


"""simple docstring"""



import argparse

import torch

from transformers import MobileBertConfig, MobileBertForPreTraining, load_tf_weights_in_mobilebert
from transformers.utils import logging


logging.set_verbosity_info()







def   __lowerCAmelCase						(						lowercase  :  Optional[int]						,							lowercase  :  Union[str, Any]						,							lowercase  :  Dict )	->						List[Any]:


    """simple docstring"""





    snake_case    :      Optional[Any]      					=    MobileBertConfig.from_json_file(SCREAMING_SNAKE_CASE_ )
    print(F'Building PyTorch model from configuration: {config}' )
    snake_case    :      str      					=    MobileBertForPreTraining(SCREAMING_SNAKE_CASE_ )
    # Load weights from tf checkpoint
    snake_case    :      Any      					=    load_tf_weights_in_mobilebert(SCREAMING_SNAKE_CASE_						,							SCREAMING_SNAKE_CASE_						,							SCREAMING_SNAKE_CASE_ )
    # Save pytorch-model
    print(F'Save PyTorch model to {pytorch_dump_path}' )
    torch.save(model.state_dict()						,							SCREAMING_SNAKE_CASE_ )


if __name__ == "__main__":
  __snake_case   			=    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(
      """--mobilebert_config_file""",
      default=None,
      type=str,
      required=True,
      help=(
          """The config json file corresponding to the pre-trained MobileBERT 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."""
  )
  __snake_case   			=    parser.parse_args()
  convert_tf_checkpoint_to_pytorch(args.tf_checkpoint_path, args.mobilebert_config_file, args.pytorch_dump_path)











import warnings

from diffusers import StableDiffusionImgaImgPipeline  # noqa F401


warnings.warn(
    """The `image_to_image.py` script is outdated. Please use directly `from diffusers import"""
    """ StableDiffusionImg2ImgPipeline` instead."""
)




