


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_				:			int   =	logging.get_logger(__name__)

UpperCAmelCase_				:			Optional[Any]   =	{
    'google/vit-base-patch16-224': 'https://huggingface.co/vit-base-patch16-224/resolve/main/config.json',
    # See all ViT models at https://huggingface.co/models?filter=vit
}
class 			SCREAMING_SNAKE_CASE__  (    lowercase__      ):
      snake_case__    :					Optional[Any]					       =						'''vit'''
      def __init__(	self    :	str					,							SCREAMING_SNAKE_CASE__    :	Tuple=7_6_8					,							SCREAMING_SNAKE_CASE__    :	str=1_2					,							SCREAMING_SNAKE_CASE__    :	Any=1_2					,							SCREAMING_SNAKE_CASE__    :	Tuple=3_0_7_2					,							SCREAMING_SNAKE_CASE__    :	Optional[int]="gelu"					,							SCREAMING_SNAKE_CASE__    :	Union[str, Any]=0.0					,							SCREAMING_SNAKE_CASE__    :	Optional[int]=0.0					,							SCREAMING_SNAKE_CASE__    :	Optional[Any]=0.02					,							SCREAMING_SNAKE_CASE__    :	int=1E-12					,							SCREAMING_SNAKE_CASE__    :	str=2_2_4					,							SCREAMING_SNAKE_CASE__    :	Optional[Any]=1_6					,							SCREAMING_SNAKE_CASE__    :	List[str]=3					,							SCREAMING_SNAKE_CASE__    :	List[str]=True					,							SCREAMING_SNAKE_CASE__    :	Dict=1_6					,							**SCREAMING_SNAKE_CASE__    :	Union[str, Any]					,							)		->      List[str]:
             super().__init__(**SCREAMING_SNAKE_CASE__					)

             a_ :		Tuple							=  hidden_size
             a_ :		Optional[int]							=  num_hidden_layers
             a_ :		int							=  num_attention_heads
             a_ :		Union[str, Any]							=  intermediate_size
             a_ :		str							=  hidden_act
             a_ :		Union[str, Any]							=  hidden_dropout_prob
             a_ :		int							=  attention_probs_dropout_prob
             a_ :		str							=  initializer_range
             a_ :		Dict							=  layer_norm_eps
             a_ :		Tuple							=  image_size
             a_ :		List[Any]							=  patch_size
             a_ :		Union[str, Any]							=  num_channels
             a_ :		Dict							=  qkv_bias
             a_ :		Optional[int]							=  encoder_stride
class 			SCREAMING_SNAKE_CASE__  (    lowercase__      ):
      snake_case__    :					str					       =						version.parse('''1.11'''      )
      @property
      def 	SCREAMING_SNAKE_CASE							(	self    :	Union[str, Any]					)		->      Mapping[str, Mapping[int, str]]:
             return OrderedDict(
                 [
                     ('pixel_values', {0: 'batch', 1: 'num_channels', 2: 'height', 3: 'width'}),
                 ]					)

      @property
      def 	SCREAMING_SNAKE_CASE							(	self    :	Union[str, Any]					)		->      float:
             return 1E-4






from __future__ import annotations

from math import pi

# Define the Reduced Planck Constant ℏ (H bar), speed of light C, value of
# Pi and the function
UpperCAmelCase_				:			str   =	1.054571817e-34  # unit of ℏ : J * s

UpperCAmelCase_				:			Dict   =	3e8  # unit of c : m * s^-1







def 	SCREAMING_SNAKE_CASE_						(      __A			:      float  ,   __A			:      float  ,   __A			:      float    )     ->		dict[str, float]:

       """simple docstring"""

       if (force, area, distance).count(0    ) != 1:
              raise ValueError('One and only one argument must be 0'    )
       if force < 0:
              raise ValueError('Magnitude of force can not be negative'    )
       if distance < 0:
              raise ValueError('Distance can not be negative'    )
       if area < 0:
              raise ValueError('Area can not be negative'    )
       if force == 0:
              a_ :		Optional[Any]							=  (REDUCED_PLANCK_CONSTANT * SPEED_OF_LIGHT * pi**2 * area) / (
                  2_40 * (distance) ** 4
              )
              return {"force": force}
       elif area == 0:
              a_ :		List[str]							=  (2_40 * force * (distance) ** 4) / (
                  REDUCED_PLANCK_CONSTANT * SPEED_OF_LIGHT * pi**2
              )
              return {"area": area}
       elif distance == 0:
              a_ :		Tuple							=  (
                  (REDUCED_PLANCK_CONSTANT * SPEED_OF_LIGHT * pi**2 * area) / (2_40 * force)
              ) ** (1 / 4)
              return {"distance": distance}
       raise ValueError('One and only one argument must be 0'    )


# Run doctest
if __name__ == "__main__":
   import doctest

   doctest.testmod()


