






from typing import TYPE_CHECKING

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


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

try:
					if not is_torch_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					snake_case_   :	str							      =[
					    '''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:
					snake_case_   :	Optional[int]							      =[
					    '''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

					snake_case_   :	str							      =_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)


"""simple docstring"""






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

lowerCAmelCase__    :    Union[str, Any]			=      [(1,)]



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



def     a_		(   ):
				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 							snake_case			(					unittest.TestCase							):




					"""simple docstring"""

					@slow
					@require_torch_gpu
					def 						__lowerCAmelCase		(							self  :      Optional[Any]     ):
									check_slow_torch_cuda()







					@require_torch_gpu
					@slow
					def 						__lowerCAmelCase		(							self  :      Any     ):
									check_slow_torch_cuda()







					@parameterized.expand(lowerCamelCase__     )
					@slow
					def 						__lowerCAmelCase		(							self  :      Union[str, Any]       ,lowerCamelCase__  :      str=None     ):
									check_slow()






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



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



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







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



