


'''simple docstring'''


from ...configuration_utils import PretrainedConfig
from ...utils import logging


_UpperCamelCase 							=  logging.get_logger(__name__)

_UpperCamelCase 							=  {
    """google/canine-s""": """https://huggingface.co/google/canine-s/resolve/main/config.json""",
    # See all CANINE models at https://huggingface.co/models?filter=canine
}







class 							__magic_name__	(       lowerCAmelCase ):

     """simple docstring"""






     lowerCamelCase__       						=				'canine'


     def __init__(   self			,     lowerCamelCase=768			,     lowerCamelCase=12			,     lowerCamelCase=12			,     lowerCamelCase=3072			,     lowerCamelCase="gelu"			,     lowerCamelCase=0.1			,     lowerCamelCase=0.1			,     lowerCamelCase=1_6384			,     lowerCamelCase=16			,     lowerCamelCase=0.02			,     lowerCamelCase=1E-12			,     lowerCamelCase=0			,     lowerCamelCase=0xE000			,     lowerCamelCase=0xE001			,     lowerCamelCase=4			,     lowerCamelCase=4			,     lowerCamelCase=8			,     lowerCamelCase=1_6384			,     lowerCamelCase=128			,     **lowerCamelCase			,     ):


          '''simple docstring'''





          super().__init__(pad_token_id=lowerCamelCase			,     bos_token_id=lowerCamelCase			,     eos_token_id=lowerCamelCase			,     **lowerCamelCase		)

          __A	:       str      =							max_position_embeddings
          __A	:       Optional[int]      =							hidden_size
          __A	:       Tuple      =							num_hidden_layers
          __A	:       Optional[Any]      =							num_attention_heads
          __A	:       Dict      =							intermediate_size
          __A	:       Optional[int]      =							hidden_act
          __A	:       int      =							hidden_dropout_prob
          __A	:       List[str]      =							attention_probs_dropout_prob
          __A	:       List[Any]      =							initializer_range
          __A	:       List[Any]      =							type_vocab_size
          __A	:       Union[str, Any]      =							layer_norm_eps

          # Character config:
          __A	:       Optional[Any]      =							downsampling_rate
          __A	:       Optional[Any]      =							upsampling_kernel_size
          __A	:       int      =							num_hash_functions
          __A	:       int      =							num_hash_buckets
          __A	:       str      =							local_transformer_stride





'''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


_UpperCamelCase 							=  get_tests_dir("""fixtures/test_sentencepiece.model""")
_UpperCamelCase 							=  get_tests_dir("""fixtures/test_sentencepiece_bpe.model""")

_UpperCamelCase 							=  """pt""" if is_torch_available() else """tf"""







@require_sentencepiece
@require_tokenizers
class 							__magic_name__	(       lowerCAmelCase    ,		unittest.TestCase ):

     """simple docstring"""






     lowerCamelCase__       						=				CamembertTokenizer
     lowerCamelCase__       						=				CamembertTokenizerFast
     lowerCamelCase__       						=				True
     lowerCamelCase__       						=				True


     def lowerCAmelCase__  (   self		):


          '''simple docstring'''





          super().setUp()

          # We have a SentencePiece fixture for testing
          __A	:       List[Any]      =							CamembertTokenizer(lowerCamelCase		)
          tokenizer.save_pretrained(self.tmpdirname		)


     def lowerCAmelCase__  (   self		):


          '''simple docstring'''





          __A	:       Optional[Any]      =							"<pad>"
          __A	:       Union[str, Any]      =							1

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


     def lowerCAmelCase__  (   self		):


          '''simple docstring'''





          __A	:       Union[str, Any]      =							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(lowerCamelCase		)			,     1004		)


     def lowerCAmelCase__  (   self		):


          '''simple docstring'''





          self.assertEqual(self.get_tokenizer().vocab_size			,     1005		)


     def lowerCAmelCase__  (   self		):


          '''simple docstring'''





          __A	:       Optional[Any]      =							CamembertTokenizer(lowerCamelCase		)
          tokenizer.save_pretrained(self.tmpdirname		)
          __A	:       Optional[int]      =							CamembertTokenizerFast.from_pretrained(self.tmpdirname		)

          __A	:       str      =							"I was born in 92000, and this is falsé."

          __A	:       List[Any]      =							tokenizer.encode(lowerCamelCase		)
          __A	:       Tuple      =							rust_tokenizer.encode(lowerCamelCase		)
          self.assertListEqual(lowerCamelCase			,     lowerCamelCase		)

          __A	:       Dict      =							tokenizer.encode(lowerCamelCase			,     add_special_tokens=lowerCamelCase		)
          __A	:       str      =							rust_tokenizer.encode(lowerCamelCase			,     add_special_tokens=lowerCamelCase		)
          self.assertListEqual(lowerCamelCase			,     lowerCamelCase		)

          # <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	:       int      =							tokenizer.convert_ids_to_tokens(lowerCamelCase		)
          __A	:       Optional[int]      =							rust_tokenizer.tokenize(lowerCamelCase		)
          self.assertListEqual(lowerCamelCase			,     lowerCamelCase		)


     def lowerCAmelCase__  (   self		):


          '''simple docstring'''





          if not self.test_rust_tokenizer:
               return

          __A	:       Tuple      =							self.get_tokenizer()
          __A	:       List[Any]      =							self.get_rust_tokenizer()

          __A	:       Any      =							"I was born in 92000, and this is falsé."

          __A	:       int      =							tokenizer.tokenize(lowerCamelCase		)
          __A	:       Any      =							rust_tokenizer.tokenize(lowerCamelCase		)
          self.assertListEqual(lowerCamelCase			,     lowerCamelCase		)

          __A	:       Any      =							tokenizer.encode(lowerCamelCase			,     add_special_tokens=lowerCamelCase		)
          __A	:       Optional[int]      =							rust_tokenizer.encode(lowerCamelCase			,     add_special_tokens=lowerCamelCase		)
          self.assertListEqual(lowerCamelCase			,     lowerCamelCase		)

          __A	:       Union[str, Any]      =							self.get_rust_tokenizer()
          __A	:       Optional[Any]      =							tokenizer.encode(lowerCamelCase		)
          __A	:       Dict      =							rust_tokenizer.encode(lowerCamelCase		)
          self.assertListEqual(lowerCamelCase			,     lowerCamelCase		)


     @slow
     def lowerCAmelCase__  (   self		):


          '''simple docstring'''





          __A	:       Any      =							{"input_ids": [[5, 54, 7196, 297, 30, 23, 776, 18, 11, 3215, 3705, 8252, 22, 3164, 1181, 2116, 29, 16, 813, 25, 791, 3314, 20, 3446, 38, 2_7575, 120, 6, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [5, 468, 17, 11, 9088, 20, 1517, 8, 2_2804, 1_8818, 10, 38, 629, 607, 607, 142, 19, 7196, 867, 56, 1_0326, 24, 2267, 20, 416, 5072, 1_5612, 233, 734, 7, 2399, 27, 16, 3015, 1649, 7, 24, 20, 4338, 2399, 27, 13, 3400, 14, 13, 6189, 8, 930, 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	:       int      =							[
              "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="camembert-base"			,     revision="3a0641d9a1aeb7e848a74299e7e4c4bca216b4cf"			,     sequences=lowerCamelCase			,     )

