
import os
from string import ascii_letters

lowerCamelCase_				:							Any       =    ascii_letters + " \t\n"
def        __lowercase(							)       ->   dict[str, None]:
       __snake_case						       =   os.path.split(os.path.realpath(__snake_case					)					)
       __snake_case						       =   {}
       with open(path[0] + '/dictionary.txt'					) as dictionary_file:
              for word in dictionary_file.read().split('\n'					):
                     __snake_case						       =   None
       return english_words


lowerCamelCase_				:							Optional[Any]       =    load_dictionary()
def        __lowercase(							__snake_case				:		str					)       ->   float:
       __snake_case						       =   message.upper()
       __snake_case						       =   remove_non_letters(__snake_case					)
       __snake_case						       =   message.split()
       __snake_case						       =   len([word for word in possible_words if word in ENGLISH_WORDS]					)
       return float(__snake_case					) / len(__snake_case					)
def        __lowercase(							__snake_case				:		str					)       ->   str:
       return "".join(symbol for symbol in message if symbol in LETTERS_AND_SPACE					)
def        __lowercase(							__snake_case				:		str    ,__snake_case				:		int = 20    ,__snake_case				:		int = 85					)       ->   bool:
       __snake_case						       =   get_english_count(__snake_case					) * 1_00 >= word_percentage
       __snake_case						       =   len(remove_non_letters(__snake_case					)					)
       __snake_case						       =   (float(__snake_case					) / len(__snake_case					)) * 1_00
       __snake_case						       =   message_letters_percentage >= letter_percentage
       return words_match and letters_match


if __name__ == "__main__":
 import doctest

 doctest.testmod()


import argparse

from transformers import ConvBertConfig, ConvBertModel, TFConvBertModel, load_tf_weights_in_convbert
from transformers.utils import logging


logging.set_verbosity_info()
def        __lowercase(							__snake_case				:		Dict    ,__snake_case				:		List[str]    ,__snake_case				:		List[Any]					)       ->   str:
       __snake_case						       =   ConvBertConfig.from_json_file(__snake_case					)
       __snake_case						       =   ConvBertModel(__snake_case					)

       __snake_case						       =   load_tf_weights_in_convbert(__snake_case    ,__snake_case    ,__snake_case					)
       model.save_pretrained(__snake_case					)

       __snake_case						       =   TFConvBertModel.from_pretrained(__snake_case    ,from_pt=__snake_case					)
       tf_model.save_pretrained(__snake_case					)


if __name__ == "__main__":
 lowerCamelCase_				:							List[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(
     "--convbert_config_file",
     default=None,
     type=str,
     required=True,
     help=(
         "The config json file corresponding to the pre-trained ConvBERT 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."
 )
 lowerCamelCase_				:							Union[str, Any]       =    parser.parse_args()
 convert_orig_tfa_checkpoint_to_pytorch(args.tf_checkpoint_path, args.convbert_config_file, args.pytorch_dump_path)
