
'''simple docstring'''
import copy
import re







class  __A				:
 lowerCamelCase				       ='''hp'''
 lowerCamelCase				       ={}
 lowerCamelCase				       =None
 @classmethod
 def 		lowercase_(   cls			:    Dict				,   lowerCamelCase			:    List[Any]				,   lowerCamelCase			:    Any       ):



   """simple docstring"""





   __A		:     List[str]												=		prefix
   __A		:     Tuple												=		defaults
   cls.build_naming_info()
 @staticmethod
 def 		lowercase_(   lowerCamelCase			:    List[str]				,   lowerCamelCase			:    Dict       ):



   """simple docstring"""





   if len(__lowerCAmelCase       ) == 0:
     return ""
   __A		:     int												=		None
   if any(char.isdigit() for char in word       ):
     raise Exception(f"Parameters should not contain numbers: \'{word}\' contains a number"       )
   if word in info["short_word"]:
     return info["short_word"][word]
   for prefix_len in range(1				,   len(__lowerCAmelCase       ) + 1       ):
     __A		:     Optional[Any]												=		word[:prefix_len]
     if prefix in info["reverse_short_word"]:
       continue
     else:
       __A		:     Optional[Any]												=		prefix
       break

   if short_word is None:
     # Paranoid fallback
     def int_to_alphabetic(lowerCamelCase			:    Union[str, Any]       ):
       __A		:     List[Any]												=		""""""
       while integer != 0:
         __A		:     Dict												=		chr(ord("""A"""       ) + integer % 10       ) + s
         integer //= 10
       return s

     __A		:     Union[str, Any]												=		0
     while True:
       __A		:     str												=		word + """#""" + int_to_alphabetic(__lowerCAmelCase       )
       if sword in info["reverse_short_word"]:
         continue
       else:
         __A		:     Optional[int]												=		sword
         break

   __A		:     Union[str, Any]												=		short_word
   __A		:     Union[str, Any]												=		word
   return short_word
 @staticmethod
 def 		lowercase_(   lowerCamelCase			:    int				,   lowerCamelCase			:    List[str]       ):



   """simple docstring"""





   __A		:     Tuple												=		param_name.split("""_"""       )

   __A		:     Optional[Any]												=		[TrialShortNamer.shortname_for_word(__lowerCAmelCase				,   __lowerCAmelCase       ) for word in words]

   # We try to create a separatorless short name, but if there is a collision we have to fallback
   # to a separated short name
   __A		:     Dict												=		["""""", """_"""]

   for separator in separators:
     __A		:     str												=		separator.join(__lowerCAmelCase       )
     if shortname not in info["reverse_short_param"]:
       __A		:     List[str]												=		shortname
       __A		:     str												=		param_name
       return shortname

   return param_name
 @staticmethod
 def 		lowercase_(   lowerCamelCase			:    Optional[Any]				,   lowerCamelCase			:    Tuple       ):



   """simple docstring"""





   __A		:     Dict												=		TrialShortNamer.shortname_for_key(__lowerCAmelCase				,   __lowerCAmelCase       )
   __A		:     Tuple												=		short_name
   __A		:     str												=		param_name
 @classmethod
 def 		lowercase_(   cls			:    int       ):



   """simple docstring"""





   if cls.NAMING_INFO is not None:
     return

   __A		:     Dict												=		{
       """short_word""": {},
       """reverse_short_word""": {},
       """short_param""": {},
       """reverse_short_param""": {},
   }

   __A		:     Optional[Any]												=		list(cls.DEFAULTS.keys()       )

   for k in field_keys:
     cls.add_new_param_name(__lowerCAmelCase				,   __lowerCAmelCase       )

   __A		:     Dict												=		info
 @classmethod
 def 		lowercase_(   cls			:    Optional[Any]				,   lowerCamelCase			:    Union[str, Any]       ):



