





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      UpperCamelCase (     SCREAMING_SNAKE_CASE   ):

    def __init__(			self :      Union[str, Any]							,    snake_case__ :      TransformeraDModel							,    snake_case__ :      AutoencoderKL							,    snake_case__ :      KarrasDiffusionSchedulers							,    snake_case__ :      Optional[Dict[int, str]] = None							,    ):

     """simple docstring"""






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

     # create a imagenet -> id dictionary for easier use
     SCREAMING_SNAKE_CASE								=			{}
     if idalabel is not None:
      for key, value in idalabel.items():
       for label in value.split(','    ):
        SCREAMING_SNAKE_CASE								=			int(snake_case__    )
      SCREAMING_SNAKE_CASE								=			dict(sorted(self.labels.items()    )    )

    def UpperCamelCase			(			self :      Optional[int]							,    snake_case__ :      Union[str, List[str]]    ):

     """simple docstring"""






     if not isinstance(snake_case__							,    snake_case__    ):
      SCREAMING_SNAKE_CASE								=			list(snake_case__    )

     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 :      List[str]							,    snake_case__ :      List[int]							,    snake_case__ :      float = 4.0							,    snake_case__ :      Optional[Union[torch.Generator, List[torch.Generator]]] = None							,    snake_case__ :      int = 5_0							,    snake_case__ :      Optional[str] = "pil"							,    snake_case__ :      bool = True							,    ):

     """simple docstring"""






     SCREAMING_SNAKE_CASE								=			len(snake_case__    )
     SCREAMING_SNAKE_CASE								=			self.transformer.config.sample_size
     SCREAMING_SNAKE_CASE								=			self.transformer.config.in_channels

     SCREAMING_SNAKE_CASE								=			randn_tensor(
         shape=(batch_size, latent_channels, latent_size, latent_size)							,    generator=snake_case__							,    device=self.device							,    dtype=self.transformer.dtype							,    )
     SCREAMING_SNAKE_CASE								=			torch.cat([latents] * 2    ) if guidance_scale > 1 else latents

     SCREAMING_SNAKE_CASE								=			torch.tensor(snake_case__							,    device=self.device    ).reshape(-1    )
     SCREAMING_SNAKE_CASE								=			torch.tensor([1_0_0_0] * batch_size							,    device=self.device    )
     SCREAMING_SNAKE_CASE								=			torch.cat([class_labels, class_null]							,    0    ) if guidance_scale > 1 else class_labels

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

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

      SCREAMING_SNAKE_CASE								=			t
      if not torch.is_tensor(snake_case__    ):
       # 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+)
       SCREAMING_SNAKE_CASE								=			latent_model_input.device.type == 'mps'
       if isinstance(snake_case__							,    snake_case__    ):
        SCREAMING_SNAKE_CASE								=			torch.floataa if is_mps else torch.floataa
       else:
        SCREAMING_SNAKE_CASE								=			torch.intaa if is_mps else torch.intaa
       SCREAMING_SNAKE_CASE								=			torch.tensor([timesteps]							,    dtype=snake_case__							,    device=latent_model_input.device    )
      elif len(timesteps.shape    ) == 0:
       SCREAMING_SNAKE_CASE								=			timesteps[None].to(latent_model_input.device    )
      # broadcast to batch dimension in a way that's compatible with ONNX/Core ML
      SCREAMING_SNAKE_CASE								=			timesteps.expand(latent_model_input.shape[0]    )
      # predict noise model_output
      SCREAMING_SNAKE_CASE								=			self.transformer(
          snake_case__							,    timestep=snake_case__							,    class_labels=snake_case__    ).sample

      # perform guidance
      if guidance_scale > 1:
       SCREAMING_SNAKE_CASE						,	SCREAMING_SNAKE_CASE								=			noise_pred[:, :latent_channels], noise_pred[:, latent_channels:]
       SCREAMING_SNAKE_CASE						,	SCREAMING_SNAKE_CASE								=			torch.split(snake_case__							,    len(snake_case__    ) // 2							,    dim=0    )

