fumiyau/python_comments_free_500000 · Datasets at Hugging Face

1c3795dc060d6509c41d3fbbb3beb2a1846b4540

4,040

py

Python

sdk/python/pulumi_azure_nextgen/devices/latest/list_iot_dps_resource_keys_for_key_name.py

test-wiz-sec/pulumi-azure-nextgen

20a695af0d020b34b0f1c336e1b69702755174cc

[ "Apache-2.0" ]

null

sdk/python/pulumi_azure_nextgen/devices/latest/list_iot_dps_resource_keys_for_key_name.py

test-wiz-sec/pulumi-azure-nextgen

20a695af0d020b34b0f1c336e1b69702755174cc

[ "Apache-2.0" ]

null

sdk/python/pulumi_azure_nextgen/devices/latest/list_iot_dps_resource_keys_for_key_name.py

test-wiz-sec/pulumi-azure-nextgen

20a695af0d020b34b0f1c336e1b69702755174cc

[ "Apache-2.0" ]

null

# coding=utf-8 # *** WARNING: this file was generated by the Pulumi SDK Generator. *** # *** Do not edit by hand unless you're certain you know what you are doing! *** import warnings import pulumi import pulumi.runtime from typing import Any, Mapping, Optional, Sequence, Union from ... import _utilities, _tables __all__ = [ 'ListIotDpsResourceKeysForKeyNameResult', 'AwaitableListIotDpsResourceKeysForKeyNameResult', 'list_iot_dps_resource_keys_for_key_name', ] @pulumi.output_type class ListIotDpsResourceKeysForKeyNameResult: """ Description of the shared access key. """ def __init__(__self__, key_name=None, primary_key=None, rights=None, secondary_key=None): if key_name and not isinstance(key_name, str): raise TypeError("Expected argument 'key_name' to be a str") pulumi.set(__self__, "key_name", key_name) if primary_key and not isinstance(primary_key, str): raise TypeError("Expected argument 'primary_key' to be a str") pulumi.set(__self__, "primary_key", primary_key) if rights and not isinstance(rights, str): raise TypeError("Expected argument 'rights' to be a str") pulumi.set(__self__, "rights", rights) if secondary_key and not isinstance(secondary_key, str): raise TypeError("Expected argument 'secondary_key' to be a str") pulumi.set(__self__, "secondary_key", secondary_key) @property @pulumi.getter(name="keyName") def key_name(self) -> str: """ Name of the key. """ return pulumi.get(self, "key_name") @property @pulumi.getter(name="primaryKey") def primary_key(self) -> Optional[str]: """ Primary SAS key value. """ return pulumi.get(self, "primary_key") @property @pulumi.getter def rights(self) -> str: """ Rights that this key has. """ return pulumi.get(self, "rights") @property @pulumi.getter(name="secondaryKey") def secondary_key(self) -> Optional[str]: """ Secondary SAS key value. """ return pulumi.get(self, "secondary_key") class AwaitableListIotDpsResourceKeysForKeyNameResult(ListIotDpsResourceKeysForKeyNameResult): # pylint: disable=using-constant-test def __await__(self): if False: yield self return ListIotDpsResourceKeysForKeyNameResult( key_name=self.key_name, primary_key=self.primary_key, rights=self.rights, secondary_key=self.secondary_key) def list_iot_dps_resource_keys_for_key_name(key_name: Optional[str] = None, provisioning_service_name: Optional[str] = None, resource_group_name: Optional[str] = None, opts: Optional[pulumi.InvokeOptions] = None) -> AwaitableListIotDpsResourceKeysForKeyNameResult: """ Use this data source to access information about an existing resource. :param str key_name: Logical key name to get key-values for. :param str provisioning_service_name: Name of the provisioning service. :param str resource_group_name: The name of the resource group that contains the provisioning service. """ __args__ = dict() __args__['keyName'] = key_name __args__['provisioningServiceName'] = provisioning_service_name __args__['resourceGroupName'] = resource_group_name if opts is None: opts = pulumi.InvokeOptions() if opts.version is None: opts.version = _utilities.get_version() __ret__ = pulumi.runtime.invoke('azure-nextgen:devices/latest:listIotDpsResourceKeysForKeyName', __args__, opts=opts, typ=ListIotDpsResourceKeysForKeyNameResult).value return AwaitableListIotDpsResourceKeysForKeyNameResult( key_name=__ret__.key_name, primary_key=__ret__.primary_key, rights=__ret__.rights, secondary_key=__ret__.secondary_key)

37.757009

171

0.67005

import warnings import pulumi import pulumi.runtime from typing import Any, Mapping, Optional, Sequence, Union from ... import _utilities, _tables __all__ = [ 'ListIotDpsResourceKeysForKeyNameResult', 'AwaitableListIotDpsResourceKeysForKeyNameResult', 'list_iot_dps_resource_keys_for_key_name', ] @pulumi.output_type class ListIotDpsResourceKeysForKeyNameResult: def __init__(__self__, key_name=None, primary_key=None, rights=None, secondary_key=None): if key_name and not isinstance(key_name, str): raise TypeError("Expected argument 'key_name' to be a str") pulumi.set(__self__, "key_name", key_name) if primary_key and not isinstance(primary_key, str): raise TypeError("Expected argument 'primary_key' to be a str") pulumi.set(__self__, "primary_key", primary_key) if rights and not isinstance(rights, str): raise TypeError("Expected argument 'rights' to be a str") pulumi.set(__self__, "rights", rights) if secondary_key and not isinstance(secondary_key, str): raise TypeError("Expected argument 'secondary_key' to be a str") pulumi.set(__self__, "secondary_key", secondary_key) @property @pulumi.getter(name="keyName") def key_name(self) -> str: return pulumi.get(self, "key_name") @property @pulumi.getter(name="primaryKey") def primary_key(self) -> Optional[str]: return pulumi.get(self, "primary_key") @property @pulumi.getter def rights(self) -> str: return pulumi.get(self, "rights") @property @pulumi.getter(name="secondaryKey") def secondary_key(self) -> Optional[str]: return pulumi.get(self, "secondary_key") class AwaitableListIotDpsResourceKeysForKeyNameResult(ListIotDpsResourceKeysForKeyNameResult): # pylint: disable=using-constant-test def __await__(self): if False: yield self return ListIotDpsResourceKeysForKeyNameResult( key_name=self.key_name, primary_key=self.primary_key, rights=self.rights, secondary_key=self.secondary_key) def list_iot_dps_resource_keys_for_key_name(key_name: Optional[str] = None, provisioning_service_name: Optional[str] = None, resource_group_name: Optional[str] = None, opts: Optional[pulumi.InvokeOptions] = None) -> AwaitableListIotDpsResourceKeysForKeyNameResult: __args__ = dict() __args__['keyName'] = key_name __args__['provisioningServiceName'] = provisioning_service_name __args__['resourceGroupName'] = resource_group_name if opts is None: opts = pulumi.InvokeOptions() if opts.version is None: opts.version = _utilities.get_version() __ret__ = pulumi.runtime.invoke('azure-nextgen:devices/latest:listIotDpsResourceKeysForKeyName', __args__, opts=opts, typ=ListIotDpsResourceKeysForKeyNameResult).value return AwaitableListIotDpsResourceKeysForKeyNameResult( key_name=__ret__.key_name, primary_key=__ret__.primary_key, rights=__ret__.rights, secondary_key=__ret__.secondary_key)

true

1c37967573dfccc4488c0576fd333668c6ce05f2

14,794

py

Python

external/logger.py

yunshengtian/ppo-mujoco

1989bc5491d2abc3d015d0ec81d34ea166c3352b

[ "MIT" ]

1

2021-01-27T08:59:31.000Z

external/logger.py

yunshengtian/ppo-mujoco

1989bc5491d2abc3d015d0ec81d34ea166c3352b

[ "MIT" ]

null

external/logger.py

yunshengtian/ppo-mujoco

1989bc5491d2abc3d015d0ec81d34ea166c3352b

[ "MIT" ]

1

2021-01-20T07:56:54.000Z

import os import sys import shutil import os.path as osp import json import time import datetime import tempfile from collections import defaultdict from contextlib import contextmanager DEBUG = 10 INFO = 20 WARN = 30 ERROR = 40 DISABLED = 50 class KVWriter(object): def writekvs(self, kvs): raise NotImplementedError class SeqWriter(object): def writeseq(self, seq): raise NotImplementedError class HumanOutputFormat(KVWriter, SeqWriter): def __init__(self, filename_or_file): if isinstance(filename_or_file, str): self.file = open(filename_or_file, 'wt') self.own_file = True else: assert hasattr(filename_or_file, 'read'), 'expected file or str, got %s'%filename_or_file self.file = filename_or_file self.own_file = False def writekvs(self, kvs): # Create strings for printing key2str = {} for (key, val) in sorted(kvs.items()): if hasattr(val, '__float__'): valstr = '%-8.3g' % val else: valstr = str(val) key2str[self._truncate(key)] = self._truncate(valstr) # Find max widths if len(key2str) == 0: print('WARNING: tried to write empty key-value dict') return else: keywidth = max(map(len, key2str.keys())) valwidth = max(map(len, key2str.values())) # Write out the data dashes = '-' * (keywidth + valwidth + 7) lines = [dashes] for (key, val) in sorted(key2str.items(), key=lambda kv: kv[0].lower()): lines.append('| %s%s | %s%s |' % ( key, ' ' * (keywidth - len(key)), val, ' ' * (valwidth - len(val)), )) lines.append(dashes) self.file.write('\n'.join(lines) + '\n') # Flush the output to the file self.file.flush() def _truncate(self, s): maxlen = 30 return s[:maxlen-3] + '...' if len(s) > maxlen else s def writeseq(self, seq): seq = list(seq) for (i, elem) in enumerate(seq): self.file.write(elem) if i < len(seq) - 1: # add space unless this is the last one self.file.write(' ') self.file.write('\n') self.file.flush() def close(self): if self.own_file: self.file.close() class JSONOutputFormat(KVWriter): def __init__(self, filename): self.file = open(filename, 'wt') def writekvs(self, kvs): for k, v in sorted(kvs.items()): if hasattr(v, 'dtype'): kvs[k] = float(v) self.file.write(json.dumps(kvs) + '\n') self.file.flush() def close(self): self.file.close() class CSVOutputFormat(KVWriter): def __init__(self, filename): self.file = open(filename, 'w+t') self.keys = [] self.sep = ',' def writekvs(self, kvs): # Add our current row to the history extra_keys = list(kvs.keys() - self.keys) extra_keys.sort() if extra_keys: self.keys.extend(extra_keys) self.file.seek(0) lines = self.file.readlines() self.file.seek(0) for (i, k) in enumerate(self.keys): if i > 0: self.file.write(',') self.file.write(k) self.file.write('\n') for line in lines[1:]: self.file.write(line[:-1]) self.file.write(self.sep * len(extra_keys)) self.file.write('\n') for (i, k) in enumerate(self.keys): if i > 0: self.file.write(',') v = kvs.get(k) if v is not None: self.file.write(str(v)) self.file.write('\n') self.file.flush() def close(self): self.file.close() class TensorBoardOutputFormat(KVWriter): """ Dumps key/value pairs into TensorBoard's numeric format. """ def __init__(self, dir): os.makedirs(dir, exist_ok=True) self.dir = dir self.step = 1 prefix = 'events' path = osp.join(osp.abspath(dir), prefix) import tensorflow as tf from tensorflow.python import pywrap_tensorflow from tensorflow.core.util import event_pb2 from tensorflow.python.util import compat self.tf = tf self.event_pb2 = event_pb2 self.pywrap_tensorflow = pywrap_tensorflow self.writer = pywrap_tensorflow.EventsWriter(compat.as_bytes(path)) def writekvs(self, kvs): def summary_val(k, v): kwargs = {'tag': k, 'simple_value': float(v)} return self.tf.Summary.Value(**kwargs) summary = self.tf.Summary(value=[summary_val(k, v) for k, v in kvs.items()]) event = self.event_pb2.Event(wall_time=time.time(), summary=summary) event.step = self.step # is there any reason why you'd want to specify the step? self.writer.WriteEvent(event) self.writer.Flush() self.step += 1 def close(self): if self.writer: self.writer.Close() self.writer = None def make_output_format(format, ev_dir, log_suffix=''): os.makedirs(ev_dir, exist_ok=True) if format == 'stdout': return HumanOutputFormat(sys.stdout) elif format == 'log': return HumanOutputFormat(osp.join(ev_dir, 'log%s.txt' % log_suffix)) elif format == 'json': return JSONOutputFormat(osp.join(ev_dir, 'progress%s.json' % log_suffix)) elif format == 'csv': return CSVOutputFormat(osp.join(ev_dir, 'progress%s.csv' % log_suffix)) elif format == 'tensorboard': return TensorBoardOutputFormat(osp.join(ev_dir, 'tb%s' % log_suffix)) else: raise ValueError('Unknown format specified: %s' % (format,)) # ================================================================ # API # ================================================================ def logkv(key, val): """ Log a value of some diagnostic Call this once for each diagnostic quantity, each iteration If called many times, last value will be used. """ get_current().logkv(key, val) def logkv_mean(key, val): """ The same as logkv(), but if called many times, values averaged. """ get_current().logkv_mean(key, val) def logkvs(d): """ Log a dictionary of key-value pairs """ for (k, v) in d.items(): logkv(k, v) def dumpkvs(): """ Write all of the diagnostics from the current iteration """ return get_current().dumpkvs() def getkvs(): return get_current().name2val def log(*args, level=INFO): """ Write the sequence of args, with no separators, to the console and output files (if you've configured an output file). """ get_current().log(*args, level=level) def debug(*args): log(*args, level=DEBUG) def info(*args): log(*args, level=INFO) def warn(*args): log(*args, level=WARN) def error(*args): log(*args, level=ERROR) def set_level(level): """ Set logging threshold on current logger. """ get_current().set_level(level) def set_comm(comm): get_current().set_comm(comm) def get_dir(): """ Get directory that log files are being written to. will be None if there is no output directory (i.e., if you didn't call start) """ return get_current().get_dir() record_tabular = logkv dump_tabular = dumpkvs @contextmanager def profile_kv(scopename): logkey = 'wait_' + scopename tstart = time.time() try: yield finally: get_current().name2val[logkey] += time.time() - tstart def profile(n): """ Usage: @profile("my_func") def my_func(): code """ def decorator_with_name(func): def func_wrapper(*args, **kwargs): with profile_kv(n): return func(*args, **kwargs) return func_wrapper return decorator_with_name # ================================================================ # Backend # ================================================================ def get_current(): if Logger.CURRENT is None: _configure_default_logger() return Logger.CURRENT class Logger(object): DEFAULT = None # A logger with no output files. (See right below class definition) # So that you can still log to the terminal without setting up any output files CURRENT = None # Current logger being used by the free functions above def __init__(self, dir, output_formats, comm=None): self.name2val = defaultdict(float) # values this iteration self.name2cnt = defaultdict(int) self.level = INFO self.dir = dir self.output_formats = output_formats self.comm = comm # Logging API, forwarded # ---------------------------------------- def logkv(self, key, val): self.name2val[key] = val def logkv_mean(self, key, val): oldval, cnt = self.name2val[key], self.name2cnt[key] self.name2val[key] = oldval*cnt/(cnt+1) + val/(cnt+1) self.name2cnt[key] = cnt + 1 def dumpkvs(self): if self.comm is None: d = self.name2val else: from external import mpi_util d = mpi_util.mpi_weighted_mean(self.comm, {name : (val, self.name2cnt.get(name, 1)) for (name, val) in self.name2val.items()}) if self.comm.rank != 0: d['dummy'] = 1 # so we don't get a warning about empty dict out = d.copy() # Return the dict for unit testing purposes for fmt in self.output_formats: if isinstance(fmt, KVWriter): fmt.writekvs(d) self.name2val.clear() self.name2cnt.clear() return out def log(self, *args, level=INFO): if self.level <= level: self._do_log(args) # Configuration # ---------------------------------------- def set_level(self, level): self.level = level def set_comm(self, comm): self.comm = comm def get_dir(self): return self.dir def close(self): for fmt in self.output_formats: fmt.close() # Misc # ---------------------------------------- def _do_log(self, args): for fmt in self.output_formats: if isinstance(fmt, SeqWriter): fmt.writeseq(map(str, args)) def get_rank_without_mpi_import(): # check environment variables here instead of importing mpi4py # to avoid calling MPI_Init() when this module is imported for varname in ['PMI_RANK', 'OMPI_COMM_WORLD_RANK']: if varname in os.environ: return int(os.environ[varname]) return 0 def configure(dir=None, format_strs=None, comm=None, log_suffix=''): """ If comm is provided, average all numerical stats across that comm """ if dir is None: dir = os.getenv('OPENAI_LOGDIR') if dir is None: dir = osp.join(tempfile.gettempdir(), datetime.datetime.now().strftime("openai-%Y-%m-%d-%H-%M-%S-%f")) assert isinstance(dir, str) dir = os.path.expanduser(dir) os.makedirs(os.path.expanduser(dir), exist_ok=True) rank = get_rank_without_mpi_import() if rank > 0: log_suffix = log_suffix + "-rank%03i" % rank if format_strs is None: if rank == 0: format_strs = os.getenv('OPENAI_LOG_FORMAT', 'stdout,log,csv').split(',') else: format_strs = os.getenv('OPENAI_LOG_FORMAT_MPI', 'log').split(',') format_strs = filter(None, format_strs) output_formats = [make_output_format(f, dir, log_suffix) for f in format_strs] Logger.CURRENT = Logger(dir=dir, output_formats=output_formats, comm=comm) if output_formats: log('Logging to %s'%dir) def _configure_default_logger(): configure() Logger.DEFAULT = Logger.CURRENT def reset(): if Logger.CURRENT is not Logger.DEFAULT: Logger.CURRENT.close() Logger.CURRENT = Logger.DEFAULT log('Reset logger') @contextmanager def scoped_configure(dir=None, format_strs=None, comm=None): prevlogger = Logger.CURRENT configure(dir=dir, format_strs=format_strs, comm=comm) try: yield finally: Logger.CURRENT.close() Logger.CURRENT = prevlogger # ================================================================ def _demo(): info("hi") debug("shouldn't appear") set_level(DEBUG) debug("should appear") dir = "/tmp/testlogging" if os.path.exists(dir): shutil.rmtree(dir) configure(dir=dir) logkv("a", 3) logkv("b", 2.5) dumpkvs() logkv("b", -2.5) logkv("a", 5.5) dumpkvs() info("^^^ should see a = 5.5") logkv_mean("b", -22.5) logkv_mean("b", -44.4) logkv("a", 5.5) dumpkvs() info("^^^ should see b = -33.3") logkv("b", -2.5) dumpkvs() logkv("a", "longasslongasslongasslongasslongasslongassvalue") dumpkvs() # ================================================================ # Readers # ================================================================ def read_json(fname): import pandas ds = [] with open(fname, 'rt') as fh: for line in fh: ds.append(json.loads(line)) return pandas.DataFrame(ds) def read_csv(fname): import pandas return pandas.read_csv(fname, index_col=None, comment='#') def read_tb(path): """ path : a tensorboard file OR a directory, where we will find all TB files of the form events.* """ import pandas import numpy as np from glob import glob import tensorflow as tf if osp.isdir(path): fnames = glob(osp.join(path, "events.*")) elif osp.basename(path).startswith("events."): fnames = [path] else: raise NotImplementedError("Expected tensorboard file or directory containing them. Got %s"%path) tag2pairs = defaultdict(list) maxstep = 0 for fname in fnames: for summary in tf.train.summary_iterator(fname): if summary.step > 0: for v in summary.summary.value: pair = (summary.step, v.simple_value) tag2pairs[v.tag].append(pair) maxstep = max(summary.step, maxstep) data = np.empty((maxstep, len(tag2pairs))) data[:] = np.nan tags = sorted(tag2pairs.keys()) for (colidx,tag) in enumerate(tags): pairs = tag2pairs[tag] for (step, value) in pairs: data[step-1, colidx] = value return pandas.DataFrame(data, columns=tags) if __name__ == "__main__": _demo()

29.411531

122

0.56773

import os import sys import shutil import os.path as osp import json import time import datetime import tempfile from collections import defaultdict from contextlib import contextmanager DEBUG = 10 INFO = 20 WARN = 30 ERROR = 40 DISABLED = 50 class KVWriter(object): def writekvs(self, kvs): raise NotImplementedError class SeqWriter(object): def writeseq(self, seq): raise NotImplementedError class HumanOutputFormat(KVWriter, SeqWriter): def __init__(self, filename_or_file): if isinstance(filename_or_file, str): self.file = open(filename_or_file, 'wt') self.own_file = True else: assert hasattr(filename_or_file, 'read'), 'expected file or str, got %s'%filename_or_file self.file = filename_or_file self.own_file = False def writekvs(self, kvs): key2str = {} for (key, val) in sorted(kvs.items()): if hasattr(val, '__float__'): valstr = '%-8.3g' % val else: valstr = str(val) key2str[self._truncate(key)] = self._truncate(valstr) if len(key2str) == 0: print('WARNING: tried to write empty key-value dict') return else: keywidth = max(map(len, key2str.keys())) valwidth = max(map(len, key2str.values())) dashes = '-' * (keywidth + valwidth + 7) lines = [dashes] for (key, val) in sorted(key2str.items(), key=lambda kv: kv[0].lower()): lines.append('| %s%s | %s%s |' % ( key, ' ' * (keywidth - len(key)), val, ' ' * (valwidth - len(val)), )) lines.append(dashes) self.file.write('\n'.join(lines) + '\n') self.file.flush() def _truncate(self, s): maxlen = 30 return s[:maxlen-3] + '...' if len(s) > maxlen else s def writeseq(self, seq): seq = list(seq) for (i, elem) in enumerate(seq): self.file.write(elem) if i < len(seq) - 1: self.file.write(' ') self.file.write('\n') self.file.flush() def close(self): if self.own_file: self.file.close() class JSONOutputFormat(KVWriter): def __init__(self, filename): self.file = open(filename, 'wt') def writekvs(self, kvs): for k, v in sorted(kvs.items()): if hasattr(v, 'dtype'): kvs[k] = float(v) self.file.write(json.dumps(kvs) + '\n') self.file.flush() def close(self): self.file.close() class CSVOutputFormat(KVWriter): def __init__(self, filename): self.file = open(filename, 'w+t') self.keys = [] self.sep = ',' def writekvs(self, kvs): extra_keys = list(kvs.keys() - self.keys) extra_keys.sort() if extra_keys: self.keys.extend(extra_keys) self.file.seek(0) lines = self.file.readlines() self.file.seek(0) for (i, k) in enumerate(self.keys): if i > 0: self.file.write(',') self.file.write(k) self.file.write('\n') for line in lines[1:]: self.file.write(line[:-1]) self.file.write(self.sep * len(extra_keys)) self.file.write('\n') for (i, k) in enumerate(self.keys): if i > 0: self.file.write(',') v = kvs.get(k) if v is not None: self.file.write(str(v)) self.file.write('\n') self.file.flush() def close(self): self.file.close() class TensorBoardOutputFormat(KVWriter): def __init__(self, dir): os.makedirs(dir, exist_ok=True) self.dir = dir self.step = 1 prefix = 'events' path = osp.join(osp.abspath(dir), prefix) import tensorflow as tf from tensorflow.python import pywrap_tensorflow from tensorflow.core.util import event_pb2 from tensorflow.python.util import compat self.tf = tf self.event_pb2 = event_pb2 self.pywrap_tensorflow = pywrap_tensorflow self.writer = pywrap_tensorflow.EventsWriter(compat.as_bytes(path)) def writekvs(self, kvs): def summary_val(k, v): kwargs = {'tag': k, 'simple_value': float(v)} return self.tf.Summary.Value(**kwargs) summary = self.tf.Summary(value=[summary_val(k, v) for k, v in kvs.items()]) event = self.event_pb2.Event(wall_time=time.time(), summary=summary) event.step = self.step self.writer.WriteEvent(event) self.writer.Flush() self.step += 1 def close(self): if self.writer: self.writer.Close() self.writer = None def make_output_format(format, ev_dir, log_suffix=''): os.makedirs(ev_dir, exist_ok=True) if format == 'stdout': return HumanOutputFormat(sys.stdout) elif format == 'log': return HumanOutputFormat(osp.join(ev_dir, 'log%s.txt' % log_suffix)) elif format == 'json': return JSONOutputFormat(osp.join(ev_dir, 'progress%s.json' % log_suffix)) elif format == 'csv': return CSVOutputFormat(osp.join(ev_dir, 'progress%s.csv' % log_suffix)) elif format == 'tensorboard': return TensorBoardOutputFormat(osp.join(ev_dir, 'tb%s' % log_suffix)) else: raise ValueError('Unknown format specified: %s' % (format,)) # ================================================================ # API # ================================================================ def logkv(key, val): get_current().logkv(key, val) def logkv_mean(key, val): get_current().logkv_mean(key, val) def logkvs(d): for (k, v) in d.items(): logkv(k, v) def dumpkvs(): return get_current().dumpkvs() def getkvs(): return get_current().name2val def log(*args, level=INFO): get_current().log(*args, level=level) def debug(*args): log(*args, level=DEBUG) def info(*args): log(*args, level=INFO) def warn(*args): log(*args, level=WARN) def error(*args): log(*args, level=ERROR) def set_level(level): get_current().set_level(level) def set_comm(comm): get_current().set_comm(comm) def get_dir(): return get_current().get_dir() record_tabular = logkv dump_tabular = dumpkvs @contextmanager def profile_kv(scopename): logkey = 'wait_' + scopename tstart = time.time() try: yield finally: get_current().name2val[logkey] += time.time() - tstart def profile(n): def decorator_with_name(func): def func_wrapper(*args, **kwargs): with profile_kv(n): return func(*args, **kwargs) return func_wrapper return decorator_with_name # ================================================================ # Backend # ================================================================ def get_current(): if Logger.CURRENT is None: _configure_default_logger() return Logger.CURRENT class Logger(object): DEFAULT = None # A logger with no output files. (See right below class definition) # So that you can still log to the terminal without setting up any output files CURRENT = None # Current logger being used by the free functions above def __init__(self, dir, output_formats, comm=None): self.name2val = defaultdict(float) # values this iteration self.name2cnt = defaultdict(int) self.level = INFO self.dir = dir self.output_formats = output_formats self.comm = comm # Logging API, forwarded # ---------------------------------------- def logkv(self, key, val): self.name2val[key] = val def logkv_mean(self, key, val): oldval, cnt = self.name2val[key], self.name2cnt[key] self.name2val[key] = oldval*cnt/(cnt+1) + val/(cnt+1) self.name2cnt[key] = cnt + 1 def dumpkvs(self): if self.comm is None: d = self.name2val else: from external import mpi_util d = mpi_util.mpi_weighted_mean(self.comm, {name : (val, self.name2cnt.get(name, 1)) for (name, val) in self.name2val.items()}) if self.comm.rank != 0: d['dummy'] = 1 # so we don't get a warning about empty dict out = d.copy() for fmt in self.output_formats: if isinstance(fmt, KVWriter): fmt.writekvs(d) self.name2val.clear() self.name2cnt.clear() return out def log(self, *args, level=INFO): if self.level <= level: self._do_log(args) def set_level(self, level): self.level = level def set_comm(self, comm): self.comm = comm def get_dir(self): return self.dir def close(self): for fmt in self.output_formats: fmt.close() def _do_log(self, args): for fmt in self.output_formats: if isinstance(fmt, SeqWriter): fmt.writeseq(map(str, args)) def get_rank_without_mpi_import(): for varname in ['PMI_RANK', 'OMPI_COMM_WORLD_RANK']: if varname in os.environ: return int(os.environ[varname]) return 0 def configure(dir=None, format_strs=None, comm=None, log_suffix=''): if dir is None: dir = os.getenv('OPENAI_LOGDIR') if dir is None: dir = osp.join(tempfile.gettempdir(), datetime.datetime.now().strftime("openai-%Y-%m-%d-%H-%M-%S-%f")) assert isinstance(dir, str) dir = os.path.expanduser(dir) os.makedirs(os.path.expanduser(dir), exist_ok=True) rank = get_rank_without_mpi_import() if rank > 0: log_suffix = log_suffix + "-rank%03i" % rank if format_strs is None: if rank == 0: format_strs = os.getenv('OPENAI_LOG_FORMAT', 'stdout,log,csv').split(',') else: format_strs = os.getenv('OPENAI_LOG_FORMAT_MPI', 'log').split(',') format_strs = filter(None, format_strs) output_formats = [make_output_format(f, dir, log_suffix) for f in format_strs] Logger.CURRENT = Logger(dir=dir, output_formats=output_formats, comm=comm) if output_formats: log('Logging to %s'%dir) def _configure_default_logger(): configure() Logger.DEFAULT = Logger.CURRENT def reset(): if Logger.CURRENT is not Logger.DEFAULT: Logger.CURRENT.close() Logger.CURRENT = Logger.DEFAULT log('Reset logger') @contextmanager def scoped_configure(dir=None, format_strs=None, comm=None): prevlogger = Logger.CURRENT configure(dir=dir, format_strs=format_strs, comm=comm) try: yield finally: Logger.CURRENT.close() Logger.CURRENT = prevlogger def _demo(): info("hi") debug("shouldn't appear") set_level(DEBUG) debug("should appear") dir = "/tmp/testlogging" if os.path.exists(dir): shutil.rmtree(dir) configure(dir=dir) logkv("a", 3) logkv("b", 2.5) dumpkvs() logkv("b", -2.5) logkv("a", 5.5) dumpkvs() info("^^^ should see a = 5.5") logkv_mean("b", -22.5) logkv_mean("b", -44.4) logkv("a", 5.5) dumpkvs() info("^^^ should see b = -33.3") logkv("b", -2.5) dumpkvs() logkv("a", "longasslongasslongasslongasslongasslongassvalue") dumpkvs() # ================================================================ # Readers # ================================================================ def read_json(fname): import pandas ds = [] with open(fname, 'rt') as fh: for line in fh: ds.append(json.loads(line)) return pandas.DataFrame(ds) def read_csv(fname): import pandas return pandas.read_csv(fname, index_col=None, comment=' def read_tb(path): import pandas import numpy as np from glob import glob import tensorflow as tf if osp.isdir(path): fnames = glob(osp.join(path, "events.*")) elif osp.basename(path).startswith("events."): fnames = [path] else: raise NotImplementedError("Expected tensorboard file or directory containing them. Got %s"%path) tag2pairs = defaultdict(list) maxstep = 0 for fname in fnames: for summary in tf.train.summary_iterator(fname): if summary.step > 0: for v in summary.summary.value: pair = (summary.step, v.simple_value) tag2pairs[v.tag].append(pair) maxstep = max(summary.step, maxstep) data = np.empty((maxstep, len(tag2pairs))) data[:] = np.nan tags = sorted(tag2pairs.keys()) for (colidx,tag) in enumerate(tags): pairs = tag2pairs[tag] for (step, value) in pairs: data[step-1, colidx] = value return pandas.DataFrame(data, columns=tags) if __name__ == "__main__": _demo()

true

1c3799c35730a1160f93225e698987adb9c3c071

4,558

py

Python

kafka-python/json_test.py

pengfei99/KafkaPyClient

b18b361aedec9b58eef27c1d6f97346a64a1f154

[ "Apache-2.0" ]

null

kafka-python/json_test.py

pengfei99/KafkaPyClient

b18b361aedec9b58eef27c1d6f97346a64a1f154

[ "Apache-2.0" ]

null

kafka-python/json_test.py

pengfei99/KafkaPyClient

b18b361aedec9b58eef27c1d6f97346a64a1f154

[ "Apache-2.0" ]

null

import json def main(): msg = """{"tableName":"students","dbName":"default","owner":"pliu","createTime":1647683673,"lastAccessTime":0,"retention":0,"sd":{"cols":[{"name":"student_id","type":"int","comment":null,"setType":true,"setName":true,"setComment":false},{"name":"firstname","type":"string","comment":null,"setType":true,"setName":true,"setComment":false},{"name":"lastname","type":"string","comment":null,"setType":true,"setName":true,"setComment":false},{"name":"year","type":"string","comment":null,"setType":true,"setName":true,"setComment":false},{"name":"major","type":"string","comment":null,"setType":true,"setName":true,"setComment":false}],"location":"file:/home/pliu/hive_data/sample_data","inputFormat":"org.apache.hadoop.mapred.TextInputFormat","outputFormat":"org.apache.hadoop.hive.ql.io.HiveIgnoreKeyTextOutputFormat","compressed":false,"numBuckets":-1,"serdeInfo":{"name":null,"serializationLib":"org.apache.hadoop.hive.serde2.lazy.LazySimpleSerDe","parameters":{"field.delim":",","serialization.format":","},"description":null,"serializerClass":null,"deserializerClass":null,"serdeType":null,"setParameters":true,"parametersSize":2,"setName":false,"setDescription":false,"setSerdeType":false,"setSerializationLib":true,"setSerializerClass":false,"setDeserializerClass":false},"bucketCols":[],"sortCols":[],"parameters":{},"skewedInfo":{"skewedColNames":[],"skewedColValues":[],"skewedColValueLocationMaps":{},"setSkewedColNames":true,"setSkewedColValues":true,"setSkewedColValueLocationMaps":true,"skewedColNamesSize":0,"skewedColNamesIterator":[],"skewedColValuesSize":0,"skewedColValuesIterator":[],"skewedColValueLocationMapsSize":0},"storedAsSubDirectories":false,"colsSize":5,"setParameters":true,"setLocation":true,"setInputFormat":true,"parametersSize":0,"setCols":true,"colsIterator":[{"name":"student_id","type":"int","comment":null,"setType":true,"setName":true,"setComment":false},{"name":"firstname","type":"string","comment":null,"setType":true,"setName":true,"setComment":false},{"name":"lastname","type":"string","comment":null,"setType":true,"setName":true,"setComment":false},{"name":"year","type":"string","comment":null,"setType":true,"setName":true,"setComment":false},{"name":"major","type":"string","comment":null,"setType":true,"setName":true,"setComment":false}],"setSkewedInfo":true,"setOutputFormat":true,"setCompressed":false,"setNumBuckets":true,"bucketColsSize":0,"bucketColsIterator":[],"sortColsSize":0,"sortColsIterator":[],"setStoredAsSubDirectories":true,"setSortCols":true,"setSerdeInfo":true,"setBucketCols":true},"partitionKeys":[],"parameters":{"totalSize":"62","EXTERNAL":"TRUE","numFiles":"1","transient_lastDdlTime":"1647683673","bucketing_version":"2","comment":"Student Names"},"viewOriginalText":null,"viewExpandedText":null,"tableType":"EXTERNAL_TABLE","privileges":{"userPrivileges":{"pliu":[{"privilege":"INSERT","createTime":-1,"grantor":"pliu","grantorType":"USER","grantOption":true,"setPrivilege":true,"setGrantOption":true,"setCreateTime":true,"setGrantor":true,"setGrantorType":true},{"privilege":"SELECT","createTime":-1,"grantor":"pliu","grantorType":"USER","grantOption":true,"setPrivilege":true,"setGrantOption":true,"setCreateTime":true,"setGrantor":true,"setGrantorType":true},{"privilege":"UPDATE","createTime":-1,"grantor":"pliu","grantorType":"USER","grantOption":true,"setPrivilege":true,"setGrantOption":true,"setCreateTime":true,"setGrantor":true,"setGrantorType":true},{"privilege":"DELETE","createTime":-1,"grantor":"pliu","grantorType":"USER","grantOption":true,"setPrivilege":true,"setGrantOption":true,"setCreateTime":true,"setGrantor":true,"setGrantorType":true}]},"groupPrivileges":null,"rolePrivileges":null,"setUserPrivileges":true,"setGroupPrivileges":false,"setRolePrivileges":false,"userPrivilegesSize":1,"groupPrivilegesSize":0,"rolePrivilegesSize":0},"temporary":false,"rewriteEnabled":false,"creationMetadata":null,"catName":"hive","ownerType":"USER","partitionKeysSize":0,"setCatName":true,"setParameters":true,"setPartitionKeys":true,"setSd":true,"setPrivileges":true,"setDbName":true,"setTableName":true,"setCreateTime":true,"setLastAccessTime":false,"parametersSize":6,"setRetention":false,"partitionKeysIterator":[],"setTemporary":true,"setRewriteEnabled":false,"setOwner":true,"setViewOriginalText":false,"setViewExpandedText":false,"setTableType":true,"setCreationMetadata":false,"setOwnerType":true}""" tmp_table = json.loads(msg) print(tmp_table['tableName']) print(tmp_table["dbName"]) if __name__ == "__main__": main()

350.615385

4,394

0.753401

import json def main(): msg = """{"tableName":"students","dbName":"default","owner":"pliu","createTime":1647683673,"lastAccessTime":0,"retention":0,"sd":{"cols":[{"name":"student_id","type":"int","comment":null,"setType":true,"setName":true,"setComment":false},{"name":"firstname","type":"string","comment":null,"setType":true,"setName":true,"setComment":false},{"name":"lastname","type":"string","comment":null,"setType":true,"setName":true,"setComment":false},{"name":"year","type":"string","comment":null,"setType":true,"setName":true,"setComment":false},{"name":"major","type":"string","comment":null,"setType":true,"setName":true,"setComment":false}],"location":"file:/home/pliu/hive_data/sample_data","inputFormat":"org.apache.hadoop.mapred.TextInputFormat","outputFormat":"org.apache.hadoop.hive.ql.io.HiveIgnoreKeyTextOutputFormat","compressed":false,"numBuckets":-1,"serdeInfo":{"name":null,"serializationLib":"org.apache.hadoop.hive.serde2.lazy.LazySimpleSerDe","parameters":{"field.delim":",","serialization.format":","},"description":null,"serializerClass":null,"deserializerClass":null,"serdeType":null,"setParameters":true,"parametersSize":2,"setName":false,"setDescription":false,"setSerdeType":false,"setSerializationLib":true,"setSerializerClass":false,"setDeserializerClass":false},"bucketCols":[],"sortCols":[],"parameters":{},"skewedInfo":{"skewedColNames":[],"skewedColValues":[],"skewedColValueLocationMaps":{},"setSkewedColNames":true,"setSkewedColValues":true,"setSkewedColValueLocationMaps":true,"skewedColNamesSize":0,"skewedColNamesIterator":[],"skewedColValuesSize":0,"skewedColValuesIterator":[],"skewedColValueLocationMapsSize":0},"storedAsSubDirectories":false,"colsSize":5,"setParameters":true,"setLocation":true,"setInputFormat":true,"parametersSize":0,"setCols":true,"colsIterator":[{"name":"student_id","type":"int","comment":null,"setType":true,"setName":true,"setComment":false},{"name":"firstname","type":"string","comment":null,"setType":true,"setName":true,"setComment":false},{"name":"lastname","type":"string","comment":null,"setType":true,"setName":true,"setComment":false},{"name":"year","type":"string","comment":null,"setType":true,"setName":true,"setComment":false},{"name":"major","type":"string","comment":null,"setType":true,"setName":true,"setComment":false}],"setSkewedInfo":true,"setOutputFormat":true,"setCompressed":false,"setNumBuckets":true,"bucketColsSize":0,"bucketColsIterator":[],"sortColsSize":0,"sortColsIterator":[],"setStoredAsSubDirectories":true,"setSortCols":true,"setSerdeInfo":true,"setBucketCols":true},"partitionKeys":[],"parameters":{"totalSize":"62","EXTERNAL":"TRUE","numFiles":"1","transient_lastDdlTime":"1647683673","bucketing_version":"2","comment":"Student Names"},"viewOriginalText":null,"viewExpandedText":null,"tableType":"EXTERNAL_TABLE","privileges":{"userPrivileges":{"pliu":[{"privilege":"INSERT","createTime":-1,"grantor":"pliu","grantorType":"USER","grantOption":true,"setPrivilege":true,"setGrantOption":true,"setCreateTime":true,"setGrantor":true,"setGrantorType":true},{"privilege":"SELECT","createTime":-1,"grantor":"pliu","grantorType":"USER","grantOption":true,"setPrivilege":true,"setGrantOption":true,"setCreateTime":true,"setGrantor":true,"setGrantorType":true},{"privilege":"UPDATE","createTime":-1,"grantor":"pliu","grantorType":"USER","grantOption":true,"setPrivilege":true,"setGrantOption":true,"setCreateTime":true,"setGrantor":true,"setGrantorType":true},{"privilege":"DELETE","createTime":-1,"grantor":"pliu","grantorType":"USER","grantOption":true,"setPrivilege":true,"setGrantOption":true,"setCreateTime":true,"setGrantor":true,"setGrantorType":true}]},"groupPrivileges":null,"rolePrivileges":null,"setUserPrivileges":true,"setGroupPrivileges":false,"setRolePrivileges":false,"userPrivilegesSize":1,"groupPrivilegesSize":0,"rolePrivilegesSize":0},"temporary":false,"rewriteEnabled":false,"creationMetadata":null,"catName":"hive","ownerType":"USER","partitionKeysSize":0,"setCatName":true,"setParameters":true,"setPartitionKeys":true,"setSd":true,"setPrivileges":true,"setDbName":true,"setTableName":true,"setCreateTime":true,"setLastAccessTime":false,"parametersSize":6,"setRetention":false,"partitionKeysIterator":[],"setTemporary":true,"setRewriteEnabled":false,"setOwner":true,"setViewOriginalText":false,"setViewExpandedText":false,"setTableType":true,"setCreationMetadata":false,"setOwnerType":true}""" tmp_table = json.loads(msg) print(tmp_table['tableName']) print(tmp_table["dbName"]) if __name__ == "__main__": main()

true

1c3799d44bc7ac749bd8a851ed33c9e6a417e9f2

1,774

py

Python

UserCode/jzhang/bubble_finder_test.py

RunzZhang/SBCcode

e75b8e751cec5fb2c28950edef0c82f005caedcb

[ "MIT" ]

4

2018-08-27T18:02:34.000Z

2020-06-09T21:19:04.000Z

UserCode/jzhang/bubble_finder_test.py

RunzZhang/SBCcode

e75b8e751cec5fb2c28950edef0c82f005caedcb

[ "MIT" ]

null

UserCode/jzhang/bubble_finder_test.py

RunzZhang/SBCcode

e75b8e751cec5fb2c28950edef0c82f005caedcb

[ "MIT" ]

4

2019-06-20T21:36:26.000Z

2020-11-10T17:23:14.000Z

# python sbc_pmttest_processall.py [run_list] # if run_list is provided, the runs in the list will be processed; otherwise # the runs in the script will be processed import SBCcode.AnalysisModules.ImageAnalysis as ia import SBCcode.DataHandling.WriteBinary as wb import numpy as np # import SBCcode as sbc import os import re import sys # datadir = '/bluearc/storage/SBC-17-data' #recondir = '/bluearc/storage/recon/devel/SBC-15/output' datadir = '/mnt/XENON_DAQ/SBC-17-data' recondir = '.' # ~ runlist = os.listdir(datadir) # ~ runlist = filter(lambda fn: (not re.search('^\d+_\d+$', fn) is None) and # ~ os.path.isdir(os.path.join(datadir, fn)), # ~ runlist) # ~ runlist = filter(lambda fn: os.path.exists(os.path.join(datadir, # ~ *[fn, 'DAQversion.txt'])), runlist) if len(sys.argv) > 1: runlist = sys.argv[1:] else: runlist = ['20170625_0'] for runname in runlist: runid_str = runname.split('_') runid = np.int32(runid_str) rundir = os.path.join(datadir,runname) eventdirlist = os.listdir(rundir) eventdirlist = filter(lambda fn: (not re.search('^\d+$', fn) is None) and os.path.isdir(os.path.join(rundir, fn)), eventdirlist) eventlist = [int(x) for x in list(eventdirlist)] eventlist = [21] if not os.path.isdir(recondir): os.mkdir(recondir) bubbleList = [] for ev in eventlist: bubbleList.append(ia.BubbleFinder(os.path.join(datadir,runname), ev, 12, 3, 15, 4).bubbles) #print(bubbleList) wb.WriteBinaryNtupleFile(os.path.join(recondir,'ImageAnalysis_' + runname + '.bin'), bubbleList, rowdef=1, initialkeys=['runid', 'ev'], drop_first_dim=True)

34.784314

100

0.638106

import SBCcode.AnalysisModules.ImageAnalysis as ia import SBCcode.DataHandling.WriteBinary as wb import numpy as np import os import re import sys datadir = '/mnt/XENON_DAQ/SBC-17-data' recondir = '.' if len(sys.argv) > 1: runlist = sys.argv[1:] else: runlist = ['20170625_0'] for runname in runlist: runid_str = runname.split('_') runid = np.int32(runid_str) rundir = os.path.join(datadir,runname) eventdirlist = os.listdir(rundir) eventdirlist = filter(lambda fn: (not re.search('^\d+$', fn) is None) and os.path.isdir(os.path.join(rundir, fn)), eventdirlist) eventlist = [int(x) for x in list(eventdirlist)] eventlist = [21] if not os.path.isdir(recondir): os.mkdir(recondir) bubbleList = [] for ev in eventlist: bubbleList.append(ia.BubbleFinder(os.path.join(datadir,runname), ev, 12, 3, 15, 4).bubbles) wb.WriteBinaryNtupleFile(os.path.join(recondir,'ImageAnalysis_' + runname + '.bin'), bubbleList, rowdef=1, initialkeys=['runid', 'ev'], drop_first_dim=True)

true

1c379ab34d3650c0fbce680f9a59effe54787ad0

857

py

Python

restriction_sites.py

Tiago-Minuzzi/lab-stuff

b4cbca8c578e3cc4035df5686254d9254a876413

[ "MIT" ]

null

restriction_sites.py

Tiago-Minuzzi/lab-stuff

b4cbca8c578e3cc4035df5686254d9254a876413

[ "MIT" ]

null

restriction_sites.py

Tiago-Minuzzi/lab-stuff

b4cbca8c578e3cc4035df5686254d9254a876413

[ "MIT" ]

null

#!/usr/bin/env python3 # -*- coding: utf-8 -*- """ Created by Tiago Minuzzi """ import sys from Bio import SeqIO from Bio.Restriction import * INFILE=sys.argv[1] with open(INFILE) as fasta: for record in SeqIO.parse(fasta, 'fasta'): fid = record.id sequencia = record.seq tamanho = len(record.seq) # Find restriction sites sitiosHIII = HindIII.search(sequencia) sitiosERI = EcoRI.search(sequencia) allsites= sitiosHIII+sitiosERI allsites=list(set(allsites)) allsites.sort() allsites.insert(0,0) for i,j in zip(allsites,allsites[1:]+[None]): sitio=f'{i+1}:{j}' sitio=sitio.replace('None',str(len(sequencia))) corte=sequencia[i:j] tam=len(corte) print(f'>{fid}|pos={sitio}|length={tam}\n{corte}')

28.566667

62

0.590432

import sys from Bio import SeqIO from Bio.Restriction import * INFILE=sys.argv[1] with open(INFILE) as fasta: for record in SeqIO.parse(fasta, 'fasta'): fid = record.id sequencia = record.seq tamanho = len(record.seq) sitiosHIII = HindIII.search(sequencia) sitiosERI = EcoRI.search(sequencia) allsites= sitiosHIII+sitiosERI allsites=list(set(allsites)) allsites.sort() allsites.insert(0,0) for i,j in zip(allsites,allsites[1:]+[None]): sitio=f'{i+1}:{j}' sitio=sitio.replace('None',str(len(sequencia))) corte=sequencia[i:j] tam=len(corte) print(f'>{fid}|pos={sitio}|length={tam}\n{corte}')

true

1c379b44da55f2f427dc8bdf12f7b203223a0aba

2,990

py

Python

run.py

felix2072/pytorch-CycleGAN-and-pix2pix

4980106ceab5e1eb7bb20c2b492d007b6310d9e1

[ "BSD-3-Clause" ]

null

run.py

felix2072/pytorch-CycleGAN-and-pix2pix

4980106ceab5e1eb7bb20c2b492d007b6310d9e1

[ "BSD-3-Clause" ]

null

run.py

felix2072/pytorch-CycleGAN-and-pix2pix

4980106ceab5e1eb7bb20c2b492d007b6310d9e1

[ "BSD-3-Clause" ]

null

import socket from options.test_options import TestOptions from data import create_dataset from models import create_model import time from util import util UDP_IP = "127.0.0.1" OUT_PORT = 5004 IN_PORT = 5005 buf = 1024 timeout = 3 if __name__ == '__main__': opt = TestOptions().parse() # get test options # hard-code some parameters for test opt.num_threads = 0 # test code only supports num_threads = 0 opt.batch_size = 1 # test code only supports batch_size = 1 opt.serial_batches = True # disable data shuffling; comment this line if results on randomly chosen images are needed. opt.no_flip = True # no flip; comment this line if results on flipped images are needed. opt.display_id = -1 # no visdom display; the test code saves the results to a HTML file. model = create_model(opt) # create a model given opt.model and other options model.setup(opt) # regular setup: load and print networks; create schedulers while True: print ("-------------------------") sock = socket.socket(socket.AF_INET, socket.SOCK_DGRAM) time.sleep(0.03) info = "need file" print(info) sock.sendto(info.encode(), (UDP_IP, OUT_PORT)) time.sleep(0.04) info = "end" print(info) sock.sendto(info.encode(), (UDP_IP, OUT_PORT)) sock.close() sock = socket.socket(socket.AF_INET, socket.SOCK_DGRAM) sock.bind((UDP_IP, IN_PORT)) while True: data, addr = sock.recvfrom(1024) if data.decode("utf-8") == "image saved": print("load base image") break dataset = create_dataset(opt) # create a dataset given opt.dataset_mode and other options #print("dataset :%s was created" % dataset) for i, data in enumerate(dataset): if i >= opt.num_test: # only apply our model to opt.num_test images. break model.set_input(data) # unpack data from data loader model.test(i) # run inference visuals = model.get_current_visuals() # get image results for label, im_data in visuals.items(): im = util.tensor2im(im_data) save_path = './datasets/{}/test_6result/fake{}.jpg'.format(opt.name,i) util.save_image(im, save_path, aspect_ratio=opt.aspect_ratio) info = "fake is ready" print(info) sock.sendto(info.encode(), (UDP_IP, OUT_PORT)) time.sleep(0.04) info = "end" print(info) sock.sendto(info.encode(), (UDP_IP, OUT_PORT)) sock.close() sock = socket.socket(socket.AF_INET, socket.SOCK_DGRAM) sock.bind((UDP_IP, IN_PORT)) while True: print("wait for VL to load image") data, addr = sock.recvfrom(1024) if data.decode("utf-8") == "got fake": print("fake image") break

36.463415

123

0.600669

import socket from options.test_options import TestOptions from data import create_dataset from models import create_model import time from util import util UDP_IP = "127.0.0.1" OUT_PORT = 5004 IN_PORT = 5005 buf = 1024 timeout = 3 if __name__ == '__main__': opt = TestOptions().parse() opt.num_threads = 0 opt.batch_size = 1 opt.serial_batches = True opt.no_flip = True opt.display_id = -1 model = create_model(opt) model.setup(opt) while True: print ("-------------------------") sock = socket.socket(socket.AF_INET, socket.SOCK_DGRAM) time.sleep(0.03) info = "need file" print(info) sock.sendto(info.encode(), (UDP_IP, OUT_PORT)) time.sleep(0.04) info = "end" print(info) sock.sendto(info.encode(), (UDP_IP, OUT_PORT)) sock.close() sock = socket.socket(socket.AF_INET, socket.SOCK_DGRAM) sock.bind((UDP_IP, IN_PORT)) while True: data, addr = sock.recvfrom(1024) if data.decode("utf-8") == "image saved": print("load base image") break dataset = create_dataset(opt) for i, data in enumerate(dataset): if i >= opt.num_test: break model.set_input(data) model.test(i) visuals = model.get_current_visuals() for label, im_data in visuals.items(): im = util.tensor2im(im_data) save_path = './datasets/{}/test_6result/fake{}.jpg'.format(opt.name,i) util.save_image(im, save_path, aspect_ratio=opt.aspect_ratio) info = "fake is ready" print(info) sock.sendto(info.encode(), (UDP_IP, OUT_PORT)) time.sleep(0.04) info = "end" print(info) sock.sendto(info.encode(), (UDP_IP, OUT_PORT)) sock.close() sock = socket.socket(socket.AF_INET, socket.SOCK_DGRAM) sock.bind((UDP_IP, IN_PORT)) while True: print("wait for VL to load image") data, addr = sock.recvfrom(1024) if data.decode("utf-8") == "got fake": print("fake image") break

true

1c379c378fffabea367feee717680ccc02e4754d

200,004

py

Python

tests/api_test.py

SCiarella/jax

a7c9b6d11fa833c748d72b3ccc11baeed9c0248c

[ "Apache-2.0" ]

null

tests/api_test.py

SCiarella/jax

a7c9b6d11fa833c748d72b3ccc11baeed9c0248c

[ "Apache-2.0" ]

6

2022-01-03T08:14:15.000Z

2022-02-14T08:13:40.000Z

tests/api_test.py

SCiarella/jax

a7c9b6d11fa833c748d72b3ccc11baeed9c0248c

[ "Apache-2.0" ]

