

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											=     8





def a(lowercase__			, lowercase__=BITS	):





	'''simple docstring'''
	snake_case_      		= x.device

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

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

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





def a(lowercase__			, lowercase__=BITS	):





	'''simple docstring'''
	snake_case_      		= x.device

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

	snake_case_      		= rearrange(_lowerCAmelCase			, 'd -> d 1 1'	)
	snake_case_      		= rearrange(_lowerCAmelCase			, 'b (c d) h w -> b c d h w'			, d=8	)
	snake_case_      		= reduce(x * mask			, 'b c d h w -> b c h w'			, 'sum'	)
	return (dec / 255).clamp(0.0			, 1.0	)





def a(self			, lowercase__			, lowercase__			, lowercase__			, lowercase__ = 0.0			, lowercase__ = True			, lowercase__=None			, lowercase__ = 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)
	snake_case_      		= timestep - self.config.num_train_timesteps // self.num_inference_steps

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

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

	# 4. Clip "predicted x_0"
	snake_case_      		= self.bit_scale
	if self.config.clip_sample:
		snake_case_      		= torch.clamp(_lowerCAmelCase			, -scale			, _lowerCAmelCase	)

	# 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)
	snake_case_      		= self._get_variance(_lowerCAmelCase			, _lowerCAmelCase	)
	snake_case_      		= eta * variance ** 0.5

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

		snake_case_      		= prev_sample + variance

	if not return_dict:
		return (prev_sample,)

	return DDIMSchedulerOutput(prev_sample=_lowerCAmelCase			, pred_original_sample=_lowerCAmelCase	)





def a(self			, lowercase__			, lowercase__			, lowercase__			, lowercase__="epsilon"			, lowercase__=None			, lowercase__ = True			, ):





	'''simple docstring'''
	snake_case_      		= timestep

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

	# 1. compute alphas, betas
	snake_case_      		= self.alphas_cumprod[t]
	snake_case_      		= self.alphas_cumprod[t - 1] if t > 0 else self.one
	snake_case_      		= 1 - alpha_prod_t
	snake_case_      		= 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":
		snake_case_      		= (sample - beta_prod_t ** 0.5 * model_output) / alpha_prod_t ** 0.5
	elif prediction_type == "sample":
		snake_case_      		= model_output
	else:
		raise ValueError(f"""Unsupported prediction_type {prediction_type}."""	)

	# 3. Clip "predicted x_0"
	snake_case_      		= self.bit_scale
	if self.config.clip_sample:
		snake_case_      		= torch.clamp(_lowerCAmelCase			, -scale			, _lowerCAmelCase	)

	# 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 * self.betas[t]) / beta_prod_t
	snake_case_      		= 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
	snake_case_      		= pred_original_sample_coeff * pred_original_sample + current_sample_coeff * sample

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

	snake_case_      		= pred_prev_sample + variance

	if not return_dict:
		return (pred_prev_sample,)

	return DDPMSchedulerOutput(prev_sample=_lowerCAmelCase			, pred_original_sample=_lowerCAmelCase	)






class SCREAMING_SNAKE_CASE  (		__snake_case							):






			"""simple docstring"""




			def __init__(    self ,				__UpperCamelCase ,				__UpperCamelCase ,				__UpperCamelCase = 1.0 ,				):

				"""simple docstring"""


				super().__init__()
				snake_case_      		= bit_scale
				snake_case_      		= (
				    ddim_bit_scheduler_step if isinstance(A_ ,				A_							) else ddpm_bit_scheduler_step
				)

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







			@torch.no_grad()
			def __call__(    self ,				__UpperCamelCase = 2_56 ,				__UpperCamelCase = 2_56 ,				__UpperCamelCase = 50 ,				__UpperCamelCase = None ,				__UpperCamelCase = 1 ,				__UpperCamelCase = "pil" ,				__UpperCamelCase = True ,				**__UpperCamelCase ,				):

				"""simple docstring"""


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

				self.scheduler.set_timesteps(A_							)

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

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

				snake_case_      		= bits_to_decimal(A_							)

				if output_type == "pil":
					snake_case_      		= self.numpy_to_pil(A_							)

				if not return_dict:
					return (image,)

				return ImagePipelineOutput(images=A_							)



import unittest

import numpy as np
import torch

from diffusers import ScoreSdeVePipeline, ScoreSdeVeScheduler, UNetaDModel
from diffusers.utils.testing_utils import enable_full_determinism, require_torch, slow, torch_device


enable_full_determinism()


class SCREAMING_SNAKE_CASE  (		unittest.TestCase							):






			"""simple docstring"""




			@property
			def 			__lowerCAmelCase (    self							):

				"""simple docstring"""


				torch.manual_seed(0							)
				snake_case_      		= UNetaDModel(
				    block_out_channels=(32, 64) ,				layers_per_block=2 ,				sample_size=32 ,				in_channels=3 ,				out_channels=3 ,				down_block_types=('DownBlock2D', 'AttnDownBlock2D') ,				up_block_types=('AttnUpBlock2D', 'UpBlock2D') ,				)
				return model







			def 			__lowerCAmelCase (    self							):

				"""simple docstring"""


				snake_case_      		= self.dummy_uncond_unet
				snake_case_      		= ScoreSdeVeScheduler()

				snake_case_      		= ScoreSdeVePipeline(unet=__UpperCamelCase ,				scheduler=__UpperCamelCase							)
				sde_ve.to(__UpperCamelCase							)
				sde_ve.set_progress_bar_config(disable=__UpperCamelCase							)

				snake_case_      		= torch.manual_seed(0							)
				snake_case_      		= sde_ve(num_inference_steps=2 ,				output_type='numpy' ,				generator=__UpperCamelCase							).images

				snake_case_      		= torch.manual_seed(0							)
				snake_case_      		= sde_ve(num_inference_steps=2 ,				output_type='numpy' ,				generator=__UpperCamelCase ,				return_dict=__UpperCamelCase							)[
				    0
				]

				snake_case_      		= image[0, -3:, -3:, -1]
				snake_case_      		= image_from_tuple[0, -3:, -3:, -1]

				assert image.shape == (1, 32, 32, 3)
				snake_case_      		= np.array([0.0, 1.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0]							)

				assert np.abs(image_slice.flatten() - expected_slice							).max() < 1E-2
				assert np.abs(image_from_tuple_slice.flatten() - expected_slice							).max() < 1E-2







@slow
@require_torch
class SCREAMING_SNAKE_CASE  (		unittest.TestCase							):






			"""simple docstring"""




			def 			__lowerCAmelCase (    self							):

				"""simple docstring"""


				snake_case_      		= 'google/ncsnpp-church-256'
				snake_case_      		= UNetaDModel.from_pretrained(__UpperCamelCase							)

				snake_case_      		= ScoreSdeVeScheduler.from_pretrained(__UpperCamelCase							)

				snake_case_      		= ScoreSdeVePipeline(unet=__UpperCamelCase ,				scheduler=__UpperCamelCase							)
				sde_ve.to(__UpperCamelCase							)
				sde_ve.set_progress_bar_config(disable=__UpperCamelCase							)

				snake_case_      		= torch.manual_seed(0							)
				snake_case_      		= sde_ve(num_inference_steps=10 ,				output_type='numpy' ,				generator=__UpperCamelCase							).images

				snake_case_      		= image[0, -3:, -3:, -1]

				assert image.shape == (1, 2_56, 2_56, 3)

				snake_case_      		= np.array([0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 0.0]							)

				assert np.abs(image_slice.flatten() - expected_slice							).max() < 1E-2
