



import inspect
import os
import unittest

import torch

import accelerate
from accelerate import debug_launcher
from accelerate.test_utils import (
    execute_subprocess_async,
    require_cpu,
    require_multi_gpu,
    require_single_gpu,
    test_sync,
)
from accelerate.utils import patch_environment


class 	a__       (       unittest.TestCase       ):


					"""simple docstring"""



					def 				__UpperCAmelCase		(		self     :List[str]    ):
										lowercase						   =       inspect.getfile(accelerate.test_utils    )
										lowercase						   =       os.path.sep.join(mod_file.split(os.path.sep    )[:-1] + ['scripts', 'test_sync.py']    )

					@require_cpu
					def 				__UpperCAmelCase		(		self     :Optional[Any]    ):
										debug_launcher(test_sync.main      ,  num_processes=1    )

					@require_cpu
					def 				__UpperCAmelCase		(		self     :int    ):
										debug_launcher(test_sync.main    )

					@require_single_gpu
					def 				__UpperCAmelCase		(		self     :Optional[Any]    ):
										test_sync.main()



					@require_multi_gpu
					def 				__UpperCAmelCase		(		self     :Any    ):
										print(F"""Found {torch.cuda.device_count()} devices."""    )
										lowercase						   =       ['torchrun', F"""--nproc_per_node={torch.cuda.device_count()}""", self.test_file_path]
										with patch_environment(omp_num_threads=1    ):
															execute_subprocess_async(lowercase__      ,  env=os.environ.copy()    )











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)






