



from typing import Optional, Tuple, Union

import torch
from einops import rearrange, reduce

from diffusers import DDIMScheduler, DDPMScheduler, DiffusionPipeline, ImagePipelineOutput, UNetaDConditionModel
from diffusers.schedulers.scheduling_ddim import DDIMSchedulerOutput
from diffusers.schedulers.scheduling_ddpm import DDPMSchedulerOutput


_lowercase						  =		8



def 		lowerCAmelCase__    (					UpperCamelCase_				:       List[str] ,       UpperCamelCase_				:       Optional[Any]=BITS	)->  Dict:
    A__		 =					x.device

    A__		 =					(x * 2_5_5).int().clamp(0 ,       2_5_5	)

    A__		 =					2 ** torch.arange(bits - 1 ,       -1 ,       -1 ,       device=UpperCamelCase_	)
    A__		 =					rearrange(UpperCamelCase_ ,       '''d -> d 1 1'''	)
    A__		 =					rearrange(UpperCamelCase_ ,       '''b c h w -> b c 1 h w'''	)

    A__		 =					((x & mask) != 0).float()
    A__		 =					rearrange(UpperCamelCase_ ,       '''b c d h w -> b (c d) h w'''	)
    A__		 =					bits * 2 - 1
    return bits



def 		lowerCAmelCase__    (					UpperCamelCase_				:       Union[str, Any] ,       UpperCamelCase_				:       Tuple=BITS	)->  List[str]:
    A__		 =					x.device

    A__		 =					(x > 0).int()
    A__		 =					2 ** torch.arange(bits - 1 ,       -1 ,       -1 ,       device=UpperCamelCase_ ,       dtype=torch.intaa	)

    A__		 =					rearrange(UpperCamelCase_ ,       '''d -> d 1 1'''	)
    A__		 =					rearrange(UpperCamelCase_ ,       '''b (c d) h w -> b c d h w''' ,       d=8	)
    A__		 =					reduce(x * mask ,       '''b c d h w -> b c h w''' ,       '''sum'''	)
    return (dec / 2_5_5).clamp(0.0 ,       1.0	)



def 		lowerCAmelCase__    (					self				:       Any ,       UpperCamelCase_				:       torch.FloatTensor ,       UpperCamelCase_				:       int ,       UpperCamelCase_				:       torch.FloatTensor ,       UpperCamelCase_				:       float = 0.0 ,       UpperCamelCase_				:       bool = True ,       UpperCamelCase_				:       Optional[int]=None ,       UpperCamelCase_				:       bool = True ,       )->  Union[DDIMSchedulerOutput, Tuple]:
    if self.num_inference_steps is None:
        raise ValueError(
            '''Number of inference steps is \'None\', you need to run \'set_timesteps\' after creating the scheduler'''	)

    # See formulas (12) and (16) of DDIM paper https://arxiv.org/pdf/2010.02502.pdf
    # Ideally, read DDIM paper in-detail understanding

    # Notation (<variable name> -> <name in paper>
    # - pred_noise_t -> e_theta(x_t, t)
    # - pred_original_sample -> f_theta(x_t, t) or x_0
    # - std_dev_t -> sigma_t
    # - eta -> η
    # - pred_sample_direction -> "direction pointing to x_t"
    # - pred_prev_sample -> "x_t-1"

    # 1. get previous step value (=t-1)
    A__		 =					timestep - self.config.num_train_timesteps // self.num_inference_steps

    # 2. compute alphas, betas
    A__		 =					self.alphas_cumprod[timestep]
    A__		 =					self.alphas_cumprod[prev_timestep] if prev_timestep >= 0 else self.final_alpha_cumprod

    A__		 =					1 - alpha_prod_t

    # 3. compute predicted original sample from predicted noise also called
    # "predicted x_0" of formula (12) from https://arxiv.org/pdf/2010.02502.pdf
    A__		 =					(sample - beta_prod_t ** 0.5 * model_output) / alpha_prod_t ** 0.5

    # 4. Clip "predicted x_0"
    A__		 =					self.bit_scale
    if self.config.clip_sample:
        A__		 =					torch.clamp(UpperCamelCase_ ,       -scale ,       UpperCamelCase_	)

    # 5. compute variance: "sigma_t(η)" -> see formula (16)
    # σ_t = sqrt((1 − α_t−1)/(1 − α_t)) * sqrt(1 − α_t/α_t−1)
    A__		 =					self._get_variance(UpperCamelCase_ ,       UpperCamelCase_	)
    A__		 =					eta * variance ** 0.5

    if use_clipped_model_output:
        # the model_output is always re-derived from the clipped x_0 in Glide
        A__		 =					(sample - alpha_prod_t ** 0.5 * pred_original_sample) / beta_prod_t ** 0.5

    # 6. compute "direction pointing to x_t" of formula (12) from https://arxiv.org/pdf/2010.02502.pdf
    A__		 =					(1 - alpha_prod_t_prev - std_dev_t**2) ** 0.5 * model_output

    # 7. compute x_t without "random noise" of formula (12) from https://arxiv.org/pdf/2010.02502.pdf
    A__		 =					alpha_prod_t_prev ** 0.5 * pred_original_sample + pred_sample_direction

    if eta > 0:
        # randn_like does not support generator https://github.com/pytorch/pytorch/issues/27072
        A__		 =					model_output.device if torch.is_tensor(UpperCamelCase_	) else '''cpu'''
        A__		 =					torch.randn(model_output.shape ,       dtype=model_output.dtype ,       generator=UpperCamelCase_	).to(UpperCamelCase_	)
        A__		 =					self._get_variance(UpperCamelCase_ ,       UpperCamelCase_	) ** 0.5 * eta * noise

