
'''simple docstring'''







import math
from dataclasses import dataclass
from typing import Optional, Tuple, Union

import numpy as np
import torch

from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput, randn_tensor
from .scheduling_utils import SchedulerMixin


@dataclass
# Copied from diffusers.schedulers.scheduling_ddpm.DDPMSchedulerOutput with DDPM->UnCLIP
class 						__UpperCAmelCase      (					_lowerCamelCase						):
							__lowercase      		=       42
							__lowercase      		=       None







def 				SCREAMING_SNAKE_CASE__	(   __A			,				__A=0.9_9_9			,				__A="cosine"			,				)       ->	str:
	if alpha_transform_type == "cosine":

		def alpha_bar_fn(__A	):
			return math.cos((t + 0.0_0_8) / 1.0_0_8 * math.pi / 2	) ** 2

	elif alpha_transform_type == "exp":

		def alpha_bar_fn(__A	):
			return math.exp(t * -1_2.0	)

	else:
		raise ValueError(F'Unsupported alpha_tranform_type: {alpha_transform_type}'	)

	_snake_case   						=		[]
	for i in range(__A	):
		_snake_case   						=		i / num_diffusion_timesteps
		_snake_case   						=		(i + 1) / num_diffusion_timesteps
		betas.append(min(1 - alpha_bar_fn(__A	) / alpha_bar_fn(__A	)			,				__A	)	)
	return torch.tensor(__A			,				dtype=torch.floataa	)





class 						__UpperCAmelCase      (					_lowerCamelCase						,   _lowerCamelCase						):


							@register_to_config
							def __init__(       self    ,   lowerCAmelCase_ = 10_00    ,   lowerCAmelCase_ = "fixed_small_log"    ,   lowerCAmelCase_ = True    ,   lowerCAmelCase_ = 1.0    ,   lowerCAmelCase_ = "epsilon"    ,   lowerCAmelCase_ = "squaredcos_cap_v2"    ,   ):





								"""simple docstring"""






								if beta_schedule != "squaredcos_cap_v2":
									raise ValueError('UnCLIPScheduler only supports `beta_schedule`: \'squaredcos_cap_v2\''  )

								_snake_case   						=		betas_for_alpha_bar(lowerCAmelCase_  )

								_snake_case   						=		1.0 - self.betas
								_snake_case   						=		torch.cumprod(self.alphas    ,   dim=0  )
								_snake_case   						=		torch.tensor(1.0  )

								# standard deviation of the initial noise distribution
								_snake_case   						=		1.0

								# setable values
								_snake_case   						=		None
								_snake_case   						=		torch.from_numpy(np.arange(0    ,   lowerCAmelCase_  )[::-1].copy()  )

								_snake_case   						=		variance_type


							def  lowerCamelCase				(       self    ,   lowerCAmelCase_    ,   lowerCAmelCase_ = None  ):





								"""simple docstring"""






								return sample


							def  lowerCamelCase				(       self    ,   lowerCAmelCase_    ,   lowerCAmelCase_ = None  ):





								"""simple docstring"""






								_snake_case   						=		num_inference_steps
								_snake_case   						=		(self.config.num_train_timesteps - 1) / (self.num_inference_steps - 1)
								_snake_case   						=		(np.arange(0    ,   lowerCAmelCase_  ) * step_ratio).round()[::-1].copy().astype(np.intaa  )
								_snake_case   						=		torch.from_numpy(lowerCAmelCase_  ).to(lowerCAmelCase_  )


							def  lowerCamelCase				(       self    ,   lowerCAmelCase_    ,   lowerCAmelCase_=None    ,   lowerCAmelCase_=None    ,   lowerCAmelCase_=None  ):





								"""simple docstring"""






								if prev_timestep is None:
									_snake_case   						=		t - 1

								_snake_case   						=		self.alphas_cumprod[t]
								_snake_case   						=		self.alphas_cumprod[prev_timestep] if prev_timestep >= 0 else self.one
								_snake_case   						=		1 - alpha_prod_t
								_snake_case   						=		1 - alpha_prod_t_prev

								if prev_timestep == t - 1:
									_snake_case   						=		self.betas[t]
								else:
									_snake_case   						=		1 - alpha_prod_t / alpha_prod_t_prev

