






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        lowerCAmelCase_				(      lowercase						,      unittest.TestCase	):







				"""simple docstring"""




				_snake_case				:   Dict        =			CamembertTokenizer
				_snake_case				:   Optional[int]        =			CamembertTokenizerFast
				_snake_case				:   Tuple        =			True
				_snake_case				:   Dict        =			True







				def 				__a   ( self     :Optional[int]     ):
					super().setUp()

					# We have a SentencePiece fixture for testing
					UpperCamelCase__ :Union[str, Any]						=      CamembertTokenizer(lowerCamelCase__     )
					tokenizer.save_pretrained(self.tmpdirname     )







				def 				__a   ( self     :List[str]     ):
					UpperCamelCase__ :List[str]						=      """<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 				__a   ( self     :List[Any]     ):
					UpperCamelCase__ :Dict						=      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__     )  ,     10_04     )







				def 				__a   ( self     :Optional[Any]     ):
					self.assertEqual(self.get_tokenizer().vocab_size  ,     10_05     )







				def 				__a   ( self     :Dict     ):
					UpperCamelCase__ :List[str]						=      CamembertTokenizer(lowerCamelCase__     )
					tokenizer.save_pretrained(self.tmpdirname     )
					UpperCamelCase__ :Union[str, Any]						=      CamembertTokenizerFast.from_pretrained(self.tmpdirname     )

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

					UpperCamelCase__ :List[Any]						=      tokenizer.encode(lowerCamelCase__     )
					UpperCamelCase__ :Dict						=      rust_tokenizer.encode(lowerCamelCase__     )
					self.assertListEqual(lowerCamelCase__  ,     lowerCamelCase__     )

					UpperCamelCase__ :str						=      tokenizer.encode(lowerCamelCase__  ,     add_special_tokens=lowerCamelCase__     )
					UpperCamelCase__ :Union[str, Any]						=      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)
					UpperCamelCase__ :Any						=      tokenizer.convert_ids_to_tokens(lowerCamelCase__     )
					UpperCamelCase__ :str						=      rust_tokenizer.tokenize(lowerCamelCase__     )
					self.assertListEqual(lowerCamelCase__  ,     lowerCamelCase__     )







				def 				__a   ( self     :Union[str, Any]     ):
					if not self.test_rust_tokenizer:
						return

					UpperCamelCase__ :Dict						=      self.get_tokenizer()
					UpperCamelCase__ :str						=      self.get_rust_tokenizer()

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

					UpperCamelCase__ :Optional[Any]						=      tokenizer.tokenize(lowerCamelCase__     )
					UpperCamelCase__ :List[Any]						=      rust_tokenizer.tokenize(lowerCamelCase__     )
					self.assertListEqual(lowerCamelCase__  ,     lowerCamelCase__     )

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

					UpperCamelCase__ :Tuple						=      self.get_rust_tokenizer()
					UpperCamelCase__ :Union[str, Any]						=      tokenizer.encode(lowerCamelCase__     )
					UpperCamelCase__ :Optional[Any]						=      rust_tokenizer.encode(lowerCamelCase__     )
					self.assertListEqual(lowerCamelCase__  ,     lowerCamelCase__     )







				@slow
				def 				__a   ( self     :List[str]     ):
					# fmt: off
					UpperCamelCase__ :Any						=      {"""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.
					UpperCamelCase__ :Tuple						=      [
					    """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__  ,     )
'''simple docstring'''


from dataclasses import dataclass, field
from typing import Tuple

from ..utils import cached_property, is_torch_available, is_torch_tpu_available, logging, requires_backends
from .benchmark_args_utils import BenchmarkArguments


if is_torch_available():
       import torch

if is_torch_tpu_available(check_device=False):
       import torch_xla.core.xla_model as xm


__snake_case						  =		logging.get_logger(__name__)





@dataclass
class       _a    (		__a      ):


  """simple docstring"""

  A_    							=						[
      '''no_inference''',
      '''no_cuda''',
      '''no_tpu''',
      '''no_speed''',
      '''no_memory''',
      '''no_env_print''',
      '''no_multi_process''',
  ]




  def __init__(		self      :       int   ,				**lowercase_      :       int      ):







    '''simple docstring'''


    for deprecated_arg in self.deprecated_args:
      if deprecated_arg in kwargs:
        lowercase_     =       deprecated_arg[3:]
        setattr(self   ,				lowercase_   ,				not kwargs.pop(lowercase_      )      )
        logger.warning(
            F"""{deprecated_arg} is depreciated. Please use --no_{positive_arg} or"""
            F""" {positive_arg}={kwargs[positive_arg]}"""      )

    lowercase_     =       kwargs.pop("""torchscript"""   ,				self.torchscript      )
    lowercase_     =       kwargs.pop("""torch_xla_tpu_print_metrics"""   ,				self.torch_xla_tpu_print_metrics      )
    lowercase_     =       kwargs.pop("""fp16_opt_level"""   ,				self.fpaa_opt_level      )
    super().__init__(**lowercase_      )

  A_    							=						field(default=__a ,				metadata={'''help''': '''Trace the models using torchscript'''}      )
  A_    							=						field(default=__a ,				metadata={'''help''': '''Print Xla/PyTorch tpu metrics'''}      )
  A_    							=						field(
      default='''O1''' ,				metadata={
          '''help''': (
              '''For fp16: Apex AMP optimization level selected in [\'O0\', \'O1\', \'O2\', and \'O3\']. '''
              '''See details at https://nvidia.github.io/apex/amp.html'''
          )
      } ,				)




  @cached_property
  def 		lowerCamelCase__  (		self      :       Dict      ):







    '''simple docstring'''


    requires_backends(self   ,				["""torch"""]      )
    logger.info("""PyTorch: setting up devices"""      )
    if not self.cuda:
      lowercase_     =       torch.device("""cpu"""      )
      lowercase_     =       0
    elif is_torch_tpu_available():
      lowercase_     =       xm.xla_device()
      lowercase_     =       0
    else:
      lowercase_     =       torch.device("""cuda""" if torch.cuda.is_available() else """cpu"""      )
      lowercase_     =       torch.cuda.device_count()
    return device, n_gpu




  @property
  def 		lowerCamelCase__  (		self      :       List[str]      ):







    '''simple docstring'''


    return is_torch_tpu_available() and self.tpu




  @property
  def 		lowerCamelCase__  (		self      :       Tuple      ):







    '''simple docstring'''


    requires_backends(self   ,				["""torch"""]      )
    # TODO(PVP): currently only single GPU is supported
    return torch.cuda.current_device()




  @property
  def 		lowerCamelCase__  (		self      :       Dict      ):







    '''simple docstring'''


    requires_backends(self   ,				["""torch"""]      )
    return self._setup_devices[0]




  @property
  def 		lowerCamelCase__  (		self      :       Any      ):







    '''simple docstring'''


    requires_backends(self   ,				["""torch"""]      )
    return self._setup_devices[1]





  @property
  def 		lowerCamelCase__  (		self      :       Optional[int]      ):







    '''simple docstring'''


    return self.n_gpu > 0

