


"""simple docstring"""



import os
import unittest

from transformers import LxmertTokenizer, LxmertTokenizerFast
from transformers.models.bert.tokenization_bert import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers

from ...test_tokenization_common import TokenizerTesterMixin
@require_tokenizers
class 					__magic_name__		(			__UpperCAmelCase			,						unittest.TestCase     ):
       __A			:      Tuple        =   LxmertTokenizer
       __A			:      List[Any]        =   LxmertTokenizerFast
       __A			:      Optional[Any]        =   True
       __A			:      Dict        =   True






       def  __snake_case						(   self  :    int				):

              '''simple docstring'''






              super().setUp()

              lowercase		:List[str]       							=				[
                  '''[UNK]''',
                  '''[CLS]''',
                  '''[SEP]''',
                  '''want''',
                  '''##want''',
                  '''##ed''',
                  '''wa''',
                  '''un''',
                  '''runn''',
                  '''##ing''',
                  ''',''',
                  '''low''',
                  '''lowest''',
              ]
              lowercase		:int       							=				os.path.join(self.tmpdirname  ,     VOCAB_FILES_NAMES['''vocab_file''']				)
              with open(self.vocab_file  ,     '''w'''  ,     encoding='''utf-8'''				) as vocab_writer:
                     vocab_writer.write(''''''.join([x + '''\n''' for x in vocab_tokens]				)				)






       def  __snake_case						(   self  :    Any  ,     snake_case__  :    int				):

              '''simple docstring'''






              lowercase		:Optional[int]       							=				'''UNwant\u00E9d,running'''
              lowercase		:Any       							=				'''unwanted, running'''
              return input_text, output_text






       def  __snake_case						(   self  :    Any				):

              '''simple docstring'''






              lowercase		:Optional[Any]       							=				self.tokenizer_class(self.vocab_file				)

              lowercase		:Union[str, Any]       							=				tokenizer.tokenize('''UNwant\u00E9d,running'''				)
              self.assertListEqual(snake_case__  ,     ['''un''', '''##want''', '''##ed''', ''',''', '''runn''', '''##ing''']				)
              self.assertListEqual(tokenizer.convert_tokens_to_ids(snake_case__				)  ,     [7, 4, 5, 1_0, 8, 9]				)







       def  __snake_case						(   self  :    List[str]				):

              '''simple docstring'''






              if not self.test_rust_tokenizer:
                     return

              lowercase		:Optional[int]       							=				self.get_tokenizer()
              lowercase		:str       							=				self.get_rust_tokenizer()

              lowercase		:Tuple       							=				'''I was born in 92000, and this is falsé.'''

              lowercase		:List[Any]       							=				tokenizer.tokenize(snake_case__				)
              lowercase		:List[Any]       							=				rust_tokenizer.tokenize(snake_case__				)
              self.assertListEqual(snake_case__  ,     snake_case__				)

              lowercase		:Union[str, Any]       							=				tokenizer.encode(snake_case__  ,     add_special_tokens=snake_case__				)
              lowercase		:Tuple       							=				rust_tokenizer.encode(snake_case__  ,     add_special_tokens=snake_case__				)
              self.assertListEqual(snake_case__  ,     snake_case__				)

              lowercase		:Union[str, Any]       							=				self.get_rust_tokenizer()
              lowercase		:int       							=				tokenizer.encode(snake_case__				)
              lowercase		:Union[str, Any]       							=				rust_tokenizer.encode(snake_case__				)
              self.assertListEqual(snake_case__  ,     snake_case__				)





"""simple docstring"""



from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_flax_available,
    is_sentencepiece_available,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