       SCREAMING_SNAKE_CASE								=			uncond_eps + guidance_scale * (cond_eps - uncond_eps)
       SCREAMING_SNAKE_CASE								=			torch.cat([half_eps, half_eps]							,    dim=0    )

       SCREAMING_SNAKE_CASE								=			torch.cat([eps, rest]							,    dim=1    )

      # learned sigma
      if self.transformer.config.out_channels // 2 == latent_channels:
       SCREAMING_SNAKE_CASE						,	SCREAMING_SNAKE_CASE								=			torch.split(snake_case__							,    snake_case__							,    dim=1    )
      else:
       SCREAMING_SNAKE_CASE								=			noise_pred

      # compute previous image: x_t -> x_t-1
      SCREAMING_SNAKE_CASE								=			self.scheduler.step(snake_case__							,    snake_case__							,    snake_case__    ).prev_sample

     if guidance_scale > 1:
      SCREAMING_SNAKE_CASE						,	SCREAMING_SNAKE_CASE								=			latent_model_input.chunk(2							,    dim=0    )
     else:
      SCREAMING_SNAKE_CASE								=			latent_model_input

     SCREAMING_SNAKE_CASE								=			1 / self.vae.config.scaling_factor * latents
     SCREAMING_SNAKE_CASE								=			self.vae.decode(snake_case__    ).sample

     SCREAMING_SNAKE_CASE								=			(samples / 2 + 0.5).clamp(0							,    1    )

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

     if output_type == "pil":
      SCREAMING_SNAKE_CASE								=			self.numpy_to_pil(snake_case__    )

     if not return_dict:
      return (samples,)

     return ImagePipelineOutput(images=snake_case__    )




from typing import Any, Dict, List, Union

from ..utils import add_end_docstrings, is_torch_available, is_vision_available, logging, requires_backends
from .base import PIPELINE_INIT_ARGS, ChunkPipeline


if is_vision_available():
      from PIL import Image

      from ..image_utils import load_image

if is_torch_available():
      import torch

      from transformers.modeling_outputs import BaseModelOutput

      from ..models.auto.modeling_auto import MODEL_FOR_ZERO_SHOT_OBJECT_DETECTION_MAPPING

a_    :						Dict    		=      logging.get_logger(__name__)




@add_end_docstrings(SCREAMING_SNAKE_CASE   )
class      UpperCamelCase (     SCREAMING_SNAKE_CASE   ):

    def __init__(			self :      Any							,    **snake_case__ :      Optional[int]    ):

     """simple docstring"""






     super().__init__(**snake_case__    )

     if self.framework == "tf":
      raise ValueError(F"""The {self.__class__} is only available in PyTorch."""    )

     requires_backends(self							,    'vision'    )
     self.check_model_type(snake_case__    )

    def __call__(			self :      List[Any]							,    snake_case__ :      Union[str, "Image.Image", List[Dict[str, Any]]]							,    snake_case__ :      Union[str, List[str]] = None							,    **snake_case__ :      Union[str, Any]							,    ):

     """simple docstring"""






     if "text_queries" in kwargs:
      SCREAMING_SNAKE_CASE								=			kwargs.pop('text_queries'    )

     if isinstance(snake_case__							,    (str, Image.Image)    ):
      SCREAMING_SNAKE_CASE								=			{'image': image, 'candidate_labels': candidate_labels}
     else:
      SCREAMING_SNAKE_CASE								=			image
     SCREAMING_SNAKE_CASE								=			super().__call__(snake_case__							,    **snake_case__    )
     return results

    def UpperCamelCase			(			self :      Union[str, Any]							,    **snake_case__ :      Optional[Any]    ):

     """simple docstring"""






