


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				:       Optional[Any]      =  logging.get_logger(__name__)

A				:       Union[str, Any]      =  {
    '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      UpperCamelCase(	_a ):
					snake_case_		:					List[Any]    					=						"""mobilenet_v1"""


					def __init__(    self						:		Tuple  ,     SCREAMING_SNAKE_CASE						:		List[Any]=3  ,     SCREAMING_SNAKE_CASE						:		Optional[int]=2_2_4  ,     SCREAMING_SNAKE_CASE						:		Any=1.0  ,     SCREAMING_SNAKE_CASE						:		int=8  ,     SCREAMING_SNAKE_CASE						:		Dict="relu6"  ,     SCREAMING_SNAKE_CASE						:		int=True  ,     SCREAMING_SNAKE_CASE						:		Any=0.999  ,     SCREAMING_SNAKE_CASE						:		Dict=0.02  ,     SCREAMING_SNAKE_CASE						:		Any=0.001  ,     **SCREAMING_SNAKE_CASE						:		List[str]  ,     )      ->						List[Any]:




											'''simple docstring'''

											super().__init__(**SCREAMING_SNAKE_CASE    )

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

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






class      UpperCamelCase(	_a ):
					snake_case_		:					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						:		Union[str, Any]    )      ->						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						:		Any    )      ->						float:




											'''simple docstring'''

											return 1e-4









from __future__ import annotations





def 						_lowerCAmelCase							(     _lowerCAmelCase			)    ->						list[int]:


						'''simple docstring'''






						__snake_case				=		2
						__snake_case				=		[]
						while i * i <= n:
												if n % i:
																		i += 1
												else:
																		n //= i
																		factors.append(_lowerCAmelCase			)
						if n > 1:
												factors.append(_lowerCAmelCase			)
						return factors


if __name__ == "__main__":
	import doctest

	doctest.testmod()





