

import argparse

import torch

from transformers import YosoConfig, YosoForMaskedLM



def 				snake_case_(_UpperCamelCase					)							->     Union[str, Any]:



  """simple docstring"""






  if "model" in orig_key:
    _snake_case						=							orig_key.replace('''model.'''			,	''''''					)
  if "norm1" in orig_key:
    _snake_case						=							orig_key.replace('''norm1'''			,	'''attention.output.LayerNorm'''					)
  if "norm2" in orig_key:
    _snake_case						=							orig_key.replace('''norm2'''			,	'''output.LayerNorm'''					)
  if "norm" in orig_key:
    _snake_case						=							orig_key.replace('''norm'''			,	'''LayerNorm'''					)
  if "transformer" in orig_key:
    _snake_case						=							orig_key.split('''.'''					)[0].split('''_'''					)[-1]
    _snake_case						=							orig_key.replace(F"""transformer_{layer_num}"""			,	F"""encoder.layer.{layer_num}"""					)
  if "mha.attn" in orig_key:
    _snake_case						=							orig_key.replace('''mha.attn'''			,	'''attention.self'''					)
  if "mha" in orig_key:
    _snake_case						=							orig_key.replace('''mha'''			,	'''attention'''					)
  if "W_q" in orig_key:
    _snake_case						=							orig_key.replace('''W_q'''			,	'''self.query'''					)
  if "W_k" in orig_key:
    _snake_case						=							orig_key.replace('''W_k'''			,	'''self.key'''					)
  if "W_v" in orig_key:
    _snake_case						=							orig_key.replace('''W_v'''			,	'''self.value'''					)
  if "ff1" in orig_key:
    _snake_case						=							orig_key.replace('''ff1'''			,	'''intermediate.dense'''					)
  if "ff2" in orig_key:
    _snake_case						=							orig_key.replace('''ff2'''			,	'''output.dense'''					)
  if "ff" in orig_key:
    _snake_case						=							orig_key.replace('''ff'''			,	'''output.dense'''					)
  if "mlm_class" in orig_key:
    _snake_case						=							orig_key.replace('''mlm.mlm_class'''			,	'''cls.predictions.decoder'''					)
  if "mlm" in orig_key:
    _snake_case						=							orig_key.replace('''mlm'''			,	'''cls.predictions.transform'''					)
  if "cls" not in orig_key:
    _snake_case						=							'''yoso.''' + orig_key

  return orig_key



def 				snake_case_(_UpperCamelCase			,	_UpperCamelCase					)							->     List[Any]:



  """simple docstring"""






  for key in orig_state_dict.copy().keys():
    _snake_case						=							orig_state_dict.pop(_UpperCamelCase					)

    if ("pooler" in key) or ("sen_class" in key):
      continue
    else:
      _snake_case						=							val

  _snake_case						=							orig_state_dict['''cls.predictions.decoder.bias''']
  _snake_case						=							torch.arange(_UpperCamelCase					).expand((1, -1)					) + 2

  return orig_state_dict



def 				snake_case_(_UpperCamelCase			,	_UpperCamelCase			,	_UpperCamelCase					)							->     Dict:



  """simple docstring"""






  _snake_case						=							torch.load(_UpperCamelCase			,	map_location='''cpu'''					)['''model_state_dict''']
  _snake_case						=							YosoConfig.from_json_file(_UpperCamelCase					)
  _snake_case						=							YosoForMaskedLM(_UpperCamelCase					)

  _snake_case						=							convert_checkpoint_helper(config.max_position_embeddings			,	_UpperCamelCase					)

  print(model.load_state_dict(_UpperCamelCase					)					)
  model.eval()
  model.save_pretrained(_UpperCamelCase					)

  print(F"""Checkpoint successfuly converted. Model saved at {pytorch_dump_path}"""					)


if __name__ == "__main__":
 __A      		=       argparse.ArgumentParser()
 # Required parameters
 parser.add_argument(
     '''--pytorch_model_path''', default=None, type=str, required=True, help='''Path to YOSO pytorch checkpoint.'''
 )
 parser.add_argument(
     '''--config_file''',
     default=None,
     type=str,
     required=True,
     help='''The json file for YOSO model config.''',
 )
 parser.add_argument(
     '''--pytorch_dump_path''', default=None, type=str, required=True, help='''Path to the output PyTorch model.'''
 )
 __A      		=       parser.parse_args()
 convert_yoso_checkpoint(args.pytorch_model_path, args.config_file, args.pytorch_dump_path)




from math import factorial



def 				snake_case_(_UpperCamelCase			,	_UpperCamelCase					)							->     int:



  """simple docstring"""






  if n < k or k < 0:
    raise ValueError('''Please enter positive integers for n and k where n >= k'''					)
  return factorial(_UpperCamelCase					) // (factorial(_UpperCamelCase					) * factorial(n - k					))


if __name__ == "__main__":
 print(
     '''The number of five-card hands possible from a standard''',
     f'''fifty-two card deck is: {combinations(52, 5)}\n''',
 )

 print(
     '''If a class of 40 students must be arranged into groups of''',
     f'''4 for group projects, there are {combinations(40, 4)} ways''',
     '''to arrange them.\n''',
 )

 print(
     '''If 10 teams are competing in a Formula One race, there''',
     f'''are {combinations(10, 3)} ways that first, second and''',
     '''third place can be awarded.''',
 )

