




from typing import List, Optional, Tuple, Union

import torch

from ...models import UNetaDModel
from ...schedulers import ScoreSdeVeScheduler
from ...utils import randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput







class 		lowercase							(     UpperCamelCase__	):
     _a											=		4_2
     _a											=		4_2

     def __init__(						self	,  _a	,  _a       )		->							Any:
           super().__init__()
           self.register_modules(unet=_a	,  scheduler=_a       )



     @torch.no_grad()
     def __call__(						self	,  _a = 1	,  _a = 2000	,  _a = None	,  _a = "pil"	,  _a = True	,  **_a	,  )		->							Union[ImagePipelineOutput, Tuple]:
           _A							:  List[Any]    =		self.unet.config.sample_size
           _A							:  Any    =		(batch_size, 3, img_size, img_size)

           _A							:  Dict    =		self.unet

           _A							:  Any    =		randn_tensor(_a	,  generator=_a       ) * self.scheduler.init_noise_sigma
           _A							:  List[str]    =		sample.to(self.device       )

           self.scheduler.set_timesteps(_a       )
           self.scheduler.set_sigmas(_a       )

           for i, t in enumerate(self.progress_bar(self.scheduler.timesteps       )       ):
                 _A							:  int    =		self.scheduler.sigmas[i] * torch.ones(shape[0]	,  device=self.device       )

                 # correction step
                 for _ in range(self.scheduler.config.correct_steps       ):
                       _A							:  Tuple    =		self.unet(_a	,  _a       ).sample
                       _A							:  int    =		self.scheduler.step_correct(_a	,  _a	,  generator=_a       ).prev_sample

                 # prediction step
                 _A							:  Optional[int]    =		model(_a	,  _a       ).sample
                 _A							:  Optional[Any]    =		self.scheduler.step_pred(_a	,  _a	,  _a	,  generator=_a       )

                 _A							:  Tuple    =		output.prev_sample, output.prev_sample_mean

           _A							:  Tuple    =		sample_mean.clamp(0	,  1       )
           _A							:  Dict    =		sample.cpu().permute(0	,  2	,  3	,  1       ).numpy()
           if output_type == "pil":
                 _A							:  Any    =		self.numpy_to_pil(_a       )

           if not return_dict:
                 return (sample,)

           return ImagePipelineOutput(images=_a       )







from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_tf_available,
    is_torch_available,
    is_vision_available,
)


_snake_case            = {
    "configuration_efficientformer": [
        "EFFICIENTFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP",
        "EfficientFormerConfig",
    ]
}

try:
  if not is_vision_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  _snake_case            = ["EfficientFormerImageProcessor"]

try:
  if not is_torch_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  _snake_case            = [
      "EFFICIENTFORMER_PRETRAINED_MODEL_ARCHIVE_LIST",
      "EfficientFormerForImageClassification",
      "EfficientFormerForImageClassificationWithTeacher",
      "EfficientFormerModel",
      "EfficientFormerPreTrainedModel",
  ]

try:
  if not is_tf_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  _snake_case            = [
      "TF_EFFICIENTFORMER_PRETRAINED_MODEL_ARCHIVE_LIST",
      "TFEfficientFormerForImageClassification",
      "TFEfficientFormerForImageClassificationWithTeacher",
      "TFEfficientFormerModel",
      "TFEfficientFormerPreTrainedModel",
  ]

if TYPE_CHECKING:
  from .configuration_efficientformer import EFFICIENTFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP, EfficientFormerConfig

  try:
    if not is_vision_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .image_processing_efficientformer import EfficientFormerImageProcessor

  try:
    if not is_torch_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_efficientformer import (
        EFFICIENTFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
        EfficientFormerForImageClassification,
        EfficientFormerForImageClassificationWithTeacher,
        EfficientFormerModel,
        EfficientFormerPreTrainedModel,
    )
  try:
    if not is_tf_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_tf_efficientformer import (
        TF_EFFICIENTFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
        TFEfficientFormerForImageClassification,
        TFEfficientFormerForImageClassificationWithTeacher,
        TFEfficientFormerModel,
        TFEfficientFormerPreTrainedModel,
    )

else:
  import sys

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

