"""simple docstring"""




import unittest

import numpy as np
import torch

from diffusers import KarrasVePipeline, KarrasVeScheduler, UNetaDModel
from diffusers.utils.testing_utils import enable_full_determinism, require_torch, slow, torch_device


enable_full_determinism()


class 						a__							(       unittest.TestCase		):



			@property
			def __SCREAMING_SNAKE_CASE							(						self   )			->   Optional[Any]:
								torch.manual_seed(0   )
								__a		 =       UNetaDModel(
								    block_out_channels=(3_2, 6_4)				,						layers_per_block=2				,						sample_size=3_2				,						in_channels=3				,						out_channels=3				,						down_block_types=('DownBlock2D', 'AttnDownBlock2D')				,						up_block_types=('AttnUpBlock2D', 'UpBlock2D')				,						)
								return model







			def __SCREAMING_SNAKE_CASE							(						self   )			->   Dict:
								__a		 =       self.dummy_uncond_unet
								__a		 =       KarrasVeScheduler()

								__a		 =       KarrasVePipeline(unet=_snake_case				,						scheduler=_snake_case   )
								pipe.to(_snake_case   )
								pipe.set_progress_bar_config(disable=_snake_case   )

								__a		 =       torch.manual_seed(0   )
								__a		 =       pipe(num_inference_steps=2				,						generator=_snake_case				,						output_type='numpy'   ).images

								__a		 =       torch.manual_seed(0   )
								__a		 =       pipe(num_inference_steps=2				,						generator=_snake_case				,						output_type='numpy'				,						return_dict=_snake_case   )[0]

								__a		 =       image[0, -3:, -3:, -1]
								__a		 =       image_from_tuple[0, -3:, -3:, -1]

								assert image.shape == (1, 3_2, 3_2, 3)
								__a		 =       np.array([0.0, 1.0, 0.0, 0.0, 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		):



			def __SCREAMING_SNAKE_CASE							(						self   )			->   Tuple:
								__a		 =       "google/ncsnpp-celebahq-256"
								__a		 =       UNetaDModel.from_pretrained(_snake_case   )
								__a		 =       KarrasVeScheduler()

								__a		 =       KarrasVePipeline(unet=_snake_case				,						scheduler=_snake_case   )
								pipe.to(_snake_case   )
								pipe.set_progress_bar_config(disable=_snake_case   )

								__a		 =       torch.manual_seed(0   )
								__a		 =       pipe(num_inference_steps=2_0				,						generator=_snake_case				,						output_type='numpy'   ).images

								__a		 =       image[0, -3:, -3:, -1]
								assert image.shape == (1, 2_5_6, 2_5_6, 3)
								__a		 =       np.array([0.578, 0.5_811, 0.5_924, 0.5_809, 0.587, 0.5_886, 0.5_861, 0.5_802, 0.586]   )

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





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

lowerCamelCase_    :		Dict							=	logging.get_logger(__name__)


@add_end_docstrings(__snake_case		)
class 						a__							(       __snake_case		):



			def __init__(						self				,						**UpperCAmelCase   )			->   List[str]:
								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				,						UpperCAmelCase				,						UpperCAmelCase = None				,						**UpperCAmelCase				,						)			->   Tuple:
								if "text_queries" in kwargs:
													__a		 =       kwargs.pop('text_queries'   )

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



			def __SCREAMING_SNAKE_CASE							(						self				,						**UpperCAmelCase   )			->   List[str]:
								__a		 =       {}
								if "threshold" in kwargs:
													__a		 =       kwargs['threshold']
								if "top_k" in kwargs:
													__a		 =       kwargs['top_k']
								return {}, {}, postprocess_params



			def __SCREAMING_SNAKE_CASE							(						self				,						UpperCAmelCase   )			->   Union[str, Any]:
								__a		 =       load_image(inputs['image']   )
								__a		 =       inputs['candidate_labels']
								if isinstance(UpperCAmelCase				,						UpperCAmelCase   ):
													__a		 =       candidate_labels.split(','   )

								__a		 =       torch.tensor([[image.height, image.width]]				,						dtype=torch.intaa   )
								for i, candidate_label in enumerate(UpperCAmelCase   ):
													__a		 =       self.tokenizer(UpperCAmelCase				,						return_tensors=self.framework   )
													__a		 =       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 __SCREAMING_SNAKE_CASE							(						self				,						UpperCAmelCase   )			->   str:
								__a		 =       model_inputs.pop('target_size'   )
								__a		 =       model_inputs.pop('candidate_label'   )
								__a		 =       model_inputs.pop('is_last'   )

								__a		 =       self.model(**UpperCAmelCase   )

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



			def __SCREAMING_SNAKE_CASE							(						self				,						UpperCAmelCase				,						UpperCAmelCase=0.1				,						UpperCAmelCase=None   )			->   Tuple:
								__a		 =       []
								for model_output in model_outputs:
													__a		 =       model_output['candidate_label']
													__a		 =       BaseModelOutput(UpperCAmelCase   )
													__a		 =       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		 =       outputs['scores'][index].item()
																		__a		 =       self._get_bounding_box(outputs['boxes'][index][0]   )

																		__a		 =       {'score': score, 'label': label, 'box': box}
																		results.append(UpperCAmelCase   )

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

								return results







			def __SCREAMING_SNAKE_CASE							(						self				,						UpperCAmelCase   )			->   Dict[str, int]:
								if self.framework != "pt":
													raise ValueError('The ZeroShotObjectDetectionPipeline is only available in PyTorch.'   )
								__a ,				__a ,				__a ,				__a		 =       box.int().tolist()
								__a		 =       {
								    'xmin': xmin,
								    'ymin': ymin,
								    'xmax': xmax,
								    'ymax': ymax,
								}
								return bbox