     SCREAMING_SNAKE_CASE								=			{}
     if "threshold" in kwargs:
      SCREAMING_SNAKE_CASE								=			kwargs['threshold']
     if "top_k" in kwargs:
      SCREAMING_SNAKE_CASE								=			kwargs['top_k']
     return {}, {}, postprocess_params

    def UpperCamelCase			(			self :      List[Any]							,    snake_case__ :      Optional[int]    ):

     """simple docstring"""






     SCREAMING_SNAKE_CASE								=			load_image(inputs['image']    )
     SCREAMING_SNAKE_CASE								=			inputs['candidate_labels']
     if isinstance(snake_case__							,    snake_case__    ):
      SCREAMING_SNAKE_CASE								=			candidate_labels.split(','    )

     SCREAMING_SNAKE_CASE								=			torch.tensor([[image.height, image.width]]							,    dtype=torch.intaa    )
     for i, candidate_label in enumerate(snake_case__    ):
      SCREAMING_SNAKE_CASE								=			self.tokenizer(snake_case__							,    return_tensors=self.framework    )
      SCREAMING_SNAKE_CASE								=			self.image_processor(snake_case__							,    return_tensors=self.framework    )
      yield {
          "is_last": i == len(snake_case__    ) - 1,
          "target_size": target_size,
          "candidate_label": candidate_label,
          **text_inputs,
          **image_features,
      }

    def UpperCamelCase			(			self :      Any							,    snake_case__ :      Dict    ):

     """simple docstring"""






     SCREAMING_SNAKE_CASE								=			model_inputs.pop('target_size'    )
     SCREAMING_SNAKE_CASE								=			model_inputs.pop('candidate_label'    )
     SCREAMING_SNAKE_CASE								=			model_inputs.pop('is_last'    )

     SCREAMING_SNAKE_CASE								=			self.model(**snake_case__    )

     SCREAMING_SNAKE_CASE								=			{'target_size': target_size, 'candidate_label': candidate_label, 'is_last': is_last, **outputs}
     return model_outputs

    def UpperCamelCase			(			self :      Union[str, Any]							,    snake_case__ :      Tuple							,    snake_case__ :      str=0.1							,    snake_case__ :      Union[str, Any]=None    ):

     """simple docstring"""






     SCREAMING_SNAKE_CASE								=			[]
     for model_output in model_outputs:
      SCREAMING_SNAKE_CASE								=			model_output['candidate_label']
      SCREAMING_SNAKE_CASE								=			BaseModelOutput(snake_case__    )
      SCREAMING_SNAKE_CASE								=			self.image_processor.post_process_object_detection(
          outputs=snake_case__							,    threshold=snake_case__							,    target_sizes=model_output['target_size']    )[0]

      for index in outputs["scores"].nonzero():
       SCREAMING_SNAKE_CASE								=			outputs['scores'][index].item()
       SCREAMING_SNAKE_CASE								=			self._get_bounding_box(outputs['boxes'][index][0]    )

       SCREAMING_SNAKE_CASE								=			{'score': score, 'label': label, 'box': box}
       results.append(snake_case__    )

     SCREAMING_SNAKE_CASE								=			sorted(snake_case__							,    key=lambda snake_case__    : x["score"]							,    reverse=snake_case__    )
     if top_k:
      SCREAMING_SNAKE_CASE								=			results[:top_k]

     return results




    def UpperCamelCase			(			self :      List[Any]							,    snake_case__ :      "torch.Tensor"    ):

     """simple docstring"""






     if self.framework != "pt":
      raise ValueError('The ZeroShotObjectDetectionPipeline is only available in PyTorch.'    )
     SCREAMING_SNAKE_CASE						,	SCREAMING_SNAKE_CASE						,	SCREAMING_SNAKE_CASE						,	SCREAMING_SNAKE_CASE								=			box.int().tolist()
     SCREAMING_SNAKE_CASE								=			{
         'xmin': xmin,
         'ymin': ymin,
         'xmax': xmax,
         'ymax': ymax,
     }
     return bbox

