

"""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


__UpperCamelCase  :      List[str]							      =		8

def   __SCREAMING_SNAKE_CASE  (      A_						,     A_=BITS ):
	lowerCAmelCase__	:						Any  				=	x.device

	lowerCAmelCase__	:						Optional[int]  				=	(x * 2_55).int().clamp(0						,     2_55 )

	lowerCAmelCase__	:						str  				=	2 ** torch.arange(bits - 1						,     -1						,     -1						,     device=A_ )
	lowerCAmelCase__	:						List[Any]  				=	rearrange(A_						,     '''d -> d 1 1''' )
	lowerCAmelCase__	:						Any  				=	rearrange(A_						,     '''b c h w -> b c 1 h w''' )

	lowerCAmelCase__	:						Dict  				=	((x & mask) != 0).float()
	lowerCAmelCase__	:						Any  				=	rearrange(A_						,     '''b c d h w -> b (c d) h w''' )
	lowerCAmelCase__	:						Union[str, Any]  				=	bits * 2 - 1
	return bits

def   __SCREAMING_SNAKE_CASE  (      A_						,     A_=BITS ):
	lowerCAmelCase__	:						str  				=	x.device

	lowerCAmelCase__	:						Dict  				=	(x > 0).int()
	lowerCAmelCase__	:						Tuple  				=	2 ** torch.arange(bits - 1						,     -1						,     -1						,     device=A_						,     dtype=torch.intaa )

	lowerCAmelCase__	:						Dict  				=	rearrange(A_						,     '''d -> d 1 1''' )
	lowerCAmelCase__	:						List[str]  				=	rearrange(A_						,     '''b (c d) h w -> b c d h w'''						,     d=8 )
	lowerCAmelCase__	:						Union[str, Any]  				=	reduce(x * mask						,     '''b c d h w -> b c h w'''						,     '''sum''' )
	return (dec / 2_55).clamp(0.0						,     1.0 )

def   __SCREAMING_SNAKE_CASE  (      self						,     A_						,     A_						,     A_						,     A_ = 0.0						,     A_ = True						,     A_=None						,     A_ = True						,     ):
	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__	:						int  				=	timestep - self.config.num_train_timesteps // self.num_inference_steps

	# 2. compute alphas, betas
	lowerCAmelCase__	:						Tuple  				=	self.alphas_cumprod[timestep]
	lowerCAmelCase__	:						Dict  				=	self.alphas_cumprod[prev_timestep] if prev_timestep >= 0 else self.final_alpha_cumprod

	lowerCAmelCase__	:						Any  				=	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__	:						Union[str, Any]  				=	(sample - beta_prod_t ** 0.5 * model_output) / alpha_prod_t ** 0.5

	# 4. Clip "predicted x_0"
	lowerCAmelCase__	:						str  				=	self.bit_scale
	if self.config.clip_sample:
		lowerCAmelCase__	:						Dict  				=	torch.clamp(A_						,     -scale						,     A_ )

	# 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__	:						str  				=	self._get_variance(A_						,     A_ )
	lowerCAmelCase__	:						List[Any]  				=	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__	:						int  				=	(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__	:						Optional[Any]  				=	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__	:						List[str]  				=	model_output.device if torch.is_tensor(A_ ) else '''cpu'''
		lowerCAmelCase__	:						Any  				=	torch.randn(model_output.shape						,     dtype=model_output.dtype						,     generator=A_ ).to(A_ )
		lowerCAmelCase__	:						Dict  				=	self._get_variance(A_						,     A_ ) ** 0.5 * eta * noise

		lowerCAmelCase__	:						Optional[int]  				=	prev_sample + variance

	if not return_dict:
		return (prev_sample,)

	return DDIMSchedulerOutput(prev_sample=A_						,     pred_original_sample=A_ )

def   __SCREAMING_SNAKE_CASE  (      self						,     A_						,     A_						,     A_						,     A_="epsilon"						,     A_=None						,     A_ = True						,     ):
	lowerCAmelCase__	:						Union[str, Any]  				=	timestep

