

'''simple docstring'''



from typing import List, Optional, Union

from ...image_utils import ImageInput
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding, PaddingStrategy, PreTokenizedInput, TextInput, TruncationStrategy
from ...utils import TensorType







class 						lowerCAmelCase__   (      a					):



 """simple docstring"""



 lowerCAmelCase__  					=      ["image_processor", "tokenizer"]
 lowerCAmelCase__  					=      "BlipImageProcessor"
 lowerCAmelCase__  					=      ("BertTokenizer", "BertTokenizerFast")
 def __init__(		self					:       int					,							__SCREAMING_SNAKE_CASE					:       Optional[Any]					,							__SCREAMING_SNAKE_CASE					:       Optional[Any]			)  -> Dict:


      """simple docstring"""




      __SCREAMING_SNAKE_CASE     =      False
      super().__init__(__SCREAMING_SNAKE_CASE					,							__SCREAMING_SNAKE_CASE			)
      __SCREAMING_SNAKE_CASE     =      self.image_processor
 def __call__(		self					:       List[str]					,							__SCREAMING_SNAKE_CASE					:       ImageInput = None					,							__SCREAMING_SNAKE_CASE					:       Union[TextInput, PreTokenizedInput, List[TextInput], List[PreTokenizedInput]] = None					,							__SCREAMING_SNAKE_CASE					:       bool = True					,							__SCREAMING_SNAKE_CASE					:       Union[bool, str, PaddingStrategy] = False					,							__SCREAMING_SNAKE_CASE					:       Union[bool, str, TruncationStrategy] = None					,							__SCREAMING_SNAKE_CASE					:       Optional[int] = None					,							__SCREAMING_SNAKE_CASE					:       int = 0					,							__SCREAMING_SNAKE_CASE					:       Optional[int] = None					,							__SCREAMING_SNAKE_CASE					:       Optional[bool] = None					,							__SCREAMING_SNAKE_CASE					:       bool = False					,							__SCREAMING_SNAKE_CASE					:       bool = False					,							__SCREAMING_SNAKE_CASE					:       bool = False					,							__SCREAMING_SNAKE_CASE					:       bool = False					,							__SCREAMING_SNAKE_CASE					:       bool = False					,							__SCREAMING_SNAKE_CASE					:       bool = True					,							__SCREAMING_SNAKE_CASE					:       Optional[Union[str, TensorType]] = None					,							**__SCREAMING_SNAKE_CASE					:       List[Any]					,							)  -> BatchEncoding:


      """simple docstring"""




      if images is None and text is None:
           raise ValueError("""You have to specify either images or text."""			)

      # Get only text
      if images is None:
           __SCREAMING_SNAKE_CASE     =      self.tokenizer
           __SCREAMING_SNAKE_CASE     =      self.tokenizer(
               text=__SCREAMING_SNAKE_CASE					,							add_special_tokens=__SCREAMING_SNAKE_CASE					,							padding=__SCREAMING_SNAKE_CASE					,							truncation=__SCREAMING_SNAKE_CASE					,							max_length=__SCREAMING_SNAKE_CASE					,							stride=__SCREAMING_SNAKE_CASE					,							pad_to_multiple_of=__SCREAMING_SNAKE_CASE					,							return_attention_mask=__SCREAMING_SNAKE_CASE					,							return_overflowing_tokens=__SCREAMING_SNAKE_CASE					,							return_special_tokens_mask=__SCREAMING_SNAKE_CASE					,							return_offsets_mapping=__SCREAMING_SNAKE_CASE					,							return_token_type_ids=__SCREAMING_SNAKE_CASE					,							return_length=__SCREAMING_SNAKE_CASE					,							verbose=__SCREAMING_SNAKE_CASE					,							return_tensors=__SCREAMING_SNAKE_CASE					,							**__SCREAMING_SNAKE_CASE					,							)
           return text_encoding

      # add pixel_values
      __SCREAMING_SNAKE_CASE     =      self.image_processor(__SCREAMING_SNAKE_CASE					,							return_tensors=__SCREAMING_SNAKE_CASE			)

      if text is not None:
           __SCREAMING_SNAKE_CASE     =      self.tokenizer(
               text=__SCREAMING_SNAKE_CASE					,							add_special_tokens=__SCREAMING_SNAKE_CASE					,							padding=__SCREAMING_SNAKE_CASE					,							truncation=__SCREAMING_SNAKE_CASE					,							max_length=__SCREAMING_SNAKE_CASE					,							stride=__SCREAMING_SNAKE_CASE					,							pad_to_multiple_of=__SCREAMING_SNAKE_CASE					,							return_attention_mask=__SCREAMING_SNAKE_CASE					,							return_overflowing_tokens=__SCREAMING_SNAKE_CASE					,							return_special_tokens_mask=__SCREAMING_SNAKE_CASE					,							return_offsets_mapping=__SCREAMING_SNAKE_CASE					,							return_token_type_ids=__SCREAMING_SNAKE_CASE					,							return_length=__SCREAMING_SNAKE_CASE					,							verbose=__SCREAMING_SNAKE_CASE					,							return_tensors=__SCREAMING_SNAKE_CASE					,							**__SCREAMING_SNAKE_CASE					,							)
      else:
           __SCREAMING_SNAKE_CASE     =      None

      if text_encoding is not None:
           encoding_image_processor.update(__SCREAMING_SNAKE_CASE			)

      return encoding_image_processor
 def       UpperCAmelCase__		(		self					:       List[str]					,							*__SCREAMING_SNAKE_CASE					:       str					,							**__SCREAMING_SNAKE_CASE					:       List[Any]			)  -> Dict:


      """simple docstring"""




      return self.tokenizer.batch_decode(*__SCREAMING_SNAKE_CASE					,							**__SCREAMING_SNAKE_CASE			)
 def       UpperCAmelCase__		(		self					:       Dict					,							*__SCREAMING_SNAKE_CASE					:       Any					,							**__SCREAMING_SNAKE_CASE					:       Tuple			)  -> str:


      """simple docstring"""




      return self.tokenizer.decode(*__SCREAMING_SNAKE_CASE					,							**__SCREAMING_SNAKE_CASE			)


 @property
 def       UpperCAmelCase__		(		self					:       List[Any]			)  -> Union[str, Any]:


      """simple docstring"""




      __SCREAMING_SNAKE_CASE     =      self.tokenizer.model_input_names
      __SCREAMING_SNAKE_CASE     =      self.image_processor.model_input_names
      return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names			)			)









'''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=' ')