null

# Copyright 2018 Google LLC # # 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 # # https://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 collections import collections.abc from contextlib import contextmanager import copy import enum from functools import partial import operator import re import subprocess import sys import types import unittest import warnings import weakref import functools import itertools as it import operator as op from absl import logging from absl.testing import absltest, parameterized import numpy as np import concurrent.futures import jax import jax.numpy as jnp from jax import float0, jit, grad, device_put, jacfwd, jacrev, hessian from jax import core, dtypes, lax from jax._src import api from jax.core import Primitive from jax.errors import UnexpectedTracerError from jax.interpreters import ad from jax.interpreters import xla from jax.interpreters import pxla from jax.interpreters.sharded_jit import PartitionSpec as P import jax._src.lib from jax._src.lib import xla_client from jax._src import test_util as jtu from jax import tree_util from jax import linear_util as lu import jax._src.util from jax._src.ad_checkpoint import saved_residuals from jax.ad_checkpoint import checkpoint as new_checkpoint, checkpoint_name from jax.config import config config.parse_flags_with_absl() FLAGS = config.FLAGS python_version = (sys.version_info[0], sys.version_info[1]) numpy_version = tuple(map(int, np.__version__.split('.')[:3])) class CPPJitTest(jtu.BufferDonationTestCase): """Shared tests between the Python and the C++ jax,jit implementations. Because the Python implementation supports more features, we need to have the Python tests that extend the C++ tests (and not the other way around). """ @property def jit(self): # Right now, the CPP tests also test the Python code-path when jaxlib is # too old. # TODO(jblespiau,phawkins): Remove this when jaxlib has been released. # This is in the future, because we are making a breaking change to # Tensorflow. return api._cpp_jit @unittest.skipIf(jax._src.lib._xla_extension_version < 40, "Test requires jaxlib 0.1.73") def test_jit_repr(self): def my_function(): return jitted = jit(my_function) self.assertEqual(repr(jitted), f"<CompiledFunction of {repr(my_function)}>") @unittest.skipIf(jax._src.lib._xla_extension_version < 40, "Test requires jaxlib 0.1.73") def test_jit_repr_errors(self): class Callable: def __call__(self): pass def __repr__(self): raise ValueError("invalid repr") # repr succeeds when underlying function repr fails. jitted = jit(Callable()) self.assertEqual(repr(jitted), "<CompiledFunction>") # repr succeeds when object is malformed. del jitted.__wrapped__ self.assertEqual(repr(jitted), "<CompiledFunction>") def test_jit_of_noncallable(self): self.assertRaisesRegex(TypeError, "Expected a callable value.*", lambda: self.jit(3)) def test_jit_of_generator(self): def gen(x): yield x self.assertRaisesRegex(TypeError, "Expected a function, got a generator function.*", lambda: self.jit(gen)) @parameterized.parameters([ # Integer support (1, 2, 3, 4, 5), # Numpy array support ( np.asarray(1, np.int32), np.asarray(2, np.int32), np.asarray(3, np.int32), np.asarray(4, np.int32), np.asarray(5, np.int32), ), ]) def test_jit_static_args(self, one, two, three, four, five): side = [] def f(x, y, z, flag=False, flag2=False): del flag2 # unused assert flag side.append(None) return 100 * x + 10 * y + z f1 = self.jit(f, static_argnums=(3, 4)) assert f1(one, two, three, True, False) == 123 assert len(side) == 1 assert f1(one, two, three, True, False) == 123 assert len(side) == 1 # Obvious cache hit. assert f1(two, one, three, True, False) == 213 assert len(side) == 1 # Should cache hit because same signature. assert f1(two, one, three, True, True) == 213 assert len(side) == 2 side[:] = [] f2 = self.jit(f, static_argnums=(0, 2, 3, 4)) assert f2(1, 2, 3, True, False) == 123 assert len(side) == 1 assert f2(1, 3, 3, True, False) == 133 assert len(side) == 1 assert f2(2, 2, 3, True, False) == 223 assert len(side) == 2 assert f2(2, 4, 3, True, False) == 243 assert len(side) == 2 assert f2(2, 4, 3, True, True) == 243 assert len(side) == 3 assert f2(2, 5, 3, True, True) == 253 assert len(side) == 3 def test_static_args_equality(self): class A(): def __hash__(self): return 1 def __eq__(self, other): return isinstance(other, A) side = [] def f(x, static_arg): del static_arg side.append(None) return x * 100 f1 = self.jit(f, static_argnums=(1,)) self.assertEqual(f1(1, A()), 100) self.assertLen(side, 1) self.assertEqual(f1(1, A()), 100) self.assertLen(side, 1) if self.jit == api._cpp_jit: f1_cpp = getattr(f1, "_cpp_jitted_f", f1) self.assertEqual(f1_cpp._cache_size(), 1) @parameterized.parameters([ (1, 2, 3), ( np.asarray(1, np.int32), np.asarray(2, np.int32), np.asarray(3, np.int32), ), ]) def test_jit_kwargs(self, one, two, three): side = [] # For the CPP jit, we need to clear the cache to prevent cache hits between # parameterized tests. if hasattr(self.jit, "cache_clear"): self.jit.cache_clear() def f(x, y, z): side.append(None) return 100 * x + 10 * y + z f = self.jit(f) assert f(one, two, three) == 123 assert len(side) == 1 assert f(one, two, three) == 123 assert len(side) == 1 assert f(one, two, z=three) == 123 assert len(side) == 2 # actually recompiles from kwarg assert f(one, two, z=three) == 123 assert len(side) == 2 # but should still cache f(one, two, z=np.zeros(3)) # doesn't crash if config.x64_enabled: # In the above call, three is of a new type (int64), thus it should # trigger a new compilation. assert len(side) == 3 def test_jit_device(self): device = jax.devices()[-1] x = self.jit(lambda x: x, device=device)(3.) self.assertIsInstance(x, xla.DeviceArray) self.assertEqual(x.device_buffer.device(), device) def test_complex_support(self): self.assertEqual(self.jit(lambda x: x + 1)(1 + 1j), 2 + 1j) def test_jit_with_many_args_works(self): @self.jit def f(args_list): return sum(args_list) self.assertEqual(f(list(range(500))), sum(range(500))) # Jit and Donate arguments def test_jit_donate_argnums_warning_raised(self): x = jnp.array([1.0, 2.0], jnp.float32) y = jnp.array([1, 2], jnp.int32) f = self.jit(lambda x, y: x.sum() + y.sum(), donate_argnums=(0, 1)) with warnings.catch_warnings(record=True) as w: warnings.simplefilter("always") f(x, y) self.assertLen(w, 1) self.assertTrue(issubclass(w[-1].category, UserWarning)) self.assertIn( "Some donated buffers were not usable: f32[2]{0}, s32[2]{0}", str(w[-1].message)) @jtu.skip_on_devices("cpu") # In/out aliasing not supported on CPU. def test_jit_donate_argnums_invalidates_input(self): # We can't just use `lambda x: x` because JAX simplifies this away to an # empty XLA computation. move = self.jit(lambda x: x + x - x, donate_argnums=0) x = jnp.ones([]) y = move(x) self.assertDeleted(x) self.assertEqual(y, 1.) @jtu.skip_on_devices("cpu") # In/out aliasing not supported on CPU. def test_jit_donate_argnums_static_argnums(self): jit_fun = self.jit( lambda a, b, c, d: ((a + b + c), (a + b + d)), static_argnums=(0, 1), donate_argnums=(2, 3)) c = jax.device_put(jnp.array([1., 1.])) d = jax.device_put(jnp.array([1., 1., 1.])) e, f = jit_fun(1, 2, c, d) np.testing.assert_allclose(e, jnp.array([4., 4.])) np.testing.assert_allclose(f, jnp.array([4., 4., 4.])) self.assertDeleted(c) self.assertDeleted(d) @jtu.skip_on_devices("cpu") # In/out aliasing not supported on CPU. def test_jnp_array_copy(self): # https://github.com/google/jax/issues/3412 @partial(self.jit, donate_argnums=(0,)) def _test(array): return array.at[0].set(77) x = jnp.asarray([0, 1]) x_copy = jnp.array(x, copy=True) with warnings.catch_warnings(): warnings.simplefilter("ignore") _test(x) # donation # Gives: RuntimeError: Invalid argument: CopyToHostAsync() called on invalid buffer. print(x_copy) # doesn't crash def test_jit_global_cache(self): def f(x): assert python_should_be_executing return x python_should_be_executing = True self.jit(f)(2) python_should_be_executing = False self.jit(f)(3) def test_jit_shallow_copy(self): def f(x): return copy.copy(x) self.jit(f)(1) def test_jit_deep_copy(self): def f(x): return copy.deepcopy(x) self.jit(f)(1) def test_disable_jit(self): effects = [] @self.jit def f(x): effects.append(1) return x with api.disable_jit(): f(2) f(2) assert len(effects) == 2 f(2) f(2) assert len(effects) == 3 def test_static_argnum_on_method(self): class A: @functools.partial(self.jit, static_argnums=(0,)) def my_func_jit(self, x): return x+2 A().my_func_jit(3) def test_static_argnum_on_static_method_is_not_supported(self): with self.assertRaisesRegex(TypeError, "Expected a callable value"): class A: @functools.partial(self.jit, static_argnums=(0,)) @classmethod def my_classmethod_jit(cls, x): return x+2 def test_staticmethod_is_not_supported(self): with self.assertRaisesRegex(TypeError, "staticmethod arguments are not supported"): class A: @functools.partial(self.jit) @staticmethod def my_staticmethod_jit(x): return x + 2 def test_concurrent_jit(self): @self.jit def f(x): return x + x - 3. xs = [np.random.randn(i) for i in range(10)] with concurrent.futures.ThreadPoolExecutor() as executor: futures = [executor.submit(partial(f, x)) for x in xs] ys = [f.result() for f in futures] for x, y in zip(xs, ys): self.assertAllClose(x * 2 - 3., y) def test_trivial_computations(self): x = jnp.array([1, 2, 3]) y = self.jit(lambda x: x)(x) self.assertIs(x, y) z1, z2 = self.jit(lambda x: (x, x))(x) self.assertIs(z1, z2) x1, x2 = jnp.array([1, 2]), jnp.array([2, 3]) z1, z2, z3 = self.jit(lambda x, y: (y, 1, x))(x1, x2) self.assertIs(z1, x2) self.assertIs(z3, x1) self.assertEqual(z2, 1) def test_trivial_computations_with_tokens(self): @self.jit def noop(arr, token): return arr, token arr = jax.numpy.ones(10) token = jax.lax.create_token() self.assertEqual(token, noop(arr, token)[1]) def test_jit_bad_input(self): def f(x): return x self.assertRaisesRegex( TypeError, ".* 'foo' of type <.*'str'> is not a valid JAX type", lambda: self.jit(f)("foo")) def test_jit_on_all_devices(self): # Verifies we can run the same computation on every device present, even # if they are, for example, different models of GPU. data = np.random.rand(1000).astype(np.float32) f = self.jit(jnp.negative) for device in jax.local_devices(): x = device_put(data, device=device) np.testing.assert_array_equal(-data, f(x)) def test_jit_nested_donate_ignored(self): jit_fun = self.jit(lambda x: self.jit(lambda y: y**2, donate_argnums=0)(x)) a = jax.device_put(jnp.array(1)) # NOTE(mattjj): stopped raising error here and instead just ignored # with self.assertRaisesRegex(ValueError, "nested.*not supported"): # jit_fun(a) jit_fun(a) # doesn't crash def test_jit_reference_dropping(self): x = jnp.ones(10) f = (lambda x: lambda: x)(x) # reference to x in f's closure g = self.jit(f) x = weakref.ref(x) # no more strong ref to x in this scope assert x() is not None # x is still around f() # f runs g() # g runs g() # g runs a second time del f # delete the raw callable assert x() is not None # x is still around g() # g still runs del g # no more references to x assert x() is None # x is gone def test_jit_raises_on_first_invocation_on_non_hashable_static_argnum(self): if self.jit != api._python_jit: raise unittest.SkipTest("this test only applies to _python_jit") f = lambda x, y: x + 3 jitted_f = self.jit(f, static_argnums=(1,)) msg = ("Non-hashable static arguments are not supported, as this can lead " "to unexpected cache-misses. Static argument (index 1) of type " "<class 'numpy.ndarray'> for function <lambda> is non-hashable.") with self.assertRaisesRegex(ValueError, re.escape(msg)): jitted_f(1, np.asarray(1)) def test_cpp_jit_raises_on_non_hashable_static_argnum(self): if self.jit != api._cpp_jit: raise unittest.SkipTest("this test only applies to _cpp_jit") f = lambda x, y: x + 3 jitted_f = api._cpp_jit(f, static_argnums=[1]) jitted_f(1, 1) msg = ("Non-hashable static arguments are not supported. An error occured " ".*while trying to hash an object of type " "<class 'numpy\\.ndarray'>, 1. The error was:\nTypeError: " "unhashable type: 'numpy\\.ndarray'") with self.assertRaisesRegex(ValueError, msg): jitted_f(1, np.asarray(1)) class HashableWithoutEq: def __hash__(self): return 1 def __eq__(self, other): raise NotImplementedError( "A Python error is as is, without stack trace") with self.assertRaisesRegex( ValueError, re.escape("static arguments should be comparable using __eq__")): jitted_f(1, HashableWithoutEq()) def test_cpp_jitted_function_returns_PyBuffer(self): if self.jit != api._cpp_jit: raise unittest.SkipTest("this test only applies to _cpp_jit") jitted_f = self.jit(lambda a: a + 1) jitted_f(1) self.assertIsInstance(jitted_f(2), xla._CppDeviceArray) @jtu.skip_on_devices("cpu") def test_explicit_backend(self): f = lambda x: x + 1 jitted_f = jit(f, backend=jtu.device_under_test()) jitted_f_cpu = jit(f, backend="cpu") result = jitted_f(1.) result_cpu = jitted_f_cpu(1.) self.assertEqual(result.device_buffer.platform(), jtu.device_under_test()) self.assertEqual(result_cpu.device_buffer.platform(), "cpu") @jtu.skip_on_devices("cpu") def test_device_to_device_copy_between_backends(self): # b/186624243 f = lambda x: x + 1 jitted_f = jit(f, backend=jtu.device_under_test()) jitted_f_cpu = jit(f, backend="cpu") x = np.arange(30).reshape(1, 10, 3) result = jitted_f(x) result_cpu = jitted_f_cpu(result) result_2 = jitted_f(result_cpu) result_cpu_2 = jitted_f_cpu(result_2) self.assertAllClose(result_2, x + 3) self.assertAllClose(result_cpu_2, x + 4) @jtu.skip_on_devices("cpu") def test_mismatched_nested_backends(self): @partial(jit, backend=jtu.device_under_test()) def f(x): return jit(lambda x: x + 1, backend="cpu")(x) with self.assertRaisesRegex( ValueError, f"Outer-jit backend specification {jtu.device_under_test()} must match " f"explicit inner-jit backend specification cpu."): f(1.) def test_omnistaging(self): # See https://github.com/google/jax/issues/5206 # TODO(frostig): remove once we always enable_custom_prng def _prng_key_as_array(key): return key.unsafe_raw_array() if config.jax_enable_custom_prng else key # TODO(frostig): remove once we always enable_custom_prng def _array_as_prng_key(arr): arr = np.array(arr, dtype=np.uint32) if config.jax_enable_custom_prng: return jax._src.prng.PRNGKeyArray( jax._src.prng.threefry_prng_impl, arr) else: return arr key_list = [None] def init(): key, subkey = jax.random.split(key_list[0]) key_list[0] = key return jax.random.normal(subkey, ()) key_list[0] = _array_as_prng_key([2384771982, 3928867769]) init() self.jit(init)() self.assertIsInstance(_prng_key_as_array(key_list[0]), core.Tracer) def test_jit_wrapped_attributes(self): def f(x: int) -> int: """docstring of f.""" return x + 1 f.some_value = 4 jf = self.jit(f) for attr in ["doc", "name", "module", "qualname", "annotations"]: self.assertEqual( {attr: getattr(f, f"__{attr}__")}, {attr: getattr(jf, f"__{attr}__")}) self.assertEqual(f.some_value, jf.some_value) def test_jit_python_builtin(self): x = jnp.array([1, 2]) expected = x + 1 jit_add = self.jit(operator.add, static_argnums=(1,)) actual = jit_add(x, 1) self.assertArraysEqual(expected, actual) def test__infer_argnums_and_argnames(self): def f(x, y=1): pass argnums, argnames = api._infer_argnums_and_argnames( f, argnums=None, argnames=None) assert argnums == () assert argnames == () argnums, argnames = api._infer_argnums_and_argnames( f, argnums=0, argnames=None) assert argnums == (0,) assert argnames == ('x',) argnums, argnames = api._infer_argnums_and_argnames( f, argnums=None, argnames='y') assert argnums == (1,) assert argnames == ('y',) argnums, argnames = api._infer_argnums_and_argnames( f, argnums=0, argnames='y') # no validation assert argnums == (0,) assert argnames == ('y',) def g(x, y, *args): pass argnums, argnames = api._infer_argnums_and_argnames( g, argnums=(1, 2), argnames=None) assert argnums == (1, 2) assert argnames == ('y',) def h(x, y, **kwargs): pass argnums, argnames = api._infer_argnums_and_argnames( h, argnums=None, argnames=('foo', 'bar')) assert argnums == () assert argnames == ('foo', 'bar') def test_jit_with_static_argnames(self): def f(x): assert x == 'foo' return 1 f_nums = self.jit(f, static_argnums=0) assert f_nums('foo') == 1 assert f_nums(x='foo') == 1 f_names = self.jit(f, static_argnames='x') assert f_names('foo') == 1 assert f_names(x='foo') == 1 def test_new_static_argnum_on_keyword_arguments(self): f = self.jit(lambda x: x, static_argnums=0) y = f(x=4) assert y == 4 def test_new_static_argnum_with_default_arguments(self): f = self.jit(lambda x=4: x, static_argnums=0) y = f() assert y == 4 def test_jit_with_mismatched_static_argnames(self): x_is_tracer, y_is_tracer = False, False def f(x, y): assert isinstance(x, core.Tracer) == x_is_tracer assert isinstance(y, core.Tracer) == y_is_tracer return 1 # If both static_argnums and static_argnames are provided, they are allowed # to disagree and `jit` will respect the user's choices. f_nums = self.jit(f, static_argnums=1, static_argnames=()) x_is_tracer, y_is_tracer = True, False assert f_nums(2, 'foo') == 1 x_is_tracer, y_is_tracer = True, True assert f_nums(1, y=2) == 1 f_names = self.jit(f, static_argnums=(), static_argnames='y') x_is_tracer, y_is_tracer = True, True assert f_names(2, 3) == 1 x_is_tracer, y_is_tracer = True, False assert f_names(1, y='foo') == 1 f_mixed = self.jit(f, static_argnums=(1,), static_argnames='x') x_is_tracer, y_is_tracer = True, False assert f_mixed(2, 'foo') == 1 x_is_tracer, y_is_tracer = True, True assert f_mixed(1, y=3) == 1 x_is_tracer, y_is_tracer = False, True assert f_mixed(x='foo', y=3) == 1 # TODO(zhangqiaorjc): Test pruning constants after DCE pass prunes primitive # applications. @parameterized.named_parameters(jtu.cases_from_list( {"testcase_name": "_num_args={}".format(num_args), "num_args": num_args} for num_args in [2, 3, 4])) def test_jit_with_pruned_args(self, num_args): def f(*args): used = np.array(2) return args[1] + used f_pruned = self.jit(f) args = range(num_args) with jtu.count_device_put() as count: np.testing.assert_allclose(f_pruned(*args), 3) self.assertEqual(count[0], 1) @unittest.skipIf(jax._src.lib._xla_extension_version <= 36, "Test requires jaxlib 0.1.71") def testBuffersAreFreedPromptly(self): # Regression test for a bug where garbage collection was delayed too long # for NumPy buffers that are aliased zero-copy by the runtime. @self.jit def f(x): return x + 1 refs = [] x = np.ones((10000,), np.float32) for step in range(1000): x = f(x) refs.append(weakref.ref(x)) x = np.asarray(x) # We expect most of the input buffers to have been garbage # collected in parallel with the execution. We can't call # block_until_ready() here because it would force a garbage collection. live_refs = len([ref for ref in refs if ref() is not None]) self.assertLessEqual(live_refs, 100) def test_jit_lower_compile(self): def f(x): return jnp.sqrt(x ** 2) + 1. f_jit = self.jit(f) f_low = f_jit.lower(1.) f_exe = f_low.compile() self.assertAllClose(f_exe(1.), 2.) def test_jit_lower_compile_in_tree_mismatch(self): def f(x): return jnp.sqrt(x ** 2) + 1. f_jit = self.jit(f) f_low = f_jit.lower(1.) f_exe = f_low.compile() self.assertRaisesRegex( TypeError, "function compiled for .*, called with .*", lambda: f_exe([1.])) def test_jit_lower_compile_trivial(self): def f(x): return x out = self.jit(f).lower(1.).compile()(4.) self.assertAllClose(out, 4.) def test_jit_lower_compile_trivial_in_tree_mismatch(self): def f(x): return x f_exe = self.jit(f).lower(1.).compile() self.assertRaisesRegex( TypeError, "function compiled for .*, called with .*", lambda: f_exe([4.])) def test_jit_lower_compile_arg_type_mismatch(self): def f(x): return jnp.sqrt(x ** 2) + 1. x = jnp.array(1, dtype=int) x_f32 = x.astype(jnp.float32) x_i32 = x.astype(jnp.int32) f_exe = self.jit(f).lower(x_f32).compile() self.assertRaisesRegex( TypeError, "Computation compiled for input types:\n.*float32.*\n" "called with:\n.*int32.*", lambda: f_exe(x_i32)) def test_jit_lower_compile_multi_arg(self): def f(*args): x, *_ = args return jnp.sqrt(x ** 2) + 1. f_exe = self.jit(f).lower(1., 1.).compile() self.assertAllClose(f_exe(1., 1.), 2.) def test_jit_lower_compile_trivial_multi_arg(self): def f(*args): x, *_ = args return x f_exe = self.jit(f).lower(1., 1.).compile() self.assertAllClose(f_exe(1., 1.), 1.) class PythonJitTest(CPPJitTest): @property def jit(self): return api._python_jit class APITest(jtu.JaxTestCase): def test_grad_bad_input(self): def f(x): return x self.assertRaisesRegex( TypeError, ".* 'foo' of type <.*'str'> is not a valid JAX type", lambda: grad(f)("foo")) def test_grad_argnums(self): def f(x, y, z, flag=False): assert flag return 1.0 * x + 2.0 * y + 3.0 * z assert grad(f)(1.0, 1.0, 1.0, flag=True) == 1.0 assert grad(f, argnums=1)(1.0, 1.0, 1.0, flag=True) == 2.0 assert grad(f, argnums=(2, 0))(1.0, 1.0, 1.0, flag=True) == (3.0, 1.0) def test_value_and_grad_argnums(self): def f(x, y, z, flag=False): assert flag return 1.0 * x + 2.0 * y + 3.0 * z y = f(1.0, 1.0, 1.0, flag=True) assert api.value_and_grad(f)(1.0, 1.0, 1.0, flag=True) == (y, 1.0) assert api.value_and_grad(f, argnums=1)(1.0, 1.0, 1.0, flag=True) == (y, 2.0) assert api.value_and_grad(f, argnums=(2, 0))(1.0, 1.0, 1.0, flag=True) == (y, (3.0, 1.0)) def test_grad_of_jit(self): side = [] @jit def f(x): side.append(None) return x * x assert grad(f)(1.0) == 2.0 assert len(side) == 1 assert grad(f)(2.0) == 4.0 assert len(side) == 1 def test_jit_of_grad(self): side = [] @jit def f(x): side.append(None) return x * x g = jit(grad(f)) assert g(1.0) == 2.0 assert len(side) == 1 assert g(2.0) == 4.0 assert len(side) == 1 def test_bad_input(self): def f(x): return x self.assertRaisesRegex( TypeError, ".* 'foo' of type <.*'str'> is not a valid JAX type", lambda: grad(f)("foo")) self.assertRaisesRegex( TypeError, ".* 'foo' of type <.*'str'> is not a valid JAX type", lambda: jit(f)("foo")) def test_grad_tuple_output(self): jtu.check_raises(lambda: grad(lambda x: (x,x))(1.0), TypeError, "Gradient only defined for scalar-output functions. ") def test_grad_unit_output(self): jtu.check_raises(lambda: grad(lambda x: ())(np.zeros(3)), TypeError, "Gradient only defined for scalar-output functions. ") def test_grad_nonscalar_output(self): jtu.check_raises(lambda: grad(lambda x: x)(np.zeros(3)), TypeError, "Gradient only defined for scalar-output functions. ") def test_unwrapped_numpy(self): def f(x): return np.exp(x) with self.assertRaisesRegex(Exception, "The numpy.ndarray conversion .*"): grad(f)(np.zeros(3)) def test_binop_mismatch(self): def f(x, y): return x + y jtu.check_raises( lambda: f(jnp.zeros(3), jnp.zeros(4)), TypeError, "add got incompatible shapes for broadcasting: (3,), (4,).") jtu.check_raises( lambda: grad(f)(np.zeros(3), np.zeros(4)), TypeError, "add got incompatible shapes for broadcasting: (3,), (4,).") def test_dot_mismatch(self): def f(x, y): return jnp.dot(x, y) self.assertRaisesRegex( TypeError, "Incompatible shapes for dot: got \$3L?,\$ and \$4L?,\$.", lambda: grad(f)(np.zeros(3), np.zeros(4))) def test_abstract_error_message(self): for castfun in [float, complex, int]: def f(x): return castfun(x) self.assertRaisesRegex( TypeError, f"[Tt]ry using `x.astype\${castfun.__name__}\$`", lambda: jit(f)(1.0)) def test_switch_value_jit(self): def f(x): y = x > 0 if y: return x else: return -x assert grad(f)(1.0) == 1.0 assert grad(f)(-1.0) == -1.0 with self.assertRaisesRegex(core.ConcretizationTypeError, "Abstract tracer value"): jit(f)(1) def test_list_index_err(self): L = [1, 2, 3] def f(n): return L[n] assert jit(f, static_argnums=(0,))(0) == L[0] self.assertRaisesRegex( TypeError, r"The __index__ method was called on the JAX Tracer object.*", lambda: jit(f)(0)) def test_range_err(self): def f(x, n): for i in range(n): x = x + i return x assert jit(f, static_argnums=(1,))(0, 5) == 10 self.assertRaisesRegex( TypeError, r"The __index__ method was called on the JAX Tracer object.*", lambda: jit(f)(0, 5)) def test_cast_int(self): f = lambda x: int(x) self.assertRaisesRegex( TypeError, "('(?:JaxprTracer|DynamicJaxprTracer)' object cannot be interpreted as an integer" "|Abstract tracer value encountered where concrete value is expected.*)", lambda: jit(f)(0)) def test_casts(self): for castfun in [hex, oct]: f = lambda x: castfun(x) self.assertRaisesRegex( TypeError, r"The __index__ method was called on the JAX Tracer object.*", lambda: jit(f)(0)) def test_unimplemented_interpreter_rules(self): foo_p = Primitive('foo') def foo(x): return foo_p.bind(x) jtu.check_raises(lambda: foo(1.0), NotImplementedError, "Evaluation rule for 'foo' not implemented") jtu.check_raises(lambda: jit(foo)(1.0), NotImplementedError, "Abstract evaluation for 'foo' not implemented") jtu.check_raises(lambda: grad(foo)(1.0), NotImplementedError, "Differentiation rule for 'foo' not implemented") foo_p.def_abstract_eval(lambda x: x) jtu.check_raises(lambda: jit(foo)(1.0), NotImplementedError, "XLA translation rule for primitive 'foo' not found") foo_p.def_impl(lambda x: x) ad.defjvp(foo_p, lambda g, x: foo(g)) jtu.check_raises(lambda: grad(foo)(1.0), NotImplementedError, "Transpose rule (for reverse-mode differentiation) for 'foo' not implemented") def test_is_subclass(self): self.assertTrue(issubclass(xla.DeviceArray, jnp.ndarray)) self.assertTrue(issubclass(xla._CppDeviceArray, jnp.ndarray)) self.assertTrue(issubclass(pxla.ShardedDeviceArray, jnp.ndarray)) self.assertTrue(issubclass(pxla._ShardedDeviceArray, jnp.ndarray)) self.assertFalse(issubclass(np.ndarray, jnp.ndarray)) self.assertFalse(issubclass(xla.DeviceArray, np.ndarray)) self.assertFalse(issubclass(xla._CppDeviceArray, np.ndarray)) self.assertFalse(issubclass(pxla.ShardedDeviceArray, np.ndarray)) self.assertFalse(issubclass(pxla._ShardedDeviceArray, np.ndarray)) def test_is_instance(self): def f(x): self.assertIsInstance(x, jnp.ndarray) self.assertNotIsInstance(x, np.ndarray) return x + 2 jit(f)(3) jax.vmap(f)(np.arange(3)) def test_device_put_and_get(self): x = np.arange(12.).reshape((3, 4)).astype("float32") dx = api.device_put(x) self.assertIsInstance(dx, xla.DeviceArray) self.assertIsInstance(dx, jnp.ndarray) self.assertNotIsInstance(dx, np.ndarray) x2 = api.device_get(dx) self.assertNotIsInstance(x2, jnp.ndarray) self.assertIsInstance(x2, np.ndarray) assert np.all(x == x2) y = [x, (2 * x, 3 * x)] dy = api.device_put(y) y2 = api.device_get(dy) self.assertIsInstance(y2, list) self.assertIsInstance(y2[0], np.ndarray) assert np.all(y2[0] == x) self.assertIsInstance(y2[1], tuple) self.assertIsInstance(y2[1][0], np.ndarray) assert np.all(y2[1][0] == 2 * x) self.assertIsInstance(y2[1][1], np.ndarray) assert np.all(y2[1][1] == 3 * x) def test_device_get_scalar(self): x = np.arange(12.).reshape((3, 4)).astype("float32") x = api.device_put(x) self.assertIsInstance(x, xla.DeviceArray) y = [x, 2] y2 = api.device_get(y) self.assertIsInstance(y2, list) self.assertIsInstance(y2[0], np.ndarray) assert np.all(y2[0] == x) self.assertIsInstance(y2[1], int) self.assertEqual(y2[1], 2) @parameterized.parameters([(3,)], [(2, 0)]) def test_device_put_across_devices(self, shape): if len(api.local_devices()) < 2: raise unittest.SkipTest("this test requires multiple devices") d1, d2 = api.local_devices()[:2] data = np.random.randn(*shape).astype(np.float32) x = api.device_put(data, device=d1) self.assertEqual(x.device_buffer.device(), d1) y = api.device_put(x, device=d2) self.assertEqual(y.device_buffer.device(), d2) np.testing.assert_array_equal(data, np.array(y)) # Make sure these don't crash api.device_put(x) api.device_put(y) @jtu.skip_on_devices("cpu") def test_device_put_across_platforms(self): default_device = jax.devices()[0] cpu_device = jax.devices("cpu")[0] np_arr = np.array([1,2,3]) scalar = 1 device_arr = jnp.array([1,2,3]) assert device_arr.device_buffer.device() is default_device for val in [np_arr, device_arr, scalar]: x = api.device_put(val, device=cpu_device) self.assertEqual(x.device_buffer.device(), cpu_device) @jtu.skip_on_devices("tpu") def test_jacobian(self): R = np.random.RandomState(0).randn A = R(4, 3) x = R(3) f = lambda x: jnp.dot(A, x) assert np.allclose(jacfwd(f)(x), A) assert np.allclose(jacrev(f)(x), A) f = lambda x: jnp.tanh(jnp.dot(A, x)) assert np.allclose(jacfwd(f)(x), jacrev(f)(x)) @jtu.skip_on_devices("tpu") def test_hessian(self): R = np.random.RandomState(0).randn A = R(4, 4) x = R(4) f = lambda x: jnp.dot(x, jnp.dot(A, x)) assert np.allclose(hessian(f)(x), A + A.T) def test_std_basis(self): basis = api._std_basis(jnp.zeros(3)) assert getattr(basis, "shape", None) == (3, 3) assert np.allclose(basis, np.eye(3)) basis = api._std_basis(jnp.zeros((3, 3))) assert getattr(basis, "shape", None) == (9, 3, 3) assert np.allclose(basis, np.eye(9).reshape(9, 3, 3)) basis = api._std_basis([0., (jnp.zeros(3), jnp.zeros((3, 4)))]) assert isinstance(basis, list) and len(basis) == 2 assert getattr(basis[0], "shape", None) == (16,) assert isinstance(basis[1], tuple) and len(basis[1]) == 2 assert getattr(basis[1][0], "shape", None) == (16, 3) assert getattr(basis[1][1], "shape", None) == (16, 3, 4) @jtu.skip_on_devices("tpu") def test_jacobian_on_pytrees(self): for jacfun in [jacfwd, jacrev]: ans = jacfun(lambda x, y: (x, y))(0., 1.) expected = (1., 0.) self.assertAllClose(ans, expected, check_dtypes=False) ans = jacfun(lambda x, y: (x, y), 1)(0., 1.) expected = (0., 1.) self.assertAllClose(ans, expected, check_dtypes=False) ans = jacfun(lambda x, y: (x, y), (0, 1))(0., 1.) expected = ((1., 0.), (0., 1.),) self.assertAllClose(ans, expected, check_dtypes=False) ans = jacfun(lambda x: x[:2])((1., 2., 3.)) expected = ((1., 0., 0.), (0., 1., 0.)) self.assertAllClose(ans, expected, check_dtypes=False) R = np.random.RandomState(0).randn x = R(2) y = R(3) ans = jacfun(lambda x, y: {'x': x, 'xy': jnp.outer(x, y)})(x, y) expected = {'x': np.eye(2), 'xy': np.kron(np.eye(2), y[:, None]).reshape(2, 3, 2)} self.assertAllClose(ans, expected, check_dtypes=False) @jtu.skip_on_devices("tpu") def test_hessian_on_pytrees(self): ans = hessian(lambda x: jnp.array(x)**2)((1., 2.)) expected = ((np.array([2., 0.]), np.array([0., 0.])), (np.array([0., 0.]), np.array([0., 2.]))) self.assertAllClose(ans, expected, check_dtypes=False) @jtu.skip_on_devices("tpu") def test_issue1372(self): def quad(x): return jnp.dot(x, x) def f(x, u): return quad(x) + quad(u) x, u = jnp.ones(5), jnp.ones(2) rev = jacrev fwd = jacfwd # Diagonal entries self.assertEqual(rev(rev(f, 0), 0)(x, u).shape, (5, 5)) self.assertEqual(rev(fwd(f, 0), 0)(x, u).shape, (5, 5)) self.assertEqual(fwd(rev(f, 0), 0)(x, u).shape, (5, 5)) self.assertEqual(fwd(fwd(f, 0), 0)(x, u).shape, (5, 5)) self.assertEqual(rev(rev(f, 1), 1)(x, u).shape, (2, 2)) self.assertEqual(rev(fwd(f, 1), 1)(x, u).shape, (2, 2)) self.assertEqual(fwd(rev(f, 1), 1)(x, u).shape, (2, 2)) self.assertEqual(fwd(fwd(f, 1), 1)(x, u).shape, (2, 2)) # Off-diagonal entries by reverse-mode on the outside self.assertEqual(rev(rev(f, 1), 0)(x, u).shape, (2, 5)) self.assertEqual(rev(fwd(f, 1), 0)(x, u).shape, (2, 5)) self.assertEqual(rev(rev(f, 0), 1)(x, u).shape, (5, 2)) self.assertEqual(rev(fwd(f, 0), 1)(x, u).shape, (5, 2)) # Off-diagonal entries by forward-mode on the outside self.assertEqual(fwd(rev(f, 1), 0)(x, u).shape, (2, 5)) self.assertEqual(fwd(fwd(f, 1), 0)(x, u).shape, (2, 5)) self.assertEqual(fwd(rev(f, 0), 1)(x, u).shape, (5, 2)) self.assertEqual(fwd(fwd(f, 0), 1)(x, u).shape, (5, 2)) def test_large_device_constant(self): ans = jit(lambda x: 2 * x)(jnp.ones(int(2e6))) # doesn't crash self.assertAllClose(ans, np.ones(int(2e6)) * 2., check_dtypes=False) def test_grad_and_aux_basic(self): g, aux = grad(lambda x: (x**3, [x**2]), has_aux=True)(3.) self.assertAllClose(g, grad(lambda x: x**3)(3.)) self.assertAllClose(aux, [9.], check_dtypes=False) def test_grad_and_aux_error(self): with self.assertRaisesRegex(TypeError, "two-element tuple"): grad(lambda x: (1, 2, 3), has_aux=True)(1.) with self.assertRaisesRegex(TypeError, "two-element tuple"): grad(lambda x: x, has_aux=True)(1.) with self.assertRaisesRegex(TypeError, "two-element tuple"): grad(lambda x: (x,), has_aux=True)(1.) def test_grad_and_aux_nested(self): def f(x): g, aux = grad(lambda x: (x**3, [x**3]), has_aux=True)(x) return aux[0] f2 = lambda x: x**3 self.assertEqual(grad(f)(4.), grad(f2)(4.)) self.assertEqual(jit(grad(f))(4.), grad(f2)(4.)) self.assertEqual(jit(grad(jit(f)))(4.), grad(f2)(4.)) def f(x): g, aux = grad(lambda x: (x**3, [x**3]), has_aux=True)(x) return aux[0] * jnp.sin(x) f2 = lambda x: x**3 * jnp.sin(x) self.assertEqual(grad(f)(4.), grad(f2)(4.)) self.assertEqual(jit(grad(f))(4.), grad(f2)(4.)) self.assertEqual(jit(grad(jit(f)))(4.), grad(f2)(4.)) def test_grad_and_aux_constant(self): g, aux = grad(lambda x: (x**3, [4.]), has_aux=True)(4.) self.assertEqual(g, grad(lambda x: x**3)(4.)) self.assertEqual(aux, [4.]) g, aux = grad(lambda x: (x**3, [x**2, 4.]), has_aux=True)(4.) self.assertEqual(g, grad(lambda x: x**3)(4.)) self.assertEqual(aux, [4.**2, 4.]) def test_grad_and_aux_no_tracers(self): # see https://github.com/google/jax/issues/1950 def f(x): aux = dict(identity=x, p1=x+1) return x ** 2, aux _, aux = jax.grad(f, has_aux=True)(3.) self.assertIsInstance(aux, dict) for val in aux.values(): self.assertNotIsInstance(val, core.Tracer) def test_jvp_mismatched_arguments(self): self.assertRaisesRegex( TypeError, ("primal and tangent arguments to jax.jvp must have the same tree " "structure"), lambda: api.jvp(lambda x, y: x * y, (np.float32(2),), ())) # If primals and tangents must both be tuples or both lists self.assertRaisesRegex( TypeError, ("primal and tangent arguments to jax.jvp must have the same tree " "structure"), lambda: api.jvp(lambda x, y: x * y, (np.float32(2),), [np.float32(2)])) self.assertRaisesRegex( TypeError, "primal and tangent arguments to jax.jvp do not match.", lambda: api.jvp(lambda x: -x, (np.float16(2),), (np.float32(4),))) # If primals and tangents are not of the same shape then raise error fun = lambda x: x+1 with self.assertRaisesRegex( ValueError, "jvp called with different primal and tangent shapes"): api.jvp(fun, (jnp.array([1.,2.,3.]),), (jnp.array([1.,2.,3.,4.]),)) with self.assertRaisesRegex( ValueError, "jvp called with different primal and tangent shapes"): api.jvp(fun, (jnp.float32(10.),), (jnp.array([1.,2.,3.], dtype=jnp.float32),)) with self.assertRaisesRegex( ValueError, "jvp called with different primal and tangent shapes"): api.jvp(fun, (jnp.array([1.,2.,3.], dtype=jnp.float32),), (jnp.float32(20.),)) with self.assertRaisesRegex( ValueError, "jvp called with different primal and tangent shapes"): api.jvp(fun, (jnp.array([1.,2.,3.]),), (20.,)) def test_jvp_non_tuple_arguments(self): def f(x, y): return x + y self.assertRaisesRegex( TypeError, "primal and tangent arguments to jax.jvp must be tuples or lists; found float and tuple.", lambda: api.jvp(f, 0., (1.,))) self.assertRaisesRegex( TypeError, "primal and tangent arguments to jax.jvp must be tuples or lists; found tuple and ndarray.", lambda: api.jvp(f, (0.,), np.array([1., 2.]))) def test_vjp_mismatched_arguments(self): _, pullback = api.vjp(lambda x, y: x * y, np.float32(3), np.float32(4)) self.assertRaisesRegex( TypeError, "Tree structure of cotangent input.*does not match", lambda: pullback((np.float32(7), np.float32(100)))) self.assertRaisesRegex( TypeError, "Type of cotangent input to vjp pullback.*is not the expected tangent type", lambda: pullback((np.float16(42)))) def test_vjp_bad_cotangent_shape(self): x = np.ones((2, 5), dtype=np.float32) y = np.ones((5, 3), dtype=np.float32) def f_jax(x, y): return jnp.matmul(x, y) res, pullback = jax.vjp(f_jax, x, y) with self.assertRaisesRegex( ValueError, "Shape of cotangent input to vjp pullback function .* must be the same as the shape of corresponding primal input .*"): pullback(np.ones((2, 4), dtype=np.float32)) def test_jvp_jit_cached(self): """Bug in caching in presence of JVP and JIT.""" def func(x): def inner(y): return y * x # Must have two calls to the inner jit (the second one hits the cache) res1 = api.jit(inner)(4.) res2 = api.jit(inner)(5.) return res1 + res2 self.assertAllClose((45., 9.), api.jvp(func, (5.,), (1.,))) def test_linear_transpose_abstract(self): x = types.SimpleNamespace(shape=(3,), dtype=np.dtype(np.float32)) y = jnp.arange(3, dtype=np.float32) transpose_fun = api.linear_transpose(lambda x: 2 * x, x) z, = transpose_fun(y) self.assertArraysEqual(2 * y, z, check_dtypes=True) def test_linear_transpose_integer(self): f = lambda x: 2 * x transpose = api.linear_transpose(f, 1) actual, = transpose(3) expected = 6 self.assertEqual(actual, expected) def test_linear_transpose_error(self): with self.assertRaisesRegex( TypeError, "linear_transpose only supports"): api.linear_transpose(lambda x: 2. * x, 1) transpose_fun = api.linear_transpose(lambda x: [x, x], 1.0) with self.assertRaisesRegex(TypeError, "cotangent tree does not match"): transpose_fun(1.0) transpose_fun = api.linear_transpose(lambda x: jnp.stack([x, x]), 1.0) with self.assertRaisesRegex(TypeError, "cotangent type does not match"): transpose_fun(1.0) transpose_fun = api.linear_transpose(lambda x: 1j * x, 1.0) with self.assertRaisesRegex(TypeError, "cotangent type does not match"): transpose_fun(1.0) transpose_fun = api.linear_transpose(lambda x: x, 1.0) with self.assertRaisesRegex(TypeError, "cotangent type does not match"): transpose_fun(1j) def test_linear_transpose_complex(self): f = lambda x: (1 + 2j) * x transpose = api.linear_transpose(f, 1j) actual, = transpose(3 + 4j) expected = -5 + 10j self.assertEqual(actual, expected) def test_linear_transpose_zeros(self): f = lambda x: x[0] transpose = api.linear_transpose(f, [1., 2.]) actual, = transpose(3.) expected = [3., 0.] self.assertEqual(actual, expected) def test_complex_grad_raises_error(self): self.assertRaises(TypeError, lambda: grad(lambda x: jnp.sin(x))(1 + 2j)) def test_holomorphic_grad(self): out = grad(lambda x: jnp.sin(x), holomorphic=True)(1 + 2j) expected = 2.0327230070196656 - 3.0518977991518j self.assertAllClose(out, expected, check_dtypes=False) def test_nonholomorphic_grad(self): zs = 0.5j * np.arange(5) + np.arange(5) def f(z): return jnp.sum(jnp.cos(jnp.abs(z))) ans = grad(f)(zs) expected = np.array([ 0. + 0.j, -0.80430663 + 0.40215331j, -0.70368982 + 0.35184491j, 0.1886467 - 0.09432335j, 0.86873727 - 0.43436864j]) self.assertAllClose(ans, expected, check_dtypes=False, atol=jtu.default_gradient_tolerance, rtol=jtu.default_gradient_tolerance) def test_complex_output_jacrev_raises_error(self): self.assertRaises(TypeError, lambda: jacrev(lambda x: jnp.sin(x))(1 + 2j)) def test_nonholomorphic_jacrev(self): # code based on https://github.com/google/jax/issues/603 zs = 0.5j * np.arange(5) + np.arange(5) def f(z): return jnp.cos(jnp.linalg.norm(2 * z)) ans = jacrev(f)(zs) expected = grad(f)(zs) self.assertAllClose(ans, expected) def test_heterogeneous_jacfwd(self): # See https://github.com/google/jax/issues/7157 # See https://github.com/google/jax/issues/7780 x = np.array([2.0], dtype=np.float16) y = np.array([3.0], dtype=np.float32) a = (x, y) def f(tup): jtu._check_dtypes_match(tup, a) x, y = tup return x, y, x + y actual = jacfwd(f)(a) desired = ((np.array(1., dtype=np.float16), np.array(0., dtype=np.float16)), (np.array(0., dtype=np.float32), np.array(1., dtype=np.float32)), (np.array(1., dtype=np.float32), np.array(1., dtype=np.float32))) jtu._check_dtypes_match(actual, desired) jtu.check_eq(actual, desired) def test_heterogeneous_jacrev(self): # See https://github.com/google/jax/issues/7157 # See https://github.com/google/jax/issues/7780 x = np.array([2.0], dtype=np.float16) y = np.array([3.0], dtype=np.float32) a = (x, y) def f(tup): jtu._check_dtypes_match(tup, a) x, y = tup return x, y, x + y actual = jacrev(f)(a) desired = ((np.array(1., dtype=np.float16), np.array(0., dtype=np.float32)), (np.array(0., dtype=np.float16), np.array(1., dtype=np.float32)), (np.array(1., dtype=np.float16), np.array(1., dtype=np.float32))) jtu._check_dtypes_match(actual, desired) jtu.check_eq(actual, desired) def test_heterogeneous_grad(self): # See https://github.com/google/jax/issues/7157 x = np.array(1.0+1j) y = np.array(2.0) a = (x, y) def f(tup): jtu._check_dtypes_match(tup, a) x, y = tup return jnp.square(jnp.abs(x)) + y actual = grad(f)(a) desired = (np.array(2 - 2j), np.array(1.)) jtu._check_dtypes_match(actual, desired) jtu.check_eq(actual, desired) def test_complex_input_jacfwd_raises_error(self): self.assertRaises(TypeError, lambda: jacfwd(lambda x: jnp.sin(x))(1 + 2j)) def test_legacy_devicearray_repr(self): dx = device_put(3.) str(dx.item()) # doesn't crash def test_devicearray_repr(self): x = device_put(jnp.zeros(3)) self.assertIsInstance(x, xla.DeviceArray) repr(x) # doesn't crash x = device_put(jnp.ones(3) + 1j * jnp.ones(3)) self.assertIsInstance(x, xla.DeviceArray) repr(x) # doesn't crash def test_devicearray_delete(self): x = device_put(1.) x.delete() self.assertRaisesRegex(RuntimeError, "DeviceArray has been deleted.", lambda: repr(x)) def test_devicearray_block_until_ready(self): x = device_put(1.) y = x.block_until_ready() # Tests mostly that block_until_ready() does not produce an error. self.assertTrue(y is x) def test_devicearray_weakref_friendly(self): x = device_put(1.) y = weakref.ref(x) self.assertEqual(y(), 1.) del x self.assertIsNone(y()) def test_namedtuple_transparency(self): # See https://github.com/google/jax/issues/446 Point = collections.namedtuple("Point", ["x", "y"]) def f(pt): return jnp.sqrt(pt.x ** 2 + pt.y ** 2) pt = Point(1., 2.) f(pt) # doesn't crash g = api.grad(f)(pt) self.assertIsInstance(g, Point) f_jit = api.jit(f) self.assertAllClose(f(pt), f_jit(pt), check_dtypes=False) def test_namedtuple_subclass_transparency(self): # See https://github.com/google/jax/issues/806 Point = collections.namedtuple("Point", ["x", "y"]) class ZeroPoint(Point): def is_zero(self): return (self.x == 0) and (self.y == 0) pt = ZeroPoint(0., 0.) def f(pt): return 0. if pt.is_zero() else jnp.sqrt(pt.x ** 2 + pt.y ** 2) f(pt) # doesn't crash _ = api.grad(f)(pt) self.assertIsInstance(pt, ZeroPoint) @parameterized.parameters(1, 2, 3) def test_shape_dtype_struct(self, i): s = api.ShapeDtypeStruct(shape=(i, 2, 3), dtype=jnp.float32) self.assertEqual(s.shape, (i, 2, 3)) self.assertEqual(s.dtype, jnp.float32) self.assertEqual(s.ndim, 3) self.assertEqual(s.size, i * 2 * 3) self.assertLen(s, i) for f in (str, repr): self.assertEqual( f(s), "ShapeDtypeStruct(shape=({}, 2, 3), dtype=float32)".format(i)) def test_shape_dtype_struct_scalar(self): s = api.ShapeDtypeStruct(shape=(), dtype=jnp.float32) self.assertEmpty(s.shape) self.assertEqual(s.size, 1) self.assertEqual(s.ndim, 0) with self.assertRaisesRegex(TypeError, "len[(][)] of unsized object"): _ = len(s) def test_shape_dtype_struct_hash(self): s1 = api.ShapeDtypeStruct(shape=(2, 3), dtype=jnp.float32) s2 = api.ShapeDtypeStruct(shape=(2, 3), dtype=jnp.float32) s3 = api.ShapeDtypeStruct(shape=(2, 4), dtype=jnp.float32) self.assertEqual(hash(s1), hash(s2)) self.assertNotEqual(hash(s1), hash(s3)) def test_eval_shape(self): def fun(x, y): return jnp.tanh(jnp.dot(x, y) + 3.) x = jnp.ones((2, 3)) y = jnp.ones((3, 4)) out_shape = api.eval_shape(fun, x, y) self.assertEqual(out_shape.shape, (2, 4)) def test_eval_shape_constants(self): def fun(): x = jnp.ones((2, 3)) y = jnp.ones((3, 4)) return jnp.tanh(jnp.dot(x, y) + 3.) out_shape = api.eval_shape(fun) self.assertEqual(out_shape.shape, (2, 4)) def test_eval_shape_tuple_unpacking(self): def fun(x, y): a, b = x return a + b + y x = (jnp.ones(2), jnp.ones(2)) y = 3. out_shape = api.eval_shape(fun, x, y) self.assertEqual(out_shape.shape, (2,)) def test_eval_shape_tuple_itemgetting(self): def fun(x, y): return x[0] + x[1] + y x = (jnp.ones(2), jnp.ones(2)) y = 3. out_shape = api.eval_shape(fun, x, y) self.assertEqual(out_shape.shape, (2,)) def test_eval_shape_output_dict(self): def fun(x, y): return {'hi': x[0] + x[1] + y} x = (jnp.ones(2), jnp.ones(2)) y = 3. out_shape = api.eval_shape(fun, x, y) out_shape = tree_util.tree_map(np.shape, out_shape) self.assertEqual(out_shape, {'hi': (2,)}) def test_eval_shape_shape_error(self): def fun(x, y): return jnp.tanh(jnp.dot(x, y) + 3.) x = jnp.ones((3, 3)) y = jnp.ones((4, 4)) self.assertRaises(TypeError, lambda: api.eval_shape(fun, x, y)) def test_eval_shape_duck_typing(self): def fun(A, b, x): return jnp.dot(A, x) + b class MyArgArray(object): def __init__(self, shape, dtype): self.shape = shape self.dtype = np.dtype(dtype) A = MyArgArray((3, 4), jnp.float32) b = MyArgArray((5,), jnp.float32) x = MyArgArray((4, 5), jnp.float32) out_shape = api.eval_shape(fun, A, b, x) self.assertEqual(out_shape.shape, (3, 5)) def test_eval_shape_duck_typing2(self): # https://github.com/google/jax/issues/5683 class EasyDict(dict): def __init__(self, *args, **kwargs): super().__init__(*args, **kwargs) self.__dict__ = self x = EasyDict(shape=(3,), dtype=np.dtype('float32')) out_shape = api.eval_shape(lambda x: x, x) # doesn't crash self.assertEqual(out_shape.shape, (3,)) def test_eval_shape_names(self): def fun(x, y): return lax.psum(x, 'i') + y class MyArgArray(object): def __init__(self, shape, dtype, named_shape): self.shape = shape self.dtype = jnp.dtype(dtype) self.named_shape = named_shape x = MyArgArray((3, 2), jnp.float32, {'i': 10}) y = MyArgArray((3, 2), jnp.float32, {'j': 5}) with core.extend_axis_env('i', 10, None): with core.extend_axis_env('j', 5, None): out_shape = api.eval_shape(fun, x, y) self.assertEqual(out_shape.named_shape, {'j': 5}) def test_issue_871(self): T = jnp.array([[1., 2.], [3., 4.], [5., 6.]]) x = jnp.array([1, 2, 3]) msg = ("linearized function called on tangent values inconsistent with " "the original primal values") y, f_jvp = api.linearize(jnp.sum, x) with self.assertRaisesRegex(ValueError, msg): f_jvp(T) y, f_jvp = api.linearize(api.jit(jnp.sum), x) with self.assertRaisesRegex(ValueError, msg): f_jvp(T) def test_grad_of_int_errors(self): # Errors without allow_int=True dfn = grad(lambda x: x ** 2) self.assertRaisesRegex( TypeError, (r"grad requires real- or complex-valued inputs $input dtype that is a " r"sub-dtype of np.inexact$, but got int.*."), lambda: dfn(3)) @unittest.skipIf(numpy_version == (1, 21, 0), "https://github.com/numpy/numpy/issues/19305") def test_jvp_of_int_identity(self): primals = (1,) tangents = (np.zeros(shape=(), dtype=float0),) _, out = api.jvp(lambda x: x, primals, tangents) self.assertEqual(out, np.zeros(shape=(), dtype=float0)) @unittest.skipIf(numpy_version == (1, 21, 0), "https://github.com/numpy/numpy/issues/19305") def test_jvp_of_int_add(self): primals = (2,) tangents = (np.zeros(shape=(), dtype=float0),) _, out_tangent = api.jvp(lambda x: x+1, primals, tangents) self.assertEqual(out_tangent, np.zeros(shape=(), dtype=float0)) @unittest.skipIf(numpy_version == (1, 21, 0), "https://github.com/numpy/numpy/issues/19305") def test_jit_jvp_of_int(self): primals = (2,) tangents = (np.zeros(shape=(), dtype=float0),) _, out_tangent = api.jvp(jax.jit(lambda x: x+1), primals, tangents) self.assertEqual(out_tangent, np.zeros(shape=(), dtype=float0)) @unittest.skipIf(numpy_version == (1, 21, 0), "https://github.com/numpy/numpy/issues/19305") def test_vjp_of_int_index(self): primal, fn_vjp = api.vjp(lambda x, i: x[i], np.ones(2)*2, 1) tangent_x, tangent_i = fn_vjp(1.) self.assertEqual(primal, 2.) self.assertAllClose(tangent_x, jnp.array([0., 1.])) self.assertEqual(tangent_i, np.zeros(shape=(), dtype=float0)) @unittest.skipIf(numpy_version == (1, 21, 0), "https://github.com/numpy/numpy/issues/19305") def test_vjp_of_int_shapes(self): out, fn_vjp = api.vjp(lambda x: lax.reshape(x, (2, 2)), np.ones((4, 1), dtype=int)) tangent, = fn_vjp(out) self.assertArraysEqual(tangent, np.zeros(shape=(4, 1), dtype=float0)) @unittest.skipIf(numpy_version == (1, 21, 0), "https://github.com/numpy/numpy/issues/19305") def test_jit_vjp_of_int(self): primal, fn_vjp = api.vjp(lambda x, y: x+y, 2, 1) tangent_x, tangent_i = jax.jit(fn_vjp)(1) self.assertEqual(primal, 3) self.assertEqual(tangent_x, np.zeros(shape=(), dtype=float0)) self.assertEqual(tangent_i, np.zeros(shape=(), dtype=float0)) @unittest.skipIf(numpy_version == (1, 21, 0), "https://github.com/numpy/numpy/issues/19305") def test_vjp_of_int_fulllike(self): # Regression test for tangent and cotangent mismatch in convert_element_type # transpose rule wrt a ConstVar f = lax.full_like out, vjp = api.vjp(f, np.zeros((2, 2)), 1) self.assertAllClose(out, jnp.ones((2, 2))) tangent_x, tangent_y = vjp(out) self.assertAllClose(tangent_x, jnp.zeros((2, 2))) self.assertEqual(tangent_y, np.zeros(shape=(), dtype=float0)) @unittest.skipIf(numpy_version == (1, 21, 0), "https://github.com/numpy/numpy/issues/19305") def test_grad_of_int(self): # Need real-valued output, but testing integer input. out = api.grad(lambda x: x+0., allow_int=True)(1) self.assertEqual(out, np.zeros(shape=(), dtype=float0)) @unittest.skipIf(numpy_version == (1, 21, 0), "https://github.com/numpy/numpy/issues/19305") def test_grad_of_bool(self): def cond(pred): return lax.cond(pred, lambda _: 1., lambda _: 2., 1.) value, grd = api.value_and_grad(cond, allow_int=True)(True) self.assertEqual(value, 1.) self.assertEqual(grd, np.zeros(shape=(), dtype=float0)) @unittest.skipIf(numpy_version == (1, 21, 0), "https://github.com/numpy/numpy/issues/19305") def test_grad_of_int_index(self): grad_x, grad_i = api.grad(lambda x, i: x[i], argnums=(0, 1), allow_int=True)(np.ones(2), 1) self.assertAllClose(grad_x, jnp.array([0., 1.])) self.assertEqual(grad_i, np.zeros(shape=(), dtype=float0)) @unittest.skipIf(numpy_version == (1, 21, 0), "https://github.com/numpy/numpy/issues/19305") def test_jit_grad_of_int(self): grad_f = api.grad(lambda x, i: x[i], argnums=(0, 1), allow_int=True) grad_x, grad_i = jax.jit(grad_f)(np.ones(2), 1) self.assertAllClose(grad_x, jnp.array([0., 1.])) self.assertEqual(grad_i, np.zeros(shape=(), dtype=float0)) @unittest.skipIf(numpy_version == (1, 21, 0), "https://github.com/numpy/numpy/issues/19305") def test_float0_reshape(self): # dtype-agnostic operations are supported float0_array = jax.grad(lambda x: jnp.sum(x+0.), allow_int=True)(np.ones((2, 4), dtype=int)) self.assertArraysEqual(float0_array.reshape((4, 2)), np.zeros((4, 2), dtype=float0)) self.assertArraysEqual(float0_array.transpose(), np.zeros((4, 2), dtype=float0)) def test_float0_error(self): # float0 is incompatible with other dtypes float0_array = jax.grad(lambda x: x+0., allow_int=True)(1) error_text = "float0s do not support any operations by design" with self.assertRaisesRegex(TypeError, error_text): # dispatch via DeviceArray _ = float0_array + jnp.zeros(()) with self.assertRaisesRegex(TypeError, error_text): # dispatch via lax _ = lax.add(float0_array, jnp.zeros(())) def test_grad_complex_result_errors(self): dfn = grad(lambda x: x ** 2 + 1j) self.assertRaisesRegex( TypeError, (r"grad requires real-valued outputs $output dtype that is a " r"sub-dtype of np.floating$, but got complex.*"), lambda: dfn(3.)) def test_holomorphic_grad_of_float_errors(self): dfn = grad(lambda x: x ** 2, holomorphic=True) self.assertRaisesRegex( TypeError, (r"grad with holomorphic=True requires inputs with complex dtype, " r"but got float.*"), lambda: dfn(3.)) def test_holomorphic_jacrev_of_float_errors(self): dfn = jacrev(lambda x: x ** 2, holomorphic=True) self.assertRaisesRegex( TypeError, (r"jacrev with holomorphic=True requires inputs with complex dtype, " r"but got float.*"), lambda: dfn(3.)) def test_holomorphic_jacfwd_of_float_errors(self): dfn = jacfwd(lambda x: x ** 2, holomorphic=True) self.assertRaisesRegex( TypeError, (r"jacfwd with holomorphic=True requires inputs with complex dtype, " r"but got float.*"), lambda: dfn(3.)) def test_jacfwd_of_complex_errors(self): dfn = jacfwd(lambda x: x ** 2) self.assertRaisesRegex( TypeError, (r"jacfwd requires real-valued inputs $input dtype that is a " r"sub-dtype of np.floating$, but got complex.*"), lambda: dfn(3. + 1j)) def test_xla_computation(self): # these tests basically check the examples in the xla_computation docstring def e(x): return jnp.sin(jnp.cos(x)) c = api.xla_computation(e)(2.) self.assertIn('cosine', c.as_hlo_text()) self.assertIn('sine', c.as_hlo_text()) def f(x): return x - lax.psum(x, 'i') axis_env = [('i', 4)] c = api.xla_computation(f, axis_env=axis_env)(2) self.assertIn('all-reduce', c.as_hlo_text()) self.assertIn('replica_groups={{0,1,2,3}}', c.as_hlo_text()) def g(x): rowsum = lax.psum(x, 'i') colsum = lax.psum(x, 'j') allsum = lax.psum(x, ('i', 'j')) return rowsum, colsum, allsum axis_env = [('i', 4), ('j', 2)] c = api.xla_computation(g, axis_env=axis_env)(5.) self.assertIn('all-reduce', c.as_hlo_text()) self.assertIn('replica_groups={{0,2,4,6},{1,3,5,7}}', c.as_hlo_text()) self.assertIn('replica_groups={{0,1},{2,3},{4,5},{6,7}}', c.as_hlo_text()) self.assertIn('replica_groups={{0,1,2,3,4,5,6,7}}', c.as_hlo_text()) def h(x): rowsum = lax.psum(x, 'i', axis_index_groups=[[0, 1], [2, 3]]) colsum = lax.psum(x, 'j') return rowsum, colsum axis_env = [('i', 4), ('j', 2)] c = api.xla_computation(h, axis_env=axis_env)(5.) self.assertIn('all-reduce', c.as_hlo_text()) self.assertIn('replica_groups={{0,2},{4,6},{1,3},{5,7}}', c.as_hlo_text()) self.assertIn('replica_groups={{0,1},{2,3},{4,5},{6,7}}', c.as_hlo_text()) def test_xla_computation_args(self): def foo(x, y, z): return x + y + z c = api.xla_computation(foo)(1., 2., 3.) self.assertEqual(len(c.program_shape().parameter_shapes()), 3) c = api.xla_computation(foo, tuple_args=True)(1., 2., 3.) param_shapes = c.program_shape().parameter_shapes() self.assertEqual(len(param_shapes), 1) self.assertEqual(param_shapes[0].xla_element_type(), xla_client.PrimitiveType.TUPLE) def test_xla_computation_duck_typing(self): def foo(x, y, z): return x + y + z x = jax.ShapeDtypeStruct((), np.float32) y = jax.ShapeDtypeStruct((), np.float32) z = jax.ShapeDtypeStruct((), np.float32) c = api.xla_computation(foo)(x, y, z) self.assertEqual(len(c.program_shape().parameter_shapes()), 3) c = api.xla_computation(foo, tuple_args=True)(1., 2., 3.) param_shapes = c.program_shape().parameter_shapes() self.assertEqual(len(param_shapes), 1) self.assertEqual(param_shapes[0].xla_element_type(), xla_client.PrimitiveType.TUPLE) def test_staging_out_multi_replica(self): def f(x): return api.pmap(jnp.mean)(x) xla_comp = api.xla_computation(f) xla_comp(jnp.arange(8)).as_hlo_text() # doesn't crash def test_xla_computation_instantiate_constant_outputs(self): def f(): return jnp.zeros((3, 4)) xla_comp = api.xla_computation(f)() out_shape, = xla_comp.program_shape().result_shape().tuple_shapes() self.assertEqual(out_shape.dimensions(), (3, 4)) def test_xla_computation_static_argnums(self): def f(x, y): return x + y xla_comp = api.xla_computation(f, static_argnums=(1,))(2, 3) hlo_text = xla_comp.as_hlo_text() self.assertIn("constant(3)", hlo_text) # The static arguments should be removed from the function being compiled, # thus the function should have only a single argument. self.assertIn("parameter.1", hlo_text) self.assertNotIn("parameter.2", hlo_text) def test_xla_computation_return_shape(self): _, shape_tree = api.xla_computation(lambda x: (x + 1, jnp.zeros(2, jnp.float32)), return_shape=True)(np.int32(1)) expected = (api.ShapeDtypeStruct(shape=(), dtype=jnp.int32), api.ShapeDtypeStruct(shape=(2,), dtype=jnp.float32)) self.assertEqual(shape_tree, expected) def test_xla_computation_partitioned(self): def f(x, y): return jnp.dot(x, y) + 1 x = jax.ShapeDtypeStruct((8, 8), np.float32) y = jax.ShapeDtypeStruct((8, 16), np.float32) xla_comp = api.xla_computation(f, in_parts=(P(2, 2), None), out_parts=P(4, 1))(x, y) hlo_text = xla_comp.as_hlo_text() self.assertIn('sharding={devices=[2,2]0,1,2,3}', hlo_text) self.assertIn('sharding={replicated}', hlo_text) self.assertIn('sharding={{devices=[4,1]0,1,2,3}}', hlo_text) def test_xla_computation_replicated_and_partitioned(self): def f(x, y): return jnp.dot(x, y), lax.psum(x, 'i') x = jax.ShapeDtypeStruct((8, 8), np.float32) y = jax.ShapeDtypeStruct((8, 16), np.float32) axis_env = [('i', 4)] xla_comp = api.xla_computation(f, axis_env=axis_env, in_parts=(P(2, 2), None), out_parts=(P(4, 1), None))(x, y) hlo_text = xla_comp.as_hlo_text() self.assertIn('all-reduce', hlo_text) self.assertIn('replica_groups={{0,1,2,3}}', hlo_text) self.assertIn('sharding={devices=[2,2]0,1,2,3}', hlo_text) self.assertIn('sharding={replicated}', hlo_text) self.assertIn('sharding={{devices=[4,1]0,1,2,3}, {replicated}}', hlo_text) def test_xla_computation_psum_constant(self): f = lambda: jax.lax.psum(1, "i") api.xla_computation(f, axis_env=[("i", 2)])() # doesn't crash @jtu.skip_on_devices("cpu", "gpu") @jtu.ignore_warning(message="Some donated buffers were not usable") def test_xla_computation_donate_argnums(self): api.xla_computation(lambda x: None, donate_argnums=(0,))(3) # doesn't crash def test_xla_computation_lower_fun_axis_env(self): axis_name = 'i' def fn(x): y = lax.all_gather( x, axis_name=axis_name) return y * lax.axis_index(axis_name).astype(jnp.float32) input_x = jnp.ones((5,6,4)) axis_env = [(axis_name, api.local_device_count())] _ = api.xla_computation(fn, axis_env=axis_env, backend='cpu')(input_x) def test_xla_computation_axis_env(self): def fn(x): z = x * jax.lax.axis_index('i').astype(jnp.float32) def inner_fn(carry, a): return carry + a, () return jax.lax.scan(inner_fn, jnp.zeros_like(z[0]), z) x = jnp.ones((5, 6, 4)) _ = jax.xla_computation(fn, axis_env=(('i', 8),), backend='cpu')(x) def test_concurrent_device_get_and_put(self): def f(x): for _ in range(100): y = jax.device_put(x) x = jax.device_get(y) return x xs = [np.random.randn(i) for i in range(10)] with concurrent.futures.ThreadPoolExecutor() as executor: futures = [executor.submit(partial(f, x)) for x in xs] ys = [f.result() for f in futures] for x, y in zip(xs, ys): self.assertAllClose(x, y) def test_dtype_warning(self): # cf. issue #1230 if config.x64_enabled: raise unittest.SkipTest("test only applies when x64 is disabled") def check_warning(warn, nowarn): with warnings.catch_warnings(record=True) as w: warnings.simplefilter("always") nowarn() # get rid of extra startup warning prev_len = len(w) nowarn() assert len(w) == prev_len warn() assert len(w) > 0 msg = str(w[-1].message) expected_prefix = "Explicitly requested dtype " self.assertEqual(expected_prefix, msg[:len(expected_prefix)]) prev_len = len(w) nowarn() assert len(w) == prev_len check_warning(lambda: jnp.array([1, 2, 3], dtype="float64"), lambda: jnp.array([1, 2, 3], dtype="float32")) check_warning(lambda: jnp.array([1, 2, 3], dtype="float64"), lambda: jnp.array([1, 2, 3], dtype=float)) check_warning(lambda: jnp.ones(3, dtype=np.float64), lambda: jnp.ones(3)) check_warning(lambda: jnp.ones(3, dtype=np.float64), lambda: jnp.ones(3, dtype=float)) check_warning(lambda: jnp.ones_like(3, dtype=np.int64), lambda: jnp.ones_like(3, dtype=np.int32)) check_warning(lambda: jnp.zeros(3, dtype="int64"), lambda: jnp.zeros(3, dtype="int32")) check_warning(lambda: jnp.zeros_like(3, dtype="float64"), lambda: jnp.zeros_like(3, dtype="float32")) check_warning(lambda: jnp.full((2, 3), 1, dtype="int64"), lambda: jnp.full((2, 3), 1)) check_warning(lambda: jnp.ones(3).astype("float64"), lambda: jnp.ones(3).astype("float32")) check_warning(lambda: jnp.eye(3, dtype=np.float64), lambda: jnp.eye(3)) check_warning(lambda: jnp.arange(3, dtype=np.float64), lambda: jnp.arange(3, dtype=np.float32)) check_warning(lambda: jnp.linspace(0, 3, dtype=np.float64), lambda: jnp.linspace(0, 3, dtype=np.float32)) check_warning(lambda: jnp.tri(2, dtype="float64"), lambda: jnp.tri(2, dtype="float32")) check_warning(lambda: jnp.arange(1).astype("float64"), lambda: jnp.arange(1).astype(float)) check_warning(lambda: jnp.arange(1.0).astype("int64"), lambda: jnp.arange(1.0).astype(int)) def test_error_for_invalid_dtype(self): with self.assertRaisesRegex(TypeError, ".*not a valid JAX array type.*"): lax.add(jnp.array(7), np.array("hello")) def test_vmap_preserves_docstr(self): def superfun(a): """Does things with stuff.""" pass self.assertRegex(api.vmap(superfun).__doc__, "\n".join([ "Vectorized version of superfun.*", "", "Original documentation:", "", superfun.__doc__, ])) def test_vmap_in_axes_list(self): # https://github.com/google/jax/issues/2367 dictionary = {'a': 5., 'b': jnp.ones(2)} x = jnp.zeros(3) y = jnp.arange(3.) def f(dct, x, y): return dct['a'] + dct['b'] + x + y out1 = api.vmap(f, (None, 0, 0))(dictionary, x, y) out2 = api.vmap(f, [None, 0, 0])(dictionary, x, y) self.assertAllClose(out1, out2) def test_vmap_in_axes_tree_prefix_error(self): # https://github.com/google/jax/issues/795 value_tree = jnp.ones(3) self.assertRaisesRegex( ValueError, "vmap in_axes specification must be a tree prefix of the corresponding " r"value, got specification $0, 0$ for value tree " + re.escape(f"{tree_util.tree_structure((value_tree,))}."), lambda: api.vmap(lambda x: x, in_axes=(0, 0))(value_tree) ) def test_vmap_in_axes_leaf_types(self): with self.assertRaisesRegex( TypeError, r"vmap in_axes must be an int, None, or .*"): api.vmap(lambda x: x, in_axes=(jnp.array([1., 2.]),))(jnp.array([1., 2.])) def test_vmap_out_axes_leaf_types(self): with self.assertRaisesRegex( TypeError, r"vmap out_axes must be an int, None, or .*"): api.vmap(lambda x: x, out_axes=(jnp.array([1., 2.]),))(jnp.array([1., 2.])) def test_vmap_unbatched_object_passthrough_issue_183(self): # https://github.com/google/jax/issues/183 fun = lambda f, x: f(x) vfun = api.vmap(fun, (None, 0)) ans = vfun(lambda x: x + 1, jnp.arange(3)) self.assertAllClose(ans, np.arange(1, 4), check_dtypes=False) def test_vmap_mismatched_axis_sizes_error_message_issue_705(self): # https://github.com/google/jax/issues/705 def h(a, b): return jnp.sum(a) + jnp.sum(b) X = np.random.randn(10, 4) U = np.random.randn(10, 2) with self.assertRaisesRegex( ValueError, "vmap got inconsistent sizes for array axes to be mapped:\n" r"arg 0 has shape $10, 4$ and axis 0 is to be mapped" "\n" r"arg 1 has shape $10, 2$ and axis 1 is to be mapped" "\n" "so\n" "arg 0 has an axis to be mapped of size 10\n" "arg 1 has an axis to be mapped of size 2"): api.vmap(h, in_axes=(0, 1))(X, U) with self.assertRaisesRegex( ValueError, "vmap got inconsistent sizes for array axes to be mapped:\n" r"arg 0 has shape $10, 4$ and axis 0 is to be mapped" "\n" r"arg 1 has shape $10, 2$ and axis 1 is to be mapped" "\n" r"arg 2 has shape $10, 4$ and axis 0 is to be mapped" "\n" "so\n" "args 0, 2 have axes to be mapped of size 10\n" "arg 1 has an axis to be mapped of size 2"): api.vmap(lambda x, y, z: None, in_axes=(0, 1, 0))(X, U, X) with self.assertRaisesRegex( ValueError, "vmap got inconsistent sizes for array axes to be mapped:\n" "the tree of axis sizes is:\n" r"$10, \[2, 2\]$"): api.vmap(h, in_axes=(0, 1))(X, [U, U]) error = (r"vmap was requested to map its argument along axis 0, which " r"implies that its rank should be at least 1, but is only 0 " r"$its shape is \($\)") with self.assertRaisesRegex(ValueError, error): # The mapped inputs cannot be scalars api.vmap(lambda x: x)(1.) with self.assertRaisesRegex( ValueError, "vmap must have at least one non-None value in in_axes"): # If the output is mapped, there must be a non-None in_axes api.vmap(lambda x: x, in_axes=None)(jnp.array([1., 2.])) error = (r"vmap was requested to map its argument along axis 1, which " r"implies that its rank should be at least 2, but is only 1 " r"$its shape is \(2,$\)") with self.assertRaisesRegex(ValueError, error): api.vmap(lambda x: x, in_axes=1)(jnp.array([1., 2.])) # Error is: TypeError: only integer scalar arrays can be converted to a scalar index with self.assertRaisesRegex( ValueError, "vmap out_axes specification must be a tree prefix of the " "corresponding value.*"): api.vmap(lambda x: x, in_axes=0, out_axes=(2, 3))(jnp.array([1., 2.])) with self.assertRaisesRegex( ValueError, r"vmap has mapped output $axis_name=foo$ but out_axes is None"): # If the output is mapped (user-named axis), then there must be some # out_axes specified. api.vmap(lambda x: x, out_axes=None, axis_name="foo")(jnp.array([1., 2.])) with self.assertRaisesRegex( ValueError, "vmap has mapped output but out_axes is None"): # If the output is mapped (unnamed axis), then there must be some out_axes # specified. api.vmap(lambda x: x, out_axes=None)(jnp.array([1., 2.])) def test_vmap_structured_in_axes(self): A, B, C, D = 2, 3, 4, 5 K = 6 # batch size x = np.ones((K, A, B)) # batch axis in different locations y = np.ones((B, K, C)) z = np.ones((C, D, K)) def foo(tree_arg): x, (y, z) = tree_arg return jnp.dot(x, jnp.dot(y, z)) tree = (x, (y, z)) vfoo = api.vmap(foo, in_axes=((0, (1, 2)),)) self.assertEqual(vfoo(tree).shape, (6, 2, 5)) Point = collections.namedtuple("Point", ["x", "y"]) tree = (x, Point(y, z)) vfoo = api.vmap(foo, in_axes=((0, Point(1, 2)),)) self.assertEqual(vfoo(tree).shape, (6, 2, 5)) def foo(tree_arg): x, dct = tree_arg y, z = dct['a'], dct['b'] return jnp.dot(x, jnp.dot(y, z)) tree = (x, {'a': y, 'b': z}) vfoo = api.vmap(foo, in_axes=((0, {'a': 1, 'b': 2}),)) self.assertEqual(vfoo(tree).shape, (6, 2, 5)) tree = (x, collections.OrderedDict([('a', y), ('b', z)])) vfoo = api.vmap( foo, in_axes=((0, collections.OrderedDict([('a', 1), ('b', 2)])),)) self.assertEqual(vfoo(tree).shape, (6, 2, 5)) def test_vmap_in_axes_bool_error(self): # https://github.com/google/jax/issues/6372 with self.assertRaisesRegex(TypeError, "must be an int"): api.vmap(lambda x: x, in_axes=False)(jnp.zeros(3)) def test_pmap_in_axes_bool_error(self): # https://github.com/google/jax/issues/6372 with self.assertRaisesRegex(TypeError, "must be an int"): api.pmap(lambda x: x, in_axes=False)(jnp.zeros(1)) def test_pmap_global_cache(self): def f(x, y): return x, y x = np.ones((1, 1, 1)) # All defaults with jtu.assert_num_jit_and_pmap_compilations(1): for _ in range(2): api.pmap(f)(x, x) # With axis name with jtu.assert_num_jit_and_pmap_compilations(1): for _ in range(2): api.pmap(f, 'i')(x, x) # With in_axes and out_axes for x_in, y_in, x_out, y_out in it.product(*((0, 1, 2) for _ in range(4))): with jtu.assert_num_jit_and_pmap_compilations(1): for _ in range(2): api.pmap(f, 'i', in_axes=(x_in, y_in), out_axes=(x_out, y_out))(x, x) # Forward-mode AD on the outside with jtu.assert_num_jit_and_pmap_compilations(1): for _ in range(2): api.jvp(api.pmap(f), (x, x), (x, x)) # Reverse-mode AD on the outside. One compilation for forward, one for backward. with jtu.assert_num_jit_and_pmap_compilations(2): for _ in range(2): api.vjp(api.pmap(f), x, x)[1]((x, x)) def test_device_array_repr(self): rep = jnp.ones(()) + 1. self.assertStartsWith(repr(rep), "DeviceArray") def test_device_array_hash(self): rep = jnp.ones(()) + 1. self.assertIsInstance(rep, jax.interpreters.xla.DeviceArray) self.assertNotIsInstance(rep, collections.abc.Hashable) with self.assertRaisesRegex(TypeError, 'unhashable type'): hash(rep) def test_grad_without_enough_args_error_message(self): # https://github.com/google/jax/issues/1696 def f(x, y): return x + y df = api.grad(f, argnums=0) self.assertRaisesRegex( TypeError, "differentiating with respect to argnums=0 requires at least 1 " "positional arguments to be passed by the caller, but got only 0 " "positional arguments.", lambda: partial(df, x=0.)(y=1.)) def test_grad_of_jit_compilation_caching(self): if not hasattr(self, "assertLogs"): raise unittest.SkipTest("test requires assertLogs (python 3)") lax.add(1, 2) # make sure some initial warnings are already printed sin = api.jit(jnp.sin) prev_level = logging.get_verbosity() try: logging.set_verbosity('DEBUG') with self.assertLogs(level=logging.DEBUG) as l: ans1 = api.grad(sin)(2.) ans2 = api.grad(sin)(3.) finally: logging.set_verbosity(prev_level) self.assertLen(l.output, 2) self.assertAllClose(ans1, np.cos(2.), check_dtypes=False) self.assertAllClose(ans2, np.cos(3.), check_dtypes=False) def test_grad_of_jit_compilation_caching2(self): # Like the above test, but instead of logging use our compile counters. @api.jit def f(x): return jnp.sin(x) with jtu.count_jit_and_pmap_compiles() as count: # noqa: F841 _ = jax.grad(f)(3.) self.assertEqual(count[0], 2) # one for fwd, one for bwd with jtu.count_jit_and_pmap_compiles() as count: # noqa: F841 _ = jax.grad(f)(3.) _ = jax.grad(f)(4.) self.assertEqual(count[0], 0) # cache hits on both fwd and bwd def test_grad_does_not_unflatten_tree_with_none(self): # https://github.com/google/jax/issues/7546 class CustomNode(list): pass def unflatten(unused_aux_data, children): self.assertIsNotNone(children[0]) return CustomNode(children) tree_util.register_pytree_node(CustomNode, lambda x: (x, None), unflatten) grad(lambda x: x[0])(CustomNode([0.])) def test_trivial_computations(self): x = jnp.array([1, 2, 3]) y = api.jit(lambda x: x)(x) self.assertIs(x, y) z1, z2 = api.jit(lambda x: (x, x))(x) self.assertIs(z1, z2) x1, x2 = jnp.array([1, 2]), jnp.array([2, 3]) z1, z2, z3 = api.jit(lambda x, y: (y, 1, x))(x1, x2) self.assertIs(z1, x2) self.assertIs(z3, x1) self.assertEqual(z2, 1) def test_nested_jit_hoisting(self): @api.jit def f(x, y): z = 2 * x return y + z, 3 @api.jit def g(x): return f(2, x) jaxpr_subcomp = xla.jaxpr_subcomp jaxprs = [] def jaxpr_subcomp_and_collect(c, jaxpr, *args, **kwargs): jaxprs.append(jaxpr) return jaxpr_subcomp(c, jaxpr, *args, **kwargs) try: xla.jaxpr_subcomp = jaxpr_subcomp_and_collect ans = g(3) finally: xla.jaxpr_subcomp = jaxpr_subcomp self.assertEqual(ans, (7, 3)) self.assertLen(jaxprs, 2) outer_jaxpr, inner_jaxpr = jaxprs self.assertLen(outer_jaxpr.eqns, 1) self.assertEqual(outer_jaxpr.eqns[0].primitive.name, 'xla_call') subjaxpr_1 = outer_jaxpr.eqns[0].params["call_jaxpr"] self.assertEqual(str(subjaxpr_1), str(inner_jaxpr)) self.assertLen(inner_jaxpr.eqns, 2) self.assertEqual(inner_jaxpr.eqns[-2].primitive.name, 'mul') self.assertEqual(inner_jaxpr.eqns[-1].primitive.name, 'add') def test_primitive_compilation_cache(self): with jtu.count_primitive_compiles() as count: lax.add(1, 2) lax.add(2, 3) self.assertEqual(count[0], 1) def test_arange_jit(self): # see https://github.com/google/jax/issues/553 def fun(x): r = jnp.arange(x.shape[0])[x] return r jit(fun)(jnp.array([0, 1, 2], dtype=jnp.int32)) # doesn't crash def helper_save_tracer(self, x): self._saved_tracer = x return x def test_escaped_tracers_different_top_level_traces(self): api.jit(self.helper_save_tracer)(0.) with self.assertRaisesRegex( UnexpectedTracerError, "Encountered an unexpected tracer"): api.jit(lambda x: self._saved_tracer)(0.) def test_escaped_tracers_cant_lift_sublevels(self): api.jit(self.helper_save_tracer)(0.) with self.assertRaisesRegex( UnexpectedTracerError, re.compile( "Encountered an unexpected tracer", re.DOTALL)): api.jit(lambda x: x)(self._saved_tracer) def test_escaped_tracers_tracer_from_higher_level(self): api.grad(self.helper_save_tracer)(0.) with self.assertRaisesRegex( UnexpectedTracerError, re.compile( "Encountered an unexpected tracer.*Tracer from a higher level", re.DOTALL)): api.grad(lambda x: x)(self._saved_tracer) def test_escaped_tracers_incompatible_sublevel(self): def func1(x): api.jit(self.helper_save_tracer)(0.) # Use the tracer return x + self._saved_tracer with self.assertRaisesRegex( UnexpectedTracerError, re.compile("Encountered an unexpected tracer", re.DOTALL)): api.jit(func1)(2.) def test_escaped_tracers_cant_lift(self): def func1(x): api.grad(self.helper_save_tracer)(0.) return x + self._saved_tracer with self.assertRaisesRegex( UnexpectedTracerError, re.compile("Encountered an unexpected tracer.*Can't lift", re.DOTALL)): api.grad(func1)(2.) def test_escaped_tracers_not_among_input_tracers(self): def func1(x): api.grad(self.helper_save_tracer)(x) # Use the tracer return x + self._saved_tracer with self.assertRaisesRegex( UnexpectedTracerError, re.compile( "Encountered an unexpected tracer.*Tracer not among input tracers", re.DOTALL)): api.jit(func1)(2.) def test_escaped_tracer_omnistaging(self): count = 1 @jit def f(): nonlocal count count = jnp.add(count, 1) f() # leaked a tracer! but currently undetected def f(x, c): jnp.add(count, 1) return None, None @jit def g(): lax.scan(f, None, None, length=2) with self.assertRaisesRegex(UnexpectedTracerError, "was created on line"): g() def test_escaped_tracer_omnistaging_top_trace(self): count = 1 def f(_, __): nonlocal count count = jnp.add(count, 1) return None, None lax.scan(f, None, None, length=2) # leaked a tracer! (of level 1!) with self.assertRaisesRegex(UnexpectedTracerError, "was created on line"): # The following call will try and raise the ones array to the count tracer # level, which is no longer live. jax.jit(jnp.add)(jnp.ones(()), count) def test_escaped_tracer_transform_name(self): with self.assertRaisesRegex(UnexpectedTracerError, "for jit"): jax.jit(self.helper_save_tracer)(1) _ = self._saved_tracer+1 with self.assertRaisesRegex(UnexpectedTracerError, "for pmap"): jax.pmap(self.helper_save_tracer)(jnp.ones((1, 2))) _ = self._saved_tracer+1 with self.assertRaisesRegex(UnexpectedTracerError, "for eval_shape"): jax.eval_shape(self.helper_save_tracer, 1) _ = self._saved_tracer+1 def test_escaped_tracer_shape_dtype(self): with self.assertRaisesRegex(core.UnexpectedTracerError, r"shape $4, 3$ and dtype int32"): jax.jit(self.helper_save_tracer)(jnp.ones((4, 3), dtype=jnp.int32)) _ = self._saved_tracer+1 def test_pmap_static_kwarg_error_message(self): # https://github.com/google/jax/issues/3007 def f(a, b): return a + b g = jax.pmap(f, static_broadcasted_argnums=(1,)) msg = (r"pmapped function has static_broadcasted_argnums=$1,$ but was " r"called with only 1 positional argument. All static broadcasted " r"arguments must be passed positionally.") with self.assertRaisesRegex(ValueError, msg): g(jnp.ones((1, 1)), b=1) def test_vmap_unmapped_last(self): @partial(jax.vmap, out_axes=-1) def f(x): return np.zeros((2,)) f(np.zeros((5,))) # TODO(jakevdp): re-enable this if possible. @unittest.skipIf(True, "broken by convert_element_type change.") def test_xla_constant_dedup(self): y = np.array([7, 14], dtype=np.float32) def f(x): return x + y + y x = np.array([1, 2], dtype=np.float32) hlo_lines = jax.xla_computation(f)(x).as_hlo_text().split('\n') hlo_lines = set([s.strip() for s in hlo_lines]) self.assertIn('constant.1 = f32[2]{0} constant({7, 14})', hlo_lines) self.assertNotIn('constant.2 = f32[2]{0} constant({7, 14})', hlo_lines) def test_eval_context(self): @jit def f(): with core.eval_context(): assert jnp.add(1, 1) == 2 f() # doesn't crash def test_concrete_error_because_arg_unary(self): @jax.jit def f(x): if x > 0: return x else: return 0 msg = r"on the value of the argument 'x'" with self.assertRaisesRegex(core.ConcretizationTypeError, msg): f(1) def test_concrete_error_because_arg_binary(self): @jax.jit def f(x, y): if x > y: return x else: return y msg = r"on the values of the arguments 'x' and 'y'" with self.assertRaisesRegex(core.ConcretizationTypeError, msg): f(1, 2) def test_concrete_error_because_arg_ternary(self): @jax.jit def f(x, y, z): if x > z: return x else: return y msg = r"on the values of the arguments 'x' and 'z'" with self.assertRaisesRegex(core.ConcretizationTypeError, msg): f(1, 2, 3) with self.assertRaisesRegex(core.ConcretizationTypeError, msg): f(1, 2, z=3) with self.assertRaisesRegex(core.ConcretizationTypeError, msg): f(1, y=2, z=3) def test_concrete_error_because_arg_varargs(self): @jax.jit def f(*args): x, y, z = args if x > z: return x else: return y msg = r"on the values of the argument 'args'" with self.assertRaisesRegex(core.ConcretizationTypeError, msg): f(1, 2, 3) def test_concrete_error_because_arg_kwargs(self): @jax.jit def f(**kwargs): x, y, z = kwargs['x'], kwargs['y'], kwargs['z'] if x > z: return x else: return y msg = r"on the values of the argument 'kwargs'" with self.assertRaisesRegex(core.ConcretizationTypeError, msg): f(x=1, y=2, z=3) def test_concrete_error_because_arg_pytree(self): @jax.jit def f(xy, z): x, y = xy if x > 0: return x else: return y msg = r"on the value of the argument 'xy'" with self.assertRaisesRegex(core.ConcretizationTypeError, msg): f((1, 2), z=3) def test_concrete_error_because_const(self): @jax.jit def f(): assert jnp.add(1, 1) > 0 msg = "on these lines" with self.assertRaisesRegex(core.ConcretizationTypeError, msg): f() def test_xla_computation_zeros_doesnt_device_put(self): with jtu.count_device_put() as count: api.xla_computation(lambda: jnp.zeros(3))() self.assertEqual(count[0], 0) def test_join_concrete_arrays_with_omnistaging(self): # https://github.com/google/jax/issues/4622 x = jnp.array([1., 2., 3.]) y = jnp.array([1., 2., 4.]) @jit def f(): core.lattice_join(core.ConcreteArray(x), core.ConcreteArray(y)) f() # doesn't crash def test_linearize_aval_error(self): # https://github.com/google/jax/issues/4622 f = lambda x: x # these should not error _, f_jvp = api.linearize(f, 1.) f_jvp(1.) _, f_jvp = api.linearize(f, np.ones(2, np.int32)) f_jvp(np.zeros(2, float0)) # these should error _, f_jvp = api.linearize(f, 1.) with self.assertRaisesRegex(ValueError, "tangent values inconsistent"): f_jvp(1) _, f_jvp = api.linearize(f, np.ones(2, np.int32)) with self.assertRaisesRegex(ValueError, "tangent values inconsistent"): f_jvp(np.ones(2, np.int32)) def test_grad_of_token_consuming_primitive(self): # https://github.com/google/jax/issues/5463 tokentest_p = core.Primitive("tokentest") tokentest_p.def_impl(partial(xla.apply_primitive, tokentest_p)) tokentest_p.def_abstract_eval(lambda x, y: x) xla.translations[tokentest_p] = lambda c, x, y: x ad.defjvp(tokentest_p, (lambda g, x, token: x), None) token = jax.lax.create_token(123) arr = jnp.ones((3, 2)) res, vjp_fun = jax.vjp(lambda x: tokentest_p.bind(x, token), arr) # Should not crash. vjp_fun(arr) def test_jit_returning_token(self): x = jax.jit(jax.lax.create_token)(1.0) self.assertIsInstance(x, jax.interpreters.xla.Token) def test_leak_checker_catches_a_jit_leak(self): with jax.checking_leaks(): lst = [] @jit def f(x): lst.append(x) return x with self.assertRaisesRegex(Exception, r"Leaked"): f(3) def test_leak_checker_catches_a_pmap_leak(self): with jax.checking_leaks(): lst = [] @api.pmap def f(x): lst.append(x) return x with self.assertRaisesRegex(Exception, r"Leaked"): f(np.ones(1)) def test_leak_checker_catches_a_grad_leak(self): with jax.checking_leaks(): lst = [] def f(x): lst.append(x) return x with self.assertRaisesRegex(Exception, r"Leaked trace"): api.grad(f)(3.) def test_leak_checker_avoids_false_positives(self): with jax.checking_leaks(): @jit def f(x): return x f(3) # doesn't crash api.vmap(f)(np.arange(3)) # doesn't crash api.grad(f)(3.) # doesn't crash @api.pmap def f(x): return x f(np.ones(1)) # doesn't crash api.vmap(f)(np.ones((1, 1))) # doesn't crash def test_leak_checker_catches_a_scan_leak(self): with jax.checking_leaks(): lst = [] to_scan = lambda c, x: (lst.append(c) or jnp.sin(c), None) with self.assertRaisesRegex(Exception, r"Leaked trace"): lax.scan(to_scan, 1., np.arange(3.)) def test_leak_checker_avoids_false_positives_scan(self): with jax.checking_leaks(): to_scan = lambda c, x: (jnp.sin(c), None) lax.scan(to_scan, 1., np.arange(3.)) # doesn't crash def test_leak_checker_avoids_false_positives_scan_jvp(self): with jax.checking_leaks(): to_scan = lambda c, x: (c, None) def f(x): lax.scan(to_scan, x, None, length=1) api.jvp(f, (3.,), (1.,)) # doesn't crash def test_leak_checker_avoids_false_positives_scan_vmap(self): with jax.checking_leaks(): to_scan = lambda c, _: (1., None) @api.vmap def f(x): lax.scan(to_scan, x, None, length=1) f(np.arange(5.)) # doesn't crash def test_leak_checker_avoids_false_positives_scan_vmap_2(self): with jax.checking_leaks(): to_scan = lambda c, _: (c, None) @api.vmap def f(x): lax.scan(to_scan, x, None, length=1) f(np.arange(5.)) # doesn't crash def test_leak_checker_catches_a_sublevel_leak(self): with jax.checking_leaks(): @jit def f(x): lst = [] @jit def g(x): lst.append(x) return x x = g(x) return x with self.assertRaisesRegex(Exception, r"Leaked sublevel"): f(3) def test_leak_checker_avoids_false_positive_custom_jvp(self): # see https://github.com/google/jax/issues/5636 with jax.checking_leaks(): @api.custom_jvp def t(y): return y def t_jvp(p, t): pass t.defjvp(t_jvp) @jit def s(y): return t(y) s(3) # doesn't crash def test_default_backend(self): first_local_device = api.local_devices()[0] self.assertEqual(first_local_device.platform, api.default_backend()) def test_dunder_jax_array(self): # https://github.com/google/jax/pull/4725 class AlexArray: def __init__(self, jax_val): self.jax_val = jax_val def __jax_array__(self): return self.jax_val dtype = property(lambda self: self.jax_val.dtype) shape = property(lambda self: self.jax_val.shape) x = AlexArray(jnp.array([1., 2., 3.])) y = jnp.sin(x) self.assertAllClose(y, jnp.sin(jnp.array([1., 2., 3.]))) y = api.grad(api.jit(lambda x: jnp.sin(x).sum()))(x) self.assertAllClose(y, jnp.cos(jnp.array([1., 2., 3.]))) x = AlexArray(jnp.array([[1., 2., 3.]])) y = api.pmap(jnp.sin)(x) self.assertAllClose(y, jnp.sin(jnp.array([[1., 2., 3.]]))) x = jnp.array(1) a = AlexArray(x) for f in [jnp.isscalar, jnp.size, jnp.shape, jnp.dtype]: self.assertEqual(f(x), f(a)) def test_constant_handler_mro(self): # https://github.com/google/jax/issues/6129 class Foo(enum.IntEnum): bar = 1 @api.pmap def f(_): return Foo.bar ans = f(jnp.arange(1)) # doesn't crash expected = jnp.arange(1) + 1 self.assertAllClose(ans, expected) def test_large_python_ints(self): with self.assertRaises(OverflowError): jnp.multiply(2 ** 100, 3.) out = lax.convert_element_type(2 ** 100, jnp.float32) # doesn't crash self.assertArraysEqual(out, np.float32(2 ** 100)) def test_dot_precision_context_manager(self): x = jnp.zeros((2, 2)) with jax.default_matmul_precision(None): jnp.dot(x, x) # doesn't crash jaxpr = jax.make_jaxpr(jnp.dot)(x, x) self.assertIn('precision=None', str(jaxpr)) with jax.default_matmul_precision("bfloat16"): x @ x # doesn't crash jaxpr = jax.make_jaxpr(op.matmul)(x, x) self.assertIn('Precision.DEFAULT', str(jaxpr)) with jax.default_matmul_precision("tensorfloat32"): jnp.dot(x, x) # doesn't crash jaxpr = jax.make_jaxpr(jnp.dot)(x, x) self.assertIn('Precision.HIGH', str(jaxpr)) with jax.default_matmul_precision("float32"): jnp.dot(x, x) # doesn't crash jaxpr = jax.make_jaxpr(jnp.dot)(x, x) self.assertIn('Precision.HIGHEST', str(jaxpr)) dot = partial(jnp.dot, precision=lax.Precision.HIGHEST) with jax.default_matmul_precision("tensorfloat32"): dot(x, x) # doesn't crash jaxpr = jax.make_jaxpr(dot)(x, x) self.assertIn('Precision.HIGHEST', str(jaxpr)) def test_dot_precision_flag(self): x = jnp.zeros((2, 2)) prev_val = config._read("jax_default_matmul_precision") try: config.FLAGS.jax_default_matmul_precision = "tensorfloat32" jnp.dot(x, x) # doesn't crash jaxpr = jax.make_jaxpr(jnp.dot)(x, x) finally: config.FLAGS.jax_default_matmul_precision = prev_val self.assertIn('Precision.HIGH', str(jaxpr)) self.assertEqual(prev_val, config._read("jax_default_matmul_precision")) prev_val = config._read("jax_default_matmul_precision") try: config.update('jax_default_matmul_precision','tensorfloat32') jnp.dot(x, x) # doesn't crash jaxpr = jax.make_jaxpr(jnp.dot)(x, x) finally: config.update('jax_default_matmul_precision', prev_val) self.assertIn('Precision.HIGH', str(jaxpr)) self.assertEqual(prev_val, config._read("jax_default_matmul_precision")) def test_dot_precision_forces_retrace(self): num_traces = 0 def g(x): nonlocal num_traces num_traces += 1 return jnp.dot(x, x) def f_cond(x): return lax.cond(True, g, g, x) @jax.jit def f_jit(x): nonlocal num_traces num_traces += 1 return jnp.dot(x, x) for f in [f_jit, f_cond]: precision = config.jax_default_matmul_precision try: num_traces = 0 x = jnp.zeros((2, 2)) f(x) self.assertEqual(num_traces, 1) f(x) self.assertEqual(num_traces, 1) with jax.default_matmul_precision("tensorfloat32"): f(x) self.assertEqual(num_traces, 2) FLAGS.jax_default_matmul_precision = "float32" f(x) self.assertGreaterEqual(num_traces, 2) nt = num_traces f(x) self.assertEqual(num_traces, nt + 1) f(x) self.assertEqual(num_traces, nt + 1) finally: FLAGS.jax_default_matmul_precision = precision def test_rank_promotion_forces_retrace(self): num_traces = 0 def g(x): nonlocal num_traces num_traces += 1 return x + x def f_cond(x): return lax.cond(True, g, g, x) @jax.jit def f_jit(x): nonlocal num_traces num_traces += 1 return x + x for f in [f_jit, f_cond]: allow_promotion = config.jax_numpy_rank_promotion try: num_traces = 0 @jax.jit def f(x): nonlocal num_traces num_traces += 1 return x + x x = jnp.zeros((2, 2)) f(x) self.assertEqual(num_traces, 1) f(x) self.assertEqual(num_traces, 1) with jax.numpy_rank_promotion("warn"): f(x) self.assertEqual(num_traces, 2) FLAGS.jax_numpy_rank_promotion = "raise" f(x) self.assertGreaterEqual(num_traces, 2) nt = num_traces f(x) self.assertEqual(num_traces, nt + 1) f(x) self.assertEqual(num_traces, nt + 1) finally: FLAGS.jax_numpy_rank_promotion = allow_promotion def test_backward_pass_ref_dropping(self): refs = [] @api.custom_vjp def f(x): return x def f_fwd(x): return x, None def f_rev(_, g): assert len(refs) != 2 or refs[0]() is None zero = np.zeros(()) refs.append(weakref.ref(zero)) return (zero,) f.defvjp(f_fwd, f_rev) api.grad(lambda x: f(f(f(x))))(1.) def test_custom_vjp_scan_batching_edge_case(self): # https://github.com/google/jax/issues/5832 @jax.custom_vjp def mul(x, coeff): return x * coeff def mul_fwd(x, coeff): return mul(x, coeff), (x, coeff) def mul_bwd(res, g): x, coeff = res g_x = g * coeff g_coeff = (x * g).sum() return g_x, g_coeff mul.defvjp(mul_fwd, mul_bwd) def scan_over_mul(x, coeff): def f_(x, t): return mul(x, coeff), None y, _ = jax.lax.scan(f_, x, jnp.arange(3)) return y key = jax.random.PRNGKey(0) key1, key2 = jax.random.split(key, 2) x_batch = jax.random.normal(key1, (3, 2)) covector_batch = jax.random.normal(key2, (3, 2)) coeff = jnp.array(1.) batched_scan_over_mul = jax.vmap(scan_over_mul, in_axes=(0, None), out_axes=0) res, vjp_fun = jax.vjp(batched_scan_over_mul, x_batch, coeff) vjp_fun(covector_batch) # doesn't crash jtu.check_grads(batched_scan_over_mul, (x_batch, coeff), order=2, modes=['rev']) def test_jit_inline(self): @partial(api.jit, inline=False) def f(x): return x * 2 jaxpr = api.make_jaxpr(f)(3) self.assertIn('xla_call', str(jaxpr)) @partial(api.jit, inline=True) def f(x): return x * 2 jaxpr = api.make_jaxpr(f)(3) self.assertNotIn('xla_call', str(jaxpr)) # Repro for https://github.com/google/jax/issues/7229. def test_compute_with_large_transfer(self): def f(x, delta): return x + jnp.asarray(delta, x.dtype) # A large and potentially unaligned array to trigger non-zero-copy and # async device array copy. xs = np.random.uniform(0., 1., size=(10, 131, 111, 3)).astype(np.float32) for x in xs: delta = np.random.uniform(-0.5, 0.5, size=()) jitted_f = api.jit(f) np.testing.assert_allclose(jitted_f(x, delta), f(x, delta)) def test_vjp_fun_jit(self): # test that the function returned by vjp can be returned # from and passed to jitted functions f = lambda x: 2. * x @partial(jit, static_argnums=0) def linearize_vjp(f, x): _, vjp_fun = api.vjp(f, x) return vjp_fun linearized = linearize_vjp(f, 1.) actual = jit(lambda f, x: f(x))(linearized, 3.) expected = (6.,) self.assertEqual(actual, expected) def test_linearize_fun_jit(self): # test that the function returned by linearize can be returned # from and passed to jitted functions f = lambda x: 2. * x @partial(jit, static_argnums=0) def linearize(f, x): _, jvp_fun = api.linearize(f, x) return jvp_fun linearized = linearize(f, 1.) actual = jit(lambda f, x: f(x))(linearized, 3.) expected = 6. self.assertEqual(actual, expected) def test_linear_transpose_fun_jit(self): # test that the function returned by linear_transpose can be returned # from and passed to jitted functions f = lambda x: 2. * x @partial(jit, static_argnums=0) def transpose(f, x): return api.linear_transpose(f, x) transposed = transpose(f, 1.) actual = jit(lambda f, x: f(x))(transposed, 3.) expected = (6.,) self.assertEqual(actual, expected) def test_leaked_tracer_issue_7613(self): # from https://github.com/google/jax/issues/7613 import numpy.random as npr def sigmoid(x): return 1. / (1. + jnp.exp(-x)) x = jnp.ones((50,)) A = jnp.array(npr.randn(50, 50)) @jax.jit def loss(A, x): h = jax.nn.sigmoid(A * x) return jnp.sum((h - x)**2) with jax.checking_leaks(): _ = jax.grad(loss)(A, x) # doesn't crash def test_vmap_caching(self): # https://github.com/google/jax/issues/7621 f = lambda x: jnp.square(x).mean() jf = jax.jit(f) x = jax.random.uniform(jax.random.PRNGKey(0), shape=(8, 4)) with jtu.count_jit_and_pmap_compiles() as count: # noqa: F841 for _ in range(5): jax.hessian(jf)(x).block_until_ready() n = count[0] # The exact number of compilations may vary depending on the number of # jit decorators in the function above, but it should not grow after an # initial warmup phase. for _ in range(5): jax.hessian(jf)(x).block_until_ready() self.assertEqual(count[0], n) def test_jnp_array_doesnt_device_put(self): with jtu.count_device_put() as count: api.make_jaxpr(lambda: jnp.array(3))() self.assertEqual(count[0], 0) class RematTest(jtu.JaxTestCase): @parameterized.named_parameters( {"testcase_name": f"{suffix}", "remat": remat} for suffix, remat in [ ('', api.remat), ('_policy', partial(api.remat, policy=lambda *_, **__: False)), ('_new', partial(new_checkpoint, policy=lambda *_, **__: False)), ]) def test_remat_basic(self, remat): @remat def g(x): return lax.sin(lax.sin(x)), 3. def f(x): x, _ = g(x) return x ans = f(2.) expected = np.sin(np.sin(2.)) self.assertAllClose(ans, expected, check_dtypes=False) ans, f_lin = api.linearize(f, 2.) expected = np.sin(np.sin(2.)) self.assertAllClose(ans, expected, check_dtypes=False) ans = f_lin(3.) expected = np.cos(np.sin(2.)) * np.cos(2.) * 3. self.assertAllClose(ans, expected, check_dtypes=False) sin_calls = [] cos_calls = [] sin_impl = lax.sin_p.impl cos_impl = lax.cos_p.impl try: lax.sin_p.def_impl(lambda x: sin_calls.append(1) or sin_impl(x)) lax.cos_p.def_impl(lambda x: cos_calls.append(1) or cos_impl(x)) f_lin(3.) finally: lax.sin_p.def_impl(sin_impl) lax.cos_p.def_impl(cos_impl) self.assertEqual(len(sin_calls), 1) self.assertEqual(len(cos_calls), 2) @parameterized.named_parameters( {"testcase_name": f"{suffix}", "remat": remat} for suffix, remat in [ ('', api.remat), ('_policy', partial(api.remat, policy=lambda *_, **__: False)), ('_new', partial(new_checkpoint, policy=lambda *_, **__: False)), ]) def test_remat_freevars(self, remat): def f1(x): y = 2 * jnp.sin(x) z = jnp.cos(x) * jnp.sin(y) return z def f2(x): y = 2 * jnp.sin(x) z = remat(lambda x: jnp.cos(x) * jnp.sin(y))(x) return z ans, f_lin = api.linearize(f2, 2.) expected, f_lin_expected = api.linearize(f1, 2.) self.assertAllClose(ans, expected, check_dtypes=False) ans = f_lin(3.) expected = f_lin_expected(3.) self.assertAllClose(ans, expected, check_dtypes=False) def test_remat_grad_python_control_flow(self): @partial(api.remat, concrete=True) def g(x): if x > 0: return lax.sin(x), 3. else: return lax.cos(x), 4. def f(x): x, _ = g(x) return x ans = f(2.) expected = np.sin(2.) self.assertAllClose(ans, expected, check_dtypes=False) ans = api.grad(f)(2.) expected = np.cos(2.) self.assertAllClose(ans, expected, check_dtypes=False) @parameterized.named_parameters( {"testcase_name": f"{suffix}", "remat": remat} for suffix, remat in [ ('', api.remat), ('_policy', partial(api.remat, policy=lambda *_, **__: False)), ('_new', partial(new_checkpoint, policy=lambda *_, **__: False)), ]) def test_remat_jit(self, remat): @remat def g(x): return lax.sin(lax.sin(x)) def f_(x): return g(x) f = api.jit(f_) ans = f(2.) expected = np.sin(np.sin(2.)) self.assertAllClose(ans, expected, check_dtypes=False) ans = api.grad(f)(2.) expected = np.cos(np.sin(2.)) * np.cos(2.) self.assertAllClose(ans, expected, check_dtypes=False) ans = api.jit(api.grad(f_))(2.) expected = np.cos(np.sin(2.)) * np.cos(2.) self.assertAllClose(ans, expected, check_dtypes=False) @parameterized.named_parameters( {"testcase_name": f"{suffix}", "remat": remat} for suffix, remat in [ ('', api.remat), ('_policy', partial(api.remat, policy=lambda *_, **__: False)), ('_new', partial(new_checkpoint, policy=lambda *_, **__: False)), ]) def test_remat_vmap(self, remat): @remat def g(x): return lax.sin(lax.sin(x)) x = np.arange(3.) ans = api.vmap(g)(x) expected = np.sin(np.sin(x)) self.assertAllClose(ans, expected, check_dtypes=False) ans = api.jacfwd(g)(x) expected = np.diag(np.cos(np.sin(x)) * np.cos(x)) self.assertAllClose(ans, expected, check_dtypes=False) ans = api.jacrev(g)(x) expected = np.diag(np.cos(np.sin(x)) * np.cos(x)) self.assertAllClose(ans, expected, check_dtypes=False) @parameterized.named_parameters( {"testcase_name": f"{suffix}", "remat": remat} for suffix, remat in [ ('', api.remat), ('_policy', partial(api.remat, policy=lambda *_, **__: False)), ('_new', partial(new_checkpoint, policy=lambda *_, **__: False)), ]) def test_remat_higher_order_autodiff(self, remat): def f(x): return lax.cos(lax.sin(x)) g = remat(f) ans = api.grad(api.grad(g))(3.) expected = api.grad(api.grad(f))(3.) self.assertAllClose(ans, expected, check_dtypes=False) def test_remat_scan(self): to_scan = lambda c, x: (jnp.sin(c), None) def f_noremat(x): y, _ = lax.scan(to_scan, x, np.arange(3.)) return y def f_yesremat(x): y, _ = lax.scan(api.remat(to_scan), x, np.arange(3.)) return y ans = f_yesremat(4.) expected = f_noremat(4.) self.assertAllClose(ans, expected, check_dtypes=False) ans = api.grad(f_yesremat)(4.) expected = api.grad(f_noremat)(4.) self.assertAllClose(ans, expected, check_dtypes=False) jaxpr = api.make_jaxpr(api.linearize(f_yesremat, 4.)[1])(1.) scan_eqn, = jaxpr.jaxpr.eqns self.assertIn(' cos ', str(scan_eqn.params['jaxpr'])) jaxpr = api.make_jaxpr(api.vjp(f_yesremat, 4.)[1])(1.) scan_eqn, = jaxpr.jaxpr.eqns self.assertIn(' cos ', str(scan_eqn.params['jaxpr'])) @parameterized.named_parameters( {"testcase_name": f"{suffix}", "remat": remat} for suffix, remat in [ ('', api.remat), ('_policy', partial(api.remat, policy=lambda *_, **__: False)), ('_new', partial(new_checkpoint, policy=lambda *_, **__: False)), ]) def test_remat_no_redundant_flops(self, remat): # see https://github.com/google/jax/pull/1749#issuecomment-558267584 @api.jit def g(x): return f(2., x) @remat def f(x, y): return jnp.sin(x) * y # We swap out sin_p's impl rule to count how many times it's invoked called = [] sin_impl = lax.sin_p.impl try: lax.sin_p.def_impl(lambda x: called.append(1) or sin_impl(x)) api.grad(g)(3.) finally: lax.sin_p.def_impl(sin_impl) num_calls = len(called) self.assertLessEqual(num_calls, 1) @parameterized.named_parameters( {"testcase_name": f"{suffix}", "remat": remat} for suffix, remat in [ ('', api.remat), ('_policy', partial(api.remat, policy=lambda *_, **__: False)), ('_new', partial(new_checkpoint, policy=lambda *_, **__: False)), ]) def test_remat_binomial_checkpointing(self, remat): def binom_checkpoint(funs): if len(funs) == 1: return funs[0] else: f1 = binom_checkpoint(funs[:len(funs)//2]) f2 = binom_checkpoint(funs[len(funs)//2:]) return remat(lambda x: f1(f2(x))) f1 = binom_checkpoint([jnp.sin, jnp.sin, jnp.sin, jnp.sin]) f2 = lambda x: jnp.sin(jnp.sin(jnp.sin(jnp.sin(x)))) x = 4. self.assertAllClose(f1(x), f2(x), check_dtypes=False) self.assertAllClose(api.grad(f1)(x), api.grad(f2)(x), check_dtypes=False) def test_remat_symbolic_zeros(self): # code from https://github.com/google/jax/issues/1907 key = jax.random.PRNGKey(0) key, split = jax.random.split(key) n = 5 def func(D0): def shift(R, dR, **unused_kwargs): return R + dR def apply_fn(R): return D0 * R Rinit = jax.random.uniform(split, (n,3), minval=0.0, maxval=5.0, dtype=jnp.float32) def move(R,i): F = apply_fn(R) return shift(R, 0.001 * F), jnp.array([0.]) move = api.remat(move) R, temp = lax.scan(move, Rinit, jnp.arange(2)) return R[0, 0] api.grad(func)(5.0) # doesn't crash @parameterized.named_parameters( {"testcase_name": f"{suffix}", "remat": remat} for suffix, remat in [ ('', api.remat), ('_policy', partial(api.remat, policy=lambda *_, **__: False)), ('_new', partial(new_checkpoint, policy=lambda *_, **__: False)), ]) def test_remat_jit2(self, remat): @api.jit def f(x): y = 2 * x @remat def g(): return y return g() self.assertAllClose(f(3), 6, check_dtypes=False) def test_remat_nontrivial_env(self): # simplified from https://github.com/google/jax/issues/2030 @api.remat def foo(state, dt=0.5, c=1): u, u_t = state u_tt = c**2 * u u_t = u_t + u_tt * dt return (u, u_t) @partial(api.jit, static_argnums=(1,)) def _multi_step(state, count, dt, c): f = lambda s, _: (foo(s, dt, c), _) return lax.scan(f, state, None, count) def multi_step(state, count, dt=1/jnp.sqrt(2), c=1): return _multi_step(state, count, dt, c) def loss(u0, target, steps, dt=1/jnp.sqrt(2), c=1): init = (u0, jnp.zeros_like(u0)) (uf, _), _ = multi_step(init, steps, dt, c) return ((uf - target) ** 2).mean() target = jnp.zeros((128, 128)) u0 = jnp.ones_like(target) loss(u0, target, 10) # doesn't crash @parameterized.named_parameters( {"testcase_name": f"{suffix}", "remat": remat} for suffix, remat in [ ('', api.remat), ('_policy', partial(api.remat, policy=lambda *_, **__: False)), ('_new', partial(new_checkpoint, policy=lambda *_, **__: False)), ]) def test_remat_jit3(self, remat): # https://github.com/google/jax/issues/2180 def f(w, x): a = jnp.dot(x, w) b = jnp.einsum("btd,bTd->btT", a, a) c = jnp.einsum("btT,btd->btd", b, a) return jnp.sum(c) w = jnp.ones([1, 1]) x = jnp.ones([1, 1, 1]) f = remat(f) api.grad(f)(w, x) # doesn't crash @api.jit def mul(a, b): return a * b def f(w, x): a = mul(w, x) b = mul(a, a) return b w = 1. x = 1. f = remat(f) api.grad(f)(w, x) # doesn't crash def test_remat_scan2(self): # https://github.com/google/jax/issues/1963 def scan_bug(x0): f = lambda x, _: (x + 1, None) def scanned_f(x, _): return lax.scan(f, x, xs=None, length=1)[0], None x, _ = jax.remat(scanned_f)(x0, None) return x jax.grad(scan_bug)(1.0) # doesn't crash def test_remat_jit_static_argnum_omnistaging(self): # https://github.com/google/jax/issues/2833 # NOTE(mattjj): after #3370, this test doesn't actually call remat... def named_call(f): def named_f(*args): f_ = lu.wrap_init(lambda: (f(*args),)) out, = core.call_p.bind(f_) return out return named_f def f(a_bool, y): if a_bool: return y + 1 else: return y api.jit(named_call(f), static_argnums=0)(True, 1) # no crash @parameterized.named_parameters( {"testcase_name": f"{suffix}", "remat": remat} for suffix, remat in [ ('', api.remat), ('_policy', partial(api.remat, policy=lambda *_, **__: False)), ('_new', partial(new_checkpoint, policy=lambda *_, **__: False)), ]) def test_remat_eval_counter(self, remat): # https://github.com/google/jax/issues/2737 add_one_p = Primitive('add_one') add_one = add_one_p.bind num_evals = 0 @contextmanager def assertEvals(n): start = num_evals yield assert num_evals - start == n def add_one_impl(x): nonlocal num_evals num_evals += 1 return x + 1 add_one_p.def_impl(add_one_impl) def add_one_jvp(pin, tin): pout = add_one(pin[0]) return pout, pout * tin[0] ad.primitive_jvps[add_one_p] = add_one_jvp add_one_p.def_abstract_eval(lambda x: x) v = np.zeros((1,)) f = remat(add_one) g = remat(lambda x: add_one(f(x))) # 2 calls needed to evaluate g with assertEvals(2): _, vjp = jax.vjp(g, v) # 2 calls made while transposing g, 1 call made while transposing f with assertEvals(3): vjp(v) @jax._src.util.curry def call(f, *args): return jax.core.call( jax.linear_util.wrap_init(lambda *args: [f(*args)]), *args, name='foo')[0] f = call(add_one) g = remat(lambda x: add_one(f(x))) # 2 calls needed to evaluate g with assertEvals(2): _, vjp = jax.vjp(g, v) # 2 calls made while transposing g, no reevaluation for transposition of f with assertEvals(2): vjp(v) @parameterized.named_parameters( {"testcase_name": f"{suffix}", "remat": remat} for suffix, remat in [ ('', api.remat), ('_policy', partial(api.remat, policy=lambda *_, **__: False)), ('_new', partial(new_checkpoint, policy=lambda *_, **__: False)), ]) def test_escaped_tracer_remat(self, remat): # b/169779185 def f(): seq = [jnp.zeros([])] def g(): seq[0] += 1 # this is line 7 btw return seq[0] remat(g)() remat(g)() with self.assertRaisesRegex(UnexpectedTracerError, "global state"): api.jit(f)() @parameterized.named_parameters( {"testcase_name": f"{suffix}", "remat": remat} for suffix, remat in [ ('', api.remat), ('_policy', partial(api.remat, policy=lambda *_, **__: False)), ('_new', partial(new_checkpoint, policy=lambda *_, **__: False)), ]) def test_no_cse_widget_on_primals(self, remat): @remat def g(x): return lax.sin(lax.sin(x)), 3. def f(x): x, _ = g(x) return x c = api.xla_computation(f)(2.) self.assertNotIn('while', c.as_hlo_text()) self.assertNotIn('conditional', c.as_hlo_text()) c = api.xla_computation(grad(f))(2.) text = c.as_hlo_text() self.assertTrue('while' in text or 'conditional' in text) def test_no_cse_widget_with_prevent_cse_false(self): @partial(api.remat, prevent_cse=False) def g(x): return lax.sin(lax.sin(x)), 3. def f(x): x, _ = g(x) return x c = api.xla_computation(f)(2.) self.assertNotIn('while', c.as_hlo_text()) self.assertNotIn('conditional', c.as_hlo_text()) c = api.xla_computation(grad(f))(2.) self.assertNotIn('while', c.as_hlo_text()) self.assertNotIn('conditional', c.as_hlo_text()) @parameterized.named_parameters( {"testcase_name": f"_{policy_name}", "policy": policy, "in_jaxpr2": in_jaxpr2, "not_in_jaxpr2": not_in_jaxpr2} for policy_name, policy, in_jaxpr2, not_in_jaxpr2 in [ ('save_anything', lambda *_, **__: True, [], [' sin ', ' cos ']), ('save_nothing', lambda *_, **__: False, [' sin ', ' cos '], []), ('save_sin', lambda p, *_, **__: str(p) == 'sin', [' cos '], [' sin ']), ]) def test_remat_custom_policy(self, policy, in_jaxpr2, not_in_jaxpr2): for square in [lambda x: x * x, api.jit(lambda x: x * x)]: f = api.remat(lambda x: jnp.sin(square(jnp.sin(x))), policy=policy) y, f_lin = api.linearize(f, 1.) ydot = f_lin(2.) jaxpr_text = str(f_lin.func.args[0]) for substr in in_jaxpr2: self.assertIn(substr, jaxpr_text) for substr in not_in_jaxpr2: self.assertNotIn(substr, jaxpr_text) y_expected, ydot_expected = api.jvp(lambda x: jnp.sin(square(jnp.sin(x))), [1.], [2.]) self.assertAllClose(y, y_expected) self.assertAllClose(ydot, ydot_expected) jtu.check_grads(f, (3.,), order=2, modes=['fwd', 'rev']) def test_remat_custom_policy_save_cos(self): save_cos = lambda prim, *_, **__: str(prim) == 'cos' f = api.remat(lambda x: jnp.sin(jnp.sin(x)), # different function policy=save_cos) _, f_lin = api.linearize(f, 1.) jaxpr_text = str(f_lin.func.args[0]) self.assertNotIn(' sin ', jaxpr_text) self.assertNotIn(' cos ', jaxpr_text) jtu.check_grads(f, (3.,), order=2, modes=['fwd', 'rev']) def test_remat_checkpoint_dots(self): @partial(api.remat, policy=jax.checkpoint_policies.checkpoint_dots) def f(x): x = jnp.dot(x, x, precision=lax.Precision.HIGHEST) x = jnp.sin(x) x = jnp.dot(x, x, precision=lax.Precision.HIGHEST) x = jnp.sin(x) x = jnp.dot(x, x, precision=lax.Precision.HIGHEST) x = jnp.sin(x) return x _, f_lin = api.linearize(f, jnp.ones((2, 2))) jaxpr_text = str(f_lin.func.args[0]) self.assertEqual(jaxpr_text.count(' sin '), 2) self.assertEqual(jaxpr_text.count(' dot_'), 6) jtu.check_grads(f, (jnp.ones((2, 2)),), order=2, modes=['fwd', 'rev']) def test_remat_checkpoint_dots_with_no_batch_dims(self): @partial(api.remat, policy=jax.checkpoint_policies.checkpoint_dots_with_no_batch_dims) def f(x): x = jnp.einsum('ij,jk->ik', x, x, precision=lax.Precision.HIGHEST) x = jnp.sin(x) x = jnp.einsum('ij,jk->ik', x, x, precision=lax.Precision.HIGHEST) x = jnp.sin(x) x = jnp.einsum('ij,jk->ik', x, x, precision=lax.Precision.HIGHEST) x = jnp.sin(x) return x _, f_lin = api.linearize(f, jnp.ones((2, 2))) jaxpr_text = str(f_lin.func.args[0]) self.assertEqual(jaxpr_text.count(' sin '), 2) self.assertEqual(jaxpr_text.count(' dot_general'), 6) jtu.check_grads(f, (jnp.ones((2, 2)),), order=2, modes=['fwd', 'rev']) def test_remat_checkpoint_dots_with_no_batch_dims2(self): @partial(api.remat, policy=jax.checkpoint_policies.checkpoint_dots_with_no_batch_dims) def f(x): x = jnp.einsum('nij,njk->nik', x, x, precision=lax.Precision.HIGHEST) x = jnp.sin(x) x = jnp.einsum('nij,njk->nik', x, x, precision=lax.Precision.HIGHEST) x = jnp.sin(x) x = jnp.einsum('nij,njk->nik', x, x, precision=lax.Precision.HIGHEST) x = jnp.sin(x) return x _, f_lin = api.linearize(f, jnp.ones((3, 2, 2))) jaxpr_text = str(f_lin.func.args[0]) self.assertEqual(jaxpr_text.count(' sin '), 2) self.assertEqual(jaxpr_text.count(' dot_general'), 9) jtu.check_grads(f, (jnp.ones((3, 2, 2)),), order=2, modes=['fwd', 'rev']) def test_remat_checkpoint_dots_jit(self): @api.jit @partial(api.remat, policy=jax.checkpoint_policies.checkpoint_dots) def f(x): x = jnp.dot(x, x, precision=lax.Precision.HIGHEST) x = jnp.sin(x * 1e-3) x = jnp.dot(x, x, precision=lax.Precision.HIGHEST) x = jnp.sin(x * 1e-3) x = jnp.dot(x, x, precision=lax.Precision.HIGHEST) x = jnp.sin(x * 1e-3) return x _, f_lin = api.linearize(f, jnp.ones((2, 2))) jaxpr_text = str(f_lin.func.args[0]) self.assertEqual(jaxpr_text.count(' sin '), 2) self.assertEqual(jaxpr_text.count(' dot_'), 6) jtu.check_grads(f, (jnp.ones((2, 2)),), order=2, modes=['fwd', 'rev']) def test_remat_checkpoint_dots_inside_scan(self): x = jnp.ones((5,)) def f(W): @partial(api.remat, policy=jax.checkpoint_policies.checkpoint_dots) def f(x): x = jnp.sin(jnp.dot(x, W, precision=lax.Precision.HIGHEST)) x = jnp.sin(jnp.dot(x, W, precision=lax.Precision.HIGHEST)) x = jnp.sin(jnp.dot(x, W, precision=lax.Precision.HIGHEST)) return x def body(x, _): return f(x), None return lax.scan(body, x, None, length=2)[0] _, f_vjp = api.vjp(f, jnp.ones((5, 5))) jaxpr_text = str(f_vjp.args[0].func.args[1]) # Two sine calls in the backward pass because while we don't save sines # within the (rematted) body function, we can save the scan carry, which # effectively saves one sine. Three cosines for the Jacoian coefficients. self.assertEqual(jaxpr_text.count(' sin '), 2) self.assertEqual(jaxpr_text.count(' cos '), 3) # Six calls to dot_general in the backward pass because we save the primal # matmuls and only compure the backward pass ones (two for each primal one). self.assertEqual(jaxpr_text.count(' dot_'), 6) jtu.check_grads(api.jit(f), (jnp.ones((5, 5)),), order=2, modes=['fwd', 'rev']) def test_remat_custom_jvp_policy(self): @api.custom_jvp def sin(x): return jnp.sin(x) def sin_jvp(primals, tangents): x, = primals g, = tangents return sin(x), jnp.cos(x) * g sin.defjvp(sin_jvp) @partial(api.remat, policy=jax.checkpoint_policies.checkpoint_dots) def f(x): x = jnp.dot(x, x, precision=lax.Precision.HIGHEST) x = sin(x * 1e-3) x = jnp.dot(x, x, precision=lax.Precision.HIGHEST) x = sin(x * 1e-3) x = jnp.dot(x, x, precision=lax.Precision.HIGHEST) x = sin(x * 1e-3) return x jtu.check_grads(f, (3.,), order=2, modes=['fwd', 'rev']) def g(x): return lax.scan(lambda x, _: (f(x), None), x, None, length=2)[0] jtu.check_grads(g, (3.,), order=2, modes=['fwd', 'rev']) def test_remat_custom_vjp_policy(self): @api.custom_vjp def sin(x): return jnp.sin(x) def sin_fwd(x): return sin(x), x def sin_bwd(x, y_bar): return (jnp.cos(x) * y_bar,) sin.defvjp(sin_fwd, sin_bwd) @partial(api.remat, policy=jax.checkpoint_policies.checkpoint_dots) def f(x): @partial(api.named_call, name="dot") def dot2(y, z): return jnp.dot(x, jnp.dot(y, z, precision=lax.Precision.HIGHEST), precision=lax.Precision.HIGHEST) x = dot2(x, x) x = sin(x * 1e-3) x = dot2(x, x) x = sin(x * 1e-3) x = dot2(x, x) x = sin(x * 1e-3) return x jtu.check_grads(f, (3.,), order=2, modes=['rev']) def g(x): return lax.scan(lambda x, _: (f(x), None), x, None, length=2)[0] jtu.check_grads(g, (3.,), order=2, modes=['rev']) def test_remat_dropvar_policy(self): def f(x): return x, x @partial(api.remat, policy=jax.checkpoint_policies.checkpoint_dots) def g(x): x = api.grad(lambda x: f(x)[0])(x) return x api.grad(g)(3.) def test_remat_custom_jvp_linear_policy(self): @api.custom_jvp def sum(x): return jnp.sum(x, axis=0) @sum.defjvp def sum_jvp(primals, tangents): (x,), (xdot,) = primals, tangents return sum(x), sum(xdot) @partial(api.remat, policy=jax.checkpoint_policies.checkpoint_dots) def f(x): return sum(x) jtu.check_grads(f, (jnp.ones(3),), order=2, modes=['fwd', 'rev']) def g(x): return lax.scan(lambda _, x: (None, f(x)), None, x)[1] jtu.check_grads(g, (jnp.ones((2, 3)),), order=2, modes=['fwd', 'rev']) def test_constants_not_hoisted(self): # The old implementation of remat worked by data dependence, and so # (potentially large) constants would not be rematerialized and could be # wastefully instantiated. This test checks that the newer remat # implementation avoids that. See https://github.com/google/jax/pull/8191. # no residuals from constants created inside jnp.einsum @partial(new_checkpoint, policy=lambda *_, **__: False) def f(x): return jnp.einsum('ii->i', x) res_avals = saved_residuals(f, jnp.ones((2, 2))) self.assertLen(res_avals, 0) # no residuals from jnp.zeros @partial(new_checkpoint, policy=lambda *_, **__: False) def f(x): return jnp.zeros_like(x) * x res_avals = saved_residuals(f, jnp.ones((2, 2))) self.assertLen(res_avals, 0) # no residuals from jnp.zeros, but input must be saved @partial(new_checkpoint, policy=lambda *_, **__: False) def f(x): return jnp.zeros_like(x) * jnp.sin(x) res_avals = saved_residuals(f, jnp.ones((2, 2))) self.assertLen(res_avals, 1) def test_name_denylist(self): def f(x): y = checkpoint_name(jnp.multiply(2., 2.), 'y') z = checkpoint_name(jnp.multiply(2., 2.), 'z') w = checkpoint_name(jnp.multiply(2., 2.), 'w') u = jnp.multiply(2., 2.) return (((x * y) * z) * w) * u policy = jax.checkpoint_policies.save_any_names_but_these('y', 'z', 'w') res = saved_residuals(new_checkpoint(f, policy=policy), 1.) self.assertLen(res, 0) # can't save anything policy = jax.checkpoint_policies.save_any_names_but_these('z', 'w') res = saved_residuals(new_checkpoint(f, policy=policy), 1.) self.assertLen(res, 1) # can save only y policy = jax.checkpoint_policies.save_any_names_but_these('w') res = saved_residuals(new_checkpoint(f, policy=policy), 1.) self.assertLen(res, 2) # can save y and z policy = jax.checkpoint_policies.save_any_names_but_these() res = saved_residuals(new_checkpoint(f, policy=policy), 1.) self.assertLen(res, 3) # can save y, z, and w def test_name_allowlist(self): def f(x): y = checkpoint_name(jnp.multiply(2., 2.), 'y') z = checkpoint_name(jnp.multiply(2., 2.), 'z') w = checkpoint_name(jnp.multiply(2., 2.), 'w') u = jnp.multiply(2., 2.) return (((x * y) * z) * w) * u policy = jax.checkpoint_policies.save_only_these_names('y', 'z', 'w') res = saved_residuals(new_checkpoint(f, policy=policy), 1.) self.assertLen(res, 3) # can save y, z, and w policy = jax.checkpoint_policies.save_only_these_names('z', 'w') res = saved_residuals(new_checkpoint(f, policy=policy), 1.) self.assertLen(res, 2) # can save z and w policy = jax.checkpoint_policies.save_only_these_names('w') res = saved_residuals(new_checkpoint(f, policy=policy), 1.) self.assertLen(res, 1) # can save w policy = jax.checkpoint_policies.save_only_these_names() res = saved_residuals(new_checkpoint(f, policy=policy), 1.) self.assertLen(res, 0) # can't save anything! def test_saved_residuals_utility(self): def f(x, y): x1, x2 = x z = checkpoint_name(jnp.sin(3.), 'z') return z * ((x1 * x2) * y) * np.array([3.]) res = saved_residuals(f, (2., 3.), y=4.) self.assertLen(res, 6) self.assertEqual(res[0][0].shape, (1,)) self.assertEqual(res[0][1], "from a constant") self.assertEqual(res[1][0].shape, ()) self.assertEqual(res[1][1], "from the argument 'x'") self.assertEqual(res[2][0].shape, ()) self.assertEqual(res[2][1], "from the argument 'x'") self.assertEqual(res[3][0].shape, ()) self.assertEqual(res[3][1], "from the argument 'y'") self.assertEqual(res[4][0].shape, ()) self.assertStartsWith(res[4][1], "named 'z'") self.assertEqual(res[5][0].shape, ()) def test_saved_residuals_utility_literals(self): res = saved_residuals(lambda x: x * 2., 3.) self.assertLen(res, 1) self.assertEqual(res[0][0].shape, ()) @parameterized.named_parameters( {"testcase_name": f"{suffix}", "remat": remat} for suffix, remat in [ ('', api.remat), ('_policy', partial(api.remat, policy=lambda *_, **__: False)), ('_new', partial(new_checkpoint, policy=lambda *_, **__: False)), ]) def test_checkpoint_dropvars(self, remat): @remat def f(x): _, x = api.jit(lambda: (x, x))() return x _ = api.grad(f)(3.) # doesn't crash def test_dce_keeps_eqns_with_used_outputs_but_no_used_inputs(self): @new_checkpoint def f(x): c = jax.jit(lambda: 3.)() return c * x _ = jax.grad(f)(3.) # doesn't crash @parameterized.named_parameters( {"testcase_name": f"{suffix}", "remat": remat} for suffix, remat in [ ('', api.remat), ('_policy', partial(api.remat, policy=lambda *_, **__: False)), ('_new', partial(new_checkpoint, policy=lambda *_, **__: False)), ]) def test_unit_dropvar_consistency_regression(self, remat): @partial(remat, policy=lambda *_, **__: False) def f(u, x): x, _ = jax.jit(lambda x: (x, u))(x) return x _ = api.linearize(partial(f, core.unit), 3.) class JaxprTest(jtu.JaxTestCase): def test_scalar_literals(self): jaxpr = api.make_jaxpr(lambda x: x + 2)(42) self.assertLen(jaxpr.jaxpr.constvars, 0) def test_abstract_inputs(self): jaxpr = api.make_jaxpr(lambda x: x + 2.)( types.SimpleNamespace(shape=(), dtype=np.dtype(np.float32))) self.assertEqual(jaxpr.in_avals[0].shape, ()) self.assertEqual(jaxpr.in_avals[0].dtype, np.float32) def test_const(self): def fun(x): return (x, 1., np.zeros(1, dtype=jnp.float32)) expected = "{ lambda a:f32[1]; b:f32[]. let in (b, 1.0, a) }" jaxpr = api.make_jaxpr(fun)(jnp.float32(0.)) self.assertMultiLineStrippedEqual(expected, str(jaxpr)) def test_cond(self): def f(x): return lax.cond(x >= 0., x + 1., lambda xt: xt + x, x + 2., lambda xf: xf - x) expected = """{ lambda ; a:f32[]. let b:bool[] = ge a 0.0 c:f32[] = add a 1.0 d:f32[] = add a 2.0 e:i32[] = convert_element_type[new_dtype=int32 weak_type=False] b f:f32[] = cond[ branches=( { lambda ; g_:f32[] h:f32[] i:f32[] j:f32[]. let k:f32[] = sub j h in (k,) } { lambda ; l:f32[] m_:f32[] n:f32[] o:f32[]. let p:f32[] = add n l in (p,) } ) linear=(False, False, False, False) ] e a a c d in (f,) }""" jaxpr = api.make_jaxpr(f)(jnp.float32(3.)) self.assertMultiLineStrippedEqual(expected, str(jaxpr)) def test_make_jaxpr_static_argnums(self): def f(x, y): return x + y jaxpr = api.make_jaxpr(f, static_argnums=(1,))(2, 3) self.assertIn('3', str(jaxpr)) def test_make_jaxpr_return_shape(self): _, shape_tree = api.make_jaxpr(lambda x: (x + 1, jnp.zeros(2, jnp.float32)), return_shape=True)(np.int32(1)) expected = (api.ShapeDtypeStruct(shape=(), dtype=jnp.int32), api.ShapeDtypeStruct(shape=(2,), dtype=jnp.float32)) self.assertEqual(shape_tree, expected) def test_make_jaxpr_axis_env(self): def f(x): return x - lax.psum(x, 'i') jaxpr = api.make_jaxpr(f, axis_env=[('i', 4)])(2) self.assertIn('psum', str(jaxpr)) def test_make_jaxpr_named(self): def f(x): return x - lax.psum(x, 'i') x = api.ShapeDtypeStruct( shape=(2, 3), dtype=jnp.dtype(jnp.float32), named_shape={'i': 10}) jaxpr = api.make_jaxpr(f, axis_env=[('i', 10)])(x) named_shapes = [v.aval.named_shape for v in jaxpr.jaxpr.eqns[1].invars] self.assertEqual(named_shapes, [{'i': 10}, {}]) @parameterized.parameters(True, False) def test_vjp_reduce_axes_jaxpr(self, gy_batched): def f(w, x): return jnp.sin(jnp.dot(x, w)) w = api.ShapeDtypeStruct( shape=(3, 4), dtype=jnp.float32, named_shape={}) x = api.ShapeDtypeStruct( shape=(3,), dtype=jnp.float32, named_shape={'batch': 2}) gy = api.ShapeDtypeStruct( shape=(4,), dtype=jnp.float32, named_shape={'batch': 2} if gy_batched else {}) # per-example jaxpr, shapes = api.make_jaxpr( lambda w, x, gy: api.vjp(f, w, x)[1](gy), axis_env=[('batch', 2)], return_shape=True)(w, x, gy) expected = (api.ShapeDtypeStruct( shape=(3, 4), dtype=jnp.float32, named_shape={'batch': 2}), x) self.assertEqual(shapes, expected) self.assertNotIn('psum', str(jaxpr)) # reduced jaxpr, shapes = api.make_jaxpr( lambda w, x, gy: api.vjp(f, w, x, reduce_axes=('batch',))[1](gy), axis_env=[('batch', 2)], return_shape=True)(w, x, gy) expected = (w, x) self.assertEqual(shapes, expected) self.assertIn('psum', str(jaxpr)) class CustomJVPTest(jtu.JaxTestCase): def test_basic(self): @api.custom_jvp def f(x): return jnp.sin(x) def f_jvp(primals, tangents): x, = primals g, = tangents return f(x), 2 * jnp.cos(x) * g f.defjvp(f_jvp) x = 3. self.assertAllClose(f(x), jnp.sin(x)) self.assertAllClose(api.jvp(f, (x,), (1.,)), (jnp.sin(x), 2 * jnp.cos(x))) self.assertAllClose(api.grad(f)(x), 2 * jnp.cos(x)) def test_invariance(self): @api.custom_jvp def f(x): return jnp.cos(2 * x) / 2. def f_jvp(primals, tangents): x, = primals g, = tangents return (f(x), 3 * g) f.defjvp(f_jvp) def f2(x): y, _ = api.jvp(f, (x,), (x,)) return y def f3(x): y, _ = api.jvp(f2, (x,), (x,)) return y x = 1. self.assertAllClose(api.jvp(f, (x,), (x,)), api.jvp(f2, (x,), (x,)), check_dtypes=False) self.assertAllClose(api.jvp(f, (x,), (x,)), api.jvp(f3, (x,), (x,)), check_dtypes=False) def test_python_control_flow(self): @api.custom_jvp def f(x): if x > 0: return jnp.sin(x) else: return jnp.cos(x) def f_jvp(primals, tangents): x, = primals g, = tangents if x > 0: return f(x), 2 * g else: return f(x), 3 * g f.defjvp(f_jvp) x = 2. self.assertAllClose(f(x), jnp.sin(x)) self.assertAllClose(f(-x), jnp.cos(-x)) self.assertAllClose(api.jvp(f, (x,), (1.,)), (jnp.sin(x), 2.), check_dtypes=False) self.assertAllClose(api.jvp(f, (-x,), (1.,)), (jnp.cos(-x), 3.), check_dtypes=False) self.assertAllClose(api.grad(f)(x), 2., check_dtypes=False) self.assertAllClose(api.grad(f)(-x), 3., check_dtypes=False) def test_vmap(self): @api.custom_jvp def f(x): assert jnp.ndim(x) == 0 return jnp.sin(x) def f_jvp(primals, tangents): x, = primals g, = tangents assert jnp.ndim(x) == jnp.ndim(g) == 0 return f(x), 2 * jnp.cos(x) * g f.defjvp(f_jvp) x = jnp.arange(3.) xx = jnp.arange(6.).reshape(2, 3) # vmap of f self.assertAllClose(api.vmap(f)(x), jnp.sin(x)) self.assertAllClose(api.vmap(api.vmap(f))(xx), jnp.sin(xx)) # vmap of jvp of f self.assertAllClose(api.vmap(lambda x: api.jvp(f, (x,), (x,)))(x), (jnp.sin(x), 2 * jnp.cos(x) * x)) self.assertAllClose(api.vmap(api.vmap(lambda x: api.jvp(f, (x,), (x,))))(xx), (jnp.sin(xx), 2 * jnp.cos(xx) * xx)) # jvp of vmap of f self.assertAllClose(api.jvp(api.vmap(f), (x,), (x,)), (jnp.sin(x), 2 * jnp.cos(x) * x)) self.assertAllClose(api.jvp(api.vmap(api.vmap(f)), (xx,), (xx,)), (jnp.sin(xx), 2 * jnp.cos(xx) * xx)) # vmap of jvp of vmap of f self.assertAllClose(api.vmap(lambda x: api.jvp(api.vmap(f), (x,), (x,)))(xx), (jnp.sin(xx), 2 * jnp.cos(xx) * xx)) def test_jit(self): @api.custom_jvp def f(x): return jnp.sin(x) def f_jvp(primals, tangents): x, = primals g, = tangents return f(x), 2 * jnp.cos(x) * g f.defjvp(f_jvp) x = 3. # jit self.assertAllClose(api.jit(f)(x), jnp.sin(x)) self.assertAllClose(api.jit(api.jit(f))(x), jnp.sin(x)) # jit of jvp self.assertAllClose(api.jit(lambda x: api.jvp(f, (x,), (x,)))(x), (jnp.sin(x), 2 * jnp.cos(x) * x), check_dtypes=False) # jvp of jit self.assertAllClose(api.jvp(api.jit(f), (x,), (x,)), (jnp.sin(x), 2 * jnp.cos(x) * x), check_dtypes=False) def test_pytrees(self): @api.custom_jvp def f(x): return {'b': jnp.sin(x['a'])} def f_jvp(primals, tangents): x, = primals g, = tangents return f(x), {'b': 2 * jnp.cos(x['a']) * g['a']} f.defjvp(f_jvp) x = {'a': 3.} self.assertAllClose(f(x)['b'], jnp.sin(x['a'])) self.assertAllClose(api.jvp(f, (x,), (x,)), ({'b': jnp.sin(x['a'])}, {'b': 2 * jnp.cos(x['a']) * x['a']}), check_dtypes=False) def test_kwargs(self): # from https://github.com/google/jax/issues/1938 @api.custom_jvp def my_fun(x, y, c=1.): return c * (x + y) def my_jvp(primals, tangents): x, y, c = primals t_x, t_y, t_c = tangents return my_fun(x, y, c), t_c my_fun.defjvp(my_jvp) f = lambda x, y: jnp.square(my_fun(x, y, c=2.)).sum() f(10., 5.) # doesn't crash api.jvp(f, (10., 5.), (1., 1.)) # doesn't crash def test_initial_style(self): @api.custom_jvp def f(x): return 3 * x def f_jvp(primals, tangents): x, = primals g, = tangents return f(x), 2 * g f.defjvp(f_jvp) def foo(x): out, _ = lax.scan(lambda c, _: (f(c), None), x, None, length=1) return out ans = api.grad(foo)(3.) expected = 2. self.assertAllClose(ans, expected, check_dtypes=False) ans = api.grad(api.jit(foo))(3.) expected = 2. self.assertAllClose(ans, expected, check_dtypes=False) ans = api.jit(api.grad(foo))(3.) expected = 2. self.assertAllClose(ans, expected, check_dtypes=False) ans = api.grad(api.grad(foo))(3.) expected = 0. self.assertAllClose(ans, expected, check_dtypes=False) ans = api.grad(api.grad(api.jit(foo)))(3.) expected = 0. self.assertAllClose(ans, expected, check_dtypes=False) ans = api.grad(api.jit(api.grad(foo)))(3.) expected = 0. self.assertAllClose(ans, expected, check_dtypes=False) ans = api.jit(api.grad(api.grad(foo)))(3.) expected = 0. self.assertAllClose(ans, expected, check_dtypes=False) def test_initial_style_vmap(self): @api.custom_jvp def f(x): assert jnp.ndim(x) == 0 return 3 * x def f_jvp(primals, tangents): x, = primals g, = tangents return f(x), 2 * g f.defjvp(f_jvp) def foo(x): out, _ = lax.scan(lambda c, _: (f(c), None), x, None, length=1) return out ans = api.vmap(foo)(jnp.ones(3)) expected = 3. * jnp.ones(3) self.assertAllClose(ans, expected, check_dtypes=False) ans = api.vmap(api.jit(foo))(jnp.ones(3)) expected = 3. * jnp.ones(3) self.assertAllClose(ans, expected, check_dtypes=False) ans = api.jit(api.vmap(foo))(jnp.ones(3)) expected = 3. * jnp.ones(3) self.assertAllClose(ans, expected, check_dtypes=False) ans = api.grad(lambda x: api.vmap(foo)(x).sum())(jnp.ones(3)) expected = 2. * jnp.ones(3) self.assertAllClose(ans, expected, check_dtypes=False) ans = api.grad(lambda x: api.vmap(api.jit(foo))(x).sum())(jnp.ones(3)) expected = 2. * jnp.ones(3) self.assertAllClose(ans, expected, check_dtypes=False) ans = api.grad(lambda x: api.jit(api.vmap(foo))(x).sum())(jnp.ones(3)) expected = 2. * jnp.ones(3) self.assertAllClose(ans, expected, check_dtypes=False) ans = api.grad(api.jit(lambda x: api.vmap(foo)(x).sum()))(jnp.ones(3)) expected = 2. * jnp.ones(3) self.assertAllClose(ans, expected, check_dtypes=False) ans = api.jit(api.grad(lambda x: api.vmap(foo)(x).sum()))(jnp.ones(3)) expected = 2. * jnp.ones(3) self.assertAllClose(ans, expected, check_dtypes=False) def test_initial_style_vmap_with_collective(self): @api.custom_jvp def f(x): return lax.psum(x, 'foo') @f.defjvp def f_jvp(xs, ts): x, = xs t, = ts return lax.psum(x, 'foo'), t def g(x): jaxpr = api.make_jaxpr(f)(x) return core.eval_jaxpr(jaxpr.jaxpr, [], x)[0] v = api.vmap(lambda _, x: g(x), axis_name='foo', in_axes=(0, None), out_axes=None)(jnp.arange(4.), 2.) self.assertAllClose(v, 8.) def test_closed_over_tracers_error_message(self): def f(x): @api.custom_jvp def g(y): return x + y def g_jvp(primals, tangents): return g(x), 2 * primals[0] g.defjvp(g_jvp) return g(1.) self.assertRaises(ad.CustomJVPException, lambda: api.jvp(f, (3.,), (1.,))) self.assertRaises(ad.CustomJVPException, lambda: api.grad(f)(3.)) def test_nondiff_arg(self): @partial(api.custom_jvp, nondiff_argnums=(0,)) def app(f, x): return f(x) def app_jvp(f, primals, tangents): (x,), (t,) = primals, tangents return app(f, x), 3 * t app.defjvp(app_jvp) ans = app(lambda x: 2 * x, 1) expected = 2 self.assertAllClose(ans, expected, check_dtypes=False) ans = api.jvp(lambda x: app(lambda y: 2 * y, x), (1.,), (1.,)) expected = (2., 3.) self.assertAllClose(ans, expected, check_dtypes=False) def test_nondiff_arg_jit_tracer(self): @partial(api.custom_jvp, nondiff_argnums=(0,)) def f(x, y): return x * y def f_jvp(x, primals, tangents): (y,), (t_y,) = primals, tangents return f(x, y), 5 * t_y f.defjvp(f_jvp) @jit def g(x, y): return f(x, y) ans = api.jvp(lambda y: g(2., y), (3.,), (1.,)) expected = (6., 5.) self.assertAllClose(ans, expected, check_dtypes=False) def test_nondiff_arg_hiding_jvp_tracer(self): def f(x): @partial(api.custom_jvp, nondiff_argnums=(0,)) def g(h, x): return h(x) @g.defjvp def g_jvp(h, primals, tangents): x, = primals t, = tangents return g(h, x), 2. * t h = lambda y: x + y # capture x return g(h, x) with self.assertRaisesRegex(ad.CustomJVPException, "Detected differentiation"): api.jvp(f, (2.,), (1.,)) def test_vmap_axes(self): raise unittest.SkipTest("TODO") # TODO(mattjj): write test def test_pmap(self): raise unittest.SkipTest("TODO") # TODO(mattjj): write test def test_missing_jvp_rule_error_message(self): @api.custom_jvp def foo(x): return x ** 2 self.assertRaisesRegex( AttributeError, r"No JVP defined for custom_jvp function foo using defjvp.", lambda: foo(2)) self.assertRaisesRegex( AttributeError, r"No JVP defined for custom_jvp function foo using defjvp.", lambda: api.jvp(foo, (2.,), (1.,))) self.assertRaisesRegex( AttributeError, r"No JVP defined for custom_jvp function foo using defjvp.", lambda: api.grad(foo)(2.)) def test_jvp_rule_inconsistent_pytree_structures_error_message(self): @api.custom_jvp def f(x): return (x**2,) @f.defjvp def foo_jvp(primals, tangents): x, = primals t, = tangents return f(x), [2 * x * t, x] f(2.) # doesn't crash self.assertRaisesRegex( TypeError, re.escape( "Custom JVP rule must produce primal and tangent outputs " "with equal container (pytree) structures, but got " "{} and {} respectively.".format( tree_util.tree_structure((1,)), tree_util.tree_structure([1, 2])) ), lambda: api.jvp(f, (2.,), (1.,))) def test_primal_tangent_aval_disagreement_error_message(self): @api.custom_jvp def f(x): return x ** 2 @f.defjvp def foo_jvp(primals, tangents): x, = primals t, = tangents return f(x), jnp.reshape(t, (1,)) f(2.) # doesn't crash self.assertRaisesRegex( TypeError, re.escape( "Custom JVP rule must produce primal and tangent outputs " "with equal shapes and dtypes, but got float32[] and float32[1] " "respectively."), lambda: api.jvp(f, (jnp.float32(2.),), (jnp.float32(1.),))) def test_jvp_rule_doesnt_return_pair_error_message(self): # https://github.com/google/jax/issues/2516 @api.custom_jvp def f(x): return x ** 2 @f.defjvp def foo_jvp(primals, tangents): x, = primals t, = tangents return t f(2.) # doesn't crash self.assertRaisesRegex( TypeError, re.escape( "Custom JVP rule must produce a pair (list or tuple of length two) " "representing primal and tangent outputs, got 1.0"), lambda: api.jvp(f, (2.,), (1.,))) def test_multiple_rule_invocations(self): @jax.custom_jvp def expit(x): return 1 / (1 + lax.exp(-x)) @expit.defjvp def _expit_jvp(primals, tangents): (x,), (t,) = primals, tangents ans = expit(x) t_out = t * ans * (1 - ans) return ans, t_out def scanned_fun(c, _): return [expit(c[0])] + [c[i-1] + c[i] for i in range(1, len(c))], None def foo(x): c, _ = lax.scan(scanned_fun, [x, 0., 0., 0., 0.], None, length=10) return c[-1] # just make sure these don't crash foo(3.) grad(foo)(3.) grad(lambda x: jax.vmap(foo)(x).sum())(jnp.arange(3.)) def test_hard_stuff(self): arr = jnp.ones((5, 2, 2)) api.jit(jax.vmap(jnp.linalg.det))(arr) # doesn't crash def test_hard_stuff2(self): @jax.custom_jvp def f(x): return lax.tie_in(x, np.zeros(x.shape, x.dtype)) @f.defjvp def f_jvp(primals, tangents): x, = primals t, = tangents return f(x), t # don't crash jax.jit(jax.vmap(f))(jnp.arange(3.)) jax.jit(jax.vmap(jax.grad(f)))(jnp.arange(3.)) jax.jit(jax.grad(lambda x: jax.vmap(f)(x).sum()))(jnp.arange(3.)) jax.grad(lambda x: jax.vmap(f)(x).sum())(jnp.arange(3.)) jax.jvp(jax.vmap(f), (jnp.arange(3.),), (jnp.ones(3),)) def test_hard_stuff3(self): @jax.custom_jvp def relu(x): return jnp.maximum(x, 0) @relu.defjvp def _relu_jvp(primals, tangents): x, = primals t, = tangents return relu(x), lax.select(x > 0, t, lax.full_like(t, 0)) def scanned_fun(c, _): return [relu(c[0])] + [c[i-1] + c[i] for i in range(1, len(c))], None def f(x): c, _ = lax.scan(scanned_fun, [x, 0., 0., 0., 0.], None, length=10) return c[-1] # don't crash jax.jit(jax.vmap(f))(jnp.arange(3.)) jax.jit(jax.vmap(jax.grad(f)))(jnp.arange(3.)) jax.jit(jax.grad(lambda x: jax.vmap(f)(x).sum()))(jnp.arange(3.)) jax.grad(lambda x: jax.vmap(f)(x).sum())(jnp.arange(3.)) jax.jvp(jax.jit(jax.vmap(f)), (jnp.arange(3.),), (jnp.ones(3),)) def test_eval_shape(self): @jax.custom_jvp def expit(x): return 1 / (1 + lax.exp(-x)) @expit.defjvp def _expit_jvp(primals, tangents): (x,), (t,) = primals, tangents ans = expit(x) t_out = t * ans * (1 - ans) return ans, t_out # don't crash api.eval_shape(expit, jnp.ones((2, 3))) api.eval_shape(api.grad(lambda x: expit(x).sum()), jnp.ones((2, 3))) def test_jaxpr_zeros(self): # from https://github.com/google/jax/issues/2657 @api.custom_jvp def f(A, b): return A @ b def f_jvp(primals, tangents): A, b = primals dA, db = tangents z = f(A, b) dz = A @ db + dA @ b return z, dz f.defjvp(f_jvp) def experiment(theta): def step(q, _): z = f(jnp.eye(3), jnp.ones(3) * theta) q += z[0] return q, q q = 0. q, _ = lax.scan(step, q, None, 4) return q grad(experiment)(1.) # doesn't crash def test_linear_in_scan(self): @api.custom_jvp def f(x): return -x @f.defjvp def f_jvp(primals, tangents): x, = primals x_dot, = tangents return f(x), f(x_dot) def foo(x): out, _ = lax.scan(lambda c, _: (f(c), None), x, None, length=1) return out ans = api.grad(foo)(3.) expected = -1. self.assertAllClose(ans, expected, check_dtypes=False) def test_custom_jvps_first_rule_is_none(self): # https://github.com/google/jax/issues/3389 @api.custom_jvp def f(x, y): return x ** 2 * y f.defjvps(None, lambda x_dot, primal_out, x, y: 2 * x * y * x_dot) ans = grad(f, 1)(2., 3.) # doesn't crash expected = 12. self.assertAllClose(ans, expected, check_dtypes=False) def test_concurrent_initial_style(self): # https://github.com/google/jax/issues/3843 def unroll(param, sequence): def scan_f(prev_state, inputs): return prev_state, jax.nn.sigmoid(param * inputs) return jnp.sum(jax.lax.scan(scan_f, None, sequence)[1]) def run(): return jax.grad(unroll)(jnp.array(1.0), jnp.array([1.0])) expected = run() # we just don't want this to crash n_workers = 2 with concurrent.futures.ThreadPoolExecutor(max_workers=n_workers) as e: futures = [] for _ in range(n_workers): futures.append(e.submit(run)) results = [f.result() for f in futures] for ans in results: self.assertAllClose(ans, expected) def test_nondiff_argnums_vmap_tracer(self): # https://github.com/google/jax/issues/3964 @partial(jax.custom_jvp, nondiff_argnums=(0, 2)) def sample(shape, param, seed): return jax.random.uniform(key=seed, shape=shape, minval=param) @sample.defjvp def sample_jvp(shape, seed, primals, tangents): param, = primals dparam, = tangents dparam = jnp.broadcast_to(dparam, shape) samples = sample(shape, param, seed) return samples, samples * dparam # dummy jvp for proof of concept # check these don't crash jax.vmap(lambda seed: sample((2,3), 1., seed))( jax.random.split(jax.random.PRNGKey(1), 10)) jax.jvp(lambda x: sample((2, 3), x, jax.random.PRNGKey(1)), (1.,), (1.,)) def test_fun_with_nested_calls_2(self): def call(f, *args): f = api.custom_jvp(f) f.defjvp(lambda primals, tangents: (f(*primals), sum(tangents))) return f(*args) def fun_with_nested_calls_2(x): def bar(y): def baz(w): q = call(lambda x: y, x) q = q + call(lambda: y) q = q + call(lambda y: w + y, y) q = call(lambda w: call(jnp.sin, x) * y, 1.0) + q return q return api.jit(baz)(x) return call(bar, x) # test these don't crash self.assertAllClose(api.jit(fun_with_nested_calls_2)(3.), fun_with_nested_calls_2(3.)) api.vmap(fun_with_nested_calls_2)(jnp.arange(3.)) def test_closure_with_vmap(self): # https://github.com/google/jax/issues/3822 alpha = np.float32(2.) def sample(seed): @api.custom_jvp def f(alpha): return jax.random.gamma(seed, alpha, shape=[]) @f.defjvp def f_jvp(primal, tangent): alpha = primal dalpha = tangent sample = f(alpha) partial_alpha = lax.random_gamma_grad(alpha, sample) return sample, partial_alpha * dalpha return f(alpha) api.vmap(sample)(jax.random.split(jax.random.PRNGKey(1), 3)) # don't crash @unittest.skipIf(numpy_version == (1, 21, 0), "https://github.com/numpy/numpy/issues/19305") def test_float0(self): @api.custom_jvp def f(x, y): return x, y def f_jvp(primals, _): # we need a defined (non-float0) tangent to trigger the rule return primals, (2., 1) f.defjvp(f_jvp) primals = (2., 3) tangents = (np.ones(()), np.zeros((), float0),) expected_tangents = (2., np.zeros((), float0)) self.assertArraysEqual(api.jvp(f, primals, tangents), (primals, expected_tangents)) @unittest.skipIf(numpy_version == (1, 21, 0), "https://github.com/numpy/numpy/issues/19305") def test_float0_initial_style(self): @api.custom_jvp def f(x, y): return x, y def f_jvp(primals, _): x, y = primals return (x, y), (2., 1) f.defjvp(f_jvp) def foo(x, y): out, _ = lax.scan(lambda c, _: (f(*c), None), (x, y), None, length=1) return out primals = (2., 3) tangents = (np.ones(()), np.zeros((), float0),) expected_tangents = (2., np.zeros((), float0)) self.assertArraysEqual(api.jvp(foo, primals, tangents), (primals, expected_tangents)) def test_remat(self): @api.custom_jvp def f(x): return jnp.sin(x) def f_jvp(primals, tangents): x, = primals g, = tangents return f(x), 2 * jnp.cos(x) * g f.defjvp(f_jvp) @api.remat def g(x): return f(f(x)) ans = g(2.) expected = np.sin(np.sin(2.)) self.assertAllClose(ans, expected, check_dtypes=False) ans = api.grad(g)(2.) expected = 4. * api.grad(lambda x: jnp.sin(jnp.sin(x)))(2.) self.assertAllClose(ans, expected, check_dtypes=False) def test_remat_higher_order(self): @api.custom_jvp def f(x): return jnp.sin(x) def f_jvp(primals, tangents): x, = primals g, = tangents return f(x), 2 * jnp.cos(x) * g f.defjvp(f_jvp) def g(x): return f(f(x)) ans = api.grad(api.grad(api.remat(g)))(2.) expected = api.grad(api.grad(g))(2.) self.assertAllClose(ans, expected, check_dtypes=False) ans = api.grad(api.remat(api.grad(g)))(2.) expected = api.grad(api.grad(g))(2.) self.assertAllClose(ans, expected, check_dtypes=False) ans = api.grad(api.grad(api.grad(api.remat(g))))(2.) expected = api.grad(api.grad(api.grad(g)))(2.) self.assertAllClose(ans, expected, check_dtypes=False) def test_initial_style_vmap_2(self): # This is like test_initial_style_vmap except the primal function closes # over an array constant. y = jnp.array([1., 2., 3.]) @api.custom_jvp def f(x): assert jnp.ndim(x) == 0 return 3 * x * jnp.sum(y) def f_jvp(primals, tangents): x, = primals g, = tangents return f(x), 2 * g f.defjvp(f_jvp) def foo(x): out, _ = lax.scan(lambda c, _: (f(c), None), x, None, length=1) return out ans = api.grad(lambda x: api.vmap(foo)(x).sum())(jnp.ones(3)) expected = 2. * jnp.ones(3) self.assertAllClose(ans, expected, check_dtypes=False) ans = api.grad(lambda x: api.vmap(api.jit(foo))(x).sum())(jnp.ones(3)) expected = 2. * jnp.ones(3) self.assertAllClose(ans, expected, check_dtypes=False) ans = api.grad(lambda x: api.jit(api.vmap(foo))(x).sum())(jnp.ones(3)) expected = 2. * jnp.ones(3) self.assertAllClose(ans, expected, check_dtypes=False) ans = api.grad(api.jit(lambda x: api.vmap(foo)(x).sum()))(jnp.ones(3)) expected = 2. * jnp.ones(3) self.assertAllClose(ans, expected, check_dtypes=False) ans = api.jit(api.grad(lambda x: api.vmap(foo)(x).sum()))(jnp.ones(3)) expected = 2. * jnp.ones(3) self.assertAllClose(ans, expected, check_dtypes=False) def test_custom_jvp_vmap_broadcasting_interaction(self): # https://github.com/google/jax/issues/6452 def f2(y, z): v1 = z v2 = jnp.sum(y) + z return jnp.logaddexp(v1, v2) def f1(y, z): v = api.vmap(lambda _y: f2(_y, z))(y) return jnp.sum(v) y = jnp.ones((3, 2)) f = lambda z: f1(y, z) z = 0.1 val, g = api.value_and_grad(f)(z) self.assertEqual(val.shape, ()) self.assertEqual(g.shape, ()) def test_custom_jvp_vmap_broadcasting_interaction_2(self): # https://github.com/google/jax/issues/5849 @api.custom_jvp def transform(box, R): if jnp.isscalar(box) or box.size == 1: return R * box elif box.ndim == 2: return jnp.einsum('ij,j->i', box, R) raise ValueError() @transform.defjvp def transform_jvp(primals, tangents): box, R = primals dbox, dR = tangents return (transform(box, R), dR + transform(dbox, R)) def periodic_general(box): def displacement_fn(Ra, Rb, **kwargs): _box = kwargs.get('box', box) return transform(_box, Ra - Rb) return displacement_fn N = 250 scalar_box = 1.0 displacement = periodic_general(scalar_box) key = jax.random.PRNGKey(0) R = jax.random.uniform(key, (N, 2)) def energy_fn(box): d = partial(displacement, box=box) d = api.vmap(api.vmap(d, (None, 0)), (0, None)) return jnp.sum(d(R, R) ** 2) self.assertEqual(grad(energy_fn)(scalar_box).shape, ()) def test_custom_jvp_implicit_broadcasting(self): # https://github.com/google/jax/issues/6357 if config.x64_enabled: raise unittest.SkipTest("test only applies when x64 is disabled") @jax.custom_jvp def projection_unit_simplex(x: jnp.ndarray) -> jnp.ndarray: """Projection onto the unit simplex.""" s = 1.0 n_features = x.shape[0] u = jnp.sort(x)[::-1] cssv = jnp.cumsum(u) - s ind = jnp.arange(n_features) + 1 cond = u - cssv / ind > 0 idx = jnp.count_nonzero(cond) threshold = cssv[idx - 1] / idx.astype(x.dtype) return jax.nn.relu(x - threshold) @projection_unit_simplex.defjvp def projection_unit_simplex_jvp(primals, tangents): x, = primals x_dot, = tangents primal_out = projection_unit_simplex(x) supp = primal_out > 0 card = jnp.count_nonzero(supp) tangent_out = supp * x_dot - (jnp.dot(supp, x_dot) / card) * supp return primal_out, tangent_out rng = np.random.RandomState(0) x = rng.rand(5).astype(np.float32) J_rev = jax.jacrev(projection_unit_simplex)(x) J_fwd = jax.jacfwd(projection_unit_simplex)(x) p = projection_unit_simplex(x) support = (p > 0).astype(jnp.int32) cardinality = jnp.count_nonzero(support) J_true = jnp.diag(support) - jnp.outer(support, support) / cardinality self.assertAllClose(J_true, J_fwd) self.assertAllClose(J_true, J_rev) proj = jax.vmap(projection_unit_simplex) def fun(X): return jnp.sum(proj(X) ** 2) rng = np.random.RandomState(0) X = rng.rand(4, 5).astype(np.float32) U = rng.rand(4, 5) U /= np.sqrt(np.sum(U ** 2)) U = U.astype(np.float32) eps = 1e-3 dir_deriv_num = (fun(X + eps * U) - fun(X - eps * U)) / (2 * eps) dir_deriv = jnp.vdot(jax.grad(fun)(X), U) self.assertAllClose(dir_deriv, dir_deriv_num, atol=1e-3) def test_vmap_inside_defjvp(self): # https://github.com/google/jax/issues/3201 seed = 47 key = jax.random.PRNGKey(seed) mat = jax.random.normal(key, (2, 3)) @jax.custom_jvp def f(mat, aux): num_rows, num_cols = mat.shape return jnp.ones((num_rows, 1)) / num_cols @f.defjvp def f_jvp(primals, tangents): mat, aux = primals vec, _ = tangents output = f(*primals) num_rows, num_cols = mat.shape size = num_rows * num_cols # ----- bd_mat = mat.reshape(1, 1, num_rows, num_cols) bd_mat = jnp.tile(bd_mat, reps=(num_rows, num_cols)) bd_mat = bd_mat.reshape(size, num_rows, num_cols) # ----- rowsum = jnp.sum(mat, axis=1, keepdims=True) colsum = jnp.sum(mat, axis=0, keepdims=True) bd_rowsum = jnp.tile(rowsum, reps=(1, num_rows)) bd_colsum = jnp.tile(colsum, reps=(num_cols, 1)) # ----- bd_vec = vec.reshape(size, 1) # ----- def operate(mx, val): buf = 0 for i in range(2): buf = buf + jnp.matmul(mx, bd_colsum) / jnp.power(aux, i) buf = jnp.matmul(bd_rowsum, buf) return buf * val # ----- # Vertorizing will raise shape error bd_buf = jax.vmap(operate, in_axes=(0, 0), out_axes=0)(bd_mat, bd_vec) # ----- bd_buf = bd_buf / aux jvp = jnp.sum(bd_buf, axis=0) jvp = jnp.mean(jvp, axis=1, keepdims=True) # ----- # JVP ends successfully, but still raise an error return (output, jvp) jax.grad(lambda mat, aux: jnp.sum(f(mat, aux)))(mat, 0.5) # doesn't crash def test_custom_jvp_unbroadcasting(self): # https://github.com/google/jax/issues/3056 a = jnp.array([1., 1.]) @jax.custom_jvp def f(x): return a * x @f.defjvp def f_jvp(primals, tangents): x, = primals dx, = tangents return a * x, a * dx shape = grad(lambda x: jnp.sum(f(x)))(jnp.array(1.)).shape self.assertEqual(shape, ()) class CustomVJPTest(jtu.JaxTestCase): def test_basic(self): @api.custom_vjp def f(x): return jnp.sin(x) def f_fwd(x): return f(x), jnp.cos(x) def f_rev(cos_x, g): return (2 * cos_x * g,) f.defvjp(f_fwd, f_rev) x = 3. self.assertAllClose(f(x), jnp.sin(x)) self.assertAllClose(api.grad(f)(x), 2 * jnp.cos(x)) self.assertAllClose(api.value_and_grad(f)(x), (jnp.sin(x), 2 * jnp.cos(x))) def test_invariance(self): @api.custom_vjp def f(x): return jnp.cos(2 * x) / 2. def f_fwd(x): return (f(x), x) def f_rev(x, g): return (g * 3,) f.defvjp(f_fwd, f_rev) def f2(x): y, _ = api.value_and_grad(f)(x) return y def f3(x): y, _ = api.value_and_grad(f2)(x) return y x = 1. self.assertAllClose(f(x), f2(x), check_dtypes=False) self.assertAllClose(f(x), f3(x), check_dtypes=False) self.assertAllClose(api.grad(f)(x), api.grad(f2)(x), check_dtypes=False) self.assertAllClose(api.grad(f)(x), api.grad(f3)(x), check_dtypes=False) def test_python_control_flow(self): @api.custom_vjp def f(x): if x > 0: return jnp.sin(x) else: return jnp.cos(x) def f_fwd(x): if x > 0: return f(x), x else: return f(x), x def f_rev(x, g): if x > 0: return (2 * g,) else: return (3 * g,) f.defvjp(f_fwd, f_rev) x = 2. self.assertAllClose(f(x), jnp.sin(x)) self.assertAllClose(f(-x), jnp.cos(-x)) self.assertAllClose(api.value_and_grad(f)(x), (jnp.sin(x), 2.), check_dtypes=False) self.assertAllClose(api.value_and_grad(f)(-x), (jnp.cos(-x), 3.), check_dtypes=False) def test_vmap(self): @api.custom_vjp def f(x): assert jnp.ndim(x) == 0 return jnp.sin(x) def f_fwd(x): assert jnp.ndim(x) == 0 return f(x), jnp.cos(x) def f_rev(cos_x, g): return (2 * cos_x * g,) f.defvjp(f_fwd, f_rev) x = jnp.arange(3.) xx = jnp.arange(6.).reshape(2, 3) # vmap of f self.assertAllClose(api.vmap(f)(x), jnp.sin(x)) self.assertAllClose(api.vmap(api.vmap(f))(xx), jnp.sin(xx)) # vmap of grad of f self.assertAllClose(api.vmap(api.grad(f))(x), 2 * jnp.cos(x)) self.assertAllClose(api.vmap(api.value_and_grad(f))(x), (jnp.sin(x), 2 * jnp.cos(x))) self.assertAllClose(api.vmap(api.vmap(api.grad(f)))(xx), 2 * jnp.cos(xx)) self.assertAllClose(api.vmap(api.vmap(api.value_and_grad(f)))(xx), (jnp.sin(xx), 2 * jnp.cos(xx))) # grad of vmap of f self.assertAllClose(api.grad(lambda x: api.vmap(f)(x).sum())(x), 2 * jnp.cos(x)) self.assertAllClose(api.grad(lambda x: api.vmap(api.vmap(f))(x).sum())(xx), 2 * jnp.cos(xx)) # vmap of grad of vmap of f self.assertAllClose(api.vmap(api.grad(lambda x: api.vmap(f)(x).sum()))(xx), 2 * jnp.cos(xx)) def test_jit(self): @api.custom_vjp def f(x): return jnp.sin(x) def f_fwd(x): return f(x), jnp.cos(x) def f_rev(cos_x, g): return (2 * cos_x * g,) f.defvjp(f_fwd, f_rev) x = 3. # jit self.assertAllClose(api.jit(f)(x), jnp.sin(x)) self.assertAllClose(api.jit(api.jit(f))(x), jnp.sin(x)) # jit of grad self.assertAllClose(api.jit(api.grad(f))(x), 2 * jnp.cos(x), check_dtypes=False) # grad of jit self.assertAllClose(api.grad(api.jit(f))(x), 2 * jnp.cos(x), check_dtypes=False) def test_pytrees(self): @api.custom_vjp def f(x): return {'b': jnp.sin(x['a'])} def f_fwd(x): return f(x), {'r': jnp.cos(x['a'])} def f_bwd(res, g): cos_x = res['r'] return ({'a': 2 * cos_x * g['b']},) f.defvjp(f_fwd, f_bwd) x = {'a': 3.} self.assertAllClose(f(x)['b'], jnp.sin(x['a'])) self.assertAllClose(api.grad(lambda x: f(x)['b'])(x), {'a': 2 * jnp.cos(x['a'])}) def test_jvp_error(self): @api.custom_vjp def f(x): return jnp.sin(x) def f_fwd(x): return f(x), jnp.cos(x) def f_rev(cos_x, g): return (2 * cos_x * g,) f.defvjp(f_fwd, f_rev) self.assertRaisesRegex( TypeError, r"can't apply forward-mode autodiff $jvp$ to a custom_vjp function.", lambda: api.jvp(f, (3.,), (1.,))) self.assertRaisesRegex( TypeError, r"can't apply forward-mode autodiff $jvp$ to a custom_vjp function.", lambda: api.jvp(api.vmap(f), (jnp.arange(3.),), (jnp.ones(3),))) self.assertRaisesRegex( TypeError, r"can't apply forward-mode autodiff $jvp$ to a custom_vjp function.", lambda: api.jvp(jit(f), (3.,), (1.,))) def test_kwargs(self): # from https://github.com/google/jax/issues/1938 @api.custom_vjp def my_fun(x, y, c=1.): return c * (x + y) my_fun.defvjp(lambda x, y, c=1.: (my_fun(c, y, c), None), lambda _, g: (g, g, g)) f = lambda x, y: jnp.square(my_fun(x, y, c=2.)).sum() f(10., 5.) # doesn't crash api.grad(f)(10., 5.) # doesn't crash def test_initial_style(self): @api.custom_vjp def f(x): return jnp.sin(x) def f_fwd(x): return f(x), jnp.cos(x) def f_rev(cos_x, g): return (2 * cos_x * g,) f.defvjp(f_fwd, f_rev) def foo(x): out, _ = lax.scan(lambda c, _: (f(c), None), x, None, length=1) return out ans = api.grad(foo)(3.) expected = 2. * jnp.cos(3.) self.assertAllClose(ans, expected, check_dtypes=False) ans = api.grad(api.grad(foo))(3.) expected = -2. * jnp.sin(3.) self.assertAllClose(ans, expected) def test_initial_style_vmap(self): @api.custom_vjp def f(x): assert jnp.ndim(x) == 0 return 3 * x def f_fwd(x): return f(x), jnp.cos(x) def f_rev(cos_x, g): return (2 * cos_x * g,) f.defvjp(f_fwd, f_rev) def foo(x): out, _ = lax.scan(lambda c, _: (f(c), None), x, None, length=1) return out ans = api.vmap(foo)(jnp.arange(3.)) expected = 3. * jnp.arange(3.) self.assertAllClose(ans, expected, check_dtypes=False) ans = api.grad(lambda x: api.vmap(foo)(x).sum())(jnp.arange(3.)) expected = 2. * jnp.cos(jnp.arange(3.)) self.assertAllClose(ans, expected, check_dtypes=False) def test_nondiff_arg(self): @partial(api.custom_vjp, nondiff_argnums=(0,)) def app(f, x): return f(x) def app_fwd(f, x): return app(f, x), jnp.cos(x) def app_rev(f, cos_x, g): return (cos_x * g,) app.defvjp(app_fwd, app_rev) ans = app(lambda x: 2 * x, 1) expected = 2 self.assertAllClose(ans, expected, check_dtypes=False) ans = api.value_and_grad(lambda x: app(lambda y: 2 * y, x))(1.) expected = (2., jnp.cos(1.)) self.assertAllClose(ans, expected, check_dtypes=False) def test_closed_over_tracer(self): # This test is similar to test_nondiff_arg_tracer except it uses lexical # closure rather than the nondiff_argnums mechanism. We decided to disallow # tracers in nondiff_argnums to greatly simplify bookkeeping while still # supporting the cases for which it is necessary. def outer(x): @api.custom_vjp def f(y): return x * y def f_fwd(y): return f(y), jnp.cos(y) def f_rev(cos_y, g): return (cos_y * g,) f.defvjp(f_fwd, f_rev) return f @jit def g(x, y): return outer(x)(y) ans = g(2, 3.) expected = 6. self.assertAllClose(ans, expected, check_dtypes=False) ans = api.grad(g, 1)(2., 3.) expected = jnp.cos(3.) self.assertAllClose(ans, expected, check_dtypes=False) def test_closed_over_tracer2(self): def outer(x): @api.custom_vjp def f(y): return x * y def f_fwd(y): return f(y), jnp.cos(y) def f_rev(cos_y, g): return (cos_y * g,) f.defvjp(f_fwd, f_rev) return f @api.vmap def g(x): return outer(x)(3.) ans = g(np.arange(3.)) expected = np.arange(3.) * 3 self.assertAllClose(ans, expected, check_dtypes=False) def test_closed_over_tracer3(self): def outer(x): @api.custom_vjp def f(y): return x * y def f_fwd(y): return f(y), (x, jnp.cos(y)) def f_rev(res, g): x, cos_y = res return (cos_y * g * x,) f.defvjp(f_fwd, f_rev) return api.grad(f) @api.vmap def g(x): return outer(x)(3.) ans = g(np.arange(3.)) expected = np.cos(3.) * np.arange(3.) self.assertAllClose(ans, expected, check_dtypes=False) def test_nondiff_arg_tracer_error(self): # This is similar to the old (now skipped) test_nondiff_arg_tracer, except # we're testing for the error message that that usage pattern now raises. @partial(api.custom_vjp, nondiff_argnums=(0,)) def f(x, y): return x * y def f_fwd(x, y): return f(x, y), jnp.cos(y) def f_rev(x, cos_y, g): return (cos_y * g,) f.defvjp(f_fwd, f_rev) @jit def g(x, y): return f(x, y) with self.assertRaisesRegex(UnexpectedTracerError, "custom_vjp"): _ = g(2, 3.) with self.assertRaisesRegex(UnexpectedTracerError, "custom_vjp"): _ = api.grad(g, 1)(2., 3.) def test_vmap_axes(self): raise unittest.SkipTest("TODO") # TODO(mattjj): write test def test_pmap(self): raise unittest.SkipTest("TODO") # TODO(mattjj): write test def test_missing_vjp_rule_error(self): @api.custom_vjp def foo(x): return x ** 2 self.assertRaisesRegex( AttributeError, r"No VJP defined for custom_vjp function foo using defvjp.", lambda: foo(2)) self.assertRaisesRegex( AttributeError, r"No VJP defined for custom_vjp function foo using defvjp.", lambda: api.grad(foo)(2.)) def test_vjp_rule_inconsistent_pytree_structures_error(self): @api.custom_vjp def f(x): return x def foo_fwd(x): return x, None def foo_bwd(_, g): return (g, g) f.defvjp(foo_fwd, foo_bwd) f(2) # doesn't crash self.assertRaisesRegex( TypeError, re.escape( "Custom VJP rule must produce an output with the same container " "(pytree) structure as the args tuple of the primal function, " "and in particular must produce a tuple of length equal to the " "number of arguments to the primal function, but got VJP output " "structure {} for primal input structure {}.".format( tree_util.tree_structure((1, 1)), tree_util.tree_structure((1,))) ), lambda: api.grad(f)(2.)) def test_vjp_bwd_returns_non_tuple_error(self): @api.custom_vjp def f(x): return x def foo_fwd(x): return x, None def foo_bwd(_, g): return 2. * g # Should be a tuple f.defvjp(foo_fwd, foo_bwd) with self.assertRaisesRegex(TypeError, "Custom VJP rule .* must produce a tuple"): api.grad(f)(3.) def test_issue2511(self): arr = jnp.ones((5, 2, 2)) foo = lambda x: api.vmap(jnp.linalg.det, (0,))(x) api.jit(foo)(arr) # doesn't crash def test_lowering_out_of_traces(self): # https://github.com/google/jax/issues/2578 class F(collections.namedtuple("F", ["a"])): def __call__(self, x): return jax.nn.relu(self.a) * x @jax.jit def g(f, x): return f(x) jax.grad(g, argnums=(1,))(F(2.0), 0.) # doesn't crash def test_clip_gradient(self): # https://github.com/google/jax/issues/2784 @api.custom_vjp def _clip_gradient(lo, hi, x): return x # identity function when not differentiating def clip_gradient_fwd(lo, hi, x): return x, (lo, hi,) def clip_gradient_bwd(res, g): lo, hi = res return (None, None, jnp.clip(g, lo, hi),) _clip_gradient.defvjp(clip_gradient_fwd, clip_gradient_bwd) def clip_gradient(x): lo = -0.1 hi = x + 0.1 return _clip_gradient(lo, hi, x) g = jax.grad(clip_gradient)(0.1) # doesn't crash self.assertAllClose(g, jnp.array(0.2)) def test_nestable_vjp(self): # Verify that https://github.com/google/jax/issues/3667 is resolved. def f(x): return x ** 2 @api.custom_vjp def g(x): return f(x) def g_fwd(x): y, f_vjp = api.vjp(f, x) return y, f_vjp def g_bwd(f_vjp, y_bar): return f_vjp(y_bar) g.defvjp(g_fwd, g_bwd) # Check that VJP can be nested in simple situations. For this to pass, # vjp has to return a PyTree. _, g_vjp = api.vjp(g, 1.0) y, = g_vjp(1.0) self.assertAllClose(y, jnp.array(2.0)) # Check that VJP can be nested in complex situations. For this to pass, # vjp can't treat the closed-over tracer x as a static argument. @jit def z(x): _, g_vjp = api.vjp(g, x) return g_vjp y, = z(1.0)(3.0) self.assertAllClose(y, jnp.array(6.0)) def test_initial_style_vmap_2(self): # https://github.com/google/jax/issues/4173 x = jnp.ones((10, 3)) # Create the custom function @api.custom_vjp def custom_fun(x): return x.sum() def forward(x): return x.sum(), (jnp.ones_like(x),) def backward(res, g): return g * res[0], custom_fun.defvjp(forward, backward) def train_fun(x): def summed_fun(x): return api.vmap(custom_fun)(x).sum() return api.grad(summed_fun)(x) def scan_body(carry, inputs): x = carry return carry, train_fun(x) scan_range = jnp.arange(4) lax.scan(scan_body, x, scan_range) # don't crash def test_initial_style_vmap_3(self): # This is like test_initial_style_vmap except the primal function closes # over an array constant. y = jnp.array([1., 2., 3.]) @api.custom_vjp def f(x): assert jnp.ndim(x) == 0 return 3 * x * jnp.sum(y) def f_fwd(x): return f(x), jnp.cos(x) def f_rev(cos_x, g): return (2 * cos_x * g,) f.defvjp(f_fwd, f_rev) def foo(x): out, _ = lax.scan(lambda c, _: (f(c), None), x, None, length=1) return out ans = api.vmap(foo)(jnp.arange(3.)) expected = 3. * jnp.arange(3.) * 6 self.assertAllClose(ans, expected, check_dtypes=False) ans = api.grad(lambda x: api.vmap(foo)(x).sum())(jnp.arange(3.)) expected = 2. * jnp.cos(jnp.arange(3.)) self.assertAllClose(ans, expected, check_dtypes=False) def test_initial_style_vmap_with_collective(self): @api.custom_vjp def f(x): return lax.psum(x, 'foo') def f_fwd(x): return lax.psum(x, 'foo'), None def f_bwd(res, dx): return dx f.defvjp(f_fwd, f_bwd) def g(x): jaxpr = api.make_jaxpr(f)(x) return core.eval_jaxpr(jaxpr.jaxpr, [], x)[0] out = api.vmap(lambda _, x: g(x), axis_name='foo', in_axes=(0, None), out_axes=None)(jnp.arange(4.), 2.) self.assertAllClose(out, 8.) def test_bwd_closes_over_tracer(self): def f(y): @jax.custom_vjp def f(x): return 2. * jnp.sin(x) def fwd(x): return f(x), () def bwd(_, g): return (2. * jnp.cos(y) * g,) # capture! f.defvjp(fwd, bwd) return jax.grad(f)(1.) ans = jax.jit(f)(2.) self.assertAllClose(ans, 2. * jnp.cos(2.)) ans = jax.vmap(f)(jnp.arange(3.)) self.assertAllClose(ans, 2. * jnp.cos(jnp.arange(3.))) ans = jax.jit(jax.vmap(f))(jnp.arange(3.)) self.assertAllClose(ans, 2. * jnp.cos(jnp.arange(3.))) ans = jax.vmap(jax.jit(f))(jnp.arange(3.)) self.assertAllClose(ans, 2. * jnp.cos(jnp.arange(3.))) ans = jax.grad(f)(4.) self.assertAllClose(ans, -2. * jnp.sin(4.)) def test_fwd_closes_over_tracer(self): def f(y): @jax.custom_vjp def f(x): return 2. * jnp.sin(x) def fwd(x): return f(x), y def bwd(y, g): return (2. * jnp.cos(y) * g,) # capture! f.defvjp(fwd, bwd) return jax.grad(f)(1.) ans = jax.jit(f)(2.) self.assertAllClose(ans, 2. * jnp.cos(2.)) ans = jax.vmap(f)(jnp.arange(3.)) self.assertAllClose(ans, 2. * jnp.cos(jnp.arange(3.))) ans = jax.jit(jax.vmap(f))(jnp.arange(3.)) self.assertAllClose(ans, 2. * jnp.cos(jnp.arange(3.))) ans = jax.vmap(jax.jit(f))(jnp.arange(3.)) self.assertAllClose(ans, 2. * jnp.cos(jnp.arange(3.))) ans = jax.grad(f)(4.) self.assertAllClose(ans, -2. * jnp.sin(4.)) @unittest.skipIf(numpy_version == (1, 21, 0), "https://github.com/numpy/numpy/issues/19305") def test_float0(self): @api.custom_vjp def f(x, _): return x def f_fwd(x, _): # we need a defined (non-float0) tangent to trigger the rule return x, (2., 1) def f_rev(*_): return (2., 1) f.defvjp(f_fwd, f_rev) x = 2. y = 3 self.assertEqual(api.grad(f, allow_int=True, argnums=(0, 1))(x, y), (2., np.zeros(shape=(), dtype=float0))) @unittest.skipIf(numpy_version == (1, 21, 0), "https://github.com/numpy/numpy/issues/19305") def test_float0_initial_style(self): @api.custom_vjp def f(x): return x def f_fwd(x): return x, (2., x) def f_rev(*_): return ((2., 1),) f.defvjp(f_fwd, f_rev) def foo(x, y): out, _ = lax.scan(lambda c, _: (f(c), None), (x, y), None, length=1) return out[0] x = 2. y = 3 self.assertEqual(api.grad(foo, allow_int=True, argnums=(0, 1))(x, y), (2., np.zeros(shape=(), dtype=float0))) def test_remat(self): @api.custom_vjp def f(x): return jnp.sin(x) def f_fwd(x): return f(x), jnp.cos(x) def f_rev(cos_x, g): return (2 * cos_x * g,) f.defvjp(f_fwd, f_rev) @api.remat def g(x): return f(f(x)) ans = g(2.) expected = np.sin(np.sin(2.)) self.assertAllClose(ans, expected, check_dtypes=False) ans = api.grad(g)(2.) expected = 4. * api.grad(lambda x: jnp.sin(jnp.sin(x)))(2.) self.assertAllClose(ans, expected, check_dtypes=False) def test_remat_higher_order(self): @api.custom_vjp def f(x): return jnp.sin(x) def f_fwd(x): return f(x), jnp.cos(x) def f_rev(cos_x, g): return (2 * cos_x * g,) f.defvjp(f_fwd, f_rev) def g(x): return f(f(x)) ans = api.grad(api.grad(api.remat(g)))(2.) expected = api.grad(api.grad(g))(2.) self.assertAllClose(ans, expected, check_dtypes=False) ans = api.grad(api.remat(api.grad(g)))(2.) expected = api.grad(api.grad(g))(2.) self.assertAllClose(ans, expected, check_dtypes=False) ans = api.grad(api.grad(api.grad(api.remat(g))))(2.) expected = api.grad(api.grad(api.grad(g)))(2.) self.assertAllClose(ans, expected, check_dtypes=False) def test_bwd_nones(self): @api.custom_vjp def f(x, y): return x * jnp.sin(y) def f_fwd(x, y): return f(x, y), jnp.cos(y) def f_rev(cos, g): return (None, 2 * cos * g) f.defvjp(f_fwd, f_rev) ans = api.grad(lambda x: f(x, x))(3.) expected = 2 * jnp.cos(3.) self.assertAllClose(ans, expected, check_dtypes=False) def test_bwd_nones_vmap(self): @api.custom_vjp def f(x, y): return x * jnp.sin(y) def f_fwd(x, y): return f(x, y), jnp.cos(y) def f_rev(cos, g): return (None, 2 * cos * g) f.defvjp(f_fwd, f_rev) ans = api.grad(lambda x: api.vmap(f)(x, x).sum())(jnp.arange(3.)) expected = 2 * jnp.cos(jnp.arange(3.)) self.assertAllClose(ans, expected, check_dtypes=False) def test_bwd_nones_pytree(self): @api.custom_vjp def f(xs, y): x1, x2 = xs return x1 * x2 * jnp.sin(y) def f_fwd(xs, y): return f(xs, y), jnp.cos(y) def f_rev(cos, g): return (None, 2 * cos * g) f.defvjp(f_fwd, f_rev) ans = api.grad(lambda x: f((x, x), x))(3.) expected = 2 * jnp.cos(3.) self.assertAllClose(ans, expected, check_dtypes=False) def test_custom_vjp_closure_4521(self): # https://github.com/google/jax/issues/4521 @api.custom_vjp def g(x, y): return None def g_fwd(x, y): return None, y def g_bwd(residuals, z_bar): assert False g.defvjp(g_fwd, g_bwd) def f(xs, y): v_g = api.vmap(g, in_axes=(0, None), out_axes=None) v_g(xs, y) def scan_body(xs, _): y = jnp.zeros(1) _, vjp_f = api.vjp(f, xs, y) vjp_f(None) return xs, None lax.scan(scan_body, jnp.ones(5), None, 100) # doesn't crash def test_float0_bwd_none(self): @api.custom_vjp def f(i, x): return jnp.sin(x) def f_fwd(i, x): return f(i, x), jnp.cos(x) def f_rev(cos_x, g): return (None, 2 * cos_x * g) f.defvjp(f_fwd, f_rev) ans = api.grad(f, 1)(jnp.array([1, 2]), 3.) # doesn't crash expected = 2 * jnp.cos(3.) self.assertAllClose(ans, expected, check_dtypes=False) def test_custom_gradient(self): @api.custom_gradient def f(x): return x ** 2, lambda g: (g * x,) self.assertAllClose(f(3.), 9., check_dtypes=False) self.assertAllClose(api.grad(f)(3.), 3., check_dtypes=False) self.assertAllClose(api.grad(api.grad(f))(3.), 1., check_dtypes=False) def test_custom_gradient_2(self): @api.custom_gradient def f(x, y): return x * y, lambda g: (y, x) self.assertAllClose(f(3., 4.), 12., check_dtypes=False) self.assertAllClose(api.grad(f, argnums=(0, 1))(3., 4.), (4., 3.), check_dtypes=False) def test_custom_gradient_3(self): @api.custom_gradient def f(x): vjp = lambda g: (jnp.cos(x) * jnp.array([3., 4., 5.]),) return jnp.sum(jnp.sin(x)), vjp self.assertAllClose(f(jnp.arange(3)), jnp.sum(jnp.sin(jnp.arange(3.))), check_dtypes=False) self.assertAllClose( api.grad(f)(jnp.arange(3.)), api.grad(lambda x: jnp.sum(jnp.sin(x)))(jnp.arange(3.)) * jnp.array([3., 4., 5.]), check_dtypes=False) def test_custom_gradient_can_return_singleton_value_in_vjp(self): @api.custom_gradient def f(x): return x ** 2, lambda g: g * x self.assertAllClose(f(3.), 9., check_dtypes=False) self.assertAllClose(api.grad(f)(3.), 3., check_dtypes=False) self.assertAllClose(api.grad(api.grad(f))(3.), 1., check_dtypes=False) def test_closure_convert(self): def cos_after(fn, x): converted_fn, aux_args = api.closure_convert(fn, x) self.assertLessEqual(len(aux_args), 1) return _cos_after(converted_fn, x, *aux_args) @partial(api.custom_vjp, nondiff_argnums=(0,)) def _cos_after(fn, x, *args): return jnp.cos(fn(x, *args)) def fwd(fn, x, *args): y = _cos_after(fn, x, *args) return y, (x, args) def rev(fn, res, g): x, args = res x_bar = 17. * x args_bars = [42. * a for a in args] return (x_bar, *args_bars) _cos_after.defvjp(fwd, rev) def dist(c, x): return jnp.sum((x - c) ** 2.) def solve(c, x): def closure(x): return dist(c, x) return cos_after(closure, x) c, x = 2. * jnp.ones(2), jnp.ones(2) expected = jnp.cos(dist(c, x)) self.assertAllClose(solve(c, x), expected, check_dtypes=False) g_c, g_x = api.grad(solve, argnums=(0, 1))(c, x) self.assertAllClose(g_c, 42. * c, check_dtypes=False) self.assertAllClose(g_x, 17. * x, check_dtypes=False) def test_closure_convert_mixed_consts(self): # Like test_closure_convert, but close over values that # participate in AD as well as values that do not. # See https://github.com/google/jax/issues/6415 def cos_after(fn, x): converted_fn, aux_args = api.closure_convert(fn, x) self.assertLessEqual(len(aux_args), 1) return _cos_after(converted_fn, x, *aux_args) @partial(api.custom_vjp, nondiff_argnums=(0,)) def _cos_after(fn, x, *args): return jnp.cos(fn(x, *args)) def fwd(fn, x, *args): y = _cos_after(fn, x, *args) return y, (x, args) def rev(fn, res, g): x, args = res x_bar = 17. * x args_bars = [42. * a for a in args] return (x_bar, *args_bars) _cos_after.defvjp(fwd, rev) def dist(c, s, x): return jnp.sum(s * (x - c) ** 2.) def solve(c, s, x): def closure(x): return dist(c, s, x) return cos_after(closure, x) c, s, x = 2. * jnp.ones(2), 3. * jnp.ones(2), jnp.ones(2) expected = jnp.cos(dist(c, s, x)) self.assertAllClose(solve(c, s, x), expected, check_dtypes=False) g_c, g_x = api.grad(solve, argnums=(0, 2))(c, s, x) self.assertAllClose(g_c, 42. * c, check_dtypes=False) self.assertAllClose(g_x, 17. * x, check_dtypes=False) def test_float0_cotangents_automatically_handled(self): @jax.custom_vjp def f(x, y): return x def f_fwd(x, y): return x, None def f_bwd(_, zbar): return (0., 1) f.defvjp(f_fwd, f_bwd) jax.jit(lambda x: jax.vjp(f, 0., x)[1](1.))(1) # doesn't crash class CustomTransposeTest(jtu.JaxTestCase): def transpose(self, f, x_example): def transposed(y): x, = api.linear_transpose(f, x_example)(y) return x return transposed def test_linear_call(self): def f(x, y): def fn(r, x): return x / r def tp(r, t): return t / r return x + api.linear_call(fn, tp, y, x) def f_ref(x, y): return x + x / y x = jnp.ones(2) * 6. y = jnp.ones(2) * 3. self.assertAllClose(f(x, y), f_ref(x, y)) f1 = lambda x: f(x, y) f1_ref = lambda x: f_ref(x, y) self.assertAllClose(self.transpose(f1, x)(x), self.transpose(f1_ref, x)(x)) def test_linear_call_incorrect_transpose(self): def f(x, y): def fn(r, x): return x / r def tp(r, t): return t / (2. * r) # nb: not the true transpose return x + api.linear_call(fn, tp, y, x) def f_ref(x, y): return x + x / y x = jnp.ones(2) * 6. y = jnp.ones(2) * 3. self.assertAllClose(f(x, y), f_ref(x, y)) f1 = lambda x: f(x, y) f1_ref = lambda x: f_ref(x, 2. * y) # nb: double the reference divisor self.assertAllClose(self.transpose(f1, x)(x), self.transpose(f1_ref, x)(x)) def test_linear_call_transpose_transpose_transpose(self): def fn(r, x): return x / r def tp(r, t): return t / (2. * r) # nb: untrue transpose def f_(x, y): return x + api.linear_call(fn, tp, y, x) x = jnp.ones(2) * 6. y = jnp.ones(2) * 3. f = lambda x: f_(x, y) ft = self.transpose(f, x) ftt = self.transpose(ft, x) fttt = self.transpose(ftt, x) self.assertAllClose(ft(x), x + tp(y, x)) self.assertAllClose(f(x), ftt(x)) self.assertAllClose(ft(x), fttt(x)) def test_linear_call_scalar_to_vector(self): def f(c, x): def fn(_, x): return [x, x] def tp(_, t): t1, t2 = t return t1 + t2 return api.linear_call(fn, tp, (), c * x) def f_ref(c, x): return [c * x, c * x] c, x = 2., 3. t = [4., 5.] self.assertAllClose(f(c, x), f_ref(c, x)) self.assertAllClose(self.transpose(partial(f, c), x)(t), self.transpose(partial(f_ref, c), x)(t)) def test_linear_call_nested(self): # identity function with an untrue transpose of 0 def id_(x): def f(_, x): return x def t(_, t): return 0. return api.linear_call(f, t, (), x) # identity function with an untrue transpose of 7, and where both # forward and transpose have custom transpositions that should # never end up invoked. def f(x): def f_(_, x): return id_(x) def t_(_, t): return id_(7.) return api.linear_call(f_, t_, (), x) x = 5. id_t = self.transpose(id_, x) id_tt = self.transpose(id_t, x) ft = self.transpose(f, x) ftt = self.transpose(ft, x) fttt = self.transpose(ftt, x) self.assertAllClose(id_(x), x) self.assertAllClose(id_t(x), 0.) self.assertAllClose(id_tt(x), x) self.assertAllClose(f(x), x) self.assertAllClose(ft(x), 7.) self.assertAllClose(ftt(x), x) self.assertAllClose(fttt(x), 7.) def test_linear_call_jit(self): def f(x, y): def fn(r, x): return x / r def tp(r, t): return t / r return x + api.linear_call(fn, tp, y, x) x = jnp.ones(2) * 6. y = jnp.ones(2) * 3. self.assertAllClose(f(x, y), jax.jit(f)(x, y)) f1 = lambda x: f(x, y) self.assertAllClose(self.transpose(f1, x)(x), jax.jit(self.transpose(f1, x))(x)) class InvertibleADTest(jtu.JaxTestCase): @jtu.ignore_warning(message="Values that an @invertible function closes") def test_invertible_basic(self): def f(x): return lax.mul(lax.mul(lax.exp(x), 4.), x) finv = jax.invertible(f) x = jnp.ones((5,)) jaxpr = jax.make_jaxpr(lambda p, ct: jax.vjp(finv, p)[1](ct))(x, x) # expected = """ # { lambda ; a b. # let c = exp a # d = mul c 4.0 # e = mul d a # f = mul b a # g = div e a # h = mul b g # i = mul f 4.0 # j = div g 4.0 # k = mul f j # _ = reduce_sum[ axes=(0,) ] k # _ = log j # l = mul i j # m = add_any h l # in (m,) } # """ # self.assertMultiLineStrippedEqual(expected, str(jaxpr)) # no jaxpr test self.assertIn('div', str(jaxpr)) self.assertIn('log', str(jaxpr)) # assumes no DCE self.assertAllClose(jax.value_and_grad(lambda x: np.sum(f(x)))(x), jax.value_and_grad(lambda x: np.sum(finv(x)))(x), check_dtypes=True) def test_invertible_blocks(self): # NB: This is the reversible ResNet block def mk_reversible_block(f, g): @jax.custom_ivjp def rev_block(x1, x2): y1 = f(x2) + x1 y2 = g(y1) + x2 return y1, y2 @rev_block.defivjp def rev_block_ivjp(xs, ys, dys): (y1, y2) = ys (dy1, dy2) = dys dgo, dx2 = dy2, dy2 go, gvjp = jax.vjp(g, y1) dy1 += gvjp(dgo)[0] del gvjp x2 = y2 - go dfo, dx1 = dy1, dy1 fo, fvjp = jax.vjp(f, x2) dx2 += fvjp(dfo)[0] del fvjp x1 = y1 - fo return (x1, x2), (dx1, dx2) return rev_block rev_block = mk_reversible_block(jnp.sin, jnp.cos) def g(x1, x2): for i in range(2): x1, x2 = rev_block(x1, x2) return x1, x2 def reduce(f, x1, x2): y1, y2 = f(x1, x2) return np.sum(y1) + np.sum(y2) x = np.ones((1,)) # FIXME: This breaks when argnums is left as default (i.e. 0), because JVP prunes # zero tangents from call primitives. self.assertAllClose(jax.value_and_grad(partial(reduce, jax.invertible(g)), argnums=(0, 1))(x, x + 2), jax.value_and_grad(partial(reduce, g), argnums=(0, 1))(x, x + 2), check_dtypes=True) def test_invertible_partial_diff(self): # Check that we don't have to differentiate with respect to inputs # of the invertible function. def f(x, y): return lax.mul(lax.mul(lax.exp(x), 4.), x), lax.add(y, 4.) finv = jax.invertible(f) o = np.ones((5,)) self.assertAllClose(jax.value_and_grad(lambda x: np.sum(f(x, o)[0]))(o), jax.value_and_grad(lambda x: np.sum(finv(x, o)[0]))(o), check_dtypes=True) def test_invertible_pytree(self): def f(x, y): return lax.add(lax.mul(lax.exp(x[0]), x[1]), y) finv = jax.invertible(f) o = np.ones((5,)) self.assertAllClose(jax.value_and_grad(lambda x: np.sum(f((x, x), x)[0]))(o), jax.value_and_grad(lambda x: np.sum(finv((x, x), x)[0]))(o), check_dtypes=True) class BufferDonationTest(jtu.BufferDonationTestCase): @jtu.skip_on_devices("cpu") # In/out aliasing not supported on CPU. def test_pmap_donate_argnums_invalidates_input(self): move = api.pmap(lambda x: x + x - x, donate_argnums=0) n = jax.local_device_count() x = api.pmap(lambda x: x)(jnp.ones([n])) y = move(x) self.assertDeleted(x) np.testing.assert_allclose(y, [1.] * n) def test_pmap_nested_donate_ignored(self): pmap_fun = jit(lambda x: api.pmap(lambda y: y ** 2, donate_argnums=0)(x)) a = api.pmap(lambda x: x)(jnp.array([1])) # NOTE(mattjj): stopped raising error here and instead just ignored # with self.assertRaisesRegex(ValueError, "nested.*not supported"): # pmap_fun(a) pmap_fun(a) # doesn't crash class NamedCallTest(jtu.JaxTestCase): def test_default_name(self): @api.named_call def my_test_function(x): return x**2 @jax.jit def f(x): return my_test_function(x) c = jax.xla_computation(f)(2) self.assertIn("my_test_function", c.as_hlo_text()) def test_non_jaxtype_arg(self): # For the test to fail without the invalid JaxType filter we need to pass # in a valid JaxType that forces the invalid Jaxtype to be raised to an # abstract value. def f(not_a_jaxtype, a_jaxtype): # then Jax needs to try and evaluate the abstractified non-JaxType if not_a_jaxtype: return a_jaxtype return 0 f = api.named_call(f, name="test") out = jax.jit(f, static_argnums=(0,))("not a Jaxtype", 1) self.assertEqual(out, 1) @parameterized.parameters(jax.jit, jax.grad, jax.vmap, jax.remat) def test_jax_transforms(self, transform): f = jnp.sum x = jnp.array([1.]) unnamed_out = transform(f)(x) named_out = transform(api.named_call(f, name="test"))(x) self.assertEqual(unnamed_out, named_out) def test_static_argnums(self): f = api.named_call(lambda x, y: y if x else None, name="test") f = jax.jit(f, static_argnums=(0,)) out = f(True, 5) self.assertEqual(out, 5) def test_partial_eval(self): f = api.named_call(lambda x, y: y if x else None, name="test") f = jax.jit(functools.partial(f, True)) out = f(5) self.assertEqual(out, 5) @parameterized.named_parameters(jtu.cases_from_list( {"testcase_name": "_jit_type={}_func={}".format(jit_type, func), "jit_type": jit_type, "func": func} for func in ['identity', 'asarray', 'device_put'] for jit_type in [None, "python", "cpp"] if not (jit_type is None and func == 'identity'))) def test_integer_overflow(self, jit_type, func): funcdict = { 'identity': lambda x: x, 'asarray': jnp.asarray, 'device_put': api.device_put, } jit = { 'python': api._python_jit, 'cpp': api._cpp_jit, None: lambda x: x, } f = jit[jit_type](funcdict[func]) int_dtype = dtypes.canonicalize_dtype(jnp.int_) int_max = np.iinfo(int_dtype).max int_min = np.iinfo(int_dtype).min self.assertEqual(f(int_max).dtype, int_dtype) self.assertEqual(f(int_min).dtype, int_dtype) self.assertRaises(OverflowError, f, int_max + 1) self.assertRaises(OverflowError, f, int_min - 1) class BackendsTest(jtu.JaxTestCase): @unittest.skipIf(not sys.executable, "test requires sys.executable") @jtu.skip_on_devices("gpu", "tpu") def test_cpu_warning_suppression(self): warning_expected = ( "import jax; " "jax.numpy.arange(10)") warning_not_expected = ( "import jax; " "jax.config.update('jax_platform_name', 'cpu'); " "jax.numpy.arange(10)") result = subprocess.run([sys.executable, '-c', warning_expected], check=True, capture_output=True) assert "No GPU/TPU found" in result.stderr.decode() result = subprocess.run([sys.executable, '-c', warning_not_expected], check=True, capture_output=True) assert "No GPU/TPU found" not in result.stderr.decode() if __name__ == '__main__': absltest.main(testLoader=jtu.JaxTestLoader())

