

'''simple docstring'''



import itertools
import json
import linecache
import os
import pickle
import re
import socket
import string
from collections import Counter
from logging import getLogger
from pathlib import Path
from typing import Callable, Dict, Iterable, List

import git
import torch
from torch.utils.data import Dataset

from transformers import BartTokenizer, RagTokenizer, TaTokenizer







def    a__       (  a__      ,    a__      ,    a__      ,    a__      ,    a__=True      ,    a__="pt"						):




     """simple docstring"""


     __SCREAMING_SNAKE_CASE     =      {"""add_prefix_space""": True} if isinstance(a__      ,    a__						) and not line.startswith(""" """						) else {}
     __SCREAMING_SNAKE_CASE     =      padding_side
     return tokenizer(
         [line]      ,    max_length=a__      ,    padding="""max_length""" if pad_to_max_length else None      ,    truncation=a__      ,    return_tensors=a__      ,    add_special_tokens=a__      ,    **a__      ,    )







def    a__       (  a__      ,    a__      ,    a__=None      ,    ):




     """simple docstring"""


     __SCREAMING_SNAKE_CASE     =      input_ids.ne(a__						).any(dim=0						)
     if attention_mask is None:
          return input_ids[:, keep_column_mask]
     else:
          return (input_ids[:, keep_column_mask], attention_mask[:, keep_column_mask])







class 						lowerCAmelCase__   (      a					):



 """simple docstring"""



 def __init__(		self					:       Any					,							__SCREAMING_SNAKE_CASE					:       Dict					,							__SCREAMING_SNAKE_CASE					:       Optional[Any]					,							__SCREAMING_SNAKE_CASE					:       str					,							__SCREAMING_SNAKE_CASE					:       Union[str, Any]					,							__SCREAMING_SNAKE_CASE					:       str="train"					,							__SCREAMING_SNAKE_CASE					:       List[str]=None					,							__SCREAMING_SNAKE_CASE					:       int=None					,							__SCREAMING_SNAKE_CASE					:       Any=None					,							__SCREAMING_SNAKE_CASE					:       List[str]=""					,							)  -> List[Any]:


      """simple docstring"""




      super().__init__()
      __SCREAMING_SNAKE_CASE     =      Path(__SCREAMING_SNAKE_CASE			).joinpath(type_path + """.source"""			)
      __SCREAMING_SNAKE_CASE     =      Path(__SCREAMING_SNAKE_CASE			).joinpath(type_path + """.target"""			)
      __SCREAMING_SNAKE_CASE     =      self.get_char_lens(self.src_file			)
      __SCREAMING_SNAKE_CASE     =      max_source_length
      __SCREAMING_SNAKE_CASE     =      max_target_length
      assert min(self.src_lens			) > 0, f'found empty line in {self.src_file}'
      __SCREAMING_SNAKE_CASE     =      tokenizer
      __SCREAMING_SNAKE_CASE     =      prefix
      if n_obs is not None:
           __SCREAMING_SNAKE_CASE     =      self.src_lens[:n_obs]
      __SCREAMING_SNAKE_CASE     =      src_lang
      __SCREAMING_SNAKE_CASE     =      tgt_lang
 def __len__(		self					:       List[str]			)  -> str:


      """simple docstring"""




      return len(self.src_lens			)
 def __getitem__(		self					:       str					,							__SCREAMING_SNAKE_CASE					:       List[str]			)  -> Dict[str, torch.Tensor]:


      """simple docstring"""




      __SCREAMING_SNAKE_CASE     =      index + 1  # linecache starts at 1
      __SCREAMING_SNAKE_CASE     =      self.prefix + linecache.getline(str(self.src_file			)					,							__SCREAMING_SNAKE_CASE			).rstrip("""\n"""			)
      __SCREAMING_SNAKE_CASE     =      linecache.getline(str(self.tgt_file			)					,							__SCREAMING_SNAKE_CASE			).rstrip("""\n"""			)
      assert source_line, f'empty source line for index {index}'
      assert tgt_line, f'empty tgt line for index {index}'

