


'''simple docstring'''



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 						_snake_case   (       a__    ,				unittest.TestCase		):
							lowerCAmelCase :List[str]						=   LEDTokenizer
							lowerCAmelCase :List[str]						=   LEDTokenizerFast
							lowerCAmelCase :Dict						=   True




							def 			snake_case__  ( self):
										super().setUp()
										UpperCAmelCase__					:   List[Any]       				=       [
										    """l""",
										    """o""",
										    """w""",
										    """e""",
										    """r""",
										    """s""",
										    """t""",
										    """i""",
										    """d""",
										    """n""",
										    """\u0120""",
										    """\u0120l""",
										    """\u0120n""",
										    """\u0120lo""",
										    """\u0120low""",
										    """er""",
										    """\u0120lowest""",
										    """\u0120newer""",
										    """\u0120wider""",
										    """<unk>""",
										]
										UpperCAmelCase__					:   Optional[Any]       				=       dict(zip(_lowerCamelCase							,							range(len(_lowerCamelCase))))
										UpperCAmelCase__					:   Dict       				=       ["""#version: 0.2""", """\u0120 l""", """\u0120l o""", """\u0120lo w""", """e r""", """"""]
										UpperCAmelCase__					:   Tuple       				=       {"""unk_token""": """<unk>"""}

										UpperCAmelCase__					:   Dict       				=       os.path.join(self.tmpdirname							,							VOCAB_FILES_NAMES["""vocab_file"""])
										UpperCAmelCase__					:   Union[str, Any]       				=       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(_lowerCamelCase) + """\n""")
										with open(self.merges_file							,							"""w"""							,							encoding="""utf-8""") as fp:
													fp.write("""\n""".join(_lowerCamelCase))




							def 			snake_case__  ( self							,							**_lowerCamelCase):
										kwargs.update(self.special_tokens_map)
										return self.tokenizer_class.from_pretrained(self.tmpdirname							,							**_lowerCamelCase)




							def 			snake_case__  ( self							,							**_lowerCamelCase):
										kwargs.update(self.special_tokens_map)
										return self.rust_tokenizer_class.from_pretrained(self.tmpdirname							,							**_lowerCamelCase)




							def 			snake_case__  ( self							,							_lowerCamelCase):
										return "lower newer", "lower newer"




							@cached_property
							def 			snake_case__  ( self):
										return LEDTokenizer.from_pretrained("""allenai/led-base-16384""")




							@cached_property
							def 			snake_case__  ( self):
										return LEDTokenizerFast.from_pretrained("""allenai/led-base-16384""")




							@require_torch
							def 			snake_case__  ( self):
										UpperCAmelCase__					:   str       				=       ["""A long paragraph for summarization.""", """Another paragraph for summarization."""]
										UpperCAmelCase__					:   Optional[Any]       				=       [0, 250, 251, 1_7818, 13, 3_9186, 1938, 4, 2]

										for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
													UpperCAmelCase__					:   str       				=       tokenizer(_lowerCamelCase							,							max_length=len(_lowerCamelCase)							,							padding=_lowerCamelCase							,							return_tensors="""pt""")
													self.assertIsInstance(_lowerCamelCase							,							_lowerCamelCase)

													self.assertEqual((2, 9)							,							batch.input_ids.shape)
													self.assertEqual((2, 9)							,							batch.attention_mask.shape)
													UpperCAmelCase__					:   Dict       				=       batch.input_ids.tolist()[0]
													self.assertListEqual(_lowerCamelCase							,							_lowerCamelCase)




							@require_torch
							def 			snake_case__  ( self):
										UpperCAmelCase__					:   List[str]       				=       ["""A long paragraph for summarization.""", """Another paragraph for summarization."""]
										for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
													UpperCAmelCase__					:   Any       				=       tokenizer(_lowerCamelCase							,							padding=_lowerCamelCase							,							return_tensors="""pt""")
													self.assertIn("""input_ids"""							,							_lowerCamelCase)
													self.assertIn("""attention_mask"""							,							_lowerCamelCase)
													self.assertNotIn("""labels"""							,							_lowerCamelCase)
													self.assertNotIn("""decoder_attention_mask"""							,							_lowerCamelCase)




							@require_torch
							def 			snake_case__  ( self):
										UpperCAmelCase__					:   str       				=       [
										    """Summary of the text.""",
										    """Another summary.""",
										]
										for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
													UpperCAmelCase__					:   int       				=       tokenizer(text_target=_lowerCamelCase							,							max_length=32							,							padding="""max_length"""							,							return_tensors="""pt""")
													self.assertEqual(32							,							targets["""input_ids"""].shape[1])




							@require_torch
							def 			snake_case__  ( self):
										for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
													UpperCAmelCase__					:   List[Any]       				=       tokenizer(
													    ["""I am a small frog""" * 1024, """I am a small frog"""]							,							padding=_lowerCamelCase							,							truncation=_lowerCamelCase							,							return_tensors="""pt""")
													self.assertIsInstance(_lowerCamelCase							,							_lowerCamelCase)
													self.assertEqual(batch.input_ids.shape							,							(2, 5122))