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import collections import collections.abc from contextlib import contextmanager import copy import enum from functools import partial import operator import re import subprocess import sys import types import unittest import warnings import weakref import functools import itertools as it import operator as op from absl import logging from absl.testing import absltest, parameterized import numpy as np import concurrent.futures import jax import jax.numpy as jnp from jax import float0, jit, grad, device_put, jacfwd, jacrev, hessian from jax import core, dtypes, lax from jax._src import api from jax.core import Primitive from jax.errors import UnexpectedTracerError from jax.interpreters import ad from jax.interpreters import xla from jax.interpreters import pxla from jax.interpreters.sharded_jit import PartitionSpec as P import jax._src.lib from jax._src.lib import xla_client from jax._src import test_util as jtu from jax import tree_util from jax import linear_util as lu import jax._src.util from jax._src.ad_checkpoint import saved_residuals from jax.ad_checkpoint import checkpoint as new_checkpoint, checkpoint_name from jax.config import config config.parse_flags_with_absl() FLAGS = config.FLAGS python_version = (sys.version_info[0], sys.version_info[1]) numpy_version = tuple(map(int, np.__version__.split('.')[:3])) class CPPJitTest(jtu.BufferDonationTestCase): @property def jit(self): return api._cpp_jit @unittest.skipIf(jax._src.lib._xla_extension_version < 40, "Test requires jaxlib 0.1.73") def test_jit_repr(self): def my_function(): return jitted = jit(my_function) self.assertEqual(repr(jitted), f"<CompiledFunction of {repr(my_function)}>") @unittest.skipIf(jax._src.lib._xla_extension_version < 40, "Test requires jaxlib 0.1.73") def test_jit_repr_errors(self): class Callable: def __call__(self): pass def __repr__(self): raise ValueError("invalid repr") jitted = jit(Callable()) self.assertEqual(repr(jitted), "<CompiledFunction>") del jitted.__wrapped__ self.assertEqual(repr(jitted), "<CompiledFunction>") def test_jit_of_noncallable(self): self.assertRaisesRegex(TypeError, "Expected a callable value.*", lambda: self.jit(3)) def test_jit_of_generator(self): def gen(x): yield x self.assertRaisesRegex(TypeError, "Expected a function, got a generator function.*", lambda: self.jit(gen)) @parameterized.parameters([ (1, 2, 3, 4, 5), ( np.asarray(1, np.int32), np.asarray(2, np.int32), np.asarray(3, np.int32), np.asarray(4, np.int32), np.asarray(5, np.int32), ), ]) def test_jit_static_args(self, one, two, three, four, five): side = [] def f(x, y, z, flag=False, flag2=False): del flag2 assert flag side.append(None) return 100 * x + 10 * y + z f1 = self.jit(f, static_argnums=(3, 4)) assert f1(one, two, three, True, False) == 123 assert len(side) == 1 assert f1(one, two, three, True, False) == 123 assert len(side) == 1 assert f1(two, one, three, True, False) == 213 assert len(side) == 1 assert f1(two, one, three, True, True) == 213 assert len(side) == 2 side[:] = [] f2 = self.jit(f, static_argnums=(0, 2, 3, 4)) assert f2(1, 2, 3, True, False) == 123 assert len(side) == 1 assert f2(1, 3, 3, True, False) == 133 assert len(side) == 1 assert f2(2, 2, 3, True, False) == 223 assert len(side) == 2 assert f2(2, 4, 3, True, False) == 243 assert len(side) == 2 assert f2(2, 4, 3, True, True) == 243 assert len(side) == 3 assert f2(2, 5, 3, True, True) == 253 assert len(side) == 3 def test_static_args_equality(self): class A(): def __hash__(self): return 1 def __eq__(self, other): return isinstance(other, A) side = [] def f(x, static_arg): del static_arg side.append(None) return x * 100 f1 = self.jit(f, static_argnums=(1,)) self.assertEqual(f1(1, A()), 100) self.assertLen(side, 1) self.assertEqual(f1(1, A()), 100) self.assertLen(side, 1) if self.jit == api._cpp_jit: f1_cpp = getattr(f1, "_cpp_jitted_f", f1) self.assertEqual(f1_cpp._cache_size(), 1) @parameterized.parameters([ (1, 2, 3), ( np.asarray(1, np.int32), np.asarray(2, np.int32), np.asarray(3, np.int32), ), ]) def test_jit_kwargs(self, one, two, three): side = [] if hasattr(self.jit, "cache_clear"): self.jit.cache_clear() def f(x, y, z): side.append(None) return 100 * x + 10 * y + z f = self.jit(f) assert f(one, two, three) == 123 assert len(side) == 1 assert f(one, two, three) == 123 assert len(side) == 1 assert f(one, two, z=three) == 123 assert len(side) == 2 assert f(one, two, z=three) == 123 assert len(side) == 2 f(one, two, z=np.zeros(3)) if config.x64_enabled: # In the above call, three is of a new type (int64), thus it should # trigger a new compilation. assert len(side) == 3 def test_jit_device(self): device = jax.devices()[-1] x = self.jit(lambda x: x, device=device)(3.) self.assertIsInstance(x, xla.DeviceArray) self.assertEqual(x.device_buffer.device(), device) def test_complex_support(self): self.assertEqual(self.jit(lambda x: x + 1)(1 + 1j), 2 + 1j) def test_jit_with_many_args_works(self): @self.jit def f(args_list): return sum(args_list) self.assertEqual(f(list(range(500))), sum(range(500))) # Jit and Donate arguments def test_jit_donate_argnums_warning_raised(self): x = jnp.array([1.0, 2.0], jnp.float32) y = jnp.array([1, 2], jnp.int32) f = self.jit(lambda x, y: x.sum() + y.sum(), donate_argnums=(0, 1)) with warnings.catch_warnings(record=True) as w: warnings.simplefilter("always") f(x, y) self.assertLen(w, 1) self.assertTrue(issubclass(w[-1].category, UserWarning)) self.assertIn( "Some donated buffers were not usable: f32[2]{0}, s32[2]{0}", str(w[-1].message)) @jtu.skip_on_devices("cpu") # In/out aliasing not supported on CPU. def test_jit_donate_argnums_invalidates_input(self): # We can't just use `lambda x: x` because JAX simplifies this away to an move = self.jit(lambda x: x + x - x, donate_argnums=0) x = jnp.ones([]) y = move(x) self.assertDeleted(x) self.assertEqual(y, 1.) @jtu.skip_on_devices("cpu") def test_jit_donate_argnums_static_argnums(self): jit_fun = self.jit( lambda a, b, c, d: ((a + b + c), (a + b + d)), static_argnums=(0, 1), donate_argnums=(2, 3)) c = jax.device_put(jnp.array([1., 1.])) d = jax.device_put(jnp.array([1., 1., 1.])) e, f = jit_fun(1, 2, c, d) np.testing.assert_allclose(e, jnp.array([4., 4.])) np.testing.assert_allclose(f, jnp.array([4., 4., 4.])) self.assertDeleted(c) self.assertDeleted(d) @jtu.skip_on_devices("cpu") def test_jnp_array_copy(self): @partial(self.jit, donate_argnums=(0,)) def _test(array): return array.at[0].set(77) x = jnp.asarray([0, 1]) x_copy = jnp.array(x, copy=True) with warnings.catch_warnings(): warnings.simplefilter("ignore") _test(x) print(x_copy) def test_jit_global_cache(self): def f(x): assert python_should_be_executing return x python_should_be_executing = True self.jit(f)(2) python_should_be_executing = False self.jit(f)(3) def test_jit_shallow_copy(self): def f(x): return copy.copy(x) self.jit(f)(1) def test_jit_deep_copy(self): def f(x): return copy.deepcopy(x) self.jit(f)(1) def test_disable_jit(self): effects = [] @self.jit def f(x): effects.append(1) return x with api.disable_jit(): f(2) f(2) assert len(effects) == 2 f(2) f(2) assert len(effects) == 3 def test_static_argnum_on_method(self): class A: @functools.partial(self.jit, static_argnums=(0,)) def my_func_jit(self, x): return x+2 A().my_func_jit(3) def test_static_argnum_on_static_method_is_not_supported(self): with self.assertRaisesRegex(TypeError, "Expected a callable value"): class A: @functools.partial(self.jit, static_argnums=(0,)) @classmethod def my_classmethod_jit(cls, x): return x+2 def test_staticmethod_is_not_supported(self): with self.assertRaisesRegex(TypeError, "staticmethod arguments are not supported"): class A: @functools.partial(self.jit) @staticmethod def my_staticmethod_jit(x): return x + 2 def test_concurrent_jit(self): @self.jit def f(x): return x + x - 3. xs = [np.random.randn(i) for i in range(10)] with concurrent.futures.ThreadPoolExecutor() as executor: futures = [executor.submit(partial(f, x)) for x in xs] ys = [f.result() for f in futures] for x, y in zip(xs, ys): self.assertAllClose(x * 2 - 3., y) def test_trivial_computations(self): x = jnp.array([1, 2, 3]) y = self.jit(lambda x: x)(x) self.assertIs(x, y) z1, z2 = self.jit(lambda x: (x, x))(x) self.assertIs(z1, z2) x1, x2 = jnp.array([1, 2]), jnp.array([2, 3]) z1, z2, z3 = self.jit(lambda x, y: (y, 1, x))(x1, x2) self.assertIs(z1, x2) self.assertIs(z3, x1) self.assertEqual(z2, 1) def test_trivial_computations_with_tokens(self): @self.jit def noop(arr, token): return arr, token arr = jax.numpy.ones(10) token = jax.lax.create_token() self.assertEqual(token, noop(arr, token)[1]) def test_jit_bad_input(self): def f(x): return x self.assertRaisesRegex( TypeError, ".* 'foo' of type <.*'str'> is not a valid JAX type", lambda: self.jit(f)("foo")) def test_jit_on_all_devices(self): # Verifies we can run the same computation on every device present, even # if they are, for example, different models of GPU. data = np.random.rand(1000).astype(np.float32) f = self.jit(jnp.negative) for device in jax.local_devices(): x = device_put(data, device=device) np.testing.assert_array_equal(-data, f(x)) def test_jit_nested_donate_ignored(self): jit_fun = self.jit(lambda x: self.jit(lambda y: y**2, donate_argnums=0)(x)) a = jax.device_put(jnp.array(1)) # NOTE(mattjj): stopped raising error here and instead just ignored # with self.assertRaisesRegex(ValueError, "nested.*not supported"): # jit_fun(a) jit_fun(a) # doesn't crash def test_jit_reference_dropping(self): x = jnp.ones(10) f = (lambda x: lambda: x)(x) g = self.jit(f) x = weakref.ref(x) # no more strong ref to x in this scope assert x() is not None # x is still around f() # f runs g() # g runs g() # g runs a second time del f # delete the raw callable assert x() is not None # x is still around g() # g still runs del g # no more references to x assert x() is None # x is gone def test_jit_raises_on_first_invocation_on_non_hashable_static_argnum(self): if self.jit != api._python_jit: raise unittest.SkipTest("this test only applies to _python_jit") f = lambda x, y: x + 3 jitted_f = self.jit(f, static_argnums=(1,)) msg = ("Non-hashable static arguments are not supported, as this can lead " "to unexpected cache-misses. Static argument (index 1) of type " "<class 'numpy.ndarray'> for function <lambda> is non-hashable.") with self.assertRaisesRegex(ValueError, re.escape(msg)): jitted_f(1, np.asarray(1)) def test_cpp_jit_raises_on_non_hashable_static_argnum(self): if self.jit != api._cpp_jit: raise unittest.SkipTest("this test only applies to _cpp_jit") f = lambda x, y: x + 3 jitted_f = api._cpp_jit(f, static_argnums=[1]) jitted_f(1, 1) msg = ("Non-hashable static arguments are not supported. An error occured " ".*while trying to hash an object of type " "<class 'numpy\\.ndarray'>, 1. The error was:\nTypeError: " "unhashable type: 'numpy\\.ndarray'") with self.assertRaisesRegex(ValueError, msg): jitted_f(1, np.asarray(1)) class HashableWithoutEq: def __hash__(self): return 1 def __eq__(self, other): raise NotImplementedError( "A Python error is as is, without stack trace") with self.assertRaisesRegex( ValueError, re.escape("static arguments should be comparable using __eq__")): jitted_f(1, HashableWithoutEq()) def test_cpp_jitted_function_returns_PyBuffer(self): if self.jit != api._cpp_jit: raise unittest.SkipTest("this test only applies to _cpp_jit") jitted_f = self.jit(lambda a: a + 1) jitted_f(1) self.assertIsInstance(jitted_f(2), xla._CppDeviceArray) @jtu.skip_on_devices("cpu") def test_explicit_backend(self): f = lambda x: x + 1 jitted_f = jit(f, backend=jtu.device_under_test()) jitted_f_cpu = jit(f, backend="cpu") result = jitted_f(1.) result_cpu = jitted_f_cpu(1.) self.assertEqual(result.device_buffer.platform(), jtu.device_under_test()) self.assertEqual(result_cpu.device_buffer.platform(), "cpu") @jtu.skip_on_devices("cpu") def test_device_to_device_copy_between_backends(self): # b/186624243 f = lambda x: x + 1 jitted_f = jit(f, backend=jtu.device_under_test()) jitted_f_cpu = jit(f, backend="cpu") x = np.arange(30).reshape(1, 10, 3) result = jitted_f(x) result_cpu = jitted_f_cpu(result) result_2 = jitted_f(result_cpu) result_cpu_2 = jitted_f_cpu(result_2) self.assertAllClose(result_2, x + 3) self.assertAllClose(result_cpu_2, x + 4) @jtu.skip_on_devices("cpu") def test_mismatched_nested_backends(self): @partial(jit, backend=jtu.device_under_test()) def f(x): return jit(lambda x: x + 1, backend="cpu")(x) with self.assertRaisesRegex( ValueError, f"Outer-jit backend specification {jtu.device_under_test()} must match " f"explicit inner-jit backend specification cpu."): f(1.) def test_omnistaging(self): # See https://github.com/google/jax/issues/5206 # TODO(frostig): remove once we always enable_custom_prng def _prng_key_as_array(key): return key.unsafe_raw_array() if config.jax_enable_custom_prng else key # TODO(frostig): remove once we always enable_custom_prng def _array_as_prng_key(arr): arr = np.array(arr, dtype=np.uint32) if config.jax_enable_custom_prng: return jax._src.prng.PRNGKeyArray( jax._src.prng.threefry_prng_impl, arr) else: return arr key_list = [None] def init(): key, subkey = jax.random.split(key_list[0]) key_list[0] = key return jax.random.normal(subkey, ()) key_list[0] = _array_as_prng_key([2384771982, 3928867769]) init() self.jit(init)() self.assertIsInstance(_prng_key_as_array(key_list[0]), core.Tracer) def test_jit_wrapped_attributes(self): def f(x: int) -> int: return x + 1 f.some_value = 4 jf = self.jit(f) for attr in ["doc", "name", "module", "qualname", "annotations"]: self.assertEqual( {attr: getattr(f, f"__{attr}__")}, {attr: getattr(jf, f"__{attr}__")}) self.assertEqual(f.some_value, jf.some_value) def test_jit_python_builtin(self): x = jnp.array([1, 2]) expected = x + 1 jit_add = self.jit(operator.add, static_argnums=(1,)) actual = jit_add(x, 1) self.assertArraysEqual(expected, actual) def test__infer_argnums_and_argnames(self): def f(x, y=1): pass argnums, argnames = api._infer_argnums_and_argnames( f, argnums=None, argnames=None) assert argnums == () assert argnames == () argnums, argnames = api._infer_argnums_and_argnames( f, argnums=0, argnames=None) assert argnums == (0,) assert argnames == ('x',) argnums, argnames = api._infer_argnums_and_argnames( f, argnums=None, argnames='y') assert argnums == (1,) assert argnames == ('y',) argnums, argnames = api._infer_argnums_and_argnames( f, argnums=0, argnames='y') # no validation assert argnums == (0,) assert argnames == ('y',) def g(x, y, *args): pass argnums, argnames = api._infer_argnums_and_argnames( g, argnums=(1, 2), argnames=None) assert argnums == (1, 2) assert argnames == ('y',) def h(x, y, **kwargs): pass argnums, argnames = api._infer_argnums_and_argnames( h, argnums=None, argnames=('foo', 'bar')) assert argnums == () assert argnames == ('foo', 'bar') def test_jit_with_static_argnames(self): def f(x): assert x == 'foo' return 1 f_nums = self.jit(f, static_argnums=0) assert f_nums('foo') == 1 assert f_nums(x='foo') == 1 f_names = self.jit(f, static_argnames='x') assert f_names('foo') == 1 assert f_names(x='foo') == 1 def test_new_static_argnum_on_keyword_arguments(self): f = self.jit(lambda x: x, static_argnums=0) y = f(x=4) assert y == 4 def test_new_static_argnum_with_default_arguments(self): f = self.jit(lambda x=4: x, static_argnums=0) y = f() assert y == 4 def test_jit_with_mismatched_static_argnames(self): x_is_tracer, y_is_tracer = False, False def f(x, y): assert isinstance(x, core.Tracer) == x_is_tracer assert isinstance(y, core.Tracer) == y_is_tracer return 1 # If both static_argnums and static_argnames are provided, they are allowed # to disagree and `jit` will respect the user's choices. f_nums = self.jit(f, static_argnums=1, static_argnames=()) x_is_tracer, y_is_tracer = True, False assert f_nums(2, 'foo') == 1 x_is_tracer, y_is_tracer = True, True assert f_nums(1, y=2) == 1 f_names = self.jit(f, static_argnums=(), static_argnames='y') x_is_tracer, y_is_tracer = True, True assert f_names(2, 3) == 1 x_is_tracer, y_is_tracer = True, False assert f_names(1, y='foo') == 1 f_mixed = self.jit(f, static_argnums=(1,), static_argnames='x') x_is_tracer, y_is_tracer = True, False assert f_mixed(2, 'foo') == 1 x_is_tracer, y_is_tracer = True, True assert f_mixed(1, y=3) == 1 x_is_tracer, y_is_tracer = False, True assert f_mixed(x='foo', y=3) == 1 @parameterized.named_parameters(jtu.cases_from_list( {"testcase_name": "_num_args={}".format(num_args), "num_args": num_args} for num_args in [2, 3, 4])) def test_jit_with_pruned_args(self, num_args): def f(*args): used = np.array(2) return args[1] + used f_pruned = self.jit(f) args = range(num_args) with jtu.count_device_put() as count: np.testing.assert_allclose(f_pruned(*args), 3) self.assertEqual(count[0], 1) @unittest.skipIf(jax._src.lib._xla_extension_version <= 36, "Test requires jaxlib 0.1.71") def testBuffersAreFreedPromptly(self): @self.jit def f(x): return x + 1 refs = [] x = np.ones((10000,), np.float32) for step in range(1000): x = f(x) refs.append(weakref.ref(x)) x = np.asarray(x) # block_until_ready() here because it would force a garbage collection. live_refs = len([ref for ref in refs if ref() is not None]) self.assertLessEqual(live_refs, 100) def test_jit_lower_compile(self): def f(x): return jnp.sqrt(x ** 2) + 1. f_jit = self.jit(f) f_low = f_jit.lower(1.) f_exe = f_low.compile() self.assertAllClose(f_exe(1.), 2.) def test_jit_lower_compile_in_tree_mismatch(self): def f(x): return jnp.sqrt(x ** 2) + 1. f_jit = self.jit(f) f_low = f_jit.lower(1.) f_exe = f_low.compile() self.assertRaisesRegex( TypeError, "function compiled for .*, called with .*", lambda: f_exe([1.])) def test_jit_lower_compile_trivial(self): def f(x): return x out = self.jit(f).lower(1.).compile()(4.) self.assertAllClose(out, 4.) def test_jit_lower_compile_trivial_in_tree_mismatch(self): def f(x): return x f_exe = self.jit(f).lower(1.).compile() self.assertRaisesRegex( TypeError, "function compiled for .*, called with .*", lambda: f_exe([4.])) def test_jit_lower_compile_arg_type_mismatch(self): def f(x): return jnp.sqrt(x ** 2) + 1. x = jnp.array(1, dtype=int) x_f32 = x.astype(jnp.float32) x_i32 = x.astype(jnp.int32) f_exe = self.jit(f).lower(x_f32).compile() self.assertRaisesRegex( TypeError, "Computation compiled for input types:\n.*float32.*\n" "called with:\n.*int32.*", lambda: f_exe(x_i32)) def test_jit_lower_compile_multi_arg(self): def f(*args): x, *_ = args return jnp.sqrt(x ** 2) + 1. f_exe = self.jit(f).lower(1., 1.).compile() self.assertAllClose(f_exe(1., 1.), 2.) def test_jit_lower_compile_trivial_multi_arg(self): def f(*args): x, *_ = args return x f_exe = self.jit(f).lower(1., 1.).compile() self.assertAllClose(f_exe(1., 1.), 1.) class PythonJitTest(CPPJitTest): @property def jit(self): return api._python_jit class APITest(jtu.JaxTestCase): def test_grad_bad_input(self): def f(x): return x self.assertRaisesRegex( TypeError, ".* 'foo' of type <.*'str'> is not a valid JAX type", lambda: grad(f)("foo")) def test_grad_argnums(self): def f(x, y, z, flag=False): assert flag return 1.0 * x + 2.0 * y + 3.0 * z assert grad(f)(1.0, 1.0, 1.0, flag=True) == 1.0 assert grad(f, argnums=1)(1.0, 1.0, 1.0, flag=True) == 2.0 assert grad(f, argnums=(2, 0))(1.0, 1.0, 1.0, flag=True) == (3.0, 1.0) def test_value_and_grad_argnums(self): def f(x, y, z, flag=False): assert flag return 1.0 * x + 2.0 * y + 3.0 * z y = f(1.0, 1.0, 1.0, flag=True) assert api.value_and_grad(f)(1.0, 1.0, 1.0, flag=True) == (y, 1.0) assert api.value_and_grad(f, argnums=1)(1.0, 1.0, 1.0, flag=True) == (y, 2.0) assert api.value_and_grad(f, argnums=(2, 0))(1.0, 1.0, 1.0, flag=True) == (y, (3.0, 1.0)) def test_grad_of_jit(self): side = [] @jit def f(x): side.append(None) return x * x assert grad(f)(1.0) == 2.0 assert len(side) == 1 assert grad(f)(2.0) == 4.0 assert len(side) == 1 def test_jit_of_grad(self): side = [] @jit def f(x): side.append(None) return x * x g = jit(grad(f)) assert g(1.0) == 2.0 assert len(side) == 1 assert g(2.0) == 4.0 assert len(side) == 1 def test_bad_input(self): def f(x): return x self.assertRaisesRegex( TypeError, ".* 'foo' of type <.*'str'> is not a valid JAX type", lambda: grad(f)("foo")) self.assertRaisesRegex( TypeError, ".* 'foo' of type <.*'str'> is not a valid JAX type", lambda: jit(f)("foo")) def test_grad_tuple_output(self): jtu.check_raises(lambda: grad(lambda x: (x,x))(1.0), TypeError, "Gradient only defined for scalar-output functions. ") def test_grad_unit_output(self): jtu.check_raises(lambda: grad(lambda x: ())(np.zeros(3)), TypeError, "Gradient only defined for scalar-output functions. ") def test_grad_nonscalar_output(self): jtu.check_raises(lambda: grad(lambda x: x)(np.zeros(3)), TypeError, "Gradient only defined for scalar-output functions. ") def test_unwrapped_numpy(self): def f(x): return np.exp(x) with self.assertRaisesRegex(Exception, "The numpy.ndarray conversion .*"): grad(f)(np.zeros(3)) def test_binop_mismatch(self): def f(x, y): return x + y jtu.check_raises( lambda: f(jnp.zeros(3), jnp.zeros(4)), TypeError, "add got incompatible shapes for broadcasting: (3,), (4,).") jtu.check_raises( lambda: grad(f)(np.zeros(3), np.zeros(4)), TypeError, "add got incompatible shapes for broadcasting: (3,), (4,).") def test_dot_mismatch(self): def f(x, y): return jnp.dot(x, y) self.assertRaisesRegex( TypeError, "Incompatible shapes for dot: got \$3L?,\$ and \$4L?,\$.", lambda: grad(f)(np.zeros(3), np.zeros(4))) def test_abstract_error_message(self): for castfun in [float, complex, int]: def f(x): return castfun(x) self.assertRaisesRegex( TypeError, f"[Tt]ry using `x.astype\${castfun.__name__}\$`", lambda: jit(f)(1.0)) def test_switch_value_jit(self): def f(x): y = x > 0 if y: return x else: return -x assert grad(f)(1.0) == 1.0 assert grad(f)(-1.0) == -1.0 with self.assertRaisesRegex(core.ConcretizationTypeError, "Abstract tracer value"): jit(f)(1) def test_list_index_err(self): L = [1, 2, 3] def f(n): return L[n] assert jit(f, static_argnums=(0,))(0) == L[0] self.assertRaisesRegex( TypeError, r"The __index__ method was called on the JAX Tracer object.*", lambda: jit(f)(0)) def test_range_err(self): def f(x, n): for i in range(n): x = x + i return x assert jit(f, static_argnums=(1,))(0, 5) == 10 self.assertRaisesRegex( TypeError, r"The __index__ method was called on the JAX Tracer object.*", lambda: jit(f)(0, 5)) def test_cast_int(self): f = lambda x: int(x) self.assertRaisesRegex( TypeError, "('(?:JaxprTracer|DynamicJaxprTracer)' object cannot be interpreted as an integer" "|Abstract tracer value encountered where concrete value is expected.*)", lambda: jit(f)(0)) def test_casts(self): for castfun in [hex, oct]: f = lambda x: castfun(x) self.assertRaisesRegex( TypeError, r"The __index__ method was called on the JAX Tracer object.*", lambda: jit(f)(0)) def test_unimplemented_interpreter_rules(self): foo_p = Primitive('foo') def foo(x): return foo_p.bind(x) jtu.check_raises(lambda: foo(1.0), NotImplementedError, "Evaluation rule for 'foo' not implemented") jtu.check_raises(lambda: jit(foo)(1.0), NotImplementedError, "Abstract evaluation for 'foo' not implemented") jtu.check_raises(lambda: grad(foo)(1.0), NotImplementedError, "Differentiation rule for 'foo' not implemented") foo_p.def_abstract_eval(lambda x: x) jtu.check_raises(lambda: jit(foo)(1.0), NotImplementedError, "XLA translation rule for primitive 'foo' not found") foo_p.def_impl(lambda x: x) ad.defjvp(foo_p, lambda g, x: foo(g)) jtu.check_raises(lambda: grad(foo)(1.0), NotImplementedError, "Transpose rule (for reverse-mode differentiation) for 'foo' not implemented") def test_is_subclass(self): self.assertTrue(issubclass(xla.DeviceArray, jnp.ndarray)) self.assertTrue(issubclass(xla._CppDeviceArray, jnp.ndarray)) self.assertTrue(issubclass(pxla.ShardedDeviceArray, jnp.ndarray)) self.assertTrue(issubclass(pxla._ShardedDeviceArray, jnp.ndarray)) self.assertFalse(issubclass(np.ndarray, jnp.ndarray)) self.assertFalse(issubclass(xla.DeviceArray, np.ndarray)) self.assertFalse(issubclass(xla._CppDeviceArray, np.ndarray)) self.assertFalse(issubclass(pxla.ShardedDeviceArray, np.ndarray)) self.assertFalse(issubclass(pxla._ShardedDeviceArray, np.ndarray)) def test_is_instance(self): def f(x): self.assertIsInstance(x, jnp.ndarray) self.assertNotIsInstance(x, np.ndarray) return x + 2 jit(f)(3) jax.vmap(f)(np.arange(3)) def test_device_put_and_get(self): x = np.arange(12.).reshape((3, 4)).astype("float32") dx = api.device_put(x) self.assertIsInstance(dx, xla.DeviceArray) self.assertIsInstance(dx, jnp.ndarray) self.assertNotIsInstance(dx, np.ndarray) x2 = api.device_get(dx) self.assertNotIsInstance(x2, jnp.ndarray) self.assertIsInstance(x2, np.ndarray) assert np.all(x == x2) y = [x, (2 * x, 3 * x)] dy = api.device_put(y) y2 = api.device_get(dy) self.assertIsInstance(y2, list) self.assertIsInstance(y2[0], np.ndarray) assert np.all(y2[0] == x) self.assertIsInstance(y2[1], tuple) self.assertIsInstance(y2[1][0], np.ndarray) assert np.all(y2[1][0] == 2 * x) self.assertIsInstance(y2[1][1], np.ndarray) assert np.all(y2[1][1] == 3 * x) def test_device_get_scalar(self): x = np.arange(12.).reshape((3, 4)).astype("float32") x = api.device_put(x) self.assertIsInstance(x, xla.DeviceArray) y = [x, 2] y2 = api.device_get(y) self.assertIsInstance(y2, list) self.assertIsInstance(y2[0], np.ndarray) assert np.all(y2[0] == x) self.assertIsInstance(y2[1], int) self.assertEqual(y2[1], 2) @parameterized.parameters([(3,)], [(2, 0)]) def test_device_put_across_devices(self, shape): if len(api.local_devices()) < 2: raise unittest.SkipTest("this test requires multiple devices") d1, d2 = api.local_devices()[:2] data = np.random.randn(*shape).astype(np.float32) x = api.device_put(data, device=d1) self.assertEqual(x.device_buffer.device(), d1) y = api.device_put(x, device=d2) self.assertEqual(y.device_buffer.device(), d2) np.testing.assert_array_equal(data, np.array(y)) # Make sure these don't crash api.device_put(x) api.device_put(y) @jtu.skip_on_devices("cpu") def test_device_put_across_platforms(self): default_device = jax.devices()[0] cpu_device = jax.devices("cpu")[0] np_arr = np.array([1,2,3]) scalar = 1 device_arr = jnp.array([1,2,3]) assert device_arr.device_buffer.device() is default_device for val in [np_arr, device_arr, scalar]: x = api.device_put(val, device=cpu_device) self.assertEqual(x.device_buffer.device(), cpu_device) @jtu.skip_on_devices("tpu") def test_jacobian(self): R = np.random.RandomState(0).randn A = R(4, 3) x = R(3) f = lambda x: jnp.dot(A, x) assert np.allclose(jacfwd(f)(x), A) assert np.allclose(jacrev(f)(x), A) f = lambda x: jnp.tanh(jnp.dot(A, x)) assert np.allclose(jacfwd(f)(x), jacrev(f)(x)) @jtu.skip_on_devices("tpu") def test_hessian(self): R = np.random.RandomState(0).randn A = R(4, 4) x = R(4) f = lambda x: jnp.dot(x, jnp.dot(A, x)) assert np.allclose(hessian(f)(x), A + A.T) def test_std_basis(self): basis = api._std_basis(jnp.zeros(3)) assert getattr(basis, "shape", None) == (3, 3) assert np.allclose(basis, np.eye(3)) basis = api._std_basis(jnp.zeros((3, 3))) assert getattr(basis, "shape", None) == (9, 3, 3) assert np.allclose(basis, np.eye(9).reshape(9, 3, 3)) basis = api._std_basis([0., (jnp.zeros(3), jnp.zeros((3, 4)))]) assert isinstance(basis, list) and len(basis) == 2 assert getattr(basis[0], "shape", None) == (16,) assert isinstance(basis[1], tuple) and len(basis[1]) == 2 assert getattr(basis[1][0], "shape", None) == (16, 3) assert getattr(basis[1][1], "shape", None) == (16, 3, 4) @jtu.skip_on_devices("tpu") def test_jacobian_on_pytrees(self): for jacfun in [jacfwd, jacrev]: ans = jacfun(lambda x, y: (x, y))(0., 1.) expected = (1., 0.) self.assertAllClose(ans, expected, check_dtypes=False) ans = jacfun(lambda x, y: (x, y), 1)(0., 1.) expected = (0., 1.) self.assertAllClose(ans, expected, check_dtypes=False) ans = jacfun(lambda x, y: (x, y), (0, 1))(0., 1.) expected = ((1., 0.), (0., 1.),) self.assertAllClose(ans, expected, check_dtypes=False) ans = jacfun(lambda x: x[:2])((1., 2., 3.)) expected = ((1., 0., 0.), (0., 1., 0.)) self.assertAllClose(ans, expected, check_dtypes=False) R = np.random.RandomState(0).randn x = R(2) y = R(3) ans = jacfun(lambda x, y: {'x': x, 'xy': jnp.outer(x, y)})(x, y) expected = {'x': np.eye(2), 'xy': np.kron(np.eye(2), y[:, None]).reshape(2, 3, 2)} self.assertAllClose(ans, expected, check_dtypes=False) @jtu.skip_on_devices("tpu") def test_hessian_on_pytrees(self): ans = hessian(lambda x: jnp.array(x)**2)((1., 2.)) expected = ((np.array([2., 0.]), np.array([0., 0.])), (np.array([0., 0.]), np.array([0., 2.]))) self.assertAllClose(ans, expected, check_dtypes=False) @jtu.skip_on_devices("tpu") def test_issue1372(self): def quad(x): return jnp.dot(x, x) def f(x, u): return quad(x) + quad(u) x, u = jnp.ones(5), jnp.ones(2) rev = jacrev fwd = jacfwd self.assertEqual(rev(rev(f, 0), 0)(x, u).shape, (5, 5)) self.assertEqual(rev(fwd(f, 0), 0)(x, u).shape, (5, 5)) self.assertEqual(fwd(rev(f, 0), 0)(x, u).shape, (5, 5)) self.assertEqual(fwd(fwd(f, 0), 0)(x, u).shape, (5, 5)) self.assertEqual(rev(rev(f, 1), 1)(x, u).shape, (2, 2)) self.assertEqual(rev(fwd(f, 1), 1)(x, u).shape, (2, 2)) self.assertEqual(fwd(rev(f, 1), 1)(x, u).shape, (2, 2)) self.assertEqual(fwd(fwd(f, 1), 1)(x, u).shape, (2, 2)) self.assertEqual(rev(rev(f, 1), 0)(x, u).shape, (2, 5)) self.assertEqual(rev(fwd(f, 1), 0)(x, u).shape, (2, 5)) self.assertEqual(rev(rev(f, 0), 1)(x, u).shape, (5, 2)) self.assertEqual(rev(fwd(f, 0), 1)(x, u).shape, (5, 2)) self.assertEqual(fwd(rev(f, 1), 0)(x, u).shape, (2, 5)) self.assertEqual(fwd(fwd(f, 1), 0)(x, u).shape, (2, 5)) self.assertEqual(fwd(rev(f, 0), 1)(x, u).shape, (5, 2)) self.assertEqual(fwd(fwd(f, 0), 1)(x, u).shape, (5, 2)) def test_large_device_constant(self): ans = jit(lambda x: 2 * x)(jnp.ones(int(2e6))) self.assertAllClose(ans, np.ones(int(2e6)) * 2., check_dtypes=False) def test_grad_and_aux_basic(self): g, aux = grad(lambda x: (x**3, [x**2]), has_aux=True)(3.) self.assertAllClose(g, grad(lambda x: x**3)(3.)) self.assertAllClose(aux, [9.], check_dtypes=False) def test_grad_and_aux_error(self): with self.assertRaisesRegex(TypeError, "two-element tuple"): grad(lambda x: (1, 2, 3), has_aux=True)(1.) with self.assertRaisesRegex(TypeError, "two-element tuple"): grad(lambda x: x, has_aux=True)(1.) with self.assertRaisesRegex(TypeError, "two-element tuple"): grad(lambda x: (x,), has_aux=True)(1.) def test_grad_and_aux_nested(self): def f(x): g, aux = grad(lambda x: (x**3, [x**3]), has_aux=True)(x) return aux[0] f2 = lambda x: x**3 self.assertEqual(grad(f)(4.), grad(f2)(4.)) self.assertEqual(jit(grad(f))(4.), grad(f2)(4.)) self.assertEqual(jit(grad(jit(f)))(4.), grad(f2)(4.)) def f(x): g, aux = grad(lambda x: (x**3, [x**3]), has_aux=True)(x) return aux[0] * jnp.sin(x) f2 = lambda x: x**3 * jnp.sin(x) self.assertEqual(grad(f)(4.), grad(f2)(4.)) self.assertEqual(jit(grad(f))(4.), grad(f2)(4.)) self.assertEqual(jit(grad(jit(f)))(4.), grad(f2)(4.)) def test_grad_and_aux_constant(self): g, aux = grad(lambda x: (x**3, [4.]), has_aux=True)(4.) self.assertEqual(g, grad(lambda x: x**3)(4.)) self.assertEqual(aux, [4.]) g, aux = grad(lambda x: (x**3, [x**2, 4.]), has_aux=True)(4.) self.assertEqual(g, grad(lambda x: x**3)(4.)) self.assertEqual(aux, [4.**2, 4.]) def test_grad_and_aux_no_tracers(self): # see https://github.com/google/jax/issues/1950 def f(x): aux = dict(identity=x, p1=x+1) return x ** 2, aux _, aux = jax.grad(f, has_aux=True)(3.) self.assertIsInstance(aux, dict) for val in aux.values(): self.assertNotIsInstance(val, core.Tracer) def test_jvp_mismatched_arguments(self): self.assertRaisesRegex( TypeError, ("primal and tangent arguments to jax.jvp must have the same tree " "structure"), lambda: api.jvp(lambda x, y: x * y, (np.float32(2),), ())) # If primals and tangents must both be tuples or both lists self.assertRaisesRegex( TypeError, ("primal and tangent arguments to jax.jvp must have the same tree " "structure"), lambda: api.jvp(lambda x, y: x * y, (np.float32(2),), [np.float32(2)])) self.assertRaisesRegex( TypeError, "primal and tangent arguments to jax.jvp do not match.", lambda: api.jvp(lambda x: -x, (np.float16(2),), (np.float32(4),))) # If primals and tangents are not of the same shape then raise error fun = lambda x: x+1 with self.assertRaisesRegex( ValueError, "jvp called with different primal and tangent shapes"): api.jvp(fun, (jnp.array([1.,2.,3.]),), (jnp.array([1.,2.,3.,4.]),)) with self.assertRaisesRegex( ValueError, "jvp called with different primal and tangent shapes"): api.jvp(fun, (jnp.float32(10.),), (jnp.array([1.,2.,3.], dtype=jnp.float32),)) with self.assertRaisesRegex( ValueError, "jvp called with different primal and tangent shapes"): api.jvp(fun, (jnp.array([1.,2.,3.], dtype=jnp.float32),), (jnp.float32(20.),)) with self.assertRaisesRegex( ValueError, "jvp called with different primal and tangent shapes"): api.jvp(fun, (jnp.array([1.,2.,3.]),), (20.,)) def test_jvp_non_tuple_arguments(self): def f(x, y): return x + y self.assertRaisesRegex( TypeError, "primal and tangent arguments to jax.jvp must be tuples or lists; found float and tuple.", lambda: api.jvp(f, 0., (1.,))) self.assertRaisesRegex( TypeError, "primal and tangent arguments to jax.jvp must be tuples or lists; found tuple and ndarray.", lambda: api.jvp(f, (0.,), np.array([1., 2.]))) def test_vjp_mismatched_arguments(self): _, pullback = api.vjp(lambda x, y: x * y, np.float32(3), np.float32(4)) self.assertRaisesRegex( TypeError, "Tree structure of cotangent input.*does not match", lambda: pullback((np.float32(7), np.float32(100)))) self.assertRaisesRegex( TypeError, "Type of cotangent input to vjp pullback.*is not the expected tangent type", lambda: pullback((np.float16(42)))) def test_vjp_bad_cotangent_shape(self): x = np.ones((2, 5), dtype=np.float32) y = np.ones((5, 3), dtype=np.float32) def f_jax(x, y): return jnp.matmul(x, y) res, pullback = jax.vjp(f_jax, x, y) with self.assertRaisesRegex( ValueError, "Shape of cotangent input to vjp pullback function .* must be the same as the shape of corresponding primal input .*"): pullback(np.ones((2, 4), dtype=np.float32)) def test_jvp_jit_cached(self): def func(x): def inner(y): return y * x # Must have two calls to the inner jit (the second one hits the cache) res1 = api.jit(inner)(4.) res2 = api.jit(inner)(5.) return res1 + res2 self.assertAllClose((45., 9.), api.jvp(func, (5.,), (1.,))) def test_linear_transpose_abstract(self): x = types.SimpleNamespace(shape=(3,), dtype=np.dtype(np.float32)) y = jnp.arange(3, dtype=np.float32) transpose_fun = api.linear_transpose(lambda x: 2 * x, x) z, = transpose_fun(y) self.assertArraysEqual(2 * y, z, check_dtypes=True) def test_linear_transpose_integer(self): f = lambda x: 2 * x transpose = api.linear_transpose(f, 1) actual, = transpose(3) expected = 6 self.assertEqual(actual, expected) def test_linear_transpose_error(self): with self.assertRaisesRegex( TypeError, "linear_transpose only supports"): api.linear_transpose(lambda x: 2. * x, 1) transpose_fun = api.linear_transpose(lambda x: [x, x], 1.0) with self.assertRaisesRegex(TypeError, "cotangent tree does not match"): transpose_fun(1.0) transpose_fun = api.linear_transpose(lambda x: jnp.stack([x, x]), 1.0) with self.assertRaisesRegex(TypeError, "cotangent type does not match"): transpose_fun(1.0) transpose_fun = api.linear_transpose(lambda x: 1j * x, 1.0) with self.assertRaisesRegex(TypeError, "cotangent type does not match"): transpose_fun(1.0) transpose_fun = api.linear_transpose(lambda x: x, 1.0) with self.assertRaisesRegex(TypeError, "cotangent type does not match"): transpose_fun(1j) def test_linear_transpose_complex(self): f = lambda x: (1 + 2j) * x transpose = api.linear_transpose(f, 1j) actual, = transpose(3 + 4j) expected = -5 + 10j self.assertEqual(actual, expected) def test_linear_transpose_zeros(self): f = lambda x: x[0] transpose = api.linear_transpose(f, [1., 2.]) actual, = transpose(3.) expected = [3., 0.] self.assertEqual(actual, expected) def test_complex_grad_raises_error(self): self.assertRaises(TypeError, lambda: grad(lambda x: jnp.sin(x))(1 + 2j)) def test_holomorphic_grad(self): out = grad(lambda x: jnp.sin(x), holomorphic=True)(1 + 2j) expected = 2.0327230070196656 - 3.0518977991518j self.assertAllClose(out, expected, check_dtypes=False) def test_nonholomorphic_grad(self): zs = 0.5j * np.arange(5) + np.arange(5) def f(z): return jnp.sum(jnp.cos(jnp.abs(z))) ans = grad(f)(zs) expected = np.array([ 0. + 0.j, -0.80430663 + 0.40215331j, -0.70368982 + 0.35184491j, 0.1886467 - 0.09432335j, 0.86873727 - 0.43436864j]) self.assertAllClose(ans, expected, check_dtypes=False, atol=jtu.default_gradient_tolerance, rtol=jtu.default_gradient_tolerance) def test_complex_output_jacrev_raises_error(self): self.assertRaises(TypeError, lambda: jacrev(lambda x: jnp.sin(x))(1 + 2j)) def test_nonholomorphic_jacrev(self): # code based on https://github.com/google/jax/issues/603 zs = 0.5j * np.arange(5) + np.arange(5) def f(z): return jnp.cos(jnp.linalg.norm(2 * z)) ans = jacrev(f)(zs) expected = grad(f)(zs) self.assertAllClose(ans, expected) def test_heterogeneous_jacfwd(self): # See https://github.com/google/jax/issues/7157 # See https://github.com/google/jax/issues/7780 x = np.array([2.0], dtype=np.float16) y = np.array([3.0], dtype=np.float32) a = (x, y) def f(tup): jtu._check_dtypes_match(tup, a) x, y = tup return x, y, x + y actual = jacfwd(f)(a) desired = ((np.array(1., dtype=np.float16), np.array(0., dtype=np.float16)), (np.array(0., dtype=np.float32), np.array(1., dtype=np.float32)), (np.array(1., dtype=np.float32), np.array(1., dtype=np.float32))) jtu._check_dtypes_match(actual, desired) jtu.check_eq(actual, desired) def test_heterogeneous_jacrev(self): # See https://github.com/google/jax/issues/7157 # See https://github.com/google/jax/issues/7780 x = np.array([2.0], dtype=np.float16) y = np.array([3.0], dtype=np.float32) a = (x, y) def f(tup): jtu._check_dtypes_match(tup, a) x, y = tup return x, y, x + y actual = jacrev(f)(a) desired = ((np.array(1., dtype=np.float16), np.array(0., dtype=np.float32)), (np.array(0., dtype=np.float16), np.array(1., dtype=np.float32)), (np.array(1., dtype=np.float16), np.array(1., dtype=np.float32))) jtu._check_dtypes_match(actual, desired) jtu.check_eq(actual, desired) def test_heterogeneous_grad(self): # See https://github.com/google/jax/issues/7157 x = np.array(1.0+1j) y = np.array(2.0) a = (x, y) def f(tup): jtu._check_dtypes_match(tup, a) x, y = tup return jnp.square(jnp.abs(x)) + y actual = grad(f)(a) desired = (np.array(2 - 2j), np.array(1.)) jtu._check_dtypes_match(actual, desired) jtu.check_eq(actual, desired) def test_complex_input_jacfwd_raises_error(self): self.assertRaises(TypeError, lambda: jacfwd(lambda x: jnp.sin(x))(1 + 2j)) def test_legacy_devicearray_repr(self): dx = device_put(3.) str(dx.item()) # doesn't crash def test_devicearray_repr(self): x = device_put(jnp.zeros(3)) self.assertIsInstance(x, xla.DeviceArray) repr(x) x = device_put(jnp.ones(3) + 1j * jnp.ones(3)) self.assertIsInstance(x, xla.DeviceArray) repr(x) # doesn't crash def test_devicearray_delete(self): x = device_put(1.) x.delete() self.assertRaisesRegex(RuntimeError, "DeviceArray has been deleted.", lambda: repr(x)) def test_devicearray_block_until_ready(self): x = device_put(1.) y = x.block_until_ready() self.assertTrue(y is x) def test_devicearray_weakref_friendly(self): x = device_put(1.) y = weakref.ref(x) self.assertEqual(y(), 1.) del x self.assertIsNone(y()) def test_namedtuple_transparency(self): Point = collections.namedtuple("Point", ["x", "y"]) def f(pt): return jnp.sqrt(pt.x ** 2 + pt.y ** 2) pt = Point(1., 2.) f(pt) g = api.grad(f)(pt) self.assertIsInstance(g, Point) f_jit = api.jit(f) self.assertAllClose(f(pt), f_jit(pt), check_dtypes=False) def test_namedtuple_subclass_transparency(self): # See https://github.com/google/jax/issues/806 Point = collections.namedtuple("Point", ["x", "y"]) class ZeroPoint(Point): def is_zero(self): return (self.x == 0) and (self.y == 0) pt = ZeroPoint(0., 0.) def f(pt): return 0. if pt.is_zero() else jnp.sqrt(pt.x ** 2 + pt.y ** 2) f(pt) # doesn't crash _ = api.grad(f)(pt) self.assertIsInstance(pt, ZeroPoint) @parameterized.parameters(1, 2, 3) def test_shape_dtype_struct(self, i): s = api.ShapeDtypeStruct(shape=(i, 2, 3), dtype=jnp.float32) self.assertEqual(s.shape, (i, 2, 3)) self.assertEqual(s.dtype, jnp.float32) self.assertEqual(s.ndim, 3) self.assertEqual(s.size, i * 2 * 3) self.assertLen(s, i) for f in (str, repr): self.assertEqual( f(s), "ShapeDtypeStruct(shape=({}, 2, 3), dtype=float32)".format(i)) def test_shape_dtype_struct_scalar(self): s = api.ShapeDtypeStruct(shape=(), dtype=jnp.float32) self.assertEmpty(s.shape) self.assertEqual(s.size, 1) self.assertEqual(s.ndim, 0) with self.assertRaisesRegex(TypeError, "len[(][)] of unsized object"): _ = len(s) def test_shape_dtype_struct_hash(self): s1 = api.ShapeDtypeStruct(shape=(2, 3), dtype=jnp.float32) s2 = api.ShapeDtypeStruct(shape=(2, 3), dtype=jnp.float32) s3 = api.ShapeDtypeStruct(shape=(2, 4), dtype=jnp.float32) self.assertEqual(hash(s1), hash(s2)) self.assertNotEqual(hash(s1), hash(s3)) def test_eval_shape(self): def fun(x, y): return jnp.tanh(jnp.dot(x, y) + 3.) x = jnp.ones((2, 3)) y = jnp.ones((3, 4)) out_shape = api.eval_shape(fun, x, y) self.assertEqual(out_shape.shape, (2, 4)) def test_eval_shape_constants(self): def fun(): x = jnp.ones((2, 3)) y = jnp.ones((3, 4)) return jnp.tanh(jnp.dot(x, y) + 3.) out_shape = api.eval_shape(fun) self.assertEqual(out_shape.shape, (2, 4)) def test_eval_shape_tuple_unpacking(self): def fun(x, y): a, b = x return a + b + y x = (jnp.ones(2), jnp.ones(2)) y = 3. out_shape = api.eval_shape(fun, x, y) self.assertEqual(out_shape.shape, (2,)) def test_eval_shape_tuple_itemgetting(self): def fun(x, y): return x[0] + x[1] + y x = (jnp.ones(2), jnp.ones(2)) y = 3. out_shape = api.eval_shape(fun, x, y) self.assertEqual(out_shape.shape, (2,)) def test_eval_shape_output_dict(self): def fun(x, y): return {'hi': x[0] + x[1] + y} x = (jnp.ones(2), jnp.ones(2)) y = 3. out_shape = api.eval_shape(fun, x, y) out_shape = tree_util.tree_map(np.shape, out_shape) self.assertEqual(out_shape, {'hi': (2,)}) def test_eval_shape_shape_error(self): def fun(x, y): return jnp.tanh(jnp.dot(x, y) + 3.) x = jnp.ones((3, 3)) y = jnp.ones((4, 4)) self.assertRaises(TypeError, lambda: api.eval_shape(fun, x, y)) def test_eval_shape_duck_typing(self): def fun(A, b, x): return jnp.dot(A, x) + b class MyArgArray(object): def __init__(self, shape, dtype): self.shape = shape self.dtype = np.dtype(dtype) A = MyArgArray((3, 4), jnp.float32) b = MyArgArray((5,), jnp.float32) x = MyArgArray((4, 5), jnp.float32) out_shape = api.eval_shape(fun, A, b, x) self.assertEqual(out_shape.shape, (3, 5)) def test_eval_shape_duck_typing2(self): class EasyDict(dict): def __init__(self, *args, **kwargs): super().__init__(*args, **kwargs) self.__dict__ = self x = EasyDict(shape=(3,), dtype=np.dtype('float32')) out_shape = api.eval_shape(lambda x: x, x) self.assertEqual(out_shape.shape, (3,)) def test_eval_shape_names(self): def fun(x, y): return lax.psum(x, 'i') + y class MyArgArray(object): def __init__(self, shape, dtype, named_shape): self.shape = shape self.dtype = jnp.dtype(dtype) self.named_shape = named_shape x = MyArgArray((3, 2), jnp.float32, {'i': 10}) y = MyArgArray((3, 2), jnp.float32, {'j': 5}) with core.extend_axis_env('i', 10, None): with core.extend_axis_env('j', 5, None): out_shape = api.eval_shape(fun, x, y) self.assertEqual(out_shape.named_shape, {'j': 5}) def test_issue_871(self): T = jnp.array([[1., 2.], [3., 4.], [5., 6.]]) x = jnp.array([1, 2, 3]) msg = ("linearized function called on tangent values inconsistent with " "the original primal values") y, f_jvp = api.linearize(jnp.sum, x) with self.assertRaisesRegex(ValueError, msg): f_jvp(T) y, f_jvp = api.linearize(api.jit(jnp.sum), x) with self.assertRaisesRegex(ValueError, msg): f_jvp(T) def test_grad_of_int_errors(self): # Errors without allow_int=True dfn = grad(lambda x: x ** 2) self.assertRaisesRegex( TypeError, (r"grad requires real- or complex-valued inputs $input dtype that is a " r"sub-dtype of np.inexact$, but got int.*."), lambda: dfn(3)) @unittest.skipIf(numpy_version == (1, 21, 0), "https://github.com/numpy/numpy/issues/19305") def test_jvp_of_int_identity(self): primals = (1,) tangents = (np.zeros(shape=(), dtype=float0),) _, out = api.jvp(lambda x: x, primals, tangents) self.assertEqual(out, np.zeros(shape=(), dtype=float0)) @unittest.skipIf(numpy_version == (1, 21, 0), "https://github.com/numpy/numpy/issues/19305") def test_jvp_of_int_add(self): primals = (2,) tangents = (np.zeros(shape=(), dtype=float0),) _, out_tangent = api.jvp(lambda x: x+1, primals, tangents) self.assertEqual(out_tangent, np.zeros(shape=(), dtype=float0)) @unittest.skipIf(numpy_version == (1, 21, 0), "https://github.com/numpy/numpy/issues/19305") def test_jit_jvp_of_int(self): primals = (2,) tangents = (np.zeros(shape=(), dtype=float0),) _, out_tangent = api.jvp(jax.jit(lambda x: x+1), primals, tangents) self.assertEqual(out_tangent, np.zeros(shape=(), dtype=float0)) @unittest.skipIf(numpy_version == (1, 21, 0), "https://github.com/numpy/numpy/issues/19305") def test_vjp_of_int_index(self): primal, fn_vjp = api.vjp(lambda x, i: x[i], np.ones(2)*2, 1) tangent_x, tangent_i = fn_vjp(1.) self.assertEqual(primal, 2.) self.assertAllClose(tangent_x, jnp.array([0., 1.])) self.assertEqual(tangent_i, np.zeros(shape=(), dtype=float0)) @unittest.skipIf(numpy_version == (1, 21, 0), "https://github.com/numpy/numpy/issues/19305") def test_vjp_of_int_shapes(self): out, fn_vjp = api.vjp(lambda x: lax.reshape(x, (2, 2)), np.ones((4, 1), dtype=int)) tangent, = fn_vjp(out) self.assertArraysEqual(tangent, np.zeros(shape=(4, 1), dtype=float0)) @unittest.skipIf(numpy_version == (1, 21, 0), "https://github.com/numpy/numpy/issues/19305") def test_jit_vjp_of_int(self): primal, fn_vjp = api.vjp(lambda x, y: x+y, 2, 1) tangent_x, tangent_i = jax.jit(fn_vjp)(1) self.assertEqual(primal, 3) self.assertEqual(tangent_x, np.zeros(shape=(), dtype=float0)) self.assertEqual(tangent_i, np.zeros(shape=(), dtype=float0)) @unittest.skipIf(numpy_version == (1, 21, 0), "https://github.com/numpy/numpy/issues/19305") def test_vjp_of_int_fulllike(self): # Regression test for tangent and cotangent mismatch in convert_element_type # transpose rule wrt a ConstVar f = lax.full_like out, vjp = api.vjp(f, np.zeros((2, 2)), 1) self.assertAllClose(out, jnp.ones((2, 2))) tangent_x, tangent_y = vjp(out) self.assertAllClose(tangent_x, jnp.zeros((2, 2))) self.assertEqual(tangent_y, np.zeros(shape=(), dtype=float0)) @unittest.skipIf(numpy_version == (1, 21, 0), "https://github.com/numpy/numpy/issues/19305") def test_grad_of_int(self): # Need real-valued output, but testing integer input. out = api.grad(lambda x: x+0., allow_int=True)(1) self.assertEqual(out, np.zeros(shape=(), dtype=float0)) @unittest.skipIf(numpy_version == (1, 21, 0), "https://github.com/numpy/numpy/issues/19305") def test_grad_of_bool(self): def cond(pred): return lax.cond(pred, lambda _: 1., lambda _: 2., 1.) value, grd = api.value_and_grad(cond, allow_int=True)(True) self.assertEqual(value, 1.) self.assertEqual(grd, np.zeros(shape=(), dtype=float0)) @unittest.skipIf(numpy_version == (1, 21, 0), "https://github.com/numpy/numpy/issues/19305") def test_grad_of_int_index(self): grad_x, grad_i = api.grad(lambda x, i: x[i], argnums=(0, 1), allow_int=True)(np.ones(2), 1) self.assertAllClose(grad_x, jnp.array([0., 1.])) self.assertEqual(grad_i, np.zeros(shape=(), dtype=float0)) @unittest.skipIf(numpy_version == (1, 21, 0), "https://github.com/numpy/numpy/issues/19305") def test_jit_grad_of_int(self): grad_f = api.grad(lambda x, i: x[i], argnums=(0, 1), allow_int=True) grad_x, grad_i = jax.jit(grad_f)(np.ones(2), 1) self.assertAllClose(grad_x, jnp.array([0., 1.])) self.assertEqual(grad_i, np.zeros(shape=(), dtype=float0)) @unittest.skipIf(numpy_version == (1, 21, 0), "https://github.com/numpy/numpy/issues/19305") def test_float0_reshape(self): # dtype-agnostic operations are supported float0_array = jax.grad(lambda x: jnp.sum(x+0.), allow_int=True)(np.ones((2, 4), dtype=int)) self.assertArraysEqual(float0_array.reshape((4, 2)), np.zeros((4, 2), dtype=float0)) self.assertArraysEqual(float0_array.transpose(), np.zeros((4, 2), dtype=float0)) def test_float0_error(self): # float0 is incompatible with other dtypes float0_array = jax.grad(lambda x: x+0., allow_int=True)(1) error_text = "float0s do not support any operations by design" with self.assertRaisesRegex(TypeError, error_text): # dispatch via DeviceArray _ = float0_array + jnp.zeros(()) with self.assertRaisesRegex(TypeError, error_text): # dispatch via lax _ = lax.add(float0_array, jnp.zeros(())) def test_grad_complex_result_errors(self): dfn = grad(lambda x: x ** 2 + 1j) self.assertRaisesRegex( TypeError, (r"grad requires real-valued outputs $output dtype that is a " r"sub-dtype of np.floating$, but got complex.*"), lambda: dfn(3.)) def test_holomorphic_grad_of_float_errors(self): dfn = grad(lambda x: x ** 2, holomorphic=True) self.assertRaisesRegex( TypeError, (r"grad with holomorphic=True requires inputs with complex dtype, " r"but got float.*"), lambda: dfn(3.)) def test_holomorphic_jacrev_of_float_errors(self): dfn = jacrev(lambda x: x ** 2, holomorphic=True) self.assertRaisesRegex( TypeError, (r"jacrev with holomorphic=True requires inputs with complex dtype, " r"but got float.*"), lambda: dfn(3.)) def test_holomorphic_jacfwd_of_float_errors(self): dfn = jacfwd(lambda x: x ** 2, holomorphic=True) self.assertRaisesRegex( TypeError, (r"jacfwd with holomorphic=True requires inputs with complex dtype, " r"but got float.*"), lambda: dfn(3.)) def test_jacfwd_of_complex_errors(self): dfn = jacfwd(lambda x: x ** 2) self.assertRaisesRegex( TypeError, (r"jacfwd requires real-valued inputs $input dtype that is a " r"sub-dtype of np.floating$, but got complex.*"), lambda: dfn(3. + 1j)) def test_xla_computation(self): # these tests basically check the examples in the xla_computation docstring def e(x): return jnp.sin(jnp.cos(x)) c = api.xla_computation(e)(2.) self.assertIn('cosine', c.as_hlo_text()) self.assertIn('sine', c.as_hlo_text()) def f(x): return x - lax.psum(x, 'i') axis_env = [('i', 4)] c = api.xla_computation(f, axis_env=axis_env)(2) self.assertIn('all-reduce', c.as_hlo_text()) self.assertIn('replica_groups={{0,1,2,3}}', c.as_hlo_text()) def g(x): rowsum = lax.psum(x, 'i') colsum = lax.psum(x, 'j') allsum = lax.psum(x, ('i', 'j')) return rowsum, colsum, allsum axis_env = [('i', 4), ('j', 2)] c = api.xla_computation(g, axis_env=axis_env)(5.) self.assertIn('all-reduce', c.as_hlo_text()) self.assertIn('replica_groups={{0,2,4,6},{1,3,5,7}}', c.as_hlo_text()) self.assertIn('replica_groups={{0,1},{2,3},{4,5},{6,7}}', c.as_hlo_text()) self.assertIn('replica_groups={{0,1,2,3,4,5,6,7}}', c.as_hlo_text()) def h(x): rowsum = lax.psum(x, 'i', axis_index_groups=[[0, 1], [2, 3]]) colsum = lax.psum(x, 'j') return rowsum, colsum axis_env = [('i', 4), ('j', 2)] c = api.xla_computation(h, axis_env=axis_env)(5.) self.assertIn('all-reduce', c.as_hlo_text()) self.assertIn('replica_groups={{0,2},{4,6},{1,3},{5,7}}', c.as_hlo_text()) self.assertIn('replica_groups={{0,1},{2,3},{4,5},{6,7}}', c.as_hlo_text()) def test_xla_computation_args(self): def foo(x, y, z): return x + y + z c = api.xla_computation(foo)(1., 2., 3.) self.assertEqual(len(c.program_shape().parameter_shapes()), 3) c = api.xla_computation(foo, tuple_args=True)(1., 2., 3.) param_shapes = c.program_shape().parameter_shapes() self.assertEqual(len(param_shapes), 1) self.assertEqual(param_shapes[0].xla_element_type(), xla_client.PrimitiveType.TUPLE) def test_xla_computation_duck_typing(self): def foo(x, y, z): return x + y + z x = jax.ShapeDtypeStruct((), np.float32) y = jax.ShapeDtypeStruct((), np.float32) z = jax.ShapeDtypeStruct((), np.float32) c = api.xla_computation(foo)(x, y, z) self.assertEqual(len(c.program_shape().parameter_shapes()), 3) c = api.xla_computation(foo, tuple_args=True)(1., 2., 3.) param_shapes = c.program_shape().parameter_shapes() self.assertEqual(len(param_shapes), 1) self.assertEqual(param_shapes[0].xla_element_type(), xla_client.PrimitiveType.TUPLE) def test_staging_out_multi_replica(self): def f(x): return api.pmap(jnp.mean)(x) xla_comp = api.xla_computation(f) xla_comp(jnp.arange(8)).as_hlo_text() # doesn't crash def test_xla_computation_instantiate_constant_outputs(self): def f(): return jnp.zeros((3, 4)) xla_comp = api.xla_computation(f)() out_shape, = xla_comp.program_shape().result_shape().tuple_shapes() self.assertEqual(out_shape.dimensions(), (3, 4)) def test_xla_computation_static_argnums(self): def f(x, y): return x + y xla_comp = api.xla_computation(f, static_argnums=(1,))(2, 3) hlo_text = xla_comp.as_hlo_text() self.assertIn("constant(3)", hlo_text) self.assertIn("parameter.1", hlo_text) self.assertNotIn("parameter.2", hlo_text) def test_xla_computation_return_shape(self): _, shape_tree = api.xla_computation(lambda x: (x + 1, jnp.zeros(2, jnp.float32)), return_shape=True)(np.int32(1)) expected = (api.ShapeDtypeStruct(shape=(), dtype=jnp.int32), api.ShapeDtypeStruct(shape=(2,), dtype=jnp.float32)) self.assertEqual(shape_tree, expected) def test_xla_computation_partitioned(self): def f(x, y): return jnp.dot(x, y) + 1 x = jax.ShapeDtypeStruct((8, 8), np.float32) y = jax.ShapeDtypeStruct((8, 16), np.float32) xla_comp = api.xla_computation(f, in_parts=(P(2, 2), None), out_parts=P(4, 1))(x, y) hlo_text = xla_comp.as_hlo_text() self.assertIn('sharding={devices=[2,2]0,1,2,3}', hlo_text) self.assertIn('sharding={replicated}', hlo_text) self.assertIn('sharding={{devices=[4,1]0,1,2,3}}', hlo_text) def test_xla_computation_replicated_and_partitioned(self): def f(x, y): return jnp.dot(x, y), lax.psum(x, 'i') x = jax.ShapeDtypeStruct((8, 8), np.float32) y = jax.ShapeDtypeStruct((8, 16), np.float32) axis_env = [('i', 4)] xla_comp = api.xla_computation(f, axis_env=axis_env, in_parts=(P(2, 2), None), out_parts=(P(4, 1), None))(x, y) hlo_text = xla_comp.as_hlo_text() self.assertIn('all-reduce', hlo_text) self.assertIn('replica_groups={{0,1,2,3}}', hlo_text) self.assertIn('sharding={devices=[2,2]0,1,2,3}', hlo_text) self.assertIn('sharding={replicated}', hlo_text) self.assertIn('sharding={{devices=[4,1]0,1,2,3}, {replicated}}', hlo_text) def test_xla_computation_psum_constant(self): f = lambda: jax.lax.psum(1, "i") api.xla_computation(f, axis_env=[("i", 2)])() @jtu.skip_on_devices("cpu", "gpu") @jtu.ignore_warning(message="Some donated buffers were not usable") def test_xla_computation_donate_argnums(self): api.xla_computation(lambda x: None, donate_argnums=(0,))(3) # doesn't crash def test_xla_computation_lower_fun_axis_env(self): axis_name = 'i' def fn(x): y = lax.all_gather( x, axis_name=axis_name) return y * lax.axis_index(axis_name).astype(jnp.float32) input_x = jnp.ones((5,6,4)) axis_env = [(axis_name, api.local_device_count())] _ = api.xla_computation(fn, axis_env=axis_env, backend='cpu')(input_x) def test_xla_computation_axis_env(self): def fn(x): z = x * jax.lax.axis_index('i').astype(jnp.float32) def inner_fn(carry, a): return carry + a, () return jax.lax.scan(inner_fn, jnp.zeros_like(z[0]), z) x = jnp.ones((5, 6, 4)) _ = jax.xla_computation(fn, axis_env=(('i', 8),), backend='cpu')(x) def test_concurrent_device_get_and_put(self): def f(x): for _ in range(100): y = jax.device_put(x) x = jax.device_get(y) return x xs = [np.random.randn(i) for i in range(10)] with concurrent.futures.ThreadPoolExecutor() as executor: futures = [executor.submit(partial(f, x)) for x in xs] ys = [f.result() for f in futures] for x, y in zip(xs, ys): self.assertAllClose(x, y) def test_dtype_warning(self): if config.x64_enabled: raise unittest.SkipTest("test only applies when x64 is disabled") def check_warning(warn, nowarn): with warnings.catch_warnings(record=True) as w: warnings.simplefilter("always") nowarn() prev_len = len(w) nowarn() assert len(w) == prev_len warn() assert len(w) > 0 msg = str(w[-1].message) expected_prefix = "Explicitly requested dtype " self.assertEqual(expected_prefix, msg[:len(expected_prefix)]) prev_len = len(w) nowarn() assert len(w) == prev_len check_warning(lambda: jnp.array([1, 2, 3], dtype="float64"), lambda: jnp.array([1, 2, 3], dtype="float32")) check_warning(lambda: jnp.array([1, 2, 3], dtype="float64"), lambda: jnp.array([1, 2, 3], dtype=float)) check_warning(lambda: jnp.ones(3, dtype=np.float64), lambda: jnp.ones(3)) check_warning(lambda: jnp.ones(3, dtype=np.float64), lambda: jnp.ones(3, dtype=float)) check_warning(lambda: jnp.ones_like(3, dtype=np.int64), lambda: jnp.ones_like(3, dtype=np.int32)) check_warning(lambda: jnp.zeros(3, dtype="int64"), lambda: jnp.zeros(3, dtype="int32")) check_warning(lambda: jnp.zeros_like(3, dtype="float64"), lambda: jnp.zeros_like(3, dtype="float32")) check_warning(lambda: jnp.full((2, 3), 1, dtype="int64"), lambda: jnp.full((2, 3), 1)) check_warning(lambda: jnp.ones(3).astype("float64"), lambda: jnp.ones(3).astype("float32")) check_warning(lambda: jnp.eye(3, dtype=np.float64), lambda: jnp.eye(3)) check_warning(lambda: jnp.arange(3, dtype=np.float64), lambda: jnp.arange(3, dtype=np.float32)) check_warning(lambda: jnp.linspace(0, 3, dtype=np.float64), lambda: jnp.linspace(0, 3, dtype=np.float32)) check_warning(lambda: jnp.tri(2, dtype="float64"), lambda: jnp.tri(2, dtype="float32")) check_warning(lambda: jnp.arange(1).astype("float64"), lambda: jnp.arange(1).astype(float)) check_warning(lambda: jnp.arange(1.0).astype("int64"), lambda: jnp.arange(1.0).astype(int)) def test_error_for_invalid_dtype(self): with self.assertRaisesRegex(TypeError, ".*not a valid JAX array type.*"): lax.add(jnp.array(7), np.array("hello")) def test_vmap_preserves_docstr(self): def superfun(a): pass self.assertRegex(api.vmap(superfun).__doc__, "\n".join([ "Vectorized version of superfun.*", "", "Original documentation:", "", superfun.__doc__, ])) def test_vmap_in_axes_list(self): dictionary = {'a': 5., 'b': jnp.ones(2)} x = jnp.zeros(3) y = jnp.arange(3.) def f(dct, x, y): return dct['a'] + dct['b'] + x + y out1 = api.vmap(f, (None, 0, 0))(dictionary, x, y) out2 = api.vmap(f, [None, 0, 0])(dictionary, x, y) self.assertAllClose(out1, out2) def test_vmap_in_axes_tree_prefix_error(self): value_tree = jnp.ones(3) self.assertRaisesRegex( ValueError, "vmap in_axes specification must be a tree prefix of the corresponding " r"value, got specification $0, 0$ for value tree " + re.escape(f"{tree_util.tree_structure((value_tree,))}."), lambda: api.vmap(lambda x: x, in_axes=(0, 0))(value_tree) ) def test_vmap_in_axes_leaf_types(self): with self.assertRaisesRegex( TypeError, r"vmap in_axes must be an int, None, or .*"): api.vmap(lambda x: x, in_axes=(jnp.array([1., 2.]),))(jnp.array([1., 2.])) def test_vmap_out_axes_leaf_types(self): with self.assertRaisesRegex( TypeError, r"vmap out_axes must be an int, None, or .*"): api.vmap(lambda x: x, out_axes=(jnp.array([1., 2.]),))(jnp.array([1., 2.])) def test_vmap_unbatched_object_passthrough_issue_183(self): fun = lambda f, x: f(x) vfun = api.vmap(fun, (None, 0)) ans = vfun(lambda x: x + 1, jnp.arange(3)) self.assertAllClose(ans, np.arange(1, 4), check_dtypes=False) def test_vmap_mismatched_axis_sizes_error_message_issue_705(self): def h(a, b): return jnp.sum(a) + jnp.sum(b) X = np.random.randn(10, 4) U = np.random.randn(10, 2) with self.assertRaisesRegex( ValueError, "vmap got inconsistent sizes for array axes to be mapped:\n" r"arg 0 has shape $10, 4$ and axis 0 is to be mapped" "\n" r"arg 1 has shape $10, 2$ and axis 1 is to be mapped" "\n" "so\n" "arg 0 has an axis to be mapped of size 10\n" "arg 1 has an axis to be mapped of size 2"): api.vmap(h, in_axes=(0, 1))(X, U) with self.assertRaisesRegex( ValueError, "vmap got inconsistent sizes for array axes to be mapped:\n" r"arg 0 has shape $10, 4$ and axis 0 is to be mapped" "\n" r"arg 1 has shape $10, 2$ and axis 1 is to be mapped" "\n" r"arg 2 has shape $10, 4$ and axis 0 is to be mapped" "\n" "so\n" "args 0, 2 have axes to be mapped of size 10\n" "arg 1 has an axis to be mapped of size 2"): api.vmap(lambda x, y, z: None, in_axes=(0, 1, 0))(X, U, X) with self.assertRaisesRegex( ValueError, "vmap got inconsistent sizes for array axes to be mapped:\n" "the tree of axis sizes is:\n" r"$10, \[2, 2\]$"): api.vmap(h, in_axes=(0, 1))(X, [U, U]) error = (r"vmap was requested to map its argument along axis 0, which " r"implies that its rank should be at least 1, but is only 0 " r"$its shape is \($\)") with self.assertRaisesRegex(ValueError, error): api.vmap(lambda x: x)(1.) with self.assertRaisesRegex( ValueError, "vmap must have at least one non-None value in in_axes"): api.vmap(lambda x: x, in_axes=None)(jnp.array([1., 2.])) error = (r"vmap was requested to map its argument along axis 1, which " r"implies that its rank should be at least 2, but is only 1 " r"$its shape is \(2,$\)") with self.assertRaisesRegex(ValueError, error): api.vmap(lambda x: x, in_axes=1)(jnp.array([1., 2.])) with self.assertRaisesRegex( ValueError, "vmap out_axes specification must be a tree prefix of the " "corresponding value.*"): api.vmap(lambda x: x, in_axes=0, out_axes=(2, 3))(jnp.array([1., 2.])) with self.assertRaisesRegex( ValueError, r"vmap has mapped output $axis_name=foo$ but out_axes is None"): api.vmap(lambda x: x, out_axes=None, axis_name="foo")(jnp.array([1., 2.])) with self.assertRaisesRegex( ValueError, "vmap has mapped output but out_axes is None"): api.vmap(lambda x: x, out_axes=None)(jnp.array([1., 2.])) def test_vmap_structured_in_axes(self): A, B, C, D = 2, 3, 4, 5 K = 6 x = np.ones((K, A, B)) y = np.ones((B, K, C)) z = np.ones((C, D, K)) def foo(tree_arg): x, (y, z) = tree_arg return jnp.dot(x, jnp.dot(y, z)) tree = (x, (y, z)) vfoo = api.vmap(foo, in_axes=((0, (1, 2)),)) self.assertEqual(vfoo(tree).shape, (6, 2, 5)) Point = collections.namedtuple("Point", ["x", "y"]) tree = (x, Point(y, z)) vfoo = api.vmap(foo, in_axes=((0, Point(1, 2)),)) self.assertEqual(vfoo(tree).shape, (6, 2, 5)) def foo(tree_arg): x, dct = tree_arg y, z = dct['a'], dct['b'] return jnp.dot(x, jnp.dot(y, z)) tree = (x, {'a': y, 'b': z}) vfoo = api.vmap(foo, in_axes=((0, {'a': 1, 'b': 2}),)) self.assertEqual(vfoo(tree).shape, (6, 2, 5)) tree = (x, collections.OrderedDict([('a', y), ('b', z)])) vfoo = api.vmap( foo, in_axes=((0, collections.OrderedDict([('a', 1), ('b', 2)])),)) self.assertEqual(vfoo(tree).shape, (6, 2, 5)) def test_vmap_in_axes_bool_error(self): with self.assertRaisesRegex(TypeError, "must be an int"): api.vmap(lambda x: x, in_axes=False)(jnp.zeros(3)) def test_pmap_in_axes_bool_error(self): with self.assertRaisesRegex(TypeError, "must be an int"): api.pmap(lambda x: x, in_axes=False)(jnp.zeros(1)) def test_pmap_global_cache(self): def f(x, y): return x, y x = np.ones((1, 1, 1)) with jtu.assert_num_jit_and_pmap_compilations(1): for _ in range(2): api.pmap(f)(x, x) with jtu.assert_num_jit_and_pmap_compilations(1): for _ in range(2): api.pmap(f, 'i')(x, x) for x_in, y_in, x_out, y_out in it.product(*((0, 1, 2) for _ in range(4))): with jtu.assert_num_jit_and_pmap_compilations(1): for _ in range(2): api.pmap(f, 'i', in_axes=(x_in, y_in), out_axes=(x_out, y_out))(x, x) with jtu.assert_num_jit_and_pmap_compilations(1): for _ in range(2): api.jvp(api.pmap(f), (x, x), (x, x)) with jtu.assert_num_jit_and_pmap_compilations(2): for _ in range(2): api.vjp(api.pmap(f), x, x)[1]((x, x)) def test_device_array_repr(self): rep = jnp.ones(()) + 1. self.assertStartsWith(repr(rep), "DeviceArray") def test_device_array_hash(self): rep = jnp.ones(()) + 1. self.assertIsInstance(rep, jax.interpreters.xla.DeviceArray) self.assertNotIsInstance(rep, collections.abc.Hashable) with self.assertRaisesRegex(TypeError, 'unhashable type'): hash(rep) def test_grad_without_enough_args_error_message(self): def f(x, y): return x + y df = api.grad(f, argnums=0) self.assertRaisesRegex( TypeError, "differentiating with respect to argnums=0 requires at least 1 " "positional arguments to be passed by the caller, but got only 0 " "positional arguments.", lambda: partial(df, x=0.)(y=1.)) def test_grad_of_jit_compilation_caching(self): if not hasattr(self, "assertLogs"): raise unittest.SkipTest("test requires assertLogs (python 3)") lax.add(1, 2) sin = api.jit(jnp.sin) prev_level = logging.get_verbosity() try: logging.set_verbosity('DEBUG') with self.assertLogs(level=logging.DEBUG) as l: ans1 = api.grad(sin)(2.) ans2 = api.grad(sin)(3.) finally: logging.set_verbosity(prev_level) self.assertLen(l.output, 2) self.assertAllClose(ans1, np.cos(2.), check_dtypes=False) self.assertAllClose(ans2, np.cos(3.), check_dtypes=False) def test_grad_of_jit_compilation_caching2(self): @api.jit def f(x): return jnp.sin(x) with jtu.count_jit_and_pmap_compiles() as count: _ = jax.grad(f)(3.) self.assertEqual(count[0], 2) with jtu.count_jit_and_pmap_compiles() as count: _ = jax.grad(f)(3.) _ = jax.grad(f)(4.) self.assertEqual(count[0], 0) def test_grad_does_not_unflatten_tree_with_none(self): class CustomNode(list): pass def unflatten(unused_aux_data, children): self.assertIsNotNone(children[0]) return CustomNode(children) tree_util.register_pytree_node(CustomNode, lambda x: (x, None), unflatten) grad(lambda x: x[0])(CustomNode([0.])) def test_trivial_computations(self): x = jnp.array([1, 2, 3]) y = api.jit(lambda x: x)(x) self.assertIs(x, y) z1, z2 = api.jit(lambda x: (x, x))(x) self.assertIs(z1, z2) x1, x2 = jnp.array([1, 2]), jnp.array([2, 3]) z1, z2, z3 = api.jit(lambda x, y: (y, 1, x))(x1, x2) self.assertIs(z1, x2) self.assertIs(z3, x1) self.assertEqual(z2, 1) def test_nested_jit_hoisting(self): @api.jit def f(x, y): z = 2 * x return y + z, 3 @api.jit def g(x): return f(2, x) jaxpr_subcomp = xla.jaxpr_subcomp jaxprs = [] def jaxpr_subcomp_and_collect(c, jaxpr, *args, **kwargs): jaxprs.append(jaxpr) return jaxpr_subcomp(c, jaxpr, *args, **kwargs) try: xla.jaxpr_subcomp = jaxpr_subcomp_and_collect ans = g(3) finally: xla.jaxpr_subcomp = jaxpr_subcomp self.assertEqual(ans, (7, 3)) self.assertLen(jaxprs, 2) outer_jaxpr, inner_jaxpr = jaxprs self.assertLen(outer_jaxpr.eqns, 1) self.assertEqual(outer_jaxpr.eqns[0].primitive.name, 'xla_call') subjaxpr_1 = outer_jaxpr.eqns[0].params["call_jaxpr"] self.assertEqual(str(subjaxpr_1), str(inner_jaxpr)) self.assertLen(inner_jaxpr.eqns, 2) self.assertEqual(inner_jaxpr.eqns[-2].primitive.name, 'mul') self.assertEqual(inner_jaxpr.eqns[-1].primitive.name, 'add') def test_primitive_compilation_cache(self): with jtu.count_primitive_compiles() as count: lax.add(1, 2) lax.add(2, 3) self.assertEqual(count[0], 1) def test_arange_jit(self): def fun(x): r = jnp.arange(x.shape[0])[x] return r jit(fun)(jnp.array([0, 1, 2], dtype=jnp.int32)) def helper_save_tracer(self, x): self._saved_tracer = x return x def test_escaped_tracers_different_top_level_traces(self): api.jit(self.helper_save_tracer)(0.) with self.assertRaisesRegex( UnexpectedTracerError, "Encountered an unexpected tracer"): api.jit(lambda x: self._saved_tracer)(0.) def test_escaped_tracers_cant_lift_sublevels(self): api.jit(self.helper_save_tracer)(0.) with self.assertRaisesRegex( UnexpectedTracerError, re.compile( "Encountered an unexpected tracer", re.DOTALL)): api.jit(lambda x: x)(self._saved_tracer) def test_escaped_tracers_tracer_from_higher_level(self): api.grad(self.helper_save_tracer)(0.) with self.assertRaisesRegex( UnexpectedTracerError, re.compile( "Encountered an unexpected tracer.*Tracer from a higher level", re.DOTALL)): api.grad(lambda x: x)(self._saved_tracer) def test_escaped_tracers_incompatible_sublevel(self): def func1(x): api.jit(self.helper_save_tracer)(0.) # Use the tracer return x + self._saved_tracer with self.assertRaisesRegex( UnexpectedTracerError, re.compile("Encountered an unexpected tracer", re.DOTALL)): api.jit(func1)(2.) def test_escaped_tracers_cant_lift(self): def func1(x): api.grad(self.helper_save_tracer)(0.) return x + self._saved_tracer with self.assertRaisesRegex( UnexpectedTracerError, re.compile("Encountered an unexpected tracer.*Can't lift", re.DOTALL)): api.grad(func1)(2.) def test_escaped_tracers_not_among_input_tracers(self): def func1(x): api.grad(self.helper_save_tracer)(x) return x + self._saved_tracer with self.assertRaisesRegex( UnexpectedTracerError, re.compile( "Encountered an unexpected tracer.*Tracer not among input tracers", re.DOTALL)): api.jit(func1)(2.) def test_escaped_tracer_omnistaging(self): count = 1 @jit def f(): nonlocal count count = jnp.add(count, 1) f() def f(x, c): jnp.add(count, 1) return None, None @jit def g(): lax.scan(f, None, None, length=2) with self.assertRaisesRegex(UnexpectedTracerError, "was created on line"): g() def test_escaped_tracer_omnistaging_top_trace(self): count = 1 def f(_, __): nonlocal count count = jnp.add(count, 1) return None, None lax.scan(f, None, None, length=2) with self.assertRaisesRegex(UnexpectedTracerError, "was created on line"): jax.jit(jnp.add)(jnp.ones(()), count) def test_escaped_tracer_transform_name(self): with self.assertRaisesRegex(UnexpectedTracerError, "for jit"): jax.jit(self.helper_save_tracer)(1) _ = self._saved_tracer+1 with self.assertRaisesRegex(UnexpectedTracerError, "for pmap"): jax.pmap(self.helper_save_tracer)(jnp.ones((1, 2))) _ = self._saved_tracer+1 with self.assertRaisesRegex(UnexpectedTracerError, "for eval_shape"): jax.eval_shape(self.helper_save_tracer, 1) _ = self._saved_tracer+1 def test_escaped_tracer_shape_dtype(self): with self.assertRaisesRegex(core.UnexpectedTracerError, r"shape $4, 3$ and dtype int32"): jax.jit(self.helper_save_tracer)(jnp.ones((4, 3), dtype=jnp.int32)) _ = self._saved_tracer+1 def test_pmap_static_kwarg_error_message(self): def f(a, b): return a + b g = jax.pmap(f, static_broadcasted_argnums=(1,)) msg = (r"pmapped function has static_broadcasted_argnums=$1,$ but was " r"called with only 1 positional argument. All static broadcasted " r"arguments must be passed positionally.") with self.assertRaisesRegex(ValueError, msg): g(jnp.ones((1, 1)), b=1) def test_vmap_unmapped_last(self): @partial(jax.vmap, out_axes=-1) def f(x): return np.zeros((2,)) f(np.zeros((5,))) @unittest.skipIf(True, "broken by convert_element_type change.") def test_xla_constant_dedup(self): y = np.array([7, 14], dtype=np.float32) def f(x): return x + y + y x = np.array([1, 2], dtype=np.float32) hlo_lines = jax.xla_computation(f)(x).as_hlo_text().split('\n') hlo_lines = set([s.strip() for s in hlo_lines]) self.assertIn('constant.1 = f32[2]{0} constant({7, 14})', hlo_lines) self.assertNotIn('constant.2 = f32[2]{0} constant({7, 14})', hlo_lines) def test_eval_context(self): @jit def f(): with core.eval_context(): assert jnp.add(1, 1) == 2 f() def test_concrete_error_because_arg_unary(self): @jax.jit def f(x): if x > 0: return x else: return 0 msg = r"on the value of the argument 'x'" with self.assertRaisesRegex(core.ConcretizationTypeError, msg): f(1) def test_concrete_error_because_arg_binary(self): @jax.jit def f(x, y): if x > y: return x else: return y msg = r"on the values of the arguments 'x' and 'y'" with self.assertRaisesRegex(core.ConcretizationTypeError, msg): f(1, 2) def test_concrete_error_because_arg_ternary(self): @jax.jit def f(x, y, z): if x > z: return x else: return y msg = r"on the values of the arguments 'x' and 'z'" with self.assertRaisesRegex(core.ConcretizationTypeError, msg): f(1, 2, 3) with self.assertRaisesRegex(core.ConcretizationTypeError, msg): f(1, 2, z=3) with self.assertRaisesRegex(core.ConcretizationTypeError, msg): f(1, y=2, z=3) def test_concrete_error_because_arg_varargs(self): @jax.jit def f(*args): x, y, z = args if x > z: return x else: return y msg = r"on the values of the argument 'args'" with self.assertRaisesRegex(core.ConcretizationTypeError, msg): f(1, 2, 3) def test_concrete_error_because_arg_kwargs(self): @jax.jit def f(**kwargs): x, y, z = kwargs['x'], kwargs['y'], kwargs['z'] if x > z: return x else: return y msg = r"on the values of the argument 'kwargs'" with self.assertRaisesRegex(core.ConcretizationTypeError, msg): f(x=1, y=2, z=3) def test_concrete_error_because_arg_pytree(self): @jax.jit def f(xy, z): x, y = xy if x > 0: return x else: return y msg = r"on the value of the argument 'xy'" with self.assertRaisesRegex(core.ConcretizationTypeError, msg): f((1, 2), z=3) def test_concrete_error_because_const(self): @jax.jit def f(): assert jnp.add(1, 1) > 0 msg = "on these lines" with self.assertRaisesRegex(core.ConcretizationTypeError, msg): f() def test_xla_computation_zeros_doesnt_device_put(self): with jtu.count_device_put() as count: api.xla_computation(lambda: jnp.zeros(3))() self.assertEqual(count[0], 0) def test_join_concrete_arrays_with_omnistaging(self): # https://github.com/google/jax/issues/4622 x = jnp.array([1., 2., 3.]) y = jnp.array([1., 2., 4.]) @jit def f(): core.lattice_join(core.ConcreteArray(x), core.ConcreteArray(y)) f() # doesn't crash def test_linearize_aval_error(self): f = lambda x: x _, f_jvp = api.linearize(f, 1.) f_jvp(1.) _, f_jvp = api.linearize(f, np.ones(2, np.int32)) f_jvp(np.zeros(2, float0)) _, f_jvp = api.linearize(f, 1.) with self.assertRaisesRegex(ValueError, "tangent values inconsistent"): f_jvp(1) _, f_jvp = api.linearize(f, np.ones(2, np.int32)) with self.assertRaisesRegex(ValueError, "tangent values inconsistent"): f_jvp(np.ones(2, np.int32)) def test_grad_of_token_consuming_primitive(self): tokentest_p = core.Primitive("tokentest") tokentest_p.def_impl(partial(xla.apply_primitive, tokentest_p)) tokentest_p.def_abstract_eval(lambda x, y: x) xla.translations[tokentest_p] = lambda c, x, y: x ad.defjvp(tokentest_p, (lambda g, x, token: x), None) token = jax.lax.create_token(123) arr = jnp.ones((3, 2)) res, vjp_fun = jax.vjp(lambda x: tokentest_p.bind(x, token), arr) vjp_fun(arr) def test_jit_returning_token(self): x = jax.jit(jax.lax.create_token)(1.0) self.assertIsInstance(x, jax.interpreters.xla.Token) def test_leak_checker_catches_a_jit_leak(self): with jax.checking_leaks(): lst = [] @jit def f(x): lst.append(x) return x with self.assertRaisesRegex(Exception, r"Leaked"): f(3) def test_leak_checker_catches_a_pmap_leak(self): with jax.checking_leaks(): lst = [] @api.pmap def f(x): lst.append(x) return x with self.assertRaisesRegex(Exception, r"Leaked"): f(np.ones(1)) def test_leak_checker_catches_a_grad_leak(self): with jax.checking_leaks(): lst = [] def f(x): lst.append(x) return x with self.assertRaisesRegex(Exception, r"Leaked trace"): api.grad(f)(3.) def test_leak_checker_avoids_false_positives(self): with jax.checking_leaks(): @jit def f(x): return x f(3) api.vmap(f)(np.arange(3)) # doesn't crash api.grad(f)(3.) @api.pmap def f(x): return x f(np.ones(1)) # doesn't crash api.vmap(f)(np.ones((1, 1))) def test_leak_checker_catches_a_scan_leak(self): with jax.checking_leaks(): lst = [] to_scan = lambda c, x: (lst.append(c) or jnp.sin(c), None) with self.assertRaisesRegex(Exception, r"Leaked trace"): lax.scan(to_scan, 1., np.arange(3.)) def test_leak_checker_avoids_false_positives_scan(self): with jax.checking_leaks(): to_scan = lambda c, x: (jnp.sin(c), None) lax.scan(to_scan, 1., np.arange(3.)) # doesn't crash def test_leak_checker_avoids_false_positives_scan_jvp(self): with jax.checking_leaks(): to_scan = lambda c, x: (c, None) def f(x): lax.scan(to_scan, x, None, length=1) api.jvp(f, (3.,), (1.,)) def test_leak_checker_avoids_false_positives_scan_vmap(self): with jax.checking_leaks(): to_scan = lambda c, _: (1., None) @api.vmap def f(x): lax.scan(to_scan, x, None, length=1) f(np.arange(5.)) # doesn't crash def test_leak_checker_avoids_false_positives_scan_vmap_2(self): with jax.checking_leaks(): to_scan = lambda c, _: (c, None) @api.vmap def f(x): lax.scan(to_scan, x, None, length=1) f(np.arange(5.)) def test_leak_checker_catches_a_sublevel_leak(self): with jax.checking_leaks(): @jit def f(x): lst = [] @jit def g(x): lst.append(x) return x x = g(x) return x with self.assertRaisesRegex(Exception, r"Leaked sublevel"): f(3) def test_leak_checker_avoids_false_positive_custom_jvp(self): # see https://github.com/google/jax/issues/5636 with jax.checking_leaks(): @api.custom_jvp def t(y): return y def t_jvp(p, t): pass t.defjvp(t_jvp) @jit def s(y): return t(y) s(3) # doesn't crash def test_default_backend(self): first_local_device = api.local_devices()[0] self.assertEqual(first_local_device.platform, api.default_backend()) def test_dunder_jax_array(self): class AlexArray: def __init__(self, jax_val): self.jax_val = jax_val def __jax_array__(self): return self.jax_val dtype = property(lambda self: self.jax_val.dtype) shape = property(lambda self: self.jax_val.shape) x = AlexArray(jnp.array([1., 2., 3.])) y = jnp.sin(x) self.assertAllClose(y, jnp.sin(jnp.array([1., 2., 3.]))) y = api.grad(api.jit(lambda x: jnp.sin(x).sum()))(x) self.assertAllClose(y, jnp.cos(jnp.array([1., 2., 3.]))) x = AlexArray(jnp.array([[1., 2., 3.]])) y = api.pmap(jnp.sin)(x) self.assertAllClose(y, jnp.sin(jnp.array([[1., 2., 3.]]))) x = jnp.array(1) a = AlexArray(x) for f in [jnp.isscalar, jnp.size, jnp.shape, jnp.dtype]: self.assertEqual(f(x), f(a)) def test_constant_handler_mro(self): class Foo(enum.IntEnum): bar = 1 @api.pmap def f(_): return Foo.bar ans = f(jnp.arange(1)) expected = jnp.arange(1) + 1 self.assertAllClose(ans, expected) def test_large_python_ints(self): with self.assertRaises(OverflowError): jnp.multiply(2 ** 100, 3.) out = lax.convert_element_type(2 ** 100, jnp.float32) # doesn't crash self.assertArraysEqual(out, np.float32(2 ** 100)) def test_dot_precision_context_manager(self): x = jnp.zeros((2, 2)) with jax.default_matmul_precision(None): jnp.dot(x, x) jaxpr = jax.make_jaxpr(jnp.dot)(x, x) self.assertIn('precision=None', str(jaxpr)) with jax.default_matmul_precision("bfloat16"): x @ x # doesn't crash jaxpr = jax.make_jaxpr(op.matmul)(x, x) self.assertIn('Precision.DEFAULT', str(jaxpr)) with jax.default_matmul_precision("tensorfloat32"): jnp.dot(x, x) jaxpr = jax.make_jaxpr(jnp.dot)(x, x) self.assertIn('Precision.HIGH', str(jaxpr)) with jax.default_matmul_precision("float32"): jnp.dot(x, x) # doesn't crash jaxpr = jax.make_jaxpr(jnp.dot)(x, x) self.assertIn('Precision.HIGHEST', str(jaxpr)) dot = partial(jnp.dot, precision=lax.Precision.HIGHEST) with jax.default_matmul_precision("tensorfloat32"): dot(x, x) jaxpr = jax.make_jaxpr(dot)(x, x) self.assertIn('Precision.HIGHEST', str(jaxpr)) def test_dot_precision_flag(self): x = jnp.zeros((2, 2)) prev_val = config._read("jax_default_matmul_precision") try: config.FLAGS.jax_default_matmul_precision = "tensorfloat32" jnp.dot(x, x) # doesn't crash jaxpr = jax.make_jaxpr(jnp.dot)(x, x) finally: config.FLAGS.jax_default_matmul_precision = prev_val self.assertIn('Precision.HIGH', str(jaxpr)) self.assertEqual(prev_val, config._read("jax_default_matmul_precision")) prev_val = config._read("jax_default_matmul_precision") try: config.update('jax_default_matmul_precision','tensorfloat32') jnp.dot(x, x) jaxpr = jax.make_jaxpr(jnp.dot)(x, x) finally: config.update('jax_default_matmul_precision', prev_val) self.assertIn('Precision.HIGH', str(jaxpr)) self.assertEqual(prev_val, config._read("jax_default_matmul_precision")) def test_dot_precision_forces_retrace(self): num_traces = 0 def g(x): nonlocal num_traces num_traces += 1 return jnp.dot(x, x) def f_cond(x): return lax.cond(True, g, g, x) @jax.jit def f_jit(x): nonlocal num_traces num_traces += 1 return jnp.dot(x, x) for f in [f_jit, f_cond]: precision = config.jax_default_matmul_precision try: num_traces = 0 x = jnp.zeros((2, 2)) f(x) self.assertEqual(num_traces, 1) f(x) self.assertEqual(num_traces, 1) with jax.default_matmul_precision("tensorfloat32"): f(x) self.assertEqual(num_traces, 2) FLAGS.jax_default_matmul_precision = "float32" f(x) self.assertGreaterEqual(num_traces, 2) nt = num_traces f(x) self.assertEqual(num_traces, nt + 1) f(x) self.assertEqual(num_traces, nt + 1) finally: FLAGS.jax_default_matmul_precision = precision def test_rank_promotion_forces_retrace(self): num_traces = 0 def g(x): nonlocal num_traces num_traces += 1 return x + x def f_cond(x): return lax.cond(True, g, g, x) @jax.jit def f_jit(x): nonlocal num_traces num_traces += 1 return x + x for f in [f_jit, f_cond]: allow_promotion = config.jax_numpy_rank_promotion try: num_traces = 0 @jax.jit def f(x): nonlocal num_traces num_traces += 1 return x + x x = jnp.zeros((2, 2)) f(x) self.assertEqual(num_traces, 1) f(x) self.assertEqual(num_traces, 1) with jax.numpy_rank_promotion("warn"): f(x) self.assertEqual(num_traces, 2) FLAGS.jax_numpy_rank_promotion = "raise" f(x) self.assertGreaterEqual(num_traces, 2) nt = num_traces f(x) self.assertEqual(num_traces, nt + 1) f(x) self.assertEqual(num_traces, nt + 1) finally: FLAGS.jax_numpy_rank_promotion = allow_promotion def test_backward_pass_ref_dropping(self): refs = [] @api.custom_vjp def f(x): return x def f_fwd(x): return x, None def f_rev(_, g): assert len(refs) != 2 or refs[0]() is None zero = np.zeros(()) refs.append(weakref.ref(zero)) return (zero,) f.defvjp(f_fwd, f_rev) api.grad(lambda x: f(f(f(x))))(1.) def test_custom_vjp_scan_batching_edge_case(self): # https://github.com/google/jax/issues/5832 @jax.custom_vjp def mul(x, coeff): return x * coeff def mul_fwd(x, coeff): return mul(x, coeff), (x, coeff) def mul_bwd(res, g): x, coeff = res g_x = g * coeff g_coeff = (x * g).sum() return g_x, g_coeff mul.defvjp(mul_fwd, mul_bwd) def scan_over_mul(x, coeff): def f_(x, t): return mul(x, coeff), None y, _ = jax.lax.scan(f_, x, jnp.arange(3)) return y key = jax.random.PRNGKey(0) key1, key2 = jax.random.split(key, 2) x_batch = jax.random.normal(key1, (3, 2)) covector_batch = jax.random.normal(key2, (3, 2)) coeff = jnp.array(1.) batched_scan_over_mul = jax.vmap(scan_over_mul, in_axes=(0, None), out_axes=0) res, vjp_fun = jax.vjp(batched_scan_over_mul, x_batch, coeff) vjp_fun(covector_batch) # doesn't crash jtu.check_grads(batched_scan_over_mul, (x_batch, coeff), order=2, modes=['rev']) def test_jit_inline(self): @partial(api.jit, inline=False) def f(x): return x * 2 jaxpr = api.make_jaxpr(f)(3) self.assertIn('xla_call', str(jaxpr)) @partial(api.jit, inline=True) def f(x): return x * 2 jaxpr = api.make_jaxpr(f)(3) self.assertNotIn('xla_call', str(jaxpr)) def test_compute_with_large_transfer(self): def f(x, delta): return x + jnp.asarray(delta, x.dtype) xs = np.random.uniform(0., 1., size=(10, 131, 111, 3)).astype(np.float32) for x in xs: delta = np.random.uniform(-0.5, 0.5, size=()) jitted_f = api.jit(f) np.testing.assert_allclose(jitted_f(x, delta), f(x, delta)) def test_vjp_fun_jit(self): f = lambda x: 2. * x @partial(jit, static_argnums=0) def linearize_vjp(f, x): _, vjp_fun = api.vjp(f, x) return vjp_fun linearized = linearize_vjp(f, 1.) actual = jit(lambda f, x: f(x))(linearized, 3.) expected = (6.,) self.assertEqual(actual, expected) def test_linearize_fun_jit(self): f = lambda x: 2. * x @partial(jit, static_argnums=0) def linearize(f, x): _, jvp_fun = api.linearize(f, x) return jvp_fun linearized = linearize(f, 1.) actual = jit(lambda f, x: f(x))(linearized, 3.) expected = 6. self.assertEqual(actual, expected) def test_linear_transpose_fun_jit(self): f = lambda x: 2. * x @partial(jit, static_argnums=0) def transpose(f, x): return api.linear_transpose(f, x) transposed = transpose(f, 1.) actual = jit(lambda f, x: f(x))(transposed, 3.) expected = (6.,) self.assertEqual(actual, expected) def test_leaked_tracer_issue_7613(self): import numpy.random as npr def sigmoid(x): return 1. / (1. + jnp.exp(-x)) x = jnp.ones((50,)) A = jnp.array(npr.randn(50, 50)) @jax.jit def loss(A, x): h = jax.nn.sigmoid(A * x) return jnp.sum((h - x)**2) with jax.checking_leaks(): _ = jax.grad(loss)(A, x) def test_vmap_caching(self): # https://github.com/google/jax/issues/7621 f = lambda x: jnp.square(x).mean() jf = jax.jit(f) x = jax.random.uniform(jax.random.PRNGKey(0), shape=(8, 4)) with jtu.count_jit_and_pmap_compiles() as count: # noqa: F841 for _ in range(5): jax.hessian(jf)(x).block_until_ready() n = count[0] # The exact number of compilations may vary depending on the number of # jit decorators in the function above, but it should not grow after an # initial warmup phase. for _ in range(5): jax.hessian(jf)(x).block_until_ready() self.assertEqual(count[0], n) def test_jnp_array_doesnt_device_put(self): with jtu.count_device_put() as count: api.make_jaxpr(lambda: jnp.array(3))() self.assertEqual(count[0], 0) class RematTest(jtu.JaxTestCase): @parameterized.named_parameters( {"testcase_name": f"{suffix}", "remat": remat} for suffix, remat in [ ('', api.remat), ('_policy', partial(api.remat, policy=lambda *_, **__: False)), ('_new', partial(new_checkpoint, policy=lambda *_, **__: False)), ]) def test_remat_basic(self, remat): @remat def g(x): return lax.sin(lax.sin(x)), 3. def f(x): x, _ = g(x) return x ans = f(2.) expected = np.sin(np.sin(2.)) self.assertAllClose(ans, expected, check_dtypes=False) ans, f_lin = api.linearize(f, 2.) expected = np.sin(np.sin(2.)) self.assertAllClose(ans, expected, check_dtypes=False) ans = f_lin(3.) expected = np.cos(np.sin(2.)) * np.cos(2.) * 3. self.assertAllClose(ans, expected, check_dtypes=False) sin_calls = [] cos_calls = [] sin_impl = lax.sin_p.impl cos_impl = lax.cos_p.impl try: lax.sin_p.def_impl(lambda x: sin_calls.append(1) or sin_impl(x)) lax.cos_p.def_impl(lambda x: cos_calls.append(1) or cos_impl(x)) f_lin(3.) finally: lax.sin_p.def_impl(sin_impl) lax.cos_p.def_impl(cos_impl) self.assertEqual(len(sin_calls), 1) self.assertEqual(len(cos_calls), 2) @parameterized.named_parameters( {"testcase_name": f"{suffix}", "remat": remat} for suffix, remat in [ ('', api.remat), ('_policy', partial(api.remat, policy=lambda *_, **__: False)), ('_new', partial(new_checkpoint, policy=lambda *_, **__: False)), ]) def test_remat_freevars(self, remat): def f1(x): y = 2 * jnp.sin(x) z = jnp.cos(x) * jnp.sin(y) return z def f2(x): y = 2 * jnp.sin(x) z = remat(lambda x: jnp.cos(x) * jnp.sin(y))(x) return z ans, f_lin = api.linearize(f2, 2.) expected, f_lin_expected = api.linearize(f1, 2.) self.assertAllClose(ans, expected, check_dtypes=False) ans = f_lin(3.) expected = f_lin_expected(3.) self.assertAllClose(ans, expected, check_dtypes=False) def test_remat_grad_python_control_flow(self): @partial(api.remat, concrete=True) def g(x): if x > 0: return lax.sin(x), 3. else: return lax.cos(x), 4. def f(x): x, _ = g(x) return x ans = f(2.) expected = np.sin(2.) self.assertAllClose(ans, expected, check_dtypes=False) ans = api.grad(f)(2.) expected = np.cos(2.) self.assertAllClose(ans, expected, check_dtypes=False) @parameterized.named_parameters( {"testcase_name": f"{suffix}", "remat": remat} for suffix, remat in [ ('', api.remat), ('_policy', partial(api.remat, policy=lambda *_, **__: False)), ('_new', partial(new_checkpoint, policy=lambda *_, **__: False)), ]) def test_remat_jit(self, remat): @remat def g(x): return lax.sin(lax.sin(x)) def f_(x): return g(x) f = api.jit(f_) ans = f(2.) expected = np.sin(np.sin(2.)) self.assertAllClose(ans, expected, check_dtypes=False) ans = api.grad(f)(2.) expected = np.cos(np.sin(2.)) * np.cos(2.) self.assertAllClose(ans, expected, check_dtypes=False) ans = api.jit(api.grad(f_))(2.) expected = np.cos(np.sin(2.)) * np.cos(2.) self.assertAllClose(ans, expected, check_dtypes=False) @parameterized.named_parameters( {"testcase_name": f"{suffix}", "remat": remat} for suffix, remat in [ ('', api.remat), ('_policy', partial(api.remat, policy=lambda *_, **__: False)), ('_new', partial(new_checkpoint, policy=lambda *_, **__: False)), ]) def test_remat_vmap(self, remat): @remat def g(x): return lax.sin(lax.sin(x)) x = np.arange(3.) ans = api.vmap(g)(x) expected = np.sin(np.sin(x)) self.assertAllClose(ans, expected, check_dtypes=False) ans = api.jacfwd(g)(x) expected = np.diag(np.cos(np.sin(x)) * np.cos(x)) self.assertAllClose(ans, expected, check_dtypes=False) ans = api.jacrev(g)(x) expected = np.diag(np.cos(np.sin(x)) * np.cos(x)) self.assertAllClose(ans, expected, check_dtypes=False) @parameterized.named_parameters( {"testcase_name": f"{suffix}", "remat": remat} for suffix, remat in [ ('', api.remat), ('_policy', partial(api.remat, policy=lambda *_, **__: False)), ('_new', partial(new_checkpoint, policy=lambda *_, **__: False)), ]) def test_remat_higher_order_autodiff(self, remat): def f(x): return lax.cos(lax.sin(x)) g = remat(f) ans = api.grad(api.grad(g))(3.) expected = api.grad(api.grad(f))(3.) self.assertAllClose(ans, expected, check_dtypes=False) def test_remat_scan(self): to_scan = lambda c, x: (jnp.sin(c), None) def f_noremat(x): y, _ = lax.scan(to_scan, x, np.arange(3.)) return y def f_yesremat(x): y, _ = lax.scan(api.remat(to_scan), x, np.arange(3.)) return y ans = f_yesremat(4.) expected = f_noremat(4.) self.assertAllClose(ans, expected, check_dtypes=False) ans = api.grad(f_yesremat)(4.) expected = api.grad(f_noremat)(4.) self.assertAllClose(ans, expected, check_dtypes=False) jaxpr = api.make_jaxpr(api.linearize(f_yesremat, 4.)[1])(1.) scan_eqn, = jaxpr.jaxpr.eqns self.assertIn(' cos ', str(scan_eqn.params['jaxpr'])) jaxpr = api.make_jaxpr(api.vjp(f_yesremat, 4.)[1])(1.) scan_eqn, = jaxpr.jaxpr.eqns self.assertIn(' cos ', str(scan_eqn.params['jaxpr'])) @parameterized.named_parameters( {"testcase_name": f"{suffix}", "remat": remat} for suffix, remat in [ ('', api.remat), ('_policy', partial(api.remat, policy=lambda *_, **__: False)), ('_new', partial(new_checkpoint, policy=lambda *_, **__: False)), ]) def test_remat_no_redundant_flops(self, remat): # see https://github.com/google/jax/pull/1749#issuecomment-558267584 @api.jit def g(x): return f(2., x) @remat def f(x, y): return jnp.sin(x) * y # We swap out sin_p's impl rule to count how many times it's invoked called = [] sin_impl = lax.sin_p.impl try: lax.sin_p.def_impl(lambda x: called.append(1) or sin_impl(x)) api.grad(g)(3.) finally: lax.sin_p.def_impl(sin_impl) num_calls = len(called) self.assertLessEqual(num_calls, 1) @parameterized.named_parameters( {"testcase_name": f"{suffix}", "remat": remat} for suffix, remat in [ ('', api.remat), ('_policy', partial(api.remat, policy=lambda *_, **__: False)), ('_new', partial(new_checkpoint, policy=lambda *_, **__: False)), ]) def test_remat_binomial_checkpointing(self, remat): def binom_checkpoint(funs): if len(funs) == 1: return funs[0] else: f1 = binom_checkpoint(funs[:len(funs)//2]) f2 = binom_checkpoint(funs[len(funs)//2:]) return remat(lambda x: f1(f2(x))) f1 = binom_checkpoint([jnp.sin, jnp.sin, jnp.sin, jnp.sin]) f2 = lambda x: jnp.sin(jnp.sin(jnp.sin(jnp.sin(x)))) x = 4. self.assertAllClose(f1(x), f2(x), check_dtypes=False) self.assertAllClose(api.grad(f1)(x), api.grad(f2)(x), check_dtypes=False) def test_remat_symbolic_zeros(self): # code from https://github.com/google/jax/issues/1907 key = jax.random.PRNGKey(0) key, split = jax.random.split(key) n = 5 def func(D0): def shift(R, dR, **unused_kwargs): return R + dR def apply_fn(R): return D0 * R Rinit = jax.random.uniform(split, (n,3), minval=0.0, maxval=5.0, dtype=jnp.float32) def move(R,i): F = apply_fn(R) return shift(R, 0.001 * F), jnp.array([0.]) move = api.remat(move) R, temp = lax.scan(move, Rinit, jnp.arange(2)) return R[0, 0] api.grad(func)(5.0) # doesn't crash @parameterized.named_parameters( {"testcase_name": f"{suffix}", "remat": remat} for suffix, remat in [ ('', api.remat), ('_policy', partial(api.remat, policy=lambda *_, **__: False)), ('_new', partial(new_checkpoint, policy=lambda *_, **__: False)), ]) def test_remat_jit2(self, remat): @api.jit def f(x): y = 2 * x @remat def g(): return y return g() self.assertAllClose(f(3), 6, check_dtypes=False) def test_remat_nontrivial_env(self): @api.remat def foo(state, dt=0.5, c=1): u, u_t = state u_tt = c**2 * u u_t = u_t + u_tt * dt return (u, u_t) @partial(api.jit, static_argnums=(1,)) def _multi_step(state, count, dt, c): f = lambda s, _: (foo(s, dt, c), _) return lax.scan(f, state, None, count) def multi_step(state, count, dt=1/jnp.sqrt(2), c=1): return _multi_step(state, count, dt, c) def loss(u0, target, steps, dt=1/jnp.sqrt(2), c=1): init = (u0, jnp.zeros_like(u0)) (uf, _), _ = multi_step(init, steps, dt, c) return ((uf - target) ** 2).mean() target = jnp.zeros((128, 128)) u0 = jnp.ones_like(target) loss(u0, target, 10) @parameterized.named_parameters( {"testcase_name": f"{suffix}", "remat": remat} for suffix, remat in [ ('', api.remat), ('_policy', partial(api.remat, policy=lambda *_, **__: False)), ('_new', partial(new_checkpoint, policy=lambda *_, **__: False)), ]) def test_remat_jit3(self, remat): # https://github.com/google/jax/issues/2180 def f(w, x): a = jnp.dot(x, w) b = jnp.einsum("btd,bTd->btT", a, a) c = jnp.einsum("btT,btd->btd", b, a) return jnp.sum(c) w = jnp.ones([1, 1]) x = jnp.ones([1, 1, 1]) f = remat(f) api.grad(f)(w, x) # doesn't crash @api.jit def mul(a, b): return a * b def f(w, x): a = mul(w, x) b = mul(a, a) return b w = 1. x = 1. f = remat(f) api.grad(f)(w, x) def test_remat_scan2(self): # https://github.com/google/jax/issues/1963 def scan_bug(x0): f = lambda x, _: (x + 1, None) def scanned_f(x, _): return lax.scan(f, x, xs=None, length=1)[0], None x, _ = jax.remat(scanned_f)(x0, None) return x jax.grad(scan_bug)(1.0) # doesn't crash def test_remat_jit_static_argnum_omnistaging(self): ): f_ = lu.wrap_init(lambda: (f(*args),)) out, = core.call_p.bind(f_) return out return named_f def f(a_bool, y): if a_bool: return y + 1 else: return y api.jit(named_call(f), static_argnums=0)(True, 1) # no crash @parameterized.named_parameters( {"testcase_name": f"{suffix}", "remat": remat} for suffix, remat in [ ('', api.remat), ('_policy', partial(api.remat, policy=lambda *_, **__: False)), ('_new', partial(new_checkpoint, policy=lambda *_, **__: False)), ]) def test_remat_eval_counter(self, remat): # https://github.com/google/jax/issues/2737 add_one_p = Primitive('add_one') add_one = add_one_p.bind num_evals = 0 @contextmanager def assertEvals(n): start = num_evals yield assert num_evals - start == n def add_one_impl(x): nonlocal num_evals num_evals += 1 return x + 1 add_one_p.def_impl(add_one_impl) def add_one_jvp(pin, tin): pout = add_one(pin[0]) return pout, pout * tin[0] ad.primitive_jvps[add_one_p] = add_one_jvp add_one_p.def_abstract_eval(lambda x: x) v = np.zeros((1,)) f = remat(add_one) g = remat(lambda x: add_one(f(x))) # 2 calls needed to evaluate g with assertEvals(2): _, vjp = jax.vjp(g, v) # 2 calls made while transposing g, 1 call made while transposing f with assertEvals(3): vjp(v) @jax._src.util.curry def call(f, *args): return jax.core.call( jax.linear_util.wrap_init(lambda *args: [f(*args)]), *args, name='foo')[0] f = call(add_one) g = remat(lambda x: add_one(f(x))) # 2 calls needed to evaluate g with assertEvals(2): _, vjp = jax.vjp(g, v) # 2 calls made while transposing g, no reevaluation for transposition of f with assertEvals(2): vjp(v) @parameterized.named_parameters( {"testcase_name": f"{suffix}", "remat": remat} for suffix, remat in [ ('', api.remat), ('_policy', partial(api.remat, policy=lambda *_, **__: False)), ('_new', partial(new_checkpoint, policy=lambda *_, **__: False)), ]) def test_escaped_tracer_remat(self, remat): # b/169779185 def f(): seq = [jnp.zeros([])] def g(): seq[0] += 1 # this is line 7 btw return seq[0] remat(g)() remat(g)() with self.assertRaisesRegex(UnexpectedTracerError, "global state"): api.jit(f)() @parameterized.named_parameters( {"testcase_name": f"{suffix}", "remat": remat} for suffix, remat in [ ('', api.remat), ('_policy', partial(api.remat, policy=lambda *_, **__: False)), ('_new', partial(new_checkpoint, policy=lambda *_, **__: False)), ]) def test_no_cse_widget_on_primals(self, remat): @remat def g(x): return lax.sin(lax.sin(x)), 3. def f(x): x, _ = g(x) return x c = api.xla_computation(f)(2.) self.assertNotIn('while', c.as_hlo_text()) self.assertNotIn('conditional', c.as_hlo_text()) c = api.xla_computation(grad(f))(2.) text = c.as_hlo_text() self.assertTrue('while' in text or 'conditional' in text) def test_no_cse_widget_with_prevent_cse_false(self): @partial(api.remat, prevent_cse=False) def g(x): return lax.sin(lax.sin(x)), 3. def f(x): x, _ = g(x) return x c = api.xla_computation(f)(2.) self.assertNotIn('while', c.as_hlo_text()) self.assertNotIn('conditional', c.as_hlo_text()) c = api.xla_computation(grad(f))(2.) self.assertNotIn('while', c.as_hlo_text()) self.assertNotIn('conditional', c.as_hlo_text()) @parameterized.named_parameters( {"testcase_name": f"_{policy_name}", "policy": policy, "in_jaxpr2": in_jaxpr2, "not_in_jaxpr2": not_in_jaxpr2} for policy_name, policy, in_jaxpr2, not_in_jaxpr2 in [ ('save_anything', lambda *_, **__: True, [], [' sin ', ' cos ']), ('save_nothing', lambda *_, **__: False, [' sin ', ' cos '], []), ('save_sin', lambda p, *_, **__: str(p) == 'sin', [' cos '], [' sin ']), ]) def test_remat_custom_policy(self, policy, in_jaxpr2, not_in_jaxpr2): for square in [lambda x: x * x, api.jit(lambda x: x * x)]: f = api.remat(lambda x: jnp.sin(square(jnp.sin(x))), policy=policy) y, f_lin = api.linearize(f, 1.) ydot = f_lin(2.) jaxpr_text = str(f_lin.func.args[0]) for substr in in_jaxpr2: self.assertIn(substr, jaxpr_text) for substr in not_in_jaxpr2: self.assertNotIn(substr, jaxpr_text) y_expected, ydot_expected = api.jvp(lambda x: jnp.sin(square(jnp.sin(x))), [1.], [2.]) self.assertAllClose(y, y_expected) self.assertAllClose(ydot, ydot_expected) jtu.check_grads(f, (3.,), order=2, modes=['fwd', 'rev']) def test_remat_custom_policy_save_cos(self): save_cos = lambda prim, *_, **__: str(prim) == 'cos' f = api.remat(lambda x: jnp.sin(jnp.sin(x)), # different function policy=save_cos) _, f_lin = api.linearize(f, 1.) jaxpr_text = str(f_lin.func.args[0]) self.assertNotIn(' sin ', jaxpr_text) self.assertNotIn(' cos ', jaxpr_text) jtu.check_grads(f, (3.,), order=2, modes=['fwd', 'rev']) def test_remat_checkpoint_dots(self): @partial(api.remat, policy=jax.checkpoint_policies.checkpoint_dots) def f(x): x = jnp.dot(x, x, precision=lax.Precision.HIGHEST) x = jnp.sin(x) x = jnp.dot(x, x, precision=lax.Precision.HIGHEST) x = jnp.sin(x) x = jnp.dot(x, x, precision=lax.Precision.HIGHEST) x = jnp.sin(x) return x _, f_lin = api.linearize(f, jnp.ones((2, 2))) jaxpr_text = str(f_lin.func.args[0]) self.assertEqual(jaxpr_text.count(' sin '), 2) self.assertEqual(jaxpr_text.count(' dot_'), 6) jtu.check_grads(f, (jnp.ones((2, 2)),), order=2, modes=['fwd', 'rev']) def test_remat_checkpoint_dots_with_no_batch_dims(self): @partial(api.remat, policy=jax.checkpoint_policies.checkpoint_dots_with_no_batch_dims) def f(x): x = jnp.einsum('ij,jk->ik', x, x, precision=lax.Precision.HIGHEST) x = jnp.sin(x) x = jnp.einsum('ij,jk->ik', x, x, precision=lax.Precision.HIGHEST) x = jnp.sin(x) x = jnp.einsum('ij,jk->ik', x, x, precision=lax.Precision.HIGHEST) x = jnp.sin(x) return x _, f_lin = api.linearize(f, jnp.ones((2, 2))) jaxpr_text = str(f_lin.func.args[0]) self.assertEqual(jaxpr_text.count(' sin '), 2) self.assertEqual(jaxpr_text.count(' dot_general'), 6) jtu.check_grads(f, (jnp.ones((2, 2)),), order=2, modes=['fwd', 'rev']) def test_remat_checkpoint_dots_with_no_batch_dims2(self): @partial(api.remat, policy=jax.checkpoint_policies.checkpoint_dots_with_no_batch_dims) def f(x): x = jnp.einsum('nij,njk->nik', x, x, precision=lax.Precision.HIGHEST) x = jnp.sin(x) x = jnp.einsum('nij,njk->nik', x, x, precision=lax.Precision.HIGHEST) x = jnp.sin(x) x = jnp.einsum('nij,njk->nik', x, x, precision=lax.Precision.HIGHEST) x = jnp.sin(x) return x _, f_lin = api.linearize(f, jnp.ones((3, 2, 2))) jaxpr_text = str(f_lin.func.args[0]) self.assertEqual(jaxpr_text.count(' sin '), 2) self.assertEqual(jaxpr_text.count(' dot_general'), 9) jtu.check_grads(f, (jnp.ones((3, 2, 2)),), order=2, modes=['fwd', 'rev']) def test_remat_checkpoint_dots_jit(self): @api.jit @partial(api.remat, policy=jax.checkpoint_policies.checkpoint_dots) def f(x): x = jnp.dot(x, x, precision=lax.Precision.HIGHEST) x = jnp.sin(x * 1e-3) x = jnp.dot(x, x, precision=lax.Precision.HIGHEST) x = jnp.sin(x * 1e-3) x = jnp.dot(x, x, precision=lax.Precision.HIGHEST) x = jnp.sin(x * 1e-3) return x _, f_lin = api.linearize(f, jnp.ones((2, 2))) jaxpr_text = str(f_lin.func.args[0]) self.assertEqual(jaxpr_text.count(' sin '), 2) self.assertEqual(jaxpr_text.count(' dot_'), 6) jtu.check_grads(f, (jnp.ones((2, 2)),), order=2, modes=['fwd', 'rev']) def test_remat_checkpoint_dots_inside_scan(self): x = jnp.ones((5,)) def f(W): @partial(api.remat, policy=jax.checkpoint_policies.checkpoint_dots) def f(x): x = jnp.sin(jnp.dot(x, W, precision=lax.Precision.HIGHEST)) x = jnp.sin(jnp.dot(x, W, precision=lax.Precision.HIGHEST)) x = jnp.sin(jnp.dot(x, W, precision=lax.Precision.HIGHEST)) return x def body(x, _): return f(x), None return lax.scan(body, x, None, length=2)[0] _, f_vjp = api.vjp(f, jnp.ones((5, 5))) jaxpr_text = str(f_vjp.args[0].func.args[1]) # Two sine calls in the backward pass because while we don't save sines self.assertEqual(jaxpr_text.count(' sin '), 2) self.assertEqual(jaxpr_text.count(' cos '), 3) self.assertEqual(jaxpr_text.count(' dot_'), 6) jtu.check_grads(api.jit(f), (jnp.ones((5, 5)),), order=2, modes=['fwd', 'rev']) def test_remat_custom_jvp_policy(self): @api.custom_jvp def sin(x): return jnp.sin(x) def sin_jvp(primals, tangents): x, = primals g, = tangents return sin(x), jnp.cos(x) * g sin.defjvp(sin_jvp) @partial(api.remat, policy=jax.checkpoint_policies.checkpoint_dots) def f(x): x = jnp.dot(x, x, precision=lax.Precision.HIGHEST) x = sin(x * 1e-3) x = jnp.dot(x, x, precision=lax.Precision.HIGHEST) x = sin(x * 1e-3) x = jnp.dot(x, x, precision=lax.Precision.HIGHEST) x = sin(x * 1e-3) return x jtu.check_grads(f, (3.,), order=2, modes=['fwd', 'rev']) def g(x): return lax.scan(lambda x, _: (f(x), None), x, None, length=2)[0] jtu.check_grads(g, (3.,), order=2, modes=['fwd', 'rev']) def test_remat_custom_vjp_policy(self): @api.custom_vjp def sin(x): return jnp.sin(x) def sin_fwd(x): return sin(x), x def sin_bwd(x, y_bar): return (jnp.cos(x) * y_bar,) sin.defvjp(sin_fwd, sin_bwd) @partial(api.remat, policy=jax.checkpoint_policies.checkpoint_dots) def f(x): @partial(api.named_call, name="dot") def dot2(y, z): return jnp.dot(x, jnp.dot(y, z, precision=lax.Precision.HIGHEST), precision=lax.Precision.HIGHEST) x = dot2(x, x) x = sin(x * 1e-3) x = dot2(x, x) x = sin(x * 1e-3) x = dot2(x, x) x = sin(x * 1e-3) return x jtu.check_grads(f, (3.,), order=2, modes=['rev']) def g(x): return lax.scan(lambda x, _: (f(x), None), x, None, length=2)[0] jtu.check_grads(g, (3.,), order=2, modes=['rev']) def test_remat_dropvar_policy(self): def f(x): return x, x @partial(api.remat, policy=jax.checkpoint_policies.checkpoint_dots) def g(x): x = api.grad(lambda x: f(x)[0])(x) return x api.grad(g)(3.) def test_remat_custom_jvp_linear_policy(self): @api.custom_jvp def sum(x): return jnp.sum(x, axis=0) @sum.defjvp def sum_jvp(primals, tangents): (x,), (xdot,) = primals, tangents return sum(x), sum(xdot) @partial(api.remat, policy=jax.checkpoint_policies.checkpoint_dots) def f(x): return sum(x) jtu.check_grads(f, (jnp.ones(3),), order=2, modes=['fwd', 'rev']) def g(x): return lax.scan(lambda _, x: (None, f(x)), None, x)[1] jtu.check_grads(g, (jnp.ones((2, 3)),), order=2, modes=['fwd', 'rev']) def test_constants_not_hoisted(self): @partial(new_checkpoint, policy=lambda *_, **__: False) def f(x): return jnp.einsum('ii->i', x) res_avals = saved_residuals(f, jnp.ones((2, 2))) self.assertLen(res_avals, 0) @partial(new_checkpoint, policy=lambda *_, **__: False) def f(x): return jnp.zeros_like(x) * x res_avals = saved_residuals(f, jnp.ones((2, 2))) self.assertLen(res_avals, 0) @partial(new_checkpoint, policy=lambda *_, **__: False) def f(x): return jnp.zeros_like(x) * jnp.sin(x) res_avals = saved_residuals(f, jnp.ones((2, 2))) self.assertLen(res_avals, 1) def test_name_denylist(self): def f(x): y = checkpoint_name(jnp.multiply(2., 2.), 'y') z = checkpoint_name(jnp.multiply(2., 2.), 'z') w = checkpoint_name(jnp.multiply(2., 2.), 'w') u = jnp.multiply(2., 2.) return (((x * y) * z) * w) * u policy = jax.checkpoint_policies.save_any_names_but_these('y', 'z', 'w') res = saved_residuals(new_checkpoint(f, policy=policy), 1.) self.assertLen(res, 0) policy = jax.checkpoint_policies.save_any_names_but_these('z', 'w') res = saved_residuals(new_checkpoint(f, policy=policy), 1.) self.assertLen(res, 1) # can save only y policy = jax.checkpoint_policies.save_any_names_but_these('w') res = saved_residuals(new_checkpoint(f, policy=policy), 1.) self.assertLen(res, 2) # can save y and z policy = jax.checkpoint_policies.save_any_names_but_these() res = saved_residuals(new_checkpoint(f, policy=policy), 1.) self.assertLen(res, 3) # can save y, z, and w def test_name_allowlist(self): def f(x): y = checkpoint_name(jnp.multiply(2., 2.), 'y') z = checkpoint_name(jnp.multiply(2., 2.), 'z') w = checkpoint_name(jnp.multiply(2., 2.), 'w') u = jnp.multiply(2., 2.) return (((x * y) * z) * w) * u policy = jax.checkpoint_policies.save_only_these_names('y', 'z', 'w') res = saved_residuals(new_checkpoint(f, policy=policy), 1.) self.assertLen(res, 3) # can save y, z, and w policy = jax.checkpoint_policies.save_only_these_names('z', 'w') res = saved_residuals(new_checkpoint(f, policy=policy), 1.) self.assertLen(res, 2) # can save z and w policy = jax.checkpoint_policies.save_only_these_names('w') res = saved_residuals(new_checkpoint(f, policy=policy), 1.) self.assertLen(res, 1) # can save w policy = jax.checkpoint_policies.save_only_these_names() res = saved_residuals(new_checkpoint(f, policy=policy), 1.) self.assertLen(res, 0) # can't save anything! def test_saved_residuals_utility(self): def f(x, y): x1, x2 = x z = checkpoint_name(jnp.sin(3.), 'z') return z * ((x1 * x2) * y) * np.array([3.]) res = saved_residuals(f, (2., 3.), y=4.) self.assertLen(res, 6) self.assertEqual(res[0][0].shape, (1,)) self.assertEqual(res[0][1], "from a constant") self.assertEqual(res[1][0].shape, ()) self.assertEqual(res[1][1], "from the argument 'x'") self.assertEqual(res[2][0].shape, ()) self.assertEqual(res[2][1], "from the argument 'x'") self.assertEqual(res[3][0].shape, ()) self.assertEqual(res[3][1], "from the argument 'y'") self.assertEqual(res[4][0].shape, ()) self.assertStartsWith(res[4][1], "named 'z'") self.assertEqual(res[5][0].shape, ()) def test_saved_residuals_utility_literals(self): res = saved_residuals(lambda x: x * 2., 3.) self.assertLen(res, 1) self.assertEqual(res[0][0].shape, ()) @parameterized.named_parameters( {"testcase_name": f"{suffix}", "remat": remat} for suffix, remat in [ ('', api.remat), ('_policy', partial(api.remat, policy=lambda *_, **__: False)), ('_new', partial(new_checkpoint, policy=lambda *_, **__: False)), ]) def test_checkpoint_dropvars(self, remat): @remat def f(x): _, x = api.jit(lambda: (x, x))() return x _ = api.grad(f)(3.) def test_dce_keeps_eqns_with_used_outputs_but_no_used_inputs(self): @new_checkpoint def f(x): c = jax.jit(lambda: 3.)() return c * x _ = jax.grad(f)(3.) # doesn't crash @parameterized.named_parameters( {"testcase_name": f"{suffix}", "remat": remat} for suffix, remat in [ ('', api.remat), ('_policy', partial(api.remat, policy=lambda *_, **__: False)), ('_new', partial(new_checkpoint, policy=lambda *_, **__: False)), ]) def test_unit_dropvar_consistency_regression(self, remat): @partial(remat, policy=lambda *_, **__: False) def f(u, x): x, _ = jax.jit(lambda x: (x, u))(x) return x _ = api.linearize(partial(f, core.unit), 3.) class JaxprTest(jtu.JaxTestCase): def test_scalar_literals(self): jaxpr = api.make_jaxpr(lambda x: x + 2)(42) self.assertLen(jaxpr.jaxpr.constvars, 0) def test_abstract_inputs(self): jaxpr = api.make_jaxpr(lambda x: x + 2.)( types.SimpleNamespace(shape=(), dtype=np.dtype(np.float32))) self.assertEqual(jaxpr.in_avals[0].shape, ()) self.assertEqual(jaxpr.in_avals[0].dtype, np.float32) def test_const(self): def fun(x): return (x, 1., np.zeros(1, dtype=jnp.float32)) expected = "{ lambda a:f32[1]; b:f32[]. let in (b, 1.0, a) }" jaxpr = api.make_jaxpr(fun)(jnp.float32(0.)) self.assertMultiLineStrippedEqual(expected, str(jaxpr)) def test_cond(self): def f(x): return lax.cond(x >= 0., x + 1., lambda xt: xt + x, x + 2., lambda xf: xf - x) expected = """{ lambda ; a:f32[]. let b:bool[] = ge a 0.0 c:f32[] = add a 1.0 d:f32[] = add a 2.0 e:i32[] = convert_element_type[new_dtype=int32 weak_type=False] b f:f32[] = cond[ branches=( { lambda ; g_:f32[] h:f32[] i:f32[] j:f32[]. let k:f32[] = sub j h in (k,) } { lambda ; l:f32[] m_:f32[] n:f32[] o:f32[]. let p:f32[] = add n l in (p,) } ) linear=(False, False, False, False) ] e a a c d in (f,) }""" jaxpr = api.make_jaxpr(f)(jnp.float32(3.)) self.assertMultiLineStrippedEqual(expected, str(jaxpr)) def test_make_jaxpr_static_argnums(self): def f(x, y): return x + y jaxpr = api.make_jaxpr(f, static_argnums=(1,))(2, 3) self.assertIn('3', str(jaxpr)) def test_make_jaxpr_return_shape(self): _, shape_tree = api.make_jaxpr(lambda x: (x + 1, jnp.zeros(2, jnp.float32)), return_shape=True)(np.int32(1)) expected = (api.ShapeDtypeStruct(shape=(), dtype=jnp.int32), api.ShapeDtypeStruct(shape=(2,), dtype=jnp.float32)) self.assertEqual(shape_tree, expected) def test_make_jaxpr_axis_env(self): def f(x): return x - lax.psum(x, 'i') jaxpr = api.make_jaxpr(f, axis_env=[('i', 4)])(2) self.assertIn('psum', str(jaxpr)) def test_make_jaxpr_named(self): def f(x): return x - lax.psum(x, 'i') x = api.ShapeDtypeStruct( shape=(2, 3), dtype=jnp.dtype(jnp.float32), named_shape={'i': 10}) jaxpr = api.make_jaxpr(f, axis_env=[('i', 10)])(x) named_shapes = [v.aval.named_shape for v in jaxpr.jaxpr.eqns[1].invars] self.assertEqual(named_shapes, [{'i': 10}, {}]) @parameterized.parameters(True, False) def test_vjp_reduce_axes_jaxpr(self, gy_batched): def f(w, x): return jnp.sin(jnp.dot(x, w)) w = api.ShapeDtypeStruct( shape=(3, 4), dtype=jnp.float32, named_shape={}) x = api.ShapeDtypeStruct( shape=(3,), dtype=jnp.float32, named_shape={'batch': 2}) gy = api.ShapeDtypeStruct( shape=(4,), dtype=jnp.float32, named_shape={'batch': 2} if gy_batched else {}) jaxpr, shapes = api.make_jaxpr( lambda w, x, gy: api.vjp(f, w, x)[1](gy), axis_env=[('batch', 2)], return_shape=True)(w, x, gy) expected = (api.ShapeDtypeStruct( shape=(3, 4), dtype=jnp.float32, named_shape={'batch': 2}), x) self.assertEqual(shapes, expected) self.assertNotIn('psum', str(jaxpr)) jaxpr, shapes = api.make_jaxpr( lambda w, x, gy: api.vjp(f, w, x, reduce_axes=('batch',))[1](gy), axis_env=[('batch', 2)], return_shape=True)(w, x, gy) expected = (w, x) self.assertEqual(shapes, expected) self.assertIn('psum', str(jaxpr)) class CustomJVPTest(jtu.JaxTestCase): def test_basic(self): @api.custom_jvp def f(x): return jnp.sin(x) def f_jvp(primals, tangents): x, = primals g, = tangents return f(x), 2 * jnp.cos(x) * g f.defjvp(f_jvp) x = 3. self.assertAllClose(f(x), jnp.sin(x)) self.assertAllClose(api.jvp(f, (x,), (1.,)), (jnp.sin(x), 2 * jnp.cos(x))) self.assertAllClose(api.grad(f)(x), 2 * jnp.cos(x)) def test_invariance(self): @api.custom_jvp def f(x): return jnp.cos(2 * x) / 2. def f_jvp(primals, tangents): x, = primals g, = tangents return (f(x), 3 * g) f.defjvp(f_jvp) def f2(x): y, _ = api.jvp(f, (x,), (x,)) return y def f3(x): y, _ = api.jvp(f2, (x,), (x,)) return y x = 1. self.assertAllClose(api.jvp(f, (x,), (x,)), api.jvp(f2, (x,), (x,)), check_dtypes=False) self.assertAllClose(api.jvp(f, (x,), (x,)), api.jvp(f3, (x,), (x,)), check_dtypes=False) def test_python_control_flow(self): @api.custom_jvp def f(x): if x > 0: return jnp.sin(x) else: return jnp.cos(x) def f_jvp(primals, tangents): x, = primals g, = tangents if x > 0: return f(x), 2 * g else: return f(x), 3 * g f.defjvp(f_jvp) x = 2. self.assertAllClose(f(x), jnp.sin(x)) self.assertAllClose(f(-x), jnp.cos(-x)) self.assertAllClose(api.jvp(f, (x,), (1.,)), (jnp.sin(x), 2.), check_dtypes=False) self.assertAllClose(api.jvp(f, (-x,), (1.,)), (jnp.cos(-x), 3.), check_dtypes=False) self.assertAllClose(api.grad(f)(x), 2., check_dtypes=False) self.assertAllClose(api.grad(f)(-x), 3., check_dtypes=False) def test_vmap(self): @api.custom_jvp def f(x): assert jnp.ndim(x) == 0 return jnp.sin(x) def f_jvp(primals, tangents): x, = primals g, = tangents assert jnp.ndim(x) == jnp.ndim(g) == 0 return f(x), 2 * jnp.cos(x) * g f.defjvp(f_jvp) x = jnp.arange(3.) xx = jnp.arange(6.).reshape(2, 3) self.assertAllClose(api.vmap(f)(x), jnp.sin(x)) self.assertAllClose(api.vmap(api.vmap(f))(xx), jnp.sin(xx)) self.assertAllClose(api.vmap(lambda x: api.jvp(f, (x,), (x,)))(x), (jnp.sin(x), 2 * jnp.cos(x) * x)) self.assertAllClose(api.vmap(api.vmap(lambda x: api.jvp(f, (x,), (x,))))(xx), (jnp.sin(xx), 2 * jnp.cos(xx) * xx)) self.assertAllClose(api.jvp(api.vmap(f), (x,), (x,)), (jnp.sin(x), 2 * jnp.cos(x) * x)) self.assertAllClose(api.jvp(api.vmap(api.vmap(f)), (xx,), (xx,)), (jnp.sin(xx), 2 * jnp.cos(xx) * xx)) self.assertAllClose(api.vmap(lambda x: api.jvp(api.vmap(f), (x,), (x,)))(xx), (jnp.sin(xx), 2 * jnp.cos(xx) * xx)) def test_jit(self): @api.custom_jvp def f(x): return jnp.sin(x) def f_jvp(primals, tangents): x, = primals g, = tangents return f(x), 2 * jnp.cos(x) * g f.defjvp(f_jvp) x = 3. self.assertAllClose(api.jit(f)(x), jnp.sin(x)) self.assertAllClose(api.jit(api.jit(f))(x), jnp.sin(x)) self.assertAllClose(api.jit(lambda x: api.jvp(f, (x,), (x,)))(x), (jnp.sin(x), 2 * jnp.cos(x) * x), check_dtypes=False) self.assertAllClose(api.jvp(api.jit(f), (x,), (x,)), (jnp.sin(x), 2 * jnp.cos(x) * x), check_dtypes=False) def test_pytrees(self): @api.custom_jvp def f(x): return {'b': jnp.sin(x['a'])} def f_jvp(primals, tangents): x, = primals g, = tangents return f(x), {'b': 2 * jnp.cos(x['a']) * g['a']} f.defjvp(f_jvp) x = {'a': 3.} self.assertAllClose(f(x)['b'], jnp.sin(x['a'])) self.assertAllClose(api.jvp(f, (x,), (x,)), ({'b': jnp.sin(x['a'])}, {'b': 2 * jnp.cos(x['a']) * x['a']}), check_dtypes=False) def test_kwargs(self): @api.custom_jvp def my_fun(x, y, c=1.): return c * (x + y) def my_jvp(primals, tangents): x, y, c = primals t_x, t_y, t_c = tangents return my_fun(x, y, c), t_c my_fun.defjvp(my_jvp) f = lambda x, y: jnp.square(my_fun(x, y, c=2.)).sum() f(10., 5.) api.jvp(f, (10., 5.), (1., 1.)) # doesn't crash def test_initial_style(self): @api.custom_jvp def f(x): return 3 * x def f_jvp(primals, tangents): x, = primals g, = tangents return f(x), 2 * g f.defjvp(f_jvp) def foo(x): out, _ = lax.scan(lambda c, _: (f(c), None), x, None, length=1) return out ans = api.grad(foo)(3.) expected = 2. self.assertAllClose(ans, expected, check_dtypes=False) ans = api.grad(api.jit(foo))(3.) expected = 2. self.assertAllClose(ans, expected, check_dtypes=False) ans = api.jit(api.grad(foo))(3.) expected = 2. self.assertAllClose(ans, expected, check_dtypes=False) ans = api.grad(api.grad(foo))(3.) expected = 0. self.assertAllClose(ans, expected, check_dtypes=False) ans = api.grad(api.grad(api.jit(foo)))(3.) expected = 0. self.assertAllClose(ans, expected, check_dtypes=False) ans = api.grad(api.jit(api.grad(foo)))(3.) expected = 0. self.assertAllClose(ans, expected, check_dtypes=False) ans = api.jit(api.grad(api.grad(foo)))(3.) expected = 0. self.assertAllClose(ans, expected, check_dtypes=False) def test_initial_style_vmap(self): @api.custom_jvp def f(x): assert jnp.ndim(x) == 0 return 3 * x def f_jvp(primals, tangents): x, = primals g, = tangents return f(x), 2 * g f.defjvp(f_jvp) def foo(x): out, _ = lax.scan(lambda c, _: (f(c), None), x, None, length=1) return out ans = api.vmap(foo)(jnp.ones(3)) expected = 3. * jnp.ones(3) self.assertAllClose(ans, expected, check_dtypes=False) ans = api.vmap(api.jit(foo))(jnp.ones(3)) expected = 3. * jnp.ones(3) self.assertAllClose(ans, expected, check_dtypes=False) ans = api.jit(api.vmap(foo))(jnp.ones(3)) expected = 3. * jnp.ones(3) self.assertAllClose(ans, expected, check_dtypes=False) ans = api.grad(lambda x: api.vmap(foo)(x).sum())(jnp.ones(3)) expected = 2. * jnp.ones(3) self.assertAllClose(ans, expected, check_dtypes=False) ans = api.grad(lambda x: api.vmap(api.jit(foo))(x).sum())(jnp.ones(3)) expected = 2. * jnp.ones(3) self.assertAllClose(ans, expected, check_dtypes=False) ans = api.grad(lambda x: api.jit(api.vmap(foo))(x).sum())(jnp.ones(3)) expected = 2. * jnp.ones(3) self.assertAllClose(ans, expected, check_dtypes=False) ans = api.grad(api.jit(lambda x: api.vmap(foo)(x).sum()))(jnp.ones(3)) expected = 2. * jnp.ones(3) self.assertAllClose(ans, expected, check_dtypes=False) ans = api.jit(api.grad(lambda x: api.vmap(foo)(x).sum()))(jnp.ones(3)) expected = 2. * jnp.ones(3) self.assertAllClose(ans, expected, check_dtypes=False) def test_initial_style_vmap_with_collective(self): @api.custom_jvp def f(x): return lax.psum(x, 'foo') @f.defjvp def f_jvp(xs, ts): x, = xs t, = ts return lax.psum(x, 'foo'), t def g(x): jaxpr = api.make_jaxpr(f)(x) return core.eval_jaxpr(jaxpr.jaxpr, [], x)[0] v = api.vmap(lambda _, x: g(x), axis_name='foo', in_axes=(0, None), out_axes=None)(jnp.arange(4.), 2.) self.assertAllClose(v, 8.) def test_closed_over_tracers_error_message(self): def f(x): @api.custom_jvp def g(y): return x + y def g_jvp(primals, tangents): return g(x), 2 * primals[0] g.defjvp(g_jvp) return g(1.) self.assertRaises(ad.CustomJVPException, lambda: api.jvp(f, (3.,), (1.,))) self.assertRaises(ad.CustomJVPException, lambda: api.grad(f)(3.)) def test_nondiff_arg(self): @partial(api.custom_jvp, nondiff_argnums=(0,)) def app(f, x): return f(x) def app_jvp(f, primals, tangents): (x,), (t,) = primals, tangents return app(f, x), 3 * t app.defjvp(app_jvp) ans = app(lambda x: 2 * x, 1) expected = 2 self.assertAllClose(ans, expected, check_dtypes=False) ans = api.jvp(lambda x: app(lambda y: 2 * y, x), (1.,), (1.,)) expected = (2., 3.) self.assertAllClose(ans, expected, check_dtypes=False) def test_nondiff_arg_jit_tracer(self): @partial(api.custom_jvp, nondiff_argnums=(0,)) def f(x, y): return x * y def f_jvp(x, primals, tangents): (y,), (t_y,) = primals, tangents return f(x, y), 5 * t_y f.defjvp(f_jvp) @jit def g(x, y): return f(x, y) ans = api.jvp(lambda y: g(2., y), (3.,), (1.,)) expected = (6., 5.) self.assertAllClose(ans, expected, check_dtypes=False) def test_nondiff_arg_hiding_jvp_tracer(self): def f(x): @partial(api.custom_jvp, nondiff_argnums=(0,)) def g(h, x): return h(x) @g.defjvp def g_jvp(h, primals, tangents): x, = primals t, = tangents return g(h, x), 2. * t h = lambda y: x + y return g(h, x) with self.assertRaisesRegex(ad.CustomJVPException, "Detected differentiation"): api.jvp(f, (2.,), (1.,)) def test_vmap_axes(self): raise unittest.SkipTest("TODO") def test_pmap(self): raise unittest.SkipTest("TODO") def test_missing_jvp_rule_error_message(self): @api.custom_jvp def foo(x): return x ** 2 self.assertRaisesRegex( AttributeError, r"No JVP defined for custom_jvp function foo using defjvp.", lambda: foo(2)) self.assertRaisesRegex( AttributeError, r"No JVP defined for custom_jvp function foo using defjvp.", lambda: api.jvp(foo, (2.,), (1.,))) self.assertRaisesRegex( AttributeError, r"No JVP defined for custom_jvp function foo using defjvp.", lambda: api.grad(foo)(2.)) def test_jvp_rule_inconsistent_pytree_structures_error_message(self): @api.custom_jvp def f(x): return (x**2,) @f.defjvp def foo_jvp(primals, tangents): x, = primals t, = tangents return f(x), [2 * x * t, x] f(2.) self.assertRaisesRegex( TypeError, re.escape( "Custom JVP rule must produce primal and tangent outputs " "with equal container (pytree) structures, but got " "{} and {} respectively.".format( tree_util.tree_structure((1,)), tree_util.tree_structure([1, 2])) ), lambda: api.jvp(f, (2.,), (1.,))) def test_primal_tangent_aval_disagreement_error_message(self): @api.custom_jvp def f(x): return x ** 2 @f.defjvp def foo_jvp(primals, tangents): x, = primals t, = tangents return f(x), jnp.reshape(t, (1,)) f(2.) # doesn't crash self.assertRaisesRegex( TypeError, re.escape( "Custom JVP rule must produce primal and tangent outputs " "with equal shapes and dtypes, but got float32[] and float32[1] " "respectively."), lambda: api.jvp(f, (jnp.float32(2.),), (jnp.float32(1.),))) def test_jvp_rule_doesnt_return_pair_error_message(self): @api.custom_jvp def f(x): return x ** 2 @f.defjvp def foo_jvp(primals, tangents): x, = primals t, = tangents return t f(2.) self.assertRaisesRegex( TypeError, re.escape( "Custom JVP rule must produce a pair (list or tuple of length two) " "representing primal and tangent outputs, got 1.0"), lambda: api.jvp(f, (2.,), (1.,))) def test_multiple_rule_invocations(self): @jax.custom_jvp def expit(x): return 1 / (1 + lax.exp(-x)) @expit.defjvp def _expit_jvp(primals, tangents): (x,), (t,) = primals, tangents ans = expit(x) t_out = t * ans * (1 - ans) return ans, t_out def scanned_fun(c, _): return [expit(c[0])] + [c[i-1] + c[i] for i in range(1, len(c))], None def foo(x): c, _ = lax.scan(scanned_fun, [x, 0., 0., 0., 0.], None, length=10) return c[-1] # just make sure these don't crash foo(3.) grad(foo)(3.) grad(lambda x: jax.vmap(foo)(x).sum())(jnp.arange(3.)) def test_hard_stuff(self): arr = jnp.ones((5, 2, 2)) api.jit(jax.vmap(jnp.linalg.det))(arr) def test_hard_stuff2(self): @jax.custom_jvp def f(x): return lax.tie_in(x, np.zeros(x.shape, x.dtype)) @f.defjvp def f_jvp(primals, tangents): x, = primals t, = tangents return f(x), t # don't crash jax.jit(jax.vmap(f))(jnp.arange(3.)) jax.jit(jax.vmap(jax.grad(f)))(jnp.arange(3.)) jax.jit(jax.grad(lambda x: jax.vmap(f)(x).sum()))(jnp.arange(3.)) jax.grad(lambda x: jax.vmap(f)(x).sum())(jnp.arange(3.)) jax.jvp(jax.vmap(f), (jnp.arange(3.),), (jnp.ones(3),)) def test_hard_stuff3(self): @jax.custom_jvp def relu(x): return jnp.maximum(x, 0) @relu.defjvp def _relu_jvp(primals, tangents): x, = primals t, = tangents return relu(x), lax.select(x > 0, t, lax.full_like(t, 0)) def scanned_fun(c, _): return [relu(c[0])] + [c[i-1] + c[i] for i in range(1, len(c))], None def f(x): c, _ = lax.scan(scanned_fun, [x, 0., 0., 0., 0.], None, length=10) return c[-1] jax.jit(jax.vmap(f))(jnp.arange(3.)) jax.jit(jax.vmap(jax.grad(f)))(jnp.arange(3.)) jax.jit(jax.grad(lambda x: jax.vmap(f)(x).sum()))(jnp.arange(3.)) jax.grad(lambda x: jax.vmap(f)(x).sum())(jnp.arange(3.)) jax.jvp(jax.jit(jax.vmap(f)), (jnp.arange(3.),), (jnp.ones(3),)) def test_eval_shape(self): @jax.custom_jvp def expit(x): return 1 / (1 + lax.exp(-x)) @expit.defjvp def _expit_jvp(primals, tangents): (x,), (t,) = primals, tangents ans = expit(x) t_out = t * ans * (1 - ans) return ans, t_out # don't crash api.eval_shape(expit, jnp.ones((2, 3))) api.eval_shape(api.grad(lambda x: expit(x).sum()), jnp.ones((2, 3))) def test_jaxpr_zeros(self): @api.custom_jvp def f(A, b): return A @ b def f_jvp(primals, tangents): A, b = primals dA, db = tangents z = f(A, b) dz = A @ db + dA @ b return z, dz f.defjvp(f_jvp) def experiment(theta): def step(q, _): z = f(jnp.eye(3), jnp.ones(3) * theta) q += z[0] return q, q q = 0. q, _ = lax.scan(step, q, None, 4) return q grad(experiment)(1.) def test_linear_in_scan(self): @api.custom_jvp def f(x): return -x @f.defjvp def f_jvp(primals, tangents): x, = primals x_dot, = tangents return f(x), f(x_dot) def foo(x): out, _ = lax.scan(lambda c, _: (f(c), None), x, None, length=1) return out ans = api.grad(foo)(3.) expected = -1. self.assertAllClose(ans, expected, check_dtypes=False) def test_custom_jvps_first_rule_is_none(self): # https://github.com/google/jax/issues/3389 @api.custom_jvp def f(x, y): return x ** 2 * y f.defjvps(None, lambda x_dot, primal_out, x, y: 2 * x * y * x_dot) ans = grad(f, 1)(2., 3.) # doesn't crash expected = 12. self.assertAllClose(ans, expected, check_dtypes=False) def test_concurrent_initial_style(self): def unroll(param, sequence): def scan_f(prev_state, inputs): return prev_state, jax.nn.sigmoid(param * inputs) return jnp.sum(jax.lax.scan(scan_f, None, sequence)[1]) def run(): return jax.grad(unroll)(jnp.array(1.0), jnp.array([1.0])) expected = run() n_workers = 2 with concurrent.futures.ThreadPoolExecutor(max_workers=n_workers) as e: futures = [] for _ in range(n_workers): futures.append(e.submit(run)) results = [f.result() for f in futures] for ans in results: self.assertAllClose(ans, expected) def test_nondiff_argnums_vmap_tracer(self): # https://github.com/google/jax/issues/3964 @partial(jax.custom_jvp, nondiff_argnums=(0, 2)) def sample(shape, param, seed): return jax.random.uniform(key=seed, shape=shape, minval=param) @sample.defjvp def sample_jvp(shape, seed, primals, tangents): param, = primals dparam, = tangents dparam = jnp.broadcast_to(dparam, shape) samples = sample(shape, param, seed) return samples, samples * dparam # dummy jvp for proof of concept # check these don't crash jax.vmap(lambda seed: sample((2,3), 1., seed))( jax.random.split(jax.random.PRNGKey(1), 10)) jax.jvp(lambda x: sample((2, 3), x, jax.random.PRNGKey(1)), (1.,), (1.,)) def test_fun_with_nested_calls_2(self): def call(f, *args): f = api.custom_jvp(f) f.defjvp(lambda primals, tangents: (f(*primals), sum(tangents))) return f(*args) def fun_with_nested_calls_2(x): def bar(y): def baz(w): q = call(lambda x: y, x) q = q + call(lambda: y) q = q + call(lambda y: w + y, y) q = call(lambda w: call(jnp.sin, x) * y, 1.0) + q return q return api.jit(baz)(x) return call(bar, x) self.assertAllClose(api.jit(fun_with_nested_calls_2)(3.), fun_with_nested_calls_2(3.)) api.vmap(fun_with_nested_calls_2)(jnp.arange(3.)) def test_closure_with_vmap(self): # https://github.com/google/jax/issues/3822 alpha = np.float32(2.) def sample(seed): @api.custom_jvp def f(alpha): return jax.random.gamma(seed, alpha, shape=[]) @f.defjvp def f_jvp(primal, tangent): alpha = primal dalpha = tangent sample = f(alpha) partial_alpha = lax.random_gamma_grad(alpha, sample) return sample, partial_alpha * dalpha return f(alpha) api.vmap(sample)(jax.random.split(jax.random.PRNGKey(1), 3)) # don't crash @unittest.skipIf(numpy_version == (1, 21, 0), "https://github.com/numpy/numpy/issues/19305") def test_float0(self): @api.custom_jvp def f(x, y): return x, y def f_jvp(primals, _): return primals, (2., 1) f.defjvp(f_jvp) primals = (2., 3) tangents = (np.ones(()), np.zeros((), float0),) expected_tangents = (2., np.zeros((), float0)) self.assertArraysEqual(api.jvp(f, primals, tangents), (primals, expected_tangents)) @unittest.skipIf(numpy_version == (1, 21, 0), "https://github.com/numpy/numpy/issues/19305") def test_float0_initial_style(self): @api.custom_jvp def f(x, y): return x, y def f_jvp(primals, _): x, y = primals return (x, y), (2., 1) f.defjvp(f_jvp) def foo(x, y): out, _ = lax.scan(lambda c, _: (f(*c), None), (x, y), None, length=1) return out primals = (2., 3) tangents = (np.ones(()), np.zeros((), float0),) expected_tangents = (2., np.zeros((), float0)) self.assertArraysEqual(api.jvp(foo, primals, tangents), (primals, expected_tangents)) def test_remat(self): @api.custom_jvp def f(x): return jnp.sin(x) def f_jvp(primals, tangents): x, = primals g, = tangents return f(x), 2 * jnp.cos(x) * g f.defjvp(f_jvp) @api.remat def g(x): return f(f(x)) ans = g(2.) expected = np.sin(np.sin(2.)) self.assertAllClose(ans, expected, check_dtypes=False) ans = api.grad(g)(2.) expected = 4. * api.grad(lambda x: jnp.sin(jnp.sin(x)))(2.) self.assertAllClose(ans, expected, check_dtypes=False) def test_remat_higher_order(self): @api.custom_jvp def f(x): return jnp.sin(x) def f_jvp(primals, tangents): x, = primals g, = tangents return f(x), 2 * jnp.cos(x) * g f.defjvp(f_jvp) def g(x): return f(f(x)) ans = api.grad(api.grad(api.remat(g)))(2.) expected = api.grad(api.grad(g))(2.) self.assertAllClose(ans, expected, check_dtypes=False) ans = api.grad(api.remat(api.grad(g)))(2.) expected = api.grad(api.grad(g))(2.) self.assertAllClose(ans, expected, check_dtypes=False) ans = api.grad(api.grad(api.grad(api.remat(g))))(2.) expected = api.grad(api.grad(api.grad(g)))(2.) self.assertAllClose(ans, expected, check_dtypes=False) def test_initial_style_vmap_2(self): y = jnp.array([1., 2., 3.]) @api.custom_jvp def f(x): assert jnp.ndim(x) == 0 return 3 * x * jnp.sum(y) def f_jvp(primals, tangents): x, = primals g, = tangents return f(x), 2 * g f.defjvp(f_jvp) def foo(x): out, _ = lax.scan(lambda c, _: (f(c), None), x, None, length=1) return out ans = api.grad(lambda x: api.vmap(foo)(x).sum())(jnp.ones(3)) expected = 2. * jnp.ones(3) self.assertAllClose(ans, expected, check_dtypes=False) ans = api.grad(lambda x: api.vmap(api.jit(foo))(x).sum())(jnp.ones(3)) expected = 2. * jnp.ones(3) self.assertAllClose(ans, expected, check_dtypes=False) ans = api.grad(lambda x: api.jit(api.vmap(foo))(x).sum())(jnp.ones(3)) expected = 2. * jnp.ones(3) self.assertAllClose(ans, expected, check_dtypes=False) ans = api.grad(api.jit(lambda x: api.vmap(foo)(x).sum()))(jnp.ones(3)) expected = 2. * jnp.ones(3) self.assertAllClose(ans, expected, check_dtypes=False) ans = api.jit(api.grad(lambda x: api.vmap(foo)(x).sum()))(jnp.ones(3)) expected = 2. * jnp.ones(3) self.assertAllClose(ans, expected, check_dtypes=False) def test_custom_jvp_vmap_broadcasting_interaction(self): def f2(y, z): v1 = z v2 = jnp.sum(y) + z return jnp.logaddexp(v1, v2) def f1(y, z): v = api.vmap(lambda _y: f2(_y, z))(y) return jnp.sum(v) y = jnp.ones((3, 2)) f = lambda z: f1(y, z) z = 0.1 val, g = api.value_and_grad(f)(z) self.assertEqual(val.shape, ()) self.assertEqual(g.shape, ()) def test_custom_jvp_vmap_broadcasting_interaction_2(self): @api.custom_jvp def transform(box, R): if jnp.isscalar(box) or box.size == 1: return R * box elif box.ndim == 2: return jnp.einsum('ij,j->i', box, R) raise ValueError() @transform.defjvp def transform_jvp(primals, tangents): box, R = primals dbox, dR = tangents return (transform(box, R), dR + transform(dbox, R)) def periodic_general(box): def displacement_fn(Ra, Rb, **kwargs): _box = kwargs.get('box', box) return transform(_box, Ra - Rb) return displacement_fn N = 250 scalar_box = 1.0 displacement = periodic_general(scalar_box) key = jax.random.PRNGKey(0) R = jax.random.uniform(key, (N, 2)) def energy_fn(box): d = partial(displacement, box=box) d = api.vmap(api.vmap(d, (None, 0)), (0, None)) return jnp.sum(d(R, R) ** 2) self.assertEqual(grad(energy_fn)(scalar_box).shape, ()) def test_custom_jvp_implicit_broadcasting(self): if config.x64_enabled: raise unittest.SkipTest("test only applies when x64 is disabled") @jax.custom_jvp def projection_unit_simplex(x: jnp.ndarray) -> jnp.ndarray: s = 1.0 n_features = x.shape[0] u = jnp.sort(x)[::-1] cssv = jnp.cumsum(u) - s ind = jnp.arange(n_features) + 1 cond = u - cssv / ind > 0 idx = jnp.count_nonzero(cond) threshold = cssv[idx - 1] / idx.astype(x.dtype) return jax.nn.relu(x - threshold) @projection_unit_simplex.defjvp def projection_unit_simplex_jvp(primals, tangents): x, = primals x_dot, = tangents primal_out = projection_unit_simplex(x) supp = primal_out > 0 card = jnp.count_nonzero(supp) tangent_out = supp * x_dot - (jnp.dot(supp, x_dot) / card) * supp return primal_out, tangent_out rng = np.random.RandomState(0) x = rng.rand(5).astype(np.float32) J_rev = jax.jacrev(projection_unit_simplex)(x) J_fwd = jax.jacfwd(projection_unit_simplex)(x) p = projection_unit_simplex(x) support = (p > 0).astype(jnp.int32) cardinality = jnp.count_nonzero(support) J_true = jnp.diag(support) - jnp.outer(support, support) / cardinality self.assertAllClose(J_true, J_fwd) self.assertAllClose(J_true, J_rev) proj = jax.vmap(projection_unit_simplex) def fun(X): return jnp.sum(proj(X) ** 2) rng = np.random.RandomState(0) X = rng.rand(4, 5).astype(np.float32) U = rng.rand(4, 5) U /= np.sqrt(np.sum(U ** 2)) U = U.astype(np.float32) eps = 1e-3 dir_deriv_num = (fun(X + eps * U) - fun(X - eps * U)) / (2 * eps) dir_deriv = jnp.vdot(jax.grad(fun)(X), U) self.assertAllClose(dir_deriv, dir_deriv_num, atol=1e-3) def test_vmap_inside_defjvp(self): seed = 47 key = jax.random.PRNGKey(seed) mat = jax.random.normal(key, (2, 3)) @jax.custom_jvp def f(mat, aux): num_rows, num_cols = mat.shape return jnp.ones((num_rows, 1)) / num_cols @f.defjvp def f_jvp(primals, tangents): mat, aux = primals vec, _ = tangents output = f(*primals) num_rows, num_cols = mat.shape size = num_rows * num_cols bd_mat = mat.reshape(1, 1, num_rows, num_cols) bd_mat = jnp.tile(bd_mat, reps=(num_rows, num_cols)) bd_mat = bd_mat.reshape(size, num_rows, num_cols) rowsum = jnp.sum(mat, axis=1, keepdims=True) colsum = jnp.sum(mat, axis=0, keepdims=True) bd_rowsum = jnp.tile(rowsum, reps=(1, num_rows)) bd_colsum = jnp.tile(colsum, reps=(num_cols, 1)) bd_vec = vec.reshape(size, 1) def operate(mx, val): buf = 0 for i in range(2): buf = buf + jnp.matmul(mx, bd_colsum) / jnp.power(aux, i) buf = jnp.matmul(bd_rowsum, buf) return buf * val bd_buf = jax.vmap(operate, in_axes=(0, 0), out_axes=0)(bd_mat, bd_vec) bd_buf = bd_buf / aux jvp = jnp.sum(bd_buf, axis=0) jvp = jnp.mean(jvp, axis=1, keepdims=True) return (output, jvp) jax.grad(lambda mat, aux: jnp.sum(f(mat, aux)))(mat, 0.5) def test_custom_jvp_unbroadcasting(self): # https://github.com/google/jax/issues/3056 a = jnp.array([1., 1.]) @jax.custom_jvp def f(x): return a * x @f.defjvp def f_jvp(primals, tangents): x, = primals dx, = tangents return a * x, a * dx shape = grad(lambda x: jnp.sum(f(x)))(jnp.array(1.)).shape self.assertEqual(shape, ()) class CustomVJPTest(jtu.JaxTestCase): def test_basic(self): @api.custom_vjp def f(x): return jnp.sin(x) def f_fwd(x): return f(x), jnp.cos(x) def f_rev(cos_x, g): return (2 * cos_x * g,) f.defvjp(f_fwd, f_rev) x = 3. self.assertAllClose(f(x), jnp.sin(x)) self.assertAllClose(api.grad(f)(x), 2 * jnp.cos(x)) self.assertAllClose(api.value_and_grad(f)(x), (jnp.sin(x), 2 * jnp.cos(x))) def test_invariance(self): @api.custom_vjp def f(x): return jnp.cos(2 * x) / 2. def f_fwd(x): return (f(x), x) def f_rev(x, g): return (g * 3,) f.defvjp(f_fwd, f_rev) def f2(x): y, _ = api.value_and_grad(f)(x) return y def f3(x): y, _ = api.value_and_grad(f2)(x) return y x = 1. self.assertAllClose(f(x), f2(x), check_dtypes=False) self.assertAllClose(f(x), f3(x), check_dtypes=False) self.assertAllClose(api.grad(f)(x), api.grad(f2)(x), check_dtypes=False) self.assertAllClose(api.grad(f)(x), api.grad(f3)(x), check_dtypes=False) def test_python_control_flow(self): @api.custom_vjp def f(x): if x > 0: return jnp.sin(x) else: return jnp.cos(x) def f_fwd(x): if x > 0: return f(x), x else: return f(x), x def f_rev(x, g): if x > 0: return (2 * g,) else: return (3 * g,) f.defvjp(f_fwd, f_rev) x = 2. self.assertAllClose(f(x), jnp.sin(x)) self.assertAllClose(f(-x), jnp.cos(-x)) self.assertAllClose(api.value_and_grad(f)(x), (jnp.sin(x), 2.), check_dtypes=False) self.assertAllClose(api.value_and_grad(f)(-x), (jnp.cos(-x), 3.), check_dtypes=False) def test_vmap(self): @api.custom_vjp def f(x): assert jnp.ndim(x) == 0 return jnp.sin(x) def f_fwd(x): assert jnp.ndim(x) == 0 return f(x), jnp.cos(x) def f_rev(cos_x, g): return (2 * cos_x * g,) f.defvjp(f_fwd, f_rev) x = jnp.arange(3.) xx = jnp.arange(6.).reshape(2, 3) # vmap of f self.assertAllClose(api.vmap(f)(x), jnp.sin(x)) self.assertAllClose(api.vmap(api.vmap(f))(xx), jnp.sin(xx)) # vmap of grad of f self.assertAllClose(api.vmap(api.grad(f))(x), 2 * jnp.cos(x)) self.assertAllClose(api.vmap(api.value_and_grad(f))(x), (jnp.sin(x), 2 * jnp.cos(x))) self.assertAllClose(api.vmap(api.vmap(api.grad(f)))(xx), 2 * jnp.cos(xx)) self.assertAllClose(api.vmap(api.vmap(api.value_and_grad(f)))(xx), (jnp.sin(xx), 2 * jnp.cos(xx))) # grad of vmap of f self.assertAllClose(api.grad(lambda x: api.vmap(f)(x).sum())(x), 2 * jnp.cos(x)) self.assertAllClose(api.grad(lambda x: api.vmap(api.vmap(f))(x).sum())(xx), 2 * jnp.cos(xx)) # vmap of grad of vmap of f self.assertAllClose(api.vmap(api.grad(lambda x: api.vmap(f)(x).sum()))(xx), 2 * jnp.cos(xx)) def test_jit(self): @api.custom_vjp def f(x): return jnp.sin(x) def f_fwd(x): return f(x), jnp.cos(x) def f_rev(cos_x, g): return (2 * cos_x * g,) f.defvjp(f_fwd, f_rev) x = 3. # jit self.assertAllClose(api.jit(f)(x), jnp.sin(x)) self.assertAllClose(api.jit(api.jit(f))(x), jnp.sin(x)) # jit of grad self.assertAllClose(api.jit(api.grad(f))(x), 2 * jnp.cos(x), check_dtypes=False) # grad of jit self.assertAllClose(api.grad(api.jit(f))(x), 2 * jnp.cos(x), check_dtypes=False) def test_pytrees(self): @api.custom_vjp def f(x): return {'b': jnp.sin(x['a'])} def f_fwd(x): return f(x), {'r': jnp.cos(x['a'])} def f_bwd(res, g): cos_x = res['r'] return ({'a': 2 * cos_x * g['b']},) f.defvjp(f_fwd, f_bwd) x = {'a': 3.} self.assertAllClose(f(x)['b'], jnp.sin(x['a'])) self.assertAllClose(api.grad(lambda x: f(x)['b'])(x), {'a': 2 * jnp.cos(x['a'])}) def test_jvp_error(self): @api.custom_vjp def f(x): return jnp.sin(x) def f_fwd(x): return f(x), jnp.cos(x) def f_rev(cos_x, g): return (2 * cos_x * g,) f.defvjp(f_fwd, f_rev) self.assertRaisesRegex( TypeError, r"can't apply forward-mode autodiff $jvp$ to a custom_vjp function.", lambda: api.jvp(f, (3.,), (1.,))) self.assertRaisesRegex( TypeError, r"can't apply forward-mode autodiff $jvp$ to a custom_vjp function.", lambda: api.jvp(api.vmap(f), (jnp.arange(3.),), (jnp.ones(3),))) self.assertRaisesRegex( TypeError, r"can't apply forward-mode autodiff $jvp$ to a custom_vjp function.", lambda: api.jvp(jit(f), (3.,), (1.,))) def test_kwargs(self): @api.custom_vjp def my_fun(x, y, c=1.): return c * (x + y) my_fun.defvjp(lambda x, y, c=1.: (my_fun(c, y, c), None), lambda _, g: (g, g, g)) f = lambda x, y: jnp.square(my_fun(x, y, c=2.)).sum() f(10., 5.) api.grad(f)(10., 5.) # doesn't crash def test_initial_style(self): @api.custom_vjp def f(x): return jnp.sin(x) def f_fwd(x): return f(x), jnp.cos(x) def f_rev(cos_x, g): return (2 * cos_x * g,) f.defvjp(f_fwd, f_rev) def foo(x): out, _ = lax.scan(lambda c, _: (f(c), None), x, None, length=1) return out ans = api.grad(foo)(3.) expected = 2. * jnp.cos(3.) self.assertAllClose(ans, expected, check_dtypes=False) ans = api.grad(api.grad(foo))(3.) expected = -2. * jnp.sin(3.) self.assertAllClose(ans, expected) def test_initial_style_vmap(self): @api.custom_vjp def f(x): assert jnp.ndim(x) == 0 return 3 * x def f_fwd(x): return f(x), jnp.cos(x) def f_rev(cos_x, g): return (2 * cos_x * g,) f.defvjp(f_fwd, f_rev) def foo(x): out, _ = lax.scan(lambda c, _: (f(c), None), x, None, length=1) return out ans = api.vmap(foo)(jnp.arange(3.)) expected = 3. * jnp.arange(3.) self.assertAllClose(ans, expected, check_dtypes=False) ans = api.grad(lambda x: api.vmap(foo)(x).sum())(jnp.arange(3.)) expected = 2. * jnp.cos(jnp.arange(3.)) self.assertAllClose(ans, expected, check_dtypes=False) def test_nondiff_arg(self): @partial(api.custom_vjp, nondiff_argnums=(0,)) def app(f, x): return f(x) def app_fwd(f, x): return app(f, x), jnp.cos(x) def app_rev(f, cos_x, g): return (cos_x * g,) app.defvjp(app_fwd, app_rev) ans = app(lambda x: 2 * x, 1) expected = 2 self.assertAllClose(ans, expected, check_dtypes=False) ans = api.value_and_grad(lambda x: app(lambda y: 2 * y, x))(1.) expected = (2., jnp.cos(1.)) self.assertAllClose(ans, expected, check_dtypes=False) def test_closed_over_tracer(self): def outer(x): @api.custom_vjp def f(y): return x * y def f_fwd(y): return f(y), jnp.cos(y) def f_rev(cos_y, g): return (cos_y * g,) f.defvjp(f_fwd, f_rev) return f @jit def g(x, y): return outer(x)(y) ans = g(2, 3.) expected = 6. self.assertAllClose(ans, expected, check_dtypes=False) ans = api.grad(g, 1)(2., 3.) expected = jnp.cos(3.) self.assertAllClose(ans, expected, check_dtypes=False) def test_closed_over_tracer2(self): def outer(x): @api.custom_vjp def f(y): return x * y def f_fwd(y): return f(y), jnp.cos(y) def f_rev(cos_y, g): return (cos_y * g,) f.defvjp(f_fwd, f_rev) return f @api.vmap def g(x): return outer(x)(3.) ans = g(np.arange(3.)) expected = np.arange(3.) * 3 self.assertAllClose(ans, expected, check_dtypes=False) def test_closed_over_tracer3(self): def outer(x): @api.custom_vjp def f(y): return x * y def f_fwd(y): return f(y), (x, jnp.cos(y)) def f_rev(res, g): x, cos_y = res return (cos_y * g * x,) f.defvjp(f_fwd, f_rev) return api.grad(f) @api.vmap def g(x): return outer(x)(3.) ans = g(np.arange(3.)) expected = np.cos(3.) * np.arange(3.) self.assertAllClose(ans, expected, check_dtypes=False) def test_nondiff_arg_tracer_error(self): @partial(api.custom_vjp, nondiff_argnums=(0,)) def f(x, y): return x * y def f_fwd(x, y): return f(x, y), jnp.cos(y) def f_rev(x, cos_y, g): return (cos_y * g,) f.defvjp(f_fwd, f_rev) @jit def g(x, y): return f(x, y) with self.assertRaisesRegex(UnexpectedTracerError, "custom_vjp"): _ = g(2, 3.) with self.assertRaisesRegex(UnexpectedTracerError, "custom_vjp"): _ = api.grad(g, 1)(2., 3.) def test_vmap_axes(self): raise unittest.SkipTest("TODO") # TODO(mattjj): write test def test_pmap(self): raise unittest.SkipTest("TODO") # TODO(mattjj): write test def test_missing_vjp_rule_error(self): @api.custom_vjp def foo(x): return x ** 2 self.assertRaisesRegex( AttributeError, r"No VJP defined for custom_vjp function foo using defvjp.", lambda: foo(2)) self.assertRaisesRegex( AttributeError, r"No VJP defined for custom_vjp function foo using defvjp.", lambda: api.grad(foo)(2.)) def test_vjp_rule_inconsistent_pytree_structures_error(self): @api.custom_vjp def f(x): return x def foo_fwd(x): return x, None def foo_bwd(_, g): return (g, g) f.defvjp(foo_fwd, foo_bwd) f(2) # doesn't crash self.assertRaisesRegex( TypeError, re.escape( "Custom VJP rule must produce an output with the same container " "(pytree) structure as the args tuple of the primal function, " "and in particular must produce a tuple of length equal to the " "number of arguments to the primal function, but got VJP output " "structure {} for primal input structure {}.".format( tree_util.tree_structure((1, 1)), tree_util.tree_structure((1,))) ), lambda: api.grad(f)(2.)) def test_vjp_bwd_returns_non_tuple_error(self): @api.custom_vjp def f(x): return x def foo_fwd(x): return x, None def foo_bwd(_, g): return 2. * g f.defvjp(foo_fwd, foo_bwd) with self.assertRaisesRegex(TypeError, "Custom VJP rule .* must produce a tuple"): api.grad(f)(3.) def test_issue2511(self): arr = jnp.ones((5, 2, 2)) foo = lambda x: api.vmap(jnp.linalg.det, (0,))(x) api.jit(foo)(arr) def test_lowering_out_of_traces(self): # https://github.com/google/jax/issues/2578 class F(collections.namedtuple("F", ["a"])): def __call__(self, x): return jax.nn.relu(self.a) * x @jax.jit def g(f, x): return f(x) jax.grad(g, argnums=(1,))(F(2.0), 0.) # doesn't crash def test_clip_gradient(self): @api.custom_vjp def _clip_gradient(lo, hi, x): return x def clip_gradient_fwd(lo, hi, x): return x, (lo, hi,) def clip_gradient_bwd(res, g): lo, hi = res return (None, None, jnp.clip(g, lo, hi),) _clip_gradient.defvjp(clip_gradient_fwd, clip_gradient_bwd) def clip_gradient(x): lo = -0.1 hi = x + 0.1 return _clip_gradient(lo, hi, x) g = jax.grad(clip_gradient)(0.1) self.assertAllClose(g, jnp.array(0.2)) def test_nestable_vjp(self): # Verify that https://github.com/google/jax/issues/3667 is resolved. def f(x): return x ** 2 @api.custom_vjp def g(x): return f(x) def g_fwd(x): y, f_vjp = api.vjp(f, x) return y, f_vjp def g_bwd(f_vjp, y_bar): return f_vjp(y_bar) g.defvjp(g_fwd, g_bwd) # Check that VJP can be nested in simple situations. For this to pass, # vjp has to return a PyTree. _, g_vjp = api.vjp(g, 1.0) y, = g_vjp(1.0) self.assertAllClose(y, jnp.array(2.0)) # Check that VJP can be nested in complex situations. For this to pass, # vjp can't treat the closed-over tracer x as a static argument. @jit def z(x): _, g_vjp = api.vjp(g, x) return g_vjp y, = z(1.0)(3.0) self.assertAllClose(y, jnp.array(6.0)) def test_initial_style_vmap_2(self): x = jnp.ones((10, 3)) @api.custom_vjp def custom_fun(x): return x.sum() def forward(x): return x.sum(), (jnp.ones_like(x),) def backward(res, g): return g * res[0], custom_fun.defvjp(forward, backward) def train_fun(x): def summed_fun(x): return api.vmap(custom_fun)(x).sum() return api.grad(summed_fun)(x) def scan_body(carry, inputs): x = carry return carry, train_fun(x) scan_range = jnp.arange(4) lax.scan(scan_body, x, scan_range) def test_initial_style_vmap_3(self): # This is like test_initial_style_vmap except the primal function closes # over an array constant. y = jnp.array([1., 2., 3.]) @api.custom_vjp def f(x): assert jnp.ndim(x) == 0 return 3 * x * jnp.sum(y) def f_fwd(x): return f(x), jnp.cos(x) def f_rev(cos_x, g): return (2 * cos_x * g,) f.defvjp(f_fwd, f_rev) def foo(x): out, _ = lax.scan(lambda c, _: (f(c), None), x, None, length=1) return out ans = api.vmap(foo)(jnp.arange(3.)) expected = 3. * jnp.arange(3.) * 6 self.assertAllClose(ans, expected, check_dtypes=False) ans = api.grad(lambda x: api.vmap(foo)(x).sum())(jnp.arange(3.)) expected = 2. * jnp.cos(jnp.arange(3.)) self.assertAllClose(ans, expected, check_dtypes=False) def test_initial_style_vmap_with_collective(self): @api.custom_vjp def f(x): return lax.psum(x, 'foo') def f_fwd(x): return lax.psum(x, 'foo'), None def f_bwd(res, dx): return dx f.defvjp(f_fwd, f_bwd) def g(x): jaxpr = api.make_jaxpr(f)(x) return core.eval_jaxpr(jaxpr.jaxpr, [], x)[0] out = api.vmap(lambda _, x: g(x), axis_name='foo', in_axes=(0, None), out_axes=None)(jnp.arange(4.), 2.) self.assertAllClose(out, 8.) def test_bwd_closes_over_tracer(self): def f(y): @jax.custom_vjp def f(x): return 2. * jnp.sin(x) def fwd(x): return f(x), () def bwd(_, g): return (2. * jnp.cos(y) * g,) # capture! f.defvjp(fwd, bwd) return jax.grad(f)(1.) ans = jax.jit(f)(2.) self.assertAllClose(ans, 2. * jnp.cos(2.)) ans = jax.vmap(f)(jnp.arange(3.)) self.assertAllClose(ans, 2. * jnp.cos(jnp.arange(3.))) ans = jax.jit(jax.vmap(f))(jnp.arange(3.)) self.assertAllClose(ans, 2. * jnp.cos(jnp.arange(3.))) ans = jax.vmap(jax.jit(f))(jnp.arange(3.)) self.assertAllClose(ans, 2. * jnp.cos(jnp.arange(3.))) ans = jax.grad(f)(4.) self.assertAllClose(ans, -2. * jnp.sin(4.)) def test_fwd_closes_over_tracer(self): def f(y): @jax.custom_vjp def f(x): return 2. * jnp.sin(x) def fwd(x): return f(x), y def bwd(y, g): return (2. * jnp.cos(y) * g,) # capture! f.defvjp(fwd, bwd) return jax.grad(f)(1.) ans = jax.jit(f)(2.) self.assertAllClose(ans, 2. * jnp.cos(2.)) ans = jax.vmap(f)(jnp.arange(3.)) self.assertAllClose(ans, 2. * jnp.cos(jnp.arange(3.))) ans = jax.jit(jax.vmap(f))(jnp.arange(3.)) self.assertAllClose(ans, 2. * jnp.cos(jnp.arange(3.))) ans = jax.vmap(jax.jit(f))(jnp.arange(3.)) self.assertAllClose(ans, 2. * jnp.cos(jnp.arange(3.))) ans = jax.grad(f)(4.) self.assertAllClose(ans, -2. * jnp.sin(4.)) @unittest.skipIf(numpy_version == (1, 21, 0), "https://github.com/numpy/numpy/issues/19305") def test_float0(self): @api.custom_vjp def f(x, _): return x def f_fwd(x, _): # we need a defined (non-float0) tangent to trigger the rule return x, (2., 1) def f_rev(*_): return (2., 1) f.defvjp(f_fwd, f_rev) x = 2. y = 3 self.assertEqual(api.grad(f, allow_int=True, argnums=(0, 1))(x, y), (2., np.zeros(shape=(), dtype=float0))) @unittest.skipIf(numpy_version == (1, 21, 0), "https://github.com/numpy/numpy/issues/19305") def test_float0_initial_style(self): @api.custom_vjp def f(x): return x def f_fwd(x): return x, (2., x) def f_rev(*_): return ((2., 1),) f.defvjp(f_fwd, f_rev) def foo(x, y): out, _ = lax.scan(lambda c, _: (f(c), None), (x, y), None, length=1) return out[0] x = 2. y = 3 self.assertEqual(api.grad(foo, allow_int=True, argnums=(0, 1))(x, y), (2., np.zeros(shape=(), dtype=float0))) def test_remat(self): @api.custom_vjp def f(x): return jnp.sin(x) def f_fwd(x): return f(x), jnp.cos(x) def f_rev(cos_x, g): return (2 * cos_x * g,) f.defvjp(f_fwd, f_rev) @api.remat def g(x): return f(f(x)) ans = g(2.) expected = np.sin(np.sin(2.)) self.assertAllClose(ans, expected, check_dtypes=False) ans = api.grad(g)(2.) expected = 4. * api.grad(lambda x: jnp.sin(jnp.sin(x)))(2.) self.assertAllClose(ans, expected, check_dtypes=False) def test_remat_higher_order(self): @api.custom_vjp def f(x): return jnp.sin(x) def f_fwd(x): return f(x), jnp.cos(x) def f_rev(cos_x, g): return (2 * cos_x * g,) f.defvjp(f_fwd, f_rev) def g(x): return f(f(x)) ans = api.grad(api.grad(api.remat(g)))(2.) expected = api.grad(api.grad(g))(2.) self.assertAllClose(ans, expected, check_dtypes=False) ans = api.grad(api.remat(api.grad(g)))(2.) expected = api.grad(api.grad(g))(2.) self.assertAllClose(ans, expected, check_dtypes=False) ans = api.grad(api.grad(api.grad(api.remat(g))))(2.) expected = api.grad(api.grad(api.grad(g)))(2.) self.assertAllClose(ans, expected, check_dtypes=False) def test_bwd_nones(self): @api.custom_vjp def f(x, y): return x * jnp.sin(y) def f_fwd(x, y): return f(x, y), jnp.cos(y) def f_rev(cos, g): return (None, 2 * cos * g) f.defvjp(f_fwd, f_rev) ans = api.grad(lambda x: f(x, x))(3.) expected = 2 * jnp.cos(3.) self.assertAllClose(ans, expected, check_dtypes=False) def test_bwd_nones_vmap(self): @api.custom_vjp def f(x, y): return x * jnp.sin(y) def f_fwd(x, y): return f(x, y), jnp.cos(y) def f_rev(cos, g): return (None, 2 * cos * g) f.defvjp(f_fwd, f_rev) ans = api.grad(lambda x: api.vmap(f)(x, x).sum())(jnp.arange(3.)) expected = 2 * jnp.cos(jnp.arange(3.)) self.assertAllClose(ans, expected, check_dtypes=False) def test_bwd_nones_pytree(self): @api.custom_vjp def f(xs, y): x1, x2 = xs return x1 * x2 * jnp.sin(y) def f_fwd(xs, y): return f(xs, y), jnp.cos(y) def f_rev(cos, g): return (None, 2 * cos * g) f.defvjp(f_fwd, f_rev) ans = api.grad(lambda x: f((x, x), x))(3.) expected = 2 * jnp.cos(3.) self.assertAllClose(ans, expected, check_dtypes=False) def test_custom_vjp_closure_4521(self): # https://github.com/google/jax/issues/4521 @api.custom_vjp def g(x, y): return None def g_fwd(x, y): return None, y def g_bwd(residuals, z_bar): assert False g.defvjp(g_fwd, g_bwd) def f(xs, y): v_g = api.vmap(g, in_axes=(0, None), out_axes=None) v_g(xs, y) def scan_body(xs, _): y = jnp.zeros(1) _, vjp_f = api.vjp(f, xs, y) vjp_f(None) return xs, None lax.scan(scan_body, jnp.ones(5), None, 100) # doesn't crash def test_float0_bwd_none(self): @api.custom_vjp def f(i, x): return jnp.sin(x) def f_fwd(i, x): return f(i, x), jnp.cos(x) def f_rev(cos_x, g): return (None, 2 * cos_x * g) f.defvjp(f_fwd, f_rev) ans = api.grad(f, 1)(jnp.array([1, 2]), 3.) expected = 2 * jnp.cos(3.) self.assertAllClose(ans, expected, check_dtypes=False) def test_custom_gradient(self): @api.custom_gradient def f(x): return x ** 2, lambda g: (g * x,) self.assertAllClose(f(3.), 9., check_dtypes=False) self.assertAllClose(api.grad(f)(3.), 3., check_dtypes=False) self.assertAllClose(api.grad(api.grad(f))(3.), 1., check_dtypes=False) def test_custom_gradient_2(self): @api.custom_gradient def f(x, y): return x * y, lambda g: (y, x) self.assertAllClose(f(3., 4.), 12., check_dtypes=False) self.assertAllClose(api.grad(f, argnums=(0, 1))(3., 4.), (4., 3.), check_dtypes=False) def test_custom_gradient_3(self): @api.custom_gradient def f(x): vjp = lambda g: (jnp.cos(x) * jnp.array([3., 4., 5.]),) return jnp.sum(jnp.sin(x)), vjp self.assertAllClose(f(jnp.arange(3)), jnp.sum(jnp.sin(jnp.arange(3.))), check_dtypes=False) self.assertAllClose( api.grad(f)(jnp.arange(3.)), api.grad(lambda x: jnp.sum(jnp.sin(x)))(jnp.arange(3.)) * jnp.array([3., 4., 5.]), check_dtypes=False) def test_custom_gradient_can_return_singleton_value_in_vjp(self): @api.custom_gradient def f(x): return x ** 2, lambda g: g * x self.assertAllClose(f(3.), 9., check_dtypes=False) self.assertAllClose(api.grad(f)(3.), 3., check_dtypes=False) self.assertAllClose(api.grad(api.grad(f))(3.), 1., check_dtypes=False) def test_closure_convert(self): def cos_after(fn, x): converted_fn, aux_args = api.closure_convert(fn, x) self.assertLessEqual(len(aux_args), 1) return _cos_after(converted_fn, x, *aux_args) @partial(api.custom_vjp, nondiff_argnums=(0,)) def _cos_after(fn, x, *args): return jnp.cos(fn(x, *args)) def fwd(fn, x, *args): y = _cos_after(fn, x, *args) return y, (x, args) def rev(fn, res, g): x, args = res x_bar = 17. * x args_bars = [42. * a for a in args] return (x_bar, *args_bars) _cos_after.defvjp(fwd, rev) def dist(c, x): return jnp.sum((x - c) ** 2.) def solve(c, x): def closure(x): return dist(c, x) return cos_after(closure, x) c, x = 2. * jnp.ones(2), jnp.ones(2) expected = jnp.cos(dist(c, x)) self.assertAllClose(solve(c, x), expected, check_dtypes=False) g_c, g_x = api.grad(solve, argnums=(0, 1))(c, x) self.assertAllClose(g_c, 42. * c, check_dtypes=False) self.assertAllClose(g_x, 17. * x, check_dtypes=False) def test_closure_convert_mixed_consts(self): # Like test_closure_convert, but close over values that # participate in AD as well as values that do not. # See https://github.com/google/jax/issues/6415 def cos_after(fn, x): converted_fn, aux_args = api.closure_convert(fn, x) self.assertLessEqual(len(aux_args), 1) return _cos_after(converted_fn, x, *aux_args) @partial(api.custom_vjp, nondiff_argnums=(0,)) def _cos_after(fn, x, *args): return jnp.cos(fn(x, *args)) def fwd(fn, x, *args): y = _cos_after(fn, x, *args) return y, (x, args) def rev(fn, res, g): x, args = res x_bar = 17. * x args_bars = [42. * a for a in args] return (x_bar, *args_bars) _cos_after.defvjp(fwd, rev) def dist(c, s, x): return jnp.sum(s * (x - c) ** 2.) def solve(c, s, x): def closure(x): return dist(c, s, x) return cos_after(closure, x) c, s, x = 2. * jnp.ones(2), 3. * jnp.ones(2), jnp.ones(2) expected = jnp.cos(dist(c, s, x)) self.assertAllClose(solve(c, s, x), expected, check_dtypes=False) g_c, g_x = api.grad(solve, argnums=(0, 2))(c, s, x) self.assertAllClose(g_c, 42. * c, check_dtypes=False) self.assertAllClose(g_x, 17. * x, check_dtypes=False) def test_float0_cotangents_automatically_handled(self): @jax.custom_vjp def f(x, y): return x def f_fwd(x, y): return x, None def f_bwd(_, zbar): return (0., 1) f.defvjp(f_fwd, f_bwd) jax.jit(lambda x: jax.vjp(f, 0., x)[1](1.))(1) # doesn't crash class CustomTransposeTest(jtu.JaxTestCase): def transpose(self, f, x_example): def transposed(y): x, = api.linear_transpose(f, x_example)(y) return x return transposed def test_linear_call(self): def f(x, y): def fn(r, x): return x / r def tp(r, t): return t / r return x + api.linear_call(fn, tp, y, x) def f_ref(x, y): return x + x / y x = jnp.ones(2) * 6. y = jnp.ones(2) * 3. self.assertAllClose(f(x, y), f_ref(x, y)) f1 = lambda x: f(x, y) f1_ref = lambda x: f_ref(x, y) self.assertAllClose(self.transpose(f1, x)(x), self.transpose(f1_ref, x)(x)) def test_linear_call_incorrect_transpose(self): def f(x, y): def fn(r, x): return x / r def tp(r, t): return t / (2. * r) return x + api.linear_call(fn, tp, y, x) def f_ref(x, y): return x + x / y x = jnp.ones(2) * 6. y = jnp.ones(2) * 3. self.assertAllClose(f(x, y), f_ref(x, y)) f1 = lambda x: f(x, y) f1_ref = lambda x: f_ref(x, 2. * y) self.assertAllClose(self.transpose(f1, x)(x), self.transpose(f1_ref, x)(x)) def test_linear_call_transpose_transpose_transpose(self): def fn(r, x): return x / r def tp(r, t): return t / (2. * r) def f_(x, y): return x + api.linear_call(fn, tp, y, x) x = jnp.ones(2) * 6. y = jnp.ones(2) * 3. f = lambda x: f_(x, y) ft = self.transpose(f, x) ftt = self.transpose(ft, x) fttt = self.transpose(ftt, x) self.assertAllClose(ft(x), x + tp(y, x)) self.assertAllClose(f(x), ftt(x)) self.assertAllClose(ft(x), fttt(x)) def test_linear_call_scalar_to_vector(self): def f(c, x): def fn(_, x): return [x, x] def tp(_, t): t1, t2 = t return t1 + t2 return api.linear_call(fn, tp, (), c * x) def f_ref(c, x): return [c * x, c * x] c, x = 2., 3. t = [4., 5.] self.assertAllClose(f(c, x), f_ref(c, x)) self.assertAllClose(self.transpose(partial(f, c), x)(t), self.transpose(partial(f_ref, c), x)(t)) def test_linear_call_nested(self): def id_(x): def f(_, x): return x def t(_, t): return 0. return api.linear_call(f, t, (), x) def f(x): def f_(_, x): return id_(x) def t_(_, t): return id_(7.) return api.linear_call(f_, t_, (), x) x = 5. id_t = self.transpose(id_, x) id_tt = self.transpose(id_t, x) ft = self.transpose(f, x) ftt = self.transpose(ft, x) fttt = self.transpose(ftt, x) self.assertAllClose(id_(x), x) self.assertAllClose(id_t(x), 0.) self.assertAllClose(id_tt(x), x) self.assertAllClose(f(x), x) self.assertAllClose(ft(x), 7.) self.assertAllClose(ftt(x), x) self.assertAllClose(fttt(x), 7.) def test_linear_call_jit(self): def f(x, y): def fn(r, x): return x / r def tp(r, t): return t / r return x + api.linear_call(fn, tp, y, x) x = jnp.ones(2) * 6. y = jnp.ones(2) * 3. self.assertAllClose(f(x, y), jax.jit(f)(x, y)) f1 = lambda x: f(x, y) self.assertAllClose(self.transpose(f1, x)(x), jax.jit(self.transpose(f1, x))(x)) class InvertibleADTest(jtu.JaxTestCase): @jtu.ignore_warning(message="Values that an @invertible function closes") def test_invertible_basic(self): def f(x): return lax.mul(lax.mul(lax.exp(x), 4.), x) finv = jax.invertible(f) x = jnp.ones((5,)) jaxpr = jax.make_jaxpr(lambda p, ct: jax.vjp(finv, p)[1](ct))(x, x) # { lambda ; a b. # let c = exp a # d = mul c 4.0 # e = mul d a # f = mul b a # g = div e a # h = mul b g # i = mul f 4.0 # j = div g 4.0 # k = mul f j # _ = reduce_sum[ axes=(0,) ] k # _ = log j # l = mul i j # m = add_any h l # in (m,) } # """ rtIn('div', str(jaxpr)) self.assertIn('log', str(jaxpr)) self.assertAllClose(jax.value_and_grad(lambda x: np.sum(f(x)))(x), jax.value_and_grad(lambda x: np.sum(finv(x)))(x), check_dtypes=True) def test_invertible_blocks(self): def mk_reversible_block(f, g): @jax.custom_ivjp def rev_block(x1, x2): y1 = f(x2) + x1 y2 = g(y1) + x2 return y1, y2 @rev_block.defivjp def rev_block_ivjp(xs, ys, dys): (y1, y2) = ys (dy1, dy2) = dys dgo, dx2 = dy2, dy2 go, gvjp = jax.vjp(g, y1) dy1 += gvjp(dgo)[0] del gvjp x2 = y2 - go dfo, dx1 = dy1, dy1 fo, fvjp = jax.vjp(f, x2) dx2 += fvjp(dfo)[0] del fvjp x1 = y1 - fo return (x1, x2), (dx1, dx2) return rev_block rev_block = mk_reversible_block(jnp.sin, jnp.cos) def g(x1, x2): for i in range(2): x1, x2 = rev_block(x1, x2) return x1, x2 def reduce(f, x1, x2): y1, y2 = f(x1, x2) return np.sum(y1) + np.sum(y2) x = np.ones((1,)) self.assertAllClose(jax.value_and_grad(partial(reduce, jax.invertible(g)), argnums=(0, 1))(x, x + 2), jax.value_and_grad(partial(reduce, g), argnums=(0, 1))(x, x + 2), check_dtypes=True) def test_invertible_partial_diff(self): # of the invertible function. def f(x, y): return lax.mul(lax.mul(lax.exp(x), 4.), x), lax.add(y, 4.) finv = jax.invertible(f) o = np.ones((5,)) self.assertAllClose(jax.value_and_grad(lambda x: np.sum(f(x, o)[0]))(o), jax.value_and_grad(lambda x: np.sum(finv(x, o)[0]))(o), check_dtypes=True) def test_invertible_pytree(self): def f(x, y): return lax.add(lax.mul(lax.exp(x[0]), x[1]), y) finv = jax.invertible(f) o = np.ones((5,)) self.assertAllClose(jax.value_and_grad(lambda x: np.sum(f((x, x), x)[0]))(o), jax.value_and_grad(lambda x: np.sum(finv((x, x), x)[0]))(o), check_dtypes=True) class BufferDonationTest(jtu.BufferDonationTestCase): @jtu.skip_on_devices("cpu") # In/out aliasing not supported on CPU. def test_pmap_donate_argnums_invalidates_input(self): move = api.pmap(lambda x: x + x - x, donate_argnums=0) n = jax.local_device_count() x = api.pmap(lambda x: x)(jnp.ones([n])) y = move(x) self.assertDeleted(x) np.testing.assert_allclose(y, [1.] * n) def test_pmap_nested_donate_ignored(self): pmap_fun = jit(lambda x: api.pmap(lambda y: y ** 2, donate_argnums=0)(x)) a = api.pmap(lambda x: x)(jnp.array([1])) # NOTE(mattjj): stopped raising error here and instead just ignored # with self.assertRaisesRegex(ValueError, "nested.*not supported"): # pmap_fun(a) pmap_fun(a) # doesn't crash class NamedCallTest(jtu.JaxTestCase): def test_default_name(self): @api.named_call def my_test_function(x): return x**2 @jax.jit def f(x): return my_test_function(x) c = jax.xla_computation(f)(2) self.assertIn("my_test_function", c.as_hlo_text()) def test_non_jaxtype_arg(self): def f(not_a_jaxtype, a_jaxtype): if not_a_jaxtype: return a_jaxtype return 0 f = api.named_call(f, name="test") out = jax.jit(f, static_argnums=(0,))("not a Jaxtype", 1) self.assertEqual(out, 1) @parameterized.parameters(jax.jit, jax.grad, jax.vmap, jax.remat) def test_jax_transforms(self, transform): f = jnp.sum x = jnp.array([1.]) unnamed_out = transform(f)(x) named_out = transform(api.named_call(f, name="test"))(x) self.assertEqual(unnamed_out, named_out) def test_static_argnums(self): f = api.named_call(lambda x, y: y if x else None, name="test") f = jax.jit(f, static_argnums=(0,)) out = f(True, 5) self.assertEqual(out, 5) def test_partial_eval(self): f = api.named_call(lambda x, y: y if x else None, name="test") f = jax.jit(functools.partial(f, True)) out = f(5) self.assertEqual(out, 5) @parameterized.named_parameters(jtu.cases_from_list( {"testcase_name": "_jit_type={}_func={}".format(jit_type, func), "jit_type": jit_type, "func": func} for func in ['identity', 'asarray', 'device_put'] for jit_type in [None, "python", "cpp"] if not (jit_type is None and func == 'identity'))) def test_integer_overflow(self, jit_type, func): funcdict = { 'identity': lambda x: x, 'asarray': jnp.asarray, 'device_put': api.device_put, } jit = { 'python': api._python_jit, 'cpp': api._cpp_jit, None: lambda x: x, } f = jit[jit_type](funcdict[func]) int_dtype = dtypes.canonicalize_dtype(jnp.int_) int_max = np.iinfo(int_dtype).max int_min = np.iinfo(int_dtype).min self.assertEqual(f(int_max).dtype, int_dtype) self.assertEqual(f(int_min).dtype, int_dtype) self.assertRaises(OverflowError, f, int_max + 1) self.assertRaises(OverflowError, f, int_min - 1) class BackendsTest(jtu.JaxTestCase): @unittest.skipIf(not sys.executable, "test requires sys.executable") @jtu.skip_on_devices("gpu", "tpu") def test_cpu_warning_suppression(self): warning_expected = ( "import jax; " "jax.numpy.arange(10)") warning_not_expected = ( "import jax; " "jax.config.update('jax_platform_name', 'cpu'); " "jax.numpy.arange(10)") result = subprocess.run([sys.executable, '-c', warning_expected], check=True, capture_output=True) assert "No GPU/TPU found" in result.stderr.decode() result = subprocess.run([sys.executable, '-c', warning_not_expected], check=True, capture_output=True) assert "No GPU/TPU found" not in result.stderr.decode() if __name__ == '__main__': absltest.main(testLoader=jtu.JaxTestLoader())

