



import argparse
import os
import pickle
import sys

import torch

from transformers import TransfoXLConfig, TransfoXLLMHeadModel, load_tf_weights_in_transfo_xl
from transformers.models.transfo_xl import tokenization_transfo_xl as data_utils
from transformers.models.transfo_xl.tokenization_transfo_xl import CORPUS_NAME, VOCAB_FILES_NAMES
from transformers.utils import CONFIG_NAME, WEIGHTS_NAME, logging


logging.set_verbosity_info()

# We do this to be able to load python 2 datasets pickles
# See e.g. https://stackoverflow.com/questions/2121874/python-pickling-after-changing-a-modules-directory/2121918#2121918
UpperCAmelCase_		 =					data_utils.TransfoXLTokenizer
UpperCAmelCase_		 =					data_utils.TransfoXLCorpus
UpperCAmelCase_		 =					data_utils
UpperCAmelCase_		 =					data_utils

def 	SCREAMING_SNAKE_CASE_							(	_snake_case	:str	,   _snake_case	:str	,   _snake_case	:List[Any]	,   _snake_case	:Dict				)  ->     str:
   if transfo_xl_dataset_file:
      # Convert a pre-processed corpus (see original TensorFlow repo)
      with open(_snake_case	,   '''rb'''				) as fp:
         _A      =       pickle.load(_snake_case	,   encoding='''latin1'''				)
      # Save vocabulary and dataset cache as Dictionaries (should be better than pickles for the long-term)
      _A      =       pytorch_dump_folder_path + '''/''' + VOCAB_FILES_NAMES['''pretrained_vocab_file''']
      print(F'''Save vocabulary to {pytorch_vocab_dump_path}'''				)
      _A      =       corpus.vocab.__dict__
      torch.save(_snake_case	,   _snake_case				)

      _A      =       corpus.__dict__
      corpus_dict_no_vocab.pop('''vocab'''	,   _snake_case				)
      _A      =       pytorch_dump_folder_path + '''/''' + CORPUS_NAME
      print(F'''Save dataset to {pytorch_dataset_dump_path}'''				)
      torch.save(_snake_case	,   _snake_case				)

   if tf_checkpoint_path:
      # Convert a pre-trained TensorFlow model
      _A      =       os.path.abspath(_snake_case				)
      _A      =       os.path.abspath(_snake_case				)

      print(F'''Converting Transformer XL checkpoint from {tf_path} with config at {config_path}.'''				)
      # Initialise PyTorch model
      if transfo_xl_config_file == "":
         _A      =       TransfoXLConfig()
      else:
         _A      =       TransfoXLConfig.from_json_file(_snake_case				)
      print(F'''Building PyTorch model from configuration: {config}'''				)
      _A      =       TransfoXLLMHeadModel(_snake_case				)

      _A      =       load_tf_weights_in_transfo_xl(_snake_case	,   _snake_case	,   _snake_case				)
      # Save pytorch-model
      _A      =       os.path.join(_snake_case	,   _snake_case				)
      _A      =       os.path.join(_snake_case	,   _snake_case				)
      print(F'''Save PyTorch model to {os.path.abspath(_snake_case				)}'''				)
      torch.save(model.state_dict()	,   _snake_case				)
      print(F'''Save configuration file to {os.path.abspath(_snake_case				)}'''				)
      with open(_snake_case	,   '''w'''	,   encoding='''utf-8'''				) as f:
         f.write(config.to_json_string()				)


if __name__ == "__main__":
   UpperCAmelCase_		 =					argparse.ArgumentParser()
   parser.add_argument(
       """--pytorch_dump_folder_path""",
       default=None,
       type=str,
       required=True,
       help="""Path to the folder to store the PyTorch model or dataset/vocab.""",
   )
   parser.add_argument(
       """--tf_checkpoint_path""",
       default="""""",
       type=str,
       help="""An optional path to a TensorFlow checkpoint path to be converted.""",
   )
   parser.add_argument(
       """--transfo_xl_config_file""",
       default="""""",
       type=str,
       help=(
           """An optional config json file corresponding to the pre-trained BERT model. \n"""
           """This specifies the model architecture."""
       ),
   )
   parser.add_argument(
       """--transfo_xl_dataset_file""",
       default="""""",
       type=str,
       help="""An optional dataset file to be converted in a vocabulary.""",
   )
   UpperCAmelCase_		 =					parser.parse_args()
   convert_transfo_xl_checkpoint_to_pytorch(
       args.tf_checkpoint_path,
       args.transfo_xl_config_file,
       args.pytorch_dump_folder_path,
       args.transfo_xl_dataset_file,
   )









'''simple docstring'''


def  _SCREAMING_SNAKE_CASE     (      UpperCamelCase__    :					list[int] ,    UpperCamelCase__    :					str				):




     """simple docstring"""


     a_     :							Tuple     						=  int(UpperCamelCase__				)

     # Initialize Result
     a_     :							Tuple     						=  []

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

     return answer


# Driver Code
if __name__ == "__main__":
 lowerCAmelCase_      :  List[str]				     =  []
 lowerCAmelCase_      :  Union[str, Any]				     =  '0'

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