"""simple docstring"""


import warnings
from typing import List

import numpy as np

from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding
from ...utils import is_flax_available, is_tf_available, is_torch_available






class    a__    (   _a	):
			snake_case_							=      ["image_processor", "tokenizer"]
			snake_case_							=      "OwlViTImageProcessor"
			snake_case_							=      ("CLIPTokenizer", "CLIPTokenizerFast")


			def __init__(     self,				_UpperCAmelCase=None,				_UpperCAmelCase=None,				**_UpperCAmelCase  ):
								'''simple docstring'''



								lowercase__				 =							None
								if "feature_extractor" in kwargs:
													warnings.warn(
													    "The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`"
													    " instead.",				_UpperCAmelCase,				)
													lowercase__				 =							kwargs.pop("feature_extractor"  )

								lowercase__				 =							image_processor if image_processor is not None else feature_extractor
								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`."  )

								super().__init__(_UpperCAmelCase,				_UpperCAmelCase  )


			def __call__(     self,				_UpperCAmelCase=None,				_UpperCAmelCase=None,				_UpperCAmelCase=None,				_UpperCAmelCase="max_length",				_UpperCAmelCase="np",				**_UpperCAmelCase  ):
								'''simple docstring'''



								if text is None and query_images is None and images is None:
													raise ValueError(
													    "You have to specify at least one text or query image or image. All three cannot be none."  )

								if text is not None:
													if isinstance(_UpperCAmelCase,				_UpperCAmelCase  ) or (isinstance(_UpperCAmelCase,				_UpperCAmelCase  ) and not isinstance(text[0],				_UpperCAmelCase  )):
																		lowercase__				 =							[self.tokenizer(_UpperCAmelCase,				padding=_UpperCAmelCase,				return_tensors=_UpperCAmelCase,				**_UpperCAmelCase  )]

													elif isinstance(_UpperCAmelCase,				_UpperCAmelCase  ) and isinstance(text[0],				_UpperCAmelCase  ):
																		lowercase__				 =							[]

																		# Maximum number of queries across batch
																		lowercase__				 =							max([len(_UpperCAmelCase  ) for t in text]  )

																		# Pad all batch samples to max number of text queries
																		for t in text:
																							if len(_UpperCAmelCase  ) != max_num_queries:
																												lowercase__				 =							t + [" "] * (max_num_queries - len(_UpperCAmelCase  ))

																							lowercase__				 =							self.tokenizer(_UpperCAmelCase,				padding=_UpperCAmelCase,				return_tensors=_UpperCAmelCase,				**_UpperCAmelCase  )
																							encodings.append(_UpperCAmelCase  )
													else:
																		raise TypeError("Input text should be a string, a list of strings or a nested list of strings"  )

													if return_tensors == "np":
																		lowercase__				 =							np.concatenate([encoding["input_ids"] for encoding in encodings],				axis=0  )
																		lowercase__				 =							np.concatenate([encoding["attention_mask"] for encoding in encodings],				axis=0  )

													elif return_tensors == "jax" and is_flax_available():
																		import jax.numpy as jnp

																		lowercase__				 =							jnp.concatenate([encoding["input_ids"] for encoding in encodings],				axis=0  )
																		lowercase__				 =							jnp.concatenate([encoding["attention_mask"] for encoding in encodings],				axis=0  )

													elif return_tensors == "pt" and is_torch_available():
																		import torch

																		lowercase__				 =							torch.cat([encoding["input_ids"] for encoding in encodings],				dim=0  )
																		lowercase__				 =							torch.cat([encoding["attention_mask"] for encoding in encodings],				dim=0  )

													elif return_tensors == "tf" and is_tf_available():
																		import tensorflow as tf

																		lowercase__				 =							tf.stack([encoding["input_ids"] for encoding in encodings],				axis=0  )
																		lowercase__				 =							tf.stack([encoding["attention_mask"] for encoding in encodings],				axis=0  )

													else:
																		raise ValueError("Target return tensor type could not be returned"  )

													lowercase__				 =							BatchEncoding()
													lowercase__				 =							input_ids
													lowercase__				 =							attention_mask

								if query_images is not None:
													lowercase__				 =							BatchEncoding()
													lowercase__				 =							self.image_processor(
													    _UpperCAmelCase,				return_tensors=_UpperCAmelCase,				**_UpperCAmelCase  ).pixel_values
													lowercase__				 =							query_pixel_values

								if images is not None:
													lowercase__				 =							self.image_processor(_UpperCAmelCase,				return_tensors=_UpperCAmelCase,				**_UpperCAmelCase  )

								if text is not None and images is not None:
													lowercase__				 =							image_features.pixel_values
													return encoding
								elif query_images is not None and images is not None:
													lowercase__				 =							image_features.pixel_values
													return encoding
								elif text is not None or query_images is not None:
													return encoding
								else:
													return BatchEncoding(data=dict(**_UpperCAmelCase  ),				tensor_type=_UpperCAmelCase  )


			def 	snake_case__	(     self,				*_UpperCAmelCase,				**_UpperCAmelCase  ):
								'''simple docstring'''