true

1c379c56877c1c8f51cd44e18e3eba0986f7c3d1

907

py

Python

tmp/wswp/crawler.py

godontop/python

a33391304e3396d2f208dfc8cec3c200e4f18136

[ "MIT" ]

null

tmp/wswp/crawler.py

godontop/python

a33391304e3396d2f208dfc8cec3c200e4f18136

[ "MIT" ]

null

tmp/wswp/crawler.py

godontop/python

a33391304e3396d2f208dfc8cec3c200e4f18136

[ "MIT" ]

null

# coding=utf-8 import datetime import re import time import urllib.error import urllib.parse import urllib.request import urllib.robotparser import sys from downloader import Downloader def crawler(seed_url, delay=1, max_depth=2, user_agent='Mozilla/5.0 (Macintosh; Intel Mac OS X 10_13_4) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/66.0.3359.139 Safari/537.36', proxies=None, num_retries=2, scrape_callback=None, cache=None): """Crawl from the given seed URL following links matched by link_regex """ crawl_queue = [seed_url] D = Downloader(delay=delay, user_agent=user_agent, proxies=proxies, num_retries=num_retries, cache=cache) if scrape_callback: crawl_queue = scrape_callback('http://python.ticp.net:2018/top-1m.csv.zip', D('http://python.ticp.net:2018/top-1m.csv.zip')) while crawl_queue: url = crawl_queue.pop() html = D(url)

37.791667

243

0.724366

import datetime import re import time import urllib.error import urllib.parse import urllib.request import urllib.robotparser import sys from downloader import Downloader def crawler(seed_url, delay=1, max_depth=2, user_agent='Mozilla/5.0 (Macintosh; Intel Mac OS X 10_13_4) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/66.0.3359.139 Safari/537.36', proxies=None, num_retries=2, scrape_callback=None, cache=None): crawl_queue = [seed_url] D = Downloader(delay=delay, user_agent=user_agent, proxies=proxies, num_retries=num_retries, cache=cache) if scrape_callback: crawl_queue = scrape_callback('http://python.ticp.net:2018/top-1m.csv.zip', D('http://python.ticp.net:2018/top-1m.csv.zip')) while crawl_queue: url = crawl_queue.pop() html = D(url)

true

1c379db5bb9eb9cc51e5d447527f55bc6ea4f4f7

4,676

py

Python

flink-ai-flow/ai_flow/application_master/master.py

MarvinMiao/flink-ai-extended

e45eecf2deea6976ba3d7ba821ffb8d9ce0a17f4

[ "Apache-2.0", "BSD-2-Clause", "MIT", "ECL-2.0", "BSD-3-Clause" ]

1

2020-12-12T15:21:05.000Z

flink-ai-flow/ai_flow/application_master/master.py

MarvinMiao/flink-ai-extended

e45eecf2deea6976ba3d7ba821ffb8d9ce0a17f4

[ "Apache-2.0", "BSD-2-Clause", "MIT", "ECL-2.0", "BSD-3-Clause" ]

1

2021-01-30T11:28:37.000Z

flink-ai-flow/ai_flow/application_master/master.py

MarvinMiao/flink-ai-extended

e45eecf2deea6976ba3d7ba821ffb8d9ce0a17f4

[ "Apache-2.0", "BSD-2-Clause", "MIT", "ECL-2.0", "BSD-3-Clause" ]

null

# # Licensed to the Apache Software Foundation (ASF) 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 limitations # under the License. # import os from typing import Text from ai_flow.rest_endpoint.service.server import AIFlowServer, HighAvailableAIFlowServer from ai_flow.store.db.base_model import base from ai_flow.store.sqlalchemy_store import SqlAlchemyStore from ai_flow.store.mongo_store import MongoStoreConnManager from ai_flow.application_master.master_config import MasterConfig, DBType from ai_flow.client.ai_flow_client import get_ai_flow_client import logging _SQLITE_DB_FILE = 'aiflow.db' _SQLITE_DB_URI = '%s%s' % ('sqlite:///', _SQLITE_DB_FILE) _MYSQL_DB_URI = 'mysql+pymysql://root:aliyunmysql@localhost:3306/aiflow' _PORT = '50051' GLOBAL_MASTER_CONFIG = {} class AIFlowMaster(object): """ AI flow master. """ def __init__(self, config_file: Text = None, enable_ha=False, server_uri: str = None, ttl_ms=10000) -> None: """ Set the master attribute according to the master config file. :param config_file: master configuration file. """ super().__init__() self.config_file = config_file self.server = None self.master_config = MasterConfig() self.enable_ha = enable_ha self.server_uri = server_uri self.ttl_ms = ttl_ms def start(self, is_block=False) -> None: """ Start the AI flow master. :param is_block: AI flow master will run non-stop if True. """ if self.config_file is not None: self.master_config.load_from_file(self.config_file) else: self.master_config.set_master_port(str(_PORT)) global GLOBAL_MASTER_CONFIG GLOBAL_MASTER_CONFIG = self.master_config logging.info("AI Flow Master Config {}".format(GLOBAL_MASTER_CONFIG)) if not self.master_config.get_enable_ha(): self.server = AIFlowServer( store_uri=self.master_config.get_db_uri(), port=str(self.master_config.get_master_port()), start_default_notification=self.master_config.start_default_notification(), notification_uri=self.master_config.get_notification_uri()) else: self.server = HighAvailableAIFlowServer( store_uri=self.master_config.get_db_uri(), port=str(self.master_config.get_master_port()), start_default_notification=self.master_config.start_default_notification(), notification_uri=self.master_config.get_notification_uri(), server_uri=self.master_config.get_master_ip() + ":" + str(self.master_config.get_master_port()), ttl_ms=self.master_config.get_ha_ttl_ms()) self.server.run(is_block=is_block) def stop(self, clear_sql_lite_db_file=True) -> None: """ Stop the AI flow master. :param clear_sql_lite_db_file: If True, the sqlite database files will be deleted When the server stops working. """ self.server.stop() if self.master_config.get_db_type() == DBType.SQLITE and clear_sql_lite_db_file: store = SqlAlchemyStore(self.master_config.get_db_uri()) base.metadata.drop_all(store.db_engine) os.remove(self.master_config.get_sql_lite_db_file()) elif self.master_config.get_db_type() == DBType.MONGODB: MongoStoreConnManager().disconnect_all() def _clear_db(self): if self.master_config.get_db_type() == DBType.SQLITE: store = SqlAlchemyStore(self.master_config.get_db_uri()) base.metadata.drop_all(store.db_engine) base.metadata.create_all(store.db_engine) elif self.master_config.get_db_type() == DBType.MONGODB: MongoStoreConnManager().drop_all() def set_master_config(): code, config, message = get_ai_flow_client().get_master_config() for k, v in config.items(): GLOBAL_MASTER_CONFIG[k] = v

42.126126

120

0.695252

import os from typing import Text from ai_flow.rest_endpoint.service.server import AIFlowServer, HighAvailableAIFlowServer from ai_flow.store.db.base_model import base from ai_flow.store.sqlalchemy_store import SqlAlchemyStore from ai_flow.store.mongo_store import MongoStoreConnManager from ai_flow.application_master.master_config import MasterConfig, DBType from ai_flow.client.ai_flow_client import get_ai_flow_client import logging _SQLITE_DB_FILE = 'aiflow.db' _SQLITE_DB_URI = '%s%s' % ('sqlite:///', _SQLITE_DB_FILE) _MYSQL_DB_URI = 'mysql+pymysql://root:aliyunmysql@localhost:3306/aiflow' _PORT = '50051' GLOBAL_MASTER_CONFIG = {} class AIFlowMaster(object): def __init__(self, config_file: Text = None, enable_ha=False, server_uri: str = None, ttl_ms=10000) -> None: super().__init__() self.config_file = config_file self.server = None self.master_config = MasterConfig() self.enable_ha = enable_ha self.server_uri = server_uri self.ttl_ms = ttl_ms def start(self, is_block=False) -> None: if self.config_file is not None: self.master_config.load_from_file(self.config_file) else: self.master_config.set_master_port(str(_PORT)) global GLOBAL_MASTER_CONFIG GLOBAL_MASTER_CONFIG = self.master_config logging.info("AI Flow Master Config {}".format(GLOBAL_MASTER_CONFIG)) if not self.master_config.get_enable_ha(): self.server = AIFlowServer( store_uri=self.master_config.get_db_uri(), port=str(self.master_config.get_master_port()), start_default_notification=self.master_config.start_default_notification(), notification_uri=self.master_config.get_notification_uri()) else: self.server = HighAvailableAIFlowServer( store_uri=self.master_config.get_db_uri(), port=str(self.master_config.get_master_port()), start_default_notification=self.master_config.start_default_notification(), notification_uri=self.master_config.get_notification_uri(), server_uri=self.master_config.get_master_ip() + ":" + str(self.master_config.get_master_port()), ttl_ms=self.master_config.get_ha_ttl_ms()) self.server.run(is_block=is_block) def stop(self, clear_sql_lite_db_file=True) -> None: self.server.stop() if self.master_config.get_db_type() == DBType.SQLITE and clear_sql_lite_db_file: store = SqlAlchemyStore(self.master_config.get_db_uri()) base.metadata.drop_all(store.db_engine) os.remove(self.master_config.get_sql_lite_db_file()) elif self.master_config.get_db_type() == DBType.MONGODB: MongoStoreConnManager().disconnect_all() def _clear_db(self): if self.master_config.get_db_type() == DBType.SQLITE: store = SqlAlchemyStore(self.master_config.get_db_uri()) base.metadata.drop_all(store.db_engine) base.metadata.create_all(store.db_engine) elif self.master_config.get_db_type() == DBType.MONGODB: MongoStoreConnManager().drop_all() def set_master_config(): code, config, message = get_ai_flow_client().get_master_config() for k, v in config.items(): GLOBAL_MASTER_CONFIG[k] = v

true

1c379de15770b17dc2ba1bf16a65246f51b2f12e

136

py

Python

abc/abc143/abc143c-1.py

c-yan/atcoder

940e49d576e6a2d734288fadaf368e486480a948

[ "MIT" ]

1

2019-08-21T00:49:34.000Z

abc/abc143/abc143c-1.py

c-yan/atcoder

940e49d576e6a2d734288fadaf368e486480a948

[ "MIT" ]

null

abc/abc143/abc143c-1.py

c-yan/atcoder

940e49d576e6a2d734288fadaf368e486480a948

[ "MIT" ]

null

N = int(input()) S = input() p = '' result = 0 for i in range(N): if p != S[i]: result += 1 p = S[i] print(result)

12.363636

19

0.448529

N = int(input()) S = input() p = '' result = 0 for i in range(N): if p != S[i]: result += 1 p = S[i] print(result)

true

1c379e95efb13865d25b98a431b6010d16bbe638

11,345

py

Python

es_test_data.py

unfor19/elasticsearch-test-data

e79be946aee74fb4f4cc77cf9209ac3a62f710be

[ "MIT" ]

1

2021-09-18T06:50:04.000Z

es_test_data.py

unfor19/elasticsearch-test-data

e79be946aee74fb4f4cc77cf9209ac3a62f710be

[ "MIT" ]

null

es_test_data.py

unfor19/elasticsearch-test-data

e79be946aee74fb4f4cc77cf9209ac3a62f710be

[ "MIT" ]

null

#!/usr/bin/python import json import time import logging import random import string import uuid import datetime import tornado.gen import tornado.httpclient import tornado.ioloop import tornado.options try: xrange range = xrange except NameError: pass async_http_client = tornado.httpclient.AsyncHTTPClient() headers = tornado.httputil.HTTPHeaders({"content-type": "application/json"}) id_counter = 0 upload_data_count = 0 _dict_data = None def delete_index(idx_name): try: url = "%s/%s?refresh=true" % (tornado.options.options.es_url, idx_name) request = tornado.httpclient.HTTPRequest(url, headers=headers, method="DELETE", request_timeout=240, auth_username=tornado.options.options.username, auth_password=tornado.options.options.password, validate_cert=tornado.options.options.validate_cert) response = tornado.httpclient.HTTPClient().fetch(request) logging.info('Deleting index "%s" done %s' % (idx_name, response.body)) except tornado.httpclient.HTTPError: pass def create_index(idx_name): schema = { "settings": { "number_of_shards": tornado.options.options.num_of_shards, "number_of_replicas": tornado.options.options.num_of_replicas }, "refresh": True } body = json.dumps(schema) url = "%s/%s" % (tornado.options.options.es_url, idx_name) try: logging.info('Trying to create index %s' % (url)) request = tornado.httpclient.HTTPRequest(url, headers=headers, method="PUT", body=body, request_timeout=240, auth_username=tornado.options.options.username, auth_password=tornado.options.options.password, validate_cert=tornado.options.options.validate_cert) response = tornado.httpclient.HTTPClient().fetch(request) logging.info('Creating index "%s" done %s' % (idx_name, response.body)) except tornado.httpclient.HTTPError: logging.info('Looks like the index exists already') pass @tornado.gen.coroutine def upload_batch(upload_data_txt): try: request = tornado.httpclient.HTTPRequest(tornado.options.options.es_url + "/_bulk", method="POST", body=upload_data_txt, headers=headers, request_timeout=tornado.options.options.http_upload_timeout, auth_username=tornado.options.options.username, auth_password=tornado.options.options.password, validate_cert=tornado.options.options.validate_cert) response = yield async_http_client.fetch(request) except Exception as ex: logging.error("upload failed, error: %s" % ex) return result = json.loads(response.body.decode('utf-8')) res_txt = "OK" if not result['errors'] else "FAILED" took = int(result['took']) logging.info("Upload: %s - upload took: %5dms, total docs uploaded: %7d" % (res_txt, took, upload_data_count)) def get_data_for_format(format): split_f = format.split(":") if not split_f: return None, None field_name = split_f[0] field_type = split_f[1] return_val = '' if field_type == "bool": return_val = random.choice([True, False]) elif field_type == "str": min = 3 if len(split_f) < 3 else int(split_f[2]) max = min + 7 if len(split_f) < 4 else int(split_f[3]) length = generate_count(min, max) return_val = "".join([random.choice(string.ascii_letters + string.digits) for x in range(length)]) elif field_type == "int": min = 0 if len(split_f) < 3 else int(split_f[2]) max = min + 100000 if len(split_f) < 4 else int(split_f[3]) return_val = generate_count(min, max) elif field_type == "ipv4": return_val = "{0}.{1}.{2}.{3}".format(generate_count(0, 245),generate_count(0, 245),generate_count(0, 245),generate_count(0, 245)) elif field_type in ["ts", "tstxt"]: now = int(time.time()) per_day = 24 * 60 * 60 min = now - 30 * per_day if len(split_f) < 3 else int(split_f[2]) max = now + 30 * per_day if len(split_f) < 4 else int(split_f[3]) ts = generate_count(min, max) return_val = int(ts * 1000) if field_type == "ts" else datetime.datetime.fromtimestamp(ts).strftime("%Y-%m-%dT%H:%M:%S.000-0000") elif field_type == "words": min = 2 if len(split_f) < 3 else int(split_f[2]) max = min + 8 if len(split_f) < 4 else int(split_f[3]) count = generate_count(min, max) words = [] for _ in range(count): word_len = random.randrange(3, 10) words.append("".join([random.choice(string.ascii_letters + string.digits) for x in range(word_len)])) return_val = " ".join(words) elif field_type == "dict": global _dict_data min = 2 if len(split_f) < 3 else int(split_f[2]) max = min + 8 if len(split_f) < 4 else int(split_f[3]) count = generate_count(min, max) return_val = " ".join([random.choice(_dict_data).strip() for _ in range(count)]) elif field_type == "text": text = ["text1", "text2", "text3"] if len(split_f) < 3 else split_f[2].split("-") min = 1 if len(split_f) < 4 else int(split_f[3]) max = min + 1 if len(split_f) < 5 else int(split_f[4]) count = generate_count(min, max) words = [] for _ in range(count): words.append(""+random.choice(text)) return_val = " ".join(words) return field_name, return_val def generate_count(min, max): if min == max: return max elif min > max: return random.randrange(max, min); else: return random.randrange(min, max); def generate_random_doc(format): global id_counter res = {} for f in format: f_key, f_val = get_data_for_format(f) if f_key: res[f_key] = f_val if not tornado.options.options.id_type: return res if tornado.options.options.id_type == 'int': res['_id'] = id_counter id_counter += 1 elif tornado.options.options.id_type == 'uuid4': res['_id'] = str(uuid.uuid4()) return res def set_index_refresh(val): params = {"index": {"refresh_interval": val}} body = json.dumps(params) url = "%s/%s/_settings" % (tornado.options.options.es_url, tornado.options.options.index_name) try: request = tornado.httpclient.HTTPRequest(url, headers=headers, method="PUT", body=body, request_timeout=240, auth_username=tornado.options.options.username, auth_password=tornado.options.options.password, validate_cert=tornado.options.options.validate_cert) http_client = tornado.httpclient.HTTPClient() http_client.fetch(request) logging.info('Set index refresh to %s' % val) except Exception as ex: logging.exception(ex) @tornado.gen.coroutine def generate_test_data(): global upload_data_count if tornado.options.options.force_init_index: delete_index(tornado.options.options.index_name) create_index(tornado.options.options.index_name) # todo: query what refresh is set to, then restore later if tornado.options.options.set_refresh: set_index_refresh("-1") if tornado.options.options.out_file: out_file = open(tornado.options.options.out_file, "w") else: out_file = None if tornado.options.options.dict_file: global _dict_data with open(tornado.options.options.dict_file, 'r') as f: _dict_data = f.readlines() logging.info("Loaded %d words from the %s" % (len(_dict_data), tornado.options.options.dict_file)) format = tornado.options.options.format.split(',') if not format: logging.error('invalid format') exit(1) ts_start = int(time.time()) upload_data_txt = "" logging.info("Generating %d docs, upload batch size is %d" % (tornado.options.options.count, tornado.options.options.batch_size)) for num in range(0, tornado.options.options.count): item = generate_random_doc(format) if out_file: out_file.write("%s\n" % json.dumps(item)) cmd = {'index': {'_index': tornado.options.options.index_name, '_type': tornado.options.options.index_type}} if '_id' in item: cmd['index']['_id'] = item['_id'] upload_data_txt += json.dumps(cmd) + "\n" upload_data_txt += json.dumps(item) + "\n" upload_data_count += 1 if upload_data_count % tornado.options.options.batch_size == 0: yield upload_batch(upload_data_txt) upload_data_txt = "" # upload remaining items in `upload_data_txt` if upload_data_txt: yield upload_batch(upload_data_txt) if tornado.options.options.set_refresh: set_index_refresh("1s") if out_file: out_file.close() took_secs = int(time.time() - ts_start) logging.info("Done - total docs uploaded: %d, took %d seconds" % (tornado.options.options.count, took_secs)) if __name__ == '__main__': tornado.options.define("es_url", type=str, default='http://localhost:9200/', help="URL of your Elasticsearch node") tornado.options.define("index_name", type=str, default='test_data', help="Name of the index to store your messages") tornado.options.define("index_type", type=str, default='test_type', help="Type") tornado.options.define("batch_size", type=int, default=1000, help="Elasticsearch bulk index batch size") tornado.options.define("num_of_shards", type=int, default=2, help="Number of shards for ES index") tornado.options.define("http_upload_timeout", type=int, default=3, help="Timeout in seconds when uploading data") tornado.options.define("count", type=int, default=100000, help="Number of docs to generate") tornado.options.define("format", type=str, default='name:str,age:int,last_updated:ts', help="message format") tornado.options.define("num_of_replicas", type=int, default=0, help="Number of replicas for ES index") tornado.options.define("force_init_index", type=bool, default=False, help="Force deleting and re-initializing the Elasticsearch index") tornado.options.define("set_refresh", type=bool, default=False, help="Set refresh rate to -1 before starting the upload") tornado.options.define("out_file", type=str, default=False, help="If set, write test data to out_file as well.") tornado.options.define("id_type", type=str, default=None, help="Type of 'id' to use for the docs, valid settings are int and uuid4, None is default") tornado.options.define("dict_file", type=str, default=None, help="Name of dictionary file to use") tornado.options.define("username", type=str, default=None, help="Username for elasticsearch") tornado.options.define("password", type=str, default=None, help="Password for elasticsearch") tornado.options.define("validate_cert", type=bool, default=True, help="SSL validate_cert for requests. Use false for self-signed certificates.") tornado.options.parse_command_line() tornado.ioloop.IOLoop.instance().run_sync(generate_test_data)

40.230496

265

0.65509

import json import time import logging import random import string import uuid import datetime import tornado.gen import tornado.httpclient import tornado.ioloop import tornado.options try: xrange range = xrange except NameError: pass async_http_client = tornado.httpclient.AsyncHTTPClient() headers = tornado.httputil.HTTPHeaders({"content-type": "application/json"}) id_counter = 0 upload_data_count = 0 _dict_data = None def delete_index(idx_name): try: url = "%s/%s?refresh=true" % (tornado.options.options.es_url, idx_name) request = tornado.httpclient.HTTPRequest(url, headers=headers, method="DELETE", request_timeout=240, auth_username=tornado.options.options.username, auth_password=tornado.options.options.password, validate_cert=tornado.options.options.validate_cert) response = tornado.httpclient.HTTPClient().fetch(request) logging.info('Deleting index "%s" done %s' % (idx_name, response.body)) except tornado.httpclient.HTTPError: pass def create_index(idx_name): schema = { "settings": { "number_of_shards": tornado.options.options.num_of_shards, "number_of_replicas": tornado.options.options.num_of_replicas }, "refresh": True } body = json.dumps(schema) url = "%s/%s" % (tornado.options.options.es_url, idx_name) try: logging.info('Trying to create index %s' % (url)) request = tornado.httpclient.HTTPRequest(url, headers=headers, method="PUT", body=body, request_timeout=240, auth_username=tornado.options.options.username, auth_password=tornado.options.options.password, validate_cert=tornado.options.options.validate_cert) response = tornado.httpclient.HTTPClient().fetch(request) logging.info('Creating index "%s" done %s' % (idx_name, response.body)) except tornado.httpclient.HTTPError: logging.info('Looks like the index exists already') pass @tornado.gen.coroutine def upload_batch(upload_data_txt): try: request = tornado.httpclient.HTTPRequest(tornado.options.options.es_url + "/_bulk", method="POST", body=upload_data_txt, headers=headers, request_timeout=tornado.options.options.http_upload_timeout, auth_username=tornado.options.options.username, auth_password=tornado.options.options.password, validate_cert=tornado.options.options.validate_cert) response = yield async_http_client.fetch(request) except Exception as ex: logging.error("upload failed, error: %s" % ex) return result = json.loads(response.body.decode('utf-8')) res_txt = "OK" if not result['errors'] else "FAILED" took = int(result['took']) logging.info("Upload: %s - upload took: %5dms, total docs uploaded: %7d" % (res_txt, took, upload_data_count)) def get_data_for_format(format): split_f = format.split(":") if not split_f: return None, None field_name = split_f[0] field_type = split_f[1] return_val = '' if field_type == "bool": return_val = random.choice([True, False]) elif field_type == "str": min = 3 if len(split_f) < 3 else int(split_f[2]) max = min + 7 if len(split_f) < 4 else int(split_f[3]) length = generate_count(min, max) return_val = "".join([random.choice(string.ascii_letters + string.digits) for x in range(length)]) elif field_type == "int": min = 0 if len(split_f) < 3 else int(split_f[2]) max = min + 100000 if len(split_f) < 4 else int(split_f[3]) return_val = generate_count(min, max) elif field_type == "ipv4": return_val = "{0}.{1}.{2}.{3}".format(generate_count(0, 245),generate_count(0, 245),generate_count(0, 245),generate_count(0, 245)) elif field_type in ["ts", "tstxt"]: now = int(time.time()) per_day = 24 * 60 * 60 min = now - 30 * per_day if len(split_f) < 3 else int(split_f[2]) max = now + 30 * per_day if len(split_f) < 4 else int(split_f[3]) ts = generate_count(min, max) return_val = int(ts * 1000) if field_type == "ts" else datetime.datetime.fromtimestamp(ts).strftime("%Y-%m-%dT%H:%M:%S.000-0000") elif field_type == "words": min = 2 if len(split_f) < 3 else int(split_f[2]) max = min + 8 if len(split_f) < 4 else int(split_f[3]) count = generate_count(min, max) words = [] for _ in range(count): word_len = random.randrange(3, 10) words.append("".join([random.choice(string.ascii_letters + string.digits) for x in range(word_len)])) return_val = " ".join(words) elif field_type == "dict": global _dict_data min = 2 if len(split_f) < 3 else int(split_f[2]) max = min + 8 if len(split_f) < 4 else int(split_f[3]) count = generate_count(min, max) return_val = " ".join([random.choice(_dict_data).strip() for _ in range(count)]) elif field_type == "text": text = ["text1", "text2", "text3"] if len(split_f) < 3 else split_f[2].split("-") min = 1 if len(split_f) < 4 else int(split_f[3]) max = min + 1 if len(split_f) < 5 else int(split_f[4]) count = generate_count(min, max) words = [] for _ in range(count): words.append(""+random.choice(text)) return_val = " ".join(words) return field_name, return_val def generate_count(min, max): if min == max: return max elif min > max: return random.randrange(max, min); else: return random.randrange(min, max); def generate_random_doc(format): global id_counter res = {} for f in format: f_key, f_val = get_data_for_format(f) if f_key: res[f_key] = f_val if not tornado.options.options.id_type: return res if tornado.options.options.id_type == 'int': res['_id'] = id_counter id_counter += 1 elif tornado.options.options.id_type == 'uuid4': res['_id'] = str(uuid.uuid4()) return res def set_index_refresh(val): params = {"index": {"refresh_interval": val}} body = json.dumps(params) url = "%s/%s/_settings" % (tornado.options.options.es_url, tornado.options.options.index_name) try: request = tornado.httpclient.HTTPRequest(url, headers=headers, method="PUT", body=body, request_timeout=240, auth_username=tornado.options.options.username, auth_password=tornado.options.options.password, validate_cert=tornado.options.options.validate_cert) http_client = tornado.httpclient.HTTPClient() http_client.fetch(request) logging.info('Set index refresh to %s' % val) except Exception as ex: logging.exception(ex) @tornado.gen.coroutine def generate_test_data(): global upload_data_count if tornado.options.options.force_init_index: delete_index(tornado.options.options.index_name) create_index(tornado.options.options.index_name) if tornado.options.options.set_refresh: set_index_refresh("-1") if tornado.options.options.out_file: out_file = open(tornado.options.options.out_file, "w") else: out_file = None if tornado.options.options.dict_file: global _dict_data with open(tornado.options.options.dict_file, 'r') as f: _dict_data = f.readlines() logging.info("Loaded %d words from the %s" % (len(_dict_data), tornado.options.options.dict_file)) format = tornado.options.options.format.split(',') if not format: logging.error('invalid format') exit(1) ts_start = int(time.time()) upload_data_txt = "" logging.info("Generating %d docs, upload batch size is %d" % (tornado.options.options.count, tornado.options.options.batch_size)) for num in range(0, tornado.options.options.count): item = generate_random_doc(format) if out_file: out_file.write("%s\n" % json.dumps(item)) cmd = {'index': {'_index': tornado.options.options.index_name, '_type': tornado.options.options.index_type}} if '_id' in item: cmd['index']['_id'] = item['_id'] upload_data_txt += json.dumps(cmd) + "\n" upload_data_txt += json.dumps(item) + "\n" upload_data_count += 1 if upload_data_count % tornado.options.options.batch_size == 0: yield upload_batch(upload_data_txt) upload_data_txt = "" if upload_data_txt: yield upload_batch(upload_data_txt) if tornado.options.options.set_refresh: set_index_refresh("1s") if out_file: out_file.close() took_secs = int(time.time() - ts_start) logging.info("Done - total docs uploaded: %d, took %d seconds" % (tornado.options.options.count, took_secs)) if __name__ == '__main__': tornado.options.define("es_url", type=str, default='http://localhost:9200/', help="URL of your Elasticsearch node") tornado.options.define("index_name", type=str, default='test_data', help="Name of the index to store your messages") tornado.options.define("index_type", type=str, default='test_type', help="Type") tornado.options.define("batch_size", type=int, default=1000, help="Elasticsearch bulk index batch size") tornado.options.define("num_of_shards", type=int, default=2, help="Number of shards for ES index") tornado.options.define("http_upload_timeout", type=int, default=3, help="Timeout in seconds when uploading data") tornado.options.define("count", type=int, default=100000, help="Number of docs to generate") tornado.options.define("format", type=str, default='name:str,age:int,last_updated:ts', help="message format") tornado.options.define("num_of_replicas", type=int, default=0, help="Number of replicas for ES index") tornado.options.define("force_init_index", type=bool, default=False, help="Force deleting and re-initializing the Elasticsearch index") tornado.options.define("set_refresh", type=bool, default=False, help="Set refresh rate to -1 before starting the upload") tornado.options.define("out_file", type=str, default=False, help="If set, write test data to out_file as well.") tornado.options.define("id_type", type=str, default=None, help="Type of 'id' to use for the docs, valid settings are int and uuid4, None is default") tornado.options.define("dict_file", type=str, default=None, help="Name of dictionary file to use") tornado.options.define("username", type=str, default=None, help="Username for elasticsearch") tornado.options.define("password", type=str, default=None, help="Password for elasticsearch") tornado.options.define("validate_cert", type=bool, default=True, help="SSL validate_cert for requests. Use false for self-signed certificates.") tornado.options.parse_command_line() tornado.ioloop.IOLoop.instance().run_sync(generate_test_data)

true

1c379ecbd15812a9820a9f9718e729fc0870bf76

2,178

py

Python

test/lib/git.py

morgante/cnrm-blueprints

34453c4acde2cd321f71b76b3e6c6b086bc8ada1

[ "Apache-2.0" ]

9

2020-07-10T18:20:19.000Z

2021-10-08T23:58:06.000Z

test/lib/git.py

morgante/cnrm-blueprints

34453c4acde2cd321f71b76b3e6c6b086bc8ada1

[ "Apache-2.0" ]

1

2021-03-17T19:20:27.000Z

test/lib/git.py

isabella232/cnrm-blueprints

19d7c459c4f71198208282da17bcade53d28cc9c

[ "Apache-2.0" ]

4

2020-07-10T23:22:20.000Z

2021-09-27T19:27:02.000Z

# Copyright 2020 Google LLC # # 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 downloads import os class Git(object): def __init__(self, user_email, user_name, env=None): if env is None: env = os.environ.copy() self.bin = "git" self.env = env downloads.exec(["chmod", "600", "/root/.ssh/id_rsa"]) downloads.exec(["git", "config", "--global", "user.email", user_email]) downloads.exec(["git", "config", "--global", "user.name", user_name]) def __repr__(self): return "Git:" + downloads.exec(["which", "git"]) def clone(self, repo, directory): downloads.exec(["git", "clone", "--recursive", repo, directory]) self.statedir = directory def checkout(self, branch): self.exec(["checkout", branch]) def commit_and_push(self, branch, file, msg): self.exec(["add", file]) self.exec(["commit", "-m", msg]) self.exec(["push", "origin", "HEAD:%s" % branch]) def create_remote_tag(self, tag): self.exec(["tag", tag]) self.exec(["push", "origin", tag]) def get_commit_message(self, commit_hash): return self.exec(["show", "--pretty=format:%s", "-s", commit_hash]) def get_last_commit_hash(self): return self.exec(["rev-parse", "HEAD"]) # return a list of changed file paths def get_changed_files(self, revision): files = self.exec( ["show", "--pretty=", "--name-only", revision]).lstrip().rstrip() return files.split("\n") def exec(self, args): return downloads.exec( [self.bin] + args, cwd=self.statedir, env=self.env ).strip()

34.571429

79

0.624885

import downloads import os class Git(object): def __init__(self, user_email, user_name, env=None): if env is None: env = os.environ.copy() self.bin = "git" self.env = env downloads.exec(["chmod", "600", "/root/.ssh/id_rsa"]) downloads.exec(["git", "config", "--global", "user.email", user_email]) downloads.exec(["git", "config", "--global", "user.name", user_name]) def __repr__(self): return "Git:" + downloads.exec(["which", "git"]) def clone(self, repo, directory): downloads.exec(["git", "clone", "--recursive", repo, directory]) self.statedir = directory def checkout(self, branch): self.exec(["checkout", branch]) def commit_and_push(self, branch, file, msg): self.exec(["add", file]) self.exec(["commit", "-m", msg]) self.exec(["push", "origin", "HEAD:%s" % branch]) def create_remote_tag(self, tag): self.exec(["tag", tag]) self.exec(["push", "origin", tag]) def get_commit_message(self, commit_hash): return self.exec(["show", "--pretty=format:%s", "-s", commit_hash]) def get_last_commit_hash(self): return self.exec(["rev-parse", "HEAD"]) def get_changed_files(self, revision): files = self.exec( ["show", "--pretty=", "--name-only", revision]).lstrip().rstrip() return files.split("\n") def exec(self, args): return downloads.exec( [self.bin] + args, cwd=self.statedir, env=self.env ).strip()

true

1c379f073a5a5d7623ac8b55b3bcc6bed55eeb70

8,763

py

Python

maple/maple/spiders/news.py

honmaple/maple-spider

b9b6b295114149436974f4fe82f75dc7f2797129

[ "MIT" ]

null

maple/maple/spiders/news.py

honmaple/maple-spider

b9b6b295114149436974f4fe82f75dc7f2797129

[ "MIT" ]

null

maple/maple/spiders/news.py

honmaple/maple-spider

b9b6b295114149436974f4fe82f75dc7f2797129

[ "MIT" ]

1

2019-04-20T03:22:26.000Z

#!/usr/bin/env python # -*- coding=UTF-8 -*- #************************************************************************* # Copyright © 2015 JiangLin. All rights reserved. # File Name: news.py # Author:JiangLin # Mail:xiyang0807@gmail.com # Created Time: 2016-04-03 23:02:32 # Last Update: 星期四 2016-4-7 12:57:8 (CST) # By: jianglin # Description: 爬取学校新闻 #************************************************************************* import scrapy from maple.items import NewsItem from scrapy.http import Request from scrapy.selector import Selector from datetime import datetime from maple.models import News, DBSession session = DBSession() def exsit_session(url): a = session.query(News.url).filter_by(url=url).first() if not a: return False else: return True class NewsSpider(scrapy.spiders.Spider): name = "news" allowed_domains = ["202.119.112.75"] start_urls = [] for page in range(1, 3): url = 'http://202.119.112.75/s/2001/t/2016/p/5/i/%d/list.htm' % page start_urls.append(url) def parse_item(self, response): p1 = response.xpath('//td[contains(@class, "content")]/p') p2 = response.xpath('//td[contains(@class, "content")]/div') p = p1 or p2 item = response.meta['item'] content = '' for text in p: c1 = text.xpath('text()').extract() c2 = text.xpath('*/text()').extract() c3 = text.xpath('*/*/text()').extract() c4 = text.xpath('*/*/*/text()').extract() c = c1 + c2 + c3 + c4 for i in c: con = i + '\n' content += con item['content'] = content item['category'] = 'hhuc' return item def parse(self, response): sites = response.xpath('//table[contains(@class, "columnStyle")]/tr') items = [] for site in sites: item = NewsItem() title = site.xpath('td[1]/a/font/text()').extract() url = site.xpath('td[1]/a/@href').extract() time = site.xpath('td[2]/text()').extract() if len(title) == 1: item['title'] = title[0] if len(url) == 1: item['url'] = 'http://202.119.112.75' + url[0] if len(time) == 1: date_time = datetime.strptime(time[0], '%Y-%m-%d') item['time'] = date_time items.append(item) for item in items: if not exsit_session(item['url']): yield Request(item['url'], meta={'item': item}, callback=self.parse_item) class BsSpider(scrapy.spiders.Spider): name = "bs" allowed_domains = ["bs.hhuc.edu.cn"] start_urls = [] for page in range(1, 3): url = 'http://bs.hhuc.edu.cn/s/2039/t/2371/p/3/i/%d/list.htm' % page start_urls.append(url) def parse_item(self, response): p1 = response.xpath('//td[contains(@class, "content")]/p') p2 = response.xpath('//td[contains(@class, "content")]/div') p = p1 or p2 item = response.meta['item'] content = '' for text in p: c1 = text.xpath('text()').extract() c2 = text.xpath('*/text()').extract() c3 = text.xpath('*/*/text()').extract() c4 = text.xpath('*/*/*/text()').extract() c = c1 + c2 + c3 + c4 for i in c: con = i + '\n' content += con item['content'] = content item['category'] = 'bs' return item def parse(self, response): sites = response.xpath('//table[contains(@class, "columnStyle")]/tr') items = [] for site in sites: item = NewsItem() title = site.xpath('td[1]/a/font/text()').extract() url = site.xpath('td[1]/a/@href').extract() time = site.xpath('td[2]/text()').extract() if len(title) == 1: item['title'] = title[0] if len(url) == 1: item['url'] = 'http://bs.hhuc.edu.cn' + url[0] if len(time) == 1: date_time = datetime.strptime(time[0], '%Y-%m-%d') item['time'] = date_time items.append(item) for item in items: if not exsit_session(item['url']): yield Request(item['url'], meta={'item': item}, callback=self.parse_item) class WulwxySpider(scrapy.spiders.Spider): name = "wulwxy" allowed_domains = ["wulwxy.hhuc.edu.cn"] start_urls = [] for page in range(1, 3): url = 'http://wulwxy.hhuc.edu.cn/s/2059/t/2561/p/4/i/%d/list.htm' % page start_urls.append(url) def parse_item(self, response): p1 = response.xpath('//td[contains(@height, "400")]/p') p2 = response.xpath('//td[contains(@height, "400")]/div') p = p1 or p2 item = response.meta['item'] content = '' for text in p: c1 = text.xpath('text()').extract() c2 = text.xpath('*/text()').extract() c3 = text.xpath('*/*/text()').extract() c4 = text.xpath('*/*/*/text()').extract() c = c1 + c2 + c3 + c4 for i in c: content += i print(content) item['content'] = content item['category'] = 'wulwxy' return item def parse(self, response): sites = response.xpath('//table[contains(@class, "columnStyle")]/tr') items = [] for site in sites: item = NewsItem() title = site.xpath('td[1]/a/font/text()').extract() url = site.xpath('td[1]/a/@href').extract() time = site.xpath('td[2]/text()').extract() if len(title) == 1: item['title'] = title[0] if len(url) == 1: item['url'] = 'http://wulwxy.hhuc.edu.cn' + url[0] if len(time) == 1: date_time = datetime.strptime(time[0], '%Y-%m-%d') item['time'] = date_time items.append(item) for item in items: if not exsit_session(item['url']): yield Request(item['url'], meta={'item': item}, callback=self.parse_item) class JidianSpider(scrapy.spiders.Spider): name = "jidian" allowed_domains = ["jidian.hhuc.edu.cn"] start_urls = [] for page in range(1, 3): url = 'http://jidian.hhuc.edu.cn/s/2029/t/2608/p/3/i/%d/list.htm' % page start_urls.append(url) def parse_item(self, response): try: p1 = response.xpath('//table[contains(@width, "98%")]\ /tr/td[contains(@valign,"top")]/p') p2 = response.xpath('//table[contains(@width, "98%")]\ /tr/td[contains(@valign,"top")]/div') except: hxs = Selector(text=response.body) p1 = hxs.xpath('//table[contains(@width, "98%")]\ /tr/td[contains(@valign,"top")]/p') p2 = hxs.xpath('//table[contains(@width, "98%")]\ /tr/td[contains(@valign,"top")]/div') p = p1 or p2 item = response.meta['item'] content = '' for text in p: c1 = text.xpath('text()').extract() c2 = text.xpath('*/text()').extract() c3 = text.xpath('*/*/text()').extract() c4 = text.xpath('*/*/*/text()').extract() c = c1 + c2 + c3 + c4 for i in c: con = i + '\n' content += con print(content) item['content'] = content item['category'] = 'jidian' return item def parse(self, response): sites = response.xpath('//table[contains(@class, "columnStyle")]/tr') items = [] for site in sites: item = NewsItem() title = site.xpath('td[1]/a/font/text()').extract() url = site.xpath('td[1]/a/@href').extract() time = site.xpath('td[2]/text()').extract() if len(title) == 1: item['title'] = title[0] if len(url) == 1: item['url'] = 'http://jidian.hhuc.edu.cn' + url[0] if len(time) == 1: date_time = datetime.strptime(time[0], '%Y-%m-%d') item['time'] = date_time items.append(item) for item in items: if not exsit_session(item['url']): yield Request(item['url'], meta={'item': item}, callback=self.parse_item)

36.665272

80

0.479516

import scrapy from maple.items import NewsItem from scrapy.http import Request from scrapy.selector import Selector from datetime import datetime from maple.models import News, DBSession session = DBSession() def exsit_session(url): a = session.query(News.url).filter_by(url=url).first() if not a: return False else: return True class NewsSpider(scrapy.spiders.Spider): name = "news" allowed_domains = ["202.119.112.75"] start_urls = [] for page in range(1, 3): url = 'http://202.119.112.75/s/2001/t/2016/p/5/i/%d/list.htm' % page start_urls.append(url) def parse_item(self, response): p1 = response.xpath('//td[contains(@class, "content")]/p') p2 = response.xpath('//td[contains(@class, "content")]/div') p = p1 or p2 item = response.meta['item'] content = '' for text in p: c1 = text.xpath('text()').extract() c2 = text.xpath('*/text()').extract() c3 = text.xpath('*/*/text()').extract() c4 = text.xpath('*/*/*/text()').extract() c = c1 + c2 + c3 + c4 for i in c: con = i + '\n' content += con item['content'] = content item['category'] = 'hhuc' return item def parse(self, response): sites = response.xpath('//table[contains(@class, "columnStyle")]/tr') items = [] for site in sites: item = NewsItem() title = site.xpath('td[1]/a/font/text()').extract() url = site.xpath('td[1]/a/@href').extract() time = site.xpath('td[2]/text()').extract() if len(title) == 1: item['title'] = title[0] if len(url) == 1: item['url'] = 'http://202.119.112.75' + url[0] if len(time) == 1: date_time = datetime.strptime(time[0], '%Y-%m-%d') item['time'] = date_time items.append(item) for item in items: if not exsit_session(item['url']): yield Request(item['url'], meta={'item': item}, callback=self.parse_item) class BsSpider(scrapy.spiders.Spider): name = "bs" allowed_domains = ["bs.hhuc.edu.cn"] start_urls = [] for page in range(1, 3): url = 'http://bs.hhuc.edu.cn/s/2039/t/2371/p/3/i/%d/list.htm' % page start_urls.append(url) def parse_item(self, response): p1 = response.xpath('//td[contains(@class, "content")]/p') p2 = response.xpath('//td[contains(@class, "content")]/div') p = p1 or p2 item = response.meta['item'] content = '' for text in p: c1 = text.xpath('text()').extract() c2 = text.xpath('*/text()').extract() c3 = text.xpath('*/*/text()').extract() c4 = text.xpath('*/*/*/text()').extract() c = c1 + c2 + c3 + c4 for i in c: con = i + '\n' content += con item['content'] = content item['category'] = 'bs' return item def parse(self, response): sites = response.xpath('//table[contains(@class, "columnStyle")]/tr') items = [] for site in sites: item = NewsItem() title = site.xpath('td[1]/a/font/text()').extract() url = site.xpath('td[1]/a/@href').extract() time = site.xpath('td[2]/text()').extract() if len(title) == 1: item['title'] = title[0] if len(url) == 1: item['url'] = 'http://bs.hhuc.edu.cn' + url[0] if len(time) == 1: date_time = datetime.strptime(time[0], '%Y-%m-%d') item['time'] = date_time items.append(item) for item in items: if not exsit_session(item['url']): yield Request(item['url'], meta={'item': item}, callback=self.parse_item) class WulwxySpider(scrapy.spiders.Spider): name = "wulwxy" allowed_domains = ["wulwxy.hhuc.edu.cn"] start_urls = [] for page in range(1, 3): url = 'http://wulwxy.hhuc.edu.cn/s/2059/t/2561/p/4/i/%d/list.htm' % page start_urls.append(url) def parse_item(self, response): p1 = response.xpath('//td[contains(@height, "400")]/p') p2 = response.xpath('//td[contains(@height, "400")]/div') p = p1 or p2 item = response.meta['item'] content = '' for text in p: c1 = text.xpath('text()').extract() c2 = text.xpath('*/text()').extract() c3 = text.xpath('*/*/text()').extract() c4 = text.xpath('*/*/*/text()').extract() c = c1 + c2 + c3 + c4 for i in c: content += i print(content) item['content'] = content item['category'] = 'wulwxy' return item def parse(self, response): sites = response.xpath('//table[contains(@class, "columnStyle")]/tr') items = [] for site in sites: item = NewsItem() title = site.xpath('td[1]/a/font/text()').extract() url = site.xpath('td[1]/a/@href').extract() time = site.xpath('td[2]/text()').extract() if len(title) == 1: item['title'] = title[0] if len(url) == 1: item['url'] = 'http://wulwxy.hhuc.edu.cn' + url[0] if len(time) == 1: date_time = datetime.strptime(time[0], '%Y-%m-%d') item['time'] = date_time items.append(item) for item in items: if not exsit_session(item['url']): yield Request(item['url'], meta={'item': item}, callback=self.parse_item) class JidianSpider(scrapy.spiders.Spider): name = "jidian" allowed_domains = ["jidian.hhuc.edu.cn"] start_urls = [] for page in range(1, 3): url = 'http://jidian.hhuc.edu.cn/s/2029/t/2608/p/3/i/%d/list.htm' % page start_urls.append(url) def parse_item(self, response): try: p1 = response.xpath('//table[contains(@width, "98%")]\ /tr/td[contains(@valign,"top")]/p') p2 = response.xpath('//table[contains(@width, "98%")]\ /tr/td[contains(@valign,"top")]/div') except: hxs = Selector(text=response.body) p1 = hxs.xpath('//table[contains(@width, "98%")]\ /tr/td[contains(@valign,"top")]/p') p2 = hxs.xpath('//table[contains(@width, "98%")]\ /tr/td[contains(@valign,"top")]/div') p = p1 or p2 item = response.meta['item'] content = '' for text in p: c1 = text.xpath('text()').extract() c2 = text.xpath('*/text()').extract() c3 = text.xpath('*/*/text()').extract() c4 = text.xpath('*/*/*/text()').extract() c = c1 + c2 + c3 + c4 for i in c: con = i + '\n' content += con print(content) item['content'] = content item['category'] = 'jidian' return item def parse(self, response): sites = response.xpath('//table[contains(@class, "columnStyle")]/tr') items = [] for site in sites: item = NewsItem() title = site.xpath('td[1]/a/font/text()').extract() url = site.xpath('td[1]/a/@href').extract() time = site.xpath('td[2]/text()').extract() if len(title) == 1: item['title'] = title[0] if len(url) == 1: item['url'] = 'http://jidian.hhuc.edu.cn' + url[0] if len(time) == 1: date_time = datetime.strptime(time[0], '%Y-%m-%d') item['time'] = date_time items.append(item) for item in items: if not exsit_session(item['url']): yield Request(item['url'], meta={'item': item}, callback=self.parse_item)

true

1c37a06c7473a70641c7ae6eeef966e4e43240bc

16,122

py

Python

src/TestRailAPIClient.py

ezywebs/robotframework-testrail-extended

7797905257a590e9764c07a915de2dcbbde1e850

[ "Apache-2.0" ]

null

src/TestRailAPIClient.py

ezywebs/robotframework-testrail-extended

7797905257a590e9764c07a915de2dcbbde1e850

[ "Apache-2.0" ]

null

src/TestRailAPIClient.py

ezywebs/robotframework-testrail-extended

7797905257a590e9764c07a915de2dcbbde1e850

[ "Apache-2.0" ]

null

# -*- coding: utf-8 -*- from requests import post, get from typing import Any, cast, Dict, List, Optional, Sequence, Union DEFAULT_TESTRAIL_HEADERS = {'Content-Type': 'application/json'} TESTRAIL_STATUS_ID_PASSED = 1 # custom types JsonDict = Dict[str, Any] # noqa: E993 JsonList = List[JsonDict] # noqa: E993 Id = Union[str, int] # noqa: E993 class TestRailAPIClient(object): """Library for working with [http://www.gurock.com/testrail/ | TestRail]. == Dependencies == | requests | https://pypi.python.org/pypi/requests | == Preconditions == 1. [ http://docs.gurock.com/testrail-api2/introduction | Enable TestRail API] """ def __init__(self, server: str, user: str, password: str, run_id: Id = None, protocol: str = 'http') -> None: """Create TestRailAPIClient instance. *Args:*\n _server_ - name of TestRail server;\n _user_ - name of TestRail user;\n _password_ - password of TestRail user;\n _run_id_ - ID of the test run;\n _protocol_ - connecting protocol to TestRail server: http or https. """ self._url = '{protocol}://{server}/testrail/index.php?/api/v2/'.format(protocol=protocol, server=server) self._user = user self._password = password if run_id is not None: self.run_id = run_id def _send_post(self, uri: str, data: Dict[str, Any]) -> Union[JsonList, JsonDict]: """Perform post request to TestRail. *Args:* \n _uri_ - URI for test case;\n _data_ - json with test result. *Returns:* \n Request result in json format. """ url = self._url + uri response = post(url, json=data, auth=(self._user, self._password), verify=False) response.raise_for_status() return response.json() def _send_get(self, uri: str, headers: Dict[str, str] = None, params: Dict[str, Any] = None) -> Union[JsonList, JsonDict]: """Perform get request to TestRail. *Args:* \n _uri_ - URI for test case;\n _headers_ - headers for http-request;\n _params_ - parameters for http-request. *Returns:* \n Request result in json format. """ url = self._url + uri response = get(url, headers=headers, params=params, auth=(self._user, self._password), verify=False) response.raise_for_status() return response.json() def get_tests(self, run_id: Id, status_ids: Union[str, Sequence[int]] = None) -> JsonList: """Get tests from TestRail test run by run_id. *Args:* \n _run_id_ - ID of the test run;\n _status_ids_ - list of the required test statuses. *Returns:* \n Tests information in json format. """ uri = 'get_tests/{run_id}'.format(run_id=run_id) if status_ids: status_ids = ','.join(str(status_id) for status_id in status_ids) params = { 'status_id': status_ids } response = self._send_get(uri=uri, headers=DEFAULT_TESTRAIL_HEADERS, params=params) return cast(JsonList, response) def get_results_for_case(self, run_id: Id, case_id: Id, limit: int = None) -> JsonList: """Get results for case by run_id and case_id. *Args:* \n _run_id_ - ID of the test run;\n _case_id_ - ID of the test case;\n _limit_ - limit of case results. *Returns:* \n Cases results in json format. """ uri = 'get_results_for_case/{run_id}/{case_id}'.format(run_id=run_id, case_id=case_id) params = { 'limit': limit } response = self._send_get(uri=uri, headers=DEFAULT_TESTRAIL_HEADERS, params=params) return cast(JsonList, response) def add_result_for_case(self, run_id: Id, case_id: Id, test_result_fields: Dict[str, Union[str, int]]) -> None: """Add results for case in TestRail test run by run_id and case_id. *Supported request fields for test result:*\n | *Name* | *Type* | *Description* | | status_id | int | The ID of the test status | | comment | string | The comment / description for the test result | | version | string | The version or build you tested against | | elapsed | timespan | The time it took to execute the test, e.g. "30s" or "1m 45s" | | defects | string | A comma-separated list of defects to link to the test result | | assignedto_id | int | The ID of a user the test should be assigned to | | Custom fields are supported as well and must be submitted with their system name, prefixed with 'custom_' | *Args:* \n _run_id_ - ID of the test run;\n _case_id_ - ID of the test case;\n _test_result_fields_ - result of the test fields dictionary. *Example:*\n | Add Result For Case | run_id=321 | case_id=123| test_result={'status_id': 3, 'comment': 'This test is untested', 'defects': 'DEF-123'} | """ uri = 'add_result_for_case/{run_id}/{case_id}'.format(run_id=run_id, case_id=case_id) self._send_post(uri, test_result_fields) def get_statuses(self) -> JsonList: """Get test statuses information from TestRail. *Returns:* \n Statuses information in json format. """ uri = 'get_statuses' response = self._send_get(uri=uri, headers=DEFAULT_TESTRAIL_HEADERS) return cast(JsonList, response) def update_case(self, case_id: Id, request_fields: Dict[str, Union[str, int, None]]) -> JsonDict: """Update an existing test case in TestRail. *Supported request fields:*\n | *Name* | *Type* | *Description* | | title | string | The title of the test case (required) | | template_id | int | The ID of the template (field layout) (requires TestRail 5.2 or later) | | type_id | int | The ID of the case type | | priority_id | int | The ID of the case priority | | estimate | timespan | The estimate, e.g. "30s" or "1m 45s" | | milestone_id | int | The ID of the milestone to link to the test case | | refs | string | A comma-separated list of references/requirements | | Custom fields are supported as well and must be submitted with their system name, prefixed with 'custom_' | *Args:* \n _case_id_ - ID of the test case;\n _request_fields_ - request fields dictionary. *Returns:* \n Case information in json format. *Example:*\n | Update Case | case_id=213 | request_fields={'title': name, 'type_id': 1, 'custom_case_description': description, 'refs': references} | """ uri = 'update_case/{case_id}'.format(case_id=case_id) response = self._send_post(uri, request_fields) return cast(JsonDict, response) def get_status_id_by_status_label(self, status_label: str) -> int: """Get test status id by status label. *Args:* \n _status_label_ - status label of the tests. *Returns:* \n Test status ID. """ statuses_info = self.get_statuses() for status in statuses_info: if status['label'].lower() == status_label.lower(): return status['id'] raise Exception(u"There is no status with label \'{}\' in TestRail".format(status_label)) def get_test_status_id_by_case_id(self, run_id: Id, case_id: Id) -> Optional[int]: """Get test last status id by case id. If there is no last test result returns None. *Args:* \n _run_id_ - ID of the test run;\n _case_id_ - ID of the test case. *Returns:* \n Test status ID. """ last_case_result = self.get_results_for_case(run_id=run_id, case_id=case_id, limit=1) return last_case_result[0]['status_id'] if last_case_result else None def get_project(self, project_id: Id) -> JsonDict: """Get project info by project id. *Args:* \n _project_id_ - ID of the project. *Returns:* \n Request result in json format. """ uri = 'get_project/{project_id}'.format(project_id=project_id) response = self._send_get(uri=uri, headers=DEFAULT_TESTRAIL_HEADERS) return cast(JsonDict, response) def get_suite(self, suite_id: Id) -> JsonDict: """Get suite info by suite id. *Args:* \n _suite_id_ - ID of the test suite. *Returns:* \n Request result in json format. """ uri = 'get_suite/{suite_id}'.format(suite_id=suite_id) response = self._send_get(uri=uri, headers=DEFAULT_TESTRAIL_HEADERS) return cast(JsonDict, response) def get_section(self, section_id: Id) -> JsonDict: """Get section info by section id. *Args:* \n _section_id_ - ID of the section. *Returns:* \n Request result in json format. """ uri = 'get_section/{section_id}'.format(section_id=section_id) response = self._send_get(uri=uri, headers=DEFAULT_TESTRAIL_HEADERS) return cast(JsonDict, response) def add_section(self, project_id: Id, name: str, suite_id: Id = None, parent_id: Id = None, description: str = None) -> JsonDict: """Creates a new section. *Args:* \n _project_id_ - ID of the project;\n _name_ - name of the section;\n _suite_id_ - ID of the test suite(ignored if the project is operating in single suite mode);\n _parent_id_ - ID of the parent section (to build section hierarchies);\n _description_ - description of the section. *Returns:* \n New section information. """ uri = 'add_section/{project_id}'.format(project_id=project_id) data: Dict[str, Union[int, str]] = {'name': name} if suite_id is not None: data['suite_id'] = suite_id if parent_id is not None: data['parent_id'] = parent_id if description is not None: data['description'] = description response = self._send_post(uri=uri, data=data) return cast(JsonDict, response) def get_sections(self, project_id: Id, suite_id: Id) -> JsonList: """Returns existing sections. *Args:* \n _project_id_ - ID of the project;\n _suite_id_ - ID of the test suite. *Returns:* \n Information about section. """ uri = 'get_sections/{project_id}&suite_id={suite_id}'.format(project_id=project_id, suite_id=suite_id) response = self._send_get(uri=uri, headers=DEFAULT_TESTRAIL_HEADERS) return cast(JsonList, response) def get_case(self, case_id: Id) -> JsonDict: """Get case info by case id. *Args:* \n _case_id_ - ID of the test case. *Returns:* \n Request result in json format. """ uri = 'get_case/{case_id}'.format(case_id=case_id) response = self._send_get(uri=uri, headers=DEFAULT_TESTRAIL_HEADERS) return cast(JsonDict, response) def get_cases(self, project_id: Id, suite_id: Id = None, section_id: Id = None) -> JsonList: """Returns a list of test cases for a test suite or specific section in a test suite. *Args:* \n _project_id_ - ID of the project;\n _suite_id_ - ID of the test suite (optional if the project is operating in single suite mode);\n _section_id_ - ID of the section (optional). *Returns:* \n Information about test cases in section. """ uri = 'get_cases/{project_id}'.format(project_id=project_id) params = {'project_id': project_id} if suite_id is not None: params['suite_id'] = suite_id if section_id is not None: params['section_id'] = section_id response = self._send_get(uri=uri, headers=DEFAULT_TESTRAIL_HEADERS, params=params) return cast(JsonList, response) def add_case(self, section_id: Id, title: str, steps: List[Dict[str, str]], description: str, refs: str, type_id: Id, priority_id: Id, **additional_data: Any) -> JsonDict: """Creates a new test case. *Args:* \n _section_id_ - ID of the section;\n _title_ - title of the test case;\n _steps_ - test steps;\n _description_ - test description;\n _refs_ - comma-separated list of references;\n _type_id_ - ID of the case type;\n _priority_id_ - ID of the case priority;\n _additional_data_ - additional parameters. *Returns:* \n Information about new test case. """ uri = 'add_case/{section_id}'.format(section_id=section_id) data = { 'title': title, 'custom_case_description': description, 'custom_steps_separated': steps, 'refs': refs, 'type_id': type_id, 'priority_id': priority_id } for key in additional_data: data[key] = additional_data[key] response = self._send_post(uri=uri, data=data) return cast(JsonDict, response) def add_test_run(self, project_id: Id, suite_id: Id = None, name: str = "Test run"): """Adds test run to specified project *Supported request fields for test result:*\n | *Name* | *Type* | *Description* | | suite_id | int | The ID of the test suite for the test run (optional if the project is operating in single suite mode, required otherwise) | | name | string | The name of the test run | | description | string | The description of the test run | | milestone_id | int | The ID of the milestone to link to the test run | | assignedto_id | int | The ID of the user the test run should be assigned to | | include_all | bool | True for including all test cases of the test suite and false for a custom case selection (default: true) | | case_ids | array | An array of case IDs for the custom case selection | | refs | string | A comma-separated list of references/requirements (Requires TestRail 6.1 or later) | *Args:* \n _project_id_ - ID of the project;\n _suite_id_ - ID of the test suite(ignored if the project is operating in single suite mode);\n _name_ - name of test run;\n *Returns:* \n Test Run information. """ uri = 'add_run/{project_id}'.format(project_id=project_id) data: Dict[str, Union[int, str]] = {'name': name} if suite_id is not None: data['suite_id'] = suite_id response = self._send_post(uri=uri, data=data) self.run_id = response['id'] return cast(JsonDict, response)

42.426316

146

0.56823

from requests import post, get from typing import Any, cast, Dict, List, Optional, Sequence, Union DEFAULT_TESTRAIL_HEADERS = {'Content-Type': 'application/json'} TESTRAIL_STATUS_ID_PASSED = 1 JsonDict = Dict[str, Any] JsonList = List[JsonDict] Id = Union[str, int] class TestRailAPIClient(object): def __init__(self, server: str, user: str, password: str, run_id: Id = None, protocol: str = 'http') -> None: self._url = '{protocol}://{server}/testrail/index.php?/api/v2/'.format(protocol=protocol, server=server) self._user = user self._password = password if run_id is not None: self.run_id = run_id def _send_post(self, uri: str, data: Dict[str, Any]) -> Union[JsonList, JsonDict]: url = self._url + uri response = post(url, json=data, auth=(self._user, self._password), verify=False) response.raise_for_status() return response.json() def _send_get(self, uri: str, headers: Dict[str, str] = None, params: Dict[str, Any] = None) -> Union[JsonList, JsonDict]: url = self._url + uri response = get(url, headers=headers, params=params, auth=(self._user, self._password), verify=False) response.raise_for_status() return response.json() def get_tests(self, run_id: Id, status_ids: Union[str, Sequence[int]] = None) -> JsonList: uri = 'get_tests/{run_id}'.format(run_id=run_id) if status_ids: status_ids = ','.join(str(status_id) for status_id in status_ids) params = { 'status_id': status_ids } response = self._send_get(uri=uri, headers=DEFAULT_TESTRAIL_HEADERS, params=params) return cast(JsonList, response) def get_results_for_case(self, run_id: Id, case_id: Id, limit: int = None) -> JsonList: uri = 'get_results_for_case/{run_id}/{case_id}'.format(run_id=run_id, case_id=case_id) params = { 'limit': limit } response = self._send_get(uri=uri, headers=DEFAULT_TESTRAIL_HEADERS, params=params) return cast(JsonList, response) def add_result_for_case(self, run_id: Id, case_id: Id, test_result_fields: Dict[str, Union[str, int]]) -> None: uri = 'add_result_for_case/{run_id}/{case_id}'.format(run_id=run_id, case_id=case_id) self._send_post(uri, test_result_fields) def get_statuses(self) -> JsonList: uri = 'get_statuses' response = self._send_get(uri=uri, headers=DEFAULT_TESTRAIL_HEADERS) return cast(JsonList, response) def update_case(self, case_id: Id, request_fields: Dict[str, Union[str, int, None]]) -> JsonDict: uri = 'update_case/{case_id}'.format(case_id=case_id) response = self._send_post(uri, request_fields) return cast(JsonDict, response) def get_status_id_by_status_label(self, status_label: str) -> int: statuses_info = self.get_statuses() for status in statuses_info: if status['label'].lower() == status_label.lower(): return status['id'] raise Exception(u"There is no status with label \'{}\' in TestRail".format(status_label)) def get_test_status_id_by_case_id(self, run_id: Id, case_id: Id) -> Optional[int]: last_case_result = self.get_results_for_case(run_id=run_id, case_id=case_id, limit=1) return last_case_result[0]['status_id'] if last_case_result else None def get_project(self, project_id: Id) -> JsonDict: uri = 'get_project/{project_id}'.format(project_id=project_id) response = self._send_get(uri=uri, headers=DEFAULT_TESTRAIL_HEADERS) return cast(JsonDict, response) def get_suite(self, suite_id: Id) -> JsonDict: uri = 'get_suite/{suite_id}'.format(suite_id=suite_id) response = self._send_get(uri=uri, headers=DEFAULT_TESTRAIL_HEADERS) return cast(JsonDict, response) def get_section(self, section_id: Id) -> JsonDict: uri = 'get_section/{section_id}'.format(section_id=section_id) response = self._send_get(uri=uri, headers=DEFAULT_TESTRAIL_HEADERS) return cast(JsonDict, response) def add_section(self, project_id: Id, name: str, suite_id: Id = None, parent_id: Id = None, description: str = None) -> JsonDict: uri = 'add_section/{project_id}'.format(project_id=project_id) data: Dict[str, Union[int, str]] = {'name': name} if suite_id is not None: data['suite_id'] = suite_id if parent_id is not None: data['parent_id'] = parent_id if description is not None: data['description'] = description response = self._send_post(uri=uri, data=data) return cast(JsonDict, response) def get_sections(self, project_id: Id, suite_id: Id) -> JsonList: uri = 'get_sections/{project_id}&suite_id={suite_id}'.format(project_id=project_id, suite_id=suite_id) response = self._send_get(uri=uri, headers=DEFAULT_TESTRAIL_HEADERS) return cast(JsonList, response) def get_case(self, case_id: Id) -> JsonDict: uri = 'get_case/{case_id}'.format(case_id=case_id) response = self._send_get(uri=uri, headers=DEFAULT_TESTRAIL_HEADERS) return cast(JsonDict, response) def get_cases(self, project_id: Id, suite_id: Id = None, section_id: Id = None) -> JsonList: uri = 'get_cases/{project_id}'.format(project_id=project_id) params = {'project_id': project_id} if suite_id is not None: params['suite_id'] = suite_id if section_id is not None: params['section_id'] = section_id response = self._send_get(uri=uri, headers=DEFAULT_TESTRAIL_HEADERS, params=params) return cast(JsonList, response) def add_case(self, section_id: Id, title: str, steps: List[Dict[str, str]], description: str, refs: str, type_id: Id, priority_id: Id, **additional_data: Any) -> JsonDict: uri = 'add_case/{section_id}'.format(section_id=section_id) data = { 'title': title, 'custom_case_description': description, 'custom_steps_separated': steps, 'refs': refs, 'type_id': type_id, 'priority_id': priority_id } for key in additional_data: data[key] = additional_data[key] response = self._send_post(uri=uri, data=data) return cast(JsonDict, response) def add_test_run(self, project_id: Id, suite_id: Id = None, name: str = "Test run"): uri = 'add_run/{project_id}'.format(project_id=project_id) data: Dict[str, Union[int, str]] = {'name': name} if suite_id is not None: data['suite_id'] = suite_id response = self._send_post(uri=uri, data=data) self.run_id = response['id'] return cast(JsonDict, response)

true

1c37a07e0ed2366f0d8ed081f69ba7572ae5a7d7

1,108

py

Python

python/paddle_fl/split_learning/core/layer_handler/layer_base.py

kaih70/PaddleFL

515906e2c61ee90f8a1c3f8e8210aac2f4177a4a

[ "Apache-2.0" ]

379

2019-09-27T14:26:42.000Z

2022-03-29T14:28:12.000Z

python/paddle_fl/split_learning/core/layer_handler/layer_base.py

Sprate/PaddleFL

583691acd5db0a7ca331cc9a72415017b18669b8

[ "Apache-2.0" ]

132

2019-10-16T03:22:03.000Z

2022-03-23T08:54:29.000Z

python/paddle_fl/split_learning/core/layer_handler/layer_base.py

Sprate/PaddleFL

583691acd5db0a7ca331cc9a72415017b18669b8

[ "Apache-2.0" ]

106

2019-09-27T12:47:18.000Z

2022-03-29T09:07:25.000Z

# Copyright (c) 2021 PaddlePaddle 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 paddle import numpy as np import logging _LOGGER = logging.getLogger(__name__) class LayerBase(paddle.nn.Layer): def __init__(self): super(LayerBase, self).__init__() @paddle.jit.to_static def forward(self, **feed): raise NotImplementedError("Failed to run forward") def get_fetch_vars(self): raise NotImplementedError("Failed to get fetch vars") def get_loss(self, inputs, predict): raise NotImplementedError("Failed to get loss")

31.657143

74

0.73556

import paddle import numpy as np import logging _LOGGER = logging.getLogger(__name__) class LayerBase(paddle.nn.Layer): def __init__(self): super(LayerBase, self).__init__() @paddle.jit.to_static def forward(self, **feed): raise NotImplementedError("Failed to run forward") def get_fetch_vars(self): raise NotImplementedError("Failed to get fetch vars") def get_loss(self, inputs, predict): raise NotImplementedError("Failed to get loss")

true

1c37a1b755ce6106a519f07b5dd18552e9e34701

2,776

py

Python

test/datacenters/test_gcp.py

aexvir/the-zoo

7816afb9a0a26c6058b030b4a987c73e952d92bd

[ "MIT" ]

90

2018-11-20T10:58:24.000Z

2022-02-19T16:12:46.000Z

test/datacenters/test_gcp.py

kiwicom/the-zoo

fee0108ea7b65112e5b572a146cff4b1c54033fd

[ "MIT" ]

348

2018-11-21T09:22:31.000Z

2021-11-03T13:45:08.000Z

test/datacenters/test_gcp.py

aexvir/the-zoo

7816afb9a0a26c6058b030b4a987c73e952d92bd

[ "MIT" ]

11

2018-12-08T18:42:07.000Z

2021-02-21T06:27:58.000Z

from unittest.mock import MagicMock import pytest from zoo.datacenters import gcp as uut from zoo.datacenters import models pytestmark = pytest.mark.django_db def test_gcp_map_to_nodes(mocker): mocker.patch("zoo.datacenters.utils.gcloud.GCPClient.__init__", return_value=None) mocker.patch( "zoo.datacenters.utils.gcloud.GCPClient.get_all_projects", return_value=[{"projectId": "pid1"}, {"projectId": "pid2"}], ) mocker.patch( "zoo.datacenters.utils.gcloud.GCPClient.get_forwarding_rules", return_value=[ { "id": "test1", "loadBalancingScheme": "EXTERNAL", "IPAddress": "1.1.1.1", "portRange": "443-443", }, { "id": "test2", "loadBalancingScheme": "INTERNAL", "IPAddress": "2.2.2.2", "portRange": "443-443", }, ], ) mocker.patch( "zoo.datacenters.utils.GCPClient.get_all_clusters", return_value=[{"name": "test", "zone": "europe-test"}], ) mocker.patch( "zoo.datacenters.utils.kube.KubernetesClient.__init__", return_value=None ) workload = MagicMock() image1 = MagicMock() image2 = MagicMock() image1.image = "test/image:0.0.1" image2.image = "test/image2:0.0.2" workload.metadata.namespace = "namespace-test" workload.metadata.name = "resource-test" workload.spec.template.spec.containers = [image1, image2] mocker.patch( "zoo.datacenters.utils.kube.KubernetesClient.iter_workloads", return_value={"test-type": [workload]}, ) uut.map_to_nodes() root = models.InfraNode.objects.get(kind=models.NodeKind.GCP_ROOT_PROJ) projects = {project.value: project for project in root.targets.all()} assert set(projects) == {"pid1", "pid2"} ctx = "gke_pid1_europe-test_test" clusters = { cluster.value: cluster for cluster in projects["pid1"].targets.filter( kind=models.NodeKind.GCP_CLUSTER_NAME ) } assert set(clusters) == {ctx} ip_rules = { cluster.value: cluster for cluster in projects["pid1"].targets.filter( kind=models.NodeKind.GCP_IP_RULE_NAME ) } assert set(ip_rules) == {"test1:1.1.1.1:443-443"} workloads = { workload.value: workload for workload in clusters["gke_pid1_europe-test_test"].targets.all() } full_name = "test-type:namespace-test/resource-test" assert set(workloads) == {f"{ctx}:{full_name}"} images = { image.value: image for image in workloads[f"{ctx}:{full_name}"].targets.all() } assert set(images) == {"test/image:0.0.1", "test/image2:0.0.2"}

30.173913

86

0.607709

from unittest.mock import MagicMock import pytest from zoo.datacenters import gcp as uut from zoo.datacenters import models pytestmark = pytest.mark.django_db def test_gcp_map_to_nodes(mocker): mocker.patch("zoo.datacenters.utils.gcloud.GCPClient.__init__", return_value=None) mocker.patch( "zoo.datacenters.utils.gcloud.GCPClient.get_all_projects", return_value=[{"projectId": "pid1"}, {"projectId": "pid2"}], ) mocker.patch( "zoo.datacenters.utils.gcloud.GCPClient.get_forwarding_rules", return_value=[ { "id": "test1", "loadBalancingScheme": "EXTERNAL", "IPAddress": "1.1.1.1", "portRange": "443-443", }, { "id": "test2", "loadBalancingScheme": "INTERNAL", "IPAddress": "2.2.2.2", "portRange": "443-443", }, ], ) mocker.patch( "zoo.datacenters.utils.GCPClient.get_all_clusters", return_value=[{"name": "test", "zone": "europe-test"}], ) mocker.patch( "zoo.datacenters.utils.kube.KubernetesClient.__init__", return_value=None ) workload = MagicMock() image1 = MagicMock() image2 = MagicMock() image1.image = "test/image:0.0.1" image2.image = "test/image2:0.0.2" workload.metadata.namespace = "namespace-test" workload.metadata.name = "resource-test" workload.spec.template.spec.containers = [image1, image2] mocker.patch( "zoo.datacenters.utils.kube.KubernetesClient.iter_workloads", return_value={"test-type": [workload]}, ) uut.map_to_nodes() root = models.InfraNode.objects.get(kind=models.NodeKind.GCP_ROOT_PROJ) projects = {project.value: project for project in root.targets.all()} assert set(projects) == {"pid1", "pid2"} ctx = "gke_pid1_europe-test_test" clusters = { cluster.value: cluster for cluster in projects["pid1"].targets.filter( kind=models.NodeKind.GCP_CLUSTER_NAME ) } assert set(clusters) == {ctx} ip_rules = { cluster.value: cluster for cluster in projects["pid1"].targets.filter( kind=models.NodeKind.GCP_IP_RULE_NAME ) } assert set(ip_rules) == {"test1:1.1.1.1:443-443"} workloads = { workload.value: workload for workload in clusters["gke_pid1_europe-test_test"].targets.all() } full_name = "test-type:namespace-test/resource-test" assert set(workloads) == {f"{ctx}:{full_name}"} images = { image.value: image for image in workloads[f"{ctx}:{full_name}"].targets.all() } assert set(images) == {"test/image:0.0.1", "test/image2:0.0.2"}

true

1c37a1c346f1ed4454020911795eff358c3de77d

2,559

py

Python

plugins/tff_backend/bizz/todo/investor.py

threefoldfoundation/app_backend

b3cea2a3ff9e10efcc90d3d6e5e8e46b9e84312a

[ "Apache-2.0" ]

null

plugins/tff_backend/bizz/todo/investor.py

threefoldfoundation/app_backend

b3cea2a3ff9e10efcc90d3d6e5e8e46b9e84312a

[ "Apache-2.0" ]

178

2017-08-02T12:58:06.000Z

2017-12-20T15:01:12.000Z

plugins/tff_backend/bizz/todo/investor.py

threefoldfoundation/app_backend

b3cea2a3ff9e10efcc90d3d6e5e8e46b9e84312a

[ "Apache-2.0" ]

2

2018-01-10T10:43:12.000Z

2018-03-18T10:42:23.000Z

# -*- coding: utf-8 -*- # Copyright 2017 GIG Technology NV # # 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. # # @@license_version:1.3@@ import logging class InvestorSteps(object): DOWNLOAD = 'DOWNLOAD' ITO_INVITES = 'ITO_INVITES' FLOW_INIT = 'FLOW_INIT' FLOW_AMOUNT = 'FLOW_AMOUNT' FLOW_SIGN = 'FLOW_SIGN' PAY = 'PAY' PAY_PROCESS = 'PAY_PROCESS' ASSIGN_TOKENS = 'ASSIGN_TOKENS' DESCRIPTIONS = { DOWNLOAD: 'Download the ThreeFold app', ITO_INVITES: 'Register using an invitation code', FLOW_INIT: 'Initiate “purchase iTokens” in the TF app', FLOW_AMOUNT: 'Select currency and how much you want to invest', FLOW_SIGN: 'Sign the purchase agreement', PAY: 'We send you payment information', PAY_PROCESS: 'We process the payment', ASSIGN_TOKENS: 'Tokens are assigned', } @classmethod def all(cls): return [cls.DOWNLOAD, cls.ITO_INVITES, cls.FLOW_INIT, cls.FLOW_AMOUNT, cls.FLOW_SIGN, cls.PAY, cls.PAY_PROCESS, cls.ASSIGN_TOKENS] @classmethod def should_archive(cls, step): return cls.ASSIGN_TOKENS == step or step is None @classmethod def get_name_for_step(cls, step): if step not in cls.DESCRIPTIONS: logging.error('Investor description for step \'%s\' not set', step) return cls.DESCRIPTIONS.get(step, step) @classmethod def get_progress(cls, last_checked_step): checked = False items = [] for step in reversed(cls.all()): if not checked and step == last_checked_step: checked = True item = { 'id': step, 'name': cls.get_name_for_step(step), 'checked': checked } items.append(item) return { 'id': 'investor', 'name': 'Become a token holder', 'items': list(reversed(items)) }

30.831325

79

0.609222

import logging class InvestorSteps(object): DOWNLOAD = 'DOWNLOAD' ITO_INVITES = 'ITO_INVITES' FLOW_INIT = 'FLOW_INIT' FLOW_AMOUNT = 'FLOW_AMOUNT' FLOW_SIGN = 'FLOW_SIGN' PAY = 'PAY' PAY_PROCESS = 'PAY_PROCESS' ASSIGN_TOKENS = 'ASSIGN_TOKENS' DESCRIPTIONS = { DOWNLOAD: 'Download the ThreeFold app', ITO_INVITES: 'Register using an invitation code', FLOW_INIT: 'Initiate “purchase iTokens” in the TF app', FLOW_AMOUNT: 'Select currency and how much you want to invest', FLOW_SIGN: 'Sign the purchase agreement', PAY: 'We send you payment information', PAY_PROCESS: 'We process the payment', ASSIGN_TOKENS: 'Tokens are assigned', } @classmethod def all(cls): return [cls.DOWNLOAD, cls.ITO_INVITES, cls.FLOW_INIT, cls.FLOW_AMOUNT, cls.FLOW_SIGN, cls.PAY, cls.PAY_PROCESS, cls.ASSIGN_TOKENS] @classmethod def should_archive(cls, step): return cls.ASSIGN_TOKENS == step or step is None @classmethod def get_name_for_step(cls, step): if step not in cls.DESCRIPTIONS: logging.error('Investor description for step \'%s\' not set', step) return cls.DESCRIPTIONS.get(step, step) @classmethod def get_progress(cls, last_checked_step): checked = False items = [] for step in reversed(cls.all()): if not checked and step == last_checked_step: checked = True item = { 'id': step, 'name': cls.get_name_for_step(step), 'checked': checked } items.append(item) return { 'id': 'investor', 'name': 'Become a token holder', 'items': list(reversed(items)) }

true

1c37a20652cba8d9ec66c21d85f77b65e8fdd40d

2,837

py

Python

edna2/tasks/test/H5ToCBFTask/H5ToCBF_exec_test.py

shibom/edna2

31e39b887be88a47bca775cd91310f5a17841bdd

[ "CC0-1.0", "MIT" ]

null

edna2/tasks/test/H5ToCBFTask/H5ToCBF_exec_test.py

shibom/edna2

31e39b887be88a47bca775cd91310f5a17841bdd

[ "CC0-1.0", "MIT" ]

2

2020-04-06T10:39:50.000Z

2021-04-14T19:24:37.000Z

edna2/tasks/test/H5ToCBFTask/H5ToCBF_exec_test.py

shibom/edna2

31e39b887be88a47bca775cd91310f5a17841bdd

[ "CC0-1.0", "MIT" ]

5

2019-06-14T07:28:38.000Z

2021-04-28T13:10:39.000Z

# # Copyright (c) European Synchrotron Radiation Facility (ESRF) # # 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. # __authors__ = ["O. Svensson"] __license__ = "MIT" __date__ = "21/04/2019" import os import unittest from edna2.tasks.H5ToCBFTask import H5ToCBFTask from edna2.utils import UtilsTest from edna2.utils import UtilsConfig class H5ToCBFExecTest(unittest.TestCase): def setUp(self): self.dataPath = UtilsTest.prepareTestDataPath(__file__) @unittest.skipIf(UtilsConfig.getSite() == 'Default', 'Cannot run h5ToCbf test with default config') def test_execute_withImageNumber(self): referenceDataPath = self.dataPath / 'H5ToCBF_withImageNumber.json' inData = UtilsTest.loadAndSubstitueTestData(referenceDataPath, loadTestImages=False) h5ToCBF = H5ToCBFTask(inData=inData) h5ToCBF.execute() self.assertTrue(h5ToCBF.isSuccess()) outData = h5ToCBF.outData self.assertTrue(os.path.exists(outData['outputCBFFile'])) @unittest.skipIf(UtilsConfig.getSite() == 'Default', 'Cannot run h5ToCbf test with default config') def test_execute_withImageRange(self): referenceDataPath = self.dataPath / 'H5ToCBF_withImageRange.json' inData = UtilsTest.loadAndSubstitueTestData(referenceDataPath, loadTestImages=False) h5ToCBF = H5ToCBFTask(inData=inData) h5ToCBF.execute() self.assertTrue(h5ToCBF.isSuccess()) outData = h5ToCBF.outData for index in range(1,11): template = outData['outputCBFFileTemplate'] filePath = template.replace('######', '{0:06d}').format(index) self.assertTrue(os.path.exists(filePath))

42.984848

82

0.701445

__authors__ = ["O. Svensson"] __license__ = "MIT" __date__ = "21/04/2019" import os import unittest from edna2.tasks.H5ToCBFTask import H5ToCBFTask from edna2.utils import UtilsTest from edna2.utils import UtilsConfig class H5ToCBFExecTest(unittest.TestCase): def setUp(self): self.dataPath = UtilsTest.prepareTestDataPath(__file__) @unittest.skipIf(UtilsConfig.getSite() == 'Default', 'Cannot run h5ToCbf test with default config') def test_execute_withImageNumber(self): referenceDataPath = self.dataPath / 'H5ToCBF_withImageNumber.json' inData = UtilsTest.loadAndSubstitueTestData(referenceDataPath, loadTestImages=False) h5ToCBF = H5ToCBFTask(inData=inData) h5ToCBF.execute() self.assertTrue(h5ToCBF.isSuccess()) outData = h5ToCBF.outData self.assertTrue(os.path.exists(outData['outputCBFFile'])) @unittest.skipIf(UtilsConfig.getSite() == 'Default', 'Cannot run h5ToCbf test with default config') def test_execute_withImageRange(self): referenceDataPath = self.dataPath / 'H5ToCBF_withImageRange.json' inData = UtilsTest.loadAndSubstitueTestData(referenceDataPath, loadTestImages=False) h5ToCBF = H5ToCBFTask(inData=inData) h5ToCBF.execute() self.assertTrue(h5ToCBF.isSuccess()) outData = h5ToCBF.outData for index in range(1,11): template = outData['outputCBFFileTemplate'] filePath = template.replace('######', '{0:06d}').format(index) self.assertTrue(os.path.exists(filePath))

true

1c37a23f90a79b0e103ea206f96f45072c69bd92

8,150

py

Python

contrib/devtools/update-translations.py

nuyulcore/NuyulX

ccf82925dc72966e911f5c5613558b654dce7d96

[ "MIT" ]

null

contrib/devtools/update-translations.py

nuyulcore/NuyulX

ccf82925dc72966e911f5c5613558b654dce7d96

[ "MIT" ]

null

contrib/devtools/update-translations.py

nuyulcore/NuyulX

ccf82925dc72966e911f5c5613558b654dce7d96

[ "MIT" ]

null

#!/usr/bin/env python # Copyright (c) 2014 Wladimir J. van der Laan # Distributed under the MIT software license, see the accompanying # file COPYING or http://www.opensource.org/licenses/mit-license.php. ''' Run this script from the root of the repository to update all translations from transifex. It will do the following automatically: - fetch all translations using the tx tool - post-process them into valid and committable format - remove invalid control characters - remove location tags (makes diffs less noisy) TODO: - auto-add new translations to the build system according to the translation process ''' from __future__ import division, print_function import subprocess import re import sys import os import io import xml.etree.ElementTree as ET # Name of transifex tool TX = 'tx' # Name of source language file SOURCE_LANG = 'nuyul_en.ts' # Directory with locale files LOCALE_DIR = 'src/qt/locale' # Minimum number of messages for translation to be considered at all MIN_NUM_MESSAGES = 10 def check_at_repository_root(): if not os.path.exists('.git'): print('No .git directory found') print('Execute this script at the root of the repository', file=sys.stderr) exit(1) def fetch_all_translations(): if subprocess.call([TX, 'pull', '-f', '-a']): print('Error while fetching translations', file=sys.stderr) exit(1) def find_format_specifiers(s): '''Find all format specifiers in a string.''' pos = 0 specifiers = [] while True: percent = s.find('%', pos) if percent < 0: break try: specifiers.append(s[percent+1]) except: print('Failed to get specifier') pos = percent+2 return specifiers def split_format_specifiers(specifiers): '''Split format specifiers between numeric (Qt) and others (strprintf)''' numeric = [] other = [] for s in specifiers: if s in {'1','2','3','4','5','6','7','8','9'}: numeric.append(s) else: other.append(s) # If both numeric format specifiers and "others" are used, assume we're dealing # with a Qt-formatted message. In the case of Qt formatting (see https://doc.qt.io/qt-5/qstring.html#arg) # only numeric formats are replaced at all. This means "(percentage: %1%)" is valid, without needing # any kind of escaping that would be necessary for strprintf. Without this, this function # would wrongly detect '%)' as a printf format specifier. if numeric: other = [] # numeric (Qt) can be present in any order, others (strprintf) must be in specified order return set(numeric),other def sanitize_string(s): '''Sanitize string for printing''' return s.replace('\n',' ') def check_format_specifiers(source, translation, errors, numerus): source_f = split_format_specifiers(find_format_specifiers(source)) # assert that no source messages contain both Qt and strprintf format specifiers # if this fails, go change the source as this is hacky and confusing! assert(not(source_f[0] and source_f[1])) try: translation_f = split_format_specifiers(find_format_specifiers(translation)) except IndexError: errors.append("Parse error in translation for '%s': '%s'" % (sanitize_string(source), sanitize_string(translation))) return False else: if source_f != translation_f: if numerus and source_f == (set(), ['n']) and translation_f == (set(), []) and translation.find('%') == -1: # Allow numerus translations to omit %n specifier (usually when it only has one possible value) return True errors.append("Mismatch between '%s' and '%s'" % (sanitize_string(source), sanitize_string(translation))) return False return True def all_ts_files(suffix=''): for filename in os.listdir(LOCALE_DIR): # process only language files, and do not process source language if not filename.endswith('.ts'+suffix) or filename == SOURCE_LANG+suffix: continue if suffix: # remove provided suffix filename = filename[0:-len(suffix)] filepath = os.path.join(LOCALE_DIR, filename) yield(filename, filepath) FIX_RE = re.compile(b'[\x00-\x09\x0b\x0c\x0e-\x1f]') def remove_invalid_characters(s): '''Remove invalid characters from translation string''' return FIX_RE.sub(b'', s) # Override cdata escape function to make our output match Qt's (optional, just for cleaner diffs for # comparison, disable by default) _orig_escape_cdata = None def escape_cdata(text): text = _orig_escape_cdata(text) text = text.replace("'", ''') text = text.replace('"', '"') return text def postprocess_translations(reduce_diff_hacks=False): print('Checking and postprocessing...') if reduce_diff_hacks: global _orig_escape_cdata _orig_escape_cdata = ET._escape_cdata ET._escape_cdata = escape_cdata for (filename,filepath) in all_ts_files(): os.rename(filepath, filepath+'.orig') have_errors = False for (filename,filepath) in all_ts_files('.orig'): # pre-fixups to cope with transifex output parser = ET.XMLParser(encoding='utf-8') # need to override encoding because 'utf8' is not understood only 'utf-8' with open(filepath + '.orig', 'rb') as f: data = f.read() # remove control characters; this must be done over the entire file otherwise the XML parser will fail data = remove_invalid_characters(data) tree = ET.parse(io.BytesIO(data), parser=parser) # iterate over all messages in file root = tree.getroot() for context in root.findall('context'): for message in context.findall('message'): numerus = message.get('numerus') == 'yes' source = message.find('source').text translation_node = message.find('translation') # pick all numerusforms if numerus: translations = [i.text for i in translation_node.findall('numerusform')] else: translations = [translation_node.text] for translation in translations: if translation is None: continue errors = [] valid = check_format_specifiers(source, translation, errors, numerus) for error in errors: print('%s: %s' % (filename, error)) if not valid: # set type to unfinished and clear string if invalid translation_node.clear() translation_node.set('type', 'unfinished') have_errors = True # Remove location tags for location in message.findall('location'): message.remove(location) # Remove entire message if it is an unfinished translation if translation_node.get('type') == 'unfinished': context.remove(message) # check if document is (virtually) empty, and remove it if so num_messages = 0 for context in root.findall('context'): for message in context.findall('message'): num_messages += 1 if num_messages < MIN_NUM_MESSAGES: print('Removing %s, as it contains only %i messages' % (filepath, num_messages)) continue # write fixed-up tree # if diff reduction requested, replace some XML to 'sanitize' to qt formatting if reduce_diff_hacks: out = io.BytesIO() tree.write(out, encoding='utf-8') out = out.getvalue() out = out.replace(b' />', b'/>') with open(filepath, 'wb') as f: f.write(out) else: tree.write(filepath, encoding='utf-8') return have_errors if __name__ == '__main__': check_at_repository_root() fetch_all_translations() postprocess_translations()

38.625592

124

0.633865

from __future__ import division, print_function import subprocess import re import sys import os import io import xml.etree.ElementTree as ET TX = 'tx' SOURCE_LANG = 'nuyul_en.ts' LOCALE_DIR = 'src/qt/locale' MIN_NUM_MESSAGES = 10 def check_at_repository_root(): if not os.path.exists('.git'): print('No .git directory found') print('Execute this script at the root of the repository', file=sys.stderr) exit(1) def fetch_all_translations(): if subprocess.call([TX, 'pull', '-f', '-a']): print('Error while fetching translations', file=sys.stderr) exit(1) def find_format_specifiers(s): pos = 0 specifiers = [] while True: percent = s.find('%', pos) if percent < 0: break try: specifiers.append(s[percent+1]) except: print('Failed to get specifier') pos = percent+2 return specifiers def split_format_specifiers(specifiers): numeric = [] other = [] for s in specifiers: if s in {'1','2','3','4','5','6','7','8','9'}: numeric.append(s) else: other.append(s) # with a Qt-formatted message. In the case of Qt formatting (see https://doc.qt.io/qt-5/qstring.html#arg) # only numeric formats are replaced at all. This means "(percentage: %1%)" is valid, without needing # any kind of escaping that would be necessary for strprintf. Without this, this function # would wrongly detect '%)' as a printf format specifier. if numeric: other = [] # numeric (Qt) can be present in any order, others (strprintf) must be in specified order return set(numeric),other def sanitize_string(s): return s.replace('\n',' ') def check_format_specifiers(source, translation, errors, numerus): source_f = split_format_specifiers(find_format_specifiers(source)) # assert that no source messages contain both Qt and strprintf format specifiers # if this fails, go change the source as this is hacky and confusing! assert(not(source_f[0] and source_f[1])) try: translation_f = split_format_specifiers(find_format_specifiers(translation)) except IndexError: errors.append("Parse error in translation for '%s': '%s'" % (sanitize_string(source), sanitize_string(translation))) return False else: if source_f != translation_f: if numerus and source_f == (set(), ['n']) and translation_f == (set(), []) and translation.find('%') == -1: # Allow numerus translations to omit %n specifier (usually when it only has one possible value) return True errors.append("Mismatch between '%s' and '%s'" % (sanitize_string(source), sanitize_string(translation))) return False return True def all_ts_files(suffix=''): for filename in os.listdir(LOCALE_DIR): # process only language files, and do not process source language if not filename.endswith('.ts'+suffix) or filename == SOURCE_LANG+suffix: continue if suffix: # remove provided suffix filename = filename[0:-len(suffix)] filepath = os.path.join(LOCALE_DIR, filename) yield(filename, filepath) FIX_RE = re.compile(b'[\x00-\x09\x0b\x0c\x0e-\x1f]') def remove_invalid_characters(s): return FIX_RE.sub(b'', s) # Override cdata escape function to make our output match Qt's (optional, just for cleaner diffs for _orig_escape_cdata = None def escape_cdata(text): text = _orig_escape_cdata(text) text = text.replace("'", ''') text = text.replace('"', '"') return text def postprocess_translations(reduce_diff_hacks=False): print('Checking and postprocessing...') if reduce_diff_hacks: global _orig_escape_cdata _orig_escape_cdata = ET._escape_cdata ET._escape_cdata = escape_cdata for (filename,filepath) in all_ts_files(): os.rename(filepath, filepath+'.orig') have_errors = False for (filename,filepath) in all_ts_files('.orig'): # pre-fixups to cope with transifex output parser = ET.XMLParser(encoding='utf-8') # need to override encoding because 'utf8' is not understood only 'utf-8' with open(filepath + '.orig', 'rb') as f: data = f.read() # remove control characters; this must be done over the entire file otherwise the XML parser will fail data = remove_invalid_characters(data) tree = ET.parse(io.BytesIO(data), parser=parser) # iterate over all messages in file root = tree.getroot() for context in root.findall('context'): for message in context.findall('message'): numerus = message.get('numerus') == 'yes' source = message.find('source').text translation_node = message.find('translation') # pick all numerusforms if numerus: translations = [i.text for i in translation_node.findall('numerusform')] else: translations = [translation_node.text] for translation in translations: if translation is None: continue errors = [] valid = check_format_specifiers(source, translation, errors, numerus) for error in errors: print('%s: %s' % (filename, error)) if not valid: # set type to unfinished and clear string if invalid translation_node.clear() translation_node.set('type', 'unfinished') have_errors = True # Remove location tags for location in message.findall('location'): message.remove(location) # Remove entire message if it is an unfinished translation if translation_node.get('type') == 'unfinished': context.remove(message) # check if document is (virtually) empty, and remove it if so num_messages = 0 for context in root.findall('context'): for message in context.findall('message'): num_messages += 1 if num_messages < MIN_NUM_MESSAGES: print('Removing %s, as it contains only %i messages' % (filepath, num_messages)) continue # write fixed-up tree # if diff reduction requested, replace some XML to 'sanitize' to qt formatting if reduce_diff_hacks: out = io.BytesIO() tree.write(out, encoding='utf-8') out = out.getvalue() out = out.replace(b' />', b'/>') with open(filepath, 'wb') as f: f.write(out) else: tree.write(filepath, encoding='utf-8') return have_errors if __name__ == '__main__': check_at_repository_root() fetch_all_translations() postprocess_translations()

true

1c37a34a195f3289c67ff26374a903d2e2c87e3b

11,126

py

Python

code/python/OverviewReportBuilder/v1/fds/sdk/OverviewReportBuilder/model/description_description.py

factset/enterprise-sdk

3fd4d1360756c515c9737a0c9a992c7451d7de7e

[ "Apache-2.0" ]

6

2022-02-07T16:34:18.000Z

2022-03-30T08:04:57.000Z

code/python/OverviewReportBuilder/v1/fds/sdk/OverviewReportBuilder/model/description_description.py

factset/enterprise-sdk

3fd4d1360756c515c9737a0c9a992c7451d7de7e

[ "Apache-2.0" ]

2

2022-02-07T05:25:57.000Z

2022-03-07T14:18:04.000Z

code/python/OverviewReportBuilder/v1/fds/sdk/OverviewReportBuilder/model/description_description.py

factset/enterprise-sdk

3fd4d1360756c515c9737a0c9a992c7451d7de7e

[ "Apache-2.0" ]

null

""" FactSet Overview Report Builder API No description provided (generated by Openapi Generator https://github.com/openapitools/openapi-generator) # noqa: E501 The version of the OpenAPI document: 1.0.0 Generated by: https://openapi-generator.tech """ import re # noqa: F401 import sys # noqa: F401 from fds.sdk.OverviewReportBuilder.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, OpenApiModel ) from fds.sdk.OverviewReportBuilder.exceptions import ApiAttributeError class DescriptionDescription(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 """ 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. """ return { 'value': (str,), # noqa: E501 } @cached_property def discriminator(): return None attribute_map = { 'value': 'value', # noqa: E501 } read_only_vars = { } _composed_schemas = {} @classmethod @convert_js_args_to_python_args def _from_openapi_data(cls, *args, **kwargs): # noqa: E501 """DescriptionDescription - a model defined in OpenAPI 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,) value (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__,) 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, *args, **kwargs): # noqa: E501 """DescriptionDescription - a model defined in OpenAPI 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,) value (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__,) 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.")

