xszheng2020/the_stack_dedup_python_hits_5 · Datasets at Hugging Face

976e1c5a15497e47a1bcbbe0427e677ccca6a94a

84

py

Python

rosreestr2coord/__init__.py

antonsukhanov/rosreestr2coord

abf8da7ed2eef32fc0926a0568b48d48bdb872a7

[ "MIT" ]

121

2016-04-27T13:03:53.000Z

2022-03-07T08:41:47.000Z

rosreestr2coord/__init__.py

antonsukhanov/rosreestr2coord

abf8da7ed2eef32fc0926a0568b48d48bdb872a7

[ "MIT" ]

54

2017-05-16T13:32:46.000Z

2022-03-09T07:28:51.000Z

rosreestr2coord/__init__.py

antonsukhanov/rosreestr2coord

abf8da7ed2eef32fc0926a0568b48d48bdb872a7

[ "MIT" ]

68

2016-12-01T15:37:55.000Z

2022-03-15T20:28:34.000Z

from rosreestr2coord.parser import Area from rosreestr2coord.version import VERSION

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5

976f33db6f8f027503a36eeafa1169b17b0744d7

39

py

Python

PiCN/Executable/Helpers/ConfigParser/__init__.py

NikolaiRutz/PiCN

7775c61caae506a88af2e4ec34349e8bd9098459

[ "BSD-3-Clause" ]

null

PiCN/Executable/Helpers/ConfigParser/__init__.py

NikolaiRutz/PiCN

7775c61caae506a88af2e4ec34349e8bd9098459

[ "BSD-3-Clause" ]

5

2020-07-15T09:01:42.000Z

2020-09-28T08:45:21.000Z

PiCN/Executable/Helpers/ConfigParser/__init__.py

NikolaiRutz/PiCN

7775c61caae506a88af2e4ec34349e8bd9098459

[ "BSD-3-Clause" ]

null

from .ConfigParser import ConfigParser

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1

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1

0

5

9777e33ebfc0e74b9c2d7d4b9e07b4f5b47aa5a6

140

py

Python

trends.py

PapaSinku/Sinku-the-Duck

535496369c3097d619f57f650a8af39c0b26e6c6

[ "MIT" ]

3

2021-11-24T16:26:56.000Z

2021-11-29T19:40:10.000Z

trends.py

PapaSinku/Sinku-the-Duck

535496369c3097d619f57f650a8af39c0b26e6c6

[ "MIT" ]

null

trends.py

PapaSinku/Sinku-the-Duck

535496369c3097d619f57f650a8af39c0b26e6c6

[ "MIT" ]

null

import tweepy def get_latest_trend(api : tweepy.API): trends_result = api.get_place_trends(1) return trends_result[0]['trends'][1]

23.333333

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5

978a2e79d5fd224f95bffe37711cb8b25fafe1b0

184

py

Python

api/utils/states.py

stephansama/usa_tax_api

2fc321aadc6c58215b21cc8752c9fc9c5cb38714

[ "MIT" ]

null

api/utils/states.py

stephansama/usa_tax_api

2fc321aadc6c58215b21cc8752c9fc9c5cb38714

[ "MIT" ]

null

api/utils/states.py

stephansama/usa_tax_api

2fc321aadc6c58215b21cc8752c9fc9c5cb38714

[ "MIT" ]

null

from sqlalchemy.orm import Session from database.models.state import State def find_state(db: Session, state_id: int): return db.query(State).filter(State.id == state_id).first()

30.666667

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0

1

0

1

0

5

97a57afc7d405e94a69ef498a10164b919dccfa0

260

py

Python

docs/tests/W0199.py

mrfyda/codacy-pylint-python3

e360f6c0407edebe274835d3a881d67e96adf8ba

[ "Apache-2.0" ]

17

2016-01-26T13:30:04.000Z

2022-03-06T21:11:42.000Z

docs/tests/W0199.py

mrfyda/codacy-pylint-python3

e360f6c0407edebe274835d3a881d67e96adf8ba

[ "Apache-2.0" ]

50

2019-08-14T16:14:45.000Z

2022-03-31T11:00:50.000Z

docs/tests/W0199.py

mrfyda/codacy-pylint-python3

e360f6c0407edebe274835d3a881d67e96adf8ba

[ "Apache-2.0" ]

15

2015-11-18T12:18:50.000Z

2021-01-17T22:21:41.000Z

##Patterns: W0199 assert (1 == 1, 2 == 2), "no error" ##Warn: W0199 assert (1 == 1, 2 == 2) assert 1 == 1, "no error" assert (1 == 1,), "no error" assert (1 == 1,) assert (1 == 1, 2 == 2, 3 == 5), "no error" assert () ##Warn: W0199 assert (True, 'error msg')

20

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null

0

1

0

1

0

5

97c8679d93d28fee4e56fad3280efd2ddb3487e5

291

py

Python

manm_cs/prob_distributions/__init__.py

hpi-epic/manm-cs

042248799e3d07ba6267c5b67133dd121f5a7331

[ "MIT" ]

null

manm_cs/prob_distributions/__init__.py

hpi-epic/manm-cs

042248799e3d07ba6267c5b67133dd121f5a7331

[ "MIT" ]

11

2021-08-08T14:21:57.000Z

2022-01-13T11:47:26.000Z

manm_cs/prob_distributions/__init__.py

hpi-epic/manm-cs

042248799e3d07ba6267c5b67133dd121f5a7331

[ "MIT" ]

null

from manm_cs.prob_distributions.continuous import GaussianDistribution from manm_cs.prob_distributions.discrete import BinomialDistribution from manm_cs.prob_distributions.discrete import CustomDiscreteDistribution from manm_cs.prob_distributions.discrete import UniformDiscreteDistribution

58.2

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0.216216

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0

5

97cc82d06ec2705d0e9df0ab5aa1f6ce804dac0f

26

py

Python

Lib/test/test_compiler/testcorpus/74_class_kwargs.py

diogommartins/cinder

79103e9119cbecef3b085ccf2878f00c26e1d175

[ "CNRI-Python-GPL-Compatible" ]

1,886

2021-05-03T23:58:43.000Z

2022-03-31T19:15:58.000Z

Lib/test/test_compiler/testcorpus/74_class_kwargs.py

diogommartins/cinder

79103e9119cbecef3b085ccf2878f00c26e1d175

[ "CNRI-Python-GPL-Compatible" ]

70

2021-05-04T23:25:35.000Z

2022-03-31T18:42:08.000Z

Lib/test/test_compiler/testcorpus/74_class_kwargs.py

diogommartins/cinder

79103e9119cbecef3b085ccf2878f00c26e1d175

[ "CNRI-Python-GPL-Compatible" ]

52

2021-05-04T21:26:03.000Z

2022-03-08T18:02:56.000Z

class Foo(x=42): pass

8.666667

16

0.576923

5

26

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1

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0.105263

0.269231

26

2

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13

0.684211

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true

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null

0

1

0

null

0

1

0

5

8ada68c64d935e82096725ea9e77df436ebeb2fc

1,673

py

Python

temboo/core/Library/Disqus/Threads/__init__.py

jordanemedlock/psychtruths

52e09033ade9608bd5143129f8a1bfac22d634dd

[ "Apache-2.0" ]

7

2016-03-07T02:07:21.000Z

2022-01-21T02:22:41.000Z

temboo/core/Library/Disqus/Threads/__init__.py

jordanemedlock/psychtruths

52e09033ade9608bd5143129f8a1bfac22d634dd

[ "Apache-2.0" ]

null

temboo/core/Library/Disqus/Threads/__init__.py

jordanemedlock/psychtruths

52e09033ade9608bd5143129f8a1bfac22d634dd

[ "Apache-2.0" ]

8

2016-06-14T06:01:11.000Z

2020-04-22T09:21:44.000Z

from temboo.Library.Disqus.Threads.CloseThread import CloseThread, CloseThreadInputSet, CloseThreadResultSet, CloseThreadChoreographyExecution from temboo.Library.Disqus.Threads.CreateThread import CreateThread, CreateThreadInputSet, CreateThreadResultSet, CreateThreadChoreographyExecution from temboo.Library.Disqus.Threads.ListPosts import ListPosts, ListPostsInputSet, ListPostsResultSet, ListPostsChoreographyExecution from temboo.Library.Disqus.Threads.ListThreads import ListThreads, ListThreadsInputSet, ListThreadsResultSet, ListThreadsChoreographyExecution from temboo.Library.Disqus.Threads.OpenThread import OpenThread, OpenThreadInputSet, OpenThreadResultSet, OpenThreadChoreographyExecution from temboo.Library.Disqus.Threads.RemoveThread import RemoveThread, RemoveThreadInputSet, RemoveThreadResultSet, RemoveThreadChoreographyExecution from temboo.Library.Disqus.Threads.RestoreThread import RestoreThread, RestoreThreadInputSet, RestoreThreadResultSet, RestoreThreadChoreographyExecution from temboo.Library.Disqus.Threads.SubscribeToThread import SubscribeToThread, SubscribeToThreadInputSet, SubscribeToThreadResultSet, SubscribeToThreadChoreographyExecution from temboo.Library.Disqus.Threads.ThreadDetails import ThreadDetails, ThreadDetailsInputSet, ThreadDetailsResultSet, ThreadDetailsChoreographyExecution from temboo.Library.Disqus.Threads.UnsubscribeFromThread import UnsubscribeFromThread, UnsubscribeFromThreadInputSet, UnsubscribeFromThreadResultSet, UnsubscribeFromThreadChoreographyExecution from temboo.Library.Disqus.Threads.VoteOnThread import VoteOnThread, VoteOnThreadInputSet, VoteOnThreadResultSet, VoteOnThreadChoreographyExecution

139.416667

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121

1,673

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0.046025

1,673

11

193

152.090909

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null

0

1

0

1

0

5

8ae385b7c62f61c7d90a52961fc489602ee0bc21

160

py

Python

corehq/apps/fixtures/__init__.py

rochakchauhan/commcare-hq

aa7ab3c2d0c51fe10f2b51b08101bb4b5a376236

[ "BSD-3-Clause" ]

null

corehq/apps/fixtures/__init__.py

rochakchauhan/commcare-hq

aa7ab3c2d0c51fe10f2b51b08101bb4b5a376236

[ "BSD-3-Clause" ]

null

corehq/apps/fixtures/__init__.py

rochakchauhan/commcare-hq

aa7ab3c2d0c51fe10f2b51b08101bb4b5a376236

[ "BSD-3-Clause" ]

null

from django.conf import settings from corehq.preindex import ExtraPreindexPlugin ExtraPreindexPlugin.register('fixtures', __file__, settings.NEW_FIXTURES_DB)

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160

7.277778

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160

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true

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null

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1

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1

0

1

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5

c1041764cf05ffed21f88e50d600bcbf8c41d247

4,239

py

Python

bottomline/blweb/tests/test_account_create.py

mcm219/BottomLine

db82eef403c79bffa3864c4db6bc336632abaca5

[ "MIT" ]

null

bottomline/blweb/tests/test_account_create.py

mcm219/BottomLine

db82eef403c79bffa3864c4db6bc336632abaca5

[ "MIT" ]

1

2021-06-14T02:20:40.000Z

bottomline/blweb/tests/test_account_create.py

mcm219/BottomLine

db82eef403c79bffa3864c4db6bc336632abaca5

[ "MIT" ]

null

from http import HTTPStatus from django.contrib.auth import get_user_model from django.contrib.auth.models import User from django.test import TestCase from django.test import Client from django.urls import reverse # Test class for unit tests on the user login interface from blweb.models import AccountType class TestAccountCreate(TestCase): # any needed setup for the tests. this function will be run before every test case def setUp(self): self.client = Client() self.username = "TEST_USER_123" self.first_name = "TEST" self.last_name = "USER" self.email = "TEST@host.com" self.phone = "(912) 123-4567" self.password1 = "rqwerwfw12321ef" # check to see if the shopper page is present def test_account_create_page_present(self): response = self.client.get('/accounts/account_signup/') self.assertEqual(response.status_code, 200) self.assertTemplateUsed(response, template_name='account_signup.html') # check that the proper view was used (shopper) def test_account_view_name(self): response = self.client.get(reverse('account_signup')) self.assertEqual(response.status_code, 200) self.assertTemplateUsed(response, template_name='account_signup.html') # check to see if the dealer page is present def test_dealer_account_create_page_present(self): response = self.client.get('/accounts/dealer_signup/') self.assertEqual(response.status_code, 200) self.assertTemplateUsed(response, template_name='dealer_signup.html') # check that the proper view was used (dealer) def test_dealer_account_view_name(self): response = self.client.get(reverse('dealer_signup')) self.assertEqual(response.status_code, 200) self.assertTemplateUsed(response, template_name='dealer_signup.html') # test that the user signup form works as expected def test_shopper_account_form(self): response = self.client.post(reverse('account_signup'), data={ 'username': self.username, 'first_name': self.first_name, 'last_name': self.last_name, 'phone': self.phone, 'email': self.email, 'password1': self.password1, 'password2': self.password1 }) self.assertEqual(response.status_code, 200) users = get_user_model().objects.all() self.assertEqual(users.count(), 1) # test that the account_type is set correctly for a dealer def test_shopper_account_type(self): response = self.client.post(reverse('account_signup'), data={ 'username': self.username, 'first_name': self.first_name, 'last_name': self.last_name, 'phone': self.phone, 'email': self.email, 'password1': self.password1, 'password2': self.password1 }) user = User.objects.get(username=self.username) self.assertEqual(AccountType.SHOPPER.value, user.profile.account_type) # test that the dealer signup form works as expected def test_dealer_account_form(self): response = self.client.post(reverse('dealer_signup'), data={ 'username': self.username, 'first_name': self.first_name, 'last_name': self.last_name, 'phone': self.phone, 'email': self.email, 'password1': self.password1, 'password2': self.password1 }) self.assertEqual(response.status_code, 200) users = get_user_model().objects.all() self.assertEqual(users.count(), 1) # test that the account_type is set correctly for a dealer def test_dealer_account_type(self): response = self.client.post(reverse('dealer_signup'), data={ 'username': self.username, 'first_name': self.first_name, 'last_name': self.last_name, 'phone': self.phone, 'email': self.email, 'password1': self.password1, 'password2': self.password1 }) user = User.objects.get(username=self.username) self.assertEqual(AccountType.DEALER.value, user.profile.account_type)

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5

c14b8efade766e85587636146fbaec8d804c5ad4

196

py

Python

example/users/admin.py

kaoslabsinc/django-iam

42c027aa76c66c9d3868a8e2f884e954c323e08d

[ "BSD-3-Clause" ]

null

example/users/admin.py

kaoslabsinc/django-iam

42c027aa76c66c9d3868a8e2f884e954c323e08d

[ "BSD-3-Clause" ]

10

2021-07-27T20:50:00.000Z

2021-08-11T16:35:08.000Z

example/users/admin.py

kaoslabsinc/django-iam

42c027aa76c66c9d3868a8e2f884e954c323e08d

[ "BSD-3-Clause" ]

1

2021-08-12T18:57:19.000Z

from django.contrib import admin from django.contrib.auth import get_user_model from iam.contrib.users.admin import IAMUserAdmin User = get_user_model() admin.site.register(User, IAMUserAdmin)

21.777778

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5

c1611124b19361f7a4c83497e279c74e0b3812d9

69

py

Python

turingquant/__init__.py

GrupoTuring/turingquant

f10ea1bc69435ec39bb2f8c0533d31d8a4b3b5f7

[ "MIT" ]

10

2020-09-01T03:28:41.000Z

2021-04-15T05:12:37.000Z

turingquant/__init__.py

GrupoTuring/turingquant

f10ea1bc69435ec39bb2f8c0533d31d8a4b3b5f7

[ "MIT" ]

26

2020-09-10T20:51:05.000Z

2021-04-10T04:34:15.000Z

turingquant/__init__.py

turing-usp/turingquant

f10ea1bc69435ec39bb2f8c0533d31d8a4b3b5f7

[ "MIT" ]

null

from . import benchmark, metrics, support, optimizers, plot_metrics

23

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2

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1

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1

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5

c1668903f01c5e844925830ced8455676f3e3b67

146

py

Python

app/util/indy.py

didx-xyz/aries-cloudapi-python

0c8004265c4bfd88f313a152f2757ec0441740a7

[ "Apache-2.0" ]

7

2021-05-19T17:50:31.000Z

2022-01-16T13:52:34.000Z

app/util/indy.py

didx-xyz/aries-cloudapi-python

0c8004265c4bfd88f313a152f2757ec0441740a7

[ "Apache-2.0" ]

181

2021-05-25T14:55:14.000Z

2022-03-30T11:37:34.000Z

app/util/indy.py

didx-xyz/aries-cloudapi-python

0c8004265c4bfd88f313a152f2757ec0441740a7

[ "Apache-2.0" ]

5

2021-06-02T06:57:52.000Z

2022-03-23T10:23:07.000Z

def did_from_credential_definition_id(credential_definition_id: str) -> str: parts = credential_definition_id.split(":") return parts[0]

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1

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null

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5

c169afea448339db168832bb87dc441181a00d57

6,454

py

Python

tests/test_configuration.py

leibowitz/django-auth-adfs

10b664aa55518fb0df806d4256e0883e012b9b5b

[ "BSD-2-Clause" ]

null

tests/test_configuration.py

leibowitz/django-auth-adfs

10b664aa55518fb0df806d4256e0883e012b9b5b

[ "BSD-2-Clause" ]

null

tests/test_configuration.py

leibowitz/django-auth-adfs

10b664aa55518fb0df806d4256e0883e012b9b5b

[ "BSD-2-Clause" ]

null

import os from django.conf import settings from django.core.exceptions import ImproperlyConfigured from django.test import TestCase, Client from httmock import with_httmock, urlmatch from mock import patch, mock_open from django_auth_adfs.backend import AdfsBackend from django_auth_adfs.config import Settings from .utils import get_base_claims, encode_jwt client = Client() @urlmatch(path=r"^/adfs/oauth2/token$") def token_response(url, request): claims = get_base_claims() token = encode_jwt(claims) return {'status_code': 200, 'content': b'{"access_token":"' + token + b'"}'} @urlmatch(path=r"^/FederationMetadata/2007-06/FederationMetadata.xml$") def metadata_response(url, request): with open(os.path.join(os.path.dirname(__file__), "FederationMetadata_valid_cert_first.xml")) as f: return {'status_code': 200, 'content': f.read()} @urlmatch(path=r"^/FederationMetadata/2007-06/FederationMetadata.xml$") def empty_metadata_response(url, request): with open(os.path.join(os.path.dirname(__file__), "FederationMetadata_empty.xml")) as f: return {'status_code': 200, 'content': f.read()} class InvalidConfigurationTests(TestCase): @with_httmock(token_response) def test_invalid_redir_uri(self): backend = AdfsBackend() with patch("django_auth_adfs.backend.settings.REDIR_URI", None): self.assertRaises(ImproperlyConfigured, backend.authenticate, authorization_code='testcode') @with_httmock(token_response) def test_required_setting(self): new_settings = settings.AUTH_ADFS new_settings["SERVER"] = None with self.settings(AUTH_ADFS=new_settings): self.assertRaises(ImproperlyConfigured, Settings) @with_httmock(token_response) def test_unknown_setting(self): new_settings = settings.AUTH_ADFS new_settings["NON_EXISTING"] = "Dummy" with self.settings(AUTH_ADFS=new_settings): self.assertRaises(ImproperlyConfigured, Settings) @with_httmock(token_response) def test_missing_setting(self): with self.settings(): del settings.AUTH_ADFS self.assertRaises(ImproperlyConfigured, Settings) @with_httmock(token_response) def test_invalid_certificate(self): with patch("django_auth_adfs.backend.settings.SIGNING_CERT", None): self.assertRaises(ImproperlyConfigured, AdfsBackend) @with_httmock(token_response) def test_invalid_certificate_path(self): mock_file_path = "/path/to/cert.pem" with patch("django_auth_adfs.backend.AdfsBackend._public_keys", []): with patch("django_auth_adfs.backend.settings.SIGNING_CERT", mock_file_path): with patch("django_auth_adfs.backend.isfile") as mock_isfile: mock_isfile.return_value = False self.assertRaises(ImproperlyConfigured, AdfsBackend) @with_httmock(token_response) def test_claim_mapping_non_existing_model_field(self): backend = AdfsBackend() mock_claim_mapping = { "nonexisting": "given_name", "last_name": "family_name", "email": "email" } with patch("django_auth_adfs.backend.settings.CLAIM_MAPPING", mock_claim_mapping): self.assertRaises(ImproperlyConfigured, backend.authenticate, authorization_code="dummycode") @with_httmock(token_response) def test_bool_claim_mapping_non_existing_model_field(self): backend = AdfsBackend() mock_claim_mapping = { "is_staffffffffff": "user_is_staff", } with patch("django_auth_adfs.backend.settings.BOOLEAN_CLAIM_MAPPING", mock_claim_mapping): self.assertRaises(ImproperlyConfigured, backend.authenticate, authorization_code="dummycode") @with_httmock(token_response) def test_claim_mapping_non_existing_claim(self): backend = AdfsBackend() mock_claim_mapping = { "first_name": "nonexisting", "last_name": "family_name", "email": "email" } with patch("django_auth_adfs.backend.settings.CLAIM_MAPPING", mock_claim_mapping): self.assertRaises(ImproperlyConfigured, backend.authenticate, authorization_code="dummycode") class ConfigurationVariationsTests(TestCase): @with_httmock(token_response) def test_invalid_redir_uri(self): backend = AdfsBackend() with patch("django_auth_adfs.backend.settings.REDIR_URI", None): self.assertRaises(ImproperlyConfigured, backend.authenticate, authorization_code='testcode') @with_httmock(token_response) def test_invalid_certificate(self): with patch("django_auth_adfs.backend.AdfsBackend._public_keys", []): with patch("django_auth_adfs.backend.settings.SIGNING_CERT", None): self.assertRaises(ImproperlyConfigured, AdfsBackend) @with_httmock(token_response) def test_claim_mapping_non_existing_model_field(self): backend = AdfsBackend() mock_claim_mapping = { "nonexisting": "given_name", "last_name": "family_name", "email": "email" } with patch("django_auth_adfs.backend.settings.CLAIM_MAPPING", mock_claim_mapping): self.assertRaises(ImproperlyConfigured, backend.authenticate, authorization_code="dummycode") @with_httmock(token_response) def test_signing_cert_file(self): cert_content = settings.AUTH_ADFS["SIGNING_CERT"] mock_file_path = "/path/to/cert.pem" with patch("django_auth_adfs.backend.AdfsBackend._public_keys", []): with patch("django_auth_adfs.backend.settings.SIGNING_CERT", mock_file_path): with patch("django_auth_adfs.backend.isfile") as mock_isfile: mock_isfile.return_value = True with patch("django_auth_adfs.backend.open", mock_open(read_data=cert_content)) as mock_file: AdfsBackend() mock_file.assert_called_once_with(mock_file_path, 'r') @with_httmock(token_response) def test_authentication(self): with patch("django_auth_adfs.backend.settings.LOGIN_REDIRECT_URL", "/test/path/"): response = client.get("/oauth2/login", {'code': 'testcode'}) self.assertEqual(response.status_code, 302) self.assertTrue(response['Location'].endswith('/test/path/'))

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c18e0ab4b12dca0bd25c4379604af5c6f48a786d

155

py

Python

webdev/home_view.py

robsonleal/django-pro

68c02c287b33d8ee94bd2c08f1a10b86470ae6c6

[ "MIT" ]

null

webdev/home_view.py

robsonleal/django-pro

68c02c287b33d8ee94bd2c08f1a10b86470ae6c6

[ "MIT" ]

7

2022-01-14T19:08:46.000Z

2022-03-16T20:04:12.000Z

webdev/home_view.py

robsonleal/django-pro

68c02c287b33d8ee94bd2c08f1a10b86470ae6c6

[ "MIT" ]

null

from django.http import HttpResponseRedirect from django.urls import reverse def home(request): return HttpResponseRedirect(reverse('tarefas:home'))

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c1b10abe1a574daff609c2d38c92d16b9acb873b

508

py

Python

flask_miracle/functions.py

tdpsk/flask-miracle-acl

426a9845854678d00108cf5f91ada9323968b524

[ "BSD-2-Clause" ]

2

2018-01-17T15:57:38.000Z

2018-02-06T00:03:16.000Z

flask_miracle/functions.py

tdpsk/flask-miracle-acl

426a9845854678d00108cf5f91ada9323968b524

[ "BSD-2-Clause" ]

null

flask_miracle/functions.py

tdpsk/flask-miracle-acl

426a9845854678d00108cf5f91ada9323968b524

[ "BSD-2-Clause" ]

null

''' flask_miracle.functions ----------------------- functions callable from within a Flask context ''' from flask import current_app def check_any(resource, permission, roles=None): return current_app.miracle_acl_manager.check_any(resource, permission, roles=None) def check_all(resource, permission, roles=None): return current_app.miracle_acl_manager.check_all(resource, permission, roles=None) def set_current_roles(roles): return current_app.miracle_acl_manager.set_current_roles(roles)

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5

c1e3c8ec39bd35ff5887de472762b4b80acf5137

96

py

Python

venv/lib/python3.8/site-packages/libpasteurize/fixes/fix_throw.py

GiulianaPola/select_repeats

17a0d053d4f874e42cf654dd142168c2ec8fbd11

[ "MIT" ]

2

2022-03-13T01:58:52.000Z

2022-03-31T06:07:54.000Z

venv/lib/python3.8/site-packages/libpasteurize/fixes/fix_throw.py

DesmoSearch/Desmobot

b70b45df3485351f471080deb5c785c4bc5c4beb

[ "MIT" ]

19

2021-11-20T04:09:18.000Z

2022-03-23T15:05:55.000Z

venv/lib/python3.8/site-packages/libpasteurize/fixes/fix_throw.py

DesmoSearch/Desmobot

b70b45df3485351f471080deb5c785c4bc5c4beb

[ "MIT" ]

null

/home/runner/.cache/pip/pool/fd/94/44/56b7be5adb54be4e2c5a3aea50daa6f50d6e15a013102374ffe3d729b9

96

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1

0

null

1

0

1

0

5

c1e690f9b64cb0dbf7d17727de17cd08dc2444d4

89

py

Python

auto.py

robertoweller/python

b01939810f7eb388f4b79bfad00abc5fb293d8dd

[ "MIT" ]

null

auto.py

robertoweller/python

b01939810f7eb388f4b79bfad00abc5fb293d8dd

[ "MIT" ]

null

auto.py

robertoweller/python

b01939810f7eb388f4b79bfad00abc5fb293d8dd

[ "MIT" ]

null

from time import sleep # Instalado "pip install selenium" from selenium import webdriver

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5

c1f1306ad05ccc4a1880c3010ccb80960bfbe9d6

514

py

Python

sample_data.py

michael-stricklin/sugar

1129c90657f329880a58f2e89d9a833ecb090f9a

[ "Apache-2.0" ]

1

2020-05-07T22:28:59.000Z

sample_data.py

michael-stricklin/sugar

1129c90657f329880a58f2e89d9a833ecb090f9a

[ "Apache-2.0" ]

null

sample_data.py

michael-stricklin/sugar

1129c90657f329880a58f2e89d9a833ecb090f9a

[ "Apache-2.0" ]

null

#!/usr/bin/env python ## ============================================================================= jsonStr = """[{"DT":"\/Date(1495333623000-0700)\/", "ST":"\/Date(1495337144000)\/", "Trend":8, "Value":245, "WT":"\/Date(1495326471000)\/"}, {"DT":"\/Date(1519423410000-0700)\/", "ST":"\/Date(1519423939000)\/", "Trend":8, "Value":245, "WT":"\/Date(1519423410000)\/"} ]"""

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e74b2ea9209d520c26774d65e4ec2f058c83691d

