

"""simple docstring"""




# DISCLAIMER: This file is strongly influenced by https://github.com/yang-song/score_sde_pytorch

import math
from typing import Union

import torch

from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import randn_tensor
from .scheduling_utils import SchedulerMixin
class      _UpperCAmelCase   (							_lowerCAmelCase		,    _lowerCAmelCase       ):
	a__		: Tuple					=					1



	@register_to_config
	def __init__(  self     :       List[Any]			,    _lowercase     :       Any=20_00			,    _lowercase     :       Union[str, Any]=0.1			,    _lowercase     :       Union[str, Any]=20			,    _lowercase     :       Optional[int]=1E-3						):
		__UpperCAmelCase      		= None
		__UpperCAmelCase      		= None
		__UpperCAmelCase      		= None



	def        a  (  self     :       List[Any]			,    _lowercase     :       str			,    _lowercase     :       Union[str, torch.device] = None						):
		__UpperCAmelCase      		= torch.linspace(1			,    self.config.sampling_eps			,    _lowercase			,    device=_lowercase						)



	def        a  (  self     :       Optional[int]			,    _lowercase     :       Any			,    _lowercase     :       Tuple			,    _lowercase     :       Dict			,    _lowercase     :       Dict=None						):
		if self.timesteps is None:
			raise ValueError(
			    '''`self.timesteps` is not set, you need to run \'set_timesteps\' after creating the scheduler'''						)

		# TODO(Patrick) better comments + non-PyTorch
		# postprocess model score
		__UpperCAmelCase      		= (
		    -0.25 * t**2 * (self.config.beta_max - self.config.beta_min) - 0.5 * t * self.config.beta_min
		)
		__UpperCAmelCase      		= torch.sqrt(1.0 - torch.exp(2.0 * log_mean_coeff						)						)
		__UpperCAmelCase      		= std.flatten()
		while len(std.shape						) < len(score.shape						):
			__UpperCAmelCase      		= std.unsqueeze(-1						)
		__UpperCAmelCase      		= -score / std

		# compute
		__UpperCAmelCase      		= -1.0 / len(self.timesteps						)

		__UpperCAmelCase      		= self.config.beta_min + t * (self.config.beta_max - self.config.beta_min)
		__UpperCAmelCase      		= beta_t.flatten()
		while len(beta_t.shape						) < len(x.shape						):
			__UpperCAmelCase      		= beta_t.unsqueeze(-1						)
		__UpperCAmelCase      		= -0.5 * beta_t * x

		__UpperCAmelCase      		= torch.sqrt(_lowercase						)
		__UpperCAmelCase      		= drift - diffusion**2 * score
		__UpperCAmelCase      		= x + drift * dt

		# add noise
		__UpperCAmelCase      		= randn_tensor(x.shape			,    layout=x.layout			,    generator=_lowercase			,    device=x.device			,    dtype=x.dtype						)
		__UpperCAmelCase      		= x_mean + diffusion * math.sqrt(-dt						) * noise

		return x, x_mean



	def __len__(  self     :       Optional[int]						):
		return self.config.num_train_timesteps




import argparse

from diffusers.pipelines.stable_diffusion.convert_from_ckpt import download_controlnet_from_original_ckpt


if __name__ == "__main__":
	_lowerCAmelCase:						int   				=       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   _lowercase(					__a				:						Optional[Any]     ):
								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:						str   				=       parser.parse_args()

	_lowerCAmelCase:						Tuple   				=       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)