43.460938

124

0.571994

import re import sys from fds.sdk.OverviewReportBuilder.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, OpenApiModel ) from fds.sdk.OverviewReportBuilder.exceptions import ApiAttributeError class DescriptionDescription(ModelNormal): allowed_values = { } validations = { } @cached_property def additional_properties_type(): return (bool, date, datetime, dict, float, int, list, str, none_type,) _nullable = False @cached_property def openapi_types(): return { 'value': (str,), } @cached_property def discriminator(): return None attribute_map = { 'value': 'value', } read_only_vars = { } _composed_schemas = {} @classmethod @convert_js_args_to_python_args def _from_openapi_data(cls, *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__,) 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, *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__,) 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

1c37a46965905b69ae23131d9faa5c47d8f12d9c

27,228

py

Python

src/datasets/utils/file_utils.py

borisdayma/datasets

ab6d9759b8b15c0109947159ff1cb6cb3486fdb8

[ "Apache-2.0" ]

1

2020-09-09T00:44:49.000Z

src/datasets/utils/file_utils.py

borisdayma/datasets

ab6d9759b8b15c0109947159ff1cb6cb3486fdb8

[ "Apache-2.0" ]

null

src/datasets/utils/file_utils.py

borisdayma/datasets

ab6d9759b8b15c0109947159ff1cb6cb3486fdb8

[ "Apache-2.0" ]

1

2020-09-04T02:33:51.000Z

""" Utilities for working with the local dataset cache. This file is adapted from the AllenNLP library at https://github.com/allenai/allennlp Copyright by the AllenNLP authors. """ import copy import gzip import json import lzma import os import re import shutil import sys import tarfile import tempfile import time import urllib from contextlib import closing, contextmanager from dataclasses import dataclass from functools import partial from hashlib import sha256 from pathlib import Path from typing import Dict, Optional, Union from urllib.parse import urlparse from zipfile import ZipFile, is_zipfile import numpy as np import posixpath import pyarrow as pa import requests from tqdm.auto import tqdm from .. import __version__, config from .filelock import FileLock from .logging import WARNING, get_logger logger = get_logger(__name__) # pylint: disable=invalid-name INCOMPLETE_SUFFIX = ".incomplete" def init_hf_modules(hf_modules_cache: Optional[Union[Path, str]] = None) -> str: """ Add hf_modules_cache to the python path. By default hf_modules_cache='~/.cache/huggingface/modules'. It can also be set with the environment variable HF_MODULES_CACHE. This is used to add modules such as `datasets_modules` """ hf_modules_cache = hf_modules_cache if hf_modules_cache is not None else config.HF_MODULES_CACHE hf_modules_cache = str(hf_modules_cache) if hf_modules_cache not in sys.path: sys.path.append(hf_modules_cache) os.makedirs(hf_modules_cache, exist_ok=True) if not os.path.exists(os.path.join(hf_modules_cache, "__init__.py")): with open(os.path.join(hf_modules_cache, "__init__.py"), "w"): pass return hf_modules_cache @contextmanager def temp_seed(seed: int, set_pytorch=False, set_tensorflow=False): """Temporarily set the random seed. This works for python numpy, pytorch and tensorflow.""" np_state = np.random.get_state() np.random.seed(seed) if set_pytorch and config.TORCH_AVAILABLE: import torch torch_state = torch.random.get_rng_state() torch.random.manual_seed(seed) if torch.cuda.is_available(): torch_cuda_states = torch.cuda.get_rng_state_all() torch.cuda.manual_seed_all(seed) if set_tensorflow and config.TF_AVAILABLE: import tensorflow as tf from tensorflow.python import context as tfpycontext tf_state = tf.random.get_global_generator() temp_gen = tf.random.Generator.from_seed(seed) tf.random.set_global_generator(temp_gen) if not tf.executing_eagerly(): raise ValueError("Setting random seed for TensorFlow is only available in eager mode") tf_context = tfpycontext.context() # eager mode context tf_seed = tf_context._seed tf_rng_initialized = hasattr(tf_context, "_rng") if tf_rng_initialized: tf_rng = tf_context._rng tf_context._set_global_seed(seed) try: yield finally: np.random.set_state(np_state) if set_pytorch and config.TORCH_AVAILABLE: torch.random.set_rng_state(torch_state) if torch.cuda.is_available(): torch.cuda.set_rng_state_all(torch_cuda_states) if set_tensorflow and config.TF_AVAILABLE: tf.random.set_global_generator(tf_state) tf_context._seed = tf_seed if tf_rng_initialized: tf_context._rng = tf_rng else: delattr(tf_context, "_rng") def is_remote_url(url_or_filename: str) -> bool: parsed = urlparse(url_or_filename) return parsed.scheme in ("http", "https", "s3", "gs", "hdfs", "ftp") def is_local_path(url_or_filename: str) -> bool: # On unix the scheme of a local path is empty (for both absolute and relative), # while on windows the scheme is the drive name (ex: "c") for absolute paths. # for details on the windows behavior, see https://bugs.python.org/issue42215 return urlparse(url_or_filename).scheme == "" or os.path.ismount(urlparse(url_or_filename).scheme + ":/") def is_relative_path(url_or_filename: str) -> bool: return urlparse(url_or_filename).scheme == "" and not os.path.isabs(url_or_filename) def hf_bucket_url(identifier: str, filename: str, use_cdn=False, dataset=True) -> str: if dataset: endpoint = config.CLOUDFRONT_DATASETS_DISTRIB_PREFIX if use_cdn else config.S3_DATASETS_BUCKET_PREFIX else: endpoint = config.CLOUDFRONT_METRICS_DISTRIB_PREFIX if use_cdn else config.S3_METRICS_BUCKET_PREFIX return "/".join((endpoint, identifier, filename)) def head_hf_s3( identifier: str, filename: str, use_cdn=False, dataset=True, max_retries=0 ) -> Union[requests.Response, Exception]: try: return http_head( hf_bucket_url(identifier=identifier, filename=filename, use_cdn=use_cdn, dataset=dataset), max_retries=max_retries, ) except Exception as e: return e def hf_github_url(path: str, name: str, dataset=True, version: Optional[str] = None) -> str: from .. import SCRIPTS_VERSION version = version or os.getenv("HF_SCRIPTS_VERSION", SCRIPTS_VERSION) if dataset: return config.REPO_DATASETS_URL.format(version=version, path=path, name=name) else: return config.REPO_METRICS_URL.format(version=version, path=path, name=name) def hf_hub_url(path: str, name: str, version: Optional[str] = None) -> str: version = version or config.HUB_DEFAULT_VERSION return config.HUB_DATASETS_URL.format(path=path, name=name, version=version) def url_or_path_join(base_name: str, *pathnames: str) -> str: if is_remote_url(base_name): return posixpath.join(base_name, *pathnames) else: return Path(base_name, *pathnames).as_posix() def url_or_path_parent(url_or_path: str) -> str: if is_remote_url(url_or_path): return url_or_path[: url_or_path.rindex("/")] else: return os.path.dirname(url_or_path) def hash_url_to_filename(url, etag=None): """ Convert `url` into a hashed filename in a repeatable way. If `etag` is specified, append its hash to the url's, delimited by a period. If the url ends with .h5 (Keras HDF5 weights) adds '.h5' to the name so that TF 2.0 can identify it as a HDF5 file (see https://github.com/tensorflow/tensorflow/blob/00fad90125b18b80fe054de1055770cfb8fe4ba3/tensorflow/python/keras/engine/network.py#L1380) """ url_bytes = url.encode("utf-8") url_hash = sha256(url_bytes) filename = url_hash.hexdigest() if etag: etag_bytes = etag.encode("utf-8") etag_hash = sha256(etag_bytes) filename += "." + etag_hash.hexdigest() if url.endswith(".py"): filename += ".py" return filename @dataclass class DownloadConfig: """Configuration for our cached path manager. Attributes: cache_dir (:obj:`str` or :obj:`Path`, optional): Specify a cache directory to save the file to (overwrite the default cache dir). force_download (:obj:`bool`, default ``False``): If True, re-dowload the file even if it's already cached in the cache dir. resume_download (:obj:`bool`, default ``False``): If True, resume the download if incompletly recieved file is found. proxies (:obj:`dict`, optional): user_agent (:obj:`str`, optional): Optional string or dict that will be appended to the user-agent on remote requests. extract_compressed_file (:obj:`bool`, default ``False``): If True and the path point to a zip or tar file, extract the compressed file in a folder along the archive. force_extract (:obj:`bool`, default ``False``): If True when extract_compressed_file is True and the archive was already extracted, re-extract the archive and override the folder where it was extracted. use_etag (:obj:`bool`, default ``True``): num_proc (:obj:`int`, optional): max_retries (:obj:`int`, default ``1``): The number of times to retry an HTTP request if it fails. use_auth_token (:obj:`str` or :obj:`bool`, optional): Optional string or boolean to use as Bearer token for remote files on the Datasets Hub. If True, will get token from ~/.huggingface. """ cache_dir: Optional[Union[str, Path]] = None force_download: bool = False resume_download: bool = False local_files_only: bool = False proxies: Optional[Dict] = None user_agent: Optional[str] = None extract_compressed_file: bool = False force_extract: bool = False use_etag: bool = True num_proc: Optional[int] = None max_retries: int = 1 use_auth_token: Optional[Union[str, bool]] = None def copy(self) -> "DownloadConfig": return self.__class__(**{k: copy.deepcopy(v) for k, v in self.__dict__.items()}) def cached_path( url_or_filename, download_config=None, **download_kwargs, ) -> str: """ Given something that might be a URL (or might be a local path), determine which. If it's a URL, download the file and cache it, and return the path to the cached file. If it's already a local path, make sure the file exists and then return the path. Return: Local path (string) Raises: FileNotFoundError: in case of non-recoverable file (non-existent or no cache on disk) ConnectionError: in case of unreachable url and no cache on disk ValueError: if it couldn't parse the url or filename correctly requests.exceptions.ConnectionError: in case of internet connection issue """ if download_config is None: download_config = DownloadConfig(**download_kwargs) cache_dir = download_config.cache_dir or os.path.join(config.HF_DATASETS_CACHE, "downloads") if isinstance(cache_dir, Path): cache_dir = str(cache_dir) if isinstance(url_or_filename, Path): url_or_filename = str(url_or_filename) if is_remote_url(url_or_filename): # URL, so get it from the cache (downloading if necessary) output_path = get_from_cache( url_or_filename, cache_dir=cache_dir, force_download=download_config.force_download, proxies=download_config.proxies, resume_download=download_config.resume_download, user_agent=download_config.user_agent, local_files_only=download_config.local_files_only, use_etag=download_config.use_etag, max_retries=download_config.max_retries, use_auth_token=download_config.use_auth_token, ) elif os.path.exists(url_or_filename): # File, and it exists. output_path = url_or_filename elif is_local_path(url_or_filename): # File, but it doesn't exist. raise FileNotFoundError("Local file {} doesn't exist".format(url_or_filename)) else: # Something unknown raise ValueError("unable to parse {} as a URL or as a local path".format(url_or_filename)) if download_config.extract_compressed_file and output_path is not None: if ( not is_zipfile(output_path) and not tarfile.is_tarfile(output_path) and not is_gzip(output_path) and not is_xz(output_path) and not is_rarfile(output_path) ): return output_path # Path where we extract compressed archives # We extract in the cache dir, and get the extracted path name by hashing the original path" abs_output_path = os.path.abspath(output_path) output_path_extracted = os.path.join(cache_dir, "extracted", hash_url_to_filename(abs_output_path)) if ( os.path.isdir(output_path_extracted) and os.listdir(output_path_extracted) and not download_config.force_extract ) or (os.path.isfile(output_path_extracted) and not download_config.force_extract): return output_path_extracted # Prevent parallel extractions lock_path = output_path + ".lock" with FileLock(lock_path): shutil.rmtree(output_path_extracted, ignore_errors=True) os.makedirs(output_path_extracted, exist_ok=True) if tarfile.is_tarfile(output_path): tar_file = tarfile.open(output_path) tar_file.extractall(output_path_extracted) tar_file.close() elif is_gzip(output_path): os.rmdir(output_path_extracted) with gzip.open(output_path, "rb") as gzip_file: with open(output_path_extracted, "wb") as extracted_file: shutil.copyfileobj(gzip_file, extracted_file) elif is_zipfile(output_path): # put zip file to the last, b/c it is possible wrongly detected as zip with ZipFile(output_path, "r") as zip_file: zip_file.extractall(output_path_extracted) zip_file.close() elif is_xz(output_path): os.rmdir(output_path_extracted) with lzma.open(output_path) as compressed_file: with open(output_path_extracted, "wb") as extracted_file: shutil.copyfileobj(compressed_file, extracted_file) elif is_rarfile(output_path): if config.RARFILE_AVAILABLE: import rarfile rf = rarfile.RarFile(output_path) rf.extractall(output_path_extracted) rf.close() else: raise EnvironmentError("Please pip install rarfile") else: raise EnvironmentError("Archive format of {} could not be identified".format(output_path)) return output_path_extracted return output_path def get_datasets_user_agent(user_agent: Optional[Union[str, dict]] = None) -> str: ua = "datasets/{}; python/{}".format(__version__, config.PY_VERSION) ua += "; pyarrow/{}".format(pa.__version__) if config.TORCH_AVAILABLE: ua += "; torch/{}".format(config.TORCH_VERSION) if config.TF_AVAILABLE: ua += "; tensorflow/{}".format(config.TF_VERSION) if config.BEAM_AVAILABLE: ua += "; apache_beam/{}".format(config.BEAM_VERSION) if isinstance(user_agent, dict): ua += "; " + "; ".join("{}/{}".format(k, v) for k, v in user_agent.items()) elif isinstance(user_agent, str): ua += "; " + user_agent return ua def get_authentication_headers_for_url(url: str, use_auth_token: Optional[Union[str, bool]] = None) -> dict: """Handle the HF authentication""" headers = {} if url.startswith("https://huggingface.co/"): token = None if isinstance(use_auth_token, str): token = use_auth_token elif bool(use_auth_token): from huggingface_hub import hf_api token = hf_api.HfFolder.get_token() if token: headers["authorization"] = "Bearer {}".format(token) return headers class OfflineModeIsEnabled(ConnectionError): pass def _raise_if_offline_mode_is_enabled(msg: Optional[str] = None): """Raise a OfflineModeIsEnabled error (subclass of ConnectionError) if HF_DATASETS_OFFLINE is True.""" if config.HF_DATASETS_OFFLINE: raise OfflineModeIsEnabled( "Offline mode is enabled." if msg is None else "Offline mode is enabled. " + str(msg) ) def _request_with_retry( method: str, url: str, max_retries: int = 0, base_wait_time: float = 0.5, max_wait_time: float = 2, timeout: float = 10.0, **params, ) -> requests.Response: """Wrapper around requests to retry in case it fails with a ConnectTimeout, with exponential backoff. Note that if the environment variable HF_DATASETS_OFFLINE is set to 1, then a OfflineModeIsEnabled error is raised. Args: method (str): HTTP method, such as 'GET' or 'HEAD' url (str): The URL of the ressource to fetch max_retries (int): Maximum number of retries, defaults to 0 (no retries) base_wait_time (float): Duration (in seconds) to wait before retrying the first time. Wait time between retries then grows exponentially, capped by max_wait_time. max_wait_time (float): Maximum amount of time between two retries, in seconds **params: Params to pass to `requests.request` """ _raise_if_offline_mode_is_enabled(f"Tried to reach {url}") tries, success = 0, False while not success: tries += 1 try: response = requests.request(method=method.upper(), url=url, timeout=timeout, **params) success = True except requests.exceptions.ConnectTimeout as err: if tries > max_retries: raise err else: logger.info(f"{method} request to {url} timed out, retrying... [{tries/max_retries}]") sleep_time = min(max_wait_time, base_wait_time * 2 ** (tries - 1)) # Exponential backoff time.sleep(sleep_time) return response def ftp_head(url, timeout=10.0): _raise_if_offline_mode_is_enabled(f"Tried to reach {url}") try: with closing(urllib.request.urlopen(url, timeout=timeout)) as r: r.read(1) except Exception: return False return True def ftp_get(url, temp_file, timeout=10.0): _raise_if_offline_mode_is_enabled(f"Tried to reach {url}") try: logger.info(f"Getting through FTP {url} into {temp_file.name}") with closing(urllib.request.urlopen(url, timeout=timeout)) as r: shutil.copyfileobj(r, temp_file) except urllib.error.URLError as e: raise ConnectionError(e) def http_get(url, temp_file, proxies=None, resume_size=0, headers=None, cookies=None, timeout=10.0, max_retries=0): headers = copy.deepcopy(headers) or {} headers["user-agent"] = get_datasets_user_agent(user_agent=headers.get("user-agent")) if resume_size > 0: headers["Range"] = "bytes=%d-" % (resume_size,) response = _request_with_retry( method="GET", url=url, stream=True, proxies=proxies, headers=headers, cookies=cookies, max_retries=max_retries, timeout=timeout, ) if response.status_code == 416: # Range not satisfiable return content_length = response.headers.get("Content-Length") total = resume_size + int(content_length) if content_length is not None else None not_verbose = bool(logger.getEffectiveLevel() > WARNING) progress = tqdm( unit="B", unit_scale=True, total=total, initial=resume_size, desc="Downloading", disable=not_verbose, ) for chunk in response.iter_content(chunk_size=1024): if chunk: # filter out keep-alive new chunks progress.update(len(chunk)) temp_file.write(chunk) progress.close() def http_head( url, proxies=None, headers=None, cookies=None, allow_redirects=True, timeout=10.0, max_retries=0 ) -> requests.Response: headers = copy.deepcopy(headers) or {} headers["user-agent"] = get_datasets_user_agent(user_agent=headers.get("user-agent")) response = _request_with_retry( method="HEAD", url=url, proxies=proxies, headers=headers, cookies=cookies, allow_redirects=allow_redirects, timeout=timeout, max_retries=max_retries, ) return response def get_from_cache( url, cache_dir=None, force_download=False, proxies=None, etag_timeout=10, resume_download=False, user_agent=None, local_files_only=False, use_etag=True, max_retries=0, use_auth_token=None, ) -> str: """ Given a URL, look for the corresponding file in the local cache. If it's not there, download it. Then return the path to the cached file. Return: Local path (string) Raises: FileNotFoundError: in case of non-recoverable file (non-existent or no cache on disk) ConnectionError: in case of unreachable url and no cache on disk """ if cache_dir is None: cache_dir = config.HF_DATASETS_CACHE if isinstance(cache_dir, Path): cache_dir = str(cache_dir) os.makedirs(cache_dir, exist_ok=True) original_url = url # Some parameters may be added connected = False response = None cookies = None etag = None # Try a first time to file the file on the local file system without eTag (None) # if we don't ask for 'force_download' then we spare a request filename = hash_url_to_filename(original_url, etag=None) cache_path = os.path.join(cache_dir, filename) if os.path.exists(cache_path) and not force_download and not use_etag: return cache_path # Prepare headers for authentication headers = get_authentication_headers_for_url(url, use_auth_token=use_auth_token) if user_agent is not None: headers["user-agent"] = user_agent # We don't have the file locally or we need an eTag if not local_files_only: if url.startswith("ftp://"): connected = ftp_head(url) try: response = http_head( url, allow_redirects=True, proxies=proxies, timeout=etag_timeout, max_retries=max_retries, headers=headers, ) if response.status_code == 200: # ok etag = response.headers.get("ETag") if use_etag else None for k, v in response.cookies.items(): # In some edge cases, we need to get a confirmation token if k.startswith("download_warning") and "drive.google.com" in url: url += "&confirm=" + v cookies = response.cookies connected = True # In some edge cases, head request returns 400 but the connection is actually ok elif ( (response.status_code == 400 and "firebasestorage.googleapis.com" in url) or (response.status_code == 405 and "drive.google.com" in url) or ( response.status_code == 403 and re.match(r"^https?://github.com/.*?/.*?/releases/download/.*?/.*?$", url) ) ): connected = True logger.info("Couldn't get ETag version for url {}".format(url)) except (EnvironmentError, requests.exceptions.Timeout): # not connected pass # connected == False = we don't have a connection, or url doesn't exist, or is otherwise inaccessible. # try to get the last downloaded one if not connected: if os.path.exists(cache_path): return cache_path if local_files_only: raise FileNotFoundError( f"Cannot find the requested files in the cached path at {cache_path} and outgoing traffic has been" " disabled. To enable file online look-ups, set 'local_files_only' to False." ) elif response is not None and response.status_code == 404: raise FileNotFoundError("Couldn't find file at {}".format(url)) _raise_if_offline_mode_is_enabled(f"Tried to reach {url}") raise ConnectionError("Couldn't reach {}".format(url)) # Try a second time filename = hash_url_to_filename(original_url, etag) cache_path = os.path.join(cache_dir, filename) if os.path.exists(cache_path) and not force_download: return cache_path # From now on, connected is True. # Prevent parallel downloads of the same file with a lock. lock_path = cache_path + ".lock" with FileLock(lock_path): if resume_download: incomplete_path = cache_path + ".incomplete" @contextmanager def _resumable_file_manager(): with open(incomplete_path, "a+b") as f: yield f temp_file_manager = _resumable_file_manager if os.path.exists(incomplete_path): resume_size = os.stat(incomplete_path).st_size else: resume_size = 0 else: temp_file_manager = partial(tempfile.NamedTemporaryFile, dir=cache_dir, delete=False) resume_size = 0 # Download to temporary file, then copy to cache dir once finished. # Otherwise you get corrupt cache entries if the download gets interrupted. with temp_file_manager() as temp_file: logger.info("%s not found in cache or force_download set to True, downloading to %s", url, temp_file.name) # GET file object if url.startswith("ftp://"): ftp_get(url, temp_file) else: http_get( url, temp_file, proxies=proxies, resume_size=resume_size, headers=headers, cookies=cookies, max_retries=max_retries, ) logger.info("storing %s in cache at %s", url, cache_path) shutil.move(temp_file.name, cache_path) logger.info("creating metadata file for %s", cache_path) meta = {"url": url, "etag": etag} meta_path = cache_path + ".json" with open(meta_path, "w", encoding="utf-8") as meta_file: json.dump(meta, meta_file) return cache_path def is_gzip(path: str) -> bool: """from https://stackoverflow.com/a/60634210""" with gzip.open(path, "r") as fh: try: fh.read(1) return True except OSError: return False def is_xz(path: str) -> bool: """https://tukaani.org/xz/xz-file-format-1.0.4.txt""" with open(path, "rb") as f: try: header_magic_bytes = f.read(6) except OSError: return False if header_magic_bytes == b"\xfd7zXZ\x00": return True else: return False def is_rarfile(path: str) -> bool: """https://github.com/markokr/rarfile/blob/master/rarfile.py""" RAR_ID = b"Rar!\x1a\x07\x00" RAR5_ID = b"Rar!\x1a\x07\x01\x00" with open(path, "rb", 1024) as fd: buf = fd.read(len(RAR5_ID)) if buf.startswith(RAR_ID) or buf.startswith(RAR5_ID): return True else: return False def add_start_docstrings(*docstr): def docstring_decorator(fn): fn.__doc__ = "".join(docstr) + (fn.__doc__ if fn.__doc__ is not None else "") return fn return docstring_decorator def add_end_docstrings(*docstr): def docstring_decorator(fn): fn.__doc__ = (fn.__doc__ if fn.__doc__ is not None else "") + "".join(docstr) return fn return docstring_decorator def estimate_dataset_size(paths): return sum(path.stat().st_size for path in paths)

36.844384

144

0.648046

import copy import gzip import json import lzma import os import re import shutil import sys import tarfile import tempfile import time import urllib from contextlib import closing, contextmanager from dataclasses import dataclass from functools import partial from hashlib import sha256 from pathlib import Path from typing import Dict, Optional, Union from urllib.parse import urlparse from zipfile import ZipFile, is_zipfile import numpy as np import posixpath import pyarrow as pa import requests from tqdm.auto import tqdm from .. import __version__, config from .filelock import FileLock from .logging import WARNING, get_logger logger = get_logger(__name__) INCOMPLETE_SUFFIX = ".incomplete" def init_hf_modules(hf_modules_cache: Optional[Union[Path, str]] = None) -> str: hf_modules_cache = hf_modules_cache if hf_modules_cache is not None else config.HF_MODULES_CACHE hf_modules_cache = str(hf_modules_cache) if hf_modules_cache not in sys.path: sys.path.append(hf_modules_cache) os.makedirs(hf_modules_cache, exist_ok=True) if not os.path.exists(os.path.join(hf_modules_cache, "__init__.py")): with open(os.path.join(hf_modules_cache, "__init__.py"), "w"): pass return hf_modules_cache @contextmanager def temp_seed(seed: int, set_pytorch=False, set_tensorflow=False): np_state = np.random.get_state() np.random.seed(seed) if set_pytorch and config.TORCH_AVAILABLE: import torch torch_state = torch.random.get_rng_state() torch.random.manual_seed(seed) if torch.cuda.is_available(): torch_cuda_states = torch.cuda.get_rng_state_all() torch.cuda.manual_seed_all(seed) if set_tensorflow and config.TF_AVAILABLE: import tensorflow as tf from tensorflow.python import context as tfpycontext tf_state = tf.random.get_global_generator() temp_gen = tf.random.Generator.from_seed(seed) tf.random.set_global_generator(temp_gen) if not tf.executing_eagerly(): raise ValueError("Setting random seed for TensorFlow is only available in eager mode") tf_context = tfpycontext.context() tf_seed = tf_context._seed tf_rng_initialized = hasattr(tf_context, "_rng") if tf_rng_initialized: tf_rng = tf_context._rng tf_context._set_global_seed(seed) try: yield finally: np.random.set_state(np_state) if set_pytorch and config.TORCH_AVAILABLE: torch.random.set_rng_state(torch_state) if torch.cuda.is_available(): torch.cuda.set_rng_state_all(torch_cuda_states) if set_tensorflow and config.TF_AVAILABLE: tf.random.set_global_generator(tf_state) tf_context._seed = tf_seed if tf_rng_initialized: tf_context._rng = tf_rng else: delattr(tf_context, "_rng") def is_remote_url(url_or_filename: str) -> bool: parsed = urlparse(url_or_filename) return parsed.scheme in ("http", "https", "s3", "gs", "hdfs", "ftp") def is_local_path(url_or_filename: str) -> bool: return urlparse(url_or_filename).scheme == "" or os.path.ismount(urlparse(url_or_filename).scheme + ":/") def is_relative_path(url_or_filename: str) -> bool: return urlparse(url_or_filename).scheme == "" and not os.path.isabs(url_or_filename) def hf_bucket_url(identifier: str, filename: str, use_cdn=False, dataset=True) -> str: if dataset: endpoint = config.CLOUDFRONT_DATASETS_DISTRIB_PREFIX if use_cdn else config.S3_DATASETS_BUCKET_PREFIX else: endpoint = config.CLOUDFRONT_METRICS_DISTRIB_PREFIX if use_cdn else config.S3_METRICS_BUCKET_PREFIX return "/".join((endpoint, identifier, filename)) def head_hf_s3( identifier: str, filename: str, use_cdn=False, dataset=True, max_retries=0 ) -> Union[requests.Response, Exception]: try: return http_head( hf_bucket_url(identifier=identifier, filename=filename, use_cdn=use_cdn, dataset=dataset), max_retries=max_retries, ) except Exception as e: return e def hf_github_url(path: str, name: str, dataset=True, version: Optional[str] = None) -> str: from .. import SCRIPTS_VERSION version = version or os.getenv("HF_SCRIPTS_VERSION", SCRIPTS_VERSION) if dataset: return config.REPO_DATASETS_URL.format(version=version, path=path, name=name) else: return config.REPO_METRICS_URL.format(version=version, path=path, name=name) def hf_hub_url(path: str, name: str, version: Optional[str] = None) -> str: version = version or config.HUB_DEFAULT_VERSION return config.HUB_DATASETS_URL.format(path=path, name=name, version=version) def url_or_path_join(base_name: str, *pathnames: str) -> str: if is_remote_url(base_name): return posixpath.join(base_name, *pathnames) else: return Path(base_name, *pathnames).as_posix() def url_or_path_parent(url_or_path: str) -> str: if is_remote_url(url_or_path): return url_or_path[: url_or_path.rindex("/")] else: return os.path.dirname(url_or_path) def hash_url_to_filename(url, etag=None): url_bytes = url.encode("utf-8") url_hash = sha256(url_bytes) filename = url_hash.hexdigest() if etag: etag_bytes = etag.encode("utf-8") etag_hash = sha256(etag_bytes) filename += "." + etag_hash.hexdigest() if url.endswith(".py"): filename += ".py" return filename @dataclass class DownloadConfig: cache_dir: Optional[Union[str, Path]] = None force_download: bool = False resume_download: bool = False local_files_only: bool = False proxies: Optional[Dict] = None user_agent: Optional[str] = None extract_compressed_file: bool = False force_extract: bool = False use_etag: bool = True num_proc: Optional[int] = None max_retries: int = 1 use_auth_token: Optional[Union[str, bool]] = None def copy(self) -> "DownloadConfig": return self.__class__(**{k: copy.deepcopy(v) for k, v in self.__dict__.items()}) def cached_path( url_or_filename, download_config=None, **download_kwargs, ) -> str: if download_config is None: download_config = DownloadConfig(**download_kwargs) cache_dir = download_config.cache_dir or os.path.join(config.HF_DATASETS_CACHE, "downloads") if isinstance(cache_dir, Path): cache_dir = str(cache_dir) if isinstance(url_or_filename, Path): url_or_filename = str(url_or_filename) if is_remote_url(url_or_filename): output_path = get_from_cache( url_or_filename, cache_dir=cache_dir, force_download=download_config.force_download, proxies=download_config.proxies, resume_download=download_config.resume_download, user_agent=download_config.user_agent, local_files_only=download_config.local_files_only, use_etag=download_config.use_etag, max_retries=download_config.max_retries, use_auth_token=download_config.use_auth_token, ) elif os.path.exists(url_or_filename): output_path = url_or_filename elif is_local_path(url_or_filename): raise FileNotFoundError("Local file {} doesn't exist".format(url_or_filename)) else: raise ValueError("unable to parse {} as a URL or as a local path".format(url_or_filename)) if download_config.extract_compressed_file and output_path is not None: if ( not is_zipfile(output_path) and not tarfile.is_tarfile(output_path) and not is_gzip(output_path) and not is_xz(output_path) and not is_rarfile(output_path) ): return output_path abs_output_path = os.path.abspath(output_path) output_path_extracted = os.path.join(cache_dir, "extracted", hash_url_to_filename(abs_output_path)) if ( os.path.isdir(output_path_extracted) and os.listdir(output_path_extracted) and not download_config.force_extract ) or (os.path.isfile(output_path_extracted) and not download_config.force_extract): return output_path_extracted # Prevent parallel extractions lock_path = output_path + ".lock" with FileLock(lock_path): shutil.rmtree(output_path_extracted, ignore_errors=True) os.makedirs(output_path_extracted, exist_ok=True) if tarfile.is_tarfile(output_path): tar_file = tarfile.open(output_path) tar_file.extractall(output_path_extracted) tar_file.close() elif is_gzip(output_path): os.rmdir(output_path_extracted) with gzip.open(output_path, "rb") as gzip_file: with open(output_path_extracted, "wb") as extracted_file: shutil.copyfileobj(gzip_file, extracted_file) elif is_zipfile(output_path): # put zip file to the last, b/c it is possible wrongly detected as zip with ZipFile(output_path, "r") as zip_file: zip_file.extractall(output_path_extracted) zip_file.close() elif is_xz(output_path): os.rmdir(output_path_extracted) with lzma.open(output_path) as compressed_file: with open(output_path_extracted, "wb") as extracted_file: shutil.copyfileobj(compressed_file, extracted_file) elif is_rarfile(output_path): if config.RARFILE_AVAILABLE: import rarfile rf = rarfile.RarFile(output_path) rf.extractall(output_path_extracted) rf.close() else: raise EnvironmentError("Please pip install rarfile") else: raise EnvironmentError("Archive format of {} could not be identified".format(output_path)) return output_path_extracted return output_path def get_datasets_user_agent(user_agent: Optional[Union[str, dict]] = None) -> str: ua = "datasets/{}; python/{}".format(__version__, config.PY_VERSION) ua += "; pyarrow/{}".format(pa.__version__) if config.TORCH_AVAILABLE: ua += "; torch/{}".format(config.TORCH_VERSION) if config.TF_AVAILABLE: ua += "; tensorflow/{}".format(config.TF_VERSION) if config.BEAM_AVAILABLE: ua += "; apache_beam/{}".format(config.BEAM_VERSION) if isinstance(user_agent, dict): ua += "; " + "; ".join("{}/{}".format(k, v) for k, v in user_agent.items()) elif isinstance(user_agent, str): ua += "; " + user_agent return ua def get_authentication_headers_for_url(url: str, use_auth_token: Optional[Union[str, bool]] = None) -> dict: headers = {} if url.startswith("https://huggingface.co/"): token = None if isinstance(use_auth_token, str): token = use_auth_token elif bool(use_auth_token): from huggingface_hub import hf_api token = hf_api.HfFolder.get_token() if token: headers["authorization"] = "Bearer {}".format(token) return headers class OfflineModeIsEnabled(ConnectionError): pass def _raise_if_offline_mode_is_enabled(msg: Optional[str] = None): if config.HF_DATASETS_OFFLINE: raise OfflineModeIsEnabled( "Offline mode is enabled." if msg is None else "Offline mode is enabled. " + str(msg) ) def _request_with_retry( method: str, url: str, max_retries: int = 0, base_wait_time: float = 0.5, max_wait_time: float = 2, timeout: float = 10.0, **params, ) -> requests.Response: _raise_if_offline_mode_is_enabled(f"Tried to reach {url}") tries, success = 0, False while not success: tries += 1 try: response = requests.request(method=method.upper(), url=url, timeout=timeout, **params) success = True except requests.exceptions.ConnectTimeout as err: if tries > max_retries: raise err else: logger.info(f"{method} request to {url} timed out, retrying... [{tries/max_retries}]") sleep_time = min(max_wait_time, base_wait_time * 2 ** (tries - 1)) # Exponential backoff time.sleep(sleep_time) return response def ftp_head(url, timeout=10.0): _raise_if_offline_mode_is_enabled(f"Tried to reach {url}") try: with closing(urllib.request.urlopen(url, timeout=timeout)) as r: r.read(1) except Exception: return False return True def ftp_get(url, temp_file, timeout=10.0): _raise_if_offline_mode_is_enabled(f"Tried to reach {url}") try: logger.info(f"Getting through FTP {url} into {temp_file.name}") with closing(urllib.request.urlopen(url, timeout=timeout)) as r: shutil.copyfileobj(r, temp_file) except urllib.error.URLError as e: raise ConnectionError(e) def http_get(url, temp_file, proxies=None, resume_size=0, headers=None, cookies=None, timeout=10.0, max_retries=0): headers = copy.deepcopy(headers) or {} headers["user-agent"] = get_datasets_user_agent(user_agent=headers.get("user-agent")) if resume_size > 0: headers["Range"] = "bytes=%d-" % (resume_size,) response = _request_with_retry( method="GET", url=url, stream=True, proxies=proxies, headers=headers, cookies=cookies, max_retries=max_retries, timeout=timeout, ) if response.status_code == 416: # Range not satisfiable return content_length = response.headers.get("Content-Length") total = resume_size + int(content_length) if content_length is not None else None not_verbose = bool(logger.getEffectiveLevel() > WARNING) progress = tqdm( unit="B", unit_scale=True, total=total, initial=resume_size, desc="Downloading", disable=not_verbose, ) for chunk in response.iter_content(chunk_size=1024): if chunk: # filter out keep-alive new chunks progress.update(len(chunk)) temp_file.write(chunk) progress.close() def http_head( url, proxies=None, headers=None, cookies=None, allow_redirects=True, timeout=10.0, max_retries=0 ) -> requests.Response: headers = copy.deepcopy(headers) or {} headers["user-agent"] = get_datasets_user_agent(user_agent=headers.get("user-agent")) response = _request_with_retry( method="HEAD", url=url, proxies=proxies, headers=headers, cookies=cookies, allow_redirects=allow_redirects, timeout=timeout, max_retries=max_retries, ) return response def get_from_cache( url, cache_dir=None, force_download=False, proxies=None, etag_timeout=10, resume_download=False, user_agent=None, local_files_only=False, use_etag=True, max_retries=0, use_auth_token=None, ) -> str: if cache_dir is None: cache_dir = config.HF_DATASETS_CACHE if isinstance(cache_dir, Path): cache_dir = str(cache_dir) os.makedirs(cache_dir, exist_ok=True) original_url = url # Some parameters may be added connected = False response = None cookies = None etag = None # Try a first time to file the file on the local file system without eTag (None) # if we don't ask for 'force_download' then we spare a request filename = hash_url_to_filename(original_url, etag=None) cache_path = os.path.join(cache_dir, filename) if os.path.exists(cache_path) and not force_download and not use_etag: return cache_path # Prepare headers for authentication headers = get_authentication_headers_for_url(url, use_auth_token=use_auth_token) if user_agent is not None: headers["user-agent"] = user_agent # We don't have the file locally or we need an eTag if not local_files_only: if url.startswith("ftp://"): connected = ftp_head(url) try: response = http_head( url, allow_redirects=True, proxies=proxies, timeout=etag_timeout, max_retries=max_retries, headers=headers, ) if response.status_code == 200: # ok etag = response.headers.get("ETag") if use_etag else None for k, v in response.cookies.items(): # In some edge cases, we need to get a confirmation token if k.startswith("download_warning") and "drive.google.com" in url: url += "&confirm=" + v cookies = response.cookies connected = True # In some edge cases, head request returns 400 but the connection is actually ok elif ( (response.status_code == 400 and "firebasestorage.googleapis.com" in url) or (response.status_code == 405 and "drive.google.com" in url) or ( response.status_code == 403 and re.match(r"^https?://github.com/.*?/.*?/releases/download/.*?/.*?$", url) ) ): connected = True logger.info("Couldn't get ETag version for url {}".format(url)) except (EnvironmentError, requests.exceptions.Timeout): # not connected pass # connected == False = we don't have a connection, or url doesn't exist, or is otherwise inaccessible. # try to get the last downloaded one if not connected: if os.path.exists(cache_path): return cache_path if local_files_only: raise FileNotFoundError( f"Cannot find the requested files in the cached path at {cache_path} and outgoing traffic has been" " disabled. To enable file online look-ups, set 'local_files_only' to False." ) elif response is not None and response.status_code == 404: raise FileNotFoundError("Couldn't find file at {}".format(url)) _raise_if_offline_mode_is_enabled(f"Tried to reach {url}") raise ConnectionError("Couldn't reach {}".format(url)) # Try a second time filename = hash_url_to_filename(original_url, etag) cache_path = os.path.join(cache_dir, filename) if os.path.exists(cache_path) and not force_download: return cache_path # From now on, connected is True. # Prevent parallel downloads of the same file with a lock. lock_path = cache_path + ".lock" with FileLock(lock_path): if resume_download: incomplete_path = cache_path + ".incomplete" @contextmanager def _resumable_file_manager(): with open(incomplete_path, "a+b") as f: yield f temp_file_manager = _resumable_file_manager if os.path.exists(incomplete_path): resume_size = os.stat(incomplete_path).st_size else: resume_size = 0 else: temp_file_manager = partial(tempfile.NamedTemporaryFile, dir=cache_dir, delete=False) resume_size = 0 # Download to temporary file, then copy to cache dir once finished. # Otherwise you get corrupt cache entries if the download gets interrupted. with temp_file_manager() as temp_file: logger.info("%s not found in cache or force_download set to True, downloading to %s", url, temp_file.name) # GET file object if url.startswith("ftp://"): ftp_get(url, temp_file) else: http_get( url, temp_file, proxies=proxies, resume_size=resume_size, headers=headers, cookies=cookies, max_retries=max_retries, ) logger.info("storing %s in cache at %s", url, cache_path) shutil.move(temp_file.name, cache_path) logger.info("creating metadata file for %s", cache_path) meta = {"url": url, "etag": etag} meta_path = cache_path + ".json" with open(meta_path, "w", encoding="utf-8") as meta_file: json.dump(meta, meta_file) return cache_path def is_gzip(path: str) -> bool: with gzip.open(path, "r") as fh: try: fh.read(1) return True except OSError: return False def is_xz(path: str) -> bool: with open(path, "rb") as f: try: header_magic_bytes = f.read(6) except OSError: return False if header_magic_bytes == b"\xfd7zXZ\x00": return True else: return False def is_rarfile(path: str) -> bool: RAR_ID = b"Rar!\x1a\x07\x00" RAR5_ID = b"Rar!\x1a\x07\x01\x00" with open(path, "rb", 1024) as fd: buf = fd.read(len(RAR5_ID)) if buf.startswith(RAR_ID) or buf.startswith(RAR5_ID): return True else: return False def add_start_docstrings(*docstr): def docstring_decorator(fn): fn.__doc__ = "".join(docstr) + (fn.__doc__ if fn.__doc__ is not None else "") return fn return docstring_decorator def add_end_docstrings(*docstr): def docstring_decorator(fn): fn.__doc__ = (fn.__doc__ if fn.__doc__ is not None else "") + "".join(docstr) return fn return docstring_decorator def estimate_dataset_size(paths): return sum(path.stat().st_size for path in paths)

true

1c37a7a27277e84f157ef1a0c27c525b20bb63fe

614

py

Python

tests/test_sox.py

ayoubBouziane/TrainingSpeech

799e95d644d69890fa69e488712f10e662827c10

[ "MIT" ]

9

2022-01-24T00:42:31.000Z

2022-03-23T06:32:43.000Z

tests/test_sox.py

wasertech/TrainingSpeech

2a0a7674aa41b3526aeafde58d820a16397923f4

[ "MIT" ]

null

tests/test_sox.py

wasertech/TrainingSpeech

2a0a7674aa41b3526aeafde58d820a16397923f4

[ "MIT" ]

3

2020-05-05T21:17:12.000Z

2022-01-30T09:20:28.000Z

import subprocess import pytest from training_speech import sox @pytest.mark.parametrize('kwargs, expected_call', [ (dict(path_to_file='/path/to/foo.mp3'), 'play -q /path/to/foo.mp3'), (dict(path_to_file='/path/to/foo.mp3', speed=1.2), 'play -q /path/to/foo.mp3 tempo 1.2'), ]) def test_convert(kwargs, expected_call, mocker): wait_mock = mocker.patch('subprocess.Popen.wait') with sox.play(**kwargs) as player: assert isinstance(player, subprocess.Popen) assert wait_mock.call_count == 0 assert ' '.join(player.args) == expected_call wait_mock.assert_called_once()

32.315789

93

0.69544

import subprocess import pytest from training_speech import sox @pytest.mark.parametrize('kwargs, expected_call', [ (dict(path_to_file='/path/to/foo.mp3'), 'play -q /path/to/foo.mp3'), (dict(path_to_file='/path/to/foo.mp3', speed=1.2), 'play -q /path/to/foo.mp3 tempo 1.2'), ]) def test_convert(kwargs, expected_call, mocker): wait_mock = mocker.patch('subprocess.Popen.wait') with sox.play(**kwargs) as player: assert isinstance(player, subprocess.Popen) assert wait_mock.call_count == 0 assert ' '.join(player.args) == expected_call wait_mock.assert_called_once()

true

1c37a83c830cb55ad1916a0c80361da3be597d97

2,801

py

Python

BagModules/bag_digital_ec/tinv.py

xyabc/bag_digital_ec

71b982fc0fbe275fc3901db2e25ab7ca62fb319f

[ "BSD-3-Clause" ]

null

BagModules/bag_digital_ec/tinv.py

xyabc/bag_digital_ec

71b982fc0fbe275fc3901db2e25ab7ca62fb319f

[ "BSD-3-Clause" ]

null

BagModules/bag_digital_ec/tinv.py

xyabc/bag_digital_ec

71b982fc0fbe275fc3901db2e25ab7ca62fb319f

[ "BSD-3-Clause" ]

2

2019-06-30T07:03:02.000Z

2020-01-07T04:55:21.000Z

# -*- coding: utf-8 -*- from typing import Dict, Any import os import pkg_resources from bag.design import Module yaml_file = pkg_resources.resource_filename(__name__, os.path.join('netlist_info', 'tinv.yaml')) # noinspection PyPep8Naming class bag_digital_ec__tinv(Module): """Module for library bag_digital_ec cell tinv. Fill in high level description here. """ def __init__(self, bag_config, parent=None, prj=None, **kwargs): Module.__init__(self, bag_config, yaml_file, parent=parent, prj=prj, **kwargs) @classmethod def get_params_info(cls): # type: () -> Dict[str, str] return dict( lch='channel length.', wp='PMOS width.', wn='NMOS width.', thp='PMOS threshold.', thn='NMOS threshold.', segp='PMOS segments.', segn='NMOS segments.', pmos_switch='True to add PMOS enable switch.', wpen='PMOS enable width.', wnen='NMOS enable width.', thpen='PMOS enable threshold.', thnen='NMOS enable threshold.', ) @classmethod def get_default_param_values(cls): # type: () -> Dict[str, Any] return dict( pmos_switch=True, wpen=None, wnen=None, thpen=None, thnen=None, ) def get_master_basename(self): # type: () -> str if self.params['pmos_switch']: return 'tinv' else: return 'tinv_pass0' def design(self, lch, wp, wn, thp, thn, segp, segn, pmos_switch, wpen, wnen, thpen, thnen): if segp < 1 or segn < 1: raise ValueError('number of segments must be >= 1.') self._set_segments('XN', 'XNEN', 'mn', lch, wn, thn, wnen, thnen, segn) if pmos_switch: self._set_segments('XP', 'XPEN', 'mp', lch, wp, thp, wpen, thpen, segp) else: self.delete_instance('XPEN') self.remove_pin('enb') self.instances['XP'].design(w=wp, l=lch, nf=segp, intent=thp) self.reconnect_instance_terminal('XP', 'D', 'out') def _set_segments(self, bot_name, top_name, mid_name, lch, w, th, wen, then, seg): if wen is None: wen = w if then is None: then = th self.instances[bot_name].design(w=w, l=lch, nf=1, intent=th) self.instances[top_name].design(w=wen, l=lch, nf=1, intent=then) if seg > 1: suffix = '<%d:0>' % (seg - 1) self.array_instance(bot_name, [bot_name + suffix], term_list=[dict(D=mid_name + suffix)]) self.array_instance(top_name, [top_name + suffix], term_list=[dict(S=mid_name + suffix)])

32.569767

96

0.555873

from typing import Dict, Any import os import pkg_resources from bag.design import Module yaml_file = pkg_resources.resource_filename(__name__, os.path.join('netlist_info', 'tinv.yaml')) class bag_digital_ec__tinv(Module): def __init__(self, bag_config, parent=None, prj=None, **kwargs): Module.__init__(self, bag_config, yaml_file, parent=parent, prj=prj, **kwargs) @classmethod def get_params_info(cls): return dict( lch='channel length.', wp='PMOS width.', wn='NMOS width.', thp='PMOS threshold.', thn='NMOS threshold.', segp='PMOS segments.', segn='NMOS segments.', pmos_switch='True to add PMOS enable switch.', wpen='PMOS enable width.', wnen='NMOS enable width.', thpen='PMOS enable threshold.', thnen='NMOS enable threshold.', ) @classmethod def get_default_param_values(cls): return dict( pmos_switch=True, wpen=None, wnen=None, thpen=None, thnen=None, ) def get_master_basename(self): if self.params['pmos_switch']: return 'tinv' else: return 'tinv_pass0' def design(self, lch, wp, wn, thp, thn, segp, segn, pmos_switch, wpen, wnen, thpen, thnen): if segp < 1 or segn < 1: raise ValueError('number of segments must be >= 1.') self._set_segments('XN', 'XNEN', 'mn', lch, wn, thn, wnen, thnen, segn) if pmos_switch: self._set_segments('XP', 'XPEN', 'mp', lch, wp, thp, wpen, thpen, segp) else: self.delete_instance('XPEN') self.remove_pin('enb') self.instances['XP'].design(w=wp, l=lch, nf=segp, intent=thp) self.reconnect_instance_terminal('XP', 'D', 'out') def _set_segments(self, bot_name, top_name, mid_name, lch, w, th, wen, then, seg): if wen is None: wen = w if then is None: then = th self.instances[bot_name].design(w=w, l=lch, nf=1, intent=th) self.instances[top_name].design(w=wen, l=lch, nf=1, intent=then) if seg > 1: suffix = '<%d:0>' % (seg - 1) self.array_instance(bot_name, [bot_name + suffix], term_list=[dict(D=mid_name + suffix)]) self.array_instance(top_name, [top_name + suffix], term_list=[dict(S=mid_name + suffix)])

true

1c37a872ef16d6404b68cac4763c8943fe47e889

387

py

Python

tests/dump/graph.full.py

noabauma/Mirheo

bf7979bfbbf402d33c26ac5dc879f880e78e7017

[ "MIT" ]

null

tests/dump/graph.full.py

noabauma/Mirheo

bf7979bfbbf402d33c26ac5dc879f880e78e7017

[ "MIT" ]

null

tests/dump/graph.full.py

noabauma/Mirheo

bf7979bfbbf402d33c26ac5dc879f880e78e7017

[ "MIT" ]

1

2021-07-14T13:24:05.000Z

#!/usr/bin/env python import mirheo as mir dt = 0.001 ranks = (1, 1, 1) domain = (4, 4, 4) u = mir.Mirheo(ranks, domain, dt, debug_level=3, log_filename='log', no_splash=True) u.save_dependency_graph_graphml("tasks.full", current=False) # sTEST: dump.graph.full # cd dump # rm -rf tasks.graphml # mir.run --runargs "-n 1" ./graph.full.py # cat tasks.full.graphml > tasks.out.txt

19.35

84

0.684755

import mirheo as mir dt = 0.001 ranks = (1, 1, 1) domain = (4, 4, 4) u = mir.Mirheo(ranks, domain, dt, debug_level=3, log_filename='log', no_splash=True) u.save_dependency_graph_graphml("tasks.full", current=False)

true

1c37a8f2f8562f4283ccad4cc4a0bd62e91558d2

4,788

py

Python

src/m9b_summing_again.py

frazeedj/03-AccumulatorsAndFunctionsWithParameters

1b83b3b33da7ec855563182478526469f682fe51

[ "MIT" ]

null

src/m9b_summing_again.py

frazeedj/03-AccumulatorsAndFunctionsWithParameters

1b83b3b33da7ec855563182478526469f682fe51

[ "MIT" ]

null

src/m9b_summing_again.py

frazeedj/03-AccumulatorsAndFunctionsWithParameters

1b83b3b33da7ec855563182478526469f682fe51

[ "MIT" ]

null

""" This module lets you practice the ACCUMULATOR pattern in its simplest classic forms: SUMMING: total = total + number Authors: David Mutchler, Dave Fisher, Vibha Alangar, Mark Hays, Amanda Stouder, their colleagues and Dylan Frazee. """ # DONE: 1. PUT YOUR NAME IN THE ABOVE LINE. def main(): """ Calls the TEST functions in this module. """ run_test_sum_powers() run_test_sum_powers_in_range() def run_test_sum_powers(): """ Tests the sum_powers function. """ # ------------------------------------------------------------------ # DONE: 2. Implement this function. # It TESTS the sum_powers function defined below. # Include at least ** 3 ** tests. # # Use the same 4-step process as in implementing previous # TEST functions, including the same way to print expected/actual. # ------------------------------------------------------------------ print() print('--------------------------------------------------') print('Testing the sum_powers function:') print('--------------------------------------------------') expected = 3.80826 answer = sum_powers(5,-0.3) print('Test 1 expected:', expected) print(' actual: ', answer) expected = 144.45655 answer = sum_powers(100,0.1) print('Test 2 expected:', expected) print(' actual: ', answer) expected = 1025 answer = sum_powers(2,10) print('Test 3 expected:', expected) print(' actual: ', answer) def sum_powers(n, p): """ What comes in: A non-negative integer n and a number p. What goes out: The sum 1**p + 2**p + 3**p + ... + n**p for the given numbers n and p. The latter may be any number (possibly a floating point number, and possibly negative). Side effects: None. Examples: -- sum_powers(5, -0.3) returns about 3.80826 -- sum_powers(100, 0.1) returns about 144.45655 """ # ------------------------------------------------------------------ # DONE: 3. Implement and test this function. # Note that you should write its TEST function first (above). # # No fair running the code of sum_powers to GENERATE # test cases; that would defeat the purpose of TESTING! # ------------------------------------------------------------------ total = 0 for k in range(n): total = total + ((k+1)**p) return(total) def run_test_sum_powers_in_range(): """ Tests the sum_powers_in_range function. """ # ------------------------------------------------------------------ # DONE: 4. Implement this function. # It TESTS the sum_powers_in_range function defined below. # Include at least ** 3 ** tests. # # Use the same 4-step process as in implementing previous # TEST functions, including the same way to print expected/actual. # ------------------------------------------------------------------ print() print('--------------------------------------------------') print('Testing the sum_powers_in_range function:') print('--------------------------------------------------') expected = 142.384776 answer = sum_powers_in_range(3,100,0.1) print('Test 1 expected:', expected) print(' actual: ', answer) expected = 2024 answer = sum_powers_in_range(2,10,3) print('Test 2 expected:', expected) print(' actual: ', answer) expected = 40 answer = sum_powers_in_range(6,7,1) print('Test 3 expected:', expected) print(' actual: ', answer) def sum_powers_in_range(m, n, p): """ What comes in: Non-negative integers m and n, with n >= m, and a number p. What goes out: the sum m**p + (m+1)**p + (m+2)**p + ... + n**p for the given numbers m, n and p. The latter may be any number (possibly a floating point number, and possibly negative). Side effects: None. Example: -- sum_powers_in_range(3, 100, 0.1) returns about 142.384776 """ # ------------------------------------------------------------------ # DONE: 5. Implement and test this function. # Note that you should write its TEST function first (above). # # No fair running the code of sum_powers_in_range to GENERATE # test cases; that would defeat the purpose of TESTING! # ------------------------------------------------------------------ total = 0 for k in range(n-2): total = total + ((m + k)**p) return(total) # ---------------------------------------------------------------------- # Calls main to start the ball rolling. # ---------------------------------------------------------------------- main()

37.40625

79

0.496032

def main(): run_test_sum_powers() run_test_sum_powers_in_range() def run_test_sum_powers(): print() print('--------------------------------------------------') print('Testing the sum_powers function:') print('--------------------------------------------------') expected = 3.80826 answer = sum_powers(5,-0.3) print('Test 1 expected:', expected) print(' actual: ', answer) expected = 144.45655 answer = sum_powers(100,0.1) print('Test 2 expected:', expected) print(' actual: ', answer) expected = 1025 answer = sum_powers(2,10) print('Test 3 expected:', expected) print(' actual: ', answer) def sum_powers(n, p): total = 0 for k in range(n): total = total + ((k+1)**p) return(total) def run_test_sum_powers_in_range(): print() print('--------------------------------------------------') print('Testing the sum_powers_in_range function:') print('--------------------------------------------------') expected = 142.384776 answer = sum_powers_in_range(3,100,0.1) print('Test 1 expected:', expected) print(' actual: ', answer) expected = 2024 answer = sum_powers_in_range(2,10,3) print('Test 2 expected:', expected) print(' actual: ', answer) expected = 40 answer = sum_powers_in_range(6,7,1) print('Test 3 expected:', expected) print(' actual: ', answer) def sum_powers_in_range(m, n, p): total = 0 for k in range(n-2): total = total + ((m + k)**p) return(total) main()

true

1c37aa413333e3d03690186888d312c16dded533

2,165

py

Python

tavern/_plugins/mqtt/request.py

BangWork/tavern

050308841461894a28b07bd2ece85a9b48ff2df4

[ "MIT" ]

null

tavern/_plugins/mqtt/request.py

BangWork/tavern

050308841461894a28b07bd2ece85a9b48ff2df4

[ "MIT" ]

null

tavern/_plugins/mqtt/request.py

BangWork/tavern

050308841461894a28b07bd2ece85a9b48ff2df4

[ "MIT" ]

null

import logging import json import functools from future.utils import raise_from from box import Box from tavern.util import exceptions from tavern.util.dict_util import format_keys, check_expected_keys from tavern.request.base import BaseRequest logger = logging.getLogger(__name__) def get_publish_args(rspec, test_block_config): """Format mqtt request args Todo: Anything else to do here? """ fspec = format_keys(rspec, test_block_config["variables"]) if "json" in rspec: if "payload" in rspec: raise exceptions.BadSchemaError( "Can only specify one of 'payload' or 'json' in MQTT request") fspec["payload"] = json.dumps(fspec.pop("json")) return fspec class MQTTRequest(BaseRequest): """Wrapper for a single mqtt request on a client Similar to RestRequest, publishes a single message. """ def __init__(self, client, rspec, test_block_config): expected = { "topic", "payload", "json", "qos", # TODO retain? } check_expected_keys(expected, rspec) publish_args = get_publish_args(rspec, test_block_config) self._prepared = functools.partial(client.publish, **publish_args) # Need to do this here because get_publish_args will modify the original # input, which we might want to use to format. No error handling because # all the error handling is done in the previous call self._original_publish_args = format_keys( rspec, test_block_config["variables"]) # TODO # From paho: # > raise TypeError('payload must be a string, bytearray, int, float or None.') # Need to be able to take all of these somehow, and also match these # against any payload received on the topic def run(self): try: return self._prepared() except ValueError as e: logger.exception("Error publishing") raise_from(exceptions.MQTTRequestException, e) @property def request_vars(self): return Box(self._original_publish_args)

27.405063

87

0.651732

import logging import json import functools from future.utils import raise_from from box import Box from tavern.util import exceptions from tavern.util.dict_util import format_keys, check_expected_keys from tavern.request.base import BaseRequest logger = logging.getLogger(__name__) def get_publish_args(rspec, test_block_config): fspec = format_keys(rspec, test_block_config["variables"]) if "json" in rspec: if "payload" in rspec: raise exceptions.BadSchemaError( "Can only specify one of 'payload' or 'json' in MQTT request") fspec["payload"] = json.dumps(fspec.pop("json")) return fspec class MQTTRequest(BaseRequest): def __init__(self, client, rspec, test_block_config): expected = { "topic", "payload", "json", "qos", } check_expected_keys(expected, rspec) publish_args = get_publish_args(rspec, test_block_config) self._prepared = functools.partial(client.publish, **publish_args) self._original_publish_args = format_keys( rspec, test_block_config["variables"]) def run(self): try: return self._prepared() except ValueError as e: logger.exception("Error publishing") raise_from(exceptions.MQTTRequestException, e) @property def request_vars(self): return Box(self._original_publish_args)

true

1c37aaa572f430e147ce57c950d49214c220c7f1

13,738

py

Python

game/data/scripts/quests/605_AllianceWithKetraOrcs/__init__.py

TheDemonLife/Lineage2Server-Interlude

d23d145db533fd899d4064026e4bc7ee45c6624a

[ "Apache-2.0" ]

10

2019-07-27T13:12:11.000Z

2022-01-15T19:13:26.000Z

game/data/scripts/quests/605_AllianceWithKetraOrcs/__init__.py

TheDemonLife/Lineage2Server-Interlude

d23d145db533fd899d4064026e4bc7ee45c6624a

[ "Apache-2.0" ]

1

2021-08-06T12:15:01.000Z

2021-08-09T10:18:47.000Z

game/data/scripts/quests/605_AllianceWithKetraOrcs/__init__.py

TheDemonLife/Lineage2Server-Interlude

d23d145db533fd899d4064026e4bc7ee45c6624a

[ "Apache-2.0" ]

2

2020-02-20T23:02:26.000Z

2020-11-22T09:27:51.000Z

# Made by Emperorc # Rate Fix by Gnat import sys from ru.catssoftware import Config from ru.catssoftware.gameserver.model.quest import State from ru.catssoftware.gameserver.model.quest import QuestState from ru.catssoftware.gameserver.model.quest.jython import QuestJython as JQuest qn = "605_AllianceWithKetraOrcs" #NPC Wahkan = 31371 #MOB #mobs for Alliance lvl 1:Varka Silenos- Recruit, Footman, Scout, Hunter, Shaman Varka_One = [ 21350, 21351, 21353, 21354, 21355 ] #mobs for Alliance lvl 2 SHOULD BE:Varka Silenos- priests, warriors, mediums, \ #magi, officers, legionnaire captains, and elite escorts #AS LISTED in npc.sql: Varka Silenos-priests, warriors, mediums, magi, officers;\ #Varka's- Commander, Elite Guard Varka_Two = [ 21357, 21358, 21360, 21361, 21362, 21369, 21370 ] #mobs for Alliance lvl 3 and up SHOULD BE:Varka Silenos- great mystics, captains, \ #grand seers, prophets, prophet's disciples, prophet's royal guards, chief magi and chief escorts #AS LISTED in npc.sql: Varka Silenos-Seer, Great Magus, General, Great Seer, #Varka's - Head Magus, Head Guard, Prophet, Prophet Guard, and Disciple of Prophet Varka_Three = [ 21364, 21365, 21366, 21368, 21371, 21372, 21373, 21374, 21375 ] #All Ketra Orc mobs Ketra_Orcs = [ 21324, 21325, 21327, 21328, 21329, 21331, 21332, 21334, 21335, \ 21336, 21338, 21339, 21340, 21342, 21343, 21344, 21345, 21346, 21347, 21348, 21349 ] Chance = { 21351:500,#Footman 21366:628,#General 21365:500,#Great Magus 21368:508,#Great Seer 21354:521,#Hunter 21361:518,#Magus 21360:509,#Medium 21362:500,#Officer 21357:500,#Priest 21350:500,#Recruit 21353:509,#Scout 21364:527,#Seer 21355:519,#Shaman 21358:500,#Warrior 21369:518,#Commander 21370:604,#Elite guard 21372:604,#Head guard 21371:627,#Head magus 21374:626,#Prophet Guard 21375:626,#Disciple of Prophet 21373:649#Prophet } Chance_mane = { 21366:664,#General 21365:568,#Great Magus 21368:568,#Great Seer 21354:522,#Hunter 21360:539,#Medium 21362:568,#Officer 21357:529,#Priest 21350:500,#Recruit 21353:510,#Scout 21364:558,#Seer 21355:519,#Shaman 21358:529,#Warrior 21369:548,#Commander 21371:713,#Head magus 21373:738#Prophet } #Quest Items Varka_Badge_Soldier, Varka_Badge_Officer, Varka_Badge_Captain = [7216, 7217, 7218] Ketra_Alliance_One, Ketra_Alliance_Two, Ketra_Alliance_Three, \ Ketra_Alliance_Four, Ketra_Alliance_Five = [7211, 7212, 7213, 7214, 7215] Varka_Alliance_One, Varka_Alliance_Two, Varka_Alliance_Three, \ Varka_Alliance_Four, Varka_Alliance_Five = [7221, 7222, 7223, 7224, 7225] Ketra_Badge_Soldier, Ketra_Badge_Officer, Ketra_Badge_Captain = [7226, 7227, 7228] Valor_Totem, Wisdom_Totem = [ 7219,7220 ] Mane = 7233 #drop system - cond:[item_id,max,drop_id] One ={ 1:[57,100,Varka_Badge_Soldier], 2:[Ketra_Alliance_One,200,Varka_Badge_Soldier], 3:[Ketra_Alliance_Two,300,Varka_Badge_Soldier], 4:[Ketra_Alliance_Three,300,Varka_Badge_Soldier], 5:[Ketra_Alliance_Four,400,Varka_Badge_Soldier] } Two ={ 2:[Ketra_Alliance_One,100,Varka_Badge_Officer], 3:[Ketra_Alliance_Two,200,Varka_Badge_Officer], 4:[Ketra_Alliance_Three,300,Varka_Badge_Officer], 5:[Ketra_Alliance_Four,400,Varka_Badge_Officer] } Three ={ 3:[Ketra_Alliance_Two,100,Varka_Badge_Captain], 4:[Ketra_Alliance_Three,200,Varka_Badge_Captain], 5:[Ketra_Alliance_Four,200,Varka_Badge_Captain] } def giveReward(st,item,chance,MAX,drop) : if st.getQuestItemsCount(item) > 0 : count = st.getQuestItemsCount(drop) if count < MAX or drop == Mane : numItems,chance = divmod(chance*Config.RATE_DROP_QUEST,1000) if st.getRandom(1000) < chance : numItems += 1 numItems = int(numItems) if numItems != 0 : if count + numItems >= MAX and drop != Mane : numItems = MAX - count st.playSound("ItemSound.quest_middle") elif drop == Mane and int((count+numItems)/100) > int(count/100) : st.playSound("ItemSound.quest_middle") else : st.playSound("ItemSound.quest_itemget") st.giveItems(drop,numItems) class Quest (JQuest) : def __init__(self,id,name,descr): JQuest.__init__(self,id,name,descr) self.questItemIds = [Varka_Badge_Soldier, Varka_Badge_Officer, Varka_Badge_Captain] def onEvent (self,event,st) : cond = st.getInt("cond") id = st.getInt("id") htmltext = event player = st.getPlayer() if event == "31371-03a.htm" : if player.getLevel() >= 74 : st.set("cond","1") st.set("id","2") st.setState(State.STARTED) st.playSound("ItemSound.quest_accept") htmltext = "31371-03a.htm" else : htmltext = "31371-02b.htm" st.exitQuest(1) player.setAllianceWithVarkaKetra(0) elif event == "31371-10-1.htm" : htmltext = "31371-10-1.htm" st.set("id","3") st.takeItems(Varka_Badge_Soldier, 100) st.giveItems(Ketra_Alliance_One, 1) player.setAllianceWithVarkaKetra(1) st.playSound("ItemSound.quest_middle") elif event == "31371-10-2.htm" : htmltext = "31371-10-2.htm" st.set("id","3") st.takeItems(Varka_Badge_Soldier, 200) st.takeItems(Varka_Badge_Officer, 100) st.takeItems(Ketra_Alliance_One, -1) st.giveItems(Ketra_Alliance_Two, 1) player.setAllianceWithVarkaKetra(2) st.playSound("ItemSound.quest_middle") elif event == "31371-10-3.htm" : htmltext = "31371-10-3.htm" st.set("id","3") st.takeItems(Varka_Badge_Soldier, 300) st.takeItems(Varka_Badge_Officer, 200) st.takeItems(Varka_Badge_Captain, 100) st.takeItems(Ketra_Alliance_Two, -1) st.giveItems(Ketra_Alliance_Three, 1) player.setAllianceWithVarkaKetra(3) st.playSound("ItemSound.quest_middle") elif event == "31371-10-4.htm" : htmltext = "31371-10-4.htm" st.set("id","3") st.takeItems(Varka_Badge_Soldier, 300) st.takeItems(Varka_Badge_Officer, 300) st.takeItems(Varka_Badge_Captain, 200) st.takeItems(Ketra_Alliance_Three, -1) st.takeItems(Valor_Totem,-1) st.giveItems(Ketra_Alliance_Four, 1) player.setAllianceWithVarkaKetra(4) st.playSound("ItemSound.quest_middle") elif event == "31371-11a.htm" : htmltext = "31371-11a.htm" elif event == "31371-19.htm" : htmltext = "31371-19.htm" elif event == "31371-11b.htm" : htmltext = "31371-11b.htm" elif event == "31371-20.htm" : htmltext = "31371-20.htm" st.takeItems(Varka_Badge_Soldier, -1) st.takeItems(Varka_Badge_Officer, -1) st.takeItems(Varka_Badge_Captain, -1) st.takeItems(Ketra_Alliance_One, -1) st.takeItems(Ketra_Alliance_Two, -1) st.takeItems(Ketra_Alliance_Three, -1) st.takeItems(Ketra_Alliance_Four, -1) st.takeItems(Ketra_Alliance_Five, -1) st.takeItems(Valor_Totem,-1) st.takeItems(Wisdom_Totem,-1) player.setAllianceWithVarkaKetra(0) st.exitQuest(1) return htmltext def onTalk (self,npc,player): htmltext = "<html><body>You are either not on a quest that involves this NPC, or you don't meet this NPC's minimum quest requirements.</body></html>" st = player.getQuestState(qn) if st : npcId = npc.getNpcId() cond = st.getInt("cond") id = st.getInt("id") VBadgeS = st.getQuestItemsCount(Varka_Badge_Soldier) VBadgeO = st.getQuestItemsCount(Varka_Badge_Officer) VBadgeC = st.getQuestItemsCount(Varka_Badge_Captain) KAlliance1 = st.getQuestItemsCount(Ketra_Alliance_One) KAlliance2 = st.getQuestItemsCount(Ketra_Alliance_Two) KAlliance3 = st.getQuestItemsCount(Ketra_Alliance_Three) KAlliance4 = st.getQuestItemsCount(Ketra_Alliance_Four) KAlliance5 = st.getQuestItemsCount(Ketra_Alliance_Five) KAlliance = KAlliance1 + KAlliance2 + KAlliance3 + KAlliance4 + KAlliance5 VAlliance = st.getQuestItemsCount(Varka_Alliance_One) + \ st.getQuestItemsCount(Varka_Alliance_Two) + st.getQuestItemsCount(Varka_Alliance_Three) + \ st.getQuestItemsCount(Varka_Alliance_Four) + st.getQuestItemsCount(Varka_Alliance_Five) Valor = st.getQuestItemsCount(Valor_Totem) Wisdom = st.getQuestItemsCount(Wisdom_Totem) if npcId == Wahkan : st.set("id","1") if player.isAlliedWithVarka() or VAlliance : htmltext= "31371-02a.htm" st.exitQuest(1) elif KAlliance == 0 : if cond != 1 : htmltext = "31371-01.htm" else : st.set("id","2") if VBadgeS < 100 : htmltext= "31371-03b.htm" elif VBadgeS >= 100 : htmltext = "31371-09.htm" elif KAlliance : st.setState(State.STARTED) st.set("id","2") if KAlliance1 : if cond != 2 : htmltext = "31371-04.htm" st.set("cond","2") player.setAllianceWithVarkaKetra(1) else : if VBadgeS < 200 or VBadgeO < 100 : htmltext = "31371-12.htm" elif VBadgeS >= 200 and VBadgeO >= 100 : htmltext = "31371-13.htm" elif KAlliance2 : if cond != 3 : htmltext = "31371-05.htm" st.set("cond","3") player.setAllianceWithVarkaKetra(2) else : if VBadgeS < 300 or VBadgeO < 200 or VBadgeC < 100 : htmltext = "31371-15.htm" elif VBadgeS >= 300 and VBadgeO >= 200 and VBadgeC >= 100 : htmltext = "31371-16.htm" elif KAlliance3 : if cond != 4 : htmltext = "31371-06.htm" st.set("cond","4") player.setAllianceWithVarkaKetra(3) else: if VBadgeS < 300 or VBadgeO < 300 or VBadgeC < 200 or Valor == 0 : htmltext = "31371-21.htm" elif VBadgeS >= 300 and VBadgeO >= 300 and VBadgeC >= 200 and Valor > 0 : htmltext = "31371-22.htm" elif KAlliance4 : if cond != 5 : htmltext = "31371-07.htm" st.set("cond","5") player.setAllianceWithVarkaKetra(4) else : if VBadgeS < 400 or VBadgeO < 400 or VBadgeC < 200 or Wisdom == 0 : htmltext = "31371-17.htm" elif VBadgeS >= 400 and VBadgeO >= 400 and VBadgeC >= 200 and Wisdom > 0 : htmltext = "31371-10-5.htm" st.takeItems(Varka_Badge_Soldier, 400) st.takeItems(Varka_Badge_Officer, 400) st.takeItems(Varka_Badge_Captain, 200) st.takeItems(Ketra_Alliance_Four, -1) st.takeItems(Wisdom_Totem,-1) st.giveItems(Ketra_Alliance_Five, 1) player.setAllianceWithVarkaKetra(5) st.set("id","3") st.playSound("ItemSound.quest_middle") elif KAlliance5 : if cond != 6 : htmltext = "31371-18.htm" st.set("cond","6") player.setAllianceWithVarkaKetra(5) else: htmltext = "31371-08.htm" return htmltext def onKill(self,npc,player,isPet): partyMember = self.getRandomPartyMemberState(player,State.STARTED) if not partyMember : return st = partyMember.getQuestState(qn) if st : if st.getState() == State.STARTED : npcId = npc.getNpcId() cond = st.getInt("cond") id = st.getInt("id") st2 = partyMember.getQuestState("606_WarWithVarkaSilenos") if not partyMember.isAlliedWithVarka() : if (npcId in Varka_One) or (npcId in Varka_Two) or (npcId in Varka_Three) : item = 0 if cond <= 5 : if npcId in Varka_One : item,MAX,drop = One[cond] elif npcId in Varka_Two and cond > 1: item,MAX,drop = Two[cond] elif npcId in Varka_Three and cond > 2 : item,MAX,drop = Three[cond] if item != 0 : if st.getQuestItemsCount(drop) == MAX : item = 0 chance = Chance[npcId] if st2 : if (st.getRandom(2) == 1 or item == 0) and npcId in Chance_mane.keys() : item = 57 MAX = 100 drop = Mane chance = Chance_mane[npcId] giveReward(st,item,chance,MAX,drop) elif id == 2 and item != 0 : giveReward(st,item,chance,MAX,drop) elif id == 2 and item != 0 : giveReward(st,item,chance,MAX,drop) return QUEST = Quest(605,qn,"Alliance With Ketra Orcs") QUEST.addStartNpc(Wahkan) QUEST.addTalkId(Wahkan) for mobId in Chance.keys() : QUEST.addKillId(mobId) for mobId in Ketra_Orcs : QUEST.addKillId(mobId)

39.705202

153

0.59521

import sys from ru.catssoftware import Config from ru.catssoftware.gameserver.model.quest import State from ru.catssoftware.gameserver.model.quest import QuestState from ru.catssoftware.gameserver.model.quest.jython import QuestJython as JQuest qn = "605_AllianceWithKetraOrcs" Wahkan = 31371 Varka_One = [ 21350, 21351, 21353, 21354, 21355 ] Varka_Two = [ 21357, 21358, 21360, 21361, 21362, 21369, 21370 ] #mobs for Alliance lvl 3 and up SHOULD BE:Varka Silenos- great mystics, captains, \ #grand seers, prophets, prophet's disciples, prophet's royal guards, chief magi and chief escorts #AS LISTED in npc.sql: Varka Silenos-Seer, Great Magus, General, Great Seer, #Varka's - Head Magus, Head Guard, Prophet, Prophet Guard, and Disciple of Prophet Varka_Three = [ 21364, 21365, 21366, 21368, 21371, 21372, 21373, 21374, 21375 ] Ketra_Orcs = [ 21324, 21325, 21327, 21328, 21329, 21331, 21332, 21334, 21335, \ 21336, 21338, 21339, 21340, 21342, 21343, 21344, 21345, 21346, 21347, 21348, 21349 ] Chance = { 21351:500, 21366:628, 21365:500, 21368:508, 21354:521, 21361:518, 21360:509, 21362:500, 21357:500, 21350:500, 21353:509, 21364:527, 21355:519, 21358:500, 21369:518, 21370:604, 21372:604, 21371:627, 21374:626, 21375:626, 21373:649 } Chance_mane = { 21366:664, 21365:568, 21368:568, 21354:522, 21360:539, 21362:568, 21357:529, 21350:500, 21353:510, 21364:558, 21355:519, 21358:529, 21369:548, 21371:713, 21373:738 } Varka_Badge_Soldier, Varka_Badge_Officer, Varka_Badge_Captain = [7216, 7217, 7218] Ketra_Alliance_One, Ketra_Alliance_Two, Ketra_Alliance_Three, \ Ketra_Alliance_Four, Ketra_Alliance_Five = [7211, 7212, 7213, 7214, 7215] Varka_Alliance_One, Varka_Alliance_Two, Varka_Alliance_Three, \ Varka_Alliance_Four, Varka_Alliance_Five = [7221, 7222, 7223, 7224, 7225] Ketra_Badge_Soldier, Ketra_Badge_Officer, Ketra_Badge_Captain = [7226, 7227, 7228] Valor_Totem, Wisdom_Totem = [ 7219,7220 ] Mane = 7233 One ={ 1:[57,100,Varka_Badge_Soldier], 2:[Ketra_Alliance_One,200,Varka_Badge_Soldier], 3:[Ketra_Alliance_Two,300,Varka_Badge_Soldier], 4:[Ketra_Alliance_Three,300,Varka_Badge_Soldier], 5:[Ketra_Alliance_Four,400,Varka_Badge_Soldier] } Two ={ 2:[Ketra_Alliance_One,100,Varka_Badge_Officer], 3:[Ketra_Alliance_Two,200,Varka_Badge_Officer], 4:[Ketra_Alliance_Three,300,Varka_Badge_Officer], 5:[Ketra_Alliance_Four,400,Varka_Badge_Officer] } Three ={ 3:[Ketra_Alliance_Two,100,Varka_Badge_Captain], 4:[Ketra_Alliance_Three,200,Varka_Badge_Captain], 5:[Ketra_Alliance_Four,200,Varka_Badge_Captain] } def giveReward(st,item,chance,MAX,drop) : if st.getQuestItemsCount(item) > 0 : count = st.getQuestItemsCount(drop) if count < MAX or drop == Mane : numItems,chance = divmod(chance*Config.RATE_DROP_QUEST,1000) if st.getRandom(1000) < chance : numItems += 1 numItems = int(numItems) if numItems != 0 : if count + numItems >= MAX and drop != Mane : numItems = MAX - count st.playSound("ItemSound.quest_middle") elif drop == Mane and int((count+numItems)/100) > int(count/100) : st.playSound("ItemSound.quest_middle") else : st.playSound("ItemSound.quest_itemget") st.giveItems(drop,numItems) class Quest (JQuest) : def __init__(self,id,name,descr): JQuest.__init__(self,id,name,descr) self.questItemIds = [Varka_Badge_Soldier, Varka_Badge_Officer, Varka_Badge_Captain] def onEvent (self,event,st) : cond = st.getInt("cond") id = st.getInt("id") htmltext = event player = st.getPlayer() if event == "31371-03a.htm" : if player.getLevel() >= 74 : st.set("cond","1") st.set("id","2") st.setState(State.STARTED) st.playSound("ItemSound.quest_accept") htmltext = "31371-03a.htm" else : htmltext = "31371-02b.htm" st.exitQuest(1) player.setAllianceWithVarkaKetra(0) elif event == "31371-10-1.htm" : htmltext = "31371-10-1.htm" st.set("id","3") st.takeItems(Varka_Badge_Soldier, 100) st.giveItems(Ketra_Alliance_One, 1) player.setAllianceWithVarkaKetra(1) st.playSound("ItemSound.quest_middle") elif event == "31371-10-2.htm" : htmltext = "31371-10-2.htm" st.set("id","3") st.takeItems(Varka_Badge_Soldier, 200) st.takeItems(Varka_Badge_Officer, 100) st.takeItems(Ketra_Alliance_One, -1) st.giveItems(Ketra_Alliance_Two, 1) player.setAllianceWithVarkaKetra(2) st.playSound("ItemSound.quest_middle") elif event == "31371-10-3.htm" : htmltext = "31371-10-3.htm" st.set("id","3") st.takeItems(Varka_Badge_Soldier, 300) st.takeItems(Varka_Badge_Officer, 200) st.takeItems(Varka_Badge_Captain, 100) st.takeItems(Ketra_Alliance_Two, -1) st.giveItems(Ketra_Alliance_Three, 1) player.setAllianceWithVarkaKetra(3) st.playSound("ItemSound.quest_middle") elif event == "31371-10-4.htm" : htmltext = "31371-10-4.htm" st.set("id","3") st.takeItems(Varka_Badge_Soldier, 300) st.takeItems(Varka_Badge_Officer, 300) st.takeItems(Varka_Badge_Captain, 200) st.takeItems(Ketra_Alliance_Three, -1) st.takeItems(Valor_Totem,-1) st.giveItems(Ketra_Alliance_Four, 1) player.setAllianceWithVarkaKetra(4) st.playSound("ItemSound.quest_middle") elif event == "31371-11a.htm" : htmltext = "31371-11a.htm" elif event == "31371-19.htm" : htmltext = "31371-19.htm" elif event == "31371-11b.htm" : htmltext = "31371-11b.htm" elif event == "31371-20.htm" : htmltext = "31371-20.htm" st.takeItems(Varka_Badge_Soldier, -1) st.takeItems(Varka_Badge_Officer, -1) st.takeItems(Varka_Badge_Captain, -1) st.takeItems(Ketra_Alliance_One, -1) st.takeItems(Ketra_Alliance_Two, -1) st.takeItems(Ketra_Alliance_Three, -1) st.takeItems(Ketra_Alliance_Four, -1) st.takeItems(Ketra_Alliance_Five, -1) st.takeItems(Valor_Totem,-1) st.takeItems(Wisdom_Totem,-1) player.setAllianceWithVarkaKetra(0) st.exitQuest(1) return htmltext def onTalk (self,npc,player): htmltext = "<html><body>You are either not on a quest that involves this NPC, or you don't meet this NPC's minimum quest requirements.</body></html>" st = player.getQuestState(qn) if st : npcId = npc.getNpcId() cond = st.getInt("cond") id = st.getInt("id") VBadgeS = st.getQuestItemsCount(Varka_Badge_Soldier) VBadgeO = st.getQuestItemsCount(Varka_Badge_Officer) VBadgeC = st.getQuestItemsCount(Varka_Badge_Captain) KAlliance1 = st.getQuestItemsCount(Ketra_Alliance_One) KAlliance2 = st.getQuestItemsCount(Ketra_Alliance_Two) KAlliance3 = st.getQuestItemsCount(Ketra_Alliance_Three) KAlliance4 = st.getQuestItemsCount(Ketra_Alliance_Four) KAlliance5 = st.getQuestItemsCount(Ketra_Alliance_Five) KAlliance = KAlliance1 + KAlliance2 + KAlliance3 + KAlliance4 + KAlliance5 VAlliance = st.getQuestItemsCount(Varka_Alliance_One) + \ st.getQuestItemsCount(Varka_Alliance_Two) + st.getQuestItemsCount(Varka_Alliance_Three) + \ st.getQuestItemsCount(Varka_Alliance_Four) + st.getQuestItemsCount(Varka_Alliance_Five) Valor = st.getQuestItemsCount(Valor_Totem) Wisdom = st.getQuestItemsCount(Wisdom_Totem) if npcId == Wahkan : st.set("id","1") if player.isAlliedWithVarka() or VAlliance : htmltext= "31371-02a.htm" st.exitQuest(1) elif KAlliance == 0 : if cond != 1 : htmltext = "31371-01.htm" else : st.set("id","2") if VBadgeS < 100 : htmltext= "31371-03b.htm" elif VBadgeS >= 100 : htmltext = "31371-09.htm" elif KAlliance : st.setState(State.STARTED) st.set("id","2") if KAlliance1 : if cond != 2 : htmltext = "31371-04.htm" st.set("cond","2") player.setAllianceWithVarkaKetra(1) else : if VBadgeS < 200 or VBadgeO < 100 : htmltext = "31371-12.htm" elif VBadgeS >= 200 and VBadgeO >= 100 : htmltext = "31371-13.htm" elif KAlliance2 : if cond != 3 : htmltext = "31371-05.htm" st.set("cond","3") player.setAllianceWithVarkaKetra(2) else : if VBadgeS < 300 or VBadgeO < 200 or VBadgeC < 100 : htmltext = "31371-15.htm" elif VBadgeS >= 300 and VBadgeO >= 200 and VBadgeC >= 100 : htmltext = "31371-16.htm" elif KAlliance3 : if cond != 4 : htmltext = "31371-06.htm" st.set("cond","4") player.setAllianceWithVarkaKetra(3) else: if VBadgeS < 300 or VBadgeO < 300 or VBadgeC < 200 or Valor == 0 : htmltext = "31371-21.htm" elif VBadgeS >= 300 and VBadgeO >= 300 and VBadgeC >= 200 and Valor > 0 : htmltext = "31371-22.htm" elif KAlliance4 : if cond != 5 : htmltext = "31371-07.htm" st.set("cond","5") player.setAllianceWithVarkaKetra(4) else : if VBadgeS < 400 or VBadgeO < 400 or VBadgeC < 200 or Wisdom == 0 : htmltext = "31371-17.htm" elif VBadgeS >= 400 and VBadgeO >= 400 and VBadgeC >= 200 and Wisdom > 0 : htmltext = "31371-10-5.htm" st.takeItems(Varka_Badge_Soldier, 400) st.takeItems(Varka_Badge_Officer, 400) st.takeItems(Varka_Badge_Captain, 200) st.takeItems(Ketra_Alliance_Four, -1) st.takeItems(Wisdom_Totem,-1) st.giveItems(Ketra_Alliance_Five, 1) player.setAllianceWithVarkaKetra(5) st.set("id","3") st.playSound("ItemSound.quest_middle") elif KAlliance5 : if cond != 6 : htmltext = "31371-18.htm" st.set("cond","6") player.setAllianceWithVarkaKetra(5) else: htmltext = "31371-08.htm" return htmltext def onKill(self,npc,player,isPet): partyMember = self.getRandomPartyMemberState(player,State.STARTED) if not partyMember : return st = partyMember.getQuestState(qn) if st : if st.getState() == State.STARTED : npcId = npc.getNpcId() cond = st.getInt("cond") id = st.getInt("id") st2 = partyMember.getQuestState("606_WarWithVarkaSilenos") if not partyMember.isAlliedWithVarka() : if (npcId in Varka_One) or (npcId in Varka_Two) or (npcId in Varka_Three) : item = 0 if cond <= 5 : if npcId in Varka_One : item,MAX,drop = One[cond] elif npcId in Varka_Two and cond > 1: item,MAX,drop = Two[cond] elif npcId in Varka_Three and cond > 2 : item,MAX,drop = Three[cond] if item != 0 : if st.getQuestItemsCount(drop) == MAX : item = 0 chance = Chance[npcId] if st2 : if (st.getRandom(2) == 1 or item == 0) and npcId in Chance_mane.keys() : item = 57 MAX = 100 drop = Mane chance = Chance_mane[npcId] giveReward(st,item,chance,MAX,drop) elif id == 2 and item != 0 : giveReward(st,item,chance,MAX,drop) elif id == 2 and item != 0 : giveReward(st,item,chance,MAX,drop) return QUEST = Quest(605,qn,"Alliance With Ketra Orcs") QUEST.addStartNpc(Wahkan) QUEST.addTalkId(Wahkan) for mobId in Chance.keys() : QUEST.addKillId(mobId) for mobId in Ketra_Orcs : QUEST.addKillId(mobId)

true

1c37ab078af008294ab1a5513be042ef121b6fe1

2,517

py

Python

src/sim/power/PowerModelState.py

majid169/gem5-RFDB

a8950687b8cb6a701a387fca4409ff273facb459

[ "BSD-3-Clause" ]

8

2020-02-04T23:39:49.000Z

2021-05-18T14:33:14.000Z

src/sim/power/PowerModelState.py

majid169/gem5-RFDB

a8950687b8cb6a701a387fca4409ff273facb459

[ "BSD-3-Clause" ]

2

2022-02-13T15:54:43.000Z

2022-03-22T06:19:28.000Z

src/sim/power/PowerModelState.py

majid169/gem5-RFDB

a8950687b8cb6a701a387fca4409ff273facb459

[ "BSD-3-Clause" ]

5

2020-04-07T03:38:31.000Z

2020-11-28T04:03:15.000Z

# Copyright (c) 2016 ARM Limited # All rights reserved. # # The license below extends only to copyright in the software and shall # not be construed as granting a license to any other intellectual # property including but not limited to intellectual property relating # to a hardware implementation of the functionality of the software # licensed hereunder. You may use the software subject to the license # terms below provided that you ensure that this notice is replicated # unmodified and in its entirety in all distributions of the software, # modified or unmodified, in source code or in binary form. # # Redistribution and use in source and binary forms, with or without # modification, are permitted provided that the following conditions are # met: redistributions of source code must retain the above copyright # notice, this list of conditions and the following disclaimer; # redistributions in binary form must reproduce the above copyright # notice, this list of conditions and the following disclaimer in the # documentation and/or other materials provided with the distribution; # neither the name of the copyright holders nor the names of its # contributors may be used to endorse or promote products derived from # this software without specific prior written permission. # # THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS # "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT # LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR # A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT # OWNER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, # SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT # LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, # DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY # THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT # (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE # OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. # # Authors: David Guillen Fandos from m5.SimObject import SimObject from m5.params import * # Represents a power model for a simobj class PowerModelState(SimObject): type = 'PowerModelState' cxx_header = "sim/power/power_model.hh" abstract = True cxx_class = 'PowerModelState' @classmethod def export_methods(cls, code): code(''' double getDynamicPower() const; double getStaticPower() const; ''')

44.946429

72

0.779102

from m5.SimObject import SimObject from m5.params import * class PowerModelState(SimObject): type = 'PowerModelState' cxx_header = "sim/power/power_model.hh" abstract = True cxx_class = 'PowerModelState' @classmethod def export_methods(cls, code): code(''' double getDynamicPower() const; double getStaticPower() const; ''')

true

1c37ab451ed1882f94ed21d6cef55d1de5b3dfb2

732

py

Python

app_one/migrations/0003_auto_20180906_1100.py

ngohoangyell/python-django-cbv-to-do-task

325ddbacce44baa6b06f50edd93615eb6c281fb9

[ "MIT" ]

1

2020-03-28T05:41:23.000Z

app_one/migrations/0003_auto_20180906_1100.py

ngohoangyell/python-django-cbv-to-do-task

325ddbacce44baa6b06f50edd93615eb6c281fb9

[ "MIT" ]

null

app_one/migrations/0003_auto_20180906_1100.py

ngohoangyell/python-django-cbv-to-do-task

325ddbacce44baa6b06f50edd93615eb6c281fb9

[ "MIT" ]

null

# Generated by Django 2.1 on 2018-09-06 04:00 import datetime from django.db import migrations, models from django.utils.timezone import utc class Migration(migrations.Migration): dependencies = [ ('app_one', '0002_auto_20180906_1100'), ] operations = [ migrations.AlterField( model_name='task', name='ending_date', field=models.DateField(default=datetime.datetime(2018, 9, 6, 4, 0, 56, 504423, tzinfo=utc)), ), migrations.AlterField( model_name='task', name='starting_date', field=models.DateField(default=datetime.datetime(2018, 9, 6, 4, 0, 56, 504423, tzinfo=utc)), ), ]

28.153846

105

0.592896

import datetime from django.db import migrations, models from django.utils.timezone import utc class Migration(migrations.Migration): dependencies = [ ('app_one', '0002_auto_20180906_1100'), ] operations = [ migrations.AlterField( model_name='task', name='ending_date', field=models.DateField(default=datetime.datetime(2018, 9, 6, 4, 0, 56, 504423, tzinfo=utc)), ), migrations.AlterField( model_name='task', name='starting_date', field=models.DateField(default=datetime.datetime(2018, 9, 6, 4, 0, 56, 504423, tzinfo=utc)), ), ]

true

1c37ac4c8498272775d81d61df98f0c918460bcc

618

py

Python

molecule/default/tests/test_default.py

avinetworks/ansible-role-avise

cce7e4e1b741601aace902e7a28a8e2e9766df36

[ "Apache-2.0" ]

3

2016-10-11T16:43:04.000Z

2016-11-21T16:59:15.000Z

molecule/default/tests/test_default.py

avinetworks/ansible-role-avise

cce7e4e1b741601aace902e7a28a8e2e9766df36

[ "Apache-2.0" ]

2

2019-09-20T05:52:14.000Z

2020-11-26T13:56:33.000Z

molecule/default/tests/test_default.py

avinetworks/ansible-role-avise

cce7e4e1b741601aace902e7a28a8e2e9766df36

[ "Apache-2.0" ]

5

2016-10-11T19:48:37.000Z

2021-09-26T16:17:10.000Z

############################################################################ # ======================================================================== # Copyright 2021 VMware, Inc. All rights reserved. VMware Confidential # ======================================================================== ### import os import testinfra.utils.ansible_runner testinfra_hosts = testinfra.utils.ansible_runner.AnsibleRunner( os.environ['MOLECULE_INVENTORY_FILE']).get_hosts('all') def test_hosts_file(host): f = host.file('/etc/hosts') assert f.exists assert f.user == 'root' assert f.group == 'root'

28.090909

76

0.456311

true

1c37ac78b3368b2cceda6e3f5b8fcb0fbd3a51ab

4,500

py

Python

openstack_dashboard/dashboards/admin/images/tests.py

ameoba/horizon

ff9e367c98a8bb79f10914abffaaa04b0a461819

[ "Apache-2.0" ]

2

2019-12-29T09:20:13.000Z

2020-01-01T13:12:34.000Z

openstack_dashboard/dashboards/admin/images/tests.py

yongquanf/horizon

9aad7fd6f66588fed7c27b720642e47a4a12854b

[ "Apache-2.0" ]

1

2015-03-12T01:03:44.000Z

openstack_dashboard/dashboards/admin/images/tests.py

yongquanf/horizon

9aad7fd6f66588fed7c27b720642e47a4a12854b

[ "Apache-2.0" ]

4

2015-05-05T08:17:28.000Z

2020-02-05T10:47:06.000Z

# vim: tabstop=4 shiftwidth=4 softtabstop=4 # Copyright 2012 Nebula, 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 django.conf import settings # noqa from django.core.urlresolvers import reverse # noqa from django import http from django.test.utils import override_settings # noqa from mox import IsA # noqa from openstack_dashboard import api from openstack_dashboard.test import helpers as test from openstack_dashboard.dashboards.admin.images import tables class ImageCreateViewTest(test.BaseAdminViewTests): def test_admin_image_create_view_uses_admin_template(self): res = self.client.get( reverse('horizon:admin:images:create')) self.assertTemplateUsed(res, 'admin/images/create.html') class ImagesViewTest(test.BaseAdminViewTests): @test.create_stubs({api.glance: ('image_list_detailed',)}) def test_images_list(self): api.glance.image_list_detailed(IsA(http.HttpRequest), marker=None, paginate=True) \ .AndReturn([self.images.list(), False]) self.mox.ReplayAll() res = self.client.get( reverse('horizon:admin:images:index')) self.assertTemplateUsed(res, 'admin/images/index.html') self.assertEqual(len(res.context['images_table'].data), len(self.images.list())) @override_settings(API_RESULT_PAGE_SIZE=2) @test.create_stubs({api.glance: ('image_list_detailed',)}) def test_images_list_get_pagination(self): images = self.images.list()[:5] api.glance.image_list_detailed(IsA(http.HttpRequest), marker=None, paginate=True) \ .AndReturn([images, True]) api.glance.image_list_detailed(IsA(http.HttpRequest), marker=None, paginate=True) \ .AndReturn([images[:2], True]) api.glance.image_list_detailed(IsA(http.HttpRequest), marker=images[2].id, paginate=True) \ .AndReturn([images[2:4], True]) api.glance.image_list_detailed(IsA(http.HttpRequest), marker=images[4].id, paginate=True) \ .AndReturn([images[4:], True]) self.mox.ReplayAll() url = reverse('horizon:admin:images:index') res = self.client.get(url) # get all self.assertEqual(len(res.context['images_table'].data), len(images)) self.assertTemplateUsed(res, 'admin/images/index.html') res = self.client.get(url) # get first page with 2 items self.assertEqual(len(res.context['images_table'].data), settings.API_RESULT_PAGE_SIZE) url = "?".join([reverse('horizon:admin:images:index'), "=".join([tables.AdminImagesTable._meta.pagination_param, images[2].id])]) res = self.client.get(url) # get second page (items 2-4) self.assertEqual(len(res.context['images_table'].data), settings.API_RESULT_PAGE_SIZE) url = "?".join([reverse('horizon:admin:images:index'), "=".join([tables.AdminImagesTable._meta.pagination_param, images[4].id])]) res = self.client.get(url) # get third page (item 5) self.assertEqual(len(res.context['images_table'].data), 1)

42.056075

78

0.555333

from django.conf import settings from django.core.urlresolvers import reverse from django import http from django.test.utils import override_settings from mox import IsA from openstack_dashboard import api from openstack_dashboard.test import helpers as test from openstack_dashboard.dashboards.admin.images import tables class ImageCreateViewTest(test.BaseAdminViewTests): def test_admin_image_create_view_uses_admin_template(self): res = self.client.get( reverse('horizon:admin:images:create')) self.assertTemplateUsed(res, 'admin/images/create.html') class ImagesViewTest(test.BaseAdminViewTests): @test.create_stubs({api.glance: ('image_list_detailed',)}) def test_images_list(self): api.glance.image_list_detailed(IsA(http.HttpRequest), marker=None, paginate=True) \ .AndReturn([self.images.list(), False]) self.mox.ReplayAll() res = self.client.get( reverse('horizon:admin:images:index')) self.assertTemplateUsed(res, 'admin/images/index.html') self.assertEqual(len(res.context['images_table'].data), len(self.images.list())) @override_settings(API_RESULT_PAGE_SIZE=2) @test.create_stubs({api.glance: ('image_list_detailed',)}) def test_images_list_get_pagination(self): images = self.images.list()[:5] api.glance.image_list_detailed(IsA(http.HttpRequest), marker=None, paginate=True) \ .AndReturn([images, True]) api.glance.image_list_detailed(IsA(http.HttpRequest), marker=None, paginate=True) \ .AndReturn([images[:2], True]) api.glance.image_list_detailed(IsA(http.HttpRequest), marker=images[2].id, paginate=True) \ .AndReturn([images[2:4], True]) api.glance.image_list_detailed(IsA(http.HttpRequest), marker=images[4].id, paginate=True) \ .AndReturn([images[4:], True]) self.mox.ReplayAll() url = reverse('horizon:admin:images:index') res = self.client.get(url) self.assertEqual(len(res.context['images_table'].data), len(images)) self.assertTemplateUsed(res, 'admin/images/index.html') res = self.client.get(url) self.assertEqual(len(res.context['images_table'].data), settings.API_RESULT_PAGE_SIZE) url = "?".join([reverse('horizon:admin:images:index'), "=".join([tables.AdminImagesTable._meta.pagination_param, images[2].id])]) res = self.client.get(url) self.assertEqual(len(res.context['images_table'].data), settings.API_RESULT_PAGE_SIZE) url = "?".join([reverse('horizon:admin:images:index'), "=".join([tables.AdminImagesTable._meta.pagination_param, images[4].id])]) res = self.client.get(url) self.assertEqual(len(res.context['images_table'].data), 1)

true

1c37ad3c6e85968813e0b668ff632ccc6145eb04

1,292

py

Python

examples/event_handling/legend_picking.py

jbbrokaw/matplotlib

86ec1b6fc5628bfb2d09797c58d7eed0ca8c2427

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

16

2016-06-14T19:45:35.000Z

2020-11-30T19:02:58.000Z

lib/mpl_examples/event_handling/legend_picking.py

yingkailiang/matplotlib

255a79b106c98c1904489afe6a754e4d943179d6

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

7

2015-05-08T19:36:25.000Z

2015-06-30T15:32:17.000Z

lib/mpl_examples/event_handling/legend_picking.py

yingkailiang/matplotlib

255a79b106c98c1904489afe6a754e4d943179d6

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

14

2015-10-05T04:15:46.000Z

2020-06-11T18:06:02.000Z

""" Enable picking on the legend to toggle the legended line on and off """ import numpy as np import matplotlib.pyplot as plt t = np.arange(0.0, 0.2, 0.1) y1 = 2*np.sin(2*np.pi*t) y2 = 4*np.sin(2*np.pi*2*t) fig, ax = plt.subplots() ax.set_title('Click on legend line to toggle line on/off') line1, = ax.plot(t, y1, lw=2, color='red', label='1 HZ') line2, = ax.plot(t, y2, lw=2, color='blue', label='2 HZ') leg = ax.legend(loc='upper left', fancybox=True, shadow=True) leg.get_frame().set_alpha(0.4) # we will set up a dict mapping legend line to orig line, and enable # picking on the legend line lines = [line1, line2] lined = dict() for legline, origline in zip(leg.get_lines(), lines): legline.set_picker(5) # 5 pts tolerance lined[legline] = origline def onpick(event): # on the pick event, find the orig line corresponding to the # legend proxy line, and toggle the visibility legline = event.artist origline = lined[legline] vis = not origline.get_visible() origline.set_visible(vis) # Change the alpha on the line in the legend so we can see what lines # have been toggled if vis: legline.set_alpha(1.0) else: legline.set_alpha(0.2) fig.canvas.draw() fig.canvas.mpl_connect('pick_event', onpick) plt.show()

28.086957

73

0.679567

import numpy as np import matplotlib.pyplot as plt t = np.arange(0.0, 0.2, 0.1) y1 = 2*np.sin(2*np.pi*t) y2 = 4*np.sin(2*np.pi*2*t) fig, ax = plt.subplots() ax.set_title('Click on legend line to toggle line on/off') line1, = ax.plot(t, y1, lw=2, color='red', label='1 HZ') line2, = ax.plot(t, y2, lw=2, color='blue', label='2 HZ') leg = ax.legend(loc='upper left', fancybox=True, shadow=True) leg.get_frame().set_alpha(0.4) lines = [line1, line2] lined = dict() for legline, origline in zip(leg.get_lines(), lines): legline.set_picker(5) lined[legline] = origline def onpick(event): legline = event.artist origline = lined[legline] vis = not origline.get_visible() origline.set_visible(vis) if vis: legline.set_alpha(1.0) else: legline.set_alpha(0.2) fig.canvas.draw() fig.canvas.mpl_connect('pick_event', onpick) plt.show()

true

1c37ae3f173da72ddbce0e44e1eaf2cc654095f9

305

py

Python

2018/11/graphics/bitcoin-nov-drop-20181128/graphic_config.py

nprapps/graphics-archive

97b0ef326b46a959df930f5522d325e537f7a655

[ "FSFAP" ]

14

2015-05-08T13:41:51.000Z

2021-02-24T12:34:55.000Z

2018/11/graphics/bitcoin-nov-drop-20181128/graphic_config.py

nprapps/graphics-archive

97b0ef326b46a959df930f5522d325e537f7a655

[ "FSFAP" ]

null

2018/11/graphics/bitcoin-nov-drop-20181128/graphic_config.py

nprapps/graphics-archive

97b0ef326b46a959df930f5522d325e537f7a655

[ "FSFAP" ]

7

2015-04-04T04:45:54.000Z

2021-02-18T11:12:48.000Z

#!/usr/bin/env python import base_filters COPY_GOOGLE_DOC_KEY = '1FKqTvQtjYjN9LlhCtkO59hUqZ6SbvUHfYpgetAS5sek' USE_ASSETS = False # Use these variables to override the default cache timeouts for this graphic # DEFAULT_MAX_AGE = 20 # ASSETS_MAX_AGE = 300 JINJA_FILTER_FUNCTIONS = base_filters.FILTERS

21.785714

77

0.819672

import base_filters COPY_GOOGLE_DOC_KEY = '1FKqTvQtjYjN9LlhCtkO59hUqZ6SbvUHfYpgetAS5sek' USE_ASSETS = False JINJA_FILTER_FUNCTIONS = base_filters.FILTERS

true

1c37ae41f6079eccf08a8ecad0c0187105559d92

388

py

Python

test1/wsgi.py

arohom/test1

2f8c662fbb347c017aba986e1cd36e2a428bade7

[ "MIT" ]

1

2019-12-15T16:56:44.000Z

test1/test1/wsgi.py

1923488289/myfisttwo

a4b30b6944407f3525787eea777c327615e0caa7

[ "MIT" ]

87

2018-01-06T10:18:31.000Z

2022-03-11T23:32:30.000Z

test1/test1/wsgi.py

1923488289/myfisttwo

a4b30b6944407f3525787eea777c327615e0caa7

[ "MIT" ]

null

""" WSGI config for test1 project. It exposes the WSGI callable as a module-level variable named ``application``. For more information on this file, see https://docs.djangoproject.com/en/1.11/howto/deployment/wsgi/ """ import os from django.core.wsgi import get_wsgi_application os.environ.setdefault("DJANGO_SETTINGS_MODULE", "test1.settings") application = get_wsgi_application()

22.823529

78

0.783505

import os from django.core.wsgi import get_wsgi_application os.environ.setdefault("DJANGO_SETTINGS_MODULE", "test1.settings") application = get_wsgi_application()

true

1c37aebef98dc918d1cecaf7e1721cdf921d976a

3,045

py

Python

source/main/transcribe.py

SN-18/scrivener

76ca835b47f84ad231a6b4bf6ab9e212fc6b8724

[ "MIT" ]

null

source/main/transcribe.py

SN-18/scrivener

76ca835b47f84ad231a6b4bf6ab9e212fc6b8724

[ "MIT" ]

27

2021-10-21T18:39:01.000Z

2021-11-05T14:17:29.000Z

source/main/transcribe.py

SN-18/scrivener

76ca835b47f84ad231a6b4bf6ab9e212fc6b8724

[ "MIT" ]

2

2021-10-30T03:51:33.000Z

2021-11-30T02:10:49.000Z

""" Copyright (c) 2021 Anshul Patel This code is licensed under MIT license (see LICENSE.MD for details) @author: Scrivener """ # Import Libraries from source.main.summarize import Summary import speech_recognition as sr import moviepy.editor as mp from source.helper.split_audio import splitwavaudio import os from source.helper.cleanup import Cleanup from source.main.punctuation import Punctuation class TranscribeVideo: """ A class used to summarize video without Closed Captions ... Attributes ---------- summary: str Summary of the video Methods ------- transcribe_video: Generate summary from video split_init: Split audio file into multiple small chunks """ def __init(self): self.summary = "" def transcribe_video(self, ip_path): """ Generate summary on punctuated transcript from video without Closed Captions """ # Read video input video = mp.VideoFileClip(ip_path) # Check if temp directory available if not os.path.exists(os.getcwd() + "/temp"): # Create temp directory os.mkdir("temp") # Generate audio file for the input video video.audio.write_audiofile(os.getcwd() + "/temp/temp_audio.wav") # Call split_init to generate small chunks of audio files num_of_files = self.split_init() transcript_text = "" # Read through all chunks of audio files for i in range(num_of_files): recognizer = sr.Recognizer() # Read single audio file chunk audio = sr.AudioFile("temp/" + str(i * 2) + "_temp_audio.wav") # Get audio data with audio as src: audio_data = recognizer.record(src) # Perform speech to text and store the text transcript_text += recognizer.recognize_google(audio_data) # Adding punctuation to transcript punctuated_transcription = Punctuation.add_punctuation_transcript( transcript_text ) # Call the summarization script on the punctuated transcript transcript_summary = Summary(punctuated_transcription) summary = transcript_summary.summarize_text() for lines in summary: print(lines) # Join summary list with ' ' self.summary = "\n".join(summary) # Perform clean up to remove temporary files clean_up = Cleanup() clean_up.delete_temp_files() # Return summary return self.summary def split_init(self): """ Split audio file into multiple small chunks """ # Get current working directory folder = os.getcwd() + "/" + "temp" file = "temp_audio.wav" # Call the script to split audio files into smaller files split_wav = splitwavaudio(folder, file) num_of_files = split_wav.multiple_split(min_per_split=2) # Return number of small files created return num_of_files

30.45

84

0.636453

from source.main.summarize import Summary import speech_recognition as sr import moviepy.editor as mp from source.helper.split_audio import splitwavaudio import os from source.helper.cleanup import Cleanup from source.main.punctuation import Punctuation class TranscribeVideo: def __init(self): self.summary = "" def transcribe_video(self, ip_path): video = mp.VideoFileClip(ip_path) if not os.path.exists(os.getcwd() + "/temp"): os.mkdir("temp") video.audio.write_audiofile(os.getcwd() + "/temp/temp_audio.wav") num_of_files = self.split_init() transcript_text = "" for i in range(num_of_files): recognizer = sr.Recognizer() audio = sr.AudioFile("temp/" + str(i * 2) + "_temp_audio.wav") with audio as src: audio_data = recognizer.record(src) transcript_text += recognizer.recognize_google(audio_data) punctuated_transcription = Punctuation.add_punctuation_transcript( transcript_text ) transcript_summary = Summary(punctuated_transcription) summary = transcript_summary.summarize_text() for lines in summary: print(lines) self.summary = "\n".join(summary) clean_up = Cleanup() clean_up.delete_temp_files() return self.summary def split_init(self): folder = os.getcwd() + "/" + "temp" file = "temp_audio.wav" split_wav = splitwavaudio(folder, file) num_of_files = split_wav.multiple_split(min_per_split=2) return num_of_files

true

1c37afebb69ae9547131b21420229a0cdf1df93e

5,438

py

Python

SpeakerIdentification.py

LL03-Identity-Dowell/100054-dowellvoiceapp

391df14aa4d438591bd7f9cb740d1f751b59e419

[ "Apache-2.0" ]

null

SpeakerIdentification.py

LL03-Identity-Dowell/100054-dowellvoiceapp

391df14aa4d438591bd7f9cb740d1f751b59e419

[ "Apache-2.0" ]

null

SpeakerIdentification.py

LL03-Identity-Dowell/100054-dowellvoiceapp

391df14aa4d438591bd7f9cb740d1f751b59e419

[ "Apache-2.0" ]

1

2021-09-16T09:19:38.000Z

import os import wave import time import pickle #import pyaudio import warnings import numpy as np import sounddevice as sd from scipy.io.wavfile import write from sklearn import preprocessing from scipy.io.wavfile import read import python_speech_features as mfcc from sklearn.mixture import GaussianMixture warnings.filterwarnings("ignore") def calculate_delta(array): rows,cols = array.shape print(rows) print(cols) deltas = np.zeros((rows,20)) N = 2 for i in range(rows): index = [] j = 1 while j <= N: if i-j < 0: first =0 else: first = i-j if i+j > rows-1: second = rows-1 else: second = i+j index.append((second,first)) j+=1 deltas[i] = ( array[index[0][0]]-array[index[0][1]] + (2 * (array[index[1][0]]-array[index[1][1]])) ) / 10 return deltas def extract_features(audio,rate): mfcc_feature = mfcc.mfcc(audio,rate, 0.025, 0.01,20,nfft = 1200, appendEnergy = True) mfcc_feature = preprocessing.scale(mfcc_feature) print(mfcc_feature) delta = calculate_delta(mfcc_feature) combined = np.hstack((mfcc_feature,delta)) return combined def record_audio_train(): Name =(input("Please Enter Your Name:")) for count in range(5): freq = 44100 # Recording duration duration = 10 # Start recorder with the given values # of duration and sample frequency recording = sd.rec(int(duration * freq), samplerate=freq, channels=2) # Record audio for the given number of seconds print ("recording started") sd.wait() print ("recording stopped") OUTPUT_FILENAME=Name+"-sample"+str(count)+".wav" WAVE_OUTPUT_FILENAME=os.path.join("training_set",OUTPUT_FILENAME) trainedfilelist = open("training_set_addition.txt", 'a') trainedfilelist.write(OUTPUT_FILENAME+"\n") write(WAVE_OUTPUT_FILENAME, freq, recording) def record_audio_test(): freq = 44100 # Recording duration duration = 5 # Start recorder with the given values # of duration and sample frequency recording = sd.rec(int(duration * freq), samplerate=freq, channels=2) # Record audio for the given number of seconds print ("recording started") sd.wait() print ("recording stopped") OUTPUT_FILENAME="sample.wav" WAVE_OUTPUT_FILENAME=os.path.join("testing_set",OUTPUT_FILENAME) trainedfilelist = open("testing_set_addition.txt", 'a') trainedfilelist.write(OUTPUT_FILENAME+"\n") write(WAVE_OUTPUT_FILENAME, freq, recording) def train_model(): source = "/home/sky_walker/Music/spkr2/training_set/" dest = "/home/sky_walker/Music/spkr2/trained_models/" train_file = "/home/sky_walker/Music/spkr2/training_set_addition.txt" file_paths = open(train_file,'r') count = 1 features = np.asarray(()) for path in file_paths: path = path.strip() print(path) sr,audio = read(source + path) print(sr) vector = extract_features(audio,sr) if features.size == 0: features = vector else: features = np.vstack((features, vector)) if count == 5: gmm = GaussianMixture(n_components = 6, max_iter = 200, covariance_type='diag',n_init = 3) gmm.fit(features) # dumping the trained gaussian model picklefile = path.split("-")[0]+".gmm" pickle.dump(gmm,open(dest + picklefile,'wb')) print('+ modeling completed for speaker:',picklefile," with data point = ",features.shape) features = np.asarray(()) count = 0 count = count + 1 def test_model(): source = "/home/sky_walker/Music/spkr2/testing_set/" modelpath = "/home/sky_walker/Music/spkr2/trained_models/" test_file = "/home/sky_walker/Music/spkr2/testing_set_addition.txt" file_paths = open(test_file,'r') gmm_files = [os.path.join(modelpath,fname) for fname in os.listdir(modelpath) if fname.endswith('.gmm')] #Load the Gaussian gender Models models = [pickle.load(open(fname,'rb')) for fname in gmm_files] speakers = [fname.split("/")[-1].split(".gmm")[0] for fname in gmm_files] # Read the test directory and get the list of test audio files for path in file_paths: path = path.strip() print(path) sr,audio = read(source + path) vector = extract_features(audio,sr) log_likelihood = np.zeros(len(models)) for i in range(len(models)): gmm = models[i] #checking with each model one by one scores = np.array(gmm.score(vector)) log_likelihood[i] = scores.sum() winner = np.argmax(log_likelihood) print("\tdetected as - ", speakers[winner]) time.sleep(1.0) #choice=int(input("\n1.Record audio for training \n 2.Train Model \n 3.Record audio for testing \n 4.Test Model\n")) while True: choice=int(input("\n 1.Record audio for training \n 2.Train Model \n 3.Record audio for testing \n 4.Test Model\n")) if(choice==1): record_audio_train() elif(choice==2): train_model() elif(choice==3): record_audio_test() elif(choice==4): test_model() if(choice>4): exit()

29.394595

118

0.624127

import os import wave import time import pickle import warnings import numpy as np import sounddevice as sd from scipy.io.wavfile import write from sklearn import preprocessing from scipy.io.wavfile import read import python_speech_features as mfcc from sklearn.mixture import GaussianMixture warnings.filterwarnings("ignore") def calculate_delta(array): rows,cols = array.shape print(rows) print(cols) deltas = np.zeros((rows,20)) N = 2 for i in range(rows): index = [] j = 1 while j <= N: if i-j < 0: first =0 else: first = i-j if i+j > rows-1: second = rows-1 else: second = i+j index.append((second,first)) j+=1 deltas[i] = ( array[index[0][0]]-array[index[0][1]] + (2 * (array[index[1][0]]-array[index[1][1]])) ) / 10 return deltas def extract_features(audio,rate): mfcc_feature = mfcc.mfcc(audio,rate, 0.025, 0.01,20,nfft = 1200, appendEnergy = True) mfcc_feature = preprocessing.scale(mfcc_feature) print(mfcc_feature) delta = calculate_delta(mfcc_feature) combined = np.hstack((mfcc_feature,delta)) return combined def record_audio_train(): Name =(input("Please Enter Your Name:")) for count in range(5): freq = 44100 duration = 10 recording = sd.rec(int(duration * freq), samplerate=freq, channels=2) print ("recording started") sd.wait() print ("recording stopped") OUTPUT_FILENAME=Name+"-sample"+str(count)+".wav" WAVE_OUTPUT_FILENAME=os.path.join("training_set",OUTPUT_FILENAME) trainedfilelist = open("training_set_addition.txt", 'a') trainedfilelist.write(OUTPUT_FILENAME+"\n") write(WAVE_OUTPUT_FILENAME, freq, recording) def record_audio_test(): freq = 44100 duration = 5 recording = sd.rec(int(duration * freq), samplerate=freq, channels=2) print ("recording started") sd.wait() print ("recording stopped") OUTPUT_FILENAME="sample.wav" WAVE_OUTPUT_FILENAME=os.path.join("testing_set",OUTPUT_FILENAME) trainedfilelist = open("testing_set_addition.txt", 'a') trainedfilelist.write(OUTPUT_FILENAME+"\n") write(WAVE_OUTPUT_FILENAME, freq, recording) def train_model(): source = "/home/sky_walker/Music/spkr2/training_set/" dest = "/home/sky_walker/Music/spkr2/trained_models/" train_file = "/home/sky_walker/Music/spkr2/training_set_addition.txt" file_paths = open(train_file,'r') count = 1 features = np.asarray(()) for path in file_paths: path = path.strip() print(path) sr,audio = read(source + path) print(sr) vector = extract_features(audio,sr) if features.size == 0: features = vector else: features = np.vstack((features, vector)) if count == 5: gmm = GaussianMixture(n_components = 6, max_iter = 200, covariance_type='diag',n_init = 3) gmm.fit(features) picklefile = path.split("-")[0]+".gmm" pickle.dump(gmm,open(dest + picklefile,'wb')) print('+ modeling completed for speaker:',picklefile," with data point = ",features.shape) features = np.asarray(()) count = 0 count = count + 1 def test_model(): source = "/home/sky_walker/Music/spkr2/testing_set/" modelpath = "/home/sky_walker/Music/spkr2/trained_models/" test_file = "/home/sky_walker/Music/spkr2/testing_set_addition.txt" file_paths = open(test_file,'r') gmm_files = [os.path.join(modelpath,fname) for fname in os.listdir(modelpath) if fname.endswith('.gmm')] models = [pickle.load(open(fname,'rb')) for fname in gmm_files] speakers = [fname.split("/")[-1].split(".gmm")[0] for fname in gmm_files] for path in file_paths: path = path.strip() print(path) sr,audio = read(source + path) vector = extract_features(audio,sr) log_likelihood = np.zeros(len(models)) for i in range(len(models)): gmm = models[i] scores = np.array(gmm.score(vector)) log_likelihood[i] = scores.sum() winner = np.argmax(log_likelihood) print("\tdetected as - ", speakers[winner]) time.sleep(1.0) while True: choice=int(input("\n 1.Record audio for training \n 2.Train Model \n 3.Record audio for testing \n 4.Test Model\n")) if(choice==1): record_audio_train() elif(choice==2): train_model() elif(choice==3): record_audio_test() elif(choice==4): test_model() if(choice>4): exit()

true

1c37b0d7ea55b6b89fcebfe22cb902e7179ecae3

2,733

py

Python

shoppingtrends/data.py

jhooey/shopping-cart-trends

e2ee65c2cd1f95942000175479a6666459dff854

[ "BSD-3-Clause" ]

1

2015-01-04T17:02:43.000Z

shoppingtrends/data.py

jhooey/shopping-cart-trends

e2ee65c2cd1f95942000175479a6666459dff854

[ "BSD-3-Clause" ]

null

shoppingtrends/data.py

jhooey/shopping-cart-trends

e2ee65c2cd1f95942000175479a6666459dff854

[ "BSD-3-Clause" ]

null

from localization import Province, Country, Store from user import User from receipt import Receipt, Item, Category import datetime def populate_all_tables(session): populate_provinces_tbl(session) def populate_provinces_tbl(session): canada = Country("CAN", "Canada") ontario = Province('Ontario','ON', 13) quebec = Province('Quebec','QC', 14.975) canada.provinces = [Province('Alberta','AB', 5), Province('British Columbia','BC', 12), Province('Manitoba','MB', 13), Province('New Brunswick','NB', 13), Province('Newfoundland and Labrador','NL', 13), Province('Northwest Territories','NT', 5), Province('Nova Scotia','NS', 15), Province('Nunavut','NU', 5), ontario, Province('Prince Edward Island','PE', 14), quebec, Province('Saskatchewan','SK', 10), Province('Yukon','YT', 5) ] session.add(canada) #Create test user jhooey = User("Jacob", "Hooey", "jhooey", "password") #Create test Stores loblaws = Store("Loblaws", "Rideau and Nelson", ontario) Maxi = Store("Maxi", "Hull St. Joseph", quebec) herbspice = Store("Herb and Spice Shop", "375 Bank Street", ontario) #Create test Receipts loblaws_receipt1 = Receipt(loblaws) loblaws_receipt2 = Receipt(loblaws, datetime.date.fromordinal(datetime.date.today().toordinal()-1)) loblaws_receipt3 = Receipt(loblaws, datetime.date.fromordinal(datetime.date.today().toordinal()-4)) #Create Test Items bananas = Item('Bananas', 'yellow fruit', False) napkins = Item('Napkins', 'paper napkins', True) #Add items to receipts loblaws_receipt1.add_item(session, bananas, 2, 0.79) loblaws_receipt1.add_item(session, napkins, 1, 2.99) loblaws_receipt2.add_item(session, bananas, 1.54, 0.79) loblaws_receipt3.add_item(session, bananas, 10.2, 0.59) loblaws_receipt3.add_item(session, napkins, 3, 1.99) #Add Receipts to test user jhooey.add_receipt(loblaws_receipt1) jhooey.add_receipt(loblaws_receipt2) jhooey.add_receipt(loblaws_receipt3) session.add_all([ loblaws, Maxi, herbspice, jhooey, bananas, napkins, Category('Food', 'Stuff you eat'), Category('Household Supplies', "Stuff you don't eat") ], ) session.commit()

34.594937

103

0.564581

from localization import Province, Country, Store from user import User from receipt import Receipt, Item, Category import datetime def populate_all_tables(session): populate_provinces_tbl(session) def populate_provinces_tbl(session): canada = Country("CAN", "Canada") ontario = Province('Ontario','ON', 13) quebec = Province('Quebec','QC', 14.975) canada.provinces = [Province('Alberta','AB', 5), Province('British Columbia','BC', 12), Province('Manitoba','MB', 13), Province('New Brunswick','NB', 13), Province('Newfoundland and Labrador','NL', 13), Province('Northwest Territories','NT', 5), Province('Nova Scotia','NS', 15), Province('Nunavut','NU', 5), ontario, Province('Prince Edward Island','PE', 14), quebec, Province('Saskatchewan','SK', 10), Province('Yukon','YT', 5) ] session.add(canada) jhooey = User("Jacob", "Hooey", "jhooey", "password") loblaws = Store("Loblaws", "Rideau and Nelson", ontario) Maxi = Store("Maxi", "Hull St. Joseph", quebec) herbspice = Store("Herb and Spice Shop", "375 Bank Street", ontario) loblaws_receipt1 = Receipt(loblaws) loblaws_receipt2 = Receipt(loblaws, datetime.date.fromordinal(datetime.date.today().toordinal()-1)) loblaws_receipt3 = Receipt(loblaws, datetime.date.fromordinal(datetime.date.today().toordinal()-4)) bananas = Item('Bananas', 'yellow fruit', False) napkins = Item('Napkins', 'paper napkins', True) loblaws_receipt1.add_item(session, bananas, 2, 0.79) loblaws_receipt1.add_item(session, napkins, 1, 2.99) loblaws_receipt2.add_item(session, bananas, 1.54, 0.79) loblaws_receipt3.add_item(session, bananas, 10.2, 0.59) loblaws_receipt3.add_item(session, napkins, 3, 1.99) jhooey.add_receipt(loblaws_receipt1) jhooey.add_receipt(loblaws_receipt2) jhooey.add_receipt(loblaws_receipt3) session.add_all([ loblaws, Maxi, herbspice, jhooey, bananas, napkins, Category('Food', 'Stuff you eat'), Category('Household Supplies', "Stuff you don't eat") ], ) session.commit()

true

1c37b17225300688a6ee6cd36fb9cade85c157be

2,612

py

Python

server/tmparser.py

averyhiebert/groundstation

6df5dbbe83c0621f1adfef1f04bbcf098bb30c79

[ "MIT" ]

3

2018-07-01T19:21:22.000Z

2020-09-28T05:52:47.000Z

server/tmparser.py

averyhiebert/groundstation

6df5dbbe83c0621f1adfef1f04bbcf098bb30c79

[ "MIT" ]

null

server/tmparser.py

averyhiebert/groundstation

6df5dbbe83c0621f1adfef1f04bbcf098bb30c79

[ "MIT" ]

1

2018-06-16T04:47:40.000Z

import re import json import time def decode_ll(s,is_lat=True): # s is a 4-character string containing compressed lat or lon data as # described in the APRS documentation. # is_lat is True for latitude and False for longitude. # # Return value is in decimal degrees, or None if not a number. if s == "NN!!": return None # Sorry for lack of readability here. Basically, the value is represented # in base 91 using ascii characters. # See page 38 of http://www.aprs.org/doc/APRS101.PDF base_91 = sum((ord(c) - 33)*(91**i) for i,c in enumerate(reversed(s))) if is_lat: return 90 - base_91*1.0/380926 return -180 + base_91*1.0/190463 def decode_alt(alt): # Decode 2-character string into altitude. # (string contains log (base 1.002) of altitude in feet, represented in # base 91 using ascii characters.) # (See page 40 of http://www.aprs.org/doc/APRS101.PDF) return 1.002**((ord(alt[0])-33)*91 + ord(alt[1]) - 33) #Parse a line of APRS data from the telemetrum def parseTM(line): # As with the BRB parser, I can't guarantee that this works with all # possible configuration options and not just those that we've tested # with. # # In the future, if someone wants to make a parser that works for all # valid APRS signals containing lat/lon/alt data, possibly by # introducing another dependency, go for it. # # APRS format documentation can be found here: # http://www.aprs.org/doc/APRS101.PDF # TeleMetrum uses "Compressed Position Data" with Compressed Altitude data = {} data["raw"] = line data["timestring"] = time.strftime("%Y-%m-%d %H:%M:%S",time.localtime()) data["timestamp"] = time.time()*1000 #Gives ms as a floating point. regex = ":!/(?P<lat>....)(?P<lon>....)'(?P<alt>..)Q(?P<info>.*)" m = re.search(regex,line) if(m): data["info"] = m.group("info") # Includes continuity, GPS lock, etc. data["error"] = False lat = decode_ll(m.group("lat"),is_lat=True) lon = decode_ll(m.group("lon"),is_lat=False) alt = decode_alt(m.group("alt")) if lat == None or lon == None: data["error"] = True if data["info"][0] == "U": data["errorMessage"] = "No GPS lock." else: data["errorMessage"] = "Unknown parsing error." data["latitude"] = lat data["longitude"] = lon data["altitude"] = alt return json.dumps(data) else: raise RuntimeError("Error parsing TeleMetrum data.")

36.788732

77

0.613323

import re import json import time def decode_ll(s,is_lat=True): if s == "NN!!": return None base_91 = sum((ord(c) - 33)*(91**i) for i,c in enumerate(reversed(s))) if is_lat: return 90 - base_91*1.0/380926 return -180 + base_91*1.0/190463 def decode_alt(alt): return 1.002**((ord(alt[0])-33)*91 + ord(alt[1]) - 33) def parseTM(line): # possible configuration options and not just those that we've tested data = {} data["raw"] = line data["timestring"] = time.strftime("%Y-%m-%d %H:%M:%S",time.localtime()) data["timestamp"] = time.time()*1000 regex = ":!/(?P<lat>....)(?P<lon>....)'(?P<alt>..)Q(?P<info>.*)" m = re.search(regex,line) if(m): data["info"] = m.group("info") # Includes continuity, GPS lock, etc. data["error"] = False lat = decode_ll(m.group("lat"),is_lat=True) lon = decode_ll(m.group("lon"),is_lat=False) alt = decode_alt(m.group("alt")) if lat == None or lon == None: data["error"] = True if data["info"][0] == "U": data["errorMessage"] = "No GPS lock." else: data["errorMessage"] = "Unknown parsing error." data["latitude"] = lat data["longitude"] = lon data["altitude"] = alt return json.dumps(data) else: raise RuntimeError("Error parsing TeleMetrum data.")

true

1c37b1be2c3811d36040ad7c8fa8645101838750

1,213

py

Python

openstack_dashboard/test/integration_tests/tests/test_login.py

hemantsonawane95/horizon-apelby

01a5e72219aeca8c1451701ee85e232ed0618751

[ "Apache-2.0" ]

930

2015-01-04T08:06:03.000Z

2022-03-13T18:47:13.000Z

openstack_dashboard/test/integration_tests/tests/test_login.py

hemantsonawane95/horizon-apelby

01a5e72219aeca8c1451701ee85e232ed0618751

[ "Apache-2.0" ]

106

2019-01-18T03:06:55.000Z

2019-11-29T05:06:18.000Z

openstack_dashboard/test/integration_tests/tests/test_login.py

hemantsonawane95/horizon-apelby

01a5e72219aeca8c1451701ee85e232ed0618751

[ "Apache-2.0" ]

1,040

2015-01-01T18:48:28.000Z

2022-03-19T08:35:18.000Z

# 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 openstack_dashboard.test.integration_tests import helpers from openstack_dashboard.test.integration_tests.pages import loginpage class TestLogin(helpers.BaseTestCase): """This is a basic scenario test: * checks that the login page is available * logs in as a regular user * checks that the user home page loads without error """ def test_login(self): login_pg = loginpage.LoginPage(self.driver, self.CONFIG) login_pg.go_to_login_page() home_pg = login_pg.login() if not home_pg.is_logged_in: self.fail("Could not determine if logged in") home_pg.log_out()

39.129032

78

0.716406

from openstack_dashboard.test.integration_tests import helpers from openstack_dashboard.test.integration_tests.pages import loginpage class TestLogin(helpers.BaseTestCase): def test_login(self): login_pg = loginpage.LoginPage(self.driver, self.CONFIG) login_pg.go_to_login_page() home_pg = login_pg.login() if not home_pg.is_logged_in: self.fail("Could not determine if logged in") home_pg.log_out()

true

1c37b27ecc836f268bc8e919c2f06e85513de2ea

3,804

py

Python

carculator_truck/geomap.py

romainsacchi/carculator_truck

2c709ac6a956570a56ad2778619aef457e8d42a2

[ "BSD-3-Clause" ]

7

2021-03-19T12:28:18.000Z

2022-02-22T11:13:08.000Z

carculator_truck/geomap.py

romainsacchi/carculator_truck

2c709ac6a956570a56ad2778619aef457e8d42a2

[ "BSD-3-Clause" ]

1

2021-05-21T09:14:53.000Z

2021-05-27T09:23:29.000Z

carculator_truck/geomap.py

romainsacchi/carculator_truck

2c709ac6a956570a56ad2778619aef457e8d42a2

[ "BSD-3-Clause" ]

1

2022-02-22T11:13:00.000Z

from wurst.geo import geomatcher from . import DATA_DIR REGION_MAPPING_FILEPATH = DATA_DIR / "regionmappingH12.csv" class Geomap: """ Map ecoinvent locations to IAM regions and vice-versa. """ def __init__(self): self.geo = self.get_IAM_geomatcher() @staticmethod def get_IAM_geomatcher(): """ Geographical boundaries for IMAGE regions are initally included in geomatcher. However, they are not properly labelled. """ d_image_regions = { "BRA": "Brazil", "CAN": "Canada", "CEU": "Central Europe", "CHN": "China Region", "EAF": "Eastern Africa", "INDIA": "India", "INDO": "Indonesia Region", "JAP": "Japan", "KOR": "Korea Region", "ME": "Middle east", "MEX": "Mexico", "NAF": "Northern Africa", "OCE": "Oceania", "RCAM": "Central America", "RSAF": "Rest of Southern Africa", "RSAM": "Rest of South America", "RSAS": "Rest of South Asia", "RUS": "Russia Region", "SAF": "South Africa", "SEAS": "South Asia", "STAN": "Central Asia", "TUR": "Turkey", "UKR": "Ukraine region", "USA": "USA", "WAF": "Western Africa", "WEU": "Western Europe", } d_map = {("IMAGE", v): ("IMAGE", k) for k, v in d_image_regions.items()} new_def = dict() for k, v in geomatcher.items(): if isinstance(k, tuple): if k[0] == "IMAGE" and k[1] in list(d_image_regions.values()): new_def[d_map[k]] = v geo = geomatcher for k in list(geomatcher.keys()): if k[0] == "IMAGE" and k[1] in list(d_image_regions.values()): geomatcher.pop(k) geo.update(new_def) with open(REGION_MAPPING_FILEPATH) as f: f.readline() csv_list = [[val.strip() for val in r.split(";")] for r in f.readlines()] split_row = [(x[1], x[2]) for x in csv_list] # List of countries not found countries_not_found = ["CC", "CX", "GG", "JE", "BL"] rmnd_to_iso = {} iso_to_rmnd = {} # Build a dictionary that maps region names (used by REMIND) to ISO country codes # And a reverse dictionary that maps ISO country codes to region names for ISO, region in split_row: if ISO not in countries_not_found: try: rmnd_to_iso[region].append(ISO) except KeyError: rmnd_to_iso[region] = [ISO] iso_to_rmnd[region] = ISO geo.add_definitions(rmnd_to_iso, "REMIND") return geo def iam_to_ecoinvent_location(self, location, contained=False): """ Find the corresponding ecoinvent region given an IAM region. :param location: name of a IAM region :type location: str :return: name of an ecoinvent region :rtype: str """ if location == "World": return ["GLO"] ecoinvent_locations = [] searchfunc = self.geo.contained if contained else self.geo.intersects for iam in ("REMIND", "IMAGE"): loc = (iam, location) try: searchfunc(loc) for r in searchfunc(loc): if not isinstance(r, tuple): ecoinvent_locations.append(r) except KeyError: pass if len(ecoinvent_locations) == 0: print("Can't find location {} using the geomatcher.".format(location)) return ecoinvent_locations

29.261538

89

0.52103

from wurst.geo import geomatcher from . import DATA_DIR REGION_MAPPING_FILEPATH = DATA_DIR / "regionmappingH12.csv" class Geomap: def __init__(self): self.geo = self.get_IAM_geomatcher() @staticmethod def get_IAM_geomatcher(): d_image_regions = { "BRA": "Brazil", "CAN": "Canada", "CEU": "Central Europe", "CHN": "China Region", "EAF": "Eastern Africa", "INDIA": "India", "INDO": "Indonesia Region", "JAP": "Japan", "KOR": "Korea Region", "ME": "Middle east", "MEX": "Mexico", "NAF": "Northern Africa", "OCE": "Oceania", "RCAM": "Central America", "RSAF": "Rest of Southern Africa", "RSAM": "Rest of South America", "RSAS": "Rest of South Asia", "RUS": "Russia Region", "SAF": "South Africa", "SEAS": "South Asia", "STAN": "Central Asia", "TUR": "Turkey", "UKR": "Ukraine region", "USA": "USA", "WAF": "Western Africa", "WEU": "Western Europe", } d_map = {("IMAGE", v): ("IMAGE", k) for k, v in d_image_regions.items()} new_def = dict() for k, v in geomatcher.items(): if isinstance(k, tuple): if k[0] == "IMAGE" and k[1] in list(d_image_regions.values()): new_def[d_map[k]] = v geo = geomatcher for k in list(geomatcher.keys()): if k[0] == "IMAGE" and k[1] in list(d_image_regions.values()): geomatcher.pop(k) geo.update(new_def) with open(REGION_MAPPING_FILEPATH) as f: f.readline() csv_list = [[val.strip() for val in r.split(";")] for r in f.readlines()] split_row = [(x[1], x[2]) for x in csv_list] countries_not_found = ["CC", "CX", "GG", "JE", "BL"] rmnd_to_iso = {} iso_to_rmnd = {} for ISO, region in split_row: if ISO not in countries_not_found: try: rmnd_to_iso[region].append(ISO) except KeyError: rmnd_to_iso[region] = [ISO] iso_to_rmnd[region] = ISO geo.add_definitions(rmnd_to_iso, "REMIND") return geo def iam_to_ecoinvent_location(self, location, contained=False): if location == "World": return ["GLO"] ecoinvent_locations = [] searchfunc = self.geo.contained if contained else self.geo.intersects for iam in ("REMIND", "IMAGE"): loc = (iam, location) try: searchfunc(loc) for r in searchfunc(loc): if not isinstance(r, tuple): ecoinvent_locations.append(r) except KeyError: pass if len(ecoinvent_locations) == 0: print("Can't find location {} using the geomatcher.".format(location)) return ecoinvent_locations

true

1c37b83833807df30f1f7fed748b6fe7b6d22bf5

1,321

py

Python

plugins/explain.py

random-access7/corobo

5e517e3ca677e1465a1003307dfe0a755fc48cfb

[ "MIT" ]

null

plugins/explain.py

random-access7/corobo

5e517e3ca677e1465a1003307dfe0a755fc48cfb

[ "MIT" ]

null

plugins/explain.py

random-access7/corobo

5e517e3ca677e1465a1003307dfe0a755fc48cfb

[ "MIT" ]

null

import re import glob import os.path from errbot import BotPlugin, re_botcmd from errbot.templating import tenv class Explain(BotPlugin): """ Explain various terms """ files = glob.glob('plugins/templates/explain/*.jinja2.md') KNOWN_KEYS = [] for fname in files: KNOWN_KEYS.append(fname.replace( 'plugins/templates/explain/', '' ).replace('.jinja2.md', '')) ERROR_MSG = ( 'Sorry, I only know about these things:\n- ' + '\n- '.join(KNOWN_KEYS) ) @re_botcmd(pattern=r'^explain\s+(\w+)(?:\s+to\s+@?([\w-]+))?$', re_cmd_name_help='explain <term>', flags=re.IGNORECASE) def explain(self, msg, match): """Explain various terms.""" # Ignore QuotesBear user = msg.frm.nick response = '' filename = 'explain/{}.jinja2.md'.format(match.group(1).lower()) if match.group(1).lower() in self.KNOWN_KEYS: if match.group(2): response += '@{}: \n'.format(match.group(2)) response += tenv().get_template(filename).render( username=user, target=match.group(2), bot_prefix=self.bot_config.BOT_PREFIX, ) else: response = self.ERROR_MSG return response

28.717391

72

0.557154

import re import glob import os.path from errbot import BotPlugin, re_botcmd from errbot.templating import tenv class Explain(BotPlugin): files = glob.glob('plugins/templates/explain/*.jinja2.md') KNOWN_KEYS = [] for fname in files: KNOWN_KEYS.append(fname.replace( 'plugins/templates/explain/', '' ).replace('.jinja2.md', '')) ERROR_MSG = ( 'Sorry, I only know about these things:\n- ' + '\n- '.join(KNOWN_KEYS) ) @re_botcmd(pattern=r'^explain\s+(\w+)(?:\s+to\s+@?([\w-]+))?$', re_cmd_name_help='explain <term>', flags=re.IGNORECASE) def explain(self, msg, match): user = msg.frm.nick response = '' filename = 'explain/{}.jinja2.md'.format(match.group(1).lower()) if match.group(1).lower() in self.KNOWN_KEYS: if match.group(2): response += '@{}: \n'.format(match.group(2)) response += tenv().get_template(filename).render( username=user, target=match.group(2), bot_prefix=self.bot_config.BOT_PREFIX, ) else: response = self.ERROR_MSG return response

true

1c37b83b83f4feed583f6c83a2af67c37998c475

1,350

py

Python

Cloud9.dev2/src/third_party/gyp/test/defines/gyptest-defines-env-regyp.py

twang15/BarrierFinder

c20ff99ffeeeabc1508682bc99ffb4c7659e7e9f

[ "MIT" ]

3

2019-02-12T04:14:39.000Z

2020-11-05T08:46:20.000Z

tools/gyp/test/defines/gyptest-defines-env-regyp.py

kans/birgo

d9aca7356933c4bb95f5649353acbc95e3083a57

[ "Apache-2.0" ]

null

tools/gyp/test/defines/gyptest-defines-env-regyp.py

kans/birgo

d9aca7356933c4bb95f5649353acbc95e3083a57

[ "Apache-2.0" ]

null

#!/usr/bin/env python # Copyright (c) 2011 Google Inc. All rights reserved. # Use of this source code is governed by a BSD-style license that can be # found in the LICENSE file. """ Verifies build of an executable with C++ define specified by a gyp define, and the use of the environment during regeneration when the gyp file changes. """ import os import TestGyp # Regenerating build files when a gyp file changes is currently only supported # by the make generator. test = TestGyp.TestGyp(formats=['make']) try: os.environ['GYP_DEFINES'] = 'value=50' test.run_gyp('defines.gyp') finally: # We clear the environ after calling gyp. When the auto-regeneration happens, # the same define should be reused anyway. Reset to empty string first in # case the platform doesn't support unsetenv. os.environ['GYP_DEFINES'] = '' del os.environ['GYP_DEFINES'] test.build('defines.gyp') expect = """\ FOO is defined VALUE is 1 2*PAREN_VALUE is 12 HASH_VALUE is a#1 """ test.run_built_executable('defines', stdout=expect) # Sleep so that the changed gyp file will have a newer timestamp than the # previously generated build files. test.sleep() test.write('defines.gyp', test.read('defines-env.gyp')) test.build('defines.gyp', test.ALL) expect = """\ VALUE is 50 """ test.run_built_executable('defines', stdout=expect) test.pass_test()

25.961538

80

0.737778

import os import TestGyp test = TestGyp.TestGyp(formats=['make']) try: os.environ['GYP_DEFINES'] = 'value=50' test.run_gyp('defines.gyp') finally: os.environ['GYP_DEFINES'] = '' del os.environ['GYP_DEFINES'] test.build('defines.gyp') expect = """\ FOO is defined VALUE is 1 2*PAREN_VALUE is 12 HASH_VALUE is a#1 """ test.run_built_executable('defines', stdout=expect) # Sleep so that the changed gyp file will have a newer timestamp than the # previously generated build files. test.sleep() test.write('defines.gyp', test.read('defines-env.gyp')) test.build('defines.gyp', test.ALL) expect = """\ VALUE is 50 """ test.run_built_executable('defines', stdout=expect) test.pass_test()

true

1c37b842c09e88da94ae6e385ae5c966222a46f9

513

py

Python

src/test.py

kynmh69/cotoha_test

6c01ca0477399a2a07bc36a694850ad3c3c40228

[ "MIT" ]

1

2020-03-14T14:02:57.000Z

src/test.py

kynmh69/cotoha_test

6c01ca0477399a2a07bc36a694850ad3c3c40228

[ "MIT" ]

null

src/test.py

kynmh69/cotoha_test

6c01ca0477399a2a07bc36a694850ad3c3c40228

[ "MIT" ]

null

import json from tkinter import Tk from gui.gui import Application from src.cotoha_api.cotoha_api import CotohaApi, CotohaApiResponse from src.logger.logger import logger_initialize, LoggerUtils EQUAL_STR = "=" * 20 if __name__ == "__main__": logger_initialize() logger = LoggerUtils.get_instance() logger.info(f"{EQUAL_STR} START {EQUAL_STR}") app = Application() app.create_window() app.create_sentence_form() app.master.mainloop() logger.info(f"{EQUAL_STR} END {EQUAL_STR}")

27

66

0.738791

import json from tkinter import Tk from gui.gui import Application from src.cotoha_api.cotoha_api import CotohaApi, CotohaApiResponse from src.logger.logger import logger_initialize, LoggerUtils EQUAL_STR = "=" * 20 if __name__ == "__main__": logger_initialize() logger = LoggerUtils.get_instance() logger.info(f"{EQUAL_STR} START {EQUAL_STR}") app = Application() app.create_window() app.create_sentence_form() app.master.mainloop() logger.info(f"{EQUAL_STR} END {EQUAL_STR}")

true

1c37b8c017e046ed568dd6da235c0bc53fc0dd6a

677

py

Python

trace_operator.py

6895mahfuzgit/Linear_Algebra_for_Machine_Learning

3f266391491d9ab99e53a3547900c6b1bd657af1

[ "Apache-2.0" ]

null

trace_operator.py

6895mahfuzgit/Linear_Algebra_for_Machine_Learning

3f266391491d9ab99e53a3547900c6b1bd657af1

[ "Apache-2.0" ]

null

trace_operator.py

6895mahfuzgit/Linear_Algebra_for_Machine_Learning

3f266391491d9ab99e53a3547900c6b1bd657af1

[ "Apache-2.0" ]

null

# -*- coding: utf-8 -*- """ Created on Fri Sep 17 02:09:53 2021 @author: Mahfuz_Shazol """ import numpy as np import torch as th A=np.array([[25,2], [5,4]]) A_trace=np.trace(A) print(A_trace) # Tr(A)=Tr(A.T) result1=np.trace(A) print(result1) result2=np.trace(A.T) print(result2) print('Tr(A)=Tr(A.T) Ans:',result1==result2) #Calculate Frobenius norm AF=(Tr(A A.T))**(1/2) A_p=th.tensor([ [-1,2], [3,-2], [5,7], ]) calculated_frobenius_norm=(th.trace(th.matmul(th.as_tensor(A),th.as_tensor(A.T))))**(1/2) print('calculated_frobenius_norm Ans:',calculated_frobenius_norm) norm_result=np.linalg.norm(A) print(norm_result)

13.54

89

0.635155

import numpy as np import torch as th A=np.array([[25,2], [5,4]]) A_trace=np.trace(A) print(A_trace) result1=np.trace(A) print(result1) result2=np.trace(A.T) print(result2) print('Tr(A)=Tr(A.T) Ans:',result1==result2) A_p=th.tensor([ [-1,2], [3,-2], [5,7], ]) calculated_frobenius_norm=(th.trace(th.matmul(th.as_tensor(A),th.as_tensor(A.T))))**(1/2) print('calculated_frobenius_norm Ans:',calculated_frobenius_norm) norm_result=np.linalg.norm(A) print(norm_result)

true

1c37b94b8d6ed6dc771e8357788977085e797788

2,143

py

Python

aliyun-python-sdk-mse/aliyunsdkmse/request/v20190531/DeleteNacosConfigRequest.py

jorsonzen/aliyun-openapi-python-sdk

0afbfa8e5f9e19455695aa799f7dcc1cd853d827

[ "Apache-2.0" ]

null

aliyun-python-sdk-mse/aliyunsdkmse/request/v20190531/DeleteNacosConfigRequest.py

jorsonzen/aliyun-openapi-python-sdk

0afbfa8e5f9e19455695aa799f7dcc1cd853d827

[ "Apache-2.0" ]

null

aliyun-python-sdk-mse/aliyunsdkmse/request/v20190531/DeleteNacosConfigRequest.py

jorsonzen/aliyun-openapi-python-sdk

0afbfa8e5f9e19455695aa799f7dcc1cd853d827

[ "Apache-2.0" ]

null

# Licensed to the Apache Software Foundation (ASF) 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 limitations # under the License. from aliyunsdkcore.request import RpcRequest from aliyunsdkmse.endpoint import endpoint_data class DeleteNacosConfigRequest(RpcRequest): def __init__(self): RpcRequest.__init__(self, 'mse', '2019-05-31', 'DeleteNacosConfig') self.set_method('POST') if hasattr(self, "endpoint_map"): setattr(self, "endpoint_map", endpoint_data.getEndpointMap()) if hasattr(self, "endpoint_regional"): setattr(self, "endpoint_regional", endpoint_data.getEndpointRegional()) def get_InstanceId(self): # String return self.get_query_params().get('InstanceId') def set_InstanceId(self, InstanceId): # String self.add_query_param('InstanceId', InstanceId) def get_DataId(self): # String return self.get_query_params().get('DataId') def set_DataId(self, DataId): # String self.add_query_param('DataId', DataId) def get_NamespaceId(self): # String return self.get_query_params().get('NamespaceId') def set_NamespaceId(self, NamespaceId): # String self.add_query_param('NamespaceId', NamespaceId) def get_Beta(self): # Boolean return self.get_query_params().get('Beta') def set_Beta(self, Beta): # Boolean self.add_query_param('Beta', Beta) def get_Group(self): # String return self.get_query_params().get('Group') def set_Group(self, Group): # String self.add_query_param('Group', Group)

36.322034

74

0.74895

from aliyunsdkcore.request import RpcRequest from aliyunsdkmse.endpoint import endpoint_data class DeleteNacosConfigRequest(RpcRequest): def __init__(self): RpcRequest.__init__(self, 'mse', '2019-05-31', 'DeleteNacosConfig') self.set_method('POST') if hasattr(self, "endpoint_map"): setattr(self, "endpoint_map", endpoint_data.getEndpointMap()) if hasattr(self, "endpoint_regional"): setattr(self, "endpoint_regional", endpoint_data.getEndpointRegional()) def get_InstanceId(self): return self.get_query_params().get('InstanceId') def set_InstanceId(self, InstanceId): self.add_query_param('InstanceId', InstanceId) def get_DataId(self): return self.get_query_params().get('DataId') def set_DataId(self, DataId): self.add_query_param('DataId', DataId) def get_NamespaceId(self): return self.get_query_params().get('NamespaceId') def set_NamespaceId(self, NamespaceId): self.add_query_param('NamespaceId', NamespaceId) def get_Beta(self): return self.get_query_params().get('Beta') def set_Beta(self, Beta): self.add_query_param('Beta', Beta) def get_Group(self): return self.get_query_params().get('Group') def set_Group(self, Group): self.add_query_param('Group', Group)

true

1c37bc1dc6d774ea34be94b34a72e489ac4ada8f

677

py

Python

scrapyd/scheduler.py

senohaha/jzSpiderNode

9ff725a00a25c74c4fc9b6096c7c4d8dd1de6ba4

[ "BSD-3-Clause" ]

null

scrapyd/scheduler.py

senohaha/jzSpiderNode

9ff725a00a25c74c4fc9b6096c7c4d8dd1de6ba4

[ "BSD-3-Clause" ]

null

scrapyd/scheduler.py

senohaha/jzSpiderNode

9ff725a00a25c74c4fc9b6096c7c4d8dd1de6ba4

[ "BSD-3-Clause" ]

null

# -*-coding: utf-8 -*- from zope.interface import implementer from .interfaces import ISpiderScheduler from .utils import get_spider_queues @implementer(ISpiderScheduler) class SpiderScheduler(object): def __init__(self, config): self.config = config self.update_projects() def schedule(self, project, spider_name, **spider_args): q = self.queues[project] # self.queues： {u'dfy': <scrapyd.spiderqueue.SqliteSpiderQueue object at 0x1c3df90>} q.add(spider_name, **spider_args) def list_projects(self): return self.queues.keys() def update_projects(self): self.queues = get_spider_queues(self.config)

28.208333

93

0.700148

from zope.interface import implementer from .interfaces import ISpiderScheduler from .utils import get_spider_queues @implementer(ISpiderScheduler) class SpiderScheduler(object): def __init__(self, config): self.config = config self.update_projects() def schedule(self, project, spider_name, **spider_args): q = self.queues[project] q.add(spider_name, **spider_args) def list_projects(self): return self.queues.keys() def update_projects(self): self.queues = get_spider_queues(self.config)

true

1c37bc73f791a3975a6afa5c87788d3edc839815

692

py

Python

strategy/minimize.py

shimomura314/non-bit-reversi

587aaeea0476c5c6339b6c96f7525c66cbc5321f

[ "MIT" ]

null

strategy/minimize.py

shimomura314/non-bit-reversi

587aaeea0476c5c6339b6c96f7525c66cbc5321f

[ "MIT" ]

null

strategy/minimize.py

shimomura314/non-bit-reversi

587aaeea0476c5c6339b6c96f7525c66cbc5321f

[ "MIT" ]

null

"""Various strategies for othello. """ import random class Minimize: """Put disk to minimize number of one's disks.""" def __init__(self): return def put_disk(self, othello): """Put disk to minimize number of one's disks.""" min_strategy = [] min_merit = float('inf') for candidate in othello.reversible.keys(): if min_merit > len(othello.reversible[candidate]): min_strategy = [candidate] min_merit = len(othello.reversible[candidate]) elif min_merit == len(othello.reversible[candidate]): min_strategy.append(candidate) return random.choice(min_strategy)

31.454545

65

0.614162

import random class Minimize: def __init__(self): return def put_disk(self, othello): min_strategy = [] min_merit = float('inf') for candidate in othello.reversible.keys(): if min_merit > len(othello.reversible[candidate]): min_strategy = [candidate] min_merit = len(othello.reversible[candidate]) elif min_merit == len(othello.reversible[candidate]): min_strategy.append(candidate) return random.choice(min_strategy)

true

1c37bc84fb69610331ac21e64650183042302f84

2,438

py

Python

Organise-Files-According-To-Their-Extensions/script_dirs.py

A-kriti/Amazing-Python-Scripts

ebf607fe39e6d9e61f30ec3439fc8d6ab1f736b9

[ "MIT" ]

930

2020-09-05T22:07:28.000Z

2022-03-30T07:56:18.000Z

Organise-Files-According-To-Their-Extensions/script_dirs.py

maheshdbabar9340/Amazing-Python-Scripts

e2272048cbe49b4bda5072bbdd8479739bb6c18d

[ "MIT" ]

893

2020-09-04T07:57:24.000Z

2022-02-08T02:12:26.000Z

Organise-Files-According-To-Their-Extensions/script_dirs.py

maheshdbabar9340/Amazing-Python-Scripts

e2272048cbe49b4bda5072bbdd8479739bb6c18d

[ "MIT" ]

497

2020-09-05T08:16:24.000Z

2022-03-31T00:55:57.000Z

import os from pathlib import Path import sys # Taking input print_string = """ Type Path of the directory OR Press enter for running the script on current directory: OR Type quit """ print(print_string + "\n\n") input_path = input("Input:") print("\n\n") # Script will terminate if input is 'quit' if input_path == "quit": sys.exit(1) # If nothing is entered then current working directory will be taken as the input path if input_path == "": input_path = os.getcwd() input_path = Path(input_path) # Changing the working directory to input path os.chdir(input_path) # Creates a dictionary "dic" with key,value pairs where key is extension and value is no. of files with that extension dic = {} for file in os.listdir(os.getcwd()): if os.path.isfile(file): extension = file.split(".")[-1] dic[extension] = dic.get(extension, 0) + 1 for key in dic: print(f"There are {dic[key]} files file with extension {key}") print("\n\n") # assigning a variable named current Path of current working directory just for simplicity. # could have used input_path too current = Path(os.getcwd()) ''' When this script would run the structure of the current directory would change.Hence, we are assigning list_dir variable the files and dirs in current working directory which the script would modify ''' list_dir = os.listdir(current) # keys of dic are extensions of the file for key in dic: # try except block for making directory if it doesn't exists already try: os.mkdir(key) except: print( f"directory named {key} already exists so it won't be overwrited \n" ) # goes through the files in list_dir # we are not using os.listdir() as the directory structure will change during the execution for file in list_dir: if file.split(".")[-1] == key and os.path.isfile(file): # prints absolute path of the file print(os.path.abspath(file)) # Renames the path of the file or moves the file in to the newly created directory Path.rename(Path(os.path.abspath(file)), current / Path("./{}/".format(key) + file)) # This block just prints a note and the current structure of the directory print( "\n Script has organised files as per their extensions into different directories! \n" ) for file in os.listdir(os.getcwd()): if not (os.path.isfile(file)): print(file)

31.25641

118

0.684988

import os from pathlib import Path import sys print_string = """ Type Path of the directory OR Press enter for running the script on current directory: OR Type quit """ print(print_string + "\n\n") input_path = input("Input:") print("\n\n") if input_path == "quit": sys.exit(1) if input_path == "": input_path = os.getcwd() input_path = Path(input_path) os.chdir(input_path) dic = {} for file in os.listdir(os.getcwd()): if os.path.isfile(file): extension = file.split(".")[-1] dic[extension] = dic.get(extension, 0) + 1 for key in dic: print(f"There are {dic[key]} files file with extension {key}") print("\n\n") current = Path(os.getcwd()) list_dir = os.listdir(current) for key in dic: try: os.mkdir(key) except: print( f"directory named {key} already exists so it won't be overwrited \n" ) for file in list_dir: if file.split(".")[-1] == key and os.path.isfile(file): print(os.path.abspath(file)) Path.rename(Path(os.path.abspath(file)), current / Path("./{}/".format(key) + file)) print( "\n Script has organised files as per their extensions into different directories! \n" ) for file in os.listdir(os.getcwd()): if not (os.path.isfile(file)): print(file)

true

1c37bcdaebfc6149a8029cccfbac3b7468196323

6,554

py

Python

awswrangler/s3/_write_dataset.py

isichei/aws-data-wrangler

0ce3836000bc5f4b5f7adffdb81392cdcf135b7a

[ "Apache-2.0" ]

2

2021-10-24T01:01:08.000Z

2022-01-12T13:23:44.000Z

awswrangler/s3/_write_dataset.py

isichei/aws-data-wrangler

0ce3836000bc5f4b5f7adffdb81392cdcf135b7a

[ "Apache-2.0" ]

67

2021-01-15T15:00:37.000Z

2022-03-21T09:27:42.000Z

awswrangler/s3/_write_dataset.py

isichei/aws-data-wrangler

0ce3836000bc5f4b5f7adffdb81392cdcf135b7a

[ "Apache-2.0" ]

3

2020-12-29T17:27:38.000Z

2021-01-15T13:47:25.000Z

"""Amazon S3 Write Dataset (PRIVATE).""" import logging from typing import Any, Callable, Dict, List, Optional, Tuple, Union import boto3 import numpy as np import pandas as pd from awswrangler import exceptions from awswrangler.s3._delete import delete_objects from awswrangler.s3._write_concurrent import _WriteProxy _logger: logging.Logger = logging.getLogger(__name__) def _to_partitions( func: Callable[..., List[str]], concurrent_partitioning: bool, df: pd.DataFrame, path_root: str, use_threads: bool, mode: str, partition_cols: List[str], bucketing_info: Optional[Tuple[List[str], int]], filename_prefix: str, boto3_session: boto3.Session, **func_kwargs: Any, ) -> Tuple[List[str], Dict[str, List[str]]]: partitions_values: Dict[str, List[str]] = {} proxy: _WriteProxy = _WriteProxy(use_threads=concurrent_partitioning) for keys, subgroup in df.groupby(by=partition_cols, observed=True): subgroup = subgroup.drop(partition_cols, axis="columns") keys = (keys,) if not isinstance(keys, tuple) else keys subdir = "/".join([f"{name}={val}" for name, val in zip(partition_cols, keys)]) prefix: str = f"{path_root}{subdir}/" if mode == "overwrite_partitions": delete_objects( path=prefix, use_threads=use_threads, boto3_session=boto3_session, s3_additional_kwargs=func_kwargs.get("s3_additional_kwargs"), ) if bucketing_info: _to_buckets( func=func, df=subgroup, path_root=prefix, bucketing_info=bucketing_info, boto3_session=boto3_session, use_threads=use_threads, proxy=proxy, filename_prefix=filename_prefix, **func_kwargs, ) else: proxy.write( func=func, df=subgroup, path_root=prefix, filename_prefix=filename_prefix, boto3_session=boto3_session, use_threads=use_threads, **func_kwargs, ) partitions_values[prefix] = [str(k) for k in keys] paths: List[str] = proxy.close() # blocking return paths, partitions_values def _to_buckets( func: Callable[..., List[str]], df: pd.DataFrame, path_root: str, bucketing_info: Tuple[List[str], int], filename_prefix: str, boto3_session: boto3.Session, use_threads: bool, proxy: Optional[_WriteProxy] = None, **func_kwargs: Any, ) -> List[str]: _proxy: _WriteProxy = proxy if proxy else _WriteProxy(use_threads=False) bucket_number_series = df.astype("O").apply( lambda row: _get_bucket_number(bucketing_info[1], [row[col_name] for col_name in bucketing_info[0]]), axis="columns", ) for bucket_number, subgroup in df.groupby(by=bucket_number_series, observed=True): _proxy.write( func=func, df=subgroup, path_root=path_root, filename_prefix=f"{filename_prefix}_bucket-{bucket_number:05d}", boto3_session=boto3_session, use_threads=use_threads, **func_kwargs, ) if proxy: return [] paths: List[str] = _proxy.close() # blocking return paths def _get_bucket_number(number_of_buckets: int, values: List[Union[str, int, bool]]) -> int: hash_code = 0 for value in values: hash_code = 31 * hash_code + _get_value_hash(value) return hash_code % number_of_buckets def _get_value_hash(value: Union[str, int, bool]) -> int: if isinstance(value, (int, np.int_)): return int(value) if isinstance(value, (str, np.str_)): value_hash = 0 for byte in value.encode(): value_hash = value_hash * 31 + byte return value_hash if isinstance(value, (bool, np.bool_)): return int(value) raise exceptions.InvalidDataFrame( "Column specified for bucketing contains invalid data type. Only string, int and bool are supported." ) def _to_dataset( func: Callable[..., List[str]], concurrent_partitioning: bool, df: pd.DataFrame, path_root: str, filename_prefix: str, index: bool, use_threads: bool, mode: str, partition_cols: Optional[List[str]], bucketing_info: Optional[Tuple[List[str], int]], boto3_session: boto3.Session, **func_kwargs: Any, ) -> Tuple[List[str], Dict[str, List[str]]]: path_root = path_root if path_root.endswith("/") else f"{path_root}/" # Evaluate mode if mode not in ["append", "overwrite", "overwrite_partitions"]: raise exceptions.InvalidArgumentValue( f"{mode} is a invalid mode, please use append, overwrite or overwrite_partitions." ) if (mode == "overwrite") or ((mode == "overwrite_partitions") and (not partition_cols)): delete_objects( path=path_root, use_threads=use_threads, boto3_session=boto3_session, s3_additional_kwargs=func_kwargs.get("s3_additional_kwargs"), ) # Writing partitions_values: Dict[str, List[str]] = {} paths: List[str] if partition_cols: paths, partitions_values = _to_partitions( func=func, concurrent_partitioning=concurrent_partitioning, df=df, path_root=path_root, use_threads=use_threads, mode=mode, bucketing_info=bucketing_info, filename_prefix=filename_prefix, partition_cols=partition_cols, boto3_session=boto3_session, index=index, **func_kwargs, ) elif bucketing_info: paths = _to_buckets( func=func, df=df, path_root=path_root, use_threads=use_threads, bucketing_info=bucketing_info, filename_prefix=filename_prefix, boto3_session=boto3_session, index=index, **func_kwargs, ) else: paths = func( df=df, path_root=path_root, filename_prefix=filename_prefix, use_threads=use_threads, boto3_session=boto3_session, index=index, **func_kwargs, ) _logger.debug("paths: %s", paths) _logger.debug("partitions_values: %s", partitions_values) return paths, partitions_values

32.606965

109

0.614434

import logging from typing import Any, Callable, Dict, List, Optional, Tuple, Union import boto3 import numpy as np import pandas as pd from awswrangler import exceptions from awswrangler.s3._delete import delete_objects from awswrangler.s3._write_concurrent import _WriteProxy _logger: logging.Logger = logging.getLogger(__name__) def _to_partitions( func: Callable[..., List[str]], concurrent_partitioning: bool, df: pd.DataFrame, path_root: str, use_threads: bool, mode: str, partition_cols: List[str], bucketing_info: Optional[Tuple[List[str], int]], filename_prefix: str, boto3_session: boto3.Session, **func_kwargs: Any, ) -> Tuple[List[str], Dict[str, List[str]]]: partitions_values: Dict[str, List[str]] = {} proxy: _WriteProxy = _WriteProxy(use_threads=concurrent_partitioning) for keys, subgroup in df.groupby(by=partition_cols, observed=True): subgroup = subgroup.drop(partition_cols, axis="columns") keys = (keys,) if not isinstance(keys, tuple) else keys subdir = "/".join([f"{name}={val}" for name, val in zip(partition_cols, keys)]) prefix: str = f"{path_root}{subdir}/" if mode == "overwrite_partitions": delete_objects( path=prefix, use_threads=use_threads, boto3_session=boto3_session, s3_additional_kwargs=func_kwargs.get("s3_additional_kwargs"), ) if bucketing_info: _to_buckets( func=func, df=subgroup, path_root=prefix, bucketing_info=bucketing_info, boto3_session=boto3_session, use_threads=use_threads, proxy=proxy, filename_prefix=filename_prefix, **func_kwargs, ) else: proxy.write( func=func, df=subgroup, path_root=prefix, filename_prefix=filename_prefix, boto3_session=boto3_session, use_threads=use_threads, **func_kwargs, ) partitions_values[prefix] = [str(k) for k in keys] paths: List[str] = proxy.close() return paths, partitions_values def _to_buckets( func: Callable[..., List[str]], df: pd.DataFrame, path_root: str, bucketing_info: Tuple[List[str], int], filename_prefix: str, boto3_session: boto3.Session, use_threads: bool, proxy: Optional[_WriteProxy] = None, **func_kwargs: Any, ) -> List[str]: _proxy: _WriteProxy = proxy if proxy else _WriteProxy(use_threads=False) bucket_number_series = df.astype("O").apply( lambda row: _get_bucket_number(bucketing_info[1], [row[col_name] for col_name in bucketing_info[0]]), axis="columns", ) for bucket_number, subgroup in df.groupby(by=bucket_number_series, observed=True): _proxy.write( func=func, df=subgroup, path_root=path_root, filename_prefix=f"{filename_prefix}_bucket-{bucket_number:05d}", boto3_session=boto3_session, use_threads=use_threads, **func_kwargs, ) if proxy: return [] paths: List[str] = _proxy.close() return paths def _get_bucket_number(number_of_buckets: int, values: List[Union[str, int, bool]]) -> int: hash_code = 0 for value in values: hash_code = 31 * hash_code + _get_value_hash(value) return hash_code % number_of_buckets def _get_value_hash(value: Union[str, int, bool]) -> int: if isinstance(value, (int, np.int_)): return int(value) if isinstance(value, (str, np.str_)): value_hash = 0 for byte in value.encode(): value_hash = value_hash * 31 + byte return value_hash if isinstance(value, (bool, np.bool_)): return int(value) raise exceptions.InvalidDataFrame( "Column specified for bucketing contains invalid data type. Only string, int and bool are supported." ) def _to_dataset( func: Callable[..., List[str]], concurrent_partitioning: bool, df: pd.DataFrame, path_root: str, filename_prefix: str, index: bool, use_threads: bool, mode: str, partition_cols: Optional[List[str]], bucketing_info: Optional[Tuple[List[str], int]], boto3_session: boto3.Session, **func_kwargs: Any, ) -> Tuple[List[str], Dict[str, List[str]]]: path_root = path_root if path_root.endswith("/") else f"{path_root}/" if mode not in ["append", "overwrite", "overwrite_partitions"]: raise exceptions.InvalidArgumentValue( f"{mode} is a invalid mode, please use append, overwrite or overwrite_partitions." ) if (mode == "overwrite") or ((mode == "overwrite_partitions") and (not partition_cols)): delete_objects( path=path_root, use_threads=use_threads, boto3_session=boto3_session, s3_additional_kwargs=func_kwargs.get("s3_additional_kwargs"), ) partitions_values: Dict[str, List[str]] = {} paths: List[str] if partition_cols: paths, partitions_values = _to_partitions( func=func, concurrent_partitioning=concurrent_partitioning, df=df, path_root=path_root, use_threads=use_threads, mode=mode, bucketing_info=bucketing_info, filename_prefix=filename_prefix, partition_cols=partition_cols, boto3_session=boto3_session, index=index, **func_kwargs, ) elif bucketing_info: paths = _to_buckets( func=func, df=df, path_root=path_root, use_threads=use_threads, bucketing_info=bucketing_info, filename_prefix=filename_prefix, boto3_session=boto3_session, index=index, **func_kwargs, ) else: paths = func( df=df, path_root=path_root, filename_prefix=filename_prefix, use_threads=use_threads, boto3_session=boto3_session, index=index, **func_kwargs, ) _logger.debug("paths: %s", paths) _logger.debug("partitions_values: %s", partitions_values) return paths, partitions_values

true

1c37bd002df98cf406408b1b8384bf64c26f3e96

4,474

py

Python

tests/apps/info/memo/commands_test.py

item4/yui

8628d0d54b94ada3cbe7d1b0f624063258bad10a

[ "MIT" ]

36

2017-06-12T01:09:46.000Z

2021-01-31T17:57:41.000Z

tests/apps/info/memo/commands_test.py

item4/yui

8628d0d54b94ada3cbe7d1b0f624063258bad10a

[ "MIT" ]

145

2017-06-21T13:31:29.000Z

2021-06-20T01:01:30.000Z

tests/apps/info/memo/commands_test.py

item4/yui

8628d0d54b94ada3cbe7d1b0f624063258bad10a

[ "MIT" ]

21

2017-07-24T15:53:19.000Z

2021-12-23T04:18:31.000Z

import pytest from yui.apps.info.memo.commands import memo_add from yui.apps.info.memo.commands import memo_delete from yui.apps.info.memo.commands import memo_show from yui.apps.info.memo.models import Memo from yui.orm.utils import get_count @pytest.mark.asyncio async def test_memo_flow(bot, fx_sess): keyword1 = '키리토' keyword2 = '밥' text1 = '키리가야 카즈토의 게임 아이디' text2 = '귀엽다' text3 = '먹어야한다' bot.add_channel('C1', 'test') bot.add_user('U1', 'tester') event = bot.create_message('C1', 'U1') await memo_show(bot, event, fx_sess, keyword1) said = bot.call_queue.pop(0) assert said.method == 'chat.postMessage' assert said.data['channel'] == 'C1' assert said.data['text'] == f'`{keyword1}`란 이름을 가진 기억 레코드가 없어요!' assert ( get_count( fx_sess.query(Memo).filter_by(keyword=keyword1), ) == 0 ) await memo_show(bot, event, fx_sess, keyword2) said = bot.call_queue.pop(0) assert said.method == 'chat.postMessage' assert said.data['channel'] == 'C1' assert said.data['text'] == f'`{keyword2}`이란 이름을 가진 기억 레코드가 없어요!' assert ( get_count( fx_sess.query(Memo).filter_by(keyword=keyword2), ) == 0 ) await memo_add(bot, event, fx_sess, keyword1, text1) said = bot.call_queue.pop(0) assert said.method == 'chat.postMessage' assert said.data['channel'] == 'C1' assert said.data['text'] == f'`{keyword1}`로 기억 레코드를 생성했어요!' assert ( get_count( fx_sess.query(Memo).filter_by(keyword=keyword1), ) == 1 ) await memo_add(bot, event, fx_sess, keyword2, text3) said = bot.call_queue.pop(0) assert said.method == 'chat.postMessage' assert said.data['channel'] == 'C1' assert said.data['text'] == f'`{keyword2}`으로 기억 레코드를 생성했어요!' assert ( get_count( fx_sess.query(Memo).filter_by(keyword=keyword2), ) == 1 ) await memo_add(bot, event, fx_sess, keyword1, text2) said = bot.call_queue.pop(0) assert said.method == 'chat.postMessage' assert said.data['channel'] == 'C1' assert said.data['text'] == f'`{keyword1}`로 기억 레코드를 생성했어요!' assert ( get_count( fx_sess.query(Memo).filter_by(keyword=keyword1), ) == 2 ) await memo_show(bot, event, fx_sess, keyword1) said = bot.call_queue.pop(0) assert said.method == 'chat.postMessage' assert said.data['channel'] == 'C1' assert said.data['text'] == f'`{keyword1}`: {text1} | {text2}' await memo_show(bot, event, fx_sess, keyword2) said = bot.call_queue.pop(0) assert said.method == 'chat.postMessage' assert said.data['channel'] == 'C1' assert said.data['text'] == f'`{keyword2}`: {text3}' await memo_delete(bot, event, fx_sess, keyword1) said = bot.call_queue.pop(0) assert said.method == 'chat.postMessage' assert said.data['channel'] == 'C1' assert said.data['text'] == f'`{keyword1}`에 관한 기억 레코드를 모두 삭제했어요!' assert ( get_count( fx_sess.query(Memo).filter_by(keyword=keyword1), ) == 0 ) assert ( get_count( fx_sess.query(Memo).filter_by(keyword=keyword2), ) == 1 ) await memo_delete(bot, event, fx_sess, keyword2) said = bot.call_queue.pop(0) assert said.method == 'chat.postMessage' assert said.data['channel'] == 'C1' assert said.data['text'] == f'`{keyword2}`에 관한 기억 레코드를 모두 삭제했어요!' assert ( get_count( fx_sess.query(Memo).filter_by(keyword=keyword1), ) == 0 ) assert ( get_count( fx_sess.query(Memo).filter_by(keyword=keyword2), ) == 0 ) @pytest.mark.asyncio async def test_length_limit(bot, fx_sess): bot.add_channel('C1', 'test') bot.add_user('U1', 'tester') event = bot.create_message('C1', 'U1') await memo_add(bot, event, fx_sess, 'long' * 100, 'test') said = bot.call_queue.pop(0) assert said.method == 'chat.postMessage' assert said.data['channel'] == 'C1' assert said.data['text'] == '기억하려는 키워드가 너무 길어요! 20자 이하의 키워드만 가능해요!' await memo_add(bot, event, fx_sess, 'test', 'long' * 1000) said = bot.call_queue.pop(0) assert said.method == 'chat.postMessage' assert said.data['channel'] == 'C1' assert said.data['text'] == '기억하려는 내용이 너무 길어요! 500자 이하의 내용만 가능해요!'