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

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

	# 3. Clip "predicted x_0"
	lowerCAmelCase__	:						List[Any]  				=	self.bit_scale
	if self.config.clip_sample:
		lowerCAmelCase__	:						Dict  				=	torch.clamp(A_						,     -scale						,     A_ )

	# 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__	:						Optional[int]  				=	(alpha_prod_t_prev ** 0.5 * self.betas[t]) / beta_prod_t
	lowerCAmelCase__	:						int  				=	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__	:						Optional[Any]  				=	pred_original_sample_coeff * pred_original_sample + current_sample_coeff * sample

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

	lowerCAmelCase__	:						Any  				=	pred_prev_sample + variance

	if not return_dict:
		return (pred_prev_sample,)

	return DDPMSchedulerOutput(prev_sample=A_						,     pred_original_sample=A_ )







class 				SCREAMING_SNAKE_CASE					(		a_       ):






					"""simple docstring"""



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

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







					@torch.no_grad()
					def __call__(       self			:				List[str]   ,lowercase_			:				Optional[int] = 2_5_6   ,lowercase_			:				Optional[int] = 2_5_6   ,lowercase_			:				Optional[int] = 5_0   ,lowercase_			:				Optional[torch.Generator] = None   ,lowercase_			:				Optional[int] = 1   ,lowercase_			:				Optional[str] = "pil"   ,lowercase_			:				bool = True   ,**lowercase_			:				Tuple   ,):
						lowerCAmelCase__	:						List[Any]  				=	torch.randn(
						    (batch_size, self.unet.config.in_channels, height, width)   ,generator=lowercase_   ,)
						lowerCAmelCase__	:						int  				=	decimal_to_bits(lowercase_		) * self.bit_scale
						lowerCAmelCase__	:						Optional[int]  				=	latents.to(self.device		)

						self.scheduler.set_timesteps(lowercase_		)

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

							# compute the previous noisy sample x_t -> x_t-1
							lowerCAmelCase__	:						int  				=	self.scheduler.step(lowercase_   ,lowercase_   ,lowercase_		).prev_sample

						lowerCAmelCase__	:						Optional[Any]  				=	bits_to_decimal(lowercase_		)

						if output_type == "pil":
							lowerCAmelCase__	:						Union[str, Any]  				=	self.numpy_to_pil(lowercase_		)

						if not return_dict:
							return (image,)

						return ImagePipelineOutput(images=lowercase_		)







"""simple docstring"""





from typing import Dict, List, Optional, Union

import numpy as np

from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import (
    center_crop,
    convert_to_rgb,
    get_resize_output_image_size,
    normalize,
    rescale,
    resize,
    to_channel_dimension_format,
)
from ...image_utils import (
    OPENAI_CLIP_MEAN,
    OPENAI_CLIP_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, is_vision_available, logging


__UpperCamelCase  :      Optional[Any]							      =		logging.get_logger(__name__)


if is_vision_available():
							import PIL







class 				SCREAMING_SNAKE_CASE					(		a_       ):






					"""simple docstring"""



					lowercase__				 =       ["pixel_values"]







					def __init__(       self			:				str   ,lowercase_			:				bool = True   ,lowercase_			:				Dict[str, int] = None   ,lowercase_			:				PILImageResampling = PILImageResampling.BICUBIC   ,lowercase_			:				bool = True   ,lowercase_			:				Dict[str, int] = None   ,lowercase_			:				bool = True   ,lowercase_			:				Union[int, float] = 1 / 2_5_5   ,lowercase_			:				bool = True   ,lowercase_			:				Optional[Union[float, List[float]]] = None   ,lowercase_			:				Optional[Union[float, List[float]]] = None   ,lowercase_			:				bool = True   ,**lowercase_			:				Optional[Any]   ,):
						super().__init__(**lowercase_		)
						lowerCAmelCase__	:						List[str]  				=	size if size is not None else {'''shortest_edge''': 2_2_4}
						lowerCAmelCase__	:						Tuple  				=	get_size_dict(lowercase_   ,default_to_square=lowercase_		)
						lowerCAmelCase__	:						Optional[int]  				=	crop_size if crop_size is not None else {'''height''': 2_2_4, '''width''': 2_2_4}
						lowerCAmelCase__	:						Optional[Any]  				=	get_size_dict(lowercase_   ,default_to_square=lowercase_   ,param_name='''crop_size'''		)

