






'''simple docstring'''







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


_A     :      Union[str, Any]   					=8





def 			SCREAMING_SNAKE_CASE_	(UpperCamelCase    ,      UpperCamelCase=BITS				)				->	Tuple:
							lowerCamelCase__ :     List[str]	  =	x.device

							lowerCamelCase__ :     Any	  =	(x * 255).int().clamp(0    ,      255				)

							lowerCamelCase__ :     Optional[int]	  =	2 ** torch.arange(bits - 1    ,      -1    ,      -1    ,      device=UpperCamelCase				)
							lowerCamelCase__ :     int	  =	rearrange(UpperCamelCase    ,      """d -> d 1 1"""				)
							lowerCamelCase__ :     List[str]	  =	rearrange(UpperCamelCase    ,      """b c h w -> b c 1 h w"""				)

							lowerCamelCase__ :     Tuple	  =	((x & mask) != 0).float()
							lowerCamelCase__ :     List[Any]	  =	rearrange(UpperCamelCase    ,      """b c d h w -> b (c d) h w"""				)
							lowerCamelCase__ :     Optional[int]	  =	bits * 2 - 1
							return bits





def 			SCREAMING_SNAKE_CASE_	(UpperCamelCase    ,      UpperCamelCase=BITS				)				->	List[Any]:
							lowerCamelCase__ :     List[Any]	  =	x.device

							lowerCamelCase__ :     Dict	  =	(x > 0).int()
							lowerCamelCase__ :     Optional[Any]	  =	2 ** torch.arange(bits - 1    ,      -1    ,      -1    ,      device=UpperCamelCase    ,      dtype=torch.intaa				)

							lowerCamelCase__ :     List[Any]	  =	rearrange(UpperCamelCase    ,      """d -> d 1 1"""				)
							lowerCamelCase__ :     List[str]	  =	rearrange(UpperCamelCase    ,      """b (c d) h w -> b c d h w"""    ,      d=8				)
							lowerCamelCase__ :     List[Any]	  =	reduce(x * mask    ,      """b c d h w -> b c h w"""    ,      """sum"""				)
							return (dec / 255).clamp(0.0    ,      1.0				)





def 			SCREAMING_SNAKE_CASE_	(self    ,      UpperCamelCase    ,      UpperCamelCase    ,      UpperCamelCase    ,      UpperCamelCase = 0.0    ,      UpperCamelCase = True    ,      UpperCamelCase=None    ,      UpperCamelCase = 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)
							lowerCamelCase__ :     Optional[int]	  =	timestep - self.config.num_train_timesteps // self.num_inference_steps

							# 2. compute alphas, betas
							lowerCamelCase__ :     str	  =	self.alphas_cumprod[timestep]
							lowerCamelCase__ :     List[str]	  =	self.alphas_cumprod[prev_timestep] if prev_timestep >= 0 else self.final_alpha_cumprod

							lowerCamelCase__ :     Optional[int]	  =	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
							lowerCamelCase__ :     Optional[int]	  =	(sample - beta_prod_t ** 0.5 * model_output) / alpha_prod_t ** 0.5

							# 4. Clip "predicted x_0"
							lowerCamelCase__ :     Dict	  =	self.bit_scale
							if self.config.clip_sample:
														lowerCamelCase__ :     Optional[Any]	  =	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)
							lowerCamelCase__ :     Tuple	  =	self._get_variance(UpperCamelCase    ,      UpperCamelCase				)
							lowerCamelCase__ :     Optional[int]	  =	eta * variance ** 0.5

							if use_clipped_model_output:
														# the model_output is always re-derived from the clipped x_0 in Glide
														lowerCamelCase__ :     int	  =	(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
							lowerCamelCase__ :     Optional[Any]	  =	(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
							lowerCamelCase__ :     Tuple	  =	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
														lowerCamelCase__ :     Dict	  =	model_output.device if torch.is_tensor(UpperCamelCase				) else """cpu"""
														lowerCamelCase__ :     str	  =	torch.randn(model_output.shape    ,      dtype=model_output.dtype    ,      generator=UpperCamelCase				).to(UpperCamelCase				)
														lowerCamelCase__ :     Optional[Any]	  =	self._get_variance(UpperCamelCase    ,      UpperCamelCase				) ** 0.5 * eta * noise

														lowerCamelCase__ :     int	  =	prev_sample + variance

							if not return_dict:
														return (prev_sample,)

							return DDIMSchedulerOutput(prev_sample=UpperCamelCase    ,      pred_original_sample=UpperCamelCase				)





def 			SCREAMING_SNAKE_CASE_	(self    ,      UpperCamelCase    ,      UpperCamelCase    ,      UpperCamelCase    ,      UpperCamelCase="epsilon"    ,      UpperCamelCase=None    ,      UpperCamelCase = True    ,      )				->	Union[DDPMSchedulerOutput, Tuple]:
							lowerCamelCase__ :     List[Any]	  =	timestep

							if model_output.shape[1] == sample.shape[1] * 2 and self.variance_type in ["learned", "learned_range"]:
														lowerCamelCase__  ,		lowerCamelCase__ :     Optional[Any]	  =	torch.split(UpperCamelCase    ,      sample.shape[1]    ,      dim=1				)
							else:
														lowerCamelCase__ :     List[str]	  =	None