239

py

Python

holocube/__init__.py

ContinuumIO/cube-explorer

d00731e57255f4d343d0b403be5e2a4cd62ac437

[ "BSD-3-Clause" ]

4

2017-09-17T18:00:22.000Z

2022-03-15T13:00:12.000Z

holocube/__init__.py

ContinuumIO/cube-explorer

d00731e57255f4d343d0b403be5e2a4cd62ac437

[ "BSD-3-Clause" ]

3

2016-03-14T20:48:34.000Z

2016-04-18T00:04:46.000Z

holocube/__init__.py

ContinuumIO/cube-explorer

d00731e57255f4d343d0b403be5e2a4cd62ac437

[ "BSD-3-Clause" ]

4

2017-10-10T23:28:48.000Z

2021-02-23T07:05:02.000Z

from .element import (GeoElement, HoloCube, GeoFeature, # noqa (API import) GeoTiles, WMTS, Contours, Text, Image, Points) from . import plotting # noqa (API import)

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e75ac09c2a77743163fc9a7eef60621ee4d5f737

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py

Python

geek/geek2.py

lcarlin/guppe

a0ee7b85e8687e8fb8243fbb509119a94bc6460f

[ "Apache-2.0" ]

1

2021-12-18T15:29:24.000Z

geek/geek2.py

lcarlin/guppe

a0ee7b85e8687e8fb8243fbb509119a94bc6460f

[ "Apache-2.0" ]

null

geek/geek2.py

lcarlin/guppe

a0ee7b85e8687e8fb8243fbb509119a94bc6460f

[ "Apache-2.0" ]

3

2021-08-23T22:45:20.000Z

2022-02-17T13:17:09.000Z

curso = 'Programacao em Python Essencial' def funcao2(): return curso

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e7ae3cc99c64af93bdc1fdd3bc4857f7f0ca2a79

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py

Python

tensor2struct/models/spider/spider_enc_bert.py

nghoanglong/tensor2struct-public

deb6f14de04ebcc5563b05b7fee86986d73bbd65

[ "MIT" ]

null

tensor2struct/models/spider/spider_enc_bert.py

nghoanglong/tensor2struct-public

deb6f14de04ebcc5563b05b7fee86986d73bbd65

[ "MIT" ]

null

tensor2struct/models/spider/spider_enc_bert.py

nghoanglong/tensor2struct-public

deb6f14de04ebcc5563b05b7fee86986d73bbd65

[ "MIT" ]

null

import collections import itertools import json import os import attr import nltk.corpus import torch import torchtext import numpy as np from tensor2struct.models import abstract_preproc from tensor2struct.utils import serialization, vocab, registry from tensor2struct.modules import rat, lstm, embedders, bert_tokenizer from tensor2struct.resources import vncorenlp from tensor2struct.models.spider.spider_match_utils import ( compute_schema_linking, compute_cell_value_linking ) from transformers import BertModel, ElectraModel from transformers import AutoModel, AutoTokenizer from transformers import logging logging.set_verbosity_error() import logging logger = logging.getLogger("tensor2struct") @attr.s class SpiderEncoderState: state = attr.ib() memory = attr.ib() question_memory = attr.ib() schema_memory = attr.ib() words_for_copying = attr.ib() pointer_memories = attr.ib() pointer_maps = attr.ib() m2c_align_mat = attr.ib() m2t_align_mat = attr.ib() # for copying tokenizer = attr.ib() def find_word_occurrences(self, token): occurrences = [i for i, w in enumerate(self.words_for_copying) if w == token] if len(occurrences) > 0: return occurrences[0] else: return None class SpiderEncoderBertPreproc(abstract_preproc.AbstractPreproc): def __init__( self, save_path, context, bert_version="bert-base-uncased", compute_sc_link=True, compute_cv_link=True, ): self.data_dir = os.path.join(save_path, "enc") self.texts = collections.defaultdict(list) self.compute_sc_link = compute_sc_link self.compute_cv_link = compute_cv_link self.context_config = context self.relations = set() # TODO: should get types from the data # column_types = ["text", "number", "time", "boolean", "others"] # self.tokenizer.add_tokens([f"<type: {t}>" for t in column_types]) self.tokenizer_config = bert_version # lazy init self.context_cache = {} @property def tokenizer(self): if not hasattr(self, "_tokenizer"): self._tokenizer = bert_tokenizer.BERTokenizer(self.tokenizer_config) return self._tokenizer def validate_item(self, item, section): num_words = ( len(item.text) + sum(len(c.name) for c in item.schema.columns) + sum(len(t.name) for t in item.schema.tables) ) if num_words > 512: logger.info(f"Found long seq in {item.schema.db_id}") return False, None else: return True, None def add_item(self, item, section, validation_info): preprocessed = self.preprocess_item(item, validation_info) self.texts[section].append(preprocessed) if section == "train": for relation_name in itertools.chain( preprocessed["schema_relations"].keys(), preprocessed["sc_relations"].keys(), preprocessed["cv_relations"].keys(), ): self.relations.add(relation_name) def clear_items(self): self.texts = collections.defaultdict(list) def preprocess_item(self, item, validation_info): q_text = " ".join(item.text) # use the original words for copying, while they are not necessarily used for encoding # question_for_copying = self.tokenizer.tokenize_and_lemmatize(q_text) question_for_copying = self.tokenizer.tokenize_with_orig(q_text) if item.schema.db_id in self.context_cache: context = self.context_cache[item.schema.db_id] else: context = registry.construct( "context", self.context_config, schema=item.schema, tokenizer=self.tokenizer, ) self.context_cache[item.schema.db_id] = context preproc_schema = context.preproc_schema schema_relations = context.compute_schema_relations() sc_relations = ( context.compute_schema_linking(q_text) if self.compute_sc_link else {} ) cv_relations = ( context.compute_cell_value_linking(q_text) if self.compute_cv_link else {} ) return { "question_text": q_text, "question_for_copying": question_for_copying, "db_id": item.schema.db_id, "schema_relations": schema_relations, "sc_relations": sc_relations, "cv_relations": cv_relations, "columns": preproc_schema.column_names, "tables": preproc_schema.table_names, "table_bounds": preproc_schema.table_bounds, "column_to_table": preproc_schema.column_to_table, "table_to_columns": preproc_schema.table_to_columns, "foreign_keys": preproc_schema.foreign_keys, "foreign_keys_tables": preproc_schema.foreign_keys_tables, "primary_keys": preproc_schema.primary_keys, } def save(self): os.makedirs(self.data_dir, exist_ok=True) # self.tokenizer.save_pretrained(self.data_dir) default_relations = registry.lookup( "context", self.context_config["name"] ).get_default_relations() self.relations = sorted(self.relations.union(default_relations)) print(f"{len(self.relations)} relations extracted") with open(os.path.join(self.data_dir, "relations.json"), "w") as f: json.dump(self.relations, f) for section, texts in self.texts.items(): with open(os.path.join(self.data_dir, section + ".jsonl"), "w") as f: for text in texts: f.write(json.dumps(text) + "\n") def load(self): # self.tokenizer = BertTokenizer.from_pretrained(self.data_dir) with open(os.path.join(self.data_dir, "relations.json"), "r") as f: relations = json.load(f) self.relations = sorted(relations) self.relations2id = {r: ind for ind, r in enumerate(self.relations)} def dataset(self, section): # for codalab eval if len(self.texts[section]) > 0: return self.texts[section] else: return [ json.loads(line) for line in open(os.path.join(self.data_dir, section + ".jsonl")) ] @registry.register("encoder", "spider-bert") class SpiderEncoderBert(torch.nn.Module): Preproc = SpiderEncoderBertPreproc batched = True def __init__( self, device, preproc, bert_token_type=False, bert_version="bert-base-uncased", summarize_header="avg", include_in_memory=("question", "column", "table"), rat_config={}, linking_config={}, ): super().__init__() self._device = device self.preproc = preproc self.bert_token_type = bert_token_type self.base_enc_hidden_size = ( 1024 if "large" in bert_version else 768 ) self.include_in_memory = include_in_memory # ways to summarize header assert summarize_header in ["first", "avg"] self.summarize_header = summarize_header self.enc_hidden_size = self.base_enc_hidden_size # matching self.schema_linking = registry.construct( "schema_linking", linking_config, preproc=preproc, device=device, ) # rat rat_modules = {"rat": rat.RAT, "none": rat.NoOpUpdate} self.rat_update = registry.instantiate( rat_modules[rat_config["name"]], rat_config, unused_keys={"name"}, device=self._device, relations2id=preproc.relations2id, hidden_size=self.enc_hidden_size, ) # aligner self.aligner = rat.AlignmentWithRAT( device=device, hidden_size=self.enc_hidden_size, relations2id=preproc.relations2id, enable_latent_relations=False, ) if "electra" in bert_version: modelclass = ElectraModel elif "bert" in bert_version: modelclass = BertModel else: raise NotImplementedError self.bert_model = modelclass.from_pretrained(bert_version) self.tokenizer = self.preproc.tokenizer # self.bert_model.resize_token_embeddings( # len(self.tokenizer) # ) # several tokens added def forward(self, descs): # TODO: abstract the operations of batching for bert batch_token_lists = [] batch_id_to_retrieve_question = [] batch_id_to_retrieve_column = [] batch_id_to_retrieve_table = [] if self.summarize_header == "avg": batch_id_to_retrieve_column_2 = [] batch_id_to_retrieve_table_2 = [] long_seq_set = set() batch_id_map = {} # some long examples are not included # 1) retrieve bert pre-trained embeddings for batch_idx, desc in enumerate(descs): qs = self.tokenizer.text_to_ids(desc["question_text"], cls=True) cols = [self.tokenizer.text_to_ids(c, cls=False) for c in desc["columns"]] tabs = [self.tokenizer.text_to_ids(t, cls=False) for t in desc["tables"]] token_list = ( qs + [c for col in cols for c in col] + [t for tab in tabs for t in tab] ) assert self.tokenizer.check_bert_input_seq(token_list) if len(token_list) > 512: long_seq_set.add(batch_idx) continue q_b = len(qs) col_b = q_b + sum(len(c) for c in cols) # leave out [CLS] and [SEP] question_indexes = list(range(q_b))[1:-1] # use the first/avg representation for column/table column_indexes = np.cumsum( [q_b] + [len(token_list) for token_list in cols[:-1]] ).tolist() table_indexes = np.cumsum( [col_b] + [len(token_list) for token_list in tabs[:-1]] ).tolist() if self.summarize_header == "avg": column_indexes_2 = np.cumsum( [q_b - 2] + [len(token_list) for token_list in cols] ).tolist()[1:] table_indexes_2 = np.cumsum( [col_b - 2] + [len(token_list) for token_list in tabs] ).tolist()[1:] # token_list is already indexed indexed_token_list = token_list batch_token_lists.append(indexed_token_list) # add index for retrieving representations question_rep_ids = torch.LongTensor(question_indexes).to(self._device) batch_id_to_retrieve_question.append(question_rep_ids) column_rep_ids = torch.LongTensor(column_indexes).to(self._device) batch_id_to_retrieve_column.append(column_rep_ids) table_rep_ids = torch.LongTensor(table_indexes).to(self._device) batch_id_to_retrieve_table.append(table_rep_ids) if self.summarize_header == "avg": assert all(i2 >= i1 for i1, i2 in zip(column_indexes, column_indexes_2)) column_rep_ids_2 = torch.LongTensor(column_indexes_2).to(self._device) batch_id_to_retrieve_column_2.append(column_rep_ids_2) assert all(i2 >= i1 for i1, i2 in zip(table_indexes, table_indexes_2)) table_rep_ids_2 = torch.LongTensor(table_indexes_2).to(self._device) batch_id_to_retrieve_table_2.append(table_rep_ids_2) batch_id_map[batch_idx] = len(batch_id_map) ( padded_token_lists, att_mask_lists, tok_type_lists, ) = self.tokenizer.pad_sequence_for_bert_batch(batch_token_lists) tokens_tensor = torch.LongTensor(padded_token_lists).to(self._device) att_masks_tensor = torch.LongTensor(att_mask_lists).to(self._device) if self.bert_token_type: tok_type_tensor = torch.LongTensor(tok_type_lists).to(self._device) bert_output = self.bert_model( tokens_tensor, attention_mask=att_masks_tensor, token_type_ids=tok_type_tensor, )[0] else: bert_output = self.bert_model( tokens_tensor, attention_mask=att_masks_tensor )[0] enc_output = bert_output column_pointer_maps = [ {i: [i] for i in range(len(desc["columns"]))} for desc in descs ] table_pointer_maps = [ {i: [i] for i in range(len(desc["tables"]))} for desc in descs ] assert len(long_seq_set) == 0 # remove them for now # 2) rat update result = [] for batch_idx, desc in enumerate(descs): # retrieve representations bert_batch_idx = batch_id_map[batch_idx] q_enc = enc_output[bert_batch_idx][ batch_id_to_retrieve_question[bert_batch_idx] ] col_enc = enc_output[bert_batch_idx][ batch_id_to_retrieve_column[bert_batch_idx] ] tab_enc = enc_output[bert_batch_idx][ batch_id_to_retrieve_table[bert_batch_idx] ] if self.summarize_header == "avg": col_enc_2 = enc_output[bert_batch_idx][ batch_id_to_retrieve_column_2[bert_batch_idx] ] tab_enc_2 = enc_output[bert_batch_idx][ batch_id_to_retrieve_table_2[bert_batch_idx] ] col_enc = (col_enc + col_enc_2) / 2.0 # avg of first and last token tab_enc = (tab_enc + tab_enc_2) / 2.0 # avg of first and last token words_for_copying = desc["question_for_copying"] assert q_enc.size()[0] == len(words_for_copying) assert col_enc.size()[0] == len(desc["columns"]) assert tab_enc.size()[0] == len(desc["tables"]) # rat update # TODO: change this, question is in the protocal of build relations desc["question"] = words_for_copying relation = self.schema_linking(desc) ( q_enc_new_item, c_enc_new_item, t_enc_new_item, ) = self.rat_update.forward_unbatched( desc, q_enc.unsqueeze(1), col_enc.unsqueeze(1), tab_enc.unsqueeze(1), relation, ) # attention memory memory = [] if "question" in self.include_in_memory: memory.append(q_enc_new_item) if "column" in self.include_in_memory: memory.append(c_enc_new_item) if "table" in self.include_in_memory: memory.append(t_enc_new_item) memory = torch.cat(memory, dim=1) # alignment matrix align_mat_item = self.aligner( desc, q_enc_new_item, c_enc_new_item, t_enc_new_item, relation ) result.append( SpiderEncoderState( state=None, words_for_copying=words_for_copying, tokenizer=self.tokenizer, memory=memory, question_memory=q_enc_new_item, schema_memory=torch.cat((c_enc_new_item, t_enc_new_item), dim=1), pointer_memories={ "column": c_enc_new_item, "table": t_enc_new_item, }, pointer_maps={ "column": column_pointer_maps[batch_idx], "table": table_pointer_maps[batch_idx], }, m2c_align_mat=align_mat_item[0], m2t_align_mat=align_mat_item[1], ) ) return result class PhoBertokens: def __init__(self, pieces): self.pieces = pieces self.normalized_pieces = None self.recovered_pieces = None self.idx_map = None self.normalize_toks() def normalize_toks(self): """ If the token is not a word piece, then find its lemma If it is, combine pieces into a word, and then find its lemma E.g., a ##b ##c will be normalized as "abc", "", "" NOTE: this is only used for schema linking """ self.startidx2pieces = dict() self.pieces2startidx = dict() cache_start = None for i, piece in enumerate(self.pieces + [""]): if piece.startswith("##"): if cache_start is None: cache_start = i - 1 self.pieces2startidx[i] = cache_start self.pieces2startidx[i - 1] = cache_start else: if cache_start is not None: self.startidx2pieces[cache_start] = i cache_start = None assert cache_start is None # combine pieces, "abc", "", "" combined_word = {} for start, end in self.startidx2pieces.items(): assert end - start + 1 < 10 pieces = [self.pieces[start]] + [self.pieces[_id].strip("##") for _id in range(start + 1, end)] word = "".join(pieces) combined_word[start] = word # remove "", only keep "abc" idx_map = {} new_toks = [] for i, piece in enumerate(self.pieces): if i in combined_word: idx_map[len(new_toks)] = i new_toks.append(combined_word[i]) elif i in self.pieces2startidx: # remove it pass else: idx_map[len(new_toks)] = i new_toks.append(piece) self.idx_map = idx_map # lemmatize "abc" normalized_toks = [] for i, tok in enumerate(new_toks): ann = vncorenlp.tokenize(tok) lemmas = [tok.lower() for sent in ann for tok in sent] lemma_word = " ".join(lemmas) normalized_toks.append(lemma_word) self.normalized_pieces = normalized_toks self.recovered_pieces = new_toks class Vitext2sqlEncoderPhoBertPreproc(abstract_preproc.AbstractPreproc): def __init__( self, save_path, context, bert_version="vinai/phobert-large", compute_sc_link=True, compute_cv_link=True, ): self.data_dir = os.path.join(save_path, "enc") self.texts = collections.defaultdict(list) self.compute_sc_link = compute_sc_link self.compute_cv_link = compute_cv_link self.context_config = context self.relations = set() # TODO: should get types from the data # column_types = ["text", "number", "time", "boolean", "others"] # self.tokenizer.add_tokens([f"<type: {t}>" for t in column_types]) self.tokenizer_config = bert_version # lazy init self.tokenizer = AutoTokenizer.from_pretrained(self.tokenizer_config) self.context_cache = {} def _tokenize(self, presplit, unsplit): toks = vncorenlp.tokenize(unsplit) if toks[0]: return toks[0] return presplit def validate_item(self, item, section): num_words = ( len(item.text) + sum(len(c.name) for c in item.schema.columns) + sum(len(t.name) for t in item.schema.tables) ) if num_words > 256 and section != "test": logger.info(f"Found long seq in {item.schema.db_id}") return False, None else: return True, None def add_item(self, item, section, validation_info): preprocessed = self.preprocess_item(item, validation_info) self.texts[section].append(preprocessed) if section == "train": for relation_name in itertools.chain( preprocessed["schema_relations"].keys(), preprocessed["sc_relations"].keys(), preprocessed["cv_relations"].keys(), ): self.relations.add(relation_name) def clear_items(self): self.texts = collections.defaultdict(list) def preprocess_item(self, item, validation_info=None): # use the original words for copying, while they are not necessarily used for encoding # question_for_copying = self.tokenizer.tokenize_and_lemmatize(q_text) question_for_copying = self._tokenize(item.text, item.orig['question']) q_text = question_for_copying context_preproc = self.preprocess_schema(item.schema) # if item.schema.db_id in self.context_cache: # context = self.context_cache[item.schema.db_id] # else: # context = registry.construct( # "context", # self.context_config, # schema=item.schema, # tokenizer=self._tokenize, # ) # self.context_cache[item.schema.db_id] = context preproc_schema = context_preproc.preproc_schema schema_relations = context_preproc.compute_schema_relations() if self.compute_sc_link: sc_relations = ( context_preproc.compute_schema_linking(q_text, preproc_schema.normalized_column_names, preproc_schema.normalized_table_names) ) else: sc_relations = {} if self.compute_cv_link: cv_relations = ( context_preproc.compute_cell_value_linking(q_text) ) else: cv_relations = {} return { "question_text": item.orig['question'], "question_for_copying": question_for_copying, "db_id": item.schema.db_id, "schema_relations": schema_relations, "sc_relations": sc_relations, "cv_relations": cv_relations, "columns": preproc_schema.column_names, "tables": preproc_schema.table_names, "table_bounds": preproc_schema.table_bounds, "column_to_table": preproc_schema.column_to_table, "table_to_columns": preproc_schema.table_to_columns, "foreign_keys": preproc_schema.foreign_keys, "foreign_keys_tables": preproc_schema.foreign_keys_tables, "primary_keys": preproc_schema.primary_keys, } def preprocess_schema(self, schema): if schema.db_id in self.context_cache: context = self.context_cache[schema.db_id] else: context = registry.construct( "context", self.context_config, schema=schema, tokenizer=self._tokenize, ) self.context_cache[schema.db_id] = context return context def save(self): os.makedirs(self.data_dir, exist_ok=True) # self.tokenizer.save_pretrained(self.data_dir) default_relations = registry.lookup( "context", self.context_config["name"] ).get_default_relations() self.relations = sorted(self.relations.union(default_relations)) print(f"{len(self.relations)} relations extracted") with open(os.path.join(self.data_dir, "relations.json"), "w", encoding='utf-8') as f: json.dump(self.relations, f, ensure_ascii=False) for section, texts in self.texts.items(): with open(os.path.join(self.data_dir, section + ".jsonl"), "w", encoding='utf-8') as f: for text in texts: f.write(json.dumps(text, ensure_ascii=False) + "\n") return def load(self): # self.tokenizer = BertTokenizer.from_pretrained(self.data_dir) with open(os.path.join(self.data_dir, "relations.json"), "r") as f: relations = json.load(f) self.relations = sorted(relations) self.relations2id = {r: ind for ind, r in enumerate(self.relations)} def dataset(self, section): # for codalab eval if len(self.texts[section]) > 0: return self.texts[section] else: return [ json.loads(line) for line in open(os.path.join(self.data_dir, section + ".jsonl")) ] @registry.register("encoder", "vitext2sql-phobert") class Vitext2SQLEncoderPhoBert(torch.nn.Module): Preproc = Vitext2sqlEncoderPhoBertPreproc batched = True def __init__( self, device, preproc, bert_token_type=False, bert_version="vinai/phobert-large", summarize_header="avg", include_in_memory=("question", "column", "table"), rat_config={}, linking_config={}, ): super().__init__() self._device = device self.preproc = preproc self.bert_token_type = bert_token_type self.base_enc_hidden_size = 1024 if bert_version == "vinai/phobert-large" else 768 self.include_in_memory = include_in_memory # ways to summarize header assert summarize_header in ["first", "avg"] self.summarize_header = summarize_header self.enc_hidden_size = self.base_enc_hidden_size # matching self.schema_linking = registry.construct( "schema_linking", linking_config, preproc=preproc, device=device, ) # rat rat_modules = {"rat": rat.RAT, "none": rat.NoOpUpdate} self.rat_update = registry.instantiate( rat_modules[rat_config["name"]], rat_config, unused_keys={"name"}, device=self._device, relations2id=preproc.relations2id, hidden_size=self.enc_hidden_size, ) # aligner self.aligner = rat.AlignmentWithRAT( device=device, hidden_size=self.enc_hidden_size, relations2id=preproc.relations2id, enable_latent_relations=False, ) self.phobert_model = AutoModel.from_pretrained(bert_version) self.tokenizer = self.preproc.tokenizer # self.bert_model.resize_token_embeddings( # len(self.tokenizer) # ) # several tokens added def forward(self, descs): # TODO: abstract the operations of batching for bert batch_token_lists = [] batch_id_to_retrieve_question = [] batch_id_to_retrieve_column = [] batch_id_to_retrieve_table = [] if self.summarize_header == "avg": batch_id_to_retrieve_column_2 = [] batch_id_to_retrieve_table_2 = [] long_seq_set = set() batch_id_map = {} # some long examples are not included # 1) retrieve bert pre-trained embeddings for batch_idx, desc in enumerate(descs): qs = self.pad_single_sentence_for_bert(desc['question_for_copying'], cls=True) cols = [self.pad_single_sentence_for_bert([c], cls=True) for c in desc['columns']] tabs = [self.pad_single_sentence_for_bert([t], cls=True) for t in desc['tables']] token_list = ( qs + [c for col in cols for c in col] + [t for tab in tabs for t in tab] ) assert self.check_bert_seq(token_list) if len(token_list) > 256: long_seq_set.add(batch_idx) continue q_b = len(qs) col_b = q_b + sum(len(c) for c in cols) # leave out [CLS] and [SEP] question_indexes = list(range(q_b))[1:-1] # use the first/avg representation for column/table column_indexes = np.cumsum( [q_b] + [len(token_list) for token_list in cols[:-1]] ).tolist() table_indexes = np.cumsum( [col_b] + [len(token_list) for token_list in tabs[:-1]] ).tolist() if self.summarize_header == "avg": column_indexes_2 = np.cumsum( [q_b - 2] + [len(token_list) for token_list in cols] ).tolist()[1:] table_indexes_2 = np.cumsum( [col_b - 2] + [len(token_list) for token_list in tabs] ).tolist()[1:] # token_list is already indexed indexed_token_list = self.tokenizer.convert_tokens_to_ids(token_list) batch_token_lists.append(indexed_token_list) # add index for retrieving representations question_rep_ids = torch.LongTensor(question_indexes).to(self._device) batch_id_to_retrieve_question.append(question_rep_ids) column_rep_ids = torch.LongTensor(column_indexes).to(self._device) batch_id_to_retrieve_column.append(column_rep_ids) table_rep_ids = torch.LongTensor(table_indexes).to(self._device) batch_id_to_retrieve_table.append(table_rep_ids) if self.summarize_header == "avg": assert all(i2 >= i1 for i1, i2 in zip(column_indexes, column_indexes_2)) column_rep_ids_2 = torch.LongTensor(column_indexes_2).to(self._device) batch_id_to_retrieve_column_2.append(column_rep_ids_2) assert all(i2 >= i1 for i1, i2 in zip(table_indexes, table_indexes_2)) table_rep_ids_2 = torch.LongTensor(table_indexes_2).to(self._device) batch_id_to_retrieve_table_2.append(table_rep_ids_2) batch_id_map[batch_idx] = len(batch_id_map) if len(batch_token_lists) != 0: ( padded_token_lists, att_mask_lists, tok_type_lists, ) = self.pad_sequence_for_bert_batch(batch_token_lists) tokens_tensor = torch.LongTensor(padded_token_lists).to(self._device) att_masks_tensor = torch.LongTensor(att_mask_lists).to(self._device) if self.bert_token_type: tok_type_tensor = torch.LongTensor(tok_type_lists).to(self._device) phobert_output = self.phobert_model(tokens_tensor, attention_mask=att_masks_tensor, token_type_ids=tok_type_tensor)[0] else: phobert_output = self.phobert_model(tokens_tensor, attention_mask=att_masks_tensor)[0] enc_output = phobert_output column_pointer_maps = [ {i: [i] for i in range(len(desc["columns"]))} for desc in descs ] table_pointer_maps = [ {i: [i] for i in range(len(desc["tables"]))} for desc in descs ] # assert len(long_seq_set) == 0 # remove them for now # 2) rat update result = [] for batch_idx, desc in enumerate(descs): # xử lý sentence dài hơn độ dài phobert if batch_idx in long_seq_set: q_enc, col_enc, tab_enc = self.encoder_long_seq(desc) else: # retrieve representations bert_batch_idx = batch_id_map[batch_idx] q_enc = enc_output[bert_batch_idx][ batch_id_to_retrieve_question[bert_batch_idx] ] col_enc = enc_output[bert_batch_idx][ batch_id_to_retrieve_column[bert_batch_idx] ] tab_enc = enc_output[bert_batch_idx][ batch_id_to_retrieve_table[bert_batch_idx] ] if self.summarize_header == "avg": col_enc_2 = enc_output[bert_batch_idx][ batch_id_to_retrieve_column_2[bert_batch_idx] ] tab_enc_2 = enc_output[bert_batch_idx][ batch_id_to_retrieve_table_2[bert_batch_idx] ] col_enc = (col_enc + col_enc_2) / 2.0 # avg of first and last token tab_enc = (tab_enc + tab_enc_2) / 2.0 # avg of first and last token words_for_copying = desc["question_for_copying"] assert q_enc.size()[0] == len(words_for_copying) assert col_enc.size()[0] == len(desc["columns"]) assert tab_enc.size()[0] == len(desc["tables"]) # rat update # TODO: change this, question is in the protocal of build relations desc["question"] = words_for_copying relation = self.schema_linking(desc) ( q_enc_new_item, c_enc_new_item, t_enc_new_item, ) = self.rat_update.forward_unbatched( desc, q_enc.unsqueeze(1), col_enc.unsqueeze(1), tab_enc.unsqueeze(1), relation, ) # attention memory memory = [] if "question" in self.include_in_memory: memory.append(q_enc_new_item) if "column" in self.include_in_memory: memory.append(c_enc_new_item) if "table" in self.include_in_memory: memory.append(t_enc_new_item) memory = torch.cat(memory, dim=1) # alignment matrix align_mat_item = self.aligner( desc, q_enc_new_item, c_enc_new_item, t_enc_new_item, relation ) result.append( SpiderEncoderState( state=None, words_for_copying=words_for_copying, tokenizer=self.tokenizer, memory=memory, question_memory=q_enc_new_item, schema_memory=torch.cat((c_enc_new_item, t_enc_new_item), dim=1), pointer_memories={ "column": c_enc_new_item, "table": t_enc_new_item, }, pointer_maps={ "column": column_pointer_maps[batch_idx], "table": table_pointer_maps[batch_idx], }, m2c_align_mat=align_mat_item[0], m2t_align_mat=align_mat_item[1], ) ) return result def encoder_long_seq(self, desc): """ Since phobert cannot handle sequence longer than 256, each column/table is encoded individually The representation of a column/table is the vector of the first token [CLS] """ qs = self.pad_single_sentence_for_bert(desc['question_for_copying'], cls=True) cols = [self.pad_single_sentence_for_bert([c], cls=True) for c in desc['columns']] tabs = [self.pad_single_sentence_for_bert([t], cls=True) for t in desc['tables']] enc_q = self._bert_encode(qs) enc_col = self._bert_encode(cols) enc_tab = self._bert_encode(tabs) return enc_q, enc_col, enc_tab def _bert_encode(self, toks): if not isinstance(toks[0], list): # encode question words indexed_tokens = self.tokenizer.convert_tokens_to_ids(toks) tokens_tensor = torch.tensor([indexed_tokens]).to(self._device) outputs = self.phobert_model(tokens_tensor) return outputs[0][0, 1:-1] # remove [CLS] and [SEP] else: max_len = max([len(it) for it in toks]) tok_ids = [] for item_toks in toks: item_toks = item_toks + [self.tokenizer.pad_token] * (max_len - len(item_toks)) indexed_tokens = self.tokenizer.convert_tokens_to_ids(item_toks) tok_ids.append(indexed_tokens) tokens_tensor = torch.tensor(tok_ids).to(self._device) outputs = self.phobert_model(tokens_tensor) return outputs[0][:, 0, :] def pad_single_sentence_for_bert(self, toks, cls=True): if cls: return [self.tokenizer.cls_token] + toks + [self.tokenizer.sep_token] else: return toks + [self.tokenizer.sep_token] def check_bert_seq(self, toks): if toks[0] == self.tokenizer.cls_token and toks[-1] == self.tokenizer.sep_token: return True else: return False def pad_sequence_for_bert_batch(self, tokens_lists): pad_id = self.tokenizer.pad_token_id max_len = max([len(it) for it in tokens_lists]) assert max_len <= 256 toks_ids = [] att_masks = [] tok_type_lists = [] for item_toks in tokens_lists: padded_item_toks = item_toks + [pad_id] * (max_len - len(item_toks)) toks_ids.append(padded_item_toks) _att_mask = [1] * len(item_toks) + [0] * (max_len - len(item_toks)) att_masks.append(_att_mask) first_sep_id = padded_item_toks.index(self.tokenizer.sep_token_id) assert first_sep_id > 0 _tok_type_list = [0] * (first_sep_id + 1) + [1] * (max_len - first_sep_id - 1) tok_type_lists.append(_tok_type_list) return toks_ids, att_masks, tok_type_lists

