






import unittest

from transformers import BarthezTokenizer, BarthezTokenizerFast, BatchEncoding
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow

from ...test_tokenization_common import TokenizerTesterMixin




@require_tokenizers
@require_sentencepiece
@slow  # see https://github.com/huggingface/transformers/issues/11457
class 				__A     (			a      ,     unittest.TestCase ):





				"""simple docstring"""







				UpperCamelCase__ :      int			 =BarthezTokenizer
				UpperCamelCase__ :      Union[str, Any]			 =BarthezTokenizerFast
				UpperCamelCase__ :      List[str]			 =True
				UpperCamelCase__ :      int			 =True


				def       __lowercase							( self    ):




									"""simple docstring"""

									super().setUp()

									__UpperCamelCase      :					Any       		=BarthezTokenizerFast.from_pretrained('moussaKam/mbarthez'    )
									tokenizer.save_pretrained(self.tmpdirname    )
									tokenizer.save_pretrained(self.tmpdirname							,  legacy_format=lowerCamelCase__    )
									__UpperCamelCase      :					str       		=tokenizer


				def       __lowercase							( self    ):




									"""simple docstring"""

									__UpperCamelCase      :					Union[str, Any]       		='<pad>'
									__UpperCamelCase      :					Dict       		=1

									self.assertEqual(self.get_tokenizer()._convert_token_to_id(lowerCamelCase__    )							,  lowerCamelCase__    )
									self.assertEqual(self.get_tokenizer()._convert_id_to_token(lowerCamelCase__    )							,  lowerCamelCase__    )


				def       __lowercase							( self    ):




									"""simple docstring"""

									__UpperCamelCase      :					List[str]       		=list(self.get_tokenizer().get_vocab().keys()    )

									self.assertEqual(vocab_keys[0]							,  '<s>'    )
									self.assertEqual(vocab_keys[1]							,  '<pad>'    )
									self.assertEqual(vocab_keys[-1]							,  '<mask>'    )
									self.assertEqual(len(lowerCamelCase__    )							,  101122    )


				def       __lowercase							( self    ):




									"""simple docstring"""

									self.assertEqual(self.get_tokenizer().vocab_size							,  101122    )


				@require_torch
				def       __lowercase							( self    ):




									"""simple docstring"""

									__UpperCamelCase      :					Tuple       		=['A long paragraph for summarization.', 'Another paragraph for summarization.']
									__UpperCamelCase      :					int       		=[0, 57, 3018, 70307, 91, 2]

									__UpperCamelCase      :					Union[str, Any]       		=self.tokenizer(
									    lowerCamelCase__							,  max_length=len(lowerCamelCase__    )							,  padding=lowerCamelCase__							,  truncation=lowerCamelCase__							,  return_tensors='pt'    )
									self.assertIsInstance(lowerCamelCase__							,  lowerCamelCase__    )

									self.assertEqual((2, 6)							,  batch.input_ids.shape    )
									self.assertEqual((2, 6)							,  batch.attention_mask.shape    )
									__UpperCamelCase      :					List[str]       		=batch.input_ids.tolist()[0]
									self.assertListEqual(lowerCamelCase__							,  lowerCamelCase__    )


				def       __lowercase							( self    ):




									"""simple docstring"""

									if not self.test_rust_tokenizer:
														return

									__UpperCamelCase      :					Union[str, Any]       		=self.get_tokenizer()
									__UpperCamelCase      :					int       		=self.get_rust_tokenizer()

									__UpperCamelCase      :					str       		='I was born in 92000, and this is falsé.'

									__UpperCamelCase      :					Tuple       		=tokenizer.tokenize(lowerCamelCase__    )
									__UpperCamelCase      :					List[str]       		=rust_tokenizer.tokenize(lowerCamelCase__    )
									self.assertListEqual(lowerCamelCase__							,  lowerCamelCase__    )

									__UpperCamelCase      :					List[Any]       		=tokenizer.encode(lowerCamelCase__							,  add_special_tokens=lowerCamelCase__    )
									__UpperCamelCase      :					Union[str, Any]       		=rust_tokenizer.encode(lowerCamelCase__							,  add_special_tokens=lowerCamelCase__    )
									self.assertListEqual(lowerCamelCase__							,  lowerCamelCase__    )

