





'''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


a        =						8

def 						__magic_name__       (							__UpperCAmelCase							,	__UpperCAmelCase=BITS      )						->					List[str]:

   '''simple docstring'''







   __SCREAMING_SNAKE_CASE      							=       x.device

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

   __SCREAMING_SNAKE_CASE      							=       2 ** torch.arange(bits - 1							,	-1							,	-1							,	device=__UpperCAmelCase      )
   __SCREAMING_SNAKE_CASE      							=       rearrange(__UpperCAmelCase							,	"""d -> d 1 1"""      )
   __SCREAMING_SNAKE_CASE      							=       rearrange(__UpperCAmelCase							,	"""b c h w -> b c 1 h w"""      )

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

def 						__magic_name__       (							__UpperCAmelCase							,	__UpperCAmelCase=BITS      )						->					Optional[int]:

   '''simple docstring'''







   __SCREAMING_SNAKE_CASE      							=       x.device

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

   __SCREAMING_SNAKE_CASE      							=       rearrange(__UpperCAmelCase							,	"""d -> d 1 1"""      )
   __SCREAMING_SNAKE_CASE      							=       rearrange(__UpperCAmelCase							,	"""b (c d) h w -> b c d h w"""							,	d=8      )
   __SCREAMING_SNAKE_CASE      							=       reduce(x * mask							,	"""b c d h w -> b c h w"""							,	"""sum"""      )
   return (dec / 255).clamp(0.0							,	1.0      )

def 						__magic_name__       (							self							,	__UpperCAmelCase							,	__UpperCAmelCase							,	__UpperCAmelCase							,	__UpperCAmelCase = 0.0							,	__UpperCAmelCase = True							,	__UpperCAmelCase=None							,	__UpperCAmelCase = 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)
   __SCREAMING_SNAKE_CASE      							=       timestep - self.config.num_train_timesteps // self.num_inference_steps

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

   __SCREAMING_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
   __SCREAMING_SNAKE_CASE      							=       (sample - beta_prod_t ** 0.5 * model_output) / alpha_prod_t ** 0.5

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

   # 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)
   __SCREAMING_SNAKE_CASE      							=       self._get_variance(__UpperCAmelCase							,	__UpperCAmelCase      )
   __SCREAMING_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
      __SCREAMING_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
   __SCREAMING_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
   __SCREAMING_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
      __SCREAMING_SNAKE_CASE      							=       model_output.device if torch.is_tensor(__UpperCAmelCase      ) else """cpu"""
      __SCREAMING_SNAKE_CASE      							=       torch.randn(model_output.shape							,	dtype=model_output.dtype							,	generator=__UpperCAmelCase      ).to(__UpperCAmelCase      )
      __SCREAMING_SNAKE_CASE      							=       self._get_variance(__UpperCAmelCase							,	__UpperCAmelCase      ) ** 0.5 * eta * noise

      __SCREAMING_SNAKE_CASE      							=       prev_sample + variance

   if not return_dict:
      return (prev_sample,)

   return DDIMSchedulerOutput(prev_sample=__UpperCAmelCase							,	pred_original_sample=__UpperCAmelCase      )

def 						__magic_name__       (							self							,	__UpperCAmelCase							,	__UpperCAmelCase							,	__UpperCAmelCase							,	__UpperCAmelCase="epsilon"							,	__UpperCAmelCase=None							,	__UpperCAmelCase = True							,	)						->					Union[DDPMSchedulerOutput, Tuple]:

   '''simple docstring'''







   __SCREAMING_SNAKE_CASE      							=       timestep

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

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

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

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

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

   __SCREAMING_SNAKE_CASE      							=       pred_prev_sample + variance

   if not return_dict:
      return (pred_prev_sample,)

   return DDPMSchedulerOutput(prev_sample=__UpperCAmelCase							,	pred_original_sample=__UpperCAmelCase      )







class __a (   _snake_case			):





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

      '''simple docstring'''

      super().__init__()
      __SCREAMING_SNAKE_CASE      							=       bit_scale
      __SCREAMING_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				:			Optional[Any]				,lowerCamelCase				:			Optional[int] = 256				,lowerCamelCase				:			Optional[int] = 256				,lowerCamelCase				:			Optional[int] = 50				,lowerCamelCase				:			Optional[torch.Generator] = None				,lowerCamelCase				:			Optional[int] = 1				,lowerCamelCase				:			Optional[str] = "pil"				,lowerCamelCase				:			bool = True				,**lowerCamelCase				:			List[Any]				,):