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0.804285

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false

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null

0

5

e7e05ef72b19342c02336453cdeb64522d5cc790

111

py

Python

pytorch_bpr/__init__.py

hadware/pytorch-bpr

a63660f7f01ac017250db30eb9e543342741a0b5

[ "MIT" ]

5

2018-08-04T09:43:12.000Z

2021-08-30T22:24:10.000Z

pytorch_bpr/__init__.py

hadware/pytorch-bpr

a63660f7f01ac017250db30eb9e543342741a0b5

[ "MIT" ]

2

2018-05-16T02:06:18.000Z

2018-05-16T15:58:45.000Z

pytorch_bpr/__init__.py

hadware/pytorch-bpr

a63660f7f01ac017250db30eb9e543342741a0b5

[ "MIT" ]

4

2018-12-19T01:52:30.000Z

2022-02-24T01:17:37.000Z

from .model import MFModel, BPRLossFunctional, DotProductScorer from .metrics import AUCEvaluator, MAPEvaluator

55.5

63

0.864865

11

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99b2f4da2ad484dd6bf27b7e49ece54495da0764

138

py

Python

tasks_proj/tests/func/test_add.py

Jamrozinski/PythonTestingWithPytest

0dceb58f0b17fefa776748c93f5df062395d00be

[ "MIT" ]

11

2021-05-06T12:39:39.000Z

2022-03-14T11:58:44.000Z

tasks_proj/tests/func/test_add.py

Jamrozinski/PythonTestingWithPytest

0dceb58f0b17fefa776748c93f5df062395d00be

[ "MIT" ]

34

2019-12-16T16:53:24.000Z

2022-01-13T02:29:30.000Z

tasks_proj/tests/func/test_add.py

Jamrozinski/PythonTestingWithPytest

0dceb58f0b17fefa776748c93f5df062395d00be

[ "MIT" ]

11

2021-06-10T21:19:42.000Z

2022-02-21T04:03:06.000Z

""" Placeholder test file. We'll add a bunch of tests here in later versions. """ def test_add(): """Placeholder test.""" pass

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99b66882c52b3aa782c73024b5f89b92b0288de3

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py

Python

chainmodel/models/base/__init__.py

aaroncox/blockmodel

14fa856a439ee3563ea4cad7b77db9bb998bdc7e

[ "MIT" ]

1

2018-03-29T09:06:03.000Z

chainmodel/models/base/__init__.py

aaroncox/blockmodel

14fa856a439ee3563ea4cad7b77db9bb998bdc7e

[ "MIT" ]

null

chainmodel/models/base/__init__.py

aaroncox/blockmodel

14fa856a439ee3563ea4cad7b77db9bb998bdc7e

[ "MIT" ]

null

from .operation import Operation, OperationIndex

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null

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1

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5

8212228a45c5109d3407c04665f24915720b09fe

170

py

Python

securemailbox/constants.py

securemailbox/api

6e6c594c1e6588f15e2d78018074808dbe0ada57

[ "Apache-2.0" ]

2

2020-04-02T02:37:53.000Z

2020-06-11T04:45:06.000Z

securemailbox/constants.py

securemailbox/api

6e6c594c1e6588f15e2d78018074808dbe0ada57

[ "Apache-2.0" ]

28

2020-02-11T03:11:25.000Z

2020-06-11T01:49:22.000Z

securemailbox/constants.py

securemailbox/api

6e6c594c1e6588f15e2d78018074808dbe0ada57

[ "Apache-2.0" ]

null

# Declare any variables (as constants) that are or could be used anywhere in the application # TODO: Determine what a good fingerprint length is FINGERPRINT_LENGTH = 40

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null

0

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5

8228aeeb4fce65919d7548bdf633e72692cffc3d

150

py

Python

pylib/yutraceback.py

in4lio/yupp

38d4002d2f07c31940b2be572a1c205d6bf63546

[ "MIT" ]

44

2015-09-15T17:14:05.000Z

2021-08-22T10:35:05.000Z

pylib/yutraceback.py

in4lio/yupp

38d4002d2f07c31940b2be572a1c205d6bf63546

[ "MIT" ]

null

pylib/yutraceback.py

in4lio/yupp

38d4002d2f07c31940b2be572a1c205d6bf63546

[ "MIT" ]

1

2015-09-22T22:27:28.000Z

from __future__ import absolute_import import sys if sys.version_info[0] < 3: from .yutraceback2 import * else: from .yutraceback3 import *

16.666667

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5.25

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39

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null

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1

0

1

0

1

0

5

823b4053887d01ef044b85f0f5fabffbde26e51a

143

py

Python

pyapp_ext/elasticsearch/checks.py

pyapp-org/pyapp.elasticsearch

6d16c906a0048aed20fb657ba5b92885853d6172

[ "BSD-3-Clause" ]

null

pyapp_ext/elasticsearch/checks.py

pyapp-org/pyapp.elasticsearch

6d16c906a0048aed20fb657ba5b92885853d6172

[ "BSD-3-Clause" ]

51

2020-08-10T08:08:20.000Z

2022-03-28T09:01:48.000Z

pyapp_ext/elasticsearch/checks.py

pyapp-org/pyapp.elasticsearch

6d16c906a0048aed20fb657ba5b92885853d6172

[ "BSD-3-Clause" ]

null

""" Elasticsearch Checks ~~~~~~~~~~~~~~~~~~~~ """ from pyapp.checks.registry import register from ._factory import factory register(factory)

14.3

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143

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0

1

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5

413b5dde964079a4c622c789cba2928abf6cf096

22

py

Python

photo_management/__init__.py

samhutchins/photo_management

7c1f3eaaebf4b6249b18e273aef78a2a9d0f48ed

[ "MIT" ]

null

photo_management/__init__.py

samhutchins/photo_management

7c1f3eaaebf4b6249b18e273aef78a2a9d0f48ed

[ "MIT" ]

null

photo_management/__init__.py

samhutchins/photo_management

7c1f3eaaebf4b6249b18e273aef78a2a9d0f48ed

[ "MIT" ]

1

2019-05-01T05:00:40.000Z

__version__ = "2021.1"

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1

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1

0

null

0

5

417896a8befda6c3783f274119230147e4a5d46e

3,805

py

Python

tests/unit/test_reindex.py

Brickstertwo/git-commands

87fa9a6573dd426eecece098fbadc3f5550c8976

[ "MIT" ]

1

2018-10-17T11:09:32.000Z

tests/unit/test_reindex.py

Brickstertwo/git-commands

87fa9a6573dd426eecece098fbadc3f5550c8976

[ "MIT" ]

122

2015-01-06T19:10:23.000Z

2017-09-26T14:22:11.000Z

tests/unit/test_reindex.py

Brickster/git-commands

87fa9a6573dd426eecece098fbadc3f5550c8976

[ "MIT" ]

null

import mock import unittest from . import testutils from ..layers import GitReindex from bin.commands import reindex class TestReindex(unittest.TestCase): layer = GitReindex @mock.patch('bin.commands.utils.directories.is_git_repository', return_value=True) @mock.patch('bin.commands.utils.execute.check_output') @mock.patch('bin.commands.utils.git.deleted_files', return_value=['file3']) @mock.patch('bin.commands.utils.execute.call') def test_reindex_noneDeleted(self, mock_call, mock_deletedfiles, mock_checkoutput, mock_isgitrepository): # setup files = ['file1', 'file2'] mock_checkoutput.return_value = '\n'.join(files) + '\n' # when reindex.reindex() # then mock_isgitrepository.assert_called_once_with() mock_checkoutput.assert_called_once_with('git diff --name-only --cached'.split()) mock_call.assert_called_once_with(['git', 'add', '--'] + files) @mock.patch('bin.commands.utils.directories.is_git_repository', return_value=True) @mock.patch('bin.commands.utils.execute.check_output') @mock.patch('bin.commands.utils.git.deleted_files') @mock.patch('bin.commands.utils.execute.call') def test_reindex_someDeleted(self, mock_call, mock_deletedfiles, mock_checkoutput, mock_isgitrepository): # setup files = ['file1', 'file2', 'file3'] mock_checkoutput.return_value = '\n'.join(files) + '\n' mock_deletedfiles.return_value = ['file2'] # when reindex.reindex() # then mock_isgitrepository.assert_called_once_with() mock_checkoutput.assert_called_once_with('git diff --name-only --cached'.split()) mock_call.assert_called_once_with(['git', 'add', '--', 'file1', 'file3']) @mock.patch('bin.commands.utils.directories.is_git_repository', return_value=True) @mock.patch('bin.commands.utils.execute.check_output') @mock.patch('bin.commands.utils.git.deleted_files') @mock.patch('bin.commands.utils.execute.call') def test_reindex_allDeleted(self, mock_call, mock_deletedfiles, mock_checkoutput, mock_isgitrepository): # setup files = ['file1', 'file2'] mock_checkoutput.return_value = '\n'.join(files) + '\n' mock_deletedfiles.return_value = files # when reindex.reindex() # then mock_isgitrepository.assert_called_once_with() mock_checkoutput.assert_called_once_with('git diff --name-only --cached'.split()) mock_call.assert_not_called() @mock.patch('bin.commands.utils.directories.is_git_repository', return_value=True) @mock.patch('bin.commands.utils.execute.check_output', return_value = '') @mock.patch('bin.commands.utils.execute.call') def test_reindex_noFilesToIndex(self, mock_call, mock_checkoutput, mock_isgitrepository): # when reindex.reindex() # then mock_isgitrepository.assert_called_once_with() mock_checkoutput.assert_called_once_with('git diff --name-only --cached'.split()) mock_call.assert_not_called() @mock.patch('bin.commands.utils.directories.is_git_repository', return_value=False) @mock.patch('bin.commands.utils.messages.error', side_effect=testutils.and_exit) @mock.patch('os.getcwd', return_value='/working/dir') def test_reindex_notAGitRepository(self, mock_getcwd, mock_error, mock_isgitrepository): # when try: reindex.reindex() self.fail('expected to exit but did not') # pragma: no cover except SystemExit: pass # then mock_isgitrepository.assert_called_once_with() mock_error.assert_called_once_with("'/working/dir' not a git repository") mock_getcwd.assert_called_once_with()

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null

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null

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41932168f921e0c1b9ea7dba826386c0884411b6

180

py

Python

tools/apps.py

IATI/new-website

b90783e32d19ac4c821c5ea018a52997a11b5286

[ "MIT" ]

4

2019-03-28T06:42:17.000Z

2021-06-06T13:10:51.000Z

tools/apps.py

IATI/new-website

b90783e32d19ac4c821c5ea018a52997a11b5286

[ "MIT" ]

177

2018-09-28T14:21:56.000Z

2022-03-30T21:45:26.000Z

tools/apps.py

IATI/new-website

b90783e32d19ac4c821c5ea018a52997a11b5286

[ "MIT" ]

8

2018-10-25T20:43:10.000Z

2022-03-17T14:19:27.000Z

"""Application configuration for the tools app.""" from django.apps import AppConfig class ToolsConfig(AppConfig): """Config class for the tools app.""" name = 'tools'

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1

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0

5

41be0ea8460d8d3027d9efcd9192922b7c84b3b6

35

py

Python

gatekeeper/exception/__init__.py

Guya-LTD/gatekeeper

a3e673cc9875ade6d91dcc8a7ea7c10ab0a3dd09

[ "RSA-MD" ]

null

gatekeeper/exception/__init__.py

Guya-LTD/gatekeeper

a3e673cc9875ade6d91dcc8a7ea7c10ab0a3dd09

[ "RSA-MD" ]

null

gatekeeper/exception/__init__.py

Guya-LTD/gatekeeper

a3e673cc9875ade6d91dcc8a7ea7c10ab0a3dd09

[ "RSA-MD" ]

null

from .value_empty import ValueEmpty

35

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5

35

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1

0

0.085714

35

1

35

0.9375

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1

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true

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1

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1

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null

0

1

0

null

0

1

0

1

0

5

41c63236648ee23c085a6faebeb0d1bcd4f26436

90

py

Python

pytmi/__init__.py

bynect/pytmi

72a6e45082c82672bd84df9adc8d2ef939eaf3d7

[ "MIT" ]

2

2021-01-24T07:59:28.000Z

2021-05-03T21:28:36.000Z

pytmi/__init__.py

bynect/pytmi

72a6e45082c82672bd84df9adc8d2ef939eaf3d7

[ "MIT" ]

1

2022-03-06T07:06:43.000Z

2022-03-06T13:12:15.000Z

pytmi/__init__.py

bynect/pytmi

72a6e45082c82672bd84df9adc8d2ef939eaf3d7

[ "MIT" ]

null

__version__ = "0.2.0" from .stream import * from .message import * from .client import *

15

22

0.7

13

90

4.538462

0.615385

0.338983

0

0.040541

0.177778

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5

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0

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0

1

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false

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0

0.75

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1

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null

1

0

1

0

null

0

1

0

1

0

5

68c20ad8e0fe82a6a64315a61ae2d0969a68efef

32

py

Python

Week2/my_func1.py

tb2010/pynet

bb206d7ff0d183f62ca8549b596011de6a28b3d4

[ "MIT" ]

null

Week2/my_func1.py

tb2010/pynet

bb206d7ff0d183f62ca8549b596011de6a28b3d4

[ "MIT" ]

null

Week2/my_func1.py

tb2010/pynet

bb206d7ff0d183f62ca8549b596011de6a28b3d4

[ "MIT" ]

null

def hw(): print 'hello'

4.571429

17

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32

3.75

1

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32

6

18

5.333333

0.75

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null

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0

null

0

1

0

null

0

1

0

1

0

5

68cc1188171968b041cab6ad5095461af528aa1e

95

py

Python

enthought/mayavi/filters/mask_points.py

enthought/etsproxy

4aafd628611ebf7fe8311c9d1a0abcf7f7bb5347

[ "BSD-3-Clause" ]

3

2016-12-09T06:05:18.000Z

2018-03-01T13:00:29.000Z

enthought/mayavi/filters/mask_points.py

enthought/etsproxy

4aafd628611ebf7fe8311c9d1a0abcf7f7bb5347

[ "BSD-3-Clause" ]

1

2020-12-02T00:51:32.000Z

2020-12-02T08:48:55.000Z

enthought/mayavi/filters/mask_points.py

enthought/etsproxy

4aafd628611ebf7fe8311c9d1a0abcf7f7bb5347

[ "BSD-3-Clause" ]

null

# proxy module from __future__ import absolute_import from mayavi.filters.mask_points import *

23.75

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0.842105

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95

5.692308

0.769231

0

0.115789

95

3

41

31.666667

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0

1

0

true

0

1

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1

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null

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1

0

null

0

1

0

1

0

1

0

5

ec059e1ee27b1feea0a3c442c225ec5e77f39c19

53

py

Python

src/ua/raccoon/task4/solution.py

DataArt/kiddo-tasks

9e15241a6a3f152b4b64a345d923224450c0adee

[ "Apache-2.0" ]

null

src/ua/raccoon/task4/solution.py

DataArt/kiddo-tasks

9e15241a6a3f152b4b64a345d923224450c0adee

[ "Apache-2.0" ]

null

src/ua/raccoon/task4/solution.py

DataArt/kiddo-tasks

9e15241a6a3f152b4b64a345d923224450c0adee

[ "Apache-2.0" ]

null

import raccoon raccoon.go_right(3) raccoon.go_up(3)

10.6

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53

4

0.6

0.45

0

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53

4

20

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true

0

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1

0

1

0

null

0

1

0

1

0

5

ec343136d9fa64e46001b91492e197518dea2fb4

1,179

py

Python

meridian/channels/sanjiao.py

sinotradition/meridian

8c6c1762b204b72346be4bbfb74dedd792ae3024

[ "Apache-2.0" ]

5

2015-12-14T15:14:23.000Z

2022-02-09T10:15:33.000Z

meridian/channels/sanjiao.py

sinotradition/meridian

8c6c1762b204b72346be4bbfb74dedd792ae3024

[ "Apache-2.0" ]

null

meridian/channels/sanjiao.py

sinotradition/meridian

8c6c1762b204b72346be4bbfb74dedd792ae3024

[ "Apache-2.0" ]

3

2015-11-27T05:23:49.000Z

2020-11-28T09:01:56.000Z

#!/usr/bin/python #coding=utf-8 ''' @author: sheng @license: ''' from meridian.acupoints import guanchong11 from meridian.acupoints import yemen42 from meridian.acupoints import zhongzhu13 from meridian.acupoints import yangchi22 from meridian.acupoints import waiguan41 from meridian.acupoints import zhigou11 from meridian.acupoints import huizong41 from meridian.acupoints import sanyangluo124 from meridian.acupoints import sidu42 from meridian.acupoints import tianjing13 from meridian.acupoints import qinglengyuan131 from meridian.acupoints import xiaoluo14 from meridian.acupoints import naohui44 from meridian.acupoints import jianliao12 from meridian.acupoints import tianliao12 from meridian.acupoints import tianyou13 from meridian.acupoints import yifeng41 from meridian.acupoints import zhimai44 from meridian.acupoints import luxi21 from meridian.acupoints import jiaosun31 from meridian.acupoints import ermen32 from meridian.acupoints import erheliao322 from meridian.acupoints import sizhukong121 SPELL=u'shǒushàoyángsānjiāojīng' CN=u'手少阳三焦经' ABBR=u'SJ' NAME='sanjiao' FULLNAME='SanjiaoChannelofHand-Shaoyang' SEQ=6 if __name__ == '__main__': pass

25.630435

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6.840278

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false

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null

1

0

null

0

1

0

1

0

5

6b51ad427e5c8862369de0e11a9b0a3265df2874

86

py

Python

preimutils/keypoint_detection/cvat/__init__.py

ArianAmani/preimutils

d4f79525caae322d94d97febc4654229a2eb7407

[ "MIT" ]

null

preimutils/keypoint_detection/cvat/__init__.py

ArianAmani/preimutils

d4f79525caae322d94d97febc4654229a2eb7407

[ "MIT" ]

null

preimutils/keypoint_detection/cvat/__init__.py

ArianAmani/preimutils

d4f79525caae322d94d97febc4654229a2eb7407

[ "MIT" ]

null

from .augment import KPImageAug from .dataset import Dataset from .utils import utils

21.5

31

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86

5.916667

0.5

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0.139535

86

3

32

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0.959459

0

1

0

true

0

1

0

1

0

1

0

null

0

1

0

null

0

1

0

1

0

1

0

5

6b6be36ad6924df9d1161a039d66a66a29028cdd

738

py

Python

vizsgaremek/pages/login_page.py

femese/conduit

3ab5cc6a3b37e28d7712c2780f62a8091df2fad5

[ "MIT" ]

null

vizsgaremek/pages/login_page.py

femese/conduit

3ab5cc6a3b37e28d7712c2780f62a8091df2fad5

[ "MIT" ]

null

vizsgaremek/pages/login_page.py

femese/conduit

3ab5cc6a3b37e28d7712c2780f62a8091df2fad5

[ "MIT" ]

null

from selenium.webdriver.common.by import By from pages.base_element import BaseElement class LoginPage: def __init__(self, driver): self.driver = driver @property def email_input(self): return BaseElement(driver=self.driver, by=By.XPATH, value="//input[@placeholder='Email']") @property def password_input(self): return BaseElement(driver=self.driver, by=By.XPATH, value="//input[@placeholder='Password']") @property def signin_button(self): return BaseElement(driver=self.driver, by=By.XPATH, value="//button[1]") def fill_login_details(self, email, password): self.email_input.send_text_to_input(email) self.password_input.send_text_to_input(password)

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null

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1

0

1

0

1

0

5

6be85cbff15545588da1f04e5ced6940592bac7b

163

py

Python

social/storage/peewee_orm.py

raccoongang/python-social-auth

81c0a542d158772bd3486d31834c10af5d5f08b0

[ "BSD-3-Clause" ]

1,987

2015-01-01T16:12:45.000Z

2022-03-29T14:24:25.000Z

social/storage/peewee_orm.py

raccoongang/python-social-auth

81c0a542d158772bd3486d31834c10af5d5f08b0

[ "BSD-3-Clause" ]

731

2015-01-01T22:55:25.000Z

2022-03-10T15:07:51.000Z

virtual/lib/python3.6/site-packages/social/storage/peewee_orm.py

dennismwaniki67/awards

80ed10541f5f751aee5f8285ab1ad54cfecba95f

[ "MIT" ]

1,082

2015-01-01T16:27:26.000Z

2022-03-22T21:18:33.000Z

from social_peewee.storage import database_proxy, BaseModel, PeeweeUserMixin, \ PeeweeNonceMixin, PeeweeAssociationMixin, PeeweeCodeMixin, BasePeeweeStorage

54.333333

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d40e1d84dd15a32beebce4ec09b11a79e61be531

249

py

Python

pava/implementation/natives/sun/java2d/opengl/WGLSurfaceData.py

laffra/pava

54d10cf7f8def2f96e254c0356623d08f221536f

[ "MIT" ]

4

2017-03-30T16:51:16.000Z

2020-10-05T12:25:47.000Z

pava/implementation/natives/sun/java2d/opengl/WGLSurfaceData.py

laffra/pava

54d10cf7f8def2f96e254c0356623d08f221536f

[ "MIT" ]

null

pava/implementation/natives/sun/java2d/opengl/WGLSurfaceData.py

laffra/pava

54d10cf7f8def2f96e254c0356623d08f221536f

[ "MIT" ]

null

def add_native_methods(clazz): def initPbuffer__long__long__boolean__int__int__(a0, a1, a2, a3, a4, a5): raise NotImplementedError() clazz.initPbuffer__long__long__boolean__int__int__ = initPbuffer__long__long__boolean__int__int__

35.571429

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33

249

4.969697

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0.347561

0.47561

0.585366

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1

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null

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5

d439dcc7d049f34d730f8e7ec305a077a4dde5b5

341

py

Python

python_programs/find_first_in_sorted_test.py

vitchyr/QuixBugs

1fd2c3402e1a2aa3ff9ec22d7ba82c07b59996c2

[ "MIT" ]

null

python_programs/find_first_in_sorted_test.py

vitchyr/QuixBugs

1fd2c3402e1a2aa3ff9ec22d7ba82c07b59996c2

[ "MIT" ]

null

python_programs/find_first_in_sorted_test.py

vitchyr/QuixBugs

1fd2c3402e1a2aa3ff9ec22d7ba82c07b59996c2

[ "MIT" ]

null

from .find_first_in_sorted import find_first_in_sorted def test_main(): assert find_first_in_sorted([3, 4, 5, 5, 5, 5, 6], 5) == 2 assert find_first_in_sorted([3, 4, 5, 5, 5, 5, 6], 4) == 1 assert find_first_in_sorted([1, 2, 3], 1) == 0 assert find_first_in_sorted([], 1) == -1 if __name__ == "__main__": test_main()

26.230769

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0.091912

0.202346

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12

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null

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1

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5

d489139d6878ea1870c3dbdc037a42b3dc3612f8

209

py

Python

diaux/__init__.py

cremerlab/ltee_diauxie

69eae1857ff93c512d79d12489a287a24351e336

[ "MIT" ]

1

2021-10-01T03:31:19.000Z

diaux/__init__.py

cremerlab/ltee_diauxie

69eae1857ff93c512d79d12489a287a24351e336

[ "MIT" ]

null

diaux/__init__.py

cremerlab/ltee_diauxie

69eae1857ff93c512d79d12489a287a24351e336

[ "MIT" ]

null

# -*- coding: utf-8 -*- from . import viz from . import io from . import fitderiv from . import gaussianprocess __author__ = """Griffin Chure""" __email__ = """griffinchure@gmail.com""" __version__ = "0.0.1"

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0

1

0

null

0

1

0

1

0

5

d48a158ecff4605803c4c9eb326a797c9b9eb703

84

py

Python

apps/plea/exceptions.py

uk-gov-mirror/ministryofjustice.manchester_traffic_offences_pleas

4c625b13fa2826bdde083a0270dcea1791f6dc18

[ "MIT" ]

