




'''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


_snake_case : Optional[Any]  				=						8
def 						snake_case_       (UpperCamelCase			:    List[Any]					,		UpperCamelCase			:    Dict=BITS ):





							'''simple docstring'''


							_a      	=							x.device

							_a      	=							(x * 255).int().clamp(0					,		255 )

							_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 						snake_case_       (UpperCamelCase			:    List[Any]					,		UpperCamelCase			:    Any=BITS ):





							'''simple docstring'''


							_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 / 255).clamp(0.0					,		1.0 )
def 						snake_case_       (self			:    Union[str, Any]					,		UpperCamelCase			:    torch.FloatTensor					,		UpperCamelCase			:    int					,		UpperCamelCase			:    torch.FloatTensor					,		UpperCamelCase			:    float = 0.0					,		UpperCamelCase			:    bool = True					,		UpperCamelCase			:    Any=None					,		UpperCamelCase			:    bool = True					,		):





							'''simple docstring'''


							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 						snake_case_       (self			:    Any					,		UpperCamelCase			:    torch.FloatTensor					,		UpperCamelCase			:    int					,		UpperCamelCase			:    torch.FloatTensor					,		UpperCamelCase			:    str="epsilon"					,		UpperCamelCase			:    Dict=None					,		UpperCamelCase			:    bool = True					,		):





							'''simple docstring'''


							_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      A       (						_a       ):







							def __init__(    self			:    Any				,     lowerCAmelCase_			:    UNetaDConditionModel				,     lowerCAmelCase_			:    Union[DDIMScheduler, DDPMScheduler]				,     lowerCAmelCase_			:    Optional[float] = 1.0				,     )     ->				int:


														"""simple docstring"""


														super().__init__()
														_a      	=							bit_scale
														_a      	=							(
														    ddim_bit_scheduler_step if isinstance(lowerCAmelCase_				,     lowerCAmelCase_   ) else ddpm_bit_scheduler_step
														)

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







							@torch.no_grad()
							def __call__(    self			:    List[Any]				,     lowerCAmelCase_			:    Optional[int] = 2_56				,     lowerCAmelCase_			:    Optional[int] = 2_56				,     lowerCAmelCase_			:    Optional[int] = 50				,     lowerCAmelCase_			:    Optional[torch.Generator] = None				,     lowerCAmelCase_			:    Optional[int] = 1				,     lowerCAmelCase_			:    Optional[str] = "pil"				,     lowerCAmelCase_			:    bool = True				,     **lowerCAmelCase_			:    Any				,     )     ->				Union[Tuple, ImagePipelineOutput]:


														"""simple docstring"""


														_a      	=							torch.randn(
														    (batch_size, self.unet.config.in_channels, height, width)				,     generator=lowerCAmelCase_				,     )
														_a      	=							decimal_to_bits(lowerCAmelCase_   ) * self.bit_scale
														_a      	=							latents.to(self.device   )

														self.scheduler.set_timesteps(lowerCAmelCase_   )

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

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

														_a      	=							bits_to_decimal(lowerCAmelCase_   )

														if output_type == "pil":
																					_a      	=							self.numpy_to_pil(lowerCAmelCase_   )

														if not return_dict:
																					return (image,)

														return ImagePipelineOutput(images=lowerCAmelCase_   )




"""simple docstring"""





import warnings

from ...utils import logging
from .image_processing_imagegpt import ImageGPTImageProcessor


__UpperCamelCase    : List[str]				 =						logging.get_logger(__name__)







class 							SCREAMING_SNAKE_CASE   (						a_			):






					"""simple docstring"""



					def __init__(			self     :							Any  ,*lowercase_     :							Dict  ,**lowercase_     :							List[str]						):
						warnings.warn(
						    '''The class ImageGPTFeatureExtractor is deprecated and will be removed in version 5 of Transformers.'''
						    ''' Please use ImageGPTImageProcessor instead.'''  ,lowercase_  ,)
						super().__init__(*lowercase_  ,**lowercase_						)




