


"""simple docstring"""







import unittest

import numpy as np
import torch

from diffusers import PNDMPipeline, PNDMScheduler, UNetaDModel
from diffusers.utils.testing_utils import enable_full_determinism, require_torch, slow, torch_device


enable_full_determinism()






class 						_A  (  unittest.TestCase  ):







    """simple docstring"""




    @property
    def      __snake_case							( self    :   Optional[int]):
      torch.manual_seed(0)
      a						:		List[Any]											=      UNetaDModel(
          block_out_channels=(32, 64)    ,			layers_per_block=2    ,			sample_size=32    ,			in_channels=3    ,			out_channels=3    ,			down_block_types=("DownBlock2D", "AttnDownBlock2D")    ,			up_block_types=("AttnUpBlock2D", "UpBlock2D")    ,			)
      return model






    def      __snake_case							( self    :   Optional[Any]):
      a						:		Any											=      self.dummy_uncond_unet
      a						:		List[str]											=      PNDMScheduler()

      a						:		Dict											=      PNDMPipeline(unet=__UpperCAmelCase    ,			scheduler=__UpperCAmelCase)
      pndm.to(__UpperCAmelCase)
      pndm.set_progress_bar_config(disable=__UpperCAmelCase)

      a						:		int											=      torch.manual_seed(0)
      a						:		Dict											=      pndm(generator=__UpperCAmelCase    ,			num_inference_steps=20    ,			output_type="numpy").images

      a						:		int											=      torch.manual_seed(0)
      a						:		List[str]											=      pndm(generator=__UpperCAmelCase    ,			num_inference_steps=20    ,			output_type="numpy"    ,			return_dict=__UpperCAmelCase)[0]

      a						:		List[str]											=      image[0, -3:, -3:, -1]
      a						:		Optional[int]											=      image_from_tuple[0, -3:, -3:, -1]

      assert image.shape == (1, 32, 32, 3)
      a						:		str											=      np.array([1.0, 1.0, 0.0, 1.0, 0.0, 1.0, 0.0, 0.0, 0.0])

      assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-2
      assert np.abs(image_from_tuple_slice.flatten() - expected_slice).max() < 1e-2






@slow
@require_torch
class 						_A  (  unittest.TestCase  ):







    """simple docstring"""




    def      __snake_case							( self    :   Tuple):
      a						:		Union[str, Any]											=      "google/ddpm-cifar10-32"

      a						:		Tuple											=      UNetaDModel.from_pretrained(__UpperCAmelCase)
      a						:		List[str]											=      PNDMScheduler()

      a						:		int											=      PNDMPipeline(unet=__UpperCAmelCase    ,			scheduler=__UpperCAmelCase)
      pndm.to(__UpperCAmelCase)
      pndm.set_progress_bar_config(disable=__UpperCAmelCase)
      a						:		Optional[int]											=      torch.manual_seed(0)
      a						:		Optional[int]											=      pndm(generator=__UpperCAmelCase    ,			output_type="numpy").images

      a						:		int											=      image[0, -3:, -3:, -1]

      assert image.shape == (1, 32, 32, 3)
      a						:		Optional[Any]											=      np.array([0.1_564, 0.14_645, 0.1_406, 0.14_715, 0.12_425, 0.14_045, 0.13_115, 0.12_175, 0.125])

      assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-2










"""simple docstring"""







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

__lowercase    =				logging.get_logger(__name__)






@add_end_docstrings(_a  )
class 						_A  (  _a  ):







    """simple docstring"""




    def __init__( self    :   List[Any]    ,			**__UpperCAmelCase    :   List[Any]):
      super().__init__(**__UpperCAmelCase)

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

      requires_backends(self    ,			"vision")
      self.check_model_type(__UpperCAmelCase)






    def __call__( self    :   str    ,			__UpperCAmelCase    :   Union[str, "Image.Image", List[Dict[str, Any]]]    ,			__UpperCAmelCase    :   Union[str, List[str]] = None    ,			**__UpperCAmelCase    :   List[Any]    ,			):
      if "text_queries" in kwargs:
        a						:		List[Any]											=      kwargs.pop("text_queries")

      if isinstance(__UpperCAmelCase    ,			(str, Image.Image)):
        a						:		Any											=      {"image": image, "candidate_labels": candidate_labels}
      else:
        a						:		Optional[int]											=      image
      a						:		Optional[int]											=      super().__call__(__UpperCAmelCase    ,			**__UpperCAmelCase)
      return results






    def      __snake_case							( self    :   Optional[int]    ,			**__UpperCAmelCase    :   List[Any]):
      a						:		str											=      {}
      if "threshold" in kwargs:
        a						:		Dict											=      kwargs["threshold"]
      if "top_k" in kwargs:
        a						:		str											=      kwargs["top_k"]
      return {}, {}, postprocess_params






    def      __snake_case							( self    :   List[Any]    ,			__UpperCAmelCase    :   Optional[Any]):
      a						:		Union[str, Any]											=      load_image(inputs["image"])
      a						:		Any											=      inputs["candidate_labels"]
      if isinstance(__UpperCAmelCase    ,			__UpperCAmelCase):
        a						:		Optional[Any]											=      candidate_labels.split(",")

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






    def      __snake_case							( self    :   Dict    ,			__UpperCAmelCase    :   Optional[int]):
      a						:		List[Any]											=      model_inputs.pop("target_size")
      a						:		Optional[int]											=      model_inputs.pop("candidate_label")
      a						:		List[Any]											=      model_inputs.pop("is_last")

      a						:		List[Any]											=      self.model(**__UpperCAmelCase)

      a						:		Union[str, Any]											=      {"target_size": target_size, "candidate_label": candidate_label, "is_last": is_last, **outputs}
      return model_outputs






    def      __snake_case							( self    :   str    ,			__UpperCAmelCase    :   Dict    ,			__UpperCAmelCase    :   Optional[int]=0.1    ,			__UpperCAmelCase    :   List[str]=None):
      a						:		Dict											=      []
      for model_output in model_outputs:
        a						:		int											=      model_output["candidate_label"]
        a						:		Any											=      BaseModelOutput(__UpperCAmelCase)
        a						:		Optional[Any]											=      self.image_processor.post_process_object_detection(
            outputs=__UpperCAmelCase    ,			threshold=__UpperCAmelCase    ,			target_sizes=model_output["target_size"])[0]

        for index in outputs["scores"].nonzero():
          a						:		Any											=      outputs["scores"][index].item()
          a						:		str											=      self._get_bounding_box(outputs["boxes"][index][0])

          a						:		Optional[Any]											=      {"score": score, "label": label, "box": box}
          results.append(__UpperCAmelCase)

      a						:		str											=      sorted(__UpperCAmelCase    ,			key=lambda __UpperCAmelCase: x["score"]    ,			reverse=__UpperCAmelCase)
      if top_k:
        a						:		Union[str, Any]											=      results[:top_k]

      return results






    def      __snake_case							( self    :   Union[str, Any]    ,			__UpperCAmelCase    :   "torch.Tensor"):
      if self.framework != "pt":
        raise ValueError("The ZeroShotObjectDetectionPipeline is only available in PyTorch.")
      a							,		a							,		a							,		a						:		List[Any]											=      box.int().tolist()
      a						:		str											=      {
          "xmin": xmin,
          "ymin": ymin,
          "xmax": xmax,
          "ymax": ymax,
      }
      return bbox






