
from collections import OrderedDict
from typing import List, Mapping

from packaging import version

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


lowercase  :					Optional[int]													=       logging.get_logger(__name__)

lowercase  :					Any													=       {
    "google/efficientnet-b7": "https://huggingface.co/google/efficientnet-b7/resolve/main/config.json",
}




class  lowerCamelCase__							(				__lowercase):




	'''simple docstring'''




	_A								=     'efficientnet'







	def __init__(					self		:Dict     ,   a		:int = 3     ,   a		:int = 6_0_0     ,   a		:float = 2.0     ,   a		:float = 3.1     ,   a		:int = 8     ,   a		:List[int] = [3, 3, 5, 3, 5, 5, 3]     ,   a		:List[int] = [3_2, 1_6, 2_4, 4_0, 8_0, 1_1_2, 1_9_2]     ,   a		:List[int] = [1_6, 2_4, 4_0, 8_0, 1_1_2, 1_9_2, 3_2_0]     ,   a		:List[int] = []     ,   a		:List[int] = [1, 2, 2, 2, 1, 2, 1]     ,   a		:List[int] = [1, 2, 2, 3, 3, 4, 1]     ,   a		:List[int] = [1, 6, 6, 6, 6, 6, 6]     ,   a		:float = 0.25     ,   a		:str = "swish"     ,   a		:int = 2_5_6_0     ,   a		:str = "mean"     ,   a		:float = 0.02     ,   a		:float = 0.001     ,   a		:float = 0.99     ,   a		:float = 0.5     ,   a		:float = 0.2     ,   **a		:Optional[int]     ,   )		-> Optional[Any]:
								super().__init__(**A_						)

								__UpperCamelCase	:   List[str]			   =					num_channels
								__UpperCamelCase	:   str			   =					image_size
								__UpperCamelCase	:   Optional[Any]			   =					width_coefficient
								__UpperCamelCase	:   List[str]			   =					depth_coefficient
								__UpperCamelCase	:   Optional[Any]			   =					depth_divisor
								__UpperCamelCase	:   Tuple			   =					kernel_sizes
								__UpperCamelCase	:   List[str]			   =					in_channels
								__UpperCamelCase	:   int			   =					out_channels
								__UpperCamelCase	:   Optional[Any]			   =					depthwise_padding
								__UpperCamelCase	:   int			   =					strides
								__UpperCamelCase	:   Optional[Any]			   =					num_block_repeats
								__UpperCamelCase	:   List[Any]			   =					expand_ratios
								__UpperCamelCase	:   Dict			   =					squeeze_expansion_ratio
								__UpperCamelCase	:   List[Any]			   =					hidden_act
								__UpperCamelCase	:   Dict			   =					hidden_dim
								__UpperCamelCase	:   str			   =					pooling_type
								__UpperCamelCase	:   Union[str, Any]			   =					initializer_range
								__UpperCamelCase	:   Union[str, Any]			   =					batch_norm_eps
								__UpperCamelCase	:   Optional[Any]			   =					batch_norm_momentum
								__UpperCamelCase	:   Optional[int]			   =					dropout_rate
								__UpperCamelCase	:   Union[str, Any]			   =					drop_connect_rate
								__UpperCamelCase	:   Optional[int]			   =					sum(A_						) * 4




class  lowerCamelCase__							(				__lowercase):




	'''simple docstring'''




	_A								=     version.parse('1.11')







	@property
	def    _lowerCamelCase				(					self		:Optional[int]						)		-> Mapping[str, Mapping[int, str]]:
								return OrderedDict(
								    [
								        ("pixel_values", {0: "batch", 1: "num_channels", 2: "height", 3: "width"}),
								    ]						)







	@property
	def    _lowerCamelCase				(					self		:List[str]						)		-> float:
								return 1E-5





from collections.abc import Callable

import numpy as np

def 			_SCREAMING_SNAKE_CASE			(							lowercase	:       Callable	,							lowercase	:       float	,							lowercase	:       float	,							lowercase	:       float	,							lowercase	:       float     ):


      '''simple docstring'''



      lowerCamelCase_		     =    int(np.ceil((x_end - xa) / step_size     )     )
      lowerCamelCase_		     =    np.zeros((n + 1,)     )
      lowerCamelCase_		     =    ya
      lowerCamelCase_		     =    xa

      for k in range(lowercase     ):
            lowerCamelCase_		     =    y[k] + step_size * ode_func(lowercase	,							y[k]     )
            lowerCamelCase_		     =    y[k] + (
                (step_size / 2) * (ode_func(lowercase	,							y[k]     ) + ode_func(x + step_size	,							lowercase     ))
            )
            x += step_size

      return y


if __name__ == "__main__":
 import doctest

 doctest.testmod()



