





'''simple docstring'''






import logging

from transformers import PretrainedConfig


A   : Union[str, Any]             =			logging.getLogger(__name__)


A   : List[Any]             =			{
    'bertabs-finetuned-cnndm': 'https://huggingface.co/remi/bertabs-finetuned-cnndm-extractive-abstractive-summarization/resolve/main/config.json',
}







class 			lowerCamelCase					(						__UpperCAmelCase  ):
					_SCREAMING_SNAKE_CASE							   =    "bertabs"


					def __init__(					self	:  Dict       ,     __snake_case	:  List[Any]=3_05_22       ,     __snake_case	:  Any=5_12       ,     __snake_case	:  Optional[int]=6       ,     __snake_case	:  str=5_12       ,     __snake_case	:  Optional[Any]=8       ,     __snake_case	:  str=5_12       ,     __snake_case	:  int=0.2       ,     __snake_case	:  int=6       ,     __snake_case	:  Dict=7_68       ,     __snake_case	:  Optional[int]=8       ,     __snake_case	:  Optional[int]=20_48       ,     __snake_case	:  Optional[int]=0.2       ,     **__snake_case	:  Dict       ,     ):





											'''simple docstring'''





											super().__init__(**__snake_case       )

											_snake_case:   Union[str, Any]  					=		vocab_size
											_snake_case:   Dict  					=		max_pos

											_snake_case:   str  					=		enc_layers
											_snake_case:   int  					=		enc_hidden_size
											_snake_case:   List[Any]  					=		enc_heads
											_snake_case:   Optional[int]  					=		enc_ff_size
											_snake_case:   int  					=		enc_dropout

											_snake_case:   Tuple  					=		dec_layers
											_snake_case:   Optional[int]  					=		dec_hidden_size
											_snake_case:   Optional[int]  					=		dec_heads
											_snake_case:   Union[str, Any]  					=		dec_ff_size
											_snake_case:   Optional[int]  					=		dec_dropout












'''simple docstring'''






from typing import TYPE_CHECKING

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


A   : Optional[Any]             =			{'configuration_deit': ['DEIT_PRETRAINED_CONFIG_ARCHIVE_MAP', 'DeiTConfig', 'DeiTOnnxConfig']}

try:
		if not is_vision_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		A   : str             =			['DeiTFeatureExtractor']
		A   : Optional[int]             =			['DeiTImageProcessor']

try:
		if not is_torch_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		A   : Optional[Any]             =			[
		    'DEIT_PRETRAINED_MODEL_ARCHIVE_LIST',
		    'DeiTForImageClassification',
		    'DeiTForImageClassificationWithTeacher',
		    'DeiTForMaskedImageModeling',
		    'DeiTModel',
		    'DeiTPreTrainedModel',
		]

try:
		if not is_tf_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		A   : int             =			[
		    'TF_DEIT_PRETRAINED_MODEL_ARCHIVE_LIST',
		    'TFDeiTForImageClassification',
		    'TFDeiTForImageClassificationWithTeacher',
		    'TFDeiTForMaskedImageModeling',
		    'TFDeiTModel',
		    'TFDeiTPreTrainedModel',
		]


if TYPE_CHECKING:
		from .configuration_deit import DEIT_PRETRAINED_CONFIG_ARCHIVE_MAP, DeiTConfig, DeiTOnnxConfig

		try:
				if not is_vision_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .feature_extraction_deit import DeiTFeatureExtractor
				from .image_processing_deit import DeiTImageProcessor

		try:
				if not is_torch_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_deit import (
				    DEIT_PRETRAINED_MODEL_ARCHIVE_LIST,
				    DeiTForImageClassification,
				    DeiTForImageClassificationWithTeacher,
				    DeiTForMaskedImageModeling,
				    DeiTModel,
				    DeiTPreTrainedModel,
				)

		try:
				if not is_tf_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_tf_deit import (
				    TF_DEIT_PRETRAINED_MODEL_ARCHIVE_LIST,
				    TFDeiTForImageClassification,
				    TFDeiTForImageClassificationWithTeacher,
				    TFDeiTForMaskedImageModeling,
				    TFDeiTModel,
				    TFDeiTPreTrainedModel,
				)


else:
		import sys

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





