





import argparse

from diffusers.pipelines.stable_diffusion.convert_from_ckpt import download_controlnet_from_original_ckpt


if __name__ == "__main__":
       a__: Any							   =						argparse.ArgumentParser()

       parser.add_argument(
           '--checkpoint_path', default=None, type=str, required=True, help='Path to the checkpoint to convert.'
       )
       parser.add_argument(
           '--original_config_file',
           type=str,
           required=True,
           help='The YAML config file corresponding to the original architecture.',
       )
       parser.add_argument(
           '--num_in_channels',
           default=None,
           type=int,
           help='The number of input channels. If `None` number of input channels will be automatically inferred.',
       )
       parser.add_argument(
           '--image_size',
           default=512,
           type=int,
           help=(
               'The image size that the model was trained on. Use 512 for Stable Diffusion v1.X and Stable Siffusion v2'
               ' Base. Use 768 for Stable Diffusion v2.'
           ),
       )
       parser.add_argument(
           '--extract_ema',
           action='store_true',
           help=(
               'Only relevant for checkpoints that have both EMA and non-EMA weights. Whether to extract the EMA weights'
               ' or not. Defaults to `False`. Add `--extract_ema` to extract the EMA weights. EMA weights usually yield'
               ' higher quality images for inference. Non-EMA weights are usually better to continue fine-tuning.'
           ),
       )
       parser.add_argument(
           '--upcast_attention',
           action='store_true',
           help=(
               'Whether the attention computation should always be upcasted. This is necessary when running stable'
               ' diffusion 2.1.'
           ),
       )
       parser.add_argument(
           '--from_safetensors',
           action='store_true',
           help='If `--checkpoint_path` is in `safetensors` format, load checkpoint with safetensors instead of PyTorch.',
       )
       parser.add_argument(
           '--to_safetensors',
           action='store_true',
           help='Whether to store pipeline in safetensors format or not.',
       )
       parser.add_argument('--dump_path', default=None, type=str, required=True, help='Path to the output model.')
       parser.add_argument('--device', type=str, help='Device to use (e.g. cpu, cuda:0, cuda:1, etc.)')


       def       UpperCamelCase__(			UpperCamelCase__     :			Dict    )->List[str]:
              if string == "True":
                     return True
              elif string == "False":
                     return False
              else:
                     raise ValueError(f"could not parse string as bool {string}"    )

       parser.add_argument(
           '--use_linear_projection', help='Override for use linear projection', required=False, type=parse_bool
       )

       parser.add_argument('--cross_attention_dim', help='Override for cross attention_dim', required=False, type=int)

       a__: Optional[Any]							   =						parser.parse_args()

       a__: str							   =						download_controlnet_from_original_ckpt(
           checkpoint_path=args.checkpoint_path,
           original_config_file=args.original_config_file,
           image_size=args.image_size,
           extract_ema=args.extract_ema,
           num_in_channels=args.num_in_channels,
           upcast_attention=args.upcast_attention,
           from_safetensors=args.from_safetensors,
           device=args.device,
           use_linear_projection=args.use_linear_projection,
           cross_attention_dim=args.cross_attention_dim,
       )
       controlnet.save_pretrained(args.dump_path, safe_serialization=args.to_safetensors)












import argparse

from transformers import BigBirdConfig, BigBirdForPreTraining, BigBirdForQuestionAnswering, load_tf_weights_in_big_bird
from transformers.utils import logging


logging.set_verbosity_info()


def       UpperCamelCase__(			UpperCamelCase__     :			Any					,	UpperCamelCase__     :			Optional[Any]					,	UpperCamelCase__     :			Dict					,	UpperCamelCase__     :			int    )->Union[str, Any]:
       # Initialise PyTorch model
       A__		   =		BigBirdConfig.from_json_file(UpperCamelCase__    )
       print(f"Building PyTorch model from configuration: {config}"    )

       if is_trivia_qa:
              A__		   =		BigBirdForQuestionAnswering(UpperCamelCase__    )
       else:
              A__		   =		BigBirdForPreTraining(UpperCamelCase__    )

       # Load weights from tf checkpoint
       load_tf_weights_in_big_bird(UpperCamelCase__					,	UpperCamelCase__					,	is_trivia_qa=UpperCamelCase__    )

       # Save pytorch-model
       print(f"Save PyTorch model to {pytorch_dump_path}"    )
       model.save_pretrained(UpperCamelCase__    )


if __name__ == "__main__":
       a__: Optional[int]							   =						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(
           '--big_bird_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.'
       )
       parser.add_argument(
           '--is_trivia_qa', action='store_true', help='Whether to convert a model with a trivia_qa head.'
       )
       a__: Tuple							   =						parser.parse_args()
       convert_tf_checkpoint_to_pytorch(
           args.tf_checkpoint_path, args.big_bird_config_file, args.pytorch_dump_path, args.is_trivia_qa
       )