26.951807

71

0.602816

import pytest from yui.apps.info.memo.commands import memo_add from yui.apps.info.memo.commands import memo_delete from yui.apps.info.memo.commands import memo_show from yui.apps.info.memo.models import Memo from yui.orm.utils import get_count @pytest.mark.asyncio async def test_memo_flow(bot, fx_sess): keyword1 = '키리토' keyword2 = '밥' text1 = '키리가야 카즈토의 게임 아이디' text2 = '귀엽다' text3 = '먹어야한다' bot.add_channel('C1', 'test') bot.add_user('U1', 'tester') event = bot.create_message('C1', 'U1') await memo_show(bot, event, fx_sess, keyword1) said = bot.call_queue.pop(0) assert said.method == 'chat.postMessage' assert said.data['channel'] == 'C1' assert said.data['text'] == f'`{keyword1}`란 이름을 가진 기억 레코드가 없어요!' assert ( get_count( fx_sess.query(Memo).filter_by(keyword=keyword1), ) == 0 ) await memo_show(bot, event, fx_sess, keyword2) said = bot.call_queue.pop(0) assert said.method == 'chat.postMessage' assert said.data['channel'] == 'C1' assert said.data['text'] == f'`{keyword2}`이란 이름을 가진 기억 레코드가 없어요!' assert ( get_count( fx_sess.query(Memo).filter_by(keyword=keyword2), ) == 0 ) await memo_add(bot, event, fx_sess, keyword1, text1) said = bot.call_queue.pop(0) assert said.method == 'chat.postMessage' assert said.data['channel'] == 'C1' assert said.data['text'] == f'`{keyword1}`로 기억 레코드를 생성했어요!' assert ( get_count( fx_sess.query(Memo).filter_by(keyword=keyword1), ) == 1 ) await memo_add(bot, event, fx_sess, keyword2, text3) said = bot.call_queue.pop(0) assert said.method == 'chat.postMessage' assert said.data['channel'] == 'C1' assert said.data['text'] == f'`{keyword2}`으로 기억 레코드를 생성했어요!' assert ( get_count( fx_sess.query(Memo).filter_by(keyword=keyword2), ) == 1 ) await memo_add(bot, event, fx_sess, keyword1, text2) said = bot.call_queue.pop(0) assert said.method == 'chat.postMessage' assert said.data['channel'] == 'C1' assert said.data['text'] == f'`{keyword1}`로 기억 레코드를 생성했어요!' assert ( get_count( fx_sess.query(Memo).filter_by(keyword=keyword1), ) == 2 ) await memo_show(bot, event, fx_sess, keyword1) said = bot.call_queue.pop(0) assert said.method == 'chat.postMessage' assert said.data['channel'] == 'C1' assert said.data['text'] == f'`{keyword1}`: {text1} | {text2}' await memo_show(bot, event, fx_sess, keyword2) said = bot.call_queue.pop(0) assert said.method == 'chat.postMessage' assert said.data['channel'] == 'C1' assert said.data['text'] == f'`{keyword2}`: {text3}' await memo_delete(bot, event, fx_sess, keyword1) said = bot.call_queue.pop(0) assert said.method == 'chat.postMessage' assert said.data['channel'] == 'C1' assert said.data['text'] == f'`{keyword1}`에 관한 기억 레코드를 모두 삭제했어요!' assert ( get_count( fx_sess.query(Memo).filter_by(keyword=keyword1), ) == 0 ) assert ( get_count( fx_sess.query(Memo).filter_by(keyword=keyword2), ) == 1 ) await memo_delete(bot, event, fx_sess, keyword2) said = bot.call_queue.pop(0) assert said.method == 'chat.postMessage' assert said.data['channel'] == 'C1' assert said.data['text'] == f'`{keyword2}`에 관한 기억 레코드를 모두 삭제했어요!' assert ( get_count( fx_sess.query(Memo).filter_by(keyword=keyword1), ) == 0 ) assert ( get_count( fx_sess.query(Memo).filter_by(keyword=keyword2), ) == 0 ) @pytest.mark.asyncio async def test_length_limit(bot, fx_sess): bot.add_channel('C1', 'test') bot.add_user('U1', 'tester') event = bot.create_message('C1', 'U1') await memo_add(bot, event, fx_sess, 'long' * 100, 'test') said = bot.call_queue.pop(0) assert said.method == 'chat.postMessage' assert said.data['channel'] == 'C1' assert said.data['text'] == '기억하려는 키워드가 너무 길어요! 20자 이하의 키워드만 가능해요!' await memo_add(bot, event, fx_sess, 'test', 'long' * 1000) said = bot.call_queue.pop(0) assert said.method == 'chat.postMessage' assert said.data['channel'] == 'C1' assert said.data['text'] == '기억하려는 내용이 너무 길어요! 500자 이하의 내용만 가능해요!'

true

1c37bd1b5ff673772f12755c5de44777390b17f0

2,664

py

Python

PythonClient/car/legacy_hello_car.py

whatseven/AirSim

fe7e4e7c782cf1077594c1ee6cc1bbfec3f66bd1

[ "MIT" ]

7

2020-05-22T18:00:19.000Z

2021-01-07T08:31:19.000Z

PythonClient/car/legacy_hello_car.py

whatseven/AirSim

fe7e4e7c782cf1077594c1ee6cc1bbfec3f66bd1

[ "MIT" ]

4

2020-08-21T07:48:06.000Z

2021-03-14T21:06:41.000Z

PythonClient/car/legacy_hello_car.py

whatseven/AirSim

fe7e4e7c782cf1077594c1ee6cc1bbfec3f66bd1

[ "MIT" ]

7

2020-05-22T20:08:22.000Z

2021-01-22T09:39:17.000Z

""" For connecting to the AirSim drone environment and testing API functionality """ import os import tempfile import pprint import setup_path import airsim # connect to the AirSim simulator client = airsim.MultirotorClient() client.confirmConnection() client.enableApiControl(True) client.armDisarm(True) state = client.getMultirotorState() s = pprint.pformat(state) print("state: %s" % s) airsim.wait_key('Press any key to takeoff') client.takeoff() state = client.getMultirotorState() print("state: %s" % pprint.pformat(state)) airsim.wait_key('Press any key to move vehicle to (-10, 10, -10) at 5 m/s') client.moveToPosition(-10, 10, -10, 5) client.hover() state = client.getMultirotorState() print("state: %s" % pprint.pformat(state)) airsim.wait_key('Press any key to take images') # get camera images from the car responses = client.simGetImages([ ImageRequest(0, airsim.AirSimImageType.DepthVis), #depth visualiztion image ImageRequest(1, airsim.AirSimImageType.DepthPerspective, True), #depth in perspective projection ImageRequest(1, airsim.AirSimImageType.Scene), #scene vision image in png format ImageRequest(1, airsim.AirSimImageType.Scene, False, False)]) #scene vision image in uncompressed RGB array print('Retrieved images: %d' % len(responses)) tmp_dir = os.path.join(tempfile.gettempdir(), "airsim_drone") print ("Saving images to %s" % tmp_dir) try: os.makedirs(tmp_dir) except OSError: if not os.path.isdir(tmp_dir): raise for idx, response in enumerate(responses): filename = os.path.join(tmp_dir, str(idx)) if response.pixels_as_float: print("Type %d, size %d" % (response.image_type, len(response.image_data_float))) AirSimClientBase.write_pfm(os.path.normpath(filename + '.pfm'), AirSimClientBase.getPfmArray(response)) elif response.compress: #png format print("Type %d, size %d" % (response.image_type, len(response.image_data_uint8))) AirSimClientBase.write_file(os.path.normpath(filename + '.png'), response.image_data_uint8) else: #uncompressed array print("Type %d, size %d" % (response.image_type, len(response.image_data_uint8))) img1d = np.fromstring(response.image_data_uint8, dtype=np.uint8) #get numpy array img_rgb = img1d.reshape(response.height, response.width, 3) #reshape array to 3 channel image array H X W X 3 AirSimClientBase.write_png(os.path.normpath(filename + '.png'), img_rgb) #write to png AirSimClientBase.wait_key('Press any key to reset to original state') client.armDisarm(False) client.reset() # that's enough fun for now. let's quit cleanly client.enableApiControl(False)

34.597403

117

0.737988

import os import tempfile import pprint import setup_path import airsim client = airsim.MultirotorClient() client.confirmConnection() client.enableApiControl(True) client.armDisarm(True) state = client.getMultirotorState() s = pprint.pformat(state) print("state: %s" % s) airsim.wait_key('Press any key to takeoff') client.takeoff() state = client.getMultirotorState() print("state: %s" % pprint.pformat(state)) airsim.wait_key('Press any key to move vehicle to (-10, 10, -10) at 5 m/s') client.moveToPosition(-10, 10, -10, 5) client.hover() state = client.getMultirotorState() print("state: %s" % pprint.pformat(state)) airsim.wait_key('Press any key to take images') responses = client.simGetImages([ ImageRequest(0, airsim.AirSimImageType.DepthVis), ImageRequest(1, airsim.AirSimImageType.DepthPerspective, True), ImageRequest(1, airsim.AirSimImageType.Scene), ImageRequest(1, airsim.AirSimImageType.Scene, False, False)]) print('Retrieved images: %d' % len(responses)) tmp_dir = os.path.join(tempfile.gettempdir(), "airsim_drone") print ("Saving images to %s" % tmp_dir) try: os.makedirs(tmp_dir) except OSError: if not os.path.isdir(tmp_dir): raise for idx, response in enumerate(responses): filename = os.path.join(tmp_dir, str(idx)) if response.pixels_as_float: print("Type %d, size %d" % (response.image_type, len(response.image_data_float))) AirSimClientBase.write_pfm(os.path.normpath(filename + '.pfm'), AirSimClientBase.getPfmArray(response)) elif response.compress: print("Type %d, size %d" % (response.image_type, len(response.image_data_uint8))) AirSimClientBase.write_file(os.path.normpath(filename + '.png'), response.image_data_uint8) else: print("Type %d, size %d" % (response.image_type, len(response.image_data_uint8))) img1d = np.fromstring(response.image_data_uint8, dtype=np.uint8) img_rgb = img1d.reshape(response.height, response.width, 3) AirSimClientBase.write_png(os.path.normpath(filename + '.png'), img_rgb) AirSimClientBase.wait_key('Press any key to reset to original state') client.armDisarm(False) client.reset() client.enableApiControl(False)

true

1c37bdc53354937df90dffa8566038d87a1e44cb

16,065

py

Python

geopandas/tests/test_extension_array.py

standakozak/geopandas

9f5413f8e992472d89d312cc4853d74c76fbbdf1

[ "BSD-3-Clause" ]

2,914

2015-01-01T14:27:43.000Z

2022-03-31T22:26:39.000Z

geopandas/tests/test_extension_array.py

standakozak/geopandas

9f5413f8e992472d89d312cc4853d74c76fbbdf1

[ "BSD-3-Clause" ]

2,040

2015-01-16T11:34:26.000Z

2022-03-31T12:13:39.000Z

geopandas/tests/test_extension_array.py

standakozak/geopandas

9f5413f8e992472d89d312cc4853d74c76fbbdf1

[ "BSD-3-Clause" ]

758

2015-01-21T20:23:32.000Z

2022-03-31T17:22:53.000Z

""" This file contains a minimal set of tests for compliance with the extension array interface test suite (by inheriting the pandas test suite), and should contain no other tests. Other tests (eg related to the spatial functionality or integration with GeoSeries/GeoDataFrame) should be added to test_array.py and others. The tests in this file are inherited from the BaseExtensionTests, and only minimal tweaks should be applied to get the tests passing (by overwriting a parent method). A set of fixtures are defined to provide data for the tests (the fixtures expected to be available to pytest by the inherited pandas tests). """ import operator import numpy as np from numpy.testing import assert_array_equal import pandas as pd from pandas.tests.extension import base as extension_tests import shapely.geometry from geopandas.array import GeometryArray, GeometryDtype, from_shapely from geopandas._compat import ignore_shapely2_warnings import pytest # ----------------------------------------------------------------------------- # Compat with extension tests in older pandas versions # ----------------------------------------------------------------------------- not_yet_implemented = pytest.mark.skip(reason="Not yet implemented") no_sorting = pytest.mark.skip(reason="Sorting not supported") # ----------------------------------------------------------------------------- # Required fixtures # ----------------------------------------------------------------------------- @pytest.fixture def dtype(): """A fixture providing the ExtensionDtype to validate.""" return GeometryDtype() def make_data(): a = np.empty(100, dtype=object) with ignore_shapely2_warnings(): a[:] = [shapely.geometry.Point(i, i) for i in range(100)] ga = from_shapely(a) return ga @pytest.fixture def data(): """Length-100 array for this type. * data[0] and data[1] should both be non missing * data[0] and data[1] should not be equal """ return make_data() @pytest.fixture def data_for_twos(): """Length-100 array in which all the elements are two.""" raise NotImplementedError @pytest.fixture def data_missing(): """Length-2 array with [NA, Valid]""" return from_shapely([None, shapely.geometry.Point(1, 1)]) @pytest.fixture(params=["data", "data_missing"]) def all_data(request, data, data_missing): """Parametrized fixture giving 'data' and 'data_missing'""" if request.param == "data": return data elif request.param == "data_missing": return data_missing @pytest.fixture def data_repeated(data): """ Generate many datasets. Parameters ---------- data : fixture implementing `data` Returns ------- Callable[[int], Generator]: A callable that takes a `count` argument and returns a generator yielding `count` datasets. """ def gen(count): for _ in range(count): yield data return gen @pytest.fixture def data_for_sorting(): """Length-3 array with a known sort order. This should be three items [B, C, A] with A < B < C """ raise NotImplementedError @pytest.fixture def data_missing_for_sorting(): """Length-3 array with a known sort order. This should be three items [B, NA, A] with A < B and NA missing. """ raise NotImplementedError @pytest.fixture def na_cmp(): """Binary operator for comparing NA values. Should return a function of two arguments that returns True if both arguments are (scalar) NA for your type. By default, uses ``operator.or`` """ return lambda x, y: x is None and y is None @pytest.fixture def na_value(): """The scalar missing value for this type. Default 'None'""" return None @pytest.fixture def data_for_grouping(): """Data for factorization, grouping, and unique tests. Expected to be like [B, B, NA, NA, A, A, B, C] Where A < B < C and NA is missing """ return from_shapely( [ shapely.geometry.Point(1, 1), shapely.geometry.Point(1, 1), None, None, shapely.geometry.Point(0, 0), shapely.geometry.Point(0, 0), shapely.geometry.Point(1, 1), shapely.geometry.Point(2, 2), ] ) @pytest.fixture(params=[True, False]) def box_in_series(request): """Whether to box the data in a Series""" return request.param @pytest.fixture( params=[ lambda x: 1, lambda x: [1] * len(x), lambda x: pd.Series([1] * len(x)), lambda x: x, ], ids=["scalar", "list", "series", "object"], ) def groupby_apply_op(request): """ Functions to test groupby.apply(). """ return request.param @pytest.fixture(params=[True, False]) def as_frame(request): """ Boolean fixture to support Series and Series.to_frame() comparison testing. """ return request.param @pytest.fixture(params=[True, False]) def as_series(request): """ Boolean fixture to support arr and Series(arr) comparison testing. """ return request.param @pytest.fixture(params=[True, False]) def use_numpy(request): """ Boolean fixture to support comparison testing of ExtensionDtype array and numpy array. """ return request.param @pytest.fixture(params=["ffill", "bfill"]) def fillna_method(request): """ Parametrized fixture giving method parameters 'ffill' and 'bfill' for Series.fillna(method=<method>) testing. """ return request.param @pytest.fixture(params=[True, False]) def as_array(request): """ Boolean fixture to support ExtensionDtype _from_sequence method testing. """ return request.param # Fixtures defined in pandas/conftest.py that are also needed: defining them # here instead of importing for compatibility @pytest.fixture( params=["sum", "max", "min", "mean", "prod", "std", "var", "median", "kurt", "skew"] ) def all_numeric_reductions(request): """ Fixture for numeric reduction names """ return request.param @pytest.fixture(params=["all", "any"]) def all_boolean_reductions(request): """ Fixture for boolean reduction names """ return request.param # only == and != are support for GeometryArray # @pytest.fixture(params=["__eq__", "__ne__", "__le__", "__lt__", "__ge__", "__gt__"]) @pytest.fixture(params=["__eq__", "__ne__"]) def all_compare_operators(request): """ Fixture for dunder names for common compare operations * >= * > * == * != * < * <= """ return request.param # ----------------------------------------------------------------------------- # Inherited tests # ----------------------------------------------------------------------------- class TestDtype(extension_tests.BaseDtypeTests): # additional tests def test_array_type_with_arg(self, data, dtype): assert dtype.construct_array_type() is GeometryArray def test_registry(self, data, dtype): s = pd.Series(np.asarray(data), dtype=object) result = s.astype("geometry") assert isinstance(result.array, GeometryArray) expected = pd.Series(data) self.assert_series_equal(result, expected) class TestInterface(extension_tests.BaseInterfaceTests): def test_array_interface(self, data): # we are overriding this base test because the creation of `expected` # potentionally doesn't work for shapely geometries # TODO can be removed with Shapely 2.0 result = np.array(data) assert result[0] == data[0] result = np.array(data, dtype=object) # expected = np.array(list(data), dtype=object) expected = np.empty(len(data), dtype=object) with ignore_shapely2_warnings(): expected[:] = list(data) assert_array_equal(result, expected) def test_contains(self, data, data_missing): # overridden due to the inconsistency between # GeometryDtype.na_value = np.nan # and None being used as NA in array # ensure data without missing values data = data[~data.isna()] # first elements are non-missing assert data[0] in data assert data_missing[0] in data_missing assert None in data_missing assert None not in data assert pd.NaT not in data_missing class TestConstructors(extension_tests.BaseConstructorsTests): pass class TestReshaping(extension_tests.BaseReshapingTests): pass class TestGetitem(extension_tests.BaseGetitemTests): pass class TestSetitem(extension_tests.BaseSetitemTests): pass class TestMissing(extension_tests.BaseMissingTests): def test_fillna_series(self, data_missing): fill_value = data_missing[1] ser = pd.Series(data_missing) result = ser.fillna(fill_value) expected = pd.Series(data_missing._from_sequence([fill_value, fill_value])) self.assert_series_equal(result, expected) # filling with array-like not yet supported # # Fill with a series # result = ser.fillna(expected) # self.assert_series_equal(result, expected) # # Fill with a series not affecting the missing values # result = ser.fillna(ser) # self.assert_series_equal(result, ser) @pytest.mark.skip("fillna method not supported") def test_fillna_limit_pad(self, data_missing): pass @pytest.mark.skip("fillna method not supported") def test_fillna_limit_backfill(self, data_missing): pass @pytest.mark.skip("fillna method not supported") def test_fillna_series_method(self, data_missing, method): pass @pytest.mark.skip("fillna method not supported") def test_fillna_no_op_returns_copy(self, data): pass class TestReduce(extension_tests.BaseNoReduceTests): @pytest.mark.skip("boolean reduce (any/all) tested in test_pandas_methods") def test_reduce_series_boolean(): pass _all_arithmetic_operators = [ "__add__", "__radd__", # '__sub__', '__rsub__', "__mul__", "__rmul__", "__floordiv__", "__rfloordiv__", "__truediv__", "__rtruediv__", "__pow__", "__rpow__", "__mod__", "__rmod__", ] @pytest.fixture(params=_all_arithmetic_operators) def all_arithmetic_operators(request): """ Fixture for dunder names for common arithmetic operations Adapted to exclude __sub__, as this is implemented as "difference". """ return request.param # an inherited test from pandas creates a Series from a list of geometries, which # triggers the warning from Shapely, out of control of GeoPandas, so ignoring here @pytest.mark.filterwarnings( "ignore:The array interface is deprecated and will no longer work in Shapely 2.0" ) class TestArithmeticOps(extension_tests.BaseArithmeticOpsTests): @pytest.mark.skip(reason="not applicable") def test_divmod_series_array(self, data, data_for_twos): pass @pytest.mark.skip(reason="not applicable") def test_add_series_with_extension_array(self, data): pass # an inherited test from pandas creates a Series from a list of geometries, which # triggers the warning from Shapely, out of control of GeoPandas, so ignoring here @pytest.mark.filterwarnings( "ignore:The array interface is deprecated and will no longer work in Shapely 2.0" ) class TestComparisonOps(extension_tests.BaseComparisonOpsTests): def _compare_other(self, s, data, op_name, other): op = getattr(operator, op_name.strip("_")) result = op(s, other) expected = s.combine(other, op) self.assert_series_equal(result, expected) def test_compare_scalar(self, data, all_compare_operators): # noqa op_name = all_compare_operators s = pd.Series(data) self._compare_other(s, data, op_name, data[0]) def test_compare_array(self, data, all_compare_operators): # noqa op_name = all_compare_operators s = pd.Series(data) other = pd.Series([data[0]] * len(data)) self._compare_other(s, data, op_name, other) class TestMethods(extension_tests.BaseMethodsTests): @no_sorting @pytest.mark.parametrize("dropna", [True, False]) def test_value_counts(self, all_data, dropna): pass @no_sorting def test_value_counts_with_normalize(self, data): pass @no_sorting def test_argsort(self, data_for_sorting): result = pd.Series(data_for_sorting).argsort() expected = pd.Series(np.array([2, 0, 1], dtype=np.int64)) self.assert_series_equal(result, expected) @no_sorting def test_argsort_missing(self, data_missing_for_sorting): result = pd.Series(data_missing_for_sorting).argsort() expected = pd.Series(np.array([1, -1, 0], dtype=np.int64)) self.assert_series_equal(result, expected) @no_sorting @pytest.mark.parametrize("ascending", [True, False]) def test_sort_values(self, data_for_sorting, ascending): ser = pd.Series(data_for_sorting) result = ser.sort_values(ascending=ascending) expected = ser.iloc[[2, 0, 1]] if not ascending: expected = expected[::-1] self.assert_series_equal(result, expected) @no_sorting @pytest.mark.parametrize("ascending", [True, False]) def test_sort_values_missing(self, data_missing_for_sorting, ascending): ser = pd.Series(data_missing_for_sorting) result = ser.sort_values(ascending=ascending) if ascending: expected = ser.iloc[[2, 0, 1]] else: expected = ser.iloc[[0, 2, 1]] self.assert_series_equal(result, expected) @no_sorting @pytest.mark.parametrize("ascending", [True, False]) def test_sort_values_frame(self, data_for_sorting, ascending): df = pd.DataFrame({"A": [1, 2, 1], "B": data_for_sorting}) result = df.sort_values(["A", "B"]) expected = pd.DataFrame( {"A": [1, 1, 2], "B": data_for_sorting.take([2, 0, 1])}, index=[2, 0, 1] ) self.assert_frame_equal(result, expected) @no_sorting def test_searchsorted(self, data_for_sorting, as_series): pass @not_yet_implemented def test_combine_le(self): pass @pytest.mark.skip(reason="addition not supported") def test_combine_add(self): pass @not_yet_implemented def test_fillna_length_mismatch(self, data_missing): msg = "Length of 'value' does not match." with pytest.raises(ValueError, match=msg): data_missing.fillna(data_missing.take([1])) @no_sorting def test_nargsort(self): pass @no_sorting def test_argsort_missing_array(self): pass @no_sorting def test_argmin_argmax(self): pass @no_sorting def test_argmin_argmax_empty_array(self): pass @no_sorting def test_argmin_argmax_all_na(self): pass @no_sorting def test_argreduce_series(self): pass @no_sorting def test_argmax_argmin_no_skipna_notimplemented(self): pass class TestCasting(extension_tests.BaseCastingTests): pass class TestGroupby(extension_tests.BaseGroupbyTests): @no_sorting @pytest.mark.parametrize("as_index", [True, False]) def test_groupby_extension_agg(self, as_index, data_for_grouping): pass @no_sorting def test_groupby_extension_transform(self, data_for_grouping): pass @no_sorting @pytest.mark.parametrize( "op", [ lambda x: 1, lambda x: [1] * len(x), lambda x: pd.Series([1] * len(x)), lambda x: x, ], ids=["scalar", "list", "series", "object"], ) def test_groupby_extension_apply(self, data_for_grouping, op): pass class TestPrinting(extension_tests.BasePrintingTests): pass @not_yet_implemented class TestParsing(extension_tests.BaseParsingTests): pass

27.414676

88

0.651292

import operator import numpy as np from numpy.testing import assert_array_equal import pandas as pd from pandas.tests.extension import base as extension_tests import shapely.geometry from geopandas.array import GeometryArray, GeometryDtype, from_shapely from geopandas._compat import ignore_shapely2_warnings import pytest not_yet_implemented = pytest.mark.skip(reason="Not yet implemented") no_sorting = pytest.mark.skip(reason="Sorting not supported") @pytest.fixture def dtype(): return GeometryDtype() def make_data(): a = np.empty(100, dtype=object) with ignore_shapely2_warnings(): a[:] = [shapely.geometry.Point(i, i) for i in range(100)] ga = from_shapely(a) return ga @pytest.fixture def data(): return make_data() @pytest.fixture def data_for_twos(): raise NotImplementedError @pytest.fixture def data_missing(): return from_shapely([None, shapely.geometry.Point(1, 1)]) @pytest.fixture(params=["data", "data_missing"]) def all_data(request, data, data_missing): if request.param == "data": return data elif request.param == "data_missing": return data_missing @pytest.fixture def data_repeated(data): def gen(count): for _ in range(count): yield data return gen @pytest.fixture def data_for_sorting(): raise NotImplementedError @pytest.fixture def data_missing_for_sorting(): raise NotImplementedError @pytest.fixture def na_cmp(): return lambda x, y: x is None and y is None @pytest.fixture def na_value(): return None @pytest.fixture def data_for_grouping(): return from_shapely( [ shapely.geometry.Point(1, 1), shapely.geometry.Point(1, 1), None, None, shapely.geometry.Point(0, 0), shapely.geometry.Point(0, 0), shapely.geometry.Point(1, 1), shapely.geometry.Point(2, 2), ] ) @pytest.fixture(params=[True, False]) def box_in_series(request): return request.param @pytest.fixture( params=[ lambda x: 1, lambda x: [1] * len(x), lambda x: pd.Series([1] * len(x)), lambda x: x, ], ids=["scalar", "list", "series", "object"], ) def groupby_apply_op(request): return request.param @pytest.fixture(params=[True, False]) def as_frame(request): return request.param @pytest.fixture(params=[True, False]) def as_series(request): return request.param @pytest.fixture(params=[True, False]) def use_numpy(request): return request.param @pytest.fixture(params=["ffill", "bfill"]) def fillna_method(request): return request.param @pytest.fixture(params=[True, False]) def as_array(request): return request.param @pytest.fixture( params=["sum", "max", "min", "mean", "prod", "std", "var", "median", "kurt", "skew"] ) def all_numeric_reductions(request): return request.param @pytest.fixture(params=["all", "any"]) def all_boolean_reductions(request): return request.param @pytest.fixture(params=["__eq__", "__ne__"]) def all_compare_operators(request): return request.param class TestDtype(extension_tests.BaseDtypeTests): def test_array_type_with_arg(self, data, dtype): assert dtype.construct_array_type() is GeometryArray def test_registry(self, data, dtype): s = pd.Series(np.asarray(data), dtype=object) result = s.astype("geometry") assert isinstance(result.array, GeometryArray) expected = pd.Series(data) self.assert_series_equal(result, expected) class TestInterface(extension_tests.BaseInterfaceTests): def test_array_interface(self, data): # TODO can be removed with Shapely 2.0 result = np.array(data) assert result[0] == data[0] result = np.array(data, dtype=object) # expected = np.array(list(data), dtype=object) expected = np.empty(len(data), dtype=object) with ignore_shapely2_warnings(): expected[:] = list(data) assert_array_equal(result, expected) def test_contains(self, data, data_missing): # overridden due to the inconsistency between # GeometryDtype.na_value = np.nan # and None being used as NA in array # ensure data without missing values data = data[~data.isna()] # first elements are non-missing assert data[0] in data assert data_missing[0] in data_missing assert None in data_missing assert None not in data assert pd.NaT not in data_missing class TestConstructors(extension_tests.BaseConstructorsTests): pass class TestReshaping(extension_tests.BaseReshapingTests): pass class TestGetitem(extension_tests.BaseGetitemTests): pass class TestSetitem(extension_tests.BaseSetitemTests): pass class TestMissing(extension_tests.BaseMissingTests): def test_fillna_series(self, data_missing): fill_value = data_missing[1] ser = pd.Series(data_missing) result = ser.fillna(fill_value) expected = pd.Series(data_missing._from_sequence([fill_value, fill_value])) self.assert_series_equal(result, expected) # filling with array-like not yet supported # # Fill with a series # result = ser.fillna(expected) # self.assert_series_equal(result, expected) # # Fill with a series not affecting the missing values # result = ser.fillna(ser) # self.assert_series_equal(result, ser) @pytest.mark.skip("fillna method not supported") def test_fillna_limit_pad(self, data_missing): pass @pytest.mark.skip("fillna method not supported") def test_fillna_limit_backfill(self, data_missing): pass @pytest.mark.skip("fillna method not supported") def test_fillna_series_method(self, data_missing, method): pass @pytest.mark.skip("fillna method not supported") def test_fillna_no_op_returns_copy(self, data): pass class TestReduce(extension_tests.BaseNoReduceTests): @pytest.mark.skip("boolean reduce (any/all) tested in test_pandas_methods") def test_reduce_series_boolean(): pass _all_arithmetic_operators = [ "__add__", "__radd__", # '__sub__', '__rsub__', "__mul__", "__rmul__", "__floordiv__", "__rfloordiv__", "__truediv__", "__rtruediv__", "__pow__", "__rpow__", "__mod__", "__rmod__", ] @pytest.fixture(params=_all_arithmetic_operators) def all_arithmetic_operators(request): return request.param # an inherited test from pandas creates a Series from a list of geometries, which # triggers the warning from Shapely, out of control of GeoPandas, so ignoring here @pytest.mark.filterwarnings( "ignore:The array interface is deprecated and will no longer work in Shapely 2.0" ) class TestArithmeticOps(extension_tests.BaseArithmeticOpsTests): @pytest.mark.skip(reason="not applicable") def test_divmod_series_array(self, data, data_for_twos): pass @pytest.mark.skip(reason="not applicable") def test_add_series_with_extension_array(self, data): pass # an inherited test from pandas creates a Series from a list of geometries, which # triggers the warning from Shapely, out of control of GeoPandas, so ignoring here @pytest.mark.filterwarnings( "ignore:The array interface is deprecated and will no longer work in Shapely 2.0" ) class TestComparisonOps(extension_tests.BaseComparisonOpsTests): def _compare_other(self, s, data, op_name, other): op = getattr(operator, op_name.strip("_")) result = op(s, other) expected = s.combine(other, op) self.assert_series_equal(result, expected) def test_compare_scalar(self, data, all_compare_operators): # noqa op_name = all_compare_operators s = pd.Series(data) self._compare_other(s, data, op_name, data[0]) def test_compare_array(self, data, all_compare_operators): # noqa op_name = all_compare_operators s = pd.Series(data) other = pd.Series([data[0]] * len(data)) self._compare_other(s, data, op_name, other) class TestMethods(extension_tests.BaseMethodsTests): @no_sorting @pytest.mark.parametrize("dropna", [True, False]) def test_value_counts(self, all_data, dropna): pass @no_sorting def test_value_counts_with_normalize(self, data): pass @no_sorting def test_argsort(self, data_for_sorting): result = pd.Series(data_for_sorting).argsort() expected = pd.Series(np.array([2, 0, 1], dtype=np.int64)) self.assert_series_equal(result, expected) @no_sorting def test_argsort_missing(self, data_missing_for_sorting): result = pd.Series(data_missing_for_sorting).argsort() expected = pd.Series(np.array([1, -1, 0], dtype=np.int64)) self.assert_series_equal(result, expected) @no_sorting @pytest.mark.parametrize("ascending", [True, False]) def test_sort_values(self, data_for_sorting, ascending): ser = pd.Series(data_for_sorting) result = ser.sort_values(ascending=ascending) expected = ser.iloc[[2, 0, 1]] if not ascending: expected = expected[::-1] self.assert_series_equal(result, expected) @no_sorting @pytest.mark.parametrize("ascending", [True, False]) def test_sort_values_missing(self, data_missing_for_sorting, ascending): ser = pd.Series(data_missing_for_sorting) result = ser.sort_values(ascending=ascending) if ascending: expected = ser.iloc[[2, 0, 1]] else: expected = ser.iloc[[0, 2, 1]] self.assert_series_equal(result, expected) @no_sorting @pytest.mark.parametrize("ascending", [True, False]) def test_sort_values_frame(self, data_for_sorting, ascending): df = pd.DataFrame({"A": [1, 2, 1], "B": data_for_sorting}) result = df.sort_values(["A", "B"]) expected = pd.DataFrame( {"A": [1, 1, 2], "B": data_for_sorting.take([2, 0, 1])}, index=[2, 0, 1] ) self.assert_frame_equal(result, expected) @no_sorting def test_searchsorted(self, data_for_sorting, as_series): pass @not_yet_implemented def test_combine_le(self): pass @pytest.mark.skip(reason="addition not supported") def test_combine_add(self): pass @not_yet_implemented def test_fillna_length_mismatch(self, data_missing): msg = "Length of 'value' does not match." with pytest.raises(ValueError, match=msg): data_missing.fillna(data_missing.take([1])) @no_sorting def test_nargsort(self): pass @no_sorting def test_argsort_missing_array(self): pass @no_sorting def test_argmin_argmax(self): pass @no_sorting def test_argmin_argmax_empty_array(self): pass @no_sorting def test_argmin_argmax_all_na(self): pass @no_sorting def test_argreduce_series(self): pass @no_sorting def test_argmax_argmin_no_skipna_notimplemented(self): pass class TestCasting(extension_tests.BaseCastingTests): pass class TestGroupby(extension_tests.BaseGroupbyTests): @no_sorting @pytest.mark.parametrize("as_index", [True, False]) def test_groupby_extension_agg(self, as_index, data_for_grouping): pass @no_sorting def test_groupby_extension_transform(self, data_for_grouping): pass @no_sorting @pytest.mark.parametrize( "op", [ lambda x: 1, lambda x: [1] * len(x), lambda x: pd.Series([1] * len(x)), lambda x: x, ], ids=["scalar", "list", "series", "object"], ) def test_groupby_extension_apply(self, data_for_grouping, op): pass class TestPrinting(extension_tests.BasePrintingTests): pass @not_yet_implemented class TestParsing(extension_tests.BaseParsingTests): pass

true

1c37be98984ec7224180548ff6d1dbe3a33de59e

1,940

py

Python

receipts/receipts/admin.py

rolisz/receipt_budget

74f73e7f8bb8b0b4fa89bfebf4c3c2c930511308

[ "BSD-3-Clause" ]

15

2016-03-02T18:16:46.000Z

2022-03-05T10:55:58.000Z

receipts/receipts/admin.py

rolisz/receipt_budget

74f73e7f8bb8b0b4fa89bfebf4c3c2c930511308

[ "BSD-3-Clause" ]

1

2017-04-10T23:46:43.000Z

receipts/receipts/admin.py

rolisz/receipt_budget

74f73e7f8bb8b0b4fa89bfebf4c3c2c930511308

[ "BSD-3-Clause" ]

11

2016-03-02T18:16:12.000Z

2020-07-19T11:57:27.000Z

from django.contrib import admin from receipts.models import Expense, ExpenseItem, Shop from django.core.urlresolvers import reverse from django.utils.safestring import mark_safe class ExpenseItemInline(admin.StackedInline): model = ExpenseItem extra = 2 template = 'admin/receipts/expense/item_stacked.html' class ShopInline(admin.StackedInline): model = Shop class ExpenseAdmin(admin.ModelAdmin): class Media: css = { 'all': ("receipts/css/bootstrap.css", "receipts/css/bootstrap-theme.css") } js = ("receipts/js/jquery.js", "receipts/js/bootstrap.min.js",) def change_view(self, request, object_id, form_url='', extra_context=None): extra_context = extra_context or {} extra_context['show_delete_link'] = True return super(ExpenseAdmin, self).change_view(request, object_id, form_url, extra_context=extra_context) def link(self, obj): print(obj.shop) url = reverse('admin:receipts_shop_change',args=(obj.shop.id,)) print(url) return mark_safe("<a href='%s'>edit</a>" % url) link.allow_tags = True link.short_description = "" inlines = [ExpenseItemInline] fields = ['date', ('shop', 'link')] readonly_fields = ['image', 'link'] class ShopAdmin(admin.ModelAdmin): class Media: css = { 'all': ("receipts/css/bootstrap.css", "receipts/css/bootstrap-theme.css") } js = ("receipts/js/jquery.js", "receipts/js/bootstrap.min.js",) def change_view(self, request, object_id, form_url='', extra_context=None): extra_context = extra_context or {} extra_context['show_delete_link'] = True return super(ShopAdmin, self).change_view(request, object_id, form_url, extra_context=extra_context) admin.site.register(Shop, ShopAdmin) admin.site.register(Expense, ExpenseAdmin) admin.site.register(ExpenseItem)

31.803279

85

0.673711

from django.contrib import admin from receipts.models import Expense, ExpenseItem, Shop from django.core.urlresolvers import reverse from django.utils.safestring import mark_safe class ExpenseItemInline(admin.StackedInline): model = ExpenseItem extra = 2 template = 'admin/receipts/expense/item_stacked.html' class ShopInline(admin.StackedInline): model = Shop class ExpenseAdmin(admin.ModelAdmin): class Media: css = { 'all': ("receipts/css/bootstrap.css", "receipts/css/bootstrap-theme.css") } js = ("receipts/js/jquery.js", "receipts/js/bootstrap.min.js",) def change_view(self, request, object_id, form_url='', extra_context=None): extra_context = extra_context or {} extra_context['show_delete_link'] = True return super(ExpenseAdmin, self).change_view(request, object_id, form_url, extra_context=extra_context) def link(self, obj): print(obj.shop) url = reverse('admin:receipts_shop_change',args=(obj.shop.id,)) print(url) return mark_safe("<a href='%s'>edit</a>" % url) link.allow_tags = True link.short_description = "" inlines = [ExpenseItemInline] fields = ['date', ('shop', 'link')] readonly_fields = ['image', 'link'] class ShopAdmin(admin.ModelAdmin): class Media: css = { 'all': ("receipts/css/bootstrap.css", "receipts/css/bootstrap-theme.css") } js = ("receipts/js/jquery.js", "receipts/js/bootstrap.min.js",) def change_view(self, request, object_id, form_url='', extra_context=None): extra_context = extra_context or {} extra_context['show_delete_link'] = True return super(ShopAdmin, self).change_view(request, object_id, form_url, extra_context=extra_context) admin.site.register(Shop, ShopAdmin) admin.site.register(Expense, ExpenseAdmin) admin.site.register(ExpenseItem)

true

1c37bf63892ff3c3dc3eea7b99f3a2fe1c154eb9

15,380

py

Python

tifresi/phase/modGabPhaseGrad.py

andimarafioti/tifresi

676db371d5c472a5f3199506bf3863367a2ecde4

[ "MIT" ]

12

2020-02-08T09:47:17.000Z

2021-07-31T09:22:41.000Z

tifresi/phase/modGabPhaseGrad.py

nperraud/stft4pghi

676db371d5c472a5f3199506bf3863367a2ecde4

[ "MIT" ]

1

2020-07-20T22:32:49.000Z

2020-07-21T15:20:11.000Z

tifresi/phase/modGabPhaseGrad.py

nperraud/stft4pghi

676db371d5c472a5f3199506bf3863367a2ecde4

[ "MIT" ]

null

# -*- coding: utf-8 -*- # ######### COPYRIGHT ######### # Credits # ####### # # Copyright(c) 2015-2018 # ---------------------- # # * `LabEx Archimède <http://labex-archimede.univ-amu.fr/>`_ # * `Laboratoire d'Informatique Fondamentale <http://www.lif.univ-mrs.fr/>`_ # (now `Laboratoire d'Informatique et Systèmes <http://www.lis-lab.fr/>`_) # * `Institut de Mathématiques de Marseille <http://www.i2m.univ-amu.fr/>`_ # * `Université d'Aix-Marseille <http://www.univ-amu.fr/>`_ # # This software is a port from LTFAT 2.1.0 : # Copyright (C) 2005-2018 Peter L. Soendergaard <peter@sonderport.dk>. # # Contributors # ------------ # # * Denis Arrivault <contact.dev_AT_lis-lab.fr> # * Florent Jaillet <contact.dev_AT_lis-lab.fr> # # Description # ----------- # # ltfatpy is a partial Python port of the # `Large Time/Frequency Analysis Toolbox <http://ltfat.sourceforge.net/>`_, # a MATLAB®/Octave toolbox for working with time-frequency analysis and # synthesis. # # Version # ------- # # * ltfatpy version = 1.0.16 # * LTFAT version = 2.1.0 # # Licence # ------- # # This program 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. # # This program 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 this program. If not, see <http://www.gnu.org/licenses/>. # # ######### COPYRIGHT ######### """Module of phase gradient computation Ported from ltfat_2.1.0/gabor/gabphasegrad.m .. moduleauthor:: Florent Jaillet """ from __future__ import print_function, division import numpy as np from ltfatpy.comp.comp_sigreshape_pre import comp_sigreshape_pre from ltfatpy.gabor.dgtlength import dgtlength from ltfatpy.gabor.gabwin import gabwin from ltfatpy.tools.postpad import postpad from ltfatpy.fourier.fftindex import fftindex from ltfatpy.comp.comp_sepdgt import comp_sepdgt from ltfatpy.fourier.pderiv import pderiv def modgabphasegrad(method, *args, **kwargs): """Modified Phase gradient of the discrete Gabor transform We modified this to work with dgtreals on the phase and abs case Phase case we did a lot of changes, abs case we added M as a mandatory parameter - Usage: | ``(tgrad, fgrad, c) = gabphasegrad('dgt', f, g, a, M, L=None)`` | ``(tgrad, fgrad) = gabphasegrad('phase', cphase, a)`` | ``(tgrad, fgrad) = gabphasegrad('abs', s, g, a, M, difforder=2)`` - Input parameters: :param str method: Method used to compute the phase gradient, see the possible values below :param numpy.ndarray f: (defined if ``method='dgt'``) Input signal :param numpy.ndarray cphase: (defined if ``method='phase'``) Phase of a :func:`~ltfatpy.gabor.dgt.dgt` of the signal :param numpy.ndarray s: (defined if ``method='abs'``) Spectrogram of the signal :param numpy.ndarray g: (defined if ``method='dgt'`` or ``method='phase'``) Window function :param int a: (defined if ``method='dgt'`` or ``method='phase'`` or ``method='abs'``) Length of time shift :param int M: (defined if ``method='dgt'``) Number of channels :param int L: (defined if ``method='dgt'``, optional) Length of transform to do :param int difforder: (defined if ``method='abs'``, optional) Order of the centered finite difference scheme used to perform the needed numerical differentiation - Output parameters: :returns: ``(tgrad, fgrad, c)`` if ``method='dgt'``, or ``(tgrad, fgrad)`` if ``method='phase'`` or ``method='abs'`` :rtype: tuple :var numpy.ndarray tgrad: Instantaneous frequency :var numpy.ndarray fgrad: Local group delay :var numpy.ndarray c: Gabor coefficients ``gabphasegrad`` computes the time-frequency gradient of the phase of the :func:`~ltfatpy.gabor.dgt.dgt` of a signal. The derivative in time **tgrad** is the instantaneous frequency while the frequency derivative **fgrad** is the local group delay. **tgrad** and **fgrad** measure the deviation from the current time and frequency, so a value of zero means that the instantaneous frequency is equal to the center frequency of the considered channel. **tgrad** is scaled such that distances are measured in samples. Similarly, **fgrad** is scaled such that the Nyquist frequency (the highest possible frequency) corresponds to a value of ``L/2``. The computation of **tgrad** and **fgrad** is inaccurate when the absolute value of the Gabor coefficients is low. This is due to the fact the the phase of complex numbers close to the machine precision is almost random. Therefore, **tgrad** and **fgrad** may attain very large random values when ``abs(c)`` is close to zero. The computation can be done using three different methods: =========== =========================================================== ``'dgt'`` Directly from the signal. ``'phase'`` From the phase of a :func:`~ltfatpy.gabor.dgt.dgt` of the signal. This is the classic method used in the phase vocoder. ``'abs'`` From the absolute value of the :func:`~ltfatpy.gabor.dgt.dgt`. Currently this method works only for Gaussian windows. =========== =========================================================== ``(tgrad, fgrad, c) = gabphasegrad('dgt', f, g, a, M)`` computes the time-frequency gradient using a :func:`~ltfatpy.gabor.dgt.dgt` of the signal **f**. The :func:`~ltfatpy.gabor.dgt.dgt` is computed using the window **g** on the lattice specified by the time shift **a** and the number of channels **M**. The algorithm used to perform this calculation computes several DGTs, and therefore this routine takes the exact same input parameters as :func:`~ltfatpy.gabor.dgt.dgt`. The window **g** may be specified as in :func:`~ltfatpy.gabor.dgt.dgt`. If the window used is ``'gauss'``, the computation will be done by a faster algorithm. ``(tgrad, fgrad, c) = gabphasegrad('dgt', f, g, a, M)`` additionally returns the Gabor coefficients ``c``, as they are always computed as a byproduct of the algorithm. ``(tgrad, fgrad) = gabphasegrad('phase', cphase, a)`` computes the phase gradient from the phase **cphase** of a :func:`~ltfatpy.gabor.dgt.dgt` of the signal. The original :func:`~ltfatpy.gabor.dgt.dgt` from which the phase is obtained must have been computed using a time-shift of **a**. ``(tgrad, fgrad) = gabphasegrad('abs', s, g, a)`` computes the phase gradient from the spectrogram **s**. The spectrogram must have been computed using the window **g** and time-shift **a**. ``(tgrad, fgrad) = gabphasegrad('abs', s, g, a, difforder=ord)`` uses a centered finite difference scheme of order ``ord`` to perform the needed numerical differentiation. Default is to use a 4th order scheme. Currently the 'abs' method only works if the window **g** is a Gaussian window specified as a string or cell array. .. seealso:: :func:`resgram`, :func:`gabreassign`, :func:`~ltfatpy.gabor.dgt.dgt` - References: :cite:`aufl95,cmdaaufl97,fl65` """ # NOTE: This function doesn't support the parameter lt (lattice type) # supported by the corresponding octave function and the lattice used is # seperable (square lattice lt = (0, 1)). # NOTE: As in the octave version of this function, if needed, the # undocumented optional keyword minlvl is available when using method=dgt. # So it can be passed using a call of the following form: # (tgrad, fgrad, c) = gabphasegrad('dgt', f, g, a, M, minlvl=val) if not isinstance(method, str): raise TypeError('First argument must be a str containing the method ' 'name, "dgt", "phase" or "abs".') method = method.lower() if method == 'dgt': raise Exception("We dont know if this works") # --------------------------- DGT method ------------------------ (f, g, a, M) = args if 'L' in kwargs: L = kwargs['L'] else: L = None if 'minlvl' in kwargs: minlvl = kwargs['minlvl'] else: minlvl = np.finfo(np.float64).tiny # # ----- step 1 : Verify f and determine its length ------- # Change f to correct shape. f, Ls, W, wasrow, remembershape = comp_sigreshape_pre(f, 0) # # ------ step 2: Verify a, M and L if not L: # ----- step 2b : Verify a, M and get L from the signal length f--- L = dgtlength(Ls, a, M) else: # ----- step 2a : Verify a, M and get L Luser = dgtlength(L, a, M) if Luser != L: raise ValueError('Incorrect transform length L = {0:d} ' 'specified. Next valid length is L = {1:d}. ' 'See the help of dgtlength for the ' 'requirements.'.format(L, Luser)) # # ----- step 3 : Determine the window g, info = gabwin(g, a, M, L) if L < info['gl']: raise ValueError('Window is too long.') # # ----- step 4: final cleanup --------------- f = postpad(f, L) # # ------ algorithm starts -------------------- # Compute the time weighted version of the window. hg = fftindex(L) * g # The computation done this way is insensitive to whether the dgt is # phaselocked or not. c = comp_sepdgt(f, g, a, M, 0) c_h = comp_sepdgt(f, hg, a, M, 0) c_s = np.abs(c) ** 2 # Remove small values because we need to divide by c_s c_s = np.maximum(c_s, minlvl * np.max(c_s)) # Compute the group delay fgrad = np.real(c_h * c.conjugate() / c_s) if info['gauss']: # The method used below only works for the Gaussian window, because # the time derivative and the time multiplicative of the Gaussian # are identical. tgrad = np.imag(c_h * c.conjugate() / c_s) / info['tfr'] else: # The code below works for any window, and not just the Gaussian dg = pderiv(g, difforder=float('inf')) / (2 * np.pi) c_d = comp_sepdgt(f, dg, a, M, 0) # NOTE: There is a bug here in the original octave file as it # contains a reshape that uses an undefined variable N. # You can get the error with LTFAT 2.1.0 in octave by running for # example: # gabphasegrad('dgt', rand(16,1), rand(16,1), 4, 16) # # So we just comment out the corresponding line here, as it appears # to be unneeded: # c_d.shape = (M, N, W) # Compute the instantaneous frequency tgrad = -np.imag(c_d * c.conjugate() / c_s) return (tgrad, fgrad, c) elif method == 'phase': # --------------------------- phase method ------------------------ (cphase, a, M) = args if not np.isrealobj(cphase): raise TypeError("Input phase must be real valued. Use the 'angle'" " function to compute the argument of complex " "numbers.") # --- linear method --- if cphase.ndim == 3: M2, N, W = cphase.shape # M2 is the number of channels from 0 to Nyquist else: M2, N = cphase.shape # M2 is the number of channels from 0 to Nyquist L = N * a b = L / M # NOTE: The following code found in the original octave version of the function # hasn't been translated here to Python as it is not used: # if 0 # # # This is the classic phase vocoder algorithm by Flanagan. # # tgrad = cphase-circshift(cphase,[0,-1]); # tgrad = tgrad- 2*pi*round(tgrad/(2*pi)); # tgrad = -tgrad/(2*pi)*L; # # # Phase-lock the angles. # TimeInd = (0:(N-1))*a; # FreqInd = (0:(M-1))/M; # # phl = FreqInd'*TimeInd; # cphase = cphase+2*pi.*phl; # # fgrad = cphase-circshift(cphase,[1,0]); # fgrad = fgrad- 2*pi*round(fgrad/(2*pi)); # fgrad = -fgrad/(2*pi)*L; # # end; # This is the classic phase vocoder algorithm by Flanagan modified to # yield a second order centered difference approximation. # Forward approximation tgrad_1 = cphase - np.roll(cphase, -1, axis=1) # numpy round function doesn't use the same convention than octave for # half-integers but the standard Python round function uses the same # convention than octave, so we use the Python standard round in the # computation below octave_round = np.vectorize(round) tgrad_1 = tgrad_1 - 2 * np.pi * octave_round(tgrad_1 / (2 * np.pi)) # Backward approximation tgrad_2 = np.roll(cphase, 1, axis=1) - cphase tgrad_2 = tgrad_2 - 2 * np.pi * octave_round(tgrad_2 / (2 * np.pi)) # Average tgrad = (tgrad_1 + tgrad_2) / 2 tgrad = -tgrad / (2 * np.pi * a) * L # Phase-lock the angles. TimeInd = np.arange(N) * a FreqInd = np.arange(M2) / M phl = np.dot(FreqInd.reshape((FreqInd.shape[0], 1)), TimeInd.reshape((1, TimeInd.shape[0]))) # NOTE: in the following lines, the shape of phl is changed so that # broadcasting works in the following addition with cphase when cphase # has more than two dimensions new_shape = np.ones((len(cphase.shape),), dtype=int) new_shape[0] = phl.shape[0] new_shape[1] = phl.shape[1] phl = phl.reshape(tuple(new_shape)) cphase = cphase + 2 * np.pi * phl cphase_to_aprox = np.concatenate([-cphase[1:2], cphase, -cphase[-2:-1]]) # Forward approximation fgrad_1 = cphase_to_aprox - np.roll(cphase_to_aprox, -1, axis=0) fgrad_1 = fgrad_1 - 2 * np.pi * octave_round(fgrad_1 / (2 * np.pi)) fgrad_1 = fgrad_1[1:-1] # Backward approximation fgrad_2 = np.roll(cphase_to_aprox, 1, axis=0) - cphase_to_aprox fgrad_2 = fgrad_2 - 2 * np.pi * octave_round(fgrad_2 / (2 * np.pi)) fgrad_2 = fgrad_2[1:-1] # Average fgrad = (fgrad_1 + fgrad_2) / 2 fgrad = fgrad / (2 * np.pi * b) * L return (tgrad, fgrad) elif method == 'abs': # --------------------------- abs method ------------------------ (s, g, a, M) = args if 'difforder' in kwargs: difforder = kwargs['difforder'] else: difforder = 2 if not np.all(s >= 0.): raise ValueError('First input argument must be positive or zero.') if s.ndim == 3: M2, N, W = s.shape else: M2, N = s.shape L = N * a g, info = gabwin(g, a, M, L) if not info['gauss']: raise ValueError('The window must be a Gaussian window (specified ' 'as a string or as a dictionary).') b = L / M # We must avoid taking the log of zero. # Therefore we add the smallest possible # number logs = np.log(s + np.finfo(s.dtype).tiny) # XXX REMOVE Add a small constant to limit the dynamic range. This # should lessen the problem of errors in the differentiation for points # close to (but not exactly) zeros points. maxmax = np.max(logs) tt = -11. logs[logs < (maxmax + tt)] = tt fgrad = pderiv(logs, 1, difforder) / (2 * np.pi) * info['tfr'] tgrad = pderiv(logs, 0, difforder) / (2 * np.pi * info['tfr']) * (M/M2) # Fix the first and last rows .. the # borders are symmetric so the centered difference is 0 tgrad[0, :] = 0 tgrad[-1, :] = 0 return (tgrad, fgrad) else: raise ValueError("First argument must be the method name, 'dgt', " "'phase' or 'abs'.")

34.954545

81

0.633745

t Jaillet <contact.dev_AT_lis-lab.fr> # # Description # ----------- # # ltfatpy is a partial Python port of the # `Large Time/Frequency Analysis Toolbox <http://ltfat.sourceforge.net/>`_, # a MATLAB®/Octave toolbox for working with time-frequency analysis and # synthesis. # # Version # ------- # # * ltfatpy version = 1.0.16 # * LTFAT version = 2.1.0 # # Licence # ------- # # This program 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. # # This program 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 this program. If not, see <http://www.gnu.org/licenses/>. # # ######### COPYRIGHT ######### from __future__ import print_function, division import numpy as np from ltfatpy.comp.comp_sigreshape_pre import comp_sigreshape_pre from ltfatpy.gabor.dgtlength import dgtlength from ltfatpy.gabor.gabwin import gabwin from ltfatpy.tools.postpad import postpad from ltfatpy.fourier.fftindex import fftindex from ltfatpy.comp.comp_sepdgt import comp_sepdgt from ltfatpy.fourier.pderiv import pderiv def modgabphasegrad(method, *args, **kwargs): # NOTE: This function doesn't support the parameter lt (lattice type) if not isinstance(method, str): raise TypeError('First argument must be a str containing the method ' 'name, "dgt", "phase" or "abs".') method = method.lower() if method == 'dgt': raise Exception("We dont know if this works") (f, g, a, M) = args if 'L' in kwargs: L = kwargs['L'] else: L = None if 'minlvl' in kwargs: minlvl = kwargs['minlvl'] else: minlvl = np.finfo(np.float64).tiny re(f, 0) Ls, a, M) else: Luser = dgtlength(L, a, M) if Luser != L: raise ValueError('Incorrect transform length L = {0:d} ' 'specified. Next valid length is L = {1:d}. ' 'See the help of dgtlength for the ' 'requirements.'.format(L, Luser)) L < info['gl']: raise ValueError('Window is too long.') ) c_h = comp_sepdgt(f, hg, a, M, 0) c_s = np.abs(c) ** 2 c_s = np.maximum(c_s, minlvl * np.max(c_s)) fgrad = np.real(c_h * c.conjugate() / c_s) if info['gauss']: tgrad = np.imag(c_h * c.conjugate() / c_s) / info['tfr'] else: dg = pderiv(g, difforder=float('inf')) / (2 * np.pi) c_d = comp_sepdgt(f, dg, a, M, 0) tgrad = -np.imag(c_d * c.conjugate() / c_s) return (tgrad, fgrad, c) elif method == 'phase': (cphase, a, M) = args if not np.isrealobj(cphase): raise TypeError("Input phase must be real valued. Use the 'angle'" " function to compute the argument of complex " "numbers.") if cphase.ndim == 3: M2, N, W = cphase.shape else: M2, N = cphase.shape L = N * a b = L / M # if 0 # # # This is the classic phase vocoder algorithm by Flanagan. # # tgrad = cphase-circshift(cphase,[0,-1]); # tgrad = tgrad- 2*pi*round(tgrad/(2*pi)); # tgrad = -tgrad/(2*pi)*L; # # # Phase-lock the angles. # TimeInd = (0:(N-1))*a; # FreqInd = (0:(M-1))/M; # # phl = FreqInd'*TimeInd; tgrad_1 = cphase - np.roll(cphase, -1, axis=1) # half-integers but the standard Python round function uses the same # convention than octave, so we use the Python standard round in the # computation below octave_round = np.vectorize(round) tgrad_1 = tgrad_1 - 2 * np.pi * octave_round(tgrad_1 / (2 * np.pi)) # Backward approximation tgrad_2 = np.roll(cphase, 1, axis=1) - cphase tgrad_2 = tgrad_2 - 2 * np.pi * octave_round(tgrad_2 / (2 * np.pi)) # Average tgrad = (tgrad_1 + tgrad_2) / 2 tgrad = -tgrad / (2 * np.pi * a) * L # Phase-lock the angles. TimeInd = np.arange(N) * a FreqInd = np.arange(M2) / M phl = np.dot(FreqInd.reshape((FreqInd.shape[0], 1)), TimeInd.reshape((1, TimeInd.shape[0]))) # NOTE: in the following lines, the shape of phl is changed so that # broadcasting works in the following addition with cphase when cphase # has more than two dimensions new_shape = np.ones((len(cphase.shape),), dtype=int) new_shape[0] = phl.shape[0] new_shape[1] = phl.shape[1] phl = phl.reshape(tuple(new_shape)) cphase = cphase + 2 * np.pi * phl cphase_to_aprox = np.concatenate([-cphase[1:2], cphase, -cphase[-2:-1]]) # Forward approximation fgrad_1 = cphase_to_aprox - np.roll(cphase_to_aprox, -1, axis=0) fgrad_1 = fgrad_1 - 2 * np.pi * octave_round(fgrad_1 / (2 * np.pi)) fgrad_1 = fgrad_1[1:-1] # Backward approximation fgrad_2 = np.roll(cphase_to_aprox, 1, axis=0) - cphase_to_aprox fgrad_2 = fgrad_2 - 2 * np.pi * octave_round(fgrad_2 / (2 * np.pi)) fgrad_2 = fgrad_2[1:-1] # Average fgrad = (fgrad_1 + fgrad_2) / 2 fgrad = fgrad / (2 * np.pi * b) * L return (tgrad, fgrad) elif method == 'abs': # --------------------------- abs method ------------------------ (s, g, a, M) = args if 'difforder' in kwargs: difforder = kwargs['difforder'] else: difforder = 2 if not np.all(s >= 0.): raise ValueError('First input argument must be positive or zero.') if s.ndim == 3: M2, N, W = s.shape else: M2, N = s.shape L = N * a g, info = gabwin(g, a, M, L) if not info['gauss']: raise ValueError('The window must be a Gaussian window (specified ' 'as a string or as a dictionary).') b = L / M # We must avoid taking the log of zero. # Therefore we add the smallest possible # number logs = np.log(s + np.finfo(s.dtype).tiny) # XXX REMOVE Add a small constant to limit the dynamic range. This # should lessen the problem of errors in the differentiation for points # close to (but not exactly) zeros points. maxmax = np.max(logs) tt = -11. logs[logs < (maxmax + tt)] = tt fgrad = pderiv(logs, 1, difforder) / (2 * np.pi) * info['tfr'] tgrad = pderiv(logs, 0, difforder) / (2 * np.pi * info['tfr']) * (M/M2) # Fix the first and last rows .. the # borders are symmetric so the centered difference is 0 tgrad[0, :] = 0 tgrad[-1, :] = 0 return (tgrad, fgrad) else: raise ValueError("First argument must be the method name, 'dgt', " "'phase' or 'abs'.")

true

1c37bfd8ec65b02baae06a02cae28fc2283c52ec

971

py

Python

main/settings/stage.py

wuuuduu/django-notifications2

544502ec02bf34b4e0ff613500fd29766aecd229

[ "BSD-3-Clause" ]

1

2020-09-08T20:13:58.000Z

main/settings/stage.py

wuuuduu/django-notifications2

544502ec02bf34b4e0ff613500fd29766aecd229

[ "BSD-3-Clause" ]

null

main/settings/stage.py

wuuuduu/django-notifications2

544502ec02bf34b4e0ff613500fd29766aecd229

[ "BSD-3-Clause" ]

null

from .base import * from .logging import ConfigureLogger LOGGING_LEVEL = 'INFO' ConfigureLogger(log_level=LOGGING_LEVEL, logging_dir=LOGGING_DIR, django_modules=PROJECT_APPS) REST_FRAMEWORK['DEFAULT_RENDERER_CLASSES'] = [ 'rest_framework.renderers.JSONRenderer', ] ALLOWED_HOSTS = [ 'api.stage.example.com' ] RAVEN_CONFIG['environment'] = 'stage' CORS_ORIGIN_WHITELIST = [ 'https://stage.example.com', 'https://www.stage.example.com', ] DATABASES = { 'default': { 'ENGINE': 'django.db.backends.mysql', 'NAME': os.getenv('DATABASES_NAME'), 'USER': os.getenv('DATABASES_USER'), 'PASSWORD': os.getenv('DATABASES_PASSWORD'), 'HOST': os.getenv('DATABASES_HOST'), 'PORT': os.getenv('DATABASES_PORT'), 'OPTIONS': {'charset': 'utf8mb4'}, } } CACHES = { 'default': { 'BACKEND': 'django.core.cache.backends.memcached.MemcachedCache', 'LOCATION': '127.0.0.1:11211', } }

23.682927

94

0.649846

from .base import * from .logging import ConfigureLogger LOGGING_LEVEL = 'INFO' ConfigureLogger(log_level=LOGGING_LEVEL, logging_dir=LOGGING_DIR, django_modules=PROJECT_APPS) REST_FRAMEWORK['DEFAULT_RENDERER_CLASSES'] = [ 'rest_framework.renderers.JSONRenderer', ] ALLOWED_HOSTS = [ 'api.stage.example.com' ] RAVEN_CONFIG['environment'] = 'stage' CORS_ORIGIN_WHITELIST = [ 'https://stage.example.com', 'https://www.stage.example.com', ] DATABASES = { 'default': { 'ENGINE': 'django.db.backends.mysql', 'NAME': os.getenv('DATABASES_NAME'), 'USER': os.getenv('DATABASES_USER'), 'PASSWORD': os.getenv('DATABASES_PASSWORD'), 'HOST': os.getenv('DATABASES_HOST'), 'PORT': os.getenv('DATABASES_PORT'), 'OPTIONS': {'charset': 'utf8mb4'}, } } CACHES = { 'default': { 'BACKEND': 'django.core.cache.backends.memcached.MemcachedCache', 'LOCATION': '127.0.0.1:11211', } }

true

1c37c0a9cb26e30c5fb60c8dbfd4c2ea7166eb2a

15,493

py

Python

daseki/common/__init__.py

cuthbertLab/daseki

48a16ab1351bd1128c06092065234ea1016a87ef

[ "BSD-3-Clause" ]

null

daseki/common/__init__.py

cuthbertLab/daseki

48a16ab1351bd1128c06092065234ea1016a87ef

[ "BSD-3-Clause" ]

null

daseki/common/__init__.py

cuthbertLab/daseki

48a16ab1351bd1128c06092065234ea1016a87ef

[ "BSD-3-Clause" ]

null

# -*- coding: utf-8 -*- # ----------------------------------------------------------------------------- # Name: common.py # Purpose: Commonly used tools across Daseki # # Authors: Michael Scott Cuthbert # # Copyright: Copyright © 2014-16 Michael Scott Cuthbert / cuthbertLab # License: BSD, see license.txt # ---------------------------------------------------------------------------- ''' Common is a collection of utility functions, objects, constants and dictionaries used throughout daseki. functions in common/ should not import anything from daseki except daseki.exceptionsDS (except in tests and doctests). For historical reasons all the (non-private) functions etc. of the common/ folder are available by importing common. ''' # pylint: disable=wildcard-import from typing import Any from daseki.common.parallel import * import enum import inspect import re import os import sys import time import tempfile import weakref from daseki.exceptionsDS import DasekiException maxRetrosheetYear = 2015 class TeamNum(enum.IntEnum): VISITOR = 0 HOME = 1 # tools for setup.py def sourceFilePath(): ''' Get the Daseki directory that contains source files. This is not the same as the outermost package development directory. ''' dn = os.path.dirname fpThis = inspect.getfile(sourceFilePath) fpDS = dn(dn(fpThis)) # use retro as a test case if 'retro' not in os.listdir(fpDS): raise DasekiException('cannot find expected daseki directory: %s' % fpDS) return fpDS def dataFilePath(): return os.path.join(sourceFilePath(), 'dataFiles') def dataRetrosheet(): return os.path.join(dataFilePath(), 'retrosheet') def dataRetrosheetEvent(): return os.path.join(dataRetrosheet(), 'event') def dataRetrosheetByType(gameType='regular'): if gameType not in ('asg', 'post', 'regular'): raise DasekiException('gameType must be asg, post, or regular, not {0}'.format(gameType)) return os.path.join(dataRetrosheetEvent(), gameType) def gameLogFilePath(): return os.path.join(dataRetrosheet(), 'gamelog') # --------------------- def getDefaultRootTempDir(): ''' returns whatever tempfile.gettempdir() returns plus 'daseki'. Creates the subdirectory if it doesn't exist: >>> from daseki import common >>> import tempfile >>> t = tempfile.gettempdir() >>> #_DOCS_SHOW t '/var/folders/x5/rymq2tx16lqbpytwb1n_cc4c0000gn/T' >>> import os >>> common.getDefaultRootTempDir() == os.path.join(t, 'daseki') True ''' # this returns the root temp dir; this does not create a new dir dstDir = os.path.join(tempfile.gettempdir(), 'daseki') # if this path already exists, we have nothing more to do if os.path.exists(dstDir): return dstDir else: # make this directory as a temp directory try: os.mkdir(dstDir) except OSError: # cannot make the directory dstDir = tempfile.gettempdir() return dstDir # --------------------- GAMEID_MATCH = re.compile(r'([A-Za-z][A-Za-z][A-Za-z])(\d\d\d\d)(\d\d)(\d\d)(\d?)') class GameId(object): ''' A GameId is a 12-character string that embeds information about when and where a game was played. It is designed to uniquely identify any game every played. We can initialize a GameId object from a string: >>> from daseki import common >>> gid = common.GameId('SDN201304090') >>> str(gid) 'SDN201304090' >>> gid <daseki.common.GameId SDN201304090> >>> gid.year 2013 >>> gid.day 9 >>> gid.gameNum # always a string because of weird split double header A, B codes '0' >>> gid.homeTeam 'SDN' Or we can construct the id from all the information: >>> gid2 = common.GameId() >>> gid2.homeTeam = 'ARI' >>> gid2.year = 2000 >>> gid2.month = 9 >>> gid2.day = 22 >>> print(gid2) ARI200009220 Last digit is optional: >>> gid = common.GameId('SDN20130409') >>> str(gid) 'SDN201304090' ''' def __init__(self, gameId=None): self.gameId = gameId self.year = 0 self.month = 0 self.day = 0 self.gameNum = '0' self.homeTeam = 'XXX' if gameId is not None: self.parse() def __repr__(self): return '<{0}.{1} {2}>'.format(self.__module__, self.__class__.__name__, str(self)) def __str__(self): return '{s.homeTeam}{s.year:4d}{s.month:02d}{s.day:02d}{s.gameNum}'.format(s=self) def parse(self): gameId = self.gameId matched = GAMEID_MATCH.match(gameId) if not matched: raise DasekiException('invalid gameId: %s' % gameId) self.homeTeam = matched.group(1).upper() self.year = int(matched.group(2)) self.month = int(matched.group(3)) self.day = int(matched.group(4)) self.gameNum = matched.group(5) if self.gameNum == '': self.gameNum = '0' # --------------------- ordinals = ['Zeroth', 'First', 'Second', 'Third', 'Fourth', 'Fifth', 'Sixth', 'Seventh', 'Eighth', 'Ninth', 'Tenth', 'Eleventh', 'Twelfth', 'Thirteenth', 'Fourteenth', 'Fifteenth', 'Sixteenth', 'Seventeenth', 'Eighteenth', 'Nineteenth', 'Twentieth', 'Twenty-first', 'Twenty-second'] def ordinalAbbreviation(value, plural=False): '''Return the ordinal abbreviations for integers >>> from daseki import common >>> common.ordinalAbbreviation(3) 'rd' >>> common.ordinalAbbreviation(255) 'th' >>> common.ordinalAbbreviation(255, plural=True) 'ths' :rtype: str ''' valueHundreths = value % 100 post = '' if valueHundreths in [11, 12, 13]: post = 'th' else: valueMod = value % 10 if valueMod == 1: post = 'st' elif valueMod in [0, 4, 5, 6, 7, 8, 9]: post = 'th' elif valueMod == 2: post = 'nd' elif valueMod == 3: post = 'rd' if post != 'st' and plural: post += 's' return post # ------------------------------------------------------------------------------- class Timer(object): ''' An object for timing. Call it to get the current time since starting. >>> from daseki import common >>> t = common.Timer() >>> now = t() >>> now_now = t() >>> now_now > now True Call `stop` to stop it. Calling `start` again will reset the number >>> t.stop() >>> stopTime = t() >>> stopNow = t() >>> stopTime == stopNow True All this had better take less than one second! >>> stopTime < 1 True ''' def __init__(self): # start on init self._tStart = time.time() self._tDif = 0 self._tStop = None def start(self): ''' Explicit start method; will clear previous values. Start always happens on initialization. ''' self._tStart = time.time() self._tStop = None # show that a new run has started so __call__ works self._tDif = 0 def stop(self): self._tStop = time.time() self._tDif = self._tStop - self._tStart def clear(self): self._tStop = None self._tDif = 0 self._tStart = None def __call__(self): '''Reports current time or, if stopped, stopped time. ''' # if stopped, gets _tDif; if not stopped, gets current time if self._tStop is None: # if not stopped yet t = time.time() - self._tStart else: t = self._tDif return t def __str__(self): if self._tStop is None: # if not stopped yet t = time.time() - self._tStart else: t = self._tDif return str(round(t, 3)) # --------- def sortModules(moduleList): ''' Sort a lost of imported module names such that most recently modified is first. In ties, last access time is used then module name Will return a different order each time depending on the last mod time :rtype: list(str) ''' sort = [] modNameToMod = {} for mod in moduleList: modNameToMod[mod.__name__] = mod fp = mod.__file__ # returns the pyc file stat = os.stat(fp) lastmod = time.localtime(stat[8]) asctime = time.asctime(lastmod) sort.append((lastmod, asctime, mod.__name__)) sort.sort() sort.reverse() # just return module list return [modNameToMod[modName] for lastmod, asctime, modName in sort] # ------------------------ class SlottedObjectMixin(object): r''' Provides template for classes implementing slots allowing it to be pickled properly. Only use SlottedObjects for objects that we expect to make so many of that memory storage and speed become an issue. For instance an object representing a single play or plate appearence. >>> import pickle >>> from daseki import common >>> class BatAngle(common.SlottedObjectMixin): ... __slots__ = ('horizontal', 'vertical') >>> s = BatAngle() >>> s.horizontal = 35 >>> s.vertical = 20 >>> #_DOCS_SHOW out = pickle.dumps(s) >>> #_DOCS_SHOW t = pickle.loads(out) >>> t = s #_DOCS_HIDE -- cannot define classes for pickling in doctests >>> t.horizontal, t.vertical (35, 20) ''' # CLASS VARIABLES # __slots__ = ('__weakref__') # SPECIAL METHODS # def __getstate__(self): if getattr(self, '__dict__', None) is not None: state = getattr(self, '__dict__').copy() else: state = {} slots = set() for cls in self.__class__.mro(): slots.update(getattr(cls, '__slots__', ())) for slot in slots: sValue = getattr(self, slot, None) if isinstance(sValue, weakref.ref): sValue = sValue() print('Warning: uncaught weakref found in %r - %s, will not be rewrapped' % (self, slot)) state[slot] = sValue if getattr(self, '__dict__', None) is not None: print('We got a dict TOO!', getattr(self, '__class__')) return state def __setstate__(self, state): # print('Restoring state {0}'.format(self.__class__)) for slot, value in state.items(): setattr(self, slot, value) class ParentMixin(SlottedObjectMixin): __slots__ = ('_parent',) def __init__(self, parent=None): self._parent = None if parent is not None: self.parent = parent def __getstate__(self): pValue = getattr(self, '_parent', None) setattr(self, '_parent', None) state = super().__getstate__() state['_parent'] = pValue return state def __setstate__(self, state): super().__setstate__(state) pValue = getattr(self, '_parent', None) try: pValue = weakref.ref(pValue) except TypeError: pass # hard reference now... setattr(self, '_parent', pValue) def parentByClass(self, className): ''' iterate through parents until one of the proper class is found. ''' p = self.parent if p is None: return None if p.__class__.__name__ == className: return p elif hasattr(p, 'parentByClass'): return p.parentByClass(className) else: return None def _getParent(self): _p = self._parent if _p is None: return _p elif isinstance(_p, weakref.ref): return _p() else: return _p def _setParent(self, referent): if referent is None: return try: self._parent = weakref.ref(referent) # if referent is None, will raise a TypeError # if referent is a weakref, will also raise a TypeError # will also raise a type error for string, ints, etc. # slight performance boost rather than checking if None except TypeError: self._parent = referent parent = property(_getParent, _setParent) # ------------------------------------------------------------------------------ def wrapWeakref(referent): ''' utility function that wraps objects as weakrefs but does not wrap already wrapped objects; also prevents wrapping the unwrapable 'None' type, etc. >>> import weakref >>> from daseki import common >>> class Mock(object): ... pass >>> a1 = Mock() >>> ref1 = common.wrapWeakref(a1) >>> ref1 <weakref at 0x101f29ae8; to 'Mock' at 0x101e45358> >>> ref2 = common.wrapWeakref(ref1) >>> ref2 <weakref at 0x101f299af; to 'Mock' at 0x101e45358> >>> ref3 = common.wrapWeakref(5) >>> ref3 5 ''' # if type(referent) is weakref.ref: # if isinstance(referent, weakref.ref): # return referent try: return weakref.ref(referent) # if referent is None, will raise a TypeError # if referent is a weakref, will also raise a TypeError # will also raise a type error for string, ints, etc. # slight performance boost rather than checking if None except TypeError: return referent def unwrapWeakref(referent): ''' Utility function that gets an object that might be an object itself or a weak reference to an object. It returns obj() if it's a weakref or another callable. and obj if it's not. >>> from daseki import common >>> class Mock(object): ... strong: Any ... weak: Any >>> a1 = Mock() >>> a2 = Mock() >>> a2.strong = a1 >>> a2.weak = common.wrapWeakref(a1) >>> common.unwrapWeakref(a2.strong) is a1 True >>> common.unwrapWeakref(a2.weak) is a1 True >>> common.unwrapWeakref(a2.strong) is common.unwrapWeakref(a2.weak) True ''' try: return referent() except TypeError: return referent def warn(*msg): ''' To print a warning to the user, send a list of strings to this method. Similar to printDebug but even if debug is off. ''' msg = formatStr(msg) sys.stderr.write(msg) def formatStr(msg, *arguments, **keywords): '''Format one or more data elements into string suitable for printing straight to stderr or other outputs >>> from daseki import common >>> a = common.formatStr('test', '1', 2, 3) >>> print(a) test 1 2 3 <BLANKLINE> ''' if 'format' in keywords: formatType = keywords['format'] else: formatType = None msg = [msg] + list(arguments) for i in range(len(msg)): x = msg[i] if isinstance(x, bytes): msg[i] = x.decode('utf-8') if not isinstance(x, str): try: msg[i] = repr(x) except TypeError: try: msg[i] = x.decode('utf-8') except AttributeError: msg[i] = '<__repr__ failed for ' + x.__class__.__name__ + '>' except AttributeError: # or something msg[i] = '<__repr__ failed for ' + x.__class__.__name__ + '>' if formatType == 'block': return '\n*** '.join(msg)+'\n' else: # catch all others return ' '.join(msg)+'\n' if __name__ == '__main__': import daseki daseki.mainTest()

28.117967

97

0.57981

from typing import Any from daseki.common.parallel import * import enum import inspect import re import os import sys import time import tempfile import weakref from daseki.exceptionsDS import DasekiException maxRetrosheetYear = 2015 class TeamNum(enum.IntEnum): VISITOR = 0 HOME = 1 def sourceFilePath(): dn = os.path.dirname fpThis = inspect.getfile(sourceFilePath) fpDS = dn(dn(fpThis)) if 'retro' not in os.listdir(fpDS): raise DasekiException('cannot find expected daseki directory: %s' % fpDS) return fpDS def dataFilePath(): return os.path.join(sourceFilePath(), 'dataFiles') def dataRetrosheet(): return os.path.join(dataFilePath(), 'retrosheet') def dataRetrosheetEvent(): return os.path.join(dataRetrosheet(), 'event') def dataRetrosheetByType(gameType='regular'): if gameType not in ('asg', 'post', 'regular'): raise DasekiException('gameType must be asg, post, or regular, not {0}'.format(gameType)) return os.path.join(dataRetrosheetEvent(), gameType) def gameLogFilePath(): return os.path.join(dataRetrosheet(), 'gamelog') def getDefaultRootTempDir(): dstDir = os.path.join(tempfile.gettempdir(), 'daseki') if os.path.exists(dstDir): return dstDir else: try: os.mkdir(dstDir) except OSError: dstDir = tempfile.gettempdir() return dstDir GAMEID_MATCH = re.compile(r'([A-Za-z][A-Za-z][A-Za-z])(\d\d\d\d)(\d\d)(\d\d)(\d?)') class GameId(object): def __init__(self, gameId=None): self.gameId = gameId self.year = 0 self.month = 0 self.day = 0 self.gameNum = '0' self.homeTeam = 'XXX' if gameId is not None: self.parse() def __repr__(self): return '<{0}.{1} {2}>'.format(self.__module__, self.__class__.__name__, str(self)) def __str__(self): return '{s.homeTeam}{s.year:4d}{s.month:02d}{s.day:02d}{s.gameNum}'.format(s=self) def parse(self): gameId = self.gameId matched = GAMEID_MATCH.match(gameId) if not matched: raise DasekiException('invalid gameId: %s' % gameId) self.homeTeam = matched.group(1).upper() self.year = int(matched.group(2)) self.month = int(matched.group(3)) self.day = int(matched.group(4)) self.gameNum = matched.group(5) if self.gameNum == '': self.gameNum = '0' ordinals = ['Zeroth', 'First', 'Second', 'Third', 'Fourth', 'Fifth', 'Sixth', 'Seventh', 'Eighth', 'Ninth', 'Tenth', 'Eleventh', 'Twelfth', 'Thirteenth', 'Fourteenth', 'Fifteenth', 'Sixteenth', 'Seventeenth', 'Eighteenth', 'Nineteenth', 'Twentieth', 'Twenty-first', 'Twenty-second'] def ordinalAbbreviation(value, plural=False): valueHundreths = value % 100 post = '' if valueHundreths in [11, 12, 13]: post = 'th' else: valueMod = value % 10 if valueMod == 1: post = 'st' elif valueMod in [0, 4, 5, 6, 7, 8, 9]: post = 'th' elif valueMod == 2: post = 'nd' elif valueMod == 3: post = 'rd' if post != 'st' and plural: post += 's' return post class Timer(object): def __init__(self): self._tStart = time.time() self._tDif = 0 self._tStop = None def start(self): self._tStart = time.time() self._tStop = None self._tDif = 0 def stop(self): self._tStop = time.time() self._tDif = self._tStop - self._tStart def clear(self): self._tStop = None self._tDif = 0 self._tStart = None def __call__(self): if self._tStop is None: t = time.time() - self._tStart else: t = self._tDif return t def __str__(self): if self._tStop is None: t = time.time() - self._tStart else: t = self._tDif return str(round(t, 3)) def sortModules(moduleList): sort = [] modNameToMod = {} for mod in moduleList: modNameToMod[mod.__name__] = mod fp = mod.__file__ stat = os.stat(fp) lastmod = time.localtime(stat[8]) asctime = time.asctime(lastmod) sort.append((lastmod, asctime, mod.__name__)) sort.sort() sort.reverse() return [modNameToMod[modName] for lastmod, asctime, modName in sort] class SlottedObjectMixin(object): __slots__ = ('__weakref__') def __getstate__(self): if getattr(self, '__dict__', None) is not None: state = getattr(self, '__dict__').copy() else: state = {} slots = set() for cls in self.__class__.mro(): slots.update(getattr(cls, '__slots__', ())) for slot in slots: sValue = getattr(self, slot, None) if isinstance(sValue, weakref.ref): sValue = sValue() print('Warning: uncaught weakref found in %r - %s, will not be rewrapped' % (self, slot)) state[slot] = sValue if getattr(self, '__dict__', None) is not None: print('We got a dict TOO!', getattr(self, '__class__')) return state def __setstate__(self, state): for slot, value in state.items(): setattr(self, slot, value) class ParentMixin(SlottedObjectMixin): __slots__ = ('_parent',) def __init__(self, parent=None): self._parent = None if parent is not None: self.parent = parent def __getstate__(self): pValue = getattr(self, '_parent', None) setattr(self, '_parent', None) state = super().__getstate__() state['_parent'] = pValue return state def __setstate__(self, state): super().__setstate__(state) pValue = getattr(self, '_parent', None) try: pValue = weakref.ref(pValue) except TypeError: pass setattr(self, '_parent', pValue) def parentByClass(self, className): p = self.parent if p is None: return None if p.__class__.__name__ == className: return p elif hasattr(p, 'parentByClass'): return p.parentByClass(className) else: return None def _getParent(self): _p = self._parent if _p is None: return _p elif isinstance(_p, weakref.ref): return _p() else: return _p def _setParent(self, referent): if referent is None: return try: self._parent = weakref.ref(referent) except TypeError: self._parent = referent parent = property(_getParent, _setParent) def wrapWeakref(referent): try: return weakref.ref(referent) except TypeError: return referent def unwrapWeakref(referent): try: return referent() except TypeError: return referent def warn(*msg): msg = formatStr(msg) sys.stderr.write(msg) def formatStr(msg, *arguments, **keywords): if 'format' in keywords: formatType = keywords['format'] else: formatType = None msg = [msg] + list(arguments) for i in range(len(msg)): x = msg[i] if isinstance(x, bytes): msg[i] = x.decode('utf-8') if not isinstance(x, str): try: msg[i] = repr(x) except TypeError: try: msg[i] = x.decode('utf-8') except AttributeError: msg[i] = '<__repr__ failed for ' + x.__class__.__name__ + '>' except AttributeError: msg[i] = '<__repr__ failed for ' + x.__class__.__name__ + '>' if formatType == 'block': return '\n*** '.join(msg)+'\n' else: return ' '.join(msg)+'\n' if __name__ == '__main__': import daseki daseki.mainTest()

true

1c37c1f7bdcfbe9db8a303114887b9cbea05e3b6

4,260

py

Python

bot/utils.py

Alpha-Omega-United/discord-bot

d395c1e139de8b59773fb0a222d08f68105a811c

[ "MIT" ]

1

2021-09-21T07:50:39.000Z

bot/utils.py

Alpha-Omega-United/discord-bot

d395c1e139de8b59773fb0a222d08f68105a811c

[ "MIT" ]

1

2021-07-30T20:31:49.000Z

2021-08-17T16:50:43.000Z

bot/utils.py

Alpha-Omega-United/discord-bot

d395c1e139de8b59773fb0a222d08f68105a811c

[ "MIT" ]

1

2021-08-10T16:41:39.000Z

"""Helper functions.""" from __future__ import annotations from dataclasses import dataclass from typing import TYPE_CHECKING import hikari from bot import constants if TYPE_CHECKING: from typing import Awaitable import tanjun def is_admin(member: hikari.Member) -> bool: """ Check if a member is an admin. Args: member (hikari.Member): Member to check Returns: bool: If the member is an admin or not """ roles = member.get_roles() return any(role.id == constants.ADMIN_ROLE_ID for role in roles) async def wait_for_interaction( ctx: tanjun.SlashContext, message: hikari.Message, timeout: int | float = 60 * 5, ) -> hikari.ComponentInteraction: """ Wait for an interaction to happen on the message. Args: ctx (tanjun.SlashContext): The context the message was sent in. message (hikari.Message): The message to wait for interactions on. timeout (int | float): How long to wait before stop waiting. Defaults to 60*5 (5 minutes). Returns: hikari.ComponentInteraction: the event, garantied to contain a ComponentInteraction Raises: TypeError: ctx.events is None """ def predicate(event: hikari.InteractionCreateEvent) -> bool: inte = event.interaction return ( isinstance(inte, hikari.ComponentInteraction) and inte.message.id == message.id ) if ctx.events is None: raise TypeError("ctx.events is None") event = await ctx.events.wait_for( hikari.InteractionCreateEvent, timeout=timeout, predicate=predicate ) return event.interaction # type: ignore @dataclass(frozen=True) class ButtonInfo: """Info about a discord button.""" label: str style: hikari.InteractiveButtonTypesT emoji: hikari.Snowflakeish | hikari.Emoji | str | hikari.UndefinedType = ( hikari.UNDEFINED ) async def confirmation_embed( ctx: tanjun.SlashContext, *, callback: Awaitable[None], embed: hikari.Embed, confirm_button: ButtonInfo, deny_button: ButtonInfo = ButtonInfo("Cancel", hikari.ButtonStyle.DANGER), ) -> None: """ Create a confirmation embed and call a callback if the user confirms. Args: ctx (tanjun.SlashContext): The context to create the popup in callback (Awaitable[None]): The callback to call if the user click confirm embed (hikari.Embed): The embed to present the user with. confirm_button (ButtonInfo): The button the confirms the selection. deny_button (ButtonInfo): The button to cancel the action. Defaults to ButtonInfo("Cancel", hikari.ButtonStyle.DANGER). """ confirm_button_id = "confirm" deny_button_id = "deny" buttons = ( ctx.rest.build_action_row() .add_button(confirm_button.style, confirm_button_id) .set_label(confirm_button.label) .set_emoji(confirm_button.emoji) .add_to_container() .add_button(deny_button.style, deny_button_id) .set_label(deny_button.label) .set_emoji(deny_button.emoji) .add_to_container() ) message = await ctx.respond( embed=embed, component=buttons, ensure_result=True ) interaction = await wait_for_interaction(ctx, message) if embed.title is None: embed.title = "" if interaction.custom_id == confirm_button_id: await callback embed.color = constants.Colors.GREEN embed.title += ": DONE" else: embed.color = constants.Colors.RED embed.title += ": Canceld" # disable buttons buttons = ( ctx.rest.build_action_row() .add_button(confirm_button.style, confirm_button_id) .set_label(confirm_button.label) .set_emoji(confirm_button.emoji) .set_is_disabled(True) .add_to_container() .add_button(deny_button.style, deny_button_id) .set_label(deny_button.label) .set_emoji(deny_button.emoji) .set_is_disabled(True) .add_to_container() ) await interaction.create_initial_response( hikari.ResponseType.MESSAGE_UPDATE, embed=embed, component=buttons )

28.026316

82

0.661972

from __future__ import annotations from dataclasses import dataclass from typing import TYPE_CHECKING import hikari from bot import constants if TYPE_CHECKING: from typing import Awaitable import tanjun def is_admin(member: hikari.Member) -> bool: roles = member.get_roles() return any(role.id == constants.ADMIN_ROLE_ID for role in roles) async def wait_for_interaction( ctx: tanjun.SlashContext, message: hikari.Message, timeout: int | float = 60 * 5, ) -> hikari.ComponentInteraction: def predicate(event: hikari.InteractionCreateEvent) -> bool: inte = event.interaction return ( isinstance(inte, hikari.ComponentInteraction) and inte.message.id == message.id ) if ctx.events is None: raise TypeError("ctx.events is None") event = await ctx.events.wait_for( hikari.InteractionCreateEvent, timeout=timeout, predicate=predicate ) return event.interaction @dataclass(frozen=True) class ButtonInfo: label: str style: hikari.InteractiveButtonTypesT emoji: hikari.Snowflakeish | hikari.Emoji | str | hikari.UndefinedType = ( hikari.UNDEFINED ) async def confirmation_embed( ctx: tanjun.SlashContext, *, callback: Awaitable[None], embed: hikari.Embed, confirm_button: ButtonInfo, deny_button: ButtonInfo = ButtonInfo("Cancel", hikari.ButtonStyle.DANGER), ) -> None: confirm_button_id = "confirm" deny_button_id = "deny" buttons = ( ctx.rest.build_action_row() .add_button(confirm_button.style, confirm_button_id) .set_label(confirm_button.label) .set_emoji(confirm_button.emoji) .add_to_container() .add_button(deny_button.style, deny_button_id) .set_label(deny_button.label) .set_emoji(deny_button.emoji) .add_to_container() ) message = await ctx.respond( embed=embed, component=buttons, ensure_result=True ) interaction = await wait_for_interaction(ctx, message) if embed.title is None: embed.title = "" if interaction.custom_id == confirm_button_id: await callback embed.color = constants.Colors.GREEN embed.title += ": DONE" else: embed.color = constants.Colors.RED embed.title += ": Canceld" buttons = ( ctx.rest.build_action_row() .add_button(confirm_button.style, confirm_button_id) .set_label(confirm_button.label) .set_emoji(confirm_button.emoji) .set_is_disabled(True) .add_to_container() .add_button(deny_button.style, deny_button_id) .set_label(deny_button.label) .set_emoji(deny_button.emoji) .set_is_disabled(True) .add_to_container() ) await interaction.create_initial_response( hikari.ResponseType.MESSAGE_UPDATE, embed=embed, component=buttons )

true

1c37c32f6b96cbaa4f9672e1a1276cb7d4d1aad4

5,702

py

Python

modules/event.py

TheApertureProject/Yume-Bot

9b1219958f1c43489c0fbc33825ae7656eeea02e

[ "MIT" ]

1

2020-06-04T17:26:13.000Z

modules/event.py

TheApertureProject/Yume-Bot

9b1219958f1c43489c0fbc33825ae7656eeea02e

[ "MIT" ]

null

modules/event.py

TheApertureProject/Yume-Bot

9b1219958f1c43489c0fbc33825ae7656eeea02e

[ "MIT" ]

null

# Copyright (c) 2019. # MIT License # # Copyright (c) 2019 YumeNetwork # # 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 random import discord from discord.ext import commands from modules.sql.guilddb import GuildDB from modules.utils import lists class Event(commands.Cog): conf = {} def __init__(self, bot): self.bot = bot self.config = bot.config @commands.Cog.listener() async def on_member_join(self, member: discord.Member): """ :param member: The member who joined the guild """ guild = GuildDB.get_one(member.guild.id) if guild.greet: channel = self.bot.get_channel(int(guild.greet_chan)) greet = random.choice(lists.greet) em = discord.Embed(timestamp=member.joined_at) em.set_author(name="Welcome", icon_url=member.avatar_url) em.set_footer(text=f'{member.name}') em.description = f"{greet}" await channel.send(embed=em) if guild.stats_channels: try: category = discord.utils.get( member.guild.categories, id=int(guild.stats_category)) except discord.HTTPException: return else: if not isinstance(category, discord.CategoryChannel): return for channel in category.channels: try: await channel.delete() except discord.Forbidden: return except discord.HTTPException: return overwrite = { member.guild.default_role: discord.PermissionOverwrite(connect=False), } await member.guild.create_voice_channel(f'Users : {len(member.guild.members)}', overwrites=overwrite, category=category) bots = [] for user in member.guild.members: if user.bot is True: bots.append(user) await member.guild.create_voice_channel(f'Bots : {len(bots)}', overwrites=overwrite, category=category) await member.guild.create_voice_channel(f'Members : {len(member.guild.members) - len(bots)}', overwrites=overwrite, category=category) @commands.Cog.listener() async def on_member_remove(self, member): """ :param member: The member who has left """ guild = GuildDB.get_one(member.guild.id) if guild.greet: try: channel = member.guild.get_channel(int(guild.greet_chan)) except discord.HTTPException: pass else: greet = random.choice(lists.leave) em = discord.Embed(timestamp=member.joined_at) em.set_author(name="Bye", icon_url=member.avatar_url) em.set_footer(text=f'{member.name}') em.description = f"{greet}" await channel.send(embed=em) if guild.stats_channels: try: category = discord.utils.get( member.guild.categories, id=int(guild.stats_category)) except discord.HTTPException: return else: if not isinstance(category, discord.CategoryChannel): return for channel in category.channels: try: await channel.delete() except discord.Forbidden: return except discord.HTTPException: return overwrite = { member.guild.default_role: discord.PermissionOverwrite(connect=False), } await member.guild.create_voice_channel(f'Users : {len(member.guild.members)}', overwrites=overwrite, category=category) bots = [] for user in member.guild.members: if user.bot is True: bots.append(user) await member.guild.create_voice_channel(f'Bots : {len(bots)}', overwrites=overwrite, category=category) await member.guild.create_voice_channel(f'Members : {len(member.guild.members) - len(bots)}', overwrites=overwrite, category=category) def setup(bot): bot.add_cog(Event(bot))

38.789116

119

0.571554

import random import discord from discord.ext import commands from modules.sql.guilddb import GuildDB from modules.utils import lists class Event(commands.Cog): conf = {} def __init__(self, bot): self.bot = bot self.config = bot.config @commands.Cog.listener() async def on_member_join(self, member: discord.Member): guild = GuildDB.get_one(member.guild.id) if guild.greet: channel = self.bot.get_channel(int(guild.greet_chan)) greet = random.choice(lists.greet) em = discord.Embed(timestamp=member.joined_at) em.set_author(name="Welcome", icon_url=member.avatar_url) em.set_footer(text=f'{member.name}') em.description = f"{greet}" await channel.send(embed=em) if guild.stats_channels: try: category = discord.utils.get( member.guild.categories, id=int(guild.stats_category)) except discord.HTTPException: return else: if not isinstance(category, discord.CategoryChannel): return for channel in category.channels: try: await channel.delete() except discord.Forbidden: return except discord.HTTPException: return overwrite = { member.guild.default_role: discord.PermissionOverwrite(connect=False), } await member.guild.create_voice_channel(f'Users : {len(member.guild.members)}', overwrites=overwrite, category=category) bots = [] for user in member.guild.members: if user.bot is True: bots.append(user) await member.guild.create_voice_channel(f'Bots : {len(bots)}', overwrites=overwrite, category=category) await member.guild.create_voice_channel(f'Members : {len(member.guild.members) - len(bots)}', overwrites=overwrite, category=category) @commands.Cog.listener() async def on_member_remove(self, member): guild = GuildDB.get_one(member.guild.id) if guild.greet: try: channel = member.guild.get_channel(int(guild.greet_chan)) except discord.HTTPException: pass else: greet = random.choice(lists.leave) em = discord.Embed(timestamp=member.joined_at) em.set_author(name="Bye", icon_url=member.avatar_url) em.set_footer(text=f'{member.name}') em.description = f"{greet}" await channel.send(embed=em) if guild.stats_channels: try: category = discord.utils.get( member.guild.categories, id=int(guild.stats_category)) except discord.HTTPException: return else: if not isinstance(category, discord.CategoryChannel): return for channel in category.channels: try: await channel.delete() except discord.Forbidden: return except discord.HTTPException: return overwrite = { member.guild.default_role: discord.PermissionOverwrite(connect=False), } await member.guild.create_voice_channel(f'Users : {len(member.guild.members)}', overwrites=overwrite, category=category) bots = [] for user in member.guild.members: if user.bot is True: bots.append(user) await member.guild.create_voice_channel(f'Bots : {len(bots)}', overwrites=overwrite, category=category) await member.guild.create_voice_channel(f'Members : {len(member.guild.members) - len(bots)}', overwrites=overwrite, category=category) def setup(bot): bot.add_cog(Event(bot))

true

1c37c515ccfe0f90bc286e7c0bc33482d12a3037

7,574

py

Python

src/oci/data_safe/models/tls_config.py

Manny27nyc/oci-python-sdk

de60b04e07a99826254f7255e992f41772902df7

[ "Apache-2.0", "BSD-3-Clause" ]

249

2017-09-11T22:06:05.000Z

2022-03-04T17:09:29.000Z

src/oci/data_safe/models/tls_config.py

Manny27nyc/oci-python-sdk

de60b04e07a99826254f7255e992f41772902df7

[ "Apache-2.0", "BSD-3-Clause" ]

228

2017-09-11T23:07:26.000Z

2022-03-23T10:58:50.000Z

src/oci/data_safe/models/tls_config.py

Manny27nyc/oci-python-sdk

de60b04e07a99826254f7255e992f41772902df7

[ "Apache-2.0", "BSD-3-Clause" ]

224

2017-09-27T07:32:43.000Z

2022-03-25T16:55:42.000Z

# coding: utf-8 # Copyright (c) 2016, 2021, Oracle and/or its affiliates. All rights reserved. # This software is dual-licensed to you under the Universal Permissive License (UPL) 1.0 as shown at https://oss.oracle.com/licenses/upl or Apache License 2.0 as shown at http://www.apache.org/licenses/LICENSE-2.0. You may choose either license. from oci.util import formatted_flat_dict, NONE_SENTINEL, value_allowed_none_or_none_sentinel # noqa: F401 from oci.decorators import init_model_state_from_kwargs @init_model_state_from_kwargs class TlsConfig(object): """ The details required to establish a TLS enabled connection. """ #: A constant which can be used with the status property of a TlsConfig. #: This constant has a value of "ENABLED" STATUS_ENABLED = "ENABLED" #: A constant which can be used with the status property of a TlsConfig. #: This constant has a value of "DISABLED" STATUS_DISABLED = "DISABLED" #: A constant which can be used with the certificate_store_type property of a TlsConfig. #: This constant has a value of "JKS" CERTIFICATE_STORE_TYPE_JKS = "JKS" def __init__(self, **kwargs): """ Initializes a new TlsConfig object with values from keyword arguments. The following keyword arguments are supported (corresponding to the getters/setters of this class): :param status: The value to assign to the status property of this TlsConfig. Allowed values for this property are: "ENABLED", "DISABLED", 'UNKNOWN_ENUM_VALUE'. Any unrecognized values returned by a service will be mapped to 'UNKNOWN_ENUM_VALUE'. :type status: str :param certificate_store_type: The value to assign to the certificate_store_type property of this TlsConfig. Allowed values for this property are: "JKS", 'UNKNOWN_ENUM_VALUE'. Any unrecognized values returned by a service will be mapped to 'UNKNOWN_ENUM_VALUE'. :type certificate_store_type: str :param store_password: The value to assign to the store_password property of this TlsConfig. :type store_password: str :param trust_store_content: The value to assign to the trust_store_content property of this TlsConfig. :type trust_store_content: str :param key_store_content: The value to assign to the key_store_content property of this TlsConfig. :type key_store_content: str """ self.swagger_types = { 'status': 'str', 'certificate_store_type': 'str', 'store_password': 'str', 'trust_store_content': 'str', 'key_store_content': 'str' } self.attribute_map = { 'status': 'status', 'certificate_store_type': 'certificateStoreType', 'store_password': 'storePassword', 'trust_store_content': 'trustStoreContent', 'key_store_content': 'keyStoreContent' } self._status = None self._certificate_store_type = None self._store_password = None self._trust_store_content = None self._key_store_content = None @property def status(self): """ **[Required]** Gets the status of this TlsConfig. Status to represent whether the database connection is TLS enabled or not. Allowed values for this property are: "ENABLED", "DISABLED", 'UNKNOWN_ENUM_VALUE'. Any unrecognized values returned by a service will be mapped to 'UNKNOWN_ENUM_VALUE'. :return: The status of this TlsConfig. :rtype: str """ return self._status @status.setter def status(self, status): """ Sets the status of this TlsConfig. Status to represent whether the database connection is TLS enabled or not. :param status: The status of this TlsConfig. :type: str """ allowed_values = ["ENABLED", "DISABLED"] if not value_allowed_none_or_none_sentinel(status, allowed_values): status = 'UNKNOWN_ENUM_VALUE' self._status = status @property def certificate_store_type(self): """ Gets the certificate_store_type of this TlsConfig. The format of the certificate store. Allowed values for this property are: "JKS", 'UNKNOWN_ENUM_VALUE'. Any unrecognized values returned by a service will be mapped to 'UNKNOWN_ENUM_VALUE'. :return: The certificate_store_type of this TlsConfig. :rtype: str """ return self._certificate_store_type @certificate_store_type.setter def certificate_store_type(self, certificate_store_type): """ Sets the certificate_store_type of this TlsConfig. The format of the certificate store. :param certificate_store_type: The certificate_store_type of this TlsConfig. :type: str """ allowed_values = ["JKS"] if not value_allowed_none_or_none_sentinel(certificate_store_type, allowed_values): certificate_store_type = 'UNKNOWN_ENUM_VALUE' self._certificate_store_type = certificate_store_type @property def store_password(self): """ Gets the store_password of this TlsConfig. The password to read the trust store and key store files, if they are password protected. :return: The store_password of this TlsConfig. :rtype: str """ return self._store_password @store_password.setter def store_password(self, store_password): """ Sets the store_password of this TlsConfig. The password to read the trust store and key store files, if they are password protected. :param store_password: The store_password of this TlsConfig. :type: str """ self._store_password = store_password @property def trust_store_content(self): """ Gets the trust_store_content of this TlsConfig. Base64 encoded string of trust store file content. :return: The trust_store_content of this TlsConfig. :rtype: str """ return self._trust_store_content @trust_store_content.setter def trust_store_content(self, trust_store_content): """ Sets the trust_store_content of this TlsConfig. Base64 encoded string of trust store file content. :param trust_store_content: The trust_store_content of this TlsConfig. :type: str """ self._trust_store_content = trust_store_content @property def key_store_content(self): """ Gets the key_store_content of this TlsConfig. Base64 encoded string of key store file content. :return: The key_store_content of this TlsConfig. :rtype: str """ return self._key_store_content @key_store_content.setter def key_store_content(self, key_store_content): """ Sets the key_store_content of this TlsConfig. Base64 encoded string of key store file content. :param key_store_content: The key_store_content of this TlsConfig. :type: str """ self._key_store_content = key_store_content def __repr__(self): return formatted_flat_dict(self) def __eq__(self, other): if other is None: return False return self.__dict__ == other.__dict__ def __ne__(self, other): return not self == other

33.964126

245

0.66319

from oci.util import formatted_flat_dict, NONE_SENTINEL, value_allowed_none_or_none_sentinel from oci.decorators import init_model_state_from_kwargs @init_model_state_from_kwargs class TlsConfig(object): STATUS_ENABLED = "ENABLED" STATUS_DISABLED = "DISABLED" CERTIFICATE_STORE_TYPE_JKS = "JKS" def __init__(self, **kwargs): self.swagger_types = { 'status': 'str', 'certificate_store_type': 'str', 'store_password': 'str', 'trust_store_content': 'str', 'key_store_content': 'str' } self.attribute_map = { 'status': 'status', 'certificate_store_type': 'certificateStoreType', 'store_password': 'storePassword', 'trust_store_content': 'trustStoreContent', 'key_store_content': 'keyStoreContent' } self._status = None self._certificate_store_type = None self._store_password = None self._trust_store_content = None self._key_store_content = None @property def status(self): return self._status @status.setter def status(self, status): allowed_values = ["ENABLED", "DISABLED"] if not value_allowed_none_or_none_sentinel(status, allowed_values): status = 'UNKNOWN_ENUM_VALUE' self._status = status @property def certificate_store_type(self): return self._certificate_store_type @certificate_store_type.setter def certificate_store_type(self, certificate_store_type): allowed_values = ["JKS"] if not value_allowed_none_or_none_sentinel(certificate_store_type, allowed_values): certificate_store_type = 'UNKNOWN_ENUM_VALUE' self._certificate_store_type = certificate_store_type @property def store_password(self): return self._store_password @store_password.setter def store_password(self, store_password): self._store_password = store_password @property def trust_store_content(self): return self._trust_store_content @trust_store_content.setter def trust_store_content(self, trust_store_content): self._trust_store_content = trust_store_content @property def key_store_content(self): return self._key_store_content @key_store_content.setter def key_store_content(self, key_store_content): self._key_store_content = key_store_content def __repr__(self): return formatted_flat_dict(self) def __eq__(self, other): if other is None: return False return self.__dict__ == other.__dict__ def __ne__(self, other): return not self == other

true

1c37c566dd1447eac08426f66222b65b38b12130

1,121

py

Python

examples/standalone_plan.py

dpcomp-org/ektelo

7629fbf106f9b9568c66a0b97f6005280022c3d8

[ "Apache-2.0" ]

null

examples/standalone_plan.py

dpcomp-org/ektelo

7629fbf106f9b9568c66a0b97f6005280022c3d8

[ "Apache-2.0" ]

1

2019-04-09T20:51:32.000Z

examples/standalone_plan.py

dpcomp-org/ektelo

7629fbf106f9b9568c66a0b97f6005280022c3d8

[ "Apache-2.0" ]

null

""" Example of the invocation of a standalone plan """ from ektelo import data from ektelo import workload from ektelo.plans import standalone from ektelo.private import transformation import os import numpy as np import yaml CSV_PATH = os.environ['EKTELO_DATA'] CONFIG_PATH = os.path.join(os.environ['EKTELO_HOME'], 'resources', 'config') # Load relation filename = os.path.join(CSV_PATH, 'cps.csv') config_file = os.path.join(CONFIG_PATH, 'cps.yml') config = yaml.load(open(config_file, 'r').read())['cps_config'] R = data.Relation(config).load_csv(filename, ',') # Choose reduced domain for relation domain = (10, 1, 7, 1, 1) # Vectorize relation x = transformation.Vectorize('CPS', reduced_domain=domain).transform(R) # Setup arbitrary constants for MWEM seed = 0 ratio = 0.5 rounds = 3 data_scale = 1e5 use_history = True epsilon = 0.1 # Create query workload W = workload.RandomRange(None, (np.prod(domain),), 25) # Calculate noisy estimate of x x_hat = standalone.Mwem(ratio, rounds, data_scale, domain, use_history).Run(W, x, epsilon, seed) # Report noisy query responses print(W.get_matrix() * x_hat)

26.690476

96

0.742194

from ektelo import data from ektelo import workload from ektelo.plans import standalone from ektelo.private import transformation import os import numpy as np import yaml CSV_PATH = os.environ['EKTELO_DATA'] CONFIG_PATH = os.path.join(os.environ['EKTELO_HOME'], 'resources', 'config') filename = os.path.join(CSV_PATH, 'cps.csv') config_file = os.path.join(CONFIG_PATH, 'cps.yml') config = yaml.load(open(config_file, 'r').read())['cps_config'] R = data.Relation(config).load_csv(filename, ',') domain = (10, 1, 7, 1, 1) x = transformation.Vectorize('CPS', reduced_domain=domain).transform(R) seed = 0 ratio = 0.5 rounds = 3 data_scale = 1e5 use_history = True epsilon = 0.1 W = workload.RandomRange(None, (np.prod(domain),), 25) x_hat = standalone.Mwem(ratio, rounds, data_scale, domain, use_history).Run(W, x, epsilon, seed) print(W.get_matrix() * x_hat)

true

1c37c5dec7c9b26fac370eb8d7e111002c09524a

171

py

Python

.history/calculator_factories_20210629130649.py

Aleff13/calculadora-tkinter

01e169d3c1d128976eb3a41ea1f53f11d6157e44

[ "MIT" ]

null

.history/calculator_factories_20210629130649.py

Aleff13/calculadora-tkinter

01e169d3c1d128976eb3a41ea1f53f11d6157e44

[ "MIT" ]

null

.history/calculator_factories_20210629130649.py

Aleff13/calculadora-tkinter

01e169d3c1d128976eb3a41ea1f53f11d6157e44

[ "MIT" ]

null

import tkinter as tk def make_root() -> tk.Tk: root = tk.Tk() root.title("Calculator") root.config(padx=10, pady=10, background="white") return root

21.375

53

0.625731

import tkinter as tk def make_root() -> tk.Tk: root = tk.Tk() root.title("Calculator") root.config(padx=10, pady=10, background="white") return root

true

1c37c69715879ce386b01293ebb811b4037e288a

6,823

py

Python

third_party/boto/manage/task.py

stdft112/depot_tools

52c7211807930272424213ff6127c209de790eca

[ "BSD-3-Clause" ]

2,151

2020-04-18T07:31:17.000Z

2022-03-31T08:39:18.000Z

third_party/boto/manage/task.py

stdft112/depot_tools

52c7211807930272424213ff6127c209de790eca

[ "BSD-3-Clause" ]

395

2020-04-18T08:22:18.000Z

2021-12-08T13:04:49.000Z

third_party/boto/manage/task.py

stdft112/depot_tools

52c7211807930272424213ff6127c209de790eca

[ "BSD-3-Clause" ]

338

2020-04-18T08:03:10.000Z

2022-03-29T12:33:22.000Z

# Copyright (c) 2006-2009 Mitch Garnaat http://garnaat.org/ # # 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, dis- # tribute, sublicense, and/or sell copies of the Software, and to permit # persons to whom the Software is furnished to do so, subject to the fol- # lowing 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 MERCHANTABIL- # ITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT # SHALL THE AUTHOR 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 boto from boto.sdb.db.property import StringProperty, DateTimeProperty, IntegerProperty from boto.sdb.db.model import Model import datetime, subprocess, StringIO, time def check_hour(val): if val == '*': return if int(val) < 0 or int(val) > 23: raise ValueError class Task(Model): """ A scheduled, repeating task that can be executed by any participating servers. The scheduling is similar to cron jobs. Each task has an hour attribute. The allowable values for hour are [0-23|*]. To keep the operation reasonably efficient and not cause excessive polling, the minimum granularity of a Task is hourly. Some examples: hour='*' - the task would be executed each hour hour='3' - the task would be executed at 3AM GMT each day. """ name = StringProperty() hour = StringProperty(required=True, validator=check_hour, default='*') command = StringProperty(required=True) last_executed = DateTimeProperty() last_status = IntegerProperty() last_output = StringProperty() message_id = StringProperty() @classmethod def start_all(cls, queue_name): for task in cls.all(): task.start(queue_name) def __init__(self, id=None, **kw): Model.__init__(self, id, **kw) self.hourly = self.hour == '*' self.daily = self.hour != '*' self.now = datetime.datetime.utcnow() def check(self): """ Determine how long until the next scheduled time for a Task. Returns the number of seconds until the next scheduled time or zero if the task needs to be run immediately. If it's an hourly task and it's never been run, run it now. If it's a daily task and it's never been run and the hour is right, run it now. """ boto.log.info('checking Task[%s]-now=%s, last=%s' % (self.name, self.now, self.last_executed)) if self.hourly and not self.last_executed: return 0 if self.daily and not self.last_executed: if int(self.hour) == self.now.hour: return 0 else: return max( (int(self.hour)-self.now.hour), (self.now.hour-int(self.hour)) )*60*60 delta = self.now - self.last_executed if self.hourly: if delta.seconds >= 60*60: return 0 else: return 60*60 - delta.seconds else: if int(self.hour) == self.now.hour: if delta.days >= 1: return 0 else: return 82800 # 23 hours, just to be safe else: return max( (int(self.hour)-self.now.hour), (self.now.hour-int(self.hour)) )*60*60 def _run(self, msg, vtimeout): boto.log.info('Task[%s] - running:%s' % (self.name, self.command)) log_fp = StringIO.StringIO() process = subprocess.Popen(self.command, shell=True, stdin=subprocess.PIPE, stdout=subprocess.PIPE, stderr=subprocess.PIPE) nsecs = 5 current_timeout = vtimeout while process.poll() == None: boto.log.info('nsecs=%s, timeout=%s' % (nsecs, current_timeout)) if nsecs >= current_timeout: current_timeout += vtimeout boto.log.info('Task[%s] - setting timeout to %d seconds' % (self.name, current_timeout)) if msg: msg.change_visibility(current_timeout) time.sleep(5) nsecs += 5 t = process.communicate() log_fp.write(t[0]) log_fp.write(t[1]) boto.log.info('Task[%s] - output: %s' % (self.name, log_fp.getvalue())) self.last_executed = self.now self.last_status = process.returncode self.last_output = log_fp.getvalue()[0:1023] def run(self, msg, vtimeout=60): delay = self.check() boto.log.info('Task[%s] - delay=%s seconds' % (self.name, delay)) if delay == 0: self._run(msg, vtimeout) queue = msg.queue new_msg = queue.new_message(self.id) new_msg = queue.write(new_msg) self.message_id = new_msg.id self.put() boto.log.info('Task[%s] - new message id=%s' % (self.name, new_msg.id)) msg.delete() boto.log.info('Task[%s] - deleted message %s' % (self.name, msg.id)) else: boto.log.info('new_vtimeout: %d' % delay) msg.change_visibility(delay) def start(self, queue_name): boto.log.info('Task[%s] - starting with queue: %s' % (self.name, queue_name)) queue = boto.lookup('sqs', queue_name) msg = queue.new_message(self.id) msg = queue.write(msg) self.message_id = msg.id self.put() boto.log.info('Task[%s] - start successful' % self.name) class TaskPoller(object): def __init__(self, queue_name): self.sqs = boto.connect_sqs() self.queue = self.sqs.lookup(queue_name) def poll(self, wait=60, vtimeout=60): while True: m = self.queue.read(vtimeout) if m: task = Task.get_by_id(m.get_body()) if task: if not task.message_id or m.id == task.message_id: boto.log.info('Task[%s] - read message %s' % (task.name, m.id)) task.run(m, vtimeout) else: boto.log.info('Task[%s] - found extraneous message, ignoring' % task.name) else: time.sleep(wait)

38.767045

104

0.597245

import boto from boto.sdb.db.property import StringProperty, DateTimeProperty, IntegerProperty from boto.sdb.db.model import Model import datetime, subprocess, StringIO, time def check_hour(val): if val == '*': return if int(val) < 0 or int(val) > 23: raise ValueError class Task(Model): name = StringProperty() hour = StringProperty(required=True, validator=check_hour, default='*') command = StringProperty(required=True) last_executed = DateTimeProperty() last_status = IntegerProperty() last_output = StringProperty() message_id = StringProperty() @classmethod def start_all(cls, queue_name): for task in cls.all(): task.start(queue_name) def __init__(self, id=None, **kw): Model.__init__(self, id, **kw) self.hourly = self.hour == '*' self.daily = self.hour != '*' self.now = datetime.datetime.utcnow() def check(self): boto.log.info('checking Task[%s]-now=%s, last=%s' % (self.name, self.now, self.last_executed)) if self.hourly and not self.last_executed: return 0 if self.daily and not self.last_executed: if int(self.hour) == self.now.hour: return 0 else: return max( (int(self.hour)-self.now.hour), (self.now.hour-int(self.hour)) )*60*60 delta = self.now - self.last_executed if self.hourly: if delta.seconds >= 60*60: return 0 else: return 60*60 - delta.seconds else: if int(self.hour) == self.now.hour: if delta.days >= 1: return 0 else: return 82800 else: return max( (int(self.hour)-self.now.hour), (self.now.hour-int(self.hour)) )*60*60 def _run(self, msg, vtimeout): boto.log.info('Task[%s] - running:%s' % (self.name, self.command)) log_fp = StringIO.StringIO() process = subprocess.Popen(self.command, shell=True, stdin=subprocess.PIPE, stdout=subprocess.PIPE, stderr=subprocess.PIPE) nsecs = 5 current_timeout = vtimeout while process.poll() == None: boto.log.info('nsecs=%s, timeout=%s' % (nsecs, current_timeout)) if nsecs >= current_timeout: current_timeout += vtimeout boto.log.info('Task[%s] - setting timeout to %d seconds' % (self.name, current_timeout)) if msg: msg.change_visibility(current_timeout) time.sleep(5) nsecs += 5 t = process.communicate() log_fp.write(t[0]) log_fp.write(t[1]) boto.log.info('Task[%s] - output: %s' % (self.name, log_fp.getvalue())) self.last_executed = self.now self.last_status = process.returncode self.last_output = log_fp.getvalue()[0:1023] def run(self, msg, vtimeout=60): delay = self.check() boto.log.info('Task[%s] - delay=%s seconds' % (self.name, delay)) if delay == 0: self._run(msg, vtimeout) queue = msg.queue new_msg = queue.new_message(self.id) new_msg = queue.write(new_msg) self.message_id = new_msg.id self.put() boto.log.info('Task[%s] - new message id=%s' % (self.name, new_msg.id)) msg.delete() boto.log.info('Task[%s] - deleted message %s' % (self.name, msg.id)) else: boto.log.info('new_vtimeout: %d' % delay) msg.change_visibility(delay) def start(self, queue_name): boto.log.info('Task[%s] - starting with queue: %s' % (self.name, queue_name)) queue = boto.lookup('sqs', queue_name) msg = queue.new_message(self.id) msg = queue.write(msg) self.message_id = msg.id self.put() boto.log.info('Task[%s] - start successful' % self.name) class TaskPoller(object): def __init__(self, queue_name): self.sqs = boto.connect_sqs() self.queue = self.sqs.lookup(queue_name) def poll(self, wait=60, vtimeout=60): while True: m = self.queue.read(vtimeout) if m: task = Task.get_by_id(m.get_body()) if task: if not task.message_id or m.id == task.message_id: boto.log.info('Task[%s] - read message %s' % (task.name, m.id)) task.run(m, vtimeout) else: boto.log.info('Task[%s] - found extraneous message, ignoring' % task.name) else: time.sleep(wait)

true

1c37c6dbd9f5765f750aee5d81ab7b88235339b9

402

py

Python

litemark/__init__.py

pyrustic/litemark

49669865b7aa23f964eec0117b7ba1936658a0d2

[ "MIT" ]

4

2021-10-14T16:20:36.000Z

2022-01-18T08:44:12.000Z

litemark/__init__.py

pyrustic/litemark

49669865b7aa23f964eec0117b7ba1936658a0d2

[ "MIT" ]

null

litemark/__init__.py

pyrustic/litemark

49669865b7aa23f964eec0117b7ba1936658a0d2

[ "MIT" ]

null

from litemark.core import scanner from litemark.core.scanner import Element from litemark.core.viewer import Viewer, get_light_style from litemark.core.style import Style from litemark.core.util import center_window __all__ = ["scan", "Element", "Viewer", "get_light_style", "Style"] def scan(text): """Returns a generator. If you need a list: list(scan(text))""" return scanner.scan(text)

28.714286

67

0.756219

from litemark.core import scanner from litemark.core.scanner import Element from litemark.core.viewer import Viewer, get_light_style from litemark.core.style import Style from litemark.core.util import center_window __all__ = ["scan", "Element", "Viewer", "get_light_style", "Style"] def scan(text): return scanner.scan(text)

true

1c37c9049f3188be7356e8c6011ee01171864c66

3,234

py

Python

tests/components/mqtt/test_server.py

logic/home-assistant

d3fed52254053a24e901cde8528c0e407d429311

[ "Apache-2.0" ]

7

2018-08-03T10:15:36.000Z

2019-03-25T13:31:55.000Z

tests/components/mqtt/test_server.py

sara0871/thepracticaldev

28de2d6f75656349de94dd897156d33fbadaa43a

[ "Apache-2.0" ]

3

2021-09-08T03:06:43.000Z

2022-03-12T00:56:04.000Z

tests/components/mqtt/test_server.py

sara0871/thepracticaldev

28de2d6f75656349de94dd897156d33fbadaa43a

[ "Apache-2.0" ]

3

2018-12-04T11:54:27.000Z

2019-08-31T14:41:32.000Z

"""The tests for the MQTT component embedded server.""" from unittest.mock import Mock, MagicMock, patch import sys import pytest from homeassistant.setup import setup_component import homeassistant.components.mqtt as mqtt from tests.common import get_test_home_assistant, mock_coro # Until https://github.com/beerfactory/hbmqtt/pull/139 is released @pytest.mark.skipif(sys.version_info[:2] >= (3, 7), reason='Package incompatible with Python 3.7') class TestMQTT: """Test the MQTT component.""" def setup_method(self, method): """Setup things to be run when tests are started.""" self.hass = get_test_home_assistant() setup_component(self.hass, 'http', { 'api_password': 'super_secret' }) def teardown_method(self, method): """Stop everything that was started.""" self.hass.stop() @patch('passlib.apps.custom_app_context', Mock(return_value='')) @patch('tempfile.NamedTemporaryFile', Mock(return_value=MagicMock())) @patch('hbmqtt.broker.Broker', Mock(return_value=MagicMock())) @patch('hbmqtt.broker.Broker.start', Mock(return_value=mock_coro())) @patch('homeassistant.components.mqtt.MQTT') def test_creating_config_with_http_pass(self, mock_mqtt): """Test if the MQTT server gets started and subscribe/publish msg.""" mock_mqtt().async_connect.return_value = mock_coro(True) self.hass.bus.listen_once = MagicMock() password = 'super_secret' self.hass.config.api = MagicMock(api_password=password) assert setup_component(self.hass, mqtt.DOMAIN, {}) assert mock_mqtt.called from pprint import pprint pprint(mock_mqtt.mock_calls) assert mock_mqtt.mock_calls[1][1][5] == 'homeassistant' assert mock_mqtt.mock_calls[1][1][6] == password @patch('passlib.apps.custom_app_context', Mock(return_value='')) @patch('tempfile.NamedTemporaryFile', Mock(return_value=MagicMock())) @patch('hbmqtt.broker.Broker', Mock(return_value=MagicMock())) @patch('hbmqtt.broker.Broker.start', Mock(return_value=mock_coro())) @patch('homeassistant.components.mqtt.MQTT') def test_creating_config_with_http_no_pass(self, mock_mqtt): """Test if the MQTT server gets started and subscribe/publish msg.""" mock_mqtt().async_connect.return_value = mock_coro(True) self.hass.bus.listen_once = MagicMock() self.hass.config.api = MagicMock(api_password=None) assert setup_component(self.hass, mqtt.DOMAIN, {}) assert mock_mqtt.called assert mock_mqtt.mock_calls[1][1][5] is None assert mock_mqtt.mock_calls[1][1][6] is None @patch('tempfile.NamedTemporaryFile', Mock(return_value=MagicMock())) @patch('hbmqtt.broker.Broker.start', return_value=mock_coro()) def test_broker_config_fails(self, mock_run): """Test if the MQTT component fails if server fails.""" from hbmqtt.broker import BrokerException mock_run.side_effect = BrokerException self.hass.config.api = MagicMock(api_password=None) assert not setup_component(self.hass, mqtt.DOMAIN, { mqtt.DOMAIN: {mqtt.CONF_EMBEDDED: {}} })

41.461538

77

0.693568

from unittest.mock import Mock, MagicMock, patch import sys import pytest from homeassistant.setup import setup_component import homeassistant.components.mqtt as mqtt from tests.common import get_test_home_assistant, mock_coro @pytest.mark.skipif(sys.version_info[:2] >= (3, 7), reason='Package incompatible with Python 3.7') class TestMQTT: def setup_method(self, method): self.hass = get_test_home_assistant() setup_component(self.hass, 'http', { 'api_password': 'super_secret' }) def teardown_method(self, method): self.hass.stop() @patch('passlib.apps.custom_app_context', Mock(return_value='')) @patch('tempfile.NamedTemporaryFile', Mock(return_value=MagicMock())) @patch('hbmqtt.broker.Broker', Mock(return_value=MagicMock())) @patch('hbmqtt.broker.Broker.start', Mock(return_value=mock_coro())) @patch('homeassistant.components.mqtt.MQTT') def test_creating_config_with_http_pass(self, mock_mqtt): mock_mqtt().async_connect.return_value = mock_coro(True) self.hass.bus.listen_once = MagicMock() password = 'super_secret' self.hass.config.api = MagicMock(api_password=password) assert setup_component(self.hass, mqtt.DOMAIN, {}) assert mock_mqtt.called from pprint import pprint pprint(mock_mqtt.mock_calls) assert mock_mqtt.mock_calls[1][1][5] == 'homeassistant' assert mock_mqtt.mock_calls[1][1][6] == password @patch('passlib.apps.custom_app_context', Mock(return_value='')) @patch('tempfile.NamedTemporaryFile', Mock(return_value=MagicMock())) @patch('hbmqtt.broker.Broker', Mock(return_value=MagicMock())) @patch('hbmqtt.broker.Broker.start', Mock(return_value=mock_coro())) @patch('homeassistant.components.mqtt.MQTT') def test_creating_config_with_http_no_pass(self, mock_mqtt): mock_mqtt().async_connect.return_value = mock_coro(True) self.hass.bus.listen_once = MagicMock() self.hass.config.api = MagicMock(api_password=None) assert setup_component(self.hass, mqtt.DOMAIN, {}) assert mock_mqtt.called assert mock_mqtt.mock_calls[1][1][5] is None assert mock_mqtt.mock_calls[1][1][6] is None @patch('tempfile.NamedTemporaryFile', Mock(return_value=MagicMock())) @patch('hbmqtt.broker.Broker.start', return_value=mock_coro()) def test_broker_config_fails(self, mock_run): from hbmqtt.broker import BrokerException mock_run.side_effect = BrokerException self.hass.config.api = MagicMock(api_password=None) assert not setup_component(self.hass, mqtt.DOMAIN, { mqtt.DOMAIN: {mqtt.CONF_EMBEDDED: {}} })

true

1c37c95e39cda9c81e5ca97fe1bf80cd91fe316a

20,863

py

Python

conans/model/conf.py

Mu-L/conan

7c24ec4bbd6e8c16cdcd879403aae742689bc36a

[ "MIT" ]

1

2019-11-04T17:23:09.000Z

conans/model/conf.py

Mu-L/conan

7c24ec4bbd6e8c16cdcd879403aae742689bc36a

[ "MIT" ]

1

2020-11-05T16:16:49.000Z

conans/model/conf.py

Mattlk13/conan

005fc53485557b0a570bb71670f2ca9c66082165

[ "MIT" ]

null

import fnmatch from collections import OrderedDict import six from conans.errors import ConanException BUILT_IN_CONFS = { "core:required_conan_version": "Raise if current version does not match the defined range.", "core.package_id:msvc_visual_incompatible": "Allows opting-out the fallback from the new msvc compiler to the Visual Studio compiler existing binaries", "core:default_profile": "Defines the default host profile ('default' by default)", "core:default_build_profile": "Defines the default build profile (None by default)", "tools.android:ndk_path": "Argument for the CMAKE_ANDROID_NDK", "tools.build:skip_test": "Do not execute CMake.test() and Meson.test() when enabled", "tools.build:jobs": "Default compile jobs number -jX Ninja, Make, /MP VS (default: max CPUs)", "tools.build:sysroot": "Pass the --sysroot=<tools.build:sysroot> flag if available. (None by default)", "tools.cmake.cmaketoolchain:generator": "User defined CMake generator to use instead of default", "tools.cmake.cmaketoolchain:find_package_prefer_config": "Argument for the CMAKE_FIND_PACKAGE_PREFER_CONFIG", "tools.cmake.cmaketoolchain:toolchain_file": "Use other existing file rather than conan_toolchain.cmake one", "tools.cmake.cmaketoolchain:user_toolchain": "Inject existing user toolchains at the beginning of conan_toolchain.cmake", "tools.cmake.cmaketoolchain:system_name": "Define CMAKE_SYSTEM_NAME in CMakeToolchain", "tools.cmake.cmaketoolchain:system_version": "Define CMAKE_SYSTEM_VERSION in CMakeToolchain", "tools.cmake.cmaketoolchain:system_processor": "Define CMAKE_SYSTEM_PROCESSOR in CMakeToolchain", "tools.env.virtualenv:auto_use": "Automatically activate virtualenv file generation", "tools.cmake.cmake_layout:build_folder_vars": "Settings and Options that will produce a different build folder and different CMake presets names", "tools.files.download:retry": "Number of retries in case of failure when downloading", "tools.files.download:retry_wait": "Seconds to wait between download attempts", "tools.gnu:make_program": "Indicate path to make program", "tools.gnu:define_libcxx11_abi": "Force definition of GLIBCXX_USE_CXX11_ABI=1 for libstdc++11", "tools.google.bazel:configs": "Define Bazel config file", "tools.google.bazel:bazelrc_path": "Defines Bazel rc-path", "tools.microsoft.msbuild:verbosity": "Verbosity level for MSBuild: 'Quiet', 'Minimal', 'Normal', 'Detailed', 'Diagnostic'", "tools.microsoft.msbuild:vs_version": "Defines the IDE version when using the new msvc compiler", "tools.microsoft.msbuild:max_cpu_count": "Argument for the /m when running msvc to build parallel projects", "tools.microsoft.msbuild:installation_path": "VS install path, to avoid auto-detect via vswhere, like C:/Program Files (x86)/Microsoft Visual Studio/2019/Community", "tools.microsoft.msbuilddeps:exclude_code_analysis": "Suppress MSBuild code analysis for patterns", "tools.microsoft.msbuildtoolchain:compile_options": "Dictionary with MSBuild compiler options", "tools.intel:installation_path": "Defines the Intel oneAPI installation root path", "tools.intel:setvars_args": "Custom arguments to be passed onto the setvars.sh|bat script from Intel oneAPI", "tools.system.package_manager:tool": "Default package manager tool: 'apt-get', 'yum', 'dnf', 'brew', 'pacman', 'choco', 'zypper', 'pkg' or 'pkgutil'", "tools.system.package_manager:mode": "Mode for package_manager tools: 'check' or 'install'", "tools.system.package_manager:sudo": "Use 'sudo' when invoking the package manager tools in Linux (False by default)", "tools.system.package_manager:sudo_askpass": "Use the '-A' argument if using sudo in Linux to invoke the system package manager (False by default)", "tools.apple.xcodebuild:verbosity": "Verbosity level for xcodebuild: 'verbose' or 'quiet", "tools.apple:enable_bitcode": "(boolean) Enable/Disable Bitcode Apple Clang flags", "tools.apple:enable_arc": "(boolean) Enable/Disable ARC Apple Clang flags", "tools.apple:enable_visibility": "(boolean) Enable/Disable Visibility Apple Clang flags", # Flags configuration "tools.build:cxxflags": "List of extra CXX flags used by different toolchains like CMakeToolchain, AutotoolsToolchain and MesonToolchain", "tools.build:cflags": "List of extra C flags used by different toolchains like CMakeToolchain, AutotoolsToolchain and MesonToolchain", "tools.build:defines": "List of extra definition flags used by different toolchains like CMakeToolchain and AutotoolsToolchain", "tools.build:sharedlinkflags": "List of extra flags used by CMakeToolchain for CMAKE_SHARED_LINKER_FLAGS_INIT variable", "tools.build:exelinkflags": "List of extra flags used by CMakeToolchain for CMAKE_EXE_LINKER_FLAGS_INIT variable", } def _is_profile_module(module_name): # These are the modules that are propagated to profiles and user recipes _user_modules = "tools.", "user." return any(module_name.startswith(user_module) for user_module in _user_modules) # FIXME: Refactor all the next classes because they are mostly the same as # conan.tools.env.environment ones class _ConfVarPlaceHolder: pass class _ConfValue(object): def __init__(self, name, value): self._name = name self._value = value self._value_type = type(value) def __repr__(self): return repr(self._value) @property def value(self): if self._value_type is list and _ConfVarPlaceHolder in self._value: v = self._value[:] v.remove(_ConfVarPlaceHolder) return v return self._value def copy(self): return _ConfValue(self._name, self._value) def dumps(self): if self._value is None: return "{}=!".format(self._name) # unset elif self._value_type is list and _ConfVarPlaceHolder in self._value: v = self._value[:] v.remove(_ConfVarPlaceHolder) return "{}={}".format(self._name, v) else: return "{}={}".format(self._name, self._value) def update(self, value): if self._value_type is dict: self._value.update(value) def remove(self, value): if self._value_type is list: self._value.remove(value) elif self._value_type is dict: self._value.pop(value, None) def append(self, value): if self._value_type is not list: raise ConanException("Only list-like values can append other values.") if isinstance(value, list): self._value.extend(value) else: self._value.append(value) def prepend(self, value): if self._value_type is not list: raise ConanException("Only list-like values can prepend other values.") if isinstance(value, list): self._value = value + self._value else: self._value.insert(0, value) def compose_conf_value(self, other): """ self has precedence, the "other" will add/append if possible and not conflicting, but self mandates what to do. If self has define(), without placeholder, that will remain. :type other: _ConfValue """ v_type = self._value_type o_type = other._value_type if v_type is list and o_type is list: try: index = self._value.index(_ConfVarPlaceHolder) except ValueError: # It doesn't have placeholder pass else: new_value = self._value[:] # do a copy new_value[index:index + 1] = other._value # replace the placeholder self._value = new_value elif self._value is None or other._value is None \ or (isinstance(self._value, six.string_types) and isinstance(self._value, six.string_types)): # TODO: Python2, remove in 2.0 # It means any of those values were an "unset" so doing nothing because we don't # really know the original value type pass elif o_type != v_type: raise ConanException("It's not possible to compose {} values " "and {} ones.".format(v_type.__name__, o_type.__name__)) # TODO: In case of any other object types? class Conf: # Putting some default expressions to check that any value could be false boolean_false_expressions = ("0", '"0"', "false", '"false"', "off") def __init__(self): # It being ordered allows for Windows case-insensitive composition self._values = OrderedDict() # {var_name: [] of values, including separators} def __bool__(self): return bool(self._values) # TODO: Python2, remove in 2.0 __nonzero__ = __bool__ def __repr__(self): return "Conf: " + repr(self._values) def __eq__(self, other): """ :type other: Conf """ return other._values == self._values # TODO: Python2, remove in 2.0 def __ne__(self, other): return not self.__eq__(other) def __getitem__(self, name): """ DEPRECATED: it's going to disappear in Conan 2.0. Use self.get() instead. """ # FIXME: Keeping backward compatibility return self.get(name) def __setitem__(self, name, value): """ DEPRECATED: it's going to disappear in Conan 2.0. """ # FIXME: Keeping backward compatibility self.define(name, value) # it's like a new definition def __delitem__(self, name): """ DEPRECATED: it's going to disappear in Conan 2.0. """ # FIXME: Keeping backward compatibility del self._values[name] def items(self): # FIXME: Keeping backward compatibility for k, v in self._values.items(): yield k, v.value @property def sha(self): # FIXME: Keeping backward compatibility return self.dumps() @staticmethod def _get_boolean_value(value): if type(value) is bool: return value elif str(value).lower() in Conf.boolean_false_expressions: return False else: return True def get(self, conf_name, default=None, check_type=None): """ Get all the values belonging to the passed conf name. :param conf_name: conf name :param default: default value in case of conf does not have the conf_name key :param check_type: check the conf type(value) is the same as the given by this param. There are two default smart conversions for bool and str types. """ conf_value = self._values.get(conf_name) if conf_value: v = conf_value.value # Some smart conversions if check_type is bool and not isinstance(v, bool): # Perhaps, user has introduced a "false", "0" or even "off" return self._get_boolean_value(v) elif check_type is str and not isinstance(v, str): return str(v) elif v is None: # value was unset return default elif check_type is not None and not isinstance(v, check_type): raise ConanException("[conf] {name} must be a {type}-like object. " "The value '{value}' introduced is a {vtype} " "object".format(name=conf_name, type=check_type.__name__, value=v, vtype=type(v).__name__)) return v else: return default def pop(self, conf_name, default=None): """ Remove any key-value given the conf name """ value = self.get(conf_name, default=default) self._values.pop(conf_name, None) return value @staticmethod def _validate_lower_case(name): if name != name.lower(): raise ConanException("Conf '{}' must be lowercase".format(name)) def copy(self): c = Conf() c._values = self._values.copy() return c def dumps(self): """ returns a string with a profile-like original definition, not the full environment values """ return "\n".join([v.dumps() for v in reversed(self._values.values())]) def define(self, name, value): self._validate_lower_case(name) self._values[name] = _ConfValue(name, value) def unset(self, name): """ clears the variable, equivalent to a unset or set XXX= """ self._values[name] = _ConfValue(name, None) def update(self, name, value): self._validate_lower_case(name) conf_value = _ConfValue(name, {}) self._values.setdefault(name, conf_value).update(value) def append(self, name, value): self._validate_lower_case(name) conf_value = _ConfValue(name, [_ConfVarPlaceHolder]) self._values.setdefault(name, conf_value).append(value) def prepend(self, name, value): self._validate_lower_case(name) conf_value = _ConfValue(name, [_ConfVarPlaceHolder]) self._values.setdefault(name, conf_value).prepend(value) def remove(self, name, value): conf_value = self._values.get(name) if conf_value: conf_value.remove(value) else: raise ConanException("Conf {} does not exist.".format(name)) def compose_conf(self, other): """ :param other: other has less priority than current one :type other: Conf """ for k, v in other._values.items(): existing = self._values.get(k) if existing is None: self._values[k] = v.copy() else: existing.compose_conf_value(v) return self def filter_user_modules(self): result = Conf() for k, v in self._values.items(): if _is_profile_module(k): result._values[k] = v return result class ConfDefinition: actions = (("+=", "append"), ("=+", "prepend"), ("=!", "unset"), ("=", "define")) def __init__(self): self._pattern_confs = OrderedDict() def __repr__(self): return "ConfDefinition: " + repr(self._pattern_confs) def __bool__(self): return bool(self._pattern_confs) __nonzero__ = __bool__ def __getitem__(self, module_name): """ DEPRECATED: it's going to disappear in Conan 2.0. Use self.get() instead. if a module name is requested for this, it goes to the None-Global config by default """ pattern, name = self._split_pattern_name(module_name) return self._pattern_confs.get(pattern, Conf()).get(name) def __delitem__(self, module_name): """ DEPRECATED: it's going to disappear in Conan 2.0. Use self.pop() instead. if a module name is requested for this, it goes to the None-Global config by default """ pattern, name = self._split_pattern_name(module_name) del self._pattern_confs.get(pattern, Conf())[name] def get(self, conf_name, default=None, check_type=None): """ Get the value of the conf name requested and convert it to the [type]-like passed. """ pattern, name = self._split_pattern_name(conf_name) return self._pattern_confs.get(pattern, Conf()).get(name, default=default, check_type=check_type) def pop(self, conf_name, default=None): """ Remove the conf name passed. """ pattern, name = self._split_pattern_name(conf_name) return self._pattern_confs.get(pattern, Conf()).pop(name, default=default) @staticmethod def _split_pattern_name(pattern_name): if pattern_name.count(":") >= 2: pattern, name = pattern_name.split(":", 1) else: pattern, name = None, pattern_name return pattern, name def get_conanfile_conf(self, ref): """ computes package-specific Conf it is only called when conanfile.buildenv is called the last one found in the profile file has top priority """ result = Conf() for pattern, conf in self._pattern_confs.items(): if pattern is None or fnmatch.fnmatch(str(ref), pattern): # Latest declared has priority, copy() necessary to not destroy data result = conf.copy().compose_conf(result) return result def update_conf_definition(self, other): """ :type other: ConfDefinition :param other: The argument profile has priority/precedence over the current one. """ for pattern, conf in other._pattern_confs.items(): self._update_conf_definition(pattern, conf) def _update_conf_definition(self, pattern, conf): existing = self._pattern_confs.get(pattern) if existing: self._pattern_confs[pattern] = conf.compose_conf(existing) else: self._pattern_confs[pattern] = conf def rebase_conf_definition(self, other): """ for taking the new global.conf and composing with the profile [conf] :type other: ConfDefinition """ for pattern, conf in other._pattern_confs.items(): new_conf = conf.filter_user_modules() # Creates a copy, filtered existing = self._pattern_confs.get(pattern) if existing: existing.compose_conf(new_conf) else: self._pattern_confs[pattern] = new_conf def update(self, key, value, profile=False, method="define"): """ Define/append/prepend/unset any Conf line >> update("tools.microsoft.msbuild:verbosity", "Detailed") """ pattern, name = self._split_pattern_name(key) if not _is_profile_module(name): if profile: raise ConanException("[conf] '{}' not allowed in profiles".format(key)) if pattern is not None: raise ConanException("Conf '{}' cannot have a package pattern".format(key)) # strip whitespaces before/after = # values are not strip() unless they are a path, to preserve potential whitespaces name = name.strip() # When loading from profile file, latest line has priority conf = Conf() if method == "unset": conf.unset(name) else: getattr(conf, method)(name, value) # Update self._update_conf_definition(pattern, conf) def as_list(self): result = [] for pattern, conf in self._pattern_confs.items(): for name, value in sorted(conf.items()): if pattern: result.append(("{}:{}".format(pattern, name), value)) else: result.append((name, value)) return result def dumps(self): result = [] for pattern, conf in self._pattern_confs.items(): if pattern is None: result.append(conf.dumps()) else: result.append("\n".join("{}:{}".format(pattern, line) if line else "" for line in conf.dumps().splitlines())) if result: result.append("") return "\n".join(result) @staticmethod def _get_evaluated_value(__v): """ Function to avoid eval() catching local variables """ try: # Isolated eval parsed_value = eval(__v) if isinstance(parsed_value, str): # xxx:xxx = "my string" # Let's respect the quotes introduced by any user parsed_value = '"{}"'.format(parsed_value) except: # It means eval() failed because of a string without quotes parsed_value = __v.strip() return parsed_value def loads(self, text, profile=False): self._pattern_confs = {} for line in text.splitlines(): line = line.strip() if not line or line.startswith("#"): continue for op, method in ConfDefinition.actions: tokens = line.split(op, 1) if len(tokens) != 2: continue pattern_name, value = tokens parsed_value = ConfDefinition._get_evaluated_value(value) self.update(pattern_name, parsed_value, profile=profile, method=method) break else: raise ConanException("Bad conf definition: {}".format(line))