									__UpperCamelCase      :					List[Any]       		=self.get_rust_tokenizer()
									__UpperCamelCase      :					Tuple       		=tokenizer.encode(lowerCamelCase__    )
									__UpperCamelCase      :					List[str]       		=rust_tokenizer.encode(lowerCamelCase__    )
									self.assertListEqual(lowerCamelCase__							,  lowerCamelCase__    )







				@slow
				def       __lowercase							( self    ):




									"""simple docstring"""

									__UpperCamelCase      :					Dict       		={'input_ids': [[0, 490, 14328, 4507, 354, 47, 43669, 95, 25, 78117, 20215, 19779, 190, 22, 400, 4, 35343, 80310, 603, 86, 24937, 105, 33438, 94762, 196, 39642, 7, 15, 15933, 173, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [0, 10534, 87, 25, 66, 3358, 196, 55289, 8, 82961, 81, 2204, 75203, 7, 15, 763, 12956, 216, 178, 14328, 9595, 1377, 69693, 7, 448, 71021, 196, 18106, 1437, 13974, 108, 9083, 4, 49315, 7, 39, 86, 1326, 2793, 46333, 4, 448, 196, 74588, 7, 49315, 7, 39, 21, 822, 38470, 74, 21, 66723, 62480, 8, 22050, 5, 2]], 'attention_mask': [[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]]}  # noqa: E501
									# fmt: on

									# moussaKam/mbarthez is a french model. So we also use french texts.
									__UpperCamelCase      :					str       		=[
									    'Le transformeur est un modèle d\'apprentissage profond introduit en 2017, '
									    'utilisé principalement dans le domaine du traitement automatique des langues (TAL).',
									    'À l\'instar des réseaux de neurones récurrents (RNN), les transformeurs sont conçus '
									    'pour gérer des données séquentielles, telles que le langage naturel, pour des tâches '
									    'telles que la traduction et la synthèse de texte.',
									]

									self.tokenizer_integration_test_util(
									    expected_encoding=lowerCamelCase__							,  model_name='moussaKam/mbarthez'							,  revision='c2e4ecbca5e3cd2c37fe1ac285ca4fbdf1366fb6'							,  sequences=lowerCamelCase__							,  )


'''simple docstring'''




import os
from shutil import copyfile
from typing import List, Optional, Tuple

from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging


if is_sentencepiece_available():
  from .tokenization_pegasus import PegasusTokenizer
else:
  __SCREAMING_SNAKE_CASE       :     Optional[Any]     =  None


__SCREAMING_SNAKE_CASE       :     Dict     =  logging.get_logger(__name__)


__SCREAMING_SNAKE_CASE       :     Union[str, Any]     =  """▁"""

__SCREAMING_SNAKE_CASE       :     str     =  {"""vocab_file""": """spiece.model""", """tokenizer_file""": """tokenizer.json"""}

__SCREAMING_SNAKE_CASE       :     int     =  {
    """vocab_file""": {"""google/pegasus-xsum""": """https://huggingface.co/google/pegasus-xsum/resolve/main/spiece.model"""},
    """tokenizer_file""": {
        """google/pegasus-xsum""": """https://huggingface.co/google/pegasus-xsum/resolve/main/tokenizer.json"""
    },
}

__SCREAMING_SNAKE_CASE       :     str     =  {
    """google/pegasus-xsum""": 512,
}

class 	lowerCamelCase_	(snake_case__    ):
      '''simple docstring'''

      __UpperCamelCase:  Optional[int]						=						VOCAB_FILES_NAMES
      __UpperCamelCase:  Dict						=						PRETRAINED_VOCAB_FILES_MAP
      __UpperCamelCase:  List[Any]						=						PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
      __UpperCamelCase:  Optional[int]						=						PegasusTokenizer
      __UpperCamelCase:  Optional[Any]						=						["input_ids", "attention_mask"]




      def __init__(       self						:				Dict						,					A						:				List[str]=None						,					A						:				Union[str, Any]=None						,					A						:				Optional[int]="<pad>"						,					A						:				Tuple="</s>"						,					A						:				Union[str, Any]="<unk>"						,					A						:				Union[str, Any]="<mask_2>"						,					A						:				Dict="<mask_1>"						,					A						:				Union[str, Any]=None						,					A						:				int=103						,					**A						:				Optional[Any]						,					):
             _UpperCAmelCase     :  Dict							      =    offset

             if additional_special_tokens is not None:
                    if not isinstance(A						,					A       ):
                           raise TypeError(
                               F"""additional_special_tokens should be of type {type(A       )}, but is"""
                               F""" {type(A       )}"""       )

