



"""simple docstring"""







import timeit

import numpy as np

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

def _a			(		_snake_case							):







      """simple docstring"""






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

      UpperCAmelCase          =	func.__name__

      return wrapper

def _a			(		_snake_case				,	_snake_case=100				,	_snake_case=None							):







      """simple docstring"""






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

            dummy_data.append((i, example)							)

      return dummy_data






def _a			(		_snake_case				,	_snake_case				,	_snake_case=100				,	_snake_case=None							):







      """simple docstring"""






      UpperCAmelCase          =	generate_examples(_snake_case				,	num_examples=_snake_case				,	seq_shapes=_snake_case							)

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

            UpperCAmelCase      , UpperCAmelCase          =	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}.'''							)

      UpperCAmelCase          =	datasets.Dataset.from_file(filename=_snake_case				,	info=datasets.DatasetInfo(features=_snake_case							)							)

      return dataset











"""simple docstring"""







from typing import List, Optional, Tuple, Union

import torch

from ...models import UNetaDModel
from ...schedulers import ScoreSdeVeScheduler
from ...utils import randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput







class  lowerCamelCase__ (	snake_case			):
    SCREAMING_SNAKE_CASE		=      42
    SCREAMING_SNAKE_CASE		=      42






    def __init__(			self     ,A     ,A ):
          super().__init__()
          self.register_modules(unet=A     ,scheduler=A )






    @torch.no_grad()
    def __call__(			self     ,A = 1     ,A = 2_000     ,A = None     ,A = "pil"     ,A = True     ,**A     ,):
          UpperCAmelCase          =	self.unet.config.sample_size
          UpperCAmelCase          =	(batch_size, 3, img_size, img_size)

          UpperCAmelCase          =	self.unet

          UpperCAmelCase          =	randn_tensor(A     ,generator=A ) * self.scheduler.init_noise_sigma
          UpperCAmelCase          =	sample.to(self.device )

          self.scheduler.set_timesteps(A )
          self.scheduler.set_sigmas(A )

          for i, t in enumerate(self.progress_bar(self.scheduler.timesteps ) ):
                UpperCAmelCase          =	self.scheduler.sigmas[i] * torch.ones(shape[0]     ,device=self.device )

                # correction step
                for _ in range(self.scheduler.config.correct_steps ):
                      UpperCAmelCase          =	self.unet(A     ,A ).sample
                      UpperCAmelCase          =	self.scheduler.step_correct(A     ,A     ,generator=A ).prev_sample

                # prediction step
                UpperCAmelCase          =	model(A     ,A ).sample
                UpperCAmelCase          =	self.scheduler.step_pred(A     ,A     ,A     ,generator=A )

                UpperCAmelCase      , UpperCAmelCase          =	output.prev_sample, output.prev_sample_mean

          UpperCAmelCase          =	sample_mean.clamp(0     ,1 )
          UpperCAmelCase          =	sample.cpu().permute(0     ,2     ,3     ,1 ).numpy()
          if output_type == "pil":
                UpperCAmelCase          =	self.numpy_to_pil(A )

          if not return_dict:
                return (sample,)

          return ImagePipelineOutput(images=A )



