

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 (       SCREAMING_SNAKE_CASE		:			Optional[int]    ,					SCREAMING_SNAKE_CASE		:			Optional[int]=BITS		)    ->    int:

 """simple docstring"""






 __lowerCAmelCase:   Optional[Any]   	=      x.device

 __lowerCAmelCase:   List[Any]   	=      (x * 2_55).int().clamp(0    ,					2_55		)

 __lowerCAmelCase:   Any   	=      2 ** torch.arange(bits - 1    ,					-1    ,					-1    ,					device=SCREAMING_SNAKE_CASE		)
 __lowerCAmelCase:   Tuple   	=      rearrange(SCREAMING_SNAKE_CASE    ,					'd -> d 1 1'		)
 __lowerCAmelCase:   Tuple   	=      rearrange(SCREAMING_SNAKE_CASE    ,					'b c h w -> b c 1 h w'		)

 __lowerCAmelCase:   Dict   	=      ((x & mask) != 0).float()
 __lowerCAmelCase:   Dict   	=      rearrange(SCREAMING_SNAKE_CASE    ,					'b c d h w -> b (c d) h w'		)
 __lowerCAmelCase:   str   	=      bits * 2 - 1
 return bits







def _a (       SCREAMING_SNAKE_CASE		:			Optional[Any]    ,					SCREAMING_SNAKE_CASE		:			int=BITS		)    ->    List[str]:

 """simple docstring"""






 __lowerCAmelCase:   str   	=      x.device

 __lowerCAmelCase:   int   	=      (x > 0).int()
 __lowerCAmelCase:   Tuple   	=      2 ** torch.arange(bits - 1    ,					-1    ,					-1    ,					device=SCREAMING_SNAKE_CASE    ,					dtype=torch.intaa		)

 __lowerCAmelCase:   Union[str, Any]   	=      rearrange(SCREAMING_SNAKE_CASE    ,					'd -> d 1 1'		)
 __lowerCAmelCase:   int   	=      rearrange(SCREAMING_SNAKE_CASE    ,					'b (c d) h w -> b c d h w'    ,					d=8		)
 __lowerCAmelCase:   List[Any]   	=      reduce(x * mask    ,					'b c d h w -> b c h w'    ,					'sum'		)
 return (dec / 2_55).clamp(0.0    ,					1.0		)







def _a (       self		:			List[str]    ,					SCREAMING_SNAKE_CASE		:			torch.FloatTensor    ,					SCREAMING_SNAKE_CASE		:			int    ,					SCREAMING_SNAKE_CASE		:			torch.FloatTensor    ,					SCREAMING_SNAKE_CASE		:			float = 0.0    ,					SCREAMING_SNAKE_CASE		:			bool = True    ,					SCREAMING_SNAKE_CASE		:			Optional[Any]=None    ,					SCREAMING_SNAKE_CASE		:			bool = True    ,					)    ->    Dict:

 """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:   List[Any]   	=      timestep - self.config.num_train_timesteps // self.num_inference_steps

 # 2. compute alphas, betas
 __lowerCAmelCase:   List[Any]   	=      self.alphas_cumprod[timestep]
 __lowerCAmelCase:   Dict   	=      self.alphas_cumprod[prev_timestep] if prev_timestep >= 0 else self.final_alpha_cumprod

 __lowerCAmelCase:   int   	=      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:   Dict   	=      (sample - beta_prod_t ** 0.5 * model_output) / alpha_prod_t ** 0.5

 # 4. Clip "predicted x_0"
 __lowerCAmelCase:   Union[str, Any]   	=      self.bit_scale
 if self.config.clip_sample:
  __lowerCAmelCase:   List[Any]   	=      torch.clamp(SCREAMING_SNAKE_CASE    ,					-scale    ,					SCREAMING_SNAKE_CASE		)

 # 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:   int   	=      self._get_variance(SCREAMING_SNAKE_CASE    ,					SCREAMING_SNAKE_CASE		)
 __lowerCAmelCase:   int   	=      eta * variance ** 0.5

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

  __lowerCAmelCase:   int   	=      prev_sample + variance

 if not return_dict:
  return (prev_sample,)

 return DDIMSchedulerOutput(prev_sample=SCREAMING_SNAKE_CASE    ,					pred_original_sample=SCREAMING_SNAKE_CASE		)







def _a (       self		:			List[str]    ,					SCREAMING_SNAKE_CASE		:			torch.FloatTensor    ,					SCREAMING_SNAKE_CASE		:			int    ,					SCREAMING_SNAKE_CASE		:			torch.FloatTensor    ,					SCREAMING_SNAKE_CASE		:			Tuple="epsilon"    ,					SCREAMING_SNAKE_CASE		:			Union[str, Any]=None    ,					SCREAMING_SNAKE_CASE		:			bool = True    ,					)    ->    Union[str, Any]:

 """simple docstring"""






