


from typing import TYPE_CHECKING

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


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

try:
						if not is_torch_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						__UpperCamelCase      						=    [
						    '''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:
						__UpperCamelCase      						=    [
						    '''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

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






from __future__ import annotations
def     UpperCamelCase_(			_A				:list					)->						int | float:
			UpperCamelCase__   =    sorted(_A					)
			UpperCamelCase__   =    len(_A					)
			UpperCamelCase__   =    length >> 1
			return (
			    (sorted_list[mid_index] + sorted_list[mid_index - 1]) / 2
			    if length % 2 == 0
			    else sorted_list[mid_index]
			)
def     UpperCamelCase_(			)->						List[Any]:
			import doctest

			doctest.testmod()


if __name__ == "__main__":
						main()


