




import json
import os
import shutil
import tempfile
import unittest

from transformers import BatchEncoding, CanineTokenizer
from transformers.testing_utils import require_tokenizers, require_torch
from transformers.tokenization_utils import AddedToken
from transformers.utils import cached_property

from ...test_tokenization_common import TokenizerTesterMixin







class 		lowercase							(     UpperCamelCase__,unittest.TestCase	):
     _a											=		CanineTokenizer
     _a											=		False

     def       a__			(						self       )		->							str:
           super().setUp()
           _A							:  Union[str, Any]    =		CanineTokenizer()
           tokenizer.save_pretrained(self.tmpdirname       )

     @cached_property
     def       a__			(						self       )		->							List[str]:
           return CanineTokenizer.from_pretrained("""google/canine-s"""       )

     def       a__			(						self	,  **_a       )		->							CanineTokenizer:
           _A							:  List[Any]    =		self.tokenizer_class.from_pretrained(self.tmpdirname	,  **_a       )
           _A							:  int    =		1024
           return tokenizer

     @require_torch
     def       a__			(						self       )		->							int:
           _A							:  Union[str, Any]    =		self.canine_tokenizer
           _A							:  Optional[int]    =		["""Life is like a box of chocolates.""", """You never know what you're gonna get."""]
           # fmt: off
           _A							:  Optional[int]    =		[5_7344, 76, 105, 102, 101, 32, 105, 115, 32, 108, 105, 107, 101, 32, 97, 32, 98, 111, 120, 32, 111, 102, 32, 99, 104, 111, 99, 111, 108, 97, 116, 101, 115, 46, 5_7345, 0, 0, 0, 0]
           # fmt: on
           _A							:  Union[str, Any]    =		tokenizer(_a	,  padding=_a	,  return_tensors="""pt"""       )
           self.assertIsInstance(_a	,  _a       )

           _A							:  str    =		list(batch.input_ids.numpy()[0]       )

           self.assertListEqual(_a	,  _a       )

           self.assertEqual((2, 39)	,  batch.input_ids.shape       )
           self.assertEqual((2, 39)	,  batch.attention_mask.shape       )

     @require_torch
     def       a__			(						self       )		->							Dict:
           _A							:  List[Any]    =		self.canine_tokenizer
           _A							:  Dict    =		["""Once there was a man.""", """He wrote a test in HuggingFace Tranformers."""]
           _A							:  Any    =		tokenizer(_a	,  padding=_a	,  return_tensors="""pt"""       )
           # check if input_ids, attention_mask and token_type_ids are returned
           self.assertIn("""input_ids"""	,  _a       )
           self.assertIn("""attention_mask"""	,  _a       )
           self.assertIn("""token_type_ids"""	,  _a       )

     @require_torch
     def       a__			(						self       )		->							List[Any]:
           _A							:  Tuple    =		self.canine_tokenizer
           _A							:  int    =		[
               """What's the weater?""",
               """It's about 25 degrees.""",
           ]
           _A							:  str    =		tokenizer(
               text_target=_a	,  max_length=32	,  padding="""max_length"""	,  truncation=_a	,  return_tensors="""pt"""       )
           self.assertEqual(32	,  targets["""input_ids"""].shape[1]       )

     def       a__			(						self       )		->							int:
           # safety check on max_len default value so we are sure the test works
           _A							:  Union[str, Any]    =		self.get_tokenizers()
           for tokenizer in tokenizers:
                 with self.subTest(F'''{tokenizer.__class__.__name__}'''       ):
                       self.assertNotEqual(tokenizer.model_max_length	,  42       )

        # Now let's start the test
           _A							:  Any    =		self.get_tokenizers()
           for tokenizer in tokenizers:
                 with self.subTest(F'''{tokenizer.__class__.__name__}'''       ):
                       # Isolate this from the other tests because we save additional tokens/etc
                       _A							:  str    =		tempfile.mkdtemp()

                       _A							:  Optional[Any]    =		""" He is very happy, UNwant\u00E9d,running"""
                       _A							:  Union[str, Any]    =		tokenizer.encode(_a	,  add_special_tokens=_a       )
                       tokenizer.save_pretrained(_a       )

                       _A							:  str    =		tokenizer.__class__.from_pretrained(_a       )
                       _A							:  Dict    =		after_tokenizer.encode(_a	,  add_special_tokens=_a       )
                       self.assertListEqual(_a	,  _a       )

                       shutil.rmtree(_a       )

