





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


_SCREAMING_SNAKE_CASE 		=						logging.get_logger(__name__)

_SCREAMING_SNAKE_CASE 		=						{
    """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 SCREAMING_SNAKE_CASE_  (   snake_case_   ):
						__magic_name__:     str	 =    "vit_msn"






						def __init__( self : List[str]						,				_A : List[Any]=768						,				_A : Tuple=12						,				_A : Optional[int]=12						,				_A : Optional[Any]=3072						,				_A : int="gelu"						,				_A : Any=0.0						,				_A : Optional[int]=0.0						,				_A : int=0.0_2						,				_A : Any=1E-06						,				_A : List[Any]=224						,				_A : Tuple=16						,				_A : Dict=3						,				_A : Union[str, Any]=True						,				**_A : int						,				)							->      Any:







							"""simple docstring"""


							super().__init__(**_A		)

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








from typing import TYPE_CHECKING

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


_SCREAMING_SNAKE_CASE 		=						{
    """configuration_mctct""": ["""MCTCT_PRETRAINED_CONFIG_ARCHIVE_MAP""", """MCTCTConfig"""],
    """feature_extraction_mctct""": ["""MCTCTFeatureExtractor"""],
    """processing_mctct""": ["""MCTCTProcessor"""],
}


try:
						if not is_torch_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						_SCREAMING_SNAKE_CASE 		=						[
						    """MCTCT_PRETRAINED_MODEL_ARCHIVE_LIST""",
						    """MCTCTForCTC""",
						    """MCTCTModel""",
						    """MCTCTPreTrainedModel""",
						]


if TYPE_CHECKING:
						from .configuration_mctct import MCTCT_PRETRAINED_CONFIG_ARCHIVE_MAP, MCTCTConfig
						from .feature_extraction_mctct import MCTCTFeatureExtractor
						from .processing_mctct import MCTCTProcessor

						try:
												if not is_torch_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .modeling_mctct import MCTCT_PRETRAINED_MODEL_ARCHIVE_LIST, MCTCTForCTC, MCTCTModel, MCTCTPreTrainedModel

else:
						import sys

						_SCREAMING_SNAKE_CASE 		=						_LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)

