


'''simple docstring'''






import unittest

from transformers import CamembertTokenizer, CamembertTokenizerFast
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, slow
from transformers.utils import is_torch_available

from ...test_tokenization_common import TokenizerTesterMixin


a_   :      str				    =      get_tests_dir("fixtures/test_sentencepiece.model")
a_   :      Optional[int]				    =      get_tests_dir("fixtures/test_sentencepiece_bpe.model")

a_   :      Union[str, Any]				    =      "pt" if is_torch_available() else "tf"




@require_sentencepiece
@require_tokenizers
class 	a       (       _SCREAMING_SNAKE_CASE					,			unittest.TestCase    ):
 _lowerCAmelCase	   =		CamembertTokenizer
 _lowerCAmelCase	   =		CamembertTokenizerFast
 _lowerCAmelCase	   =		True
 _lowerCAmelCase	   =		True
 def 							__UpperCAmelCase						(	self  )   ->							int:
      super().setUp()

      # We have a SentencePiece fixture for testing
      _a      =							CamembertTokenizer(__UpperCAmelCase  )
      tokenizer.save_pretrained(self.tmpdirname  )
 def 							__UpperCAmelCase						(	self  )   ->							Union[str, Any]:
      _a      =							'<pad>'
      _a      =							1

      self.assertEqual(self.get_tokenizer()._convert_token_to_id(__UpperCAmelCase  ) ,			__UpperCAmelCase  )
      self.assertEqual(self.get_tokenizer()._convert_id_to_token(__UpperCAmelCase  ) ,			__UpperCAmelCase  )
 def 							__UpperCAmelCase						(	self  )   ->							Dict:
      _a      =							list(self.get_tokenizer().get_vocab().keys()  )

      self.assertEqual(vocab_keys[0] ,			'<s>NOTUSED'  )
      self.assertEqual(vocab_keys[1] ,			'<pad>'  )
      self.assertEqual(vocab_keys[-1] ,			'<mask>'  )
      self.assertEqual(len(__UpperCAmelCase  ) ,			10_04  )
 def 							__UpperCAmelCase						(	self  )   ->							List[Any]:
      self.assertEqual(self.get_tokenizer().vocab_size ,			10_05  )
 def 							__UpperCAmelCase						(	self  )   ->							List[str]:
      _a      =							CamembertTokenizer(__UpperCAmelCase  )
      tokenizer.save_pretrained(self.tmpdirname  )
      _a      =							CamembertTokenizerFast.from_pretrained(self.tmpdirname  )

      _a      =							'I was born in 92000, and this is falsé.'

      _a      =							tokenizer.encode(__UpperCAmelCase  )
      _a      =							rust_tokenizer.encode(__UpperCAmelCase  )
      self.assertListEqual(__UpperCAmelCase ,			__UpperCAmelCase  )

      _a      =							tokenizer.encode(__UpperCAmelCase ,			add_special_tokens=__UpperCAmelCase  )
      _a      =							rust_tokenizer.encode(__UpperCAmelCase ,			add_special_tokens=__UpperCAmelCase  )
      self.assertListEqual(__UpperCAmelCase ,			__UpperCAmelCase  )

      # <unk> tokens are not the same for `rust` than for `slow`.
      # Because spm gives back raw token instead of `unk` in EncodeAsPieces
      # tokens = tokenizer.tokenize(sequence)
      _a      =							tokenizer.convert_ids_to_tokens(__UpperCAmelCase  )
      _a      =							rust_tokenizer.tokenize(__UpperCAmelCase  )
      self.assertListEqual(__UpperCAmelCase ,			__UpperCAmelCase  )
 def 							__UpperCAmelCase						(	self  )   ->							Any:


      if not self.test_rust_tokenizer:
           return

      _a      =							self.get_tokenizer()
      _a      =							self.get_rust_tokenizer()

      _a      =							'I was born in 92000, and this is falsé.'

      _a      =							tokenizer.tokenize(__UpperCAmelCase  )
      _a      =							rust_tokenizer.tokenize(__UpperCAmelCase  )
      self.assertListEqual(__UpperCAmelCase ,			__UpperCAmelCase  )

      _a      =							tokenizer.encode(__UpperCAmelCase ,			add_special_tokens=__UpperCAmelCase  )
      _a      =							rust_tokenizer.encode(__UpperCAmelCase ,			add_special_tokens=__UpperCAmelCase  )
      self.assertListEqual(__UpperCAmelCase ,			__UpperCAmelCase  )

      _a      =							self.get_rust_tokenizer()
      _a      =							tokenizer.encode(__UpperCAmelCase  )
      _a      =							rust_tokenizer.encode(__UpperCAmelCase  )
      self.assertListEqual(__UpperCAmelCase ,			__UpperCAmelCase  )