                    _UpperCAmelCase     :  Optional[int]							      =    (
                        ([mask_token_sent] + additional_special_tokens)
                        if mask_token_sent not in additional_special_tokens and mask_token_sent is not None
                        else additional_special_tokens
                    )
                    # fill additional tokens with ..., <unk_token_102> in case not all additional tokens are already taken
                    additional_special_tokens_extended += [
                        F"""<unk_{i}>""" for i in range(len(A       )						,					self.offset - 1       )
                    ]

                    if len(set(A       )       ) != len(A       ):
                           raise ValueError(
                               "Please make sure that the provided additional_special_tokens do not contain an incorrectly"
                               F""" shifted list of <unk_x> tokens. Found {additional_special_tokens_extended}."""       )
                    _UpperCAmelCase     :  Any							      =    additional_special_tokens_extended
             else:
                    _UpperCAmelCase     :  Dict							      =    [mask_token_sent] if mask_token_sent is not None else []
                    additional_special_tokens += [F"""<unk_{i}>""" for i in range(2						,					self.offset       )]

             super().__init__(
                 A						,					tokenizer_file=A						,					pad_token=A						,					eos_token=A						,					unk_token=A						,					mask_token=A						,					mask_token_sent=A						,					offset=A						,					additional_special_tokens=A						,					**A						,					)
             _UpperCAmelCase     :  Optional[Any]							      =    vocab_file
             _UpperCAmelCase     :  Optional[Any]							      =    False if not self.vocab_file else True




      def      _A	(       self						:				List[str]						,					A						:				Optional[Any]       ):
             _UpperCAmelCase     :  Any							      =    set(self.all_special_ids       )  # call it once instead of inside list comp
             all_special_ids.remove(self.unk_token_id       )  # <unk> is only sometimes special

             if all_special_ids != set(range(len(self.additional_special_tokens       ) + 3       )       ):
                    raise ValueError(
                        "There should be 3 special tokens: mask_token, pad_token, and eos_token +"
                        F""" {len(self.additional_special_tokens       )} additional_special_tokens, but got {all_special_ids}"""       )

             return [1 if x in all_special_ids else 0 for x in seq]




      def      _A	(       self						:				str						,					A						:				List						,					A						:				Optional[List] = None						,					A						:				bool = False       ):
             if already_has_special_tokens:
                    return self._special_token_mask(A       )
             elif token_ids_a is None:
                    return self._special_token_mask(A       ) + [1]
             else:
                    return self._special_token_mask(token_ids_a + token_ids_a       ) + [1]




      def      _A	(       self						:				Optional[int]						,					A						:				Union[str, Any]						,					A						:				int=None       ):
             if token_ids_a is None:
                    return token_ids_a + [self.eos_token_id]
             # We don't expect to process pairs, but leave the pair logic for API consistency
             return token_ids_a + token_ids_a + [self.eos_token_id]





      def      _A	(       self						:				Union[str, Any]						,					A						:				str						,					A						:				Optional[str] = None       ):
             if not self.can_save_slow_tokenizer:
                    raise ValueError(
                        "Your fast tokenizer does not have the necessary information to save the vocabulary for a slow "
                        "tokenizer."       )

             if not os.path.isdir(A       ):
                    logger.error(F"""Vocabulary path ({save_directory}) should be a directory"""       )
                    return
             _UpperCAmelCase     :  List[Any]							      =    os.path.join(
                 A						,					(filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"]       )

             if os.path.abspath(self.vocab_file       ) != os.path.abspath(A       ):
                    copyfile(self.vocab_file						,					A       )

             return (out_vocab_file,)

