code: 
  
  
  
  
  
  import importlib
  import inspect
  from functools import wraps
  from typing import TYPE_CHECKING, Optional, Union
  
  from .download.streaming_download_manager import (
      xbasename,
      xdirname,
      xet_parse,
      xexists,
      xgetsize,
      xglob,
      xgzip_open,
      xisdir,
      xisfile,
      xjoin,
      xlistdir,
      xnumpy_load,
      xopen,
      xpandas_read_csv,
      xpandas_read_excel,
      xPath,
      xrelpath,
      xsio_loadmat,
      xsplit,
      xsplitext,
      xwalk,
      xxml_dom_minidom_parse,
  )
  from .utils.logging import get_logger
  from .utils.patching import patch_submodule
  from .utils.py_utils import get_imports
  
  
  _UpperCamelCase       = get_logger(__name__)
  
  
  if TYPE_CHECKING:
       from .builder import DatasetBuilder
  
  
  
  
  
  
  
  def       lowerCAmelCase__(	lowercase       :    int   ,     lowercase       :    Optional[Union[str, bool]] = None				) ->    Optional[Any]:
    __snake_case   :				int        =       importlib.import_module(lowercase				)
  
    # TODO(QL): always update the module to add subsequent new authentication
    if hasattr(lowercase   ,     "_patched_for_streaming"				) and module._patched_for_streaming:
      return
  
    def wrap_auth(lowercase       :    List[str]				):
      @wraps(lowercase				)
      def wrapper(*lowercase       :    Union[str, Any]   ,     **lowercase       :    Optional[int]				):
        return function(*lowercase   ,     use_auth_token=lowercase   ,     **lowercase				)
  
      __snake_case   :				Any        =       "wrap_auth"
      return wrapper
  
    # open files in a streaming fashion
    patch_submodule(lowercase   ,     "open"   ,     wrap_auth(lowercase				)				).start()
    patch_submodule(lowercase   ,     "os.listdir"   ,     wrap_auth(lowercase				)				).start()
    patch_submodule(lowercase   ,     "os.walk"   ,     wrap_auth(lowercase				)				).start()
    patch_submodule(lowercase   ,     "glob.glob"   ,     wrap_auth(lowercase				)				).start()
    # allow to navigate in remote zip files
    patch_submodule(lowercase   ,     "os.path.join"   ,     lowercase				).start()
    patch_submodule(lowercase   ,     "os.path.dirname"   ,     lowercase				).start()
    patch_submodule(lowercase   ,     "os.path.basename"   ,     lowercase				).start()
    patch_submodule(lowercase   ,     "os.path.relpath"   ,     lowercase				).start()
    patch_submodule(lowercase   ,     "os.path.split"   ,     lowercase				).start()
    patch_submodule(lowercase   ,     "os.path.splitext"   ,     lowercase				).start()
    # allow checks on paths
    patch_submodule(lowercase   ,     "os.path.exists"   ,     wrap_auth(lowercase				)				).start()
    patch_submodule(lowercase   ,     "os.path.isdir"   ,     wrap_auth(lowercase				)				).start()
    patch_submodule(lowercase   ,     "os.path.isfile"   ,     wrap_auth(lowercase				)				).start()
    patch_submodule(lowercase   ,     "os.path.getsize"   ,     wrap_auth(lowercase				)				).start()
    patch_submodule(lowercase   ,     "pathlib.Path"   ,     lowercase				).start()
    # file readers
    patch_submodule(lowercase   ,     "gzip.open"   ,     wrap_auth(lowercase				)				).start()
    patch_submodule(lowercase   ,     "numpy.load"   ,     wrap_auth(lowercase				)				).start()
    patch_submodule(lowercase   ,     "pandas.read_csv"   ,     wrap_auth(lowercase				)   ,     attrs=["__version__"]				).start()
    patch_submodule(lowercase   ,     "pandas.read_excel"   ,     wrap_auth(lowercase				)   ,     attrs=["__version__"]				).start()
    patch_submodule(lowercase   ,     "scipy.io.loadmat"   ,     wrap_auth(lowercase				)   ,     attrs=["__version__"]				).start()
    patch_submodule(lowercase   ,     "xml.etree.ElementTree.parse"   ,     wrap_auth(lowercase				)				).start()
    patch_submodule(lowercase   ,     "xml.dom.minidom.parse"   ,     wrap_auth(lowercase				)				).start()
    __snake_case   :				Any        =       True
  
