_floor_divide__tensor) scripted_floor_divide__scalar = torch.jit.script(_wrapped_floor_divide__scalar) for a, b in product(range(-10, 10), range(-10, 10)): for op in (lambda x: x * .5, lambda x: math.floor(x)): a = op(a) b = op(b) # Skips zero divisors if b == 0: continue expected_idiv = a / b expected_ifloordiv = a // b expected_itruncdiv = math.trunc(a / b) a_t = torch.tensor(a, device=device) b_t = torch.tensor(b, device=device) if a_t.is_floating_point(): tmp0 = a_t.clone() tmp0 /= b tmp1 = a_t.clone() tmp1 /= b_t self.assertEqual(tmp0.item(), expected_idiv) self.assertEqual(tmp1.item(), expected_idiv) self.assertEqual(scripted_true_divide__tensor(a_t.clone(), b_t).item(), expected_idiv) self.assertEqual(scripted_true_divide__scalar(a_t.clone()).item(), a / 5) else: tmp = a_t.clone() with self.assertRaises(RuntimeError): tmp /= b with self.assertRaises(RuntimeError): tmp /= b_t with self.assertRaises(RuntimeError): scripted_true_divide__tensor(tmp, b_t) with self.assertRaises(RuntimeError): scripted_true_divide__scalar(tmp) if not a_t.is_floating_point() and b_t.is_floating_point(): # Inplace modification fails because a float tensor is required # if the divisor is a float tensor with self.assertRaises(RuntimeError), self.assertWarnsOnceRegex(UserWarning, "floor_divide"): a_t.clone().floor_divide_(b_t) with self.assertRaises(RuntimeError), self.assertWarnsOnceRegex(UserWarning, "floor_divide"): scripted_floor_divide_tensor(a_t.clone(), b_t) tmp = a_t.clone() with self.assertRaises(RuntimeError), self.assertWarnsOnceRegex(UserWarning, "floor_divide"): tmp //= b_t else: # Inplace modification is OK when both or neither tensor is # a float tensor with self.assertWarnsOnceRegex(UserWarning, "floor_divide"): self.assertEqual(a_t.clone().floor_divide_(b_t).item(), expected_itruncdiv) self.assertEqual(scripted_floor_divide__tensor(a_t.clone(), b_t).item(), expected_itruncdiv) tmp = a_t.clone() with self.assertWarnsOnceRegex(UserWarning, "floor_divide"): tmp //= b_t self.assertEqual(tmp.item(), expected_itruncdiv) with self.assertWarnsOnceRegex(UserWarning, "floor_divide"): self.assertEqual(scripted_floor_divide__scalar(a_t), math.trunc(a / 5)) # Tests binary op equivalence with Python builtin ops # Also tests that reverse operations are equivalent to forward ops # NOTE: division ops are tested separately above def test_binary_ops_with_scalars(self, device): for ops in ((operator.add, torch.add), (operator.sub, torch.sub), (operator.mul, torch.mul), (operator.truediv, torch.div)): python_op, torch_op = ops for a, b in product(range(-10, 10), range(-10, 10)): for op in (lambda x: x * .5, lambda x: math.floor(x)): a = op(a) b = op(b) # Skips zero divisors if b == 0 or a == 0: continue a_tensor = torch.tensor(a, device=device) b_tensor = torch.tensor(b, device=device) a_tensor_cpu = a_tensor.cpu() b_tensor_cpu = b_tensor.cpu() vals = (a, b, a_tensor, b_tensor, a_tensor_cpu, b_tensor_cpu) for args in product(vals, vals): first, second = args first_scalar = first if not isinstance(first, torch.Tensor) else first.item() second_scalar = second if not isinstance(second, torch.Tensor) else second.item() expected = python_op(first_scalar, second_scalar) self.assertEqual(expected, python_op(first, second)) self.assertEqual(expected, torch_op(first, second)) @dtypes(*product(torch.testing.get_all_dtypes(include_complex=False), torch.testing.get_all_dtypes(include_complex=False))) def test_maximum_minimum_type_promotion(self, device, dtypes): a = torch.tensor((0, 1), device=device, dtype=dtypes[0]) b = torch.tensor((1, 0), device=device, dtype=dtypes[1]) for op in (torch.maximum, torch.max, torch.fmax, torch.minimum, torch.min, torch.fmin): result = op(a, b) self.assertEqual(result.dtype, torch.result_type(a, b)) @dtypes(*(torch.testing.get_all_int_dtypes() + [torch.bool])) def test_maximum_minimum_int_and_bool(self, device, dtype): ops = ((torch.maximum, torch.max, np.maximum), (torch.minimum, torch.min, np.minimum), (torch.fmax, None, np.fmax), (torch.fmin, None, np.fmin)) rng = np.random.default_rng() a_np = np.array(rng.integers(-100, 100, size=10), dtype=torch_to_numpy_dtype_dict[dtype]) b_np = np.array(rng.integers(-100, 100, size=10), dtype=torch_to_numpy_dtype_dict[dtype]) for torch_op, alias, numpy_op in ops: a_tensor = torch.from_numpy(a_np).to(device=device, dtype=dtype) b_tensor = torch.from_numpy(b_np).to(device=device, dtype=dtype) tensor_result = torch_op(a_tensor, b_tensor) out = torch.empty_like(a_tensor) torch_op(a_tensor, b_tensor, out=out) numpy_result = numpy_op(a_np, b_np) if alias is not None: alias_result = alias(a_tensor, b_tensor) self.assertEqual(alias_result, tensor_result) self.assertEqual(tensor_result, numpy_result) self.assertEqual(out, numpy_result) @precisionOverride({torch.bfloat16: 1e-2}) @dtypes(*(torch.testing.get_all_fp_dtypes())) def test_maximum_minimum_float(self, device, dtype): ops = ((torch.maximum, torch.max, np.maximum), (torch.minimum, torch.min, np.minimum), (torch.fmax, None, np.fmax), (torch.fmin, None, np.fmin)) if dtype == torch.bfloat16: a_np = np.random.randn(10).astype(np.float64) b_np = np.random.randn(10).astype(np.float64) else: a_np = np.random.randn(10).astype(torch_to_numpy_dtype_dict[dtype]) b_np = np.random.randn(10).astype(torch_to_numpy_dtype_dict[dtype]) for torch_op, alias, numpy_op in ops: numpy_result = numpy_op(a_np, b_np) a_tensor = torch.from_numpy(a_np).to(device=device, dtype=dtype) b_tensor = torch.from_numpy(b_np).to(device=device, dtype=dtype) tensor_result = torch_op(a_tensor, b_tensor) out = torch.empty_like(a_tensor) torch_op(a_tensor, b_tensor, out=out) if alias is not None: alias_result = alias(a_tensor, b_tensor) self.assertEqual(alias_result, tensor_result, exact_dtype=False) self.assertEqual(tensor_result, numpy_result, exact_dtype=False) self.assertEqual(out, numpy_result, exact_dtype=False) @dtypes(*(torch.testing.get_all_fp_dtypes())) def test_maximum_minimum_float_nan_and_inf(self, device, dtype): # np.maximum and np.minimum functions compare input arrays element-wisely. # if one of the elements being compared is a NaN, then that element is returned. ops = ((torch.maximum, torch.max, np.maximum), (torch.minimum, torch.min, np.minimum), (torch.fmax, None, np.fmax), (torch.fmin, None, np.fmin)) a_vals = (float('inf'), -float('inf'), float('nan'), float('inf'), float('nan'), float('nan'), 1, float('nan')) b_vals = (-float('inf'), float('inf'), float('inf'), float('nan'), float('nan'), 0, float('nan'), -5) if dtype == torch.bfloat16: a_np = np.array(a_vals, dtype=np.float64) b_np = np.array(b_vals, dtype=np.float64) else: a_np = np.array(a_vals, dtype=torch_to_numpy_dtype_dict[dtype]) b_np = np.array(b_vals, dtype=torch_to_numpy_dtype_dict[dtype]) for torch_op, alias, numpy_op in ops: numpy_result = numpy_op(a_np, b_np) a_tensor = torch.from_numpy(a_np).to(device=device, dtype=dtype) b_tensor = torch.from_numpy(b_np).to(device=device, dtype=dtype) tensor_result = torch_op(a_tensor, b_tensor) out = torch.empty_like(a_tensor) torch_op(a_tensor, b_tensor, out=out) if alias is not None: alias_result = alias(a_tensor, b_tensor) self.assertEqual(alias_result, tensor_result) if dtype == torch.bfloat16: self.assertEqual(tensor_result, numpy_result, exact_dtype=False) self.assertEqual(out, numpy_result, exact_dtype=False) else: self.assertEqual(tensor_result, numpy_result) self.assertEqual(out, numpy_result) @dtypes(*product(torch.testing.get_all_complex_dtypes(), torch.testing.get_all_dtypes())) def test_maximum_minimum_complex(self, device, dtypes): for torch_op in (torch.maximum, torch.minimum, torch.max, torch.min, torch.fmax, torch.fmin): with self.assertRaisesRegex(RuntimeError, '.+not implemented for.+'): torch_op(torch.ones(1, device=device, dtype=dtypes[0]), torch.ones(1, device=device, dtype=dtypes[1])) with self.assertRaisesRegex(RuntimeError, '.+not implemented for.+'): torch_op(torch.ones(1, device=device, dtype=dtypes[1]), torch.ones(1, device=device, dtype=dtypes[0])) @onlyCUDA def test_maximum_minimum_cross_device(self, device): a = torch.tensor((1, 2, -1)) b = torch.tensor((3, 0, 4), device=device) ops = (torch.maximum, torch.minimum) for torch_op in ops: with self.assertRaisesRegex(RuntimeError, "Expected all tensors to be on the same device"): torch_op(a, b) with self.assertRaisesRegex(RuntimeError, "Expected all tensors to be on the same device"): torch_op(b, a) # test cuda tensor and cpu scalar ops = ((torch.maximum, np.maximum), (torch.minimum, np.minimum)) a_np = np.array(1) b_np = np.array([3, 0, 4]) for torch_op, numpy_op in ops: a_tensor = torch.from_numpy(a_np) b_tensor = torch.from_numpy(b_np).to(device=device) tensor_result_1 = torch_op(a_tensor, b_tensor) numpy_result_1 = numpy_op(a_np, b_np) tensor_result_2 = torch_op(b_tensor, a_tensor) numpy_result_2 = numpy_op(b_np, a_np) self.assertEqual(tensor_result_1, numpy_result_1) self.assertEqual(tensor_result_2, numpy_result_2) # TODO: tests like this should be generic @dtypesIfCUDA(torch.half, torch.float, torch.double) @dtypes(torch.float, torch.double) def test_mul_intertype_scalar(self, device, dtype): x = torch.tensor(1.5, dtype=dtype, device=device) y = torch.tensor(3, dtype=torch.int32, device=device) self.assertEqual(x * y, 4.5) self.assertEqual(y * x, 4.5) with self.assertRaisesRegex(RuntimeError, "can't be cast to the desired output type"): y *= x x *= y self.assertEqual(x, 4.5) @onlyCPU @dtypes(*torch.testing.get_all_dtypes()) def test_sub(self, device, dtype): m1 = torch.tensor([2.34, 4.44], dtype=dtype, device=device) m2 = torch.tensor([1.23, 2.33], dtype=dtype, device=device) if dtype == torch.bool: self.assertRaises(RuntimeError, lambda: m1 - m2) elif (dtype == torch.bfloat16 or dtype == torch.half): # bfloat16 has a lower precision so we have to have a separate check for it self.assertEqual(m1 - m2, torch.tensor([1.11, 2.11], dtype=dtype), atol=0.01, rtol=0) else: self.assertEqual(m1 - m2, torch.tensor([1.11, 2.11], dtype=dtype)) # TODO: what is this test testing? @onlyCPU @dtypes(torch.float) def test_csub(self, device, dtype): # with a tensor a = torch.randn(100, 90, dtype=dtype, device=device) b = a.clone().normal_() res_add = torch.add(a, b, alpha=-1) res_csub = a.clone() res_csub.sub_(b) self.assertEqual(res_add, res_csub) # with a scalar a = torch.randn(100, 100, dtype=dtype, device=device) scalar = 123.5 res_add = torch.add(a, -scalar) res_csub = a.clone() res_csub.sub_(scalar) self.assertEqual(res_add, res_csub) # TODO: reconcile with minimum/maximum tests @dtypesIfCUDA(torch.half, torch.float, torch.double) @dtypes(torch.float, torch.double) def test_min_max_binary_op_nan(self, device, dtype): a = torch.rand(1000, dtype=dtype, device=device) b = torch.rand(1000, dtype=dtype, device=device) # 0:250: a -- nan, b -- not nan a[:250] = float('nan') # 250:500: a -- not nan, b -- nan b[250:500] = float('nan') # 500:750: a and b both nan a[500:750] = float('nan') b[500:750] = float('nan') # 750:1000: neither nan ma = torch.max(a, b) mi = torch.min(a, b) for i in range(750): self.assertTrue(torch.isnan(ma[i]), "max(a, b): {}, a: {}, b: {}".format(ma[i], a[i], b[i])) self.assertTrue(torch.isnan(mi[i]), "min(a, b): {}, a: {}, b: {}".format(mi[i], a[i], b[i])) for i in range(750, 1000): self.assertFalse(torch.isnan(ma[i]), "max(a, b): {}, a: {}, b: {}".format(ma[i], a[i], b[i])) self.assertFalse(torch.isnan(mi[i]), "min(a, b): {}, a: {}, b: {}".format(mi[i], a[i], b[i])) @dtypes(*product(torch.testing.get_all_dtypes(include_complex=False), torch.testing.get_all_dtypes(include_complex=False))) def test_copysign(self, device, dtypes): def _test_copysign_numpy(a, b): torch_result = torch.copysign(a, b) if a.dtype == torch.bfloat16: np_a = a.to(torch.float).cpu().numpy() else: np_a = a.cpu().numpy() if b.dtype == torch.bfloat16: np_b = b.to(torch.float).cpu().numpy() else: np_b = b.cpu().numpy() expected = torch.from_numpy(np.copysign(np_a, np_b)) # To handle inconsistencies of type promotion between PyTorch and Numpy # Applied for both arguments having integral precision and bfloat16 types = [torch.bool, torch.bfloat16] + torch.testing.get_all_int_dtypes() if a.dtype in types or b.dtype in types: promoted_type = torch.promote_types(torch_result.dtype, expected.dtype) torch_result = torch_result.to(promoted_type) expected = expected.to(promoted_type) # Verify Value self.assertEqual(torch_result, expected) # Verify Sign # Use double copysign to verify the correctnes of 0.0 and -0.0, since # it always True for self.assertEqual(0.0 == -0.0). So, we use 1 as the # magnitude to verify the sign between torch and numpy results, elementwise. # Special case: NaN conversions between FP32 and FP16 is not bitwise # equivalent to pass this assertion. if a.dtype != torch.float16 and b.dtype != torch.float16: self.assertEqual(torch.copysign(torch.tensor(1.0), torch_result), torch.copysign(torch.tensor(1.0), expected)) # Compare Result with NumPy # Type promotion a = make_tensor((10, 10), device=device, dtype=dtypes[0], low=-9, high=9) b = make_tensor((10, 10), device=device, dtype=dtypes[1], low=-9, high=9) _test_copysign_numpy(a, b) # Broadcast a = make_tensor((10, 1, 10), device=device, dtype=dtypes[0], low=-9, high=9) b = make_tensor((10, 10), device=device, dtype=dtypes[1], low=-9, high=9) _test_copysign_numpy(a, b) a = make_tensor((10, 10), device=device, dtype=dtypes[0], low=-9, high=9) b = make_tensor((10, 1, 10), device=device, dtype=dtypes[1], low=-9, high=9) _test_copysign_numpy(a, b) # 0.0/-0.0/inf/-inf/nan cases = [0.0, -0.0, float('inf'), float('-inf'), float('nan')] # torch.bfloat16 can not hold '-nan' # torch.half can not hold '-nan' on CUDA types = [torch.float32, torch.float64] if device == 'cpu': types.append(torch.float16) if dtypes[0] in types: b = make_tensor((10, 10), device=device, dtype=dtypes[1], low=-9, high=9) for case in cases: _test_copysign_numpy(torch.tensor([case], device=device, dtype=dtypes[0]), b) if dtypes[1] in torch.testing.get_all_fp_dtypes(): a = make_tensor((10, 10), device=device, dtype=dtypes[0], low=-9, high=9) for case in cases: _test_copysign_numpy(a, torch.tensor([case], device=device, dtype=dtypes[1])) @dtypes(torch.bfloat16, torch.float) def test_div(self, device, dtype): for op, method, inplace in ((torch.div, torch.Tensor.div, torch.Tensor.div_), (torch.true_divide, torch.Tensor.true_divide, torch.Tensor.true_divide_)): m1 = torch.randn(10, 10, dtype=torch.float, device=device).to(dtype=dtype) res1 = m1.clone() inplace(res1[:, 3], 2) res2 = m1.clone() for i in range(m1.size(0)): res2[i, 3] = res2[i, 3] / 2 self.assertEqual(res1, res2) if dtype == torch.bfloat16: a1 = torch.tensor([4.2, 6.2], dtype=dtype, device=device) a2 = torch.tensor([2., 2.], dtype=dtype, device=device) self.assertEqual(op(a1, a2), torch.tensor([2.1, 3.1], dtype=dtype, device=device), atol=0.01, rtol=0) self.assertEqual(method(a1, a2), op(a1, a2)) @dtypes(torch.bfloat16, torch.float) def test_true_divide_out(self, device, dtype): a1 = torch.tensor([4.2, 6.2], dtype=dtype, device=device) a2 = torch.tensor([2., 2.], dtype=dtype, device=device) res = torch.empty_like(a1) self.assertEqual(torch.true_divide(a1, a2, out=res), torch.tensor([2.1, 3.1], dtype=dtype, device=device), atol=0.01, rtol=0) @onlyCUDA @dtypes(torch.half) def test_divmul_scalar(self, device, dtype): x = torch.tensor(100., device=device, dtype=dtype) x_ref = x.float() scale = 1e5 res = x.div(scale) expected = x_ref.div(scale) self.assertEqual(res, expected.to(dtype), atol=0., rtol=0.) x = torch.tensor(1e-5, device=device, dtype=dtype) x_ref = x.float() res = x.mul(scale) expected = x_ref.mul(scale) self.assertEqual(res, expected.to(dtype), atol=0., rtol=0.) res = scale * x self.assertEqual(res, expected.to(dtype), atol=0., rtol=0.) @dtypesIfCUDA(*set(torch.testing.get_all_math_dtypes('cuda')) - {torch.complex64, torch.complex128}) @dtypes(*set(torch.testing.get_all_math_dtypes('cpu')) - {torch.complex64, torch.complex128}) def test_floor_divide_tensor(self, device, dtype): x = torch.randn(10, device=device).mul(30).to(dtype) y = torch.arange(1, 11, dtype=dtype, device=device) with self.assertWarnsOnceRegex(UserWarning, "floor_divide"): z = x // y z_alt = torch.trunc(x.double() / y.double()).to(dtype) self.assertEqual(z.dtype, x.dtype) self.assertEqual(z, z_alt) @dtypesIfCUDA(*set(torch.testing.get_all_math_dtypes('cuda')) - {torch.complex64, torch.complex128}) @dtypes(*set(torch.testing.get_all_math_dtypes('cpu')) - {torch.complex64, torch.complex128}) def test_floor_divide_scalar(self, device, dtype): x = torch.randn(100, device=device).mul(10).to(dtype) with self.assertWarnsOnceRegex(UserWarning, "floor_divide"): z = x // 3 z_alt = torch.tensor([math.trunc(v.item() / 3.) for v in x], dtype=x.dtype, device=device) self.assertEqual(z.dtype, x.dtype) self.assertEqual(z, z_alt) # Note: this tests fails on XLA @onlyOnCPUAndCUDA @dtypes(torch.float, torch.long) def test_floor_divide_out(self, device, dtype): x = torch.randn(10, device=device).mul(10).to(dtype) y = torch.arange(1, 11, dtype=dtype, device=device) o = torch.empty(10, dtype=dtype, device=device) with self.assertWarnsOnceRegex(UserWarning, "floor_divide"): torch.floor_divide(x, y, out=o) self.assertEqual(o, x // y) # Tests scalar with out torch.floor_divide(x, 2, out=o) self.assertEqual(o, x // 2) if dtype == torch.int: o = torch.empty(10, dtype=torch.float, device=device) torch.floor_divide(x, y, out=o) self.assertEqual(o, torch.floor_divide(x.float(), y.float())) @onlyCPU @dtypes(*torch.testing.get_all_math_dtypes('cpu')) def test_rdiv(self, device, dtype): if dtype is torch.float16: return elif dtype.is_complex: x = torch.rand(100, dtype=dtype, device=device).add(1).mul(4) else: x = torch.rand(100, device=device).add(1).mul(4).to(dtype) y = 30 / x z = torch.tensor([30 / v.item() for v in x], device=device) self.assertEqual(y, z, exact_dtype=False) @dtypes(*torch.testing.get_all_fp_dtypes(include_bfloat16=False)) def test_fmod_remainder_by_zero_float(self, device, dtype): fn_list = (torch.fmod, torch.remainder) for fn in fn_list: # check floating-point tensor fmod/remainder to zero is nan on both CPU and GPU x = make_tensor((10, 10), device=device, dtype=dtype, low=-9, high=9) zero = torch.zeros_like(x) self.assertTrue(torch.all(fn(x, 0.0).isnan())) self.assertTrue(torch.all(fn(x, zero).isnan())) @onlyOnCPUAndCUDA # Check Issue https://github.com/pytorch/pytorch/issues/48130 @skipCUDAIfRocm # Error happens on both ROCM and XLA @dtypes(*torch.testing.get_all_int_dtypes()) def test_fmod_remainder_by_zero_integral(self, device, dtype): fn_list = (torch.fmod, torch.remainder) for fn in fn_list: # check integral tensor fmod/remainder to zero x = make_tensor((10, 10), device=device, dtype=dtype, low=-9, high=9) zero = torch.zeros_like(x) # RuntimeError on CPU if self.device_type == 'cpu': with self.assertRaisesRegex(RuntimeError, "ZeroDivisionError"): fn(x, zero) # Different value for different dtype on CUDA: # Due to it's an undefined behavior, CUDA returns a pattern of all 1s # for integral dividend (other than int64) divided by zero. For int64, # CUDA returns all 1s for negative dividend, half 1s for positive dividend. # uint8: 0xff -> 255 # int32: 0xffffffff -> -1 else: if dtype == torch.int64: self.assertEqual(fn(x, zero) == 4294967295, x >= 0) self.assertEqual(fn(x, zero) == -1, x < 0) else: value = 255 if dtype == torch.uint8 else -1 self.assertTrue(torch.all(fn(x, zero) == value)) @dtypes(*torch.testing.get_all_dtypes(include_bfloat16=False, include_bool=False, include_complex=False)) def test_fmod_remainder(self, device, dtype): # Use numpy as reference def _helper(x, mod, fns_list): for fn, inplace_fn, ref_fn in fns_list: np_x = x.cpu().numpy() if torch.is_tensor(x) else x np_mod = mod.cpu().numpy() if torch.is_tensor(mod) else mod exp = ref_fn(np_x, np_mod) exp = torch.from_numpy(exp) res = fn(x, mod) self.assertEqual(res, exp, exact_dtype=False) if torch.is_tensor(x): # out out = torch.empty(0, device=device, dtype=res.dtype) fn(x, mod, out=out) self.assertEqual(out, exp, exact_dtype=False) self.assertEqual(out.size(), torch.Size([10, 10])) # in-place (Type cast runtime error) try: inplace_fn(x, mod) self.assertEqual(x, exp, exact_dtype=False) except RuntimeError as e: self.assertRegex(str(e), "result type (Half|Float|Double) " "can't be cast to the desired output " "type (Byte|Char|Short|Int|Long)") x = make_tensor((10, 10), device=device, dtype=dtype, low=-9, high=9) # mod with same dtype as x mod = make_tensor((10, 10), device=device, dtype=dtype, low=-9, high=9) # Exclude 0 mod[mod == 0] = 1 # Mods: Integer, Float, Tensor, Non-contiguous Tensor mods = [3, 2.3, mod, mod.t()] # mod with floating-point dtype if dtype in torch.testing.get_all_int_dtypes(): mod_float = make_tensor((10, 10), device=device, dtype=torch.float, low=-9, high=9) mod[mod == 0] = 1 mods.append(mod_float) for dividend, mod in product([x, x.t()], mods): _helper(dividend, mod, ((torch.fmod, torch.Tensor.fmod_, np.fmod), (torch.remainder, torch.Tensor.remainder_, np.remainder),)) # Tests for torch.remainder(scalar, tensor) for dividend, mod in product([5, 3.14], mods): if