

'''simple docstring'''



import os
import re
import warnings
from shutil import copyfile
from typing import List, Optional, Tuple

from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging


if is_sentencepiece_available():
  from .tokenization_ta import TaTokenizer
else:
  UpperCAmelCase :       List[Any]			   =     None


UpperCAmelCase :       Optional[int]			   =     logging.get_logger(__name__)

UpperCAmelCase :       Optional[Any]			   =     {'vocab_file': 'spiece.model', 'tokenizer_file': 'tokenizer.json'}

UpperCAmelCase :       int			   =     {
    'vocab_file': {
        't5-small': 'https://huggingface.co/t5-small/resolve/main/spiece.model',
        't5-base': 'https://huggingface.co/t5-base/resolve/main/spiece.model',
        't5-large': 'https://huggingface.co/t5-large/resolve/main/spiece.model',
        't5-3b': 'https://huggingface.co/t5-3b/resolve/main/spiece.model',
        't5-11b': 'https://huggingface.co/t5-11b/resolve/main/spiece.model',
    },
    'tokenizer_file': {
        't5-small': 'https://huggingface.co/t5-small/resolve/main/tokenizer.json',
        't5-base': 'https://huggingface.co/t5-base/resolve/main/tokenizer.json',
        't5-large': 'https://huggingface.co/t5-large/resolve/main/tokenizer.json',
        't5-3b': 'https://huggingface.co/t5-3b/resolve/main/tokenizer.json',
        't5-11b': 'https://huggingface.co/t5-11b/resolve/main/tokenizer.json',
    },
}


# TODO(PVP) - this should be removed in Transformers v5
UpperCAmelCase :       Optional[int]			   =     {
    't5-small': 5_1_2,
    't5-base': 5_1_2,
    't5-large': 5_1_2,
    't5-3b': 5_1_2,
    't5-11b': 5_1_2,
}







class 						lowerCAmelCase__   (      a					):



 """simple docstring"""



 lowerCAmelCase__  					=      VOCAB_FILES_NAMES
 lowerCAmelCase__  					=      PRETRAINED_VOCAB_FILES_MAP
 lowerCAmelCase__  					=      PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
 lowerCAmelCase__  					=      ["input_ids", "attention_mask"]
 lowerCAmelCase__  					=      TaTokenizer

 lowerCAmelCase__  					=      []
 def __init__(		self					:       Any					,							__SCREAMING_SNAKE_CASE					:       Dict=None					,							__SCREAMING_SNAKE_CASE					:       Union[str, Any]=None					,							__SCREAMING_SNAKE_CASE					:       List[str]="</s>"					,							__SCREAMING_SNAKE_CASE					:       Optional[int]="<unk>"					,							__SCREAMING_SNAKE_CASE					:       Optional[Any]="<pad>"					,							__SCREAMING_SNAKE_CASE					:       Tuple=100					,							__SCREAMING_SNAKE_CASE					:       Tuple=None					,							**__SCREAMING_SNAKE_CASE					:       Dict					,							)  -> List[str]:


      """simple docstring"""




      if extra_ids > 0 and additional_special_tokens is None:
           __SCREAMING_SNAKE_CASE     =      [f'<extra_id_{i}>' for i in range(__SCREAMING_SNAKE_CASE			)]
      elif extra_ids > 0 and additional_special_tokens is not None:
           # Check that we have the right number of extra special tokens
           __SCREAMING_SNAKE_CASE     =      len(set(filter(lambda __SCREAMING_SNAKE_CASE			: bool("""extra_id_""" in str(__SCREAMING_SNAKE_CASE			)			)					,							__SCREAMING_SNAKE_CASE			)			)			)
           if extra_tokens != extra_ids:
                raise ValueError(
                    f'Both extra_ids ({extra_ids}) and additional_special_tokens ({additional_special_tokens}) are'
                    """ provided to T5Tokenizer. In this case the additional_special_tokens must include the extra_ids"""
                    """ tokens"""			)

      super().__init__(
          __SCREAMING_SNAKE_CASE					,							tokenizer_file=__SCREAMING_SNAKE_CASE					,							eos_token=__SCREAMING_SNAKE_CASE					,							unk_token=__SCREAMING_SNAKE_CASE					,							pad_token=__SCREAMING_SNAKE_CASE					,							extra_ids=__SCREAMING_SNAKE_CASE					,							additional_special_tokens=__SCREAMING_SNAKE_CASE					,							**__SCREAMING_SNAKE_CASE					,							)

