




from typing import List, Optional, Tuple, Union

import torch

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







class 		lowercase							(     UpperCamelCase__	):
     _a											=		42
     _a											=		42

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



     @torch.no_grad()
     def __call__(						self	,  _a = 1	,  _a = 50	,  _a = None	,  _a = "pil"	,  _a = True	,  **_a	,  )		->							Union[Tuple, ImagePipelineOutput]:
           _A							:  int    =		self.unet.config.sample_size
           _A							:  Any    =		(batch_size, 3, img_size, img_size)

           _A							:  Optional[Any]    =		self.unet

           # sample x_0 ~ N(0, sigma_0^2 * I)
           _A							:  int    =		randn_tensor(_a	,  generator=_a	,  device=self.device       ) * self.scheduler.init_noise_sigma

           self.scheduler.set_timesteps(_a       )

           for t in self.progress_bar(self.scheduler.timesteps       ):
                 # here sigma_t == t_i from the paper
                 _A							:  int    =		self.scheduler.schedule[t]
                 _A							:  int    =		self.scheduler.schedule[t - 1] if t > 0 else 0

                 # 1. Select temporarily increased noise level sigma_hat
                 # 2. Add new noise to move from sample_i to sample_hat
                 _A    ,     _A							:  Any    =		self.scheduler.add_noise_to_input(_a	,  _a	,  generator=_a       )

                 # 3. Predict the noise residual given the noise magnitude `sigma_hat`
                 # The model inputs and output are adjusted by following eq. (213) in [1].
                 _A							:  Tuple    =		(sigma_hat / 2) * model((sample_hat + 1) / 2	,  sigma_hat / 2       ).sample

                 # 4. Evaluate dx/dt at sigma_hat
                 # 5. Take Euler step from sigma to sigma_prev
                 _A							:  Tuple    =		self.scheduler.step(_a	,  _a	,  _a	,  _a       )

                 if sigma_prev != 0:
                       # 6. Apply 2nd order correction
                       # The model inputs and output are adjusted by following eq. (213) in [1].
                       _A							:  Dict    =		(sigma_prev / 2) * model((step_output.prev_sample + 1) / 2	,  sigma_prev / 2       ).sample
                       _A							:  str    =		self.scheduler.step_correct(
                           _a	,  _a	,  _a	,  _a	,  step_output.prev_sample	,  step_output["""derivative"""]	,  )
                 _A							:  str    =		step_output.prev_sample

           _A							:  Tuple    =		(sample / 2 + 0.5).clamp(0	,  1       )
           _A							:  Dict    =		sample.cpu().permute(0	,  2	,  3	,  1       ).numpy()
           if output_type == "pil":
                 _A							:  int    =		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_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__)