torch.is_tensor(mod): _helper(dividend, mod, ((torch.remainder, torch.Tensor.remainder_, np.remainder),)) @dtypes(torch.float, torch.double) def test_remainder_fmod_large_dividend(self, device, dtype): alarge = 1e9 pi = 3.14159265358979 for avalue in [alarge, -alarge]: for bvalue in [pi, -pi]: a = torch.tensor([avalue], dtype=dtype, device=device) b = torch.tensor([bvalue], dtype=dtype, device=device) c = torch.remainder(a, b) d = torch.fmod(a, b) self.assertTrue((b[0] > 0) == (c[0] > 0)) # remainder has same sign as divisor self.assertTrue((a[0] > 0) == (d[0] > 0)) # fmod has same sign as dividend self.assertTrue(abs(c[0]) < abs(b[0])) # remainder is within range of divisor self.assertTrue(abs(d[0]) < abs(b[0])) # fmod is within range of divisor if ((a[0] > 0) == (b[0] > 0)): self.assertTrue(c[0] == d[0]) # remainder is same as fmod else: self.assertTrue(abs(c[0] - d[0]) == abs(b[0])) # differ by one divisor @dtypesIfCPU(torch.bfloat16, torch.float32, torch.float64) @dtypes(torch.float32, torch.float64) def test_hypot(self, device, dtype): inputs = [ (torch.randn(10, device=device).to(dtype), torch.randn(10, device=device).to(dtype)), (torch.randn((3, 3, 3), device=device).to(dtype), torch.randn((3, 3, 3), device=device).to(dtype)), (torch.randn((10, 1), device=device).to(dtype), torch.randn((10, 1), device=device).to(dtype).transpose(0, 1)), (torch.randint(100, (10, ), device=device, dtype=torch.long), torch.randn(10, device=device).to(dtype)) ] for input in inputs: actual = torch.hypot(input[0], input[1]) if dtype == torch.bfloat16: expected = torch.sqrt(input[0] * input[0] + input[1] * input[1]) else: expected = np.hypot(input[0].cpu().numpy(), input[1].cpu().numpy()) self.assertEqual(actual, expected, exact_dtype=False) @onlyOnCPUAndCUDA @dtypes(torch.uint8, torch.int8, torch.int16, torch.int32, torch.int64) def test_gcd(self, device, dtype): # Tests gcd(0, 0), gcd(0, a) cases t1 = torch.tensor([0, 10, 0], dtype=dtype, device=device) t2 = torch.tensor([0, 0, 10], dtype=dtype, device=device) actual = torch.gcd(t1, t2) expected = np.gcd([0, 10, 0], [0, 0, 10]) self.assertEqual(actual, expected, exact_dtype=False) if dtype == torch.uint8: # Test unsigned integers with potential sign issues (i.e., uint8 with value >= 128) a = torch.tensor([190, 210], device=device, dtype=dtype) b = torch.tensor([190, 220], device=device, dtype=dtype) actual = torch.gcd(a, b) expected = torch.tensor([190, 10], device=device, dtype=dtype) self.assertEqual(actual, expected) else: # Compares with NumPy a = torch.randint(-20, 20, (1024,), device=device, dtype=dtype) b = torch.randint(-20, 20, (1024,), device=device, dtype=dtype) actual = torch.gcd(a, b) expected = np.gcd(a.cpu().numpy(), b.cpu().numpy()) self.assertEqual(actual, expected) @onlyOnCPUAndCUDA @dtypes(torch.int16, torch.int32, torch.int64) def test_lcm(self, device, dtype): # Tests lcm(0, 0), lcm(0, a) cases t1 = torch.tensor([0, 10, 0], dtype=dtype, device=device) t2 = torch.tensor([0, 0, 10], dtype=dtype, device=device) actual = torch.lcm(t1, t2) expected = np.lcm([0, 10, 0], [0, 0, 10]) self.assertEqual(actual, expected, exact_dtype=False) # Compares with NumPy a = torch.randint(-20, 20, (1024,), device=device, dtype=dtype) b = torch.randint(-20, 20, (1024,), device=device, dtype=dtype) actual = torch.lcm(a, b) expected = np.lcm(a.cpu().numpy(), b.cpu().numpy()) self.assertEqual(actual, expected, exact_dtype=False) @onlyOnCPUAndCUDA @dtypes(torch.float32, torch.float64) def test_nextafter(self, device, dtype): # Test special cases t1 = torch.tensor([0, 0, 10], device=device, dtype=dtype) t2 = torch.tensor([inf, -inf, 10], device=device, dtype=dtype) actual = torch.nextafter(t1, t2) expected = np.nextafter(t1.cpu().numpy(), t2.cpu().numpy()) self.assertEqual(actual, expected, atol=0, rtol=0) actual = torch.nextafter(t2, t1) expected = np.nextafter(t2.cpu().numpy(), t1.cpu().numpy()) self.assertEqual(actual, expected, atol=0, rtol=0) t1 = torch.tensor([0, nan], device=device, dtype=dtype) t2 = torch.tensor([nan, 0], device=device, dtype=dtype) self.assertTrue(torch.nextafter(t1, t2).isnan().all()) a = torch.randn(100, device=device, dtype=dtype) b = torch.randn(100, device=device, dtype=dtype) actual = torch.nextafter(a, b) expected = np.nextafter(a.cpu().numpy(), b.cpu().numpy()) self.assertEqual(actual, expected, atol=0, rtol=0) def _test_cop(self, torchfn, mathfn, dtype, device): def reference_implementation(res2): for i, j in iter_indices(sm1): idx1d = i * sm1.size(0) + j res2[i, j] = mathfn(sm1[i, j], sm2[idx1d]) return res2 # contiguous m1 = torch.randn(10, 10, 10, dtype=dtype, device=device) m2 = torch.randn(10, 10 * 10, dtype=dtype, device=device) sm1 = m1[4] sm2 = m2[4] res1 = torchfn(sm1, sm2.view(10, 10)) res2 = reference_implementation(res1.clone()) self.assertEqual(res1, res2) # non-contiguous m1 = torch.randn(10, 10, 10, dtype=dtype, device=device) m2 = torch.randn(10 * 10, 10 * 10, dtype=dtype, device=device) sm1 = m1[:, 4] sm2 = m2[:, 4] # view as sm1.size() sm2.set_(sm2.storage(), sm2.storage_offset(), sm1.size(), (sm2.stride()[0] * 10, sm2.stride()[0])) res1 = torchfn(sm1, sm2) # reference_implementation assumes 1-d sm2 sm2.set_(sm2.storage(), sm2.storage_offset(), m2[:, 4].size(), m2[:, 4].stride()) res2 = reference_implementation(res1.clone()) self.assertEqual(res1, res2) @onlyCPU @dtypes(torch.float) def test_cdiv(self, device, dtype): self._test_cop(torch.div, lambda x, y: x / y, dtype, device) @onlyCPU @dtypes(torch.float) def test_cremainder(self, device, dtype): self._test_cop(torch.remainder, lambda x, y: x % y, dtype, device) @onlyCPU @dtypes(torch.float) def test_cmul(self, device, dtype): self._test_cop(torch.mul, lambda x, y: x * y, dtype, device) @onlyCPU @dtypes(torch.float) def test_cpow(self, device, dtype): self._test_cop(torch.pow, lambda x, y: nan if x < 0 else math.pow(x, y), dtype, device) @onlyCPU @dtypes(torch.uint8, torch.int8, torch.int16, torch.int32, torch.int64) def test_floor_divide_zero(self, device, dtype): a = torch.tensor([0, 1], dtype=dtype, device=device) b = torch.tensor([0, 1], dtype=dtype, device=device) with self.assertRaisesRegex(RuntimeError, 'ZeroDivisionError'): with self.assertWarnsOnceRegex(UserWarning, "floor_divide"): a // b @unittest.skipIf(TEST_WITH_ASAN, "Integer overflows are not allowed under ASAN") @dtypes(*torch.testing.get_all_dtypes()) def test_muldiv_scalar(self, device, dtype): x = make_tensor((10, 3), device, dtype, low=None, high=None) s = make_tensor((1,), 'cpu', dtype, low=None, high=None).item() y = torch.full_like(x, s) self.assertEqual(x * s, x * y) self.assertEqual(s * x, y * x) self.assertEqual(x / s, x / y) self.assertEqual(s / x, y / x) @dtypes(*tuple(itertools.combinations_with_replacement(torch.testing.get_all_dtypes(), 2))) def test_comparison_ops_type_promotion_and_broadcasting(self, device, dtypes): # issue #42660 # testing all combinations of broadcasting and type promotion # with a range of dtypes and input shapes, and with extremal values def compare_with_numpy_bin_op(torch_fn, np_fn, x, y, out=None): # working around the fact that numpy doesn't support bfloat16 # by letting numpy treat them as float32's x_np = x if x.dtype != torch.bfloat16 else x.to(torch.float32) y_np = y.cpu().numpy() if y.dtype != torch.bfloat16 else y.to(torch.float32).cpu().numpy() self.compare_with_numpy(lambda inp: torch_fn(inp, y, out=out) if out else torch_fn(inp, y), lambda inp: np_fn(inp, y_np, out=out) if out else np_fn(inp, y_np), x_np) complex_op_denylist = [torch.lt, torch.le, torch.gt, torch.ge] # complex not supported input_sizes = [ (1,), (10,), (10, 1), (1, 10), (4, 10), (64, 10), (12, 3)] op_pairs = [(torch.lt, np.less), (torch.le, np.less_equal), (torch.gt, np.greater), (torch.ge, np.greater_equal), (torch.eq, np.equal), (torch.ne, np.not_equal), (torch.logical_and, np.logical_and), (torch.logical_or, np.logical_or), (torch.logical_xor, np.logical_xor)] for size1 in input_sizes: size2 = (2,) + size1 # perform broadcasting for with_extremal in [False, True]: a = _generate_input(size1, dtypes[0], device, with_extremal) b = _generate_input(size2, dtypes[1], device, with_extremal) for torch_op, numpy_op in op_pairs: if (dtypes[0].is_complex or dtypes[1].is_complex) and torch_op in complex_op_denylist: continue # functional version of op compare_with_numpy_bin_op(torch_op, numpy_op, a, b) # functional comparison ops always return bool tensors self.assertEqual(torch_op(a, b).dtype, torch.bool) # out version of op out = torch.zeros(1, dtype=torch.complex128) # all casts to complex128 are safe compare_with_numpy_bin_op(torch_op, numpy_op, a, b, out=out) @onlyOnCPUAndCUDA @dtypes(torch.int8, torch.int16, torch.int32, torch.int64) def test_signed_shift(self, device, dtype): "Ensure that signed integer bit shifting works as expected." a = torch.tensor([-10, 10], device=device, dtype=dtype) # [11...1110110, 1010] expected_l = torch.tensor([-40, 40], device=device, dtype=dtype) # [11...11011000, 101000] self.assertEqual(a << 2, expected_l) self.compare_with_numpy(lambda x: x << 2, lambda x: np.left_shift(x, 2), a) expected_r = torch.tensor([-5, 5], device=device, dtype=dtype) # [1111...111011, 101] self.assertEqual(a >> 1, expected_r) self.compare_with_numpy(lambda x: x >> 1, lambda x: np.right_shift(x, 1), a) def test_bitwise_and(self, device): for dtype in (torch.uint8, torch.int8, torch.int16, torch.int32, torch.int64): a = torch.tensor([1, -2, 3], dtype=dtype, device=device) b = torch.tensor([2, 1, 3], dtype=dtype, device=device) expected_res = torch.tensor([0, 0, 3], dtype=dtype, device=device) b_scalar = 2 expected_res_scalar = torch.tensor([0, 2, 2], dtype=dtype, device=device) # standard version self.assertEqual(torch.bitwise_and(a, b), expected_res) self.assertEqual(torch.bitwise_and(a, b_scalar), expected_res_scalar) # out c = torch.empty(0, dtype=dtype, device=device) torch.bitwise_and(a, b, out=c) self.assertEqual(c, expected_res) torch.bitwise_and(a, b_scalar, out=c) self.assertEqual(c, expected_res_scalar) # in-place a1 = a.clone() a1.bitwise_and_(b) self.assertEqual(a1, expected_res) a.bitwise_and_(b_scalar) self.assertEqual(a, expected_res_scalar) self.assertEqual(torch.tensor([False, True, False], device=device), torch.bitwise_and(torch.tensor([True, True, False], device=device), torch.tensor([False, True, False], device=device))) def test_bitwise_or(self, device): for dtype in (torch.uint8, torch.int8, torch.int16, torch.int32, torch.int64): a = torch.tensor([1, -2, 3], dtype=dtype, device=device) b = torch.tensor([2, 1, 3], dtype=dtype, device=device) expected_res = torch.tensor([3, -1, 3], dtype=dtype, device=device) b_scalar = 2 expected_res_scalar = torch.tensor([3, -2, 3], dtype=dtype, device=device) # standard version self.assertEqual(torch.bitwise_or(a, b), expected_res) self.assertEqual(torch.bitwise_or(a, b_scalar), expected_res_scalar) # out c = torch.empty(0, dtype=dtype, device=device) torch.bitwise_or(a, b, out=c) self.assertEqual(c, expected_res) torch.bitwise_or(a, b_scalar, out=c) self.assertEqual(c, expected_res_scalar) # in-place a1 = a.clone() a1.bitwise_or_(b) self.assertEqual(a1, expected_res) a.bitwise_or_(b_scalar) self.assertEqual(a, expected_res_scalar) self.assertEqual(torch.tensor([True, True, False], device=device), torch.bitwise_or(torch.tensor([True, True, False], device=device), torch.tensor([False, True, False], device=device))) def test_bitwise_xor(self, device): for dtype in (torch.uint8, torch.int8, torch.int16, torch.int32, torch.int64): a = torch.tensor([1, -2, 3], dtype=dtype, device=device) b = torch.tensor([2, 1, 3], dtype=dtype, device=device) expected_res = torch.tensor([3, -1, 0], dtype=dtype, device=device) b_scalar = 2 expected_res_scalar = torch.tensor([3, -4, 1], dtype=dtype, device=device) # standard version self.assertEqual(torch.bitwise_xor(a, b), expected_res) self.assertEqual(torch.bitwise_xor(a, b_scalar), expected_res_scalar) # out c = torch.empty(0, dtype=dtype, device=device) torch.bitwise_xor(a, b, out=c) self.assertEqual(c, expected_res) torch.bitwise_xor(a, b_scalar, out=c) self.assertEqual(c, expected_res_scalar) # in-place a1 = a.clone() a1.bitwise_xor_(b) self.assertEqual(a1, expected_res) a.bitwise_xor_(b_scalar) self.assertEqual(a, expected_res_scalar) self.assertEqual(torch.tensor([True, False, False], device=device), torch.bitwise_xor(torch.tensor([True, True, False], device=device), torch.tensor([False, True, False], device=device))) @dtypes(torch.uint8, torch.int8, torch.int16, torch.int32, torch.int64) def test_bitwise_shift(self, device, dtype): ops = [ (torch.bitwise_left_shift, np.left_shift), (operator.lshift, operator.lshift), (torch.bitwise_right_shift, np.right_shift), (operator.rshift, operator.rshift), ] for torch_op, numpy_op in ops: a = torch.tensor([19, -20, -21, 22], dtype=dtype, device=device) b = torch.tensor([2, 1, 3, 1], dtype=dtype, device=device) a_np = a.cpu().numpy() b_np = b.cpu().numpy() # Tensor x Tensor self.assertEqual(torch_op(a, b), torch.tensor(numpy_op(a_np, b_np), device=device)) # Tensor x int scalar self.assertEqual(torch_op(a, 2), torch.tensor(numpy_op(a_np, 2), device=device)) def test_bitwise_shift_float(self, device): ops = [ (torch.bitwise_left_shift, lambda x, y: x * 2. ** y), (operator.lshift, lambda x, y: x * 2. ** y), (torch.bitwise_right_shift, lambda x, y: x / 2. ** y), (operator.rshift, lambda x, y: x / 2. ** y), ] for torch_op, expected_op in ops: # int tensor x float a = torch.tensor([19, -20, -21, 22], dtype=torch.int64, device=device) self.assertEqual(torch_op(a, 1.8), torch.floor(expected_op(a, 1)).to(a.dtype)) # float tensor x int scalar a = torch.tensor([19.1, -20.2, -21.3, 22.4], dtype=torch.float32, device=device) self.assertEqual(torch_op(a, 2), expected_op(a, 2)) # float tensor x float scalar a = torch.tensor([19.1, -20.2, -21.3, 22.4], dtype=torch.float32, device=device) self.assertEqual(torch_op(a, 2.2), expected_op(a, 2.2)) @onlyOnCPUAndCUDA @dtypes(*list(product(torch.testing.get_all_dtypes(include_complex=False), torch.testing.get_all_dtypes(include_complex=False)))) def test_heaviside(self, device, dtypes): input_dtype = dtypes[0] values_dtype = dtypes[1] rng = np.random.default_rng() input = np.array(rng.integers(-10, 10, size=10), dtype=torch_to_numpy_dtype_dict[input_dtype if (input_dtype != torch.bfloat16) else torch.float64]) input[0] = input[3] = input[7] = 0 values = np.array(rng.integers(-10, 10, size=10), dtype=torch_to_numpy_dtype_dict[values_dtype if (values_dtype != torch.bfloat16) else torch.float64]) np_result = torch.from_numpy(np.heaviside(input, values)).to(device=device, dtype=input_dtype) input = torch.from_numpy(input).to(device=device, dtype=input_dtype) values = torch.from_numpy(values).to(device=device, dtype=values_dtype) out = torch.empty_like(input) if input_dtype == values_dtype: torch_result = torch.heaviside(input, values) self.assertEqual(np_result, torch_result) torch_result = input.heaviside(values) self.assertEqual(np_result, torch_result) torch.heaviside(input, values, out=out) self.assertEqual(np_result, out) input.heaviside_(values) self.assertEqual(np_result, input) else: with self.assertRaisesRegex(RuntimeError, 'heaviside is not yet implemented for tensors with different dtypes.'): torch.heaviside(input, values) with self.assertRaisesRegex(RuntimeError, 'heaviside is not yet implemented for tensors with different dtypes.'): input.heaviside(values) with self.assertRaisesRegex(RuntimeError, 'heaviside is not yet implemented for tensors with different dtypes.'): torch.heaviside(input, values, out=out) with self.assertRaisesRegex(RuntimeError, 'heaviside is not yet implemented for tensors with different dtypes.'): input.heaviside_(values) @onlyCUDA def test_heaviside_cross_device(self, device): x = torch.tensor([-9, 5, 0, 6, -2, 2], device=device) y = torch.tensor(0) result = torch.heaviside(x, y) expect = torch.tensor([0, 1, 0, 1, 0, 1], device=device) self.assertEqual(result, expect) result = torch.heaviside(y, x) expect = torch.tensor([-9, 5, 0, 6, -2, 2], device=device) self.assertEqual(result, expect) x = torch.tensor([-9, 5, 0, 6, -2, 2]) y = torch.tensor(0, device=device) with self.assertRaisesRegex(RuntimeError, 'Expected all tensors to be on the same device'): torch.heaviside(x, y) with self.assertRaisesRegex(RuntimeError, 'Expected all tensors to be on the same device'): torch.heaviside(y, x) @dtypes(*list(product(torch.testing.get_all_complex_dtypes(), torch.testing.get_all_complex_dtypes()))) def test_heaviside_complex(self, device, dtypes): input_dtype = dtypes[0] values_dtype = dtypes[1] data = (complex(0, -6), complex(-1, 3), complex(1, 1)) input = torch.tensor(data, device=device, dtype=input_dtype) values = torch.tensor(data, device=device, dtype=values_dtype) out = torch.empty_like(input) real = input.real with self.assertRaisesRegex(RuntimeError, 'heaviside is not yet implemented for complex tensors.'): torch.heaviside(input, real) with self.assertRaisesRegex(RuntimeError, 'heaviside is not yet implemented for complex tensors.'): real.heaviside(values) with self.assertRaisesRegex(RuntimeError, 'heaviside is not yet implemented for complex tensors.'): input.heaviside_(values) with self.assertRaisesRegex(RuntimeError, 'heaviside is not yet implemented for complex tensors.'): torch.heaviside(real, real, out=out) def _test_logical(self, device, dtypes, op, a_, b_, expected_res_): expected_res = torch.tensor(expected_res_, dtype=dtypes[0], device=device) a = torch.tensor(a_, dtype=dtypes[0], device=device) b = torch.tensor(b_, dtype=dtypes[1], device=device) # new tensor self.assertEqual(expected_res.bool(), getattr(a, op)(b)) # out c = torch.empty(0, dtype=torch.bool, device=device) getattr(torch, op)(a, b, out=c) self.assertEqual(expected_res.bool(), c) # in-place # TODO: remove when different dtypes as operands are supported if dtypes[0] != dtypes[1]: with self.assertRaises(RuntimeError): getattr(a, op + '_')(b) return getattr(a, op + '_')(b) self.assertEqual(expected_res, a) @dtypes(*product(torch.testing.get_all_dtypes(), torch.testing.get_all_dtypes())) def test_logical_xor(self, device, dtypes): self._test_logical(device, dtypes, 'logical_xor', [10, 0, 1, 0], [1, 0, 0, 10], [0, 0, 1, 1]) @dtypes(*product(torch.testing.get_all_dtypes(), torch.testing.get_all_dtypes())) def test_logical_and(self, device, dtypes): self._test_logical(device, dtypes, 'logical_and', [10, 0, 1, 0], [1, 0, 0, 10], [1, 0, 0, 0]) @dtypes(*product(torch.testing.get_all_dtypes(), torch.testing.get_all_dtypes())) def test_logical_or(self, device, dtypes): self._test_logical(device, dtypes, 'logical_or', [10, 0, 1, 0], [1, 0, 0, 10], [1, 0, 1, 1]) def test_remainder_overflow(self, device): # Check Integer Overflows x = torch.tensor(23500, dtype=torch.int64, device=device) q = 392486996410368 self.assertEqual(x % q, x) self.assertEqual(-x % q, q - x) self.assertEqual(x % -q, x - q) self.assertEqual(-x % -q, -x) def test_rpow(self, device): m = torch.randn(10, 10, device=device) self.assertEqual(torch.pow(2, m), 2**m) # test with scalar m = torch.randn(1, device=device).squeeze() assert m.dim() == 0, "m is intentionally a scalar" self.assertEqual(torch.pow(2, m), 2**m) @onlyCPU def test_ldexp(self, device): # random values mantissas = torch.randn(64, device=device) exponents = torch.randint(-31, 31, (64,), device=device, dtype=torch.int32) # basic test np_outcome = np.ldexp(mantissas.numpy(), exponents.numpy()) pt_outcome_1 = torch.ldexp(mantissas, exponents) pt_outcome_2 = mantissas.ldexp(exponents) self.assertEqual(np_outcome, pt_outcome_1) self.assertEqual(np_outcome, pt_outcome_2) mantissas.ldexp_(exponents) self.assertEqual(np_outcome, mantissas) # test bounds mantissas = torch.tensor([float('inf'), float('-inf'), float('inf'), float('nan')], device=device) exponents = torch.randint(0, 31, (4,), device=device, dtype=torch.int32) np_outcome = np.ldexp(mantissas.numpy(), exponents.numpy()) pt_outcome = torch.ldexp(mantissas, exponents) self.assertEqual(np_outcome, pt_outcome) @dtypes(torch.float, torch.double, torch.cfloat, torch.cdouble) def test_lerp(self, device, dtype): start_end_weight_shapes = [(), (5,), (5, 5)] for shapes in product(start_end_weight_shapes, start_end_weight_shapes, start_end_weight_shapes): start = torch.randn(shapes[0], device=device, dtype=dtype) end = torch.randn(shapes[1], device=device, dtype=dtype) # Tensor weights weights = [torch.randn(shapes[2], device=device, dtype=dtype), random.random()] if dtype.is_complex: weights += [complex(0, 1), complex(0.4, 1.2)] for weight in weights: actual = torch.lerp(start, end, weight) actual_method = start.lerp(end, weight) self.assertEqual(actual, actual_method) actual_out = torch.tensor(1., dtype=dtype, device=device) torch.lerp(start, end, weight, out=actual_out) self.assertEqual(actual, actual_out) expected = start + weight * (end - start) self.assertEqual(expected, actual) def _test_logaddexp(self, device, dtype, base2): if base2: ref_func = np.logaddexp2 our_func = torch.logaddexp2 else: ref_func = np.logaddexp our_func = torch.logaddexp def _test_helper(a, b): ref = ref_func(a.cpu().numpy(), b.cpu().numpy()) v = our_func(a, b) self.assertEqual(ref, v) # simple test a = torch.randn(64, 