3

2015-12-22T16:37:14.000Z

2018-01-22T18:44:38.000Z

apps/plea/exceptions.py

uk-gov-mirror/ministryofjustice.manchester_traffic_offences_pleas

4c625b13fa2826bdde083a0270dcea1791f6dc18

[ "MIT" ]

145

2015-03-04T11:17:50.000Z

2022-03-21T12:10:13.000Z

apps/plea/exceptions.py

uk-gov-mirror/ministryofjustice.manchester_traffic_offences_pleas

4c625b13fa2826bdde083a0270dcea1791f6dc18

[ "MIT" ]

3

2015-12-29T14:59:12.000Z

2021-04-11T06:24:11.000Z

""" Exceptions ========== """ class AuditEventException(BaseException): pass

8.4

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84

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42

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1

null

0

1

0

null

0

1

0

5

00fd85e90a3a7e35f0039301dedc375fc5bb1a43

5,678

py

Python

andres@programo.ual.es/figureMoG.py

andresmasegosa/PRML-CoreSets

fb768debb15e3ff6f5b65b7224915a41c1493f3d

[ "MIT" ]

null

andres@programo.ual.es/figureMoG.py

andresmasegosa/PRML-CoreSets

fb768debb15e3ff6f5b65b7224915a41c1493f3d

[ "MIT" ]

null

andres@programo.ual.es/figureMoG.py

andresmasegosa/PRML-CoreSets

fb768debb15e3ff6f5b65b7224915a41c1493f3d

[ "MIT" ]

null

import matplotlib.pyplot as plt import numpy as np from sklearn.cluster import KMeans import inferpy as inf from sklearn import metrics from datareduction.variational_gaussian_mixture_DR import VariationalGaussianMixture_DR from prml.rv import VariationalGaussianMixture from prml.features import PolynomialFeatures from prml.linear import ( VariationalLinearRegressor, VariationalLogisticRegressor ) from scipy import random, linalg ############## GENERATE DATA ######################## N=1000 K=2 M=10 D=2 np.random.seed(10) cov = np.random.rand(D,D) cov = np.dot(cov,cov.transpose()) x_train = np.random.multivariate_normal(np.repeat(5,D),cov,int(N/K)) x_test = np.random.multivariate_normal(np.repeat(5,D),cov,int(N/K)) y_test = np.repeat(0,int(N/K)) for i in range(1,K): x_train=np.append(x_train, np.random.multivariate_normal(np.repeat(10*i,D),cov,int(N/K)),axis=0) x_test=np.append(x_test, np.random.multivariate_normal(np.repeat(10*i,D),cov,int(N/K)),axis=0) y_test = np.append(y_test, np.repeat(i, int(N / K))) np.take(x_train,np.random.permutation(x_train.shape[0]),axis=0,out=x_train) a=0 b=15 c=0 d=15 #plt.scatter(x_train[:,0],x_train[:,1]) # plt.figure(0) # np.random.seed(1234) # vgmm = VariationalGaussianMixture(n_components=2) # vgmm.fit(x_train) # vgmm.mu # # plt.scatter(x_train[:, 0], x_train[:, 1], c=vgmm.classify(x_train)) # x0, x1 = np.meshgrid(np.linspace(a, b, 100), np.linspace(c, d, 100)) # x = np.array([x0, x1]).reshape(2, -1).T # plt.contour(x0, x1, np.exp(vgmm.logpdf(x)).reshape(100, 100)) # plt.xlim(-5, 10, 100) # plt.ylim(-5, 10, 100) # plt.gca().set_aspect('equal', adjustable='box') # plt.savefig("./figs/MoG_Artificial_TrueVI.pdf",bbox_inches='tight') plt.figure(0) np.random.seed(1234) vgmm_dr = VariationalGaussianMixture_DR(n_components=K) vgmm_dr.fit(x_train, n_clusters=2, cluster_method="SS") vgmm_dr.mu plt.scatter(x_train[:, 0], x_train[:, 1], c=vgmm_dr.classify(x_train)) x0, x1 = np.meshgrid(np.linspace(a, b, 1000), np.linspace(c, d, 1000)) x = np.array([x0, x1]).reshape(2, -1).T plt.contour(x0, x1, np.exp(vgmm_dr.logpdf(x)).reshape(1000, 1000)) plt.scatter(vgmm_dr.X_dr['X'][:,0],vgmm_dr.X_dr['X'][:,1], c='k', s=50.0, marker='+') plt.xlim(a, b, 100) plt.ylim(c, d, 100) plt.gca().set_aspect('equal', adjustable='box') plt.savefig("./figs/MoG_Artificial_SS_M_2.pdf",bbox_inches='tight') plt.figure(1) np.random.seed(12) vgmm_dr = VariationalGaussianMixture_DR(n_components=K) vgmm_dr.fit(x_train, n_clusters=10, cluster_method="SS") vgmm_dr.mu plt.scatter(x_train[:, 0], x_train[:, 1], c=vgmm_dr.classify(x_train)) x0, x1 = np.meshgrid(np.linspace(a, b, 1000), np.linspace(c, d, 1000)) x = np.array([x0, x1]).reshape(2, -1).T plt.contour(x0, x1, np.exp(vgmm_dr.logpdf(x)).reshape(1000, 1000)) plt.scatter(vgmm_dr.X_dr['X'][:,0],vgmm_dr.X_dr['X'][:,1], c='k', s=50.0, marker='+') plt.xlim(a, b, 100) plt.ylim(c, d, 100) plt.gca().set_aspect('equal', adjustable='box') plt.savefig("./figs/MoG_Artificial_SS_M_ 10.pdf",bbox_inches='tight') plt.figure(2) np.random.seed(10) vgmm_dr = VariationalGaussianMixture_DR(n_components=K) vgmm_dr.fit(x_train, n_clusters=2, cluster_method="NoSS") vgmm_dr.mu plt.scatter(x_train[:, 0], x_train[:, 1], c=vgmm_dr.classify(x_train)) x0, x1 = np.meshgrid(np.linspace(a, b, 1000), np.linspace(c, d, 1000)) x = np.array([x0, x1]).reshape(2, -1).T plt.contour(x0, x1, np.exp(vgmm_dr.logpdf(x)).reshape(1000, 1000)) plt.scatter(vgmm_dr.X_dr['X'][:,0],vgmm_dr.X_dr['X'][:,1], c='k', s=50.0, marker='+') plt.xlim(a, b, 100) plt.ylim(c, d, 100) plt.gca().set_aspect('equal', adjustable='box') plt.savefig("./figs/MoG_Artificial_NoSS_M_2.pdf",bbox_inches='tight') plt.figure(3) np.random.seed(10) vgmm_dr = VariationalGaussianMixture_DR(n_components=K) vgmm_dr.fit(x_train, n_clusters=10, cluster_method="NoSS") vgmm_dr.mu plt.scatter(x_train[:, 0], x_train[:, 1], c=vgmm_dr.classify(x_train)) x0, x1 = np.meshgrid(np.linspace(a, b, 1000), np.linspace(c, d, 1000)) x = np.array([x0, x1]).reshape(2, -1).T plt.contour(x0, x1, np.exp(vgmm_dr.logpdf(x)).reshape(1000, 1000)) plt.scatter(vgmm_dr.X_dr['X'][:,0],vgmm_dr.X_dr['X'][:,1], c='k', s=50.0, marker='+') plt.xlim(a, b, 100) plt.ylim(c, d, 100) plt.gca().set_aspect('equal', adjustable='box') plt.savefig("./figs/MoG_Artificial_NoSS_M_10.pdf",bbox_inches='tight') plt.figure(4) np.random.seed(0) vgmm_dr = VariationalGaussianMixture_DR(n_components=K) vgmm_dr.fit(x_train, n_clusters=10, cluster_method="random") vgmm_dr.mu plt.scatter(x_train[:, 0], x_train[:, 1], c=vgmm_dr.classify(x_train)) x0, x1 = np.meshgrid(np.linspace(a, b, 1000), np.linspace(c, d, 1000)) x = np.array([x0, x1]).reshape(2, -1).T plt.contour(x0, x1, np.exp(vgmm_dr.logpdf(x)).reshape(1000, 1000)) plt.scatter(vgmm_dr.X_dr['X'][:,0],vgmm_dr.X_dr['X'][:,1], c='k', s=100.0, marker='+') plt.xlim(a, b, 100) plt.ylim(c, d, 100) plt.gca().set_aspect('equal', adjustable='box') plt.savefig("./figs/MoG_Artificial_Random_M_10_0.pdf",bbox_inches='tight') plt.figure(5) np.random.seed(123456) vgmm_dr = VariationalGaussianMixture_DR(n_components=K) vgmm_dr.fit(x_train, n_clusters=10, cluster_method="random") vgmm_dr.mu plt.scatter(x_train[:, 0], x_train[:, 1], c=vgmm_dr.classify(x_train)) x0, x1 = np.meshgrid(np.linspace(a, b, 1000), np.linspace(c, d, 1000)) x = np.array([x0, x1]).reshape(2, -1).T plt.contour(x0, x1, np.exp(vgmm_dr.logpdf(x)).reshape(1000, 1000)) plt.scatter(vgmm_dr.X_dr['X'][:,0],vgmm_dr.X_dr['X'][:,1], c='k', s=100.0, marker='+') plt.xlim(a, b, 100) plt.ylim(c, d, 100) plt.gca().set_aspect('equal', adjustable='box') plt.savefig("./figs/MoG_Artificial_Random_M_10_1.pdf",bbox_inches='tight') plt.show()

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null

0

1

0

null

0

5

2e0850ae1066f3630c75f1c1ed6542cba97f1387

86

py

Python

DailyChallenge/LC_1551.py

iphyer/LeetcodeSummary

ad5229bbb8e76083e5c7f0312fa0c8ff78d516a9

[ "MIT" ]

null

DailyChallenge/LC_1551.py

iphyer/LeetcodeSummary

ad5229bbb8e76083e5c7f0312fa0c8ff78d516a9

[ "MIT" ]

null

DailyChallenge/LC_1551.py

iphyer/LeetcodeSummary

ad5229bbb8e76083e5c7f0312fa0c8ff78d516a9

[ "MIT" ]

null

class Solution: def minOperations(self, n: int) -> int: return n*n//4

21.5

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1

0

5

2e2093240745b6e4a46f73bdc8d0d1c3c7122248

431

py

Python

sales_register/domain/ports/repositories/salesman_repository.py

tamercuba/purchase-system

cfd3e4fecbd96c130f620d11491fa14979c0d996

[ "MIT" ]

null

sales_register/domain/ports/repositories/salesman_repository.py

tamercuba/purchase-system

cfd3e4fecbd96c130f620d11491fa14979c0d996

[ "MIT" ]

6

2021-05-15T21:44:19.000Z

2021-05-23T22:20:13.000Z

sales_register/domain/ports/repositories/salesman_repository.py

tamercuba/sales-register

cfd3e4fecbd96c130f620d11491fa14979c0d996

[ "MIT" ]

null

from abc import abstractmethod from domain.entities import Salesman class ISalesmanRepository: @abstractmethod def new(self, salesman: Salesman) -> Salesman: pass @abstractmethod def get_by_cpf(self, cpf: str) -> Salesman: pass @abstractmethod def get_by_id(self, _id: str) -> Salesman: pass @abstractmethod def get_by_email(self, email: str) -> Salesman: pass

19.590909

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2e33a7e59396af0100ca054bb931be9af6b61e56

1,782

py

Python

tests/test_main.py

source-foundry/ufolint

88c744d7f8d45c62701c58f1a028f0283670571f

[ "MIT" ]

22

2017-08-07T13:58:28.000Z

2021-11-21T17:01:01.000Z

tests/test_main.py

source-foundry/ufolint

88c744d7f8d45c62701c58f1a028f0283670571f

[ "MIT" ]

119

2017-08-03T14:08:02.000Z

2022-03-23T06:04:33.000Z

tests/test_main.py

source-foundry/ufolint

88c744d7f8d45c62701c58f1a028f0283670571f

[ "MIT" ]

4

2017-08-08T12:20:58.000Z

2020-11-25T14:38:27.000Z

#!/usr/bin/env python # -*- coding: utf-8 -*- import sys import os import pytest from ufolint.app import main ufo3_test_success_path = os.path.join('tests', 'testfiles', 'ufo', 'passes', 'UFO3-Pass.ufo') def test_ufolint_app_main_function_missing_args(capsys): with pytest.raises(SystemExit) as pytest_wrapped_e: sys.argv = ['ufolint'] main() out, err = capsys.readouterr() assert '[ufolint] ERROR:' in err assert pytest_wrapped_e.type == SystemExit assert pytest_wrapped_e.value.code == 1 def test_ufolint_app_main_function_help_request(capsys): with pytest.raises(SystemExit) as pytest_wrapped_e: sys.argv = ['ufolint', '--help'] main() out, err = capsys.readouterr() assert len(out) > 1 assert pytest_wrapped_e.type == SystemExit assert pytest_wrapped_e.value.code == 0 def test_ufolint_app_main_function_version_request(capsys): with pytest.raises(SystemExit) as pytest_wrapped_e: sys.argv = ['ufolint', '--version'] main() out, err = capsys.readouterr() assert 'ufolint v' in out assert pytest_wrapped_e.type == SystemExit assert pytest_wrapped_e.value.code == 0 def test_ufolint_app_main_function_usage_request(capsys): with pytest.raises(SystemExit) as pytest_wrapped_e: sys.argv = ['ufolint', '--usage'] main() out, err = capsys.readouterr() assert 'ufolint' in out assert pytest_wrapped_e.type == SystemExit assert pytest_wrapped_e.value.code == 0 def test_ufolint_app_main_function_mainrunner(): with pytest.raises(SystemExit) as pytest_wrapped_e: sys.argv = ['ufolint', ufo3_test_success_path] main() assert pytest_wrapped_e.type == SystemExit assert pytest_wrapped_e.value.code == 0

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1

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null

0

5

2e3f0fdc089b3ea2c874d91573d9a7ec2ccf91e2

83

py

Python

gan/__init__.py

haihabi/RainMapGenerator

e6ebcb01a4703d4af6a64ccd62bbc5d32d36b617

[ "MIT" ]

2

2021-07-25T19:22:29.000Z

2021-10-31T10:19:39.000Z

gan/__init__.py

haihabi/RainMapGenerator

e6ebcb01a4703d4af6a64ccd62bbc5d32d36b617

[ "MIT" ]

1

2022-03-10T02:54:45.000Z

2022-03-11T02:05:20.000Z

gan/__init__.py

haihabi/RainMapGenerator

e6ebcb01a4703d4af6a64ccd62bbc5d32d36b617

[ "MIT" ]

null

from gan.gan_training import GANTraining from gan.config import GANType, GANConfig

27.666667

41

0.855422

12

83

5.833333

0.666667

0.2

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2

42

41.5

0.945946

0

1

0

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0

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1

0

null

0

1

0

1

0

1

0

5

2e80793bf03809ed2d2d72dc536124a383764414

122

py

Python

app/resources/checks.py

dpfg/kicker-scorer-api

38dcf85e8e80a7a6a2213fb80f7f480c74f87cac

[ "MIT" ]

null

app/resources/checks.py

dpfg/kicker-scorer-api

38dcf85e8e80a7a6a2213fb80f7f480c74f87cac

[ "MIT" ]

null

app/resources/checks.py

dpfg/kicker-scorer-api

38dcf85e8e80a7a6a2213fb80f7f480c74f87cac

[ "MIT" ]

null

from flask_restful import Resource, Api class HealthCheck(Resource): def get(self): return {'alive': 'true'}

20.333333

39

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15

122

5.466667

0.933333

0

0.204918

122

5

40

24.4

0.845361

0

0.07377

0

1

0.25

false

0

0.25

1

0

1

0

null

0

1

0

null

0

1

0

1

0

5

cf4c6d4d9c96da8a7824dfec19ee2fda9e546278

75

py

Python

src/Squareroot.py

leivapaola/Calculator

1d7e91f93c3f308c289e34c5872591bfd8bf7cdb

[ "MIT" ]

null

src/Squareroot.py

leivapaola/Calculator

1d7e91f93c3f308c289e34c5872591bfd8bf7cdb

[ "MIT" ]

null

src/Squareroot.py

leivapaola/Calculator

1d7e91f93c3f308c289e34c5872591bfd8bf7cdb

[ "MIT" ]

null

import math def sqrt(a): return "{:.8f}".format(math.sqrt(float(a)))

12.5

47

0.613333

12

75

3.833333

0.75

0

0.015873

0.16

75

5

48

15

0.714286

0

0.08

0

1

0.333333

false

0

0.333333

1

0

1

0

null

0

1

0

null

0

1

0

1

0

5

cf689e446d7e94d05ee92f2d233eacdd597fd230

1,274

py

Python

AutomationFramework/tests/interfaces/test_if_lag.py

sbarguil/Testing-framework

f3ef69f1c4f0aeafd02e222d846162c711783b15

[ "Apache-2.0" ]

1

2020-04-23T15:22:16.000Z

AutomationFramework/tests/interfaces/test_if_lag.py

sbarguil/Testing-framework

f3ef69f1c4f0aeafd02e222d846162c711783b15

[ "Apache-2.0" ]

44

2020-08-13T19:35:41.000Z

2021-03-01T09:08:00.000Z

AutomationFramework/tests/interfaces/test_if_lag.py

sbarguil/Testing-framework

f3ef69f1c4f0aeafd02e222d846162c711783b15

[ "Apache-2.0" ]

6

2020-04-23T15:29:38.000Z

2022-03-03T14:23:38.000Z

import pytest from AutomationFramework.page_objects.interfaces.interfaces import Interfaces from AutomationFramework.tests.base_test import BaseTest class TestInterfacesLag(BaseTest): test_case_file = 'if_lag.yml' @pytest.mark.parametrize('create_page_object_arg', [{'test_case_file': test_case_file, 'test_case_name': 'if_lag_type', 'page_object_class': Interfaces}]) def test_if_lag_type(self, create_page_object): create_page_object.execute_generic_interfaces_edit_config_test_case() assert create_page_object.generic_validate_test_case_params(), create_page_object.get_test_case_description() @pytest.mark.parametrize('create_page_object_arg', [{'test_case_file': test_case_file, 'test_case_name': 'if_lag_min_links', 'page_object_class': Interfaces}]) def test_if_lag_min_links(self, create_page_object): create_page_object.execute_generic_interfaces_edit_config_test_case() assert create_page_object.generic_validate_test_case_params(), create_page_object.get_test_case_description()

57.909091

117

0.672684

144

1,274

5.388889

0.263889

0.134021

0.206186

0.082474

0.734536

0.639175

0

0.256672

1,274

21

118

60.666667

0.81943

0

0.470588

0

0.134223

0.034537

0

0.117647

1

0.117647

false

0

0.176471

0

0.411765

0

null

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1

0

1

0

1

0

null

0

5

cf694e8f1e908c0b528754f0ad1baa5a2fb588a3

412

bzl

Python

crate_universe/crates.bzl

silas-enf/rules_rust

41b39f0c9951dfda3bd0a95df31695578dd3f5ea

[ "Apache-2.0" ]

1

2017-06-12T02:10:48.000Z

crate_universe/crates.bzl

silas-enf/rules_rust

41b39f0c9951dfda3bd0a95df31695578dd3f5ea

[ "Apache-2.0" ]

null

crate_universe/crates.bzl

silas-enf/rules_rust

41b39f0c9951dfda3bd0a95df31695578dd3f5ea

[ "Apache-2.0" ]

null

"""**DEPRECATED** - Instead, use `@rules_rust//crate_universe:repositories.bzl""" load(":repositories.bzl", "crate_universe_dependencies") def crate_deps_repository(**kwargs): # buildifier: disable=print print("`crate_deps_repository` is deprecated. See setup instructions for how to update: https://bazelbuild.github.io/rules_rust/crate_universe.html#setup") crate_universe_dependencies(**kwargs)

45.777778

159

0.771845

50

412

6.12

0.62

0.169935

0.091503

0.143791

0

0.092233

412

8

160

51.5

0.818182

0.247573

0

0.25

0.625

0.164474

0

1

0.25

true

0

0.25

0

null

0

1

0

1

0

null

0

1

0

5

cf6fb23d869726ef4c4ccbc7676a8756b13fbfd2

4,345

py

Python

evalsrlfet.py

hldai/SRFET-prep

e729b2cdb268b201cfe63fd6ed3ee932f8ff3ad0

[ "MIT" ]

null

evalsrlfet.py

hldai/SRFET-prep

e729b2cdb268b201cfe63fd6ed3ee932f8ff3ad0

[ "MIT" ]

null

evalsrlfet.py

hldai/SRFET-prep

e729b2cdb268b201cfe63fd6ed3ee932f8ff3ad0

[ "MIT" ]

null

import os import datetime import torch import logging import argparse from exp import srlfetexp, expdata from utils.loggingutils import init_universal_logging import config def __eval1(): dataset = 'figer' # dataset = 'bbn' datafiles = config.FIGER_FILES if dataset == 'figer' else config.BBN_FILES word_vecs_file = config.WIKI_FETEL_WORDVEC_FILE model_file_prefix = os.path.join(config.DATA_DIR, 'models/pretrained-srl-{}'.format(dataset)) # sub_set = 'test' sub_set = 'train' if sub_set == 'test': mentions_file = datafiles['test-mentions'] sents_file = datafiles['test-sents'] srl_file = datafiles['test-srl'] dep_file = datafiles['test-sents-dep'] else: if dataset == 'bbn': mentions_file = datafiles['train-mentions'] sents_file = datafiles['train-sents'] srl_file = datafiles['train-srl'] dep_file = datafiles['train-sents-dep'] else: mentions_file = os.path.join(config.DATA_DIR, 'figer/wiki-valcands-figer-mentions.json') sents_file = os.path.join(config.DATA_DIR, 'figer/wiki-valcands-figer-sents.json') srl_file = os.path.join(config.DATA_DIR, 'figer/wiki-valcands-figer-srl.txt') dep_file = os.path.join(config.DATA_DIR, 'figer/wiki-valcands-figer-tok-dep.txt') output_preds_file = os.path.join( config.DATA_DIR, '{}/{}-{}-pretrained-srl-preds.txt'.format(dataset, dataset, sub_set)) single_type_path = True if dataset == 'bbn' else False gres = expdata.ResData(datafiles['type-vocab'], word_vecs_file) srlfetexp.eval_trained(device, gres, model_file_prefix, mentions_file, sents_file, srl_file, dep_file, single_type_path, output_preds_file) def __eval(): # dataset = 'figer' dataset = 'bbn' datafiles = config.FIGER_FILES if dataset == 'figer' else config.BBN_FILES word_vecs_file = config.WIKI_FETEL_WORDVEC_FILE model_file_prefix = os.path.join(config.DATA_DIR, 'models/srl-{}'.format(dataset)) # sub_set = 'test' # sub_set = 'train' sub_sets = ['test', 'train'] for sub_set in sub_sets: if sub_set == 'test': mentions_file = datafiles['test-mentions'] sents_file = datafiles['test-sents'] srl_file = datafiles['test-srl'] dep_file = datafiles['test-sents-dep'] else: if dataset == 'bbn': mentions_file = datafiles['train-mentions'] sents_file = datafiles['train-sents'] srl_file = datafiles['train-srl'] dep_file = datafiles['train-sents-dep'] else: mentions_file = os.path.join(config.DATA_DIR, 'figer/wiki-valcands-figer-mentions.json') sents_file = os.path.join(config.DATA_DIR, 'figer/wiki-valcands-figer-sents.json') srl_file = os.path.join(config.DATA_DIR, 'figer/wiki-valcands-figer-srl.txt') dep_file = os.path.join(config.DATA_DIR, 'figer/wiki-valcands-figer-tok-dep.txt') output_preds_file = os.path.join( config.DATA_DIR, '{}/{}-{}-srl-preds.txt'.format(dataset, dataset, sub_set)) single_type_path = True if dataset == 'bbn' else False gres = expdata.ResData(datafiles['type-vocab'], word_vecs_file) srlfetexp.eval_trained(device, gres, model_file_prefix, mentions_file, sents_file, srl_file, dep_file, single_type_path, output_preds_file) if __name__ == '__main__': str_today = datetime.date.today().strftime('%y-%m-%d') # log_file = os.path.join(config.LOG_DIR, '{}-{}-{}.log'.format(os.path.splitext( # os.path.basename(__file__))[0], str_today, config.MACHINE_NAME)) log_file = None init_universal_logging(log_file, mode='a', to_stdout=True) parser = argparse.ArgumentParser(description='dhl') parser.add_argument('idx', type=int, default=0, nargs='?') parser.add_argument('-d', type=int, default=[], nargs='+') args = parser.parse_args() cuda_device_str = 'cuda' if len(args.d) == 0 else 'cuda:{}'.format(args.d[0]) device = torch.device(cuda_device_str) if torch.cuda.device_count() > 0 else torch.device('cpu') if args.idx == 0: __eval() if args.idx == 1: __eval1()

43.019802

110

0.643268

569

4,345

4.667838

0.179262

0.078313

0.048946

0.078313

0.743976

0.736446

0.708584

0

0.002659

0.220944

4,345

100

111

43.45

0.781979

0.053855

0

0.582278

0

0.166464

0.089934

0

1

0.025316

false

0

0.101266

0

0.126582

0

null

0

1

0

null

0

5

d8c0336659eea66eed544f66d3eae64cdb94d104

189

py

Python

rentomatic/repository/memrepo.py

swilltec/rentomatic

2e7184bfa4c23fa580651c09a7317629e5d5df0c

[ "MIT" ]

null

rentomatic/repository/memrepo.py

swilltec/rentomatic

2e7184bfa4c23fa580651c09a7317629e5d5df0c

[ "MIT" ]

null

rentomatic/repository/memrepo.py

swilltec/rentomatic

2e7184bfa4c23fa580651c09a7317629e5d5df0c

[ "MIT" ]

null

from rentomatic.domain import room as r class MemRepo: def __init__(self, data): self.data = data def list(self): return [r.Room.from_dict(i) for i in self.data]

18.9

55

0.645503

30

189

3.9

0.633333

0.205128

0

0.259259

189

9

56

21

0.835714

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0.333333

false

0

0.166667

0.833333

0

null

1

0

1

0

null

0

1

0

1

0

5

2b04d498c74a07c0ac2a37af802f2973bdb057fa

223

py

Python

minirun/app.py

danhorsley/barebones

f832ecdda1931e261f1d66c8fee825d78c737326

[ "CC-BY-4.0" ]

null

minirun/app.py

danhorsley/barebones

f832ecdda1931e261f1d66c8fee825d78c737326

[ "CC-BY-4.0" ]

null

minirun/app.py

danhorsley/barebones

f832ecdda1931e261f1d66c8fee825d78c737326

[ "CC-BY-4.0" ]

null

from flask import Flask def create_app(): """Create and configure instance of the Flask application""" app = Flask(__name__) @app.route('/') def barebones(): return 'the barebones' return app

18.583333

64

0.641256

27

223

5.111111

0.592593

0.217391

0

0.251121

223

12

65

18.583333

0.826347

0.242152

0

0.085366

0

1

0.285714

false

0

0.142857

0.714286

0

1

0

null

1

0

null

0

1

0

1

0

5

2b06da83212a058b9a255d1b6b069424d41bb402

416

py

Python

estimators/__init__.py

dizcza/entropy-estimators

a12b9c5a0be5e7314a8392f1eafefd76a78b82f9

[ "MIT" ]

1

2021-04-13T06:41:14.000Z

estimators/__init__.py

dizcza/entropy-estimators

a12b9c5a0be5e7314a8392f1eafefd76a78b82f9

[ "MIT" ]

null

estimators/__init__.py

dizcza/entropy-estimators

a12b9c5a0be5e7314a8392f1eafefd76a78b82f9

[ "MIT" ]

