





import argparse

from diffusers.pipelines.stable_diffusion.convert_from_ckpt import download_controlnet_from_original_ckpt


if __name__ == "__main__":
			UpperCAmelCase__  :     Union[str, 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=5_12,
			    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        _lowercase   (  _UpperCAmelCase			)  ->					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)

			UpperCAmelCase__  :     Any   			=parser.parse_args()

			UpperCAmelCase__  :     Optional[Any]   			=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)












from typing import List, Optional, Tuple, Union

import torch

from ...utils import logging, randn_tensor
from ..pipeline_utils import AudioPipelineOutput, DiffusionPipeline


UpperCAmelCase__  :     Dict   			=logging.get_logger(__name__)  # pylint: disable=invalid-name

class  __A       (  a      ):


				def __init__(						self				,	UpperCAmelCase_				,	UpperCAmelCase_							):
								super().__init__()
								self.register_modules(unet=UpperCAmelCase_				,	scheduler=UpperCAmelCase_							)


				@torch.no_grad()
				def __call__(						self				,	UpperCAmelCase_ = 1				,	UpperCAmelCase_ = 100				,	UpperCAmelCase_ = None				,	UpperCAmelCase_ = None				,	UpperCAmelCase_ = True				,	):

								if audio_length_in_s is None:
												lowerCamelCase			=self.unet.config.sample_size / self.unet.config.sample_rate

								lowerCamelCase			=audio_length_in_s * self.unet.config.sample_rate

								lowerCamelCase			=2 ** len(self.unet.up_blocks							)
								if sample_size < 3 * down_scale_factor:
												raise ValueError(
												    f"""{audio_length_in_s} is too small. Make sure it's bigger or equal to"""
												    f""" {3 * down_scale_factor / self.unet.config.sample_rate}."""							)

								lowerCamelCase			=int(UpperCAmelCase_							)
								if sample_size % down_scale_factor != 0:
												lowerCamelCase			=(
												    (audio_length_in_s * self.unet.config.sample_rate) // down_scale_factor + 1
												) * down_scale_factor
												logger.info(
												    f"""{audio_length_in_s} is increased to {sample_size / self.unet.config.sample_rate} so that it can be handled"""
												    f""" by the model. It will be cut to {original_sample_size / self.unet.config.sample_rate} after the denoising"""
												    """ process."""							)
								lowerCamelCase			=int(UpperCAmelCase_							)

								lowerCamelCase			=next(iter(self.unet.parameters()							)							).dtype
								lowerCamelCase			=(batch_size, self.unet.config.in_channels, sample_size)
								if isinstance(UpperCAmelCase_				,	UpperCAmelCase_							) and len(UpperCAmelCase_							) != batch_size:
												raise ValueError(
												    f"""You have passed a list of generators of length {len(UpperCAmelCase_							)}, but requested an effective batch"""
												    f""" size of {batch_size}. Make sure the batch size matches the length of the generators."""							)

								lowerCamelCase			=randn_tensor(UpperCAmelCase_				,	generator=UpperCAmelCase_				,	device=self.device				,	dtype=UpperCAmelCase_							)

								# set step values
								self.scheduler.set_timesteps(UpperCAmelCase_				,	device=audio.device							)
								lowerCamelCase			=self.scheduler.timesteps.to(UpperCAmelCase_							)

								for t in self.progress_bar(self.scheduler.timesteps							):
												# 1. predict noise model_output
												lowerCamelCase			=self.unet(UpperCAmelCase_				,	UpperCAmelCase_							).sample

												# 2. compute previous image: x_t -> t_t-1
												lowerCamelCase			=self.scheduler.step(UpperCAmelCase_				,	UpperCAmelCase_				,	UpperCAmelCase_							).prev_sample

								lowerCamelCase			=audio.clamp(-1				,	1							).float().cpu().numpy()

								lowerCamelCase			=audio[:, :, :original_sample_size]

								if not return_dict:
												return (audio,)

								return AudioPipelineOutput(audios=UpperCAmelCase_							)





