



"""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


snake_case   					=				8




def 			snake_case		(		lowerCAmelCase_  ,						lowerCAmelCase_=BITS	)						->				List[str]:
  _snake_case   	=      x.device

  _snake_case   	=      (x * 255).int().clamp(0  ,						255	)

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

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




def 			snake_case		(		lowerCAmelCase_  ,						lowerCAmelCase_=BITS	)						->				Union[str, Any]:
  _snake_case   	=      x.device

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

  _snake_case   	=      rearrange(lowerCAmelCase_  ,						'''d -> d 1 1'''	)
  _snake_case   	=      rearrange(lowerCAmelCase_  ,						'''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 / 255).clamp(0.0  ,						1.0	)




def 			snake_case		(		self  ,						lowerCAmelCase_  ,						lowerCAmelCase_  ,						lowerCAmelCase_  ,						lowerCAmelCase_ = 0.0  ,						lowerCAmelCase_ = True  ,						lowerCAmelCase_=None  ,						lowerCAmelCase_ = True  ,						)						->				Union[DDIMSchedulerOutput, Tuple]:
  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(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)
  _snake_case   	=      self._get_variance(lowerCAmelCase_  ,						lowerCAmelCase_	)
  _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(lowerCAmelCase_	) else '''cpu'''
    _snake_case   	=      torch.randn(model_output.shape  ,						dtype=model_output.dtype  ,						generator=lowerCAmelCase_	).to(lowerCAmelCase_	)
    _snake_case   	=      self._get_variance(lowerCAmelCase_  ,						lowerCAmelCase_	) ** 0.5 * eta * noise

    _snake_case   	=      prev_sample + variance

  if not return_dict:
    return (prev_sample,)

  return DDIMSchedulerOutput(prev_sample=lowerCAmelCase_  ,						pred_original_sample=lowerCAmelCase_	)




def 			snake_case		(		self  ,						lowerCAmelCase_  ,						lowerCAmelCase_  ,						lowerCAmelCase_  ,						lowerCAmelCase_="epsilon"  ,						lowerCAmelCase_=None  ,						lowerCAmelCase_ = True  ,						)						->				Union[DDPMSchedulerOutput, Tuple]:
  _snake_case   	=      timestep

  if model_output.shape[1] == sample.shape[1] * 2 and self.variance_type in ["learned", "learned_range"]:
    _snake_case	,     _snake_case   	=      torch.split(lowerCAmelCase_  ,						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(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
  _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=lowerCAmelCase_	).to(model_output.device	)
    _snake_case   	=      (self._get_variance(lowerCAmelCase_  ,						predicted_variance=lowerCAmelCase_	) ** 0.5) * noise

  _snake_case   	=      pred_prev_sample + variance

  if not return_dict:
    return (pred_prev_sample,)

  return DDPMSchedulerOutput(prev_sample=lowerCAmelCase_  ,						pred_original_sample=lowerCAmelCase_	)







class     UpperCAmelCase    (	__SCREAMING_SNAKE_CASE  ):



   def __init__(       self    :	Dict							,       __lowerCamelCase    :	UNetaDConditionModel							,       __lowerCamelCase    :	Union[DDIMScheduler, DDPMScheduler]							,       __lowerCamelCase    :	Optional[float] = 1.0							,       ):



     """simple docstring"""
     super().__init__()
     _snake_case   	=      bit_scale
     _snake_case   	=      (
         ddim_bit_scheduler_step if isinstance(__lowerCamelCase							,       __lowerCamelCase				) else ddpm_bit_scheduler_step
     )

     self.register_modules(unet=__lowerCamelCase							,       scheduler=__lowerCamelCase				)





   @torch.no_grad()
   def __call__(       self    :	Union[str, Any]							,       __lowerCamelCase    :	Optional[int] = 2_5_6							,       __lowerCamelCase    :	Optional[int] = 2_5_6							,       __lowerCamelCase    :	Optional[int] = 5_0							,       __lowerCamelCase    :	Optional[torch.Generator] = None							,       __lowerCamelCase    :	Optional[int] = 1							,       __lowerCamelCase    :	Optional[str] = "pil"							,       __lowerCamelCase    :	bool = True							,       **__lowerCamelCase    :	List[str]							,       ):