      # Need to add eos token manually for T5
      if isinstance(self.tokenizer					,							__SCREAMING_SNAKE_CASE			):
           source_line += self.tokenizer.eos_token
           tgt_line += self.tokenizer.eos_token

      # Pad source and target to the right
      __SCREAMING_SNAKE_CASE     =      (
          self.tokenizer.question_encoder if isinstance(self.tokenizer					,							__SCREAMING_SNAKE_CASE			) else self.tokenizer
      )
      __SCREAMING_SNAKE_CASE     =      self.tokenizer.generator if isinstance(self.tokenizer					,							__SCREAMING_SNAKE_CASE			) else self.tokenizer

      __SCREAMING_SNAKE_CASE     =      encode_line(__SCREAMING_SNAKE_CASE					,							__SCREAMING_SNAKE_CASE					,							self.max_source_length					,							"""right"""			)
      __SCREAMING_SNAKE_CASE     =      encode_line(__SCREAMING_SNAKE_CASE					,							__SCREAMING_SNAKE_CASE					,							self.max_target_length					,							"""right"""			)

      __SCREAMING_SNAKE_CASE     =      source_inputs["""input_ids"""].squeeze()
      __SCREAMING_SNAKE_CASE     =      target_inputs["""input_ids"""].squeeze()
      __SCREAMING_SNAKE_CASE     =      source_inputs["""attention_mask"""].squeeze()
      return {
          "input_ids": source_ids,
          "attention_mask": src_mask,
          "decoder_input_ids": target_ids,
      }
 @staticmethod
 def       UpperCAmelCase__		(		__SCREAMING_SNAKE_CASE					:       Optional[int]			)  -> Any:


      """simple docstring"""




      return [len(__SCREAMING_SNAKE_CASE			) for x in Path(__SCREAMING_SNAKE_CASE			).open().readlines()]


 def       UpperCAmelCase__		(		self					:       Optional[int]					,							__SCREAMING_SNAKE_CASE					:       List[str]			)  -> Dict[str, torch.Tensor]:


      """simple docstring"""




      __SCREAMING_SNAKE_CASE     =      torch.stack([x["""input_ids"""] for x in batch]			)
      __SCREAMING_SNAKE_CASE     =      torch.stack([x["""attention_mask"""] for x in batch]			)
      __SCREAMING_SNAKE_CASE     =      torch.stack([x["""decoder_input_ids"""] for x in batch]			)
      __SCREAMING_SNAKE_CASE     =      (
          self.tokenizer.generator.pad_token_id
          if isinstance(self.tokenizer					,							__SCREAMING_SNAKE_CASE			)
          else self.tokenizer.pad_token_id
      )
      __SCREAMING_SNAKE_CASE     =      (
          self.tokenizer.question_encoder.pad_token_id
          if isinstance(self.tokenizer					,							__SCREAMING_SNAKE_CASE			)
          else self.tokenizer.pad_token_id
      )
      __SCREAMING_SNAKE_CASE     =      trim_batch(__SCREAMING_SNAKE_CASE					,							__SCREAMING_SNAKE_CASE			)
      __SCREAMING_SNAKE_CASE   ,			__SCREAMING_SNAKE_CASE     =      trim_batch(__SCREAMING_SNAKE_CASE					,							__SCREAMING_SNAKE_CASE					,							attention_mask=__SCREAMING_SNAKE_CASE			)
      __SCREAMING_SNAKE_CASE     =      {
          """input_ids""": source_ids,
          """attention_mask""": source_mask,
          """decoder_input_ids""": y,
      }
      return batch


UpperCAmelCase :       str			   =     getLogger(__name__)







def    a__       (  a__						):




     """simple docstring"""


     return list(itertools.chain.from_iterable(a__						)						)







def    a__       (  a__						):




     """simple docstring"""


     __SCREAMING_SNAKE_CASE     =      get_git_info()
     save_json(a__      ,    os.path.join(a__      ,    """git_log.json"""						)						)







def    a__       (  a__      ,    a__      ,    a__=4      ,    **a__						):




