




import argparse

import torch

from transformers import RoFormerConfig, RoFormerForMaskedLM, load_tf_weights_in_roformer
from transformers.utils import logging


logging.set_verbosity_info()





def   SCREAMING_SNAKE_CASE__						(   snake_case_,  snake_case_,  snake_case_  )   ->  Any:


	"""simple docstring"""



	a		=			RoFormerConfig.from_json_file(snake_case_  )
	print(f"""Building PyTorch model from configuration: {config}"""  )
	a		=			RoFormerForMaskedLM(snake_case_  )

	# Load weights from tf checkpoint
	load_tf_weights_in_roformer(snake_case_,  snake_case_,  snake_case_  )

	# Save pytorch-model
	print(f"""Save PyTorch model to {pytorch_dump_path}"""  )
	torch.save(model.state_dict(),  snake_case_,  _use_new_zipfile_serialization=snake_case_  )


if __name__ == "__main__":
				UpperCamelCase__						:      str								=					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(
				    """--bert_config_file""",
				    default=None,
				    type=str,
				    required=True,
				    help=(
				        """The config json file corresponding to the pre-trained BERT 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."""
				)
				UpperCamelCase__						:      Any								=					parser.parse_args()
				convert_tf_checkpoint_to_pytorch(args.tf_checkpoint_path, args.bert_config_file, args.pytorch_dump_path)













import random
import sys

UpperCamelCase__						:      List[str]								=					"""ABCDEFGHIJKLMNOPQRSTUVWXYZ"""





def   SCREAMING_SNAKE_CASE__						(   )   ->  None:


	"""simple docstring"""



	a		=			input('''Enter message: '''  )
	a		=			'''LFWOAYUISVKMNXPBDCRJTQEGHZ'''
	a		=			input('''Encrypt/Decrypt [e/d]: '''  )

	check_valid_key(snake_case_  )

	if resp.lower().startswith('''e'''  ):
		a		=			'''encrypt'''
		a		=			encrypt_message(snake_case_,  snake_case_  )
	elif resp.lower().startswith('''d'''  ):
		a		=			'''decrypt'''
		a		=			decrypt_message(snake_case_,  snake_case_  )

	print(f"""\n{mode.title()}ion: \n{translated}"""  )





def   SCREAMING_SNAKE_CASE__						(   snake_case_  )   ->  None:


	"""simple docstring"""



	a		=			list(snake_case_  )
	a		=			list(snake_case_  )
	key_list.sort()
	letters_list.sort()

	if key_list != letters_list:
		sys.exit('''Error in the key or symbol set.'''  )





def   SCREAMING_SNAKE_CASE__						(   snake_case_,  snake_case_  )   ->  str:


	"""simple docstring"""



	return translate_message(snake_case_,  snake_case_,  '''encrypt'''  )





def   SCREAMING_SNAKE_CASE__						(   snake_case_,  snake_case_  )   ->  str:


	"""simple docstring"""



	return translate_message(snake_case_,  snake_case_,  '''decrypt'''  )





def   SCREAMING_SNAKE_CASE__						(   snake_case_,  snake_case_,  snake_case_  )   ->  str:


	"""simple docstring"""



	a		=			''''''
	a		=			LETTERS
	a		=			key

	if mode == "decrypt":
		a	, a		=			chars_b, chars_a

	for symbol in message:
		if symbol.upper() in chars_a:
			a		=			chars_a.find(symbol.upper()  )
			if symbol.isupper():
				translated += chars_b[sym_index].upper()
			else:
				translated += chars_b[sym_index].lower()
		else:
			translated += symbol

	return translated





def   SCREAMING_SNAKE_CASE__						(   )   ->  str:


	"""simple docstring"""



	a		=			list(snake_case_  )
	random.shuffle(snake_case_  )
	return "".join(snake_case_  )


if __name__ == "__main__":
				main()





