






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


_SCREAMING_SNAKE_CASE					:  Tuple             =      get_tests_dir('''fixtures/test_sentencepiece.model''')
_SCREAMING_SNAKE_CASE					:  List[str]             =      get_tests_dir('''fixtures/test_sentencepiece_bpe.model''')

_SCREAMING_SNAKE_CASE					:  Dict             =      '''pt''' if is_torch_available() else '''tf'''






@require_sentencepiece
@require_tokenizers
class 	UpperCAmelCase__		(	A__						,					unittest.TestCase	):






  """simple docstring"""


  a									= CamembertTokenizer
  a									= CamembertTokenizerFast
  a									= True
  a									= True






  def 				lowercase_		(							self						:		str )		->							Tuple:
       super().setUp()

       # We have a SentencePiece fixture for testing
       SCREAMING_SNAKE_CASE__   =   CamembertTokenizer(__lowerCamelCase )
       tokenizer.save_pretrained(self.tmpdirname )






  def 				lowercase_		(							self						:		List[Any] )		->							Optional[int]:
       SCREAMING_SNAKE_CASE__   =   '''<pad>'''
       SCREAMING_SNAKE_CASE__   =   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						:		int )		->							int:
       SCREAMING_SNAKE_CASE__   =   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 				lowercase_		(							self						:		Dict )		->							Tuple:
       self.assertEqual(self.get_tokenizer().vocab_size     ,       1005 )






  def 				lowercase_		(							self						:		str )		->							Dict:
       SCREAMING_SNAKE_CASE__   =   CamembertTokenizer(__lowerCamelCase )
       tokenizer.save_pretrained(self.tmpdirname )
       SCREAMING_SNAKE_CASE__   =   CamembertTokenizerFast.from_pretrained(self.tmpdirname )

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

       SCREAMING_SNAKE_CASE__   =   tokenizer.encode(__lowerCamelCase )
       SCREAMING_SNAKE_CASE__   =   rust_tokenizer.encode(__lowerCamelCase )
       self.assertListEqual(__lowerCamelCase     ,       __lowerCamelCase )

       SCREAMING_SNAKE_CASE__   =   tokenizer.encode(__lowerCamelCase     ,       add_special_tokens=__lowerCamelCase )
       SCREAMING_SNAKE_CASE__   =   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)
       SCREAMING_SNAKE_CASE__   =   tokenizer.convert_ids_to_tokens(__lowerCamelCase )
       SCREAMING_SNAKE_CASE__   =   rust_tokenizer.tokenize(__lowerCamelCase )
       self.assertListEqual(__lowerCamelCase     ,       __lowerCamelCase )






  def 				lowercase_		(							self						:		Dict )		->							int:
       if not self.test_rust_tokenizer:
            return

       SCREAMING_SNAKE_CASE__   =   self.get_tokenizer()
       SCREAMING_SNAKE_CASE__   =   self.get_rust_tokenizer()

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

       SCREAMING_SNAKE_CASE__   =   tokenizer.tokenize(__lowerCamelCase )
       SCREAMING_SNAKE_CASE__   =   rust_tokenizer.tokenize(__lowerCamelCase )
       self.assertListEqual(__lowerCamelCase     ,       __lowerCamelCase )

       SCREAMING_SNAKE_CASE__   =   tokenizer.encode(__lowerCamelCase     ,       add_special_tokens=__lowerCamelCase )
       SCREAMING_SNAKE_CASE__   =   rust_tokenizer.encode(__lowerCamelCase     ,       add_special_tokens=__lowerCamelCase )
       self.assertListEqual(__lowerCamelCase     ,       __lowerCamelCase )

       SCREAMING_SNAKE_CASE__   =   self.get_rust_tokenizer()
       SCREAMING_SNAKE_CASE__   =   tokenizer.encode(__lowerCamelCase )
       SCREAMING_SNAKE_CASE__   =   rust_tokenizer.encode(__lowerCamelCase )
       self.assertListEqual(__lowerCamelCase     ,       __lowerCamelCase )







  @slow
  def 				lowercase_		(							self						:		List[str] )		->							List[str]:
       # fmt: off
       SCREAMING_SNAKE_CASE__   =   {'''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.
       SCREAMING_SNAKE_CASE__   =   [
           '''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     ,       )





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


_SCREAMING_SNAKE_CASE					:  List[str]             =      logging.get_logger(__name__)

_SCREAMING_SNAKE_CASE					:  Tuple             =      {
    '''transfo-xl-wt103''': '''https://huggingface.co/transfo-xl-wt103/resolve/main/config.json''',
}






class 	UpperCAmelCase__		(	A__	):