     """simple docstring"""
     _snake_case   	=      torch.randn(
         (batch_size, self.unet.config.in_channels, height, width)							,       generator=__lowerCamelCase							,       )
     _snake_case   	=      decimal_to_bits(__lowerCamelCase				) * self.bit_scale
     _snake_case   	=      latents.to(self.device				)

     self.scheduler.set_timesteps(__lowerCamelCase				)

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

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

     _snake_case   	=      bits_to_decimal(__lowerCamelCase				)

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

     if not return_dict:
       return (image,)

     return ImagePipelineOutput(images=__lowerCamelCase				)








'''simple docstring'''



import itertools
from dataclasses import dataclass
from typing import Optional

import pandas as pd
import pyarrow as pa

import datasets
from datasets.table import table_cast



@dataclass
class 				_snake_case					(   datasets.BuilderConfig			):
  SCREAMING_SNAKE_CASE			:       Optional[datasets.Features]				=     None



class 				_snake_case					(   datasets.ArrowBasedBuilder			):
  SCREAMING_SNAKE_CASE			:       Tuple				=     PandasConfig

  def   _SCREAMING_SNAKE_CASE				(				self	):





      '''simple docstring'''

      return datasets.DatasetInfo(features=self.config.features	)

  def   _SCREAMING_SNAKE_CASE				(				self      , _SCREAMING_SNAKE_CASE	):





      '''simple docstring'''

      if not self.config.data_files:
          raise ValueError(F'At least one data file must be specified, but got data_files={self.config.data_files}'	)
      lowerCAmelCase 						=							dl_manager.download_and_extract(self.config.data_files	)
      if isinstance(_SCREAMING_SNAKE_CASE      , (str, list, tuple)	):
          lowerCAmelCase 						=							data_files
          if isinstance(_SCREAMING_SNAKE_CASE      , _SCREAMING_SNAKE_CASE	):
              lowerCAmelCase 						=							[files]
          # Use `dl_manager.iter_files` to skip hidden files in an extracted archive
          lowerCAmelCase 						=							[dl_manager.iter_files(_SCREAMING_SNAKE_CASE	) for file in files]
          return [datasets.SplitGenerator(name=datasets.Split.TRAIN      , gen_kwargs={'files': files}	)]
      lowerCAmelCase 						=							[]
      for split_name, files in data_files.items():
          if isinstance(_SCREAMING_SNAKE_CASE      , _SCREAMING_SNAKE_CASE	):
              lowerCAmelCase 						=							[files]
          # Use `dl_manager.iter_files` to skip hidden files in an extracted archive
          lowerCAmelCase 						=							[dl_manager.iter_files(_SCREAMING_SNAKE_CASE	) for file in files]
          splits.append(datasets.SplitGenerator(name=_SCREAMING_SNAKE_CASE      , gen_kwargs={'files': files}	)	)
      return splits

  def   _SCREAMING_SNAKE_CASE				(				self      , _SCREAMING_SNAKE_CASE	):





      '''simple docstring'''

      if self.config.features is not None:
          # more expensive cast to support nested features with keys in a different order
          # allows str <-> int/float or str to Audio for example
          lowerCAmelCase 						=							table_cast(_SCREAMING_SNAKE_CASE      , self.config.features.arrow_schema	)
      return pa_table


  def   _SCREAMING_SNAKE_CASE				(				self      , _SCREAMING_SNAKE_CASE	):





      '''simple docstring'''

      for i, file in enumerate(itertools.chain.from_iterable(_SCREAMING_SNAKE_CASE	)	):
          with open(_SCREAMING_SNAKE_CASE      , 'rb'	) as f:
              lowerCAmelCase 						=							pa.Table.from_pandas(pd.read_pickle(_SCREAMING_SNAKE_CASE	)	)
              yield i, self._cast_table(_SCREAMING_SNAKE_CASE	)