           _A							:  Tuple    =		self.get_tokenizers(model_max_length=42       )
           for tokenizer in tokenizers:
                 with self.subTest(F'''{tokenizer.__class__.__name__}'''       ):
                       # Isolate this from the other tests because we save additional tokens/etc
                       _A							:  Optional[int]    =		tempfile.mkdtemp()

                       _A							:  List[Any]    =		""" He is very happy, UNwant\u00E9d,running"""

                       _A							:  List[Any]    =		tokenizer.additional_special_tokens

                       # We can add a new special token for Canine as follows:
                       _A							:  int    =		chr(0xe0_07       )
                       additional_special_tokens.append(_a       )
                       tokenizer.add_special_tokens({"""additional_special_tokens""": additional_special_tokens}       )
                       _A							:  int    =		tokenizer.encode(_a	,  add_special_tokens=_a       )
                       tokenizer.save_pretrained(_a       )

                       _A							:  List[str]    =		tokenizer.__class__.from_pretrained(_a       )
                       _A							:  Dict    =		after_tokenizer.encode(_a	,  add_special_tokens=_a       )
                       self.assertListEqual(_a	,  _a       )
                       self.assertIn(_a	,  after_tokenizer.additional_special_tokens       )
                       self.assertEqual(after_tokenizer.model_max_length	,  42       )

                       _A							:  Optional[Any]    =		tokenizer.__class__.from_pretrained(_a	,  model_max_length=43       )
                       self.assertEqual(tokenizer.model_max_length	,  43       )

                       shutil.rmtree(_a       )

     def       a__			(						self       )		->							List[str]:
           _A							:  Optional[int]    =		self.get_tokenizers(do_lower_case=_a       )
           for tokenizer in tokenizers:
                 with self.subTest(F'''{tokenizer.__class__.__name__}'''       ):
                       _A    ,     _A							:  int    =		self.get_clean_sequence(_a       )

                       # a special token for Canine can be defined as follows:
                       _A							:  Tuple    =		0xe0_05
                       _A							:  str    =		chr(_a       )

                       tokenizer.add_special_tokens({"""cls_token""": special_token}       )
                       _A							:  str    =		tokenizer.encode(_a	,  add_special_tokens=_a       )
                       self.assertEqual(len(_a       )	,  1       )

                       _A							:  List[Any]    =		tokenizer.decode(ids + encoded_special_token	,  clean_up_tokenization_spaces=_a       )
                       _A							:  List[Any]    =		tokenizer.encode(_a	,  add_special_tokens=_a       )

                       _A							:  str    =		tokenizer.encode(_a	,  add_special_tokens=_a       )
                       _A							:  str    =		tokenizer.encode(_a	,  add_special_tokens=_a       )
                       self.assertEqual(_a	,  input_encoded + special_token_id       )

                       _A							:  str    =		tokenizer.decode(_a	,  skip_special_tokens=_a       )
                       self.assertTrue(special_token not in decoded       )

     def       a__			(						self       )		->							str:
           _A							:  str    =		self.get_tokenizers(do_lower_case=_a       )
           for tokenizer in tokenizers:
                 with self.subTest(F'''{tokenizer.__class__.__name__}'''       ):
                       _A							:  Tuple    =		chr(0xe0_05       )
                       _A							:  int    =		chr(0xe0_06       )

                       # `add_tokens` method stores special tokens only in `tokenizer.unique_no_split_tokens`. (in tokenization_utils.py)
                       tokenizer.add_tokens([SPECIAL_TOKEN_1]	,  special_tokens=_a       )
                       # `add_special_tokens` method stores special tokens in `tokenizer.additional_special_tokens`,
                       # which also occur in `tokenizer.all_special_tokens`. (in tokenization_utils_base.py)
                       tokenizer.add_special_tokens({"""additional_special_tokens""": [SPECIAL_TOKEN_2]}       )

                       _A							:  Union[str, Any]    =		tokenizer.tokenize(_a       )
                       _A							:  List[Any]    =		tokenizer.tokenize(_a       )

                       self.assertEqual(len(_a       )	,  1       )
                       self.assertEqual(len(_a       )	,  1       )
                       self.assertEqual(token_a[0]	,  _a       )
                       self.assertEqual(token_a[0]	,  _a       )