      __SCREAMING_SNAKE_CASE     =      vocab_file
      __SCREAMING_SNAKE_CASE     =      False if not self.vocab_file else True
      __SCREAMING_SNAKE_CASE     =      extra_ids
 @staticmethod
 def       UpperCAmelCase__		(		__SCREAMING_SNAKE_CASE					:       int					,							__SCREAMING_SNAKE_CASE					:       List[str]					,							__SCREAMING_SNAKE_CASE					:       Any			)  -> Tuple:


      """simple docstring"""




      if pretrained_model_name_or_path in TaTokenizerFast.max_model_input_sizes:
           __SCREAMING_SNAKE_CASE     =      TaTokenizerFast.max_model_input_sizes[pretrained_model_name_or_path]
           if init_max_model_length is not None and init_max_model_length != max_model_length:
                return init_max_model_length
           elif init_max_model_length is None:
                warnings.warn(
                    """This tokenizer was incorrectly instantiated with a model max length of"""
                    f' {deprecated_max_model_length} which will be corrected in Transformers v5.\nFor now, this'
                    """ behavior is kept to avoid breaking backwards compatibility when padding/encoding with"""
                    """ `truncation is True`.\n- Be aware that you SHOULD NOT rely on"""
                    f' {pretrained_model_name_or_path} automatically truncating your input to'
                    f' {deprecated_max_model_length} when padding/encoding.\n- If you want to encode/pad to sequences'
                    f' longer than {deprecated_max_model_length} you can either instantiate this tokenizer with'
                    """ `model_max_length` or pass `max_length` when encoding/padding.\n- To avoid this warning, please"""
                    """ instantiate this tokenizer with `model_max_length` set to your preferred value."""					,							__SCREAMING_SNAKE_CASE					,							)

      return max_model_length
 def       UpperCAmelCase__		(		self					:       str					,							__SCREAMING_SNAKE_CASE					:       str					,							__SCREAMING_SNAKE_CASE					:       Optional[str] = None			)  -> Tuple[str]:


      """simple docstring"""




      if not self.can_save_slow_tokenizer:
           raise ValueError(
               """Your fast tokenizer does not have the necessary information to save the vocabulary for a slow """
               """tokenizer."""			)

      if not os.path.isdir(__SCREAMING_SNAKE_CASE			):
           logger.error(f'Vocabulary path ({save_directory}) should be a directory'			)
           return
      __SCREAMING_SNAKE_CASE     =      os.path.join(
          __SCREAMING_SNAKE_CASE					,							(filename_prefix + """-""" if filename_prefix else """""") + VOCAB_FILES_NAMES["""vocab_file"""]			)

      if os.path.abspath(self.vocab_file			) != os.path.abspath(__SCREAMING_SNAKE_CASE			):
           copyfile(self.vocab_file					,							__SCREAMING_SNAKE_CASE			)
           logger.info(f'Copy vocab file to {out_vocab_file}'			)

      return (out_vocab_file,)
 def       UpperCAmelCase__		(		self					:       Dict					,							__SCREAMING_SNAKE_CASE					:       List[int]					,							__SCREAMING_SNAKE_CASE					:       Optional[List[int]] = None			)  -> List[int]:


      """simple docstring"""




      __SCREAMING_SNAKE_CASE     =      token_ids_a + [self.eos_token_id]
      if token_ids_a is None:
           return self.prefix_tokens + token_ids_a
      else:
           __SCREAMING_SNAKE_CASE     =      token_ids_a + [self.eos_token_id]
           return self.prefix_tokens + token_ids_a + token_ids_a
 def       UpperCAmelCase__		(		self					:       str					,							__SCREAMING_SNAKE_CASE					:       List[int]					,							__SCREAMING_SNAKE_CASE					:       Optional[List[int]] = None			)  -> List[int]:


      """simple docstring"""




      __SCREAMING_SNAKE_CASE     =      [self.eos_token_id]

      if token_ids_a is None:
           return len(token_ids_a + eos			) * [0]
      return len(token_ids_a + eos + token_ids_a + eos			) * [0]
 def       UpperCAmelCase__		(		self					:       List[str]			)  -> Dict:


      """simple docstring"""




      return list(
          set(filter(lambda __SCREAMING_SNAKE_CASE			: bool(re.search(r"""<extra_id_\d+>"""					,							__SCREAMING_SNAKE_CASE			)			) is not None					,							self.additional_special_tokens			)			)			)


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


      """simple docstring"""




      return [self.convert_tokens_to_ids(__SCREAMING_SNAKE_CASE			) for token in self.get_sentinel_tokens()]









'''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=' ')