2, dtype=dtype, device=device) - 0.5 b = torch.randn(64, 2, dtype=dtype, device=device) - 0.5 _test_helper(a, b) _test_helper(a[:3], b[:3]) # large value test for numerical stability a *= 10000 b *= 10000 _test_helper(a, b) _test_helper(a[:3], b[:3]) a = torch.tensor([float('inf'), float('-inf'), float('inf'), float("nan")], dtype=dtype, device=device) b = torch.tensor([float('inf'), float('-inf'), float('-inf'), float("nan")], dtype=dtype, device=device) _test_helper(a, b) @dtypes(torch.float32, torch.float64) def test_logaddexp(self, device, dtype): self._test_logaddexp(device, dtype, base2=False) @dtypes(torch.float32, torch.float64) def test_logaddexp2(self, device, dtype): self._test_logaddexp(device, dtype, base2=True) def test_add(self, device): dtypes = [torch.float, torch.double] + torch.testing.get_all_complex_dtypes() for dtype in dtypes: # [res] torch.add([res,] tensor1, tensor2) m1 = torch.randn(100, 100, dtype=dtype, device=device) v1 = torch.randn(100, dtype=dtype, device=device) # contiguous res1 = torch.add(m1[4], v1) res2 = res1.clone().zero_() for i in range(m1.size(1)): res2[i] = m1[4, i] + v1[i] self.assertEqual(res1, res2) m1 = torch.randn(100, 100, device=device) v1 = torch.randn(100, device=device) # non-contiguous res1 = torch.add(m1[:, 4], v1) res2 = res1.clone().zero_() for i in range(m1.size(0)): res2[i] = m1[i, 4] + v1[i] self.assertEqual(res1, res2) # [res] torch.add([res,] tensor, value) m1 = torch.randn(10, 10, device=device) # contiguous res1 = m1.clone() res1[3].add_(2) res2 = m1.clone() for i in range(m1.size(1)): res2[3, i] = res2[3, i] + 2 self.assertEqual(res1, res2) # non-contiguous m1 = torch.randn(10, 10, device=device) res1 = m1.clone() res1[:, 3].add_(2) res2 = m1.clone() for i in range(m1.size(0)): res2[i, 3] = res2[i, 3] + 2 self.assertEqual(res1, res2) # inter-type m1 = torch.randn(10, 10, dtype=dtype, device=device) self.assertEqual(m1 + 3, m1 + torch.tensor(3)) self.assertEqual(3 + m1, torch.tensor(3) + m1) # contiguous + non-contiguous m1 = torch.randn(10, 10, dtype=dtype, device=device) m2 = torch.randn(10, 10, dtype=dtype, device=device).t() res = m1 + m2 self.assertTrue(res.is_contiguous()) self.assertEqual(res, m1 + m2.contiguous()) # 1d + empty m1 = torch.tensor([1.0], dtype=dtype, device=device) m2 = torch.tensor([], dtype=dtype, device=device) self.assertEqual(m1 + m2, []) # inter-type unint8 one = torch.tensor(1, dtype=torch.uint8, device=device) self.assertEqual(torch.add(one, 1), 2) self.assertEqual(torch.add(one, 1).dtype, torch.uint8) # bool m1 = torch.tensor([True, False, False, True, False, False], dtype=torch.bool, device=device) m2 = torch.tensor([True, True, False, False, False, True], dtype=torch.bool, device=device) expected = torch.tensor([True, True, False, True, False, True], dtype=torch.bool, device=device) self.assertEqual(m1 + m2, expected) # fused multiply add a = torch.zeros(2, 3, dtype=torch.bool, device=device) res = torch.add(a, a, alpha=0) expected = torch.zeros(2, 3, device=device).bool() self.assertEqual(res, expected) # bfloat16 m1 = torch.tensor([1., 2.], dtype=torch.bfloat16) m2 = torch.tensor([3., 4.], dtype=torch.bfloat16) self.assertEqual(m1 + m2, torch.tensor([4., 6.], dtype=torch.bfloat16)) # different alpha types m1 = torch.tensor([2 + 3j, 4 + 5j], dtype=torch.complex64, device=device) m2 = torch.tensor([4 + 5j, 2 + 3j], dtype=torch.complex64, device=device) # add complex numbers with float alpha res = torch.add(m1, m2, alpha=0.1) expected = torch.tensor([2.4000 + 3.5000j, 4.2000 + 5.3000j], dtype=torch.complex64, device=device) self.assertEqual(res, expected) # add complex numbers with complex alpha res = torch.add(m1, m2, alpha=complex(0.1, 0.2)) expected = torch.tensor([1.4000 + 4.3000j, 3.6000 + 5.7000j], dtype=torch.complex64, device=device) self.assertEqual(res, expected) # add complex numbers with integer alpha res = torch.add(m1, m2, alpha=2) expected = torch.tensor([10. + 13.j, 8. + 11.j], dtype=torch.complex64, device=device) self.assertEqual(res, expected) # mismatched alpha m1 = torch.tensor([1], dtype=torch.int8, device=device) m2 = torch.tensor([2], dtype=torch.int8, device=device) self.assertRaisesRegex(RuntimeError, r"Boolean alpha only supported for Boolean results\.", lambda: torch.add(m1, m2, alpha=True)) self.assertRaisesRegex(RuntimeError, r"For integral input tensors, argument alpha must not be a floating point number\.", lambda: torch.add(m1, m2, alpha=1.0)) # mismatched alpha, float / double tensor and complex alpha msg = r"For non-complex input tensors, argument alpha must not be a complex number\." m1 = torch.tensor([3., 4.], device=device) m2 = torch.tensor([4., 3.], device=device) self.assertRaisesRegex(RuntimeError, msg, lambda: torch.add(m1, m2, alpha=complex(0.1, 0.2))) m1 = torch.tensor([3., 4.], dtype=torch.double, device=device) m2 = torch.tensor([4., 3.], dtype=torch.double, device=device) self.assertRaisesRegex(RuntimeError, msg, lambda: torch.add(m1, m2, alpha=complex(0.1, 0.2))) # complex m1 = torch.tensor((4.0000 + 4.0000j), dtype=torch.complex64) m2 = torch.tensor(4., dtype=torch.float64) self.assertRaisesRegex(RuntimeError, r"result type ComplexFloat can't be cast to the desired output type Double", lambda: torch.add(m1, m1, out=m2)) @onlyCUDA def test_addsub_half_tensor(self, device): x = torch.tensor([60000.0], dtype=torch.half, device=device) for op, y, alpha in ( (torch.add, torch.tensor([-60000.0], dtype=torch.half, device=device), 2), (torch.sub, torch.tensor([60000.0], dtype=torch.half, device=device), 2), (torch.add, -70000.0, 1), (torch.sub, 70000.0, 1), ): actual = op(x, y, alpha=alpha) self.assertTrue(not (actual.isnan() or actual.isinf())) def test_sub_typing(self, device): m1 = torch.tensor([True, False, False, True, False, False], dtype=torch.bool, device=device) m2 = torch.tensor([True, True, False, False, False, True], dtype=torch.bool, device=device) self.assertRaisesRegex(RuntimeError, r"Subtraction, the `\-` operator, with two bool tensors is not supported. " r"Use the `\^` or `logical_xor` operator instead.", lambda: m1 - m2) self.assertRaisesRegex(RuntimeError, r"Subtraction, the `\-` operator, with a bool tensor is not supported. " r"If you are trying to invert a mask, use the `\~` or `logical_not` operator instead.", lambda: 1 - m1) self.assertRaisesRegex(RuntimeError, r"Subtraction, the `\-` operator, with a bool tensor is not supported. " r"If you are trying to invert a mask, use the `\~` or `logical_not` operator instead.", lambda: m2 - 1) # mismatched alpha m1 = torch.tensor([1], dtype=torch.int8, device=device) m2 = torch.tensor([2], dtype=torch.int8, device=device) self.assertRaisesRegex(RuntimeError, r"Boolean alpha only supported for Boolean results\.", lambda: torch.sub(m1, m2, alpha=True)) self.assertRaisesRegex(RuntimeError, r"For integral input tensors, argument alpha must not be a floating point number\.", lambda: torch.sub(m1, m2, alpha=1.0)) def test_mul(self, device): m1 = torch.randn(10, 10, device=device) res1 = m1.clone() res1[:, 3].mul_(2) res2 = m1.clone() for i in range(res1.size(0)): res2[i, 3] = res2[i, 3] * 2 self.assertEqual(res1, res2) a1 = torch.tensor([True, False, False, True], dtype=torch.bool, device=device) a2 = torch.tensor([True, False, True, False], dtype=torch.bool, device=device) self.assertEqual(a1 * a2, torch.tensor([True, False, False, False], dtype=torch.bool, device=device)) if device == 'cpu': a1 = torch.tensor([0.1, 0.1], dtype=torch.bfloat16, device=device) a2 = torch.tensor([1.1, 0.1], dtype=torch.bfloat16, device=device) self.assertEqual(a1 * a2, torch.tensor([0.11, 0.01], dtype=torch.bfloat16, device=device), atol=0.01, rtol=0) self.assertEqual(a1.mul(a2), a1 * a2) def test_bool_tensor_comparison_ops(self, device): a = torch.tensor([True, False, True, False, True, False], dtype=torch.bool, device=device) b = torch.tensor([True, False, True, True, True, True], dtype=torch.bool, device=device) self.assertEqual(a == b, torch.tensor([1, 1, 1, 0, 1, 0], dtype=torch.bool, device=device)) self.assertEqual(a != b, torch.tensor([0, 0, 0, 1, 0, 1], dtype=torch.bool, device=device)) self.assertEqual(a < b, torch.tensor([0, 0, 0, 1, 0, 1], dtype=torch.bool, device=device)) self.assertEqual(a > b, torch.tensor([0, 0, 0, 0, 0, 0], dtype=torch.bool, device=device)) self.assertEqual(a >= b, torch.tensor([1, 1, 1, 0, 1, 0], dtype=torch.bool, device=device)) self.assertEqual(a <= b, torch.tensor([1, 1, 1, 1, 1, 1], dtype=torch.bool, device=device)) self.assertEqual(a > False, torch.tensor([1, 0, 1, 0, 1, 0], dtype=torch.bool, device=device)) self.assertEqual(a == torch.tensor(True, dtype=torch.bool, device=device), torch.tensor([1, 0, 1, 0, 1, 0], dtype=torch.bool, device=device)) self.assertEqual(a == torch.tensor(0, dtype=torch.bool, device=device), torch.tensor([0, 1, 0, 1, 0, 1], dtype=torch.bool, device=device)) self.assertFalse(a.equal(b)) @dtypes(*torch.testing.get_all_dtypes(include_complex=False)) def test_logical(self, device, dtype): if dtype != torch.bool: x = torch.tensor([1, 2, 3, 4], device=device, dtype=dtype) b = torch.tensor([2], device=device, dtype=dtype) self.assertEqual(x.lt(2), torch.tensor([True, False, False, False])) self.assertEqual(x.le(2), torch.tensor([True, True, False, False])) self.assertEqual(x.ge(2), torch.tensor([False, True, True, True])) self.assertEqual(x.gt(2), torch.tensor([False, False, True, True])) self.assertEqual(x.eq(2), torch.tensor([False, True, False, False])) self.assertEqual(x.ne(2), torch.tensor([True, False, True, True])) self.assertEqual(x.lt(b), torch.tensor([True, False, False, False])) self.assertEqual(x.le(b), torch.tensor([True, True, False, False])) self.assertEqual(x.ge(b), torch.tensor([False, True, True, True])) self.assertEqual(x.gt(b), torch.tensor([False, False, True, True])) self.assertEqual(x.eq(b), torch.tensor([False, True, False, False])) self.assertEqual(x.ne(b), torch.tensor([True, False, True, True])) else: x = torch.tensor([True, False, True, False], device=device) self.assertEqual(x.lt(True), torch.tensor([False, True, False, True])) self.assertEqual(x.le(True), torch.tensor([True, True, True, True])) self.assertEqual(x.ge(True), torch.tensor([True, False, True, False])) self.assertEqual(x.gt(True), torch.tensor([False, False, False, False])) self.assertEqual(x.eq(True), torch.tensor([True, False, True, False])) self.assertEqual(x.ne(True), torch.tensor([False, True, False, True])) def test_atan2(self, device): def _test_atan2_with_size(size, device): a = torch.rand(size=size, device=device, dtype=torch.double) b = torch.rand(size=size, device=device, dtype=torch.double) actual = a.atan2(b) x = a.view(-1) y = b.view(-1) expected = torch.tensor([math.atan2(x[i].item(), y[i].item()) for i in range(x.numel())], device=device, dtype=torch.double) self.assertEqual(expected, actual.view(-1), rtol=0, atol=0.02) _test_atan2_with_size((2, 2), device) _test_atan2_with_size((3, 3), device) _test_atan2_with_size((5, 5), device) def test_atan2_edgecases(self, device): def _test_atan2(x, y, expected, device, dtype): expected_tensor = torch.tensor([expected], dtype=dtype, device=device) x_tensor = torch.tensor([x], dtype=dtype, device=device) y_tensor = torch.tensor([y], dtype=dtype, device=device) actual = torch.atan2(y_tensor, x_tensor) self.assertEqual(expected_tensor, actual, rtol=0, atol=0.02) for dtype in [torch.float, torch.double]: _test_atan2(0, 0, 0, device, dtype) _test_atan2(0, 1, math.pi / 2, device, dtype) _test_atan2(0, -1, math.pi / -2, device, dtype) _test_atan2(-1, 0, math.pi, device, dtype) _test_atan2(1, 0, 0, device, dtype) _test_atan2(-1, -1, math.pi * -3 / 4 , device, dtype) _test_atan2(1, 1, math.pi / 4 , device, dtype) _test_atan2(1, -1, math.pi / -4 , device, dtype) _test_atan2(-1, 1, math.pi * 3 / 4 , device, dtype) def test_trapz(self, device): def test_dx(sizes, dim, dx, device): t = torch.randn(sizes, device=device) actual = torch.trapz(t, dx=dx, dim=dim) expected = np.trapz(t.cpu().numpy(), dx=dx, axis=dim) self.assertEqual(expected.shape, actual.shape) self.assertEqual(expected, actual, exact_dtype=False) def test_x(sizes, dim, x, device): t = torch.randn(sizes, device=device) actual = torch.trapz(t, x=torch.tensor(x, device=device), dim=dim) expected = np.trapz(t.cpu().numpy(), x=x, axis=dim) self.assertEqual(expected.shape, actual.shape) self.assertEqual(expected, actual.cpu(), exact_dtype=False) test_dx((2, 3, 4), 1, 1, device) test_dx((10, 2), 0, 0.1, device) test_dx((1, 10), 0, 2.3, device) test_dx((0, 2), 0, 1.0, device) test_dx((0, 2), 1, 1.0, device) test_x((2, 3, 4), 1, [1.0, 2.0, 3.0], device) test_x((10, 2), 0, [2.0, 3.0, 4.0, 7.0, 11.0, 14.0, 22.0, 26.0, 26.1, 30.3], device) test_x((1, 10), 0, [1.0], device) test_x((0, 2), 0, [], device) test_x((0, 2), 1, [1.0, 2.0], device) with self.assertRaisesRegex( IndexError, 'Dimension out of range'): test_x((2, 3), 2, [], device) test_dx((2, 3), 2, 1.0, device) with self.assertRaisesRegex( RuntimeError, 'There must be one `x` value for each sample point'): test_x((2, 3), 1, [1.0, 2.0], device) test_x((2, 3), 1, [1.0, 2.0, 3.0, 4.0], device) @dtypes(torch.double) def test_pow_scalar_overloads_mem_overlap(self, device, dtype): sz = 3 doubles = torch.randn(2 * sz, dtype=dtype, device=device) self.check_internal_mem_overlap( lambda t: t.pow_(42), 1, dtype, device) self.unary_check_input_output_mem_overlap( doubles, sz, lambda input, out: torch.pow(input, 42, out=out)) self.unary_check_input_output_mem_overlap( doubles, sz, lambda input, out: torch.pow(42, input, out=out)) @dtypes(*list(product(torch.testing.get_all_dtypes(include_bool=False), torch.testing.get_all_dtypes(include_bool=False)))) def test_float_power(self, device, dtypes): def to_np(value): if isinstance(value, torch.Tensor) and value.dtype == torch.bfloat16: return value.to(torch.float).cpu().numpy() return value.cpu().numpy() if isinstance(value, torch.Tensor) else value base_dtype = dtypes[0] exp_dtype = dtypes[1] out_dtype = torch.complex128 if base_dtype.is_complex or exp_dtype.is_complex else torch.float64 base = make_tensor((30,), device, base_dtype, low=1, high=100) # Complex and real results do not agree between PyTorch and NumPy when computing negative and zero power of 0 # Related: https://github.com/pytorch/pytorch/issues/48000 # base[0] = base[3] = base[7] = 0 exp = make_tensor((30,), device, exp_dtype, low=-2, high=2) exp[0] = exp[4] = exp[6] = 0 expected = torch.from_numpy(np.float_power(to_np(base), to_np(exp))) exponents = [-2.8, -2, -1, -0.5, 0.5, 1, 2] complex_exponents = exponents + [-2.5j, -1.0j, 1.0j, 2.5j, 1.0 + 1.0j, -1.0 - 1.5j, 3.3j] for op in (torch.float_power, torch.Tensor.float_power, torch.Tensor.float_power_): # Case of Tensor x Tensor if op is torch.Tensor.float_power_ and base_dtype != out_dtype: with self.assertRaisesRegex(RuntimeError, "operation's result requires dtype"): op(base.clone(), exp) else: result = op(base.clone(), exp) self.assertEqual(expected, result) if op is torch.float_power: out = torch.empty_like(base).to(device=device, dtype=out_dtype) op(base, exp, out=out) self.assertEqual(expected, out) # Case of Tensor x Scalar for i in complex_exponents if exp_dtype.is_complex else exponents: out_dtype_scalar_exp = torch.complex128 if base_dtype.is_complex or type(i) == complex else torch.float64 expected_scalar_exp = torch.from_numpy(np.float_power(to_np(base), i)) if op is torch.Tensor.float_power_ and base_dtype != out_dtype_scalar_exp: with self.assertRaisesRegex(RuntimeError, "operation's result requires dtype"): op(base.clone(), i) else: result = op(base.clone(), i) self.assertEqual(expected_scalar_exp, result) if op is torch.float_power: out = torch.empty_like(base).to(device=device, dtype=out_dtype_scalar_exp) op(base, i, out=out) self.assertEqual(expected_scalar_exp, out) # Case of Scalar x Tensor for i in complex_exponents if base_dtype.is_complex else exponents: out_dtype_scalar_base = torch.complex128 if exp_dtype.is_complex or type(i) == complex else torch.float64 expected_scalar_base = torch.from_numpy(np.float_power(i, to_np(exp))) result = torch.float_power(i, exp) self.assertEqual(expected_scalar_base, result) out = torch.empty_like(exp).to(device=device, dtype=out_dtype_scalar_base) torch.float_power(i, exp, out=out) self.assertEqual(expected_scalar_base, out) def test_float_power_exceptions(self, device): def _promo_helper(x, y): for i in (x, y): if type(i) == complex: return torch.complex128 elif type(i) == torch.Tensor and i.is_complex(): return torch.complex128 return torch.double test_cases = ((torch.tensor([-2, -1, 0, 1, 2], device=device), -.25), (torch.tensor([-1.0j, 0j, 1.0j, 1.0 + 1.0j, -1.0 - 1.5j], device=device), 2.)) for base, exp in test_cases: for out_dtype in (torch.long, torch.float, torch.double, torch.cdouble): out = torch.empty(1, device=device, dtype=out_dtype) required_dtype = _promo_helper(base, exp) if out.dtype == required_dtype: torch.float_power(base, exp, out=out) else: with self.assertRaisesRegex(RuntimeError, "operation's result requires dtype"): torch.float_power(base, exp, out=out) if base.dtype == required_dtype: torch.Tensor.float_power_(base.clone(), exp) else: with self.assertRaisesRegex(RuntimeError, "operation's result requires dtype"): torch.Tensor.float_power_(base.clone(), exp) @skipIf(not TEST_SCIPY, "Scipy required for the test.") @dtypes(*product(torch.testing.get_all_dtypes(include_complex=False, include_bfloat16=False), torch.testing.get_all_dtypes(include_complex=False, include_bfloat16=False))) def test_xlogy_xlog1py(self, device, dtypes): x_dtype, y_dtype = dtypes def out_variant_helper(torch_fn, x, y): expected = torch_fn(x, y) out = torch.empty_like(expected) torch_fn(x, y, out=out) self.assertEqual(expected, out) def xlogy_inplace_variant_helper(x, y): if x.dtype in torch.testing.get_all_int_dtypes() + [torch.bool]: with self.assertRaisesRegex(RuntimeError, "can't be cast to the desired output type"): x.clone().xlogy_(y) else: expected = torch.empty_like(x) torch.xlogy(x, y, out=expected) inplace_out = x.clone().xlogy_(y) self.assertEqual(expected, inplace_out) def test_helper(torch_fn, reference_fn, inputs, scalar=None): x, y, z = inputs torch_fn_partial = partial(torch_fn, x) reference_fn_partial = partial(reference_fn, x.cpu().numpy()) self.compare_with_numpy(torch_fn_partial, reference_fn_partial, x, exact_dtype=False) self.compare_with_numpy(torch_fn_partial, reference_fn_partial, y, exact_dtype=False) self.compare_with_numpy(torch_fn_partial, reference_fn_partial, z, exact_dtype=False) val = scalar if scalar is not None else x out_variant_helper(torch_fn, val, x) out_variant_helper(torch_fn, val, y) out_variant_helper(torch_fn, val, z) # Tensor-Tensor Test (tensor of same and different shape) x = make_tensor((3, 2, 4, 5), device, x_dtype, low=0.5, high=1000) y = make_tensor((3, 2, 4, 5), device, y_dtype, low=0.5, high=1000) z = make_tensor((4, 5), device, y_dtype, low=0.5, high=1000) x_1p = make_tensor((3, 2, 4, 5), device, x_dtype, low=-0.5, high=1000) y_1p = make_tensor((3, 2, 4, 5), device, y_dtype, low=-0.5, high=1000) z_1p = make_tensor((4, 5), device, y_dtype, low=-0.5, high=1000) xlogy_fns = torch.xlogy, scipy.special.xlogy xlog1py_fns = torch.special.xlog1py, scipy.special.xlog1py test_helper(*xlogy_fns, (x, y, z)) xlogy_inplace_variant_helper(x, x) xlogy_inplace_variant_helper(x, y) xlogy_inplace_variant_helper(x, z) test_helper(*xlog1py_fns, (x_1p, y_1p, z_1p)) # Scalar-Tensor Test test_helper(*xlogy_fns, (x, y, z), 3.14) test_helper(*xlog1py_fns, (x_1p, y_1p, z_1p), 3.14) # Special Values Tensor-Tensor t = torch.tensor([-1., 0., 1., 2., float('inf'), -float('inf'), float('nan')], device=device) zeros = torch.zeros(7, dtype=y_dtype, device=device) def test_zeros_special_helper(torch_fn, reference_fn, scalar=False): zeros_t = 0 if scalar else zeros zeros_np = 0 if scalar else zeros.cpu().numpy() torch_fn_partial = partial(torch_fn, zeros_t) reference_fn_partial = partial(reference_fn, zeros_np) self.compare_with_numpy(torch_fn_partial, reference_fn_partial, t, exact_dtype=False) out_variant_helper(torch_fn, zeros_t, t) test_zeros_special_helper(*xlogy_fns) xlogy_inplace_variant_helper(zeros, t) test_zeros_special_helper(*xlog1py_fns) # Special Values Scalar-Tensor test_zeros_special_helper(*xlogy_fns, scalar=True) test_zeros_special_helper(*xlog1py_fns, scalar=True) def test_xlogy_xlog1py_scalar_type_promotion(self, device): # Test that python numbers don't participate in type promotion at the same # priority level as 0-dim tensors t = torch.randn((), dtype=torch.float32, device=device) self.assertEqual(t.dtype, torch.xlogy(t, 5).dtype) self.assertEqual(t.dtype, torch.xlogy(t, 5.).dtype) self.assertEqual(t.dtype, torch.special.xlog1py(t, 5).dtype) self.assertEqual(t.dtype, torch.special.xlog1py(t, 5.).dtype) self.assertEqual(t.dtype, torch.xlogy(5, t).dtype) self.assertEqual(t.dtype, torch.xlogy(5., t).dtype) self.assertEqual(t.dtype, torch.special.xlog1py(5, t).dtype) self.assertEqual(t.dtype, torch.special.xlog1py(5., t).dtype) @skipIf(not TEST_SCIPY, "Scipy required for the test.") def test_xlogy_xlog1py_bfloat16(self, device): def _compare_helper(x, y, torch_fn, reference_fn): x_np = x if isinstance(x, float) else x.cpu().to(torch.float).numpy() y_np = y if isinstance(y, float) else y.cpu().to(torch.float).numpy() expected = torch.from_numpy(reference_fn(x_np, y_np)) actual = torch_fn(x, y) self.assertEqual(expected, actual, exact_dtype=False) x_dtype, y_dtype = torch.bfloat16, torch.bfloat16 # Tensor-Tensor Test (tensor of same and different shape) x = make_tensor((3, 2, 4, 5), device, x_dtype, low=0.5, high=1000) y = make_tensor((3, 2, 4, 5), device, y_dtype, low=0.5, high=1000) z = make_tensor((4, 5), device, y_dtype, low=0.5, high=1000) x_1p = make_tensor((3, 2, 4, 5), device, x_dtype, low=-0.8, high=1000) y_1p = make_tensor((3, 2, 4, 5), device, y_dtype, low=-0.8, high=1000) z_1p = make_tensor((4, 5), device, y_dtype, low=-0.8, high=1000) xlogy_fns = torch.xlogy, scipy.special.xlogy xlog1py_fns = torch.special.xlog1py, scipy.special.xlog1py _compare_helper(x, x, *xlogy_fns) _compare_helper(x, y, *xlogy_fns) _compare_helper(x, z, *xlogy_fns) _compare_helper(x, 3.14, *xlogy_fns) _compare_helper(y, 3.14, *xlogy_fns) _compare_helper(z, 3.14, *xlogy_fns) _compare_helper(x_1p, x_1p, *xlog1py_fns) _compare_helper(x_1p, y_1p, *xlog1py_fns) _compare_helper(x_1p, z_1p, *xlog1py_fns) _compare_helper(x_1p, 3.14, *xlog1py_fns) _compare_helper(y_1p, 3.14, *xlog1py_fns) _compare_helper(z_1p, 3.14, *xlog1py_fns) # Special Values Tensor-Tensor t = torch.tensor([-1., 0., 1., 2., float('inf'), -float('inf'), float('nan')], device=device) zeros = torch.tensor(7, dtype=y_dtype, device=device) _compare_helper(t, zeros, *xlogy_fns) _compare_helper(t, 0., *xlogy_fns) _compare_helper(t, zeros, *xlog1py_fns) _compare_helper(t, 0., *xlog1py_fns) @dtypes(*product(torch.testing.get_all_dtypes(include_complex=False, include_half=False, include_bfloat16=False), torch.testing.get_all_dtypes(include_complex=False, include_half=False, include_bfloat16=False))) @skipIf(not TEST_SCIPY, "Scipy required for the test.") def test_zeta(self, device, dtypes): x_dtype, q_dtype = dtypes def test_helper(x, q): x_np = x if isinstance(x, float) else x.cpu().numpy() q_np = q if isinstance(q, float) else q.cpu().numpy() expected = torch.from_numpy(scipy.special.zeta(x_np, q_np)) actual = torch.special.zeta(x, q) rtol, atol = None, None if self.device_type == 'cpu': rtol, atol = 1e-6, 1e-6 self.assertEqual(expected, actual, rtol=rtol, atol=atol, exact_dtype=False) # x tensor - q tensor same size x = make_tensor((2, 3, 4), device, x_dtype) q = make_tensor((2, 3, 4), device, q_dtype) test_helper(x, q) # x tensor - q tensor broadcast lhs x = make_tensor((2, 1, 4), device, x_dtype) q = make_tensor((2, 3, 4), device, q_dtype) test_helper(x, q) # x tensor - q tensor broadcast rhs x = make_tensor((2, 3, 4), device, x_dtype) q = make_tensor((2, 1, 4), device, q_dtype) test_helper(x, q) # x tensor - q tensor broadcast all x = make_tensor((2, 3, 1), device, x_dtype) q = make_tensor((2, 1, 4), device, q_dtype) test_helper(x, q) # x scalar - q tensor for x in np.linspace(-5, 5, num=10).tolist(): if not q_dtype.is_floating_point: q_dtype = torch.get_default_dtype() q = make_tensor((2, 3, 4), device, q_dtype) test_helper(x, q) # x tensor - q scalar for q in np.linspace(-5, 5, num=10).tolist(): if not x_dtype.is_floating_point: x_dtype = torch.get_default_dtype() x = make_tensor((2, 3, 4), device, x_dtype) test_helper(x, q) tensor_binary_ops = [ '__lt__', '__le__', '__gt__', '__ge__', '__eq__', '__ne__', '__add__', '__radd__', '__iadd__', '__sub__', '__rsub__', '__isub__', '__mul__', '__rmul__', '__imul__', '__matmul__', '__rmatmul__', '__truediv__', '__rtruediv__', '__itruediv__', '__floordiv__', '__rfloordiv__', '__ifloordiv__', '__mod__', '__rmod__', '__imod__', '__pow__', '__rpow__', '__ipow__', '__lshift__', '__rlshift__', '__ilshift__', '__rshift__', '__rrshift__', '__irshift__', '__and__', '__iand__', '__xor__', '__ixor__', '__or__', '__ior__', # Unsupported operators # '__imatmul__', # '__divmod__', '__rdivmod__', '__idivmod__', # '__rand__', '__ror__', '__rxor__', ] # Test that binary math operations return NotImplemented for unknown types. def generate_not_implemented_tests(cls): class UnknownType: pass # TODO: refactor to inline these _types = [ torch.half, torch.float, torch.double, torch.int8, torch.short, torch.int, torch.long, torch.uint8 ] # TODO: refactor to use make_tensor def _small_2d(dtype, device, has_zeros=True, fill_ones=False, oneish=False): t = _make_tensor((5, 5), dtype, device, fill_ones=fill_ones) if oneish: return t.clamp(min=_number(.99, 1, dtype), max=1.01) if not has_zeros: return t.clamp(min=(_number(_div_min, 1, dtype))) return t def create_test_func(op): @dtypes(*_types) def test(self, device, dtype): # Generate the inputs tensor = _small_2d(dtype, device) # Runs the tensor op on the device result = getattr(tensor, op)(UnknownType()) self.assertEqual(result, NotImplemented) return test for op in tensor_binary_ops: test_name = "test_{}_not_implemented".format(op) assert not hasattr(cls, test_name), "{0} already in {1}".format( test_name, cls.__name__) setattr(cls, test_name, create_test_func(op)) generate_not_implemented_tests(TestBinaryUfuncs) instantiate_device_type_tests(TestBinaryUfuncs, globals()) if __name__ == '__main__': run_tests()

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import torch import numpy as np import itertools from itertools import product import math import random import unittest import warnings import operator from functools import partial from torch._six import inf, nan from torch.testing._internal.common_utils import ( TestCase, iter_indices, TEST_WITH_ASAN, run_tests, torch_to_numpy_dtype_dict, make_tensor, TEST_SCIPY, set_default_dtype) from torch.testing._internal.common_device_type import ( instantiate_device_type_tests, onlyCUDA, onlyCPU, dtypes, dtypesIfCUDA, dtypesIfCPU, deviceCountAtLeast, precisionOverride, onlyOnCPUAndCUDA, skipCUDAIfRocm, skipIf) from torch.testing import all_types_and_complex_and if TEST_SCIPY: import scipy.special def _generate_input(shape, dtype, device, with_extremal): if shape == (): x = torch.tensor((), dtype=dtype, device=device) else: if dtype.is_floating_point or dtype.is_complex: if dtype == torch.bfloat16: x = torch.randn(*shape, device=device) * random.randint(30, 100) x = x.to(torch.bfloat16) else: x = torch.randn(*shape, dtype=dtype, device=device) * random.randint(30, 100) x[torch.randn(*shape) > 0.5] = 0 if with_extremal and dtype.is_floating_point: x[torch.randn(*shape) > 0.5] = float('nan') x[torch.randn(*shape) > 0.5] = float('inf') x[torch.randn(*shape) > 0.5] = float('-inf') elif with_extremal and dtype.is_complex: x[torch.randn(*shape) > 0.5] = complex('nan') x[torch.randn(*shape) > 0.5] = complex('inf') x[torch.randn(*shape) > 0.5] = complex('-inf') elif dtype == torch.bool: x = torch.zeros(shape, dtype=dtype, device=device) x[torch.randn(*shape) > 0.5] = True else: x = torch.randint(15, 100, shape, dtype=dtype, device=device) return x def _convert_t(dtype, device): if device == 'cpu' and dtype in {torch.half, torch.bfloat16}: return torch.float return dtype def _make_tensor(shape, dtype, device, fill_ones=False) -> torch.Tensor: if fill_ones: return torch.ones(*shape, dtype=_convert_t(dtype, device), device=device) if not (dtype.is_floating_point or dtype.is_complex): t = torch.randint(0, 10, shape, device=device) if dtype != torch.uint8: t = t - 5 return t.to(_convert_t(dtype, device)) if dtype == torch.half and device == 'cpu': return torch.randn(*shape, dtype=torch.float, device=device).half().float() if dtype == torch.bfloat16 and device == 'cpu': return torch.randn(*shape, dtype=torch.float, device=device).bfloat16().float() return torch.randn(shape, dtype=dtype, device=device).to(dtype=dtype) class TestBinaryUfuncs(TestCase): def test_add_broadcast_empty(self, device): self.assertRaises(RuntimeError, lambda: torch.randn(5, 0, device=device) + torch.randn(0, 5, device=device)) self.assertEqual(torch.randn(5, 0, device=device), torch.randn(0, device=device) + torch.randn(5, 0, device=device)) self.assertEqual(torch.randn(5, 0, 0, device=device), torch.randn(0, device=device) + torch.randn(5, 0, 1, device=device)) self.assertEqual(torch.randn(5, 0, 6, device=device), torch.randn((), device=device) + torch.randn(5, 0, 6, device=device)) self.assertEqual(torch.randn(0, device=device), torch.randn(0, device=device) + torch.randn(1, device=device)) self.assertEqual(torch.randn(0, 7, 0, 6, 5, 0, 7, device=device), torch.randn(0, 7, 0, 6, 5, 0, 1, device=device) + torch.randn(1, 1, 5, 1, 7, device=device)) self.assertRaises(RuntimeError, lambda: torch.randn(7, 0, device=device) + torch.randn(2, 1, device=device)) def test_addcmul_scalars_as_floats(self, device): x = torch.tensor(2.) y = torch.tensor(3., device=device) # 3 + (3 * 3) * 2 self.assertEqual(y.addcmul(y, y, value=x), 21) x = torch.tensor(2., requires_grad=True) self.assertRaises(Exception, lambda: y.addcmul(y, y, value=x)) # TODO: update to work on CUDA, too @onlyCPU def test_comparison_ops(self, device): x = torch.randn(5, 5) y = torch.randn(5, 5) eq = x == y for idx in iter_indices(x): self.assertEqual(x[idx] == y[idx], eq[idx] == 1) ne = x != y for idx in iter_indices(x): self.assertEqual(x[idx] != y[idx], ne[idx] == 1) lt = x < y for idx in iter_indices(x): self.assertEqual(x[idx] < y[idx], lt[idx] == 1) le = x <= y for idx in iter_indices(x): self.assertEqual(x[idx] <= y[idx], le[idx] == 1) gt = x > y for idx in iter_indices(x): self.assertEqual(x[idx] > y[idx], gt[idx] == 1) ge = x >= y for idx in iter_indices(x): self.assertEqual(x[idx] >= y[idx], ge[idx] == 1) # TODO: update to work on CUDA, too @onlyCPU def test_comparison_ops_must_take_bool_output(self, device): for op in [torch.lt, torch.le, torch.gt, torch.ge, torch.eq, torch.ne, torch.logical_and, torch.logical_or, torch.logical_xor]: self.assertEqual(op(torch.tensor([True]), torch.tensor([False])).dtype, torch.bool) # TODO: update to work on CUDA, too @onlyCPU def test_inplace_comparison_ops_require_inputs_have_same_dtype(self, device): with self.assertRaisesRegex(RuntimeError, 'Expected object of scalar type'): for op in ['lt_', 'le_', 'gt_', 'ge_', 'eq_', 'ne_', 'logical_xor_', 'logical_and_', 'logical_or_']: x = torch.tensor([1], dtype=torch.int) y = torch.tensor([2], dtype=torch.long) in_place_method = getattr(x, op) in_place_method(y) # TODO: update to work on CUDA, too @onlyCPU def test_comparison_ops_check_for_scalar_overflow(self, device): s = 1 << 20 t = torch.tensor([1 << 5], dtype=torch.uint8) with self.assertRaisesRegex(RuntimeError, 'value cannot be converted to type'): self.assertTrue(t < s) with self.assertRaisesRegex(RuntimeError, 'value cannot be converted to type'): self.assertTrue(s < t) with self.assertRaisesRegex(RuntimeError, 'value cannot be converted to type'): self.assertTrue(t <= s) with self.assertRaisesRegex(RuntimeError, 'value cannot be converted to type'): self.assertTrue(s <= t) with self.assertRaisesRegex(RuntimeError, 'value cannot be converted to type'): self.assertTrue(t > s) with self.assertRaisesRegex(RuntimeError, 'value cannot be converted to type'): self.assertTrue(s > t) with self.assertRaisesRegex(RuntimeError, 'value cannot be converted to type'): self.assertTrue(t >= s) with self.assertRaisesRegex(RuntimeError, 'value cannot be converted to type'): self.assertTrue(s >= t) with self.assertRaisesRegex(RuntimeError, 'value cannot be converted to type'): self.assertTrue(t == s) with self.assertRaisesRegex(RuntimeError, 'value cannot be converted to type'): self.assertTrue(s == t) with self.assertRaisesRegex(RuntimeError, 'value cannot be converted to type'): self.assertTrue(t != s) with self.assertRaisesRegex(RuntimeError, 'value cannot be converted to type'): self.assertTrue(s != t) # TODO: update to work on CUDA, too @onlyCPU def test_comparison_ops_check_for_zerodim_tensor_overflow(self, device): t1 = torch.tensor([1 << 5], dtype=torch.uint8) t2 = torch.tensor([1 << 30], dtype=torch.int32) ts1 = torch.tensor(1 << 20, dtype=torch.int32) ts2 = torch.tensor(1 << 40, dtype=torch.int64) with self.assertRaisesRegex(RuntimeError, 'value cannot be converted to type'): self.assertTrue(t1 < ts1) with self.assertRaisesRegex(RuntimeError, 'value cannot be converted to type'): self.assertTrue(ts2 < t2) with self.assertRaisesRegex(RuntimeError, 'value cannot be converted to type'): self.assertTrue(t1 <= ts1) with self.assertRaisesRegex(RuntimeError, 'value cannot be converted to type'): self.assertTrue(ts2 <= t2) with self.assertRaisesRegex(RuntimeError, 'value cannot be converted to type'): self.assertTrue(t1 > ts1) with self.assertRaisesRegex(RuntimeError, 'value cannot be converted to type'): self.assertTrue(ts2 > t2) with self.assertRaisesRegex(RuntimeError, 'value cannot be converted to type'): self.assertTrue(t1 >= ts1) with self.assertRaisesRegex(RuntimeError, 'value cannot be converted to type'): self.assertTrue(ts2 >= t2) with self.assertRaisesRegex(RuntimeError, 'value cannot be converted to type'): self.assertTrue(t1 == ts1) with self.assertRaisesRegex(RuntimeError, 'value cannot be converted to type'): self.assertTrue(ts2 == t2) with self.assertRaisesRegex(RuntimeError, 'value cannot be converted to type'): self.assertTrue(t1 != ts1) with self.assertRaisesRegex(RuntimeError, 'value cannot be converted to type'): self.assertTrue(ts2 != t2) # TODO: update to work on CUDA, too @onlyCPU def test_bitwise_ops(self, device): x = torch.randn(5, 5).gt(0) y = torch.randn(5, 5).gt(0) and_result = x & y for idx in iter_indices(x): if and_result[idx]: self.assertTrue(x[idx] and y[idx]) else: self.assertFalse(x[idx] and y[idx]) or_result = x | y for idx in iter_indices(x): if or_result[idx]: self.assertTrue(x[idx] or y[idx]) else: self.assertFalse(x[idx] or y[idx]) xor_result = x ^ y for idx in iter_indices(x): if xor_result[idx]: self.assertTrue(x[idx] ^ y[idx]) else: self.assertFalse(x[idx] ^ y[idx]) x_clone = x.clone() x_clone &= y self.assertEqual(x_clone, and_result) x_clone = x.clone() x_clone |= y self.assertEqual(x_clone, or_result) x_clone = x.clone() x_clone ^= y self.assertEqual(x_clone, xor_result) def test_inplace_division(self, device): t = torch.rand(5, 5, device=device) id_before = id(t) t /= 2 id_after = id(t) self.assertEqual(id_before, id_after) @dtypes(*torch.testing.get_all_dtypes(include_bool=False, include_complex=False)) def test_div_rounding_modes(self, device, dtype): if dtype.is_floating_point: low, high = -10.0, 10.0 else: info = torch.iinfo(dtype) low, high = info.min, info.max a = make_tensor((100,), device, dtype, low=low, high=high) b = make_tensor((100,), device, dtype, low=low, high=high) # Avoid division by zero so we can test (a / b) * b == a if dtype.is_floating_point: eps = 0.1 b[(-eps < b) & (b < eps)] = eps else: b[b == 0] = 1 if not dtype.is_floating_point: # floor(a / b) * b can be < a, so fixup slightly to avoid underflow a = torch.where(a < 0, a + b, a) d_true = torch.divide(a, b, rounding_mode=None) self.assertTrue(d_true.is_floating_point()) self.assertEqual(d_true * b, a.to(d_true.dtype)) d_floor = torch.divide(a, b, rounding_mode='floor') if dtype not in (torch.bfloat16, torch.half): self.assertEqual(d_floor * b + torch.remainder(a, b), a) else: self.assertEqual(d_floor * b + torch.remainder(a.float(), b.float()), a, exact_dtype=False) d_trunc = torch.divide(a, b, rounding_mode='trunc') rounding_unsupported = ( dtype == torch.half and device != 'cuda' or dtype == torch.bfloat16 and device != 'cpu') d_ref = d_true.float() if rounding_unsupported else d_true self.assertEqual(d_trunc, d_ref.trunc().to(dtype)) @dtypes(torch.bfloat16, torch.half, torch.float32, torch.float64) def test_div_rounding_nonfinite(self, device, dtype): # Compare division of special floating point values against NumPy num = torch.tensor([1.0, -1.0, 0, 0.1, -0.1, np.pi, -np.pi, np.inf, -np.inf, np.nan], dtype=dtype) # Divide by zero is tested seperately denom = num[num != 0] a, b = num[None, :].clone(), denom[:, None].clone() # Compare bfloat16 against NumPy float exact_dtype = dtype != torch.bfloat16 if exact_dtype: an, bn = a.cpu().numpy(), b.cpu().numpy() else: an, bn = a.float().cpu().numpy(), b.float().cpu().numpy() for mode, np_ref in ((None, np.true_divide), ("floor", np.floor_divide)): with np.errstate(all='ignore'): expect = np_ref(an, bn) kwargs = dict(rounding_mode=mode) if mode is not None else {} with set_default_dtype(torch.double): actual = torch.divide(a, b, **kwargs) self.assertEqual(actual, torch.from_numpy(expect), exact_device=False, exact_dtype=exact_dtype) # Compare contiguous (likely vectorized) against non-contiguous (not vectorized) a_noncontig = torch.empty([2 * i for i in a.shape], dtype=dtype, device=device)[::2, ::2] a_noncontig[:] = a b_noncontig = torch.empty([2 * i for i in b.shape], dtype=dtype, device=device)[::2, ::2] b_noncontig[:] = b for rounding_mode in (None, "trunc", "floor"): expect = torch.divide(a_noncontig, b_noncontig, rounding_mode=rounding_mode) actual = torch.divide(a, b, rounding_mode=rounding_mode) self.assertEqual(actual, expect) @dtypes(torch.bfloat16, torch.half, torch.float32, torch.float64) def test_divide_by_zero_rounding(self, device, dtype): a = torch.tensor([1.0, -1.0, 0, 0.1, -0.1, np.pi, -np.pi, np.inf, -np.inf, np.nan], dtype=dtype) exact_dtype = (dtype != torch.bfloat16) if exact_dtype: an = a.cpu().numpy() else: an = a.float().cpu().numpy() zero = torch.zeros_like(a) # NOTE: NumPy's floor_divide rounding changed in 1.20.0 to be consistent with divide expect = np.divide(an, 0) for rounding_mode in (None, 'floor'): actual = torch.divide(a, 0, rounding_mode=rounding_mode) self.assertEqual(actual, expect, exact_dtype=exact_dtype) actual = torch.divide(a, zero, rounding_mode=rounding_mode) self.assertEqual(actual, expect, exact_dtype=exact_dtype) @dtypes(*torch.testing.get_all_dtypes( include_bool=False, include_complex=False, include_bfloat16=False)) def test_div_rounding_numpy(self, device, dtype): info = (torch.finfo(dtype) if dtype.is_floating_point else torch.iinfo(dtype)) low, high = info.min, info.max a = make_tensor((4096,), device, dtype, low=low, high=high) b = make_tensor((4096,), device, dtype, low=low, high=high) b[b == 0] = 1 exact_dtype = dtype != torch.bfloat16 if exact_dtype: an, bn = a.cpu().numpy(), b.cpu().numpy() else: an, bn = a.float().cpu().numpy(), b.float().cpu().numpy() for mode, np_ref in ( (None, np.true_divide), ("floor", np.floor_divide), ("trunc", lambda a, b: np.trunc(np.true_divide(a, b)).astype(a.dtype)) ): with np.errstate(all='ignore'): expect = torch.from_numpy(np_ref(an, bn)) kwargs = dict(rounding_mode=mode) if mode is not None else {} with set_default_dtype(torch.double): actual = torch.divide(a, b, **kwargs) self.assertEqual(actual, expect, exact_device=False, exact_dtype=exact_dtype) expect = expect[::2] with set_default_dtype(torch.double): actual = torch.divide(a[::2], b[::2], **kwargs) self.assertEqual(actual, expect, exact_device=False, exact_dtype=exact_dtype) @onlyCUDA def test_cross_device_inplace_error_msg(self, device): a = torch.tensor(2.) b = torch.tensor(2., device=device) with self.assertRaisesRegex(RuntimeError, "Expected all tensors to be on the same device"): a += b @onlyOnCPUAndCUDA def test_out_resize_warning(self, device): a = torch.tensor((1, 2, 3), device=device, dtype=torch.float32) b = torch.tensor((4, 5, 6), device=device, dtype=torch.float32) unary_inputs = (a,) binary_inputs = (a, b) unary_ops = (torch.ceil, torch.exp) binary_ops = (torch.add, torch.sub) for op in (unary_ops + binary_ops): with warnings.catch_warnings(record=True) as w: warnings.simplefilter("always") inputs = unary_inputs if op in unary_ops else binary_inputs # No warnings op(*inputs, out=torch.empty(3, device=device)) op(*inputs, out=torch.empty(0, device=device)) self.assertEqual(len(w), 0) # Cases that throw warnings op(*inputs, out=torch.empty(2, device=device)) self.assertEqual(len(w), 1) # Verifies that the inplace dunders (like idiv) actually are in place @onlyOnCPUAndCUDA def test_inplace_dunders(self, device): t = torch.randn((1,), device=device) expected = t.data_ptr() t += 1 t -= 1 t *= 1 t /= 1 with self.assertWarnsOnceRegex(UserWarning, 'floor_divide'): t //= 1 t %= 1 self.assertEqual(expected, t.data_ptr()) def check_internal_mem_overlap(self, inplace_op, num_inputs, dtype, device, expected_failure=False): if isinstance(inplace_op, str): inplace_op = getattr(torch.Tensor, inplace_op) input = torch.randn(1, dtype=dtype, device=device).expand(3, 3) inputs = [input] + [torch.randn_like(input) for i in range(num_inputs - 1)] if not expected_failure: with self.assertRaisesRegex(RuntimeError, 'single memory location'): inplace_op(*inputs) else: with self.assertRaises(AssertionError): with self.assertRaisesRegex(RuntimeError, 'single memory location'): inplace_op(*inputs) def unary_check_input_output_mem_overlap(self, data, sz, op, expected_failure=False): def _test(op, output, input): output_exp = torch.empty_like(output) op(input, out=output_exp) self.assertEqual(op(input, out=output), output_exp, msg=op.