



'''simple docstring'''






from typing import List, Optional, Tuple, Union

import PIL
import torch
from torchvision import transforms

from diffusers.pipeline_utils import DiffusionPipeline, ImagePipelineOutput
from diffusers.schedulers import DDIMScheduler
from diffusers.utils import randn_tensor


lowerCAmelCase: Optional[Any]							=					transforms.Compose(
    [
        transforms.Resize((2_5_6, 2_5_6)),
        transforms.ToTensor(),
        transforms.Normalize([0.5], [0.5]),
    ]
)
def 				lowerCamelCase__	(						_A    ):
  if isinstance(_A			,      torch.Tensor    ):
    return image
  elif isinstance(_A			,      PIL.Image.Image    ):
    a						: str					       =					[image]

  a						: Dict					       =					[trans(img.convert('RGB'    )    ) for img in image]
  a						: Optional[int]					       =					torch.stack(_A    )
  return image

class 							a__( lowerCamelCase__     ):







    def __init__(				self			:  List[Any]						,      __snake_case			:  Optional[Any]						,      __snake_case			:  str     ):
      super().__init__()

      # make sure scheduler can always be converted to DDIM
      a						: str					       =					DDIMScheduler.from_config(scheduler.config     )

      self.register_modules(unet=__snake_case						,      scheduler=__snake_case     )







    def       lowercase_	(				self			:  Union[str, Any]						,      __snake_case			:  Union[str, Any]     ):
      if strength < 0 or strength > 1:
        raise ValueError(F"""The value of strength should in [0.0, 1.0] but is {strength}"""     )







    def       lowercase_	(				self			:  Optional[Any]						,      __snake_case			:  Dict						,      __snake_case			:  Any						,      __snake_case			:  List[str]     ):
      # get the original timestep using init_timestep
      a						: Union[str, Any]					       =					min(int(num_inference_steps * strength     )						,      __snake_case     )

      a						: str					       =					max(num_inference_steps - init_timestep						,      0     )
      a						: int					       =					self.scheduler.timesteps[t_start:]

      return timesteps, num_inference_steps - t_start







    def       lowercase_	(				self			:  Union[str, Any]						,      __snake_case			:  str						,      __snake_case			:  str						,      __snake_case			:  int						,      __snake_case			:  Tuple						,      __snake_case			:  Dict						,      __snake_case			:  Dict=None     ):
      if not isinstance(__snake_case						,      (torch.Tensor, PIL.Image.Image, list)     ):
        raise ValueError(
            F"""`image` has to be of type `torch.Tensor`, `PIL.Image.Image` or list but is {type(__snake_case     )}"""     )

      a						: Union[str, Any]					       =					image.to(device=__snake_case						,      dtype=__snake_case     )

      if isinstance(__snake_case						,      __snake_case     ) and len(__snake_case     ) != batch_size:
        raise ValueError(
            F"""You have passed a list of generators of length {len(__snake_case     )}, but requested an effective batch"""
            F""" size of {batch_size}. Make sure the batch size matches the length of the generators."""     )

      a						: List[Any]					       =					init_latents.shape
      a						: str					       =					randn_tensor(__snake_case						,      generator=__snake_case						,      device=__snake_case						,      dtype=__snake_case     )

      # get latents
      print('add noise to latents at timestep'						,      __snake_case     )
      a						: List[Any]					       =					self.scheduler.add_noise(__snake_case						,      __snake_case						,      __snake_case     )
      a						: Dict					       =					init_latents

      return latents







    @torch.no_grad()
    def __call__(				self			:  Any						,      __snake_case			:  Union[torch.FloatTensor, PIL.Image.Image] = None						,      __snake_case			:  float = 0.8						,      __snake_case			:  int = 1						,      __snake_case			:  Optional[Union[torch.Generator, List[torch.Generator]]] = None						,      __snake_case			:  float = 0.0						,      __snake_case			:  int = 50						,      __snake_case			:  Optional[bool] = None						,      __snake_case			:  Optional[str] = "pil"						,      __snake_case			:  bool = True						,      ):
      self.check_inputs(__snake_case     )

      # 2. Preprocess image
      a						: Tuple					       =					preprocess(__snake_case     )

      # 3. set timesteps
      self.scheduler.set_timesteps(__snake_case						,      device=self.device     )
      a						: Optional[Any]					       =					self.get_timesteps(__snake_case						,      __snake_case						,      self.device     )
      a						: List[str]					       =					timesteps[:1].repeat(__snake_case     )

      # 4. Prepare latent variables
      a						: List[str]					       =					self.prepare_latents(__snake_case						,      __snake_case						,      __snake_case						,      self.unet.dtype						,      self.device						,      __snake_case     )
      a						: Optional[Any]					       =					latents

      # 5. Denoising loop
      for t in self.progress_bar(__snake_case     ):
        # 1. predict noise model_output
        a						: List[Any]					       =					self.unet(__snake_case						,      __snake_case     ).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
        a						: Optional[int]					       =					self.scheduler.step(
            __snake_case						,      __snake_case						,      __snake_case						,      eta=__snake_case						,      use_clipped_model_output=__snake_case						,      generator=__snake_case						,      ).prev_sample

      a						: Optional[Any]					       =					(image / 2 + 0.5).clamp(0						,      1     )
      a						: Union[str, Any]					       =					image.cpu().permute(0						,      2						,      3						,      1     ).numpy()
      if output_type == "pil":
        a						: List[str]					       =					self.numpy_to_pil(__snake_case     )

      if not return_dict:
        return (image, latent_timestep.item())

      return ImagePipelineOutput(images=__snake_case     )




'''simple docstring'''






from typing import TYPE_CHECKING

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


lowerCAmelCase: Any							=						{
    'configuration_poolformer': [
        'POOLFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP',
        'PoolFormerConfig',
        'PoolFormerOnnxConfig',
    ]
}

try:
     if not is_vision_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     lowerCAmelCase: List[str]							=						['PoolFormerFeatureExtractor']
     lowerCAmelCase: Tuple							=						['PoolFormerImageProcessor']

try:
     if not is_torch_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     lowerCAmelCase: str							=						[
         'POOLFORMER_PRETRAINED_MODEL_ARCHIVE_LIST',
         'PoolFormerForImageClassification',
         'PoolFormerModel',
         'PoolFormerPreTrainedModel',
     ]


if TYPE_CHECKING:
     from .configuration_poolformer import (
         POOLFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP,
         PoolFormerConfig,
         PoolFormerOnnxConfig,
     )

     try:
          if not is_vision_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .feature_extraction_poolformer import PoolFormerFeatureExtractor
          from .image_processing_poolformer import PoolFormerImageProcessor

     try:
          if not is_torch_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .modeling_poolformer import (
              POOLFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
              PoolFormerForImageClassification,
              PoolFormerModel,
              PoolFormerPreTrainedModel,
          )


else:
     import sys

     lowerCAmelCase: Dict							=						_LazyModule(__name__, globals()['__file__'], _import_structure)