
from typing import List

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


__magic_name__:					Any							=     logging.get_logger(__name__)

__magic_name__:					Optional[int]							=     {
    "snap-research/efficientformer-l1-300": (
        "https://huggingface.co/snap-research/efficientformer-l1-300/resolve/main/config.json"
    ),
}





class 	snake_case__ (							__lowerCamelCase				):
	lowercase__						:					Optional[Any]											=   'efficientformer'
	def __init__(    self      ,       lowerCAmelCase__ = [3, 2, 6, 4]      ,       lowerCAmelCase__ = [48, 96, 2_24, 4_48]      ,       lowerCAmelCase__ = [True, True, True, True]      ,       lowerCAmelCase__ = 4_48      ,       lowerCAmelCase__ = 32      ,       lowerCAmelCase__ = 4      ,       lowerCAmelCase__ = 7      ,       lowerCAmelCase__ = 5      ,       lowerCAmelCase__ = 8      ,       lowerCAmelCase__ = 4      ,       lowerCAmelCase__ = 0.0      ,       lowerCAmelCase__ = 16      ,       lowerCAmelCase__ = 3      ,       lowerCAmelCase__ = 3      ,       lowerCAmelCase__ = 3      ,       lowerCAmelCase__ = 2      ,       lowerCAmelCase__ = 1      ,       lowerCAmelCase__ = 0.0      ,       lowerCAmelCase__ = 1      ,       lowerCAmelCase__ = True      ,       lowerCAmelCase__ = True      ,       lowerCAmelCase__ = 1e-5      ,       lowerCAmelCase__ = "gelu"      ,       lowerCAmelCase__ = 0.0_2      ,       lowerCAmelCase__ = 1e-1_2      ,       lowerCAmelCase__ = 2_24      ,       lowerCAmelCase__ = 1e-0_5      ,       **lowerCAmelCase__      ,       )							->				Optional[int]:
							super().__init__(**UpperCamelCase_			)

							__magic_name__      :       Union[str, Any]       						= hidden_act
							__magic_name__      :       Dict       						= hidden_dropout_prob
							__magic_name__      :       Tuple       						= hidden_sizes
							__magic_name__      :       Optional[int]       						= num_hidden_layers
							__magic_name__      :       Tuple       						= num_attention_heads
							__magic_name__      :       List[str]       						= initializer_range
							__magic_name__      :       Optional[int]       						= layer_norm_eps
							__magic_name__      :       Optional[int]       						= patch_size
							__magic_name__      :       Optional[int]       						= num_channels
							__magic_name__      :       Optional[int]       						= depths
							__magic_name__      :       Optional[Any]       						= mlp_expansion_ratio
							__magic_name__      :       Optional[int]       						= downsamples
							__magic_name__      :       Tuple       						= dim
							__magic_name__      :       int       						= key_dim
							__magic_name__      :       Optional[Any]       						= attention_ratio
							__magic_name__      :       int       						= resolution
							__magic_name__      :       Dict       						= pool_size
							__magic_name__      :       Any       						= downsample_patch_size
							__magic_name__      :       List[Any]       						= downsample_stride
							__magic_name__      :       str       						= downsample_pad
							__magic_name__      :       Any       						= drop_path_rate
							__magic_name__      :       Dict       						= num_metaad_blocks
							__magic_name__      :       List[Any]       						= distillation
							__magic_name__      :       Tuple       						= use_layer_scale
							__magic_name__      :       Union[str, Any]       						= layer_scale_init_value
							__magic_name__      :       List[Any]       						= image_size
							__magic_name__      :       List[str]       						= batch_norm_eps








from typing import TYPE_CHECKING

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


__magic_name__:					Tuple							=     {
    "configuration_tapas": ["TAPAS_PRETRAINED_CONFIG_ARCHIVE_MAP", "TapasConfig"],
    "tokenization_tapas": ["TapasTokenizer"],
}

try:
		if not is_torch_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		__magic_name__:					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:
		__magic_name__:					int							=     [
		    "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

		__magic_name__:					Tuple							=     _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)