2

2020-05-13T11:59:49.000Z

2020-07-30T08:41:36.000Z

from .NPEET.npeet.entropy_estimators import mi as npeet_mi from .NPEET.npeet.entropy_estimators import entropy as npeet_entropy from .NPEET.npeet.entropy_estimators import entropyd as discrete_entropy from .NPEET.npeet.entropy_estimators import midd as discrete_mi from .gcmi.python.gcmi import gcmi_cc as gcmi_mi from .gcmi.python.gcmi import ent_g as gcmi_entropy from .mine import mine_mi from ._micd import micd

46.222222

72

0.846154

69

416

4.898551

0.246377

0.177515

0.16568

0.248521

0.633136

0.260355

0

0.105769

416

8

73

52

0.908602

0

1

0

true

0

1

0

1

0

null

0

1

0

1

0

null

0

1

0

1

0

1

0

5

2b1cafde7a3419d6f4606e9ce8e7ddd634d76751

216

py

Python

onyx/core/Screen.py

OnyxAI/onyx

52f4bd5c5dd102acc51a83a20f281d7146893c2a

[ "MIT" ]

2

2020-04-14T21:16:07.000Z

2020-07-09T07:30:44.000Z

onyx/core/Screen.py

OnyxAI/onyx

52f4bd5c5dd102acc51a83a20f281d7146893c2a

[ "MIT" ]

2

2020-04-01T12:33:36.000Z

2020-04-01T12:33:49.000Z

onyx/core/Screen.py

OnyxAI/onyx

52f4bd5c5dd102acc51a83a20f281d7146893c2a

[ "MIT" ]

null

from . import api from onyx.api.screen import Screen, ScreenStore, ScreenLayouts api.add_resource(Screen, '/screen') api.add_resource(ScreenStore, '/screen/store') api.add_resource(ScreenLayouts, '/screen/layouts')

30.857143

62

0.791667

28

216

6

0.392857

0.107143

0.25

0

0.078704

216

6

63

36

0.844221

0

0.162037

0

1

0

true

0

0.4

0

0.4

0

1

0

null

0

1

0

1

0

null

0

1

0

1

0

5

2b1dc54d3211c45549e4e5e008a605b54c90f6b5

280

py

Python

backend/apps/food/admin.py

MgArreaza13/wonderhumans

865b65e4afa1ac32976a8c53959a7c58543dbe60

[ "MIT" ]

null

backend/apps/food/admin.py

MgArreaza13/wonderhumans

865b65e4afa1ac32976a8c53959a7c58543dbe60

[ "MIT" ]

null

backend/apps/food/admin.py

MgArreaza13/wonderhumans

865b65e4afa1ac32976a8c53959a7c58543dbe60

[ "MIT" ]

null

# From Django from django.contrib import admin # My models from apps.food import models as food_models admin.site.register(food_models.FoodRun) admin.site.register(food_models.FoodDonation) admin.site.register(food_models.FoodVolunteer) admin.site.register(food_models.FeedFood)

28

46

0.839286

41

280

5.609756

0.390244

0.217391

0.295652

0.365217

0.469565

0

0.075

280

10

47

28

0.888031

0.075

0

1

0

true

0

0.333333

0

0.333333

0

null

1

0

null

0

1

0

1

0

5

2b76250ca7eada3a0745f1cda7bd7b9d64e348ae

132

py

Python

test/test_scenario_runner.py

xfuzzycomp/FuzzyAsteroids

636707499b4689bdecd8af32231c3ffd43f6583b

[ "MIT" ]

1

2021-09-14T20:38:08.000Z

test/test_scenario_runner.py

xfuzzycomp/FuzzyAsteroids

636707499b4689bdecd8af32231c3ffd43f6583b

[ "MIT" ]

null

test/test_scenario_runner.py

xfuzzycomp/FuzzyAsteroids

636707499b4689bdecd8af32231c3ffd43f6583b

[ "MIT" ]

null

from unittest import TestCase from src.fuzzy_asteroids.runner import ScenarioRunner class TestScenarioRunner(TestCase): pass

16.5

53

0.825758

15

132

7.2

0.8

0

0.136364

132

7

54

18.857143

0.947368

0

1

0

true

0.25

0.5

0

0.75

0

1

0

null

0

1

0

null

0

1

0

5

9961cd5a8c96cbf08459844dcaf0d5f6727c0da4

453

py

Python

data_crawler/data_prep.py

viethuong12/NLP

c3d1f1cc8b1eb2c64302a88cfd2223c1b9823a45

[ "MIT" ]

9

2019-01-18T14:12:03.000Z

2020-05-28T15:35:06.000Z

data_crawler/data_prep.py

ltkk/programing-language-identify

c28c0edfe48741c7ee93ed61f4c0660db1e3b394

[ "MIT" ]

null

data_crawler/data_prep.py

ltkk/programing-language-identify

c28c0edfe48741c7ee93ed61f4c0660db1e3b394

[ "MIT" ]

1

2020-04-22T16:15:41.000Z

import re class CodePreprocess: def __init__(self): pass @staticmethod def remove_comment(code): return re.sub(r"(\/\/.+)|(#.+)|('.+)|(\/\*[^(\*\/)]+?\*\/)|(\"{3}[^(\"{3})]+?\"{3})", ' ', code) @staticmethod def remove_space(code): return re.sub("\s+", ' ', code.strip()) def preprocess(self, code): code = self.remove_comment(code) code = self.remove_space(code) return code

22.65

104

0.509934

48

453

4.645833

0.416667

0.134529

0.188341

0.134529

0

0.008772

0.245033

453

19

105

23.842105

0.643275

0

0.142857

0

0.128035

0.099338

0

1

0.285714

false

0.071429

0.142857

0.642857

0

null

0

1

0

null

0

1

0

1

0

1

0

5

99855e31ab0942f6cf178501a7253212ef79a95c

177

py

Python

Codewars/8kyu/square-n-sum/Python/test.py

RevansChen/online-judge

ad1b07fee7bd3c49418becccda904e17505f3018

[ "MIT" ]

7

2017-09-20T16:40:39.000Z

2021-08-31T18:15:08.000Z

Codewars/8kyu/square-n-sum/Python/test.py

RevansChen/online-judge

ad1b07fee7bd3c49418becccda904e17505f3018

[ "MIT" ]

null

Codewars/8kyu/square-n-sum/Python/test.py

RevansChen/online-judge

ad1b07fee7bd3c49418becccda904e17505f3018

[ "MIT" ]

null

# Python - 3.6.0 Test.expect(square_sum([1, 2]), 'squareSum did not return a value') Test.assert_equals(square_sum([1, 2]), 5) Test.assert_equals(square_sum([0, 3, 4, 5]), 50)

29.5

67

0.683616

34

177

3.411765

0.617647

0.232759

0.172414

0.189655

0.431034

0

0.089744

0.118644

177

5

68

35.4

0.653846

0.079096

0

0.198758

0

0.666667

1

0

true

0

null

1

0

1

0

1

0

null

0

1

0

1

0

5

99a07373c77a9d3f4908c387ae9f9ca60718a9df

225

py

Python

MegaAdversarial/src/attacks/base.py

SamplingAndEnsemblingSolvers/SamplingAndEnsemblingSolvers

5ad3cae76c3cc9cec4d347807012e61121ea61b9

[ "MIT" ]

25

2021-03-16T13:40:45.000Z

2021-08-12T04:54:39.000Z

MegaAdversarial/src/attacks/base.py

MetaSolver/icml2021

619774abe4a834ae371434af8b23379e9524e7da

[ "BSD-3-Clause" ]

null

MegaAdversarial/src/attacks/base.py

MetaSolver/icml2021

619774abe4a834ae371434af8b23379e9524e7da

[ "BSD-3-Clause" ]

1

2021-03-31T02:58:03.000Z

from .attack import Attack, Attack2Ensemble class Clean(Attack): def forward(self, x, y, kwargs): return x, y class Clean2Ensemble(Attack2Ensemble): def forward(self, x, y, kwargs_arr): return x, y

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1

0

null

0

1

0

1

0

5

99a4e71c268d5f0bbfa2b7c124eb4259ac1e7d54

131

py

Python

storemanager/users/admin.py

sylviawanjiku/drf_sm

bf82ceacca746494cded9823e7befc65e8c98bbf

[ "MIT" ]

1

2019-01-14T15:55:57.000Z

storemanager/users/admin.py

sylviawanjiku/drf_sm

bf82ceacca746494cded9823e7befc65e8c98bbf

[ "MIT" ]

null

storemanager/users/admin.py

sylviawanjiku/drf_sm

bf82ceacca746494cded9823e7befc65e8c98bbf

[ "MIT" ]

null

from django.contrib import admin from .models import User,UserProfile admin.site.register(User) admin.site.register(UserProfile)

18.714286

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0.824427

18

131

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0.555556

0.166667

0.314815

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0.091603

131

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21.833333

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0

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1

0

null

0

1

0

1

0

5

5114366b7cca1c5dd89beb0e2c7d3c89a61a2c6a

226

py

Python

active_reward_learning/envs/mujoco/ant_maze_env.py

david-lindner/idrl

54cfad330b0598ad4f6621796f2411644e50a6ba

[ "MIT" ]

9

2021-11-20T18:14:38.000Z

2022-03-20T16:29:48.000Z

active_reward_learning/envs/mujoco/ant_maze_env.py

david-lindner/idrl

54cfad330b0598ad4f6621796f2411644e50a6ba

[ "MIT" ]

null

active_reward_learning/envs/mujoco/ant_maze_env.py

david-lindner/idrl

54cfad330b0598ad4f6621796f2411644e50a6ba

[ "MIT" ]

null

"""Adapted from https://github.com/rll/rllab.""" from active_reward_learning.envs.mujoco.ant import AntEnv from active_reward_learning.envs.mujoco.maze_env import MazeEnv class AntMazeEnv(MazeEnv): MODEL_CLASS = AntEnv

25.111111

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0.79646

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1

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null

0

1

0

1

0

5

5ab583f951fe6eb662f4e617abdecef1f0f02ae1

41

py

Python

wzdat/dashboard/__init__.py

haje01/wzdat

fad4aa411d63f643127842fdbf9450eb6d967503

[ "BSD-3-Clause" ]

15

2015-03-17T00:45:34.000Z

2021-04-14T12:31:39.000Z

wzdat/dashboard/__init__.py

haje01/wzdat

fad4aa411d63f643127842fdbf9450eb6d967503

[ "BSD-3-Clause" ]

null

wzdat/dashboard/__init__.py

haje01/wzdat

fad4aa411d63f643127842fdbf9450eb6d967503

[ "BSD-3-Clause" ]

2

2016-08-23T06:25:44.000Z

2021-04-14T12:31:42.000Z

# -*- coding: utf-8 -*- """Dashboard."""

13.666667

23

0.463415

4

41

4.75

1

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0.028571

0.146341

41

2

24

20.5

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null

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null

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null

0

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1

0

null

0

1

0

5

51cfa111f4dc7b5214726e7993000d784a529a4a

983

py

Python

core/nnlib/loss.py

FeryET/scratch_nn_lib

7810b33eac5343770cf50187442c0124166508be

[ "MIT" ]

null

core/nnlib/loss.py

FeryET/scratch_nn_lib

7810b33eac5343770cf50187442c0124166508be

[ "MIT" ]

null

core/nnlib/loss.py

FeryET/scratch_nn_lib

7810b33eac5343770cf50187442c0124166508be

[ "MIT" ]

null

import numpy as np from abc import ABC, abstractmethod # Defining base loss class class Loss(ABC): @abstractmethod def __call__(self, pred, target): pass @abstractmethod def gradient(self, *args, **kwargs): pass class MSELoss(Loss): def __call__(self, pred, target): return np.square(pred-target).mean(axis=0) / 2 def gradient(self, pred, target): return (pred - target).mean(axis=0) class L2RegularizationLoss(Loss): def __call__(self, weights): return sum([np.square(w).sum() for w in weights]) / 2 def gradient(self, w): return w class CrossEntropyLoss(Loss): def __call__(self, pred, target): return -np.sum(target * np.log(np.maximum(pred, 1e-9)), axis=1).mean() def gradient(self, pred, target): return target/pred + (1-target)/(1-pred) class CrossEntropyLossWithSoftmax(CrossEntropyLoss): def gradient(self, pred, target): return pred - target

22.340909

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0.648016

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983

4.889764

0.314961

0.144928

0.135266

0.161031

0.36715

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0.238325

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0.01321

0.229908

983

44

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22.340909

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null

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null

0

1

0

1

0

1

0

5

51d5471532c82cf1d459ecafda500c32b0cf7eec

90

py

Python

api/index/__init__.py

abbd122/Travel

c0edef468cd2dc5f3413d3f0d5e13957171f8b4e

[ "MIT" ]

null

api/index/__init__.py

abbd122/Travel

c0edef468cd2dc5f3413d3f0d5e13957171f8b4e

[ "MIT" ]

2

2021-03-10T01:11:14.000Z

2021-10-06T08:20:04.000Z

api/index/__init__.py

abbd122/Travel

c0edef468cd2dc5f3413d3f0d5e13957171f8b4e

[ "MIT" ]

null

from flask import Blueprint home_blu = Blueprint('index', __name__) from . import views

15

39

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90

5.333333

0.75

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0.155556

90

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1

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null

0

1

0

1

0

5

51d6312ff882a4f81e193fdce2d1a9696d140d49

12,747

py

Python

parser.py

martimfj/VBA-Compiler

c5a88843a0d13d561f4baba312bbaf7f8a2e5ca0

[ "MIT" ]

null

parser.py

martimfj/VBA-Compiler

c5a88843a0d13d561f4baba312bbaf7f8a2e5ca0

[ "MIT" ]

9

2019-02-23T13:19:30.000Z

2019-06-08T14:34:23.000Z

parser.py

martimfj/VBA-Compiler

c5a88843a0d13d561f4baba312bbaf7f8a2e5ca0

[ "MIT" ]

null

from node import * from symbol_table import SymbolTable from prepro import PrePro from lexer import Tokenizer class Parser: @staticmethod def parseProgram(): statements = [] if Parser.tokens.actual.type == "SUB": Parser.tokens.selectNext() if Parser.tokens.actual.type == "MAIN": Parser.tokens.selectNext() if Parser.tokens.actual.value == "(": Parser.tokens.selectNext() if Parser.tokens.actual.value == ")": Parser.tokens.selectNext() if Parser.tokens.actual.type == "LINEFEED": Parser.tokens.selectNext() while Parser.tokens.actual.type != "END": statements.append(Parser.parseStatement()) if Parser.tokens.actual.type == "LINEFEED": Parser.tokens.selectNext() if Parser.tokens.actual.type == "END": Parser.tokens.selectNext() if Parser.tokens.actual.type == "SUB": Parser.tokens.selectNext() else: raise ValueError("Parser Error (Program): Expected SUB, got token {}".format(repr(Parser.tokens.actual.value))) else: raise ValueError("Parser Error (Program): Expected END, got token {}".format(repr(Parser.tokens.actual.value))) else: raise ValueError("Parser Error (Program): Expected '\n', got token {}".format(repr(Parser.tokens.actual.value))) else: raise ValueError("Parser Error (Program): Expected ), got token {}".format(repr(Parser.tokens.actual.value))) else: raise ValueError("Parser Error (Program): Expected (, got token {}".format(repr(Parser.tokens.actual.value))) else: raise ValueError("Parser Error (Program): Expected MAIN, got token {}".format(repr(Parser.tokens.actual.value))) else: raise ValueError("Parser Error (Program): Expected SUB, got token {}".format(repr(Parser.tokens.actual.value))) return Program('program', statements) @staticmethod def parseStatement(): if Parser.tokens.actual.type == "IDENTIFIER": identifier = Identifier(Parser.tokens.actual.value) Parser.tokens.selectNext() if Parser.tokens.actual.type == "EQUAL": Parser.tokens.selectNext() return Assigment("=", [identifier, Parser.parseRelExpression()]) else: raise NameError("Parser Error (Statement): Name {} not defined".format(repr(identifier.value))) elif Parser.tokens.actual.type == "PRINT": Parser.tokens.selectNext() return Print('print', [Parser.parseRelExpression()]) elif Parser.tokens.actual.type == "WHILE": Parser.tokens.selectNext() rel_exp = Parser.parseRelExpression() if Parser.tokens.actual.type == "LINEFEED": Parser.tokens.selectNext() statements = [] while Parser.tokens.actual.type != "WEND": statements.append(Parser.parseStatement()) if Parser.tokens.actual.type == "LINEFEED": Parser.tokens.selectNext() else: raise ValueError("Parser Error (Statement): Expected '\n', got token {}".format(repr(Parser.tokens.actual.value))) if Parser.tokens.actual.type == "WEND": #Just an excuse to consume the token and SelectNext Parser.tokens.selectNext() return While("WHILE", [rel_exp, statements]) else: raise ValueError("Parser Error (Statement): Expected WEND, got token {}".format(repr(Parser.tokens.actual.value))) else: raise ValueError("Parser Error (Statement): Expected '\n', got token {}".format(repr(Parser.tokens.actual.value))) elif Parser.tokens.actual.type == "IF": Parser.tokens.selectNext() rel_exp = Parser.parseRelExpression() statements_else = None if Parser.tokens.actual.type == "THEN": Parser.tokens.selectNext() if Parser.tokens.actual.type == "LINEFEED": Parser.tokens.selectNext() statements_if = [] while Parser.tokens.actual.type not in ["ELSE", "END"]: statements_if.append(Parser.parseStatement()) if Parser.tokens.actual.type == "LINEFEED": Parser.tokens.selectNext() else: raise ValueError("Parser Error (Statement): Expected '\n', got token {}".format(repr(Parser.tokens.actual.value))) if Parser.tokens.actual.type == "ELSE": Parser.tokens.selectNext() if Parser.tokens.actual.type == "LINEFEED": Parser.tokens.selectNext() statements_else = [] while Parser.tokens.actual.type != "END": statements_else.append(Parser.parseStatement()) if Parser.tokens.actual.type == "LINEFEED": Parser.tokens.selectNext() else: raise ValueError("Parser Error (Statement): Expected '\n', got token {}".format(repr(Parser.tokens.actual.value))) else: raise ValueError("Parser Error (Statement): Expected '\n', got token {}".format(repr(Parser.tokens.actual.value))) if Parser.tokens.actual.type == "END": Parser.tokens.selectNext() if Parser.tokens.actual.type == "IF": Parser.tokens.selectNext() return If("IF", [rel_exp, statements_if, statements_else]) else: raise ValueError("Parser Error (Statement): Expected IF, got token {}".format(repr(Parser.tokens.actual.value))) else: raise ValueError("Parser Error (Statement): Expected END, got token {}".format(repr(Parser.tokens.actual.value))) else: raise ValueError("Parser Error (Statement): Expected '\n', got token {}".format(repr(Parser.tokens.actual.value))) else: raise ValueError("Parser Error (Statement): Expected THEN, got token {}".format(repr(Parser.tokens.actual.value))) elif Parser.tokens.actual.type == "DIM": Parser.tokens.selectNext() if Parser.tokens.actual.type == "IDENTIFIER": identifier = Identifier(Parser.tokens.actual.value) Parser.tokens.selectNext() if Parser.tokens.actual.type == "AS": Parser.tokens.selectNext() return VarDec("VarDec", [identifier, Parser.parseType()]) else: raise ValueError("Parser Error (Statement): Expected AS, got token {}".format(repr(Parser.tokens.actual.value))) else: raise ValueError("Parser Error (Statement): Expected an IDENTIFIER, got token {}".format(repr(Parser.tokens.actual.value))) else: return NoOp() @staticmethod def parseExpression(): output = Parser.parseTerm() while Parser.tokens.actual.value in ["+", "-", "OR"]: if Parser.tokens.actual.value == "+": Parser.tokens.selectNext() output = BinOp("+", [output, Parser.parseTerm()]) elif Parser.tokens.actual.value == "-": Parser.tokens.selectNext() output = BinOp("-", [output, Parser.parseTerm()]) elif Parser.tokens.actual.value == "OR": Parser.tokens.selectNext() output = BinOp("OR", [output, Parser.parseTerm()]) return output @staticmethod def parseTerm(): output = Parser.parseFactor() while Parser.tokens.actual.value in ["*", "/", "AND"]: if Parser.tokens.actual.value == "*": Parser.tokens.selectNext() output = BinOp("*", [output, Parser.parseFactor()]) elif Parser.tokens.actual.value == "/": Parser.tokens.selectNext() output = BinOp("/", [output, Parser.parseFactor()]) elif Parser.tokens.actual.value == "AND": Parser.tokens.selectNext() output = BinOp("AND", [output, Parser.parseFactor()]) return output @staticmethod def parseFactor(): output = 0 if Parser.tokens.actual.type == "INT": output = IntVal(Parser.tokens.actual.value) Parser.tokens.selectNext() elif Parser.tokens.actual.type == "IDENTIFIER": output = Identifier(Parser.tokens.actual.value) Parser.tokens.selectNext() elif Parser.tokens.actual.type == "INPUT": output = Input("Input") Parser.tokens.selectNext() elif Parser.tokens.actual.type == "BRACKETS": if Parser.tokens.actual.value == "(": Parser.tokens.selectNext() output = Parser.parseRelExpression() if Parser.tokens.actual.value == ")": Parser.tokens.selectNext() else: raise ValueError("Parser Error (Factor): Expected ), got token {}".format(repr(Parser.tokens.actual.value))) else: raise ValueError("Parser Error (Factor): Expected (, got token {}".format(repr(Parser.tokens.actual.value))) elif Parser.tokens.actual.value in ["+", "-", "NOT"]: if Parser.tokens.actual.value == "+": Parser.tokens.selectNext() output = UnOp("+", [Parser.parseFactor()]) elif Parser.tokens.actual.value == "-": Parser.tokens.selectNext() output = UnOp("-", [Parser.parseFactor()]) elif Parser.tokens.actual.value == "NOT": Parser.tokens.selectNext() output = UnOp("NOT", [Parser.parseFactor()]) elif Parser.tokens.actual.value in ["TRUE", "FALSE"]: output = BoolValue(Parser.tokens.actual.value) Parser.tokens.selectNext() else: raise ValueError("Parser Error (Factor): Token {} is invalid".format(repr(Parser.tokens.actual.value))) return output @staticmethod def parseType(): if Parser.tokens.actual.type == "INTEGER": Parser.tokens.selectNext() return Type("INT") elif Parser.tokens.actual.type == "BOOLEAN": Parser.tokens.selectNext() return Type("BOOLEAN") else: raise ValueError("Parser Error (Type): Token {} type is not supported".format(repr(Parser.tokens.actual.type))) @staticmethod def parseRelExpression(): output = Parser.parseExpression() while Parser.tokens.actual.value in ["=", ">", "<"]: if Parser.tokens.actual.value == "=": Parser.tokens.selectNext() output = BinOp("=", [output, Parser.parseExpression()]) elif Parser.tokens.actual.value == ">": Parser.tokens.selectNext() output = BinOp(">", [output, Parser.parseExpression()]) elif Parser.tokens.actual.value == "<": Parser.tokens.selectNext() output = BinOp("<", [output, Parser.parseExpression()]) return output @staticmethod def run(code): st = SymbolTable() Parser.tokens = Tokenizer(PrePro.filtra(code)) Parser.tokens.selectNext() res = Parser.parseProgram() Parser.tokens.selectNext() if Parser.tokens.actual.value != "EOF": raise ValueError("Run (EOF Check): Expected EOF, got token {}: {}".format(repr(Parser.tokens.actual.value), Parser.tokens.position)) res.evaluate(st)

44.726316

150

0.530635

1,140

12,747

5.922807

0.086842

0.243483

0.229265

0.17032

0.799615

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0.711345

0.654769

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0.593158

0

0.00012

0.346278

12,747

285

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44.726316

0.810152

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0

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false

0

0.017544

0

0.114035

0.004386

0

null

1

0

1

0

null

0

5

51dec74e4e62275dc6361aae665fe2952003c8a9

120

py

Python

mysite/stopwatch/admin.py

MaksymPylypenko/Smart-Stopwatch

416af5fa5051ea9bb7375d49877b6be31739d95b

[ "MIT" ]

null

mysite/stopwatch/admin.py

MaksymPylypenko/Smart-Stopwatch

416af5fa5051ea9bb7375d49877b6be31739d95b

[ "MIT" ]

null

mysite/stopwatch/admin.py

MaksymPylypenko/Smart-Stopwatch

416af5fa5051ea9bb7375d49877b6be31739d95b

[ "MIT" ]

null

from django.contrib import admin # Register your models here. from .models import Record admin.site.register(Record)

15

32

0.791667

17

120

5.588235

0.647059

0

0.141667

120

7

33

17.142857

0.92233

0.216667

0

1

0

true

0

0.666667

0

0.666667

0

1

0

null

0

1

0

null

0

1

0

1

0

1

0

5

51e25c551cf7801a20bc126ed51130b042a3adb7

835

py

Python

Outils/views/home.py

Inedit20/Climatelabs

5621a0eb7a0aa634b5203c172edbe65706537a31

[ "bzip2-1.0.6" ]

null

Outils/views/home.py

Inedit20/Climatelabs

5621a0eb7a0aa634b5203c172edbe65706537a31

[ "bzip2-1.0.6" ]

null

Outils/views/home.py

Inedit20/Climatelabs

5621a0eb7a0aa634b5203c172edbe65706537a31

[ "bzip2-1.0.6" ]

null

from django.shortcuts import redirect, render from django.views.generic import TemplateView from django.utils import timezone from django.utils.translation import ugettext #from ..models import #from stories.filters import CasesFilter from django.contrib import messages from django.contrib.auth import update_session_auth_hash from django.contrib.auth.forms import PasswordChangeForm def Introduction(request): template_name = 'Pages/Introduction.html' return render(request, template_name) def Guide(request): template_name = 'Pages/guide.html' return render(request, template_name) def documentation(request): template_name = 'Pages/doc.html' return render(request, template_name) def roles(request): template_name = 'Pages/roles.html' return render(request, template_name)

21.973684

56

0.77485

104

835

6.115385

0.355769

0.188679

0.238994

0.150943

0.234277

0.179245

0

0.150898

835

37

57

22.567568

0.897038

0.073054

0

0.210526

0

0.089378

0.029793

0

1

0.210526

false

0.052632

0.368421

0

0.789474

0

null

0

1

0

null

0

1

0

1

0

1

0

5

51e972283266c3f36cb5ef411f77adf0b117562a

226

py

Python

todo/templatetags/todo_user_can_toggle_task_done.py

paiuolo/django-todo

17d35460b6dfa8c5a45a9eeafbec262233f1586d

[ "BSD-3-Clause" ]

null

todo/templatetags/todo_user_can_toggle_task_done.py

paiuolo/django-todo

17d35460b6dfa8c5a45a9eeafbec262233f1586d

[ "BSD-3-Clause" ]

null

todo/templatetags/todo_user_can_toggle_task_done.py

paiuolo/django-todo

17d35460b6dfa8c5a45a9eeafbec262233f1586d

[ "BSD-3-Clause" ]

null

from django import template from ..utils import user_can_toggle_task_done register = template.Library() @register.simple_tag def todo_user_can_toggle_task_done(user, task): return user_can_toggle_task_done(user, task)

