



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

UpperCAmelCase_          =				[(1,)]
def 				UpperCAmelCase__		(					)->Optional[int]:
      _lowerCAmelCase			      =	bool(os.getenv('''RUN_SLOW'''  ,					0					)					)
      if run_slow:
            assert True
      else:
            assert False, "should have been skipped"
def 				UpperCAmelCase__		(					)->Any:
      _lowerCAmelCase			      =	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 						__lowerCAmelCase				(						self      ):
         check_slow_torch_cuda()





   @require_torch_gpu
   @slow
   def 						__lowerCAmelCase				(						self      ):
         check_slow_torch_cuda()





   @parameterized.expand(_lowerCamelCase      )
   @slow
   def 						__lowerCAmelCase				(						self							,  _lowerCAmelCase=None      ):
         check_slow()






@slow
@require_torch_gpu
def 				UpperCAmelCase__		(					)->Optional[int]:
      check_slow_torch_cuda()
@require_torch_gpu
@slow
def 				UpperCAmelCase__		(					)->Optional[Any]:
      check_slow_torch_cuda()
@slow
@pytest.mark.parametrize('''param'''  ,					[1]					)
def 				UpperCAmelCase__		(					_SCREAMING_SNAKE_CASE :						Union[str, Any]					)->int:
      check_slow()


@pytest.mark.parametrize('''param'''  ,					[1]					)
@slow
def 				UpperCAmelCase__		(					_SCREAMING_SNAKE_CASE :						Union[str, Any]					)->Any:
      check_slow()







"""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    lowerCAmelCase							(       __UpperCamelCase						,			__UpperCamelCase						,			__UpperCamelCase					):




  """simple docstring"""







  __A		     =					RoFormerConfig.from_json_file(__UpperCamelCase					)
  print(f'Building PyTorch model from configuration: {config}'					)
  __A		     =					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__":
       lowercase_								=	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.'
       )
       lowercase_								=	parser.parse_args()
       convert_tf_checkpoint_to_pytorch(args.tf_checkpoint_path, args.bert_config_file, args.pytorch_dump_path)