						lowerCAmelCase__	:						Dict  				=	do_resize
						lowerCAmelCase__	:						Optional[int]  				=	size
						lowerCAmelCase__	:						Dict  				=	resample
						lowerCAmelCase__	:						Optional[Any]  				=	do_center_crop
						lowerCAmelCase__	:						Dict  				=	crop_size
						lowerCAmelCase__	:						Tuple  				=	do_rescale
						lowerCAmelCase__	:						str  				=	rescale_factor
						lowerCAmelCase__	:						List[str]  				=	do_normalize
						lowerCAmelCase__	:						Tuple  				=	image_mean if image_mean is not None else OPENAI_CLIP_MEAN
						lowerCAmelCase__	:						Optional[Any]  				=	image_std if image_std is not None else OPENAI_CLIP_STD
						lowerCAmelCase__	:						Optional[Any]  				=	do_convert_rgb







					def        __lowerCAmelCase (       self			:				Dict   ,lowercase_			:				np.ndarray   ,lowercase_			:				Dict[str, int]   ,lowercase_			:				PILImageResampling = PILImageResampling.BICUBIC   ,lowercase_			:				Optional[Union[str, ChannelDimension]] = None   ,**lowercase_			:				Optional[Any]   ,):
						lowerCAmelCase__	:						Dict  				=	get_size_dict(lowercase_   ,default_to_square=lowercase_		)
						if "shortest_edge" not in size:
							raise ValueError(F'The `size` parameter must contain the key `shortest_edge`. Got {size.keys()}'		)
						lowerCAmelCase__	:						Optional[Any]  				=	get_resize_output_image_size(lowercase_   ,size=size['''shortest_edge''']   ,default_to_square=lowercase_		)
						return resize(lowercase_   ,size=lowercase_   ,resample=lowercase_   ,data_format=lowercase_   ,**lowercase_		)







					def        __lowerCAmelCase (       self			:				Tuple   ,lowercase_			:				np.ndarray   ,lowercase_			:				Dict[str, int]   ,lowercase_			:				Optional[Union[str, ChannelDimension]] = None   ,**lowercase_			:				str   ,):
						lowerCAmelCase__	:						List[str]  				=	get_size_dict(lowercase_		)
						if "height" not in size or "width" not in size:
							raise ValueError(F'The `size` parameter must contain the keys (height, width). Got {size.keys()}'		)
						return center_crop(lowercase_   ,size=(size['''height'''], size['''width'''])   ,data_format=lowercase_   ,**lowercase_		)







					def        __lowerCAmelCase (       self			:				Optional[int]   ,lowercase_			:				np.ndarray   ,lowercase_			:				Union[int, float]   ,lowercase_			:				Optional[Union[str, ChannelDimension]] = None   ,**lowercase_			:				Union[str, Any]   ,):
						return rescale(lowercase_   ,scale=lowercase_   ,data_format=lowercase_   ,**lowercase_		)







					def        __lowerCAmelCase (       self			:				Dict   ,lowercase_			:				np.ndarray   ,lowercase_			:				Union[float, List[float]]   ,lowercase_			:				Union[float, List[float]]   ,lowercase_			:				Optional[Union[str, ChannelDimension]] = None   ,**lowercase_			:				int   ,):
						return normalize(lowercase_   ,mean=lowercase_   ,std=lowercase_   ,data_format=lowercase_   ,**lowercase_		)