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


			def 	snake_case__	(     self,				*_UpperCAmelCase,				**_UpperCAmelCase  ):
								'''simple docstring'''



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


			def 	snake_case__	(     self,				*_UpperCAmelCase,				**_UpperCAmelCase  ):
								'''simple docstring'''



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


			def 	snake_case__	(     self,				*_UpperCAmelCase,				**_UpperCAmelCase  ):
								'''simple docstring'''



								return self.tokenizer.batch_decode(*_UpperCAmelCase,				**_UpperCAmelCase  )


			def 	snake_case__	(     self,				*_UpperCAmelCase,				**_UpperCAmelCase  ):
								'''simple docstring'''



								return self.tokenizer.decode(*_UpperCAmelCase,				**_UpperCAmelCase  )


			@property
			def 	snake_case__	(     self  ):
								'''simple docstring'''



								warnings.warn(
								    "`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead.",				_UpperCAmelCase,				)
								return self.image_processor_class


			@property
			def 	snake_case__	(     self  ):
								'''simple docstring'''



								warnings.warn(
								    "`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead.",				_UpperCAmelCase,				)
								return self.image_processor





"""simple docstring"""


from collections import deque






class    a__    :


   def __init__(     self,				_UpperCAmelCase,				_UpperCAmelCase,				_UpperCAmelCase  ):
        '''simple docstring'''



        lowercase__				 =							process_name  # process name
        lowercase__				 =							arrival_time  # arrival time of the process
        # completion time of finished process or last interrupted time
        lowercase__				 =							arrival_time
        lowercase__				 =							burst_time  # remaining burst time
        lowercase__				 =							0  # total time of the process wait in ready queue
        lowercase__				 =							0  # time from arrival time to completion time






class    a__    :


   def __init__(     self,				_UpperCAmelCase,				_UpperCAmelCase,				_UpperCAmelCase,				_UpperCAmelCase,				):
        '''simple docstring'''



        lowercase__				 =							number_of_queues
        # time slice of queues that round robin algorithm applied
        lowercase__				 =							time_slices
        # unfinished process is in this ready_queue
        lowercase__				 =							queue
        # current time
        lowercase__				 =							current_time
        # finished process is in this sequence queue
        lowercase__				 =							deque()


   def 	snake_case__	(     self  ):
        '''simple docstring'''



        lowercase__				 =							[]
        for i in range(len(self.finish_queue  )  ):
             sequence.append(self.finish_queue[i].process_name  )
        return sequence


   def 	snake_case__	(     self,				_UpperCAmelCase  ):
        '''simple docstring'''



        lowercase__				 =							[]
        for i in range(len(_UpperCAmelCase  )  ):
             waiting_times.append(queue[i].waiting_time  )
        return waiting_times


   def 	snake_case__	(     self,				_UpperCAmelCase  ):
        '''simple docstring'''



        lowercase__				 =							[]
        for i in range(len(_UpperCAmelCase  )  ):
             turnaround_times.append(queue[i].turnaround_time  )
        return turnaround_times


   def 	snake_case__	(     self,				_UpperCAmelCase  ):
        '''simple docstring'''



        lowercase__				 =							[]
        for i in range(len(_UpperCAmelCase  )  ):
             completion_times.append(queue[i].stop_time  )
        return completion_times


   def 	snake_case__	(     self,				_UpperCAmelCase  ):
        '''simple docstring'''



        return [q.burst_time for q in queue]


   def 	snake_case__	(     self,				_UpperCAmelCase  ):
        '''simple docstring'''



        process.waiting_time += self.current_time - process.stop_time
        return process.waiting_time


   def 	snake_case__	(     self,				_UpperCAmelCase  ):
        '''simple docstring'''



        lowercase__				 =							deque()  # sequence deque of finished process
        while len(_UpperCAmelCase  ) != 0:
             lowercase__				 =							ready_queue.popleft()  # current process

             # if process's arrival time is later than current time, update current time
             if self.current_time < cp.arrival_time:
                  self.current_time += cp.arrival_time