        A__		 =					prev_sample + variance

    if not return_dict:
        return (prev_sample,)

    return DDIMSchedulerOutput(prev_sample=UpperCamelCase_ ,       pred_original_sample=UpperCamelCase_	)



def 		lowerCAmelCase__    (					self				:       List[Any] ,       UpperCamelCase_				:       torch.FloatTensor ,       UpperCamelCase_				:       int ,       UpperCamelCase_				:       torch.FloatTensor ,       UpperCamelCase_				:       Optional[Any]="epsilon" ,       UpperCamelCase_				:       Optional[int]=None ,       UpperCamelCase_				:       bool = True ,       )->  Union[DDPMSchedulerOutput, Tuple]:
    A__		 =					timestep

    if model_output.shape[1] == sample.shape[1] * 2 and self.variance_type in ["learned", "learned_range"]:
        A__ ,	A__		 =					torch.split(UpperCamelCase_ ,       sample.shape[1] ,       dim=1	)
    else:
        A__		 =					None

    # 1. compute alphas, betas
    A__		 =					self.alphas_cumprod[t]
    A__		 =					self.alphas_cumprod[t - 1] if t > 0 else self.one
    A__		 =					1 - alpha_prod_t
    A__		 =					1 - alpha_prod_t_prev

    # 2. compute predicted original sample from predicted noise also called
    # "predicted x_0" of formula (15) from https://arxiv.org/pdf/2006.11239.pdf
    if prediction_type == "epsilon":
        A__		 =					(sample - beta_prod_t ** 0.5 * model_output) / alpha_prod_t ** 0.5
    elif prediction_type == "sample":
        A__		 =					model_output
    else:
        raise ValueError(f"Unsupported prediction_type {prediction_type}."	)

    # 3. Clip "predicted x_0"
    A__		 =					self.bit_scale
    if self.config.clip_sample:
        A__		 =					torch.clamp(UpperCamelCase_ ,       -scale ,       UpperCamelCase_	)

    # 4. Compute coefficients for pred_original_sample x_0 and current sample x_t
    # See formula (7) from https://arxiv.org/pdf/2006.11239.pdf
    A__		 =					(alpha_prod_t_prev ** 0.5 * self.betas[t]) / beta_prod_t
    A__		 =					self.alphas[t] ** 0.5 * beta_prod_t_prev / beta_prod_t

    # 5. Compute predicted previous sample µ_t
    # See formula (7) from https://arxiv.org/pdf/2006.11239.pdf
    A__		 =					pred_original_sample_coeff * pred_original_sample + current_sample_coeff * sample

    # 6. Add noise
    A__		 =					0
    if t > 0:
        A__		 =					torch.randn(
            model_output.size() ,       dtype=model_output.dtype ,       layout=model_output.layout ,       generator=UpperCamelCase_	).to(model_output.device	)
        A__		 =					(self._get_variance(UpperCamelCase_ ,       predicted_variance=UpperCamelCase_	) ** 0.5) * noise

    A__		 =					pred_prev_sample + variance

    if not return_dict:
        return (pred_prev_sample,)

    return DDPMSchedulerOutput(prev_sample=UpperCamelCase_ ,       pred_original_sample=UpperCamelCase_	)



class     _UpperCAmelCase			(       A__     ):




     def __init__(				self ,						a__ ,						a__ ,						a__ = 1.0 ,						):
         super().__init__()
         A__		 =					bit_scale
         A__		 =					(
             ddim_bit_scheduler_step if isinstance(a__ ,						a__) else ddpm_bit_scheduler_step
         )

         self.register_modules(unet=a__ ,						scheduler=a__)




     @torch.no_grad()
     def __call__(				self ,						a__ = 2_5_6 ,						a__ = 2_5_6 ,						a__ = 5_0 ,						a__ = None ,						a__ = 1 ,						a__ = "pil" ,						a__ = True ,						**a__ ,						):
         A__		 =					torch.randn(
             (batch_size, self.unet.config.in_channels, height, width) ,						generator=a__ ,						)
         A__		 =					decimal_to_bits(a__) * self.bit_scale
         A__		 =					latents.to(self.device)

         self.scheduler.set_timesteps(a__)

         for t in self.progress_bar(self.scheduler.timesteps):
             # predict the noise residual
             A__		 =					self.unet(a__ ,						a__).sample

             # compute the previous noisy sample x_t -> x_t-1
             A__		 =					self.scheduler.step(a__ ,						a__ ,						a__).prev_sample

         A__		 =					bits_to_decimal(a__)

         if output_type == "pil":
             A__		 =					self.numpy_to_pil(a__)

         if not return_dict:
             return (image,)

         return ImagePipelineOutput(images=a__)





import argparse

from diffusers.pipelines.stable_diffusion.convert_from_ckpt import download_controlnet_from_original_ckpt


if __name__ == "__main__":
   _lowercase						  =		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 		lowerCAmelCase__    (					UpperCamelCase_				:       Any	)->  Union[str, 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)

   _lowercase						  =		parser.parse_args()

   _lowercase						  =		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)
