



from __future__ import annotations




def   lowerCamelCase_       (					UpperCAmelCase__      ):



						"""simple docstring"""




						a_					=		[0] * len(snake_case_      )
						for i in range(1       ,    len(snake_case_      )      ):
												a_					=		duration_times[i - 1] + waiting_times[i - 1]
						return waiting_times




def   lowerCamelCase_       (					UpperCAmelCase__       ,    UpperCAmelCase__      ):



						"""simple docstring"""




						return [
						    duration_time + waiting_times[i]
						    for i, duration_time in enumerate(snake_case_      )
						]




def   lowerCamelCase_       (					UpperCAmelCase__      ):



						"""simple docstring"""




						return sum(snake_case_      ) / len(snake_case_      )




def   lowerCamelCase_       (					UpperCAmelCase__      ):



						"""simple docstring"""




						return sum(snake_case_      ) / len(snake_case_      )


if __name__ == "__main__":
	# process id's
	A_    :					List[str]             =[1, 2, 3]

	# ensure that we actually have processes
	if len(processes) == 0:
		print("""Zero amount of processes""")
		raise SystemExit(0)

	# duration time of all processes
	A_    :					Dict             =[19, 8, 9]

	# ensure we can match each id to a duration time
	if len(duration_times) != len(processes):
		print("""Unable to match all id's with their duration time""")
		raise SystemExit(0)

	# get the waiting times and the turnaround times
	A_    :					int             =calculate_waiting_times(duration_times)
	A_    :					Optional[int]             =calculate_turnaround_times(duration_times, waiting_times)

	# get the average times
	A_    :					List[str]             =calculate_average_waiting_time(waiting_times)
	A_    :					Dict             =calculate_average_turnaround_time(turnaround_times)

	# print all the results
	print("""Process ID\tDuration Time\tWaiting Time\tTurnaround Time""")
	for i, process in enumerate(processes):
		print(
		    F'''{process}\t\t{duration_times[i]}\t\t{waiting_times[i]}\t\t'''
		    F'''{turnaround_times[i]}'''
		)
	print(F'''Average waiting time = {average_waiting_time}''')
	print(F'''Average turn around time = {average_turnaround_time}''')



from typing import List

from ...processing_utils import ProcessorMixin
from ...utils import is_torch_available


if is_torch_available():
	import torch





class        lowercase_				( UpperCamelCase__):






							"""simple docstring"""







							snake_case_        =		['''image_processor''', '''tokenizer''']
							snake_case_        =		'''OneFormerImageProcessor'''
							snake_case_        =		('''CLIPTokenizer''', '''CLIPTokenizerFast''')


							def __init__(   self			,    _UpperCAmelCase=None			,    _UpperCAmelCase=None			,    _UpperCAmelCase = 77			,    _UpperCAmelCase = 77			,    **_UpperCAmelCase		):







													"""simple docstring"""


													if image_processor is None:
																			raise ValueError("""You need to specify an `image_processor`."""		)
													if tokenizer is None:
																			raise ValueError("""You need to specify a `tokenizer`."""		)

													a_					=		max_seq_length
													a_					=		task_seq_length

													super().__init__(_UpperCAmelCase			,    _UpperCAmelCase		)


							def 						lowercase__     (   self			,    _UpperCAmelCase=None			,    _UpperCAmelCase=77		):







													"""simple docstring"""


													if text_list is None:
																			raise ValueError("""tokens cannot be None."""		)

													a_					=		self.tokenizer(_UpperCAmelCase			,    padding="""max_length"""			,    max_length=_UpperCAmelCase			,    truncation=_UpperCAmelCase		)

													a_			,   a_					=		tokens["""attention_mask"""], tokens["""input_ids"""]

													a_					=		[]
													for attn_mask, input_id in zip(_UpperCAmelCase			,    _UpperCAmelCase		):
																			a_					=		torch.tensor(_UpperCAmelCase		) * torch.tensor(_UpperCAmelCase		)
																			token_inputs.append(token.unsqueeze(0		)		)

													a_					=		torch.cat(_UpperCAmelCase			,    dim=0		)
													return token_inputs


							def __call__(   self			,    _UpperCAmelCase=None			,    _UpperCAmelCase=None			,    _UpperCAmelCase=None			,    **_UpperCAmelCase		):







