'''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,
						)


_lowerCamelCase							:						List[Any]							     =  logging.get_logger(__name__)


_lowerCamelCase							:						int							     =  Dict[str, Any]
_lowerCamelCase							:						List[str]							     =  List[Prediction]





@add_end_docstrings(UpperCAmelCase_							)
class 					lowerCamelCase_							(	UpperCAmelCase_							):




					'''simple docstring'''






					def __init__(   self,  *__UpperCamelCase,  **__UpperCamelCase			):
							super().__init__(*__UpperCamelCase,  **__UpperCamelCase			)

							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     __SCREAMING_SNAKE_CASE     (   self,  **__UpperCamelCase			):
							_snake_case     	=	{}
							_snake_case     	=	{}
							if "subtask" in kwargs:
									_snake_case     	=	kwargs["subtask"]
									_snake_case     	=	kwargs["subtask"]
							if "threshold" in kwargs:
									_snake_case     	=	kwargs["threshold"]
							if "mask_threshold" in kwargs:
									_snake_case     	=	kwargs["mask_threshold"]
							if "overlap_mask_area_threshold" in kwargs:
									_snake_case     	=	kwargs["overlap_mask_area_threshold"]

							return preprocess_kwargs, {}, postprocess_kwargs






					def __call__(   self,  __UpperCamelCase,  **__UpperCamelCase			):
							return super().__call__(__UpperCamelCase,  **__UpperCamelCase			)






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






					def     __SCREAMING_SNAKE_CASE     (   self,  __UpperCamelCase			):
							_snake_case     	=	model_inputs.pop("target_size"			)
							_snake_case     	=	self.model(**__UpperCamelCase			)
							_snake_case     	=	target_size
							return model_outputs






					def     __SCREAMING_SNAKE_CASE     (   self,  __UpperCamelCase,  __UpperCamelCase=None,  __UpperCamelCase=0.9,  __UpperCamelCase=0.5,  __UpperCamelCase=0.5			):
							_snake_case     	=	None
							if subtask in {"panoptic", None} and hasattr(self.image_processor,  "post_process_panoptic_segmentation"			):
									_snake_case     	=	self.image_processor.post_process_panoptic_segmentation
							elif subtask in {"instance", None} and hasattr(self.image_processor,  "post_process_instance_segmentation"			):
									_snake_case     	=	self.image_processor.post_process_instance_segmentation

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

									_snake_case     	=	[]
									_snake_case     	=	outputs["segmentation"]

									for segment in outputs["segments_info"]:
											_snake_case     	=	(segmentation == segment["id"]) * 255
											_snake_case     	=	Image.fromarray(mask.numpy().astype(np.uinta			),  mode="L"			)
											_snake_case     	=	self.model.config.idalabel[segment["label_id"]]
											_snake_case     	=	segment["score"]
											annotation.append({"score": score, "label": label, "mask": mask}			)

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

									_snake_case     	=	[]
									_snake_case     	=	outputs.numpy()
									_snake_case     	=	np.unique(__UpperCamelCase			)

									for label in labels:
											_snake_case     	=	(segmentation == label) * 255
											_snake_case     	=	Image.fromarray(mask.astype(np.uinta			),  mode="L"			)
											_snake_case     	=	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'''







import math





class 					lowerCamelCase_							:




					'''simple docstring'''






					def __init__(   self,  __UpperCamelCase,  __UpperCamelCase,  __UpperCamelCase			):
							_snake_case     	=	x
							_snake_case     	=	y
							_snake_case     	=	z






					def __repr__(   self			):
							return f'''Point({self.x}, {self.y}, {self.z})'''






def 		_lowercase		(     lowercase_						,							lowercase_  ):



		'''simple docstring'''







		return math.sqrt(abs((b.x - a.x) ** 2 + (b.y - a.y) ** 2 + (b.z - a.z) ** 2  )  )


if __name__ == "__main__":
						import doctest

						doctest.testmod()





