
"""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


_a		       =							8







def 	__a     (		__lowerCamelCase,		__lowerCamelCase=BITS  ):
   UpperCAmelCase_					:					Any			     =    x.device

   UpperCAmelCase_					:					str			     =    (x * 255).int().clamp(0,		255  )

   UpperCAmelCase_					:					Tuple			     =    2 ** torch.arange(bits - 1,		-1,		-1,		device=_a  )
   UpperCAmelCase_					:					Tuple			     =    rearrange(_a,		"d -> d 1 1"  )
   UpperCAmelCase_					:					Any			     =    rearrange(_a,		"b c h w -> b c 1 h w"  )

   UpperCAmelCase_					:					List[str]			     =    ((x & mask) != 0).float()
   UpperCAmelCase_					:					Optional[int]			     =    rearrange(_a,		"b c d h w -> b (c d) h w"  )
   UpperCAmelCase_					:					Union[str, Any]			     =    bits * 2 - 1
   return bits







def 	__a     (		__lowerCamelCase,		__lowerCamelCase=BITS  ):
   UpperCAmelCase_					:					str			     =    x.device

   UpperCAmelCase_					:					Any			     =    (x > 0).int()
   UpperCAmelCase_					:					Any			     =    2 ** torch.arange(bits - 1,		-1,		-1,		device=_a,		dtype=torch.intaa  )

   UpperCAmelCase_					:					List[str]			     =    rearrange(_a,		"d -> d 1 1"  )
   UpperCAmelCase_					:					Dict			     =    rearrange(_a,		"b (c d) h w -> b c d h w",		d=8  )
   UpperCAmelCase_					:					Optional[Any]			     =    reduce(x * mask,		"b c d h w -> b c h w",		"sum"  )
   return (dec / 255).clamp(0.0,		1.0  )







def 	__a     (		self,		__lowerCamelCase,		__lowerCamelCase,		__lowerCamelCase,		__lowerCamelCase = 0.0,		__lowerCamelCase = True,		__lowerCamelCase=None,		__lowerCamelCase = 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)
   UpperCAmelCase_					:					List[Any]			     =    timestep - self.config.num_train_timesteps // self.num_inference_steps

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

   UpperCAmelCase_					:					Tuple			     =    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
   UpperCAmelCase_					:					Optional[int]			     =    (sample - beta_prod_t ** 0.5 * model_output) / alpha_prod_t ** 0.5

   # 4. Clip "predicted x_0"
   UpperCAmelCase_					:					Tuple			     =    self.bit_scale
   if self.config.clip_sample:
      UpperCAmelCase_					:					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)
   UpperCAmelCase_					:					Optional[int]			     =    self._get_variance(_a,		_a  )
   UpperCAmelCase_					:					Union[str, Any]			     =    eta * variance ** 0.5

   if use_clipped_model_output:
      # the model_output is always re-derived from the clipped x_0 in Glide
      UpperCAmelCase_					:					Union[str, Any]			     =    (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
   UpperCAmelCase_					:					Optional[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
   UpperCAmelCase_					:					Tuple			     =    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
      UpperCAmelCase_					:					List[str]			     =    model_output.device if torch.is_tensor(_a  ) else '''cpu'''
      UpperCAmelCase_					:					Tuple			     =    torch.randn(model_output.shape,		dtype=model_output.dtype,		generator=_a  ).to(_a  )
      UpperCAmelCase_					:					Optional[Any]			     =    self._get_variance(_a,		_a  ) ** 0.5 * eta * noise

      UpperCAmelCase_					:					Optional[int]			     =    prev_sample + variance

   if not return_dict:
      return (prev_sample,)

   return DDIMSchedulerOutput(prev_sample=_a,		pred_original_sample=_a  )







def 	__a     (		self,		__lowerCamelCase,		__lowerCamelCase,		__lowerCamelCase,		__lowerCamelCase="epsilon",		__lowerCamelCase=None,		__lowerCamelCase = True,		):
   UpperCAmelCase_					:					Optional[Any]			     =    timestep

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

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

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

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

   UpperCAmelCase_					:					List[str]			     =    pred_prev_sample + variance

   if not return_dict:
      return (pred_prev_sample,)

   return DDPMSchedulerOutput(prev_sample=_a,		pred_original_sample=_a  )


class A_	(lowerCAmelCase__      ):




  '''simple docstring'''







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

     """simple docstring"""
     super().__init__()
     UpperCAmelCase_					:					Union[str, Any]			     =    bit_scale
     UpperCAmelCase_					:					Any			     =    (
         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				,   lowercase_ = 256				,   lowercase_ = 256				,   lowercase_ = 50				,   lowercase_ = None				,   lowercase_ = 1				,   lowercase_ = "pil"				,   lowercase_ = True				,   **lowercase_				,   ):

     """simple docstring"""
     UpperCAmelCase_					:					int			     =    torch.randn(
         (batch_size, self.unet.config.in_channels, height, width)				,   generator=lowercase_				,   )
     UpperCAmelCase_					:					str			     =    decimal_to_bits(lowercase_  ) * self.bit_scale
     UpperCAmelCase_					:					Dict			     =    latents.to(self.device  )

     self.scheduler.set_timesteps(lowercase_  )

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

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

     UpperCAmelCase_					:					Tuple			     =    bits_to_decimal(lowercase_  )

     if output_type == "pil":
        UpperCAmelCase_					:					List[Any]			     =    self.numpy_to_pil(lowercase_  )

     if not return_dict:
        return (image,)

     return ImagePipelineOutput(images=lowercase_  )







"""simple docstring"""

import numpy as np




def __lowercase			(							_a    ):
    return (2 / (1 + np.exp(-2 * vector    ))) - 1


if __name__ == "__main__":
     import doctest

     doctest.testmod()