__name__) # output is identical to input: _test(op, output=data[0:sz], input=data[0:sz]) # output and input are independent: _test(op, output=data[0:sz], input=data[sz:2 * sz]) # output partially overlaps with input: if not expected_failure: with self.assertRaisesRegex(RuntimeError, 'unsupported operation'): _test(op, data[0:sz], data[1:sz + 1]) else: with self.assertRaises(AssertionError): with self.assertRaisesRegex(RuntimeError, 'unsupported operation'): _test(op, data[0:sz], data[1:sz + 1]) def binary_check_input_output_mem_overlap(self, op, device, expected_failure=False): sz = 3 data = torch.randn(2 * sz, device=device) other = torch.randn(sz, device=device) self.unary_check_input_output_mem_overlap( data, sz, lambda input, out: op(other, input, out=out), expected_failure=expected_failure) self.unary_check_input_output_mem_overlap( data, sz, lambda input, out: op(input, other, out=out), expected_failure=expected_failure) @dtypes(torch.double) def test_binary_op_mem_overlap(self, device, dtype): ops = [ ("add", True, True, 'cpu'), ("add", True, True, 'cuda'), ("mul", True, True, 'cpu'), ("mul", True, True, 'cuda'), ("sub", True, True, 'cpu'), ("sub", True, True, 'cuda'), ("div", True, True, 'cpu'), ("div", True, True, 'cuda'), ("pow", True, True, 'cpu'), ("pow", True, True, 'cuda'), ("fmod", True, True, 'cpu'), ("fmod", True, True, 'cuda'), ("atan2", True, True, 'cpu'), ("atan2", True, True, 'cuda'), ("hypot", True, True, 'cpu'), ("hypot", True, True, 'cuda'), ("igamma", True, True, 'cpu'), ("igamma", True, True, 'cuda'), ("igammac", True, True, 'cpu'), ("igammac", True, True, 'cuda'), ("nextafter", True, True, 'cpu'), ("nextafter", True, True, 'cuda'), ("le", True, True, 'cpu'), ("le", True, True, 'cuda'), ("lt", True, True, 'cpu'), ("lt", True, True, 'cuda'), ("ge", True, True, 'cpu'), ("ge", True, True, 'cuda'), ("gt", True, True, 'cpu'), ("gt", True, True, 'cuda'), ("eq", True, True, 'cpu'), ("eq", True, True, 'cuda'), ("ne", True, True, 'cpu'), ("ne", True, True, 'cuda'), ("logical_and", True, True, 'cpu'), ("logical_and", True, True, 'cuda'), ("logical_or", True, True, 'cpu'), ("logical_or", True, True, 'cuda'), ("logical_xor", True, True, 'cpu'), ("logical_xor", True, True, 'cuda'), ] for (fn, has_input_output_mem_overlap_check, has_internal_mem_overlap_check, dev) in ops: if dev != device: continue out_op = getattr(torch, fn) inplace_op = getattr(torch.Tensor, fn + '_') self.check_internal_mem_overlap( inplace_op, 2, dtype, device, expected_failure=not has_internal_mem_overlap_check) self.binary_check_input_output_mem_overlap(out_op, device, expected_failure=not has_input_output_mem_overlap_check) def _do_pow_for_exponents(self, m1, exponents, pow_fn, atol): for num in exponents: if isinstance(num, int) and num < 0 and not m1.is_floating_point() and not m1.is_complex(): with self.assertRaisesRegex(RuntimeError, r'Integers to negative integer powers are not allowed\.'): torch.pow(m1[4], num) else: # base - tensor, exponent - number # contiguous res1 = torch.pow(m1[4], num) res2 = res1.clone().zero_() # `math.pow` has issues with complex exponentiation so we need to resort to normal `pow`. for i in range(res2.size(0)): res2[i] = pow_fn(m1[4][i], num) rtol = 0 if atol is not None else None self.assertEqual(res1, res2, atol=atol, rtol=rtol) # non-contiguous res1 = torch.pow(m1[:, 4], num) res2 = res1.clone().zero_() for i in range(res2.size(0)): res2[i] = pow_fn(m1[i, 4], num) self.assertEqual(res1, res2, atol=atol, rtol=rtol) # scalar ** tensor to enforce correct handling of dtypes for __rpow__(). expected_dtype = torch.result_type(num, m1) res1 = num ** m1[4] res2 = torch.tensor(num, dtype=expected_dtype, device=m1.device) ** m1[4] self.assertEqual(res1, res2) self.assertEqual(res1.dtype, expected_dtype) @dtypes(*all_types_and_complex_and(torch.half, torch.bfloat16)) def test_pow(self, device, dtype): m1 = torch.empty(0, dtype=dtype, device=device) if m1.is_floating_point() or m1.is_complex(): m1 = make_tensor((100, 100), low=0, high=1, dtype=dtype, device=device) + 0.5 else: # math.pow will overflow and throw exceptions for large integers range_high = 4 if dtype in (torch.int8, torch.uint8) else 10 m1 = make_tensor((100, 100), low=1, high=range_high, dtype=dtype, device=device) exponents = [-2.8, -2, -1, -0.5, 0, 0.5, 1, 2, 3, 4, 3.3] complex_exponents = [-2.5j, -1.0j, 0j, 1.0j, 2.5j, 1.0 + 1.0j, -1.0 - 1.5j, 3.3j] if m1.is_complex(): self._do_pow_for_exponents(m1, exponents + complex_exponents, pow, 10e-4) else: self._do_pow_for_exponents(m1, exponents, math.pow, None) self._do_pow_for_exponents(m1, complex_exponents, pow, 10e-4) # base - number, exponent - tensor # contiguous res1 = torch.pow(3, m1[4]) res2 = res1.clone().zero_() for i in range(res2.size(0)): res2[i] = pow(3, m1[4, i]) self.assertEqual(res1, res2) # non-contiguous res1 = torch.pow(3, m1[:, 4]) res2 = res1.clone().zero_() for i in range(res2.size(0)): res2[i] = pow(3, m1[i][4]) self.assertEqual(res1, res2) # TODO: refactor all these tests using opinfos properly def _test_pow(self, base, exponent, np_exponent=None): if np_exponent is None: np_exponent = exponent def to_np(value): if isinstance(value, torch.Tensor): return value.cpu().numpy() return value try: np_res = np.power(to_np(base), to_np(np_exponent)) expected = torch.from_numpy(np_res) if isinstance(np_res, np.ndarray) else torch.tensor(np_res, dtype=base.dtype) except ValueError as e: err_msg = "Integers to negative integer powers are not allowed." self.assertEqual(str(e), err_msg) out = torch.empty_like(base) test_cases = [ lambda: base.pow(exponent), lambda: base.pow_(exponent), lambda: torch.pow(base, exponent), lambda: torch.pow(base, exponent, out=out) ] for test_case in test_cases: self.assertRaisesRegex(RuntimeError, err_msg, test_case) else: if isinstance(base, torch.Tensor): actual = base.pow(exponent) self.assertEqual(actual, expected.to(actual)) actual = base.clone() # When base is a 0-dim cpu tensor and exp is a cuda tensor, we exp `pow` to work but `pow_` to fail, since # `pow` will try to create the output tensor on a cuda device, but `pow_` needs to use the cpu tensor as the output if (isinstance(exponent, torch.Tensor) and base.dim() == 0 and base.device.type == 'cpu' and exponent.device.type == 'cuda'): regex = 'Expected all tensors to be on the same device, but found at least two devices, cuda.* and cpu!' self.assertRaisesRegex(RuntimeError, regex, base.pow_, exponent) elif torch.can_cast(torch.result_type(base, exponent), base.dtype): actual2 = actual.pow_(exponent) self.assertEqual(actual, expected) self.assertEqual(actual2, expected) else: self.assertRaisesRegex(RuntimeError, "Found dtype \\w+ but expected \\w+", lambda: actual.pow_(exponent)) actual = torch.pow(base, exponent) self.assertEqual(actual, expected.to(actual)) actual2 = torch.pow(base, exponent, out=actual) self.assertEqual(actual, expected.to(actual)) self.assertEqual(actual2, expected.to(actual)) # Tests pow() for integral, floating-type tensors, with integral, floating-type # exponents (tensor or scalar), respectively. noncontiguous tensors are also tested. def test_int_and_float_pow(self, device): def _test_int_and_float_pow(dt, low, high, dev): test_cases = ( ((4, 4), 0, (4, 1)), ((3, 1), 4, (3, 1)), ((2,), 4, (1,)), ((1,), 2, ()), ((513, 513), 4, (513,)), ((5, 5, 5), 5, (5,)), ((), 2, ()), ) for base_shape, exp_scalar, exp_shape in test_cases: base_tensor = make_tensor(base_shape, dtype=dt, device=dev, low=low, high=high) # int tensors don't take negative exponents if dt in [torch.uint8, torch.int8, torch.int16, torch.int32, torch.int64]: exp_tensor = make_tensor(exp_shape, dtype=dt, device=dev, low=0, high=high) else: exp_tensor = make_tensor(exp_shape, dtype=dt, device=dev, low=low, high=high) self._test_pow(base_tensor, exp_scalar) self._test_pow(base_tensor, exp_tensor) base_tensor = make_tensor(base_shape, dtype=dt, device=dev, low=low, high=high, noncontiguous=True) if dt in [torch.uint8, torch.int8, torch.int16, torch.int32, torch.int64]: exp_tensor = make_tensor(exp_shape, dtype=dt, device=dev, low=0, high=high, noncontiguous=True) else: exp_tensor = make_tensor(exp_shape, dtype=dt, device=dev, low=low, high=high, noncontiguous=True) self._test_pow(base_tensor, exp_scalar) self._test_pow(base_tensor, exp_tensor) _test_int_and_float_pow(torch.int8, -2, 2, device) _test_int_and_float_pow(torch.uint8, 0, 3, device) _test_int_and_float_pow(torch.int16, -5, 5, device) _test_int_and_float_pow(torch.int64, -10, 10, device) _test_int_and_float_pow(torch.int32, -10, 10, device) _test_int_and_float_pow(torch.float16, 0., 5., device) _test_int_and_float_pow(torch.float32, 0., 10., device) _test_int_and_float_pow(torch.float64, 0., 10., device) _test_int_and_float_pow(torch.float32, -10., 10., device) _test_int_and_float_pow(torch.float64, -10., 10., device) # Tests that a Runtime error occurs when a base tensor cannot be resized # by pow's inplace variant due to PyTorch's broadcasting semantics. def test_pow_inplace_resizing_exception(self, device): test_cases = ( ((), (3,)), ((2,), (2, 1)), ((2, 1), (2, 2)), ((2, 2), (2, 1, 1)), ) test_inputs = list((make_tensor(base_size, dtype=torch.float64, device=device, high=10., low=0.), make_tensor(exp_size, dtype=torch.float64, device=device, high=10., low=0.)) for base_size, exp_size in test_cases) for base, exponent in test_inputs: regex = "doesn't match the broadcast shape" self.assertRaisesRegex(RuntimeError, regex, base.pow_, exponent) def test_int_tensor_pow_neg_ints(self, device): ints = [torch.iinfo(torch.int32).min, -3, -2, -1, 0, 1, 2, 3, torch.iinfo(torch.int32).max] neg_ints = [torch.iinfo(torch.int32).min, -3, -2, -1] tensor = torch.tensor(ints, dtype=torch.int32, device=device) for pow in neg_ints: self._test_pow(tensor, pow) def test_long_tensor_pow_floats(self, device): ints = [0, 1, 23, 4567] floats = [0.0, 1 / 3, 1 / 2, 1.0, 3 / 2, 2.0] tensor = torch.tensor(ints, dtype=torch.int64, device=device) for pow in floats: self._test_pow(tensor, pow) @dtypes(*[torch.float32, torch.float64]) def test_float_scalar_pow_float_tensor(self, device, dtype): floats = [2.0, -3 / 2, -1.0, -1 / 2, -1 / 3, 0.0, 1 / 3, 1 / 2, 1.0, 3 / 2, 2.0] exponent_shapes = ( (1,), (2, 2), (2, 1), (2, 2, 2), ) tensors = list(make_tensor(shape, dtype=dtype, device=device, low=0) for shape in exponent_shapes) floats_tensor = torch.tensor(floats, dtype=dtype, device=device) for base in floats: self._test_pow(base, floats_tensor) for tensor in tensors: self._test_pow(base, tensor) @onlyCUDA def test_cuda_tensor_pow_scalar_tensor(self, device): cuda_tensors = [torch.randn((3, 3), device=device), torch.tensor(3.0, device=device)] scalar_tensors = [torch.tensor(5.0, device='cpu'), torch.tensor(-3), torch.tensor(1)] for base, exp in product(cuda_tensors, scalar_tensors): self._test_pow(base, exp) @onlyCUDA def test_cpu_tensor_pow_cuda_scalar_tensor(self, device): cuda_tensors = [torch.tensor(5.0, device='cuda'), torch.tensor(-3, device='cuda')] for exp in cuda_tensors: base = torch.randn((3, 3), device='cpu') regex = 'Expected all tensors to be on the same device, but found at least two devices, cuda.* and cpu!' self.assertRaisesRegex(RuntimeError, regex, torch.pow, base, exp) for exp in cuda_tensors: base = torch.tensor(3.0, device='cpu') self._test_pow(base, exp) @onlyCUDA @dtypes(torch.complex64, torch.complex128) def test_pow_cuda_complex_extremal_failing(self, device, dtype): t = torch.tensor(complex(-1., float('inf')), dtype=dtype, device=device) with self.assertRaises(AssertionError): cuda_out = t.pow(2) cpu_out = t.cpu().pow(2) self.assertEqual(cpu_out, cuda_out) @onlyOnCPUAndCUDA @dtypes(*(torch.testing.get_all_dtypes(include_bool=False, include_bfloat16=False))) def test_complex_scalar_pow_tensor(self, device, dtype): complexes = [0.5j, 1. + 1.j, -1.5j, 2.2 - 1.6j, 1 + 0j] first_exp = make_tensor((100,), device, dtype, low=-2, high=2) second_exp = make_tensor((100,), device, dtype, low=-2, high=2, noncontiguous=True) first_exp[0] = first_exp[10] = first_exp[20] = 0 second_exp[0] = second_exp[10] = second_exp[20] = 0 for base in complexes: self._test_pow(base, first_exp) self._test_pow(base, second_exp) @onlyOnCPUAndCUDA def test_pow_scalar_type_promotion(self, device): inputs = [17, [17]] for input in inputs: input_tensor_uint8 = torch.tensor(input, dtype=torch.uint8, device=device) out_uint8_computation = torch.pow(2, input_tensor_uint8, out=torch.tensor(0, dtype=torch.int64, device=device)) input_tensor_int64 = torch.tensor(input, dtype=torch.int64, device=device) out_int64_computation = torch.pow(2, input_tensor_int64, out=torch.tensor(0, dtype=torch.int64, device=device)) self.assertNotEqual(out_uint8_computation, out_int64_computation) self.assertEqual(out_uint8_computation.to(dtype=torch.uint8), out_int64_computation.to(dtype=torch.uint8)) def test_tensor_pow_tensor(self, dev): def rotate(l, n): return l[-n:] + l[:-n] def test_tensor_pow_tensor(values, torch_type, numpy_type): vals_tensor = torch.tensor(values, dtype=torch_type, device=dev) for i in range(len(values)): pows = rotate(values, i) pows_tensor = torch.tensor(pows, dtype=torch_type, device=dev) self._test_pow(vals_tensor, pows_tensor) ints = [0, 1, 2, 3] test_tensor_pow_tensor(ints, torch.uint8, np.uint8) test_tensor_pow_tensor(ints, torch.int8, np.int8) test_tensor_pow_tensor(ints, torch.int16, np.int16) test_tensor_pow_tensor(ints, torch.int32, np.int32) test_tensor_pow_tensor(ints, torch.int64, np.int64) floats = [-3.0, -2.0, -1.0, -1 / 2, -1 / 3, 0.0, 1 / 3, 1 / 2, 1.0, 2.0, 3.0] test_tensor_pow_tensor(floats, torch.float16, np.float16) test_tensor_pow_tensor(floats, torch.float32, np.float32) test_tensor_pow_tensor(floats, torch.float64, np.float64) def test_logical_xor_with_nontrivial_alignment(self, device): size = 128 a = (torch.randn(size, device=device) > 0) b = (torch.randn(size, device=device) > 0) c = (torch.randn(size, device=device) > 0) non_trivial_alignment = [1, 2, 4, 8, 15] for i in non_trivial_alignment: for j in non_trivial_alignment: for k in non_trivial_alignment: a_ = a[i: 100 + i] b_ = b[j: 100 + j] c_ = c[k: 100 + k] torch.logical_xor(a_, b_, out=c_) for x, y, z in zip(a_.tolist(), b_.tolist(), c_.tolist()): self.assertEqual(x ^ y, z) @dtypes(torch.float) def test_add_with_tail(self, device, dtype): for tail_size in [1, 63, 67, 130]: size = 4096 + tail_size a = torch.randn(size, device=device, dtype=dtype) b = torch.randn(size, device=device, dtype=dtype) c = a + b for x, y, z in zip(a.tolist(), b.tolist(), c.tolist()): self.assertEqual(x + y, z) @deviceCountAtLeast(2) @onlyCUDA def test_cross_device_binary_ops(self, devices): vals = (1., (2.,)) cpu_tensor = torch.randn(2, 2) def do_test(op, a, b): with self.assertRaisesRegex(RuntimeError, "Expected all tensors.+"): op(a, b) with self.assertRaisesRegex(RuntimeError, "Expected all tensors.+"): op(b, a) with self.assertRaisesRegex(RuntimeError, "Expected all tensors.+"): op(a, cpu_tensor) with self.assertRaisesRegex(RuntimeError, "Expected all tensors.+"): op(cpu_tensor, a) for op in (operator.add, torch.add, operator.sub, torch.sub, operator.mul, torch.mul, operator.truediv, torch.true_divide, operator.floordiv, torch.floor_divide): for a, b in product(vals, vals): a = torch.tensor(a, device=devices[0]) b = torch.tensor(b, device=devices[1]) do_test(op, a, b) @deviceCountAtLeast(2) @onlyCUDA def test_binary_op_scalar_device_unspecified(self, devices): scalar_val = torch.tensor(1.) for default_device in devices: with torch.cuda.device(default_device): for device in devices: device_obj = torch.device(device) x = torch.rand(3, device=device) y0 = x * scalar_val self.assertEqual(y0.device, device_obj) y1 = scalar_val * x self.assertEqual(y1.device, device_obj) self.assertEqual(y0, y1) def test_div_and_floordiv_vs_python(self, device): def _scalar_helper(python_op, torch_op): for a, b in product(range(-10, 10), range(-10, 10)): for op in (lambda x: x * .5, lambda x: math.floor(x)): a = op(a) b = op(b) if b == 0: continue expected = python_op(a, b) for op in (operator.truediv, torch.true_divide): actual_scalar = torch_op(a, b) a_t = torch.tensor(a, device=device) b_t = torch.tensor(b, device=device) actual_tensor = torch_op(a_t, b_t) actual_first_tensor = torch_op(a_t, b) actual_second_tensor = torch_op(a, b_t) self.assertEqual(actual_scalar, expected_div) self.assertEqual(actual_tensor.item(), expected_div) self.assertEqual(actual_first_tensor, actual_tensor) self.assertEqual(actual_second_tensor, actual_tensor) _scalar_helper(operator.truediv, operator.truediv) _scalar_helper(operator.truediv, torch.true_divide) with self.assertWarnsOnceRegex(UserWarning, 'floor_divide'): _scalar_helper(lambda a, b: math.trunc(a / b), operator.floordiv) _scalar_helper(lambda a, b: math.trunc(a / b), torch.floor_divide) @onlyOnCPUAndCUDA def test_div_and_floordiv_script_vs_python(self, device): def _wrapped_div(a, b): return a / b def _wrapped_floordiv(a, b): return a // b scripted_div = torch.jit.script(_wrapped_div) scripted_floordiv = torch.jit.script(_wrapped_floordiv) for a, b in product(range(-10, 10), range(-10, 10)): for op in (lambda x: x * .5, lambda x: math.floor(x)): a = op(a) b = op(b) if b == 0: continue expected_div = a / b expected_truncdiv = math.trunc(a / b) a_t = torch.tensor(a, device=device) b_t = torch.tensor(b, device=device) self.assertEqual(scripted_div(a_t, b_t), expected_div) with self.assertWarnsOnceRegex(UserWarning, 'floor_divide'): self.assertEqual(scripted_floordiv(a_t, b_t), expected_truncdiv) def _wrapped_div_scalar(a): return a / 5 def _wrapped_rdiv_scalar(a): return 5 / a def _wrapped_floordiv_scalar(a): return a // 5 def _wrapped_rfloordiv_scalar(a): return 5 // a scripted_div_scalar = torch.jit.script(_wrapped_div_scalar) scripted_rdiv_scalar = torch.jit.script(_wrapped_rdiv_scalar) scripted_floordiv_scalar = torch.jit.script(_wrapped_floordiv_scalar) scripted_rfloordiv_scalar = torch.jit.script(_wrapped_rfloordiv_scalar) for a in range(-10, 10): for op in (lambda x: x * .5, lambda x: math.floor(x)): a = op(a) a_t = torch.tensor(a, device=device) self.assertEqual(a / 5, scripted_div_scalar(a_t)) with self.assertWarnsOnceRegex(UserWarning, 'floor_divide'): self.assertEqual(math.trunc(a / 5), scripted_floordiv_scalar(a_t)) if a == 0: continue self.assertEqual(5 / a, scripted_rdiv_scalar(a_t)) if a_t.is_floating_point(): with self.assertRaises(RuntimeError): scripted_rfloordiv_scalar(a_t) else: # See issue gh-52387 self.assertEqual(5 // a, scripted_rfloordiv_scalar(a_t)) # NOTE: torch.floor_divide currently truncates instead of flooring # the quotient. See https://github.com/pytorch/pytorch/issues/43874. @onlyOnCPUAndCUDA def test_idiv_and_ifloordiv_vs_python(self, device): def _wrapped_idiv_tensor(a, b): a /= b return a def _wrapped_idiv_scalar(a): a /= 5 return a def _wrapped_true_divide__tensor(a, b): a.true_divide_(b) return a def _wrapped_true_divide__scalar(a): a.true_divide_(5) return a def _wrapped_floor_divide__tensor(a, b): a.floor_divide_(b) return a def _wrapped_floor_divide__scalar(a): a.floor_divide_(5) return a # The following functions are unsupported by the JIT def _wrapped_ifloordiv_tensor(a, b): a //= b return a def _wrapped_ifloordiv_scalar(a): a //= 5 return a with self.assertRaises(torch.jit.frontend.NotSupportedError): scripted_ifloordiv_tensor = torch.jit.script(_wrapped_ifloordiv_tensor) with self.assertRaises(torch.jit.frontend.NotSupportedError): scripted_ifloordiv_scalar = torch.jit.script(_wrapped_ifloordiv_scalar) scripted_idiv_tensor = torch.jit.script(_wrapped_idiv_tensor) scripted_idiv_scalar = torch.jit.script(_wrapped_idiv_scalar) scripted_true_divide__tensor = torch.jit.script(_wrapped_true_divide__tensor) scripted_true_divide__scalar = torch.jit.script(_wrapped_true_divide__scalar) scripted_floor_divide__tensor = torch.jit.script(_wrapped_floor_divide__tensor) scripted_floor_divide__scalar = torch.jit.script(_wrapped_floor_divide__scalar) for a, b in product(range(-10, 10), range(-10, 10)): for op in (lambda x: x * .5, lambda x: math.floor(x)): a = op(a) b = op(b) # Skips zero divisors if b == 0: continue expected_idiv = a / b expected_ifloordiv = a // b expected_itruncdiv = math.trunc(a / b) a_t = torch.tensor(a, device=device) b_t = torch.tensor(b, device=device) if a_t.is_floating_point(): tmp0 = a_t.clone() tmp0 /= b tmp1 = a_t.clone() tmp1 /= b_t self.assertEqual(tmp0.item(), expected_idiv) self.assertEqual(tmp1.item(), expected_idiv) self.assertEqual(scripted_true_divide__tensor(a_t.clone(), b_t).item(), expected_idiv) self.assertEqual(scripted_true_divide__scalar(a_t.clone()).item(), a / 5) else: tmp = a_t.clone() with self.assertRaises(RuntimeError): tmp /= b with self.assertRaises(RuntimeError): tmp /= b_t with self.assertRaises(RuntimeError): scripted_true_divide__tensor(tmp, b_t) with self.assertRaises(RuntimeError): scripted_true_divide__scalar(tmp) if not a_t.is_floating_point() and b_t.is_floating_point(): # Inplace modification fails because a float tensor is required # if the divisor is a float tensor with self.assertRaises(RuntimeError), self.assertWarnsOnceRegex(UserWarning, "floor_divide"): a_t.clone().floor_divide_(b_t) with self.assertRaises(RuntimeError), self.assertWarnsOnceRegex(UserWarning, "floor_divide"): scripted_floor_divide_tensor(a_t.clone(), b_t) tmp = a_t.clone() with self.assertRaises(RuntimeError), self.assertWarnsOnceRegex(UserWarning, "floor_divide"): tmp //= b_t else: # Inplace modification is OK when both or neither tensor is # a float tensor with self.assertWarnsOnceRegex(UserWarning, "floor_divide"): self.assertEqual(a_t.clone().floor_divide_(b_t).item(), expected_itruncdiv) self.assertEqual(scripted_floor_divide__tensor(a_t.clone(), b_t).item(), expected_itruncdiv) tmp = a_t.clone() with self.assertWarnsOnceRegex(UserWarning, "floor_divide"): tmp //= b_t self.assertEqual(tmp.item(), expected_itruncdiv) with self.assertWarnsOnceRegex(UserWarning, "floor_divide"): self.assertEqual(scripted_floor_divide__scalar(a_t), math.trunc(a / 5)) # Tests binary op equivalence with Python builtin ops # Also tests that reverse operations are equivalent to forward ops # NOTE: division ops are tested separately above def test_binary_ops_with_scalars(self, device): for ops in ((operator.add, torch.add), (operator.sub, torch.sub), (operator.mul, torch.mul), (operator.truediv, torch.div)): python_op, torch_op = ops for a, b in product(range(-10, 10), range(-10, 10)): for op in (lambda x: x * .5, lambda x: math.floor(x)): a = op(a) b = op(b) # Skips zero divisors if b == 0 or a == 0: continue a_tensor = torch.tensor(a, device=device) b_tensor = torch.tensor(b, device=device) a_tensor_cpu = a_tensor.cpu() b_tensor_cpu = b_tensor.cpu() vals = (a, b, a_tensor, b_tensor, a_tensor_cpu, b_tensor_cpu) for args in product(vals, vals): first, second = args first_scalar = first if not isinstance(first, torch.Tensor) else first.item() second_scalar = second if not isinstance(second, torch.Tensor) else second.item() expected = python_op(first_scalar, second_scalar) self.assertEqual(expected, python_op(first, second)) self.assertEqual(expected, torch_op(first, second)) @dtypes(*product(torch.testing.get_all_dtypes(include_complex=False), torch.testing.get_all_dtypes(include_complex=False))) def test_maximum_minimum_type_promotion(self, device, dtypes): a = torch.tensor((0, 1), device=device, dtype=dtypes[0]) b = torch.tensor((1, 0), device=device, dtype=dtypes[1]) for op in (torch.maximum, torch.max, torch.fmax, torch.minimum, torch.min, torch.fmin): result = op(a, b) self.assertEqual(result.dtype, torch.result_type(a, b)) @dtypes(*(torch.testing.get_all_int_dtypes() + [torch.bool])) def test_maximum_minimum_int_and_bool(self, device, dtype): ops = ((torch.maximum, torch.max, np.maximum), (torch.minimum, torch.min, np.minimum), (torch.fmax, None, np.fmax), (torch.fmin, None, np.fmin)) rng = np.random.default_rng() a_np = np.array(rng.integers(-100, 100, size=10), dtype=torch_to_numpy_dtype_dict[dtype]) b_np = np.array(rng.integers(-100, 100, size=10), dtype=torch_to_numpy_dtype_dict[dtype]) for torch_op, alias, numpy_op in ops: a_tensor = torch.from_numpy(a_np).to(device=device, dtype=dtype) b_tensor = torch.from_numpy(b_np).to(device=device, dtype=dtype) tensor_result = torch_op(a_tensor, b_tensor) out = torch.empty_like(a_tensor) torch_op(a_tensor, b_tensor, out=out) numpy_result = numpy_op(a_np, b_np) if alias is not None: alias_result = alias(a_tensor, b_tensor) self.assertEqual(alias_result, tensor_result) self.assertEqual(tensor_result, numpy_result) self.assertEqual(out, numpy_result) @precisionOverride({torch.bfloat16: 1e-2}) @dtypes(*(torch.testing.get_all_fp_dtypes())) def test_maximum_minimum_float(self, device, dtype): ops = ((torch.maximum, torch.max, np.maximum), (torch.minimum, torch.min, np.minimum), (torch.fmax, None, np.fmax), (torch.fmin, None, np.fmin)) if dtype == torch.bfloat16: a_np = np.random.randn(10).astype(np.float64) b_np = np.random.randn(10).astype(np.float64) else: a_np = np.random.randn(10).astype(torch_to_numpy_dtype_dict[dtype]) b_np = np.random.randn(10).astype(torch_to_numpy_dtype_dict[dtype]) for torch_op, alias, numpy_op in ops: numpy_result = numpy_op(a_np, b_np) a_tensor = torch.from_numpy(a_np).to(device=device, dtype=dtype) b_tensor = torch.from_numpy(b_np).to(device=device, dtype=dtype) tensor_result = torch_op(a_tensor, b_tensor) out = torch.empty_like(a_tensor) torch_op(a_tensor, b_tensor, out=out) if alias is not None: alias_result = alias(a_tensor, b_tensor) self.assertEqual(alias_result, tensor_result, exact_dtype=False) self.assertEqual(tensor_result, numpy_result, exact_dtype=False) self.assertEqual(out, numpy_result, exact_dtype=False) @dtypes(*(torch.testing.get_all_fp_dtypes())) def test_maximum_minimum_float_nan_and_inf(self, device, dtype): # np.maximum and np.minimum functions compare input arrays element-wisely. # if one of the elements being compared is a NaN, then that element is returned. ops = ((torch.maximum, torch.max, np.maximum), (torch.minimum, torch.min, np.minimum), (torch.fmax, None, np.fmax), (torch.fmin, None, np.fmin)) a_vals = (float('inf'), -float('inf'), float('nan'), float('inf'), float('nan'), float('nan'), 1, float('nan')) b_vals = (-float('inf'), float('inf'), float('inf'), float('nan'), float('nan'), 0, float('nan'), -5) if dtype == torch.bfloat16: a_np = np.array(a_vals, dtype=np.float64) b_np = np.array(b_vals, dtype=np.float64) else: a_np = np.array(a_vals, dtype=torch_to_numpy_dtype_dict[dtype]) b_np = np.array(b_vals, dtype=torch_to_numpy_dtype_dict[dtype]) for torch_op, alias, numpy_op in ops: numpy_result = numpy_op(a_np, b_np) a_tensor = torch.from_numpy(a_np).to(device=device, dtype=dtype) b_tensor = torch.from_numpy(b_np).to(device=device, dtype=dtype) tensor_result = torch_op(a_tensor, b_tensor) out = torch.empty_like(a_tensor) torch_op(a_tensor, b_tensor, out=out) if alias is not None: alias_result = alias(a_tensor, b_tensor) self.assertEqual(alias_result, tensor_result) if dtype == torch.bfloat16: self.assertEqual(tensor_result, numpy_result, exact_dtype=False) self.assertEqual(out, numpy_result, exact_dtype=False) else: self.assertEqual(tensor_result, numpy_result) self.assertEqual(out, numpy_result) @dtypes(*product(torch.testing.get_all_complex_dtypes(), torch.testing.get_all_dtypes())) def test_maximum_minimum_complex(self, device, dtypes): for torch_op in (torch.maximum, torch.minimum, torch.max, torch.min, torch.fmax, torch.fmin): with self.assertRaisesRegex(RuntimeError, '.+not implemented for.+'): torch_op(torch.ones(1, device=device, dtype=dtypes[0]), torch.ones(1, device=device, dtype=dtypes[1])) with self.assertRaisesRegex(RuntimeError, '.+not implemented for.+'): torch_op(torch.ones(1, device=device, dtype=dtypes[1]), torch.ones(1, device=device, dtype=dtypes[0])) @onlyCUDA def test_maximum_minimum_cross_device(self, device): a = torch.tensor((1, 2, -1)) b = torch.tensor((3, 0, 4), device=device) ops = (torch.maximum, torch.minimum) for torch_op in ops: with self.assertRaisesRegex(RuntimeError, "Expected all tensors to be on the same device"): torch_op(a, b) with self.assertRaisesRegex(RuntimeError, "Expected all tensors to be on the same device"): torch_op(b, a) # test cuda tensor and cpu scalar ops = ((torch.maximum, np.maximum), (torch.minimum, np.minimum)) a_np = np.array(1) b_np = np.array([3, 0, 4]) for torch_op, numpy_op in ops: a_tensor = torch.from_numpy(a_np) b_tensor = torch.from_numpy(b_np).to(device=device) tensor_result_1 = torch_op(a_tensor, b_tensor) numpy_result_1 = numpy_op(a_np, b_np) tensor_result_2 = torch_op(b_tensor, a_tensor) numpy_result_2 = numpy_op(b_np, a_np) self.assertEqual(tensor_result_1, numpy_result_1) self.assertEqual(tensor_result_2, numpy_result_2) # TODO: tests like this should be generic @dtypesIfCUDA(torch.half, torch.float, torch.double) @dtypes(torch.float, torch.double) def test_mul_intertype_scalar(self, device, dtype): x = torch.tensor(1.5, dtype=dtype, device=device) y = torch.tensor(3, dtype=torch.int32, device=device) self.assertEqual(x * y, 4.5) self.assertEqual(y * x, 4.5) with self.assertRaisesRegex(RuntimeError, "can't be cast to the desired output type"): y *= x x *= y self.assertEqual(x, 4.5) @onlyCPU @dtypes(*torch.testing.get_all_dtypes()) def test_sub(self, device, dtype): m1 = torch.tensor([2.34, 4.44], dtype=dtype, device=device) m2 = torch.tensor([1.23, 2.33], dtype=dtype, device=device) if dtype == torch.bool: self.assertRaises(RuntimeError, lambda: m1 - m2) elif (dtype == torch.bfloat16 or dtype == torch.half): self.assertEqual(m1 - m2, torch.tensor([1.11, 2.11], dtype=dtype), atol=0.01, rtol=0) else: self.assertEqual(m1 - m2, torch.tensor([1.11, 2.11], dtype=dtype)) @onlyCPU @dtypes(torch.float) def test_csub(self, device, dtype): a = torch.randn(100, 90, dtype=dtype, device=device) b = a.clone().normal_() res_add = torch.add(a, b, alpha=-1) res_csub = a.clone() res_csub.sub_(b) self.assertEqual(res_add, res_csub) a = torch.randn(100, 100, dtype=dtype, device=device) scalar = 123.5 res_add = torch.add(a, -scalar) res_csub = a.clone() res_csub.sub_(scalar) self.assertEqual(res_add, res_csub) @dtypesIfCUDA(torch.half, torch.float, torch.double) @dtypes(torch.float, torch.double) def test_min_max_binary_op_nan(self, device, dtype): a = torch.rand(1000, dtype=dtype, device=device) b = torch.rand(1000, dtype=dtype, device=device) a[:250] = float('nan') b[250:500] = float('nan') a[500:750] = float('nan') b[500:750] = float('nan') ma = torch.max(a, b) mi = torch.min(a, b) for i in range(750): self.assertTrue(torch.isnan(ma[i]), "max(a, b): {}, a: {}, b: {}".format(ma[i], a[i], b[i])) self.assertTrue(torch.isnan(mi[i]), "min(a, b): {}, a: {}, b: {}".format(mi[i], a[i], b[i])) for i in range(750, 1000): self.assertFalse(torch.isnan(ma[i]), "max(a, b): {}, a: {}, b: {}".format(ma[i], a[i], b[i])) self.assertFalse(torch.isnan(mi[i]), "min(a, b): {}, a: {}, b: {}".format(mi[i], a[i], b[i])) @dtypes(*product(torch.testing.get_all_dtypes(include_complex=False), torch.testing.get_all_dtypes(include_complex=False))) def test_copysign(self, device, dtypes): def _test_copysign_numpy(a, b): torch_result = torch.copysign(a, b) if a.dtype == torch.bfloat16: np_a = a.to(torch.float).cpu().numpy() else: np_a = a.cpu().numpy() if b.dtype == torch.bfloat16: np_b = b.to(torch.float).cpu().numpy() else: np_b = b.cpu().numpy() expected = torch.from_numpy(np.copysign(np_a, np_b)) types = [torch.bool, torch.bfloat16] + torch.testing.get_all_int_dtypes() if a.dtype in types or b.dtype in types: promoted_type = torch.promote_types(torch_result.dtype, expected.dtype) torch_result = torch_result.to(promoted_type) expected = expected.to(promoted_type) self.assertEqual(torch_result, expected) if a.dtype != torch.float16 and b.dtype != torch.float16: self.assertEqual(torch.copysign(torch.tensor(1.0), torch_result), torch.copysign(torch.tensor(1.0), expected)) a = make_tensor((10, 10), device=device, dtype=dtypes[0], low=-9, high=9) b = make_tensor((10, 10), device=device, dtype=dtypes[1], low=-9, high=9) _test_copysign_numpy(a, b) a = make_tensor((10, 1, 10), device=device, dtype=dtypes[0], low=-9, high=9) b = make_tensor((10, 10), device=device, dtype=dtypes[1], low=-9, high=9) _test_copysign_numpy(a, b) a = make_tensor((10, 10), device=device, dtype=dtypes[0], low=-9, high=9) b = make_tensor((10, 1, 10), device=device, dtype=dtypes[1], low=-9, high=9) _test_copysign_numpy(a, b) cases = [0.0, -0.0, float('inf'), float('-inf'), float('nan')] types = [torch.float32, torch.float64] if device == 'cpu': types.append(torch.float16) if dtypes[0] in types: b = make_tensor((10, 10), device=device, dtype=dtypes[1], low=-9, high=9) for case in cases: _test_copysign_numpy(torch.tensor([case], device=device, dtype=dtypes[0]), b) if dtypes[1] in torch.testing.get_all_fp_dtypes(): a = make_tensor((10, 10), device=device, dtype=dtypes[0], low=-9, high=9) for case in cases: _test_copysign_numpy(a, torch.tensor([case], device=device, dtype=dtypes[1])) @dtypes(torch.bfloat16, torch.float) def test_div(self, device, dtype): for op, method, inplace in ((torch.div, torch.Tensor.div, torch.Tensor.div_), (torch.true_divide, torch.Tensor.true_divide, torch.Tensor.true_divide_)): m1 = torch.randn(10, 10, dtype=torch.float, device=device).to(dtype=dtype) res1 = m1.clone() inplace(res1[:, 3], 2) res2 = m1.clone() for i in range(m1.size(0)): res2[i, 3] = res2[i, 3] / 2 self.assertEqual(res1, res2) if dtype == torch.bfloat16: a1 = torch.tensor([4.2, 6.2], dtype=dtype, device=device) a2 = torch.tensor([2., 2.], dtype=dtype, device=device) self.assertEqual(op(a1, a2), torch.tensor([2.1, 3.1], dtype=dtype, device=device), atol=0.01, rtol=0) self.assertEqual(method(a1, a2), op(a1, a2)) @dtypes(torch.bfloat16, torch.float) def test_true_divide_out(self, device, dtype): a1 = torch.tensor([4.2, 6.2], dtype=dtype, device=device) a2 = torch.tensor([2., 2.], dtype=dtype, device=device) res = torch.empty_like(a1) self.assertEqual(torch.true_divide(a1, a2, out=res), torch.tensor([2.1, 3.1], dtype=dtype, device=device), atol=0.01, rtol=0) @onlyCUDA @dtypes(torch.half) def test_divmul_scalar(self, device, dtype): x = torch.tensor(100., device=device, dtype=dtype) x_ref = x.float() scale = 1e5 res = x.div(scale) expected = x_ref.div(scale) self.assertEqual(res, expected.to(dtype), atol=0., rtol=0.) x = torch.tensor(1e-5, device=device, dtype=dtype) x_ref = x.float() res = x.mul(scale) expected = x_ref.mul(scale) self.assertEqual(res, expected.to(dtype), atol=0., rtol=0.) res = scale * x self.assertEqual(res, expected.to(dtype), atol=0., rtol=0.) @dtypesIfCUDA(*set(torch.testing.get_all_math_dtypes('cuda')) - {torch.complex64, torch.complex128}) @dtypes(*set(torch.testing.get_all_math_dtypes('cpu')) - {torch.complex64, torch.complex128}) def test_floor_divide_tensor(self, device, dtype): x = torch.randn(10, device=device).mul(30).to(dtype) y = torch.arange(1, 11, dtype=dtype, device=device) with self.assertWarnsOnceRegex(UserWarning, "floor_divide"): z = x // y z_alt = torch.trunc(x.double() / y.double()).to(dtype) self.assertEqual(z.dtype, x.dtype) self.assertEqual(z, z_alt) @dtypesIfCUDA(*set(torch.testing.get_all_math_dtypes('cuda')) - {torch.complex64, torch.complex128}) @dtypes(*set(torch.testing.get_all_math_dtypes('cpu')) - {torch.complex64, torch.complex128}) def test_floor_divide_scalar(self, device, dtype): x = torch.randn(100, device=device).mul(10).to(dtype) with self.assertWarnsOnceRegex(UserWarning, "floor_divide"): z = x // 3 z_alt = torch.tensor([math.trunc(v.item() / 3.) for v in x], dtype=x.dtype, device=device) self.assertEqual(z.dtype, x.dtype) self.assertEqual(z, z_alt) @onlyOnCPUAndCUDA @dtypes(torch.float, torch.long) def test_floor_divide_out(self, device, dtype): x = torch.randn(10, device=device).mul(10).to(dtype) y = torch.arange(1, 11, dtype=dtype, device=device) o = torch.empty(10, dtype=dtype, device=device) with self.assertWarnsOnceRegex(UserWarning, "floor_divide"): torch.floor_divide(x, y, out=o) self.assertEqual(o, x // y) torch.floor_divide(x, 2, out=o) self.assertEqual(o, x // 2) if dtype == torch.int: o = torch.empty(10, dtype=torch.float, device=device) torch.floor_divide(x, y, out=o) self.assertEqual(o, torch.floor_divide(x.float(), y.float())) @onlyCPU @dtypes(*torch.testing.get_all_math_dtypes('cpu')) def test_rdiv(self, device, dtype): if dtype is torch.float16: return elif dtype.is_complex: x = torch.rand(100, dtype=dtype, device=device).add(1).mul(4) else: x = torch.rand(100, device=device).add(1).mul(4).to(dtype) y = 30 / x z = torch.tensor([30 / v.item() for v in x], device=device) self.assertEqual(y, z, exact_dtype=False) @dtypes(*torch.testing.get_all_fp_dtypes(include_bfloat16=False)) def test_fmod_remainder_by_zero_float(self, device, dtype): fn_list = (torch.fmod, torch.remainder) for fn in fn_list: x = make_tensor((10, 10), device=device, dtype=dtype, low=-9, high=9) zero = torch.zeros_like(x) self.assertTrue(torch.all(fn(x, 0.0).isnan())) self.assertTrue(torch.all(fn(x, zero).isnan())) @onlyOnCPUAndCUDA @skipCUDAIfRocm @dtypes(*torch.testing.get_all_int_dtypes()) def test_fmod_remainder_by_zero_integral(self, device, dtype): fn_list = (torch.fmod, torch.remainder) for fn in fn_list: x = make_tensor((10, 10), device=device, dtype=dtype, low=-9, high=9) zero = torch.zeros_like(x) if self.device_type == 'cpu': with self.assertRaisesRegex(RuntimeError, "ZeroDivisionError"): fn(x, zero) # for integral dividend (other than int64) divided by zero. For int64, # CUDA returns all 1s for negative dividend, half 1s for positive dividend. # uint8: 0xff -> 255 # int32: 0xffffffff -> -1 else: if dtype == torch.int64: self.assertEqual(fn(x, zero) == 4294967295, x >= 0) self.assertEqual(fn(x, zero) == -1, x < 0) else: value = 255 if dtype == torch.uint8 else -1 self.assertTrue(torch.all(fn(x, zero) == value)) @dtypes(*torch.testing.get_all_dtypes(include_bfloat16=False, include_bool=False, include_complex=False)) def test_fmod_remainder(self, device, dtype): # Use numpy as reference def _helper(x, mod, fns_list): for fn, inplace_fn, ref_fn in fns_list: np_x = x.cpu().numpy() if torch.is_tensor(x) else x np_mod = mod.cpu().numpy() if torch.is_tensor(mod) else mod exp = ref_fn(np_x, np_mod) exp = torch.from_numpy(exp) res = fn(x, mod) self.assertEqual(res, exp, exact_dtype=False) if torch.is_tensor(x): # out out = torch.empty(0, device=device, dtype=res.dtype) fn(x, mod, out=out) self.assertEqual(out, exp, exact_dtype=False) self.assertEqual(out.size(), torch.Size([10, 10])) # in-place (Type cast runtime error) try: inplace_fn(x, mod) self.assertEqual(x, exp, exact_dtype=False) except RuntimeError as e: self.assertRegex(str(e), "result type (Half|Float|Double) " "can't be cast to the desired output " "type (Byte|Char|Short|Int|Long)") x = make_tensor((10, 10), device=device, dtype=dtype, low=-9, high=9) mod = make_tensor((10, 10), device=device, dtype=dtype, low=-9, high=9) mod[mod == 0] = 1 mods = [3, 2.3, mod, mod.t()] if dtype in torch.testing.get_all_int_dtypes(): mod_float = make_tensor((10, 10), device=device, dtype=torch.float, low=-9, high=9) mod[mod == 0] = 1 mods.append(mod_float) for dividend, mod in product([x, x.t()], mods): _helper(dividend, mod, ((torch.fmod, torch.Tensor.fmod_, np.fmod), (torch.remainder, torch.Tensor.remainder_, np.remainder),)) for dividend, mod in product([5, 3.14], mods): if torch.is_tensor(mod): _helper(dividend, mod, ((torch.remainder, torch.Tensor.remainder_, np.remainder),)) @dtypes(torch.float, torch.double) def test_remainder_fmod_large_dividend(self, device, dtype): alarge = 1e9 pi = 3.14159265358979 for avalue in [alarge, -alarge]: for bvalue in [pi, -pi]: a = torch.tensor([avalue], dtype=dtype, device=device) b = torch.tensor([bvalue], dtype=dtype, device=device) c = torch.remainder(a, b) d = torch.fmod(a, b) self.assertTrue((b[0] > 0) == (c[0] > 0)) self.assertTrue((a[0] > 0) == (d[0] > 0)) self.assertTrue(abs(c[0]) < abs(b[0])) self.assertTrue(abs(d[0]) < abs(b[0])) if ((a[0] > 0) == (b[0] > 0)): self.assertTrue(c[0] == d[0]) else: self.assertTrue(abs(c[0] - d[0]) == abs(b[0])) @dtypesIfCPU(torch.bfloat16, torch.float32, torch.float64) @dtypes(torch.float32, torch.float64) def test_hypot(self, device, dtype): inputs = [ (torch.randn(10, device=device).to(dtype), torch.randn(10, device=device).to(dtype)), (torch.randn((3, 3, 3), device=device).to(dtype), torch.randn((3, 3, 3), device=device).to(dtype)), (torch.randn((10, 1), device=device).to(dtype), torch.randn((10, 1), device=device).to(dtype).transpose(0, 1)), (torch.randint(100, (10, ), device=device, dtype=torch.long), torch.randn(10, device=device).to(dtype)) ] for input in inputs: actual = torch.hypot(input[0], input[1]) if dtype == torch.bfloat16: expected = torch.sqrt(input[0] * input[0] + input[1] * input[1]) else: expected = np.hypot(input[0].cpu().numpy(), input[1].cpu().numpy()) self.assertEqual(actual, expected, exact_dtype=False) @onlyOnCPUAndCUDA @dtypes(torch.uint8, torch.int8, torch.int16, torch.int32, torch.int64) def test_gcd(self, device, dtype): t1 = torch.tensor([0, 10, 0], dtype=dtype, device=device) t2 = torch.tensor([0, 0, 10], dtype=dtype, device=device) actual = torch.gcd(t1, t2) expected = np.gcd([0, 10, 0], [0, 0, 10]) self.assertEqual(actual, expected, exact_dtype=False) if dtype == torch.uint8: a = torch.tensor([190, 210], device=device, dtype=dtype) b = torch.tensor([190, 220], device=device, dtype=dtype) actual = torch.gcd(a, b) expected = torch.tensor([190, 10], device=device, dtype=dtype) self.assertEqual(actual, expected) else: a = torch.randint(-20, 20, (1024,), device=device, dtype=dtype) b = torch.randint(-20, 20, (1024,), device=device, dtype=dtype) actual = torch.gcd(a, b) expected = np.gcd(a.cpu().numpy(), b.cpu().numpy()) self.assertEqual(actual, expected) @onlyOnCPUAndCUDA @dtypes(torch.int16, torch.int32, torch.int64) def test_lcm(self, device, dtype): t1 = torch.tensor([0, 10, 0], dtype=dtype, device=device) t2 = torch.tensor([0, 0, 10], dtype=dtype, device=device) actual = torch.lcm(t1, t2) expected = np.lcm([0, 10, 0], [0, 0, 10]) self.assertEqual(actual, expected, exact_dtype=False) a = torch.randint(-20, 20, (1024,), device=device, dtype=dtype) b = torch.randint(-20, 20, (1024,), device=device, dtype=dtype) actual = torch.lcm(a, b) expected = np.lcm(a.cpu().numpy(), b.cpu().numpy()) self.assertEqual(actual, expected, exact_dtype=False) @onlyOnCPUAndCUDA @dtypes(torch.float32, torch.float64) def test_nextafter(self, device, dtype): t1 = torch.tensor([0, 0, 10], device=device, dtype=dtype) t2 = torch.tensor([inf, -inf, 10], device=device, dtype=dtype) actual = torch.nextafter(t1, t2) expected = np.nextafter(t1.cpu().numpy(), t2.cpu().numpy()) self.assertEqual(actual, expected, atol=0, rtol=0) actual = torch.nextafter(t2, t1) expected = np.nextafter(t2.cpu().numpy(), t1.cpu().numpy()) self.assertEqual(actual, expected, atol=0, rtol=0) t1 = torch.tensor([0, nan], device=device, dtype=dtype) t2 = torch.tensor([nan, 0], device=device, dtype=dtype) self.assertTrue(torch.nextafter(t1, t2).isnan().all()) a = torch.randn(100, device=device, dtype=dtype) b = torch.randn(100, device=device, dtype=dtype) actual = torch.nextafter(a, b) expected = np.nextafter(a.cpu().numpy(), b.cpu().numpy()) self.assertEqual(actual, expected, atol=0, rtol=0) def _test_cop(self, torchfn, mathfn, dtype, device): def reference_implementation(res2): for i, j in iter_indices(sm1): idx1d = i * sm1.size(0) + j res2[i, j] = mathfn(sm1[i, j], sm2[idx1d]) return res2 m1 = torch.randn(10, 10, 10, dtype=dtype, device=device) m2 = torch.randn(10, 10 * 10, dtype=dtype, device=device) sm1 = m1[4] sm2 = m2[4] res1 = torchfn(sm1, sm2.view(10, 10)) res2 = reference_implementation(res1.clone()) self.assertEqual(res1, res2) m1 = torch.randn(10, 10, 10, dtype=dtype, device=device) m2 = torch.randn(10 * 10, 10 * 10, dtype=dtype, device=device) sm1 = m1[:, 4] sm2 = m2[:, 4] sm2.set_(sm2.storage(), sm2.storage_offset(), sm1.size(), (sm2.stride()[0] * 10, sm2.stride()[0])) res1 = torchfn(sm1, sm2) sm2.set_(sm2.storage(), sm2.storage_offset(), m2[:, 4].size(), m2[:, 4].stride()) res2 = reference_implementation(res1.clone()) self.assertEqual(res1, res2) @onlyCPU @dtypes(torch.float) def test_cdiv(self, device, dtype): self._test_cop(torch.div, lambda x, y: x / y, dtype, device) @onlyCPU @dtypes(torch.float) def test_cremainder(self, device, dtype): self._test_cop(torch.remainder, lambda x, y: x % y, dtype, device) @onlyCPU @dtypes(torch.float) def test_cmul(self, device, dtype): self._test_cop(torch.mul, lambda x, y: x * y, dtype, device) @onlyCPU @dtypes(torch.float) def test_cpow(self, device, dtype): self._test_cop(torch.pow, lambda x, y: nan if x < 0 else math.pow(x, y), dtype, device) @onlyCPU @dtypes(torch.uint8, torch.int8, torch.int16, torch.int32, torch.int64) def test_floor_divide_zero(self, device, dtype): a = torch.tensor([0, 1], dtype=dtype, device=device) b = torch.tensor([0, 1], dtype=dtype, device=device) with self.assertRaisesRegex(RuntimeError, 'ZeroDivisionError'): with self.assertWarnsOnceRegex(UserWarning, "floor_divide"): a // b @unittest.skipIf(TEST_WITH_ASAN, "Integer overflows are not allowed under ASAN") @dtypes(*torch.testing.get_all_dtypes()) def test_muldiv_scalar(self, device, dtype): x = make_tensor((10, 3), device, dtype, low=None, high=None) s = make_tensor((1,), 'cpu', dtype, low=None, high=None).item() y = torch.full_like(x, s) self.assertEqual(x * s, x * y) self.assertEqual(s * x, y * x) self.assertEqual(x / s, x / y) self.assertEqual(s / x, y / x) @dtypes(*tuple(itertools.combinations_with_replacement(torch.testing.get_all_dtypes(), 2))) def test_comparison_ops_type_promotion_and_broadcasting(self, device, dtypes): def compare_with_numpy_bin_op(torch_fn, np_fn, x, y, out=None): # by letting numpy treat them as float32's x_np = x if x.dtype != torch.bfloat16 else x.to(torch.float32) y_np = y.cpu().numpy() if y.dtype != torch.bfloat16 else y.to(torch.float32).cpu().numpy() self.compare_with_numpy(lambda inp: torch_fn(inp, y, out=out) if out else torch_fn(inp, y), lambda inp: np_fn(inp, y_np, out=out) if out else np_fn(inp, y_np), x_np) complex_op_denylist = [torch.lt, torch.le, torch.gt, torch.ge] input_sizes = [ (1,), (10,), (10, 1), (1, 10), (4, 10), (64, 10), (12, 3)] op_pairs = [(torch.lt, np.less), (torch.le, np.less_equal), (torch.gt, np.greater), (torch.ge, np.greater_equal), (torch.eq, np.equal), (torch.ne, np.not_equal), (torch.logical_and, np.logical_and), (torch.logical_or, np.logical_or), (torch.logical_xor, np.logical_xor)] for size1 in input_sizes: size2 = (2,) + size1 for with_extremal in [False, True]: a = _generate_input(size1, dtypes[0], device, with_extremal) b = _generate_input(size2, dtypes[1], device, with_extremal) for torch_op, numpy_op in op_pairs: if (dtypes[0].is_complex or dtypes[1].is_complex) and torch_op in complex_op_denylist: continue compare_with_numpy_bin_op(torch_op, numpy_op, a, b) self.assertEqual(torch_op(a, b).dtype, torch.bool) out = torch.zeros(1, dtype=torch.complex128) compare_with_numpy_bin_op(torch_op, numpy_op, a, b, out=out) @onlyOnCPUAndCUDA @dtypes(torch.int8, torch.int16, torch.int32, torch.int64) def test_signed_shift(self, device, dtype): a = torch.tensor([-10, 10], device=device, dtype=dtype) expected_l = torch.tensor([-40, 40], device=device, dtype=dtype) self.assertEqual(a << 2, expected_l) self.compare_with_numpy(lambda x: x << 2, lambda x: np.left_shift(x, 2), a) expected_r = torch.tensor([-5, 5], device=device, dtype=dtype) self.assertEqual(a >> 1, expected_r) self.compare_with_numpy(lambda x: x >> 1, lambda x: np.right_shift(x, 1), a) def test_bitwise_and(self, device): for dtype in (torch.uint8, torch.int8, torch.int16, torch.int32, torch.int64): a = torch.tensor([1, -2, 3], dtype=dtype, device=device) b = torch.tensor([2, 1, 3], dtype=dtype, device=device) expected_res = torch.tensor([0, 0, 3], dtype=dtype, device=device) b_scalar = 2 expected_res_scalar = torch.tensor([0, 2, 2], dtype=dtype, device=device) self.assertEqual(torch.bitwise_and(a, b), expected_res) self.assertEqual(torch.bitwise_and(a, b_scalar), expected_res_scalar) c = torch.empty(0, dtype=dtype, device=device) torch.bitwise_and(a, b, out=c) self.assertEqual(c, expected_res) torch.bitwise_and(a, b_scalar, out=c) self.assertEqual(c, expected_res_scalar) a1 = a.clone() a1.bitwise_and_(b) self.assertEqual(a1, expected_res) a.bitwise_and_(b_scalar) self.assertEqual(a, expected_res_scalar) self.assertEqual(torch.tensor([False, True, False], device=device), torch.bitwise_and(torch.tensor([True, True, False], device=device), torch.tensor([False, True, False], device=device))) def test_bitwise_or(self, device): for dtype in (torch.uint8, torch.int8, torch.int16, torch.int32, torch.int64): a = torch.tensor([1, -2, 3], dtype=dtype, device=device) b = torch.tensor([2, 1, 3], dtype=dtype, device=device) expected_res = torch.tensor([3, -1, 3], dtype=dtype, device=device) b_scalar = 2 expected_res_scalar = torch.tensor([3, -2, 3], dtype=dtype, device=device) self.assertEqual(torch.bitwise_or(a, b), expected_res) self.assertEqual(torch.bitwise_or(a, b_scalar), expected_res_scalar) c = torch.empty(0, dtype=dtype, device=device) torch.bitwise_or(a, b, out=c) self.assertEqual(c, expected_res) torch.bitwise_or(a, b_scalar, out=c) self.assertEqual(c, expected_res_scalar) a1 = a.clone() a1.bitwise_or_(b) self.assertEqual(a1, expected_res) a.bitwise_or_(b_scalar) self.assertEqual(a, expected_res_scalar) self.assertEqual(torch.tensor([True, True, False], device=device), torch.bitwise_or(torch.tensor([True, True, False], device=device), torch.tensor([False, True, False], device=device))) def test_bitwise_xor(self, device): for dtype in (torch.uint8, torch.int8, torch.int16, torch.int32, torch.int64): a = torch.tensor([1, -2, 3], dtype=dtype, device=device) b = torch.tensor([2, 1, 3], dtype=dtype, device=device) expected_res = torch.tensor([3, -1, 0], dtype=dtype, device=device) b_scalar = 2 expected_res_scalar = torch.tensor([3, -4, 1], dtype=dtype, device=device) self.assertEqual(torch.bitwise_xor(a, b), expected_res) self.assertEqual(torch.bitwise_xor(a, b_scalar), expected_res_scalar) c = torch.empty(0, dtype=dtype, device=device) torch.bitwise_xor(a, b, out=c) self.assertEqual(c, expected_res) torch.bitwise_xor(a, b_scalar, out=c) self.assertEqual(c, expected_res_scalar) a1 = a.clone() a1.bitwise_xor_(b) self.assertEqual(a1, expected_res) a.bitwise_xor_(b_scalar) self.assertEqual(a, expected_res_scalar) self.assertEqual(torch.tensor([True, False, False], device=device), torch.bitwise_xor(torch.tensor([True, True, False], device=device), torch.tensor([False, True, False], device=device))) @dtypes(torch.uint8, torch.int8, torch.int16, torch.int32, torch.int64) def test_bitwise_shift(self, device, dtype): ops = [ (torch.bitwise_left_shift, np.left_shift), (operator.lshift, operator.lshift), (torch.bitwise_right_shift, np.right_shift), (operator.rshift, operator.rshift), ] for torch_op, numpy_op in ops: a = torch.tensor([19, -20, -21, 22], dtype=dtype, device=device) b = torch.tensor([2, 1, 3, 1], dtype=dtype, device=device) a_np = a.cpu().numpy() b_np = b.cpu().numpy() self.assertEqual(torch_op(a, b), torch.tensor(numpy_op(a_np, b_np), device=device)) self.assertEqual(torch_op(a, 2), torch.tensor(numpy_op(a_np, 2), device=device)) def test_bitwise_shift_float(self, device): ops = [ (torch.bitwise_left_shift, lambda x, y: x * 2. ** y), (operator.lshift, lambda x, y: x * 2. ** y), (torch.bitwise_right_shift, lambda x, y: x / 2. ** y), (operator.rshift, lambda x, y: x / 2. ** y), ] for torch_op, expected_op in ops: a = torch.tensor([19, -20, -21, 22], dtype=torch.int64, device=device) self.assertEqual(torch_op(a, 1.8), torch.floor(expected_op(a, 1)).to(a.dtype)) a = torch.tensor([19.1, -20.2, -21.3, 22.4], dtype=torch.float32, device=device) self.assertEqual(torch_op(a, 2), expected_op(a, 2)) a = torch.tensor([19.1, -20.2, -21.3, 22.4], dtype=torch.float32, device=device) self.assertEqual(torch_op(a, 2.2), expected_op(a, 2.2)) @onlyOnCPUAndCUDA @dtypes(*list(product(torch.testing.get_all_dtypes(include_complex=False), torch.testing.get_all_dtypes(include_complex=False)))) def test_heaviside(self, device, dtypes): input_dtype = dtypes[0] values_dtype = dtypes[1] rng = np.random.default_rng() input = np.array(rng.integers(-10, 10, size=10), dtype=torch_to_numpy_dtype_dict[input_dtype if (input_dtype != torch.bfloat16) else torch.float64]) input[0] = input[3] = input[7] = 0 values = np.array(rng.integers(-10, 10, size=10), dtype=torch_to_numpy_dtype_dict[values_dtype if (values_dtype != torch.bfloat16) else torch.float64]) np_result = torch.from_numpy(np.heaviside(input, values)).to(device=device, dtype=input_dtype) input = torch.from_numpy(input).to(device=device, dtype=input_dtype) values = torch.from_numpy(values).to(device=device, dtype=values_dtype) out = torch.empty_like(input) if input_dtype == values_dtype: torch_result = torch.heaviside(input, values) self.assertEqual(np_result, torch_result) torch_result = input.heaviside(values) self.assertEqual(np_result, torch_result) torch.heaviside(input, values, out=out) self.assertEqual(np_result, out) input.heaviside_(values) self.assertEqual(np_result, input) else: with self.assertRaisesRegex(RuntimeError, 'heaviside is not yet implemented for tensors with different dtypes.'): torch.heaviside(input, values) with self.assertRaisesRegex(RuntimeError, 'heaviside is not yet implemented for tensors with different dtypes.'): input.heaviside(values) with self.assertRaisesRegex(RuntimeError, 'heaviside is not yet implemented for tensors with different dtypes.'): torch.heaviside(input, values, out=out) with self.assertRaisesRegex(RuntimeError, 'heaviside is not yet implemented for tensors with different dtypes.'): input.heaviside_(values) @onlyCUDA def test_heaviside_cross_device(self, device): x = torch.tensor([-9, 5, 0, 6, -2, 2], device=device) y = torch.tensor(0) result = torch.heaviside(x, y) expect = torch.tensor([0, 1, 0, 1, 0, 1], device=device) self.assertEqual(result, expect) result = torch.heaviside(y, x) expect = torch.tensor([-9, 5, 0, 6, -2, 2], device=device) self.assertEqual(result, expect) x = torch.tensor([-9, 5, 0, 6, -2, 2]) y = torch.tensor(0, device=device) with self.assertRaisesRegex(RuntimeError, 'Expected all tensors to be on the same device'): torch.heaviside(x, y) with self.assertRaisesRegex(RuntimeError, 'Expected all tensors to be on the same device'): torch.heaviside(y, x) @dtypes(*list(product(torch.testing.get_all_complex_dtypes(), torch.testing.get_all_complex_dtypes()))) def test_heaviside_complex(self, device, dtypes): input_dtype = dtypes[0] values_dtype = dtypes[1] data = (complex(0, -6), complex(-1, 3), complex(1, 1)) input = torch.tensor(data, device=device, dtype=input_dtype) values = torch.tensor(data, device=device, dtype=values_dtype) out = torch.empty_like(input) real = input.real with self.assertRaisesRegex(RuntimeError, 'heaviside is not yet implemented for complex tensors.'): torch.heaviside(input, real) with self.assertRaisesRegex(RuntimeError, 'heaviside is not yet implemented for complex tensors.'): real.heaviside(values) with self.assertRaisesRegex(RuntimeError, 'heaviside is not yet implemented for complex tensors.'): input.heaviside_(values) with self.assertRaisesRegex(RuntimeError, 'heaviside is not yet implemented for complex tensors.'): torch.heaviside(real, real, out=out) def _test_logical(self, device, dtypes, op, a_, b_, expected_res_): expected_res = torch.tensor(expected_res_, dtype=dtypes[0], device=device) a = torch.tensor(a_, dtype=dtypes[0], device=device) b = torch.tensor(b_, dtype=dtypes[1], device=device) self.assertEqual(expected_res.bool(), getattr(a, op)(b)) c = torch.empty(0, dtype=torch.bool, device=device) getattr(torch, op)(a, b, out=c) self.assertEqual(expected_res.bool(), c) if dtypes[0] != dtypes[1]: with self.assertRaises(RuntimeError): getattr(a, op + '_')(b) return getattr(a, op + '_')(b) self.assertEqual(expected_res, a) @dtypes(*product(torch.testing.get_all_dtypes(), torch.testing.get_all_dtypes())) def test_logical_xor(self, device, dtypes): self._test_logical(device, dtypes, 'logical_xor', [10, 0, 1, 0], [1, 0, 0, 10], [0, 0, 1, 1]) @dtypes(*product(torch.testing.get_all_dtypes(), torch.testing.get_all_dtypes())) def test_logical_and(self, device, dtypes): self._test_logical(device, dtypes, 'logical_and', [10, 0, 1, 0], [1, 0, 0, 10], [1, 0, 0, 0]) @dtypes(*product(torch.testing.get_all_dtypes(), torch.testing.get_all_dtypes())) def test_logical_or(self, device, dtypes): self._test_logical(device, dtypes, 'logical_or', [10, 0, 1, 0], [1, 0, 0, 10], [1, 0, 1, 1]) def test_remainder_overflow(self, device): x = torch.tensor(23500, dtype=torch.int64, device=device) q = 392486996410368 self.assertEqual(x % q, x) self.assertEqual(-x % q, q - x) self.assertEqual(x % -q, x - q) self.assertEqual(-x % -q, -x) def test_rpow(self, device): m = torch.randn(10, 10, device=device) self.assertEqual(torch.pow(2, m), 2**m) m = torch.randn(1, device=device).squeeze() assert m.dim() == 0, "m is intentionally a scalar" self.assertEqual(torch.pow(2, m), 2**m) @onlyCPU def test_ldexp(self, device): mantissas = torch.randn(64, device=device) exponents = torch.randint(-31, 31, (64,), device=device, dtype=torch.int32) np_outcome = np.ldexp(mantissas.numpy(), exponents.numpy()) pt_outcome_1 = torch.ldexp(mantissas, exponents) pt_outcome_2 = mantissas.ldexp(exponents) self.assertEqual(np_outcome, pt_outcome_1) self.assertEqual(np_outcome, pt_outcome_2) mantissas.ldexp_(exponents) self.assertEqual(np_outcome, mantissas) mantissas = torch.tensor([float('inf'), float('-inf'), float('inf'), float('nan')], device=device) exponents = torch.randint(0, 31, (4,), device=device, dtype=torch.int32) np_outcome = np.ldexp(mantissas.numpy(), exponents.numpy()) pt_outcome = torch.ldexp(mantissas, exponents) self.assertEqual(np_outcome, pt_outcome) @dtypes(torch.float, torch.double, torch.cfloat, torch.cdouble) def test_lerp(self, device, dtype): start_end_weight_shapes = [(), (5,), (5, 5)] for shapes in product(start_end_weight_shapes, start_end_weight_shapes, start_end_weight_shapes): start = torch.randn(shapes[0], device=device, dtype=dtype) end = torch.randn(shapes[1], device=device, dtype=dtype) weights = [torch.randn(shapes[2], device=device, dtype=dtype), random.random()] if dtype.is_complex: weights += [complex(0, 1), complex(0.4, 1.2)] for weight in weights: actual = torch.lerp(start, end, weight) actual_method = start.lerp(end, weight) self.assertEqual(actual, actual_method) actual_out = torch.tensor(1., dtype=dtype, device=device) torch.lerp(start, end, weight, out=actual_out) self.assertEqual(actual, actual_out) expected = start + weight * (end - start) self.assertEqual(expected, actual) def _test_logaddexp(self, device, dtype, base2): if base2: ref_func = np.logaddexp2 our_func = torch.logaddexp2 else: ref_func = np.logaddexp our_func = torch.logaddexp def _test_helper(a, b): ref = ref_func(a.cpu().numpy(), b.cpu().numpy()) v = our_func(a, b) self.assertEqual(ref, v) a = torch.randn(64, 2, dtype=dtype, device=device) - 0.5 b = torch.randn(64, 2, dtype=dtype, device=device) - 0.5 _test_helper(a, b) _test_helper(a[:3], b[:3]) a *= 10000 b *= 10000 _test_helper(a, b) _test_helper(a[:3], b[:3]) a = torch.tensor([float('inf'), float('-inf'), float('inf'), float("nan")], dtype=dtype, device=device) b = torch.tensor([float('inf'), float('-inf'), float('-inf'), float("nan")], dtype=dtype, device=device) _test_helper(a, b) @dtypes(torch.float32, torch.float64) def test_logaddexp(self, device, dtype): self._test_logaddexp(device, dtype, base2=False) @dtypes(torch.float32, torch.float64) def test_logaddexp2(self, device, dtype): self._test_logaddexp(device, dtype, base2=True) def test_add(self, device): dtypes = [torch.float, torch.double] + torch.testing.get_all_complex_dtypes() for dtype in dtypes: m1 = torch.randn(100, 100, dtype=dtype, device=device) v1 = torch.randn(100, dtype=dtype, device=device) res1 = torch.add(m1[4], v1) res2 = res1.clone().zero_() for i in range(m1.size(1)): res2[i] = m1[4, i] + v1[i] self.assertEqual(res1, res2) m1 = torch.randn(100, 100, device=device) v1 = torch.randn(100, device=device) res1 = torch.add(m1[:, 4], v1) res2 = res1.clone().zero_() for i in range(m1.size(0)): res2[i] = m1[i, 4] + v1[i] self.assertEqual(res1, res2) m1 = torch.randn(10, 10, device=device) res1 = m1.clone() res1[3].add_(2) res2 = m1.clone() for i in range(m1.size(1)): res2[3, i] = res2[3, i] + 2 self.assertEqual(res1, res2) m1 = torch.randn(10, 10, device=device) res1 = m1.clone() res1[:, 3].add_(2) res2 = m1.clone() for i in range(m1.size(0)): res2[i, 3] = res2[i, 3] + 2 self.assertEqual(res1, res2) m1 = torch.randn(10, 10, dtype=dtype, device=device) self.assertEqual(m1 + 3, m1 + torch.tensor(3)) self.assertEqual(3 + m1, torch.tensor(3) + m1) m1 = torch.randn(10, 10, dtype=dtype, device=device) m2 = torch.randn(10, 10, dtype=dtype, device=device).t() res = m1 + m2 self.assertTrue(res.is_contiguous()) self.assertEqual(res, m1 + m2.contiguous()) m1 = torch.tensor([1.0], dtype=dtype, device=device) m2 = torch.tensor([], dtype=dtype, device=device) self.assertEqual(m1 + m2, []) one = torch.tensor(1, dtype=torch.uint8, device=device) self.assertEqual(torch.add(one, 1), 2) self.assertEqual(torch.add(one, 1).dtype, torch.uint8) m1 = torch.tensor([True, False, False, True, False, False], dtype=torch.bool, device=device) m2 = torch.tensor([True, True, False, False, False, True], dtype=torch.bool, device=device) expected = torch.tensor([True, True, False, True, False, True], dtype=torch.bool, device=device) self.assertEqual(m1 + m2, expected) a = torch.zeros(2, 3, dtype=torch.bool, device=device) res = torch.add(a, a, alpha=0) expected = torch.zeros(2, 3, device=device).bool() self.assertEqual(res, expected) m1 = torch.tensor([1., 2.], dtype=torch.bfloat16) m2 = torch.tensor([3., 4.], dtype=torch.bfloat16) self.assertEqual(m1 + m2, torch.tensor([4., 6.], dtype=torch.bfloat16)) m1 = torch.tensor([2 + 3j, 4 + 5j], dtype=torch.complex64, device=device) m2 = torch.tensor([4 + 5j, 2 + 3j], dtype=torch.complex64, device=device) res = torch.add(m1, m2, alpha=0.1) expected = torch.tensor([2.4000 + 3.5000j, 4.2000 + 5.3000j], dtype=torch.complex64, device=device) self.assertEqual(res, expected) res = torch.add(m1, m2, alpha=complex(0.1, 0.2)) expected = torch.tensor([1.4000 + 4.3000j, 3.6000 + 5.7000j], dtype=torch.complex64, device=device) self.assertEqual(res, expected) res = torch.add(m1, m2, alpha=2) expected = torch.tensor([10. + 13.j, 8. + 11.j], dtype=torch.complex64, device=device) self.assertEqual(res, expected) m1 = torch.tensor([1], dtype=torch.int8, device=device) m2 = torch.tensor([2], dtype=torch.int8, device=device) self.assertRaisesRegex(RuntimeError, r"Boolean alpha only supported for Boolean results\.", lambda: torch.add(m1, m2, alpha=True)) self.assertRaisesRegex(RuntimeError, r"For integral input tensors, argument alpha must not be a floating point number\.", lambda: torch.add(m1, m2, alpha=1.0)) msg = r"For non-complex input tensors, argument alpha must not be a complex number\." m1 = torch.tensor([3., 4.], device=device) m2 = torch.tensor([4., 3.], device=device) self.assertRaisesRegex(RuntimeError, msg, lambda: torch.add(m1, m2, alpha=complex(0.1, 0.2))) m1 = torch.tensor([3., 4.], dtype=torch.double, device=device) m2 = torch.tensor([4., 3.], dtype=torch.double, device=device) self.assertRaisesRegex(RuntimeError, msg, lambda: torch.add(m1, m2, alpha=complex(0.1, 0.2))) m1 = torch.tensor((4.0000 + 4.0000j), dtype=torch.complex64) m2 = torch.tensor(4., dtype=torch.float64) self.assertRaisesRegex(RuntimeError, r"result type ComplexFloat can't be cast to the desired output type Double", lambda: torch.add(m1, m1, out=m2)) @onlyCUDA def test_addsub_half_tensor(self, device): x = torch.tensor([60000.0], dtype=torch.half, device=device) for op, y, alpha in ( (torch.add, torch.tensor([-60000.0], dtype=torch.half, device=device), 2), (torch.sub, torch.tensor([60000.0], dtype=torch.half, device=device), 2), (torch.add, -70000.0, 1), (torch.sub, 70000.0, 1), ): actual = op(x, y, alpha=alpha) self.assertTrue(not (actual.isnan() or actual.isinf())) def test_sub_typing(self, device): m1 = torch.tensor([True, False, False, True, False, False], dtype=torch.bool, device=device) m2 = torch.tensor([True, True, False, False, False, True], dtype=torch.bool, device=device) self.assertRaisesRegex(RuntimeError, r"Subtraction, the `\-` operator, with two bool tensors is not supported. " r"Use the `\^` or `logical_xor` operator instead.", lambda: m1 - m2) self.assertRaisesRegex(RuntimeError, r"Subtraction, the `\-` operator, with a bool tensor is not supported. " r"If you are trying to invert a mask, use the `\~` or `logical_not` operator instead.", lambda: 1 - m1) self.assertRaisesRegex(RuntimeError, r"Subtraction, the `\-` operator, with a bool tensor is not supported. " r"If you are trying to invert a mask, use the `\~` or `logical_not` operator instead.", lambda: m2 - 1) # mismatched alpha m1 = torch.tensor([1], dtype=torch.int8, device=device) m2 = torch.tensor([2], dtype=torch.int8, device=device) self.assertRaisesRegex(RuntimeError, r"Boolean alpha only supported for Boolean results\.", lambda: torch.sub(m1, m2, alpha=True)) self.assertRaisesRegex(RuntimeError, r"For integral input tensors, argument alpha must not be a floating point number\.", lambda: torch.sub(m1, m2, alpha=1.0)) def test_mul(self, device): m1 = torch.randn(10, 10, device=device) res1 = m1.clone() res1[:, 3].mul_(2) res2 = m1.clone() for i in range(res1.size(0)): res2[i, 3] = res2[i, 3] * 2 self.assertEqual(res1, res2) a1 = torch.tensor([True, False, False, True], dtype=torch.bool, device=device) a2 = torch.tensor([True, False, True, False], dtype=torch.bool, device=device) self.assertEqual(a1 * a2, torch.tensor([True, False, False, False], dtype=torch.bool, device=device)) if device == 'cpu': a1 = torch.tensor([0.1, 0.1], dtype=torch.bfloat16, device=device) a2 = torch.tensor([1.1, 0.1], dtype=torch.bfloat16, device=device) self.assertEqual(a1 * a2, torch.tensor([0.11, 0.01], dtype=torch.bfloat16, device=device), atol=0.01, rtol=0) self.assertEqual(a1.mul(a2), a1 * a2) def test_bool_tensor_comparison_ops(self, device): a = torch.tensor([True, False, True, False, True, False], dtype=torch.bool, device=device) b = torch.tensor([True, False, True, True, True, True], dtype=torch.bool, device=device) self.assertEqual(a == b, torch.tensor([1, 1, 1, 0, 1, 0], dtype=torch.bool, device=device)) self.assertEqual(a != b, torch.tensor([0, 0, 0, 1, 0, 1], dtype=torch.bool, device=device)) self.assertEqual(a < b, torch.tensor([0, 0, 0, 1, 0, 1], dtype=torch.bool, device=device)) self.assertEqual(a > b, torch.tensor([0, 0, 0, 0, 0, 0], dtype=torch.bool, device=device)) self.assertEqual(a >= b, torch.tensor([1, 1, 1, 0, 1, 0], dtype=torch.bool, device=device)) self.assertEqual(a <= b, torch.tensor([1, 1, 1, 1, 1, 1], dtype=torch.bool, device=device)) self.assertEqual(a > False, torch.tensor([1, 0, 1, 0, 1, 0], dtype=torch.bool, device=device)) self.assertEqual(a == torch.tensor(True, dtype=torch.bool, device=device), torch.tensor([1, 0, 1, 0, 1, 0], dtype=torch.bool, device=device)) self.assertEqual(a == torch.tensor(0, dtype=torch.bool, device=device), torch.tensor([0, 1, 0, 1, 0, 1], dtype=torch.bool, device=device)) self.assertFalse(a.equal(b)) @dtypes(*torch.testing.get_all_dtypes(include_complex=False)) def test_logical(self, device, dtype): if dtype != torch.bool: x = torch.tensor([1, 2, 3, 4], device=device, dtype=dtype) b = torch.tensor([2], device=device, dtype=dtype) self.assertEqual(x.lt(2), torch.tensor([True, False, False, False])) self.assertEqual(x.le(2), torch.tensor([True, True, False, False])) self.assertEqual(x.ge(2), torch.tensor([False, True, True, True])) self.assertEqual(x.gt(2), torch.tensor([False, False, True, True])) self.assertEqual(x.eq(2), torch.tensor([False, True, False, False])) self.assertEqual(x.ne(2), torch.tensor([True, False, True, True])) self.assertEqual(x.lt(b), torch.tensor([True, False, False, False])) self.assertEqual(x.le(b), torch.tensor([True, True, False, False])) self.assertEqual(x.ge(b), torch.tensor([False, True, True, True])) self.assertEqual(x.gt(b), torch.tensor([False, False, True, True])) self.assertEqual(x.eq(b), torch.tensor([False, True, False, False])) self.assertEqual(x.ne(b), torch.tensor([True, False, True, True])) else: x = torch.tensor([True, False, True, False], device=device) self.assertEqual(x.lt(True), torch.tensor([False, True, False, True])) self.assertEqual(x.le(True), torch.tensor([True, True, True, True])) self.assertEqual(x.ge(True), torch.tensor([True, False, True, False])) self.assertEqual(x.gt(True), torch.tensor([False, False, False, False])) self.assertEqual(x.eq(True), torch.tensor([True, False, True, False])) self.assertEqual(x.ne(True), torch.tensor([False, True, False, True])) def test_atan2(self, device): def _test_atan2_with_size(size, device): a = torch.rand(size=size, device=device, dtype=torch.double) b = torch.rand(size=size, device=device, dtype=torch.double) actual = a.atan2(b) x = a.view(-1) y = b.view(-1) expected = torch.tensor([math.atan2(x[i].item(), y[i].item()) for i in range(x.numel())], device=device, dtype=torch.double) self.assertEqual(expected, actual.view(-1), rtol=0, atol=0.02) _test_atan2_with_size((2, 2), device) _test_atan2_with_size((3, 3), device) _test_atan2_with_size((5, 5), device) def test_atan2_edgecases(self, device): def _test_atan2(x, y, expected, device, dtype): expected_tensor = torch.tensor([expected], dtype=dtype, device=device) x_tensor = torch.tensor([x], dtype=dtype, device=device) y_tensor = torch.tensor([y], dtype=dtype, device=device) actual = torch.atan2(y_tensor, x_tensor) self.assertEqual(expected_tensor, actual, rtol=0, atol=0.02) for dtype in [torch.float, torch.double]: _test_atan2(0, 0, 0, device, dtype) _test_atan2(0, 1, math.pi / 2, device, dtype) _test_atan2(0, -1, math.pi / -2, device, dtype) _test_atan2(-1, 0, math.pi, device, dtype) _test_atan2(1, 0, 0, device, dtype) _test_atan2(-1, -1, math.pi * -3 / 4 , device, dtype) _test_atan2(1, 1, math.pi / 4 , device, dtype) _test_atan2(1, -1, math.pi / -4 , device, dtype) _test_atan2(-1, 1, math.pi * 3 / 4 , device, dtype) def test_trapz(self, device): def test_dx(sizes, dim, dx, device): t = torch.randn(sizes, device=device) actual = torch.trapz(t, dx=dx, dim=dim) expected = np.trapz(t.cpu().numpy(), dx=dx, axis=dim) self.assertEqual(expected.shape, actual.shape) self.assertEqual(expected, actual, exact_dtype=False) def test_x(sizes, dim, x, device): t = torch.randn(sizes, device=device) actual = torch.trapz(t, x=torch.tensor(x, device=device), dim=dim) expected = np.trapz(t.cpu().numpy(), x=x, axis=dim) self.assertEqual(expected.shape, actual.shape) self.assertEqual(expected, actual.cpu(), exact_dtype=False) test_dx((2, 3, 4), 1, 1, device) test_dx((10, 2), 0, 0.1, device) test_dx((1, 10), 0, 2.3, device) test_dx((0, 2), 0, 1.0, device) test_dx((0, 2), 1, 1.0, device) test_x((2, 3, 4), 1, [1.0, 2.0, 3.0], device) test_x((10, 2), 0, [2.0, 3.0, 4.0, 7.0, 11.0, 14.0, 22.0, 26.0, 26.1, 30.3], device) test_x((1, 10), 0, [1.0], device) test_x((0, 2), 0, [], device) test_x((0, 2), 1, [1.0, 2.0], device) with self.assertRaisesRegex( IndexError, 'Dimension out of range'): test_x((2, 3), 2, [], device) test_dx((2, 3), 2, 1.0, device) with self.assertRaisesRegex( RuntimeError, 'There must be one `x` value for each sample point'): test_x((2, 3), 1, [1.0, 2.0], device) test_x((2, 3), 1, [1.0, 2.0, 3.0, 4.0], device) @dtypes(torch.double) def test_pow_scalar_overloads_mem_overlap(self, device, dtype): sz = 3 doubles = torch.randn(2 * sz, dtype=dtype, device=device) self.check_internal_mem_overlap( lambda t: t.pow_(42), 1, dtype, device) self.unary_check_input_output_mem_overlap( doubles, sz, lambda input, out: torch.pow(input, 42, out=out)) self.unary_check_input_output_mem_overlap( doubles, sz, lambda input, out: torch.pow(42, input, out=out)) @dtypes(*list(product(torch.testing.get_all_dtypes(include_bool=False), torch.testing.get_all_dtypes(include_bool=False)))) def test_float_power(self, device, dtypes): def to_np(value): if isinstance(value, torch.Tensor) and value.dtype == torch.bfloat16: return value.to(torch.float).cpu().numpy() return value.cpu().numpy() if isinstance(value, torch.Tensor) else value base_dtype = dtypes[0] exp_dtype = dtypes[1] out_dtype = torch.complex128 if base_dtype.is_complex or exp_dtype.is_complex else torch.float64 base = make_tensor((30,), device, base_dtype, low=1, high=100) # Complex and real results do not agree between PyTorch and NumPy when computing negative and zero power of 0 # Related: https://github.com/pytorch/pytorch/issues/48000 # base[0] = base[3] = base[7] = 0 exp = make_tensor((30,), device, exp_dtype, low=-2, high=2) exp[0] = exp[4] = exp[6] = 0 expected = torch.from_numpy(np.float_power(to_np(base), to_np(exp))) exponents = [-2.8, -2, -1, -0.5, 0.5, 1, 2] complex_exponents = exponents + [-2.5j, -1.0j, 1.0j, 2.5j, 1.0 + 1.0j, -1.0 - 1.5j, 3.3j] for op in (torch.float_power, torch.Tensor.float_power, torch.Tensor.float_power_): # Case of Tensor x Tensor if op is torch.Tensor.float_power_ and base_dtype != out_dtype: with self.assertRaisesRegex(RuntimeError, "operation's result requires dtype"): op(base.clone(), exp) else: result = op(base.clone(), exp) self.assertEqual(expected, result) if op is torch.float_power: out = torch.empty_like(base).to(device=device, dtype=out_dtype) op(base, exp, out=out) self.assertEqual(expected, out) for i in complex_exponents if exp_dtype.is_complex else exponents: out_dtype_scalar_exp = torch.complex128 if base_dtype.is_complex or type(i) == complex else torch.float64 expected_scalar_exp = torch.from_numpy(np.float_power(to_np(base), i)) if op is torch.Tensor.float_power_ and base_dtype != out_dtype_scalar_exp: with self.assertRaisesRegex(RuntimeError, "operation's result requires dtype"): op(base.clone(), i) else: result = op(base.clone(), i) self.assertEqual(expected_scalar_exp, result) if op is torch.float_power: out = torch.empty_like(base).to(device=device, dtype=out_dtype_scalar_exp) op(base, i, out=out) self.assertEqual(expected_scalar_exp, out) # Case of Scalar x Tensor for i in complex_exponents if base_dtype.is_complex else exponents: out_dtype_scalar_base = torch.complex128 if exp_dtype.is_complex or type(i) == complex else torch.float64 expected_scalar_base = torch.from_numpy(np.float_power(i, to_np(exp))) result = torch.float_power(i, exp) self.assertEqual(expected_scalar_base, result) out = torch.empty_like(exp).to(device=device, dtype=out_dtype_scalar_base) torch.float_power(i, exp, out=out) self.assertEqual(expected_scalar_base, out) def test_float_power_exceptions(self, device): def _promo_helper(x, y): for i in (x, y): if type(i) == complex: return torch.complex128 elif type(i) == torch.Tensor and i.is_complex(): return torch.complex128 return torch.double test_cases = ((torch.tensor([-2, -1, 0, 1, 2], device=device), -.25), (torch.tensor([-1.0j, 0j, 1.0j, 1.0 + 1.0j, -1.0 - 1.5j], device=device), 2.)) for base, exp in test_cases: for out_dtype in (torch.long, torch.float, torch.double, torch.cdouble): out = torch.empty(1, device=device, dtype=out_dtype) required_dtype = _promo_helper(base, exp) if out.dtype == required_dtype: torch.float_power(base, exp, out=out) else: with self.assertRaisesRegex(RuntimeError, "operation's result requires dtype"): torch.float_power(base, exp, out=out) if base.dtype == required_dtype: torch.Tensor.float_power_(base.clone(), exp) else: with self.assertRaisesRegex(RuntimeError, "operation's result requires dtype"): torch.Tensor.float_power_(base.clone(), exp) @skipIf(not TEST_SCIPY, "Scipy required for the test.") @dtypes(*product(torch.testing.get_all_dtypes(include_complex=False, include_bfloat16=False), torch.testing.get_all_dtypes(include_complex=False, include_bfloat16=False))) def test_xlogy_xlog1py(self, device, dtypes): x_dtype, y_dtype = dtypes def out_variant_helper(torch_fn, x, y): expected = torch_fn(x, y) out = torch.empty_like(expected) torch_fn(x, y, out=out) self.assertEqual(expected, out) def xlogy_inplace_variant_helper(x, y): if x.dtype in torch.testing.get_all_int_dtypes() + [torch.bool]: with self.assertRaisesRegex(RuntimeError, "can't be cast to the desired output type"): x.clone().xlogy_(y) else: expected = torch.empty_like(x) torch.xlogy(x, y, out=expected) inplace_out = x.clone().xlogy_(y) self.assertEqual(expected, inplace_out) def test_helper(torch_fn, reference_fn, inputs, scalar=None): x, y, z = inputs torch_fn_partial = partial(torch_fn, x) reference_fn_partial = partial(reference_fn, x.cpu().numpy()) self.compare_with_numpy(torch_fn_partial, reference_fn_partial, x, exact_dtype=False) self.compare_with_numpy(torch_fn_partial, reference_fn_partial, y, exact_dtype=False) self.compare_with_numpy(torch_fn_partial, reference_fn_partial, z, exact_dtype=False) val = scalar if scalar is not None else x out_variant_helper(torch_fn, val, x) out_variant_helper(torch_fn, val, y) out_variant_helper(torch_fn, val, z) x = make_tensor((3, 2, 4, 5), device, x_dtype, low=0.5, high=1000) y = make_tensor((3, 2, 4, 5), device, y_dtype, low=0.5, high=1000) z = make_tensor((4, 5), device, y_dtype, low=0.5, high=1000) x_1p = make_tensor((3, 2, 4, 5), device, x_dtype, low=-0.5, high=1000) y_1p = make_tensor((3, 2, 4, 5), device, y_dtype, low=-0.5, high=1000) z_1p = make_tensor((4, 5), device, y_dtype, low=-0.5, high=1000) xlogy_fns = torch.xlogy, scipy.special.xlogy xlog1py_fns = torch.special.xlog1py, scipy.special.xlog1py test_helper(*xlogy_fns, (x, y, z)) xlogy_inplace_variant_helper(x, x) xlogy_inplace_variant_helper(x, y) xlogy_inplace_variant_helper(x, z) test_helper(*xlog1py_fns, (x_1p, y_1p, z_1p)) test_helper(*xlogy_fns, (x, y, z), 3.14) test_helper(*xlog1py_fns, (x_1p, y_1p, z_1p), 3.14) t = torch.tensor([-1., 0., 1., 2., float('inf'), -float('inf'), float('nan')], device=device) zeros = torch.zeros(7, dtype=y_dtype, device=device) def test_zeros_special_helper(torch_fn, reference_fn, scalar=False): zeros_t = 0 if scalar else zeros zeros_np = 0 if scalar else zeros.cpu().numpy() torch_fn_partial = partial(torch_fn, zeros_t) reference_fn_partial = partial(reference_fn, zeros_np) self.compare_with_numpy(torch_fn_partial, reference_fn_partial, t, exact_dtype=False) out_variant_helper(torch_fn, zeros_t, t) test_zeros_special_helper(*xlogy_fns) xlogy_inplace_variant_helper(zeros, t) test_zeros_special_helper(*xlog1py_fns) test_zeros_special_helper(*xlogy_fns, scalar=True) test_zeros_special_helper(*xlog1py_fns, scalar=True) def test_xlogy_xlog1py_scalar_type_promotion(self, device): # priority level as 0-dim tensors t = torch.randn((), dtype=torch.float32, device=device) self.assertEqual(t.dtype, torch.xlogy(t, 5).dtype) self.assertEqual(t.dtype, torch.xlogy(t, 5.).dtype) self.assertEqual(t.dtype, torch.special.xlog1py(t, 5).dtype) self.assertEqual(t.dtype, torch.special.xlog1py(t, 5.).dtype) self.assertEqual(t.dtype, torch.xlogy(5, t).dtype) self.assertEqual(t.dtype, torch.xlogy(5., t).dtype) self.assertEqual(t.dtype, torch.special.xlog1py(5, t).dtype) self.assertEqual(t.dtype, torch.special.xlog1py(5., t).dtype) @skipIf(not TEST_SCIPY, "Scipy required for the test.") def test_xlogy_xlog1py_bfloat16(self, device): def _compare_helper(x, y, torch_fn, reference_fn): x_np = x if isinstance(x, float) else x.cpu().to(torch.float).numpy() y_np = y if isinstance(y, float) else y.cpu().to(torch.float).numpy() expected = torch.from_numpy(reference_fn(x_np, y_np)) actual = torch_fn(x, y) self.assertEqual(expected, actual, exact_dtype=False) x_dtype, y_dtype = torch.bfloat16, torch.bfloat16 # Tensor-Tensor Test (tensor of same and different shape) x = make_tensor((3, 2, 4, 5), device, x_dtype, low=0.5, high=1000) y = make_tensor((3, 2, 4, 5), device, y_dtype, low=0.5, high=1000) z = make_tensor((4, 5), device, y_dtype, low=0.5, high=1000) x_1p = make_tensor((3, 2, 4, 5), device, x_dtype, low=-0.8, high=1000) y_1p = make_tensor((3, 2, 4, 5), device, y_dtype, low=-0.8, high=1000) z_1p = make_tensor((4, 5), device, y_dtype, low=-0.8, high=1000) xlogy_fns = torch.xlogy, scipy.special.xlogy xlog1py_fns = torch.special.xlog1py, scipy.special.xlog1py _compare_helper(x, x, *xlogy_fns) _compare_helper(x, y, *xlogy_fns) _compare_helper(x, z, *xlogy_fns) _compare_helper(x, 3.14, *xlogy_fns) _compare_helper(y, 3.14, *xlogy_fns) _compare_helper(z, 3.14, *xlogy_fns) _compare_helper(x_1p, x_1p, *xlog1py_fns) _compare_helper(x_1p, y_1p, *xlog1py_fns) _compare_helper(x_1p, z_1p, *xlog1py_fns) _compare_helper(x_1p, 3.14, *xlog1py_fns) _compare_helper(y_1p, 3.14, *xlog1py_fns) _compare_helper(z_1p, 3.14, *xlog1py_fns) # Special Values Tensor-Tensor t = torch.tensor([-1., 0., 1., 2., float('inf'), -float('inf'), float('nan')], device=device) zeros = torch.tensor(7, dtype=y_dtype, device=device) _compare_helper(t, zeros, *xlogy_fns) _compare_helper(t, 0., *xlogy_fns) _compare_helper(t, zeros, *xlog1py_fns) _compare_helper(t, 0., *xlog1py_fns) @dtypes(*product(torch.testing.get_all_dtypes(include_complex=False, include_half=False, include_bfloat16=False), torch.testing.get_all_dtypes(include_complex=False, include_half=False, include_bfloat16=False))) @skipIf(not TEST_SCIPY, "Scipy required for the test.") def test_zeta(self, device, dtypes): x_dtype, q_dtype = dtypes def test_helper(x, q): x_np = x if isinstance(x, float) else x.cpu().numpy() q_np = q if isinstance(q, float) else q.cpu().numpy() expected = torch.from_numpy(scipy.special.zeta(x_np, q_np)) actual = torch.special.zeta(x, q) rtol, atol = None, None if self.device_type == 'cpu': rtol, atol = 1e-6, 1e-6 self.assertEqual(expected, actual, rtol=rtol, atol=atol, exact_dtype=False) # x tensor - q tensor same size x = make_tensor((2, 3, 4), device, x_dtype) q = make_tensor((2, 3, 4), device, q_dtype) test_helper(x, q) # x tensor - q tensor broadcast lhs x = make_tensor((2, 1, 4), device, x_dtype) q = make_tensor((2, 3, 4), device, q_dtype) test_helper(x, q) # x tensor - q tensor broadcast rhs x = make_tensor((2, 3, 4), device, x_dtype) q = make_tensor((2, 1, 4), device, q_dtype) test_helper(x, q) # x tensor - q tensor broadcast all x = make_tensor((2, 3, 1), device, x_dtype) q = make_tensor((2, 1, 4), device, q_dtype) test_helper(x, q) # x scalar - q tensor for x in np.linspace(-5, 5, num=10).tolist(): if not q_dtype.is_floating_point: q_dtype = torch.get_default_dtype() q = make_tensor((2, 3, 4), device, q_dtype) test_helper(x, q) # x tensor - q scalar for q in np.linspace(-5, 5, num=10).tolist(): if not x_dtype.is_floating_point: x_dtype = torch.get_default_dtype() x = make_tensor((2, 3, 4), device, x_dtype) test_helper(x, q) tensor_binary_ops = [ '__lt__', '__le__', '__gt__', '__ge__', '__eq__', '__ne__', '__add__', '__radd__', '__iadd__', '__sub__', '__rsub__', '__isub__', '__mul__', '__rmul__', '__imul__', '__matmul__', '__rmatmul__', '__truediv__', '__rtruediv__', '__itruediv__', '__floordiv__', '__rfloordiv__', '__ifloordiv__', '__mod__', '__rmod__', '__imod__', '__pow__', '__rpow__', '__ipow__', '__lshift__', '__rlshift__', '__ilshift__', '__rshift__', '__rrshift__', '__irshift__', '__and__', '__iand__', '__xor__', '__ixor__', '__or__', '__ior__', # Unsupported operators # '__imatmul__', # '__divmod__', '__rdivmod__', '__idivmod__', # '__rand__', '__ror__', '__rxor__', ] # Test that binary math operations return NotImplemented for unknown types. def generate_not_implemented_tests(cls): class UnknownType: pass # TODO: refactor to inline these _types = [ torch.half, torch.float, torch.double, torch.int8, torch.short, torch.int, torch.long, torch.uint8 ] # TODO: refactor to use make_tensor def _small_2d(dtype, device, has_zeros=True, fill_ones=False, oneish=False): t = _make_tensor((5, 5), dtype, device, fill_ones=fill_ones) if oneish: return t.clamp(min=_number(.99, 1, dtype), max=1.01) if not has_zeros: return t.clamp(min=(_number(_div_min, 1, dtype))) return t def create_test_func(op): @dtypes(*_types) def test(self, device, dtype): # Generate the inputs tensor = _small_2d(dtype, device) # Runs the tensor op on the device result = getattr(tensor, op)(UnknownType()) self.assertEqual(result, NotImplemented) return test for op in tensor_binary_ops: test_name = "test_{}_not_implemented".format(op) assert not hasattr(cls, test_name), "{0} already in {1}".format( test_name, cls.__name__) setattr(cls, test_name, create_test_func(op)) generate_not_implemented_tests(TestBinaryUfuncs) instantiate_device_type_tests(TestBinaryUfuncs, globals()) if __name__ == '__main__': run_tests()

true