

"""simple docstring"""


import inspect
from typing import List, Optional, Tuple, Union

import numpy as np
import PIL
import torch
import torch.utils.checkpoint

from ...models import UNetaDModel, VQModel
from ...schedulers import (
    DDIMScheduler,
    DPMSolverMultistepScheduler,
    EulerAncestralDiscreteScheduler,
    EulerDiscreteScheduler,
    LMSDiscreteScheduler,
    PNDMScheduler,
)
from ...utils import PIL_INTERPOLATION, randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput






def    a__   (   __SCREAMING_SNAKE_CASE			)     ->     str:
  __lowerCAmelCase				,			__lowerCAmelCase:	Optional[int]   				=      image.size
  __lowerCAmelCase				,			__lowerCAmelCase:	Union[str, Any]   				=      (x - x % 3_2 for x in (w, h))  # resize to integer multiple of 32
  __lowerCAmelCase:	Dict   				=      image.resize((w, h) ,			resample=PIL_INTERPOLATION["lanczos"]			)
  __lowerCAmelCase:	Union[str, Any]   				=      np.array(lowerCAmelCase__			).astype(np.floataa			) / 2_5_5.0
  __lowerCAmelCase:	Union[str, Any]   				=      image[None].transpose(0 ,			3 ,			1 ,			2			)
  __lowerCAmelCase:	Dict   				=      torch.from_numpy(lowerCAmelCase__			)
  return 2.0 * image - 1.0





class    snake_case     (    _SCREAMING_SNAKE_CASE  ):

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



      '''simple docstring'''


      super().__init__()
      self.register_modules(vqvae=UpperCamelCase__					,  unet=UpperCamelCase__					,  scheduler=UpperCamelCase__)




    @torch.no_grad()
    def __call__(     self							:    Any					,  UpperCamelCase__							:    List[str] = None					,  UpperCamelCase__							:    List[Any] = 1					,  UpperCamelCase__							:    Optional[Any] = 1_0_0					,  UpperCamelCase__							:    List[Any] = 0.0					,  UpperCamelCase__							:    int = None					,  UpperCamelCase__							:    List[Any] = "pil"					,  UpperCamelCase__							:    Any = True					,  )->       Union[Tuple, ImagePipelineOutput]:



      '''simple docstring'''


      if isinstance(UpperCamelCase__					,  PIL.Image.Image):
        __lowerCAmelCase:	Any   				=      1
      elif isinstance(UpperCamelCase__					,  torch.Tensor):
        __lowerCAmelCase:	str   				=      image.shape[0]
      else:
        raise ValueError(f"`image` has to be of type `PIL.Image.Image` or `torch.Tensor` but is {type(UpperCamelCase__)}")

      if isinstance(UpperCamelCase__					,  PIL.Image.Image):
        __lowerCAmelCase:	Dict   				=      preprocess(UpperCamelCase__)

      __lowerCAmelCase				,			__lowerCAmelCase:	Optional[Any]   				=      image.shape[-2:]

      # in_channels should be 6: 3 for latents, 3 for low resolution image
      __lowerCAmelCase:	Union[str, Any]   				=      (batch_size, self.unet.config.in_channels // 2, height, width)
      __lowerCAmelCase:	Any   				=      next(self.unet.parameters()).dtype

      __lowerCAmelCase:	Tuple   				=      randn_tensor(UpperCamelCase__					,  generator=UpperCamelCase__					,  device=self.device					,  dtype=UpperCamelCase__)

      __lowerCAmelCase:	Dict   				=      image.to(device=self.device					,  dtype=UpperCamelCase__)

      # set timesteps and move to the correct device
      self.scheduler.set_timesteps(UpperCamelCase__					,  device=self.device)
      __lowerCAmelCase:	Tuple   				=      self.scheduler.timesteps

      # scale the initial noise by the standard deviation required by the scheduler
      __lowerCAmelCase:	Any   				=      latents * self.scheduler.init_noise_sigma

      # prepare extra kwargs for the scheduler step, since not all schedulers have the same signature.
      # eta (η) is only used with the DDIMScheduler, it will be ignored for other schedulers.
      # eta corresponds to η in DDIM paper: https://arxiv.org/abs/2010.02502
      # and should be between [0, 1]
      __lowerCAmelCase:	Any   				=      "eta" in set(inspect.signature(self.scheduler.step).parameters.keys())
      __lowerCAmelCase:	str   				=      {}
      if accepts_eta:
        __lowerCAmelCase:	Any   				=      eta

      for t in self.progress_bar(UpperCamelCase__):
        # concat latents and low resolution image in the channel dimension.
        __lowerCAmelCase:	Optional[int]   				=      torch.cat([latents, image]					,  dim=1)
        __lowerCAmelCase:	List[str]   				=      self.scheduler.scale_model_input(UpperCamelCase__					,  UpperCamelCase__)
        # predict the noise residual
        __lowerCAmelCase:	List[str]   				=      self.unet(UpperCamelCase__					,  UpperCamelCase__).sample
        # compute the previous noisy sample x_t -> x_t-1
        __lowerCAmelCase:	Tuple   				=      self.scheduler.step(UpperCamelCase__					,  UpperCamelCase__					,  UpperCamelCase__					,  **UpperCamelCase__).prev_sample

      # decode the image latents with the VQVAE
      __lowerCAmelCase:	int   				=      self.vqvae.decode(UpperCamelCase__).sample
      __lowerCAmelCase:	Any   				=      torch.clamp(UpperCamelCase__					,  -1.0					,  1.0)
      __lowerCAmelCase:	Any   				=      image / 2 + 0.5
      __lowerCAmelCase:	List[Any]   				=      image.cpu().permute(0					,  2					,  3					,  1).numpy()

      if output_type == "pil":
        __lowerCAmelCase:	str   				=      self.numpy_to_pil(UpperCamelCase__)

      if not return_dict:
        return (image,)

      return ImagePipelineOutput(images=UpperCamelCase__)





"""simple docstring"""


from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


__A						       =      {
    "configuration_swinv2": ["SWINV2_PRETRAINED_CONFIG_ARCHIVE_MAP", "Swinv2Config"],
}


try:
			if not is_torch_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			__A						       =      [
			    "SWINV2_PRETRAINED_MODEL_ARCHIVE_LIST",
			    "Swinv2ForImageClassification",
			    "Swinv2ForMaskedImageModeling",
			    "Swinv2Model",
			    "Swinv2PreTrainedModel",
			]


if TYPE_CHECKING:
			from .configuration_swinva import SWINV2_PRETRAINED_CONFIG_ARCHIVE_MAP, SwinvaConfig

			try:
						if not is_torch_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .modeling_swinva import (
						    SWINV2_PRETRAINED_MODEL_ARCHIVE_LIST,
						    SwinvaForImageClassification,
						    SwinvaForMaskedImageModeling,
						    SwinvaModel,
						    SwinvaPreTrainedModel,
						)


else:
			import sys

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