								# For t > 0, compute predicted variance βt (see formula (6) and (7) from https://arxiv.org/pdf/2006.11239.pdf)
								# and sample from it to get previous sample
								# x_{t-1} ~ N(pred_prev_sample, variance) == add variance to pred_sample
								_snake_case   						=		beta_prod_t_prev / beta_prod_t * beta

								if variance_type is None:
									_snake_case   						=		self.config.variance_type

								# hacks - were probably added for training stability
								if variance_type == "fixed_small_log":
									_snake_case   						=		torch.log(torch.clamp(lowerCAmelCase_    ,   min=1E-20  )  )
									_snake_case   						=		torch.exp(0.5 * variance  )
								elif variance_type == "learned_range":
									# NOTE difference with DDPM scheduler
									_snake_case   						=		variance.log()
									_snake_case   						=		beta.log()

									_snake_case   						=		(predicted_variance + 1) / 2
									_snake_case   						=		frac * max_log + (1 - frac) * min_log

								return variance


							def  lowerCamelCase				(       self    ,   lowerCAmelCase_    ,   lowerCAmelCase_    ,   lowerCAmelCase_    ,   lowerCAmelCase_ = None    ,   lowerCAmelCase_=None    ,   lowerCAmelCase_ = True    ,   ):





								"""simple docstring"""






								_snake_case   						=		timestep

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

								# 1. compute alphas, betas
								if prev_timestep is None:
									_snake_case   						=		t - 1

								_snake_case   						=		self.alphas_cumprod[t]
								_snake_case   						=		self.alphas_cumprod[prev_timestep] if prev_timestep >= 0 else self.one
								_snake_case   						=		1 - alpha_prod_t
								_snake_case   						=		1 - alpha_prod_t_prev

								if prev_timestep == t - 1:
									_snake_case   						=		self.betas[t]
									_snake_case   						=		self.alphas[t]
								else:
									_snake_case   						=		1 - alpha_prod_t / alpha_prod_t_prev
									_snake_case   						=		1 - beta

								# 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 self.config.prediction_type == "epsilon":
									_snake_case   						=		(sample - beta_prod_t ** 0.5 * model_output) / alpha_prod_t ** 0.5
								elif self.config.prediction_type == "sample":
									_snake_case   						=		model_output
								else:
									raise ValueError(
									    F'prediction_type given as {self.config.prediction_type} must be one of `epsilon` or `sample`'
									    ' for the UnCLIPScheduler.'  )

								# 3. Clip "predicted x_0"
								if self.config.clip_sample:
									_snake_case   						=		torch.clamp(
									    lowerCAmelCase_    ,   -self.config.clip_sample_range    ,   self.config.clip_sample_range  )

								# 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
								_snake_case   						=		(alpha_prod_t_prev ** 0.5 * beta) / beta_prod_t
								_snake_case   						=		alpha ** 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
								_snake_case   						=		pred_original_sample_coeff * pred_original_sample + current_sample_coeff * sample

								# 6. Add noise
								_snake_case   						=		0
								if t > 0:
									_snake_case   						=		randn_tensor(
									    model_output.shape    ,   dtype=model_output.dtype    ,   generator=lowerCAmelCase_    ,   device=model_output.device  )

									_snake_case   						=		self._get_variance(
									    lowerCAmelCase_    ,   predicted_variance=lowerCAmelCase_    ,   prev_timestep=lowerCAmelCase_    ,   )

									if self.variance_type == "fixed_small_log":
										_snake_case   						=		variance
									elif self.variance_type == "learned_range":
										_snake_case   						=		(0.5 * variance).exp()
									else:
										raise ValueError(
										    F'variance_type given as {self.variance_type} must be one of `fixed_small_log` or `learned_range`'
										    ' for the UnCLIPScheduler.'  )

									_snake_case   						=		variance * variance_noise

								_snake_case   						=		pred_prev_sample + variance

								if not return_dict:
									return (pred_prev_sample,)

								return UnCLIPSchedulerOutput(prev_sample=lowerCAmelCase_    ,   pred_original_sample=lowerCAmelCase_  )




							def  lowerCamelCase				(       self    ,   lowerCAmelCase_    ,   lowerCAmelCase_    ,   lowerCAmelCase_    ,   ):





