





'''simple docstring'''

import argparse

import torch

from transformers import BertConfig, BertForPreTraining, load_tf_weights_in_bert
from transformers.utils import logging


logging.set_verbosity_info()




def    _a    (							lowerCAmelCase_			,      lowerCAmelCase_			,      lowerCAmelCase_	):





      """simple docstring"""



      _snake_case		:	int					     =     BertConfig.from_json_file(lowerCAmelCase_	)
      print(f'''Building PyTorch model from configuration: {config}'''	)
      _snake_case		:	Optional[Any]					     =     BertForPreTraining(lowerCAmelCase_	)

      # Load weights from tf checkpoint
      load_tf_weights_in_bert(lowerCAmelCase_			,      lowerCAmelCase_			,      lowerCAmelCase_	)

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


if __name__ == "__main__":
   UpperCAmelCase					:		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.'
   )
   UpperCAmelCase					:		List[str]		=							parser.parse_args()
   convert_tf_checkpoint_to_pytorch(args.tf_checkpoint_path, args.bert_config_file, args.pytorch_dump_path)









'''simple docstring'''

from ...utils import is_torch_available, is_transformers_available


if is_transformers_available() and is_torch_available():
   from .pipeline_vq_diffusion import LearnedClassifierFreeSamplingEmbeddings, VQDiffusionPipeline