   """simple docstring"""





   cls.build_naming_info()
   assert cls.PREFIX is not None
   __A		:     Tuple												=		[copy.copy(cls.PREFIX       )]

   for k, v in params.items():
     if k not in cls.DEFAULTS:
       raise Exception(f"You should provide a default value for the param name {k} with value {v}"       )
     if v == cls.DEFAULTS[k]:
       # The default value is not added to the name
       continue

     __A		:     Optional[int]												=		cls.NAMING_INFO["""short_param"""][k]

     if isinstance(__lowerCAmelCase				,   __lowerCAmelCase       ):
       __A		:     Dict												=		1 if v else 0

     __A		:     Any												=		"""""" if isinstance(__lowerCAmelCase				,   (int, float)       ) else """-"""
     __A		:     List[Any]												=		f"{key}{sep}{v}"
     name.append(__lowerCAmelCase       )

   return "_".join(__lowerCAmelCase       )






 @classmethod
 def 		lowercase_(   cls			:    Optional[int]				,   lowerCamelCase			:    Optional[int]       ):



   """simple docstring"""





   __A		:     Optional[Any]												=		repr[len(cls.PREFIX       ) + 1 :]
   if repr == "":
     __A		:     Any												=		[]
   else:
     __A		:     List[Any]												=		repr.split("""_"""       )

   __A		:     Dict												=		{}

   for value in values:
     if "-" in value:
       __A							,					__A		:     Optional[Any]												=		value.split("""-"""       )
     else:
       __A		:     Tuple												=		re.sub("""[0-9.]"""				,   """"""				,   __lowerCAmelCase       )
       __A		:     int												=		float(re.sub("""[^0-9.]"""				,   """"""				,   __lowerCAmelCase       )       )

     __A		:     Dict												=		cls.NAMING_INFO["""reverse_short_param"""][p_k]

     __A		:     Optional[Any]												=		p_v

   for k in cls.DEFAULTS:
     if k not in parameters:
       __A		:     Union[str, Any]												=		cls.DEFAULTS[k]

   return parameters





'''simple docstring'''
import argparse

import torch

from transformers import RemBertConfig, RemBertModel, load_tf_weights_in_rembert
from transformers.utils import logging


logging.set_verbosity_info()







def        A_     (						__SCREAMING_SNAKE_CASE						:  Optional[Any]			,     __SCREAMING_SNAKE_CASE						:  List[str]			,     __SCREAMING_SNAKE_CASE						:  Optional[Any] )				->       List[Any]:





  """simple docstring"""

  __A		:     Optional[int]												=		RemBertConfig.from_json_file(__SCREAMING_SNAKE_CASE )
  print("""Building PyTorch model from configuration: {}""".format(str(__SCREAMING_SNAKE_CASE ) ) )
  __A		:     str												=		RemBertModel(__SCREAMING_SNAKE_CASE )

  # Load weights from tf checkpoint
  load_tf_weights_in_rembert(__SCREAMING_SNAKE_CASE			,     __SCREAMING_SNAKE_CASE			,     __SCREAMING_SNAKE_CASE )

  # Save pytorch-model
  print("""Save PyTorch model to {}""".format(__SCREAMING_SNAKE_CASE ) )
  torch.save(model.state_dict()			,     __SCREAMING_SNAKE_CASE )


if __name__ == "__main__":
     A__   :		Optional[Any] 		=argparse.ArgumentParser()
     # Required parameters
     parser.add_argument(
         '--tf_checkpoint_path', default=None, type=str, required=True, help='Path to the TensorFlow checkpoint path.'
     )
     parser.add_argument(
         '--rembert_config_file',
         default=None,
         type=str,
         required=True,
         help=(
             'The config json file corresponding to the pre-trained RemBERT model. \n'
             'This specifies the model architecture.'
         ),
     )
     parser.add_argument(
         '--pytorch_dump_path', default=None, type=str, required=True, help='Path to the output PyTorch model.'
     )
     A__   :		Dict 		=parser.parse_args()
     convert_rembert_tf_checkpoint_to_pytorch(args.tf_checkpoint_path, args.rembert_config_file, args.pytorch_dump_path)



