code: 
  
  
  
  
  
  
  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  							=  {
      "apple/mobilevitv2-1.0": "https://huggingface.co/apple/mobilevitv2-1.0/resolve/main/config.json",
  }
  
  
  
  class 							_A (    UpperCamelCase							):
      """simple docstring"""
  
      lowerCamelCase  :       Dict   =   'mobilevitv2'
  
  
  
      def __init__(							self			:    Tuple   ,    __SCREAMING_SNAKE_CASE			:    Dict=3   ,    __SCREAMING_SNAKE_CASE			:    Optional[Any]=256   ,    __SCREAMING_SNAKE_CASE			:    int=2   ,    __SCREAMING_SNAKE_CASE			:    int=2.0   ,    __SCREAMING_SNAKE_CASE			:    str="swish"   ,    __SCREAMING_SNAKE_CASE			:    Optional[Any]=3   ,    __SCREAMING_SNAKE_CASE			:    Dict=32   ,    __SCREAMING_SNAKE_CASE			:    List[Any]=0.1   ,    __SCREAMING_SNAKE_CASE			:    Optional[int]=0.02   ,    __SCREAMING_SNAKE_CASE			:    Union[str, Any]=1e-5   ,    __SCREAMING_SNAKE_CASE			:    Any=512   ,    __SCREAMING_SNAKE_CASE			:    Tuple=[6, 12, 18]   ,    __SCREAMING_SNAKE_CASE			:    int=0.1   ,    __SCREAMING_SNAKE_CASE			:    str=255   ,    __SCREAMING_SNAKE_CASE			:    Any=[2, 4, 3]   ,    __SCREAMING_SNAKE_CASE			:    Optional[Any]=[128, 192, 256]   ,    __SCREAMING_SNAKE_CASE			:    Union[str, Any]=1.0   ,    __SCREAMING_SNAKE_CASE			:    Any=2   ,    __SCREAMING_SNAKE_CASE			:    str=0.0   ,    __SCREAMING_SNAKE_CASE			:    Union[str, Any]=0.0   ,    **__SCREAMING_SNAKE_CASE			:    List[str]   ,    )			->       str:
             super().__init__(**__SCREAMING_SNAKE_CASE			)
  
             __UpperCAmelCase    							=num_channels
             __UpperCAmelCase    							=image_size
             __UpperCAmelCase    							=patch_size
             __UpperCAmelCase    							=expand_ratio
             __UpperCAmelCase    							=hidden_act
             __UpperCAmelCase    							=conv_kernel_size
             __UpperCAmelCase    							=output_stride
             __UpperCAmelCase    							=initializer_range
             __UpperCAmelCase    							=layer_norm_eps
             __UpperCAmelCase    							=n_attn_blocks
             __UpperCAmelCase    							=base_attn_unit_dims
             __UpperCAmelCase    							=width_multiplier
             __UpperCAmelCase    							=ffn_multiplier
             __UpperCAmelCase    							=ffn_dropout
             __UpperCAmelCase    							=attn_dropout
             __UpperCAmelCase    							=classifier_dropout_prob
  
             # decode head attributes for semantic segmentation
             __UpperCAmelCase    							=aspp_out_channels
             __UpperCAmelCase    							=atrous_rates
             __UpperCAmelCase    							=aspp_dropout_prob
             __UpperCAmelCase    							=semantic_loss_ignore_index
  
  
  
  
  class 							_A (    UpperCamelCase							):
      """simple docstring"""
  
      lowerCamelCase  :       str   =   version.parse('1.11'							)
  
  
  
      @property
      def _a       (							self			:    Union[str, Any]			)			->       Mapping[str, Mapping[int, str]]:
             return OrderedDict([("""pixel_values""", {0: """batch""", 1: """num_channels""", 2: """height""", 3: """width"""})]			)
  
  
  
      @property
      def _a       (							self			:    Union[str, Any]			)			->       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			:    int			)			->       float:
             return 1e-4
  
  
style_context: 
  
  
  
  
  
  
  import math as m
  
  
  
  
  
  
  
  def 			lowercase__  (				A_:	Tuple     ,			A_:	str     ,			A_:	Union[str, Any]=0.0_0_1		)  ->							List[str]:
  
  
  
  
  
  
  
         """simple docstring"""
  
  
  
  
  
  
         return (f(a + h		) - f(a - h		)) / (2 * h)
  
  
  
  
  
  
  
  def 			lowercase__  (				A_:	str     ,			A_:	Union[str, Any]=0     ,			A_:	List[Any]=100     ,			A_:	Optional[Any]=0.0_0_0_1     ,			A_:	Tuple=1e-6     ,			A_:	Union[str, Any]=False		)  ->							Union[str, Any]:
  
  
  
  
  
  
  
         """simple docstring"""
  
  
  
  
  
  
         __UpperCAmelCase    							=xa  # set the initial guess
         __UpperCAmelCase    							=[a]
         __UpperCAmelCase    							=abs(f(A_		)		)
         __UpperCAmelCase    							=lambda A_		: calc_derivative(A_     ,			A_     ,			h=A_		)  # noqa: E731  Derivative of f(x)
         for _ in range(A_		):
                if fa(A_		) == 0:
                       raise ValueError("""No converging solution found"""		)
                __UpperCAmelCase    							=a - f(A_		) / fa(A_		)  # Calculate the next estimate
                if logsteps:
                       steps.append(A_		)
                if error < maxerror:
                       break
         else:
                raise ValueError("""Iteration limit reached, no converging solution found"""		)
         if logsteps:
                # If logstep is true, then log intermediate steps
                return a, error, steps
         return a, error
  
  
  if __name__ == "__main__":
     from matplotlib import pyplot as plt
  
     __A  							=  lambda x: m.tanh(x) ** 2 - m.exp(3 * x)  # noqa: E731
     __A							,    __A							,    __A  							=  newton_raphson(
         f, xa=10, maxiter=10_00, step=1E-6, logsteps=True
     )
     plt.plot([abs(f(x)) for x in steps])
     plt.xlabel("step")
     plt.ylabel("error")
     plt.show()
     print(F"""solution = {{{solution:f}}}, error = {{{error:f}}}""")
  
  
  
  
  