								"""simple docstring"""






								_snake_case   						=		self.alphas_cumprod.to(device=original_samples.device    ,   dtype=original_samples.dtype  )
								_snake_case   						=		timesteps.to(original_samples.device  )

								_snake_case   						=		alphas_cumprod[timesteps] ** 0.5
								_snake_case   						=		sqrt_alpha_prod.flatten()
								while len(sqrt_alpha_prod.shape  ) < len(original_samples.shape  ):
									_snake_case   						=		sqrt_alpha_prod.unsqueeze(-1  )

								_snake_case   						=		(1 - alphas_cumprod[timesteps]) ** 0.5
								_snake_case   						=		sqrt_one_minus_alpha_prod.flatten()
								while len(sqrt_one_minus_alpha_prod.shape  ) < len(original_samples.shape  ):
									_snake_case   						=		sqrt_one_minus_alpha_prod.unsqueeze(-1  )

								_snake_case   						=		sqrt_alpha_prod * original_samples + sqrt_one_minus_alpha_prod * noise
								return noisy_samples



'''simple docstring'''







import argparse

import intel_extension_for_pytorch as ipex
import torch

from diffusers import DPMSolverMultistepScheduler, StableDiffusionPipeline


lowercase    :							Optional[Any]      =					argparse.ArgumentParser("Stable Diffusion script with intel optimization", add_help=False)
parser.add_argument("--dpm", action="store_true", help="Enable DPMSolver or not")
parser.add_argument("--steps", default=None, type=int, help="Num inference steps")
lowercase    :							Tuple      =					parser.parse_args()


lowercase    :							Optional[int]      =					"cpu"
lowercase    :							Optional[Any]      =					"a lovely <dicoo> in red dress and hat, in the snowly and brightly night, with many brighly buildings"

lowercase    :							Optional[int]      =					"path-to-your-trained-model"
lowercase    :							List[str]      =					StableDiffusionPipeline.from_pretrained(model_id)
if args.dpm:
					lowercase    :							str      =					DPMSolverMultistepScheduler.from_config(pipe.scheduler.config)
lowercase    :							Dict      =					pipe.to(device)

# to channels last
lowercase    :							Optional[Any]      =					pipe.unet.to(memory_format=torch.channels_last)
lowercase    :							int      =					pipe.vae.to(memory_format=torch.channels_last)
lowercase    :							Optional[Any]      =					pipe.text_encoder.to(memory_format=torch.channels_last)
if pipe.requires_safety_checker:
					lowercase    :							Optional[int]      =					pipe.safety_checker.to(memory_format=torch.channels_last)

# optimize with ipex
lowercase    :							Any      =					torch.randn(2, 4, 64, 64)
lowercase    :							Optional[int]      =					torch.rand(1) * 999
lowercase    :							Optional[Any]      =					torch.randn(2, 77, 768)
lowercase    :							Optional[Any]      =					(sample, timestep, encoder_hidden_status)
try:
					lowercase    :							List[Any]      =					ipex.optimize(pipe.unet.eval(), dtype=torch.bfloataa, inplace=True, sample_input=input_example)
except Exception:
					lowercase    :							List[str]      =					ipex.optimize(pipe.unet.eval(), dtype=torch.bfloataa, inplace=True)
lowercase    :							Tuple      =					ipex.optimize(pipe.vae.eval(), dtype=torch.bfloataa, inplace=True)
lowercase    :							Optional[Any]      =					ipex.optimize(pipe.text_encoder.eval(), dtype=torch.bfloataa, inplace=True)
if pipe.requires_safety_checker:
					lowercase    :							Tuple      =					ipex.optimize(pipe.safety_checker.eval(), dtype=torch.bfloataa, inplace=True)

# compute
lowercase    :							List[str]      =					666
lowercase    :							Tuple      =					torch.Generator(device).manual_seed(seed)
lowercase    :							Union[str, Any]      =					{"generator": generator}
if args.steps is not None:
					lowercase    :							Dict      =					args.steps

with torch.cpu.amp.autocast(enabled=True, dtype=torch.bfloataa):
					lowercase    :							List[str]      =					pipe(prompt, **generate_kwargs).images[0]

# save image
image.save("generated.png")

