
from typing import TYPE_CHECKING

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


__SCREAMING_SNAKE_CASE     :				List[str]             =       {
    'configuration_flaubert': ['FLAUBERT_PRETRAINED_CONFIG_ARCHIVE_MAP', 'FlaubertConfig', 'FlaubertOnnxConfig'],
    'tokenization_flaubert': ['FlaubertTokenizer'],
}

try:
		if not is_torch_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		__SCREAMING_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:
		__SCREAMING_SNAKE_CASE     :				str             =       [
		    '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

		__SCREAMING_SNAKE_CASE     :				Any             =       _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)


import warnings

from ...utils import logging
from .image_processing_conditional_detr import ConditionalDetrImageProcessor


__SCREAMING_SNAKE_CASE     :				List[Any]             =       logging.get_logger(__name__)


class 						__magic_name__				(  snake_case      ):





		def __init__(							self      :		str  ,					*lowerCamelCase__      :		Dict  ,					**lowerCamelCase__      :		int			):
							warnings.warn(
							    '''The class ConditionalDetrFeatureExtractor is deprecated and will be removed in version 5 of Transformers.'''
							    ''' Please use ConditionalDetrImageProcessor instead.'''  ,					lowerCamelCase__  ,					)
							super().__init__(*lowerCamelCase__  ,					**lowerCamelCase__			)
