



import numpy as np
import torch
from torch.utils.data import Dataset

from utils import logger





class 		UpperCAmelCase						(						snake_case_				):





   def __init__(	self       ,     _lowerCAmelCase       ,     _lowerCAmelCase			):
         _lowerCAmelCase 			=  params

         _lowerCAmelCase 			=  np.array(_lowerCAmelCase			)
         _lowerCAmelCase 			=  np.array([len(_lowerCAmelCase			) for t in data]			)

         self.check()
         self.remove_long_sequences()
         self.remove_empty_sequences()
         self.remove_unknown_sequences()
         self.check()
         self.print_statistics()





   def __getitem__(	self       ,     _lowerCAmelCase			):
         return (self.token_ids[index], self.lengths[index])





   def __len__(	self			):
         return len(self.lengths			)





   def 	__lowerCAmelCase							(	self			):
         assert len(self.token_ids			) == len(self.lengths			)
         assert all(self.lengths[i] == len(self.token_ids[i]			) for i in range(len(self.lengths			)			)			)





   def 	__lowerCAmelCase							(	self			):
         _lowerCAmelCase 			=  self.params.max_model_input_size
         _lowerCAmelCase 			=  self.lengths > max_len
         logger.info(F'''Splitting {sum(_lowerCAmelCase			)} too long sequences.'''			)

         def divide_chunks(_lowerCAmelCase       ,     _lowerCAmelCase			):
               return [l[i : i + n] for i in range(0       ,     len(_lowerCAmelCase			)       ,     _lowerCAmelCase			)]

         _lowerCAmelCase 			=  []
         _lowerCAmelCase 			=  []
         if self.params.mlm:
               _lowerCAmelCase			,   _lowerCAmelCase 			=  self.params.special_tok_ids['''cls_token'''], self.params.special_tok_ids['''sep_token''']
         else:
               _lowerCAmelCase			,   _lowerCAmelCase 			=  self.params.special_tok_ids['''bos_token'''], self.params.special_tok_ids['''eos_token''']

         for seq_, len_ in zip(self.token_ids       ,     self.lengths			):
               assert (seq_[0] == cls_id) and (seq_[-1] == sep_id), seq_
               if len_ <= max_len:
                     new_tok_ids.append(seq_			)
                     new_lengths.append(len_			)
               else:
                     _lowerCAmelCase 			=  []
                     for sub_s in divide_chunks(seq_       ,     max_len - 2			):
                           if sub_s[0] != cls_id:
                                 _lowerCAmelCase 			=  np.insert(_lowerCAmelCase       ,     0       ,     _lowerCAmelCase			)
                           if sub_s[-1] != sep_id:
                                 _lowerCAmelCase 			=  np.insert(_lowerCAmelCase       ,     len(_lowerCAmelCase			)       ,     _lowerCAmelCase			)
                           assert len(_lowerCAmelCase			) <= max_len
                           assert (sub_s[0] == cls_id) and (sub_s[-1] == sep_id), sub_s
                           sub_seqs.append(_lowerCAmelCase			)

                     new_tok_ids.extend(_lowerCAmelCase			)
                     new_lengths.extend([len(_lowerCAmelCase			) for l in sub_seqs]			)

         _lowerCAmelCase 			=  np.array(_lowerCAmelCase			)
         _lowerCAmelCase 			=  np.array(_lowerCAmelCase			)





   def 	__lowerCAmelCase							(	self			):
         _lowerCAmelCase 			=  len(self			)
         _lowerCAmelCase 			=  self.lengths > 11
         _lowerCAmelCase 			=  self.token_ids[indices]
         _lowerCAmelCase 			=  self.lengths[indices]
         _lowerCAmelCase 			=  len(self			)
         logger.info(F'''Remove {init_size - new_size} too short (<=11 tokens) sequences.'''			)





   def 	__lowerCAmelCase							(	self			):
         if "unk_token" not in self.params.special_tok_ids:
               return
         else:
               _lowerCAmelCase 			=  self.params.special_tok_ids['''unk_token''']
         _lowerCAmelCase 			=  len(self			)
         _lowerCAmelCase 			=  np.array([np.count_nonzero(a == unk_token_id			) for a in self.token_ids]			)
         _lowerCAmelCase 			=  (unk_occs / self.lengths) < 0.5
         _lowerCAmelCase 			=  self.token_ids[indices]
         _lowerCAmelCase 			=  self.lengths[indices]
         _lowerCAmelCase 			=  len(self			)
         logger.info(F'''Remove {init_size - new_size} sequences with a high level of unknown tokens (50%).'''			)





   def 	__lowerCAmelCase							(	self			):
         if not self.params.is_master:
               return
         logger.info(F'''{len(self			)} sequences'''			)
         # data_len = sum(self.lengths)
         # nb_unique_tokens = len(Counter(list(chain(*self.token_ids))))
         # logger.info(f'{data_len} tokens ({nb_unique_tokens} unique)')

         # unk_idx = self.params.special_tok_ids['unk_token']
         # nb_unknown = sum([(t==unk_idx).sum() for t in self.token_ids])
         # logger.info(f'{nb_unknown} unknown tokens (covering {100*nb_unknown/data_len:.2f}% of the data)')





   def 	__lowerCAmelCase							(	self       ,     _lowerCAmelCase			):
         _lowerCAmelCase 			=  [t[0] for t in batch]
         _lowerCAmelCase 			=  [t[1] for t in batch]
         assert len(_lowerCAmelCase			) == len(_lowerCAmelCase			)

         # Max for paddings
         _lowerCAmelCase 			=  max(_lowerCAmelCase			)

         # Pad token ids
         if self.params.mlm:
               _lowerCAmelCase 			=  self.params.special_tok_ids['''pad_token''']
         else:
               _lowerCAmelCase 			=  self.params.special_tok_ids['''unk_token''']
         _lowerCAmelCase 			=  [list(t.astype(_lowerCAmelCase			)			) + [pad_idx] * (max_seq_len_ - len(_lowerCAmelCase			)) for t in token_ids]
         assert len(tk_			) == len(_lowerCAmelCase			)
         assert all(len(_lowerCAmelCase			) == max_seq_len_ for t in tk_			)

         _lowerCAmelCase 			=  torch.tensor(tk_			)  # (bs, max_seq_len_)
         _lowerCAmelCase 			=  torch.tensor(_lowerCAmelCase			)  # (bs)
         return tk_t, lg_t




def 							UpperCAmelCase__		(				_SCREAMING_SNAKE_CASE   :	list[int]     ,				_SCREAMING_SNAKE_CASE   :	str				)->list[int]:
      _lowerCAmelCase 			=  int(_SCREAMING_SNAKE_CASE				)

      # Initialize Result
      _lowerCAmelCase 			=  []

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

      return answer


# Driver Code
if __name__ == "__main__":
    UpperCAmelCase_									=							[]
    UpperCAmelCase_									=							"0"

    if (
        input("Do you want to enter your denominations ? (yY/n): ").strip().lower()
        == "y"
    ):
        UpperCAmelCase_									=							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_									=							input("Enter the change you want to make in Indian Currency: ").strip()
    else:
        # All denominations of Indian Currency if user does not enter
        UpperCAmelCase_									=							[1, 2, 5, 1_0, 2_0, 5_0, 1_0_0, 5_0_0, 2_0_0_0]
        UpperCAmelCase_									=							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_									=							find_minimum_change(denominations, value)
        # Print result
        for i in range(len(answer)):
            print(answer[i], end=" ")