




"""simple docstring"""



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


A_    =							logging.get_logger(__name__)

A_    =							{
    '''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 							__SCREAMING_SNAKE_CASE						(				UpperCamelCase		):
							snake_case_     =							'mobilenet_v1'


							def __init__(			self      :						Tuple			,    snake_case      :						int=3			,    snake_case      :						Union[str, Any]=224			,    snake_case      :						Any=1.0			,    snake_case      :						Any=8			,    snake_case      :						List[Any]="relu6"			,    snake_case      :						List[str]=True			,    snake_case      :						Dict=0.999			,    snake_case      :						List[Any]=0.02			,    snake_case      :						List[Any]=0.001			,    **snake_case      :						str			,    ):
													'''simple docstring'''





													super().__init__(**_UpperCAmelCase							)

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

													A__						:  List[Any]		  =  num_channels
													A__						:  str		  =  image_size
													A__						:  Dict		  =  depth_multiplier
													A__						:  Union[str, Any]		  =  min_depth
													A__						:  List[str]		  =  hidden_act
													A__						:  Union[str, Any]		  =  tf_padding
													A__						:  List[Any]		  =  classifier_dropout_prob
													A__						:  Optional[int]		  =  initializer_range
													A__						:  Optional[int]		  =  layer_norm_eps






class 							__SCREAMING_SNAKE_CASE						(				UpperCamelCase		):
							snake_case_     =							version.parse('1.11'		)


							@property
							def 						_UpperCamelCase       (			self      :						List[Any]							):
													'''simple docstring'''





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


							@property
							def 						_UpperCamelCase       (			self      :						List[Any]							):
													'''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 						_UpperCamelCase       (			self      :						Tuple							):
													'''simple docstring'''





													return 1e-4














"""simple docstring"""



from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


A_    =							{'''configuration_sew''': ['''SEW_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''SEWConfig''']}

try:
						if not is_torch_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						A_    =							[
						    '''SEW_PRETRAINED_MODEL_ARCHIVE_LIST''',
						    '''SEWForCTC''',
						    '''SEWForSequenceClassification''',
						    '''SEWModel''',
						    '''SEWPreTrainedModel''',
						]

if TYPE_CHECKING:
						from .configuration_sew import SEW_PRETRAINED_CONFIG_ARCHIVE_MAP, SEWConfig

						try:
												if not is_torch_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .modeling_sew import (
												    SEW_PRETRAINED_MODEL_ARCHIVE_LIST,
												    SEWForCTC,
												    SEWForSequenceClassification,
												    SEWModel,
												    SEWPreTrainedModel,
												)


else:
						import sys

						A_    =							_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)






