





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 				SCREAMING_SNAKE_CASE__						(       UpperCamelCase__		,					unittest.TestCase    ):
      snake_case__       :  Tuple       =		CanineTokenizer
      snake_case__       :  Union[str, Any]       =		False
      def  SCREAMING_SNAKE_CASE							( self       : Tuple  )	->    str:
             super().setUp()
             a_      :	Union[str, Any]							=						CanineTokenizer()
             tokenizer.save_pretrained(self.tmpdirname  )
      @cached_property
      def  SCREAMING_SNAKE_CASE							( self       : Optional[Any]  )	->    List[str]:
             return CanineTokenizer.from_pretrained('google/canine-s'  )
      def  SCREAMING_SNAKE_CASE							( self       : Any  ,							**SCREAMING_SNAKE_CASE__       : Dict  )	->    CanineTokenizer:
             a_      :	List[Any]							=						self.tokenizer_class.from_pretrained(self.tmpdirname  ,							**_a  )
             a_      :	int							=						1_0_2_4
             return tokenizer
      @require_torch
      def  SCREAMING_SNAKE_CASE							( self       : List[str]  )	->    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_7_3_4_4, 7_6, 1_0_5, 1_0_2, 1_0_1, 3_2, 1_0_5, 1_1_5, 3_2, 1_0_8, 1_0_5, 1_0_7, 1_0_1, 3_2, 9_7, 3_2, 9_8, 1_1_1, 1_2_0, 3_2, 1_1_1, 1_0_2, 3_2, 9_9, 1_0_4, 1_1_1, 9_9, 1_1_1, 1_0_8, 9_7, 1_1_6, 1_0_1, 1_1_5, 4_6, 5_7_3_4_5, 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, 3_9)  ,							batch.input_ids.shape  )
             self.assertEqual((2, 3_9)  ,							batch.attention_mask.shape  )
      @require_torch
      def  SCREAMING_SNAKE_CASE							( self       : List[str]  )	->    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  SCREAMING_SNAKE_CASE							( self       : Tuple  )	->    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=3_2  ,							padding='max_length'  ,							truncation=_a  ,							return_tensors='pt'  )
             self.assertEqual(3_2  ,							targets['input_ids'].shape[1]  )
      def  SCREAMING_SNAKE_CASE							( self       : Union[str, Any]  )	->    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  ,							4_2  )

        # 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=4_2  )
             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(0Xe007  )
                           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  ,							4_2  )

                           a_      :	Optional[Any]							=						tokenizer.__class__.from_pretrained(_a  ,							model_max_length=4_3  )
                           self.assertEqual(tokenizer.model_max_length  ,							4_3  )

                           shutil.rmtree(_a  )
      def  SCREAMING_SNAKE_CASE							( self       : List[str]  )	->    List[str]:
             a_      :	Optional[int]							=						self.get_tokenizers(do_lower_case=_a  )
             for tokenizer in tokenizers:
                    with self.subTest(F"""{tokenizer.__class__.__name__}"""  ):
                           a_      :	int							=						self.get_clean_sequence(_a  )

                           # a special token for Canine can be defined as follows:
                           a_      :	Tuple							=						0Xe005
                           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  SCREAMING_SNAKE_CASE							( self       : List[Any]  )	->    str:
             a_      :	str							=						self.get_tokenizers(do_lower_case=_a  )
             for tokenizer in tokenizers:
                    with self.subTest(F"""{tokenizer.__class__.__name__}"""  ):
                           a_      :	Tuple							=						chr(0Xe005  )
                           a_      :	int							=						chr(0Xe006  )

                           # `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  SCREAMING_SNAKE_CASE							( self       : Union[str, Any]  )	->    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]							=						0Xe006
                           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  SCREAMING_SNAKE_CASE							( self       : Union[str, Any]  )	->    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]							=						0Xe006
                           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							=						0Xe007
                           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  SCREAMING_SNAKE_CASE							( self       : Optional[Any]  )	->    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  SCREAMING_SNAKE_CASE							( self       : Optional[Any]  )	->    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]							=						0Xe006
             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  SCREAMING_SNAKE_CASE							( self       : int  )	->    Optional[int]:
             pass
      def  SCREAMING_SNAKE_CASE							( self       : Dict  )	->    int:
             pass
      def  SCREAMING_SNAKE_CASE							( self       : Union[str, Any]  )	->    Union[str, Any]:
             pass
      def  SCREAMING_SNAKE_CASE							( self       : int  )	->    int:
             pass
      def  SCREAMING_SNAKE_CASE							( self       : Dict  )	->    int:
             pass
      def  SCREAMING_SNAKE_CASE							( self       : Tuple  )	->    List[Any]:
             pass
      def  SCREAMING_SNAKE_CASE							( self       : Optional[int]  )	->    Union[str, Any]:
             pass

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






from .dependency_versions_table import deps
from .utils.versions import require_version, require_version_core


# define which module versions we always want to check at run time
# (usually the ones defined in `install_requires` in setup.py)
#
# order specific notes:
# - tqdm must be checked before tokenizers

UpperCAmelCase_   :				Dict						      = [
    'python',
    'tqdm',
    'regex',
    'requests',
    'packaging',
    'filelock',
    'numpy',
    'tokenizers',
    'huggingface-hub',
    'safetensors',
    'accelerate',
    'pyyaml',
]

for pkg in pkgs_to_check_at_runtime:
   if pkg in deps:
      if pkg == "tokenizers":
         # must be loaded here, or else tqdm check may fail
         from .utils import is_tokenizers_available

         if not is_tokenizers_available():
            continue  # not required, check version only if installed
      elif pkg == "accelerate":
         # must be loaded here, or else tqdm check may fail
         from .utils import is_accelerate_available

         # Maybe switch to is_torch_available in the future here so that Accelerate is hard dep of
         # Transformers with PyTorch
         if not is_accelerate_available():
            continue  # not required, check version only if installed

      require_version_core(deps[pkg])
   else:
      raise ValueError(F'can\'t find {pkg} in {deps.keys()}, check dependency_versions_table.py')







def  SCREAMING_SNAKE_CASE_		(    __A				:    Optional[Any]						,					__A				:    int=None       )			->	int:

       """simple docstring"""

       require_version(deps[pkg]						,					__A       )


