
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      =logging.get_logger(__name__)

lowerCamelCase      ={
    "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 				_lowerCamelCase (      UpperCamelCase_ ):





     """simple docstring"""







     SCREAMING_SNAKE_CASE_		   =  '''mobilenet_v2'''






     def __init__(							self						,						__SCREAMING_SNAKE_CASE=3						,						__SCREAMING_SNAKE_CASE=2_2_4						,						__SCREAMING_SNAKE_CASE=1.0						,						__SCREAMING_SNAKE_CASE=8						,						__SCREAMING_SNAKE_CASE=8						,						__SCREAMING_SNAKE_CASE=6						,						__SCREAMING_SNAKE_CASE=3_2						,						__SCREAMING_SNAKE_CASE=True						,						__SCREAMING_SNAKE_CASE=True						,						__SCREAMING_SNAKE_CASE="relu6"						,						__SCREAMING_SNAKE_CASE=True						,						__SCREAMING_SNAKE_CASE=0.8						,						__SCREAMING_SNAKE_CASE=0.02						,						__SCREAMING_SNAKE_CASE=0.001						,						__SCREAMING_SNAKE_CASE=2_5_5						,						**__SCREAMING_SNAKE_CASE						,						)  ->   List[str]:




           """simple docstring"""







           super().__init__(**__SCREAMING_SNAKE_CASE  )

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

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




class 				_lowerCamelCase (      UpperCamelCase_ ):





     """simple docstring"""







     SCREAMING_SNAKE_CASE_		   =  version.parse('''1.11''' )






     @property
     def    __SCREAMING_SNAKE_CASE			(							self  )  ->   Mapping[str, Mapping[int, str]]:




           """simple docstring"""







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






     @property
     def    __SCREAMING_SNAKE_CASE			(							self  )  ->   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  )  ->   float:




           """simple docstring"""







           return 1e-4




from __future__ import annotations

from collections.abc import Callable


def 							SCREAMING_SNAKE_CASE_      (			UpperCamelCase__ ,		UpperCamelCase__ ,		UpperCamelCase__ ,		UpperCamelCase__ = 1_0_0 ,		):
      UpperCamelCase__    :   Union[str, Any]					=				x_start
      UpperCamelCase__    :   List[Any]					=				fnc(UpperCamelCase__		)
      UpperCamelCase__    :   Any					=				0.0
      for _ in range(UpperCamelCase__		):
            # Approximates small segments of curve as linear and solve
            # for trapezoidal area
            UpperCamelCase__    :   str					=				(x_end - x_start) / steps + xa
            UpperCamelCase__    :   Dict					=				fnc(UpperCamelCase__		)
            area += abs(fxa + fxa		) * (xa - xa) / 2
            # Increment step
            UpperCamelCase__    :   Tuple					=				xa
            UpperCamelCase__    :   Union[str, Any]					=				fxa
      return area


if __name__ == "__main__":


    def 							SCREAMING_SNAKE_CASE_      (			UpperCamelCase__		):
          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:")
    lowerCamelCase      =1_0
    while i <= 1_0_0_0_0_0:
        print(F'''with {i} steps: {trapezoidal_area(f, -5, 5, i)}''')
        i *= 1_0


