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


lowerCamelCase__    :		Union[str, Any]          =   logging.get_logger(__name__)

lowerCamelCase__    :		Optional[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       _snake_case   (	UpperCAmelCase_							):
 __lowerCAmelCase  :     Any        =			'donut-swin'

 __lowerCAmelCase  :     int        =			{
     'num_attention_heads': 'num_heads',
     'num_hidden_layers': 'num_layers',
 }


 def __init__(					self					,  SCREAMING_SNAKE_CASE_=2_24					,  SCREAMING_SNAKE_CASE_=4					,  SCREAMING_SNAKE_CASE_=3					,  SCREAMING_SNAKE_CASE_=96					,  SCREAMING_SNAKE_CASE_=[2, 2, 6, 2]					,  SCREAMING_SNAKE_CASE_=[3, 6, 12, 24]					,  SCREAMING_SNAKE_CASE_=7					,  SCREAMING_SNAKE_CASE_=4.0					,  SCREAMING_SNAKE_CASE_=True					,  SCREAMING_SNAKE_CASE_=0.0					,  SCREAMING_SNAKE_CASE_=0.0					,  SCREAMING_SNAKE_CASE_=0.1					,  SCREAMING_SNAKE_CASE_="gelu"					,  SCREAMING_SNAKE_CASE_=False					,  SCREAMING_SNAKE_CASE_=0.0_2					,  SCREAMING_SNAKE_CASE_=1E-5					,  **SCREAMING_SNAKE_CASE_					,  ):


   '''simple docstring'''




   super().__init__(**SCREAMING_SNAKE_CASE_)

   lowercase__  :   List[Any]    		=      image_size
   lowercase__  :   str    		=      patch_size
   lowercase__  :   List[Any]    		=      num_channels
   lowercase__  :   int    		=      embed_dim
   lowercase__  :   Union[str, Any]    		=      depths
   lowercase__  :   Union[str, Any]    		=      len(SCREAMING_SNAKE_CASE_)
   lowercase__  :   Any    		=      num_heads
   lowercase__  :   Union[str, Any]    		=      window_size
   lowercase__  :   List[Any]    		=      mlp_ratio
   lowercase__  :   int    		=      qkv_bias
   lowercase__  :   Union[str, Any]    		=      hidden_dropout_prob
   lowercase__  :   List[Any]    		=      attention_probs_dropout_prob
   lowercase__  :   str    		=      drop_path_rate
   lowercase__  :   List[Any]    		=      hidden_act
   lowercase__  :   Any    		=      use_absolute_embeddings
   lowercase__  :   Tuple    		=      layer_norm_eps
   lowercase__  :   str    		=      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
   lowercase__  :   List[Any]    		=      int(embed_dim * 2 ** (len(SCREAMING_SNAKE_CASE_) - 1))








from math import factorial

lowerCamelCase__    :		dict[str, int]          =   {str(digit): factorial(digit) for digit in range(1_0)}

def 		UpperCamelCase	(      lowercase_   )		->	int:


  '''simple docstring'''






  if not isinstance(lowercase_ , lowercase_   ):
    raise TypeError("""Parameter number must be int"""   )

  if number < 0:
    raise ValueError("""Parameter number must be greater than or equal to 0"""   )

  # Converts number in string to iterate on its digits and adds its factorial.
  return sum(DIGIT_FACTORIAL[digit] for digit in str(lowercase_   )   )

def 		UpperCamelCase	(      lowercase_ = 60 , lowercase_ = 1_00_00_00   )		->	int:


  '''simple docstring'''






  if not isinstance(lowercase_ , lowercase_   ) or not isinstance(lowercase_ , lowercase_   ):
    raise TypeError("""Parameters chain_length and number_limit must be int"""   )

  if chain_length <= 0 or number_limit <= 0:
    raise ValueError(
        """Parameters chain_length and number_limit must be greater than 0"""   )

  # the counter for the chains with the exact desired length
  lowercase__  :   List[Any]    		=      0
  # the cached sizes of the previous chains
  lowercase__  :   dict[int, int]    		=      {}

  for start_chain_element in range(1 , lowercase_   ):
    # The temporary set will contain the elements of the chain
    lowercase__  :   Optional[Any]    		=      set()
    lowercase__  :   Union[str, Any]    		=      0

    # Stop computing the chain when you find a cached size, a repeating item or the
    # length is greater then the desired one.
    lowercase__  :   Union[str, Any]    		=      start_chain_element
    while (
        chain_element not in chain_sets_lengths
        and chain_element not in chain_set
        and chain_set_length <= chain_length
    ):
      chain_set.add(lowercase_   )
      chain_set_length += 1
      lowercase__  :   str    		=      digit_factorial_sum(lowercase_   )

    if chain_element in chain_sets_lengths:
      chain_set_length += chain_sets_lengths[chain_element]

    lowercase__  :   str    		=      chain_set_length

    # If chain contains the exact amount of elements increase the counter
    if chain_set_length == chain_length:
      chains_counter += 1

  return chains_counter


if __name__ == "__main__":
     import doctest

     doctest.testmod()
     print(f'''{solution()}''')






