

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 				snake_case_(_UpperCamelCase			,	_UpperCamelCase=BITS					)							->     Union[str, Any]:



		"""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 				snake_case_(_UpperCamelCase			,	_UpperCamelCase=BITS					)							->     List[str]:



		"""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 				snake_case_(self			,	_UpperCamelCase			,	_UpperCamelCase			,	_UpperCamelCase			,	_UpperCamelCase = 0.0			,	_UpperCamelCase = True			,	_UpperCamelCase=None			,	_UpperCamelCase = True			,	)							->     Tuple:



		"""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 				snake_case_(self			,	_UpperCamelCase			,	_UpperCamelCase			,	_UpperCamelCase			,	_UpperCamelCase="epsilon"			,	_UpperCamelCase=None			,	_UpperCamelCase = True			,	)							->     Optional[int]:



		"""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   lowercase_   (  __SCREAMING_SNAKE_CASE					):



						def __init__(      self				:    Union[str, Any]      , A__				:    List[str]      , A__				:    Optional[Any]      , A__				:    Tuple = 1.0      , )   ->					Tuple:
								super().__init__()
								_snake_case						=							bit_scale
								_snake_case						=							(
								    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				:    Optional[int]      , A__				:    Optional[Any] = 256      , A__				:    Optional[Any] = 256      , A__				:    Tuple = 50      , A__				:    int = None      , A__				:    Optional[int] = 1      , A__				:    Optional[int] = "pil"      , A__				:    str = True      , **A__				:    Any      , )   ->					Union[Tuple, ImagePipelineOutput]:
								_snake_case						=							torch.randn(
								    (batch_size, self.unet.config.in_channels, height, width)      , generator=__UpperCAmelCase      , )
								_snake_case						=							decimal_to_bits(__UpperCAmelCase		) * self.bit_scale
								_snake_case						=							latents.to(self.device		)

								self.scheduler.set_timesteps(__UpperCAmelCase		)

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

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

								_snake_case						=							bits_to_decimal(__UpperCAmelCase		)

								if output_type == "pil":
										_snake_case						=							self.numpy_to_pil(__UpperCAmelCase		)

								if not return_dict:
										return (image,)

								return ImagePipelineOutput(images=__UpperCAmelCase		)




from ..utils import DummyObject, requires_backends



class   lowercase_   (  metaclass=__lowercase					):
      UpperCamelCase_      :		Optional[int]										=    ["speech"]



      def __init__(      self				:    str      , *A__				:    List[str]      , **A__				:    Tuple		)   ->					Optional[Any]:
        requires_backends(self      , ['''speech''']		)



class   lowercase_   (  metaclass=__lowercase					):
      UpperCamelCase_      :		Optional[Any]										=    ["speech"]



      def __init__(      self				:    Dict      , *A__				:    int      , **A__				:    int		)   ->					Tuple:
        requires_backends(self      , ['''speech''']		)