 __lowerCAmelCase:   List[Any]   	=      timestep

 if model_output.shape[1] == sample.shape[1] * 2 and self.variance_type in ["learned", "learned_range"]:
  __lowerCAmelCase:   Optional[Any]   	=      torch.split(SCREAMING_SNAKE_CASE    ,					sample.shape[1]    ,					dim=1		)
 else:
  __lowerCAmelCase:   Tuple   	=      None

 # 1. compute alphas, betas
 __lowerCAmelCase:   Optional[int]   	=      self.alphas_cumprod[t]
 __lowerCAmelCase:   int   	=      self.alphas_cumprod[t - 1] if t > 0 else self.one
 __lowerCAmelCase:   List[str]   	=      1 - alpha_prod_t
 __lowerCAmelCase:   List[str]   	=      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:   int   	=      (sample - beta_prod_t ** 0.5 * model_output) / alpha_prod_t ** 0.5
 elif prediction_type == "sample":
  __lowerCAmelCase:   Dict   	=      model_output
 else:
  raise ValueError(f'''Unsupported prediction_type {prediction_type}.'''		)

 # 3. Clip "predicted x_0"
 __lowerCAmelCase:   List[str]   	=      self.bit_scale
 if self.config.clip_sample:
  __lowerCAmelCase:   Union[str, Any]   	=      torch.clamp(SCREAMING_SNAKE_CASE    ,					-scale    ,					SCREAMING_SNAKE_CASE		)

 # 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:   Union[str, Any]   	=      (alpha_prod_t_prev ** 0.5 * self.betas[t]) / beta_prod_t
 __lowerCAmelCase:   str   	=      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:   Optional[int]   	=      pred_original_sample_coeff * pred_original_sample + current_sample_coeff * sample

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

 __lowerCAmelCase:   Any   	=      pred_prev_sample + variance

 if not return_dict:
  return (pred_prev_sample,)

 return DDPMSchedulerOutput(prev_sample=SCREAMING_SNAKE_CASE    ,					pred_original_sample=SCREAMING_SNAKE_CASE		)






class 							A_ (		lowerCAmelCase_					):


     def __init__(							self			:		Union[str, Any]			,  UpperCAmelCase			:		UNetaDConditionModel			,  UpperCAmelCase			:		Union[DDIMScheduler, DDPMScheduler]			,  UpperCAmelCase			:		Optional[float] = 1.0			,  )							->	int:
      super().__init__()
      __lowerCAmelCase:   Optional[int]   	=      bit_scale
      __lowerCAmelCase:   int   	=      (
          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			:		List[str]			,  UpperCAmelCase			:		Optional[int] = 2_5_6			,  UpperCAmelCase			:		Optional[int] = 2_5_6			,  UpperCAmelCase			:		Optional[int] = 5_0			,  UpperCAmelCase			:		Optional[torch.Generator] = None			,  UpperCAmelCase			:		Optional[int] = 1			,  UpperCAmelCase			:		Optional[str] = "pil"			,  UpperCAmelCase			:		bool = True			,  **UpperCAmelCase			:		Tuple			,  )							->	Union[Tuple, ImagePipelineOutput]:
      __lowerCAmelCase:   int   	=      torch.randn(
          (batch_size, self.unet.config.in_channels, height, width)			,  generator=_snake_case			,  )
      __lowerCAmelCase:   Tuple   	=      decimal_to_bits(_snake_case					) * self.bit_scale
      __lowerCAmelCase:   Optional[Any]   	=      latents.to(self.device					)

      self.scheduler.set_timesteps(_snake_case					)

      for t in self.progress_bar(self.scheduler.timesteps					):
       # predict the noise residual
       __lowerCAmelCase:   Union[str, Any]   	=      self.unet(_snake_case			,  _snake_case					).sample

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

      __lowerCAmelCase:   Optional[int]   	=      bits_to_decimal(_snake_case					)

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

      if not return_dict:
       return (image,)

      return ImagePipelineOutput(images=_snake_case					)










from collections import deque

from .hash_table import HashTable




class 					snake_case__						(       lowerCAmelCase_     ):






     """simple docstring"""


     def __init__(       self  :  Optional[Any], *_snake_case  :  Optional[Any], **_snake_case  :  List[Any]					)   ->Optional[int]:
            super().__init__(*_snake_case, **_snake_case					)






     def    lowercase_   (       self  :  Optional[Any], _snake_case  :  Tuple, _snake_case  :  Dict					)   ->Dict:
            snake_case__							:	int   	=	deque([]					) if self.values[key] is None else self.values[key]
            self.values[key].appendleft(_snake_case					)
            snake_case__							:	Dict   	=	self.values[key]






     def    lowercase_   (       self  :  Any					)   ->Optional[Any]:
            return (
                sum(self.charge_factor - len(_snake_case					) for slot in self.values					)
                / self.size_table
                * self.charge_factor
            )






     def    lowercase_   (       self  :  Union[str, Any], _snake_case  :  str, _snake_case  :  Optional[int]=None					)   ->Optional[Any]:
            if not (
                len(self.values[key]					) == self.charge_factor and self.values.count(_snake_case					) == 0
            ):
                   return key
            return super()._collision_resolution(_snake_case, _snake_case					)






