

'''simple docstring'''






import argparse

import torch

from transformers import YosoConfig, YosoForMaskedLM


def 	_A  (					A__   ):






 """simple docstring"""



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

 return orig_key


def 	_A  (					A__  ,       A__   ):






 """simple docstring"""



 for key in orig_state_dict.copy().keys():
  __lowercase													=				orig_state_dict.pop(A__   )

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

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

 return orig_state_dict


def 	_A  (					A__  ,       A__  ,       A__   ):






 """simple docstring"""



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

 __lowercase													=				convert_checkpoint_helper(config.max_position_embeddings  ,       A__   )

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

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


if __name__ == "__main__":
 lowerCAmelCase__      		=			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.'''
 )
 lowerCAmelCase__      		=			parser.parse_args()
 convert_yoso_checkpoint(args.pytorch_model_path, args.config_file, args.pytorch_dump_path)




'''simple docstring'''






def 	_A  (					):






 """simple docstring"""



 for n in range(1  ,       1000000   ):
  yield n * (n + 1) // 2


def 	_A  (					A__   ):






 """simple docstring"""



 __lowercase													=				1
 __lowercase													=				2
 while i * i <= n:
  __lowercase													=				0
  while n % i == 0:
   n //= i
   multiplicity += 1
  divisors_count *= multiplicity + 1
  i += 1
 if n > 1:
  divisors_count *= 2
 return divisors_count


def 	_A  (					):






 """simple docstring"""



 return next(i for i in triangle_number_generator() if count_divisors(A__   ) > 500   )


if __name__ == "__main__":
 print(solution())