20.545455

48

0.818584

35

226

4.885714

0.485714

0.122807

0.22807

0.298246

0.461988

0.339181

0

0.115044

226

10

49

22.6

0.855

0

1

0.166667

false

0

0.333333

0.166667

0.666667

0

null

0

1

0

null

0

1

0

5

51f3a25e5a2827638686a70211ff3a3dec01e61f

2,830

py

Python

SheldonGameTest.py

sofiacarballo/rock_paper_scissors_kata

ed50d8eaebbe625b335a10826ca1dc8bd293d0c5

[ "MIT" ]

null

SheldonGameTest.py

sofiacarballo/rock_paper_scissors_kata

ed50d8eaebbe625b335a10826ca1dc8bd293d0c5

[ "MIT" ]

null

SheldonGameTest.py

sofiacarballo/rock_paper_scissors_kata

ed50d8eaebbe625b335a10826ca1dc8bd293d0c5

[ "MIT" ]

null

import unittest from SheldonGame import SheldonGame class SheldonGameTest(unittest.TestCase): def test_scissors_wins_paper(self): game = SheldonGame() result = game.calculate_sheldon_result('scissors', 'paper') self.assertEqual('Scissors wins', result) def test_scissors_wins_lizard(self): game = SheldonGame() result = game.calculate_sheldon_result('scissors', 'lizard') self.assertEqual('Scissors wins', result) def test_paper_wins_rock(self): game = SheldonGame() result = game.calculate_sheldon_result('paper', 'rock') self.assertEqual('Paper wins', result) def test_paper_wins_spock(self): game = SheldonGame() result = game.calculate_sheldon_result('paper', 'spock') self.assertEqual('Paper wins', result) def test_rock_wins_lizard(self): game = SheldonGame() result = game.calculate_sheldon_result('rock', 'lizard') self.assertEqual('Rock wins', result) def test_rock_wins_scissors(self): game = SheldonGame() result = game.calculate_sheldon_result('rock', 'scissors') self.assertEqual('Rock wins', result) def test_lizard_wins_spock(self): game = SheldonGame() result = game.calculate_sheldon_result('lizard', 'spock') self.assertEqual('Lizard wins', result) def test_lizard_wins_paper(self): game = SheldonGame() result = game.calculate_sheldon_result('lizard', 'paper') self.assertEqual('Lizard wins', result) def test_spock_wins_scissors(self): game = SheldonGame() result = game.calculate_sheldon_result('spock', 'scissors') self.assertEqual('Spock wins', result) def test_spock_wins_rock(self): game = SheldonGame() result = game.calculate_sheldon_result('spock', 'rock') self.assertEqual('Spock wins', result) def test_paper_ties(self): game = SheldonGame() result = game.calculate_sheldon_result('paper', 'paper') self.assertEqual('Tie game', result) def test_rock_ties(self): game = SheldonGame() result = game.calculate_sheldon_result('rock', 'rock') self.assertEqual('Tie game', result) def test_scissors_ties(self): game = SheldonGame() result = game.calculate_sheldon_result('scissors', 'scissors') self.assertEqual('Tie game', result) def test_lizard_ties(self): game = SheldonGame() result = game.calculate_sheldon_result('lizard', 'lizard') self.assertEqual('Tie game', result) def test_spock_ties(self): game = SheldonGame() result = game.calculate_sheldon_result('spock', 'spock') self.assertEqual('Tie game', result) if __name__ == '__main__': unittest.main()

32.906977

70

0.659364

310

2,830

5.767742

0.083871

0.058725

0.159396

0.209732

0.869128

0.850671

0.8283

0.539709

0.338926

0

0.225088

2,830

85

71

33.294118

0.815321

0

0.461538

0

0.113781

0

0.230769

1

0.230769

false

0

0.030769

0

0.276923

0

null

0

1

0

null

0

1

0

5

51fd446bd648efeef62e5a9f5f429160d00cf1f3

247

py

Python

configs/deepim/lmPbrSO/FlowNet512_1.5AugCosyAAEGray_AggressiveV2_Flat_lmPbr_SO/FlowNet512_1.5AugCosyAAEGray_AggressiveV2_Flat_Pbr_09_duck_lmo_test.py

THU-DA-6D-Pose-Group/self6dpp

c267cfa55e440e212136a5e9940598720fa21d16

[ "Apache-2.0" ]

33

2021-12-15T07:11:47.000Z

2022-03-29T08:58:32.000Z

configs/deepim/lmPbrSO/FlowNet512_1.5AugCosyAAEGray_AggressiveV2_Flat_lmPbr_SO/FlowNet512_1.5AugCosyAAEGray_AggressiveV2_Flat_Pbr_09_duck_lmo_test.py

THU-DA-6D-Pose-Group/self6dpp

c267cfa55e440e212136a5e9940598720fa21d16

[ "Apache-2.0" ]

3

2021-12-15T11:39:54.000Z

2022-03-29T07:24:23.000Z

configs/deepim/lmPbrSO/FlowNet512_1.5AugCosyAAEGray_AggressiveV2_Flat_lmPbr_SO/FlowNet512_1.5AugCosyAAEGray_AggressiveV2_Flat_Pbr_09_duck_lmo_test.py

THU-DA-6D-Pose-Group/self6dpp

c267cfa55e440e212136a5e9940598720fa21d16

[ "Apache-2.0" ]

null

_base_ = "./FlowNet512_1.5AugCosyAAEGray_AggressiveV2_Flat_Pbr_01_ape_lmo_test.py" OUTPUT_DIR = "output/deepim/lmPbrSO/FlowNet512_1.5AugCosyAAEGray_AggressiveV2_Flat_lmPbr_SO/duck" DATASETS = dict(TRAIN=("lm_pbr_duck_train",), TEST=("lmo_test",))

61.75

97

0.834008

35

247

5.314286

0.657143

0.11828

0.27957

0.408602

0.451613

0

0.059072

0.040486

247

3

98

82.333333

0.725738

0

0.720648

0.619433

0

1

0

false

0

null

0

1

0

1

0

1

null

0

5

a40ce5905f7a2d02f8236abfe7a768454bcf89a4

153

py

Python

graphene_mongo_extras/tests/conftest.py

riverfr0zen/graphene-mongo-extras

8db79fc15b116cde3c6455a0a871af67c2f26d6b

[ "MIT" ]

15

2019-04-29T09:06:13.000Z

2021-05-25T17:15:32.000Z

graphene_mongo_extras/tests/conftest.py

riverfr0zen/graphene-mongo-extras

8db79fc15b116cde3c6455a0a871af67c2f26d6b

[ "MIT" ]

8

2019-05-12T11:04:25.000Z

2020-06-02T14:46:28.000Z

graphene_mongo_extras/tests/conftest.py

riverfr0zen/graphene-mongo-extras

8db79fc15b116cde3c6455a0a871af67c2f26d6b

[ "MIT" ]

null

import pytest from mongoengine import connect @pytest.fixture def setup_mongo(): connect(host='mongomock://localhost', db='graphene-mongo-extras')

19.125

69

0.764706

19

153

6.105263

0.789474

0

0.111111

153

7

70

21.857143

0.852941

0

0.27451

0

1

0.2

true

0

0.4

0

0.6

0

1

0

null

0

1

0

null

0

1

0

1

0

1

0

5

cfc49ccc3b7879ad8585c6589a30e7aa19ab4cb2

39

py

Python

scraper/__init__.py

NicolasAbroad/wnscraper

87d5aa8e3a26aa0846a289d378848e1eb1d13304

[ "Apache-2.0" ]

null

scraper/__init__.py

NicolasAbroad/wnscraper

87d5aa8e3a26aa0846a289d378848e1eb1d13304

[ "Apache-2.0" ]

null

scraper/__init__.py

NicolasAbroad/wnscraper

87d5aa8e3a26aa0846a289d378848e1eb1d13304

[ "Apache-2.0" ]

null

import sys sys.path.append('scraper')

9.75

26

0.74359

6

39

4.833333

0.833333

0

0.102564

39

4

26

9.75

0.828571

0

0.175

0

1

0

true

0

0.5

0

0.5

0

1

0

null

0

1

0

null

0

1

0

1

0

5

cfe0447ef615227d306e3b6bb4df986674399c3e

48

py

Python

github-bot/longhorn_github_bot/__main__.py

meldafrawi/bot

c87e3a51427eb749ba0d4647a3f1a9cfc1961621

[ "Apache-2.0" ]

null

github-bot/longhorn_github_bot/__main__.py

meldafrawi/bot

c87e3a51427eb749ba0d4647a3f1a9cfc1961621

[ "Apache-2.0" ]

null

github-bot/longhorn_github_bot/__main__.py

meldafrawi/bot

c87e3a51427eb749ba0d4647a3f1a9cfc1961621

[ "Apache-2.0" ]

5

2020-07-24T20:29:27.000Z

2022-03-21T08:19:16.000Z

from longhorn_github_bot import app app.run()

9.6

35

0.791667

8

48

4.5

0.875

0

0.145833

48

4

36

12

0.878049

0

1

0

true

0

0.5

0

0.5

0

1

0

null

0

1

0

null

0

1

0

1

0

5

cffa4aa81c467e01540e9ca911f629040556f17c

385

py

Python

tests/test_sim_finger.py

xuanhien070594/trifinger_simulation

31d43764955ad3aa7af8ec20512605dcac8dbb9a

[ "BSD-3-Clause" ]

null

tests/test_sim_finger.py

xuanhien070594/trifinger_simulation

31d43764955ad3aa7af8ec20512605dcac8dbb9a

[ "BSD-3-Clause" ]

null

tests/test_sim_finger.py

xuanhien070594/trifinger_simulation

31d43764955ad3aa7af8ec20512605dcac8dbb9a

[ "BSD-3-Clause" ]

null

from trifinger_simulation.sim_finger import int_to_rgba def test_int_to_rgba(): assert int_to_rgba(0x000000) == (0.0, 0.0, 0.0, 1.0) assert int_to_rgba(0xFFFFFF) == (1.0, 1.0, 1.0, 1.0) assert int_to_rgba(0x006C66) == (0, 108 / 255, 102 / 255, 1.0) assert int_to_rgba(0x006C66, alpha=42) == ( 0, 108 / 255, 102 / 255, 42 / 255, )

25.666667

66

0.587013

66

385

3.19697

0.333333

0.14218

0.255924

0.28436

0.49763

0.350711

0.327014

0

0.250883

0.264935

385

14

67

27.5

0.4947

0

0.083117

0

0.363636

1

0.090909

true

0

0.090909

0

0.181818

0

null

0

1

0

1

0

1

0

null

0

1

0

5

5c50671ec40cdbcbfab963e2c39c45bc813a2538

30

py

Python

main.py

Navazdeen/potential-memory

3ba1d1db0c9e0f7098bccd079dce997235a84ec3

[ "MIT" ]

null

main.py

Navazdeen/potential-memory

3ba1d1db0c9e0f7098bccd079dce997235a84ec3

[ "MIT" ]

null

main.py

Navazdeen/potential-memory

3ba1d1db0c9e0f7098bccd079dce997235a84ec3

[ "MIT" ]

null

def palindrome(word): pass

7.5

21

0.7

4

30

5.25

1

0

0.2

30

3

22

10

0.875

0

1

0.5

false

0.5

0

0.5

0

1

0

null

0

1

0

null

0

1

0

1

0

5

5c7b6df2164b9f4df42ec7f1c1a48e7183b76139

21,297

py

Python

src/generator/actions.py

altostratous/ancc

583bb1fd0aa6f0ef649b89909b623ae291ebc6f6

[ "MIT" ]

null

src/generator/actions.py

altostratous/ancc

583bb1fd0aa6f0ef649b89909b623ae291ebc6f6

[ "MIT" ]

null

src/generator/actions.py

altostratous/ancc

583bb1fd0aa6f0ef649b89909b623ae291ebc6f6

[ "MIT" ]

null

from generator.defines import Mnemonic from generator.utils import indval, immval from grammar.models import Literal from core.defines import DataType, DeclarationType from scanner.errors import SemanticError class Action(Literal): def __init__(self, text): super().__init__(text, [[]]) @property def is_action(self): return True def do(self, parser): pass class PushNumAction(Action): def do(self, parser): assert parser.lookahead_token.text == 'NUM' parser.semantic_stack += [immval(parser.lookahead_token.attribute)] class PushAddOpAction(Action): def do(self, parser): assert parser.lookahead_token.text == '+' parser.semantic_stack += [parser.lookahead_token.text] class PushSubOpAction(Action): def do(self, parser): assert parser.lookahead_token.text == '-' parser.semantic_stack += [parser.lookahead_token.text] class PushRelOpAction(Action): def do(self, parser): assert parser.lookahead_token.text == 'RELOP' parser.semantic_stack += [parser.lookahead_token.attribute] class AddOpAction(Action): def do(self, parser): tmp = parser.get_temp() if parser.semantic_stack[-1] == 'None' or parser.semantic_stack[-3] == 'None': raise SemanticError('Cannot add/sub a void value', parser.scanner) if parser.scanner.get_token_by_address(parser.semantic_stack[-1]) and parser.scanner.get_token_by_address(parser.semantic_stack[-1]).declaration_type == DeclarationType.FUNCTION: raise SemanticError('Cannot add/sub a function value', parser.scanner) if parser.scanner.get_token_by_address(parser.semantic_stack[-1]) and parser.scanner.get_token_by_address(parser.semantic_stack[-1]).declaration_type == DeclarationType.ARRAY: raise SemanticError('Cannot add/sub an array value', parser.scanner) if parser.scanner.get_token_by_address(parser.semantic_stack[-3]) and parser.scanner.get_token_by_address(parser.semantic_stack[-3]).declaration_type == DeclarationType.FUNCTION: raise SemanticError('Cannot add/sub a function value', parser.scanner) if parser.scanner.get_token_by_address(parser.semantic_stack[-3]) and parser.scanner.get_token_by_address(parser.semantic_stack[-3]).declaration_type == DeclarationType.ARRAY: raise SemanticError('Cannot add/sub an array value', parser.scanner) if parser.semantic_stack[-2] == '+': parser.program.add_inst(Mnemonic.ADD, parser.semantic_stack[-3], parser.semantic_stack[-1], tmp) elif parser.semantic_stack[-2] == '-': parser.program.add_inst(Mnemonic.SUBTRACT, parser.semantic_stack[-3], parser.semantic_stack[-1], tmp) else: assert 0, 'Either + or - must have been provided' parser.semantic_stack.pop() parser.semantic_stack.pop() parser.semantic_stack.pop() parser.semantic_stack += [tmp] class RelOpAction(Action): def do(self, parser): tmp = parser.get_temp() if parser.semantic_stack[-1] == 'None' or parser.semantic_stack[-3] == 'None': raise SemanticError('Cannot compare a void value', parser.scanner) if parser.semantic_stack[-2] == 'L': parser.program.add_inst(Mnemonic.LESS_THAN, parser.semantic_stack[-3], parser.semantic_stack[-1], tmp) elif parser.semantic_stack[-2] == 'E': parser.program.add_inst(Mnemonic.EQUALS, parser.semantic_stack[-3], parser.semantic_stack[-1], tmp) else: assert 0, 'Either < or == must have been provided' parser.semantic_stack.pop() parser.semantic_stack.pop() parser.semantic_stack.pop() parser.semantic_stack += [tmp] class MultOpAction(Action): def do(self, parser): tmp = parser.get_temp() if parser.semantic_stack[-1] == 'None' or parser.semantic_stack[-2] == 'None': raise SemanticError('Cannot mult a void value', parser.scanner) parser.program.add_inst(Mnemonic.MULTIPLY, parser.semantic_stack[-2], parser.semantic_stack[-1], tmp) parser.semantic_stack.pop() parser.semantic_stack.pop() parser.semantic_stack += [tmp] class IfSaveAction(Action): def do(self, parser): parser.semantic_stack += [parser.program.pc] parser.program.add_fake_inst() class IfJumpSaveAction(Action): def do(self, parser): # Jump if parser.semantic_stack[-2] == 'None': raise SemanticError('Cannot use a void value as if condition', parser.scanner) parser.program.edit_inst(parser.semantic_stack.pop(), Mnemonic.JUMP_FALSE, parser.semantic_stack.pop(), parser.program.pc + 1) # Save parser.semantic_stack += [parser.program.pc] parser.program.add_fake_inst() class IfJumpAction(Action): def do(self, parser): parser.program.edit_inst(parser.semantic_stack.pop(), Mnemonic.JUMP, parser.program.pc) class WhileLabelAction(Action): def do(self, parser): parser.program.add_inst(Mnemonic.JUMP, parser.program.pc + 2) # Save parser.break_stack += [parser.program.pc] parser.program.add_fake_inst() parser.continue_stack += [parser.program.pc] parser.semantic_stack += [parser.program.pc] class WhileSaveAction(Action): def do(self, parser): parser.semantic_stack += [parser.program.pc] parser.program.add_fake_inst() class WhileAction(Action): def do(self, parser): if parser.semantic_stack[-2] == 'None': raise SemanticError('Cannot use a void value as while condition', parser.scanner) parser.program.edit_inst(parser.semantic_stack.pop(), Mnemonic.JUMP_FALSE, parser.semantic_stack.pop(), parser.program.pc + 1) parser.program.add_inst(Mnemonic.JUMP, parser.semantic_stack.pop()) parser.program.edit_inst(parser.break_stack.pop(), Mnemonic.JUMP, parser.program.pc) parser.continue_stack.pop() class SwitchPushTestAction(Action): def do(self, parser): test = parser.get_temp() default = parser.get_temp() parser.program.add_inst(Mnemonic.ASSIGN, immval(1), default) parser.program.add_inst(Mnemonic.ASSIGN, immval(0), test) parser.program.add_inst(Mnemonic.JUMP, parser.program.pc + 2) parser.semantic_stack.append(parser.program.pc) parser.break_stack.append(parser.program.pc) parser.program.add_fake_inst() parser.semantic_stack.append(default) parser.semantic_stack.append(test) class SwitchPopAction(Action): def do(self, parser): parser.semantic_stack.pop() parser.semantic_stack.pop() parser.semantic_stack.pop() parser.program.edit_inst(parser.semantic_stack.pop(), Mnemonic.JUMP, parser.program.pc) class SwitchTestAction(Action): def do(self, parser): test = parser.semantic_stack[-2] default = parser.semantic_stack[-3] is_equal = parser.get_temp() parser.program.add_inst( Mnemonic.EQUALS, parser.semantic_stack[-1], immval(parser.lookahead_token.attribute), is_equal ) parser.program.add_inst( Mnemonic.JUMP_FALSE, is_equal, parser.program.pc + 3 ) parser.program.add_inst(Mnemonic.ASSIGN, immval(1), test) parser.program.add_inst(Mnemonic.ASSIGN, immval(0), default) class SwitchSaveAction(Action): def do(self, parser): parser.semantic_stack.append(parser.program.pc) parser.program.add_fake_inst() class SwitchPatchJumpOnTestAction(Action): def do(self, parser): default = parser.semantic_stack[-4] if default == 'None': raise SemanticError('Cannot use a void value as switch condition', parser.scanner) parser.program.edit_inst(parser.semantic_stack.pop(), Mnemonic.JUMP_FALSE, default, parser.program.pc) class SwitchPatchJumpOnNotTestAction(Action): def do(self, parser): test = parser.semantic_stack[-3] if test == 'None': raise SemanticError('Cannot use a void value as switch condition', parser.scanner) parser.program.edit_inst(parser.semantic_stack.pop(), Mnemonic.JUMP_FALSE, test, parser.program.pc) class PushIDAction(Action): def do(self, parser): assert parser.lookahead_token.text == 'ID' parser.semantic_stack += [parser.lookahead_token.attribute] class AssignAction(Action): def do(self, parser): if parser.scanner.get_token_by_address(parser.semantic_stack[-2]) and parser.scanner.get_token_by_address(parser.semantic_stack[-2]).declaration_type == DeclarationType.ARRAY: raise SemanticError('Assignment to array is not allowed', parser.scanner) if parser.scanner.get_token_by_address(parser.semantic_stack[-2]) and parser.scanner.get_token_by_address(parser.semantic_stack[-2]).declaration_type == DeclarationType.FUNCTION: raise SemanticError('Assignment to array is not allowed', parser.scanner) if parser.scanner.get_token_by_address(parser.semantic_stack[-1]) and parser.scanner.get_token_by_address(parser.semantic_stack[-1]).declaration_type == DeclarationType.ARRAY: raise SemanticError('Assignment from function is not allowed', parser.scanner) if parser.scanner.get_token_by_address(parser.semantic_stack[-1]) and parser.scanner.get_token_by_address(parser.semantic_stack[-1]).declaration_type == DeclarationType.FUNCTION: raise SemanticError('Assignment to function is not allowed', parser.scanner) parser.program.add_inst(Mnemonic.ASSIGN, parser.semantic_stack.pop(), parser.semantic_stack[-1]) class PopIDAction(Action): def do(self, parser): parser.semantic_stack.pop() class BreakAction(Action): def do(self, parser): if len(parser.break_stack) == 0: raise SemanticError('`break` statement has no parent `while` or `switch`', parser.scanner) parser.program.add_inst(Mnemonic.JUMP, parser.break_stack[-1]) class ContinueAction(Action): def do(self, parser): if len(parser.continue_stack) == 0: raise SemanticError('`continue` statement has no parent `while`', parser.scanner) parser.program.add_inst(Mnemonic.JUMP, parser.continue_stack[-1]) class ArrayDefinitionAction(Action): def do(self, parser): assert parser.lookahead_token.text == 'NUM' parser.scanner.get_token_by_address(parser.semantic_stack[-1]).declaration_type = DeclarationType.ARRAY parser.scanner.analyze_semantics() addr = parser.scanner.malloc(parser.lookahead_token.attribute) parser.program.add_inst(Mnemonic.ASSIGN, immval(addr), parser.semantic_stack[-1]) class AssignArrayAction(Action): def do(self, parser): if parser.scanner.get_token_by_address(parser.semantic_stack[-1]) and parser.scanner.get_token_by_address(parser.semantic_stack[-1]).declaration_type == DeclarationType.ARRAY: raise SemanticError('Assignment from array is not allowed', parser.scanner) if parser.scanner.get_token_by_address(parser.semantic_stack[-1]) and parser.scanner.get_token_by_address(parser.semantic_stack[-1]).declaration_type == DeclarationType.FUNCTION: raise SemanticError('Assignment from function is not allowed', parser.scanner) tmp = parser.get_temp() parser.program.add_inst(Mnemonic.ADD, parser.semantic_stack[-3], parser.semantic_stack[-2], tmp) parser.program.add_inst(Mnemonic.ASSIGN, parser.semantic_stack.pop(), indval(tmp)) parser.semantic_stack.pop() parser.semantic_stack.pop() parser.semantic_stack += [indval(tmp)] class ArrayAccessAction(Action): def do(self, parser): tmp = parser.get_temp() if parser.scanner.get_token_by_address(parser.semantic_stack[-2]) and parser.scanner.get_token_by_address(parser.semantic_stack[-2]).declaration_type == DeclarationType.FUNCTION: raise SemanticError('Subscription from function is not allowed', parser.scanner) parser.program.add_inst(Mnemonic.ADD, parser.semantic_stack.pop(), parser.semantic_stack.pop(), tmp) parser.semantic_stack += [indval(tmp)] class IncreaseScopeAction(Action): def do(self, parser): parser.scope += 1 class DecreaseScopeAction(Action): def do(self, parser): parser.scope -= 1 class FunctionSaveAction(Action): def do(self, parser): parser.scanner.get_token_by_address(parser.semantic_stack[-1]).declaration_type = DeclarationType.FUNCTION parser.function_stack += [parser.scanner.get_token_by_address(parser.semantic_stack[-1])] parser.scanner.analyze_semantics() activity_record_address = parser.scanner.malloc(2) start_pc_address = activity_record_address return_address_address = activity_record_address + 1 parser.return_stack.append(return_address_address) # write the record address to the function symbol memory parser.program.add_inst(Mnemonic.ASSIGN, immval(activity_record_address), parser.semantic_stack[-1]) # write the start address to the first word of activity record parser.program.add_inst(Mnemonic.ASSIGN, immval(parser.program.pc + 2), start_pc_address) parser.semantic_stack.append(parser.program.pc) parser.program.add_fake_inst() # skip running the function on the first pass class FunctionAction(Action): def do(self, parser): parser.program.edit_inst(parser.semantic_stack.pop(), Mnemonic.JUMP, parser.program.pc + 1) parser.program.add_inst(Mnemonic.JUMP, indval(parser.return_stack.pop())) if parser.function_stack[-1].has_return == False and parser.function_stack[-1].data_type != DataType.VOID: raise SemanticError('Missing return statement inside the function', parser.scanner) if len(parser.function_stack) == 1 and parser.function_stack[-1].lexeme == 'main' and parser.function_stack[-1].data_type != DataType.VOID: raise SemanticError('Invalid prototype for function main', parser.scanner) parser.function_stack.pop() class FunctionReturnAction(Action): def do(self, parser): parser.function_stack[-1].has_return = True parser.program.add_inst(Mnemonic.JUMP, indval(parser.return_stack[-1])) class CallMainAction(Action): def do(self, parser): main_symbol_address = parser.scanner.get_symbol_address('main') activity_record_address = parser.get_temp() return_address_address = parser.get_temp() parser.program.add_inst(Mnemonic.ASSIGN, main_symbol_address, activity_record_address) parser.program.add_inst(Mnemonic.ADD, activity_record_address, immval(1), return_address_address) parser.program.add_inst(Mnemonic.ASSIGN, immval(parser.program.pc + 3), indval(return_address_address)) start_pc = parser.get_temp() parser.program.add_inst(Mnemonic.ASSIGN, indval(activity_record_address), start_pc) parser.program.add_inst(Mnemonic.JUMP, indval(start_pc)) parser.program.add_nop() class PullIDAction(Action): def do(self, parser): assert parser.lookahead_token.text == 'ID' parser.program.add_pop(parser.lookahead_token.attribute) class CallAction(Action): def do(self, parser): function_symbol_address = parser.semantic_stack[-1] function_data_type = parser.scanner.get_token_by_address(function_symbol_address).data_type activity_record_address = parser.get_temp() return_address_address = parser.get_temp() parser.program.add_inst(Mnemonic.ASSIGN, function_symbol_address, activity_record_address) parser.program.add_inst(Mnemonic.ADD, activity_record_address, immval(1), return_address_address) parser.program.add_inst(Mnemonic.ASSIGN, immval(parser.program.pc + 3), indval(return_address_address)) start_pc = parser.get_temp() parser.program.add_inst(Mnemonic.ASSIGN, indval(activity_record_address), start_pc) parser.program.add_inst(Mnemonic.JUMP, indval(start_pc)) if function_data_type == DataType.INTEGER: return_value = parser.get_temp() parser.program.add_pop(return_value) parser.semantic_stack[-1] = return_value else: parser.semantic_stack[-1] = 'None' if parser.argument_stack[-1]: raise SemanticError('Too few arguments passed', parser.scanner) parser.argument_stack.pop() class CallBeforeAction(Action): def do(self, parser): function_symbol_address = parser.semantic_stack[-1] prototype = parser.scanner.get_token_by_address(function_symbol_address).prototype if parser.scanner.get_token_by_address(function_symbol_address).declaration_type != DeclarationType.FUNCTION: raise SemanticError('Dude it is not a function!', parser.scanner) parser.argument_stack += [prototype[:]] class PushParameterAction(Action): def do(self, parser): top_of_stack = parser.semantic_stack.pop() if top_of_stack == 'None': raise SemanticError('Cannot pass a void argument to a function', parser.scanner) if not parser.argument_stack[-1]: raise SemanticError('Too many arguments passed', parser.scanner) if isinstance(top_of_stack, str) and top_of_stack[0] == '#': if parser.argument_stack[-1][0].declaration_type == DeclarationType.ARRAY: print(parser.argument_stack[-1]) raise SemanticError('Cannot convert an integer literal to an array', parser.scanner) if isinstance(top_of_stack, int) and parser.scanner.get_token_by_address(top_of_stack) and parser.scanner.get_token_by_address(top_of_stack).declaration_type == DeclarationType.ARRAY: if parser.argument_stack[-1][0].declaration_type == DeclarationType.VARIABLE: raise SemanticError('Cannot convert an array to an integer type', parser.scanner) if parser.scanner.get_token_by_address(top_of_stack) and parser.scanner.get_token_by_address(top_of_stack).declaration_type == DeclarationType.FUNCTION: raise SemanticError('Cannot pass a function to a function', parser.scanner) parser.argument_stack[-1] = parser.argument_stack[-1][1:] parser.program.add_push(top_of_stack) class DefinePrintAction(Action): def do(self, parser): # # FunctionSave activity_record_address = parser.scanner.malloc(2) start_pc_address = activity_record_address return_address_address = activity_record_address + 1 parser.semantic_stack.append(return_address_address) # write the record address to the function symbol memory parser.program.add_inst(Mnemonic.ASSIGN, immval(activity_record_address), 0) # write the start address to the first word of activity record parser.program.add_inst(Mnemonic.ASSIGN, immval(parser.program.pc + 2), start_pc_address) parser.semantic_stack.append(parser.program.pc) parser.program.add_fake_inst() # skip running the function on the first pass # # PullID # # Assembly temporary = parser.get_temp() parser.program.add_pop(temporary) parser.program.add_inst(Mnemonic.PRINT, temporary) # # Function parser.program.edit_inst(parser.semantic_stack.pop(), Mnemonic.JUMP, parser.program.pc + 1) parser.program.add_inst(Mnemonic.JUMP, indval(parser.semantic_stack.pop())) class PushReturnValueAction(Action): def do(self, parser): if parser.function_stack[-1].data_type == DataType.VOID: raise SemanticError('Invalid return value for a void function', parser.scanner) parser.program.add_push(parser.semantic_stack.pop()) class NoReturnAction(Action): def do(self, parser): if parser.function_stack[-1].data_type != DataType.VOID: raise SemanticError('Invalid return value for a non-void function', parser.scanner) class VarDefinitionAction(Action): def do(self, parser): parser.scanner.get_token_by_address(parser.semantic_stack[-1]).declaration_type = DeclarationType.VARIABLE parser.scanner.analyze_semantics() class NewParamAction(Action): def do(self, parser): token = parser.lookahead_token if token.data_type == DataType.VOID: raise SemanticError("Cannot declare a variable with void type", parser.scanner) token.declaration_type = DeclarationType.VARIABLE if len(parser.function_stack) == 1 and parser.function_stack[-1].lexeme == 'main': raise SemanticError('Invalid prototype for function main', parser.scanner) parser.function_stack[-1].prototype.append(token) class ArrayParamAction(Action): def do(self, parser): parser.function_stack[-1].prototype[-1].declaration_type = DeclarationType.ARRAY

