
'''simple docstring'''



from typing import Optional, Tuple, Union

import torch

from diffusers import DiffusionPipeline, ImagePipelineOutput

class    _a				(_lowerCamelCase):





       """simple docstring"""




       def __init__(							self       ,							A__       ,							A__      )    ->			Union[str, Any]:
          super().__init__()
          self.register_modules(unet=A__       ,							scheduler=A__      )






       @torch.no_grad()
       def __call__(							self       ,							A__ = 1       ,							A__ = None       ,							A__ = 50       ,							A__ = "pil"       ,							A__ = True       ,							**A__       ,							)    ->			Union[ImagePipelineOutput, Tuple]:
          _SCREAMING_SNAKE_CASE            =				torch.randn(
              (batch_size, self.unet.config.in_channels, self.unet.config.sample_size, self.unet.config.sample_size)       ,							generator=A__       ,							)
          _SCREAMING_SNAKE_CASE            =				image.to(self.device      )

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

          for t in self.progress_bar(self.scheduler.timesteps      ):
             # 1. predict noise model_output
             _SCREAMING_SNAKE_CASE            =				self.unet(A__       ,							A__      ).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
             _SCREAMING_SNAKE_CASE            =				self.scheduler.step(A__       ,							A__       ,							A__      ).prev_sample

          _SCREAMING_SNAKE_CASE            =				(image / 2 + 0.5).clamp(0       ,							1      )
          _SCREAMING_SNAKE_CASE            =				image.cpu().permute(0       ,							2       ,							3       ,							1      ).numpy()
          if output_type == "pil":
             _SCREAMING_SNAKE_CASE            =				self.numpy_to_pil(A__      )

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

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








'''simple docstring'''



from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available


UpperCamelCase__      :		int       							=  {"tokenization_herbert": ["HerbertTokenizer"]}

try:
     if not is_tokenizers_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     UpperCamelCase__      :		Tuple       							=  ["HerbertTokenizerFast"]


if TYPE_CHECKING:
     from .tokenization_herbert import HerbertTokenizer

     try:
          if not is_tokenizers_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .tokenization_herbert_fast import HerbertTokenizerFast

else:
     import sys

     UpperCamelCase__      :		Tuple       							=  _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)