             # update waiting time of current process
             self.update_waiting_time(_UpperCAmelCase  )
             # update current time
             self.current_time += cp.burst_time
             # finish the process and set the process's burst-time 0
             lowercase__				 =							0
             # set the process's turnaround time because it is finished
             lowercase__				 =							self.current_time - cp.arrival_time
             # set the completion time
             lowercase__				 =							self.current_time
             # add the process to queue that has finished queue
             finished.append(_UpperCAmelCase  )

        self.finish_queue.extend(_UpperCAmelCase  )  # add finished process to finish queue
        # FCFS will finish all remaining processes
        return finished


   def 	snake_case__	(     self,				_UpperCAmelCase,				_UpperCAmelCase  ):
        '''simple docstring'''



        lowercase__				 =							deque()  # sequence deque of terminated process
        # just for 1 cycle and unfinished processes will go back to queue
        for _ in range(len(_UpperCAmelCase  )  ):
             lowercase__				 =							ready_queue.popleft()  # current process

             # if process's arrival time is later than current time, update current time
             if self.current_time < cp.arrival_time:
                  self.current_time += cp.arrival_time

             # update waiting time of unfinished processes
             self.update_waiting_time(_UpperCAmelCase  )
             # if the burst time of process is bigger than time-slice
             if cp.burst_time > time_slice:
                  # use CPU for only time-slice
                  self.current_time += time_slice
                  # update remaining burst time
                  cp.burst_time -= time_slice
                  # update end point time
                  lowercase__				 =							self.current_time
                  # locate the process behind the queue because it is not finished
                  ready_queue.append(_UpperCAmelCase  )
             else:
                  # use CPU for remaining burst time
                  self.current_time += cp.burst_time
                  # set burst time 0 because the process is finished
                  lowercase__				 =							0
                  # set the finish time
                  lowercase__				 =							self.current_time
                  # update the process' turnaround time because it is finished
                  lowercase__				 =							self.current_time - cp.arrival_time
                  # add the process to queue that has finished queue
                  finished.append(_UpperCAmelCase  )

        self.finish_queue.extend(_UpperCAmelCase  )  # add finished process to finish queue
        # return finished processes queue and remaining processes queue
        return finished, ready_queue


   def 	snake_case__	(     self  ):
        '''simple docstring'''



        for i in range(self.number_of_queues - 1  ):
             lowercase__ ,						lowercase__				 =							self.round_robin(
                 self.ready_queue,				self.time_slices[i]  )
        #  the last queue has first_come_first_served algorithm
        self.first_come_first_served(self.ready_queue  )

        return self.finish_queue


if __name__ == "__main__":
 import doctest

 lowerCAmelCase_:					Optional[int]     =					Process("P1", 0, 5_3)
 lowerCAmelCase_:					Union[str, Any]     =					Process("P2", 0, 1_7)
 lowerCAmelCase_:					str     =					Process("P3", 0, 6_8)
 lowerCAmelCase_:					int     =					Process("P4", 0, 2_4)
 lowerCAmelCase_:					Dict     =					3
 lowerCAmelCase_:					Any     =					[1_7, 2_5]
 lowerCAmelCase_:					Tuple     =					deque([Pa, Pa, Pa, Pa])

 if len(time_slices) != number_of_queues - 1:
  raise SystemExit(0)

 doctest.testmod(extraglobs={"queue": deque([Pa, Pa, Pa, Pa])})

 lowerCAmelCase_:					Any     =					Process("P1", 0, 5_3)
 lowerCAmelCase_:					Tuple     =					Process("P2", 0, 1_7)
 lowerCAmelCase_:					Optional[int]     =					Process("P3", 0, 6_8)
 lowerCAmelCase_:					List[Any]     =					Process("P4", 0, 2_4)
 lowerCAmelCase_:					Union[str, Any]     =					3
 lowerCAmelCase_:					Any     =					[1_7, 2_5]
 lowerCAmelCase_:					Optional[Any]     =					deque([Pa, Pa, Pa, Pa])
 lowerCAmelCase_:					Union[str, Any]     =					MLFQ(number_of_queues, time_slices, queue, 0)
 lowerCAmelCase_:					Tuple     =					mlfq.multi_level_feedback_queue()

 # print total waiting times of processes(P1, P2, P3, P4)
 print(
     F'waiting time:\
        \t\t\t{MLFQ.calculate_waiting_time(mlfq, [Pa, Pa, Pa, Pa])}'
 )
 # print completion times of processes(P1, P2, P3, P4)
 print(
     F'completion time:\
        \t\t{MLFQ.calculate_completion_time(mlfq, [Pa, Pa, Pa, Pa])}'
 )
 # print total turnaround times of processes(P1, P2, P3, P4)
 print(
     F'turnaround time:\
        \t\t{MLFQ.calculate_turnaround_time(mlfq, [Pa, Pa, Pa, Pa])}'
 )
 # print sequence of finished processes
 print(
     F'sequence of finished processes:\
        {mlfq.calculate_sequence_of_finish_queue()}'
 )