					def        __lowerCAmelCase (       self			:				Optional[int]   ,lowercase_			:				ImageInput   ,lowercase_			:				bool = None   ,lowercase_			:				Dict[str, int] = None   ,lowercase_			:				PILImageResampling = None   ,lowercase_			:				bool = None   ,lowercase_			:				int = None   ,lowercase_			:				bool = None   ,lowercase_			:				float = None   ,lowercase_			:				bool = None   ,lowercase_			:				Optional[Union[float, List[float]]] = None   ,lowercase_			:				Optional[Union[float, List[float]]] = None   ,lowercase_			:				bool = None   ,lowercase_			:				Optional[Union[str, TensorType]] = None   ,lowercase_			:				Optional[ChannelDimension] = ChannelDimension.FIRST   ,**lowercase_			:				List[Any]   ,):
						lowerCAmelCase__	:						Optional[int]  				=	do_resize if do_resize is not None else self.do_resize
						lowerCAmelCase__	:						Optional[int]  				=	size if size is not None else self.size
						lowerCAmelCase__	:						Union[str, Any]  				=	get_size_dict(lowercase_   ,param_name='''size'''   ,default_to_square=lowercase_		)
						lowerCAmelCase__	:						Union[str, Any]  				=	resample if resample is not None else self.resample
						lowerCAmelCase__	:						Optional[Any]  				=	do_center_crop if do_center_crop is not None else self.do_center_crop
						lowerCAmelCase__	:						Optional[int]  				=	crop_size if crop_size is not None else self.crop_size
						lowerCAmelCase__	:						Dict  				=	get_size_dict(lowercase_   ,param_name='''crop_size'''   ,default_to_square=lowercase_		)
						lowerCAmelCase__	:						int  				=	do_rescale if do_rescale is not None else self.do_rescale
						lowerCAmelCase__	:						List[str]  				=	rescale_factor if rescale_factor is not None else self.rescale_factor
						lowerCAmelCase__	:						Optional[int]  				=	do_normalize if do_normalize is not None else self.do_normalize
						lowerCAmelCase__	:						str  				=	image_mean if image_mean is not None else self.image_mean
						lowerCAmelCase__	:						str  				=	image_std if image_std is not None else self.image_std
						lowerCAmelCase__	:						int  				=	do_convert_rgb if do_convert_rgb is not None else self.do_convert_rgb

						lowerCAmelCase__	:						Any  				=	make_list_of_images(lowercase_		)

						if not valid_images(lowercase_		):
							raise ValueError(
							    '''Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, '''
							    '''torch.Tensor, tf.Tensor or jax.ndarray.'''		)

						if do_resize and size is None:
							raise ValueError('''Size must be specified if do_resize is True.'''		)

						if do_center_crop and crop_size is None:
							raise ValueError('''Crop size must be specified if do_center_crop is True.'''		)

						if do_rescale and rescale_factor is None:
							raise ValueError('''Rescale factor must be specified if do_rescale is True.'''		)

						if do_normalize and (image_mean is None or image_std is None):
							raise ValueError('''Image mean and std must be specified if do_normalize is True.'''		)

						# PIL RGBA images are converted to RGB
						if do_convert_rgb:
							lowerCAmelCase__	:						Tuple  				=	[convert_to_rgb(lowercase_		) for image in images]

						# All transformations expect numpy arrays.
						lowerCAmelCase__	:						Optional[int]  				=	[to_numpy_array(lowercase_		) for image in images]

						if do_resize:
							lowerCAmelCase__	:						Optional[int]  				=	[self.resize(image=lowercase_   ,size=lowercase_   ,resample=lowercase_		) for image in images]

						if do_center_crop:
							lowerCAmelCase__	:						Tuple  				=	[self.center_crop(image=lowercase_   ,size=lowercase_		) for image in images]

						if do_rescale:
							lowerCAmelCase__	:						Tuple  				=	[self.rescale(image=lowercase_   ,scale=lowercase_		) for image in images]

						if do_normalize:
							lowerCAmelCase__	:						Union[str, Any]  				=	[self.normalize(image=lowercase_   ,mean=lowercase_   ,std=lowercase_		) for image in images]

						lowerCAmelCase__	:						Optional[Any]  				=	[to_channel_dimension_format(lowercase_   ,lowercase_		) for image in images]

						lowerCAmelCase__	:						List[Any]  				=	{'''pixel_values''': images}
						return BatchFeature(data=lowercase_   ,tensor_type=lowercase_		)




