






"""simple docstring"""


import unicodedata
from dataclasses import dataclass
from typing import Optional, Union

import numpy as np

from transformers.data.data_collator import DataCollatorMixin
from transformers.file_utils import PaddingStrategy
from transformers.tokenization_utils_base import PreTrainedTokenizerBase




def       snake_case  (A_				:Any     ,      A_				:str     ,      A_				:str     ,      A_				:Optional[Any]				):







  '''simple docstring'''

  if isinstance(A_     ,      A_				):
    a					:      Optional[int]    =       np.full((len(A_				), sequence_length, 2)     ,      A_				)
  else:
    a					:      str    =       np.full((len(A_				), sequence_length)     ,      A_				)

  for i, tensor in enumerate(A_				):
    if padding_side == "right":
      if isinstance(A_     ,      A_				):
        a					:      int    =       tensor[:sequence_length]
      else:
        a					:      Optional[Any]    =       tensor[:sequence_length]
    else:
      if isinstance(A_     ,      A_				):
        a					:      List[Any]    =       tensor[:sequence_length]
      else:
        a					:      int    =       tensor[:sequence_length]

  return out_tensor.tolist()




def       snake_case  (A_				:Dict				):







  '''simple docstring'''

  a					:      Any    =       ord(A_				)
  if (cp >= 3_3 and cp <= 4_7) or (cp >= 5_8 and cp <= 6_4) or (cp >= 9_1 and cp <= 9_6) or (cp >= 1_2_3 and cp <= 1_2_6):
    return True
  a					:      Dict    =       unicodedata.category(A_				)
  if cat.startswith('P'				):
    return True
  return False






@dataclass
class 						snake_case  (  UpperCAmelCase	):
    __magic_name__   					=					42
    __magic_name__   					=					True
    __magic_name__   					=					None
    __magic_name__   					=					None
    __magic_name__   					=					-100
    __magic_name__   					=					"pt"





    def     lowerCamelCase__				(							self     : List[str]    ,		A     : Optional[Any]      ):


      '''simple docstring'''




      import torch

      a					:      Union[str, Any]    =       'label' if 'label' in features[0].keys() else 'labels'
      a					:      int    =       [feature[label_name] for feature in features] if label_name in features[0].keys() else None
      a					:      int    =       self.tokenizer.pad(
          A    ,		padding=self.padding    ,		max_length=self.max_length    ,		pad_to_multiple_of=self.pad_to_multiple_of    ,		return_tensors='pt' if labels is None else None    ,		)

      if labels is None:
        return batch

      a					:      List[str]    =       torch.tensor(batch['entity_ids']      ).shape[1]
      a					:      int    =       self.tokenizer.padding_side
      if padding_side == "right":
        a					:      Union[str, Any]    =       [
            list(A      ) + [self.label_pad_token_id] * (sequence_length - len(A      )) for label in labels
        ]
      else:
        a					:      List[Any]    =       [
            [self.label_pad_token_id] * (sequence_length - len(A      )) + list(A      ) for label in labels
        ]

      a					:      str    =       [feature['ner_tags'] for feature in features]
      a					:      Optional[Any]    =       padding_tensor(A    ,		-1    ,		A    ,		A      )
      a					:      int    =       [feature['original_entity_spans'] for feature in features]
      a					:      Optional[Any]    =       padding_tensor(A    ,		(-1, -1)    ,		A    ,		A      )
      a					:      Dict    =       {k: torch.tensor(A    ,		dtype=torch.intaa      ) for k, v in batch.items()}

      return batch












"""simple docstring"""


def       snake_case  (A_				:list[int]     ,      A_				:str				):







  '''simple docstring'''

  a					:      Optional[int]    =       int(A_				)

  # Initialize Result
  a					:      int    =       []

  # 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    :   str   			=						'0'

   if (
       input('Do you want to enter your denominations ? (yY/n): ').strip().lower()
       == "y"
   ):
      _UpperCamelCase    :   Dict   			=						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    :   Optional[int]   			=						input('Enter the change you want to make in Indian Currency: ').strip()
   else:
      # All denominations of Indian Currency if user does not enter
      _UpperCamelCase    :   Union[str, Any]   			=						[1, 2, 5, 10, 20, 50, 100, 500, 2000]
      _UpperCamelCase    :   Dict   			=						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    :   Tuple   			=						find_minimum_change(denominations, value)
      # Print result
      for i in range(len(answer)):
         print(answer[i], end=' ')




