


"""simple docstring"""







import functools
import operator

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


lowerCamelCase_   :   Any									=  logging.get_logger(__name__)

lowerCamelCase_   :   List[str]									=  {
    """asapp/sew-d-tiny-100k""": """https://huggingface.co/asapp/sew-d-tiny-100k/resolve/main/config.json""",
    # See all SEW-D models at https://huggingface.co/models?filter=sew-d
}






class       __A				(       _SCREAMING_SNAKE_CASE			):






 """simple docstring"""




 __lowerCAmelCase 					=  "sew-d"


 def __init__(		self ,    __A=32 ,    __A=768 ,    __A=12 ,    __A=12 ,    __A=3072 ,    __A=2 ,    __A=512 ,    __A=256 ,    __A=True ,    __A=True ,    __A=("p2c", "c2p") ,    __A="layer_norm" ,    __A="gelu_python" ,    __A=0.1 ,    __A=0.1 ,    __A=0.1 ,    __A=0.0 ,    __A=0.1 ,    __A=0.02 ,    __A=1E-7 ,    __A=1E-5 ,    __A="group" ,    __A="gelu" ,    __A=(64, 128, 128, 128, 128, 256, 256, 256, 256, 512, 512, 512, 512) ,    __A=(5, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1) ,    __A=(10, 3, 1, 3, 1, 3, 1, 3, 1, 2, 1, 2, 1) ,    __A=False ,    __A=128 ,    __A=16 ,    __A=True ,    __A=0.05 ,    __A=10 ,    __A=2 ,    __A=0.0 ,    __A=10 ,    __A=0 ,    __A="mean" ,    __A=False ,    __A=False ,    __A=256 ,    __A=0 ,    __A=1 ,    __A=2 ,    **__A ,    )					->     Optional[Any]:
     super().__init__(**__A ,    pad_token_id=__A ,    bos_token_id=__A ,    eos_token_id=__A						)
     a 		=hidden_size
     a 		=feat_extract_norm
     a 		=feat_extract_activation
     a 		=list(__A						)
     a 		=list(__A						)
     a 		=list(__A						)
     a 		=conv_bias
     a 		=num_conv_pos_embeddings
     a 		=num_conv_pos_embedding_groups
     a 		=len(self.conv_dim						)
     a 		=num_hidden_layers
     a 		=intermediate_size
     a 		=squeeze_factor
     a 		=max_position_embeddings
     a 		=position_buckets
     a 		=share_att_key
     a 		=relative_attention
     a 		=norm_rel_ebd
     a 		=list(__A						)
     a 		=hidden_act
     a 		=num_attention_heads
     a 		=hidden_dropout
     a 		=attention_dropout
     a 		=activation_dropout
     a 		=feat_proj_dropout
     a 		=final_dropout
     a 		=layer_norm_eps
     a 		=feature_layer_norm_eps
     a 		=initializer_range
     a 		=vocab_size

     if (
         (len(self.conv_stride						) != self.num_feat_extract_layers)
         or (len(self.conv_kernel						) != self.num_feat_extract_layers)
         or (len(self.conv_dim						) != self.num_feat_extract_layers)
     ):
         raise ValueError(
             '''Configuration for convolutional layers is incorrect.'''
             '''It is required that `len(config.conv_dim)` == `len(config.conv_stride)` == `len(config.conv_kernel)`,'''
             f'''but is `len(config.conv_dim) = {len(self.conv_dim						)}`, `len(config.conv_stride)'''
             f'''= {len(self.conv_stride						)}`, `len(config.conv_kernel) = {len(self.conv_kernel						)}`.'''						)

     # fine-tuning config parameters for SpecAugment: https://arxiv.org/abs/1904.08779
     a 		=apply_spec_augment
     a 		=mask_time_prob
     a 		=mask_time_length
     a 		=mask_time_min_masks
     a 		=mask_feature_prob
     a 		=mask_feature_length
     a 		=mask_feature_min_masks

     # ctc loss
     a 		=ctc_loss_reduction
     a 		=ctc_zero_infinity

     # sequence classification
     a 		=use_weighted_layer_sum
     a 		=classifier_proj_size





 @property
 def       SCREAMING_SNAKE_CASE							(		self						)					->     Dict:
     return functools.reduce(operator.mul ,    self.conv_stride ,    1						)


'''simple docstring'''



def    a__       (  a__      ,    a__						):




     """simple docstring"""


     __SCREAMING_SNAKE_CASE     =      int(a__						)

     # Initialize Result
     __SCREAMING_SNAKE_CASE     =      []

     # Traverse through all denomination
     for denomination in reversed(a__						):
          # Find denominations
          while int(a__						) >= int(a__						):
               total_value -= int(a__						)
               answer.append(a__						)  # Append the "answers" array

     return answer


# Driver Code
if __name__ == "__main__":
  UpperCAmelCase :       Dict			   =     []
  UpperCAmelCase :       List[str]			   =     '0'

  if (
      input('Do you want to enter your denominations ? (yY/n): ').strip().lower()
      == "y"
  ):
    UpperCAmelCase :       List[str]			   =     int(input('Enter the number of denominations you want to add: ').strip())

    for i in range(0, n):
      denominations.append(int(input(f"""Denomination {i}: """).strip()))
    UpperCAmelCase :       str			   =     input('Enter the change you want to make in Indian Currency: ').strip()
  else:
    # All denominations of Indian Currency if user does not enter
    UpperCAmelCase :       int			   =     [1, 2, 5, 1_0, 2_0, 5_0, 1_0_0, 5_0_0, 2_0_0_0]
    UpperCAmelCase :       Any			   =     input('Enter the change you want to make: ').strip()

  if int(value) == 0 or int(value) < 0:
    print('The total value cannot be zero or negative.')

  else:
    print(f"""Following is minimal change for {value}: """)
    UpperCAmelCase :       Any			   =     find_minimum_change(denominations, value)
    # Print result
    for i in range(len(answer)):
      print(answer[i], end=' ')






