



from collections import OrderedDict
from typing import Mapping

from packaging import version

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


__lowerCamelCase     :							Dict  							=				logging.get_logger(__name__)

__lowerCamelCase     :							Dict  							=				{
    'google/mobilenet_v1_1.0_224': 'https://huggingface.co/google/mobilenet_v1_1.0_224/resolve/main/config.json',
    'google/mobilenet_v1_0.75_192': 'https://huggingface.co/google/mobilenet_v1_0.75_192/resolve/main/config.json',
    # See all MobileNetV1 models at https://huggingface.co/models?filter=mobilenet_v1
}





class  __magic_name__   (							lowerCamelCase__						):
		lowercase      :       Optional[int]          ='''mobilenet_v1'''






		def __init__(						self      :     Optional[Any]				,		UpperCamelCase__      :     List[str]=3				,		UpperCamelCase__      :     List[Any]=2_24				,		UpperCamelCase__      :     Dict=1.0				,		UpperCamelCase__      :     Any=8				,		UpperCamelCase__      :     Tuple="relu6"				,		UpperCamelCase__      :     Any=True				,		UpperCamelCase__      :     Any=0.9_99				,		UpperCamelCase__      :     Dict=0.02				,		UpperCamelCase__      :     Optional[int]=0.0_01				,		**UpperCamelCase__      :     Union[str, Any]				,		)				->					List[Any]:
								'''simple docstring'''







								super().__init__(**__lowerCamelCase      )

								if depth_multiplier <= 0:
														raise ValueError("depth_multiplier must be greater than zero."      )

								UpperCAmelCase         =     num_channels
								UpperCAmelCase         =     image_size
								UpperCAmelCase         =     depth_multiplier
								UpperCAmelCase         =     min_depth
								UpperCAmelCase         =     hidden_act
								UpperCAmelCase         =     tf_padding
								UpperCAmelCase         =     classifier_dropout_prob
								UpperCAmelCase         =     initializer_range
								UpperCAmelCase         =     layer_norm_eps





class  __magic_name__   (							lowerCamelCase__						):
		lowercase      :       Dict          =version.parse('''1.11'''						)






		@property
		def SCREAMING_SNAKE_CASE_			(						self      :     List[Any]      )				->					Mapping[str, Mapping[int, str]]:
								'''simple docstring'''







								return OrderedDict([("pixel_values", {0: "batch"})]      )






		@property
		def SCREAMING_SNAKE_CASE_			(						self      :     Dict      )				->					Mapping[str, Mapping[int, str]]:
								'''simple docstring'''







								if self.task == "image-classification":
														return OrderedDict([("logits", {0: "batch"})]      )
								else:
														return OrderedDict([("last_hidden_state", {0: "batch"}), ("pooler_output", {0: "batch"})]      )







		@property
		def SCREAMING_SNAKE_CASE_			(						self      :     Tuple      )				->					float:
								'''simple docstring'''







								return 1e-4








from typing import TYPE_CHECKING

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


__lowerCamelCase     :							Any  							=				{
    "configuration_mega": ["MEGA_PRETRAINED_CONFIG_ARCHIVE_MAP", "MegaConfig", "MegaOnnxConfig"],
}

try:
		if not is_torch_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		__lowerCamelCase     :							Optional[int]  							=				[
		    "MEGA_PRETRAINED_MODEL_ARCHIVE_LIST",
		    "MegaForCausalLM",
		    "MegaForMaskedLM",
		    "MegaForMultipleChoice",
		    "MegaForQuestionAnswering",
		    "MegaForSequenceClassification",
		    "MegaForTokenClassification",
		    "MegaModel",
		    "MegaPreTrainedModel",
		]

if TYPE_CHECKING:
		from .configuration_mega import MEGA_PRETRAINED_CONFIG_ARCHIVE_MAP, MegaConfig, MegaOnnxConfig

		try:
				if not is_torch_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_mega import (
				    MEGA_PRETRAINED_MODEL_ARCHIVE_LIST,
				    MegaForCausalLM,
				    MegaForMaskedLM,
				    MegaForMultipleChoice,
				    MegaForQuestionAnswering,
				    MegaForSequenceClassification,
				    MegaForTokenClassification,
				    MegaModel,
				    MegaPreTrainedModel,
				)

else:
		import sys

		__lowerCamelCase     :							int  							=				_LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)



