

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							:  int     =				logging.get_logger(__name__)


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





@add_end_docstrings(snake_case_		)
class    __UpperCAmelCase	(			snake_case_		):



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

     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 			__magic_name__       (				self				:	int,						**__A				:	Any		):
     UpperCAmelCase			:				int										=					{}
     UpperCAmelCase			:				Optional[int]										=					{}
     if "subtask" in kwargs:
       UpperCAmelCase			:				Dict										=					kwargs['''subtask''']
       UpperCAmelCase			:				Tuple										=					kwargs['''subtask''']
     if "threshold" in kwargs:
       UpperCAmelCase			:				Optional[int]										=					kwargs['''threshold''']
     if "mask_threshold" in kwargs:
       UpperCAmelCase			:				Any										=					kwargs['''mask_threshold''']
     if "overlap_mask_area_threshold" in kwargs:
       UpperCAmelCase			:				Tuple										=					kwargs['''overlap_mask_area_threshold''']

     return preprocess_kwargs, {}, postprocess_kwargs



   def __call__(				self				:	List[Any],						__A				:	List[Any],						**__A				:	Optional[Any]		):
     return super().__call__(__A,						**__A		)



   def 			__magic_name__       (				self				:	List[Any],						__A				:	Optional[Any],						__A				:	str=None		):
     UpperCAmelCase			:				Optional[Any]										=					load_image(__A		)
     UpperCAmelCase			:				Union[str, Any]										=					[(image.height, image.width)]
     if self.model.config.__class__.__name__ == "OneFormerConfig":
       if subtask is None:
         UpperCAmelCase			:				Dict										=					{}
       else:
         UpperCAmelCase			:				List[Any]										=					{'''task_inputs''': [subtask]}
       UpperCAmelCase			:				List[Any]										=					self.image_processor(images=[image],						return_tensors='''pt''',						**__A		)
       UpperCAmelCase			:				str										=					self.tokenizer(
           inputs['''task_inputs'''],						padding='''max_length''',						max_length=self.model.config.task_seq_len,						return_tensors=self.framework,						)['''input_ids''']
     else:
       UpperCAmelCase			:				List[Any]										=					self.image_processor(images=[image],						return_tensors='''pt'''		)
     UpperCAmelCase			:				Tuple										=					target_size
     return inputs



   def 			__magic_name__       (				self				:	List[Any],						__A				:	Tuple		):
     UpperCAmelCase			:				List[Any]										=					model_inputs.pop('''target_size'''		)
     UpperCAmelCase			:				int										=					self.model(**__A		)
     UpperCAmelCase			:				List[str]										=					target_size
     return model_outputs





   def 			__magic_name__       (				self				:	Union[str, Any],						__A				:	Optional[int],						__A				:	List[Any]=None,						__A				:	List[str]=0.9,						__A				:	Union[str, Any]=0.5,						__A				:	List[Any]=0.5		):
     UpperCAmelCase			:				List[str]										=					None
     if subtask in {"panoptic", None} and hasattr(self.image_processor,						'''post_process_panoptic_segmentation'''		):
       UpperCAmelCase			:				Any										=					self.image_processor.post_process_panoptic_segmentation
     elif subtask in {"instance", None} and hasattr(self.image_processor,						'''post_process_instance_segmentation'''		):
       UpperCAmelCase			:				Optional[Any]										=					self.image_processor.post_process_instance_segmentation

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

       UpperCAmelCase			:				List[Any]										=					[]
       UpperCAmelCase			:				Optional[int]										=					outputs['''segmentation''']

       for segment in outputs["segments_info"]:
         UpperCAmelCase			:				Dict										=					(segmentation == segment['''id''']) * 2_5_5
         UpperCAmelCase			:				Optional[int]										=					Image.fromarray(mask.numpy().astype(np.uinta		),						mode='''L'''		)
         UpperCAmelCase			:				Tuple										=					self.model.config.idalabel[segment['''label_id''']]
         UpperCAmelCase			:				Optional[Any]										=					segment['''score''']
         annotation.append({'''score''': score, '''label''': label, '''mask''': mask}		)

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

       UpperCAmelCase			:				Union[str, Any]										=					[]
       UpperCAmelCase			:				List[str]										=					outputs.numpy()
       UpperCAmelCase			:				Optional[Any]										=					np.unique(__A		)

       for label in labels:
         UpperCAmelCase			:				int										=					(segmentation == label) * 2_5_5
         UpperCAmelCase			:				Dict										=					Image.fromarray(mask.astype(np.uinta		),						mode='''L'''		)
         UpperCAmelCase			:				Dict										=					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}""")

