



'''simple docstring'''



from ...configuration_utils import PretrainedConfig
from ...utils import logging


lowerCAmelCase_     :       List[str]								=						logging.get_logger(__name__)

lowerCAmelCase_     :       Tuple								=						{
    'sayakpaul/vit-msn-base': 'https://huggingface.co/sayakpaul/vit-msn-base/resolve/main/config.json',
    # See all ViT MSN models at https://huggingface.co/models?filter=vit_msn
}
class 	lowerCamelCase_							(					snake_case_			):
						_lowerCAmelCase     :   int	     =				'vit_msn'







						def __init__(  self   :      Any				,    lowerCAmelCase__   :      List[str]=7_68				,    lowerCAmelCase__   :      Dict=12				,    lowerCAmelCase__   :      Any=12				,    lowerCAmelCase__   :      Tuple=30_72				,    lowerCAmelCase__   :      Dict="gelu"				,    lowerCAmelCase__   :      int=0.0				,    lowerCAmelCase__   :      Any=0.0				,    lowerCAmelCase__   :      Optional[Any]=0.02				,    lowerCAmelCase__   :      List[str]=1e-06				,    lowerCAmelCase__   :      List[Any]=2_24				,    lowerCAmelCase__   :      Tuple=16				,    lowerCAmelCase__   :      Optional[Any]=3				,    lowerCAmelCase__   :      Any=True				,    **lowerCAmelCase__   :      int				,    ):

													"""simple docstring"""
													super().__init__(**lowerCAmelCase__				)

													SCREAMING_SNAKE_CASE					:      Tuple     					=			hidden_size
													SCREAMING_SNAKE_CASE					:      int     					=			num_hidden_layers
													SCREAMING_SNAKE_CASE					:      Union[str, Any]     					=			num_attention_heads
													SCREAMING_SNAKE_CASE					:      Any     					=			intermediate_size
													SCREAMING_SNAKE_CASE					:      List[str]     					=			hidden_act
													SCREAMING_SNAKE_CASE					:      str     					=			hidden_dropout_prob
													SCREAMING_SNAKE_CASE					:      List[str]     					=			attention_probs_dropout_prob
													SCREAMING_SNAKE_CASE					:      Any     					=			initializer_range
													SCREAMING_SNAKE_CASE					:      Dict     					=			layer_norm_eps
													SCREAMING_SNAKE_CASE					:      Union[str, Any]     					=			image_size
													SCREAMING_SNAKE_CASE					:      Dict     					=			patch_size
													SCREAMING_SNAKE_CASE					:      Union[str, Any]     					=			num_channels
													SCREAMING_SNAKE_CASE					:      Tuple     					=			qkv_bias









'''simple docstring'''



from typing import TYPE_CHECKING

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


lowerCAmelCase_     :       Tuple								=						{
    'configuration_tapas': ['TAPAS_PRETRAINED_CONFIG_ARCHIVE_MAP', 'TapasConfig'],
    'tokenization_tapas': ['TapasTokenizer'],
}

try:
							if not is_torch_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							lowerCAmelCase_     :       Dict								=						[
							    'TAPAS_PRETRAINED_MODEL_ARCHIVE_LIST',
							    'TapasForMaskedLM',
							    'TapasForQuestionAnswering',
							    'TapasForSequenceClassification',
							    'TapasModel',
							    'TapasPreTrainedModel',
							    'load_tf_weights_in_tapas',
							]
try:
							if not is_tf_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							lowerCAmelCase_     :       str								=						[
							    'TF_TAPAS_PRETRAINED_MODEL_ARCHIVE_LIST',
							    'TFTapasForMaskedLM',
							    'TFTapasForQuestionAnswering',
							    'TFTapasForSequenceClassification',
							    'TFTapasModel',
							    'TFTapasPreTrainedModel',
							]


if TYPE_CHECKING:
							from .configuration_tapas import TAPAS_PRETRAINED_CONFIG_ARCHIVE_MAP, TapasConfig
							from .tokenization_tapas import TapasTokenizer

							try:
														if not is_torch_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_tapas import (
														    TAPAS_PRETRAINED_MODEL_ARCHIVE_LIST,
														    TapasForMaskedLM,
														    TapasForQuestionAnswering,
														    TapasForSequenceClassification,
														    TapasModel,
														    TapasPreTrainedModel,
														    load_tf_weights_in_tapas,
														)

							try:
														if not is_tf_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_tf_tapas import (
														    TF_TAPAS_PRETRAINED_MODEL_ARCHIVE_LIST,
														    TFTapasForMaskedLM,
														    TFTapasForQuestionAnswering,
														    TFTapasForSequenceClassification,
														    TFTapasModel,
														    TFTapasPreTrainedModel,
														)


else:
							import sys

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




