




'''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


UpperCAmelCase_							:	Any	     =		8







def       _UpperCamelCase			(_lowerCamelCase				:  Union[str, Any] ,						_lowerCamelCase				:  Tuple=BITS						)-> List[str]:




     '''simple docstring'''





     __snake_case            =	x.device

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

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

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







def       _UpperCamelCase			(_lowerCamelCase				:  List[str] ,						_lowerCamelCase				:  List[str]=BITS						)-> Optional[Any]:




     '''simple docstring'''





     __snake_case            =	x.device

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

     __snake_case            =	rearrange(__a ,						'''d -> d 1 1'''						)
     __snake_case            =	rearrange(__a ,						'''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 / 2_55).clamp(0.0 ,						1.0						)







def       _UpperCamelCase			(self				:  Optional[int] ,						_lowerCamelCase				:  torch.FloatTensor ,						_lowerCamelCase				:  int ,						_lowerCamelCase				:  torch.FloatTensor ,						_lowerCamelCase				:  float = 0.0 ,						_lowerCamelCase				:  bool = True ,						_lowerCamelCase				:  int=None ,						_lowerCamelCase				:  bool = 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(__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)
     __snake_case            =	self._get_variance(__a ,						__a						)
     __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(__a						) else '''cpu'''
          __snake_case            =	torch.randn(model_output.shape ,						dtype=model_output.dtype ,						generator=__a						).to(__a						)
          __snake_case            =	self._get_variance(__a ,						__a						) ** 0.5 * eta * noise

          __snake_case            =	prev_sample + variance

     if not return_dict:
          return (prev_sample,)

     return DDIMSchedulerOutput(prev_sample=__a ,						pred_original_sample=__a						)







def       _UpperCamelCase			(self				:  List[Any] ,						_lowerCamelCase				:  torch.FloatTensor ,						_lowerCamelCase				:  int ,						_lowerCamelCase				:  torch.FloatTensor ,						_lowerCamelCase				:  str="epsilon" ,						_lowerCamelCase				:  Optional[Any]=None ,						_lowerCamelCase				:  bool = 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            =	torch.split(__a ,						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(__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
     __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=__a						).to(model_output.device						)
          __snake_case            =	(self._get_variance(__a ,						predicted_variance=__a						) ** 0.5) * noise

     __snake_case            =	pred_prev_sample + variance

     if not return_dict:
          return (pred_prev_sample,)

     return DDPMSchedulerOutput(prev_sample=__a ,						pred_original_sample=__a						)







class      lowerCAmelCase (			__lowercase):


       def __init__( self     ,	__SCREAMING_SNAKE_CASE     ,	__SCREAMING_SNAKE_CASE     ,	__SCREAMING_SNAKE_CASE = 1.0     ,	)   ->	Optional[Any]:



            '''simple docstring'''




            super().__init__()
            __snake_case            =	bit_scale
            __snake_case            =	(
                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     ,	__SCREAMING_SNAKE_CASE = 256     ,	__SCREAMING_SNAKE_CASE = 256     ,	__SCREAMING_SNAKE_CASE = 50     ,	__SCREAMING_SNAKE_CASE = None     ,	__SCREAMING_SNAKE_CASE = 1     ,	__SCREAMING_SNAKE_CASE = "pil"     ,	__SCREAMING_SNAKE_CASE = True     ,	**__SCREAMING_SNAKE_CASE     ,	)   ->	Union[Tuple, ImagePipelineOutput]:



            '''simple docstring'''




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

            self.scheduler.set_timesteps(_a	)

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

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

            __snake_case            =	bits_to_decimal(_a	)

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

            if not return_dict:
                 return (image,)

            return ImagePipelineOutput(images=_a	)









class 							UpperCAmelCase_						:
							"""simple docstring"""







							def __init__(     self			,				_a			,				_a			,				_a			)       ->       List[str]:
												_a      :       List[Any]   =						name
												_a      :       List[str]   =						value
												_a      :       List[str]   =						weight


							def __repr__(     self			)       ->       Optional[int]:
												return F"""{self.__class__.__name__}({self.name}, {self.value}, {self.weight})"""


							def 							__lowercase							(     self			)       ->       List[Any]:
												return self.value


							def 							__lowercase							(     self			)       ->       int:
												return self.name


							def 							__lowercase							(     self			)       ->       Optional[int]:
												return self.weight




							def 							__lowercase							(     self			)       ->       Optional[Any]:
												return self.value / self.weight



def   __UpperCAmelCase					(      __a :     Optional[int]						,__a :     Tuple						,__a :     List[str]      )     ->	List[str]:

					"""simple docstring"""

					_a      :       Optional[int]   =						[]
					for i in range(len(__a      )      ):
										menu.append(Things(name[i]						,value[i]						,weight[i]      )      )
					return menu



def   __UpperCAmelCase					(      __a :     int						,__a :     Union[str, Any]						,__a :     int      )     ->	Union[str, Any]:

					"""simple docstring"""

					_a      :       Union[str, Any]   =						sorted(__a						,key=__a						,reverse=__a      )
					_a      :       Any   =						[]
					_a ,   _a      :       Optional[int]   =						0.0, 0.0
					for i in range(len(__a      )      ):
										if (total_cost + items_copy[i].get_weight()) <= max_cost:
															result.append(items_copy[i]      )
															total_cost += items_copy[i].get_weight()
															total_value += items_copy[i].get_value()
					return (result, total_value)



def   __UpperCAmelCase					(      )     ->	int:

					"""simple docstring"""



if __name__ == "__main__":
			import doctest

			doctest.testmod()



