




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


lowerCamelCase       :	str									=     8

def     _SCREAMING_SNAKE_CASE    (							lowercase			: str			,							lowercase			: Optional[Any]=BITS  ):


      '''simple docstring'''



      lowerCamelCase_	  =					x.device

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

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

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

def     _SCREAMING_SNAKE_CASE    (							lowercase			: List[str]			,							lowercase			: Optional[int]=BITS  ):


      '''simple docstring'''



      lowerCamelCase_	  =					x.device

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

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

def     _SCREAMING_SNAKE_CASE    (							self			: Optional[Any]			,							lowercase			: torch.FloatTensor			,							lowercase			: int			,							lowercase			: torch.FloatTensor			,							lowercase			: float = 0.0			,							lowercase			: bool = True			,							lowercase			: Dict=None			,							lowercase			: bool = 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)
      lowerCamelCase_	  =					timestep - self.config.num_train_timesteps // self.num_inference_steps

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

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

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

      # 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)
      lowerCamelCase_	  =					self._get_variance(lowercase			,							lowercase  )
      lowerCamelCase_	  =					eta * variance ** 0.5

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

            lowerCamelCase_	  =					prev_sample + variance

      if not return_dict:
            return (prev_sample,)

      return DDIMSchedulerOutput(prev_sample=lowercase			,							pred_original_sample=lowercase  )

def     _SCREAMING_SNAKE_CASE    (							self			: Optional[Any]			,							lowercase			: torch.FloatTensor			,							lowercase			: int			,							lowercase			: torch.FloatTensor			,							lowercase			: Tuple="epsilon"			,							lowercase			: Any=None			,							lowercase			: bool = True			,							):


      '''simple docstring'''



      lowerCamelCase_	  =					timestep

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

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

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

      # 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
      lowerCamelCase_	  =					(alpha_prod_t_prev ** 0.5 * self.betas[t]) / beta_prod_t
      lowerCamelCase_	  =					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
      lowerCamelCase_	  =					pred_original_sample_coeff * pred_original_sample + current_sample_coeff * sample

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

      lowerCamelCase_	  =					pred_prev_sample + variance

      if not return_dict:
            return (pred_prev_sample,)

      return DDPMSchedulerOutput(prev_sample=lowercase			,							pred_original_sample=lowercase  )







class 						A(		UpperCamelCase     ):



 '''simple docstring'''


 def __init__(       self      :						Optional[int]		,							A_      :						UNetaDConditionModel		,							A_      :						Union[DDIMScheduler, DDPMScheduler]		,							A_      :						Optional[float] = 1.0		,							)     ->			Union[str, Any]:

       """simple docstring"""






       super().__init__()
       lowerCamelCase_	  =					bit_scale
       lowerCamelCase_	  =					(
           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      :						Optional[int]		,							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_      :						Any		,							)     ->			Union[Tuple, ImagePipelineOutput]:

       """simple docstring"""






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

       self.scheduler.set_timesteps(A_						)

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

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

       lowerCamelCase_	  =					bits_to_decimal(A_						)

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

       if not return_dict:
             return (image,)

       return ImagePipelineOutput(images=A_						)







def     _SCREAMING_SNAKE_CASE    (							lowercase			: list[int]			,							lowercase			: list[int]  ):


      '''simple docstring'''



      lowerCamelCase_	  =					len(lowercase  )
      print('The following activities are selected:'  )

      # The first activity is always selected
      lowerCamelCase_	  =					0
      print(lowercase			,							end=','  )

      # Consider rest of the activities
      for j in range(lowercase  ):
            # If this activity has start time greater than
            # or equal to the finish time of previously
            # selected activity, then select it
            if start[j] >= finish[i]:
                  print(lowercase			,							end=','  )
                  lowerCamelCase_	  =					j


if __name__ == "__main__":
 import doctest

 doctest.testmod()

 lowerCamelCase       :	Tuple									=     [1, 3, 0, 5, 8, 5]
 lowerCamelCase       :	int									=     [2, 4, 6, 7, 9, 9]
 print_max_activities(start, finish)



