






"""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__					: List[Any]=	8







def   lowerCAmelCase_( SCREAMING_SNAKE_CASE ,	SCREAMING_SNAKE_CASE=BITS					)					->	Optional[Any]:
    """simple docstring"""

    UpperCamelCase__         =    x.device

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

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

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







def   lowerCAmelCase_( SCREAMING_SNAKE_CASE ,	SCREAMING_SNAKE_CASE=BITS					)					->	int:
    """simple docstring"""

    UpperCamelCase__         =    x.device

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

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







def   lowerCAmelCase_( self ,	SCREAMING_SNAKE_CASE ,	SCREAMING_SNAKE_CASE ,	SCREAMING_SNAKE_CASE ,	SCREAMING_SNAKE_CASE = 0.0 ,	SCREAMING_SNAKE_CASE = True ,	SCREAMING_SNAKE_CASE=None ,	SCREAMING_SNAKE_CASE = 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)
    UpperCamelCase__         =    timestep - self.config.num_train_timesteps // self.num_inference_steps

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

    UpperCamelCase__         =    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__         =    (sample - beta_prod_t ** 0.5 * model_output) / alpha_prod_t ** 0.5

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

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

        UpperCamelCase__         =    prev_sample + variance

    if not return_dict:
        return (prev_sample,)

    return DDIMSchedulerOutput(prev_sample=_lowerCAmelCase ,	pred_original_sample=_lowerCAmelCase					)







def   lowerCAmelCase_( self ,	SCREAMING_SNAKE_CASE ,	SCREAMING_SNAKE_CASE ,	SCREAMING_SNAKE_CASE ,	SCREAMING_SNAKE_CASE="epsilon" ,	SCREAMING_SNAKE_CASE=None ,	SCREAMING_SNAKE_CASE = True ,	)					->	Union[DDPMSchedulerOutput, Tuple]:
    """simple docstring"""

    UpperCamelCase__         =    timestep

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

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

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

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

    UpperCamelCase__         =    pred_prev_sample + variance

    if not return_dict:
        return (pred_prev_sample,)

    return DDPMSchedulerOutput(prev_sample=_lowerCAmelCase ,	pred_original_sample=_lowerCAmelCase					)





class    __lowerCamelCase    (  __snake_case  ):

   def __init__(					self ,				snake_case_ ,				snake_case_ ,				snake_case_ = 1.0 ,				)		->       Dict:
       super().__init__()
       UpperCamelCase__         =    bit_scale
       UpperCamelCase__         =    (
           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 ,				snake_case_ = 256 ,				snake_case_ = 256 ,				snake_case_ = 50 ,				snake_case_ = None ,				snake_case_ = 1 ,				snake_case_ = "pil" ,				snake_case_ = True ,				**snake_case_ ,				)		->       Optional[int]:
       UpperCamelCase__         =    torch.randn(
           (batch_size, self.unet.config.in_channels, height, width) ,				generator=A_ ,				)
       UpperCamelCase__         =    decimal_to_bits(A_      ) * self.bit_scale
       UpperCamelCase__         =    latents.to(self.device      )

       self.scheduler.set_timesteps(A_      )

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

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

       UpperCamelCase__         =    bits_to_decimal(A_      )

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

       if not return_dict:
           return (image,)

       return ImagePipelineOutput(images=A_      )












"""simple docstring"""





from copy import deepcopy
class    __lowerCamelCase    :

   def __init__(					self ,				snake_case_ = None ,				snake_case_ = None      )		->       None:

       if arr is None and size is not None:
           UpperCamelCase__         =    size
           UpperCamelCase__         =    [0] * size
       elif arr is not None:
           self.init(snake_case_      )
       else:
           raise ValueError('Either arr or size must be specified'      )

   def 						SCREAMING_SNAKE_CASE__ (					self ,				snake_case_      )		->       None:
       UpperCamelCase__         =    len(snake_case_      )
       UpperCamelCase__         =    deepcopy(snake_case_      )
       for i in range(1 ,				self.size      ):
           UpperCamelCase__         =    self.next_(snake_case_      )
           if j < self.size:
               self.tree[j] += self.tree[i]

   def 						SCREAMING_SNAKE_CASE__ (					self      )		->       list[int]:
       UpperCamelCase__         =    self.tree[:]
       for i in range(self.size - 1 ,				0 ,				-1      ):
           UpperCamelCase__         =    self.next_(snake_case_      )
           if j < self.size:
               arr[j] -= arr[i]
       return arr

   @staticmethod
   def 						SCREAMING_SNAKE_CASE__ (					snake_case_      )		->       int:
       return index + (index & (-index))

   @staticmethod
   def 						SCREAMING_SNAKE_CASE__ (					snake_case_      )		->       int:
       return index - (index & (-index))

   def 						SCREAMING_SNAKE_CASE__ (					self ,				snake_case_ ,				snake_case_      )		->       None:
       if index == 0:
           self.tree[0] += value
           return
       while index < self.size:
           self.tree[index] += value
           UpperCamelCase__         =    self.next_(snake_case_      )

   def 						SCREAMING_SNAKE_CASE__ (					self ,				snake_case_ ,				snake_case_      )		->       None:
       self.add(snake_case_ ,				value - self.get(snake_case_      )      )

   def 						SCREAMING_SNAKE_CASE__ (					self ,				snake_case_      )		->       int:
       if right == 0:
           return 0
       UpperCamelCase__         =    self.tree[0]
       right -= 1  # make right inclusive
       while right > 0:
           result += self.tree[right]
           UpperCamelCase__         =    self.prev(snake_case_      )
       return result

   def 						SCREAMING_SNAKE_CASE__ (					self ,				snake_case_ ,				snake_case_      )		->       int:
       return self.prefix(snake_case_      ) - self.prefix(snake_case_      )

   def 						SCREAMING_SNAKE_CASE__ (					self ,				snake_case_      )		->       int:
       return self.query(snake_case_ ,				index + 1      )







   def 						SCREAMING_SNAKE_CASE__ (					self ,				snake_case_      )		->       int:
       value -= self.tree[0]
       if value < 0:
           return -1

       UpperCamelCase__         =    1  # Largest power of 2 <= size
       while j * 2 < self.size:
           j *= 2

       UpperCamelCase__         =    0

       while j > 0:
           if i + j < self.size and self.tree[i + j] <= value:
               value -= self.tree[i + j]
               i += j
           j //= 2
       return i


if __name__ == "__main__":
  import doctest

  doctest.testmod()




