


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 		lowercase_  (     _UpperCamelCase       ,			_UpperCamelCase=BITS  ):


			'''simple docstring'''







			__lowercase 					=   x.device

			__lowercase 					=   (x * 2_55).int().clamp(0       ,			2_55  )

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

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


def 		lowercase_  (     _UpperCamelCase       ,			_UpperCamelCase=BITS  ):


			'''simple docstring'''







			__lowercase 					=   x.device

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

			__lowercase 					=   rearrange(UpperCAmelCase__       ,			'''d -> d 1 1'''  )
			__lowercase 					=   rearrange(UpperCAmelCase__       ,			'''b (c d) h w -> b c d h w'''       ,			d=8  )
			__lowercase 					=   reduce(x * mask       ,			'''b c d h w -> b c h w'''       ,			'''sum'''  )
			return (dec / 2_55).clamp(0.0       ,			1.0  )


def 		lowercase_  (     self       ,			_UpperCamelCase       ,			_UpperCamelCase       ,			_UpperCamelCase       ,			_UpperCamelCase = 0.0       ,			_UpperCamelCase = True       ,			_UpperCamelCase=None       ,			_UpperCamelCase = 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)
			__lowercase 					=   timestep - self.config.num_train_timesteps // self.num_inference_steps

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

			__lowercase 					=   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
			__lowercase 					=   (sample - beta_prod_t ** 0.5 * model_output) / alpha_prod_t ** 0.5

			# 4. Clip "predicted x_0"
			__lowercase 					=   self.bit_scale
			if self.config.clip_sample:
						__lowercase 					=   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)
			__lowercase 					=   self._get_variance(UpperCAmelCase__       ,			UpperCAmelCase__  )
			__lowercase 					=   eta * variance ** 0.5

			if use_clipped_model_output:
						# the model_output is always re-derived from the clipped x_0 in Glide
						__lowercase 					=   (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
			__lowercase 					=   (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
			__lowercase 					=   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
						__lowercase 					=   model_output.device if torch.is_tensor(UpperCAmelCase__  ) else '''cpu'''
						__lowercase 					=   torch.randn(model_output.shape       ,			dtype=model_output.dtype       ,			generator=UpperCAmelCase__  ).to(UpperCAmelCase__  )
						__lowercase 					=   self._get_variance(UpperCAmelCase__       ,			UpperCAmelCase__  ) ** 0.5 * eta * noise

						__lowercase 					=   prev_sample + variance

			if not return_dict:
						return (prev_sample,)

			return DDIMSchedulerOutput(prev_sample=UpperCAmelCase__       ,			pred_original_sample=UpperCAmelCase__  )


def 		lowercase_  (     self       ,			_UpperCamelCase       ,			_UpperCamelCase       ,			_UpperCamelCase       ,			_UpperCamelCase="epsilon"       ,			_UpperCamelCase=None       ,			_UpperCamelCase = True       ,			):


			'''simple docstring'''







			__lowercase 					=   timestep

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

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

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

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

			__lowercase 					=   pred_prev_sample + variance

			if not return_dict:
						return (pred_prev_sample,)

			return DDPMSchedulerOutput(prev_sample=UpperCAmelCase__       ,			pred_original_sample=UpperCAmelCase__  )





class 	lowerCamelCase_       (				lowerCAmelCase__				):



					'''simple docstring'''







					def __init__(				self ,       snake_case_ ,       snake_case_ ,       snake_case_ = 1.0 ,       )   ->			Union[str, Any]:







								'''simple docstring'''

								super().__init__()
								__lowercase 					=   bit_scale
								__lowercase 					=   (
								    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 ,       snake_case_ = 2_5_6 ,       snake_case_ = 2_5_6 ,       snake_case_ = 5_0 ,       snake_case_ = None ,       snake_case_ = 1 ,       snake_case_ = "pil" ,       snake_case_ = True ,       **snake_case_ ,       )   ->			Optional[Any]:







								'''simple docstring'''

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

								self.scheduler.set_timesteps(__UpperCamelCase						)

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

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

								__lowercase 					=   bits_to_decimal(__UpperCamelCase						)

								if output_type == "pil":
											__lowercase 					=   self.numpy_to_pil(__UpperCamelCase						)

								if not return_dict:
											return (image,)

								return ImagePipelineOutput(images=__UpperCamelCase						)






from dataclasses import dataclass
from typing import List, Optional, Union

import numpy as np
import PIL

from ...utils import BaseOutput, OptionalDependencyNotAvailable, is_torch_available, is_transformers_available
from .timesteps import (
    fastaa_timesteps,
    smartaa_timesteps,
    smartaa_timesteps,
    smartaaa_timesteps,
    smartaaa_timesteps,
    superaa_timesteps,
    superaa_timesteps,
    superaaa_timesteps,
)
@dataclass
class 	lowerCamelCase_       (				lowerCAmelCase__				):



					'''simple docstring'''







					__UpperCAmelCase            =				42
					__UpperCAmelCase            =				42
					__UpperCAmelCase            =				42


try:
	if not (is_transformers_available() and is_torch_available()):
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	from ...utils.dummy_torch_and_transformers_objects import *  # noqa F403
else:
	from .pipeline_if import IFPipeline
	from .pipeline_if_imgaimg import IFImgaImgPipeline
	from .pipeline_if_imgaimg_superresolution import IFImgaImgSuperResolutionPipeline
	from .pipeline_if_inpainting import IFInpaintingPipeline
	from .pipeline_if_inpainting_superresolution import IFInpaintingSuperResolutionPipeline
	from .pipeline_if_superresolution import IFSuperResolutionPipeline
	from .safety_checker import IFSafetyChecker
	from .watermark import IFWatermarker


