from typing import List, Optional, Tuple, Union

import torch

from ...utils import randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput



class 					snake_case__       (				_UpperCamelCase							):



   def __init__(				self   :   Union[str, Any]			,	A__   :   Any			,	A__   :   str   )							->     Tuple:




         '''simple docstring'''




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



   @torch.no_grad()
   def __call__(				self   :   Optional[int]			,	A__   :   int = 1			,	A__   :   Optional[Union[torch.Generator, List[torch.Generator]]] = None			,	A__   :   int = 10_00			,	A__   :   Optional[str] = "pil"			,	A__   :   bool = True			,	)							->     Union[ImagePipelineOutput, Tuple]:




         '''simple docstring'''




         if isinstance(self.unet.config.sample_size			,	A__   ):
               snake_case_     :			List[str]       =       (
                   batch_size,
                   self.unet.config.in_channels,
                   self.unet.config.sample_size,
                   self.unet.config.sample_size,
               )
         else:
               snake_case_     :			Dict       =       (batch_size, self.unet.config.in_channels, *self.unet.config.sample_size)

         if self.device.type == "mps":
               # randn does not work reproducibly on mps
               snake_case_     :			List[str]       =       randn_tensor(A__			,	generator=A__   )
               snake_case_     :			Dict       =       image.to(self.device   )
         else:
               snake_case_     :			Optional[int]       =       randn_tensor(A__			,	generator=A__			,	device=self.device   )

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

         for t in self.progress_bar(self.scheduler.timesteps   ):
               # 1. predict noise model_output
               snake_case_     :			Union[str, Any]       =       self.unet(A__			,	A__   ).sample

               # 2. compute previous image: x_t -> x_t-1
               snake_case_     :			List[str]       =       self.scheduler.step(A__			,	A__			,	A__			,	generator=A__   ).prev_sample

         snake_case_     :			Optional[int]       =       (image / 2 + 0.5).clamp(0			,	1   )
         snake_case_     :			List[Any]       =       image.cpu().permute(0			,	2			,	3			,	1   ).numpy()
         if output_type == "pil":
               snake_case_     :			Dict       =       self.numpy_to_pil(A__   )

         if not return_dict:
               return (image,)

         return ImagePipelineOutput(images=A__   )




from typing import TYPE_CHECKING

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


UpperCAmelCase							=       {
    "configuration_nystromformer": ["NYSTROMFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP", "NystromformerConfig"],
}

try:
     if not is_torch_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     UpperCAmelCase							=       [
         "NYSTROMFORMER_PRETRAINED_MODEL_ARCHIVE_LIST",
         "NystromformerForMaskedLM",
         "NystromformerForMultipleChoice",
         "NystromformerForQuestionAnswering",
         "NystromformerForSequenceClassification",
         "NystromformerForTokenClassification",
         "NystromformerLayer",
         "NystromformerModel",
         "NystromformerPreTrainedModel",
     ]


if TYPE_CHECKING:
     from .configuration_nystromformer import NYSTROMFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP, NystromformerConfig

     try:
          if not is_torch_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .modeling_nystromformer import (
              NYSTROMFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
              NystromformerForMaskedLM,
              NystromformerForMultipleChoice,
              NystromformerForQuestionAnswering,
              NystromformerForSequenceClassification,
              NystromformerForTokenClassification,
              NystromformerLayer,
              NystromformerModel,
              NystromformerPreTrainedModel,
          )


else:
     import sys

     UpperCAmelCase							=       _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)



