
import os
from datetime import datetime as dt

from github import Github


A	:      int  					=					[
    'good first issue',
    'feature request',
    'wip',
]


def        __lowerCAmelCase     (     )			->    Any:
				__a     				=       Github(os.environ['''GITHUB_TOKEN''']    )
				__a     				=       g.get_repo('''huggingface/accelerate'''    )
				__a     				=       repo.get_issues(state='''open'''    )

				for issue in open_issues:
								__a     				=       sorted([comment for comment in issue.get_comments()]		,       key=lambda a__    : i.created_at		,       reverse=lowerCAmelCase__    )
								__a     				=       comments[0] if len(lowerCAmelCase__    ) > 0 else None
								__a     				=       dt.utcnow()
								__a     				=       (current_time - issue.updated_at).days
								__a     				=       (current_time - issue.created_at).days
								if (
								    last_comment is not None
								    and last_comment.user.login == "github-actions[bot]"
								    and days_since_updated > 7
								    and days_since_creation >= 30
								    and not any(label.name.lower() in LABELS_TO_EXEMPT for label in issue.get_labels()    )
								):
												# Close issue since it has been 7 days of inactivity since bot mention.
												issue.edit(state='''closed'''    )
								elif (
								    days_since_updated > 23
								    and days_since_creation >= 30
								    and not any(label.name.lower() in LABELS_TO_EXEMPT for label in issue.get_labels()    )
								):
												# Add stale comment
												issue.create_comment(
												    '''This issue has been automatically marked as stale because it has not had '''
												    '''recent activity. If you think this still needs to be addressed '''
												    '''please comment on this thread.\n\nPlease note that issues that do not follow the '''
												    '''[contributing guidelines](https://github.com/huggingface/accelerate/blob/main/CONTRIBUTING.md) '''
												    '''are likely to be ignored.'''    )


if __name__ == "__main__":
					main()




import json
import os
import unittest

from transformers import BatchEncoding, LEDTokenizer, LEDTokenizerFast
from transformers.models.led.tokenization_led import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers, require_torch
from transformers.utils import cached_property

from ...test_tokenization_common import TokenizerTesterMixin



@require_tokenizers
class        lowerCAmelCase__					(  _lowerCamelCase ,				unittest.TestCase       ):
				A_   :     Dict								=     LEDTokenizer
				A_   :     Union[str, Any]								=     LEDTokenizerFast
				A_   :     Tuple								=     True


				def __UpperCamelCase						(							self      :  str       )		->     str:
											super().setUp()
											A      						=    [
											    'l',
											    'o',
											    'w',
											    'e',
											    'r',
											    's',
											    't',
											    'i',
											    'd',
											    'n',
											    '\u0120',
											    '\u0120l',
											    '\u0120n',
											    '\u0120lo',
											    '\u0120low',
											    'er',
											    '\u0120lowest',
											    '\u0120newer',
											    '\u0120wider',
											    '<unk>',
											]
											A      						=    dict(zip(__UpperCamelCase						, range(len(__UpperCamelCase       )       )       )       )
											A      						=    ['#version: 0.2', '\u0120 l', '\u0120l o', '\u0120lo w', 'e r', '']
											A      						=    {'unk_token': '<unk>'}

											A      						=    os.path.join(self.tmpdirname						, VOCAB_FILES_NAMES['vocab_file']       )
											A      						=    os.path.join(self.tmpdirname						, VOCAB_FILES_NAMES['merges_file']       )
											with open(self.vocab_file						, 'w'						, encoding='utf-8'       ) as fp:
																		fp.write(json.dumps(__UpperCamelCase       ) + '\n'       )
											with open(self.merges_file						, 'w'						, encoding='utf-8'       ) as fp:
																		fp.write('\n'.join(__UpperCamelCase       )       )


				def __UpperCamelCase						(							self      :  str						, **__UpperCamelCase      :  str       )		->     Tuple:
											kwargs.update(self.special_tokens_map       )
											return self.tokenizer_class.from_pretrained(self.tmpdirname						, **__UpperCamelCase       )


				def __UpperCamelCase						(							self      :  List[str]						, **__UpperCamelCase      :  List[Any]       )		->     str:
											kwargs.update(self.special_tokens_map       )
											return self.rust_tokenizer_class.from_pretrained(self.tmpdirname						, **__UpperCamelCase       )


				def __UpperCamelCase						(							self      :  Optional[Any]						, __UpperCamelCase      :  Optional[int]       )		->     Dict:
											return "lower newer", "lower newer"


				@cached_property
				def __UpperCamelCase						(							self      :  Union[str, Any]       )		->     Any:
											return LEDTokenizer.from_pretrained('allenai/led-base-16384'       )


				@cached_property
				def __UpperCamelCase						(							self      :  str       )		->     str:
											return LEDTokenizerFast.from_pretrained('allenai/led-base-16384'       )


				@require_torch
				def __UpperCamelCase						(							self      :  str       )		->     Optional[Any]:
											A      						=    ['A long paragraph for summarization.', 'Another paragraph for summarization.']
											A      						=    [0, 250, 251, 17_818, 13, 39_186, 1_938, 4, 2]

											for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
																		A      						=    tokenizer(__UpperCamelCase						, max_length=len(__UpperCamelCase       )						, padding=__UpperCamelCase						, return_tensors='pt'       )
																		self.assertIsInstance(__UpperCamelCase						, __UpperCamelCase       )

																		self.assertEqual((2, 9)						, batch.input_ids.shape       )
																		self.assertEqual((2, 9)						, batch.attention_mask.shape       )
																		A      						=    batch.input_ids.tolist()[0]
																		self.assertListEqual(__UpperCamelCase						, __UpperCamelCase       )


