


import json
import os
import tempfile

from transformers.testing_utils import check_json_file_has_correct_format



class   lowerCamelCase				:







		'''simple docstring'''



		_snake_case   :   int							=  None



		def  __UpperCAmelCase      (							self       )     ->							List[Any]:
							UpperCAmelCase_   :				List[Any]						=						self.feature_extraction_class(**self.feat_extract_dict       )
							UpperCAmelCase_   :				List[Any]						=						json.loads(feat_extract.to_json_string()       )
							for key, value in self.feat_extract_dict.items():
												self.assertEqual(obj[key]    ,		_UpperCamelCase       )



		def  __UpperCAmelCase      (							self       )     ->							Optional[int]:
							UpperCAmelCase_   :				List[Any]						=						self.feature_extraction_class(**self.feat_extract_dict       )

							with tempfile.TemporaryDirectory() as tmpdirname:
												UpperCAmelCase_   :				Tuple						=						os.path.join(_UpperCamelCase    ,		'feat_extract.json'       )
												feat_extract_first.to_json_file(_UpperCamelCase       )
												UpperCAmelCase_   :				int						=						self.feature_extraction_class.from_json_file(_UpperCamelCase       )

							self.assertEqual(feat_extract_second.to_dict()    ,		feat_extract_first.to_dict()       )



		def  __UpperCAmelCase      (							self       )     ->							Any:
							UpperCAmelCase_   :				str						=						self.feature_extraction_class(**self.feat_extract_dict       )

							with tempfile.TemporaryDirectory() as tmpdirname:
												UpperCAmelCase_   :				Dict						=						feat_extract_first.save_pretrained(_UpperCamelCase       )[0]
												check_json_file_has_correct_format(_UpperCamelCase       )
												UpperCAmelCase_   :				Dict						=						self.feature_extraction_class.from_pretrained(_UpperCamelCase       )

							self.assertEqual(feat_extract_second.to_dict()    ,		feat_extract_first.to_dict()       )



		def  __UpperCAmelCase      (							self       )     ->							Tuple:
							UpperCAmelCase_   :				Any						=						self.feature_extraction_class()
							self.assertIsNotNone(_UpperCamelCase       )














import argparse

import torch

from transformers import RemBertConfig, RemBertModel, load_tf_weights_in_rembert
from transformers.utils import logging


logging.set_verbosity_info()




def 					lowercase__				(					__snake_case   :							Optional[Any]					,	__snake_case   :							Any					,	__snake_case   :							int  ):


					'''simple docstring'''


					UpperCAmelCase_   :				Union[str, Any]						=						RemBertConfig.from_json_file(__snake_case  )
					print('Building PyTorch model from configuration: {}'.format(str(__snake_case  )  )  )
					UpperCAmelCase_   :				Dict						=						RemBertModel(__snake_case  )

					# Load weights from tf checkpoint
					load_tf_weights_in_rembert(__snake_case					,	__snake_case					,	__snake_case  )

					# Save pytorch-model
					print('Save PyTorch model to {}'.format(__snake_case  )  )
					torch.save(model.state_dict()					,	__snake_case  )


if __name__ == "__main__":
		__UpperCAmelCase     							=						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(
		    '--rembert_config_file',
		    default=None,
		    type=str,
		    required=True,
		    help=(
		        'The config json file corresponding to the pre-trained RemBERT 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.'
		)
		__UpperCAmelCase     							=						parser.parse_args()
		convert_rembert_tf_checkpoint_to_pytorch(args.tf_checkpoint_path, args.rembert_config_file, args.pytorch_dump_path)