45.216561

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2,631

21,297

5.461041

0.078677

0.112055

0.152074

0.045935

0.824401

0.769905

0.749234

0.710329

0.676712

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0.193924

21,297

471

192

45.216561

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0.014205

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0.272727

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0

null

0

1

0

1

0

null

0

5

5ca57d12ab6c0109285b8e0a5a14dca47041536f

109

py

Python

0967 Sort List by Hamming Weight.py

ansabgillani/binarysearchcomproblems

12fe8632f8cbb5058c91a55bae53afa813a3247e

[ "MIT" ]

1

2020-12-29T21:17:26.000Z

0967 Sort List by Hamming Weight.py

ansabgillani/binarysearchcomproblems

12fe8632f8cbb5058c91a55bae53afa813a3247e

[ "MIT" ]

null

0967 Sort List by Hamming Weight.py

ansabgillani/binarysearchcomproblems

12fe8632f8cbb5058c91a55bae53afa813a3247e

[ "MIT" ]

4

2021-09-09T17:42:43.000Z

2022-03-18T04:54:03.000Z

class Solution: def solve(self, nums): return sorted(nums, key=lambda x: [bin(x).count("1"), x])

27.25

65

0.605505

17

109

3.882353

0.823529

0

0.011628

0.211009

109

3

66

36.333333

0.755814

0

0.009174

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null

0

1

0

1

0

5

5caec5060ec9bc6be2b57e0c0edf609199b8c625

136

py

Python

config.py

lytex/taiga-extra

ddd2ef62515c13c34d311d0597a6df0110e3998a

[ "MIT" ]

null

config.py

lytex/taiga-extra

ddd2ef62515c13c34d311d0597a6df0110e3998a

[ "MIT" ]

null

config.py

lytex/taiga-extra

ddd2ef62515c13c34d311d0597a6df0110e3998a

[ "MIT" ]

null

TAIGA_USER = 'e1312060@urhen.com' TAIGA_PASSWORD = 'test_taiga_user' PROJECT_SLUG = 'test_taiga_user-fake-project-1' DONE_SLUG = 'Done'

27.2

47

0.786765

21

136

4.714286

0.571429

0.272727

0.262626

0

0.064516

0.088235

136

4

48

34

0.733871

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0.492647

0.220588

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0.25

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null

1

0

1

0

null

0

1

0

5

5ce36475d9d3eadb2a57338d7681379367fb641d

42

py

Python

gimme_input/user_input_error.py

dcdanko/gimme_input

988d28519bbd1ce796f40c10d00bcc23297c9ffd

[ "MIT" ]

null

gimme_input/user_input_error.py

dcdanko/gimme_input

988d28519bbd1ce796f40c10d00bcc23297c9ffd

[ "MIT" ]

null

gimme_input/user_input_error.py

dcdanko/gimme_input

988d28519bbd1ce796f40c10d00bcc23297c9ffd

[ "MIT" ]

null

class UserInputError( Exception): pass

8.4

33

0.761905

4

42

8

1

0

0.166667

42

5

34

8.4

0.914286

0

1

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0.5

0

1

0

null

0

1

0

null

0

1

0

5

7a260c8ff1d768349cec350fbb396df43f254eed

141

py

Python

colossus/constants.py

gcallah/colossus

ee5319091cd19c96987825258a57e6d6f9d8fc51

[ "MIT" ]

3

2020-03-30T14:21:44.000Z

2020-11-23T06:51:55.000Z

colossus/constants.py

gcallah/colossus

ee5319091cd19c96987825258a57e6d6f9d8fc51

[ "MIT" ]

null

colossus/constants.py

gcallah/colossus

ee5319091cd19c96987825258a57e6d6f9d8fc51

[ "MIT" ]

2

2019-10-25T20:50:20.000Z

2019-11-05T02:40:23.000Z

import os from django.conf import settings AUTHORIZED_USERS_FILE_PATH = os.path.join(settings.BASE_DIR, 'colossus', 'authorized_users.txt')

28.2

96

0.815603

21

141

5.238095

0.714286

0.272727

0

0.085106

141

4

97

35.25

0.852713

0

0.198582

0

1

0

false

0

0.666667

0

0.666667

0

1

0

null

1

0

1

0

null

0

1

0

1

0

5

7a6b3dbdbb2a778cc24ffd31fa3bb72ea7dfe40c

156

py

Python

utils/boolean_utils.py

thanhnv2303/ethereum-etl

94381feadf1f1602a95db44aea5e944559628271

[ "MIT" ]

null

utils/boolean_utils.py

thanhnv2303/ethereum-etl

94381feadf1f1602a95db44aea5e944559628271

[ "MIT" ]

null

utils/boolean_utils.py

thanhnv2303/ethereum-etl

94381feadf1f1602a95db44aea5e944559628271

[ "MIT" ]

null

def to_bool(value): value = str(value) if value == "True" or value == "TRUE" or value == "true": return True else: return False

22.285714

61

0.551282

21

156

4.047619

0.52381

0.317647

0.258824

0.376471

0.364706

0

0.320513

156

6

62

26

0.801887

0

0.076923

0

1

0.166667

false

0

0.5

0

1

0

null

1

0

1

0

null

0

5

8f8f999f50d62cef56a399ea068da277541cd98a

66

py

Python

pyhcl/simulator/__init__.py

raybdzhou/PyChip-py-hcl

08edc6ad4d2978eb417482f6f92678f8f9a1e3c7

[ "MIT" ]

1

2021-12-10T14:02:54.000Z

pyhcl/simulator/__init__.py

raybdzhou/PyChip-py-hcl

08edc6ad4d2978eb417482f6f92678f8f9a1e3c7

[ "MIT" ]

null

pyhcl/simulator/__init__.py

raybdzhou/PyChip-py-hcl

08edc6ad4d2978eb417482f6f92678f8f9a1e3c7

[ "MIT" ]

1

2022-03-04T03:36:01.000Z

from .sim import Simulator, DpiConfig from .simlite import Simlite

33

37

0.833333

9

66

6.111111

0.666667

0

0.121212

66

2

38

33

0.948276

0

1

0

true

0

1

0

1

0

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null

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1

0

null

0

1

0

1

0

1

0

5

8f9c4e7d3582ae60db9646d0d520489e6e31fcba

286

py

Python

swig/python/gdal-utils/scripts/gdal_merge.py

FeU-aKlos/gdal

bba6781133815248c9329842d365f8812b74c33f

[ "Apache-2.0" ]

3,100

2015-01-02T10:33:40.000Z

2022-03-31T02:06:51.000Z

swig/python/gdal-utils/scripts/gdal_merge.py

FeU-aKlos/gdal

bba6781133815248c9329842d365f8812b74c33f

[ "Apache-2.0" ]

3,496

2015-01-06T16:53:30.000Z

2022-03-31T20:18:51.000Z

swig/python/gdal-utils/scripts/gdal_merge.py

FeU-aKlos/gdal

bba6781133815248c9329842d365f8812b74c33f

[ "Apache-2.0" ]

2,036

2015-01-08T20:22:12.000Z

2022-03-31T10:24:08.000Z

#!/usr/bin/env python3 import sys # import osgeo_utils.gdal_merge as a convenience to use as a script from osgeo_utils.gdal_merge import * # noqa from osgeo_utils.gdal_merge import main from osgeo.gdal import deprecation_warn deprecation_warn('gdal_merge') sys.exit(main(sys.argv))

23.833333

67

0.800699

48

286

4.583333

0.479167

0.163636

0.190909

0.259091

0.263636

0

0.003984

0.122378

286

11

68

26

0.87251

0.321678

0

0.052356

0

1

0

true

0

0.666667

0

0.666667

0

null

0

1

0

null

0

1

0

1

0

1

0

5

8fa289be1ef334529d9901619505cbbfe51aa775

50

py

Python

sepmachine/pipeline/__init__.py

williamfzc/sepmachine

53bc83b2985ba2bdce9915b7f4a822d8690981a3

[ "MIT" ]

16

2020-03-20T12:37:01.000Z

2022-02-09T09:54:27.000Z

sepmachine/pipeline/__init__.py

williamfzc/sepmachine

53bc83b2985ba2bdce9915b7f4a822d8690981a3

[ "MIT" ]

4

2020-04-07T12:14:47.000Z

2020-07-20T13:33:53.000Z

sepmachine/pipeline/__init__.py

williamfzc/sepmachine

53bc83b2985ba2bdce9915b7f4a822d8690981a3

[ "MIT" ]

8

2020-03-08T09:05:47.000Z

2021-12-10T09:46:38.000Z

from sepmachine.pipeline.base import BasePipeline

25

49

0.88

6

50

7.333333

1

0

0.08

50

1

50

0.956522

0

1

0

true

0

1

0

1

0

1

0

null

0

1

0

null

0

1

0

1

0

1

0

5

8fc23422d113bfae109169154f223becdb5c2ba0

235

py

Python

comps/admin.py

dlanghorne0428/dancesport-tracker-projec

e55d91a4f03c26d6ee8c28846a809064adfdb158

[ "MIT" ]

null

comps/admin.py

dlanghorne0428/dancesport-tracker-projec

e55d91a4f03c26d6ee8c28846a809064adfdb158

[ "MIT" ]

87

2020-04-15T22:29:03.000Z

2022-01-02T02:21:28.000Z

comps/admin.py

dlanghorne0428/dancesport-tracker-projec

e55d91a4f03c26d6ee8c28846a809064adfdb158

[ "MIT" ]

null

from django.contrib import admin from .models.comp import Comp from .models.heat import Heat from .models.heatlist_dancer import Heatlist_Dancer admin.site.register(Comp) admin.site.register(Heat) admin.site.register(Heatlist_Dancer)

26.111111

51

0.834043

35

235

5.514286

0.342857

0.15544

0.264249

0

0.085106

235

8

52

29.375

0.897674

0

1

0

true

0

0.571429

0

0.571429

0

null

0

1

0

1

0

null

0

1

0

1

0

1

0

5

8fe2666ba63cd48dc5010ed6fd7db2d6567ab5aa

126

py

Python

tests/test_metadata.py

odudex/krux

db421a3f107c0263221e5f1e877e9c38925bb17c

[ "MIT" ]

null

tests/test_metadata.py

odudex/krux

db421a3f107c0263221e5f1e877e9c38925bb17c

[ "MIT" ]

13

2022-03-21T05:35:03.000Z

2022-03-31T14:31:46.000Z

tests/test_metadata.py

odudex/krux

db421a3f107c0263221e5f1e877e9c38925bb17c

[ "MIT" ]

null

def test_vars_exist(): from krux import metadata getattr(metadata, "VERSION") getattr(metadata, "SIGNER_PUBKEY")

21

38

0.714286

15

126

5.8

0.8

0.344828

0

0.18254

126

5

39

25.2

0.84466

0

0.15873

0

1

0.25

true

0

0.25

0

0.5

0

1

0

null

1

0

1

0

null

0

1

0

5

8ffa8e8b9ee6b2e2dffaab0b26cb88f2c70f80aa

230

py

Python

soft_delete_model_mixin/managers.py

frankhood/django-soft-delete-model-mixin

dd0596b791792969f8f9bc7b79d08a80f87c7d8b

[ "MIT" ]

null

soft_delete_model_mixin/managers.py

frankhood/django-soft-delete-model-mixin

dd0596b791792969f8f9bc7b79d08a80f87c7d8b

[ "MIT" ]

null

soft_delete_model_mixin/managers.py

frankhood/django-soft-delete-model-mixin

dd0596b791792969f8f9bc7b79d08a80f87c7d8b

[ "MIT" ]

null

from django.db import models from .querysets import SoftDeleteQuerySet class SoftDeleteModelManager(models.Manager): def get_queryset(self): return SoftDeleteQuerySet(self.model, using=self._db).not_deleted_items()

25.555556

81

0.791304

27

230

6.592593

0.740741

0

0.130435

230

8

82

28.75

0.89

0

1

0.2

false

0

0.4

0.2

1

0

1

0

null

0

1

0

null

0

1

0

5

8ffc3e63f0d422c9dcce5c1ead34ff3edd355329

124

py

Python

models/__init__.py

ymfa/SummaRuNNer

ec1cd4c9eb033a6da32920ace8a571c93adc5e6d

[ "MIT" ]

1

2019-09-16T12:51:43.000Z

models/__init__.py

kmm2204/SummaRuNNer

932dc1af23c783bc6032a555fd600ac0c9c6fb4c

[ "MIT" ]

null

models/__init__.py

kmm2204/SummaRuNNer

932dc1af23c783bc6032a555fd600ac0c9c6fb4c

[ "MIT" ]

1

2019-02-11T20:20:54.000Z

from .BasicModule import BasicModule from .RNN_RNN import RNN_RNN from .CNN_RNN import CNN_RNN from .AttnRNN import AttnRNN

24.8

36

0.83871

20

124

5

0.3

0.12

0

0.129032

124

4

37

31

0.925926

0

1

0

true

0

1

0

1

0

1

0

null

0

1

0

null

0

1

0

1

0

1

0

5

64f0d2a9e8f922819fffa7c15c637db67fc120ae

1,436

py

Python

swtoolkit/api/interfaces/ifeature.py

szcyd-chian/soliwordsapi

87d496f82f40febee3bdf4de878064a98a82c005

[ "MIT" ]

16

2020-11-03T14:40:30.000Z

2022-03-02T15:38:40.000Z

swtoolkit/api/interfaces/ifeature.py

szcyd-chian/soliwordsapi

87d496f82f40febee3bdf4de878064a98a82c005

[ "MIT" ]

2

2021-03-02T12:10:24.000Z

2021-11-19T21:34:47.000Z

swtoolkit/api/interfaces/ifeature.py

szcyd-chian/soliwordsapi

87d496f82f40febee3bdf4de878064a98a82c005

[ "MIT" ]

8

2020-11-11T12:25:58.000Z

2022-03-28T06:06:44.000Z

import win32com.client import pythoncom class IFeature: def __init__(self, system_object): self._instance = system_object @property def name(self): return self._instance.Name @property def description(self): return self._instance.Description @property def identity(self): return self._instance.GetID @property def type_(self): return self.get_type_name() def get_type_name(self): return self._instance.GetTypeName def get_type_name2(self): return self._instance.GetTypeName2 def select2(self, append, mark): arg1 = win32com.client.VARIANT(pythoncom.VT_BOOL, append) arg2 = win32com.client.VARIANT(pythoncom.VT_I4, mark) return self._instance.Select2(arg1, arg2) def add_comment(self, comment): arg = win32com.client.VARIANT(pythoncom.VT_BSTR, comment) return self._instance.AddComment(arg) def get_children(self): return self._instance.GetChildren def get_parents(self): return self._instance.Parents def get_owner_feature(self): return self._instance.GetOwnerFeature def get_next_feature(self): return self._instance.GetNextFeature def get_box(self): arg = win32com.client.VARIANT( pythoncom.VT_BYREF | pythoncom.VT_VARIANT, None ) self._instance.GetBox(arg) return arg.value

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5

8f215a2d5878cc7286cad918a497f98bcf65ea08

128

py

Python

environment/__init__.py

JannerM/spatial-reasoning

e163003a33177e41ca02d5feefee3fdfca5ba154

[ "MIT" ]

54

2017-07-14T01:08:57.000Z

2021-07-09T12:46:57.000Z

environment/__init__.py

jannerm/spatial-reasoning

e163003a33177e41ca02d5feefee3fdfca5ba154

[ "MIT" ]

null

environment/__init__.py

jannerm/spatial-reasoning

e163003a33177e41ca02d5feefee3fdfca5ba154

[ "MIT" ]

16

2017-07-16T03:18:19.000Z

2021-05-28T13:04:12.000Z

import library, figure_library from MDP import * from ValueIteration import ValueIteration from SpriteFigure import SpriteFigure

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5

8f7f363c11639ed097974ff2f18cae991a6b3457

107

py

Python

scripts/nltk_setup.py

ubclaunchpad/sleuth

7b7be0b7097a26169e17037f4220fd0ce039bde1

[ "MIT" ]

12

2017-09-17T02:14:35.000Z

2022-01-09T10:14:59.000Z

scripts/nltk_setup.py

ubclaunchpad/sleuth

7b7be0b7097a26169e17037f4220fd0ce039bde1

[ "MIT" ]

92

2017-09-16T23:50:45.000Z

2018-01-02T01:56:33.000Z

scripts/nltk_setup.py

ubclaunchpad/sleuth

7b7be0b7097a26169e17037f4220fd0ce039bde1

[ "MIT" ]

5

2017-12-26T01:47:36.000Z

2021-12-31T11:15:07.000Z

''' Download NLTK data ''' import nltk nltk.download('punkt') nltk.download('averaged_perceptron_tagger')

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5

56b746e727e238b0bc7becf5b2b3e348a3598d39

21

py

Python

models/__init__.py

rickyHong/Puzzle-all-repl

fb02b4f66ba3256f3b35ed1895f20e9615fe6689

[ "MIT" ]

6

2019-02-22T20:28:34.000Z

2021-10-17T10:36:09.000Z

models/__init__.py

rickyHong/Puzzle-all-repl

fb02b4f66ba3256f3b35ed1895f20e9615fe6689

[ "MIT" ]

null

models/__init__.py

rickyHong/Puzzle-all-repl

fb02b4f66ba3256f3b35ed1895f20e9615fe6689

[ "MIT" ]

1

2019-10-30T21:10:57.000Z

from .lenet import *

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7112add7a84498f363732ca17a80e332b3b02454

27,425

py

Python

eval/eccv20.py

rwe0214/xfr

a49d9e80a1bc45c25c72c394c60f6274599321aa

[ "MIT" ]

52

2020-08-04T11:33:09.000Z

2021-12-05T14:16:22.000Z

eval/eccv20.py

rwe0214/xfr

a49d9e80a1bc45c25c72c394c60f6274599321aa

[ "MIT" ]

10

2020-08-24T08:34:15.000Z

2021-12-05T06:59:50.000Z

eval/eccv20.py

rwe0214/xfr

a49d9e80a1bc45c25c72c394c60f6274599321aa

[ "MIT" ]

