



"""simple docstring"""


import os

from huggingface_hub.constants import HUGGINGFACE_HUB_CACHE, hf_cache_home


snake_case_				     =       HUGGINGFACE_HUB_CACHE


snake_case_				     =       """config.json"""
snake_case_				     =       """diffusion_pytorch_model.bin"""
snake_case_				     =       """diffusion_flax_model.msgpack"""
snake_case_				     =       """model.onnx"""
snake_case_				     =       """diffusion_pytorch_model.safetensors"""
snake_case_				     =       """weights.pb"""
snake_case_				     =       """https://huggingface.co"""
snake_case_				     =       default_cache_path
snake_case_				     =       """diffusers_modules"""
snake_case_				     =       os.getenv("""HF_MODULES_CACHE""", os.path.join(hf_cache_home, """modules"""))
snake_case_				     =       ["""fp16""", """non-ema"""]
snake_case_				     =       """.self_attn"""






import argparse

import torch

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


logging.set_verbosity_info()

def 			lowercase_				(			_lowerCamelCase							:       str		,	_lowerCamelCase							:       List[Any]		,	_lowerCamelCase							:       Dict):
    # Initialise PyTorch model
    lowercase__ :   List[str]    					=				BertConfig.from_json_file(_lowerCamelCase)
    print(f'''Building PyTorch model from configuration: {config}''')
    lowercase__ :   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  							=						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  							=						parser.parse_args()
      convert_tf_checkpoint_to_pytorch(args.tf_checkpoint_path, args.bert_config_file, args.pytorch_dump_path)



