



import argparse

import torch

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


logging.set_verbosity_info()



def    __lowerCamelCase						(      _lowercase			,       _lowercase			,       _lowercase	)   ->    str:
							# Initialise PyTorch model
							UpperCAmelCase  :	Optional[int]							  =	BertConfig.from_json_file(lowerCamelCase_	)
							print(F'''Building PyTorch model from configuration: {config}'''	)
							UpperCAmelCase  :	str							  =	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__":
							a     :    List[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     :    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

