





from collections import deque







class     __lowerCAmelCase	:







      def __init__(   self		,				lowerCAmelCase__		,				lowerCAmelCase__		,				lowerCAmelCase__							)       ->						None:



        '''simple docstring'''




        a__     : Any  =process_name  # process name
        a__     : Any  =arrival_time  # arrival time of the process
        # completion time of finished process or last interrupted time
        a__     : Optional[Any]  =arrival_time
        a__     : Union[str, Any]  =burst_time  # remaining burst time
        a__     : Tuple  =0  # total time of the process wait in ready queue
        a__     : Dict  =0  # time from arrival time to completion time







class     __lowerCAmelCase	:







      def __init__(   self		,				lowerCAmelCase__		,				lowerCAmelCase__		,				lowerCAmelCase__		,				lowerCAmelCase__		,				)       ->						None:



        '''simple docstring'''




        a__     : Any  =number_of_queues
        # time slice of queues that round robin algorithm applied
        a__     : Optional[Any]  =time_slices
        # unfinished process is in this ready_queue
        a__     : List[str]  =queue
        # current time
        a__     : Tuple  =current_time
        # finished process is in this sequence queue
        a__     : deque[Process]  =deque()







      def        _lowercase  (   self							)       ->						list[str]:



        '''simple docstring'''




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







      def        _lowercase  (   self		,				lowerCAmelCase__							)       ->						list[int]:



        '''simple docstring'''




        a__     : Union[str, Any]  =[]
        for i in range(len(lowerCAmelCase__							)							):
          waiting_times.append(queue[i].waiting_time							)
        return waiting_times







      def        _lowercase  (   self		,				lowerCAmelCase__							)       ->						list[int]:



        '''simple docstring'''




        a__     : List[str]  =[]
        for i in range(len(lowerCAmelCase__							)							):
          turnaround_times.append(queue[i].turnaround_time							)
        return turnaround_times







      def        _lowercase  (   self		,				lowerCAmelCase__							)       ->						list[int]:



        '''simple docstring'''




        a__     : int  =[]
        for i in range(len(lowerCAmelCase__							)							):
          completion_times.append(queue[i].stop_time							)
        return completion_times







      def        _lowercase  (   self		,				lowerCAmelCase__							)       ->						list[int]:



        '''simple docstring'''




        return [q.burst_time for q in queue]







      def        _lowercase  (   self		,				lowerCAmelCase__							)       ->						int:



        '''simple docstring'''




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







      def        _lowercase  (   self		,				lowerCAmelCase__							)       ->						deque[Process]:



        '''simple docstring'''




        a__     : deque[Process]  =deque()  # sequence deque of finished process
        while len(lowerCAmelCase__							) != 0:
          a__     : Any  =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(lowerCAmelCase__							)
          # update current time
          self.current_time += cp.burst_time
          # finish the process and set the process's burst-time 0
          a__     : List[Any]  =0
          # set the process's turnaround time because it is finished
          a__     : List[Any]  =self.current_time - cp.arrival_time
          # set the completion time
          a__     : Dict  =self.current_time
          # add the process to queue that has finished queue
          finished.append(lowerCAmelCase__							)

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







      def        _lowercase  (   self		,				lowerCAmelCase__		,				lowerCAmelCase__							)       ->						tuple[deque[Process], deque[Process]]:



        '''simple docstring'''




        a__     : deque[Process]  =deque()  # sequence deque of terminated process
        # just for 1 cycle and unfinished processes will go back to queue
        for _ in range(len(lowerCAmelCase__							)							):
          a__     : Any  =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(lowerCAmelCase__							)
          # 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
            a__     : Tuple  =self.current_time
            # locate the process behind the queue because it is not finished
            ready_queue.append(lowerCAmelCase__							)
          else:
            # use CPU for remaining burst time
            self.current_time += cp.burst_time
            # set burst time 0 because the process is finished
            a__     : Tuple  =0
            # set the finish time
            a__     : Tuple  =self.current_time
            # update the process' turnaround time because it is finished
            a__     : Optional[Any]  =self.current_time - cp.arrival_time
            # add the process to queue that has finished queue
            finished.append(lowerCAmelCase__							)

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







      def        _lowercase  (   self							)       ->						deque[Process]:



        '''simple docstring'''




        for i in range(self.number_of_queues - 1							):
          a__     : Any  =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

    UpperCAmelCase     :     str							=	Process("""P1""", 0, 53)
    UpperCAmelCase     :     Any							=	Process("""P2""", 0, 17)
    UpperCAmelCase     :     Optional[Any]							=	Process("""P3""", 0, 68)
    UpperCAmelCase     :     List[str]							=	Process("""P4""", 0, 24)
    UpperCAmelCase     :     Union[str, Any]							=	3
    UpperCAmelCase     :     List[str]							=	[17, 25]
    UpperCAmelCase     :     Optional[int]							=	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])})