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169

0.629056

import fnmatch from collections import OrderedDict import six from conans.errors import ConanException BUILT_IN_CONFS = { "core:required_conan_version": "Raise if current version does not match the defined range.", "core.package_id:msvc_visual_incompatible": "Allows opting-out the fallback from the new msvc compiler to the Visual Studio compiler existing binaries", "core:default_profile": "Defines the default host profile ('default' by default)", "core:default_build_profile": "Defines the default build profile (None by default)", "tools.android:ndk_path": "Argument for the CMAKE_ANDROID_NDK", "tools.build:skip_test": "Do not execute CMake.test() and Meson.test() when enabled", "tools.build:jobs": "Default compile jobs number -jX Ninja, Make, /MP VS (default: max CPUs)", "tools.build:sysroot": "Pass the --sysroot=<tools.build:sysroot> flag if available. (None by default)", "tools.cmake.cmaketoolchain:generator": "User defined CMake generator to use instead of default", "tools.cmake.cmaketoolchain:find_package_prefer_config": "Argument for the CMAKE_FIND_PACKAGE_PREFER_CONFIG", "tools.cmake.cmaketoolchain:toolchain_file": "Use other existing file rather than conan_toolchain.cmake one", "tools.cmake.cmaketoolchain:user_toolchain": "Inject existing user toolchains at the beginning of conan_toolchain.cmake", "tools.cmake.cmaketoolchain:system_name": "Define CMAKE_SYSTEM_NAME in CMakeToolchain", "tools.cmake.cmaketoolchain:system_version": "Define CMAKE_SYSTEM_VERSION in CMakeToolchain", "tools.cmake.cmaketoolchain:system_processor": "Define CMAKE_SYSTEM_PROCESSOR in CMakeToolchain", "tools.env.virtualenv:auto_use": "Automatically activate virtualenv file generation", "tools.cmake.cmake_layout:build_folder_vars": "Settings and Options that will produce a different build folder and different CMake presets names", "tools.files.download:retry": "Number of retries in case of failure when downloading", "tools.files.download:retry_wait": "Seconds to wait between download attempts", "tools.gnu:make_program": "Indicate path to make program", "tools.gnu:define_libcxx11_abi": "Force definition of GLIBCXX_USE_CXX11_ABI=1 for libstdc++11", "tools.google.bazel:configs": "Define Bazel config file", "tools.google.bazel:bazelrc_path": "Defines Bazel rc-path", "tools.microsoft.msbuild:verbosity": "Verbosity level for MSBuild: 'Quiet', 'Minimal', 'Normal', 'Detailed', 'Diagnostic'", "tools.microsoft.msbuild:vs_version": "Defines the IDE version when using the new msvc compiler", "tools.microsoft.msbuild:max_cpu_count": "Argument for the /m when running msvc to build parallel projects", "tools.microsoft.msbuild:installation_path": "VS install path, to avoid auto-detect via vswhere, like C:/Program Files (x86)/Microsoft Visual Studio/2019/Community", "tools.microsoft.msbuilddeps:exclude_code_analysis": "Suppress MSBuild code analysis for patterns", "tools.microsoft.msbuildtoolchain:compile_options": "Dictionary with MSBuild compiler options", "tools.intel:installation_path": "Defines the Intel oneAPI installation root path", "tools.intel:setvars_args": "Custom arguments to be passed onto the setvars.sh|bat script from Intel oneAPI", "tools.system.package_manager:tool": "Default package manager tool: 'apt-get', 'yum', 'dnf', 'brew', 'pacman', 'choco', 'zypper', 'pkg' or 'pkgutil'", "tools.system.package_manager:mode": "Mode for package_manager tools: 'check' or 'install'", "tools.system.package_manager:sudo": "Use 'sudo' when invoking the package manager tools in Linux (False by default)", "tools.system.package_manager:sudo_askpass": "Use the '-A' argument if using sudo in Linux to invoke the system package manager (False by default)", "tools.apple.xcodebuild:verbosity": "Verbosity level for xcodebuild: 'verbose' or 'quiet", "tools.apple:enable_bitcode": "(boolean) Enable/Disable Bitcode Apple Clang flags", "tools.apple:enable_arc": "(boolean) Enable/Disable ARC Apple Clang flags", "tools.apple:enable_visibility": "(boolean) Enable/Disable Visibility Apple Clang flags", # Flags configuration "tools.build:cxxflags": "List of extra CXX flags used by different toolchains like CMakeToolchain, AutotoolsToolchain and MesonToolchain", "tools.build:cflags": "List of extra C flags used by different toolchains like CMakeToolchain, AutotoolsToolchain and MesonToolchain", "tools.build:defines": "List of extra definition flags used by different toolchains like CMakeToolchain and AutotoolsToolchain", "tools.build:sharedlinkflags": "List of extra flags used by CMakeToolchain for CMAKE_SHARED_LINKER_FLAGS_INIT variable", "tools.build:exelinkflags": "List of extra flags used by CMakeToolchain for CMAKE_EXE_LINKER_FLAGS_INIT variable", } def _is_profile_module(module_name): # These are the modules that are propagated to profiles and user recipes _user_modules = "tools.", "user." return any(module_name.startswith(user_module) for user_module in _user_modules) # FIXME: Refactor all the next classes because they are mostly the same as # conan.tools.env.environment ones class _ConfVarPlaceHolder: pass class _ConfValue(object): def __init__(self, name, value): self._name = name self._value = value self._value_type = type(value) def __repr__(self): return repr(self._value) @property def value(self): if self._value_type is list and _ConfVarPlaceHolder in self._value: v = self._value[:] v.remove(_ConfVarPlaceHolder) return v return self._value def copy(self): return _ConfValue(self._name, self._value) def dumps(self): if self._value is None: return "{}=!".format(self._name) # unset elif self._value_type is list and _ConfVarPlaceHolder in self._value: v = self._value[:] v.remove(_ConfVarPlaceHolder) return "{}={}".format(self._name, v) else: return "{}={}".format(self._name, self._value) def update(self, value): if self._value_type is dict: self._value.update(value) def remove(self, value): if self._value_type is list: self._value.remove(value) elif self._value_type is dict: self._value.pop(value, None) def append(self, value): if self._value_type is not list: raise ConanException("Only list-like values can append other values.") if isinstance(value, list): self._value.extend(value) else: self._value.append(value) def prepend(self, value): if self._value_type is not list: raise ConanException("Only list-like values can prepend other values.") if isinstance(value, list): self._value = value + self._value else: self._value.insert(0, value) def compose_conf_value(self, other): v_type = self._value_type o_type = other._value_type if v_type is list and o_type is list: try: index = self._value.index(_ConfVarPlaceHolder) except ValueError: # It doesn't have placeholder pass else: new_value = self._value[:] new_value[index:index + 1] = other._value self._value = new_value elif self._value is None or other._value is None \ or (isinstance(self._value, six.string_types) and isinstance(self._value, six.string_types)): # really know the original value type pass elif o_type != v_type: raise ConanException("It's not possible to compose {} values " "and {} ones.".format(v_type.__name__, o_type.__name__)) class Conf: boolean_false_expressions = ("0", '"0"', "false", '"false"', "off") def __init__(self): self._values = OrderedDict() def __bool__(self): return bool(self._values) __nonzero__ = __bool__ def __repr__(self): return "Conf: " + repr(self._values) def __eq__(self, other): return other._values == self._values def __ne__(self, other): return not self.__eq__(other) def __getitem__(self, name): return self.get(name) def __setitem__(self, name, value): self.define(name, value) def __delitem__(self, name): # FIXME: Keeping backward compatibility del self._values[name] def items(self): # FIXME: Keeping backward compatibility for k, v in self._values.items(): yield k, v.value @property def sha(self): # FIXME: Keeping backward compatibility return self.dumps() @staticmethod def _get_boolean_value(value): if type(value) is bool: return value elif str(value).lower() in Conf.boolean_false_expressions: return False else: return True def get(self, conf_name, default=None, check_type=None): conf_value = self._values.get(conf_name) if conf_value: v = conf_value.value # Some smart conversions if check_type is bool and not isinstance(v, bool): # Perhaps, user has introduced a "false", "0" or even "off" return self._get_boolean_value(v) elif check_type is str and not isinstance(v, str): return str(v) elif v is None: # value was unset return default elif check_type is not None and not isinstance(v, check_type): raise ConanException("[conf] {name} must be a {type}-like object. " "The value '{value}' introduced is a {vtype} " "object".format(name=conf_name, type=check_type.__name__, value=v, vtype=type(v).__name__)) return v else: return default def pop(self, conf_name, default=None): value = self.get(conf_name, default=default) self._values.pop(conf_name, None) return value @staticmethod def _validate_lower_case(name): if name != name.lower(): raise ConanException("Conf '{}' must be lowercase".format(name)) def copy(self): c = Conf() c._values = self._values.copy() return c def dumps(self): return "\n".join([v.dumps() for v in reversed(self._values.values())]) def define(self, name, value): self._validate_lower_case(name) self._values[name] = _ConfValue(name, value) def unset(self, name): self._values[name] = _ConfValue(name, None) def update(self, name, value): self._validate_lower_case(name) conf_value = _ConfValue(name, {}) self._values.setdefault(name, conf_value).update(value) def append(self, name, value): self._validate_lower_case(name) conf_value = _ConfValue(name, [_ConfVarPlaceHolder]) self._values.setdefault(name, conf_value).append(value) def prepend(self, name, value): self._validate_lower_case(name) conf_value = _ConfValue(name, [_ConfVarPlaceHolder]) self._values.setdefault(name, conf_value).prepend(value) def remove(self, name, value): conf_value = self._values.get(name) if conf_value: conf_value.remove(value) else: raise ConanException("Conf {} does not exist.".format(name)) def compose_conf(self, other): for k, v in other._values.items(): existing = self._values.get(k) if existing is None: self._values[k] = v.copy() else: existing.compose_conf_value(v) return self def filter_user_modules(self): result = Conf() for k, v in self._values.items(): if _is_profile_module(k): result._values[k] = v return result class ConfDefinition: actions = (("+=", "append"), ("=+", "prepend"), ("=!", "unset"), ("=", "define")) def __init__(self): self._pattern_confs = OrderedDict() def __repr__(self): return "ConfDefinition: " + repr(self._pattern_confs) def __bool__(self): return bool(self._pattern_confs) __nonzero__ = __bool__ def __getitem__(self, module_name): pattern, name = self._split_pattern_name(module_name) return self._pattern_confs.get(pattern, Conf()).get(name) def __delitem__(self, module_name): pattern, name = self._split_pattern_name(module_name) del self._pattern_confs.get(pattern, Conf())[name] def get(self, conf_name, default=None, check_type=None): pattern, name = self._split_pattern_name(conf_name) return self._pattern_confs.get(pattern, Conf()).get(name, default=default, check_type=check_type) def pop(self, conf_name, default=None): pattern, name = self._split_pattern_name(conf_name) return self._pattern_confs.get(pattern, Conf()).pop(name, default=default) @staticmethod def _split_pattern_name(pattern_name): if pattern_name.count(":") >= 2: pattern, name = pattern_name.split(":", 1) else: pattern, name = None, pattern_name return pattern, name def get_conanfile_conf(self, ref): result = Conf() for pattern, conf in self._pattern_confs.items(): if pattern is None or fnmatch.fnmatch(str(ref), pattern): # Latest declared has priority, copy() necessary to not destroy data result = conf.copy().compose_conf(result) return result def update_conf_definition(self, other): for pattern, conf in other._pattern_confs.items(): self._update_conf_definition(pattern, conf) def _update_conf_definition(self, pattern, conf): existing = self._pattern_confs.get(pattern) if existing: self._pattern_confs[pattern] = conf.compose_conf(existing) else: self._pattern_confs[pattern] = conf def rebase_conf_definition(self, other): for pattern, conf in other._pattern_confs.items(): new_conf = conf.filter_user_modules() # Creates a copy, filtered existing = self._pattern_confs.get(pattern) if existing: existing.compose_conf(new_conf) else: self._pattern_confs[pattern] = new_conf def update(self, key, value, profile=False, method="define"): pattern, name = self._split_pattern_name(key) if not _is_profile_module(name): if profile: raise ConanException("[conf] '{}' not allowed in profiles".format(key)) if pattern is not None: raise ConanException("Conf '{}' cannot have a package pattern".format(key)) # strip whitespaces before/after = # values are not strip() unless they are a path, to preserve potential whitespaces name = name.strip() # When loading from profile file, latest line has priority conf = Conf() if method == "unset": conf.unset(name) else: getattr(conf, method)(name, value) # Update self._update_conf_definition(pattern, conf) def as_list(self): result = [] for pattern, conf in self._pattern_confs.items(): for name, value in sorted(conf.items()): if pattern: result.append(("{}:{}".format(pattern, name), value)) else: result.append((name, value)) return result def dumps(self): result = [] for pattern, conf in self._pattern_confs.items(): if pattern is None: result.append(conf.dumps()) else: result.append("\n".join("{}:{}".format(pattern, line) if line else "" for line in conf.dumps().splitlines())) if result: result.append("") return "\n".join(result) @staticmethod def _get_evaluated_value(__v): try: # Isolated eval parsed_value = eval(__v) if isinstance(parsed_value, str): # xxx:xxx = "my string" # Let's respect the quotes introduced by any user parsed_value = '"{}"'.format(parsed_value) except: parsed_value = __v.strip() return parsed_value def loads(self, text, profile=False): self._pattern_confs = {} for line in text.splitlines(): line = line.strip() if not line or line.startswith("#"): continue for op, method in ConfDefinition.actions: tokens = line.split(op, 1) if len(tokens) != 2: continue pattern_name, value = tokens parsed_value = ConfDefinition._get_evaluated_value(value) self.update(pattern_name, parsed_value, profile=profile, method=method) break else: raise ConanException("Bad conf definition: {}".format(line))

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1c37c9f2571d4f730113497bd724d86eecbeae20

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py

Python

lecture_05/homework5/tasks/oop_1.py

RomanSafe/epam_python_training

3aac68062e1764af844cb3e96f9481791acffc9d

[ "MIT" ]

null

lecture_05/homework5/tasks/oop_1.py

RomanSafe/epam_python_training

3aac68062e1764af844cb3e96f9481791acffc9d

[ "MIT" ]

2

2020-12-30T19:39:36.000Z

2020-12-30T21:49:33.000Z

lecture_05/homework5/tasks/oop_1.py

RomanSafe/epam_python_training

3aac68062e1764af844cb3e96f9481791acffc9d

[ "MIT" ]

null

""" Необходимо создать 3 класса и взаимосвязь между ними (Student, Teacher, Homework) Наследование в этой задаче использовать не нужно. Для работы с временем использовать модуль datetime 1. Homework принимает на вход 2 атрибута: текст задания и количество дней на это задание Атрибуты: text - текст задания deadline - хранит объект datetime.timedelta с количеством дней на выполнение created - c точной датой и временем создания Методы: is_active - проверяет не истело ли время на выполнение задания, возвращает boolean 2. Student Атрибуты: last_name first_name Методы: do_homework - принимает объект Homework и возвращает его же, если задание уже просрочено, то печатет 'You are late' и возвращает None 3. Teacher Атрибуты: last_name first_name Методы: create_homework - текст задания и количество дней на это задание, возвращает экземпляр Homework Обратите внимание, что для работы этого метода не требуется сам объект. PEP8 соблюдать строго. Всем перечисленным выше атрибутам и методам классов сохранить названия. К названием остальных переменных, классов и тд. подходить ответственно - давать логичные подходящие имена. """ import datetime from typing import NewType, Union # for static typing Timedelta = NewType("Timedelta", datetime.timedelta) class Homework: """Describes an instance of homework. Atributes: text: text of the current homework; deadline: a datetime.timedelta object with quantity days till deadline for the current homework; created: the date and time of the instance's creation. """ def __init__(self, text: str, deadline: int) -> None: """Creates a class instance.""" self.text = text self.deadline = datetime.timedelta(days=deadline) self.created = datetime.datetime.now() def is_active(self) -> bool: """Checks is there time till deadline of the current homework. Returns: If the deadline has not expired return True, overwise False. """ return datetime.datetime.now() - self.created < self.deadline class Student: """Describes an instance of a student. Atributes: first_name: the name of a student; last_name: the sername of a student. """ def __init__(self, first_name: str, last_name: str) -> None: """Creates a class instance.""" self.first_name = first_name self.last_name = last_name def do_homework(self, homework: Homework) -> Union[Homework, None]: """Checks is the deadline of the given homework expired or not. Args: homework: an instance of the Homework class that a student is going to do. Returns: the recieved instance of the Homework class if it's deadline hasn't expired, overwise prints "You are late" and returns None. """ if homework.is_active(): return homework print("You are late") return None class Teacher: """Describes an instance of a teacher. atributes: first_name: the name of a teacher; last_name: the sername of a teacher. """ def __init__(self, first_name: str, last_name: str) -> None: """Create a class instance.""" self.first_name = first_name self.last_name = last_name @staticmethod def create_homework(text: str, deadline: int) -> Homework: """Creates an instance of the Homework class. Args: text: text of created homework. deadline: a term to complete the homework in days. Returns: an instance of Homework class. """ return Homework(text, deadline) if __name__ == "__main__": teacher = Teacher("Daniil", "Shadrin") student = Student("Roman", "Petrov") teacher.last_name # Daniil student.first_name # Petrov expired_homework = teacher.create_homework("Learn functions", 0) expired_homework.created # Example: 2019-05-26 16:44:30.688762 expired_homework.deadline # 0:00:00 expired_homework.text # 'Learn functions' # create function from method and use it create_homework_too = teacher.create_homework oop_homework = create_homework_too("create 2 simple classes", 5) oop_homework.deadline # 5 days, 0:00:00 student.do_homework(oop_homework) student.do_homework(expired_homework) # You are late

28.922078

86

0.680287

import datetime from typing import NewType, Union Timedelta = NewType("Timedelta", datetime.timedelta) class Homework: def __init__(self, text: str, deadline: int) -> None: self.text = text self.deadline = datetime.timedelta(days=deadline) self.created = datetime.datetime.now() def is_active(self) -> bool: return datetime.datetime.now() - self.created < self.deadline class Student: def __init__(self, first_name: str, last_name: str) -> None: self.first_name = first_name self.last_name = last_name def do_homework(self, homework: Homework) -> Union[Homework, None]: if homework.is_active(): return homework print("You are late") return None class Teacher: def __init__(self, first_name: str, last_name: str) -> None: self.first_name = first_name self.last_name = last_name @staticmethod def create_homework(text: str, deadline: int) -> Homework: return Homework(text, deadline) if __name__ == "__main__": teacher = Teacher("Daniil", "Shadrin") student = Student("Roman", "Petrov") teacher.last_name student.first_name expired_homework = teacher.create_homework("Learn functions", 0) expired_homework.created expired_homework.deadline expired_homework.text create_homework_too = teacher.create_homework oop_homework = create_homework_too("create 2 simple classes", 5) oop_homework.deadline student.do_homework(oop_homework) student.do_homework(expired_homework)

true

1c37cb5565ca95fa508b29ea62af0b67f57a39d8

550

py

Python

test_fizzbuzz.py

milton63/fizzbuzz

411bd9fc720c081da1ef5eab5d273abab31b8fc5

[ "MIT" ]

null

test_fizzbuzz.py

milton63/fizzbuzz

411bd9fc720c081da1ef5eab5d273abab31b8fc5

[ "MIT" ]

1

2020-05-27T15:38:47.000Z

test_fizzbuzz.py

milton63/fizzbuzz

411bd9fc720c081da1ef5eab5d273abab31b8fc5

[ "MIT" ]

1

2020-05-27T13:51:15.000Z

from fizzbuzz import fizzbuzz def test_number(): assert fizzbuzz(1) == 1 def test_div_3(): assert fizzbuzz(3) == 'Fizz' def test_div_5(): assert fizzbuzz(5) == 'Buzz' def test_div_3and5(): assert fizzbuzz(15) == 'Fizz Buzz' def test_range(): result = [fizzbuzz(x) for x in range(1, 16)] assert result == [ 1, 2, 'Fizz', 4, 'Buzz', 'Fizz', 7, 8, 'Fizz', 'Buzz', 11, 'Fizz', 13, 14, 'Fizz Buzz']

13.75

48

0.469091

from fizzbuzz import fizzbuzz def test_number(): assert fizzbuzz(1) == 1 def test_div_3(): assert fizzbuzz(3) == 'Fizz' def test_div_5(): assert fizzbuzz(5) == 'Buzz' def test_div_3and5(): assert fizzbuzz(15) == 'Fizz Buzz' def test_range(): result = [fizzbuzz(x) for x in range(1, 16)] assert result == [ 1, 2, 'Fizz', 4, 'Buzz', 'Fizz', 7, 8, 'Fizz', 'Buzz', 11, 'Fizz', 13, 14, 'Fizz Buzz']

true

1c37cc07ef8124bd64a11be641a95a2aad421fcc

2,000

py

Python

src/ops/inventory/caching.py

Asjidkalam/ops-cli

951b6e53452aef60cd7a67b95cb3bf227d81c02d

[ "Apache-2.0" ]

182

2019-02-02T22:57:41.000Z

2022-03-19T11:40:15.000Z

src/ops/inventory/caching.py

Asjidkalam/ops-cli

951b6e53452aef60cd7a67b95cb3bf227d81c02d

[ "Apache-2.0" ]

66

2019-02-04T14:43:53.000Z

2021-10-05T14:19:56.000Z

src/ops/inventory/caching.py

Asjidkalam/ops-cli

951b6e53452aef60cd7a67b95cb3bf227d81c02d

[ "Apache-2.0" ]

48

2019-02-05T14:22:10.000Z

2021-09-29T13:41:11.000Z

# Copyright 2019 Adobe. All rights reserved. # This file is licensed 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 REPRESENTATIONS # OF ANY KIND, either express or implied. See the License for the specific language # governing permissions and limitations under the License. import hashlib import json import os import time from six import PY3 def cache_callback_result(directory, func, max_age, cache_key_args): directory = os.path.expanduser(directory) path = get_cache_path(directory, cache_key_args) if is_valid(path, max_age): return read(path) return write(path, func()) def get_cache_path(dir, args): m = hashlib.md5() json_dump = json.dumps(args) if PY3: json_dump = json_dump.encode('utf-8') m.update(json_dump) return os.path.join(dir, m.hexdigest()) def is_valid(filename, max_age): """ Determines if the cache files have expired, or if it is still valid """ filename = os.path.expanduser(filename) if os.path.isfile(filename): mod_time = os.path.getmtime(filename) current_time = time.time() if (mod_time + max_age) > current_time: return True return False def write(filename, data): """ Writes data in JSON format to a file """ json_data = json.dumps(data, sort_keys=True, indent=2) cache = open(os.path.expanduser(filename), 'w') cache.write(json_data) cache.close() return data def read(filename): """ Reads the inventory from the cache file and returns it as a JSON object """ cache = open(os.path.expanduser(filename), 'r') json_inventory = cache.read() return json.loads(json_inventory)

28.985507

88

0.7035

import hashlib import json import os import time from six import PY3 def cache_callback_result(directory, func, max_age, cache_key_args): directory = os.path.expanduser(directory) path = get_cache_path(directory, cache_key_args) if is_valid(path, max_age): return read(path) return write(path, func()) def get_cache_path(dir, args): m = hashlib.md5() json_dump = json.dumps(args) if PY3: json_dump = json_dump.encode('utf-8') m.update(json_dump) return os.path.join(dir, m.hexdigest()) def is_valid(filename, max_age): filename = os.path.expanduser(filename) if os.path.isfile(filename): mod_time = os.path.getmtime(filename) current_time = time.time() if (mod_time + max_age) > current_time: return True return False def write(filename, data): json_data = json.dumps(data, sort_keys=True, indent=2) cache = open(os.path.expanduser(filename), 'w') cache.write(json_data) cache.close() return data def read(filename): cache = open(os.path.expanduser(filename), 'r') json_inventory = cache.read() return json.loads(json_inventory)

true

1c37cc8897a039705453174914ac87e0aa6dd676

6,216

py

Python

lib/googlecloudsdk/api_lib/transfer/operations_util.py

google-cloud-sdk-unofficial/google-cloud-sdk

2a48a04df14be46c8745050f98768e30474a1aac

[ "Apache-2.0" ]

2

2019-11-10T09:17:07.000Z

2019-12-18T13:44:08.000Z

lib/googlecloudsdk/api_lib/transfer/operations_util.py

google-cloud-sdk-unofficial/google-cloud-sdk

2a48a04df14be46c8745050f98768e30474a1aac

[ "Apache-2.0" ]

null

lib/googlecloudsdk/api_lib/transfer/operations_util.py

google-cloud-sdk-unofficial/google-cloud-sdk

2a48a04df14be46c8745050f98768e30474a1aac

[ "Apache-2.0" ]

1

2020-07-25T01:40:19.000Z

# -*- coding: utf-8 -*- # # Copyright 2021 Google LLC. 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. """Utils for common operations API interactions.""" from __future__ import absolute_import from __future__ import division from __future__ import unicode_literals from apitools.base.py import encoding from googlecloudsdk.api_lib.transfer import jobs_util from googlecloudsdk.api_lib.util import apis from googlecloudsdk.command_lib.transfer import name_util from googlecloudsdk.core import log from googlecloudsdk.core import properties from googlecloudsdk.core.console import console_attr from googlecloudsdk.core.console import progress_tracker from googlecloudsdk.core.util import retry from googlecloudsdk.core.util import scaled_integer _LAST_RETRIAL = -1 _UNKNOWN_VALUE = 'UNKNOWN' def _get_operation_to_poll(job_name, operation_name): """Returns operation name or last operation of job name.""" if (not job_name and not operation_name) or (job_name and operation_name): raise ValueError( 'job_name or operation_name must be provided but not both.') if job_name: latest_operation_name = jobs_util.block_until_operation_created(job_name) log.status.Print('Latest Operation: {}'.format(latest_operation_name)) return latest_operation_name return operation_name def _is_operation_in_progress(result, retryer_state): """Takes Operation Apitools object and returns if it is not marked done.""" del retryer_state # Unused. return not result.done def api_get(name): """Returns operation details from API as Apitools object.""" client = apis.GetClientInstance('storagetransfer', 'v1') messages = apis.GetMessagesModule('storagetransfer', 'v1') formatted_operation_name = name_util.add_operation_prefix(name) return client.transferOperations.Get( messages.StoragetransferTransferOperationsGetRequest( name=formatted_operation_name)) def block_until_done(job_name=None, operation_name=None): """Does not return until API responds that operation is done. Args: job_name (str|None): If provided, poll job's last operation. operation_name (str|None): Poll this operation name. Raises: ValueError: One of job_name or operation_name must be provided. """ polling_operation_name = _get_operation_to_poll(job_name, operation_name) with progress_tracker.ProgressTracker( message='Waiting for operation to complete'): retry.Retryer().RetryOnResult( api_get, args=[polling_operation_name], should_retry_if=_is_operation_in_progress, sleep_ms=( properties.VALUES.transfer.no_async_polling_interval_ms.GetInt()), ) def _print_progress(operation, retryer_state): """Gets operation from API and prints its progress updating in-place.""" metadata = encoding.MessageToDict(operation.metadata) if 'counters' in metadata: skipped_bytes = int(metadata['counters'].get('bytesFromSourceSkippedBySync', 0)) skipped_string = scaled_integer.FormatBinaryNumber( skipped_bytes, decimal_places=1) copied_bytes = int(metadata['counters'].get('bytesCopiedToSink', 0)) total_bytes = int(metadata['counters'].get('bytesFoundFromSource', 0)) if total_bytes: progress_percent = int(round(copied_bytes / total_bytes, 2) * 100) else: progress_percent = 0 progress_string = '{}% ({} of {})'.format( progress_percent, scaled_integer.FormatBinaryNumber(copied_bytes, decimal_places=1), scaled_integer.FormatBinaryNumber(total_bytes, decimal_places=1)) else: progress_string = 'Progress: {}'.format(_UNKNOWN_VALUE) skipped_string = _UNKNOWN_VALUE if 'errorBreakdowns' in metadata: error_count = sum( [int(error['errorCount']) for error in metadata['errorBreakdowns']]) else: error_count = 0 spin_marks = console_attr.ProgressTrackerSymbolsAscii().spin_marks if retryer_state.retrial == _LAST_RETRIAL: spin_mark = '' else: spin_mark = spin_marks[retryer_state.retrial % len(spin_marks)] log.status.write(('{} | {} | Skipped: {} | Errors: {} {}\r').format( metadata['status'], progress_string, skipped_string, error_count, spin_mark)) def _poll_progress(name): """Prints progress of operation and blocks until transfer is complete. Args: name (str|None): Poll this operation name. Returns: Apitools Operation object containing the completed operation's metadata. """ complete_operation = retry.Retryer( jitter_ms=0, status_update_func=_print_progress).RetryOnResult( api_get, args=[name], should_retry_if=_is_operation_in_progress, sleep_ms=1000) _print_progress( complete_operation, retry.RetryerState( retrial=_LAST_RETRIAL, time_passed_ms=None, time_to_wait_ms=None)) return complete_operation def display_monitoring_view(name): """Prints and updates operation statistics, blocking until copy complete.""" initial_operation = api_get(name) initial_metadata = encoding.MessageToDict(initial_operation.metadata) log.status.Print('Operation name: ' + name_util.remove_operation_prefix(initial_operation.name)) log.status.Print( 'Parent job: ' + name_util.remove_job_prefix(initial_metadata['transferJobName'])) if 'startTime' in initial_metadata: log.status.Print('Start time: ' + initial_metadata['startTime']) final_operation = _poll_progress(initial_operation.name) final_metadata = encoding.MessageToDict(final_operation.metadata) if 'endTime' in final_metadata: log.status.Print('\nEnd time: ' + final_metadata['endTime'])

36.350877

80

0.741956

from __future__ import absolute_import from __future__ import division from __future__ import unicode_literals from apitools.base.py import encoding from googlecloudsdk.api_lib.transfer import jobs_util from googlecloudsdk.api_lib.util import apis from googlecloudsdk.command_lib.transfer import name_util from googlecloudsdk.core import log from googlecloudsdk.core import properties from googlecloudsdk.core.console import console_attr from googlecloudsdk.core.console import progress_tracker from googlecloudsdk.core.util import retry from googlecloudsdk.core.util import scaled_integer _LAST_RETRIAL = -1 _UNKNOWN_VALUE = 'UNKNOWN' def _get_operation_to_poll(job_name, operation_name): if (not job_name and not operation_name) or (job_name and operation_name): raise ValueError( 'job_name or operation_name must be provided but not both.') if job_name: latest_operation_name = jobs_util.block_until_operation_created(job_name) log.status.Print('Latest Operation: {}'.format(latest_operation_name)) return latest_operation_name return operation_name def _is_operation_in_progress(result, retryer_state): del retryer_state return not result.done def api_get(name): client = apis.GetClientInstance('storagetransfer', 'v1') messages = apis.GetMessagesModule('storagetransfer', 'v1') formatted_operation_name = name_util.add_operation_prefix(name) return client.transferOperations.Get( messages.StoragetransferTransferOperationsGetRequest( name=formatted_operation_name)) def block_until_done(job_name=None, operation_name=None): polling_operation_name = _get_operation_to_poll(job_name, operation_name) with progress_tracker.ProgressTracker( message='Waiting for operation to complete'): retry.Retryer().RetryOnResult( api_get, args=[polling_operation_name], should_retry_if=_is_operation_in_progress, sleep_ms=( properties.VALUES.transfer.no_async_polling_interval_ms.GetInt()), ) def _print_progress(operation, retryer_state): metadata = encoding.MessageToDict(operation.metadata) if 'counters' in metadata: skipped_bytes = int(metadata['counters'].get('bytesFromSourceSkippedBySync', 0)) skipped_string = scaled_integer.FormatBinaryNumber( skipped_bytes, decimal_places=1) copied_bytes = int(metadata['counters'].get('bytesCopiedToSink', 0)) total_bytes = int(metadata['counters'].get('bytesFoundFromSource', 0)) if total_bytes: progress_percent = int(round(copied_bytes / total_bytes, 2) * 100) else: progress_percent = 0 progress_string = '{}% ({} of {})'.format( progress_percent, scaled_integer.FormatBinaryNumber(copied_bytes, decimal_places=1), scaled_integer.FormatBinaryNumber(total_bytes, decimal_places=1)) else: progress_string = 'Progress: {}'.format(_UNKNOWN_VALUE) skipped_string = _UNKNOWN_VALUE if 'errorBreakdowns' in metadata: error_count = sum( [int(error['errorCount']) for error in metadata['errorBreakdowns']]) else: error_count = 0 spin_marks = console_attr.ProgressTrackerSymbolsAscii().spin_marks if retryer_state.retrial == _LAST_RETRIAL: spin_mark = '' else: spin_mark = spin_marks[retryer_state.retrial % len(spin_marks)] log.status.write(('{} | {} | Skipped: {} | Errors: {} {}\r').format( metadata['status'], progress_string, skipped_string, error_count, spin_mark)) def _poll_progress(name): complete_operation = retry.Retryer( jitter_ms=0, status_update_func=_print_progress).RetryOnResult( api_get, args=[name], should_retry_if=_is_operation_in_progress, sleep_ms=1000) _print_progress( complete_operation, retry.RetryerState( retrial=_LAST_RETRIAL, time_passed_ms=None, time_to_wait_ms=None)) return complete_operation def display_monitoring_view(name): initial_operation = api_get(name) initial_metadata = encoding.MessageToDict(initial_operation.metadata) log.status.Print('Operation name: ' + name_util.remove_operation_prefix(initial_operation.name)) log.status.Print( 'Parent job: ' + name_util.remove_job_prefix(initial_metadata['transferJobName'])) if 'startTime' in initial_metadata: log.status.Print('Start time: ' + initial_metadata['startTime']) final_operation = _poll_progress(initial_operation.name) final_metadata = encoding.MessageToDict(final_operation.metadata) if 'endTime' in final_metadata: log.status.Print('\nEnd time: ' + final_metadata['endTime'])

true

1c37cc960048a6de7328e5484c890c05c68dd0cc

1,325

py

Python

Chapter05/virtualenvs/myproject_env/project/django-myproject/myproject/urls.py

PacktPublishing/Django-2-Web-Development-Cookbook-Third-Edition

f129613e2b1d00f5c76649025ae4d568f6286f2c

[ "MIT" ]

75

2018-12-03T02:35:29.000Z

2021-11-08T13:13:34.000Z

Chapter05/virtualenvs/myproject_env/project/django-myproject/myproject/urls.py

PacktPublishing/Django-2-Web-Development-Cookbook-Third-Edition

f129613e2b1d00f5c76649025ae4d568f6286f2c

[ "MIT" ]

3

2019-08-11T13:35:01.000Z

2020-09-29T06:52:36.000Z

Chapter04/virtualenvs/myproject_env/project/django-myproject/myproject/urls.py

PacktPublishing/Django-2-Web-Development-Cookbook-Third-Edition

f129613e2b1d00f5c76649025ae4d568f6286f2c

[ "MIT" ]

45

2018-11-03T14:03:22.000Z

2021-08-25T07:39:33.000Z

"""myproject URL Configuration The `urlpatterns` list routes URLs to views. For more information please see: https://docs.djangoproject.com/en/2.1/topics/http/urls/ Examples: Function views 1. Add an import: from my_app import views 2. Add a URL to urlpatterns: path('', views.home, name='home') Class-based views 1. Add an import: from other_app.views import Home 2. Add a URL to urlpatterns: path('', Home.as_view(), name='home') Including another URLconf 1. Import the include() function: from django.urls import include, path 2. Add a URL to urlpatterns: path('blog/', include('blog.urls')) """ from django.contrib import admin from django.conf.urls.i18n import i18n_patterns from django.urls import include, path urlpatterns = [ path('admin/', admin.site.urls), path('bulletins/', include('bulletin_board.urls')), path('cv/', include('cv.urls')), path('email/', include('email_messages.urls')), path('like/', include('likes.urls')), path('locations/', include('locations.urls')), path('movies/', include('movies.urls')), path('quotes/', include('quotes.urls')), ] urlpatterns += i18n_patterns( path('search/', include('haystack.urls')), path("js-settings/", render_js, {"template_name": "settings.js"}, name="js_settings"), )

35.810811

77

0.676981

from django.contrib import admin from django.conf.urls.i18n import i18n_patterns from django.urls import include, path urlpatterns = [ path('admin/', admin.site.urls), path('bulletins/', include('bulletin_board.urls')), path('cv/', include('cv.urls')), path('email/', include('email_messages.urls')), path('like/', include('likes.urls')), path('locations/', include('locations.urls')), path('movies/', include('movies.urls')), path('quotes/', include('quotes.urls')), ] urlpatterns += i18n_patterns( path('search/', include('haystack.urls')), path("js-settings/", render_js, {"template_name": "settings.js"}, name="js_settings"), )

true

1c37cce45e3f42c0c9228dc0917458e271696929

2,932

py

Python

src/sentry/lang/native/utils.py

learninto/sentry

4f9f564841498b3af49c1677d6b61f3e47b01923

[ "BSD-3-Clause" ]

null

src/sentry/lang/native/utils.py

learninto/sentry

4f9f564841498b3af49c1677d6b61f3e47b01923

[ "BSD-3-Clause" ]

null

src/sentry/lang/native/utils.py

learninto/sentry

4f9f564841498b3af49c1677d6b61f3e47b01923

[ "BSD-3-Clause" ]

null

from __future__ import absolute_import import re import six import logging from sentry.stacktraces.processing import find_stacktraces_in_data from sentry.utils.safe import get_path logger = logging.getLogger(__name__) # Regex to parse OS versions from a minidump OS string. VERSION_RE = re.compile(r"(\d+\.\d+\.\d+)\s+(.*)") # Regex to guess whether we're dealing with Windows or Unix paths. WINDOWS_PATH_RE = re.compile(r"^([a-z]:\\|\\\\)", re.IGNORECASE) # Event platforms that could contain native stacktraces NATIVE_PLATFORMS = ("cocoa", "native") # Debug image types that can be handled by the symbolicator NATIVE_IMAGE_TYPES = ( "apple", # Deprecated in favor of "macho" "symbolic", # Generic if type is not known "elf", # Linux "macho", # macOS, iOS "pe", # Windows ) def is_native_platform(platform): return platform in NATIVE_PLATFORMS def is_native_image(image): return ( bool(image) and image.get("type") in NATIVE_IMAGE_TYPES and image.get("image_addr") is not None and image.get("image_size") is not None and (image.get("debug_id") or image.get("id") or image.get("uuid")) is not None ) def native_images_from_data(data): return get_path(data, "debug_meta", "images", default=(), filter=is_native_image) def is_native_event(data): if is_native_platform(data.get("platform")): return True for stacktrace in find_stacktraces_in_data(data): if any(is_native_platform(x) for x in stacktrace.platforms): return True return False def is_minidump_event(data): exceptions = get_path(data, "exception", "values", filter=True) return get_path(exceptions, 0, "mechanism", "type") in ("minidump", "unreal") def image_name(pkg): if not pkg: return pkg split = "\\" if WINDOWS_PATH_RE.match(pkg) else "/" return pkg.rsplit(split, 1)[-1] def get_sdk_from_event(event): sdk_info = get_path(event, "debug_meta", "sdk_info") if sdk_info: return sdk_info os = get_path(event, "contexts", "os") if os and os.get("type") == "os": return get_sdk_from_os(os) def get_sdk_from_os(data): if data.get("name") is None or data.get("version") is None: return try: version = six.text_type(data["version"]).split("-", 1)[0] + ".0" * 3 system_version = tuple(int(x) for x in version.split(".")[:3]) except ValueError: return return { "sdk_name": data["name"], "version_major": system_version[0], "version_minor": system_version[1], "version_patchlevel": system_version[2], "build": data.get("build"), } def signal_from_data(data): exceptions = get_path(data, "exception", "values", filter=True) signal = get_path(exceptions, 0, "mechanism", "meta", "signal", "number") if signal is not None: return int(signal) return None

27.148148

87

0.658595

from __future__ import absolute_import import re import six import logging from sentry.stacktraces.processing import find_stacktraces_in_data from sentry.utils.safe import get_path logger = logging.getLogger(__name__) VERSION_RE = re.compile(r"(\d+\.\d+\.\d+)\s+(.*)") WINDOWS_PATH_RE = re.compile(r"^([a-z]:\\|\\\\)", re.IGNORECASE) # Event platforms that could contain native stacktraces NATIVE_PLATFORMS = ("cocoa", "native") # Debug image types that can be handled by the symbolicator NATIVE_IMAGE_TYPES = ( "apple", # Deprecated in favor of "macho" "symbolic", # Generic if type is not known "elf", # Linux "macho", # macOS, iOS "pe", # Windows ) def is_native_platform(platform): return platform in NATIVE_PLATFORMS def is_native_image(image): return ( bool(image) and image.get("type") in NATIVE_IMAGE_TYPES and image.get("image_addr") is not None and image.get("image_size") is not None and (image.get("debug_id") or image.get("id") or image.get("uuid")) is not None ) def native_images_from_data(data): return get_path(data, "debug_meta", "images", default=(), filter=is_native_image) def is_native_event(data): if is_native_platform(data.get("platform")): return True for stacktrace in find_stacktraces_in_data(data): if any(is_native_platform(x) for x in stacktrace.platforms): return True return False def is_minidump_event(data): exceptions = get_path(data, "exception", "values", filter=True) return get_path(exceptions, 0, "mechanism", "type") in ("minidump", "unreal") def image_name(pkg): if not pkg: return pkg split = "\\" if WINDOWS_PATH_RE.match(pkg) else "/" return pkg.rsplit(split, 1)[-1] def get_sdk_from_event(event): sdk_info = get_path(event, "debug_meta", "sdk_info") if sdk_info: return sdk_info os = get_path(event, "contexts", "os") if os and os.get("type") == "os": return get_sdk_from_os(os) def get_sdk_from_os(data): if data.get("name") is None or data.get("version") is None: return try: version = six.text_type(data["version"]).split("-", 1)[0] + ".0" * 3 system_version = tuple(int(x) for x in version.split(".")[:3]) except ValueError: return return { "sdk_name": data["name"], "version_major": system_version[0], "version_minor": system_version[1], "version_patchlevel": system_version[2], "build": data.get("build"), } def signal_from_data(data): exceptions = get_path(data, "exception", "values", filter=True) signal = get_path(exceptions, 0, "mechanism", "meta", "signal", "number") if signal is not None: return int(signal) return None

true

1c37cd0577c3baf926e58ab3b3f22495cf34232b

31

py

Python

arranger/closest/__init__.py

dezimynona/icml2021submission

009eb6c6b617536bda7a247cbf5d6b7c0c131f19

[ "MIT" ]

1

2021-07-11T17:20:02.000Z

arranger/closest/__init__.py

dezimynona/icml2021submission

009eb6c6b617536bda7a247cbf5d6b7c0c131f19

[ "MIT" ]

null

arranger/closest/__init__.py

dezimynona/icml2021submission

009eb6c6b617536bda7a247cbf5d6b7c0c131f19

[ "MIT" ]

1

2021-02-03T19:22:27.000Z

"""Closest-pitch algorithm."""

15.5

30

0.677419

true

1c37cdca199ced6425daca6cc92bee848283a476

698

py

Python

sdk/python-sdk/setup.py

tw-bc-group/verity-sdk

e932209ab849f04a389bdda0718cd6227187e5cf

[ "Apache-2.0" ]

null

sdk/python-sdk/setup.py

tw-bc-group/verity-sdk

e932209ab849f04a389bdda0718cd6227187e5cf

[ "Apache-2.0" ]

2

2021-09-02T19:02:06.000Z

2021-09-02T19:02:24.000Z

sdk/python-sdk/setup.py

tw-bc-group/verity-sdk

e932209ab849f04a389bdda0718cd6227187e5cf

[ "Apache-2.0" ]

1

2021-01-13T10:43:14.000Z

import setuptools from verity_sdk.version import VERSION with open('README.md', 'r') as fh: long_description = fh.read() setuptools.setup( name="verity-sdk", version=VERSION, # see verity_sdk/version.py author="Evernym, Inc.", author_email="dev@evernym.com", description='The official Python SDK for Evernym\'s Verity', license="Apache-2.0", url="https://github.com/evernym/verity-sdk", install_requires=[ 'python3-indy~=1.15.0', 'requests~=2.22', 'base58~=2.0.0' ], python_requires='~=3.6', long_description=long_description, long_description_content_type='text/markdown', packages=setuptools.find_packages(), )

25.851852

64

0.667622

import setuptools from verity_sdk.version import VERSION with open('README.md', 'r') as fh: long_description = fh.read() setuptools.setup( name="verity-sdk", version=VERSION, author="Evernym, Inc.", author_email="dev@evernym.com", description='The official Python SDK for Evernym\'s Verity', license="Apache-2.0", url="https://github.com/evernym/verity-sdk", install_requires=[ 'python3-indy~=1.15.0', 'requests~=2.22', 'base58~=2.0.0' ], python_requires='~=3.6', long_description=long_description, long_description_content_type='text/markdown', packages=setuptools.find_packages(), )

true

1c37ce6927cbfac725faaa0c541bb5f99388ebb2

1,428

py

Python

utils/average_plate.py

khainn/ALPR_System

73d8adc6bc7cb91507d1bf047ded20be844923ef

[ "Apache-2.0" ]

71

2019-01-04T03:42:04.000Z

2022-03-28T16:38:58.000Z

utils/average_plate.py

khainn/ALPR_System

73d8adc6bc7cb91507d1bf047ded20be844923ef

[ "Apache-2.0" ]

14

2019-11-05T18:20:05.000Z

2022-02-10T00:30:54.000Z

utils/average_plate.py

khainn/ALPR_System

73d8adc6bc7cb91507d1bf047ded20be844923ef

[ "Apache-2.0" ]

38

2019-03-26T09:05:20.000Z

2022-03-25T12:46:36.000Z

""" Gets the recognized plate in several frames and calculates the most possible plate value """ import math from collections import Counter def getDistance(pointA, pointB): """ calculates the distance between two points in the image """ return math.sqrt(math.pow((pointA[0] - pointB[0]), 2) + math.pow((pointA[1] - pointB[1]), 2)) def tracking(previous_coordinate, current_coordinate): distance = getDistance(previous_coordinate, current_coordinate) return distance def get_average_plate_value(plates, plates_length): """ inputs an array of plates and returns the most possible value (average value) of the array """ # plates_length is an array containing the number of characters detected on each plate in plate array plates_to_be_considered = [] number_char_on_plate = Counter(plates_length).most_common(1)[0][0] for plate in plates: if (len(plate) == number_char_on_plate): plates_to_be_considered.append(plate) temp = '' for plate in plates_to_be_considered: temp = temp + plate counter = 0 final_plate = '' for i in range(number_char_on_plate): my_list = [] for i in range(len(plates_to_be_considered)): my_list.append(temp[i*number_char_on_plate + counter]) final_plate = final_plate + str(Counter(my_list).most_common(1)[0][0]) counter += 1 return final_plate

34

105

0.691877

import math from collections import Counter def getDistance(pointA, pointB): return math.sqrt(math.pow((pointA[0] - pointB[0]), 2) + math.pow((pointA[1] - pointB[1]), 2)) def tracking(previous_coordinate, current_coordinate): distance = getDistance(previous_coordinate, current_coordinate) return distance def get_average_plate_value(plates, plates_length): plates_to_be_considered = [] number_char_on_plate = Counter(plates_length).most_common(1)[0][0] for plate in plates: if (len(plate) == number_char_on_plate): plates_to_be_considered.append(plate) temp = '' for plate in plates_to_be_considered: temp = temp + plate counter = 0 final_plate = '' for i in range(number_char_on_plate): my_list = [] for i in range(len(plates_to_be_considered)): my_list.append(temp[i*number_char_on_plate + counter]) final_plate = final_plate + str(Counter(my_list).most_common(1)[0][0]) counter += 1 return final_plate

true

1c37cf8f0e69896397ea2c6e841faf4fa2ce3678

7,883

py

Python

main.py

namansnghl/goblin-hunter-pygame

c740549ff70e4233d3c17558a5a6e62f0025a3cf

[ "MIT" ]

null

main.py

namansnghl/goblin-hunter-pygame

c740549ff70e4233d3c17558a5a6e62f0025a3cf

[ "MIT" ]

null

main.py

namansnghl/goblin-hunter-pygame

c740549ff70e4233d3c17558a5a6e62f0025a3cf

[ "MIT" ]

null

import pygame pygame.init() screen_width = 987 screen_height = 598 # setting window size. win = pygame.display.set_mode((screen_width, screen_height)) # game window title pygame.display.set_caption("Goblin Hunter") # loading character movements bg = pygame.image.load('img/bg.jpg') idle = pygame.image.load('img/standing.png') class player: ''' Creates the main character of game Object params: x ---> initial x coordinate of player y ---> initial y coordinate of player width ---> width of player height ---> height of player jump_height ---> jumping height of player. Default 10 vel ---> Velocity of player. Default 8 Methods: draw() ---> draws the character movement at location ''' walkRight = list(map(pygame.image.load, '{folder}R1.png {folder}R2.png {folder}R3.png {folder}R4.png {folder}R5.png {folder}R6.png {folder}R7.png {folder}R8.png {folder}R9.png'.format(folder='img/').split())) walkLeft = list(map(pygame.image.load, '{folder}L1.png {folder}L2.png {folder}L3.png {folder}L4.png {folder}L5.png {folder}L6.png {folder}L7.png {folder}L8.png {folder}L9.png'.format(folder='img/').split())) def __init__(self, x, y, width, height, jump_height=10, vel=8): self.x = x self.y = y self.width = width self.height = height self.vel = vel self.left = False self.right = False self.walkCount = 0 self.jumpcount = jump_height self.jump = False self.standing = True self.hitbox = (self.x+17, self.y+11, 29, 52) def draw(self, win): # since we have only 9 sprites/movement we set 3 frame = 1 image and walkCount <= 27 if (self.walkCount+1) >= 27: self.walkCount = 0 if not(self.standing): # logic to choose character image as per movements if self.left: win.blit(self.walkLeft[self.walkCount//3], (int(self.x), int(self.y))) self.walkCount += 1 elif self.right: win.blit(self.walkRight[self.walkCount//3], (int(self.x), int(self.y))) self.walkCount += 1 else: if self.right: win.blit(self.walkRight[0], (int(self.x), int(self.y))) else: win.blit(self.walkLeft[0], (int(self.x), int(self.y))) self.hitbox = (self.x+17, self.y+11, 29, 52) pygame.draw.rect(win, (255, 0, 150), tuple(map(int, self.hitbox)), 2) class fire_bullet: ''' Creates the bullets fired Object params: x ---> initial x coordinate y ---> initial y coordinate radius ---> bullet size color ---> bullet color facing ---> direction player is facing while shooting. -1 = left, +1 = right vel ---> Velocity of bullet. Default 10 Methods: draw() ---> makes bullet animation ''' def __init__(self, x, y, radius, color, facing, vel=10): self.x, self.y, self.facing = x, y, facing self.color = color self.radius = radius self.vel = vel*facing def draw(self, win): pygame.draw.circle(win, self.color, (self.x, self.y), self.radius) class enemy: ''' Creates the main character of game Object params: x ---> initial x coordinate of enemy y ---> initial y coordinate of enemy width ---> width of enemy height ---> height of enemy end ---> right end position of enemy. Default 10 vel ---> Velocity of enemy. Default 4 Methods: draw() ---> draws the character movement at location move() ---> enemy movement direction logic ''' walkRight = list(map(pygame.image.load, '{folder}R1E.png {folder}R2E.png {folder}R3E.png {folder}R4E.png {folder}R5E.png {folder}R6E.png {folder}R7E.png {folder}R8E.png {folder}R9E.png {folder}R10E.png {folder}R11E.png'.format(folder='img/').split())) walkLeft = list(map(pygame.image.load, '{folder}L1E.png {folder}L2E.png {folder}L3E.png {folder}L4E.png {folder}L5E.png {folder}L6E.png {folder}L7E.png {folder}L8E.png {folder}L9E.png {folder}L10E.png {folder}L11E.png'.format(folder='img/').split())) def __init__(self, x, y, width, height, end, vel=4): self.x = x self.y = y self.width = width self.height = height self.end = end self.vel = vel self.walkCount = 0 self.path = [self.x, self.end] self.hitbox = (self.x+17, self.y+5, 30, 53) def draw(self, win): self.move() if (self.walkCount+1) >= 33: self.walkCount = 0 if self.vel > 0: win.blit(self.walkRight[self.walkCount//3], (int(self.x), int(self.y))) self.walkCount += 1 else: win.blit(self.walkLeft[self.walkCount//3], (int(self.x), int(self.y))) self.walkCount += 1 self.hitbox = (self.x+17, self.y+5, 30, 53) pygame.draw.rect(win, (255, 0, 150), self.hitbox, 2) def move(self): if self.x+self.vel in range(self.path[0], self.path[1]): self.x += self.vel else: self.vel = self.vel * -1 self.walkCount = 0 def hit(self): print('hit') def redrawGameWindow(): # function to draw objects on window win.blit(bg, (0, 0)) hero.draw(win) # hero goblin.draw(win) for bullet in bullets: # bullets bullet.draw(win) pygame.display.update() clock = pygame.time.Clock() jump_height = 9 hero = player(20, 416, 64, 64, jump_height, vel=6) goblin = enemy(0, 420, 64, 64, screen_width-50, vel=3) run = True shoot = 0 bullets = [] # game begins while run: clock.tick(27) # 27fps. 9 images per movement. 1 move = 3 frames for event in pygame.event.get(): # Exiting game if event.type == pygame.QUIT: run = False keys = pygame.key.get_pressed() if shoot > 0: shoot += 1 if shoot > 5: shoot = 0 for bullet in bullets: # bullet hits goblin logic if bullet.y-bullet.radius < goblin.hitbox[1]+goblin.hitbox[3] and bullet.y+bullet.radius > goblin.hitbox[1]: if bullet.x-bullet.radius < goblin.hitbox[0]+goblin.hitbox[2] and bullet.x+bullet.radius > goblin.hitbox[0]: goblin.hit() bullets.pop(bullets.index(bullet)) if bullet.x in range(0, screen_width): bullet.x += bullet.vel else: # delete when beyond screen bullets.pop(bullets.index(bullet)) if keys[pygame.K_SPACE] and len(bullets) < 6 and shoot == 0: # bullet creation if hero.left: facing = -1 else: facing = 1 bullets.append(fire_bullet( round(hero.x+hero.width//2), round(hero.y+hero.height//2), 5, (252, 177, 3), facing, vel=7)) shoot = 1 if keys[pygame.K_LEFT] and hero.x > 0: # movement control hero.x -= hero.vel hero.left, hero.right = True, False hero.standing = False elif keys[pygame.K_RIGHT] and hero.x < (screen_width-hero.width-hero.vel): hero.x += hero.vel hero.left, hero.right = False, True hero.standing = False else: hero.walkCount = 0 hero.standing = True if not(hero.jump): # Logic to make a jump and fall back if keys[pygame.K_UP]: hero.jump = True hero.left, hero.right = False, False hero.walkCount = 0 else: if hero.jumpcount >= -(jump_height): neg = 1 if hero.jumpcount < 0: neg = -1 hero.y -= (hero.jumpcount**2)*0.5*neg hero.jumpcount -= 1 else: hero.jump = False hero.jumpcount = jump_height redrawGameWindow() pygame.quit()

32.709544

236

0.578714

import pygame pygame.init() screen_width = 987 screen_height = 598 win = pygame.display.set_mode((screen_width, screen_height)) pygame.display.set_caption("Goblin Hunter") bg = pygame.image.load('img/bg.jpg') idle = pygame.image.load('img/standing.png') class player: walkRight = list(map(pygame.image.load, '{folder}R1.png {folder}R2.png {folder}R3.png {folder}R4.png {folder}R5.png {folder}R6.png {folder}R7.png {folder}R8.png {folder}R9.png'.format(folder='img/').split())) walkLeft = list(map(pygame.image.load, '{folder}L1.png {folder}L2.png {folder}L3.png {folder}L4.png {folder}L5.png {folder}L6.png {folder}L7.png {folder}L8.png {folder}L9.png'.format(folder='img/').split())) def __init__(self, x, y, width, height, jump_height=10, vel=8): self.x = x self.y = y self.width = width self.height = height self.vel = vel self.left = False self.right = False self.walkCount = 0 self.jumpcount = jump_height self.jump = False self.standing = True self.hitbox = (self.x+17, self.y+11, 29, 52) def draw(self, win): if (self.walkCount+1) >= 27: self.walkCount = 0 if not(self.standing): if self.left: win.blit(self.walkLeft[self.walkCount//3], (int(self.x), int(self.y))) self.walkCount += 1 elif self.right: win.blit(self.walkRight[self.walkCount//3], (int(self.x), int(self.y))) self.walkCount += 1 else: if self.right: win.blit(self.walkRight[0], (int(self.x), int(self.y))) else: win.blit(self.walkLeft[0], (int(self.x), int(self.y))) self.hitbox = (self.x+17, self.y+11, 29, 52) pygame.draw.rect(win, (255, 0, 150), tuple(map(int, self.hitbox)), 2) class fire_bullet: def __init__(self, x, y, radius, color, facing, vel=10): self.x, self.y, self.facing = x, y, facing self.color = color self.radius = radius self.vel = vel*facing def draw(self, win): pygame.draw.circle(win, self.color, (self.x, self.y), self.radius) class enemy: walkRight = list(map(pygame.image.load, '{folder}R1E.png {folder}R2E.png {folder}R3E.png {folder}R4E.png {folder}R5E.png {folder}R6E.png {folder}R7E.png {folder}R8E.png {folder}R9E.png {folder}R10E.png {folder}R11E.png'.format(folder='img/').split())) walkLeft = list(map(pygame.image.load, '{folder}L1E.png {folder}L2E.png {folder}L3E.png {folder}L4E.png {folder}L5E.png {folder}L6E.png {folder}L7E.png {folder}L8E.png {folder}L9E.png {folder}L10E.png {folder}L11E.png'.format(folder='img/').split())) def __init__(self, x, y, width, height, end, vel=4): self.x = x self.y = y self.width = width self.height = height self.end = end self.vel = vel self.walkCount = 0 self.path = [self.x, self.end] self.hitbox = (self.x+17, self.y+5, 30, 53) def draw(self, win): self.move() if (self.walkCount+1) >= 33: self.walkCount = 0 if self.vel > 0: win.blit(self.walkRight[self.walkCount//3], (int(self.x), int(self.y))) self.walkCount += 1 else: win.blit(self.walkLeft[self.walkCount//3], (int(self.x), int(self.y))) self.walkCount += 1 self.hitbox = (self.x+17, self.y+5, 30, 53) pygame.draw.rect(win, (255, 0, 150), self.hitbox, 2) def move(self): if self.x+self.vel in range(self.path[0], self.path[1]): self.x += self.vel else: self.vel = self.vel * -1 self.walkCount = 0 def hit(self): print('hit') def redrawGameWindow(): win.blit(bg, (0, 0)) hero.draw(win) goblin.draw(win) for bullet in bullets: bullet.draw(win) pygame.display.update() clock = pygame.time.Clock() jump_height = 9 hero = player(20, 416, 64, 64, jump_height, vel=6) goblin = enemy(0, 420, 64, 64, screen_width-50, vel=3) run = True shoot = 0 bullets = [] while run: clock.tick(27) for event in pygame.event.get(): if event.type == pygame.QUIT: run = False keys = pygame.key.get_pressed() if shoot > 0: shoot += 1 if shoot > 5: shoot = 0 for bullet in bullets: if bullet.y-bullet.radius < goblin.hitbox[1]+goblin.hitbox[3] and bullet.y+bullet.radius > goblin.hitbox[1]: if bullet.x-bullet.radius < goblin.hitbox[0]+goblin.hitbox[2] and bullet.x+bullet.radius > goblin.hitbox[0]: goblin.hit() bullets.pop(bullets.index(bullet)) if bullet.x in range(0, screen_width): bullet.x += bullet.vel else: bullets.pop(bullets.index(bullet)) if keys[pygame.K_SPACE] and len(bullets) < 6 and shoot == 0: if hero.left: facing = -1 else: facing = 1 bullets.append(fire_bullet( round(hero.x+hero.width//2), round(hero.y+hero.height//2), 5, (252, 177, 3), facing, vel=7)) shoot = 1 if keys[pygame.K_LEFT] and hero.x > 0: hero.x -= hero.vel hero.left, hero.right = True, False hero.standing = False elif keys[pygame.K_RIGHT] and hero.x < (screen_width-hero.width-hero.vel): hero.x += hero.vel hero.left, hero.right = False, True hero.standing = False else: hero.walkCount = 0 hero.standing = True if not(hero.jump): if keys[pygame.K_UP]: hero.jump = True hero.left, hero.right = False, False hero.walkCount = 0 else: if hero.jumpcount >= -(jump_height): neg = 1 if hero.jumpcount < 0: neg = -1 hero.y -= (hero.jumpcount**2)*0.5*neg hero.jumpcount -= 1 else: hero.jump = False hero.jumpcount = jump_height redrawGameWindow() pygame.quit()

true

1c37cff4afe4b2afdd67d7d83fdde90f400a23b5

35,293

py

Python

python/lvmcam/araviscam/BlackflyCam.py

sdss/lvmcam

c5f421a546a0072a0dbb3d7b2ebc74316f339f64

[ "BSD-3-Clause" ]

3

2021-11-17T02:40:02.000Z

2022-03-22T08:30:45.000Z

python/lvmcam/araviscam/BlackflyCam.py

sdss/lvmcam

c5f421a546a0072a0dbb3d7b2ebc74316f339f64

[ "BSD-3-Clause" ]

8

2021-11-25T10:18:31.000Z

2021-12-17T13:04:52.000Z

python/lvmcam/araviscam/BlackflyCam.py

sdss/lvmcam

c5f421a546a0072a0dbb3d7b2ebc74316f339f64

[ "BSD-3-Clause" ]

null

#!/usr/bin/env python3 """ Python3 class to work with Aravis/GenICam cameras, subclass of sdss-basecam. .. module:: araviscam .. moduleauthor:: Richard J. Mathar <mathar@mpia.de> """ import sys import math import asyncio import numpy import astropy from basecam.mixins import ImageAreaMixIn from basecam import ( CameraSystem, BaseCamera, CameraEvent, CameraConnectionError, models, ExposureError, ) from lvmcam.actor import modules # Since the aravis wrapper for GenICam cameras (such as the Blackfly) # is using glib2 GObjects to represent cameras and streams, the # PyGObject module allows to call the C functions of aravis in python. # https://pygobject.readthedocs.io/en/latest/ from lvmcam.araviscam.aravis import Aravis import basecam.models.card as card from lvmcam.actor.commands import expose # https://pypi.org/project/sdss-basecam/ # https://githum.com/sdss/basecam/ # from sdsstools import read_yaml_file __all__ = ["BlackflyCameraSystem", "BlackflyCamera", "BlackflyImageAreaMixIn"] class BlackflyCameraSystem(CameraSystem): """A collection of GenICam cameras, possibly online :param camera_class : `.BaseCamera` subclass The subclass of `.BaseCamera` to use with this camera system. :param camera_config : A dictionary with the configuration parameters for the multiple cameras that can be present in the system, or the path to a YAML file. Refer to the documentation for details on the accepted format. :type camera_config : dict or path :param include : List of camera UIDs that can be connected. :type include : list :param exclude : list List of camera UIDs that will be ignored. :param logger : ~logging.Logger The logger instance to use. If `None`, a new logger will be created. :param log_header : A string to be prefixed to each message logged. :type log_header : str :param log_file : The path to which to log. :type log_file : str :param verbose : Whether to log to stdout. :type verbose : bool :param ip_list: A list of IP-Adresses to be checked/pinged. :type ip_list: List of strings. """ __version__ = "0.0.301" # A list of ip addresses in the usual "xxx.yyy.zzz.ttt" or "name.subnet.net" # format that have been added manually/explicitly and may not be found by the # usual broadcase auto-detection (i.e., possibly on some other global network). ips_nonlocal = [] def __init__( self, camera_class=None, camera_config=None, include=None, exclude=None, logger=None, log_header=None, log_file=None, verbose=False, ip_list=None, ): super().__init__( camera_class=camera_class, camera_config=camera_config, include=include, exclude=exclude, logger=logger, log_header=log_header, log_file=log_file, verbose=verbose, ) # If the ctor is fed with an explicit list of IP addresses, add them to # the scanner (with delayed inspection in list_available_cameras). if ip_list is not None: self.ips_nonlocal.extend(ip_list) # debuging: print yaml configuration # print(self._config) # @modules.timeit def list_available_cameras(self): """Gather serial numbers of online Aravis/Genicam devices. :return: a list of serial numbers (as strings). This list may be empty if no cameras are online/switched on. For cameras explicitly addressed by IP, the serial numbers have the format sn@ip, with an @ between number and address. :rtype: list .. todo:: optionally implement a specific filter for Blackfly's if Basler cameras should not be listed. """ # Start with (pessimistic) initially empty set of online devices serialNums = [] addrs = [] # Broadcast ethernet/bus for recognized cameras. # Warning/todo: this gathers also cameras that are not of the Blackfly class, # and in conjunction with the SDSS may also recognize the Basler cameras.. Aravis.update_device_list() Ndev = Aravis.get_n_devices() # print(str(Ndev) + " cameras online") # get_device_id returns a string of type, SN, MAC etc for i in range(Ndev): cam = Aravis.Camera.new(Aravis.get_device_id(i)) uid = cam.get_string("DeviceSerialNumber") serialNums.append(uid) addrs.append("") # Try to ping cameras explicitly proposed with ctor. for ip in self.ips_nonlocal: try: cam = Aravis.Camera.new(ip) uid = cam.get_string("DeviceSerialNumber") # If is this was already in the scan: discard, else add if uid not in serialNums: serialNums.append(uid) addrs.append("@" + ip) except: # apparently no such camera at this address.... pass # we zip the two lists to the format 'serialnumber{@ip}' ids = [] for cam in range(len(serialNums)): ids.append(serialNums[cam] + addrs[cam]) return ids from basecam.models.builtin import basic_fz_fits_model class BlackflyCamera(BaseCamera): """A FLIR (formerly Point Grey Research) Blackfly camera. Given the pixel scale on the benches of LVMi and the assumption of 9 um pixel sizes of the LVMi cameras, we assume that the cameras have roughly 1 arsec per pixel, so they are used without binning. In addition we let the camera flip the standard image orientation of the data values assuming that values are stored into a FITS interface (where the first values in the sequential data are the bottom row). So this is not done in this python code but by the camera. """ # fits_model=basic_fz_fits_model def __init__( self, uid, camera_system, name=None, force=False, image_namer=None, camera_params={}, ): super().__init__( uid=uid, camera_system=camera_system, name=name, force=force, image_namer=image_namer, camera_params=camera_params, ) self.header = [] @modules.atimeit async def _connect_internal(self, **kwargs): """Connect to a camera and upload basic binning and ROI parameters. :param kwargs: recognizes the key uid with integer value, the serial number If the key uid is absent, tries to attach to the first camera. This is a subdictionary of 'cameras' in practise. """ # print(self.name) # search for an optional uid key in the arguments try: uid = kwargs["uid"] except: uid = None # reverse lookup of the uid in the list of known cameras cs = BlackflyCameraSystem(BlackflyCamera) slist = cs.list_available_cameras() if uid is None: # uid was not specified: grab the first device that is found # print("no uid provided, attaching to first camera") idx = 0 else: # print("searching " + uid + " in " + str(slist) ) idx = -1 for id in slist: # remove the optional ip address of the id slistuid = id.split("@")[0] if slistuid == uid: idx = slist.index(id) # not found if idx < 0: raise CameraConnectionError("SN " + uid + " not connected") cam = None try: if "@" in slist[idx]: # if the camera was not on local network use the address part cam = Aravis.Camera.new(slist[idx].split("@")[1]) else: # otherwise the index is the same as the search order... cam = Aravis.Camera.new(Aravis.get_device_id(idx)) except: raise CameraConnectionError(" not connected") # search for an optional gain key in the arguments # todo: one could interpret gain=0 here as to call set_gain_auto(ARV_AUTO_ON) try: gain = kwargs["gain"] if gain > 0.0: # todo: it might make sense to squeeze this into the minimum # and maximum range of the camera's gain if outside that range. self.device.set_gain_auto(0) cam.set_gain(gain) except Exception as ex: # print("failed to set gain " + str(ex)) pass # see arvenums.h for the list of pixel formats. This is MONO_16 here, always cam.set_pixel_format(0x01100007) # search for an optional x and y binning factor try: var = kwargs["binning"] cam.set_binning(var[0], var[1]) except Exception as ex: # print("failed to set binning " + str(ex)) # horizontal and vertical binning set to 1 cam.set_binning(1, 1) # scan the general list of genicam featured values # of the four native types for typp, arvLst in kwargs.items(): if arvLst is not None: if typp == "bool": for genkey, genval in arvLst.items(): try: cam.set_boolean(genkey, int(genval)) except: # probably a typo in the yaml file... todo: log this # print("failed for " + str(genkey)+str(genval)) pass elif typp == "int": for genkey, genval in arvLst.items(): try: cam.set_integer(genkey, genval) except: # probably a typo in the yaml file... todo: log this # print("failed for " + str(genkey)+str(genval)) pass elif typp == "float": for genkey, genval in arvLst.items(): try: cam.set_float(genkey, genval) except: # probably a typo in the yaml file... todo: log this # print("failed for " + str(genkey)+str(genval)) pass elif typp == "string": for genkey, genval in arvLst.items(): try: cam.set_string(genkey, genval) except: # probably a typo in the yaml file... todo: log this # print("failed for " + str(genkey)+str(genval)) pass dev = cam.get_device() # Take full frames by default (maximizing probability of LVM guide camera # to find guide stars in the field) roiBounds = [-1, -1] try: roiBounds[0] = dev.get_integer_feature_value("WidthMax") roiBounds[1] = dev.get_integer_feature_value("HeightMax") # print(" ROI " + str(roiBounds[0]) + " x " + str(roiBounds[1]) ) cam.set_region(0, 0, roiBounds[0], roiBounds[1]) except Exception as ex: # print("failed to set ROI " + str(ex)) pass self.device = cam self.regionBounds = roiBounds @modules.atimeit async def _disconnect_internal(self): """Close connection to camera.""" self.device = None # @modules.atimeit async def _expose_grabFrame(self, exposure): """Read a single unbinned full frame. The class splits the parent class' exposure into this function and the part which generates the FITS file, because applications in guiders are usually only interested in the frame's data, and would not take the detour of generating a FITS file and reading it back from disk. :param exposure: On entry, exposure.exptim is the intended exposure time in [sec] On exit, exposure.data is the numpy array of the 16bit data arranged in FITS order (i.e., the data of the bottom row appear first...) :return: The dictionary with the window location and size (x=,y=,width=,height=) """ # To avoid being left over by other programs with no change # to set the exposure time, we switch the auto=0=off first self.device.set_exposure_time_auto(0) # Aravis assumes exptime in micro second integers exptime_ms = int(0.5 + exposure.exptime * 1e6) self.device.set_exposure_time(exptime_ms) # timeout (factor 2: assuming there may be two frames in auto mode taken # internally) # And 5 seconds margin for any sort of transmission overhead over PoE tout_ms = int(1.0e6 * (2.0 * exposure.exptime + 5)) self.notify(CameraEvent.EXPOSURE_INTEGRATING) # the buffer allocated/created within the acquisition() buf = await self.loop.run_in_executor(None, self.device.acquisition, tout_ms) if buf is None: raise ExposureError( "Exposing for " + str(exposure.exptime) + " sec failed. Timout " + str(tout_ms / 1.0e6) ) # Decipher which methods this aravis buffer has... # print(dir(buf)) # reg becomes a x=, y=, width= height= dictionary # these are in standard X11 coordinates where upper left =(0,0) reg = buf.get_image_region() # print('region',reg) data = buf.get_data() exposure.data = numpy.ndarray( buffer=data, dtype=numpy.uint16, shape=(1, reg.height, reg.width) ) # print("exposure data shape", exposure.data.shape) return reg @modules.atimeit async def _expose_internal(self, exposure): """Read a single unbinned full frame and store in a FITS file. :param exposure: On entry exposure.exptim is the intended exposure time in [sec] On exit, exposure.data contains the 16bit data of a single frame :return: There is no return value """ # fill exposure.data with the frame's 16bit data # reg becomes a x=, y=, width= height= dictionary # these are in standard X11 coordinates where upper left =(0,0) reg = await self._expose_grabFrame(exposure) # print('region',reg) binxy = {} try: # becomes a dictionary with dx=... dy=... for the 2 horiz/vert binn fact binxy = self.device.get_binning() except Exception as ex: binxy = None # append FITS header cards # For the x/y coordinates transform from X11 to FITS coordinates # Todo: reports the camera y-flipped reg.y if ReversY=true above?? addHeaders = [ ("BinX", binxy.dx, "[ct] Horizontal Bin Factor 1, 2 or 4"), ("BinY", binxy.dy, "[ct] Vertical Bin Factor 1, 2 or 4"), ("Width", reg.width, "[ct] Pixel Columns"), ("Height", reg.height, "[ct] Pixel Rows"), ("RegX", 1 + reg.x, "[ct] Pixel Region Horiz start"), # The lower left FITS corner is the upper left X11 corner... ( "RegY", self.regionBounds[1] - (reg.y + reg.height - 1), "[ct] Pixel Region Vert start", ), ] dev = self.device.get_device() # print(dir(dev)) # print(dir(self)) # print(self.camera_system.get_camera(self.name)) # print(self.camera_system._config[self.name]) try: gain = dev.get_float_feature_value("Gain") addHeaders.append(("Gain", gain, "Gain")) except Exception as ex: # print("failed to read gain" + str(ex)) pass imgrev = [False, False] try: imgrev[0] = self.device.get_boolean("ReverseX") addHeaders.append(("ReverseX", imgrev[0] != 0, " Flipped left-right")) imgrev[1] = self.device.get_boolean("ReverseY") addHeaders.append(("ReverseY", imgrev[1] != 0, " Flipped up-down")) # print("reversed" + str(imgrev[0]) + str(imgrev[1]) ) except Exception as ex: # print("failed to read ReversXY" + str(ex)) pass # This is an enumeration in the GenICam. See features list of # `arv-tool-0.8 --address=192.168.70.50 features` binMod = [-1, -1] try: binMod[0] = dev.get_integer_feature_value("BinningHorizontalMode") if binMod[0] == 0: addHeaders.append( ("BinModeX", "Averag", "Horiz Bin Mode Sum or Averag") ) else: addHeaders.append(("BinModeX", "Sum", "Horiz Bin Mode Sum or Averag")) binMod[1] = dev.get_integer_feature_value("BinningVerticalMode") if binMod[1] == 0: addHeaders.append(("BinModeY", "Averag", "Vert Bin Mode Sum or Averag")) else: addHeaders.append(("BinModeY", "Sum", "Vert Bin Mode Sum or Averag")) except Exception as ex: # print("failed to read binmode" + str(ex)) pass tmp = False try: tmp = self.device.get_boolean("BlackLevelClampingEnable") addHeaders.append( ("CAMBLCLM", tmp != 0, "Black Level Clamping en/disabled") ) # print("BlackLevelClampingEnable" + str(imgrev[0]) + str(imgrev[1]) ) except Exception as ex: # print("failed to read BlackLevelClampingEnable" + str(ex)) pass try: camtyp = self.device.get_model_name() addHeaders.append(("CAMTYP", camtyp, "Camera model")) except: pass # call _expose_wcs() to gather WCS header keywords addHeaders.extend(self._expose_wcs(exposure, reg)) # for headr in addHeaders: # exposure.fits_model[0].header_model.append(models.Card(headr)) self.header = addHeaders # print(repr(exposure.to_hdu()[0].header)) # unref() is currently usupported in this GObject library. # Hope that this does not lead to any memory leak.... # buf.unref() return # @modules.timeit def _expose_wcs(self, exposure, reg): """Gather information for the WCS FITS keywords :param exposure: On entry exposure.exptim is the intended exposure time in [sec] On exit, exposure.data contains the 16bit data of a single frame :param reg The binning and region information """ # the section/dictionary of the yaml file for this camera yamlconfig = self.camera_system._config[self.name] wcsHeaders = [] # The distance from the long edge of the FLIR camera to the center # of the focus (fiber) is 7.144+4.0 mm according to SDSS-V_0110 figure 6 # and 11.14471 according to figure 3-1 of LVMi-0081 # For the *w or *e cameras the pixel row 1 (in FITS) is that far # away in the y-coordinate and in the middle of the x-coordinate. # For the *c cameras at the fiber bundle we assume them to be in the beam center. wcsHeaders.append(("CRPIX1", reg.width / 2, "[px] RA center along axis 1")) if self.name[-1] == "c": wcsHeaders.append( ("CRPIX2", reg.height / 2, "[px] DEC center along axis 2") ) else: # convert 11.14471 mm to microns and to to pixels crefy = 11.14471 * 1000.0 / yamlconfig["pixsize"] wcsHeaders.append(("CRPIX2", -crefy, "[px] DEC center along axis 2")) return wcsHeaders class BlackflyImageAreaMixIn(ImageAreaMixIn): """Allows to select image region and binning factors""" async def _get_image_area_internal(self): pass async def _set_image_area_internal(self, area=None): pass async def _get_binning_internal(self): pass async def _set_binning_internal(self, hbin, vbin): pass # async def singleFrame( # exptim, # name, # verb=False, # ip_add=None, # config="cameras.yaml", # targ=None, # kmirr=0.0, # flen=None, # ): # """Expose once and write the image to a FITS file. # :param exptim: The exposure time in seconds. Non-negative. # :type exptim: float # :param verb: Verbosity on or off # :type verb: boolean # :param ip_add: list of explicit IP's (like 192.168.70.51 or lvmt.irws2.mpia.de) # :type ip_add: list of strings # :param config: Name of the YAML file with the cameras configuration # :type config: string of the file name # :param targ: alpha/delta ra/dec of the sidereal target # :type targ: astropy.coordinates.SkyCoord # :param kmirr: Kmirr angle in degrees (0 if up, positive with right hand rule along North on bench) # :type kmirr: float # :param flen: focal length of telescope/siderostat in mm # If not provided it will be taken from the configuration file # :type flen: float # """ # cs = BlackflyCameraSystem( # BlackflyCamera, camera_config=config, verbose=verb, ip_list=ip_add # ) # cam = await cs.add_camera(name=name) # # print("cameras", cs.cameras) # # print("config" ,config) # exp = await cam.expose(exptim, "LAB TEST") # if targ is not None and kmirr is not None: # # if there is already a (partial) header information, keep it, # # otherwise create one ab ovo. # if exp.wcs is None: # wcshdr = astropy.io.fits.Header() # else: # wcshdr = exp.wcs.to_header() # key = astropy.io.fits.Card("CUNIT1", "deg", "WCS units along axis 1") # wcshdr.append(key) # key = astropy.io.fits.Card("CUNIT2", "deg", "WCS units along axis 2") # wcshdr.append(key) # key = astropy.io.fits.Card("CTYPE1", "RA---TAN", "WCS type axis 1") # wcshdr.append(key) # key = astropy.io.fits.Card("CTYPE2", "DEC--TAN", "WCS type axis 2") # wcshdr.append(key) # key = astropy.io.fits.Card("CRVAL1", targ.ra.deg, "[deg] RA at reference pixel") # wcshdr.append(key) # key = astropy.io.fits.Card( # "CRVAL2", targ.dec.deg, "[deg] DEC at reference pixel" # ) # wcshdr.append(key) # # field angle: degrees, then radians # # direction of NCP on the detectors (where we have already flipped pixels # # on all detectors so fieldrot=kmirr=0 implies North is up and East is left) # # With right-handed-rule: zero if N=up (y-axis), 90 deg if N=right (x-axis) # # so the direction is the vector ( sin(f), cos(f)) before the K-mirror. # # Action of K-mirror is ( cos(2*m), sin(2*m); sin(2*m), -cos(2*m)) # # and action of prism is (-1 0 ; 0 1), i.e. to flip the horizontal coordinate. # # todo: get starting value from a siderostat field rotation tracking model # fieldrot = 0.0 # if name[-1] == "c": # # without prism, assuming center camera placed horizontally # if name[:4] == "spec": # # without K-mirror # pass # else: # # with K-mirror # # in the configuration the y-axis of the image has been flipped, # # the combined action of (1, 0; 0, -1) and the K-mirror is (cos(2m), sin(2m); -sin(2m), cos(2m)) # # and applied to the input vector this is (sin(2m+f), cos(2m+f)) # fieldrot += 2.0 * kmirr # else: # # with prism # if name[:4] == "spec": # # without K-mirror # # Applied to input beam this gives (-sin(f), cos(f)) but prism effect # # had been undone by vertical flip in the FLIR image. # pass # else: # # with K-mirror # # Combined action of K-mirror and prism is (-cos(2*m), -sin(2*m);sin(2*m), -cos(2*m)). # # Applied to input beam this gives (-sin(2*m+f), -cos(2*m+f)) = (sin(2*m+f+pi), cos(2*m+f+pi)). # fieldrot += 2.0 * kmirr + 180.0 # if name[-1] == "w": # # Camera is vertically, # # so up in the lab is right in the image # fieldrot += 90 # else: # # Camera is vertically, # # so up in the lab is left in the image # fieldrot -= 90 # fieldrot = math.radians(fieldrot) # # the section/dictionary of the yaml file for this camera # yamlconfig = cs._config[name] # if flen is None: # flen = yamlconfig["flen"] # # pixel scale per arcseconds is focal length *pi/180 /3600 # # = flen * mm *pi/180 /3600 # # = flen * um *pi/180 /3.6, so in microns per arcsec... # pixscal = math.radians(flen) / 3.6 # # degrees per pixel is arcseconds per pixel/3600 = (mu/pix)/(mu/arcsec)/3600 # degperpix = yamlconfig["pixsize"] / pixscal / 3600.0 # # for the right handed coordinates # # (pixx,pixy) = (cos f', -sin f'; sin f', cos f')*(DEC,RA) where f' =90deg -fieldrot # # (pixx,pixy) = (sin f, -cos f; cos f , sin f)*(DEC,RA) # # (sin f, cos f; -cos f, sin f)*(pixx,pixy) = (DEC,RA) # # (-cos f, sin f; sin f, cos f)*(pixx,pixy) = (RA,DEC) # # Note that the det of the WCS matrix is negativ (because RA/DEC is left-handed...) # cosperpix = degperpix * math.cos(fieldrot) # sinperpix = degperpix * math.sin(fieldrot) # key = astropy.io.fits.Card("CD1_1", -cosperpix, "[deg/px] WCS matrix diagonal") # wcshdr.append(key) # key = astropy.io.fits.Card("CD2_2", cosperpix, "[deg/px] WCS matrix diagonal") # wcshdr.append(key) # key = astropy.io.fits.Card( # "CD1_2", sinperpix, "[deg/px] WCS matrix outer diagonal" # ) # wcshdr.append(key) # key = astropy.io.fits.Card( # "CD2_1", sinperpix, "[deg/px] WCS matrix outer diagonal" # ) # wcshdr.append(key) # exp.wcs = astropy.wcs.WCS(wcshdr) # # print(exp.wcs.to_header_string()) # for headr in wcshdr.cards: # exp.fits_model[0].header_model.append(models.Card(headr)) # await exp.write() # if verb: # print("wrote ", exp.filename) # # A debugging aid, demonstrator and simple test run # # This allows to call this file as an executable from the command line. # # The last command line argument must be the name of the camera # # as used in the configuration file. # # Example # # BlackflyCam.py [-e seconds] [-v] [-c ../etc/cameras.yaml] [-r 2h10m10s] [-d -20d10m3s] # # [-K kmirrdegrees] [-s "LCO"|"MPIA"|"APO"|"KHU"] [-f focallengthmm] {spec.age|spec.agw|...} # if __name__ == "__main__": # import argparse # parser = argparse.ArgumentParser() # parser.add_argument( # "-e", # "--exptime", # type=float, # default=5.0, # help="Expose for for exptime seconds", # ) # parser.add_argument( # "-v", "--verbose", action="store_true", help="print some notes to stdout" # ) # # With the -i switch we can add an explicit IP-Adress for a # # camera if we want to read a camera that is not reachable # # by the broadcast scanner. # parser.add_argument("-i", "--ip", help="IP address of camera") # # Name of an optional YAML file # parser.add_argument( # "-c", "--cfg", default="cameras.yaml", help="YAML file of lvmt cameras" # ) # # right ascension in degrees # parser.add_argument("-r", "--ra", help="RA J2000 in degrees or in xxhxxmxxs format") # # declination in degrees # parser.add_argument( # "-d", "--dec", help="DEC J2000 in degrees or in +-xxdxxmxxs format" # ) # # K-mirror angle in degrees # # Note this is only relevant for 3 of the 4 tables/telescopes # parser.add_argument("-K", "--Kmirr", type=float, help="K-mirror angle in degrees") # # focal length of telescope in mm # # Default is the LCO triple lens configuration of 1.8 meters # parser.add_argument( # "-f", "--flen", type=float, default=1839.8, help="focal length in mm" # ) # # shortcut for site coordinates: observatory # # parser.add_argument("-s", '--site', default="LCO", help="LCO or MPIA or APO or KHU") # # the last argument is mandatory: must be the name of exactly one camera # # as used in the configuration file # parser.add_argument("camname", default="sci.agw") # args = parser.parse_args() # ip_cmdLine = [] # if args.ip is not None: # ip_cmdLine.append(args.ip) # # check ranges and combine ra/dec into a single SkyCoord # if args.ra is not None and args.dec is not None: # if args.ra.find("h") < 0: # # apparently simple floating point representation # targ = astropy.coordinates.SkyCoord( # ra=float(args.ra), dec=float(args.dec), unit="deg" # ) # else: # targ = astropy.coordinates.SkyCoord(args.ra + " " + args.dec) # else: # targ = None # # print(targ) # # The following 2 lines test that listing the connected cameras works... # # bsys = BlackflyCameraSystem(camera_class=BlackflyCamera) # # bsys.list_available_cameras() # asyncio.run( # singleFrame( # args.exptime, # args.camname, # verb=args.verbose, # ip_add=ip_cmdLine, # config=args.cfg, # targ=targ, # kmirr=args.Kmirr, # flen=args.flen, # ) # ) class WcsHdrCards(card.MacroCard): def macro(self, exposure, context={}): wcshdr = get_wcshdr(modules.variables.cs_list[0], modules.variables.camname, modules.variables.targ, modules.variables.kmirr, modules.variables.flen) return wcshdr # @modules.timeit def get_wcshdr( cs, name, targ, kmirr, flen, ): if targ is not None and kmirr is not None: # wcshdr = astropy.io.fits.Header() wcshdr = [] key = astropy.io.fits.Card("CUNIT1", "deg", "WCS units along axis 1") wcshdr.append(key) key = astropy.io.fits.Card("CUNIT2", "deg", "WCS units along axis 2") wcshdr.append(key) key = astropy.io.fits.Card("CTYPE1", "RA---TAN", "WCS type axis 1") wcshdr.append(key) key = astropy.io.fits.Card("CTYPE2", "DEC--TAN", "WCS type axis 2") wcshdr.append(key) key = astropy.io.fits.Card("CRVAL1", targ.ra.deg, "[deg] RA at reference pixel") wcshdr.append(key) key = astropy.io.fits.Card( "CRVAL2", targ.dec.deg, "[deg] DEC at reference pixel" ) wcshdr.append(key) # field angle: degrees, then radians # direction of NCP on the detectors (where we have already flipped pixels # on all detectors so fieldrot=kmirr=0 implies North is up and East is left) # With right-handed-rule: zero if N=up (y-axis), 90 deg if N=right (x-axis) # so the direction is the vector ( sin(f), cos(f)) before the K-mirror. # Action of K-mirror is ( cos(2*m), sin(2*m); sin(2*m), -cos(2*m)) # and action of prism is (-1 0 ; 0 1), i.e. to flip the horizontal coordinate. # todo: get starting value from a siderostat field rotation tracking model fieldrot = 0.0 if name[-1] == "c": # without prism, assuming center camera placed horizontally if name[:4] == "spec": # without K-mirror pass else: # with K-mirror # in the configuration the y-axis of the image has been flipped, # the combined action of (1, 0; 0, -1) and the K-mirror is (cos(2m), sin(2m); -sin(2m), cos(2m)) # and applied to the input vector this is (sin(2m+f), cos(2m+f)) fieldrot += 2.0 * kmirr else: # with prism if name[:4] == "spec": # without K-mirror # Applied to input beam this gives (-sin(f), cos(f)) but prism effect # had been undone by vertical flip in the FLIR image. pass else: # with K-mirror # Combined action of K-mirror and prism is (-cos(2*m), -sin(2*m);sin(2*m), -cos(2*m)). # Applied to input beam this gives (-sin(2*m+f), -cos(2*m+f)) = (sin(2*m+f+pi), cos(2*m+f+pi)). fieldrot += 2.0 * kmirr + 180.0 if name[-1] == "w": # Camera is vertically, # so up in the lab is right in the image fieldrot += 90 else: # Camera is vertically, # so up in the lab is left in the image fieldrot -= 90 fieldrot = math.radians(fieldrot) # the section/dictionary of the yaml file for this camera yamlconfig = cs._config[name] if flen is None: flen = yamlconfig["flen"] # pixel scale per arcseconds is focal length *pi/180 /3600 # = flen * mm *pi/180 /3600 # = flen * um *pi/180 /3.6, so in microns per arcsec... pixscal = math.radians(flen) / 3.6 # degrees per pixel is arcseconds per pixel/3600 = (mu/pix)/(mu/arcsec)/3600 degperpix = yamlconfig["pixsize"] / pixscal / 3600.0 # for the right handed coordinates # (pixx,pixy) = (cos f', -sin f'; sin f', cos f')*(DEC,RA) where f' =90deg -fieldrot # (pixx,pixy) = (sin f, -cos f; cos f , sin f)*(DEC,RA) # (sin f, cos f; -cos f, sin f)*(pixx,pixy) = (DEC,RA) # (-cos f, sin f; sin f, cos f)*(pixx,pixy) = (RA,DEC) # Note that the det of the WCS matrix is negativ (because RA/DEC is left-handed...) cosperpix = degperpix * math.cos(fieldrot) sinperpix = degperpix * math.sin(fieldrot) key = astropy.io.fits.Card("CD1_1", -cosperpix, "[deg/px] WCS matrix diagonal") wcshdr.append(key) key = astropy.io.fits.Card("CD2_2", cosperpix, "[deg/px] WCS matrix diagonal") wcshdr.append(key) key = astropy.io.fits.Card( "CD1_2", sinperpix, "[deg/px] WCS matrix outer diagonal" ) wcshdr.append(key) key = astropy.io.fits.Card( "CD2_1", sinperpix, "[deg/px] WCS matrix outer diagonal" ) wcshdr.append(key) return wcshdr else: return None

39.170921

157

0.572096

import sys import math import asyncio import numpy import astropy from basecam.mixins import ImageAreaMixIn from basecam import ( CameraSystem, BaseCamera, CameraEvent, CameraConnectionError, models, ExposureError, ) from lvmcam.actor import modules from lvmcam.araviscam.aravis import Aravis import basecam.models.card as card from lvmcam.actor.commands import expose __all__ = ["BlackflyCameraSystem", "BlackflyCamera", "BlackflyImageAreaMixIn"] class BlackflyCameraSystem(CameraSystem): __version__ = "0.0.301" ips_nonlocal = [] def __init__( self, camera_class=None, camera_config=None, include=None, exclude=None, logger=None, log_header=None, log_file=None, verbose=False, ip_list=None, ): super().__init__( camera_class=camera_class, camera_config=camera_config, include=include, exclude=exclude, logger=logger, log_header=log_header, log_file=log_file, verbose=verbose, ) if ip_list is not None: self.ips_nonlocal.extend(ip_list) def list_available_cameras(self): serialNums = [] addrs = [] Aravis.update_device_list() Ndev = Aravis.get_n_devices() for i in range(Ndev): cam = Aravis.Camera.new(Aravis.get_device_id(i)) uid = cam.get_string("DeviceSerialNumber") serialNums.append(uid) addrs.append("") for ip in self.ips_nonlocal: try: cam = Aravis.Camera.new(ip) uid = cam.get_string("DeviceSerialNumber") if uid not in serialNums: serialNums.append(uid) addrs.append("@" + ip) except: pass ids = [] for cam in range(len(serialNums)): ids.append(serialNums[cam] + addrs[cam]) return ids from basecam.models.builtin import basic_fz_fits_model class BlackflyCamera(BaseCamera): def __init__( self, uid, camera_system, name=None, force=False, image_namer=None, camera_params={}, ): super().__init__( uid=uid, camera_system=camera_system, name=name, force=force, image_namer=image_namer, camera_params=camera_params, ) self.header = [] @modules.atimeit async def _connect_internal(self, **kwargs): try: uid = kwargs["uid"] except: uid = None cs = BlackflyCameraSystem(BlackflyCamera) slist = cs.list_available_cameras() if uid is None: idx = 0 else: idx = -1 for id in slist: slistuid = id.split("@")[0] if slistuid == uid: idx = slist.index(id) if idx < 0: raise CameraConnectionError("SN " + uid + " not connected") cam = None try: if "@" in slist[idx]: cam = Aravis.Camera.new(slist[idx].split("@")[1]) else: cam = Aravis.Camera.new(Aravis.get_device_id(idx)) except: raise CameraConnectionError(" not connected") try: gain = kwargs["gain"] if gain > 0.0: self.device.set_gain_auto(0) cam.set_gain(gain) except Exception as ex: # print("failed to set gain " + str(ex)) pass # see arvenums.h for the list of pixel formats. This is MONO_16 here, always cam.set_pixel_format(0x01100007) # search for an optional x and y binning factor try: var = kwargs["binning"] cam.set_binning(var[0], var[1]) except Exception as ex: # print("failed to set binning " + str(ex)) # horizontal and vertical binning set to 1 cam.set_binning(1, 1) # scan the general list of genicam featured values # of the four native types for typp, arvLst in kwargs.items(): if arvLst is not None: if typp == "bool": for genkey, genval in arvLst.items(): try: cam.set_boolean(genkey, int(genval)) except: # probably a typo in the yaml file... todo: log this # print("failed for " + str(genkey)+str(genval)) pass elif typp == "int": for genkey, genval in arvLst.items(): try: cam.set_integer(genkey, genval) except: # probably a typo in the yaml file... todo: log this # print("failed for " + str(genkey)+str(genval)) pass elif typp == "float": for genkey, genval in arvLst.items(): try: cam.set_float(genkey, genval) except: # probably a typo in the yaml file... todo: log this # print("failed for " + str(genkey)+str(genval)) pass elif typp == "string": for genkey, genval in arvLst.items(): try: cam.set_string(genkey, genval) except: # probably a typo in the yaml file... todo: log this # print("failed for " + str(genkey)+str(genval)) pass dev = cam.get_device() # Take full frames by default (maximizing probability of LVM guide camera # to find guide stars in the field) roiBounds = [-1, -1] try: roiBounds[0] = dev.get_integer_feature_value("WidthMax") roiBounds[1] = dev.get_integer_feature_value("HeightMax") # print(" ROI " + str(roiBounds[0]) + " x " + str(roiBounds[1]) ) cam.set_region(0, 0, roiBounds[0], roiBounds[1]) except Exception as ex: # print("failed to set ROI " + str(ex)) pass self.device = cam self.regionBounds = roiBounds @modules.atimeit async def _disconnect_internal(self): self.device = None # @modules.atimeit async def _expose_grabFrame(self, exposure): # To avoid being left over by other programs with no change # to set the exposure time, we switch the auto=0=off first self.device.set_exposure_time_auto(0) # Aravis assumes exptime in micro second integers exptime_ms = int(0.5 + exposure.exptime * 1e6) self.device.set_exposure_time(exptime_ms) # timeout (factor 2: assuming there may be two frames in auto mode taken # internally) # And 5 seconds margin for any sort of transmission overhead over PoE tout_ms = int(1.0e6 * (2.0 * exposure.exptime + 5)) self.notify(CameraEvent.EXPOSURE_INTEGRATING) # the buffer allocated/created within the acquisition() buf = await self.loop.run_in_executor(None, self.device.acquisition, tout_ms) if buf is None: raise ExposureError( "Exposing for " + str(exposure.exptime) + " sec failed. Timout " + str(tout_ms / 1.0e6) ) # Decipher which methods this aravis buffer has... # print(dir(buf)) # reg becomes a x=, y=, width= height= dictionary # these are in standard X11 coordinates where upper left =(0,0) reg = buf.get_image_region() # print('region',reg) data = buf.get_data() exposure.data = numpy.ndarray( buffer=data, dtype=numpy.uint16, shape=(1, reg.height, reg.width) ) # print("exposure data shape", exposure.data.shape) return reg @modules.atimeit async def _expose_internal(self, exposure): # fill exposure.data with the frame's 16bit data reg = await self._expose_grabFrame(exposure) binxy = {} try: binxy = self.device.get_binning() except Exception as ex: binxy = None addHeaders = [ ("BinX", binxy.dx, "[ct] Horizontal Bin Factor 1, 2 or 4"), ("BinY", binxy.dy, "[ct] Vertical Bin Factor 1, 2 or 4"), ("Width", reg.width, "[ct] Pixel Columns"), ("Height", reg.height, "[ct] Pixel Rows"), ("RegX", 1 + reg.x, "[ct] Pixel Region Horiz start"), ( "RegY", self.regionBounds[1] - (reg.y + reg.height - 1), "[ct] Pixel Region Vert start", ), ] dev = self.device.get_device() try: gain = dev.get_float_feature_value("Gain") addHeaders.append(("Gain", gain, "Gain")) except Exception as ex: pass imgrev = [False, False] try: imgrev[0] = self.device.get_boolean("ReverseX") addHeaders.append(("ReverseX", imgrev[0] != 0, " Flipped left-right")) imgrev[1] = self.device.get_boolean("ReverseY") addHeaders.append(("ReverseY", imgrev[1] != 0, " Flipped up-down")) except Exception as ex: pass binMod = [-1, -1] try: binMod[0] = dev.get_integer_feature_value("BinningHorizontalMode") if binMod[0] == 0: addHeaders.append( ("BinModeX", "Averag", "Horiz Bin Mode Sum or Averag") ) else: addHeaders.append(("BinModeX", "Sum", "Horiz Bin Mode Sum or Averag")) binMod[1] = dev.get_integer_feature_value("BinningVerticalMode") if binMod[1] == 0: addHeaders.append(("BinModeY", "Averag", "Vert Bin Mode Sum or Averag")) else: addHeaders.append(("BinModeY", "Sum", "Vert Bin Mode Sum or Averag")) except Exception as ex: pass tmp = False try: tmp = self.device.get_boolean("BlackLevelClampingEnable") addHeaders.append( ("CAMBLCLM", tmp != 0, "Black Level Clamping en/disabled") ) except Exception as ex: pass try: camtyp = self.device.get_model_name() addHeaders.append(("CAMTYP", camtyp, "Camera model")) except: pass addHeaders.extend(self._expose_wcs(exposure, reg)) self.header = addHeaders return def _expose_wcs(self, exposure, reg): yamlconfig = self.camera_system._config[self.name] wcsHeaders = [] wcsHeaders.append(("CRPIX1", reg.width / 2, "[px] RA center along axis 1")) if self.name[-1] == "c": wcsHeaders.append( ("CRPIX2", reg.height / 2, "[px] DEC center along axis 2") ) else: crefy = 11.14471 * 1000.0 / yamlconfig["pixsize"] wcsHeaders.append(("CRPIX2", -crefy, "[px] DEC center along axis 2")) return wcsHeaders class BlackflyImageAreaMixIn(ImageAreaMixIn): async def _get_image_area_internal(self): pass async def _set_image_area_internal(self, area=None): pass async def _get_binning_internal(self): pass async def _set_binning_internal(self, hbin, vbin): pass # :param exptim: The exposure time in seconds. Non-negative. # :type exptim: float # :param verb: Verbosity on or off # :type verb: boolean # :param ip_add: list of explicit IP's (like 192.168.70.51 or lvmt.irws2.mpia.de) # :type ip_add: list of strings # :param config: Name of the YAML file with the cameras configuration # :type config: string of the file name # :param targ: alpha/delta ra/dec of the sidereal target # :type targ: astropy.coordinates.SkyCoord # :param kmirr: Kmirr angle in degrees (0 if up, positive with right hand rule along North on bench) # :type kmirr: float # :param flen: focal length of telescope/siderostat in mm # If not provided it will be taken from the configuration file # :type flen: float # """ # cs = BlackflyCameraSystem( # BlackflyCamera, camera_config=config, verbose=verb, ip_list=ip_add # ) # cam = await cs.add_camera(name=name) # # print("cameras", cs.cameras) # # print("config" ,config) # exp = await cam.expose(exptim, "LAB TEST") # if targ is not None and kmirr is not None: # # if there is already a (partial) header information, keep it, # # otherwise create one ab ovo. # if exp.wcs is None: # wcshdr = astropy.io.fits.Header() # else: # wcshdr = exp.wcs.to_header() # key = astropy.io.fits.Card("CUNIT1", "deg", "WCS units along axis 1") # wcshdr.append(key) # key = astropy.io.fits.Card("CUNIT2", "deg", "WCS units along axis 2") # wcshdr.append(key) # key = astropy.io.fits.Card("CTYPE1", "RA---TAN", "WCS type axis 1") # wcshdr.append(key) # key = astropy.io.fits.Card("CTYPE2", "DEC--TAN", "WCS type axis 2") # wcshdr.append(key) # key = astropy.io.fits.Card("CRVAL1", targ.ra.deg, "[deg] RA at reference pixel") # wcshdr.append(key) # key = astropy.io.fits.Card( # "CRVAL2", targ.dec.deg, "[deg] DEC at reference pixel" # ) # wcshdr.append(key) # # field angle: degrees, then radians # # direction of NCP on the detectors (where we have already flipped pixels # # on all detectors so fieldrot=kmirr=0 implies North is up and East is left) # # With right-handed-rule: zero if N=up (y-axis), 90 deg if N=right (x-axis) # # so the direction is the vector ( sin(f), cos(f)) before the K-mirror. # # Action of K-mirror is ( cos(2*m), sin(2*m); sin(2*m), -cos(2*m)) # # and action of prism is (-1 0 ; 0 1), i.e. to flip the horizontal coordinate. # # todo: get starting value from a siderostat field rotation tracking model # fieldrot = 0.0 # if name[-1] == "c": # # without prism, assuming center camera placed horizontally # if name[:4] == "spec": # # without K-mirror # pass # else: # # with K-mirror # # in the configuration the y-axis of the image has been flipped, # # the combined action of (1, 0; 0, -1) and the K-mirror is (cos(2m), sin(2m); -sin(2m), cos(2m)) # # and applied to the input vector this is (sin(2m+f), cos(2m+f)) # fieldrot += 2.0 * kmirr # else: # # with prism # if name[:4] == "spec": # # without K-mirror # # Applied to input beam this gives (-sin(f), cos(f)) but prism effect # # had been undone by vertical flip in the FLIR image. # pass # else: # # with K-mirror # # Combined action of K-mirror and prism is (-cos(2*m), -sin(2*m);sin(2*m), -cos(2*m)). # # Applied to input beam this gives (-sin(2*m+f), -cos(2*m+f)) = (sin(2*m+f+pi), cos(2*m+f+pi)). # fieldrot += 2.0 * kmirr + 180.0 # if name[-1] == "w": # # Camera is vertically, # # so up in the lab is right in the image # fieldrot += 90 # else: # # Camera is vertically, # # so up in the lab is left in the image # fieldrot -= 90 # fieldrot = math.radians(fieldrot) # # the section/dictionary of the yaml file for this camera # yamlconfig = cs._config[name] # if flen is None: # flen = yamlconfig["flen"] # # pixel scale per arcseconds is focal length *pi/180 /3600 # # = flen * mm *pi/180 /3600 # # = flen * um *pi/180 /3.6, so in microns per arcsec... # pixscal = math.radians(flen) / 3.6 # # degrees per pixel is arcseconds per pixel/3600 = (mu/pix)/(mu/arcsec)/3600 # degperpix = yamlconfig["pixsize"] / pixscal / 3600.0 # # for the right handed coordinates # # (pixx,pixy) = (cos f', -sin f'; sin f', cos f')*(DEC,RA) where f' =90deg -fieldrot pass else: fieldrot += 2.0 * kmirr + 180.0 if name[-1] == "w": fieldrot += 90 else: fieldrot -= 90 fieldrot = math.radians(fieldrot) yamlconfig = cs._config[name] if flen is None: flen = yamlconfig["flen"] pixscal = math.radians(flen) / 3.6 degperpix = yamlconfig["pixsize"] / pixscal / 3600.0 # (pixx,pixy) = (sin f, -cos f; cos f , sin f)*(DEC,RA) # (sin f, cos f; -cos f, sin f)*(pixx,pixy) = (DEC,RA) # (-cos f, sin f; sin f, cos f)*(pixx,pixy) = (RA,DEC) # Note that the det of the WCS matrix is negativ (because RA/DEC is left-handed...) cosperpix = degperpix * math.cos(fieldrot) sinperpix = degperpix * math.sin(fieldrot) key = astropy.io.fits.Card("CD1_1", -cosperpix, "[deg/px] WCS matrix diagonal") wcshdr.append(key) key = astropy.io.fits.Card("CD2_2", cosperpix, "[deg/px] WCS matrix diagonal") wcshdr.append(key) key = astropy.io.fits.Card( "CD1_2", sinperpix, "[deg/px] WCS matrix outer diagonal" ) wcshdr.append(key) key = astropy.io.fits.Card( "CD2_1", sinperpix, "[deg/px] WCS matrix outer diagonal" ) wcshdr.append(key) return wcshdr else: return None

true

1c37d00a7158416e048e17ac299d114678c5fcb7

548

py

Python

is-prime/solution.py

astone648/CodeWars

13f0bf9108433909abd5cf7270515cc63a06ebd1

[ "MIT" ]

null

is-prime/solution.py

astone648/CodeWars

13f0bf9108433909abd5cf7270515cc63a06ebd1

[ "MIT" ]

null

is-prime/solution.py

astone648/CodeWars

13f0bf9108433909abd5cf7270515cc63a06ebd1

[ "MIT" ]

null

from random import * import math def is_prime(num): if num < 1: return False; elif num == 1: return False; elif num > 2 and num % 2 == 0: return False; else: for n in range(3,int(math.sqrt(num)//1)+1): if num % n == 0: return False return True def testPrime(num): if is_prime(num): print(str(num) + ' is prime.') else: print(str(num) + ' is not prime.') randArrayLength = 25 for n in range(randArrayLength): testPrime(randrange(0, 100))

21.076923

51

0.54562

from random import * import math def is_prime(num): if num < 1: return False; elif num == 1: return False; elif num > 2 and num % 2 == 0: return False; else: for n in range(3,int(math.sqrt(num)//1)+1): if num % n == 0: return False return True def testPrime(num): if is_prime(num): print(str(num) + ' is prime.') else: print(str(num) + ' is not prime.') randArrayLength = 25 for n in range(randArrayLength): testPrime(randrange(0, 100))

true

1c37d0215a8ae853b15095889bc7d195c9b05519

10,236

py

Python

homeassistant/components/emulated_hue/__init__.py

dzmitov/core

7697ef7f5ec357ae5ab76237dc52af55fc044c36

[ "Apache-2.0" ]

1

2021-01-14T11:42:12.000Z

homeassistant/components/emulated_hue/__init__.py

dzmitov/core

7697ef7f5ec357ae5ab76237dc52af55fc044c36

[ "Apache-2.0" ]

null

homeassistant/components/emulated_hue/__init__.py

dzmitov/core

7697ef7f5ec357ae5ab76237dc52af55fc044c36

[ "Apache-2.0" ]

1

2020-09-23T16:41:16.000Z

"""Support for local control of entities by emulating a Philips Hue bridge.""" import logging from aiohttp import web import voluptuous as vol from homeassistant import util from homeassistant.components.http import real_ip from homeassistant.const import EVENT_HOMEASSISTANT_START, EVENT_HOMEASSISTANT_STOP from homeassistant.exceptions import HomeAssistantError import homeassistant.helpers.config_validation as cv from homeassistant.util.json import load_json, save_json from .hue_api import ( HueAllGroupsStateView, HueAllLightsStateView, HueFullStateView, HueGroupView, HueOneLightChangeView, HueOneLightStateView, HueUnauthorizedUser, HueUsernameView, ) from .upnp import DescriptionXmlView, UPNPResponderThread DOMAIN = "emulated_hue" _LOGGER = logging.getLogger(__name__) NUMBERS_FILE = "emulated_hue_ids.json" CONF_ADVERTISE_IP = "advertise_ip" CONF_ADVERTISE_PORT = "advertise_port" CONF_ENTITIES = "entities" CONF_ENTITY_HIDDEN = "hidden" CONF_ENTITY_NAME = "name" CONF_EXPOSE_BY_DEFAULT = "expose_by_default" CONF_EXPOSED_DOMAINS = "exposed_domains" CONF_HOST_IP = "host_ip" CONF_LISTEN_PORT = "listen_port" CONF_OFF_MAPS_TO_ON_DOMAINS = "off_maps_to_on_domains" CONF_TYPE = "type" CONF_UPNP_BIND_MULTICAST = "upnp_bind_multicast" TYPE_ALEXA = "alexa" TYPE_GOOGLE = "google_home" DEFAULT_LISTEN_PORT = 8300 DEFAULT_UPNP_BIND_MULTICAST = True DEFAULT_OFF_MAPS_TO_ON_DOMAINS = ["script", "scene"] DEFAULT_EXPOSE_BY_DEFAULT = True DEFAULT_EXPOSED_DOMAINS = [ "switch", "light", "group", "input_boolean", "media_player", "fan", ] DEFAULT_TYPE = TYPE_GOOGLE CONFIG_ENTITY_SCHEMA = vol.Schema( { vol.Optional(CONF_ENTITY_NAME): cv.string, vol.Optional(CONF_ENTITY_HIDDEN): cv.boolean, } ) CONFIG_SCHEMA = vol.Schema( { DOMAIN: vol.Schema( { vol.Optional(CONF_HOST_IP): cv.string, vol.Optional(CONF_LISTEN_PORT, default=DEFAULT_LISTEN_PORT): cv.port, vol.Optional(CONF_ADVERTISE_IP): cv.string, vol.Optional(CONF_ADVERTISE_PORT): cv.port, vol.Optional(CONF_UPNP_BIND_MULTICAST): cv.boolean, vol.Optional(CONF_OFF_MAPS_TO_ON_DOMAINS): cv.ensure_list, vol.Optional(CONF_EXPOSE_BY_DEFAULT): cv.boolean, vol.Optional(CONF_EXPOSED_DOMAINS): cv.ensure_list, vol.Optional(CONF_TYPE, default=DEFAULT_TYPE): vol.Any( TYPE_ALEXA, TYPE_GOOGLE ), vol.Optional(CONF_ENTITIES): vol.Schema( {cv.entity_id: CONFIG_ENTITY_SCHEMA} ), } ) }, extra=vol.ALLOW_EXTRA, ) ATTR_EMULATED_HUE_NAME = "emulated_hue_name" async def async_setup(hass, yaml_config): """Activate the emulated_hue component.""" config = Config(hass, yaml_config.get(DOMAIN, {})) app = web.Application() app["hass"] = hass real_ip.setup_real_ip(app, False, []) # We misunderstood the startup signal. You're not allowed to change # anything during startup. Temp workaround. # pylint: disable=protected-access app._on_startup.freeze() await app.startup() runner = None site = None DescriptionXmlView(config).register(app, app.router) HueUsernameView().register(app, app.router) HueUnauthorizedUser().register(app, app.router) HueAllLightsStateView(config).register(app, app.router) HueOneLightStateView(config).register(app, app.router) HueOneLightChangeView(config).register(app, app.router) HueAllGroupsStateView(config).register(app, app.router) HueGroupView(config).register(app, app.router) HueFullStateView(config).register(app, app.router) upnp_listener = UPNPResponderThread( config.host_ip_addr, config.listen_port, config.upnp_bind_multicast, config.advertise_ip, config.advertise_port, ) async def stop_emulated_hue_bridge(event): """Stop the emulated hue bridge.""" upnp_listener.stop() if site: await site.stop() if runner: await runner.cleanup() async def start_emulated_hue_bridge(event): """Start the emulated hue bridge.""" upnp_listener.start() nonlocal site nonlocal runner runner = web.AppRunner(app) await runner.setup() site = web.TCPSite(runner, config.host_ip_addr, config.listen_port) try: await site.start() except OSError as error: _LOGGER.error( "Failed to create HTTP server at port %d: %s", config.listen_port, error ) else: hass.bus.async_listen_once( EVENT_HOMEASSISTANT_STOP, stop_emulated_hue_bridge ) hass.bus.async_listen_once(EVENT_HOMEASSISTANT_START, start_emulated_hue_bridge) return True class Config: """Hold configuration variables for the emulated hue bridge.""" def __init__(self, hass, conf): """Initialize the instance.""" self.hass = hass self.type = conf.get(CONF_TYPE) self.numbers = None self.cached_states = {} if self.type == TYPE_ALEXA: _LOGGER.warning( "Emulated Hue running in legacy mode because type has been " "specified. More info at https://goo.gl/M6tgz8" ) # Get the IP address that will be passed to the Echo during discovery self.host_ip_addr = conf.get(CONF_HOST_IP) if self.host_ip_addr is None: self.host_ip_addr = util.get_local_ip() _LOGGER.info( "Listen IP address not specified, auto-detected address is %s", self.host_ip_addr, ) # Get the port that the Hue bridge will listen on self.listen_port = conf.get(CONF_LISTEN_PORT) if not isinstance(self.listen_port, int): self.listen_port = DEFAULT_LISTEN_PORT _LOGGER.info( "Listen port not specified, defaulting to %s", self.listen_port ) # Get whether or not UPNP binds to multicast address (239.255.255.250) # or to the unicast address (host_ip_addr) self.upnp_bind_multicast = conf.get( CONF_UPNP_BIND_MULTICAST, DEFAULT_UPNP_BIND_MULTICAST ) # Get domains that cause both "on" and "off" commands to map to "on" # This is primarily useful for things like scenes or scripts, which # don't really have a concept of being off self.off_maps_to_on_domains = conf.get(CONF_OFF_MAPS_TO_ON_DOMAINS) if not isinstance(self.off_maps_to_on_domains, list): self.off_maps_to_on_domains = DEFAULT_OFF_MAPS_TO_ON_DOMAINS # Get whether or not entities should be exposed by default, or if only # explicitly marked ones will be exposed self.expose_by_default = conf.get( CONF_EXPOSE_BY_DEFAULT, DEFAULT_EXPOSE_BY_DEFAULT ) # Get domains that are exposed by default when expose_by_default is # True self.exposed_domains = set( conf.get(CONF_EXPOSED_DOMAINS, DEFAULT_EXPOSED_DOMAINS) ) # Calculated effective advertised IP and port for network isolation self.advertise_ip = conf.get(CONF_ADVERTISE_IP) or self.host_ip_addr self.advertise_port = conf.get(CONF_ADVERTISE_PORT) or self.listen_port self.entities = conf.get(CONF_ENTITIES, {}) self._entities_with_hidden_attr_in_config = {} for entity_id in self.entities: hidden_value = self.entities[entity_id].get(CONF_ENTITY_HIDDEN, None) if hidden_value is not None: self._entities_with_hidden_attr_in_config[entity_id] = hidden_value def entity_id_to_number(self, entity_id): """Get a unique number for the entity id.""" if self.type == TYPE_ALEXA: return entity_id if self.numbers is None: self.numbers = _load_json(self.hass.config.path(NUMBERS_FILE)) # Google Home for number, ent_id in self.numbers.items(): if entity_id == ent_id: return number number = "1" if self.numbers: number = str(max(int(k) for k in self.numbers) + 1) self.numbers[number] = entity_id save_json(self.hass.config.path(NUMBERS_FILE), self.numbers) return number def number_to_entity_id(self, number): """Convert unique number to entity id.""" if self.type == TYPE_ALEXA: return number if self.numbers is None: self.numbers = _load_json(self.hass.config.path(NUMBERS_FILE)) # Google Home assert isinstance(number, str) return self.numbers.get(number) def get_entity_name(self, entity): """Get the name of an entity.""" if ( entity.entity_id in self.entities and CONF_ENTITY_NAME in self.entities[entity.entity_id] ): return self.entities[entity.entity_id][CONF_ENTITY_NAME] return entity.attributes.get(ATTR_EMULATED_HUE_NAME, entity.name) def is_entity_exposed(self, entity): """Determine if an entity should be exposed on the emulated bridge. Async friendly. """ if entity.attributes.get("view") is not None: # Ignore entities that are views return False if entity.entity_id in self._entities_with_hidden_attr_in_config: return not self._entities_with_hidden_attr_in_config[entity.entity_id] if not self.expose_by_default: return False # Expose an entity if the entity's domain is exposed by default and # the configuration doesn't explicitly exclude it from being # exposed, or if the entity is explicitly exposed if entity.domain in self.exposed_domains: return True return False def _load_json(filename): """Load JSON, handling invalid syntax.""" try: return load_json(filename) except HomeAssistantError: pass return {}

33.126214

88

0.661293

import logging from aiohttp import web import voluptuous as vol from homeassistant import util from homeassistant.components.http import real_ip from homeassistant.const import EVENT_HOMEASSISTANT_START, EVENT_HOMEASSISTANT_STOP from homeassistant.exceptions import HomeAssistantError import homeassistant.helpers.config_validation as cv from homeassistant.util.json import load_json, save_json from .hue_api import ( HueAllGroupsStateView, HueAllLightsStateView, HueFullStateView, HueGroupView, HueOneLightChangeView, HueOneLightStateView, HueUnauthorizedUser, HueUsernameView, ) from .upnp import DescriptionXmlView, UPNPResponderThread DOMAIN = "emulated_hue" _LOGGER = logging.getLogger(__name__) NUMBERS_FILE = "emulated_hue_ids.json" CONF_ADVERTISE_IP = "advertise_ip" CONF_ADVERTISE_PORT = "advertise_port" CONF_ENTITIES = "entities" CONF_ENTITY_HIDDEN = "hidden" CONF_ENTITY_NAME = "name" CONF_EXPOSE_BY_DEFAULT = "expose_by_default" CONF_EXPOSED_DOMAINS = "exposed_domains" CONF_HOST_IP = "host_ip" CONF_LISTEN_PORT = "listen_port" CONF_OFF_MAPS_TO_ON_DOMAINS = "off_maps_to_on_domains" CONF_TYPE = "type" CONF_UPNP_BIND_MULTICAST = "upnp_bind_multicast" TYPE_ALEXA = "alexa" TYPE_GOOGLE = "google_home" DEFAULT_LISTEN_PORT = 8300 DEFAULT_UPNP_BIND_MULTICAST = True DEFAULT_OFF_MAPS_TO_ON_DOMAINS = ["script", "scene"] DEFAULT_EXPOSE_BY_DEFAULT = True DEFAULT_EXPOSED_DOMAINS = [ "switch", "light", "group", "input_boolean", "media_player", "fan", ] DEFAULT_TYPE = TYPE_GOOGLE CONFIG_ENTITY_SCHEMA = vol.Schema( { vol.Optional(CONF_ENTITY_NAME): cv.string, vol.Optional(CONF_ENTITY_HIDDEN): cv.boolean, } ) CONFIG_SCHEMA = vol.Schema( { DOMAIN: vol.Schema( { vol.Optional(CONF_HOST_IP): cv.string, vol.Optional(CONF_LISTEN_PORT, default=DEFAULT_LISTEN_PORT): cv.port, vol.Optional(CONF_ADVERTISE_IP): cv.string, vol.Optional(CONF_ADVERTISE_PORT): cv.port, vol.Optional(CONF_UPNP_BIND_MULTICAST): cv.boolean, vol.Optional(CONF_OFF_MAPS_TO_ON_DOMAINS): cv.ensure_list, vol.Optional(CONF_EXPOSE_BY_DEFAULT): cv.boolean, vol.Optional(CONF_EXPOSED_DOMAINS): cv.ensure_list, vol.Optional(CONF_TYPE, default=DEFAULT_TYPE): vol.Any( TYPE_ALEXA, TYPE_GOOGLE ), vol.Optional(CONF_ENTITIES): vol.Schema( {cv.entity_id: CONFIG_ENTITY_SCHEMA} ), } ) }, extra=vol.ALLOW_EXTRA, ) ATTR_EMULATED_HUE_NAME = "emulated_hue_name" async def async_setup(hass, yaml_config): config = Config(hass, yaml_config.get(DOMAIN, {})) app = web.Application() app["hass"] = hass real_ip.setup_real_ip(app, False, []) # anything during startup. Temp workaround. # pylint: disable=protected-access app._on_startup.freeze() await app.startup() runner = None site = None DescriptionXmlView(config).register(app, app.router) HueUsernameView().register(app, app.router) HueUnauthorizedUser().register(app, app.router) HueAllLightsStateView(config).register(app, app.router) HueOneLightStateView(config).register(app, app.router) HueOneLightChangeView(config).register(app, app.router) HueAllGroupsStateView(config).register(app, app.router) HueGroupView(config).register(app, app.router) HueFullStateView(config).register(app, app.router) upnp_listener = UPNPResponderThread( config.host_ip_addr, config.listen_port, config.upnp_bind_multicast, config.advertise_ip, config.advertise_port, ) async def stop_emulated_hue_bridge(event): upnp_listener.stop() if site: await site.stop() if runner: await runner.cleanup() async def start_emulated_hue_bridge(event): upnp_listener.start() nonlocal site nonlocal runner runner = web.AppRunner(app) await runner.setup() site = web.TCPSite(runner, config.host_ip_addr, config.listen_port) try: await site.start() except OSError as error: _LOGGER.error( "Failed to create HTTP server at port %d: %s", config.listen_port, error ) else: hass.bus.async_listen_once( EVENT_HOMEASSISTANT_STOP, stop_emulated_hue_bridge ) hass.bus.async_listen_once(EVENT_HOMEASSISTANT_START, start_emulated_hue_bridge) return True class Config: def __init__(self, hass, conf): self.hass = hass self.type = conf.get(CONF_TYPE) self.numbers = None self.cached_states = {} if self.type == TYPE_ALEXA: _LOGGER.warning( "Emulated Hue running in legacy mode because type has been " "specified. More info at https://goo.gl/M6tgz8" ) # Get the IP address that will be passed to the Echo during discovery self.host_ip_addr = conf.get(CONF_HOST_IP) if self.host_ip_addr is None: self.host_ip_addr = util.get_local_ip() _LOGGER.info( "Listen IP address not specified, auto-detected address is %s", self.host_ip_addr, ) # Get the port that the Hue bridge will listen on self.listen_port = conf.get(CONF_LISTEN_PORT) if not isinstance(self.listen_port, int): self.listen_port = DEFAULT_LISTEN_PORT _LOGGER.info( "Listen port not specified, defaulting to %s", self.listen_port ) # Get whether or not UPNP binds to multicast address (239.255.255.250) # or to the unicast address (host_ip_addr) self.upnp_bind_multicast = conf.get( CONF_UPNP_BIND_MULTICAST, DEFAULT_UPNP_BIND_MULTICAST ) # Get domains that cause both "on" and "off" commands to map to "on" # This is primarily useful for things like scenes or scripts, which # don't really have a concept of being off self.off_maps_to_on_domains = conf.get(CONF_OFF_MAPS_TO_ON_DOMAINS) if not isinstance(self.off_maps_to_on_domains, list): self.off_maps_to_on_domains = DEFAULT_OFF_MAPS_TO_ON_DOMAINS self.expose_by_default = conf.get( CONF_EXPOSE_BY_DEFAULT, DEFAULT_EXPOSE_BY_DEFAULT ) self.exposed_domains = set( conf.get(CONF_EXPOSED_DOMAINS, DEFAULT_EXPOSED_DOMAINS) ) self.advertise_ip = conf.get(CONF_ADVERTISE_IP) or self.host_ip_addr self.advertise_port = conf.get(CONF_ADVERTISE_PORT) or self.listen_port self.entities = conf.get(CONF_ENTITIES, {}) self._entities_with_hidden_attr_in_config = {} for entity_id in self.entities: hidden_value = self.entities[entity_id].get(CONF_ENTITY_HIDDEN, None) if hidden_value is not None: self._entities_with_hidden_attr_in_config[entity_id] = hidden_value def entity_id_to_number(self, entity_id): if self.type == TYPE_ALEXA: return entity_id if self.numbers is None: self.numbers = _load_json(self.hass.config.path(NUMBERS_FILE)) for number, ent_id in self.numbers.items(): if entity_id == ent_id: return number number = "1" if self.numbers: number = str(max(int(k) for k in self.numbers) + 1) self.numbers[number] = entity_id save_json(self.hass.config.path(NUMBERS_FILE), self.numbers) return number def number_to_entity_id(self, number): if self.type == TYPE_ALEXA: return number if self.numbers is None: self.numbers = _load_json(self.hass.config.path(NUMBERS_FILE)) assert isinstance(number, str) return self.numbers.get(number) def get_entity_name(self, entity): if ( entity.entity_id in self.entities and CONF_ENTITY_NAME in self.entities[entity.entity_id] ): return self.entities[entity.entity_id][CONF_ENTITY_NAME] return entity.attributes.get(ATTR_EMULATED_HUE_NAME, entity.name) def is_entity_exposed(self, entity): if entity.attributes.get("view") is not None: return False if entity.entity_id in self._entities_with_hidden_attr_in_config: return not self._entities_with_hidden_attr_in_config[entity.entity_id] if not self.expose_by_default: return False # the configuration doesn't explicitly exclude it from being if entity.domain in self.exposed_domains: return True return False def _load_json(filename): try: return load_json(filename) except HomeAssistantError: pass return {}

true

1c37d11febd64d8252bc97e1eb9d9befd448a37b

531

py

Python

finpro/urls.py

aditya1702/FinPro

bd0b6a8abc0ad613b39f0e814e5d5dea746ded50

[ "MIT" ]

null

finpro/urls.py

aditya1702/FinPro

bd0b6a8abc0ad613b39f0e814e5d5dea746ded50

[ "MIT" ]

null

finpro/urls.py

aditya1702/FinPro

bd0b6a8abc0ad613b39f0e814e5d5dea746ded50

[ "MIT" ]

null

from django.conf.urls import url from FinPro import views from django.contrib.auth import views as auth_views from django.urls import path, include from django.contrib import admin urlpatterns = [ path('admin/', admin.site.urls), url(r'^dashboard', views.DashboardPageView.as_view(), name = 'dashboard'), url(r'^maps', views.GlobalPageView.as_view(), name = 'maps'), url(r'^company-page', views.CompanyPageView.as_view(), name = 'company-page'), url(r'^login', views.LoginPageView.as_view(), name = 'login') ]

37.928571

82

0.717514

from django.conf.urls import url from FinPro import views from django.contrib.auth import views as auth_views from django.urls import path, include from django.contrib import admin urlpatterns = [ path('admin/', admin.site.urls), url(r'^dashboard', views.DashboardPageView.as_view(), name = 'dashboard'), url(r'^maps', views.GlobalPageView.as_view(), name = 'maps'), url(r'^company-page', views.CompanyPageView.as_view(), name = 'company-page'), url(r'^login', views.LoginPageView.as_view(), name = 'login') ]

true

1c37d15a08615b8399b234c97c570adaf8660123

2,834

py

Python

eoxserver/services/ows/wcs/v11/describecoverage.py

constantinius/eoxserver_combined

68f261133fed65a4e8a6ddba82b0d2845171e4bf

[ "OML" ]

null

eoxserver/services/ows/wcs/v11/describecoverage.py

constantinius/eoxserver_combined

68f261133fed65a4e8a6ddba82b0d2845171e4bf

[ "OML" ]

null

eoxserver/services/ows/wcs/v11/describecoverage.py

constantinius/eoxserver_combined

68f261133fed65a4e8a6ddba82b0d2845171e4bf

[ "OML" ]

null

#------------------------------------------------------------------------------- # # Project: EOxServer <http://eoxserver.org> # Authors: Fabian Schindler <fabian.schindler@eox.at> # #------------------------------------------------------------------------------- # Copyright (C) 2013 EOX IT Services GmbH # # 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 of this Software or works derived from this 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. #------------------------------------------------------------------------------- from eoxserver.core import Component, implements from eoxserver.core.decoders import xml, kvp, typelist from eoxserver.services.ows.interfaces import ( ServiceHandlerInterface, GetServiceHandlerInterface, PostServiceHandlerInterface ) from eoxserver.services.ows.wcs.basehandlers import ( WCSDescribeCoverageHandlerBase ) from eoxserver.services.ows.wcs.v11.parameters import ( WCS11CoverageDescrptionRenderParams ) from eoxserver.services.ows.wcs.v11.util import nsmap class WCS11DescribeCoverageHandler(WCSDescribeCoverageHandlerBase, Component): implements(ServiceHandlerInterface) implements(GetServiceHandlerInterface) implements(PostServiceHandlerInterface) versions = ("1.1.0", "1.1.1", "1.1.2",) def get_decoder(self, request): if request.method == "GET": return WCS11DescribeCoverageKVPDecoder(request.GET) elif request.method == "POST": return WCS11DescribeCoverageXMLDecoder(request.body) def get_params(self, coverages, decoder): return WCS11CoverageDescrptionRenderParams(coverages) class WCS11DescribeCoverageKVPDecoder(kvp.Decoder): coverage_ids = kvp.Parameter("identifier", type=typelist(separator=","), num=1) class WCS11DescribeCoverageXMLDecoder(xml.Decoder): coverage_ids = xml.Parameter("wcs:Identifier/text()", num="+") namespaces = nsmap

40.485714

83

0.702893

from eoxserver.core import Component, implements from eoxserver.core.decoders import xml, kvp, typelist from eoxserver.services.ows.interfaces import ( ServiceHandlerInterface, GetServiceHandlerInterface, PostServiceHandlerInterface ) from eoxserver.services.ows.wcs.basehandlers import ( WCSDescribeCoverageHandlerBase ) from eoxserver.services.ows.wcs.v11.parameters import ( WCS11CoverageDescrptionRenderParams ) from eoxserver.services.ows.wcs.v11.util import nsmap class WCS11DescribeCoverageHandler(WCSDescribeCoverageHandlerBase, Component): implements(ServiceHandlerInterface) implements(GetServiceHandlerInterface) implements(PostServiceHandlerInterface) versions = ("1.1.0", "1.1.1", "1.1.2",) def get_decoder(self, request): if request.method == "GET": return WCS11DescribeCoverageKVPDecoder(request.GET) elif request.method == "POST": return WCS11DescribeCoverageXMLDecoder(request.body) def get_params(self, coverages, decoder): return WCS11CoverageDescrptionRenderParams(coverages) class WCS11DescribeCoverageKVPDecoder(kvp.Decoder): coverage_ids = kvp.Parameter("identifier", type=typelist(separator=","), num=1) class WCS11DescribeCoverageXMLDecoder(xml.Decoder): coverage_ids = xml.Parameter("wcs:Identifier/text()", num="+") namespaces = nsmap

true

1c37d16e5211f0fdc491488938e9fe8fd5027f52

453

py

Python

public/migrations/0001_initial.py

Andrew-Chen-Wang/django-infinite-scroll

2e8871daf7fe37cbbd15a078fb99d8a22e12012f

[ "MIT" ]

null

public/migrations/0001_initial.py

Andrew-Chen-Wang/django-infinite-scroll

2e8871daf7fe37cbbd15a078fb99d8a22e12012f

[ "MIT" ]

3

2021-03-30T14:15:27.000Z

2021-06-10T19:56:36.000Z

public/migrations/0001_initial.py

Andrew-Chen-Wang/django-infinite-scroll

2e8871daf7fe37cbbd15a078fb99d8a22e12012f

[ "MIT" ]

null

# Generated by Django 3.0.8 on 2020-07-29 04:01 from django.db import migrations, models class Migration(migrations.Migration): initial = True dependencies = [ ] operations = [ migrations.CreateModel( name='English', fields=[ ('id', models.BigAutoField(primary_key=True, serialize=False)), ('name', models.TextField(max_length=9001)), ], ), ]

20.590909

79

0.560706

from django.db import migrations, models class Migration(migrations.Migration): initial = True dependencies = [ ] operations = [ migrations.CreateModel( name='English', fields=[ ('id', models.BigAutoField(primary_key=True, serialize=False)), ('name', models.TextField(max_length=9001)), ], ), ]

true

1c37d323b295d8307a87e53028346c16fffb06ba

678

py

Python

src/python/tensorflow_cloud/version.py

ucdmkt/cloud

5920c6cbe2f0f56600760d6857f90a170caf3359

[ "Apache-2.0" ]

null

src/python/tensorflow_cloud/version.py

ucdmkt/cloud

5920c6cbe2f0f56600760d6857f90a170caf3359

[ "Apache-2.0" ]

null

src/python/tensorflow_cloud/version.py

ucdmkt/cloud

5920c6cbe2f0f56600760d6857f90a170caf3359

[ "Apache-2.0" ]

null

# Copyright 2020 Google LLC. 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. """Contains the version string of TensorFlow Cloud.""" __version__ = "0.1.7.dev"

39.882353

74

0.756637

__version__ = "0.1.7.dev"

true

1c37d3c5e34b3c2b0571209fb3be9fff642cd9c6

21,393

py

Python

pipeline/configs/grb-citeseer/config.py

THUDM/grb

2f438ccc9e62ffb33a26ca98a95e504985443055

[ "MIT" ]

51

2021-06-09T06:33:51.000Z

2022-03-14T07:55:06.000Z

pipeline/configs/grb-citeseer/config.py

THUDM/grb

2f438ccc9e62ffb33a26ca98a95e504985443055

[ "MIT" ]

3

2021-08-12T13:12:47.000Z

2021-12-08T02:16:02.000Z

pipeline/configs/grb-citeseer/config.py

THUDM/grb

2f438ccc9e62ffb33a26ca98a95e504985443055

[ "MIT" ]

