






"""simple docstring"""







import importlib
import sys
from argparse import REMAINDER, ArgumentParser
from pathlib import Path

import torch_xla.distributed.xla_multiprocessing as xmp






def       _lowerCAmelCase       (      ):







       '''simple docstring'''
       UpperCAmelCase	    =			ArgumentParser(
           description=(
               """PyTorch TPU distributed training launch """
               """helper utility that will spawn up """
               """multiple distributed processes"""
           )		)

       # Optional arguments for the launch helper
       parser.add_argument("""--num_cores"""	,      type=lowerCAmelCase	,      default=1	,      help="""Number of TPU cores to use (1 or 8)."""		)

       # positional
       parser.add_argument(
           """training_script"""	,      type=lowerCAmelCase	,      help=(
               """The full path to the single TPU training """
               """program/script to be launched in parallel, """
               """followed by all the arguments for the """
               """training script"""
           )	,      )

       # rest from the training program
       parser.add_argument("""training_script_args"""	,      nargs=lowerCAmelCase		)

       return parser.parse_args()






def       _lowerCAmelCase       (      ):







       '''simple docstring'''
       UpperCAmelCase	    =			parse_args()

       # Import training_script as a module.
       UpperCAmelCase	    =			Path(args.training_script		)
       sys.path.append(str(script_fpath.parent.resolve()		)		)
       UpperCAmelCase	    =			script_fpath.stem
       UpperCAmelCase	    =			importlib.import_module(lowerCAmelCase		)

       # Patch sys.argv
       UpperCAmelCase	    =			[args.training_script] + args.training_script_args + ["""--tpu_num_cores""", str(args.num_cores		)]
       xmp.spawn(mod._mp_fn	,      args=()	,      nprocs=args.num_cores		)


if __name__ == "__main__":
 main()








"""simple docstring"""







import argparse

from diffusers.pipelines.stable_diffusion.convert_from_ckpt import download_controlnet_from_original_ckpt


if __name__ == "__main__":
 lowerCAmelCase_	:    Tuple            =      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=5_1_2,
     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       _lowerCAmelCase       (      lowerCAmelCase		):







        '''simple docstring'''
        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)

 lowerCAmelCase_	:    int            =      parser.parse_args()

 lowerCAmelCase_	:    Optional[int]            =      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)