UpperCAmelCase      		=			{
    '''configuration_xlm_roberta''': [
        '''XLM_ROBERTA_PRETRAINED_CONFIG_ARCHIVE_MAP''',
        '''XLMRobertaConfig''',
        '''XLMRobertaOnnxConfig''',
    ],
}

try:
 if not is_sentencepiece_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 UpperCAmelCase      		=			['''XLMRobertaTokenizer''']

try:
 if not is_tokenizers_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 UpperCAmelCase      		=			['''XLMRobertaTokenizerFast''']

try:
 if not is_torch_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 UpperCAmelCase      		=			[
     '''XLM_ROBERTA_PRETRAINED_MODEL_ARCHIVE_LIST''',
     '''XLMRobertaForCausalLM''',
     '''XLMRobertaForMaskedLM''',
     '''XLMRobertaForMultipleChoice''',
     '''XLMRobertaForQuestionAnswering''',
     '''XLMRobertaForSequenceClassification''',
     '''XLMRobertaForTokenClassification''',
     '''XLMRobertaModel''',
     '''XLMRobertaPreTrainedModel''',
 ]

try:
 if not is_tf_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 UpperCAmelCase      		=			[
     '''TF_XLM_ROBERTA_PRETRAINED_MODEL_ARCHIVE_LIST''',
     '''TFXLMRobertaForCausalLM''',
     '''TFXLMRobertaForMaskedLM''',
     '''TFXLMRobertaForMultipleChoice''',
     '''TFXLMRobertaForQuestionAnswering''',
     '''TFXLMRobertaForSequenceClassification''',
     '''TFXLMRobertaForTokenClassification''',
     '''TFXLMRobertaModel''',
     '''TFXLMRobertaPreTrainedModel''',
 ]

try:
 if not is_flax_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 UpperCAmelCase      		=			[
     '''FLAX_XLM_ROBERTA_PRETRAINED_MODEL_ARCHIVE_LIST''',
     '''FlaxXLMRobertaForMaskedLM''',
     '''FlaxXLMRobertaForCausalLM''',
     '''FlaxXLMRobertaForMultipleChoice''',
     '''FlaxXLMRobertaForQuestionAnswering''',
     '''FlaxXLMRobertaForSequenceClassification''',
     '''FlaxXLMRobertaForTokenClassification''',
     '''FlaxXLMRobertaModel''',
     '''FlaxXLMRobertaPreTrainedModel''',
 ]

if TYPE_CHECKING:
 from .configuration_xlm_roberta import (
     XLM_ROBERTA_PRETRAINED_CONFIG_ARCHIVE_MAP,
     XLMRobertaConfig,
     XLMRobertaOnnxConfig,
 )

 try:
  if not is_sentencepiece_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .tokenization_xlm_roberta import XLMRobertaTokenizer

 try:
  if not is_tokenizers_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .tokenization_xlm_roberta_fast import XLMRobertaTokenizerFast

 try:
  if not is_torch_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_xlm_roberta import (
      XLM_ROBERTA_PRETRAINED_MODEL_ARCHIVE_LIST,
      XLMRobertaForCausalLM,
      XLMRobertaForMaskedLM,
      XLMRobertaForMultipleChoice,
      XLMRobertaForQuestionAnswering,
      XLMRobertaForSequenceClassification,
      XLMRobertaForTokenClassification,
      XLMRobertaModel,
      XLMRobertaPreTrainedModel,
  )

 try:
  if not is_tf_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_tf_xlm_roberta import (
      TF_XLM_ROBERTA_PRETRAINED_MODEL_ARCHIVE_LIST,
      TFXLMRobertaForCausalLM,
      TFXLMRobertaForMaskedLM,
      TFXLMRobertaForMultipleChoice,
      TFXLMRobertaForQuestionAnswering,
      TFXLMRobertaForSequenceClassification,
      TFXLMRobertaForTokenClassification,
      TFXLMRobertaModel,
      TFXLMRobertaPreTrainedModel,
  )

 try:
  if not is_flax_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_flax_xlm_roberta import (
      FLAX_XLM_ROBERTA_PRETRAINED_MODEL_ARCHIVE_LIST,
      FlaxXLMRobertaForCausalLM,
      FlaxXLMRobertaForMaskedLM,
      FlaxXLMRobertaForMultipleChoice,
      FlaxXLMRobertaForQuestionAnswering,
      FlaxXLMRobertaForSequenceClassification,
      FlaxXLMRobertaForTokenClassification,
      FlaxXLMRobertaModel,
      FlaxXLMRobertaPreTrainedModel,
  )

else:
 import sys

 UpperCAmelCase      		=			_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)