													"""simple docstring"""


													if task_inputs is None:
																			raise ValueError("""You have to specify the task_input. Found None."""		)
													elif images is None:
																			raise ValueError("""You have to specify the image. Found None."""		)

													if not all(task in ["""semantic""", """instance""", """panoptic"""] for task in task_inputs		):
																			raise ValueError("""task_inputs must be semantic, instance, or panoptic."""		)

													a_					=		self.image_processor(_UpperCAmelCase			,    _UpperCAmelCase			,    _UpperCAmelCase			,    **_UpperCAmelCase		)

													if isinstance(_UpperCAmelCase			,    _UpperCAmelCase		):
																			a_					=		[task_inputs]

													if isinstance(_UpperCAmelCase			,    _UpperCAmelCase		) and all(isinstance(_UpperCAmelCase			,    _UpperCAmelCase		) for task_input in task_inputs		):
																			a_					=		[]
																			for task in task_inputs:
																									a_					=		f"the task is {task}"
																									task_token_inputs.append(_UpperCAmelCase		)
																			a_					=		self._preprocess_text(_UpperCAmelCase			,    max_length=self.task_seq_length		)
													else:
																			raise TypeError("""Task Inputs should be a string or a list of strings."""		)

													if hasattr(_UpperCAmelCase			,    """text_inputs"""		):
																			a_					=		encoded_inputs.text_inputs

																			a_					=		[]
																			for texts in texts_list:
																									a_					=		self._preprocess_text(_UpperCAmelCase			,    max_length=self.max_seq_length		)
																									text_inputs.append(text_input_list.unsqueeze(0		)		)

																			a_					=		torch.cat(_UpperCAmelCase			,    dim=0		)

													return encoded_inputs


							def 						lowercase__     (   self			,    _UpperCAmelCase=None			,    _UpperCAmelCase=None			,    _UpperCAmelCase=None			,    **_UpperCAmelCase		):







													"""simple docstring"""


													if task_inputs is None:
																			raise ValueError("""You have to specify the task_input. Found None."""		)
													elif images is None:
																			raise ValueError("""You have to specify the image. Found None."""		)

													if not all(task in ["""semantic""", """instance""", """panoptic"""] for task in task_inputs		):
																			raise ValueError("""task_inputs must be semantic, instance, or panoptic."""		)

													a_					=		self.image_processor.encode_inputs(_UpperCAmelCase			,    _UpperCAmelCase			,    _UpperCAmelCase			,    **_UpperCAmelCase		)

													if isinstance(_UpperCAmelCase			,    _UpperCAmelCase		):
																			a_					=		[task_inputs]

													if isinstance(_UpperCAmelCase			,    _UpperCAmelCase		) and all(isinstance(_UpperCAmelCase			,    _UpperCAmelCase		) for task_input in task_inputs		):
																			a_					=		[]
																			for task in task_inputs:
																									a_					=		f"the task is {task}"
																									task_token_inputs.append(_UpperCAmelCase		)
																			a_					=		self._preprocess_text(_UpperCAmelCase			,    max_length=self.task_seq_length		)
													else:
																			raise TypeError("""Task Inputs should be a string or a list of strings."""		)

													if hasattr(_UpperCAmelCase			,    """text_inputs"""		):
																			a_					=		encoded_inputs.text_inputs

																			a_					=		[]
																			for texts in texts_list:
																									a_					=		self._preprocess_text(_UpperCAmelCase			,    max_length=self.max_seq_length		)
																									text_inputs.append(text_input_list.unsqueeze(0		)		)

																			a_					=		torch.cat(_UpperCAmelCase			,    dim=0		)

													return encoded_inputs


							def 						lowercase__     (   self			,    *_UpperCAmelCase			,    **_UpperCAmelCase		):







													"""simple docstring"""


													return self.image_processor.post_process_semantic_segmentation(*_UpperCAmelCase			,    **_UpperCAmelCase		)


							def 						lowercase__     (   self			,    *_UpperCAmelCase			,    **_UpperCAmelCase		):







													"""simple docstring"""


													return self.image_processor.post_process_instance_segmentation(*_UpperCAmelCase			,    **_UpperCAmelCase		)






							def 						lowercase__     (   self			,    *_UpperCAmelCase			,    **_UpperCAmelCase		):







													"""simple docstring"""


													return self.image_processor.post_process_panoptic_segmentation(*_UpperCAmelCase			,    **_UpperCAmelCase		)