


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

import torch

from ...models import AutoencoderKL, TransformeraDModel
from ...schedulers import KarrasDiffusionSchedulers
from ...utils import randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput






class _lowerCamelCase(						_a   ):
       def __init__(					self,      lowerCamelCase,      lowerCamelCase,      lowerCamelCase,      lowerCamelCase = None,      )       ->   Optional[int]:




           """simple docstring"""

           super().__init__()
           self.register_modules(transformer=__UpperCAmelCase,      vae=__UpperCAmelCase,      scheduler=__UpperCAmelCase)

           # create a imagenet -> id dictionary for easier use
           _lowercase       :							Optional[int]      		=		{}
           if idalabel is not None:
               for key, value in idalabel.items():
                   for label in value.split(','):
                       _lowercase       :							Dict      		=		int(__UpperCAmelCase)
               _lowercase       :							Any      		=		dict(sorted(self.labels.items()))
       def 			UpperCamelCase     (					self,      lowerCamelCase)       ->   str:




           """simple docstring"""

           if not isinstance(__UpperCAmelCase,      __UpperCAmelCase):
               _lowercase       :							Optional[int]      		=		list(__UpperCAmelCase)

           for l in label:
               if l not in self.labels:
                   raise ValueError(
                       F'''{l} does not exist. Please make sure to select one of the following labels: \n {self.labels}.''')

           return [self.labels[l] for l in label]




       @torch.no_grad()
       def __call__(					self,      lowerCamelCase,      lowerCamelCase = 4.0,      lowerCamelCase = None,      lowerCamelCase = 50,      lowerCamelCase = "pil",      lowerCamelCase = True,      )       ->   List[Any]:




           """simple docstring"""

           _lowercase       :							Optional[Any]      		=		len(__UpperCAmelCase)
           _lowercase       :							Tuple      		=		self.transformer.config.sample_size
           _lowercase       :							Union[str, Any]      		=		self.transformer.config.in_channels

           _lowercase       :							Any      		=		randn_tensor(
               shape=(batch_size, latent_channels, latent_size, latent_size),      generator=__UpperCAmelCase,      device=self.device,      dtype=self.transformer.dtype,      )
           _lowercase       :							Tuple      		=		torch.cat([latents] * 2) if guidance_scale > 1 else latents

           _lowercase       :							Tuple      		=		torch.tensor(__UpperCAmelCase,      device=self.device).reshape(-1)
           _lowercase       :							Optional[int]      		=		torch.tensor([10_00] * batch_size,      device=self.device)
           _lowercase       :							int      		=		torch.cat([class_labels, class_null],      0) if guidance_scale > 1 else class_labels

           # set step values
           self.scheduler.set_timesteps(__UpperCAmelCase)

           for t in self.progress_bar(self.scheduler.timesteps):
               if guidance_scale > 1:
                   _lowercase       :							Tuple      		=		latent_model_input[: len(__UpperCAmelCase) // 2]
                   _lowercase       :							List[str]      		=		torch.cat([half, half],      dim=0)
               _lowercase       :							List[str]      		=		self.scheduler.scale_model_input(__UpperCAmelCase,      __UpperCAmelCase)

               _lowercase       :							Tuple      		=		t
               if not torch.is_tensor(__UpperCAmelCase):
                   # TODO: this requires sync between CPU and GPU. So try to pass timesteps as tensors if you can
                   # This would be a good case for the `match` statement (Python 3.10+)
                   _lowercase       :							List[str]      		=		latent_model_input.device.type == 'mps'
                   if isinstance(__UpperCAmelCase,      __UpperCAmelCase):
                       _lowercase       :							List[str]      		=		torch.floataa if is_mps else torch.floataa
                   else:
                       _lowercase       :							List[str]      		=		torch.intaa if is_mps else torch.intaa
                   _lowercase       :							Optional[Any]      		=		torch.tensor([timesteps],      dtype=__UpperCAmelCase,      device=latent_model_input.device)
               elif len(timesteps.shape) == 0:
                   _lowercase       :							Optional[int]      		=		timesteps[None].to(latent_model_input.device)
               # broadcast to batch dimension in a way that's compatible with ONNX/Core ML
               _lowercase       :							Optional[int]      		=		timesteps.expand(latent_model_input.shape[0])
               # predict noise model_output
               _lowercase       :							Optional[int]      		=		self.transformer(
                   __UpperCAmelCase,      timestep=__UpperCAmelCase,      class_labels=__UpperCAmelCase).sample

               # perform guidance
               if guidance_scale > 1:
                   _lowercase       :							Any      		=		noise_pred[:, :latent_channels], noise_pred[:, latent_channels:]
                   _lowercase       :							Union[str, Any]      		=		torch.split(__UpperCAmelCase,      len(__UpperCAmelCase) // 2,      dim=0)

                   _lowercase       :							Any      		=		uncond_eps + guidance_scale * (cond_eps - uncond_eps)
                   _lowercase       :							int      		=		torch.cat([half_eps, half_eps],      dim=0)

                   _lowercase       :							Union[str, Any]      		=		torch.cat([eps, rest],      dim=1)

               # learned sigma
               if self.transformer.config.out_channels // 2 == latent_channels:
                   _lowercase       :							List[str]      		=		torch.split(__UpperCAmelCase,      __UpperCAmelCase,      dim=1)
               else:
                   _lowercase       :							str      		=		noise_pred

               # compute previous image: x_t -> x_t-1
               _lowercase       :							Dict      		=		self.scheduler.step(__UpperCAmelCase,      __UpperCAmelCase,      __UpperCAmelCase).prev_sample

           if guidance_scale > 1:
               _lowercase       :							str      		=		latent_model_input.chunk(2,      dim=0)
           else:
               _lowercase       :							Optional[int]      		=		latent_model_input

           _lowercase       :							Tuple      		=		1 / self.vae.config.scaling_factor * latents
           _lowercase       :							Dict      		=		self.vae.decode(__UpperCAmelCase).sample

           _lowercase       :							Union[str, Any]      		=		(samples / 2 + 0.5).clamp(0,      1)

           # we always cast to float32 as this does not cause significant overhead and is compatible with bfloat16
           _lowercase       :							List[str]      		=		samples.cpu().permute(0,      2,      3,      1).float().numpy()

           if output_type == "pil":
               _lowercase       :							int      		=		self.numpy_to_pil(__UpperCAmelCase)

           if not return_dict:
               return (samples,)

           return ImagePipelineOutput(images=__UpperCAmelCase)









"""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__)