11

2021-06-10T08:30:05.000Z

2022-03-28T02:10:11.000Z

"""Configuration for reproducing leaderboard of grb-citeseer dataset.""" import torch import torch.nn.functional as F from grb.evaluator import metric model_list = ["gcn", "gcn_ln", "gcn_at", "graphsage", "graphsage_ln", "graphsage_at", "sgcn", "sgcn_ln", "sgcn_at", "robustgcn", "robustgcn_at", "tagcn", "tagcn_ln", "tagcn_at", "appnp", "appnp_ln", "appnp_at", "gin", "gin_ln", "gin_at", "gat", "gat_ln", "gat_at", "gcnguard", "gatguard", "gcnsvd"] model_list_basic = ["gcn", "graphsage", "sgcn", "tagcn", "appnp", "gin", "gat"] modification_attack_list = ["dice", "rand", "flip", "fga", "nea", "pgd", "prbcd", "stack"] injection_attack_list = ["rand", "fgsm", "pgd", "speit", "tdgia"] model_sur_list = ["gcn"] def build_model(model_name, num_features, num_classes): """Hyper-parameters are determined by auto training, refer to grb.utils.trainer.AutoTrainer.""" if model_name in ["gcn", "gcn_ln", "gcn_at", "gcn_ln_at"]: from grb.model.torch import GCN model = GCN(in_features=num_features, out_features=num_classes, hidden_features=128, n_layers=3, layer_norm=True if "ln" in model_name else False, dropout=0.7) train_params = { "lr" : 0.001, "n_epoch" : 5000, "early_stop" : True, "early_stop_patience": 500, "train_mode" : "inductive", } return model, train_params if model_name in ["graphsage", "graphsage_ln", "graphsage_at", "graphsage_ln_at"]: from grb.model.torch import GraphSAGE model = GraphSAGE(in_features=num_features, out_features=num_classes, hidden_features=256, n_layers=5, layer_norm=True if "ln" in model_name else False, dropout=0.5) train_params = { "lr" : 0.0001, "n_epoch" : 5000, "early_stop" : True, "early_stop_patience": 500, "train_mode" : "inductive", } return model, train_params if model_name in ["sgcn", "sgcn_ln", "sgcn_at", "sgcn_ln_at"]: from grb.model.torch import SGCN model = SGCN(in_features=num_features, out_features=num_classes, hidden_features=256, n_layers=4, k=4, layer_norm=True if "ln" in model_name else False, dropout=0.5) train_params = { "lr" : 0.01, "n_epoch" : 5000, "early_stop" : True, "early_stop_patience": 500, "train_mode" : "inductive", } return model, train_params if model_name in ["tagcn", "tagcn_ln", "tagcn_at", "tagcn_ln_at"]: from grb.model.torch import TAGCN model = TAGCN(in_features=num_features, out_features=num_classes, hidden_features=256, n_layers=3, k=2, layer_norm=True if "ln" in model_name else False, dropout=0.5) train_params = { "lr" : 0.005, "n_epoch" : 5000, "early_stop" : True, "early_stop_patience": 500, "train_mode" : "inductive", } return model, train_params if model_name in ["appnp", "appnp_ln", "appnp_at", "appnp_ln_at"]: from grb.model.torch import APPNP model = APPNP(in_features=num_features, out_features=num_classes, hidden_features=128, n_layers=3, k=3, layer_norm=True if "ln" in model_name else False, dropout=0.5) train_params = { "lr" : 0.001, "n_epoch" : 5000, "early_stop" : True, "early_stop_patience": 500, "train_mode" : "inductive", } return model, train_params if model_name in ["gin", "gin_ln", "gin_at", "gin_ln_at"]: from grb.model.torch import GIN model = GIN(in_features=num_features, out_features=num_classes, hidden_features=256, n_layers=2, layer_norm=True if "ln" in model_name else False, dropout=0.6) train_params = { "lr" : 0.0001, "n_epoch" : 5000, "early_stop" : True, "early_stop_patience": 500, "train_mode" : "inductive", } return model, train_params if model_name in ["gat", "gat_ln", "gat_at", "gat_ln_at"]: from grb.model.dgl import GAT model = GAT(in_features=num_features, out_features=num_classes, hidden_features=64, n_layers=3, n_heads=6, layer_norm=True if "ln" in model_name else False, dropout=0.6) train_params = { "lr" : 0.005, "n_epoch" : 5000, "early_stop" : True, "early_stop_patience": 500, "train_mode" : "inductive", } return model, train_params if model_name in ["robustgcn", "robustgcn_at"]: from grb.defense import RobustGCN model = RobustGCN(in_features=num_features, out_features=num_classes, hidden_features=128, n_layers=3, dropout=0.5) train_params = { "lr" : 0.001, "n_epoch" : 5000, "early_stop" : True, "early_stop_patience": 500, "train_mode" : "inductive", } return model, train_params if model_name in ["gcnsvd", "gcnsvd_ln"]: from grb.defense.gcnsvd import GCNSVD model = GCNSVD(in_features=num_features, out_features=num_classes, hidden_features=128, n_layers=3, dropout=0.5) train_params = { "lr" : 0.001, "n_epoch" : 5000, "early_stop" : True, "early_stop_patience": 500, "train_mode" : "inductive", } return model, train_params if model_name in ["gcnguard"]: from grb.defense import GCNGuard model = GCNGuard(in_features=num_features, out_features=num_classes, hidden_features=128, n_layers=3, dropout=0.5) train_params = { "lr" : 0.001, "n_epoch" : 5000, "early_stop" : True, "early_stop_patience": 500, "train_mode" : "inductive", } return model, train_params if model_name in ["gatguard"]: from grb.defense import GATGuard model = GATGuard(in_features=num_features, out_features=num_classes, hidden_features=64, n_heads=6, n_layers=3, dropout=0.5) train_params = { "lr" : 0.001, "n_epoch" : 5000, "early_stop" : True, "early_stop_patience": 500, "train_mode" : "inductive", } return model, train_params def build_optimizer(model, lr): optimizer = torch.optim.Adam(model.parameters(), lr=lr) return optimizer def build_loss(): return F.nll_loss def build_metric(): return metric.eval_acc def build_attack(attack_name, device="cpu", args=None, mode="modification"): if mode == "modification": if attack_name == "dice": from grb.attack.modification import DICE attack = DICE(n_edge_mod=args.n_edge_mod, ratio_delete=0.6, device=device) return attack if attack_name == "fga": from grb.attack.modification import FGA attack = FGA(n_edge_mod=args.n_edge_mod, device=device) return attack if attack_name == "flip": from grb.attack.modification import FLIP attack = FLIP(n_edge_mod=args.n_edge_mod, flip_type=args.flip_type, mode="descend", device=device) return attack if attack_name == "rand": from grb.attack.modification import RAND attack = RAND(n_edge_mod=args.n_edge_mod, device=device) return attack if attack_name == "nea": from grb.attack.modification import NEA attack = NEA(n_edge_mod=args.n_edge_mod, device=device) return attack if attack_name == "stack": from grb.attack.modification import STACK attack = STACK(n_edge_mod=args.n_edge_mod, device=device) return attack if attack_name == "pgd": from grb.attack.modification import PGD attack = PGD(epsilon=args.epsilon, n_epoch=args.attack_epoch, n_node_mod=args.n_node_mod, n_edge_mod=args.n_edge_mod, feat_lim_min=args.feat_lim_min, feat_lim_max=args.feat_lim_max, early_stop=args.early_stop, device=device) return attack if attack_name == "prbcd": from grb.attack.modification import PRBCD attack = PRBCD(epsilon=args.epsilon, n_epoch=args.attack_epoch, n_node_mod=args.n_node_mod, n_edge_mod=args.n_edge_mod, feat_lim_min=args.feat_lim_min, feat_lim_max=args.feat_lim_max, early_stop=args.early_stop, device=device) return attack elif mode == "injection": if attack_name == "rand": from grb.attack.injection import RAND attack = RAND(n_inject_max=args.n_inject_max, n_edge_max=args.n_edge_max, feat_lim_min=args.feat_lim_min, feat_lim_max=args.feat_lim_max, device=device) return attack elif attack_name == "fgsm": from grb.attack.injection import FGSM attack = FGSM(epsilon=args.attack_lr, n_epoch=args.attack_epoch, n_inject_max=args.n_inject_max, n_edge_max=args.n_edge_max, feat_lim_min=args.feat_lim_min, feat_lim_max=args.feat_lim_max, early_stop=args.early_stop, device=device) return attack elif attack_name == "pgd": from grb.attack.injection import PGD attack = PGD(epsilon=args.attack_lr, n_epoch=args.attack_epoch, n_inject_max=args.n_inject_max, n_edge_max=args.n_edge_max, feat_lim_min=args.feat_lim_min, feat_lim_max=args.feat_lim_max, early_stop=args.early_stop, device=device) return attack elif attack_name == "speit": from grb.attack.injection import SPEIT attack = SPEIT(lr=args.attack_lr, n_epoch=args.attack_epoch, n_inject_max=args.n_inject_max, n_edge_max=args.n_edge_max, feat_lim_min=args.feat_lim_min, feat_lim_max=args.feat_lim_max, early_stop=args.early_stop, device=device) return attack elif attack_name == "tdgia": from grb.attack.injection import TDGIA attack = TDGIA(lr=args.attack_lr, n_epoch=args.attack_epoch, n_inject_max=args.n_inject_max, n_edge_max=args.n_edge_max, feat_lim_min=args.feat_lim_min, feat_lim_max=args.feat_lim_max, early_stop=args.early_stop, inject_mode='random', sequential_step=1.0, device=device) return attack elif attack_name == "tdgia_random": from grb.attack.injection.tdgia import TDGIA attack = TDGIA(lr=args.attack_lr, n_epoch=args.attack_epoch, n_inject_max=args.n_inject_max, n_edge_max=args.n_edge_max, feat_lim_min=args.feat_lim_min, feat_lim_max=args.feat_lim_max, early_stop=args.early_stop, inject_mode='random', device=device) return attack elif attack_name == "tdgia_uniform": from grb.attack.injection import TDGIA attack = TDGIA(lr=args.attack_lr, n_epoch=args.attack_epoch, n_inject_max=args.n_inject_max, n_edge_max=args.n_edge_max, feat_lim_min=args.feat_lim_min, feat_lim_max=args.feat_lim_max, early_stop=args.early_stop, inject_mode='uniform', sequential_step=1.0, device=device) return attack else: raise NotImplementedError def build_model_autotrain(model_name): if model_name == "gcn": from grb.model.torch import GCN def params_search(trial): model_params = { "hidden_features": trial.suggest_categorical("hidden_features", [32, 64, 128, 256]), "n_layers" : trial.suggest_categorical("n_layers", [2, 3, 4, 5]), "dropout" : trial.suggest_categorical("dropout", [0.5, 0.6, 0.7, 0.8]), } other_params = { "lr" : trial.suggest_categorical("lr", [1e-2, 1e-3, 5e-3, 1e-4]), "n_epoch" : 5000, "early_stop" : True, "early_stop_patience": 500, } return model_params, other_params return GCN, params_search if model_name == "graphsage": from grb.model.torch import GraphSAGE def params_search(trial): model_params = { "hidden_features": trial.suggest_categorical("hidden_features", [32, 64, 128, 256]), "n_layers" : trial.suggest_categorical("n_layers", [2, 3, 4, 5]), "dropout" : trial.suggest_categorical("dropout", [0.5, 0.6, 0.7, 0.8]), } other_params = { "lr" : trial.suggest_categorical("lr", [1e-2, 1e-3, 5e-3, 1e-4]), "n_epoch" : 5000, "early_stop" : True, "early_stop_patience": 500, } return model_params, other_params return GraphSAGE, params_search if model_name == "sgcn": from grb.model.torch import SGCN def params_search(trial): model_params = { "hidden_features": trial.suggest_categorical("hidden_features", [32, 64, 128, 256]), "n_layers" : trial.suggest_categorical("n_layers", [2, 3, 4, 5]), "dropout" : trial.suggest_categorical("dropout", [0.5, 0.6, 0.7, 0.8]), } other_params = { "lr" : trial.suggest_categorical("lr", [1e-2, 1e-3, 5e-3, 1e-4]), "n_epoch" : 5000, "early_stop" : True, "early_stop_patience": 500, } return model_params, other_params return SGCN, params_search if model_name == "tagcn": from grb.model.torch import TAGCN def params_search(trial): model_params = { "hidden_features": trial.suggest_categorical("hidden_features", [32, 64, 128, 256]), "n_layers" : trial.suggest_categorical("n_layers", [2, 3, 4, 5]), "k" : trial.suggest_categorical("k", [2, 3, 4, 5]), "dropout" : trial.suggest_categorical("dropout", [0.5, 0.6, 0.7, 0.8]), } other_params = { "lr" : trial.suggest_categorical("lr", [1e-2, 1e-3, 5e-3, 1e-4]), "n_epoch" : 5000, "early_stop" : True, "early_stop_patience": 500, } return model_params, other_params return TAGCN, params_search if model_name == "appnp": from grb.model.torch import APPNP def params_search(trial): model_params = { "hidden_features": trial.suggest_categorical("hidden_features", [32, 64, 128, 256]), "n_layers" : trial.suggest_categorical("n_layers", [2, 3, 4, 5]), "k" : trial.suggest_categorical("k", [2, 3, 4, 5]), "dropout" : trial.suggest_categorical("dropout", [0.5, 0.6, 0.7, 0.8]), } other_params = { "lr" : trial.suggest_categorical("lr", [1e-2, 1e-3, 5e-3, 1e-4]), "n_epoch" : 5000, "early_stop" : True, "early_stop_patience": 500, } return model_params, other_params return APPNP, params_search if model_name == "gin": from grb.model.torch import GIN def params_search(trial): model_params = { "hidden_features": trial.suggest_categorical("hidden_features", [32, 64, 128, 256]), "n_layers" : trial.suggest_categorical("n_layers", [2, 3, 4, 5]), "dropout" : trial.suggest_categorical("dropout", [0.5, 0.6, 0.7, 0.8]), } other_params = { "lr" : trial.suggest_categorical("lr", [1e-2, 1e-3, 5e-3, 1e-4]), "n_epoch" : 5000, "early_stop" : True, "early_stop_patience": 500, } return model_params, other_params return GIN, params_search if model_name == "gat": from grb.model.dgl import GAT def params_search(trial): model_params = { "hidden_features": trial.suggest_categorical("hidden_features", [32, 64, 128, 256]), "n_layers" : trial.suggest_categorical("n_layers", [2, 3, 4, 5]), "n_heads" : trial.suggest_categorical("n_heads", [2, 4, 6, 8]), "dropout" : trial.suggest_categorical("dropout", [0.5, 0.6, 0.7, 0.8]), } other_params = { "lr" : trial.suggest_categorical("lr", [1e-2, 1e-3, 5e-3, 1e-4]), "n_epoch" : 5000, "early_stop" : True, "early_stop_patience": 500, } return model_params, other_params return GAT, params_search

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import torch import torch.nn.functional as F from grb.evaluator import metric model_list = ["gcn", "gcn_ln", "gcn_at", "graphsage", "graphsage_ln", "graphsage_at", "sgcn", "sgcn_ln", "sgcn_at", "robustgcn", "robustgcn_at", "tagcn", "tagcn_ln", "tagcn_at", "appnp", "appnp_ln", "appnp_at", "gin", "gin_ln", "gin_at", "gat", "gat_ln", "gat_at", "gcnguard", "gatguard", "gcnsvd"] model_list_basic = ["gcn", "graphsage", "sgcn", "tagcn", "appnp", "gin", "gat"] modification_attack_list = ["dice", "rand", "flip", "fga", "nea", "pgd", "prbcd", "stack"] injection_attack_list = ["rand", "fgsm", "pgd", "speit", "tdgia"] model_sur_list = ["gcn"] def build_model(model_name, num_features, num_classes): if model_name in ["gcn", "gcn_ln", "gcn_at", "gcn_ln_at"]: from grb.model.torch import GCN model = GCN(in_features=num_features, out_features=num_classes, hidden_features=128, n_layers=3, layer_norm=True if "ln" in model_name else False, dropout=0.7) train_params = { "lr" : 0.001, "n_epoch" : 5000, "early_stop" : True, "early_stop_patience": 500, "train_mode" : "inductive", } return model, train_params if model_name in ["graphsage", "graphsage_ln", "graphsage_at", "graphsage_ln_at"]: from grb.model.torch import GraphSAGE model = GraphSAGE(in_features=num_features, out_features=num_classes, hidden_features=256, n_layers=5, layer_norm=True if "ln" in model_name else False, dropout=0.5) train_params = { "lr" : 0.0001, "n_epoch" : 5000, "early_stop" : True, "early_stop_patience": 500, "train_mode" : "inductive", } return model, train_params if model_name in ["sgcn", "sgcn_ln", "sgcn_at", "sgcn_ln_at"]: from grb.model.torch import SGCN model = SGCN(in_features=num_features, out_features=num_classes, hidden_features=256, n_layers=4, k=4, layer_norm=True if "ln" in model_name else False, dropout=0.5) train_params = { "lr" : 0.01, "n_epoch" : 5000, "early_stop" : True, "early_stop_patience": 500, "train_mode" : "inductive", } return model, train_params if model_name in ["tagcn", "tagcn_ln", "tagcn_at", "tagcn_ln_at"]: from grb.model.torch import TAGCN model = TAGCN(in_features=num_features, out_features=num_classes, hidden_features=256, n_layers=3, k=2, layer_norm=True if "ln" in model_name else False, dropout=0.5) train_params = { "lr" : 0.005, "n_epoch" : 5000, "early_stop" : True, "early_stop_patience": 500, "train_mode" : "inductive", } return model, train_params if model_name in ["appnp", "appnp_ln", "appnp_at", "appnp_ln_at"]: from grb.model.torch import APPNP model = APPNP(in_features=num_features, out_features=num_classes, hidden_features=128, n_layers=3, k=3, layer_norm=True if "ln" in model_name else False, dropout=0.5) train_params = { "lr" : 0.001, "n_epoch" : 5000, "early_stop" : True, "early_stop_patience": 500, "train_mode" : "inductive", } return model, train_params if model_name in ["gin", "gin_ln", "gin_at", "gin_ln_at"]: from grb.model.torch import GIN model = GIN(in_features=num_features, out_features=num_classes, hidden_features=256, n_layers=2, layer_norm=True if "ln" in model_name else False, dropout=0.6) train_params = { "lr" : 0.0001, "n_epoch" : 5000, "early_stop" : True, "early_stop_patience": 500, "train_mode" : "inductive", } return model, train_params if model_name in ["gat", "gat_ln", "gat_at", "gat_ln_at"]: from grb.model.dgl import GAT model = GAT(in_features=num_features, out_features=num_classes, hidden_features=64, n_layers=3, n_heads=6, layer_norm=True if "ln" in model_name else False, dropout=0.6) train_params = { "lr" : 0.005, "n_epoch" : 5000, "early_stop" : True, "early_stop_patience": 500, "train_mode" : "inductive", } return model, train_params if model_name in ["robustgcn", "robustgcn_at"]: from grb.defense import RobustGCN model = RobustGCN(in_features=num_features, out_features=num_classes, hidden_features=128, n_layers=3, dropout=0.5) train_params = { "lr" : 0.001, "n_epoch" : 5000, "early_stop" : True, "early_stop_patience": 500, "train_mode" : "inductive", } return model, train_params if model_name in ["gcnsvd", "gcnsvd_ln"]: from grb.defense.gcnsvd import GCNSVD model = GCNSVD(in_features=num_features, out_features=num_classes, hidden_features=128, n_layers=3, dropout=0.5) train_params = { "lr" : 0.001, "n_epoch" : 5000, "early_stop" : True, "early_stop_patience": 500, "train_mode" : "inductive", } return model, train_params if model_name in ["gcnguard"]: from grb.defense import GCNGuard model = GCNGuard(in_features=num_features, out_features=num_classes, hidden_features=128, n_layers=3, dropout=0.5) train_params = { "lr" : 0.001, "n_epoch" : 5000, "early_stop" : True, "early_stop_patience": 500, "train_mode" : "inductive", } return model, train_params if model_name in ["gatguard"]: from grb.defense import GATGuard model = GATGuard(in_features=num_features, out_features=num_classes, hidden_features=64, n_heads=6, n_layers=3, dropout=0.5) train_params = { "lr" : 0.001, "n_epoch" : 5000, "early_stop" : True, "early_stop_patience": 500, "train_mode" : "inductive", } return model, train_params def build_optimizer(model, lr): optimizer = torch.optim.Adam(model.parameters(), lr=lr) return optimizer def build_loss(): return F.nll_loss def build_metric(): return metric.eval_acc def build_attack(attack_name, device="cpu", args=None, mode="modification"): if mode == "modification": if attack_name == "dice": from grb.attack.modification import DICE attack = DICE(n_edge_mod=args.n_edge_mod, ratio_delete=0.6, device=device) return attack if attack_name == "fga": from grb.attack.modification import FGA attack = FGA(n_edge_mod=args.n_edge_mod, device=device) return attack if attack_name == "flip": from grb.attack.modification import FLIP attack = FLIP(n_edge_mod=args.n_edge_mod, flip_type=args.flip_type, mode="descend", device=device) return attack if attack_name == "rand": from grb.attack.modification import RAND attack = RAND(n_edge_mod=args.n_edge_mod, device=device) return attack if attack_name == "nea": from grb.attack.modification import NEA attack = NEA(n_edge_mod=args.n_edge_mod, device=device) return attack if attack_name == "stack": from grb.attack.modification import STACK attack = STACK(n_edge_mod=args.n_edge_mod, device=device) return attack if attack_name == "pgd": from grb.attack.modification import PGD attack = PGD(epsilon=args.epsilon, n_epoch=args.attack_epoch, n_node_mod=args.n_node_mod, n_edge_mod=args.n_edge_mod, feat_lim_min=args.feat_lim_min, feat_lim_max=args.feat_lim_max, early_stop=args.early_stop, device=device) return attack if attack_name == "prbcd": from grb.attack.modification import PRBCD attack = PRBCD(epsilon=args.epsilon, n_epoch=args.attack_epoch, n_node_mod=args.n_node_mod, n_edge_mod=args.n_edge_mod, feat_lim_min=args.feat_lim_min, feat_lim_max=args.feat_lim_max, early_stop=args.early_stop, device=device) return attack elif mode == "injection": if attack_name == "rand": from grb.attack.injection import RAND attack = RAND(n_inject_max=args.n_inject_max, n_edge_max=args.n_edge_max, feat_lim_min=args.feat_lim_min, feat_lim_max=args.feat_lim_max, device=device) return attack elif attack_name == "fgsm": from grb.attack.injection import FGSM attack = FGSM(epsilon=args.attack_lr, n_epoch=args.attack_epoch, n_inject_max=args.n_inject_max, n_edge_max=args.n_edge_max, feat_lim_min=args.feat_lim_min, feat_lim_max=args.feat_lim_max, early_stop=args.early_stop, device=device) return attack elif attack_name == "pgd": from grb.attack.injection import PGD attack = PGD(epsilon=args.attack_lr, n_epoch=args.attack_epoch, n_inject_max=args.n_inject_max, n_edge_max=args.n_edge_max, feat_lim_min=args.feat_lim_min, feat_lim_max=args.feat_lim_max, early_stop=args.early_stop, device=device) return attack elif attack_name == "speit": from grb.attack.injection import SPEIT attack = SPEIT(lr=args.attack_lr, n_epoch=args.attack_epoch, n_inject_max=args.n_inject_max, n_edge_max=args.n_edge_max, feat_lim_min=args.feat_lim_min, feat_lim_max=args.feat_lim_max, early_stop=args.early_stop, device=device) return attack elif attack_name == "tdgia": from grb.attack.injection import TDGIA attack = TDGIA(lr=args.attack_lr, n_epoch=args.attack_epoch, n_inject_max=args.n_inject_max, n_edge_max=args.n_edge_max, feat_lim_min=args.feat_lim_min, feat_lim_max=args.feat_lim_max, early_stop=args.early_stop, inject_mode='random', sequential_step=1.0, device=device) return attack elif attack_name == "tdgia_random": from grb.attack.injection.tdgia import TDGIA attack = TDGIA(lr=args.attack_lr, n_epoch=args.attack_epoch, n_inject_max=args.n_inject_max, n_edge_max=args.n_edge_max, feat_lim_min=args.feat_lim_min, feat_lim_max=args.feat_lim_max, early_stop=args.early_stop, inject_mode='random', device=device) return attack elif attack_name == "tdgia_uniform": from grb.attack.injection import TDGIA attack = TDGIA(lr=args.attack_lr, n_epoch=args.attack_epoch, n_inject_max=args.n_inject_max, n_edge_max=args.n_edge_max, feat_lim_min=args.feat_lim_min, feat_lim_max=args.feat_lim_max, early_stop=args.early_stop, inject_mode='uniform', sequential_step=1.0, device=device) return attack else: raise NotImplementedError def build_model_autotrain(model_name): if model_name == "gcn": from grb.model.torch import GCN def params_search(trial): model_params = { "hidden_features": trial.suggest_categorical("hidden_features", [32, 64, 128, 256]), "n_layers" : trial.suggest_categorical("n_layers", [2, 3, 4, 5]), "dropout" : trial.suggest_categorical("dropout", [0.5, 0.6, 0.7, 0.8]), } other_params = { "lr" : trial.suggest_categorical("lr", [1e-2, 1e-3, 5e-3, 1e-4]), "n_epoch" : 5000, "early_stop" : True, "early_stop_patience": 500, } return model_params, other_params return GCN, params_search if model_name == "graphsage": from grb.model.torch import GraphSAGE def params_search(trial): model_params = { "hidden_features": trial.suggest_categorical("hidden_features", [32, 64, 128, 256]), "n_layers" : trial.suggest_categorical("n_layers", [2, 3, 4, 5]), "dropout" : trial.suggest_categorical("dropout", [0.5, 0.6, 0.7, 0.8]), } other_params = { "lr" : trial.suggest_categorical("lr", [1e-2, 1e-3, 5e-3, 1e-4]), "n_epoch" : 5000, "early_stop" : True, "early_stop_patience": 500, } return model_params, other_params return GraphSAGE, params_search if model_name == "sgcn": from grb.model.torch import SGCN def params_search(trial): model_params = { "hidden_features": trial.suggest_categorical("hidden_features", [32, 64, 128, 256]), "n_layers" : trial.suggest_categorical("n_layers", [2, 3, 4, 5]), "dropout" : trial.suggest_categorical("dropout", [0.5, 0.6, 0.7, 0.8]), } other_params = { "lr" : trial.suggest_categorical("lr", [1e-2, 1e-3, 5e-3, 1e-4]), "n_epoch" : 5000, "early_stop" : True, "early_stop_patience": 500, } return model_params, other_params return SGCN, params_search if model_name == "tagcn": from grb.model.torch import TAGCN def params_search(trial): model_params = { "hidden_features": trial.suggest_categorical("hidden_features", [32, 64, 128, 256]), "n_layers" : trial.suggest_categorical("n_layers", [2, 3, 4, 5]), "k" : trial.suggest_categorical("k", [2, 3, 4, 5]), "dropout" : trial.suggest_categorical("dropout", [0.5, 0.6, 0.7, 0.8]), } other_params = { "lr" : trial.suggest_categorical("lr", [1e-2, 1e-3, 5e-3, 1e-4]), "n_epoch" : 5000, "early_stop" : True, "early_stop_patience": 500, } return model_params, other_params return TAGCN, params_search if model_name == "appnp": from grb.model.torch import APPNP def params_search(trial): model_params = { "hidden_features": trial.suggest_categorical("hidden_features", [32, 64, 128, 256]), "n_layers" : trial.suggest_categorical("n_layers", [2, 3, 4, 5]), "k" : trial.suggest_categorical("k", [2, 3, 4, 5]), "dropout" : trial.suggest_categorical("dropout", [0.5, 0.6, 0.7, 0.8]), } other_params = { "lr" : trial.suggest_categorical("lr", [1e-2, 1e-3, 5e-3, 1e-4]), "n_epoch" : 5000, "early_stop" : True, "early_stop_patience": 500, } return model_params, other_params return APPNP, params_search if model_name == "gin": from grb.model.torch import GIN def params_search(trial): model_params = { "hidden_features": trial.suggest_categorical("hidden_features", [32, 64, 128, 256]), "n_layers" : trial.suggest_categorical("n_layers", [2, 3, 4, 5]), "dropout" : trial.suggest_categorical("dropout", [0.5, 0.6, 0.7, 0.8]), } other_params = { "lr" : trial.suggest_categorical("lr", [1e-2, 1e-3, 5e-3, 1e-4]), "n_epoch" : 5000, "early_stop" : True, "early_stop_patience": 500, } return model_params, other_params return GIN, params_search if model_name == "gat": from grb.model.dgl import GAT def params_search(trial): model_params = { "hidden_features": trial.suggest_categorical("hidden_features", [32, 64, 128, 256]), "n_layers" : trial.suggest_categorical("n_layers", [2, 3, 4, 5]), "n_heads" : trial.suggest_categorical("n_heads", [2, 4, 6, 8]), "dropout" : trial.suggest_categorical("dropout", [0.5, 0.6, 0.7, 0.8]), } other_params = { "lr" : trial.suggest_categorical("lr", [1e-2, 1e-3, 5e-3, 1e-4]), "n_epoch" : 5000, "early_stop" : True, "early_stop_patience": 500, } return model_params, other_params return GAT, params_search

true

1c37d3f21b7a001c9055b55e762d87a2946c1496

261

py

Python

Sources/02XXX/2941/2941.py

DDManager/Baekjoon-Online-Judge

7dd6d76838d3309bfe5bef46f1778c5776ebdf2a

[ "MIT" ]

1

2019-07-02T09:07:58.000Z

Sources/02XXX/2941/2941.py

DDManager/Baekjoon-Online-Judge

7dd6d76838d3309bfe5bef46f1778c5776ebdf2a

[ "MIT" ]

null

Sources/02XXX/2941/2941.py

DDManager/Baekjoon-Online-Judge

7dd6d76838d3309bfe5bef46f1778c5776ebdf2a

[ "MIT" ]

1

2022-02-13T04:17:10.000Z

## # BOJ 2941번 Python 3 소스 코드 # 작성자 : 동동매니저 (DDManager) # # ※ 실행 결과 # 사용 메모리 : 32,876 KB / 294,912 KB # 소요 시간 : 112 ms / 5,000 ms # # Copyright 2020. DDManager all rights reserved. ## import re print(len(re.sub("(dz=|c=|c-|d-|lj|nj|s=|z=)","0",input())))

20.076923

60

0.582375

import re print(len(re.sub("(dz=|c=|c-|d-|lj|nj|s=|z=)","0",input())))

true

1c37d3f6f4efd7eafe388a457039c5630ca8ffcf

828

py

Python

test/functional/bitcoin_cli.py

karthik2883/ABCCoin

e6daef308ba7bc81a59ba3aff8bc503cdece5cc6

[ "MIT" ]

1

2018-04-25T12:18:41.000Z

test/functional/bitcoin_cli.py

karthik2883/ABCCoin

e6daef308ba7bc81a59ba3aff8bc503cdece5cc6

[ "MIT" ]

null

test/functional/bitcoin_cli.py

karthik2883/ABCCoin

e6daef308ba7bc81a59ba3aff8bc503cdece5cc6

[ "MIT" ]

null

#!/usr/bin/env python3 # Copyright (c) 2017 The Bitcoin Core developers # Distributed under the MIT software license, see the accompanying # file COPYING or http://www.opensource.org/licenses/mit-license.php. """Test bitcoin-cli""" from test_framework.test_framework import BitcoinTestFramework from test_framework.util import assert_equal class TestBitcoinCli(BitcoinTestFramework): def set_test_params(self): self.setup_clean_chain = True self.num_nodes = 1 def run_test(self): """Main test logic""" self.log.info("Compare responses from getinfo RPC and `abccoin-cli getinfo`") cli_get_info = self.nodes[0].cli.getinfo() rpc_get_info = self.nodes[0].getinfo() assert_equal(cli_get_info, rpc_get_info) if __name__ == '__main__': TestBitcoinCli().main()

31.846154

85

0.721014

from test_framework.test_framework import BitcoinTestFramework from test_framework.util import assert_equal class TestBitcoinCli(BitcoinTestFramework): def set_test_params(self): self.setup_clean_chain = True self.num_nodes = 1 def run_test(self): self.log.info("Compare responses from getinfo RPC and `abccoin-cli getinfo`") cli_get_info = self.nodes[0].cli.getinfo() rpc_get_info = self.nodes[0].getinfo() assert_equal(cli_get_info, rpc_get_info) if __name__ == '__main__': TestBitcoinCli().main()

true

1c37d427226bb485f6d7aeb1d6435345e62f6017

52

py

Python

src/allocation/adapters/email.py

jeantardelli/architecture-patterns-with-python

d48c7d6d4a44073b815c7e6770e44cf2e231e35b

[ "MIT" ]

1

2021-04-07T18:04:56.000Z

src/allocation/adapters/email.py

jeantardelli/architecture-patterns-with-python

d48c7d6d4a44073b815c7e6770e44cf2e231e35b

[ "MIT" ]

null

src/allocation/adapters/email.py

jeantardelli/architecture-patterns-with-python

d48c7d6d4a44073b815c7e6770e44cf2e231e35b

[ "MIT" ]

null

def send(*args): print("SENDING EMAIL:", *args)

17.333333

34

0.615385

def send(*args): print("SENDING EMAIL:", *args)

true

1c37d42984d41533828aed23483e53f4a7a0b343

1,610

py

Python

icv/data/core/segmap.py

dmxj/icv

0b074ec9475f2c70038d2e8b7166414fd5b93e61

[ "MIT" ]

5

2019-09-10T04:02:19.000Z

2020-07-24T07:46:08.000Z

icv/data/core/segmap.py

dmxj/icv

0b074ec9475f2c70038d2e8b7166414fd5b93e61

[ "MIT" ]

null

icv/data/core/segmap.py

dmxj/icv

0b074ec9475f2c70038d2e8b7166414fd5b93e61

[ "MIT" ]

1

2020-03-20T03:44:04.000Z

# -*- coding: UTF-8 -*- import numpy as np class Segmap(object): DEFAULT_SEGMENT_COLORS = [ (0, 0, 0), # black (230, 25, 75), # red (60, 180, 75), # green (255, 225, 25), # yellow (0, 130, 200), # blue (245, 130, 48), # orange (145, 30, 180), # purple (70, 240, 240), # cyan (240, 50, 230), # magenta (210, 245, 60), # lime (250, 190, 190), # pink (0, 128, 128), # teal (230, 190, 255), # lavender (170, 110, 40), # brown (255, 250, 200), # beige (128, 0, 0), # maroon (170, 255, 195), # mint (128, 128, 0), # olive (255, 215, 180), # coral (0, 0, 128), # navy (128, 128, 128), # grey (255, 255, 255), # white # -- (115, 12, 37), # dark red (30, 90, 37), # dark green (127, 112, 12), # dark yellow (0, 65, 100), # dark blue (122, 65, 24), # dark orange (72, 15, 90), # dark purple (35, 120, 120), # dark cyan (120, 25, 115), # dark magenta (105, 122, 30), # dark lime (125, 95, 95), # dark pink (0, 64, 64), # dark teal (115, 95, 127), # dark lavender (85, 55, 20), # dark brown (127, 125, 100), # dark beige (64, 0, 0), # dark maroon (85, 127, 97), # dark mint (64, 64, 0), # dark olive (127, 107, 90), # dark coral (0, 0, 64), # dark navy (64, 64, 64), # dark grey ] def __init__(self,arr): pass

28.245614

40

0.418634

import numpy as np class Segmap(object): DEFAULT_SEGMENT_COLORS = [ (0, 0, 0), (230, 25, 75), (60, 180, 75), (255, 225, 25), (0, 130, 200), (245, 130, 48), (145, 30, 180), (70, 240, 240), (240, 50, 230), (210, 245, 60), (250, 190, 190), (0, 128, 128), (230, 190, 255), (170, 110, 40), (255, 250, 200), (128, 0, 0), (170, 255, 195), (128, 128, 0), (255, 215, 180), (0, 0, 128), (128, 128, 128), (255, 255, 255), (115, 12, 37), (30, 90, 37), (127, 112, 12), (0, 65, 100), (122, 65, 24), (72, 15, 90), (35, 120, 120), (120, 25, 115), (105, 122, 30), (125, 95, 95), (0, 64, 64), (115, 95, 127), (85, 55, 20), (127, 125, 100), (64, 0, 0), (85, 127, 97), (64, 64, 0), (127, 107, 90), (0, 0, 64), (64, 64, 64), ] def __init__(self,arr): pass

true

1c37d4c8dca32f72c89cbd0b0de7d852eb42ca60

10,464

py

Python

tensor2tensor/mesh_tensorflow/mtf_layers_test.py

ReDeiPirati/tensor2tensor

39f44893b82a5052c9eddba760fc4094d3d706bb

[ "Apache-2.0" ]

4

2019-04-20T23:28:41.000Z

2021-01-03T03:21:43.000Z

tensor2tensor/mesh_tensorflow/mtf_layers_test.py

ReDeiPirati/tensor2tensor

39f44893b82a5052c9eddba760fc4094d3d706bb

[ "Apache-2.0" ]

null

tensor2tensor/mesh_tensorflow/mtf_layers_test.py

ReDeiPirati/tensor2tensor

39f44893b82a5052c9eddba760fc4094d3d706bb

[ "Apache-2.0" ]

1

2019-01-29T18:44:17.000Z

# coding=utf-8 # Copyright 2018 The Tensor2Tensor 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. """Tests for Mesh TensorFlow layers.""" from __future__ import absolute_import from __future__ import division from __future__ import print_function from absl.testing import parameterized from tensor2tensor.layers import common_layers from tensor2tensor.mesh_tensorflow import mesh_tensorflow as mtf from tensor2tensor.mesh_tensorflow import mtf_layers from tensor2tensor.mesh_tensorflow import placement_mesh_impl import tensorflow as tf class MtfLayersTest(parameterized.TestCase, tf.test.TestCase): @parameterized.parameters( (4, True), (8, False), ) def testDense(self, units, use_bias): batch = 2 channels = 3 inputs = tf.random_normal([batch, channels]) graph = mtf.Graph() mesh = mtf.Mesh(graph, "my_mesh") batch_dim = mtf.Dimension("batch", batch) channels_dim = mtf.Dimension("channels", channels) depth_dim = mtf.Dimension("depth", units) mtf_inputs = mtf.import_tf_tensor( mesh, inputs, shape=mtf.Shape([batch_dim, channels_dim])) mtf_outputs = mtf_layers.dense(mtf_inputs, output_dim=depth_dim, reduced_dims=[channels_dim], activation=mtf.relu, use_bias=use_bias) mesh_impl = placement_mesh_impl.PlacementMeshImpl( shape=[], layout={}, devices=[""]) lowering = mtf.Lowering(graph, {mesh: mesh_impl}) actual_outputs = lowering.export_to_tf_tensor(mtf_outputs) expected_outputs = tf.keras.layers.Dense(units=units, activation=tf.nn.relu, use_bias=use_bias)(inputs) tf_group = lowering.copy_masters_to_slices() init = tf.global_variables_initializer() with self.test_session() as sess: sess.run(init) sess.run(tf_group) actual, expected = sess.run([actual_outputs, expected_outputs]) self.assertEqual(actual.shape, expected.shape) def testLayerNorm(self): batch = 2 channels = 3 inputs = tf.random_normal([batch, channels]) graph = mtf.Graph() mesh = mtf.Mesh(graph, "my_mesh") batch_dim = mtf.Dimension("batch", batch) channels_dim = mtf.Dimension("channels", channels) mtf_inputs = mtf.import_tf_tensor( mesh, inputs, shape=mtf.Shape([batch_dim, channels_dim])) mtf_outputs = mtf_layers.layer_norm(mtf_inputs, dim=channels_dim) mesh_impl = placement_mesh_impl.PlacementMeshImpl( shape=[], layout={}, devices=[""]) lowering = mtf.Lowering(graph, {mesh: mesh_impl}) actual_outputs = lowering.export_to_tf_tensor(mtf_outputs) expected_outputs = common_layers.layer_norm(inputs) tf_group = lowering.copy_masters_to_slices() init = tf.global_variables_initializer() with self.test_session() as sess: sess.run(init) sess.run(tf_group) actual, expected = sess.run([actual_outputs, expected_outputs]) self.assertEqual(actual.shape, expected.shape) def testWeightsNonzero(self): inputs = tf.constant([[3, 1, 0], [1, 0, 0]]) graph = mtf.Graph() mesh = mtf.Mesh(graph, "my_mesh") batch_dim = mtf.Dimension("batch", inputs.shape.as_list()[0]) channels_dim = mtf.Dimension("channels", inputs.shape.as_list()[1]) mtf_inputs = mtf.import_tf_tensor( mesh, inputs, shape=mtf.Shape([batch_dim, channels_dim])) mtf_outputs = mtf_layers.weights_nonzero(mtf_inputs) mesh_impl = placement_mesh_impl.PlacementMeshImpl( shape=[], layout={}, devices=[""]) lowering = mtf.Lowering(graph, {mesh: mesh_impl}) actual_outputs = lowering.export_to_tf_tensor(mtf_outputs) expected_outputs = common_layers.weights_nonzero(inputs) tf_group = lowering.copy_masters_to_slices() with self.test_session() as sess: sess.run(tf_group) actual, expected = sess.run([actual_outputs, expected_outputs]) self.assertAllEqual(actual, expected) def testDenseReluDense(self): batch = 2 channels = 3 hidden = 5 inputs = tf.random_normal([batch, channels]) graph = mtf.Graph() mesh = mtf.Mesh(graph, "my_mesh") batch_dim = mtf.Dimension("batch", batch) channels_dim = mtf.Dimension("channels", channels) hidden_dim = mtf.Dimension("hidden", hidden) mtf_inputs = mtf.import_tf_tensor( mesh, inputs, shape=mtf.Shape([batch_dim, channels_dim])) mtf_outputs = mtf_layers.dense_relu_dense(mtf_inputs, hidden_channels=hidden_dim) mesh_impl = placement_mesh_impl.PlacementMeshImpl( shape=[], layout={}, devices=[""]) lowering = mtf.Lowering(graph, {mesh: mesh_impl}) actual_outputs = lowering.export_to_tf_tensor(mtf_outputs) tf_group = lowering.copy_masters_to_slices() init = tf.global_variables_initializer() with self.test_session() as sess: sess.run(init) sess.run(tf_group) actual = sess.run(actual_outputs) self.assertEqual(actual.shape, inputs.shape) @parameterized.parameters( (4, 2), ) def testMaskedLocalAttention1D(self, kv_channels, heads): batch = 2 length_q = 16 length_m = 16 channels = 3 query = tf.random_normal([batch, length_q, channels]) memory = tf.random_normal([batch, length_m, channels]) graph = mtf.Graph() mesh = mtf.Mesh(graph, "my_mesh") batch_dim = mtf.Dimension("batch", batch) length_q_dim = mtf.Dimension("length_q", length_q) length_m_dim = mtf.Dimension("length_m", length_m) channels_dim = mtf.Dimension("channels", channels) kv_channels_dim = mtf.Dimension("kv_channels", kv_channels) heads_dim = mtf.Dimension("heads", heads) mtf_query = mtf.import_tf_tensor( mesh, query, shape=mtf.Shape([batch_dim, length_q_dim, channels_dim])) mtf_memory = mtf.import_tf_tensor( mesh, memory, shape=mtf.Shape([batch_dim, length_m_dim, channels_dim])) mtf_outputs = mtf_layers.masked_local_attention_1d( mtf_query, mtf_memory, kv_channels=kv_channels_dim, heads=heads_dim, block_length=2) mesh_impl = placement_mesh_impl.PlacementMeshImpl( shape=[], layout={}, devices=[""]) lowering = mtf.Lowering(graph, {mesh: mesh_impl}) actual_outputs = lowering.export_to_tf_tensor(mtf_outputs) tf_group = lowering.copy_masters_to_slices() init = tf.global_variables_initializer() with self.test_session() as sess: sess.run(init) sess.run(tf_group) actual = sess.run(actual_outputs) self.assertEqual(actual.shape, (batch, length_q, channels)) @parameterized.parameters( (2, 4, 5, 7, 3, 1), ) def testDotProductAttention( self, batch, heads, length_q, length_kv, depth_k, depth_v): query = tf.random_normal([batch, heads, length_q, depth_k]) key = tf.random_normal([batch, heads, length_kv, depth_k]) value = tf.random_normal([batch, heads, length_kv, depth_v]) graph = mtf.Graph() mesh = mtf.Mesh(graph, "my_mesh") batch_dim = mtf.Dimension("batch", batch) heads_dim = mtf.Dimension("heads", heads) length_q_dim = mtf.Dimension("length_q", length_q) length_kv_dim = mtf.Dimension("length_kv", length_kv) depth_k_dim = mtf.Dimension("depth_k", depth_k) depth_v_dim = mtf.Dimension("depth_v", depth_v) mtf_query = mtf.import_tf_tensor( mesh, query, shape=mtf.Shape( [batch_dim, heads_dim, length_q_dim, depth_k_dim])) mtf_key = mtf.import_tf_tensor( mesh, key, shape=mtf.Shape( [batch_dim, heads_dim, length_kv_dim, depth_k_dim])) mtf_value = mtf.import_tf_tensor( mesh, value, shape=mtf.Shape( [batch_dim, heads_dim, length_kv_dim, depth_v_dim])) mtf_outputs = mtf_layers.dot_product_attention( mtf_query, mtf_key, mtf_value, mask=None) mesh_impl = placement_mesh_impl.PlacementMeshImpl( shape=[], layout={}, devices=[""]) lowering = mtf.Lowering(graph, {mesh: mesh_impl}) actual_outputs = lowering.export_to_tf_tensor(mtf_outputs) tf_group = lowering.copy_masters_to_slices() init = tf.global_variables_initializer() with self.test_session() as sess: sess.run(init) sess.run(tf_group) actual = sess.run(actual_outputs) self.assertEqual(actual.shape, (batch, heads, length_q, depth_v)) @parameterized.parameters( (16, 4), (32, 8), ) def testMultiheadAttention(self, kv_channels, heads): batch = 2 length = 8 channels = 3 query = tf.random_normal([batch, length, channels]) graph = mtf.Graph() mesh = mtf.Mesh(graph, "my_mesh") batch_dim = mtf.Dimension("batch", batch) length_dim = mtf.Dimension("length", length) channels_dim = mtf.Dimension("channels", channels) kv_channels_dim = mtf.Dimension("kv_channels", kv_channels) heads_dim = mtf.Dimension("heads", heads) mtf_query = mtf.import_tf_tensor( mesh, query, shape=mtf.Shape([batch_dim, length_dim, channels_dim])) mtf_outputs = mtf_layers.multihead_attention( mtf_query, memory_antecedent=None, mask=None, kv_channels=kv_channels_dim, heads=heads_dim) mesh_impl = placement_mesh_impl.PlacementMeshImpl( shape=[], layout={}, devices=[""]) lowering = mtf.Lowering(graph, {mesh: mesh_impl}) actual_outputs = lowering.export_to_tf_tensor(mtf_outputs) tf_group = lowering.copy_masters_to_slices() init = tf.global_variables_initializer() with self.test_session() as sess: sess.run(init) sess.run(tf_group) actual = sess.run(actual_outputs) self.assertEqual(actual.shape, query.shape) if __name__ == "__main__": tf.test.main()

35.713311

74

0.676223

from __future__ import absolute_import from __future__ import division from __future__ import print_function from absl.testing import parameterized from tensor2tensor.layers import common_layers from tensor2tensor.mesh_tensorflow import mesh_tensorflow as mtf from tensor2tensor.mesh_tensorflow import mtf_layers from tensor2tensor.mesh_tensorflow import placement_mesh_impl import tensorflow as tf class MtfLayersTest(parameterized.TestCase, tf.test.TestCase): @parameterized.parameters( (4, True), (8, False), ) def testDense(self, units, use_bias): batch = 2 channels = 3 inputs = tf.random_normal([batch, channels]) graph = mtf.Graph() mesh = mtf.Mesh(graph, "my_mesh") batch_dim = mtf.Dimension("batch", batch) channels_dim = mtf.Dimension("channels", channels) depth_dim = mtf.Dimension("depth", units) mtf_inputs = mtf.import_tf_tensor( mesh, inputs, shape=mtf.Shape([batch_dim, channels_dim])) mtf_outputs = mtf_layers.dense(mtf_inputs, output_dim=depth_dim, reduced_dims=[channels_dim], activation=mtf.relu, use_bias=use_bias) mesh_impl = placement_mesh_impl.PlacementMeshImpl( shape=[], layout={}, devices=[""]) lowering = mtf.Lowering(graph, {mesh: mesh_impl}) actual_outputs = lowering.export_to_tf_tensor(mtf_outputs) expected_outputs = tf.keras.layers.Dense(units=units, activation=tf.nn.relu, use_bias=use_bias)(inputs) tf_group = lowering.copy_masters_to_slices() init = tf.global_variables_initializer() with self.test_session() as sess: sess.run(init) sess.run(tf_group) actual, expected = sess.run([actual_outputs, expected_outputs]) self.assertEqual(actual.shape, expected.shape) def testLayerNorm(self): batch = 2 channels = 3 inputs = tf.random_normal([batch, channels]) graph = mtf.Graph() mesh = mtf.Mesh(graph, "my_mesh") batch_dim = mtf.Dimension("batch", batch) channels_dim = mtf.Dimension("channels", channels) mtf_inputs = mtf.import_tf_tensor( mesh, inputs, shape=mtf.Shape([batch_dim, channels_dim])) mtf_outputs = mtf_layers.layer_norm(mtf_inputs, dim=channels_dim) mesh_impl = placement_mesh_impl.PlacementMeshImpl( shape=[], layout={}, devices=[""]) lowering = mtf.Lowering(graph, {mesh: mesh_impl}) actual_outputs = lowering.export_to_tf_tensor(mtf_outputs) expected_outputs = common_layers.layer_norm(inputs) tf_group = lowering.copy_masters_to_slices() init = tf.global_variables_initializer() with self.test_session() as sess: sess.run(init) sess.run(tf_group) actual, expected = sess.run([actual_outputs, expected_outputs]) self.assertEqual(actual.shape, expected.shape) def testWeightsNonzero(self): inputs = tf.constant([[3, 1, 0], [1, 0, 0]]) graph = mtf.Graph() mesh = mtf.Mesh(graph, "my_mesh") batch_dim = mtf.Dimension("batch", inputs.shape.as_list()[0]) channels_dim = mtf.Dimension("channels", inputs.shape.as_list()[1]) mtf_inputs = mtf.import_tf_tensor( mesh, inputs, shape=mtf.Shape([batch_dim, channels_dim])) mtf_outputs = mtf_layers.weights_nonzero(mtf_inputs) mesh_impl = placement_mesh_impl.PlacementMeshImpl( shape=[], layout={}, devices=[""]) lowering = mtf.Lowering(graph, {mesh: mesh_impl}) actual_outputs = lowering.export_to_tf_tensor(mtf_outputs) expected_outputs = common_layers.weights_nonzero(inputs) tf_group = lowering.copy_masters_to_slices() with self.test_session() as sess: sess.run(tf_group) actual, expected = sess.run([actual_outputs, expected_outputs]) self.assertAllEqual(actual, expected) def testDenseReluDense(self): batch = 2 channels = 3 hidden = 5 inputs = tf.random_normal([batch, channels]) graph = mtf.Graph() mesh = mtf.Mesh(graph, "my_mesh") batch_dim = mtf.Dimension("batch", batch) channels_dim = mtf.Dimension("channels", channels) hidden_dim = mtf.Dimension("hidden", hidden) mtf_inputs = mtf.import_tf_tensor( mesh, inputs, shape=mtf.Shape([batch_dim, channels_dim])) mtf_outputs = mtf_layers.dense_relu_dense(mtf_inputs, hidden_channels=hidden_dim) mesh_impl = placement_mesh_impl.PlacementMeshImpl( shape=[], layout={}, devices=[""]) lowering = mtf.Lowering(graph, {mesh: mesh_impl}) actual_outputs = lowering.export_to_tf_tensor(mtf_outputs) tf_group = lowering.copy_masters_to_slices() init = tf.global_variables_initializer() with self.test_session() as sess: sess.run(init) sess.run(tf_group) actual = sess.run(actual_outputs) self.assertEqual(actual.shape, inputs.shape) @parameterized.parameters( (4, 2), ) def testMaskedLocalAttention1D(self, kv_channels, heads): batch = 2 length_q = 16 length_m = 16 channels = 3 query = tf.random_normal([batch, length_q, channels]) memory = tf.random_normal([batch, length_m, channels]) graph = mtf.Graph() mesh = mtf.Mesh(graph, "my_mesh") batch_dim = mtf.Dimension("batch", batch) length_q_dim = mtf.Dimension("length_q", length_q) length_m_dim = mtf.Dimension("length_m", length_m) channels_dim = mtf.Dimension("channels", channels) kv_channels_dim = mtf.Dimension("kv_channels", kv_channels) heads_dim = mtf.Dimension("heads", heads) mtf_query = mtf.import_tf_tensor( mesh, query, shape=mtf.Shape([batch_dim, length_q_dim, channels_dim])) mtf_memory = mtf.import_tf_tensor( mesh, memory, shape=mtf.Shape([batch_dim, length_m_dim, channels_dim])) mtf_outputs = mtf_layers.masked_local_attention_1d( mtf_query, mtf_memory, kv_channels=kv_channels_dim, heads=heads_dim, block_length=2) mesh_impl = placement_mesh_impl.PlacementMeshImpl( shape=[], layout={}, devices=[""]) lowering = mtf.Lowering(graph, {mesh: mesh_impl}) actual_outputs = lowering.export_to_tf_tensor(mtf_outputs) tf_group = lowering.copy_masters_to_slices() init = tf.global_variables_initializer() with self.test_session() as sess: sess.run(init) sess.run(tf_group) actual = sess.run(actual_outputs) self.assertEqual(actual.shape, (batch, length_q, channels)) @parameterized.parameters( (2, 4, 5, 7, 3, 1), ) def testDotProductAttention( self, batch, heads, length_q, length_kv, depth_k, depth_v): query = tf.random_normal([batch, heads, length_q, depth_k]) key = tf.random_normal([batch, heads, length_kv, depth_k]) value = tf.random_normal([batch, heads, length_kv, depth_v]) graph = mtf.Graph() mesh = mtf.Mesh(graph, "my_mesh") batch_dim = mtf.Dimension("batch", batch) heads_dim = mtf.Dimension("heads", heads) length_q_dim = mtf.Dimension("length_q", length_q) length_kv_dim = mtf.Dimension("length_kv", length_kv) depth_k_dim = mtf.Dimension("depth_k", depth_k) depth_v_dim = mtf.Dimension("depth_v", depth_v) mtf_query = mtf.import_tf_tensor( mesh, query, shape=mtf.Shape( [batch_dim, heads_dim, length_q_dim, depth_k_dim])) mtf_key = mtf.import_tf_tensor( mesh, key, shape=mtf.Shape( [batch_dim, heads_dim, length_kv_dim, depth_k_dim])) mtf_value = mtf.import_tf_tensor( mesh, value, shape=mtf.Shape( [batch_dim, heads_dim, length_kv_dim, depth_v_dim])) mtf_outputs = mtf_layers.dot_product_attention( mtf_query, mtf_key, mtf_value, mask=None) mesh_impl = placement_mesh_impl.PlacementMeshImpl( shape=[], layout={}, devices=[""]) lowering = mtf.Lowering(graph, {mesh: mesh_impl}) actual_outputs = lowering.export_to_tf_tensor(mtf_outputs) tf_group = lowering.copy_masters_to_slices() init = tf.global_variables_initializer() with self.test_session() as sess: sess.run(init) sess.run(tf_group) actual = sess.run(actual_outputs) self.assertEqual(actual.shape, (batch, heads, length_q, depth_v)) @parameterized.parameters( (16, 4), (32, 8), ) def testMultiheadAttention(self, kv_channels, heads): batch = 2 length = 8 channels = 3 query = tf.random_normal([batch, length, channels]) graph = mtf.Graph() mesh = mtf.Mesh(graph, "my_mesh") batch_dim = mtf.Dimension("batch", batch) length_dim = mtf.Dimension("length", length) channels_dim = mtf.Dimension("channels", channels) kv_channels_dim = mtf.Dimension("kv_channels", kv_channels) heads_dim = mtf.Dimension("heads", heads) mtf_query = mtf.import_tf_tensor( mesh, query, shape=mtf.Shape([batch_dim, length_dim, channels_dim])) mtf_outputs = mtf_layers.multihead_attention( mtf_query, memory_antecedent=None, mask=None, kv_channels=kv_channels_dim, heads=heads_dim) mesh_impl = placement_mesh_impl.PlacementMeshImpl( shape=[], layout={}, devices=[""]) lowering = mtf.Lowering(graph, {mesh: mesh_impl}) actual_outputs = lowering.export_to_tf_tensor(mtf_outputs) tf_group = lowering.copy_masters_to_slices() init = tf.global_variables_initializer() with self.test_session() as sess: sess.run(init) sess.run(tf_group) actual = sess.run(actual_outputs) self.assertEqual(actual.shape, query.shape) if __name__ == "__main__": tf.test.main()

true

1c37d5112ebf0b75c7c97a7889e7af24572cb0ae

13,851

py

Python

examples/domain_adaptation/digits/mdd.py

wuaodi/Transfer-Learning-Library

29a946143e63b66a1da9ffa685bfe95f5640028a

[ "MIT" ]

1

2021-04-08T00:13:13.000Z

examples/domain_adaptation/digits/mdd.py

wuaodi/Transfer-Learning-Library

29a946143e63b66a1da9ffa685bfe95f5640028a

[ "MIT" ]

null

examples/domain_adaptation/digits/mdd.py

wuaodi/Transfer-Learning-Library

29a946143e63b66a1da9ffa685bfe95f5640028a

[ "MIT" ]

null

import random import time import warnings import sys import argparse import shutil import os.path as osp import torch import torch.nn as nn import torch.backends.cudnn as cudnn from torch.optim import Adam from torch.optim.lr_scheduler import LambdaLR from torch.utils.data import DataLoader import torchvision.transforms as T import torch.nn.functional as F sys.path.append('../../..') from dalib.adaptation.mdd import ClassificationMarginDisparityDiscrepancy\ as MarginDisparityDiscrepancy, GeneralModule import common.vision.datasets.digits as datasets import common.vision.models.digits as models from common.vision.transforms import ResizeImage from common.utils.data import ForeverDataIterator from common.utils.metric import accuracy, ConfusionMatrix from common.utils.meter import AverageMeter, ProgressMeter from common.utils.logger import CompleteLogger from common.utils.analysis import collect_feature, tsne, a_distance device = torch.device("cuda" if torch.cuda.is_available() else "cpu") def main(args: argparse.Namespace): logger = CompleteLogger(args.log, args.phase) if args.seed is not None: random.seed(args.seed) torch.manual_seed(args.seed) cudnn.deterministic = True warnings.warn('You have chosen to seed training. ' 'This will turn on the CUDNN deterministic setting, ' 'which can slow down your training considerably! ' 'You may see unexpected behavior when restarting ' 'from checkpoints.') cudnn.benchmark = True # Data loading code if args.num_channels == 3: mode = 'RGB' mean = std = [0.5, 0.5, 0.5] else: mode = 'L' mean = std = [0.5, ] normalize = T.Normalize(mean=mean, std=std) train_transform = T.Compose([ ResizeImage(args.image_size), # T.RandomRotation(10), # TODO need results T.ToTensor(), normalize ]) val_transform = T.Compose([ ResizeImage(args.image_size), T.ToTensor(), normalize ]) source_dataset = datasets.__dict__[args.source] train_source_dataset = source_dataset(root=args.source_root, mode=mode, download=True, transform=train_transform) train_source_loader = DataLoader(train_source_dataset, batch_size=args.batch_size, shuffle=True, num_workers=args.workers, drop_last=True) target_dataset = datasets.__dict__[args.target] train_target_dataset = target_dataset(root=args.target_root, mode=mode, download=True, transform=train_transform) train_target_loader = DataLoader(train_target_dataset, batch_size=args.batch_size, shuffle=True, num_workers=args.workers, drop_last=True) val_dataset = target_dataset(root=args.target_root, mode=mode, split='test', download=True, transform=val_transform) val_loader = DataLoader(val_dataset, batch_size=args.batch_size, shuffle=False, num_workers=args.workers) train_source_iter = ForeverDataIterator(train_source_loader) train_target_iter = ForeverDataIterator(train_target_loader) # create model print("=> using pre-trained model '{}'".format(args.arch)) arch = models.__dict__[args.arch]() bottleneck = nn.Sequential( nn.Flatten(), nn.Linear(arch.bottleneck_dim, arch.bottleneck_dim), nn.BatchNorm1d(arch.bottleneck_dim), nn.ReLU(), nn.Dropout(0.5) ) head = arch.head() adv_head = arch.head() classifier = GeneralModule(arch.backbone(), arch.num_classes, bottleneck, head, adv_head, finetune=False) mdd = MarginDisparityDiscrepancy(args.margin).to(device) # define optimizer and lr scheduler optimizer = Adam(classifier.get_parameters(), args.lr, betas=args.betas, weight_decay=args.wd) lr_scheduler = LambdaLR(optimizer, lambda x: args.lr * (1. + args.lr_gamma * float(x)) ** (-args.lr_decay)) # resume from the best checkpoint if args.phase != 'train': checkpoint = torch.load(logger.get_checkpoint_path('best'), map_location='cpu') classifier.load_state_dict(checkpoint) # analysis the model if args.phase == 'analysis': # extract features from both domains feature_extractor = torch.nn.Sequential(classifier.backbone, classifier.bottleneck).to(device) source_feature = collect_feature(train_source_loader, feature_extractor, device, 10) target_feature = collect_feature(val_loader, feature_extractor, device, 10) # plot t-SNE tSNE_filename = osp.join(logger.visualize_directory, 'TSNE.png') tsne.visualize(source_feature, target_feature, tSNE_filename) print("Saving t-SNE to", tSNE_filename) # calculate A-distance, which is a measure for distribution discrepancy A_distance = a_distance.calculate(source_feature, target_feature, device) print("A-distance =", A_distance) return if args.phase == 'test': acc1 = validate(val_loader, classifier, args) print(acc1) return # start training best_acc1 = 0. for epoch in range(args.epochs): print(lr_scheduler.get_lr()) # train for one epoch train(train_source_iter, train_target_iter, classifier, mdd, optimizer, lr_scheduler, epoch, args) # evaluate on validation set acc1 = validate(val_loader, classifier, args) # remember best acc@1 and save checkpoint torch.save(classifier.state_dict(), logger.get_checkpoint_path('latest')) if acc1 > best_acc1: shutil.copy(logger.get_checkpoint_path('latest'), logger.get_checkpoint_path('best')) best_acc1 = max(acc1, best_acc1) print("best_acc1 = {:3.1f}".format(best_acc1)) logger.close() def train(train_source_iter: ForeverDataIterator, train_target_iter: ForeverDataIterator, model, mdd: MarginDisparityDiscrepancy, optimizer: Adam, lr_scheduler: LambdaLR, epoch: int, args: argparse.Namespace): batch_time = AverageMeter('Time', ':4.2f') data_time = AverageMeter('Data', ':3.1f') losses = AverageMeter('Loss', ':3.2f') trans_losses = AverageMeter('Trans Loss', ':3.2f') cls_accs = AverageMeter('Cls Acc', ':3.1f') tgt_accs = AverageMeter('Tgt Acc', ':3.1f') cls_adv_accs = AverageMeter('Cls Adv Acc', ':3.1f') tgt_adv_accs = AverageMeter('Tgt Adv Acc', ':3.1f') progress = ProgressMeter( args.iters_per_epoch, [batch_time, data_time, losses, trans_losses, cls_accs, tgt_accs, cls_adv_accs, tgt_adv_accs], prefix="Epoch: [{}]".format(epoch)) # switch to train mode model.train() mdd.train() end = time.time() for i in range(args.iters_per_epoch): x_s, labels_s = next(train_source_iter) x_t, labels_t = next(train_target_iter) x_s = x_s.to(device) x_t = x_t.to(device) labels_s = labels_s.to(device) labels_t = labels_t.to(device) # measure data loading time data_time.update(time.time() - end) # compute output x = torch.cat((x_s, x_t), dim=0) outputs, outputs_adv = model(x) y_s, y_t = outputs.chunk(2, dim=0) y_s_adv, y_t_adv = outputs_adv.chunk(2, dim=0) # compute cross entropy loss on source domain cls_loss = F.cross_entropy(y_s, labels_s) # compute margin disparity discrepancy between domains # for adversarial classifier, minimize negative mdd is equal to maximize mdd transfer_loss = -mdd(y_s, y_s_adv, y_t, y_t_adv) loss = cls_loss + transfer_loss * args.trade_off model.step() cls_acc = accuracy(y_s, labels_s)[0] tgt_acc = accuracy(y_t, labels_t)[0] cls_adv_acc = accuracy(y_s_adv, labels_s)[0] tgt_adv_acc = accuracy(y_t_adv, labels_t)[0] losses.update(loss.item(), x_s.size(0)) cls_accs.update(cls_acc.item(), x_s.size(0)) tgt_accs.update(tgt_acc.item(), x_t.size(0)) cls_adv_accs.update(cls_adv_acc.item(), x_s.size(0)) tgt_adv_accs.update(tgt_adv_acc.item(), x_t.size(0)) trans_losses.update(transfer_loss.item(), x_s.size(0)) # compute gradient and do SGD step optimizer.zero_grad() loss.backward() optimizer.step() # lr_scheduler.step() # measure elapsed time batch_time.update(time.time() - end) end = time.time() if i % args.print_freq == 0: progress.display(i) def validate(val_loader: DataLoader, model, args: argparse.Namespace) -> float: batch_time = AverageMeter('Time', ':6.3f') losses = AverageMeter('Loss', ':.4e') top1 = AverageMeter('Acc@1', ':6.2f') top5 = AverageMeter('Acc@5', ':6.2f') progress = ProgressMeter( len(val_loader), [batch_time, losses, top1, top5], prefix='Test: ') # switch to evaluate mode model.eval() if args.per_class_eval: classes = val_loader.dataset.classes confmat = ConfusionMatrix(len(classes)) else: confmat = None with torch.no_grad(): end = time.time() for i, (images, target) in enumerate(val_loader): images = images.to(device) target = target.to(device) # compute output output, _ = model(images) loss = F.cross_entropy(output, target) # measure accuracy and record loss acc1, acc5 = accuracy(output, target, topk=(1, 5)) if confmat: confmat.update(target, output.argmax(1)) losses.update(loss.item(), images.size(0)) top1.update(acc1.item(), images.size(0)) top5.update(acc5.item(), images.size(0)) # measure elapsed time batch_time.update(time.time() - end) end = time.time() if i % args.print_freq == 0: progress.display(i) print(' * Acc@1 {top1.avg:.3f} Acc@5 {top5.avg:.3f}' .format(top1=top1, top5=top5)) if confmat: print(confmat.format(classes)) return top1.avg if __name__ == '__main__': architecture_names = sorted( name for name in models.__dict__ if name.islower() and not name.startswith("__") and callable(models.__dict__[name]) ) dataset_names = sorted( name for name in datasets.__dict__ if not name.startswith("__") and callable(datasets.__dict__[name]) ) parser = argparse.ArgumentParser(description='Source Only for Unsupervised Domain Adaptation') # dataset parameters parser.add_argument('source_root', help='root path of the source dataset') parser.add_argument('target_root', help='root path of the target dataset') parser.add_argument('-s', '--source', help='source domain(s)') parser.add_argument('-t', '--target', help='target domain(s)') parser.add_argument('--image-size', type=int, default=28, help='the size of input image') parser.add_argument('--num-channels', default=1, choices=[1, 3], type=int, help='the number of image channels') # model parameters parser.add_argument('-a', '--arch', metavar='ARCH', default='lenet', choices=architecture_names, help='backbone architecture: ' + ' | '.join(architecture_names) + ' (default: lenet)') parser.add_argument('--margin', type=float, default=4., help="margin gamma") parser.add_argument('--trade-off', default=1., type=float, help='the trade-off hyper-parameter for transfer loss') # training parameters parser.add_argument('-b', '--batch-size', default=128, type=int, metavar='N', help='mini-batch size (default: 32)') parser.add_argument('--lr', '--learning-rate', default=1e-4, type=float, metavar='LR', help='initial learning rate', dest='lr') parser.add_argument('--lr-gamma', default=0.0002, type=float) parser.add_argument('--lr-decay', default=0.75, type=float, help='parameter for lr scheduler') parser.add_argument('--betas', default=(0.9, 0.999), nargs='+', help='betas') parser.add_argument('--wd', '--weight-decay', default=0.0, type=float, metavar='W', help='weight decay (default: 5e-4)') parser.add_argument('-j', '--workers', default=2, type=int, metavar='N', help='number of data loading workers (default: 4)') parser.add_argument('--epochs', default=100, type=int, metavar='N', help='number of total epochs to run') parser.add_argument('-i', '--iters-per-epoch', default=500, type=int, help='Number of iterations per epoch') parser.add_argument('-p', '--print-freq', default=100, type=int, metavar='N', help='print frequency (default: 100)') parser.add_argument('--seed', default=None, type=int, help='seed for initializing training. ') parser.add_argument('--per-class-eval', action='store_true', help='whether output per-class accuracy during evaluation') parser.add_argument("--log", type=str, default='mdd', help="Where to save logs, checkpoints and debugging images.") parser.add_argument("--phase", type=str, default='train', choices=['train', 'test', 'analysis'], help="When phase is 'test', only test the model." "When phase is 'analysis', only analysis the model.") args = parser.parse_args() print(args) main(args)

41.10089

120

0.637571

import random import time import warnings import sys import argparse import shutil import os.path as osp import torch import torch.nn as nn import torch.backends.cudnn as cudnn from torch.optim import Adam from torch.optim.lr_scheduler import LambdaLR from torch.utils.data import DataLoader import torchvision.transforms as T import torch.nn.functional as F sys.path.append('../../..') from dalib.adaptation.mdd import ClassificationMarginDisparityDiscrepancy\ as MarginDisparityDiscrepancy, GeneralModule import common.vision.datasets.digits as datasets import common.vision.models.digits as models from common.vision.transforms import ResizeImage from common.utils.data import ForeverDataIterator from common.utils.metric import accuracy, ConfusionMatrix from common.utils.meter import AverageMeter, ProgressMeter from common.utils.logger import CompleteLogger from common.utils.analysis import collect_feature, tsne, a_distance device = torch.device("cuda" if torch.cuda.is_available() else "cpu") def main(args: argparse.Namespace): logger = CompleteLogger(args.log, args.phase) if args.seed is not None: random.seed(args.seed) torch.manual_seed(args.seed) cudnn.deterministic = True warnings.warn('You have chosen to seed training. ' 'This will turn on the CUDNN deterministic setting, ' 'which can slow down your training considerably! ' 'You may see unexpected behavior when restarting ' 'from checkpoints.') cudnn.benchmark = True if args.num_channels == 3: mode = 'RGB' mean = std = [0.5, 0.5, 0.5] else: mode = 'L' mean = std = [0.5, ] normalize = T.Normalize(mean=mean, std=std) train_transform = T.Compose([ ResizeImage(args.image_size), (), normalize ]) val_transform = T.Compose([ ResizeImage(args.image_size), T.ToTensor(), normalize ]) source_dataset = datasets.__dict__[args.source] train_source_dataset = source_dataset(root=args.source_root, mode=mode, download=True, transform=train_transform) train_source_loader = DataLoader(train_source_dataset, batch_size=args.batch_size, shuffle=True, num_workers=args.workers, drop_last=True) target_dataset = datasets.__dict__[args.target] train_target_dataset = target_dataset(root=args.target_root, mode=mode, download=True, transform=train_transform) train_target_loader = DataLoader(train_target_dataset, batch_size=args.batch_size, shuffle=True, num_workers=args.workers, drop_last=True) val_dataset = target_dataset(root=args.target_root, mode=mode, split='test', download=True, transform=val_transform) val_loader = DataLoader(val_dataset, batch_size=args.batch_size, shuffle=False, num_workers=args.workers) train_source_iter = ForeverDataIterator(train_source_loader) train_target_iter = ForeverDataIterator(train_target_loader) print("=> using pre-trained model '{}'".format(args.arch)) arch = models.__dict__[args.arch]() bottleneck = nn.Sequential( nn.Flatten(), nn.Linear(arch.bottleneck_dim, arch.bottleneck_dim), nn.BatchNorm1d(arch.bottleneck_dim), nn.ReLU(), nn.Dropout(0.5) ) head = arch.head() adv_head = arch.head() classifier = GeneralModule(arch.backbone(), arch.num_classes, bottleneck, head, adv_head, finetune=False) mdd = MarginDisparityDiscrepancy(args.margin).to(device) optimizer = Adam(classifier.get_parameters(), args.lr, betas=args.betas, weight_decay=args.wd) lr_scheduler = LambdaLR(optimizer, lambda x: args.lr * (1. + args.lr_gamma * float(x)) ** (-args.lr_decay)) if args.phase != 'train': checkpoint = torch.load(logger.get_checkpoint_path('best'), map_location='cpu') classifier.load_state_dict(checkpoint) if args.phase == 'analysis': feature_extractor = torch.nn.Sequential(classifier.backbone, classifier.bottleneck).to(device) source_feature = collect_feature(train_source_loader, feature_extractor, device, 10) target_feature = collect_feature(val_loader, feature_extractor, device, 10) tSNE_filename = osp.join(logger.visualize_directory, 'TSNE.png') tsne.visualize(source_feature, target_feature, tSNE_filename) print("Saving t-SNE to", tSNE_filename) A_distance = a_distance.calculate(source_feature, target_feature, device) print("A-distance =", A_distance) return if args.phase == 'test': acc1 = validate(val_loader, classifier, args) print(acc1) return best_acc1 = 0. for epoch in range(args.epochs): print(lr_scheduler.get_lr()) train(train_source_iter, train_target_iter, classifier, mdd, optimizer, lr_scheduler, epoch, args) acc1 = validate(val_loader, classifier, args) torch.save(classifier.state_dict(), logger.get_checkpoint_path('latest')) if acc1 > best_acc1: shutil.copy(logger.get_checkpoint_path('latest'), logger.get_checkpoint_path('best')) best_acc1 = max(acc1, best_acc1) print("best_acc1 = {:3.1f}".format(best_acc1)) logger.close() def train(train_source_iter: ForeverDataIterator, train_target_iter: ForeverDataIterator, model, mdd: MarginDisparityDiscrepancy, optimizer: Adam, lr_scheduler: LambdaLR, epoch: int, args: argparse.Namespace): batch_time = AverageMeter('Time', ':4.2f') data_time = AverageMeter('Data', ':3.1f') losses = AverageMeter('Loss', ':3.2f') trans_losses = AverageMeter('Trans Loss', ':3.2f') cls_accs = AverageMeter('Cls Acc', ':3.1f') tgt_accs = AverageMeter('Tgt Acc', ':3.1f') cls_adv_accs = AverageMeter('Cls Adv Acc', ':3.1f') tgt_adv_accs = AverageMeter('Tgt Adv Acc', ':3.1f') progress = ProgressMeter( args.iters_per_epoch, [batch_time, data_time, losses, trans_losses, cls_accs, tgt_accs, cls_adv_accs, tgt_adv_accs], prefix="Epoch: [{}]".format(epoch)) model.train() mdd.train() end = time.time() for i in range(args.iters_per_epoch): x_s, labels_s = next(train_source_iter) x_t, labels_t = next(train_target_iter) x_s = x_s.to(device) x_t = x_t.to(device) labels_s = labels_s.to(device) labels_t = labels_t.to(device) data_time.update(time.time() - end) x = torch.cat((x_s, x_t), dim=0) outputs, outputs_adv = model(x) y_s, y_t = outputs.chunk(2, dim=0) y_s_adv, y_t_adv = outputs_adv.chunk(2, dim=0) cls_loss = F.cross_entropy(y_s, labels_s) transfer_loss = -mdd(y_s, y_s_adv, y_t, y_t_adv) loss = cls_loss + transfer_loss * args.trade_off model.step() cls_acc = accuracy(y_s, labels_s)[0] tgt_acc = accuracy(y_t, labels_t)[0] cls_adv_acc = accuracy(y_s_adv, labels_s)[0] tgt_adv_acc = accuracy(y_t_adv, labels_t)[0] losses.update(loss.item(), x_s.size(0)) cls_accs.update(cls_acc.item(), x_s.size(0)) tgt_accs.update(tgt_acc.item(), x_t.size(0)) cls_adv_accs.update(cls_adv_acc.item(), x_s.size(0)) tgt_adv_accs.update(tgt_adv_acc.item(), x_t.size(0)) trans_losses.update(transfer_loss.item(), x_s.size(0)) optimizer.zero_grad() loss.backward() optimizer.step() batch_time.update(time.time() - end) end = time.time() if i % args.print_freq == 0: progress.display(i) def validate(val_loader: DataLoader, model, args: argparse.Namespace) -> float: batch_time = AverageMeter('Time', ':6.3f') losses = AverageMeter('Loss', ':.4e') top1 = AverageMeter('Acc@1', ':6.2f') top5 = AverageMeter('Acc@5', ':6.2f') progress = ProgressMeter( len(val_loader), [batch_time, losses, top1, top5], prefix='Test: ') model.eval() if args.per_class_eval: classes = val_loader.dataset.classes confmat = ConfusionMatrix(len(classes)) else: confmat = None with torch.no_grad(): end = time.time() for i, (images, target) in enumerate(val_loader): images = images.to(device) target = target.to(device) output, _ = model(images) loss = F.cross_entropy(output, target) acc1, acc5 = accuracy(output, target, topk=(1, 5)) if confmat: confmat.update(target, output.argmax(1)) losses.update(loss.item(), images.size(0)) top1.update(acc1.item(), images.size(0)) top5.update(acc5.item(), images.size(0)) batch_time.update(time.time() - end) end = time.time() if i % args.print_freq == 0: progress.display(i) print(' * Acc@1 {top1.avg:.3f} Acc@5 {top5.avg:.3f}' .format(top1=top1, top5=top5)) if confmat: print(confmat.format(classes)) return top1.avg if __name__ == '__main__': architecture_names = sorted( name for name in models.__dict__ if name.islower() and not name.startswith("__") and callable(models.__dict__[name]) ) dataset_names = sorted( name for name in datasets.__dict__ if not name.startswith("__") and callable(datasets.__dict__[name]) ) parser = argparse.ArgumentParser(description='Source Only for Unsupervised Domain Adaptation') parser.add_argument('source_root', help='root path of the source dataset') parser.add_argument('target_root', help='root path of the target dataset') parser.add_argument('-s', '--source', help='source domain(s)') parser.add_argument('-t', '--target', help='target domain(s)') parser.add_argument('--image-size', type=int, default=28, help='the size of input image') parser.add_argument('--num-channels', default=1, choices=[1, 3], type=int, help='the number of image channels') parser.add_argument('-a', '--arch', metavar='ARCH', default='lenet', choices=architecture_names, help='backbone architecture: ' + ' | '.join(architecture_names) + ' (default: lenet)') parser.add_argument('--margin', type=float, default=4., help="margin gamma") parser.add_argument('--trade-off', default=1., type=float, help='the trade-off hyper-parameter for transfer loss') parser.add_argument('-b', '--batch-size', default=128, type=int, metavar='N', help='mini-batch size (default: 32)') parser.add_argument('--lr', '--learning-rate', default=1e-4, type=float, metavar='LR', help='initial learning rate', dest='lr') parser.add_argument('--lr-gamma', default=0.0002, type=float) parser.add_argument('--lr-decay', default=0.75, type=float, help='parameter for lr scheduler') parser.add_argument('--betas', default=(0.9, 0.999), nargs='+', help='betas') parser.add_argument('--wd', '--weight-decay', default=0.0, type=float, metavar='W', help='weight decay (default: 5e-4)') parser.add_argument('-j', '--workers', default=2, type=int, metavar='N', help='number of data loading workers (default: 4)') parser.add_argument('--epochs', default=100, type=int, metavar='N', help='number of total epochs to run') parser.add_argument('-i', '--iters-per-epoch', default=500, type=int, help='Number of iterations per epoch') parser.add_argument('-p', '--print-freq', default=100, type=int, metavar='N', help='print frequency (default: 100)') parser.add_argument('--seed', default=None, type=int, help='seed for initializing training. ') parser.add_argument('--per-class-eval', action='store_true', help='whether output per-class accuracy during evaluation') parser.add_argument("--log", type=str, default='mdd', help="Where to save logs, checkpoints and debugging images.") parser.add_argument("--phase", type=str, default='train', choices=['train', 'test', 'analysis'], help="When phase is 'test', only test the model." "When phase is 'analysis', only analysis the model.") args = parser.parse_args() print(args) main(args)

true

1c37d528349c0e2504d4cd5a8e297d8f7bf3ae0e

894

py

Python

lib/btc.py

PiDisplay/PiDisplay

b0365ef76e24e7661ba5dcae48dcbb7262c3a57a

[ "MIT" ]

3

2021-06-01T18:51:04.000Z

2021-06-02T00:40:09.000Z

lib/btc.py

PiDisplay/PiDisplay

b0365ef76e24e7661ba5dcae48dcbb7262c3a57a

[ "MIT" ]

null

lib/btc.py

PiDisplay/PiDisplay

b0365ef76e24e7661ba5dcae48dcbb7262c3a57a

[ "MIT" ]

2

2021-06-01T19:07:24.000Z

2021-06-01T19:34:00.000Z

# A few basic helper functions for interfacing with Bitcoin Core from os import getenv from bitcoinrpc.authproxy import AuthServiceProxy, JSONRPCException class BtcRPC: def __init__(self): btcurl = "http://%s:%s@%s:%s" % (getenv('BITCOIN_RPC_USER'), getenv( 'BITCOIN_RPC_PASS'), getenv('BITCOIN_IP'), getenv('BITCOIN_RPC_PORT')) self.connection = AuthServiceProxy(btcurl) def connection_locked(self): try: self.get_blockchain_info() return True except JSONRPCException: return False def get_blockchain_info(self): response = self.connection.getblockchaininfo() return response def get_sync_progress(self): response = self.connection.getblockchaininfo() return response["verificationprogress"] * 100 def get_connection(self): return self.connection

30.827586

82

0.674497

from os import getenv from bitcoinrpc.authproxy import AuthServiceProxy, JSONRPCException class BtcRPC: def __init__(self): btcurl = "http://%s:%s@%s:%s" % (getenv('BITCOIN_RPC_USER'), getenv( 'BITCOIN_RPC_PASS'), getenv('BITCOIN_IP'), getenv('BITCOIN_RPC_PORT')) self.connection = AuthServiceProxy(btcurl) def connection_locked(self): try: self.get_blockchain_info() return True except JSONRPCException: return False def get_blockchain_info(self): response = self.connection.getblockchaininfo() return response def get_sync_progress(self): response = self.connection.getblockchaininfo() return response["verificationprogress"] * 100 def get_connection(self): return self.connection

true

1c37d6d656cce791bd21776a49639d19298b0aaa

245

py

Python

BI-IOS/semester-project/webapp/beecon/campaigns/libs/cinemas/DataSource.py

josefdolezal/fit-cvut

6b6abea4232b946246d33290718d6c5007926b63

[ "MIT" ]

20

2016-05-15T10:39:53.000Z

2022-03-29T00:06:06.000Z

BI-IOS/semester-project/webapp/beecon/campaigns/libs/cinemas/DataSource.py

josefdolezal/fit-cvut

6b6abea4232b946246d33290718d6c5007926b63

[ "MIT" ]

3

2017-05-27T16:44:01.000Z

2019-01-02T21:02:59.000Z

BI-IOS/semester-project/webapp/beecon/campaigns/libs/cinemas/DataSource.py

josefdolezal/fit-cvut

6b6abea4232b946246d33290718d6c5007926b63

[ "MIT" ]

11

2018-08-22T21:16:32.000Z

2021-04-10T22:42:34.000Z

import requests class CinemaCity: def __init__( self, url ): self._compile_url( url ) def movie_schedule( self ): content = requests.get( self.url ).content return content def _compile_url( self, url ): self.url = url

16.333333

46

0.669388

import requests class CinemaCity: def __init__( self, url ): self._compile_url( url ) def movie_schedule( self ): content = requests.get( self.url ).content return content def _compile_url( self, url ): self.url = url

true

1c37d8ccc994b2bd1d945e8270c804c1e0702049

182

py

Python

0-notes/job-search/Cracking the Coding Interview/C14Databases/questions/14.6-question.py

eengineergz/Lambda

1fe511f7ef550aed998b75c18a432abf6ab41c5f

[ "MIT" ]

null

0-notes/job-search/Cracking the Coding Interview/C14Databases/questions/14.6-question.py

eengineergz/Lambda

1fe511f7ef550aed998b75c18a432abf6ab41c5f

[ "MIT" ]

null

0-notes/job-search/Cracking the Coding Interview/C14Databases/questions/14.6-question.py

eengineergz/Lambda

1fe511f7ef550aed998b75c18a432abf6ab41c5f

[ "MIT" ]

null

# 14.6 Entity-Relationship Diagram # Draw an entity-relationship diagram for database with companies, people, and professionals. # professionals = people who work for companies

36.4

93

0.785714

true

1c37da71d4bd83672d5725fe6e8e0b080dc05d5f

20,444

py

Python

monai/engines/evaluator.py

themantalope/MONAI

9378e52b9c2283fa71cf8572b08f274071753053

[ "Apache-2.0" ]

3

2020-07-02T18:39:36.000Z

2021-06-16T09:35:53.000Z

monai/engines/evaluator.py

themantalope/MONAI

9378e52b9c2283fa71cf8572b08f274071753053

[ "Apache-2.0" ]

28

2020-06-26T12:47:52.000Z

2020-09-08T00:33:42.000Z

monai/engines/evaluator.py

Nic-Ma/MONAI

f398298b5aadc076102261a687a158f6ac17ad1c

[ "Apache-2.0" ]

null

# Copyright 2020 - 2021 MONAI Consortium # 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 TYPE_CHECKING, Callable, Dict, Iterable, List, Optional, Sequence, Tuple, Union import torch from torch.utils.data import DataLoader from monai.config import IgniteInfo from monai.engines.utils import IterationEvents, default_metric_cmp_fn, default_prepare_batch from monai.engines.workflow import Workflow from monai.inferers import Inferer, SimpleInferer from monai.networks.utils import eval_mode, train_mode from monai.transforms import Transform from monai.utils import ForwardMode, ensure_tuple, min_version, optional_import from monai.utils.enums import CommonKeys as Keys from monai.utils.module import look_up_option if TYPE_CHECKING: from ignite.engine import Engine, EventEnum from ignite.metrics import Metric else: Engine, _ = optional_import("ignite.engine", IgniteInfo.OPT_IMPORT_VERSION, min_version, "Engine") Metric, _ = optional_import("ignite.metrics", IgniteInfo.OPT_IMPORT_VERSION, min_version, "Metric") EventEnum, _ = optional_import("ignite.engine", IgniteInfo.OPT_IMPORT_VERSION, min_version, "EventEnum") __all__ = ["Evaluator", "SupervisedEvaluator", "EnsembleEvaluator"] class Evaluator(Workflow): """ Base class for all kinds of evaluators, inherits from Workflow. Args: device: an object representing the device on which to run. val_data_loader: Ignite engine use data_loader to run, must be Iterable or torch.DataLoader. epoch_length: number of iterations for one epoch, default to `len(val_data_loader)`. non_blocking: if True and this copy is between CPU and GPU, the copy may occur asynchronously with respect to the host. For other cases, this argument has no effect. prepare_batch: function to parse image and label for current iteration. iteration_update: the callable function for every iteration, expect to accept `engine` and `batchdata` as input parameters. if not provided, use `self._iteration()` instead. postprocessing: execute additional transformation for the model output data. Typically, several Tensor based transforms composed by `Compose`. key_val_metric: compute metric when every iteration completed, and save average value to engine.state.metrics when epoch completed. key_val_metric is the main metric to compare and save the checkpoint into files. additional_metrics: more Ignite metrics that also attach to Ignite Engine. metric_cmp_fn: function to compare current key metric with previous best key metric value, it must accept 2 args (current_metric, previous_best) and return a bool result: if `True`, will update `best_metric` and `best_metric_epoch` with current metric and epoch, default to `greater than`. val_handlers: every handler is a set of Ignite Event-Handlers, must have `attach` function, like: CheckpointHandler, StatsHandler, SegmentationSaver, etc. amp: whether to enable auto-mixed-precision evaluation, default is False. mode: model forward mode during evaluation, should be 'eval' or 'train', which maps to `model.eval()` or `model.train()`, default to 'eval'. event_names: additional custom ignite events that will register to the engine. new events can be a list of str or `ignite.engine.events.EventEnum`. event_to_attr: a dictionary to map an event to a state attribute, then add to `engine.state`. for more details, check: https://pytorch.org/ignite/generated/ignite.engine.engine.Engine.html #ignite.engine.engine.Engine.register_events. decollate: whether to decollate the batch-first data to a list of data after model computation, recommend `decollate=True` when `postprocessing` uses components from `monai.transforms`. default to `True`. """ def __init__( self, device: torch.device, val_data_loader: Union[Iterable, DataLoader], epoch_length: Optional[int] = None, non_blocking: bool = False, prepare_batch: Callable = default_prepare_batch, iteration_update: Optional[Callable] = None, postprocessing: Optional[Transform] = None, key_val_metric: Optional[Dict[str, Metric]] = None, additional_metrics: Optional[Dict[str, Metric]] = None, metric_cmp_fn: Callable = default_metric_cmp_fn, val_handlers: Optional[Sequence] = None, amp: bool = False, mode: Union[ForwardMode, str] = ForwardMode.EVAL, event_names: Optional[List[Union[str, EventEnum]]] = None, event_to_attr: Optional[dict] = None, decollate: bool = True, ) -> None: super().__init__( device=device, max_epochs=1, data_loader=val_data_loader, epoch_length=epoch_length, non_blocking=non_blocking, prepare_batch=prepare_batch, iteration_update=iteration_update, postprocessing=postprocessing, key_metric=key_val_metric, additional_metrics=additional_metrics, metric_cmp_fn=metric_cmp_fn, handlers=val_handlers, amp=amp, event_names=event_names, event_to_attr=event_to_attr, decollate=decollate, ) self.mode = look_up_option(mode, ForwardMode) if mode == ForwardMode.EVAL: self.mode = eval_mode elif mode == ForwardMode.TRAIN: self.mode = train_mode else: raise ValueError(f"unsupported mode: {mode}, should be 'eval' or 'train'.") def run(self, global_epoch: int = 1) -> None: """ Execute validation/evaluation based on Ignite Engine. Args: global_epoch: the overall epoch if during a training. evaluator engine can get it from trainer. """ # init env value for current validation process self.state.max_epochs = global_epoch self.state.epoch = global_epoch - 1 self.state.iteration = 0 super().run() def get_validation_stats(self) -> Dict[str, float]: return {"best_validation_metric": self.state.best_metric, "best_validation_epoch": self.state.best_metric_epoch} class SupervisedEvaluator(Evaluator): """ Standard supervised evaluation method with image and label(optional), inherits from evaluator and Workflow. Args: device: an object representing the device on which to run. val_data_loader: Ignite engine use data_loader to run, must be Iterable, typically be torch.DataLoader. network: network to evaluate in the evaluator, should be regular PyTorch `torch.nn.Module`. epoch_length: number of iterations for one epoch, default to `len(val_data_loader)`. non_blocking: if True and this copy is between CPU and GPU, the copy may occur asynchronously with respect to the host. For other cases, this argument has no effect. prepare_batch: function to parse image and label for current iteration. iteration_update: the callable function for every iteration, expect to accept `engine` and `batchdata` as input parameters. if not provided, use `self._iteration()` instead. inferer: inference method that execute model forward on input data, like: SlidingWindow, etc. postprocessing: execute additional transformation for the model output data. Typically, several Tensor based transforms composed by `Compose`. key_val_metric: compute metric when every iteration completed, and save average value to engine.state.metrics when epoch completed. key_val_metric is the main metric to compare and save the checkpoint into files. additional_metrics: more Ignite metrics that also attach to Ignite Engine. metric_cmp_fn: function to compare current key metric with previous best key metric value, it must accept 2 args (current_metric, previous_best) and return a bool result: if `True`, will update `best_metric` and `best_metric_epoch` with current metric and epoch, default to `greater than`. val_handlers: every handler is a set of Ignite Event-Handlers, must have `attach` function, like: CheckpointHandler, StatsHandler, SegmentationSaver, etc. amp: whether to enable auto-mixed-precision evaluation, default is False. mode: model forward mode during evaluation, should be 'eval' or 'train', which maps to `model.eval()` or `model.train()`, default to 'eval'. event_names: additional custom ignite events that will register to the engine. new events can be a list of str or `ignite.engine.events.EventEnum`. event_to_attr: a dictionary to map an event to a state attribute, then add to `engine.state`. for more details, check: https://pytorch.org/ignite/generated/ignite.engine.engine.Engine.html #ignite.engine.engine.Engine.register_events. decollate: whether to decollate the batch-first data to a list of data after model computation, recommend `decollate=True` when `postprocessing` uses components from `monai.transforms`. default to `True`. """ def __init__( self, device: torch.device, val_data_loader: Union[Iterable, DataLoader], network: torch.nn.Module, epoch_length: Optional[int] = None, non_blocking: bool = False, prepare_batch: Callable = default_prepare_batch, iteration_update: Optional[Callable] = None, inferer: Optional[Inferer] = None, postprocessing: Optional[Transform] = None, key_val_metric: Optional[Dict[str, Metric]] = None, additional_metrics: Optional[Dict[str, Metric]] = None, metric_cmp_fn: Callable = default_metric_cmp_fn, val_handlers: Optional[Sequence] = None, amp: bool = False, mode: Union[ForwardMode, str] = ForwardMode.EVAL, event_names: Optional[List[Union[str, EventEnum]]] = None, event_to_attr: Optional[dict] = None, decollate: bool = True, ) -> None: super().__init__( device=device, val_data_loader=val_data_loader, epoch_length=epoch_length, non_blocking=non_blocking, prepare_batch=prepare_batch, iteration_update=iteration_update, postprocessing=postprocessing, key_val_metric=key_val_metric, additional_metrics=additional_metrics, metric_cmp_fn=metric_cmp_fn, val_handlers=val_handlers, amp=amp, mode=mode, event_names=event_names, event_to_attr=event_to_attr, decollate=decollate, ) self.network = network self.inferer = SimpleInferer() if inferer is None else inferer def _iteration(self, engine: Engine, batchdata: Dict[str, torch.Tensor]) -> Dict[str, torch.Tensor]: """ callback function for the Supervised Evaluation processing logic of 1 iteration in Ignite Engine. Return below items in a dictionary: - IMAGE: image Tensor data for model input, already moved to device. - LABEL: label Tensor data corresponding to the image, already moved to device. - PRED: prediction result of model. Args: engine: Ignite Engine, it can be a trainer, validator or evaluator. batchdata: input data for this iteration, usually can be dictionary or tuple of Tensor data. Raises: ValueError: When ``batchdata`` is None. """ if batchdata is None: raise ValueError("Must provide batch data for current iteration.") batch = self.prepare_batch(batchdata, engine.state.device, engine.non_blocking) if len(batch) == 2: inputs, targets = batch args: Tuple = () kwargs: Dict = {} else: inputs, targets, args, kwargs = batch # put iteration outputs into engine.state engine.state.output = {Keys.IMAGE: inputs, Keys.LABEL: targets} # execute forward computation with self.mode(self.network): if self.amp: with torch.cuda.amp.autocast(): engine.state.output[Keys.PRED] = self.inferer(inputs, self.network, *args, **kwargs) else: engine.state.output[Keys.PRED] = self.inferer(inputs, self.network, *args, **kwargs) engine.fire_event(IterationEvents.FORWARD_COMPLETED) engine.fire_event(IterationEvents.MODEL_COMPLETED) return engine.state.output class EnsembleEvaluator(Evaluator): """ Ensemble evaluation for multiple models, inherits from evaluator and Workflow. It accepts a list of models for inference and outputs a list of predictions for further operations. Args: device: an object representing the device on which to run. val_data_loader: Ignite engine use data_loader to run, must be Iterable, typically be torch.DataLoader. epoch_length: number of iterations for one epoch, default to `len(val_data_loader)`. network: networks to evaluate in order in the evaluator, should be regular PyTorch `torch.nn.Module`. pred_keys: the keys to store every prediction data. the length must exactly match the number of networks. non_blocking: if True and this copy is between CPU and GPU, the copy may occur asynchronously with respect to the host. For other cases, this argument has no effect. prepare_batch: function to parse image and label for current iteration. iteration_update: the callable function for every iteration, expect to accept `engine` and `batchdata` as input parameters. if not provided, use `self._iteration()` instead. inferer: inference method that execute model forward on input data, like: SlidingWindow, etc. postprocessing: execute additional transformation for the model output data. Typically, several Tensor based transforms composed by `Compose`. key_val_metric: compute metric when every iteration completed, and save average value to engine.state.metrics when epoch completed. key_val_metric is the main metric to compare and save the checkpoint into files. additional_metrics: more Ignite metrics that also attach to Ignite Engine. metric_cmp_fn: function to compare current key metric with previous best key metric value, it must accept 2 args (current_metric, previous_best) and return a bool result: if `True`, will update `best_metric` and `best_metric_epoch` with current metric and epoch, default to `greater than`. val_handlers: every handler is a set of Ignite Event-Handlers, must have `attach` function, like: CheckpointHandler, StatsHandler, SegmentationSaver, etc. amp: whether to enable auto-mixed-precision evaluation, default is False. mode: model forward mode during evaluation, should be 'eval' or 'train', which maps to `model.eval()` or `model.train()`, default to 'eval'. event_names: additional custom ignite events that will register to the engine. new events can be a list of str or `ignite.engine.events.EventEnum`. event_to_attr: a dictionary to map an event to a state attribute, then add to `engine.state`. for more details, check: https://pytorch.org/ignite/generated/ignite.engine.engine.Engine.html #ignite.engine.engine.Engine.register_events. decollate: whether to decollate the batch-first data to a list of data after model computation, recommend `decollate=True` when `postprocessing` uses components from `monai.transforms`. default to `True`. """ def __init__( self, device: torch.device, val_data_loader: Union[Iterable, DataLoader], networks: Sequence[torch.nn.Module], pred_keys: Sequence[str], epoch_length: Optional[int] = None, non_blocking: bool = False, prepare_batch: Callable = default_prepare_batch, iteration_update: Optional[Callable] = None, inferer: Optional[Inferer] = None, postprocessing: Optional[Transform] = None, key_val_metric: Optional[Dict[str, Metric]] = None, additional_metrics: Optional[Dict[str, Metric]] = None, metric_cmp_fn: Callable = default_metric_cmp_fn, val_handlers: Optional[Sequence] = None, amp: bool = False, mode: Union[ForwardMode, str] = ForwardMode.EVAL, event_names: Optional[List[Union[str, EventEnum]]] = None, event_to_attr: Optional[dict] = None, decollate: bool = True, ) -> None: super().__init__( device=device, val_data_loader=val_data_loader, epoch_length=epoch_length, non_blocking=non_blocking, prepare_batch=prepare_batch, iteration_update=iteration_update, postprocessing=postprocessing, key_val_metric=key_val_metric, additional_metrics=additional_metrics, metric_cmp_fn=metric_cmp_fn, val_handlers=val_handlers, amp=amp, mode=mode, event_names=event_names, event_to_attr=event_to_attr, decollate=decollate, ) self.networks = ensure_tuple(networks) self.pred_keys = ensure_tuple(pred_keys) self.inferer = SimpleInferer() if inferer is None else inferer def _iteration(self, engine: Engine, batchdata: Dict[str, torch.Tensor]) -> Dict[str, torch.Tensor]: """ callback function for the Supervised Evaluation processing logic of 1 iteration in Ignite Engine. Return below items in a dictionary: - IMAGE: image Tensor data for model input, already moved to device. - LABEL: label Tensor data corresponding to the image, already moved to device. - pred_keys[0]: prediction result of network 0. - pred_keys[1]: prediction result of network 1. - ... ... - pred_keys[N]: prediction result of network N. Args: engine: Ignite Engine, it can be a trainer, validator or evaluator. batchdata: input data for this iteration, usually can be dictionary or tuple of Tensor data. Raises: ValueError: When ``batchdata`` is None. """ if batchdata is None: raise ValueError("Must provide batch data for current iteration.") batch = self.prepare_batch(batchdata, engine.state.device, engine.non_blocking) if len(batch) == 2: inputs, targets = batch args: Tuple = () kwargs: Dict = {} else: inputs, targets, args, kwargs = batch # put iteration outputs into engine.state engine.state.output = {Keys.IMAGE: inputs, Keys.LABEL: targets} for idx, network in enumerate(self.networks): with self.mode(network): if self.amp: with torch.cuda.amp.autocast(): engine.state.output.update( {self.pred_keys[idx]: self.inferer(inputs, network, *args, **kwargs)} ) else: engine.state.output.update({self.pred_keys[idx]: self.inferer(inputs, network, *args, **kwargs)}) engine.fire_event(IterationEvents.FORWARD_COMPLETED) engine.fire_event(IterationEvents.MODEL_COMPLETED) return engine.state.output

51.496222

120

0.674134

from typing import TYPE_CHECKING, Callable, Dict, Iterable, List, Optional, Sequence, Tuple, Union import torch from torch.utils.data import DataLoader from monai.config import IgniteInfo from monai.engines.utils import IterationEvents, default_metric_cmp_fn, default_prepare_batch from monai.engines.workflow import Workflow from monai.inferers import Inferer, SimpleInferer from monai.networks.utils import eval_mode, train_mode from monai.transforms import Transform from monai.utils import ForwardMode, ensure_tuple, min_version, optional_import from monai.utils.enums import CommonKeys as Keys from monai.utils.module import look_up_option if TYPE_CHECKING: from ignite.engine import Engine, EventEnum from ignite.metrics import Metric else: Engine, _ = optional_import("ignite.engine", IgniteInfo.OPT_IMPORT_VERSION, min_version, "Engine") Metric, _ = optional_import("ignite.metrics", IgniteInfo.OPT_IMPORT_VERSION, min_version, "Metric") EventEnum, _ = optional_import("ignite.engine", IgniteInfo.OPT_IMPORT_VERSION, min_version, "EventEnum") __all__ = ["Evaluator", "SupervisedEvaluator", "EnsembleEvaluator"] class Evaluator(Workflow): def __init__( self, device: torch.device, val_data_loader: Union[Iterable, DataLoader], epoch_length: Optional[int] = None, non_blocking: bool = False, prepare_batch: Callable = default_prepare_batch, iteration_update: Optional[Callable] = None, postprocessing: Optional[Transform] = None, key_val_metric: Optional[Dict[str, Metric]] = None, additional_metrics: Optional[Dict[str, Metric]] = None, metric_cmp_fn: Callable = default_metric_cmp_fn, val_handlers: Optional[Sequence] = None, amp: bool = False, mode: Union[ForwardMode, str] = ForwardMode.EVAL, event_names: Optional[List[Union[str, EventEnum]]] = None, event_to_attr: Optional[dict] = None, decollate: bool = True, ) -> None: super().__init__( device=device, max_epochs=1, data_loader=val_data_loader, epoch_length=epoch_length, non_blocking=non_blocking, prepare_batch=prepare_batch, iteration_update=iteration_update, postprocessing=postprocessing, key_metric=key_val_metric, additional_metrics=additional_metrics, metric_cmp_fn=metric_cmp_fn, handlers=val_handlers, amp=amp, event_names=event_names, event_to_attr=event_to_attr, decollate=decollate, ) self.mode = look_up_option(mode, ForwardMode) if mode == ForwardMode.EVAL: self.mode = eval_mode elif mode == ForwardMode.TRAIN: self.mode = train_mode else: raise ValueError(f"unsupported mode: {mode}, should be 'eval' or 'train'.") def run(self, global_epoch: int = 1) -> None: self.state.max_epochs = global_epoch self.state.epoch = global_epoch - 1 self.state.iteration = 0 super().run() def get_validation_stats(self) -> Dict[str, float]: return {"best_validation_metric": self.state.best_metric, "best_validation_epoch": self.state.best_metric_epoch} class SupervisedEvaluator(Evaluator): def __init__( self, device: torch.device, val_data_loader: Union[Iterable, DataLoader], network: torch.nn.Module, epoch_length: Optional[int] = None, non_blocking: bool = False, prepare_batch: Callable = default_prepare_batch, iteration_update: Optional[Callable] = None, inferer: Optional[Inferer] = None, postprocessing: Optional[Transform] = None, key_val_metric: Optional[Dict[str, Metric]] = None, additional_metrics: Optional[Dict[str, Metric]] = None, metric_cmp_fn: Callable = default_metric_cmp_fn, val_handlers: Optional[Sequence] = None, amp: bool = False, mode: Union[ForwardMode, str] = ForwardMode.EVAL, event_names: Optional[List[Union[str, EventEnum]]] = None, event_to_attr: Optional[dict] = None, decollate: bool = True, ) -> None: super().__init__( device=device, val_data_loader=val_data_loader, epoch_length=epoch_length, non_blocking=non_blocking, prepare_batch=prepare_batch, iteration_update=iteration_update, postprocessing=postprocessing, key_val_metric=key_val_metric, additional_metrics=additional_metrics, metric_cmp_fn=metric_cmp_fn, val_handlers=val_handlers, amp=amp, mode=mode, event_names=event_names, event_to_attr=event_to_attr, decollate=decollate, ) self.network = network self.inferer = SimpleInferer() if inferer is None else inferer def _iteration(self, engine: Engine, batchdata: Dict[str, torch.Tensor]) -> Dict[str, torch.Tensor]: if batchdata is None: raise ValueError("Must provide batch data for current iteration.") batch = self.prepare_batch(batchdata, engine.state.device, engine.non_blocking) if len(batch) == 2: inputs, targets = batch args: Tuple = () kwargs: Dict = {} else: inputs, targets, args, kwargs = batch engine.state.output = {Keys.IMAGE: inputs, Keys.LABEL: targets} with self.mode(self.network): if self.amp: with torch.cuda.amp.autocast(): engine.state.output[Keys.PRED] = self.inferer(inputs, self.network, *args, **kwargs) else: engine.state.output[Keys.PRED] = self.inferer(inputs, self.network, *args, **kwargs) engine.fire_event(IterationEvents.FORWARD_COMPLETED) engine.fire_event(IterationEvents.MODEL_COMPLETED) return engine.state.output class EnsembleEvaluator(Evaluator): def __init__( self, device: torch.device, val_data_loader: Union[Iterable, DataLoader], networks: Sequence[torch.nn.Module], pred_keys: Sequence[str], epoch_length: Optional[int] = None, non_blocking: bool = False, prepare_batch: Callable = default_prepare_batch, iteration_update: Optional[Callable] = None, inferer: Optional[Inferer] = None, postprocessing: Optional[Transform] = None, key_val_metric: Optional[Dict[str, Metric]] = None, additional_metrics: Optional[Dict[str, Metric]] = None, metric_cmp_fn: Callable = default_metric_cmp_fn, val_handlers: Optional[Sequence] = None, amp: bool = False, mode: Union[ForwardMode, str] = ForwardMode.EVAL, event_names: Optional[List[Union[str, EventEnum]]] = None, event_to_attr: Optional[dict] = None, decollate: bool = True, ) -> None: super().__init__( device=device, val_data_loader=val_data_loader, epoch_length=epoch_length, non_blocking=non_blocking, prepare_batch=prepare_batch, iteration_update=iteration_update, postprocessing=postprocessing, key_val_metric=key_val_metric, additional_metrics=additional_metrics, metric_cmp_fn=metric_cmp_fn, val_handlers=val_handlers, amp=amp, mode=mode, event_names=event_names, event_to_attr=event_to_attr, decollate=decollate, ) self.networks = ensure_tuple(networks) self.pred_keys = ensure_tuple(pred_keys) self.inferer = SimpleInferer() if inferer is None else inferer def _iteration(self, engine: Engine, batchdata: Dict[str, torch.Tensor]) -> Dict[str, torch.Tensor]: if batchdata is None: raise ValueError("Must provide batch data for current iteration.") batch = self.prepare_batch(batchdata, engine.state.device, engine.non_blocking) if len(batch) == 2: inputs, targets = batch args: Tuple = () kwargs: Dict = {} else: inputs, targets, args, kwargs = batch engine.state.output = {Keys.IMAGE: inputs, Keys.LABEL: targets} for idx, network in enumerate(self.networks): with self.mode(network): if self.amp: with torch.cuda.amp.autocast(): engine.state.output.update( {self.pred_keys[idx]: self.inferer(inputs, network, *args, **kwargs)} ) else: engine.state.output.update({self.pred_keys[idx]: self.inferer(inputs, network, *args, **kwargs)}) engine.fire_event(IterationEvents.FORWARD_COMPLETED) engine.fire_event(IterationEvents.MODEL_COMPLETED) return engine.state.output

true

1c37daaffae2c398d95511df9e2def643d477990

3,678

py

Python

scripts/figure4/preprocessing_dream5_invitro.py

jiawu/Roller

a70e350905a59c2254dcefda7ab23c6417cf8f7d

[ "MIT" ]

null

scripts/figure4/preprocessing_dream5_invitro.py

jiawu/Roller

a70e350905a59c2254dcefda7ab23c6417cf8f7d

[ "MIT" ]

2

2015-07-13T18:51:22.000Z

2015-07-16T15:35:24.000Z

scripts/figure4/preprocessing_dream5_invitro.py

jiawu/Roller

a70e350905a59c2254dcefda7ab23c6417cf8f7d

[ "MIT" ]

null

import pandas as pd import pdb import scipy.stats as stats def zscore_data(df): p = df.values z = pd.DataFrame(stats.zscore(p,axis=0,ddof=1),index=df.index, columns=df.columns) z['Time'] = df['Time'] return(z) db_path = '../data/invitro/net3_expression_data.tsv' my_df = pd.read_csv(db_path, sep='\t') my_df = my_df[~my_df['Time'].isnull()] gp = my_df.groupby(['#Experiment','Time']) #exp_list = [25,26,47,50,55,98, 105] #my_df = my_df[my_df['#Experiment'].isin(exp_list)] final_df = pd.DataFrame() ## Append certain rows with the same pertubation etc, alternating between repeats #final_df = final_df.append(my_df[ (my_df['#Experiment'] == 25) & (my_df['Repeat'] == 1) ].iloc[:5]) #final_df = final_df.append(my_df[ (my_df['#Experiment'] == 25) & (my_df['Repeat'] == 2) ].iloc[:5]) #final_df = final_df.append(my_df[ (my_df['#Experiment'] == 26) & (my_df['Repeat'] == 1) ].iloc[:5]) #final_df = final_df.append(my_df[ (my_df['#Experiment'] == 26) & (my_df['Repeat'] == 2) ].iloc[:5]) final_df = final_df.append(my_df[ (my_df['#Experiment'] == 47) & (my_df['Perturbations'].isnull())].iloc[:5]) final_df = final_df.append(my_df[ (my_df['#Experiment'] == 47) & (my_df['Perturbations']=='P13')].iloc[:5]) final_df = final_df.append(my_df[ (my_df['#Experiment'] == 49) & (my_df['Perturbations'].isnull())].iloc[:5]) temp_t0 = my_df[ (my_df['#Experiment'] == 49) & (my_df['Perturbations'].isnull())].iloc[0,:] final_df = final_df.append(temp_t0) final_df = final_df.append(my_df[ (my_df['#Experiment'] == 49) & (my_df['Perturbations'] == 'P16')].iloc[:4]) final_df = final_df.append(temp_t0) final_df = final_df.append(my_df[ (my_df['#Experiment'] == 49) & (my_df['Perturbations'] == 'P17')].iloc[:4]) final_df = final_df.append(my_df[ (my_df['#Experiment'] == 50) & (my_df['Perturbations'] =='P8') & (my_df['Repeat'] == 1) ].iloc[:5] ) final_df = final_df.append(my_df[ (my_df['#Experiment'] == 50) & (my_df['Perturbations'] =='P8') & (my_df['Repeat'] == 2) ].iloc[:5] ) final_df = final_df.append(my_df[ (my_df['#Experiment'] == 50) & (my_df['Perturbations'] =='P8') & (my_df['Repeat'] == 3) ].iloc[:5] ) #final_df = final_df.append(my_df[ (my_df['#Experiment'] == 55) & (my_df['Perturbations'] =='P24') & (my_df['Repeat'] == 1) ].iloc[:5] ) #final_df = final_df.append(my_df[ (my_df['#Experiment'] == 98) & (my_df['DeletedGenes'].isnull()) & (my_df['Repeat'] == 1 )].iloc[:5] ) #final_df = final_df.append(my_df[ (my_df['#Experiment'] == 98) & (my_df['DeletedGenes'].isnull()) & (my_df['Repeat'] == 2 )].iloc[:5] ) final_df = final_df.append(my_df[ (my_df['#Experiment'] == 105)].iloc[:30] ) gene_names = pd.read_csv('../data/invitro/net3_gene_ids.tsv', sep='\t') node_list = ['G%d'% (x) for x in range(1, 4512)] node_list2 = gene_names['Name'].str.lower().tolist() unmapped_df = final_df[['Time']+node_list] unmapped_df.columns = ['Time'] + node_list2 om_df = pd.read_csv('../data/invitro/iomranian_parsed_timeseries.tsv', sep='\t') om_df = om_df[om_df['Time'] != 90] intersecting_genes = set(om_df.columns.tolist()).intersection(set(unmapped_df.columns.tolist())) intersecting_genes = sorted(list(intersecting_genes)) intersecting_genes.insert(0, intersecting_genes.pop(intersecting_genes.index('Time'))) mapped_df = unmapped_df[intersecting_genes] norm_df = zscore_data(mapped_df) # Change the time index so that it matches up with omranian... x = [10,20,30,40,50] t = [b for a in range(14) for b in x] pdb.set_trace() norm_df['Time'] = t om_df_parsed = zscore_data(om_df[intersecting_genes]) om_df_parsed = om_df_parsed.append(norm_df) om_df_parsed.to_csv('../data/invitro/iomranian_parsed_timeseries.tsv', index=False, sep='\t')

43.270588

136

0.669657

import pandas as pd import pdb import scipy.stats as stats def zscore_data(df): p = df.values z = pd.DataFrame(stats.zscore(p,axis=0,ddof=1),index=df.index, columns=df.columns) z['Time'] = df['Time'] return(z) db_path = '../data/invitro/net3_expression_data.tsv' my_df = pd.read_csv(db_path, sep='\t') my_df = my_df[~my_df['Time'].isnull()] gp = my_df.groupby(['#Experiment','Time']) final_df = pd.DataFrame() erturbations'].isnull())].iloc[:5]) final_df = final_df.append(my_df[ (my_df['#Experiment'] == 47) & (my_df['Perturbations']=='P13')].iloc[:5]) final_df = final_df.append(my_df[ (my_df['#Experiment'] == 49) & (my_df['Perturbations'].isnull())].iloc[:5]) temp_t0 = my_df[ (my_df['#Experiment'] == 49) & (my_df['Perturbations'].isnull())].iloc[0,:] final_df = final_df.append(temp_t0) final_df = final_df.append(my_df[ (my_df['#Experiment'] == 49) & (my_df['Perturbations'] == 'P16')].iloc[:4]) final_df = final_df.append(temp_t0) final_df = final_df.append(my_df[ (my_df['#Experiment'] == 49) & (my_df['Perturbations'] == 'P17')].iloc[:4]) final_df = final_df.append(my_df[ (my_df['#Experiment'] == 50) & (my_df['Perturbations'] =='P8') & (my_df['Repeat'] == 1) ].iloc[:5] ) final_df = final_df.append(my_df[ (my_df['#Experiment'] == 50) & (my_df['Perturbations'] =='P8') & (my_df['Repeat'] == 2) ].iloc[:5] ) final_df = final_df.append(my_df[ (my_df['#Experiment'] == 50) & (my_df['Perturbations'] =='P8') & (my_df['Repeat'] == 3) ].iloc[:5] ) final_df = final_df.append(my_df[ (my_df['#Experiment'] == 105)].iloc[:30] ) gene_names = pd.read_csv('../data/invitro/net3_gene_ids.tsv', sep='\t') node_list = ['G%d'% (x) for x in range(1, 4512)] node_list2 = gene_names['Name'].str.lower().tolist() unmapped_df = final_df[['Time']+node_list] unmapped_df.columns = ['Time'] + node_list2 om_df = pd.read_csv('../data/invitro/iomranian_parsed_timeseries.tsv', sep='\t') om_df = om_df[om_df['Time'] != 90] intersecting_genes = set(om_df.columns.tolist()).intersection(set(unmapped_df.columns.tolist())) intersecting_genes = sorted(list(intersecting_genes)) intersecting_genes.insert(0, intersecting_genes.pop(intersecting_genes.index('Time'))) mapped_df = unmapped_df[intersecting_genes] norm_df = zscore_data(mapped_df) x = [10,20,30,40,50] t = [b for a in range(14) for b in x] pdb.set_trace() norm_df['Time'] = t om_df_parsed = zscore_data(om_df[intersecting_genes]) om_df_parsed = om_df_parsed.append(norm_df) om_df_parsed.to_csv('../data/invitro/iomranian_parsed_timeseries.tsv', index=False, sep='\t')

true

1c37dab1d05e53faea5ed821d46f44a67edb994a

1,831

py

Python

apps/users/admin.py

christianalcantara/book_backend

5c98aad01a1ea7d7985cafa14c6de7eb3d0b48af

[ "MIT" ]

1

2021-02-23T00:55:14.000Z

apps/users/admin.py

christianalcantara/book_backend

5c98aad01a1ea7d7985cafa14c6de7eb3d0b48af

[ "MIT" ]

1

2021-02-23T00:33:05.000Z

apps/users/admin.py

christianalcantara/book_backend

5c98aad01a1ea7d7985cafa14c6de7eb3d0b48af

[ "MIT" ]

null

from django.contrib import admin from django.contrib.auth.admin import UserAdmin as BaseUserAdmin from django.contrib.auth.models import Group from apps.users.forms import UserChangeForm, UserCreationForm from apps.users.models import User class UserAdmin(BaseUserAdmin): form = UserChangeForm add_form = UserCreationForm # The fields to be used in displaying the User model. # These override the definitions on the base UserAdmin # that reference specific fields on auth.User. list_display = ["full_name", "email"] fieldsets = [ ["Auth", {"fields": ["email", "password"]}], ["Personal info", {"fields": ["last_name", "first_name", "avatar"]}], [ "Settings", { "fields": [ "groups", "is_admin", "is_active", "is_staff", "is_superuser", "is_customer", ] }, ], ["Important dates", {"fields": ["last_login", "registered_at"]}], ] # add_fieldsets is not a standard ModelAdmin attribute. UserAdmin # overrides get_fieldsets to use this attribute when creating a user. add_fieldsets = [ [ None, { "classes": ["wide"], "fields": [ "email", "first_name", "last_name", "password1", "password2", ], }, ], ] search_fields = ["email"] ordering = ["email"] readonly_fields = ["last_login", "registered_at"] # Now register the new UserAdmin... admin.site.register(User, UserAdmin) # Unregister the Group model from admin. admin.site.unregister(Group)

30.016393

77

0.534134

from django.contrib import admin from django.contrib.auth.admin import UserAdmin as BaseUserAdmin from django.contrib.auth.models import Group from apps.users.forms import UserChangeForm, UserCreationForm from apps.users.models import User class UserAdmin(BaseUserAdmin): form = UserChangeForm add_form = UserCreationForm list_display = ["full_name", "email"] fieldsets = [ ["Auth", {"fields": ["email", "password"]}], ["Personal info", {"fields": ["last_name", "first_name", "avatar"]}], [ "Settings", { "fields": [ "groups", "is_admin", "is_active", "is_staff", "is_superuser", "is_customer", ] }, ], ["Important dates", {"fields": ["last_login", "registered_at"]}], ] add_fieldsets = [ [ None, { "classes": ["wide"], "fields": [ "email", "first_name", "last_name", "password1", "password2", ], }, ], ] search_fields = ["email"] ordering = ["email"] readonly_fields = ["last_login", "registered_at"] admin.site.register(User, UserAdmin) admin.site.unregister(Group)

true

1c37dbc948feee822d32407b551e6cb846df0a97

8,535

py

Python

gslib/addlhelp/encoding.py

stanhu/gsutil

e8403ed5e07caed3027455c7b883fef733612360

[ "Apache-2.0" ]

649

2015-01-08T01:50:15.000Z

2022-03-31T08:33:38.000Z

gslib/addlhelp/encoding.py

stanhu/gsutil

e8403ed5e07caed3027455c7b883fef733612360

[ "Apache-2.0" ]

798

2015-01-02T07:46:09.000Z

2022-03-31T20:37:19.000Z

gslib/addlhelp/encoding.py

stanhu/gsutil

e8403ed5e07caed3027455c7b883fef733612360

[ "Apache-2.0" ]

315

2015-01-02T10:26:53.000Z

2022-03-27T02:18:58.000Z

# -*- coding: utf-8 -*- # Copyright 2014 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. """Additional help about CRC32C and installing crcmod.""" from __future__ import absolute_import from __future__ import print_function from __future__ import division from __future__ import unicode_literals from gslib.help_provider import HelpProvider _DETAILED_HELP_TEXT = (""" OVERVIEW To reduce the chance for `filename encoding interoperability problems <https://en.wikipedia.org/wiki/Filename#Encoding_indication_interoperability>`_ gsutil uses `UTF-8 <https://en.wikipedia.org/wiki/UTF-8>`_ character encoding when uploading and downloading files. Because UTF-8 is in widespread (and growing) use, for most users nothing needs to be done to use UTF-8. Users with files stored in other encodings (such as `Latin 1 <https://en.wikipedia.org/wiki/ISO/IEC_8859-1>`_) must convert those filenames to UTF-8 before attempting to upload the files. The most common place where users who have filenames that use some other encoding encounter a gsutil error is while uploading files using the recursive (-R) option on the gsutil cp , mv, or rsync commands. When this happens you'll get an error like this: CommandException: Invalid Unicode path encountered ('dir1/dir2/file_name_with_\\xf6n_bad_chars'). gsutil cannot proceed with such files present. Please remove or rename this file and try again. Note that the invalid Unicode characters have been hex-encoded in this error message because otherwise trying to print them would result in another error. If you encounter such an error you can either remove the problematic file(s) or try to rename them and re-run the command. If you have a modest number of such files the simplest thing to do is to think of a different name for the file and manually rename the file (using local filesystem tools). If you have too many files for that to be practical, you can use a bulk rename tool or script. Unicode errors for valid Unicode filepaths can be caused by lack of Python locale configuration on Linux and Mac OSes. If your file paths are Unicode and you get encoding errors, ensure the LANG environment variable is set correctly. Typically, the LANG variable should be set to something like "en_US.UTF-8" or "de_DE.UTF-8". Note also that there's no restriction on the character encoding used in file content - it can be UTF-8, a different encoding, or non-character data (like audio or video content). The gsutil UTF-8 character encoding requirement applies only to filenames. USING UNICODE FILENAMES ON WINDOWS Windows support for Unicode in the command shell (cmd.exe or powershell) is somewhat painful, because Windows uses a Windows-specific character encoding called `cp1252 <https://en.wikipedia.org/wiki/Windows-1252>`_. To use Unicode characters you need to run this command in the command shell before the first time you use gsutil in that shell: chcp 65001 If you neglect to do this before using gsutil, the progress messages while uploading files with Unicode names or listing buckets with Unicode object names will look garbled (i.e., with different glyphs than you expect in the output). If you simply run the chcp command and re-run the gsutil command, the output should no longer look garbled. gsutil attempts to translate between cp1252 encoding and UTF-8 in the main places that Unicode encoding/decoding problems have been encountered to date (traversing the local file system while uploading files, and printing Unicode names while listing buckets). However, because gsutil must perform translation, it is likely there are other erroneous edge cases when using Windows with Unicode. If you encounter problems, you might consider instead using cygwin (on Windows) or Linux or macOS - all of which support Unicode. USING UNICODE FILENAMES ON MACOS macOS stores filenames in decomposed form (also known as `NFD normalization <https://en.wikipedia.org/wiki/Unicode_equivalence>`_). For example, if a filename contains an accented "e" character, that character will be converted to an "e" followed by an accent before being saved to the filesystem. As a consequence, it's possible to have different name strings for files uploaded from an operating system that doesn't enforce decomposed form (like Ubuntu) from one that does (like macOS). The following example shows how this behavior could lead to unexpected results. Say you create a file with non-ASCII characters on Ubuntu. Ubuntu stores that filename in its composed form. When you upload the file to the cloud, it is stored as named. But if you use gsutil rysnc to bring the file to a macOS machine and edit the file, then when you use gsutil rsync to bring this version back to the cloud, you end up with two different objects, instead of replacing the original. This is because macOS converted the filename to a decomposed form, and Cloud Storage sees this as a different object name. CROSS-PLATFORM ENCODING PROBLEMS OF WHICH TO BE AWARE Using UTF-8 for all object names and filenames will ensure that gsutil doesn't encounter character encoding errors while operating on the files. Unfortunately, it's still possible that files uploaded / downloaded this way can have interoperability problems, for a number of reasons unrelated to gsutil. For example: - Windows filenames are case-insensitive, while Google Cloud Storage, Linux, and macOS are not. Thus, for example, if you have two filenames on Linux differing only in case and upload both to Google Cloud Storage and then subsequently download them to Windows, you will end up with just one file whose contents came from the last of these files to be written to the filesystem. - macOS performs character encoding decomposition based on tables stored in the OS, and the tables change between Unicode versions. Thus the encoding used by an external library may not match that performed by the OS. It is possible that two object names may translate to a single local filename. - Windows console support for Unicode is difficult to use correctly. For a more thorough list of such issues see `this presentation <http://www.i18nguy.com/unicode/filename-issues-iuc33.pdf>`_ These problems mostly arise when sharing data across platforms (e.g., uploading data from a Windows machine to Google Cloud Storage, and then downloading from Google Cloud Storage to a machine running macOS). Unfortunately these problems are a consequence of the lack of a filename encoding standard, and users need to be aware of the kinds of problems that can arise when copying filenames across platforms. There is one precaution users can exercise to prevent some of these problems: When using the Windows console specify wildcards or folders (using the -R option) rather than explicitly named individual files. CONVERTING FILENAMES TO UNICODE Open-source tools are available to convert filenames for non-Unicode files. For example, to convert from latin1 (a common Windows encoding) to Unicode, you can use `Windows iconv <http://gnuwin32.sourceforge.net/packages/libiconv.htm>`_. For Unix-based systems, you can use `libiconv <https://www.gnu.org/software/libiconv/>`_. """) class CommandOptions(HelpProvider): """Additional help about filename encoding and interoperability problems.""" # Help specification. See help_provider.py for documentation. help_spec = HelpProvider.HelpSpec( help_name='encoding', help_name_aliases=[ 'encodings', 'utf8', 'utf-8', 'latin1', 'unicode', 'interoperability', ], help_type='additional_help', help_one_line_summary='Filename encoding and interoperability problems', help_text=_DETAILED_HELP_TEXT, subcommand_help_text={}, )

49.051724

81

0.765436

from __future__ import absolute_import from __future__ import print_function from __future__ import division from __future__ import unicode_literals from gslib.help_provider import HelpProvider _DETAILED_HELP_TEXT = (""" OVERVIEW To reduce the chance for `filename encoding interoperability problems <https://en.wikipedia.org/wiki/Filename#Encoding_indication_interoperability>`_ gsutil uses `UTF-8 <https://en.wikipedia.org/wiki/UTF-8>`_ character encoding when uploading and downloading files. Because UTF-8 is in widespread (and growing) use, for most users nothing needs to be done to use UTF-8. Users with files stored in other encodings (such as `Latin 1 <https://en.wikipedia.org/wiki/ISO/IEC_8859-1>`_) must convert those filenames to UTF-8 before attempting to upload the files. The most common place where users who have filenames that use some other encoding encounter a gsutil error is while uploading files using the recursive (-R) option on the gsutil cp , mv, or rsync commands. When this happens you'll get an error like this: CommandException: Invalid Unicode path encountered ('dir1/dir2/file_name_with_\\xf6n_bad_chars'). gsutil cannot proceed with such files present. Please remove or rename this file and try again. Note that the invalid Unicode characters have been hex-encoded in this error message because otherwise trying to print them would result in another error. If you encounter such an error you can either remove the problematic file(s) or try to rename them and re-run the command. If you have a modest number of such files the simplest thing to do is to think of a different name for the file and manually rename the file (using local filesystem tools). If you have too many files for that to be practical, you can use a bulk rename tool or script. Unicode errors for valid Unicode filepaths can be caused by lack of Python locale configuration on Linux and Mac OSes. If your file paths are Unicode and you get encoding errors, ensure the LANG environment variable is set correctly. Typically, the LANG variable should be set to something like "en_US.UTF-8" or "de_DE.UTF-8". Note also that there's no restriction on the character encoding used in file content - it can be UTF-8, a different encoding, or non-character data (like audio or video content). The gsutil UTF-8 character encoding requirement applies only to filenames. USING UNICODE FILENAMES ON WINDOWS Windows support for Unicode in the command shell (cmd.exe or powershell) is somewhat painful, because Windows uses a Windows-specific character encoding called `cp1252 <https://en.wikipedia.org/wiki/Windows-1252>`_. To use Unicode characters you need to run this command in the command shell before the first time you use gsutil in that shell: chcp 65001 If you neglect to do this before using gsutil, the progress messages while uploading files with Unicode names or listing buckets with Unicode object names will look garbled (i.e., with different glyphs than you expect in the output). If you simply run the chcp command and re-run the gsutil command, the output should no longer look garbled. gsutil attempts to translate between cp1252 encoding and UTF-8 in the main places that Unicode encoding/decoding problems have been encountered to date (traversing the local file system while uploading files, and printing Unicode names while listing buckets). However, because gsutil must perform translation, it is likely there are other erroneous edge cases when using Windows with Unicode. If you encounter problems, you might consider instead using cygwin (on Windows) or Linux or macOS - all of which support Unicode. USING UNICODE FILENAMES ON MACOS macOS stores filenames in decomposed form (also known as `NFD normalization <https://en.wikipedia.org/wiki/Unicode_equivalence>`_). For example, if a filename contains an accented "e" character, that character will be converted to an "e" followed by an accent before being saved to the filesystem. As a consequence, it's possible to have different name strings for files uploaded from an operating system that doesn't enforce decomposed form (like Ubuntu) from one that does (like macOS). The following example shows how this behavior could lead to unexpected results. Say you create a file with non-ASCII characters on Ubuntu. Ubuntu stores that filename in its composed form. When you upload the file to the cloud, it is stored as named. But if you use gsutil rysnc to bring the file to a macOS machine and edit the file, then when you use gsutil rsync to bring this version back to the cloud, you end up with two different objects, instead of replacing the original. This is because macOS converted the filename to a decomposed form, and Cloud Storage sees this as a different object name. CROSS-PLATFORM ENCODING PROBLEMS OF WHICH TO BE AWARE Using UTF-8 for all object names and filenames will ensure that gsutil doesn't encounter character encoding errors while operating on the files. Unfortunately, it's still possible that files uploaded / downloaded this way can have interoperability problems, for a number of reasons unrelated to gsutil. For example: - Windows filenames are case-insensitive, while Google Cloud Storage, Linux, and macOS are not. Thus, for example, if you have two filenames on Linux differing only in case and upload both to Google Cloud Storage and then subsequently download them to Windows, you will end up with just one file whose contents came from the last of these files to be written to the filesystem. - macOS performs character encoding decomposition based on tables stored in the OS, and the tables change between Unicode versions. Thus the encoding used by an external library may not match that performed by the OS. It is possible that two object names may translate to a single local filename. - Windows console support for Unicode is difficult to use correctly. For a more thorough list of such issues see `this presentation <http://www.i18nguy.com/unicode/filename-issues-iuc33.pdf>`_ These problems mostly arise when sharing data across platforms (e.g., uploading data from a Windows machine to Google Cloud Storage, and then downloading from Google Cloud Storage to a machine running macOS). Unfortunately these problems are a consequence of the lack of a filename encoding standard, and users need to be aware of the kinds of problems that can arise when copying filenames across platforms. There is one precaution users can exercise to prevent some of these problems: When using the Windows console specify wildcards or folders (using the -R option) rather than explicitly named individual files. CONVERTING FILENAMES TO UNICODE Open-source tools are available to convert filenames for non-Unicode files. For example, to convert from latin1 (a common Windows encoding) to Unicode, you can use `Windows iconv <http://gnuwin32.sourceforge.net/packages/libiconv.htm>`_. For Unix-based systems, you can use `libiconv <https://www.gnu.org/software/libiconv/>`_. """) class CommandOptions(HelpProvider): help_spec = HelpProvider.HelpSpec( help_name='encoding', help_name_aliases=[ 'encodings', 'utf8', 'utf-8', 'latin1', 'unicode', 'interoperability', ], help_type='additional_help', help_one_line_summary='Filename encoding and interoperability problems', help_text=_DETAILED_HELP_TEXT, subcommand_help_text={}, )

true

1c37dc2b9a245831e5c847624b92e7308a27130d

151

py

Python

rootfs/usr/lib/python3/dist-packages/numpy/distutils/compat.py

kappaIO-Dev/kappaIO-sdk-armhf-crosscompile

66fc5fc21e6235f7a3be72a7ccac68e2224b7fb2

[ "MIT" ]

343

2015-01-07T05:58:44.000Z

2022-03-15T14:55:21.000Z

rootfs/usr/lib/python3/dist-packages/numpy/distutils/compat.py

kappaIO-Dev/kappaIO-sdk-armhf-crosscompile

66fc5fc21e6235f7a3be72a7ccac68e2224b7fb2

[ "MIT" ]

61

2015-03-19T18:20:21.000Z

2019-10-23T12:58:23.000Z

rootfs/usr/lib/python3/dist-packages/numpy/distutils/compat.py

kappaIO-Dev/kappaIO-sdk-armhf-crosscompile

66fc5fc21e6235f7a3be72a7ccac68e2224b7fb2

[ "MIT" ]

66

2015-01-20T15:35:05.000Z

2021-11-25T16:49:41.000Z

"""Small modules to cope with python 2 vs 3 incompatibilities inside numpy.distutils """ import sys def get_exception(): return sys.exc_info()[1]

18.875

68

0.741722

import sys def get_exception(): return sys.exc_info()[1]

true

1c37dcad788bd730e9bf79ccc6bb167532afc494

2,963

py

Python

Stage/backend/Datatables.py

zuoziji/transaction

7a59817a699d9df32e13d43edda630520af7860d

[ "Apache-2.0" ]

null

Stage/backend/Datatables.py

zuoziji/transaction

7a59817a699d9df32e13d43edda630520af7860d

[ "Apache-2.0" ]

9

2021-02-08T20:19:53.000Z

2022-03-11T23:16:46.000Z

Stage/backend/Datatables.py

zuoziji/transaction

7a59817a699d9df32e13d43edda630520af7860d

[ "Apache-2.0" ]

2

2019-03-03T14:27:54.000Z

2019-07-22T09:00:35.000Z

# -*- coding: utf-8 -*- from config_fh import get_db_engine connection = get_db_engine().raw_connection() cursor = connection.cursor() class DataTablesServer(object): def __init__(self, request, columns, index, table): self.columns = columns self.index = index self.table = table self.request_values = request.values self.dbh = cursor self.resultData = None self.cadinalityFiltered = 0 self.cadinality = 0 self.run_queries() def output_result(self): output = {} output['sEcho'] = str(int(self.request_values['sEcho'])) output['iTotalRecords'] = str(self.cardinality) output['iTotalDisplayRecords'] = str(self.cadinalityFiltered) aaData_rows = [] for row in self.resultData: aaData_row = {} for i in range(len(self.columns)): aaData_row[self.columns[i]] = row[i] aaData_rows.append(aaData_row) output['aaData'] = aaData_rows return output def run_queries(self): dataCursor = self.dbh dataCursor.execute(""" SELECT SQL_CALC_FOUND_ROWS %(columns)s FROM %(table)s %(where)s %(order)s %(limit)s""" % dict( columns=', '.join(self.columns), table=self.table, where=self.filtering(), order=self.ordering(), limit=self.paging() )) self.resultData = dataCursor.fetchall() cadinalityFilteredCursor = self.dbh cadinalityFilteredCursor.execute(""" SELECT FOUND_ROWS() """) self.cadinalityFiltered = cadinalityFilteredCursor.fetchone()[0] cadinalityCursor = self.dbh cadinalityCursor.execute("""SELECT COUNT(%s) FROM %s""" % (self.index, self.table)) self.cardinality = cadinalityCursor.fetchone()[0] def filtering(self): filter = "" if (self.request_values.has_key('sSearch')) and (self.request_values['sSearch'] != ""): filter = "WHERE " for i in range(len(self.columns)): filter += "%s LIKE '%%%s%%' OR " % (self.columns[i], self.request_values['sSearch']) filter = filter[:-3] return filter def ordering(self): order = "" if (self.request_values['iSortCol_0'] != "") and (int(self.request_values['iSortingCols']) > 0): order = "ORDER BY " for i in range(int(self.request_values['iSortingCols'])): order += "%s %s, " % (self.columns[int(self.request_values['iSortCol_' + str(i)])], self.request_values['sSortDir_' + str(i)]) return order[:-2] def paging(self): limit = "" if (self.request_values['iDisplayStart'] != "") and (self.request_values['iDisplayLength'] != -1): limit = "LIMIT %s, %s" % (self.request_values['iDisplayStart'], self.request_values['iDisplayLength']) return limit

38.480519

114

0.585218

from config_fh import get_db_engine connection = get_db_engine().raw_connection() cursor = connection.cursor() class DataTablesServer(object): def __init__(self, request, columns, index, table): self.columns = columns self.index = index self.table = table self.request_values = request.values self.dbh = cursor self.resultData = None self.cadinalityFiltered = 0 self.cadinality = 0 self.run_queries() def output_result(self): output = {} output['sEcho'] = str(int(self.request_values['sEcho'])) output['iTotalRecords'] = str(self.cardinality) output['iTotalDisplayRecords'] = str(self.cadinalityFiltered) aaData_rows = [] for row in self.resultData: aaData_row = {} for i in range(len(self.columns)): aaData_row[self.columns[i]] = row[i] aaData_rows.append(aaData_row) output['aaData'] = aaData_rows return output def run_queries(self): dataCursor = self.dbh dataCursor.execute(""" SELECT SQL_CALC_FOUND_ROWS %(columns)s FROM %(table)s %(where)s %(order)s %(limit)s""" % dict( columns=', '.join(self.columns), table=self.table, where=self.filtering(), order=self.ordering(), limit=self.paging() )) self.resultData = dataCursor.fetchall() cadinalityFilteredCursor = self.dbh cadinalityFilteredCursor.execute(""" SELECT FOUND_ROWS() """) self.cadinalityFiltered = cadinalityFilteredCursor.fetchone()[0] cadinalityCursor = self.dbh cadinalityCursor.execute("""SELECT COUNT(%s) FROM %s""" % (self.index, self.table)) self.cardinality = cadinalityCursor.fetchone()[0] def filtering(self): filter = "" if (self.request_values.has_key('sSearch')) and (self.request_values['sSearch'] != ""): filter = "WHERE " for i in range(len(self.columns)): filter += "%s LIKE '%%%s%%' OR " % (self.columns[i], self.request_values['sSearch']) filter = filter[:-3] return filter def ordering(self): order = "" if (self.request_values['iSortCol_0'] != "") and (int(self.request_values['iSortingCols']) > 0): order = "ORDER BY " for i in range(int(self.request_values['iSortingCols'])): order += "%s %s, " % (self.columns[int(self.request_values['iSortCol_' + str(i)])], self.request_values['sSortDir_' + str(i)]) return order[:-2] def paging(self): limit = "" if (self.request_values['iDisplayStart'] != "") and (self.request_values['iDisplayLength'] != -1): limit = "LIMIT %s, %s" % (self.request_values['iDisplayStart'], self.request_values['iDisplayLength']) return limit

true

1c37deb30c87f7186536d3181d22fa842c9470ef

119

py

Python

executiveorder/__init__.py

chrisengelsma/executive_orders

5f0c7102b9abce3d44b54e5dd4c57bd0bb404037

[ "MIT" ]

2

2017-03-23T02:26:05.000Z

2017-08-24T02:07:17.000Z

executiveorder/__init__.py

chrisengelsma/executive_orders

5f0c7102b9abce3d44b54e5dd4c57bd0bb404037

[ "MIT" ]

null

executiveorder/__init__.py

chrisengelsma/executive_orders

5f0c7102b9abce3d44b54e5dd4c57bd0bb404037

[ "MIT" ]

null

#!/usr/bin/env python # -*- coding: utf-8 -*- from executiveorder import * from util import * from processors import *

19.833333

28

0.697479

from executiveorder import * from util import * from processors import *

true

1c37df03de31a22741263c2f30a64b198279daf0

3,874

py

Python

rs/localization_files/ES.py

alexander-marquardt/lexalink

d554f3a00699c8a4cdf1b28dd033655f929470fa

[ "Apache-2.0", "BSD-3-Clause" ]

1

2017-02-09T07:12:25.000Z

rs/localization_files/ES.py

alexander-marquardt/lexalink

d554f3a00699c8a4cdf1b28dd033655f929470fa

[ "Apache-2.0", "BSD-3-Clause" ]

null

rs/localization_files/ES.py

alexander-marquardt/lexalink

d554f3a00699c8a4cdf1b28dd033655f929470fa

[ "Apache-2.0", "BSD-3-Clause" ]

null

# -*- coding: utf-8 -*- ################################################################################ # LexaLink Copyright information - do not remove this copyright notice # Copyright (C) 2012 # # Lexalink - a free social network and dating platform for the Google App Engine. # # Original author: Alexander Marquardt # Documentation and additional information: http://www.LexaLink.com # Git source code repository: https://github.com/lexalink/LexaLink.git # # Please consider contributing your enhancements and modifications to the LexaLink community, # # 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. ################################################################################ # Spain ES_regions = [ (( u'AN', u'Andalucía'), [(u'AL', u'Almería'), (u'CA', u'Cádiz'), (u'CO', u'Córdoba'), (u'GR', u'Granada'), (u'H', u'Huelva'), (u'J', u'Jaén'), (u'MA', u'Málaga'), (u'SE', u'Sevilla')]), ((u'AR', u'Aragón'), [(u'HU', u'Huesca'), (u'TE', u'Teruel'), (u'Z', u'Zaragoza')]), ((u'O', u'Asturias'), [(u'O', u'Asturias')]), ((u'IB', u'Beleares'), # the following region codes are invented (for islands)-- these are not official # regions. But doesn't matter for purposes of indexing into the database. [(u'MA', u'Mallorca'), (u'ME', u'Menorca'), (u'IB', u'Ibiza'), (u'FO', u'Formentera')]), ((u'CN', u'Canarias'), [(u'GC', u'Las Palmas'), (u'TF', u'Santa Cruz de Tenerife')]), ((u'S', u'Cantabria'), [(u'S', u'Cantabria')]), ((u'CL', u'Castilla y León'), [(u'AV', u'Avila'), (u'BU', u'Burgos'), (u'LE', u'León'), (u'P', u'Palencia'), (u'SA', u'Salamanca'), (u'SG', u'Segovia'), (u'SO', u'Soria'), (u'VA', u'Valladolid'), (u'ZA', u'Zamora')]), ((u'CM', u'Castilla la Mancha'), [(u'AB', u'Albacete'), (u'CR', u'Ciudad Real'), (u'LE', u'León'), (u'CU', u'Cuenca'), (u'GU', u'Guadalajara'), (u'TO', u'Toledo')]), ((u'CT', u'Cataluña'), [(u'B', u'Barcelona'), (u'GI', u'Girona'), (u'L', u'Lleida'), (u'T', u'Tarragona')]), ((u'CE', u'Ceuta'), [(u'CE', u'Ceuta')]), ((u'EX', u'Extremadura'), [(u'BA', u'Badajoz'), (u'CC', u'Cáceres')]), ((u'GA', u'Galicia'), [(u'C', u'A Coruña'), (u'LU', u'Lugo'), (u'OR', u'Orense'), (u'PO', u'Pontevedra')]), ((u'LO', u'La Rioja'), [(u'LO', u'La Rioja')]), ((u'M', u'Madrid'), [(u'M', u'Área Metropolitana'), (u'1', u'Comarca de Las Vegas'), (u'2', u'Comarca Sur'), (u'3', u'Cuenca Alta del Manzanares'), (u'4', u'Cuenca del Guadarrama'), (u'5', u'Cuenca del Henares'), (u'6', u'Cuenca del Medio Jarama'), (u'7', u'Sierra Norte'), (u'8', u'Sierra Oeste'), ]), ((u'ML', u'Melilla'), [(u'ML', u'Melilla')]), ((u'MU', u'Murcia'), [(u'MU', u'Murcia')]), ((u'NA', u'Navarra'), [(u'NA', u'Navarra')]), ((u'PV', u'País Vasco'), [(u'VI', u'Álava'), (u'SS', u'Giupúzcoa'), (u'BI', u'Vizcaya')]), ((u'CV', u'Valencia'), [(u'A', u'Alicante'), (u'CS', u'Castellón'), (u'V', u'Valencia')]) ]

29.12782

94

0.497935

true