  """simple docstring"""


  a									= "transfo-xl"
  a									= ["mems"]
  a									= {
      "n_token": "vocab_size",
      "hidden_size": "d_model",
      "num_attention_heads": "n_head",
      "num_hidden_layers": "n_layer",
  }






  def __init__(							self						:		Any     ,       __lowerCamelCase						:		int=26_7735     ,       __lowerCamelCase						:		Any=[2_0000, 4_0000, 20_0000]     ,       __lowerCamelCase						:		Dict=1024     ,       __lowerCamelCase						:		Optional[int]=1024     ,       __lowerCamelCase						:		List[Any]=16     ,       __lowerCamelCase						:		Union[str, Any]=64     ,       __lowerCamelCase						:		Dict=4096     ,       __lowerCamelCase						:		int=4     ,       __lowerCamelCase						:		Dict=False     ,       __lowerCamelCase						:		Tuple=18     ,       __lowerCamelCase						:		Optional[int]=1600     ,       __lowerCamelCase						:		str=1000     ,       __lowerCamelCase						:		Any=True     ,       __lowerCamelCase						:		Dict=True     ,       __lowerCamelCase						:		List[Any]=0     ,       __lowerCamelCase						:		int=-1     ,       __lowerCamelCase						:		Tuple=True     ,       __lowerCamelCase						:		List[Any]=0.1     ,       __lowerCamelCase						:		int=0.0     ,       __lowerCamelCase						:		int=True     ,       __lowerCamelCase						:		str="normal"     ,       __lowerCamelCase						:		List[str]=0.01     ,       __lowerCamelCase						:		Any=0.01     ,       __lowerCamelCase						:		List[str]=0.02     ,       __lowerCamelCase						:		List[str]=1e-5     ,       __lowerCamelCase						:		Union[str, Any]=0     ,       **__lowerCamelCase						:		int     ,       )		->							Optional[Any]:
       SCREAMING_SNAKE_CASE__   =   vocab_size
       SCREAMING_SNAKE_CASE__   =   []
       self.cutoffs.extend(__lowerCamelCase )
       if proj_share_all_but_first:
            SCREAMING_SNAKE_CASE__   =   [False] + [True] * len(self.cutoffs )
       else:
            SCREAMING_SNAKE_CASE__   =   [False] + [False] * len(self.cutoffs )
       SCREAMING_SNAKE_CASE__   =   d_model
       SCREAMING_SNAKE_CASE__   =   d_embed
       SCREAMING_SNAKE_CASE__   =   d_head
       SCREAMING_SNAKE_CASE__   =   d_inner
       SCREAMING_SNAKE_CASE__   =   div_val
       SCREAMING_SNAKE_CASE__   =   pre_lnorm
       SCREAMING_SNAKE_CASE__   =   n_layer
       SCREAMING_SNAKE_CASE__   =   n_head
       SCREAMING_SNAKE_CASE__   =   mem_len
       SCREAMING_SNAKE_CASE__   =   same_length
       SCREAMING_SNAKE_CASE__   =   attn_type
       SCREAMING_SNAKE_CASE__   =   clamp_len
       SCREAMING_SNAKE_CASE__   =   sample_softmax
       SCREAMING_SNAKE_CASE__   =   adaptive
       SCREAMING_SNAKE_CASE__   =   dropout
       SCREAMING_SNAKE_CASE__   =   dropatt
       SCREAMING_SNAKE_CASE__   =   untie_r
       SCREAMING_SNAKE_CASE__   =   init
       SCREAMING_SNAKE_CASE__   =   init_range
       SCREAMING_SNAKE_CASE__   =   proj_init_std
       SCREAMING_SNAKE_CASE__   =   init_std
       SCREAMING_SNAKE_CASE__   =   layer_norm_epsilon
       super().__init__(eos_token_id=__lowerCamelCase     ,       **__lowerCamelCase )






  @property
  def 				lowercase_		(							self						:		str )		->							Dict:
       # Message copied from Transformer-XL documentation
       logger.info(f'''The model {self.model_type} is one of the few models that has no sequence length limit.''' )
       return -1







  @max_position_embeddings.setter
  def 				lowercase_		(							self						:		List[str]     ,       __lowerCamelCase						:		Any )		->							List[Any]:
       # Message copied from Transformer-XL documentation
       raise NotImplementedError(
           f'''The model {self.model_type} is one of the few models that has no sequence length limit.''' )


