
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


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

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





@require_sentencepiece
@require_tokenizers
class 							A__		(	__SCREAMING_SNAKE_CASE  ,   unittest.TestCase):
      _UpperCAmelCase				:    str           =			CamembertTokenizer
      _UpperCAmelCase				:    str           =			CamembertTokenizerFast
      _UpperCAmelCase				:    Tuple           =			True
      _UpperCAmelCase				:    Dict           =			True
      def 						UpperCamelCase__							(      self      ):
       super().setUp()

       # We have a SentencePiece fixture for testing
       lowerCamelCase		:	Optional[Any]							=      CamembertTokenizer(__magic_name__      )
       tokenizer.save_pretrained(self.tmpdirname      )
      def 						UpperCamelCase__							(      self      ):
       lowerCamelCase		:	Any							=      """<pad>"""
       lowerCamelCase		:	Union[str, Any]							=      1

       self.assertEqual(self.get_tokenizer()._convert_token_to_id(__magic_name__      )    ,  __magic_name__      )
       self.assertEqual(self.get_tokenizer()._convert_id_to_token(__magic_name__      )    ,  __magic_name__      )
      def 						UpperCamelCase__							(      self      ):
       lowerCamelCase		:	List[str]							=      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(__magic_name__      )    ,  1_0_0_4      )
      def 						UpperCamelCase__							(      self      ):
       self.assertEqual(self.get_tokenizer().vocab_size    ,  1_0_0_5      )
      def 						UpperCamelCase__							(      self      ):
       lowerCamelCase		:	List[str]							=      CamembertTokenizer(__magic_name__      )
       tokenizer.save_pretrained(self.tmpdirname      )
       lowerCamelCase		:	Dict							=      CamembertTokenizerFast.from_pretrained(self.tmpdirname      )

       lowerCamelCase		:	List[Any]							=      """I was born in 92000, and this is falsé."""

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

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

       # <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)
       lowerCamelCase		:	Any							=      tokenizer.convert_ids_to_tokens(__magic_name__      )
       lowerCamelCase		:	str							=      rust_tokenizer.tokenize(__magic_name__      )
       self.assertListEqual(__magic_name__    ,  __magic_name__      )
      def 						UpperCamelCase__							(      self      ):
       if not self.test_rust_tokenizer:
        return

       lowerCamelCase		:	str							=      self.get_tokenizer()
       lowerCamelCase		:	List[str]							=      self.get_rust_tokenizer()

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

       lowerCamelCase		:	List[str]							=      tokenizer.tokenize(__magic_name__      )
       lowerCamelCase		:	Optional[Any]							=      rust_tokenizer.tokenize(__magic_name__      )
       self.assertListEqual(__magic_name__    ,  __magic_name__      )

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

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






      @slow
      def 						UpperCamelCase__							(      self      ):
       # fmt: off
       lowerCamelCase		:	Dict							=      {"""input_ids""": [[5, 5_4, 7_1_9_6, 2_9_7, 3_0, 2_3, 7_7_6, 1_8, 1_1, 3_2_1_5, 3_7_0_5, 8_2_5_2, 2_2, 3_1_6_4, 1_1_8_1, 2_1_1_6, 2_9, 1_6, 8_1_3, 2_5, 7_9_1, 3_3_1_4, 2_0, 3_4_4_6, 3_8, 2_7_5_7_5, 1_2_0, 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_6_8, 1_7, 1_1, 9_0_8_8, 2_0, 1_5_1_7, 8, 2_2_8_0_4, 1_8_8_1_8, 1_0, 3_8, 6_2_9, 6_0_7, 6_0_7, 1_4_2, 1_9, 7_1_9_6, 8_6_7, 5_6, 1_0_3_2_6, 2_4, 2_2_6_7, 2_0, 4_1_6, 5_0_7_2, 1_5_6_1_2, 2_3_3, 7_3_4, 7, 2_3_9_9, 2_7, 1_6, 3_0_1_5, 1_6_4_9, 7, 2_4, 2_0, 4_3_3_8, 2_3_9_9, 2_7, 1_3, 3_4_0_0, 1_4, 1_3, 6_1_8_9, 8, 9_3_0, 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.
       lowerCamelCase		:	Union[str, Any]							=      [
           """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=__magic_name__    ,  model_name="""camembert-base"""    ,  revision="""3a0641d9a1aeb7e848a74299e7e4c4bca216b4cf"""    ,  sequences=__magic_name__    ,  )






from collections import OrderedDict
from typing import Mapping

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


_lowerCamelCase					   =logging.get_logger(__name__)

_lowerCamelCase					   ={
    """camembert-base""": """https://huggingface.co/camembert-base/resolve/main/config.json""",
    """umberto-commoncrawl-cased-v1""": (
        """https://huggingface.co/Musixmatch/umberto-commoncrawl-cased-v1/resolve/main/config.json"""
    ),
    """umberto-wikipedia-uncased-v1""": (
        """https://huggingface.co/Musixmatch/umberto-wikipedia-uncased-v1/resolve/main/config.json"""
    ),
}





class 							A__		(	__SCREAMING_SNAKE_CASE):
      _UpperCAmelCase				:    Tuple           =			"""camembert"""
      def __init__(      self    ,  __magic_name__=3_0_5_2_2    ,  __magic_name__=7_6_8    ,  __magic_name__=1_2    ,  __magic_name__=1_2    ,  __magic_name__=3_0_7_2    ,  __magic_name__="gelu"    ,  __magic_name__=0.1    ,  __magic_name__=0.1    ,  __magic_name__=5_1_2    ,  __magic_name__=2    ,  __magic_name__=0.02    ,  __magic_name__=1e-12    ,  __magic_name__=1    ,  __magic_name__=0    ,  __magic_name__=2    ,  __magic_name__="absolute"    ,  __magic_name__=True    ,  __magic_name__=None    ,  **__magic_name__    ,  ):
       super().__init__(pad_token_id=__magic_name__    ,  bos_token_id=__magic_name__    ,  eos_token_id=__magic_name__    ,  **__magic_name__      )

       lowerCamelCase		:	int							=      vocab_size
       lowerCamelCase		:	int							=      hidden_size
       lowerCamelCase		:	int							=      num_hidden_layers
       lowerCamelCase		:	int							=      num_attention_heads
       lowerCamelCase		:	Optional[int]							=      hidden_act
       lowerCamelCase		:	List[Any]							=      intermediate_size
       lowerCamelCase		:	Tuple							=      hidden_dropout_prob
       lowerCamelCase		:	Optional[int]							=      attention_probs_dropout_prob
       lowerCamelCase		:	Optional[int]							=      max_position_embeddings
       lowerCamelCase		:	str							=      type_vocab_size
       lowerCamelCase		:	Optional[Any]							=      initializer_range
       lowerCamelCase		:	int							=      layer_norm_eps
       lowerCamelCase		:	Any							=      position_embedding_type
       lowerCamelCase		:	Optional[int]							=      use_cache
       lowerCamelCase		:	Union[str, Any]							=      classifier_dropout





class 							A__		(	__SCREAMING_SNAKE_CASE):
      @property
      def 						UpperCamelCase__							(      self      ):
       if self.task == "multiple-choice":
        lowerCamelCase		:	List[str]							=      {0: """batch""", 1: """choice""", 2: """sequence"""}
       else:
        lowerCamelCase		:	List[str]							=      {0: """batch""", 1: """sequence"""}
       return OrderedDict(
           [
               ("""input_ids""", dynamic_axis),
               ("""attention_mask""", dynamic_axis),
           ]      )




