



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					)							->     List[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					)							->     Optional[Any]:



  """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			,	)							->     Union[DDIMSchedulerOutput, 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			,	)							->     Union[DDPMSchedulerOutput, Tuple]:



  """simple docstring"""






  _snake_case						=							timestep

  if model_output.shape[1] == sample.shape[1] * 2 and self.variance_type in ["learned", "learned_range"]:
    _snake_case,					_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_   (  A__					):



      def __init__(      self				:    List[Any]      , A__				:    UNetaDConditionModel      , A__				:    Union[DDIMScheduler, DDPMScheduler]      , A__				:    Optional[float] = 1.0      , )   ->					List[str]:
        super().__init__()
        _snake_case						=							bit_scale
        _snake_case						=							(
            ddim_bit_scheduler_step if isinstance(__snake_case      , __snake_case		) else ddpm_bit_scheduler_step
        )

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







      @torch.no_grad()
      def __call__(      self				:    Optional[Any]      , A__				:    Optional[int] = 256      , A__				:    Optional[int] = 256      , A__				:    Optional[int] = 50      , A__				:    Optional[torch.Generator] = None      , A__				:    Optional[int] = 1      , A__				:    Optional[str] = "pil"      , A__				:    bool = True      , **A__				:    int      , )   ->					Union[Tuple, ImagePipelineOutput]:
        _snake_case						=							torch.randn(
            (batch_size, self.unet.config.in_channels, height, width)      , generator=__snake_case      , )
        _snake_case						=							decimal_to_bits(__snake_case		) * self.bit_scale
        _snake_case						=							latents.to(self.device		)

        self.scheduler.set_timesteps(__snake_case		)

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

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

        _snake_case						=							bits_to_decimal(__snake_case		)

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

        if not return_dict:
          return (image,)

        return ImagePipelineOutput(images=__snake_case		)




import os
import sys
from contextlib import contextmanager


# Windows only
if os.name == "nt":
 import ctypes
 import msvcrt  # noqa



 class   lowercase_   (  ctypes.Structure					):
       # _fields is a specific attr expected by ctypes
       UpperCamelCase_      :		List[Any]										=    [("size", ctypes.c_int), ("visible", ctypes.c_byte)]



def 				snake_case_()							->     int:



  """simple docstring"""






  if os.name == "nt":
    _snake_case						=							CursorInfo()
    _snake_case						=							ctypes.windll.kernelaa.GetStdHandle(-11					)
    ctypes.windll.kernelaa.GetConsoleCursorInfo(_UpperCamelCase			,	ctypes.byref(_UpperCamelCase					)					)
    _snake_case						=							False
    ctypes.windll.kernelaa.SetConsoleCursorInfo(_UpperCamelCase			,	ctypes.byref(_UpperCamelCase					)					)
  elif os.name == "posix":
    sys.stdout.write('''\033[?25l'''					)
    sys.stdout.flush()



def 				snake_case_()							->     Optional[Any]:



  """simple docstring"""






  if os.name == "nt":
    _snake_case						=							CursorInfo()
    _snake_case						=							ctypes.windll.kernelaa.GetStdHandle(-11					)
    ctypes.windll.kernelaa.GetConsoleCursorInfo(_UpperCamelCase			,	ctypes.byref(_UpperCamelCase					)					)
    _snake_case						=							True
    ctypes.windll.kernelaa.SetConsoleCursorInfo(_UpperCamelCase			,	ctypes.byref(_UpperCamelCase					)					)
  elif os.name == "posix":
    sys.stdout.write('''\033[?25h'''					)
    sys.stdout.flush()



@contextmanager
def 				snake_case_()							->     int:



  """simple docstring"""






  try:
    hide_cursor()
    yield
  finally:
    show_cursor()