 @slow
 def 							__UpperCAmelCase						(	self  )   ->							List[str]:
      _a      =							{'input_ids': [[5, 54, 71_96, 2_97, 30, 23, 7_76, 18, 11, 32_15, 37_05, 82_52, 22, 31_64, 11_81, 21_16, 29, 16, 8_13, 25, 7_91, 33_14, 20, 34_46, 38, 2_75_75, 1_20, 6, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [5, 4_68, 17, 11, 90_88, 20, 15_17, 8, 2_28_04, 1_88_18, 10, 38, 6_29, 6_07, 6_07, 1_42, 19, 71_96, 8_67, 56, 1_03_26, 24, 22_67, 20, 4_16, 50_72, 1_56_12, 2_33, 7_34, 7, 23_99, 27, 16, 30_15, 16_49, 7, 24, 20, 43_38, 23_99, 27, 13, 34_00, 14, 13, 61_89, 8, 9_30, 9, 6]], '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, 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]]}  # noqa: E501
      # fmt: on

      # camembert is a french model. So we also use french texts.
      _a      =							[
          '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=__UpperCAmelCase ,			model_name='camembert-base' ,			revision='3a0641d9a1aeb7e848a74299e7e4c4bca216b4cf' ,			sequences=__UpperCAmelCase ,			)





'''simple docstring'''






import unicodedata
from dataclasses import dataclass
from typing import Optional, Union

import numpy as np

from transformers.data.data_collator import DataCollatorMixin
from transformers.file_utils import PaddingStrategy
from transformers.tokenization_utils_base import PreTrainedTokenizerBase






def 		_A			(lowerCAmelCase__					:int			,  lowerCAmelCase__					:Any			,  lowerCAmelCase__					:Optional[Any]			,  lowerCAmelCase__					:Dict						)				->					Optional[int]:




     '''simple docstring'''



     if isinstance(lowerCAmelCase__			,  lowerCAmelCase__						):
          _a      =							np.full((len(lowerCAmelCase__						), sequence_length, 2)			,  lowerCAmelCase__						)
     else:
          _a      =							np.full((len(lowerCAmelCase__						), sequence_length)			,  lowerCAmelCase__						)

     for i, tensor in enumerate(lowerCAmelCase__						):
          if padding_side == "right":
               if isinstance(lowerCAmelCase__			,  lowerCAmelCase__						):
                    _a      =							tensor[:sequence_length]
               else:
                    _a      =							tensor[:sequence_length]
          else:
               if isinstance(lowerCAmelCase__			,  lowerCAmelCase__						):
                    _a      =							tensor[:sequence_length]
               else:
                    _a      =							tensor[:sequence_length]

     return out_tensor.tolist()






def 		_A			(lowerCAmelCase__					:Any						)				->					Union[str, Any]:




     '''simple docstring'''



     _a      =							ord(lowerCAmelCase__						)
     if (cp >= 33 and cp <= 47) or (cp >= 58 and cp <= 64) or (cp >= 91 and cp <= 96) or (cp >= 1_23 and cp <= 1_26):
          return True
     _a      =							unicodedata.category(lowerCAmelCase__						)
     if cat.startswith('P'						):
          return True
     return False






@dataclass
class 	a       (       _SCREAMING_SNAKE_CASE    ):
 _lowerCAmelCase	   =		42
 _lowerCAmelCase	   =		True
 _lowerCAmelCase	   =		None
 _lowerCAmelCase	   =		None
 _lowerCAmelCase	   =		-1_0_0
 _lowerCAmelCase	   =		"pt"
 def 							__UpperCAmelCase						(	self ,			__magic_name__  )   ->							Any:
      import torch

      _a      =							'label' if 'label' in features[0].keys() else 'labels'
      _a      =							[feature[label_name] for feature in features] if label_name in features[0].keys() else None
      _a      =							self.tokenizer.pad(
          __magic_name__ ,			padding=self.padding ,			max_length=self.max_length ,			pad_to_multiple_of=self.pad_to_multiple_of ,			return_tensors='pt' if labels is None else None ,			)

      if labels is None:
           return batch

      _a      =							torch.tensor(batch['entity_ids']  ).shape[1]
      _a      =							self.tokenizer.padding_side
      if padding_side == "right":
           _a      =							[
               list(__magic_name__  ) + [self.label_pad_token_id] * (sequence_length - len(__magic_name__  )) for label in labels
           ]
      else:
           _a      =							[
               [self.label_pad_token_id] * (sequence_length - len(__magic_name__  )) + list(__magic_name__  ) for label in labels
           ]

      _a      =							[feature['ner_tags'] for feature in features]
      _a      =							padding_tensor(__magic_name__ ,			-1 ,			__magic_name__ ,			__magic_name__  )
      _a      =							[feature['original_entity_spans'] for feature in features]
      _a      =							padding_tensor(__magic_name__ ,			(-1, -1) ,			__magic_name__ ,			__magic_name__  )
      _a      =							{k: torch.tensor(__magic_name__ ,			dtype=torch.intaa  ) for k, v in batch.items()}

      return batch

