



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


UpperCamelCase_										=					logging.get_logger(__name__)

UpperCamelCase_										=					{
    '''google/mobilenet_v2_1.4_224''': '''https://huggingface.co/google/mobilenet_v2_1.4_224/resolve/main/config.json''',
    '''google/mobilenet_v2_1.0_224''': '''https://huggingface.co/google/mobilenet_v2_1.0_224/resolve/main/config.json''',
    '''google/mobilenet_v2_0.75_160''': '''https://huggingface.co/google/mobilenet_v2_0.75_160/resolve/main/config.json''',
    '''google/mobilenet_v2_0.35_96''': '''https://huggingface.co/google/mobilenet_v2_0.35_96/resolve/main/config.json''',
    # See all MobileNetV2 models at https://huggingface.co/models?filter=mobilenet_v2
}







class 	_snake_case							(			lowerCamelCase_      ):






   '''simple docstring'''






   A__	:      int    =		"""mobilenet_v2"""


   def __init__(  self:			int   ,lowerCamelCase_:			Optional[Any]=3   ,lowerCamelCase_:			Dict=224   ,lowerCamelCase_:			Dict=1.0   ,lowerCamelCase_:			Any=8   ,lowerCamelCase_:			Any=8   ,lowerCamelCase_:			Optional[Any]=6   ,lowerCamelCase_:			List[str]=32   ,lowerCamelCase_:			int=True   ,lowerCamelCase_:			Optional[Any]=True   ,lowerCamelCase_:			Dict="relu6"   ,lowerCamelCase_:			List[Any]=True   ,lowerCamelCase_:			str=0.8   ,lowerCamelCase_:			Optional[int]=0.0_2   ,lowerCamelCase_:			str=0.0_0_1   ,lowerCamelCase_:			int=255   ,**lowerCamelCase_:			Optional[Any]   ,)     ->	Any:
       super().__init__(**lowerCAmelCase__				)

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

       UpperCAmelCase_			:    List[str]   						= num_channels
       UpperCAmelCase_			:    str   						= image_size
       UpperCAmelCase_			:    Optional[int]   						= depth_multiplier
       UpperCAmelCase_			:    List[str]   						= depth_divisible_by
       UpperCAmelCase_			:    int   						= min_depth
       UpperCAmelCase_			:    Any   						= expand_ratio
       UpperCAmelCase_			:    List[str]   						= output_stride
       UpperCAmelCase_			:    List[str]   						= first_layer_is_expansion
       UpperCAmelCase_			:    Union[str, Any]   						= finegrained_output
       UpperCAmelCase_			:    str   						= hidden_act
       UpperCAmelCase_			:    Any   						= tf_padding
       UpperCAmelCase_			:    List[Any]   						= classifier_dropout_prob
       UpperCAmelCase_			:    List[Any]   						= initializer_range
       UpperCAmelCase_			:    Dict   						= layer_norm_eps
       UpperCAmelCase_			:    List[str]   						= semantic_loss_ignore_index







class 	_snake_case							(			lowerCamelCase_      ):






   '''simple docstring'''






   A__	:      Dict    =		version.parse("1.11"      )


   @property
   def 	A__  (  self:			Tuple				)     ->	Mapping[str, Mapping[int, str]]:
       return OrderedDict([("""pixel_values""", {0: """batch"""})]				)


   @property
   def 	A__  (  self:			List[str]				)     ->	Mapping[str, Mapping[int, str]]:
       if self.task == "image-classification":
           return OrderedDict([("""logits""", {0: """batch"""})]				)
       else:
           return OrderedDict([("""last_hidden_state""", {0: """batch"""}), ("""pooler_output""", {0: """batch"""})]				)






   @property
   def 	A__  (  self:			Optional[Any]				)     ->	float:
       return 1e-4




"""simple docstring"""




from __future__ import annotations

from collections.abc import Callable


def     lowercase			(SCREAMING_SNAKE_CASE_		:				Callable[[int | float], int | float] ,			SCREAMING_SNAKE_CASE_		:				int | float ,			SCREAMING_SNAKE_CASE_		:				int | float ,			SCREAMING_SNAKE_CASE_		:				int = 1_00 ,			)   ->						float:
						SCREAMING_SNAKE_CASE													=					x_start
						SCREAMING_SNAKE_CASE													=					fnc(SCREAMING_SNAKE_CASE_  )
						SCREAMING_SNAKE_CASE													=					0.0
						for _ in range(SCREAMING_SNAKE_CASE_  ):
												# Approximates small segments of curve as linear and solve
												# for trapezoidal area
												SCREAMING_SNAKE_CASE													=					(x_end - x_start) / steps + xa
												SCREAMING_SNAKE_CASE													=					fnc(SCREAMING_SNAKE_CASE_  )
												area += abs(fxa + fxa  ) * (xa - xa) / 2
												# Increment step
												SCREAMING_SNAKE_CASE													=					xa
												SCREAMING_SNAKE_CASE													=					fxa
						return area


if __name__ == "__main__":


	def     lowercase			(SCREAMING_SNAKE_CASE_		:				Optional[int]  )   ->						Dict:
							return x**3 + x**2

	print('''f(x) = x^3 + x^2''')
	print('''The area between the curve, x = -5, x = 5 and the x axis is:''')
	__UpperCamelCase						=      10
	while i <= 100000:
		print(f'''with {i} steps: {trapezoidal_area(f, -5, 5, i)}''')
		i *= 10


