


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__       			=				logging.get_logger(__name__)


SCREAMING_SNAKE_CASE__       			=				Dict[str, Any]
SCREAMING_SNAKE_CASE__       			=				List[Prediction]







@add_end_docstrings(snake_case_     )
class     __lowerCamelCase     (  snake_case_     ):


							"""simple docstring"""

							def __init__(  self						,       *UpperCAmelCase						,       **UpperCAmelCase  )     ->       Optional[int]:






								'''simple docstring'''






								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 				A__      (  self						,       **UpperCAmelCase  )     ->       Optional[int]:






								'''simple docstring'''






								lowercase_			 = {}
								lowercase_			 = {}
								if "subtask" in kwargs:
									lowercase_			 = kwargs["subtask"]
									lowercase_			 = kwargs["subtask"]
								if "threshold" in kwargs:
									lowercase_			 = kwargs["threshold"]
								if "mask_threshold" in kwargs:
									lowercase_			 = kwargs["mask_threshold"]
								if "overlap_mask_area_threshold" in kwargs:
									lowercase_			 = kwargs["overlap_mask_area_threshold"]

								return preprocess_kwargs, {}, postprocess_kwargs




							def __call__(  self						,       UpperCAmelCase						,       **UpperCAmelCase  )     ->       Union[Predictions, List[Prediction]]:






								'''simple docstring'''






								return super().__call__(UpperCAmelCase						,       **UpperCAmelCase  )




							def 				A__      (  self						,       UpperCAmelCase						,       UpperCAmelCase=None  )     ->       Union[str, Any]:






								'''simple docstring'''






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




							def 				A__      (  self						,       UpperCAmelCase  )     ->       Union[str, Any]:






								'''simple docstring'''






								lowercase_			 = model_inputs.pop("target_size"  )
								lowercase_			 = self.model(**UpperCAmelCase  )
								lowercase_			 = target_size
								return model_outputs




							def 				A__      (  self						,       UpperCAmelCase						,       UpperCAmelCase=None						,       UpperCAmelCase=0.9						,       UpperCAmelCase=0.5						,       UpperCAmelCase=0.5  )     ->       Optional[int]:






								'''simple docstring'''






								lowercase_			 = None
								if subtask in {"panoptic", None} and hasattr(self.image_processor						,       "post_process_panoptic_segmentation"  ):
									lowercase_			 = self.image_processor.post_process_panoptic_segmentation
								elif subtask in {"instance", None} and hasattr(self.image_processor						,       "post_process_instance_segmentation"  ):
									lowercase_			 = self.image_processor.post_process_instance_segmentation

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

									lowercase_			 = []
									lowercase_			 = outputs["segmentation"]

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

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

									lowercase_			 = []
									lowercase_			 = outputs.numpy()
									lowercase_			 = np.unique(UpperCAmelCase  )

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





from __future__ import annotations

from statistics import mean




def SCREAMING_SNAKE_CASE_  (				__lowerCamelCase:		list[int]  ):







	'''simple docstring'''



	lowercase_			 = 2
	lowercase_			 = list(__lowerCamelCase  )
	lowercase_			 = [0] * len(__lowerCamelCase  )
	lowercase_			 = 0
	while True:
		lowercase_			 = True
		for i, burst_time in enumerate(__lowerCamelCase  ):
			if rem_burst_times[i] > 0:
				lowercase_			 = False
				if rem_burst_times[i] > quantum:
					t += quantum
					rem_burst_times[i] -= quantum
				else:
					t += rem_burst_times[i]
					lowercase_			 = t - burst_time
					lowercase_			 = 0
		if done is True:
			return waiting_times




def SCREAMING_SNAKE_CASE_  (				__lowerCamelCase:		list[int]  ,			__lowerCamelCase:		list[int]  ):







	'''simple docstring'''



	return [burst + waiting for burst, waiting in zip(__lowerCamelCase  ,			__lowerCamelCase  )]


if __name__ == "__main__":
							SCREAMING_SNAKE_CASE__       			=				[3, 5, 7]
							SCREAMING_SNAKE_CASE__       			=				calculate_waiting_times(burst_times)
							SCREAMING_SNAKE_CASE__       			=				calculate_turn_around_times(burst_times, waiting_times)
							print("""Process ID \tBurst Time \tWaiting Time \tTurnaround Time""")
							for i, burst_time in enumerate(burst_times):
														print(
														    f"""  {i + 1}\t\t  {burst_time}\t\t  {waiting_times[i]}\t\t  """
														    f"""{turn_around_times[i]}"""
														)
							print(f"""\nAverage waiting time = {mean(waiting_times):.5f}""")
							print(f"""Average turn around time = {mean(turn_around_times):.5f}""")

