





import os
import unittest

import pytest
from parameterized import parameterized

from transformers.testing_utils import require_torch, require_torch_gpu, slow, torch_device


# skipping in unittest tests

lowercase     :			Tuple     						=	[(1,)]


def      SCREAMING_SNAKE_CASE      (   ):
       _UpperCamelCase														=    bool(os.getenv('''RUN_SLOW'''     ,							0       )       )
       if run_slow:
              assert True
       else:
              assert False, "should have been skipped"


def      SCREAMING_SNAKE_CASE      (   ):
       _UpperCamelCase														=    bool(os.getenv('''RUN_SLOW'''     ,							0       )       )
       if run_slow and torch_device == "cuda":
              assert True
       else:
              assert False, "should have been skipped"

@require_torch
class 			__A(    unittest.TestCase			):

     @slow
     @require_torch_gpu
     def 						_UpperCamelCase  (   self   ):





            """simple docstring"""

            check_slow_torch_cuda()

     @require_torch_gpu
     @slow
     def 						_UpperCamelCase  (   self   ):





            """simple docstring"""

            check_slow_torch_cuda()



     @parameterized.expand(A_   )
     @slow
     def 						_UpperCamelCase  (   self,							A=None   ):





            """simple docstring"""

            check_slow()






@slow
@require_torch_gpu
def      SCREAMING_SNAKE_CASE      (   ):
       check_slow_torch_cuda()


@require_torch_gpu
@slow
def      SCREAMING_SNAKE_CASE      (   ):
       check_slow_torch_cuda()


@slow
@pytest.mark.parametrize('''param'''     ,							[1]       )
def      SCREAMING_SNAKE_CASE      (   lowerCAmelCase       ):
       check_slow()


@pytest.mark.parametrize('''param'''     ,							[1]       )
@slow
def      SCREAMING_SNAKE_CASE      (   lowerCAmelCase       ):
       check_slow()










from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tf_available, is_torch_available


_A									=		{
    'configuration_flaubert': ['FLAUBERT_PRETRAINED_CONFIG_ARCHIVE_MAP', 'FlaubertConfig', 'FlaubertOnnxConfig'],
    'tokenization_flaubert': ['FlaubertTokenizer'],
}

try:
   if not is_torch_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   _A									=		[
       'FLAUBERT_PRETRAINED_MODEL_ARCHIVE_LIST',
       'FlaubertForMultipleChoice',
       'FlaubertForQuestionAnswering',
       'FlaubertForQuestionAnsweringSimple',
       'FlaubertForSequenceClassification',
       'FlaubertForTokenClassification',
       'FlaubertModel',
       'FlaubertWithLMHeadModel',
       'FlaubertPreTrainedModel',
   ]

try:
   if not is_tf_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   _A									=		[
       'TF_FLAUBERT_PRETRAINED_MODEL_ARCHIVE_LIST',
       'TFFlaubertForMultipleChoice',
       'TFFlaubertForQuestionAnsweringSimple',
       'TFFlaubertForSequenceClassification',
       'TFFlaubertForTokenClassification',
       'TFFlaubertModel',
       'TFFlaubertPreTrainedModel',
       'TFFlaubertWithLMHeadModel',
   ]


if TYPE_CHECKING:
   from .configuration_flaubert import FLAUBERT_PRETRAINED_CONFIG_ARCHIVE_MAP, FlaubertConfig, FlaubertOnnxConfig
   from .tokenization_flaubert import FlaubertTokenizer

   try:
      if not is_torch_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_flaubert import (
          FLAUBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
          FlaubertForMultipleChoice,
          FlaubertForQuestionAnswering,
          FlaubertForQuestionAnsweringSimple,
          FlaubertForSequenceClassification,
          FlaubertForTokenClassification,
          FlaubertModel,
          FlaubertPreTrainedModel,
          FlaubertWithLMHeadModel,
      )

   try:
      if not is_tf_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_tf_flaubert import (
          TF_FLAUBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
          TFFlaubertForMultipleChoice,
          TFFlaubertForQuestionAnsweringSimple,
          TFFlaubertForSequenceClassification,
          TFFlaubertForTokenClassification,
          TFFlaubertModel,
          TFFlaubertPreTrainedModel,
          TFFlaubertWithLMHeadModel,
      )

else:
   import sys

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