  
  
  
  
  
  
  def       lowerCAmelCase__(	lowercase       :    "DatasetBuilder"				) ->    List[Any]:
    extend_module_for_streaming(builder.__module__   ,     use_auth_token=builder.use_auth_token				)
    # if needed, we also have to extend additional internal imports (like wmt14 -> wmt_utils)
    if not builder.__module__.startswith("datasets."				):  # check that it's not a packaged builder like csv
      for imports in get_imports(inspect.getfile(builder.__class__				)				):
        if imports[0] == "internal":
          __snake_case   :				int        =       imports[1]
          __snake_case   :				Optional[Any]        =       ".".join(builder.__module__.split("."				)[:-1] + [internal_import_name]				)
          extend_module_for_streaming(lowercase   ,     use_auth_token=builder.use_auth_token				)
  
      # builders can inherit from other builders that might use streaming functionality
      # (for example, ImageFolder and AudioFolder inherit from FolderBuilder which implements examples generation)
      # but these parents builders are not patched automatically as they are not instantiated, so we patch them here
    from .builder import DatasetBuilder
  
    __snake_case   :				Optional[Any]        =       [
        cls.__module__
        for cls in type(lowercase				).__mro__[1:]  # make sure it's not the same module we've already patched
        if issubclass(cls   ,     lowercase				) and cls.__module__ != DatasetBuilder.__module__
    ]  # check it's not a standard builder from datasets.builder
    for module in parent_builder_modules:
      extend_module_for_streaming(lowercase   ,     use_auth_token=builder.use_auth_token				)
  
  
style_context: 
  
  
  
  
  
  from typing import Union
  
  import torch
  from PIL import Image
  from torchvision import transforms as tfms
  from tqdm.auto import tqdm
  from transformers import CLIPTextModel, CLIPTokenizer
  
  from diffusers import (
      AutoencoderKL,
      DDIMScheduler,
      DiffusionPipeline,
      LMSDiscreteScheduler,
      PNDMScheduler,
      UNetaDConditionModel,
  )
  
  
  
  
  class 				_lowerCamelCase (    a    ):
  
  
  
        """simple docstring"""
  
  
  
  
  
  
        def __init__( self				,						UpperCAmelCase				,						UpperCAmelCase				,						UpperCAmelCase				,						UpperCAmelCase				,						UpperCAmelCase				,						)       ->   Optional[Any]:
  
  
  
  
  
  
          '''simple docstring'''
  
  
  
  
  
  
          super().__init__()
  
          self.register_modules(vae=UpperCAmelCase				,						text_encoder=UpperCAmelCase				,						tokenizer=UpperCAmelCase				,						unet=UpperCAmelCase				,						scheduler=UpperCAmelCase   )
  
  
  
  
  
  
  
        def 						UpperCAmelCase		( self				,						UpperCAmelCase   )       ->   str:
  
  
  
  
  
  
          '''simple docstring'''
  
  
  
  
  
  
          with torch.no_grad():
            __snake_case   :				Tuple        =       self.vae.encode(tfms.ToTensor()(UpperCAmelCase   ).unsqueeze(0   ).to(self.device   ) * 2 - 1   )
            __snake_case   :				Optional[int]        =       0.18_215 * latent.latent_dist.sample()
          return latent
  
  
  
  
  
  
  
        def 						UpperCAmelCase		( self				,						UpperCAmelCase   )       ->   str:
  
  
  
  
  
  
          '''simple docstring'''
  
  
  
  
  
  
          __snake_case   :				Optional[Any]        =       (1 / 0.18_215) * latent
          with torch.no_grad():
            __snake_case   :				Any        =       self.vae.decode(UpperCAmelCase   ).sample
          __snake_case   :				Optional[int]        =       (img / 2 + 0.5).clamp(0				,						1   )
          __snake_case   :				Tuple        =       img.detach().cpu().permute(0				,						2				,						3				,						1   ).numpy()
          __snake_case   :				Dict        =       (img * 255).round().astype("uint8"   )
          return Image.fromarray(img[0]   )
  
  
  
  
  
  
  
        def 						UpperCAmelCase		( self				,						UpperCAmelCase   )       ->   Dict:
  
  
  
  
  
  
          '''simple docstring'''
  
  
  
  
  
  
          __snake_case   :				int        =       self.tokenizer(
              UpperCAmelCase				,						padding="max_length"				,						max_length=self.tokenizer.model_max_length				,						truncation=UpperCAmelCase				,						return_tensors="pt"				,						)
  
