






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












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}}}""")