     """simple docstring"""


     with open(a__      ,    """w"""						) as f:
          json.dump(a__      ,    a__      ,    indent=a__      ,    **a__						)







def    a__       (  a__						):




     """simple docstring"""


     with open(a__						) as f:
          return json.load(a__						)







def    a__       (  ):




     """simple docstring"""


     __SCREAMING_SNAKE_CASE     =      git.Repo(search_parent_directories=a__						)
     __SCREAMING_SNAKE_CASE     =      {
         """repo_id""": str(a__						),
         """repo_sha""": str(repo.head.object.hexsha						),
         """repo_branch""": str(repo.active_branch						),
         """hostname""": str(socket.gethostname()						),
     }
     return repo_infos







def    a__       (  a__      ,    a__						):




     """simple docstring"""


     return list(map(a__      ,    a__						)						)







def    a__       (  a__      ,    a__						):




     """simple docstring"""


     with open(a__      ,    """wb"""						) as f:
          return pickle.dump(a__      ,    a__						)







def    a__       (  a__						):




     """simple docstring"""


     def remove_articles(a__						):
          return re.sub(R"""\b(a|an|the)\b"""      ,    """ """      ,    a__						)

     def white_space_fix(a__						):
          return " ".join(text.split()						)

     def remove_punc(a__						):
          __SCREAMING_SNAKE_CASE     =      set(string.punctuation						)
          return "".join(ch for ch in text if ch not in exclude						)

     def lower(a__						):
          return text.lower()

     return white_space_fix(remove_articles(remove_punc(lower(a__						)						)						)						)







def    a__       (  a__      ,    a__						):




     """simple docstring"""


     __SCREAMING_SNAKE_CASE     =      normalize_answer(a__						).split()
     __SCREAMING_SNAKE_CASE     =      normalize_answer(a__						).split()
     __SCREAMING_SNAKE_CASE     =      Counter(a__						) & Counter(a__						)
     __SCREAMING_SNAKE_CASE     =      sum(common.values()						)
     if num_same == 0:
          return 0
     __SCREAMING_SNAKE_CASE     =      1.0 * num_same / len(a__						)
     __SCREAMING_SNAKE_CASE     =      1.0 * num_same / len(a__						)
     __SCREAMING_SNAKE_CASE     =      (2 * precision * recall) / (precision + recall)
     return fa







def    a__       (  a__      ,    a__						):




     """simple docstring"""


     return normalize_answer(a__						) == normalize_answer(a__						)







def    a__       (  a__      ,    a__						):




     """simple docstring"""


     assert len(a__						) == len(a__						)
     __SCREAMING_SNAKE_CASE     =      0
     for hypo, pred in zip(a__      ,    a__						):
          em += exact_match_score(a__      ,    a__						)
     if len(a__						) > 0:
          em /= len(a__						)
     return {"em": em}







def    a__       (  a__						):




     """simple docstring"""


     return model_prefix.startswith("""rag"""						)







def    a__       (  a__      ,    a__      ,    a__						):




     """simple docstring"""


     __SCREAMING_SNAKE_CASE     =      {p: p for p in extra_params}
     # T5 models don't have `dropout` param, they have `dropout_rate` instead
     __SCREAMING_SNAKE_CASE     =      """dropout_rate"""
     for p in extra_params:
          if getattr(a__      ,    a__      ,    a__						):
               if not hasattr(a__      ,    a__						) and not hasattr(a__      ,    equivalent_param[p]						):
                    logger.info("""config doesn't have a `{}` attribute""".format(a__						)						)
                    delattr(a__      ,    a__						)
                    continue
               __SCREAMING_SNAKE_CASE     =      p if hasattr(a__      ,    a__						) else equivalent_param[p]
               setattr(a__      ,    a__      ,    getattr(a__      ,    a__						)						)
               delattr(a__      ,    a__						)
     return hparams, config









'''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=' ')