							@require_torch
							def 			snake_case__  ( self):
										UpperCAmelCase__					:   Any       				=       ["""A long paragraph for summarization."""]
										UpperCAmelCase__					:   str       				=       [
										    """Summary of the text.""",
										]
										for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
													UpperCAmelCase__					:   str       				=       tokenizer(_lowerCamelCase							,							return_tensors="""pt""")
													UpperCAmelCase__					:   int       				=       tokenizer(text_target=_lowerCamelCase							,							return_tensors="""pt""")
													UpperCAmelCase__					:   List[Any]       				=       inputs["""input_ids"""]
													UpperCAmelCase__					:   Tuple       				=       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 			snake_case__  ( self):
										for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
													UpperCAmelCase__					:   Optional[int]       				=       ["""Summary of the text.""", """Another summary."""]
													UpperCAmelCase__					:   Union[str, Any]       				=       [[0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, -1, -1]]

													UpperCAmelCase__					:   Tuple       				=       tokenizer(_lowerCamelCase							,							padding=_lowerCamelCase)
													UpperCAmelCase__					:   str       				=       [[0] * len(_lowerCamelCase) for x in encoded_output["""input_ids"""]]
													UpperCAmelCase__					:   str       				=       tokenizer.pad(_lowerCamelCase)
													self.assertSequenceEqual(outputs["""global_attention_mask"""]							,							_lowerCamelCase)




							def 			snake_case__  ( self):
										pass




							def 			snake_case__  ( self):
										for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
													with self.subTest(f'''{tokenizer.__class__.__name__} ({pretrained_name})'''):
																UpperCAmelCase__					:   Dict       				=       self.rust_tokenizer_class.from_pretrained(_lowerCamelCase							,							**_lowerCamelCase)
																UpperCAmelCase__					:   List[str]       				=       self.tokenizer_class.from_pretrained(_lowerCamelCase							,							**_lowerCamelCase)
																UpperCAmelCase__					:   Dict       				=       """A, <mask> AllenNLP sentence."""
																UpperCAmelCase__					:   Union[str, Any]       				=       tokenizer_r.encode_plus(_lowerCamelCase							,							add_special_tokens=_lowerCamelCase							,							return_token_type_ids=_lowerCamelCase)
																UpperCAmelCase__					:   Tuple       				=       tokenizer_p.encode_plus(_lowerCamelCase							,							add_special_tokens=_lowerCamelCase							,							return_token_type_ids=_lowerCamelCase)
																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"""])							,							)

																UpperCAmelCase__					:   Any       				=       tokenizer_r.convert_ids_to_tokens(tokens_r["""input_ids"""])
																UpperCAmelCase__					:   Dict       				=       tokenizer_p.convert_ids_to_tokens(tokens_p["""input_ids"""])
																self.assertSequenceEqual(tokens_p["""input_ids"""]							,							[0, 250, 6, 5_0264, 3823, 487, 2_1992, 3645, 4, 2])
																self.assertSequenceEqual(tokens_r["""input_ids"""]							,							[0, 250, 6, 5_0264, 3823, 487, 2_1992, 3645, 4, 2])

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



'''simple docstring'''



import os
from datetime import datetime as dt

from github import Github


__A		=[
    'good first issue',
    'good second issue',
    'good difficult issue',
    'enhancement',
    'new pipeline/model',
    'new scheduler',
    'wip',
]







def       _UpperCamelCase (						):
			UpperCAmelCase__					:   Union[str, Any]       				=       Github(os.environ["""GITHUB_TOKEN"""]   )
			UpperCAmelCase__					:   Any       				=       g.get_repo("""huggingface/diffusers"""   )
			UpperCAmelCase__					:   Optional[int]       				=       repo.get_issues(state="""open"""   )

			for issue in open_issues:
						UpperCAmelCase__					:   Any       				=       sorted(issue.get_comments()	, key=lambda UpperCamelCase__   : i.created_at	, reverse=UpperCamelCase__   )
						UpperCAmelCase__					:   List[Any]       				=       comments[0] if len(UpperCamelCase__   ) > 0 else None
						if (
						    last_comment is not None
						    and last_comment.user.login == "github-actions[bot]"
						    and (dt.utcnow() - issue.updated_at).days > 7
						    and (dt.utcnow() - issue.created_at).days >= 3_0
						    and not any(label.name.lower() in LABELS_TO_EXEMPT for label in issue.get_labels()   )
						):
									# Closes the issue after 7 days of inactivity since the Stalebot notification.
									issue.edit(state="""closed"""   )
						elif (
						    "stale" in issue.get_labels()
						    and last_comment is not None
						    and last_comment.user.login != "github-actions[bot]"
						):
									# Opens the issue if someone other than Stalebot commented.
									issue.edit(state="""open"""   )
									issue.remove_from_labels("""stale"""   )
						elif (
						    (dt.utcnow() - issue.updated_at).days > 2_3
						    and (dt.utcnow() - issue.created_at).days >= 3_0
						    and not any(label.name.lower() in LABELS_TO_EXEMPT for label in issue.get_labels()   )
						):
									# Post a Stalebot notification after 23 days of inactivity.
									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/diffusers/blob/main/CONTRIBUTING.md) """
									    """are likely to be ignored."""   )
									issue.add_to_labels("""stale"""   )


if __name__ == "__main__":
		main()