				@require_torch
				def __UpperCamelCase						(							self      :  Optional[int]       )		->     str:
											A      						=    ['A long paragraph for summarization.', 'Another paragraph for summarization.']
											for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
																		A      						=    tokenizer(__UpperCamelCase						, padding=__UpperCamelCase						, return_tensors='pt'       )
																		self.assertIn('input_ids'						, __UpperCamelCase       )
																		self.assertIn('attention_mask'						, __UpperCamelCase       )
																		self.assertNotIn('labels'						, __UpperCamelCase       )
																		self.assertNotIn('decoder_attention_mask'						, __UpperCamelCase       )


				@require_torch
				def __UpperCamelCase						(							self      :  List[Any]       )		->     Union[str, Any]:
											A      						=    [
											    'Summary of the text.',
											    'Another summary.',
											]
											for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
																		A      						=    tokenizer(text_target=__UpperCamelCase						, max_length=32						, padding='max_length'						, return_tensors='pt'       )
																		self.assertEqual(32						, targets['input_ids'].shape[1]       )


				@require_torch
				def __UpperCamelCase						(							self      :  List[str]       )		->     Any:
											for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
																		A      						=    tokenizer(
																		    ['I am a small frog' * 1_024, 'I am a small frog']						, padding=__UpperCamelCase						, truncation=__UpperCamelCase						, return_tensors='pt'       )
																		self.assertIsInstance(__UpperCamelCase						, __UpperCamelCase       )
																		self.assertEqual(batch.input_ids.shape						, (2, 5_122)       )


				@require_torch
				def __UpperCamelCase						(							self      :  int       )		->     Optional[Any]:
											A      						=    ['A long paragraph for summarization.']
											A      						=    [
											    'Summary of the text.',
											]
											for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
																		A      						=    tokenizer(__UpperCamelCase						, return_tensors='pt'       )
																		A      						=    tokenizer(text_target=__UpperCamelCase						, return_tensors='pt'       )
																		A      						=    inputs['input_ids']
																		A      						=    targets['input_ids']
																		self.assertTrue((input_ids[:, 0] == tokenizer.bos_token_id).all().item()       )
																		self.assertTrue((labels[:, 0] == tokenizer.bos_token_id).all().item()       )
																		self.assertTrue((input_ids[:, -1] == tokenizer.eos_token_id).all().item()       )
																		self.assertTrue((labels[:, -1] == tokenizer.eos_token_id).all().item()       )


				@require_torch
				def __UpperCamelCase						(							self      :  Union[str, Any]       )		->     Tuple:
											for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
																		A      						=    ['Summary of the text.', 'Another summary.']
																		A      						=    [[0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, -1, -1]]

																		A      						=    tokenizer(__UpperCamelCase						, padding=__UpperCamelCase       )
																		A      						=    [[0] * len(__UpperCamelCase       ) for x in encoded_output['input_ids']]
																		A      						=    tokenizer.pad(__UpperCamelCase       )
																		self.assertSequenceEqual(outputs['global_attention_mask']						, __UpperCamelCase       )


				def __UpperCamelCase						(							self      :  str       )		->     int:
											pass


				def __UpperCamelCase						(							self      :  Dict       )		->     Any:
											for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
																		with self.subTest(f'''{tokenizer.__class__.__name__} ({pretrained_name})'''       ):
																									A      						=    self.rust_tokenizer_class.from_pretrained(__UpperCamelCase						, **__UpperCamelCase       )
																									A      						=    self.tokenizer_class.from_pretrained(__UpperCamelCase						, **__UpperCamelCase       )
																									A      						=    'A, <mask> AllenNLP sentence.'
																									A      						=    tokenizer_r.encode_plus(__UpperCamelCase						, add_special_tokens=__UpperCamelCase						, return_token_type_ids=__UpperCamelCase       )
																									A      						=    tokenizer_p.encode_plus(__UpperCamelCase						, add_special_tokens=__UpperCamelCase						, return_token_type_ids=__UpperCamelCase       )
																									self.assertEqual(sum(tokens_r['token_type_ids']       )						, sum(tokens_p['token_type_ids']       )       )
																									self.assertEqual(
																									    sum(tokens_r['attention_mask']       ) / len(tokens_r['attention_mask']       )						, sum(tokens_p['attention_mask']       ) / len(tokens_p['attention_mask']       )						, )

																									A      						=    tokenizer_r.convert_ids_to_tokens(tokens_r['input_ids']       )
																									A      						=    tokenizer_p.convert_ids_to_tokens(tokens_p['input_ids']       )
																									self.assertSequenceEqual(tokens_p['input_ids']						, [0, 250, 6, 50_264, 3_823, 487, 21_992, 3_645, 4, 2]       )
																									self.assertSequenceEqual(tokens_r['input_ids']						, [0, 250, 6, 50_264, 3_823, 487, 21_992, 3_645, 4, 2]       )

																									self.assertSequenceEqual(
																									    __UpperCamelCase						, ['<s>', 'A', ',', '<mask>', 'ĠAllen', 'N', 'LP', 'Ġsentence', '.', '</s>']       )
																									self.assertSequenceEqual(
																									    __UpperCamelCase						, ['<s>', 'A', ',', '<mask>', 'ĠAllen', 'N', 'LP', 'Ġsentence', '.', '</s>']       )