12

2020-08-05T03:11:56.000Z

2021-12-14T23:52:50.000Z

import sys import os.path import torch import PIL import numpy as np import pdb import uuid import torch.nn.functional as F import tempfile from scipy.spatial.distance import pdist, squareform import copy import random np.random.seed(42) # for repeatable take import vipy.image from vipy.image import ImageDetection import vipy.visualize import vipy.util import vipy.linalg from vipy.dataset.vggface2 import VGGFace2 sys.path.append('../python') from xfr.models.whitebox import WhiteboxSTResnet, Whitebox from xfr.models.resnet import stresnet101 import xfr.models.whitebox import xfr.show sys.path.append('../demo') try: from test_whitebox import _blend_saliency_map except ModuleNotFoundError as e: raise RuntimeError("This script needs to be called from the eval " "directory.") from e sys.path.append('../python/strface') import strface.detection face_detector = strface.detection.FasterRCNN(model_dir='../python/strface/models/detection', gpu_index=0 if torch.cuda.is_available() else -1, conf_threshold=None, rotate_flags=None, rotate_thresh=None, fusion_thresh=None, test_scales=800, max_size=1300) def _detector(imgfile): """Faster RCNN face detector wrapper""" im = np.array(PIL.Image.open(imgfile)) return face_detector(im) def _vggface2_topk_frontal_nonmates(wb, topk): np.random.seed(42) # for repeatable take n_minibatch = 2 vggface2 = VGGFace2('/proj/janus6/vggface2') imlist = vipy.util.chunklistbysize([im for im in vggface2.frontalset(n_frontal=n_minibatch)], n_minibatch) imlist_preprocessed = [torch.cat([wb.net.preprocess(f_detection(im).pil()) for im in iml], dim=0) for iml in imlist] # minibatch tensor X = [torch.squeeze(torch.sum(wb.net.encode(imchunk), dim=0)).detach().numpy() for imchunk in imlist_preprocessed] # minibatch encode template X = vipy.linalg.row_normalized(np.array(X)) X_subjectid = [imchunk[0].category() for imchunk in imlist] d_subjectid_to_topk_frontal_nonmates = {} for (k, d) in enumerate(squareform(pdist(X, metric='euclidean'))): j_sorted = np.argsort(d)[1:] # increasing, do not include self distance=0 on diagonal d_subjectid_to_topk_frontal_nonmates[X_subjectid[k]] = [X_subjectid[j] for j in j_sorted[0:topk]] vipy.util.save(d_subjectid_to_topk_frontal_nonmates, '_vggface2_topk_frontal_nonmates.pkl') # cache return d_subjectid_to_topk_frontal_nonmates def _vggface2_topk_nonmates(wb, topk): np.random.seed(42) # for repeatable take n_minibatch = 2 vggface2 = VGGFace2('/proj/janus6/vggface2') imlist = vipy.util.chunklistbysize([im for im in vggface2.take_per_subject(n_minibatch)], n_minibatch) imlist_preprocessed = [torch.cat([wb.net.preprocess(f_detection(im).pil()) for im in iml], dim=0) for iml in imlist] # minibatch tensor X = [torch.squeeze(torch.sum(wb.net.encode(imchunk), dim=0)).detach().numpy() for imchunk in imlist_preprocessed] # minibatch encode template X = vipy.linalg.row_normalized(np.array(X)) X_subjectid = [imchunk[0].category() for imchunk in imlist] d_subjectid_to_topk_frontal_nonmates = {} for (k, d) in enumerate(squareform(pdist(X, metric='euclidean'))): j_sorted = np.argsort(d)[1:] # increasing, do not include self distance=0 on diagonal d_subjectid_to_topk_frontal_nonmates[X_subjectid[k]] = [X_subjectid[j] for j in j_sorted[0:topk]] vipy.util.save(d_subjectid_to_topk_frontal_nonmates, '_vggface2_topk_nonmates.pkl') # cache return d_subjectid_to_topk_frontal_nonmates def _vggface2_nonmates(): np.random.seed(42) # for repeatable take return VGGFace2('/proj/janus6/vggface2').take_per_subject(1) def _triplet_mate_frontalpose_nonmate_top1_probe_mixedpose(n_subjects=32): np.random.seed(42) # for repeatable take vggface2 = VGGFace2('/proj/janus6/vggface2') frontalset = [im for im in vggface2.frontalset(n_frontal=1)] matelist = frontalset[0:n_subjects] if n_subjects == 16: matelist[3] = frontalset[n_subjects+1] matelist[5] = frontalset[n_subjects+5] matelist[6] = frontalset[n_subjects+4] matelist[11] = frontalset[n_subjects+7] matelist[12] = frontalset[n_subjects+2] matelist[13] = frontalset[n_subjects+9] matelist[15] = frontalset[n_subjects+6] d_subjectid_to_topk_frontal_nonmates = vipy.util.load('_vggface2_topk_frontal_nonmates.pkl') # cached nonmateidlist = [] for m in matelist: for n in d_subjectid_to_topk_frontal_nonmates[m.category()]: if n not in nonmateidlist: nonmateidlist.append(n) break d_frontalset = {x.category():x for x in frontalset} # for id lookup nonmatelist = [d_frontalset[k] for k in nonmateidlist] # ordered probelist = [vggface2.take(n_subjects, im_mate.category()) for im_mate in matelist] assert(len(nonmatelist) == n_subjects) assert(len(probelist) == n_subjects) assert(len(probelist[0]) == n_subjects) assert(len(matelist) == n_subjects) return (matelist, nonmatelist, probelist) def _k_mates_with_m_probes(n_subjects, n_probes): np.random.seed(42) # for repeatable take vggface2 = VGGFace2('/proj/janus6/vggface2') subjects = np.random.choice(vggface2.subjects(), n_subjects) imsubjects = {s:list(vggface2.subjectset(s)) for s in subjects} matelist = [imsubjects[s][0] for s in subjects] probelist = [imsubjects[s][1:n_probes+1] for s in subjects] return (matelist, probelist) def _n_subjects_k_mates_with_m_probes(n_subjects, k_mates, m_probes, mateset=None): np.random.seed(42) # for repeatable take vggface2 = VGGFace2('/proj/janus6/vggface2') subjects = np.random.choice(vggface2.subjects(), n_subjects) if mateset is None else mateset imsubjects = {s:list(vggface2.subjectset(s)) for s in subjects} matelist = [imsubjects[s][0:k_mates] for s in subjects] probelist = [imsubjects[s][k_mates:m_probes+k_mates] for s in subjects] return (matelist, probelist) def _all_nonmates(n=None, mateset=set()): np.random.seed(42) # for repeatable take vggface2 = VGGFace2('/proj/janus6/vggface2') subjects = vggface2.subjects() nonmates = subjects if n is None else subjects[0:n] nonmatelist = [next(vggface2.subjectset(s)) for s in nonmates if s not in mateset] return (nonmatelist) def _triplet_mate_frontalpose_nonmate_top1_probe_frontalpose(): n_subjects = 9 np.random.seed(42) # for repeatable take vggface2 = VGGFace2('/proj/janus6/vggface2') frontalset = [im for im in vggface2.frontalset(n_frontal=n_subjects+1)] subjectid = list(set([im.category() for im in frontalset])) # unique matelist = [im for im in frontalset if im.category() in subjectid[0:n_subjects]] d_mate = vipy.util.groupbyasdict(matelist, lambda im: im.category()) matelist = [v[0] for (k,v) in d_mate.items()] probelist = [v[1:] for (k,v) in d_mate.items()] d_subjectid_to_topk_frontal_nonmates = vipy.util.load('_vggface2_topk_frontal_nonmates.pkl') # cached nonmateidlist = [] for m in matelist: for n in d_subjectid_to_topk_frontal_nonmates[m.category()]: if n not in nonmateidlist: # select unique identity from top-k nonmateidlist.append(n) break nonmatelist = [x for x in frontalset if x.category() in nonmateidlist] # ordered d_nonmate = vipy.util.groupbyasdict(nonmatelist, lambda im: im.category()) nonmatelist = [d_nonmate[k][0] for k in nonmateidlist] # ordered assert(len(nonmatelist) == n_subjects) assert(len(probelist) == n_subjects) assert(len(probelist[0]) == n_subjects) assert(len(matelist) == n_subjects) return (matelist, nonmatelist, probelist) def _triplet_mate_frontalpose_nonmate_topk_probe_frontalpose(): n_subjects = 9 vggface2 = VGGFace2('/proj/janus6/vggface2', seed=42) frontalset = [im for im in vggface2.frontalset(n_frontal=n_subjects+1)] subjectid = sorted(list(set([im.category() for im in frontalset]))) # unique matelist = [im for im in frontalset if im.category() in subjectid[0:n_subjects]] d_mate = vipy.util.groupbyasdict(matelist, lambda im: im.category()) matelist = [v[0] for (k,v) in d_mate.items()] probelist = [v[1:] for (k,v) in d_mate.items()] d_subjectid_to_topk_frontal_nonmates = vipy.util.load('_vggface2_topk_nonmates.pkl') # cached nonmateidlist = d_subjectid_to_topk_frontal_nonmates[matelist[8].category()][0:n_subjects] nonmatelist = [vggface2.take(1, k)[0] for k in nonmateidlist] matelist = matelist[8] probelist = [probelist[8]] return (matelist, nonmatelist, probelist) def _triplet_montage(wb, matelist, nonmatelist, probelist, outfile, f_saliency=None): X_mate = [wb.net.encode(wb.net.preprocess(im.pil())) for im in matelist] X_nonmate = [wb.net.encode(wb.net.preprocess(im.pil())) for im in nonmatelist] # Create saliency for each matrix entry, overwrite probelist for (i, (x_mate, im_mate)) in enumerate(zip(X_mate, matelist)): for (j, (x_nonmate, im_nonmate)) in enumerate(zip(X_nonmate, nonmatelist)): wb.net.set_triplet_classifier(x_mate, x_nonmate) if f_saliency is not None: img_saliency = f_saliency(probelist[i][j]) probelist[i][j].buffer(img_saliency) # Montage imlist = [ImageDetection(xmin=0, ymin=0, xmax=256, ymax=256).buffer(np.uint8(np.zeros( (256,256,3) )))] imlist = imlist + nonmatelist for (im_mate, im_matedprobes) in zip(matelist, probelist): imlist.append(im_mate) imlist = imlist + im_matedprobes img_montage = vipy.visualize.montage(imlist, 112, 112, gridrows=len(matelist)+1, gridcols=len(nonmatelist)+1, skip=False, border=1, crop=False) return vipy.util.imwrite(img_montage, outfile) def f_saliency_whitebox_ebp(wb, im): P = torch.zeros( (1, wb.net.num_classes()) ); P[0][0] = 1.0; # one-hot prior probability img_saliency = wb.ebp(wb.net.preprocess(im.pil()), P) if np.max(img_saliency) == 255: img_saliency = img_saliency.astype(np.float32)/255.0 return np.array(_blend_saliency_map(np.array(im.pil().resize(img_saliency.shape)), img_saliency, gamma=0.5)) def f_saliency_whitebox_cebp(wb, im): img_saliency = wb.contrastive_ebp(wb.net.preprocess(im.pil()), k_poschannel=0, k_negchannel=1) if np.max(img_saliency) == 255: img_saliency = img_saliency.astype(np.float32)/255.0 return np.array(_blend_saliency_map(np.array(im.pil().resize(img_saliency.shape)), img_saliency, gamma=0.5)) def f_saliency_whitebox_tcebp(wb, im): img_saliency = wb.truncated_contrastive_ebp(wb.net.preprocess(im.pil()), k_poschannel=0, k_negchannel=1, percentile=20) if np.max(img_saliency) == 255: img_saliency = img_saliency.astype(np.float32)/255.0 return np.array(_blend_saliency_map(np.array(im.pil().resize(img_saliency.shape)), img_saliency, gamma=0.5)) def f_saliency_whitebox_weighted_subtree(wb, im): img_probe = wb.net.preprocess(im.pil()) (img_saliency, P_img, P_subtree, k_subtree) = wb.weighted_subtree_ebp(img_probe, k_poschannel=0, k_negchannel=1, topk=64, do_max_subtree=False, subtree_mode='all', do_mated_similarity_gating=True, verbose=False) img_saliency = np.float32(img_saliency)/255.0 return np.array(_blend_saliency_map(np.array(im.pil().resize(img_saliency.shape)), img_saliency, gamma=0.5)) def f_saliency_whitebox_weighted_subtree_lightcnn(wb, im): img_probe = wb.net.preprocess(im.pil()) (img_saliency, P_img, P_subtree, k_subtree) = wb.weighted_subtree_ebp(img_probe, k_poschannel=0, k_negchannel=1, topk=64, do_max_subtree=False, subtree_mode='affineonly_with_prior', do_mated_similarity_gating=True, verbose=False) img_saliency = np.float32(img_saliency)/255.0 return np.array(_blend_saliency_map(np.array(im.pil().resize(img_saliency.shape)), img_saliency, gamma=0.5)) def f_detection(im): bb = _detector(im.filename()) if len(bb) > 0: bb = bb[0] im = im.boundingbox(xmin=bb[0], ymin=bb[1], width=bb[2], height=bb[3]).dilate(1.1).crop().mindim(256).centercrop(224, 224) else: im = im.mindim(256).centercrop(224, 224) print(im) return im def f_detection_nocrop(im): bb = _detector(im.filename()) if len(bb) > 0: bb = bb[0] im = im.boundingbox(xmin=bb[0], ymin=bb[1], width=bb[2], height=bb[3]) return im def figure1(): """16x16 frontal mates, frontal non-mates, any probe, resnet-101 whitebox""" wb = Whitebox(WhiteboxSTResnet(stresnet101('../models/resnet101v4_28NOV17_train.pth'))) if not os.path.exists('_vggface2_topk_frontal_nonmates.pkl'): _vggface2_topk_frontal_nonmates(wb, topk=32) # recompute once n_subjects = 16 (matelist, nonmatelist, probelist) = _triplet_mate_frontalpose_nonmate_top1_probe_mixedpose(n_subjects) # Detection and color correction matelist = [f_detection(im).rgb() for im in matelist] nonmatelist = [f_detection(im).rgb() for im in nonmatelist] probelist = [[f_detection(im).rgb() for im in iml] for iml in probelist] probelist_clean = copy.deepcopy(probelist) # Figure 1a probelist = copy.deepcopy(probelist_clean) f_montage = _triplet_montage(wb, matelist, nonmatelist, probelist, 'figure1a_%d.jpg' % n_subjects, f_saliency=None) print('[eccv20.figure1]: Saving montage to "%s"' % f_montage) probelist_1a = copy.deepcopy(probelist) # Figure 1b probelist = copy.deepcopy(probelist_clean) f_saliency = lambda im: f_saliency_whitebox_ebp(wb, im) f_montage = _triplet_montage(wb, matelist, nonmatelist, probelist, 'figure1b_%d.jpg' % n_subjects, f_saliency=f_saliency) print('[eccv20.figure1]: Saving montage to "%s"' % f_montage) probelist_1b = copy.deepcopy(probelist) # Figure 1c probelist = copy.deepcopy(probelist_clean) f_saliency = lambda im: f_saliency_whitebox_cebp(wb, im) f_montage = _triplet_montage(wb, matelist, nonmatelist, probelist, 'figure1c_%d.jpg' % n_subjects, f_saliency=f_saliency) print('[eccv20.figure1]: Saving montage to "%s"' % f_montage) probelist_1c = copy.deepcopy(probelist) # Figure 1d probelist = copy.deepcopy(probelist_clean) f_saliency = lambda im: f_saliency_whitebox_tcebp(wb, im) f_montage = _triplet_montage(wb, matelist, nonmatelist, probelist, 'figure1d_%d.jpg' % n_subjects, f_saliency=f_saliency) print('[eccv20.figure1]: Saving montage to "%s"' % f_montage) probelist_1d = copy.deepcopy(probelist) # Figure 1e probelist = copy.deepcopy(probelist_clean) f_saliency = lambda im: f_saliency_whitebox_weighted_subtree(wb, im) f_montage = _triplet_montage(wb, matelist, nonmatelist, probelist, 'figure1e_%d.jpg' % n_subjects, f_saliency=f_saliency) print('[eccv20.figure1]: Saving montage to "%s"' % f_montage) probelist_1e = copy.deepcopy(probelist) # Figure 1f probelist = copy.deepcopy(probelist_clean) matelist = [matelist[0]]*n_subjects probelist = [probelist_1a[0]] + [probelist_1b[0]] + [probelist_1c[0]] + [probelist_1d[0]] + [probelist_1e[0]] f_montage = _triplet_montage(wb, matelist, nonmatelist, probelist, 'figure1f_%d.jpg' % n_subjects, f_saliency=None) print('[eccv20.figure1]: Saving montage to "%s"' % f_montage) def figure2(): """One mate, top-k nonmates, row-wise by approach""" n_subjects = 10 wb = Whitebox(WhiteboxSTResnet(stresnet101('../models/resnet101v4_28NOV17_train.pth'))) if not os.path.exists('_vggface2_topk_nonmates.pkl'): _vggface2_topk_nonmates(wb, topk=32) # recompute once (matelist, nonmatelist, probelist) = _triplet_mate_frontalpose_nonmate_topk_probe_frontalpose() # Detection and color correction matelist = [f_detection(im).rgb() for im in matelist] nonmatelist = [f_detection(im).rgb() for im in nonmatelist] probelist = [[f_detection(im).rgb() for im in iml] for iml in probelist] probelist_clean = copy.deepcopy(probelist) # Figure 2a probelist = copy.deepcopy(probelist_clean) f_montage = _triplet_montage(wb, matelist, nonmatelist, probelist, 'figure2a_%d.jpg' % n_subjects, f_saliency=None) probelist_2a = copy.deepcopy(probelist) print('[eccv20.figure2a]: Saving montage to "%s"' % f_montage) # Figure 2b probelist = copy.deepcopy(probelist_clean) f_saliency = lambda im: f_saliency_whitebox_ebp(wb, im) f_montage = _triplet_montage(wb, matelist, nonmatelist, probelist, 'figure2b_%d.jpg' % n_subjects, f_saliency=f_saliency) probelist_2b = copy.deepcopy(probelist) # Figure 2c probelist = copy.deepcopy(probelist_clean) f_saliency = lambda im: f_saliency_whitebox_cebp(wb, im) f_montage = _triplet_montage(wb, matelist, nonmatelist, probelist, 'figure2c_%d.jpg' % n_subjects, f_saliency=f_saliency) probelist_2c = copy.deepcopy(probelist) # Figure 2d probelist = copy.deepcopy(probelist_clean) f_saliency = lambda im: f_saliency_whitebox_tcebp(wb, im) f_montage = _triplet_montage(wb, matelist, nonmatelist, probelist, 'figure2d_%d.jpg' % n_subjects, f_saliency=f_saliency) probelist_2d = copy.deepcopy(probelist) # Figure 2e probelist = copy.deepcopy(probelist_clean) f_saliency = lambda im: f_saliency_whitebox_weighted_subtree(wb, im) f_montage = _triplet_montage(wb, matelist, nonmatelist, probelist, 'figure2e_%d.jpg' % n_subjects, f_saliency=f_saliency) probelist_2e = copy.deepcopy(probelist) # Figure 2f probelist = copy.deepcopy(probelist_clean) matelist = [matelist[0]]*n_subjects probelist = [probelist_2a[0]] + [probelist_2b[0]] + [probelist_2c[0]] + [probelist_2d[0]] + [probelist_2e[0]] f_montage = _triplet_montage(wb, matelist, nonmatelist, probelist, 'figure2f_%d.jpg' % n_subjects, f_saliency=None) print('[eccv20.figure2]: Saving montage to "%s"' % f_montage) def figure3(): """same as figure1, but light-cnn""" n_subjects = 16 net = xfr.models.lightcnn.LightCNN_29Layers_v2(num_classes=80013) statedict = xfr.models.lightcnn.Load_Checkpoint('../models/LightCNN_29Layers_V2_checkpoint.pth.tar') net.load_state_dict(statedict) wb = xfr.models.whitebox.Whitebox(xfr.models.whitebox.WhiteboxLightCNN(net), ebp_subtree_mode='affineonly_with_prior', eps=1E-16, ebp_version=5) # FIXME: the version matters if not os.path.exists('_vggface2_topk_frontal_nonmates.pkl'): _vggface2_topk_frontal_nonmates(wb, topk=32) # recompute once (matelist, nonmatelist, probelist) = _triplet_mate_frontalpose_nonmate_top1_probe_mixedpose(n_subjects) # Detection and color correction matelist = [f_detection(im).rgb() for im in matelist] nonmatelist = [f_detection(im).rgb() for im in nonmatelist] probelist = [[f_detection(im).rgb() for im in iml] for iml in probelist] probelist_clean = copy.deepcopy(probelist) # Figure 3a probelist = copy.deepcopy(probelist_clean) f_montage = _triplet_montage(wb, matelist, nonmatelist, probelist, 'figure3a_%d.jpg' % n_subjects, f_saliency=None) print('[eccv20.figure3]: Saving montage to "%s"' % f_montage) probelist_1a = copy.deepcopy(probelist) # Figure 3b probelist = copy.deepcopy(probelist_clean) f_saliency = lambda im: f_saliency_whitebox_ebp(wb, im) f_montage = _triplet_montage(wb, matelist, nonmatelist, probelist, 'figure3b_%d.jpg' % n_subjects, f_saliency=f_saliency) print('[eccv20.figure3]: Saving montage to "%s"' % f_montage) probelist_1b = copy.deepcopy(probelist) # Figure 3c probelist = copy.deepcopy(probelist_clean) f_saliency = lambda im: f_saliency_whitebox_cebp(wb, im) f_montage = _triplet_montage(wb, matelist, nonmatelist, probelist, 'figure3c_%d.jpg' % n_subjects, f_saliency=f_saliency) print('[eccv20.figure3]: Saving montage to "%s"' % f_montage) probelist_1c = copy.deepcopy(probelist) # Figure 3d probelist = copy.deepcopy(probelist_clean) f_saliency = lambda im: f_saliency_whitebox_tcebp(wb, im) f_montage = _triplet_montage(wb, matelist, nonmatelist, probelist, 'figure3d_%d.jpg' % n_subjects, f_saliency=f_saliency) print('[eccv20.figure3]: Saving montage to "%s"' % f_montage) probelist_1d = copy.deepcopy(probelist) # Figure 3e probelist = copy.deepcopy(probelist_clean) f_saliency = lambda im: f_saliency_whitebox_weighted_subtree_lightcnn(wb, im) f_montage = _triplet_montage(wb, matelist, nonmatelist, probelist, 'figure3e_%d.jpg' % n_subjects, f_saliency=f_saliency) print('[eccv20.figure3]: Saving montage to "%s"' % f_montage) probelist_1e = copy.deepcopy(probelist) # Figure 3f probelist = copy.deepcopy(probelist_clean) matelist = [matelist[0]]*n_subjects probelist = [probelist_1a[0]] + [probelist_1b[0]] + [probelist_1c[0]] + [probelist_1d[0]] + [probelist_1e[0]] f_montage = _triplet_montage(wb, matelist, nonmatelist, probelist, 'figure3f_%d.jpg' % n_subjects, f_saliency=None) print('[eccv20.figure3]: Saving montage to "%s"' % f_montage) def figure4(): """One mate, top-k nonmates, row-wise by approach""" n_subjects = 10 net = xfr.models.lightcnn.LightCNN_29Layers_v2(num_classes=80013) statedict = xfr.models.lightcnn.Load_Checkpoint('../models/LightCNN_29Layers_V2_checkpoint.pth.tar') net.load_state_dict(statedict) wb = xfr.models.whitebox.Whitebox(xfr.models.whitebox.WhiteboxLightCNN(net), ebp_subtree_mode='affineonly_with_prior', eps=1E-16, ebp_version=5) # FIXME: the version matters if not os.path.exists('_vggface2_topk_nonmates.pkl'): _vggface2_topk_nonmates(wb, topk=32) # recompute once (matelist, nonmatelist, probelist) = _triplet_mate_frontalpose_nonmate_topk_probe_frontalpose() # Detection and color correction matelist = [f_detection(im).rgb() for im in matelist] nonmatelist = [f_detection(im).rgb() for im in nonmatelist] probelist = [[f_detection(im).rgb() for im in iml] for iml in probelist] probelist_clean = copy.deepcopy(probelist) # Figure 4a probelist = copy.deepcopy(probelist_clean) f_montage = _triplet_montage(wb, matelist, nonmatelist, probelist, 'figure4a_%d.jpg' % n_subjects, f_saliency=None) probelist_4a = copy.deepcopy(probelist) # Figure 4b probelist = copy.deepcopy(probelist_clean) f_saliency = lambda im: f_saliency_whitebox_ebp(wb, im) f_montage = _triplet_montage(wb, matelist, nonmatelist, probelist, 'figure4b_%d.jpg' % n_subjects, f_saliency=f_saliency) probelist_4b = copy.deepcopy(probelist) # Figure 4c probelist = copy.deepcopy(probelist_clean) f_saliency = lambda im: f_saliency_whitebox_cebp(wb, im) f_montage = _triplet_montage(wb, matelist, nonmatelist, probelist, 'figure4c_%d.jpg' % n_subjects, f_saliency=f_saliency) probelist_4c = copy.deepcopy(probelist) # Figure 4d probelist = copy.deepcopy(probelist_clean) f_saliency = lambda im: f_saliency_whitebox_tcebp(wb, im) f_montage = _triplet_montage(wb, matelist, nonmatelist, probelist, 'figure4d_%d.jpg' % n_subjects, f_saliency=f_saliency) probelist_4d = copy.deepcopy(probelist) # Figure 4e probelist = copy.deepcopy(probelist_clean) f_saliency = lambda im: f_saliency_whitebox_weighted_subtree(wb, im) f_montage = _triplet_montage(wb, matelist, nonmatelist, probelist, 'figure4e_%d.jpg' % n_subjects, f_saliency=f_saliency) probelist_4e = copy.deepcopy(probelist) # Figure 4f probelist = copy.deepcopy(probelist_clean) matelist = [matelist[0]]*n_subjects probelist = [probelist_4a[0]] + [probelist_4b[0]] + [probelist_4c[0]] + [probelist_4d[0]] + [probelist_4e[0]] f_montage = _triplet_montage(wb, matelist, nonmatelist, probelist, 'figure4f_%d.jpg' % n_subjects, f_saliency=None) print('[eccv40.figure4]: Saving montage to "%s"' % f_montage) def figure5(): """Same as figure 3, but probe is now repeated""" n_subjects = 16 net = xfr.models.lightcnn.LightCNN_29Layers_v2(num_classes=80013) statedict = xfr.models.lightcnn.Load_Checkpoint('../models/LightCNN_29Layers_V2_checkpoint.pth.tar') net.load_state_dict(statedict) wb = xfr.models.whitebox.Whitebox(xfr.models.whitebox.WhiteboxLightCNN(net), ebp_subtree_mode='affineonly_with_prior', eps=1E-16, ebp_version=5) # FIXME: the version matters if not os.path.exists('_vggface2_topk_frontal_nonmates.pkl'): _vggface2_topk_frontal_nonmates(wb, topk=32) # recompute once (matelist, nonmatelist, probelist) = _triplet_mate_frontalpose_nonmate_top1_probe_mixedpose(n_subjects) probelist_repeated = [] for (k,p) in enumerate(probelist): probelist_repeated.append([copy.deepcopy(probelist[k][0]) for j in range(0,len(probelist[k]))]) probelist = probelist_repeated # Detection and color correction matelist = [f_detection(im).rgb() for im in matelist] nonmatelist = [f_detection(im).rgb() for im in nonmatelist] probelist = [[f_detection(im).rgb() for im in iml] for iml in probelist] probelist_clean = copy.deepcopy(probelist) # Figure 5a probelist = copy.deepcopy(probelist_clean) f_montage = _triplet_montage(wb, matelist, nonmatelist, probelist, 'figure5a_%d.jpg' % n_subjects, f_saliency=None) print('[eccv20.figure5]: Saving montage to "%s"' % f_montage) probelist_1a = copy.deepcopy(probelist) # Figure 5b probelist = copy.deepcopy(probelist_clean) f_saliency = lambda im: f_saliency_whitebox_ebp(wb, im) f_montage = _triplet_montage(wb, matelist, nonmatelist, probelist, 'figure5b_%d.jpg' % n_subjects, f_saliency=f_saliency) print('[eccv20.figure5]: Saving montage to "%s"' % f_montage) probelist_1b = copy.deepcopy(probelist) # Figure 5c probelist = copy.deepcopy(probelist_clean) f_saliency = lambda im: f_saliency_whitebox_cebp(wb, im) f_montage = _triplet_montage(wb, matelist, nonmatelist, probelist, 'figure5c_%d.jpg' % n_subjects, f_saliency=f_saliency) print('[eccv20.figure5]: Saving montage to "%s"' % f_montage) probelist_1c = copy.deepcopy(probelist) # Figure 5d probelist = copy.deepcopy(probelist_clean) f_saliency = lambda im: f_saliency_whitebox_tcebp(wb, im) f_montage = _triplet_montage(wb, matelist, nonmatelist, probelist, 'figure5d_%d.jpg' % n_subjects, f_saliency=f_saliency) print('[eccv20.figure5]: Saving montage to "%s"' % f_montage) probelist_1d = copy.deepcopy(probelist) # Figure 5e probelist = copy.deepcopy(probelist_clean) f_saliency = lambda im: f_saliency_whitebox_weighted_subtree_lightcnn(wb, im) f_montage = _triplet_montage(wb, matelist, nonmatelist, probelist, 'figure5e_%d.jpg' % n_subjects, f_saliency=f_saliency) print('[eccv20.figure5]: Saving montage to "%s"' % f_montage) probelist_1e = copy.deepcopy(probelist) # Figure 5f probelist = copy.deepcopy(probelist_clean) matelist = [matelist[0]]*n_subjects probelist = [probelist_1a[0]] + [probelist_1b[0]] + [probelist_1c[0]] + [probelist_1d[0]] + [probelist_1e[0]] f_montage = _triplet_montage(wb, matelist, nonmatelist, probelist, 'figure5f_%d.jpg' % n_subjects, f_saliency=None) print('[eccv20.figure5]: Saving montage to "%s"' % f_montage)

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0

5

712f048ddca2ef1b5e0be84744b007715a192505

2,359

py

Python

torch/nn/intrinsic/quantized/_reference/modules/conv_relu.py

jamestwebber/pytorch

cac9ae1506feabfc87d37a208b3d39ed46c59483

[ "Intel" ]

7

2021-05-29T16:31:51.000Z

2022-02-21T18:52:25.000Z

torch/nn/intrinsic/quantized/_reference/modules/conv_relu.py

jamestwebber/pytorch

cac9ae1506feabfc87d37a208b3d39ed46c59483

[ "Intel" ]

1

2022-01-18T12:17:29.000Z

torch/nn/intrinsic/quantized/_reference/modules/conv_relu.py

jamestwebber/pytorch

cac9ae1506feabfc87d37a208b3d39ed46c59483

[ "Intel" ]

2

2021-07-02T10:18:21.000Z

2021-08-18T10:10:28.000Z

import torch import torch.nn.quantized._reference as nnqr import torch.nn.functional as F class ConvReLU1d(nnqr.Conv1d): _FLOAT_MODULE = torch.nn.intrinsic.ConvReLU1d # type: ignore[assignment] def forward(self, x: torch.Tensor) -> torch.Tensor: x_dequant = x.dequantize() weight_dequant = self._qweight.dequantize() float_result = F.conv1d( x_dequant, weight_dequant, self._bias, self._conv1d_stride, self._conv1d_padding, self._conv1d_dilation, self.groups) float_result = F.relu(float_result, inplace=True) # NEEDFIX: we don't have dtype in the Linear module APIs right now! result = torch.quantize_per_tensor( float_result, self.scale, self.zero_point, torch.quint8) return result def _get_name(self): return "QuantizedConvReLU1d(Reference)" class ConvReLU2d(nnqr.Conv2d): _FLOAT_MODULE = torch.nn.intrinsic.ConvReLU2d # type: ignore[assignment] def forward(self, x: torch.Tensor) -> torch.Tensor: x_dequant = x.dequantize() weight_dequant = self._qweight.dequantize() float_result = F.conv2d( x_dequant, weight_dequant, self._bias, self.stride, self.padding, self.dilation, self.groups) float_result = F.relu(float_result, inplace=True) # NEEDFIX: we don't have dtype in the Linear module APIs right now! result = torch.quantize_per_tensor( float_result, self.scale, self.zero_point, torch.quint8) return result def _get_name(self): return "QuantizedConvReLU2d(Reference)" class ConvReLU3d(nnqr.Conv3d): _FLOAT_MODULE = torch.nn.intrinsic.ConvReLU3d # type: ignore[assignment] def forward(self, x: torch.Tensor) -> torch.Tensor: x_dequant = x.dequantize() weight_dequant = self._qweight.dequantize() float_result = F.conv3d( x_dequant, weight_dequant, self._bias, self.stride, self.padding, self.dilation, self.groups) float_result = F.relu(float_result, inplace=True) # NEEDFIX: we don't have dtype in the Linear module APIs right now! result = torch.quantize_per_tensor( float_result, self.scale, self.zero_point, torch.quint8) return result def _get_name(self): return "QuantizedConvReLU3d(Reference)"

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5.241497

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0.085659

0.066191

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2,359

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0.835165

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0.466667

0

null

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null

0

5

852fa3ca506e752f7c2751109ee500cadbabe212

98

py

Python

howl/howlcore/exceptions.py

volzotan/django-howl

3b11c530da95d152844934da09592619b3d4497f

[ "MIT" ]

null

howl/howlcore/exceptions.py

volzotan/django-howl

3b11c530da95d152844934da09592619b3d4497f

[ "MIT" ]

null

howl/howlcore/exceptions.py

volzotan/django-howl

3b11c530da95d152844934da09592619b3d4497f

[ "MIT" ]

null

class SensorReadError(Exception): pass class CommunicationErrorException(Exception): pass

19.6

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0.795918

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98

9.75

0.625

0.333333

0

0.142857

98

5

46

19.6

0.928571

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0.5

0

1

0

true

0.5

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null

1

0

1

0

null

0

1

0

5

854fdbc0ee58c6abdacb5a1d2f3607fcfc8686a7

94

py

Python

RawQuant/__init__.py

pwilmart/RawQuant

f647368c48af7884aaa91c066659db167928be18

[ "MIT" ]

12

2018-01-16T00:20:40.000Z

2021-12-07T16:48:34.000Z

RawQuant/__init__.py

pwilmart/RawQuant

f647368c48af7884aaa91c066659db167928be18

[ "MIT" ]

18

2018-05-17T05:06:23.000Z

2019-01-04T16:32:43.000Z

RawQuant/__init__.py

pwilmart/RawQuant

f647368c48af7884aaa91c066659db167928be18

[ "MIT" ]

4

2018-05-11T09:54:55.000Z

2020-04-30T18:38:59.000Z

from RawQuant.RawQuant import * from RawQuant import RawFileReader __version__ = '0.2.3'

18.8

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5.583333

0.666667

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0.170213

94

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false

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