


'''simple docstring'''
from typing import Any, Dict, List, Union

import numpy as np

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


if is_vision_available():
							from PIL import Image

							from ..image_utils import load_image

if is_torch_available():
							from ..models.auto.modeling_auto import (
							    MODEL_FOR_IMAGE_SEGMENTATION_MAPPING,
							    MODEL_FOR_INSTANCE_SEGMENTATION_MAPPING,
							    MODEL_FOR_SEMANTIC_SEGMENTATION_MAPPING,
							    MODEL_FOR_UNIVERSAL_SEGMENTATION_MAPPING,
							)


__SCREAMING_SNAKE_CASE				:							Tuple		       =		logging.get_logger(__name__)


__SCREAMING_SNAKE_CASE				:							Dict		       =		Dict[str, Any]
__SCREAMING_SNAKE_CASE				:							List[Any]		       =		List[Prediction]






@add_end_docstrings(lowercase	)
class 				__lowerCAmelCase      (      lowercase	):

						"""simple docstring"""






						def __init__(     self       :			Dict   ,     *lowerCAmelCase       :			int   ,     **lowerCAmelCase       :			Union[str, Any]  ):
													super().__init__(*lowerCAmelCase   ,     **lowerCAmelCase  )

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

													requires_backends(self   ,     "vision"  )
													self.check_model_type(
													    dict(
													        MODEL_FOR_IMAGE_SEGMENTATION_MAPPING.items()
													        + MODEL_FOR_SEMANTIC_SEGMENTATION_MAPPING.items()
													        + MODEL_FOR_INSTANCE_SEGMENTATION_MAPPING.items()
													        + MODEL_FOR_UNIVERSAL_SEGMENTATION_MAPPING.items()  )  )






						def 							_UpperCAmelCase					(     self       :			List[Any]   ,     **lowerCAmelCase       :			Dict  ):
													A_ 						=		{}
													A_ 						=		{}
													if "subtask" in kwargs:
																				A_ 						=		kwargs["subtask"]
																				A_ 						=		kwargs["subtask"]
													if "threshold" in kwargs:
																				A_ 						=		kwargs["threshold"]
													if "mask_threshold" in kwargs:
																				A_ 						=		kwargs["mask_threshold"]
													if "overlap_mask_area_threshold" in kwargs:
																				A_ 						=		kwargs["overlap_mask_area_threshold"]

													return preprocess_kwargs, {}, postprocess_kwargs






						def __call__(     self       :			str   ,     lowerCAmelCase       :			List[Any]   ,     **lowerCAmelCase       :			Tuple  ):
													return super().__call__(lowerCAmelCase   ,     **lowerCAmelCase  )






						def 							_UpperCAmelCase					(     self       :			Dict   ,     lowerCAmelCase       :			int   ,     lowerCAmelCase       :			Tuple=None  ):
													A_ 						=		load_image(lowerCAmelCase  )
													A_ 						=		[(image.height, image.width)]
													if self.model.config.__class__.__name__ == "OneFormerConfig":
																				if subtask is None:
																											A_ 						=		{}
																				else:
																											A_ 						=		{"task_inputs": [subtask]}
																				A_ 						=		self.image_processor(images=[image]   ,     return_tensors="pt"   ,     **lowerCAmelCase  )
																				A_ 						=		self.tokenizer(
																				    inputs["task_inputs"]   ,     padding="max_length"   ,     max_length=self.model.config.task_seq_len   ,     return_tensors=self.framework   ,     )["input_ids"]
													else:
																				A_ 						=		self.image_processor(images=[image]   ,     return_tensors="pt"  )
													A_ 						=		target_size
													return inputs






						def 							_UpperCAmelCase					(     self       :			Optional[int]   ,     lowerCAmelCase       :			Optional[Any]  ):
													A_ 						=		model_inputs.pop("target_size"  )
													A_ 						=		self.model(**lowerCAmelCase  )
													A_ 						=		target_size
													return model_outputs






						def 							_UpperCAmelCase					(     self       :			Optional[Any]   ,     lowerCAmelCase       :			Dict   ,     lowerCAmelCase       :			Union[str, Any]=None   ,     lowerCAmelCase       :			Tuple=0.9   ,     lowerCAmelCase       :			Optional[int]=0.5   ,     lowerCAmelCase       :			Dict=0.5  ):
													A_ 						=		None
													if subtask in {"panoptic", None} and hasattr(self.image_processor   ,     "post_process_panoptic_segmentation"  ):
																				A_ 						=		self.image_processor.post_process_panoptic_segmentation
													elif subtask in {"instance", None} and hasattr(self.image_processor   ,     "post_process_instance_segmentation"  ):
																				A_ 						=		self.image_processor.post_process_instance_segmentation

													if fn is not None:
																				A_ 						=		fn(
																				    lowerCAmelCase   ,     threshold=lowerCAmelCase   ,     mask_threshold=lowerCAmelCase   ,     overlap_mask_area_threshold=lowerCAmelCase   ,     target_sizes=model_outputs["target_size"]   ,     )[0]

																				A_ 						=		[]
																				A_ 						=		outputs["segmentation"]

																				for segment in outputs["segments_info"]:
																											A_ 						=		(segmentation == segment["id"]) * 2_55
																											A_ 						=		Image.fromarray(mask.numpy().astype(np.uinta  )   ,     mode="L"  )
																											A_ 						=		self.model.config.idalabel[segment["label_id"]]
																											A_ 						=		segment["score"]
																											annotation.append({"score": score, "label": label, "mask": mask}  )

													elif subtask in {"semantic", None} and hasattr(self.image_processor   ,     "post_process_semantic_segmentation"  ):
																				A_ 						=		self.image_processor.post_process_semantic_segmentation(
																				    lowerCAmelCase   ,     target_sizes=model_outputs["target_size"]  )[0]

																				A_ 						=		[]
																				A_ 						=		outputs.numpy()
																				A_ 						=		np.unique(lowerCAmelCase  )

																				for label in labels:
																											A_ 						=		(segmentation == label) * 2_55
																											A_ 						=		Image.fromarray(mask.astype(np.uinta  )   ,     mode="L"  )
																											A_ 						=		self.model.config.idalabel[label]
																											annotation.append({"score": None, "label": label, "mask": mask}  )
													else:
																				raise ValueError(F"Subtask {subtask} is not supported for model {type(self.model  )}"  )
													return annotation









'''simple docstring'''
def 			a_							(  UpperCamelCase_   ):
							if not isinstance(UpperCamelCase_	,     UpperCamelCase_   ):
														A_ 						=		f"Input value of [number={number}] must be an integer"
														raise TypeError(UpperCamelCase_   )
							if number < 0 or number % 2 == 1:
														return False
							A_ 						=		int(number**0.5   )
							return number == number_sqrt * (number_sqrt + 1)


if __name__ == "__main__":
							import doctest

							doctest.testmod()





