




"""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       UpperCAmelCase__ (snake_case__							:      Dict					):
  """simple docstring"""

  _snake_case							:	Any			=						image.size
  _snake_case							:	Tuple			=						(x - x % 32 for x in (w, h))  # resize to integer multiple of 32
  _snake_case							:	int			=						image.resize((w, h)	,     resample=PIL_INTERPOLATION["""lanczos"""]					)
  _snake_case							:	Tuple			=						np.array(snake_case__					).astype(np.floataa					) / 2_55.0
  _snake_case							:	str			=						image[None].transpose(0	,     3	,     1	,     2					)
  _snake_case							:	str			=						torch.from_numpy(snake_case__					)
  return 2.0 * image - 1.0






class       lowercase(	__a				):







 '''simple docstring'''





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




   '''simple docstring'''



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


 @torch.no_grad()
 def __call__(       self:	Dict,   a_:	Union[torch.Tensor, PIL.Image.Image] = None,   a_:	Optional[int] = 1,   a_:	Optional[int] = 100,   a_:	Optional[float] = 0.0,   a_:	Optional[Union[torch.Generator, List[torch.Generator]]] = None,   a_:	Optional[str] = "pil",   a_:	bool = True,   ):




   '''simple docstring'''



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

   if isinstance(a_,   PIL.Image.Image	):
     _snake_case							:	Optional[int]			=						preprocess(a_	)

   _snake_case							:	Any			=						image.shape[-2:]

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

   _snake_case							:	int			=						randn_tensor(a_,   generator=a_,   device=self.device,   dtype=a_	)

   _snake_case							:	List[str]			=						image.to(device=self.device,   dtype=a_	)

   # set timesteps and move to the correct device
   self.scheduler.set_timesteps(a_,   device=self.device	)
   _snake_case							:	Optional[int]			=						self.scheduler.timesteps

   # scale the initial noise by the standard deviation required by the scheduler
   _snake_case							:	str			=						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]
   _snake_case							:	Tuple			=						"""eta""" in set(inspect.signature(self.scheduler.step	).parameters.keys()	)
   _snake_case							:	Optional[int]			=						{}
   if accepts_eta:
     _snake_case							:	Optional[Any]			=						eta

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

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

   if output_type == "pil":
     _snake_case							:	List[str]			=						self.numpy_to_pil(a_	)

   if not return_dict:
     return (image,)

   return ImagePipelineOutput(images=a_	)








"""simple docstring"""


from typing import TYPE_CHECKING

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


A_      			=       {
    '''configuration_owlvit''': [
        '''OWLVIT_PRETRAINED_CONFIG_ARCHIVE_MAP''',
        '''OwlViTConfig''',
        '''OwlViTOnnxConfig''',
        '''OwlViTTextConfig''',
        '''OwlViTVisionConfig''',
    ],
    '''processing_owlvit''': ['''OwlViTProcessor'''],
}


try:
     if not is_vision_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     A_      			=       ['''OwlViTFeatureExtractor''']
     A_      			=       ['''OwlViTImageProcessor''']

try:
     if not is_torch_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     A_      			=       [
         '''OWLVIT_PRETRAINED_MODEL_ARCHIVE_LIST''',
         '''OwlViTModel''',
         '''OwlViTPreTrainedModel''',
         '''OwlViTTextModel''',
         '''OwlViTVisionModel''',
         '''OwlViTForObjectDetection''',
     ]

if TYPE_CHECKING:
     from .configuration_owlvit import (
         OWLVIT_PRETRAINED_CONFIG_ARCHIVE_MAP,
         OwlViTConfig,
         OwlViTOnnxConfig,
         OwlViTTextConfig,
         OwlViTVisionConfig,
     )
     from .processing_owlvit import OwlViTProcessor

     try:
          if not is_vision_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .feature_extraction_owlvit import OwlViTFeatureExtractor
          from .image_processing_owlvit import OwlViTImageProcessor

     try:
          if not is_torch_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .modeling_owlvit import (
              OWLVIT_PRETRAINED_MODEL_ARCHIVE_LIST,
              OwlViTForObjectDetection,
              OwlViTModel,
              OwlViTPreTrainedModel,
              OwlViTTextModel,
              OwlViTVisionModel,
          )

else:
     import sys

     A_      			=       _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)


