





# limitations under the License.


from typing import Optional, Tuple, Union

import torch

from diffusers import DiffusionPipeline, ImagePipelineOutput







class 				__lowerCAmelCase       (    UpperCamelCase__):







      def __init__(    self		,      lowerCAmelCase__		,      lowerCAmelCase__							)   ->						str:



        '''simple docstring'''




        super().__init__()
        self.register_modules(unet=lowerCAmelCase__		,      scheduler=lowerCAmelCase__							)







      @torch.no_grad()
      def __call__(    self		,      lowerCAmelCase__ = 1		,      lowerCAmelCase__ = None		,      lowerCAmelCase__ = 5_0		,      lowerCAmelCase__ = "pil"		,      lowerCAmelCase__ = True		,      **lowerCAmelCase__		,      )   ->						Union[ImagePipelineOutput, Tuple]:



        '''simple docstring'''




        a__	:					int										=torch.randn(
            (batch_size, self.unet.config.in_channels, self.unet.config.sample_size, self.unet.config.sample_size)		,      generator=lowerCAmelCase__		,      )
        a__	:					Union[str, Any]										=image.to(self.device							)

        # set step values
        self.scheduler.set_timesteps(lowerCAmelCase__							)

        for t in self.progress_bar(self.scheduler.timesteps							):
          # 1. predict noise model_output
          a__	:					Any										=self.unet(lowerCAmelCase__		,      lowerCAmelCase__							).sample

          # 2. predict previous mean of image x_t-1 and add variance depending on eta
          # eta corresponds to η in paper and should be between [0, 1]
          # do x_t -> x_t-1
          a__	:					List[str]										=self.scheduler.step(lowerCAmelCase__		,      lowerCAmelCase__		,      lowerCAmelCase__							).prev_sample

        a__	:					Union[str, Any]										=(image / 2 + 0.5).clamp(0		,      1							)
        a__	:					str										=image.cpu().permute(0		,      2		,      3		,      1							).numpy()
        if output_type == "pil":
          a__	:					Any										=self.numpy_to_pil(lowerCAmelCase__							)

        if not return_dict:
          return (image,), "This is a local test"

        return ImagePipelineOutput(images=lowerCAmelCase__							), "This is a local test"













from typing import Optional

import pyspark

from .. import Features, NamedSplit
from ..download import DownloadMode
from ..packaged_modules.spark.spark import Spark
from .abc import AbstractDatasetReader







class 				__lowerCAmelCase       (    UpperCamelCase__):







      def __init__(    self		,      lowerCAmelCase__		,      lowerCAmelCase__ = None		,      lowerCAmelCase__ = None		,      lowerCAmelCase__ = True		,      lowerCAmelCase__ = None		,      lowerCAmelCase__ = False		,      lowerCAmelCase__ = None		,      lowerCAmelCase__ = True		,      lowerCAmelCase__ = "arrow"		,      **lowerCAmelCase__		,      )   ->						Optional[int]:



        '''simple docstring'''




        super().__init__(
            split=lowerCAmelCase__		,      features=lowerCAmelCase__		,      cache_dir=lowerCAmelCase__		,      keep_in_memory=lowerCAmelCase__		,      streaming=lowerCAmelCase__		,      **lowerCAmelCase__		,      )
        a__	:					int										=load_from_cache_file
        a__	:					Tuple										=file_format
        a__	:					List[Any]										=Spark(
            df=lowerCAmelCase__		,      features=lowerCAmelCase__		,      cache_dir=lowerCAmelCase__		,      working_dir=lowerCAmelCase__		,      **lowerCAmelCase__		,      )







      def    _lowercase  (    self							)   ->						str:



        '''simple docstring'''




        if self.streaming:
          return self.builder.as_streaming_dataset(split=self.split							)
        a__	:					str										=None if self._load_from_cache_file else DownloadMode.FORCE_REDOWNLOAD
        self.builder.download_and_prepare(
            download_mode=lowerCAmelCase__		,      file_format=self._file_format		,      )
        return self.builder.as_dataset(split=self.split							)






