"""simple docstring"""





from typing import Optional, Tuple, Union

import torch

from diffusers.pipeline_utils import DiffusionPipeline, ImagePipelineOutput


class 					UpperCAmelCase_     (   __UpperCAmelCase):


      def __init__(       self		,      a		,      a							)	-> List[str]:
             super().__init__()
             self.register_modules(unet=lowerCamelCase__		,      scheduler=lowerCamelCase__							)


      @torch.no_grad()
      def __call__(       self		,      a = 1		,      a = None		,      a = 5_0		,      a = "pil"		,      a = True		,      **a		,      )	-> Union[ImagePipelineOutput, Tuple]:
             lowercase__      :		List[Any]          =     torch.randn(
                 (batch_size, self.unet.config.in_channels, self.unet.config.sample_size, self.unet.config.sample_size)		,      generator=lowerCamelCase__		,      )
             lowercase__      :		List[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
                    lowercase__      :		List[str]          =     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
                    lowercase__      :		Optional[int]          =     self.scheduler.step(lowerCamelCase__		,      lowerCamelCase__		,      lowerCamelCase__							).prev_sample

             lowercase__      :		List[str]          =     (image / 2 + 0.5).clamp(0		,      1							)
             lowercase__      :		List[str]          =     image.cpu().permute(0		,      2		,      3		,      1							).numpy()
             if output_type == "pil":
                    lowercase__      :		Optional[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"


"""simple docstring"""





from typing import TYPE_CHECKING

from ....utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available


_UpperCamelCase       :						int     =   {
    "configuration_retribert": ["RETRIBERT_PRETRAINED_CONFIG_ARCHIVE_MAP", "RetriBertConfig"],
    "tokenization_retribert": ["RetriBertTokenizer"],
}

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

try:
       if not is_torch_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       _UpperCamelCase       :						int     =   [
           "RETRIBERT_PRETRAINED_MODEL_ARCHIVE_LIST",
           "RetriBertModel",
           "RetriBertPreTrainedModel",
       ]


if TYPE_CHECKING:
       from .configuration_retribert import RETRIBERT_PRETRAINED_CONFIG_ARCHIVE_MAP, RetriBertConfig
       from .tokenization_retribert import RetriBertTokenizer

       try:
              if not is_tokenizers_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .tokenization_retribert_fast import RetriBertTokenizerFast

       try:
              if not is_torch_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .modeling_retribert import (
                  RETRIBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
                  RetriBertModel,
                  RetriBertPreTrainedModel,
              )

else:
       import sys

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

