
"""simple docstring"""




import argparse

import torch

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


logging.set_verbosity_info()

def     UpperCamelCase_    (						SCREAMING_SNAKE_CASE_,	SCREAMING_SNAKE_CASE_,	SCREAMING_SNAKE_CASE_     ):
					SCREAMING_SNAKE_CASE           =      RoFormerConfig.from_json_file(SCREAMING_SNAKE_CASE_     )
					print(f'''Building PyTorch model from configuration: {config}'''     )
					SCREAMING_SNAKE_CASE           =      RoFormerForMaskedLM(SCREAMING_SNAKE_CASE_     )

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

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


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


'''simple docstring'''





import os
import unittest

import pytest
from parameterized import parameterized

from transformers.testing_utils import require_torch, require_torch_gpu, slow, torch_device


# skipping in unittest tests

a__		: Tuple  							=							[(1,)]





def 							snake_case				(		)->      Union[str, Any]:







      """simple docstring"""
      __A													=      bool(os.getenv('RUN_SLOW'   ,	0						)						)
      if run_slow:
            assert True
      else:
            assert False, "should have been skipped"





def 							snake_case				(		)->      Tuple:







      """simple docstring"""
      __A													=      bool(os.getenv('RUN_SLOW'   ,	0						)						)
      if run_slow and torch_device == "cuda":
            assert True
      else:
            assert False, "should have been skipped"


@require_torch
class 			UpperCamelCase__	(     unittest.TestCase):






 @slow
 @require_torch_gpu
 def 						lowercase_    (      self	:Dict      )					->      List[Any]:







       '''simple docstring'''



       check_slow_torch_cuda()






 @require_torch_gpu
 @slow
 def 						lowercase_    (      self	:int      )					->      int:







       '''simple docstring'''



       check_slow_torch_cuda()






 @parameterized.expand(_A      )
 @slow
 def 						lowercase_    (      self	:Dict						,					_A	:Union[str, Any]=None      )					->      Dict:







       '''simple docstring'''



       check_slow()





@slow
@require_torch_gpu
def 							snake_case				(		)->      Optional[int]:







      """simple docstring"""
      check_slow_torch_cuda()





@require_torch_gpu
@slow
def 							snake_case				(		)->      List[str]:







      """simple docstring"""
      check_slow_torch_cuda()





@slow
@pytest.mark.parametrize('param'   ,	[1]						)
def 							snake_case				(		UpperCAmelCase						)->      int:







      """simple docstring"""
      check_slow()





@pytest.mark.parametrize('param'   ,	[1]						)
@slow
def 							snake_case				(		UpperCAmelCase						)->      Any:







      """simple docstring"""
      check_slow()