     @require_tokenizers
     def       a__			(						self       )		->							Union[str, Any]:
           _A							:  List[str]    =		self.get_tokenizers(do_lower_case=_a       )
           for tokenizer in tokenizers:
                 with self.subTest(F'''{tokenizer.__class__.__name__}'''       ):
                       # a special token for Canine can be defined as follows:
                       _A							:  Optional[Any]    =		0xe0_06
                       _A							:  str    =		chr(_a       )

                       _A							:  Any    =		AddedToken(_a	,  lstrip=_a       )
                       tokenizer.add_special_tokens({"""additional_special_tokens""": [new_token]}       )

                       with tempfile.TemporaryDirectory() as tmp_dir_name:
                             tokenizer.save_pretrained(_a       )
                             tokenizer.from_pretrained(_a       )

     def       a__			(						self       )		->							List[str]:
           _A							:  Optional[int]    =		[]
           if self.test_slow_tokenizer:
                 tokenizer_list.append((self.tokenizer_class, self.get_tokenizer())       )

           if self.test_rust_tokenizer:
                 tokenizer_list.append((self.rust_tokenizer_class, self.get_rust_tokenizer())       )

           for tokenizer_class, tokenizer_utils in tokenizer_list:
                 with tempfile.TemporaryDirectory() as tmp_dir:
                       tokenizer_utils.save_pretrained(_a       )

                       with open(os.path.join(_a	,  """special_tokens_map.json"""       )	,  encoding="""utf-8"""       ) as json_file:
                             _A							:  Union[str, Any]    =		json.load(_a       )

                       with open(os.path.join(_a	,  """tokenizer_config.json"""       )	,  encoding="""utf-8"""       ) as json_file:
                             _A							:  int    =		json.load(_a       )

                       # a special token for Canine can be defined as follows:
                       _A							:  List[Any]    =		0xe0_06
                       _A							:  Optional[int]    =		chr(_a       )

                       _A							:  str    =		[new_token_a]
                       _A							:  str    =		[new_token_a]

                       with open(os.path.join(_a	,  """special_tokens_map.json"""       )	,  """w"""	,  encoding="""utf-8"""       ) as outfile:
                             json.dump(_a	,  _a       )
                       with open(os.path.join(_a	,  """tokenizer_config.json"""       )	,  """w"""	,  encoding="""utf-8"""       ) as outfile:
                             json.dump(_a	,  _a       )

                       # the following checks allow us to verify that our test works as expected, i.e. that the tokenizer takes
                       # into account the new value of additional_special_tokens given in the "tokenizer_config.json" and
                       # "special_tokens_map.json" files
                       _A							:  Optional[Any]    =		tokenizer_class.from_pretrained(_a	,  extra_ids=0       )
                       self.assertIn(_a	,  tokenizer_without_change_in_init.additional_special_tokens       )
                       # self.assertIn("an_additional_special_token",tokenizer_without_change_in_init.get_vocab()) # ByT5Tokenization no vocab
                       self.assertEqual(
                           [new_token_a]	,  tokenizer_without_change_in_init.convert_ids_to_tokens(
                               tokenizer_without_change_in_init.convert_tokens_to_ids([new_token_a]       )       )	,  )

                       _A							:  Dict    =		0xe0_07
                       _A							:  Optional[Any]    =		chr(_a       )
                       # Now we test that we can change the value of additional_special_tokens in the from_pretrained
                       _A							:  int    =		[AddedToken(_a	,  lstrip=_a       )]
                       _A							:  List[str]    =		tokenizer_class.from_pretrained(
                           _a	,  additional_special_tokens=_a	,  extra_ids=0       )

                       self.assertIn(_a	,  tokenizer.additional_special_tokens       )
                       # self.assertIn(new_token_2,tokenizer.get_vocab()) # ByT5Tokenization no vocab
                       self.assertEqual(
                           [new_token_a]	,  tokenizer.convert_ids_to_tokens(tokenizer.convert_tokens_to_ids([new_token_a]       )       )       )

