



def 		__snake_case						(    _UpperCAmelCase				):
					"""simple docstring"""






					lowercase						   =       word.upper()
					lowercase						   =       0
					lowercase						   =       {}
					lowercase						   =       []

					for letter in word:
										if letter not in letter_nums:
															lowercase						   =       str(_UpperCAmelCase				)
															next_num += 1
										word_pattern.append(letter_nums[letter]				)
					return ".".join(_UpperCAmelCase				)


if __name__ == "__main__":
		import pprint
		import time

		__magic_name__       =     time.time()
		with open('''dictionary.txt''') as in_file:
				__magic_name__       =     in_file.read().splitlines()

		__magic_name__       =     {}
		for word in word_list:
				__magic_name__       =     get_word_pattern(word)
				if pattern in all_patterns:
						all_patterns[pattern].append(word)
				else:
						__magic_name__       =     [word]

		with open('''word_patterns.txt''', '''w''') as out_file:
				out_file.write(pprint.pformat(all_patterns))

		__magic_name__       =     round(time.time() - start_time, 2)
		print(F"""Done!  {len(all_patterns):,} word patterns found in {total_time} seconds.""")
		# Done!  9,581 word patterns found in 0.58 seconds.











import argparse

import torch

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


logging.set_verbosity_info()





def 		__snake_case						(    _UpperCAmelCase       ,			_UpperCAmelCase       ,			_UpperCAmelCase				):
					"""simple docstring"""






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

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

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


if __name__ == "__main__":
		__magic_name__       =     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.'''
		)
		__magic_name__       =     parser.parse_args()
		convert_tf_checkpoint_to_pytorch(args.tf_checkpoint_path, args.bert_config_file, args.pytorch_dump_path)






