





from ...configuration_utils import PretrainedConfig
from ...utils import logging


SCREAMING_SNAKE_CASE__       :			Optional[Any]			      =						logging.get_logger(__name__)

SCREAMING_SNAKE_CASE__       :			List[Any]			      =						{
    """naver-clova-ix/donut-base""": """https://huggingface.co/naver-clova-ix/donut-base/resolve/main/config.json""",
    # See all Donut models at https://huggingface.co/models?filter=donut-swin
}







class   __lowerCAmelCase			(					_UpperCamelCase  ):
   _UpperCamelCase :						List[Any]     		=				"""donut-swin"""

   _UpperCamelCase :						int     		=				{
       """num_attention_heads""": """num_heads""",
       """num_hidden_layers""": """num_layers""",
   }





   def __init__( self					,			snake_case=224					,			snake_case=4					,			snake_case=3					,			snake_case=96					,			snake_case=[2, 2, 6, 2]					,			snake_case=[3, 6, 12, 24]					,			snake_case=7					,			snake_case=4.0					,			snake_case=True					,			snake_case=0.0					,			snake_case=0.0					,			snake_case=0.1					,			snake_case="gelu"					,			snake_case=False					,			snake_case=0.02					,			snake_case=1E-5					,			**snake_case					,			)				->  Dict:
      """simple docstring"""





      super().__init__(**snake_case      )

      a__	:						List[str]      		=  image_size
      a__	:						str      		=  patch_size
      a__	:						List[Any]      		=  num_channels
      a__	:						Optional[Any]      		=  embed_dim
      a__	:						Union[str, Any]      		=  depths
      a__	:						Tuple      		=  len(snake_case      )
      a__	:						Any      		=  num_heads
      a__	:						Optional[int]      		=  window_size
      a__	:						int      		=  mlp_ratio
      a__	:						Optional[Any]      		=  qkv_bias
      a__	:						List[str]      		=  hidden_dropout_prob
      a__	:						Optional[Any]      		=  attention_probs_dropout_prob
      a__	:						List[str]      		=  drop_path_rate
      a__	:						List[Any]      		=  hidden_act
      a__	:						Tuple      		=  use_absolute_embeddings
      a__	:						Any      		=  layer_norm_eps
      a__	:						Optional[int]      		=  initializer_range
      # we set the hidden_size attribute in order to make Swin work with VisionEncoderDecoderModel
      # this indicates the channel dimension after the last stage of the model
      a__	:						List[Any]      		=  int(embed_dim * 2 ** (len(snake_case      ) - 1)      )








from collections.abc import Callable
from math import pi, sqrt
from random import uniform
from statistics import mean






def       _A  (      lowerCamelCase   ):

   # A local function to see if a dot lands in the circle.
   def is_in_circle(lowerCamelCase							, lowerCamelCase   ) -> bool:
      a__	:						Any      		=  sqrt((x**2) + (y**2)   )
      # Our circle has a radius of 1, so a distance
      # greater than 1 would land outside the circle.
      return distance_from_centre <= 1

   # The proportion of guesses that landed in the circle
   a__	:						Union[str, Any]      		=  mean(
       int(is_in_circle(uniform(-1.0							, 1.0   )							, uniform(-1.0							, 1.0   )   )   )
       for _ in range(lowerCamelCase   )   )
   # The ratio of the area for circle to square is pi/4.
   a__	:						Any      		=  proportion * 4
   print(F"""The estimated value of pi is {pi_estimate}"""   )
   print(F"""The numpy value of pi is {pi}"""   )
   print(F"""The total error is {abs(pi - pi_estimate   )}"""   )






def       _A  (      lowerCamelCase							, lowerCamelCase							, lowerCamelCase = 0.0							, lowerCamelCase = 1.0							, ):
   return mean(
       function_to_integrate(uniform(lowerCamelCase							, lowerCamelCase   )   ) for _ in range(lowerCamelCase   )   ) * (max_value - min_value)






def       _A  (      lowerCamelCase							, lowerCamelCase = 0.0							, lowerCamelCase = 1.0   ):

   def identity_function(lowerCamelCase   ) -> float:
      return x

   a__	:						int      		=  area_under_curve_estimator(
       lowerCamelCase							, lowerCamelCase							, lowerCamelCase							, lowerCamelCase   )
   a__	:						int      		=  (max_value * max_value - min_value * min_value) / 2

   print("******************"   )
   print(F"""Estimating area under y=x where x varies from {min_value} to {max_value}"""   )
   print(F"""Estimated value is {estimated_value}"""   )
   print(F"""Expected value is {expected_value}"""   )
   print(F"""Total error is {abs(estimated_value - expected_value   )}"""   )
   print("******************"   )






def       _A  (      lowerCamelCase   ):

   def function_to_integrate(lowerCamelCase   ) -> float:
      return sqrt(4.0 - x * x   )

   a__	:						int      		=  area_under_curve_estimator(
       lowerCamelCase							, lowerCamelCase							, 0.0							, 2.0   )

   print("******************"   )
   print("Estimating pi using area_under_curve_estimator"   )
   print(F"""Estimated value is {estimated_value}"""   )
   print(F"""Expected value is {pi}"""   )
   print(F"""Total error is {abs(estimated_value - pi   )}"""   )
   print("******************"   )


if __name__ == "__main__":
  import doctest

  doctest.testmod()

