
from typing import TYPE_CHECKING

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


__SCREAMING_SNAKE_CASE     :				List[str]             =       {
    'configuration_deberta_v2': ['DEBERTA_V2_PRETRAINED_CONFIG_ARCHIVE_MAP', 'DebertaV2Config', 'DebertaV2OnnxConfig'],
    'tokenization_deberta_v2': ['DebertaV2Tokenizer'],
}

try:
		if not is_tokenizers_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		__SCREAMING_SNAKE_CASE     :				Tuple             =       ['DebertaV2TokenizerFast']

try:
		if not is_tf_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		__SCREAMING_SNAKE_CASE     :				Dict             =       [
		    'TF_DEBERTA_V2_PRETRAINED_MODEL_ARCHIVE_LIST',
		    'TFDebertaV2ForMaskedLM',
		    'TFDebertaV2ForQuestionAnswering',
		    'TFDebertaV2ForSequenceClassification',
		    'TFDebertaV2ForTokenClassification',
		    'TFDebertaV2Model',
		    'TFDebertaV2PreTrainedModel',
		]

try:
		if not is_torch_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		__SCREAMING_SNAKE_CASE     :				Optional[Any]             =       [
		    'DEBERTA_V2_PRETRAINED_MODEL_ARCHIVE_LIST',
		    'DebertaV2ForMaskedLM',
		    'DebertaV2ForMultipleChoice',
		    'DebertaV2ForQuestionAnswering',
		    'DebertaV2ForSequenceClassification',
		    'DebertaV2ForTokenClassification',
		    'DebertaV2Model',
		    'DebertaV2PreTrainedModel',
		]


if TYPE_CHECKING:
		from .configuration_deberta_va import (
		    DEBERTA_V2_PRETRAINED_CONFIG_ARCHIVE_MAP,
		    DebertaVaConfig,
		    DebertaVaOnnxConfig,
		)
		from .tokenization_deberta_va import DebertaVaTokenizer

		try:
				if not is_tokenizers_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .tokenization_deberta_va_fast import DebertaVaTokenizerFast

		try:
				if not is_tf_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_tf_deberta_va import (
				    TF_DEBERTA_V2_PRETRAINED_MODEL_ARCHIVE_LIST,
				    TFDebertaVaForMaskedLM,
				    TFDebertaVaForQuestionAnswering,
				    TFDebertaVaForSequenceClassification,
				    TFDebertaVaForTokenClassification,
				    TFDebertaVaModel,
				    TFDebertaVaPreTrainedModel,
				)

		try:
				if not is_torch_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_deberta_va import (
				    DEBERTA_V2_PRETRAINED_MODEL_ARCHIVE_LIST,
				    DebertaVaForMaskedLM,
				    DebertaVaForMultipleChoice,
				    DebertaVaForQuestionAnswering,
				    DebertaVaForSequenceClassification,
				    DebertaVaForTokenClassification,
				    DebertaVaModel,
				    DebertaVaPreTrainedModel,
				)

else:
		import sys

		__SCREAMING_SNAKE_CASE     :				Dict             =       _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__			)