     @require_tokenizers
     def       a__			(						self       )		->							int:
           _A							:  Optional[Any]    =		self.get_tokenizers(do_lower_case=_a       )
           for tokenizer in tokenizers:
                 with self.subTest(F'''{tokenizer.__class__.__name__}'''       ):
                       _A							:  Tuple    =		"""hello world"""
                       if self.space_between_special_tokens:
                             _A							:  Tuple    =		"""[CLS] hello world [SEP]"""
                       else:
                             _A							:  int    =		input
                       _A							:  Optional[int]    =		tokenizer.encode(_a	,  add_special_tokens=_a       )
                       _A							:  Dict    =		tokenizer.decode(_a	,  spaces_between_special_tokens=self.space_between_special_tokens       )
                       self.assertIn(_a	,  [output, output.lower()]       )

     def       a__			(						self       )		->							Optional[int]:
           _A							:  int    =		self.get_tokenizers()
           for tokenizer in tokenizers:
                 with self.subTest(F'''{tokenizer.__class__.__name__}'''       ):
                       _A							:  Any    =		[
                           """bos_token""",
                           """eos_token""",
                           """unk_token""",
                           """sep_token""",
                           """pad_token""",
                           """cls_token""",
                           """mask_token""",
                       ]

           _A							:  int    =		"""a"""
           _A							:  Optional[int]    =		ord(_a       )

           for attr in attributes_list:
                 setattr(_a	,  attr + """_id"""	,  _a       )
                 self.assertEqual(getattr(_a	,  _a       )	,  _a       )
                 self.assertEqual(getattr(_a	,  attr + """_id"""       )	,  _a       )

                 setattr(_a	,  attr + """_id"""	,  _a       )
                 self.assertEqual(getattr(_a	,  _a       )	,  _a       )
                 self.assertEqual(getattr(_a	,  attr + """_id"""       )	,  _a       )

           setattr(_a	,  """additional_special_tokens_ids"""	,  []       )
           self.assertListEqual(getattr(_a	,  """additional_special_tokens"""       )	,  []       )
           self.assertListEqual(getattr(_a	,  """additional_special_tokens_ids"""       )	,  []       )

           _A							:  Optional[Any]    =		0xe0_06
           _A							:  Dict    =		chr(_a       )
           setattr(_a	,  """additional_special_tokens_ids"""	,  [additional_special_token_id]       )
           self.assertListEqual(getattr(_a	,  """additional_special_tokens"""       )	,  [additional_special_token]       )
           self.assertListEqual(getattr(_a	,  """additional_special_tokens_ids"""       )	,  [additional_special_token_id]       )

     def       a__			(						self       )		->							Optional[int]:
           pass

     def       a__			(						self       )		->							int:
           pass

     def       a__			(						self       )		->							Union[str, Any]:
           pass

     def       a__			(						self       )		->							int:
           pass

     def       a__			(						self       )		->							int:
           pass

     def       a__			(						self       )		->							List[Any]:
           pass

     def       a__			(						self       )		->							Union[str, Any]:
           pass



     def       a__			(						self       )		->							str:
           pass







from datetime import datetime as dt
import os

from github import Github


_snake_case            = [
    "good first issue",
    "good second issue",
    "good difficult issue",
    "feature request",
    "new model",
    "wip",
]







def        lowerCAmelCase_			(  ):
      _A							:  int    =		Github(os.environ["""GITHUB_TOKEN"""]  )
      _A							:  Tuple    =		g.get_repo("""huggingface/transformers"""  )
      _A							:  Dict    =		repo.get_issues(state="""open"""  )

      for issue in open_issues:
            _A							:  Optional[Any]    =		sorted([comment for comment in issue.get_comments()],key=lambda snake_case_  : i.created_at,reverse=snake_case_  )
            _A							:  Dict    =		comments[0] if len(snake_case_  ) > 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 >= 30
                and not any(label.name.lower() in LABELS_TO_EXEMPT for label in issue.get_labels()  )
            ):
                  # print(f"Would close issue {issue.number} since it has been 7 days of inactivity since bot mention.")
                  issue.edit(state="""closed"""  )
            elif (
                (dt.utcnow() - issue.updated_at).days > 23
                and (dt.utcnow() - issue.created_at).days >= 30
                and not any(label.name.lower() in LABELS_TO_EXEMPT for label in issue.get_labels()  )
            ):
                  # print(f"Would add stale comment to {issue.number}")
                  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/transformers/blob/main/CONTRIBUTING.md) """
                      """are likely to be ignored."""  )


if __name__ == "__main__":
  main()

