




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_				:    Dict										=     logging.get_logger(__name__)

a_				:    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 __lowercase(      lowerCamelCase__				):




					'''simple docstring'''


					__a						:						Union[str, Any]     		= 'mobilenet_v1'
					def __init__(    self						,	__a=3						,	__a=224						,	__a=1.0						,	__a=8						,	__a="relu6"						,	__a=True						,	__a=0.999						,	__a=0.02						,	__a=0.001						,	**__a						,	):
								super().__init__(**__lowerCamelCase     )

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

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



class __lowercase(      lowerCamelCase__				):




					'''simple docstring'''


					__a						:						Any     		= version.parse('1.11'				)
					@property
					def     snake_case_				(    self     ):
								return OrderedDict([('pixel_values', {0: 'batch'})]     )
					@property
					def     snake_case_				(    self     ):






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





					@property
					def     snake_case_				(    self     ):
								return 1E-4









"""simple docstring"""
import math





def 						UpperCAmelCase				(			A__:    int = 100     )  ->     int:
			__lowerCamelCase					:	Optional[Any]	       =				sum(i * i for i in range(1							,				n + 1     )     )
			__lowerCamelCase					:	Union[str, Any]	       =				int(math.pow(sum(range(1							,				n + 1     )     )							,				2     )     )
			return square_of_sum - sum_of_squares


if __name__ == "__main__":
					print(F"""{solution() = }""")