							# 1. compute alphas, betas
							lowerCamelCase__ :     str	  =	self.alphas_cumprod[t]
							lowerCamelCase__ :     List[str]	  =	self.alphas_cumprod[t - 1] if t > 0 else self.one
							lowerCamelCase__ :     str	  =	1 - alpha_prod_t
							lowerCamelCase__ :     List[Any]	  =	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":
														lowerCamelCase__ :     Union[str, Any]	  =	(sample - beta_prod_t ** 0.5 * model_output) / alpha_prod_t ** 0.5
							elif prediction_type == "sample":
														lowerCamelCase__ :     Optional[Any]	  =	model_output
							else:
														raise ValueError(f'''Unsupported prediction_type {prediction_type}.'''				)

							# 3. Clip "predicted x_0"
							lowerCamelCase__ :     str	  =	self.bit_scale
							if self.config.clip_sample:
														lowerCamelCase__ :     List[Any]	  =	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
							lowerCamelCase__ :     Tuple	  =	(alpha_prod_t_prev ** 0.5 * self.betas[t]) / beta_prod_t
							lowerCamelCase__ :     Tuple	  =	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
							lowerCamelCase__ :     int	  =	pred_original_sample_coeff * pred_original_sample + current_sample_coeff * sample

							# 6. Add noise
							lowerCamelCase__ :     Optional[Any]	  =	0
							if t > 0:
														lowerCamelCase__ :     Optional[Any]	  =	torch.randn(
														    model_output.size()    ,      dtype=model_output.dtype    ,      layout=model_output.layout    ,      generator=UpperCamelCase				).to(model_output.device				)
														lowerCamelCase__ :     str	  =	(self._get_variance(UpperCamelCase    ,      predicted_variance=UpperCamelCase				) ** 0.5) * noise

							lowerCamelCase__ :     Optional[int]	  =	pred_prev_sample + variance

							if not return_dict:
														return (pred_prev_sample,)

							return DDPMSchedulerOutput(prev_sample=UpperCamelCase    ,      pred_original_sample=UpperCamelCase				)



class _lowercase     (    _lowercase		):




							def __init__(							self:    List[str]      ,							UpperCamelCase__:    UNetaDConditionModel      ,							UpperCamelCase__:    Union[DDIMScheduler, DDPMScheduler]      ,							UpperCamelCase__:    Optional[float] = 1.0      ,							):
														super().__init__()
														lowerCamelCase__ :     Optional[int]	  =	bit_scale
														lowerCamelCase__ :     List[Any]	  =	(
														    ddim_bit_scheduler_step if isinstance(UpperCamelCase__      ,							UpperCamelCase__					) else ddpm_bit_scheduler_step
														)

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




							@torch.no_grad()
							def __call__(							self:    Union[str, Any]      ,							UpperCamelCase__:    Optional[int] = 256      ,							UpperCamelCase__:    Optional[int] = 256      ,							UpperCamelCase__:    Optional[int] = 50      ,							UpperCamelCase__:    Optional[torch.Generator] = None      ,							UpperCamelCase__:    Optional[int] = 1      ,							UpperCamelCase__:    Optional[str] = "pil"      ,							UpperCamelCase__:    bool = True      ,							**UpperCamelCase__:    int      ,							):
														lowerCamelCase__ :     List[Any]	  =	torch.randn(
														    (batch_size, self.unet.config.in_channels, height, width)      ,							generator=UpperCamelCase__      ,							)
														lowerCamelCase__ :     Union[str, Any]	  =	decimal_to_bits(UpperCamelCase__					) * self.bit_scale
														lowerCamelCase__ :     Union[str, Any]	  =	latents.to(self.device					)

														self.scheduler.set_timesteps(UpperCamelCase__					)

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

																					# compute the previous noisy sample x_t -> x_t-1
																					lowerCamelCase__ :     Any	  =	self.scheduler.step(UpperCamelCase__      ,							UpperCamelCase__      ,							UpperCamelCase__					).prev_sample

														lowerCamelCase__ :     Dict	  =	bits_to_decimal(UpperCamelCase__					)

														if output_type == "pil":
																					lowerCamelCase__ :     int	  =	self.numpy_to_pil(UpperCamelCase__					)

														if not return_dict:
																					return (image,)

														return ImagePipelineOutput(images=UpperCamelCase__					)









'''simple docstring'''







import argparse

from transformers import CLIPImageProcessor, CLIPVisionModelWithProjection

from diffusers import UnCLIPImageVariationPipeline, UnCLIPPipeline


if __name__ == "__main__":
			_A     :      List[Any]   					=argparse.ArgumentParser()

			parser.add_argument('''--dump_path''', default=None, type=str, required=True, help='''Path to the output model.''')

			parser.add_argument(
			    '''--txt2img_unclip''',
			    default='''kakaobrain/karlo-v1-alpha''',
			    type=str,
			    required=False,
			    help='''The pretrained txt2img unclip.''',
			)

			_A     :      Dict   					=parser.parse_args()

			_A     :      List[str]   					=UnCLIPPipeline.from_pretrained(args.txtaimg_unclip)

			_A     :      Any   					=CLIPImageProcessor()
			_A     :      Union[str, Any]   					=CLIPVisionModelWithProjection.from_pretrained('''openai/clip-vit-large-patch14''')

			_A     :      Union[str, Any]   					=UnCLIPImageVariationPipeline(
			    decoder=txtaimg.decoder,
			    text_encoder=txtaimg.text_encoder,
			    tokenizer=txtaimg.tokenizer,
			    text_proj=txtaimg.text_proj,
			    feature_extractor=feature_extractor,
			    image_encoder=image_encoder,
			    super_res_first=txtaimg.super_res_first,
			    super_res_last=txtaimg.super_res_last,
			    decoder_scheduler=txtaimg.decoder_scheduler,
			    super_res_scheduler=txtaimg.super_res_scheduler,
			)

			imgaimg.save_pretrained(args.dump_path)