          __snake_case   :				List[Any]        =       self.text_encoder(text_input.input_ids.to(self.device   )   )[0]
  
          __snake_case   :				Union[str, Any]        =       self.tokenizer(
              ""				,						padding="max_length"				,						max_length=self.tokenizer.model_max_length				,						truncation=UpperCAmelCase				,						return_tensors="pt"				,						)
  
          __snake_case   :				int        =       self.text_encoder(uncond_input.input_ids.to(self.device   )   )[0]
  
          return torch.cat([uncond_embedding, text_embedding]   )
  
  
  
  
  
  
  
        def __call__( self				,						UpperCAmelCase				,						UpperCAmelCase				,						UpperCAmelCase = 0.3				,						UpperCAmelCase = 0.6				,						UpperCAmelCase = 0.5				,						UpperCAmelCase = 42				,						UpperCAmelCase = 50				,						UpperCAmelCase = 7.5				,						)       ->   Image.Image:
  
  
  
  
  
  
          '''simple docstring'''
  
  
  
  
  
  
          __snake_case   :				Tuple        =       steps - int(kmin * steps   )
          __snake_case   :				Optional[int]        =       steps - int(kmax * steps   )
  
          __snake_case   :				Tuple        =       self.prep_text(UpperCAmelCase   )
  
          self.scheduler.set_timesteps(UpperCAmelCase   )
  
          __snake_case    ,   __snake_case   :				Optional[Any]        =       img.size
          __snake_case   :				List[Any]        =       self.encode(UpperCAmelCase   )
  
          torch.manual_seed(UpperCAmelCase   )
          __snake_case   :				str        =       torch.randn(
              (1, self.unet.config.in_channels, height // 8, width // 8)				,						).to(self.device   )
  
          __snake_case   :				int        =       self.scheduler.add_noise(
              UpperCAmelCase				,						UpperCAmelCase				,						timesteps=self.scheduler.timesteps[tmax]				,						)
  
          __snake_case   :				Optional[Any]        =       torch.cat([latents] * 2   )
  
          __snake_case   :				Union[str, Any]        =       self.scheduler.scale_model_input(UpperCAmelCase				,						self.scheduler.timesteps[tmax]   )
  
          with torch.no_grad():
            __snake_case   :				int        =       self.unet(
                UpperCAmelCase				,						self.scheduler.timesteps[tmax]				,						encoder_hidden_states=UpperCAmelCase				,						).sample
  
          __snake_case    ,   __snake_case   :				Union[str, Any]        =       pred.chunk(2   )
          __snake_case   :				Optional[Any]        =       pred_uncond + guidance_scale * (pred_text - pred_uncond)
  
          __snake_case   :				int        =       self.scheduler.step(UpperCAmelCase				,						self.scheduler.timesteps[tmax]				,						UpperCAmelCase   ).prev_sample
  
          for i, t in enumerate(tqdm(self.scheduler.timesteps   )   ):
            if i > tmax:
              if i < tmin:  # layout generation phase
                __snake_case   :				int        =       self.scheduler.add_noise(
                    UpperCAmelCase				,						UpperCAmelCase				,						timesteps=UpperCAmelCase				,						)
  
                __snake_case   :				str        =       (mix_factor * latents) + (
                    1 - mix_factor
                ) * orig_latents  # interpolating between layout noise and conditionally generated noise to preserve layout sematics
                __snake_case   :				List[Any]        =       torch.cat([input] * 2   )
  
              else:  # content generation phase
                __snake_case   :				str        =       torch.cat([latents] * 2   )
  
              __snake_case   :				Tuple        =       self.scheduler.scale_model_input(UpperCAmelCase				,						UpperCAmelCase   )
  
              with torch.no_grad():
                __snake_case   :				List[str]        =       self.unet(
                    UpperCAmelCase				,						UpperCAmelCase				,						encoder_hidden_states=UpperCAmelCase				,						).sample
  
              __snake_case    ,   __snake_case   :				str        =       pred.chunk(2   )
              __snake_case   :				List[Any]        =       pred_uncond + guidance_scale * (pred_text - pred_uncond)
  
              __snake_case   :				List[Any]        =       self.scheduler.step(UpperCAmelCase				,						UpperCAmelCase				,						UpperCAmelCase   ).prev_sample
  
          return self.decode(UpperCAmelCase   )
  
  