

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   __UpperCAmelCase					(      __a :     Union[str, Any]						,__a :     Tuple=BITS      )     ->	List[str]:

					"""simple docstring"""

					_a      :       Optional[Any]   =						x.device

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

					_a      :       Optional[int]   =						2 ** torch.arange(bits - 1						,-1						,-1						,device=__a      )
					_a      :       Union[str, Any]   =						rearrange(__a						,'''d -> d 1 1'''      )
					_a      :       Tuple   =						rearrange(__a						,'''b c h w -> b c 1 h w'''      )

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



def   __UpperCAmelCase					(      __a :     List[str]						,__a :     List[str]=BITS      )     ->	Optional[Any]:

					"""simple docstring"""

					_a      :       Any   =						x.device

					_a      :       Union[str, Any]   =						(x > 0).int()
					_a      :       Dict   =						2 ** torch.arange(bits - 1						,-1						,-1						,device=__a						,dtype=torch.intaa      )

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



def   __UpperCAmelCase					(      self :     Optional[int]						,__a :     torch.FloatTensor						,__a :     int						,__a :     torch.FloatTensor						,__a :     float = 0.0						,__a :     bool = True						,__a :     int=None						,__a :     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)
					_a      :       List[str]   =						timestep - self.config.num_train_timesteps // self.num_inference_steps

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

					_a      :       Union[str, Any]   =						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
					_a      :       Union[str, Any]   =						(sample - beta_prod_t ** 0.5 * model_output) / alpha_prod_t ** 0.5

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

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

										_a      :       Dict   =						prev_sample + variance

					if not return_dict:
										return (prev_sample,)

					return DDIMSchedulerOutput(prev_sample=__a						,pred_original_sample=__a      )



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

					"""simple docstring"""

					_a      :       int   =						timestep

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

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

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

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

					_a      :       str   =						pred_prev_sample + variance

					if not return_dict:
										return (pred_prev_sample,)

					return DDPMSchedulerOutput(prev_sample=__a						,pred_original_sample=__a      )







class 							UpperCAmelCase_						(     __lowercase  ):
							"""simple docstring"""







							def __init__(     self			,				_a			,				_a			,				_a = 1.0			,				)       ->       Optional[Any]:
												super().__init__()
												_a      :       Optional[Any]   =						bit_scale
												_a      :       Dict   =						(
												    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			,				_a = 2_5_6			,				_a = 2_5_6			,				_a = 5_0			,				_a = None			,				_a = 1			,				_a = "pil"			,				_a = True			,				**_a			,				)       ->       Union[Tuple, ImagePipelineOutput]:
												_a      :       Dict   =						torch.randn(
												    (batch_size, self.unet.config.in_channels, height, width)			,				generator=_a			,				)
												_a      :       List[str]   =						decimal_to_bits(_a			) * self.bit_scale
												_a      :       str   =						latents.to(self.device			)

												self.scheduler.set_timesteps(_a			)

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

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

												_a      :       str   =						bits_to_decimal(_a			)

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

												if not return_dict:
																	return (image,)

												return ImagePipelineOutput(images=_a			)







'''simple docstring'''



from __future__ import annotations

A__						:   int				=    10



def      UpperCAmelCase__   (      UpperCAmelCase_       :							list[int]				)	->						list[int]:
     __lowerCamelCase  :      List[Any]														=						1
     __lowerCamelCase  :      Any														=						max(UpperCAmelCase_				)
     while placement <= max_digit:
          # declare and initialize empty buckets
          __lowerCamelCase  :      list[list]														=						[[] for _ in range(UpperCAmelCase_				)]
          # split list_of_ints between the buckets
          for i in list_of_ints:
               __lowerCamelCase  :      List[Any]														=						int((i / placement) % RADIX				)
               buckets[tmp].append(UpperCAmelCase_				)
          # put each buckets' contents into list_of_ints
          __lowerCamelCase  :      Tuple														=						0
          for b in range(UpperCAmelCase_				):
               for i in buckets[b]:
                    __lowerCamelCase  :      List[Any]														=						i
                    a += 1
        # move to next
          placement *= RADIX
     return list_of_ints


if __name__ == "__main__":
      import doctest

      doctest.testmod()






