


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   _UpperCAmelCase       (     UpperCAmelCase      :			List[str]      ):
      """simple docstring"""




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




class 							_UpperCamelCase (						A						):

   '''simple docstring'''







   def __init__(  self  : Union[str, Any]       ,  _lowerCamelCase  : VQModel       ,  _lowerCamelCase  : UNetaDModel       ,  _lowerCamelCase  : Union[
           DDIMScheduler,
           PNDMScheduler,
           LMSDiscreteScheduler,
           EulerDiscreteScheduler,
           EulerAncestralDiscreteScheduler,
           DPMSolverMultistepScheduler,
       ]       ,  ):






         '''simple docstring'''



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


   @torch.no_grad()
   def __call__(  self  : List[str]       ,  _lowerCamelCase  : Union[torch.Tensor, PIL.Image.Image] = None       ,  _lowerCamelCase  : Optional[int] = 1       ,  _lowerCamelCase  : Optional[int] = 1_0_0       ,  _lowerCamelCase  : Optional[float] = 0.0       ,  _lowerCamelCase  : Optional[Union[torch.Generator, List[torch.Generator]]] = None       ,  _lowerCamelCase  : Optional[str] = "pil"       ,  _lowerCamelCase  : bool = True       ,  ):






         '''simple docstring'''



         if isinstance(_lowerCamelCase       ,  PIL.Image.Image							):
               __lowerCamelCase     :      Tuple       						=						1
         elif isinstance(_lowerCamelCase       ,  torch.Tensor							):
               __lowerCamelCase     :      Optional[int]       						=						image.shape[0]
         else:
               raise ValueError(F"""`image` has to be of type `PIL.Image.Image` or `torch.Tensor` but is {type(_lowerCamelCase							)}"""							)

         if isinstance(_lowerCamelCase       ,  PIL.Image.Image							):
               __lowerCamelCase     :      Optional[Any]       						=						preprocess(_lowerCamelCase							)

         __lowerCamelCase						, __lowerCamelCase     :      Optional[int]       						=						image.shape[-2:]

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

         __lowerCamelCase     :      List[Any]       						=						randn_tensor(_lowerCamelCase       ,  generator=_lowerCamelCase       ,  device=self.device       ,  dtype=_lowerCamelCase							)

         __lowerCamelCase     :      str       						=						image.to(device=self.device       ,  dtype=_lowerCamelCase							)

         # set timesteps and move to the correct device
         self.scheduler.set_timesteps(_lowerCamelCase       ,  device=self.device							)
         __lowerCamelCase     :      Dict       						=						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     :      int       						=						{}
         if accepts_eta:
               __lowerCamelCase     :      Union[str, Any]       						=						eta

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

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

         if output_type == "pil":
               __lowerCamelCase     :      List[Any]       						=						self.numpy_to_pil(_lowerCamelCase							)

         if not return_dict:
               return (image,)

         return ImagePipelineOutput(images=_lowerCamelCase							)







from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


__UpperCamelCase    :						Any     =							{
    'configuration_timesformer': ['TIMESFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP', 'TimesformerConfig'],
}

try:
    if not is_torch_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    __UpperCamelCase    :						int     =							[
        'TIMESFORMER_PRETRAINED_MODEL_ARCHIVE_LIST',
        'TimesformerModel',
        'TimesformerForVideoClassification',
        'TimesformerPreTrainedModel',
    ]

if TYPE_CHECKING:
    from .configuration_timesformer import TIMESFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP, TimesformerConfig

    try:
        if not is_torch_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .modeling_timesformer import (
            TIMESFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
            TimesformerForVideoClassification,
            TimesformerModel,
            TimesformerPreTrainedModel,
        )

else:
    import sys

    __UpperCamelCase    :						List[Any]     =							_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)



