



"""simple docstring"""
import unittest

from transformers import is_torch_available, load_tool

from .test_tools_common import ToolTesterMixin


if is_torch_available():
						import torch
class 							lowerCamelCase    (     unittest.TestCase       ,		_lowerCAmelCase							):






					'''simple docstring'''





					def   lowerCAmelCase_							(			self:				Tuple		)  ->      Any:
											snake_case_					:Any		=     load_tool("""speech-to-text"""		)
											self.tool.setup()






					def   lowerCAmelCase_							(			self:				Optional[int]		)  ->      Union[str, Any]:
											snake_case_					:List[Any]		=     self.tool(torch.ones(3_000		)		)
											self.assertEqual(snake_case		,	""" you"""		)






					def   lowerCAmelCase_							(			self:				Union[str, Any]		)  ->      Tuple:
											snake_case_					:List[str]		=     self.tool(audio=torch.ones(3_000		)		)
											self.assertEqual(snake_case		,	""" you"""		)




'''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			(	__A ,     __A ,     __A       )				->						str:


      '''simple docstring'''



      UpperCamelCase__      =							RoFormerConfig.from_json_file(__A       )
      print(F'''Building PyTorch model from configuration: {config}'''       )
      UpperCamelCase__      =							RoFormerForMaskedLM(__A       )

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

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


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





