
'''simple docstring'''



import argparse

import torch

from transformers import YosoConfig, YosoForMaskedLM
def    lowerCAmelCase_	(				SCREAMING_SNAKE_CASE_						)     ->     Optional[int]:




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

   return orig_key
def    lowerCAmelCase_	(				SCREAMING_SNAKE_CASE_    ,      SCREAMING_SNAKE_CASE_						)     ->     int:




   """simple docstring"""
   for key in orig_state_dict.copy().keys():
      _SCREAMING_SNAKE_CASE      =				orig_state_dict.pop(__A						)

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

   _SCREAMING_SNAKE_CASE      =				orig_state_dict["""cls.predictions.decoder.bias"""]
   _SCREAMING_SNAKE_CASE      =				torch.arange(__A						).expand((1, -1)						) + 2

   return orig_state_dict
def    lowerCAmelCase_	(				SCREAMING_SNAKE_CASE_    ,      SCREAMING_SNAKE_CASE_    ,      SCREAMING_SNAKE_CASE_						)     ->     str:




   """simple docstring"""
   _SCREAMING_SNAKE_CASE      =				torch.load(__A    ,      map_location="""cpu"""						)["""model_state_dict"""]
   _SCREAMING_SNAKE_CASE      =				YosoConfig.from_json_file(__A						)
   _SCREAMING_SNAKE_CASE      =				YosoForMaskedLM(__A						)

   _SCREAMING_SNAKE_CASE      =				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__":
     UpperCamelCase__      :		List[Any]       							=  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."
     )
     UpperCamelCase__      :		List[Any]       							=  parser.parse_args()
     convert_yoso_checkpoint(args.pytorch_model_path, args.config_file, args.pytorch_dump_path)








'''simple docstring'''



import sys

UpperCamelCase__      :		int       							=  (
    "73167176531330624919225119674426574742355349194934"
    "96983520312774506326239578318016984801869478851843"
    "85861560789112949495459501737958331952853208805511"
    "12540698747158523863050715693290963295227443043557"
    "66896648950445244523161731856403098711121722383113"
    "62229893423380308135336276614282806444486645238749"
    "30358907296290491560440772390713810515859307960866"
    "70172427121883998797908792274921901699720888093776"
    "65727333001053367881220235421809751254540594752243"
    "52584907711670556013604839586446706324415722155397"
    "53697817977846174064955149290862569321978468622482"
    "83972241375657056057490261407972968652414535100474"
    "82166370484403199890008895243450658541227588666881"
    "16427171479924442928230863465674813919123162824586"
    "17866458359124566529476545682848912883142607690042"
    "24219022671055626321111109370544217506941658960408"
    "07198403850962455444362981230987879927244284909188"
    "84580156166097919133875499200524063689912560717606"
    "05886116467109405077541002256983155200055935729725"
    "71636269561882670428252483600823257530420752963450"
)
def    lowerCAmelCase_	(				SCREAMING_SNAKE_CASE_ = N						)     ->     int:




   """simple docstring"""
   _SCREAMING_SNAKE_CASE      =				-sys.maxsize - 1
   for i in range(len(SCREAMING_SNAKE_CASE_						) - 12						):
      _SCREAMING_SNAKE_CASE      =				1
      for j in range(13						):
         product *= int(n[i + j]						)
      if product > largest_product:
         _SCREAMING_SNAKE_CASE      =				product
   return largest_product


if __name__ == "__main__":
     print(f"""{solution() = }""")