      '''simple docstring'''

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

      self.scheduler.set_timesteps(lowerCamelCase    )

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

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

      __SCREAMING_SNAKE_CASE      							=       bits_to_decimal(lowerCamelCase    )

      if output_type == "pil":
         __SCREAMING_SNAKE_CASE      							=       self.numpy_to_pil(lowerCamelCase    )

      if not return_dict:
         return (image,)

      return ImagePipelineOutput(images=lowerCamelCase    )












'''simple docstring'''

import timeit

import numpy as np

import datasets
from datasets.arrow_writer import ArrowWriter
from datasets.features.features import _ArrayXD

def 						__magic_name__       (							__UpperCAmelCase      )						->					Tuple:

   '''simple docstring'''







   def wrapper(*__UpperCAmelCase							,	**__UpperCAmelCase      ):
      __SCREAMING_SNAKE_CASE      							=       timeit.default_timer()
      __SCREAMING_SNAKE_CASE      							=       func(*__UpperCAmelCase							,	**__UpperCAmelCase      )
      __SCREAMING_SNAKE_CASE      							=       timeit.default_timer() - starttime
      return delta

   __SCREAMING_SNAKE_CASE      							=       func.__name__

   return wrapper

def 						__magic_name__       (							__UpperCAmelCase							,	__UpperCAmelCase=100							,	__UpperCAmelCase=None      )						->					Optional[Any]:

   '''simple docstring'''







   __SCREAMING_SNAKE_CASE      							=       []
   __SCREAMING_SNAKE_CASE      							=       seq_shapes or {}
   for i in range(__UpperCAmelCase      ):
      __SCREAMING_SNAKE_CASE      							=       {}
      for col_id, (k, v) in enumerate(features.items()      ):
         if isinstance(__UpperCAmelCase							,	_ArrayXD      ):
            __SCREAMING_SNAKE_CASE      							=       np.random.rand(*v.shape      ).astype(v.dtype      )
         elif isinstance(__UpperCAmelCase							,	datasets.Value      ):
            if v.dtype == "string":
               __SCREAMING_SNAKE_CASE      							=       """The small grey turtle was surprisingly fast when challenged."""
            else:
               __SCREAMING_SNAKE_CASE      							=       np.random.randint(10							,	size=1      ).astype(v.dtype      ).item()
         elif isinstance(__UpperCAmelCase							,	datasets.Sequence      ):
            while isinstance(__UpperCAmelCase							,	datasets.Sequence      ):
               __SCREAMING_SNAKE_CASE      							=       v.feature
            __SCREAMING_SNAKE_CASE      							=       seq_shapes[k]
            __SCREAMING_SNAKE_CASE      							=       np.random.rand(*__UpperCAmelCase      ).astype(v.dtype      )
         __SCREAMING_SNAKE_CASE      							=       data

      dummy_data.append((i, example)      )

   return dummy_data







def 						__magic_name__       (							__UpperCAmelCase							,	__UpperCAmelCase							,	__UpperCAmelCase=100							,	__UpperCAmelCase=None      )						->					str:

   '''simple docstring'''







   __SCREAMING_SNAKE_CASE      							=       generate_examples(__UpperCAmelCase							,	num_examples=__UpperCAmelCase							,	seq_shapes=__UpperCAmelCase      )

   with ArrowWriter(features=__UpperCAmelCase							,	path=__UpperCAmelCase      ) as writer:
      for key, record in dummy_data:
         __SCREAMING_SNAKE_CASE      							=       features.encode_example(__UpperCAmelCase      )
         writer.write(__UpperCAmelCase      )

      __SCREAMING_SNAKE_CASE							,     __SCREAMING_SNAKE_CASE      							=       writer.finalize()

   if not num_final_examples == num_examples:
      raise ValueError(
          f"""Error writing the dataset, wrote {num_final_examples} examples but should have written {num_examples}."""      )

   __SCREAMING_SNAKE_CASE      							=       datasets.Dataset.from_file(filename=__UpperCAmelCase							,	info=datasets.DatasetInfo(features=__UpperCAmelCase      )      )

   return dataset