    UpperCAmelCase     :     str							=	Process("""P1""", 0, 53)
    UpperCAmelCase     :     Union[str, Any]							=	Process("""P2""", 0, 17)
    UpperCAmelCase     :     Optional[int]							=	Process("""P3""", 0, 68)
    UpperCAmelCase     :     Optional[int]							=	Process("""P4""", 0, 24)
    UpperCAmelCase     :     int							=	3
    UpperCAmelCase     :     Union[str, Any]							=	[17, 25]
    UpperCAmelCase     :     List[str]							=	deque([Pa, Pa, Pa, Pa])
    UpperCAmelCase     :     Any							=	MLFQ(number_of_queues, time_slices, queue, 0)
    UpperCAmelCase     :     List[str]							=	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()}"""
    )













import unittest

from transformers import (
    MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING,
    TF_MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING,
    TextClassificationPipeline,
    pipeline,
)
from transformers.testing_utils import is_pipeline_test, nested_simplify, require_tf, require_torch, slow

from .test_pipelines_common import ANY


# These 2 model types require different inputs than those of the usual text models.
UpperCAmelCase     :     List[Any]							=	{"""LayoutLMv2Config""", """LayoutLMv3Config"""}







@is_pipeline_test
class     __lowerCAmelCase	(   unittest.TestCase):
      _lowercase    : str            =      MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING
      _lowercase    : Tuple            =      TF_MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING

      if model_mapping is not None:
            _lowercase    : Dict            =      {config: model for config, model in model_mapping.items() if config.__name__ not in _TO_SKIP}
      if tf_model_mapping is not None:
            _lowercase    : int            =      {
                config: model for config, model in tf_model_mapping.items() if config.__name__ not in _TO_SKIP
            }







      @require_torch
      def        _lowercase  (   self							)       ->						Tuple:



        '''simple docstring'''




        a__     : str  =pipeline(
            task="text-classification"		,				model="hf-internal-testing/tiny-random-distilbert"		,				framework="pt"							)

        a__     : Any  =text_classifier("This is great !"							)
        self.assertEqual(nested_simplify(lowerCAmelCase__							)		,				[{"label": "LABEL_0", "score": 0.5_04}]							)

        a__     : Any  =text_classifier("This is great !"		,				top_k=2							)
        self.assertEqual(
            nested_simplify(lowerCAmelCase__							)		,				[{"label": "LABEL_0", "score": 0.5_04}, {"label": "LABEL_1", "score": 0.4_96}]							)

        a__     : Tuple  =text_classifier(["This is great !", "This is bad"]		,				top_k=2							)
        self.assertEqual(
            nested_simplify(lowerCAmelCase__							)		,				[
                [{"label": "LABEL_0", "score": 0.5_04}, {"label": "LABEL_1", "score": 0.4_96}],
                [{"label": "LABEL_0", "score": 0.5_04}, {"label": "LABEL_1", "score": 0.4_96}],
            ]		,				)

        a__     : List[Any]  =text_classifier("This is great !"		,				top_k=1							)
        self.assertEqual(nested_simplify(lowerCAmelCase__							)		,				[{"label": "LABEL_0", "score": 0.5_04}]							)

        # Legacy behavior
        a__     : Any  =text_classifier("This is great !"		,				return_all_scores=lowerCAmelCase__							)
        self.assertEqual(nested_simplify(lowerCAmelCase__							)		,				[{"label": "LABEL_0", "score": 0.5_04}]							)

        a__     : List[str]  =text_classifier("This is great !"		,				return_all_scores=lowerCAmelCase__							)
        self.assertEqual(
            nested_simplify(lowerCAmelCase__							)		,				[[{"label": "LABEL_0", "score": 0.5_04}, {"label": "LABEL_1", "score": 0.4_96}]]							)

        a__     : Optional[int]  =text_classifier(["This is great !", "Something else"]		,				return_all_scores=lowerCAmelCase__							)
        self.assertEqual(
            nested_simplify(lowerCAmelCase__							)		,				[
                [{"label": "LABEL_0", "score": 0.5_04}, {"label": "LABEL_1", "score": 0.4_96}],
                [{"label": "LABEL_0", "score": 0.5_04}, {"label": "LABEL_1", "score": 0.4_96}],
            ]		,				)

        a__     : int  =text_classifier(["This is great !", "Something else"]		,				return_all_scores=lowerCAmelCase__							)
        self.assertEqual(
            nested_simplify(lowerCAmelCase__							)		,				[
                {"label": "LABEL_0", "score": 0.5_04},
                {"label": "LABEL_0", "score": 0.5_04},
            ]		,				)







      @require_torch
      def        _lowercase  (   self							)       ->						List[Any]:



        '''simple docstring'''




        import torch

        a__     : Dict  =pipeline(
            task="text-classification"		,				model="hf-internal-testing/tiny-random-distilbert"		,				framework="pt"		,				device=torch.device("cpu"							)		,				)

        a__     : Optional[Any]  =text_classifier("This is great !"							)
        self.assertEqual(nested_simplify(lowerCAmelCase__							)		,				[{"label": "LABEL_0", "score": 0.5_04}]							)







      @require_tf
      def        _lowercase  (   self							)       ->						Union[str, Any]:



        '''simple docstring'''




        a__     : Optional[int]  =pipeline(
            task="text-classification"		,				model="hf-internal-testing/tiny-random-distilbert"		,				framework="tf"							)

        a__     : Optional[Any]  =text_classifier("This is great !"							)
        self.assertEqual(nested_simplify(lowerCAmelCase__							)		,				[{"label": "LABEL_0", "score": 0.5_04}]							)







      @slow
      @require_torch
      def        _lowercase  (   self							)       ->						str:



        '''simple docstring'''




        a__     : Union[str, Any]  =pipeline("text-classification"							)

        a__     : Union[str, Any]  =text_classifier("This is great !"							)
        self.assertEqual(nested_simplify(lowerCAmelCase__							)		,				[{"label": "POSITIVE", "score": 1.0}]							)
        a__     : Optional[Any]  =text_classifier("This is bad !"							)
        self.assertEqual(nested_simplify(lowerCAmelCase__							)		,				[{"label": "NEGATIVE", "score": 1.0}]							)
        a__     : Dict  =text_classifier("Birds are a type of animal"							)
        self.assertEqual(nested_simplify(lowerCAmelCase__							)		,				[{"label": "POSITIVE", "score": 0.9_88}]							)







      @slow
      @require_tf
      def        _lowercase  (   self							)       ->						Optional[int]:



        '''simple docstring'''




        a__     : Tuple  =pipeline("text-classification"		,				framework="tf"							)

        a__     : str  =text_classifier("This is great !"							)
        self.assertEqual(nested_simplify(lowerCAmelCase__							)		,				[{"label": "POSITIVE", "score": 1.0}]							)
        a__     : str  =text_classifier("This is bad !"							)
        self.assertEqual(nested_simplify(lowerCAmelCase__							)		,				[{"label": "NEGATIVE", "score": 1.0}]							)
        a__     : Dict  =text_classifier("Birds are a type of animal"							)
        self.assertEqual(nested_simplify(lowerCAmelCase__							)		,				[{"label": "POSITIVE", "score": 0.9_88}]							)







      def        _lowercase  (   self		,				lowerCAmelCase__		,				lowerCAmelCase__		,				lowerCAmelCase__							)       ->						Optional[int]:



        '''simple docstring'''




        a__     : List[Any]  =TextClassificationPipeline(model=lowerCAmelCase__		,				tokenizer=lowerCAmelCase__							)
        return text_classifier, ["HuggingFace is in", "This is another test"]







      def        _lowercase  (   self		,				lowerCAmelCase__		,				lowerCAmelCase__							)       ->						List[str]:



        '''simple docstring'''




        a__     : Tuple  =text_classifier.model
        # Small inputs because BartTokenizer tiny has maximum position embeddings = 22
        a__     : List[Any]  ="HuggingFace is in"
        a__     : int  =text_classifier(lowerCAmelCase__							)

        self.assertEqual(nested_simplify(lowerCAmelCase__							)		,				[{"label": ANY(lowerCAmelCase__							), "score": ANY(lowerCAmelCase__							)}]							)
        self.assertTrue(outputs[0]["label"] in model.config.idalabel.values()							)

        a__     : Optional[int]  =["HuggingFace is in ", "Paris is in France"]
        a__     : Optional[Any]  =text_classifier(lowerCAmelCase__							)
        self.assertEqual(
            nested_simplify(lowerCAmelCase__							)		,				[{"label": ANY(lowerCAmelCase__							), "score": ANY(lowerCAmelCase__							)}, {"label": ANY(lowerCAmelCase__							), "score": ANY(lowerCAmelCase__							)}]		,				)
        self.assertTrue(outputs[0]["label"] in model.config.idalabel.values()							)
        self.assertTrue(outputs[1]["label"] in model.config.idalabel.values()							)

        # Forcing to get all results with `top_k=None`
        # This is NOT the legacy format
        a__     : Union[str, Any]  =text_classifier(lowerCAmelCase__		,				top_k=lowerCAmelCase__							)
        a__     : Optional[Any]  =len(model.config.idalabel.values()							)
        self.assertEqual(
            nested_simplify(lowerCAmelCase__							)		,				[[{"label": ANY(lowerCAmelCase__							), "score": ANY(lowerCAmelCase__							)}] * N, [{"label": ANY(lowerCAmelCase__							), "score": ANY(lowerCAmelCase__							)}] * N]		,				)

        a__     : List[str]  ={"text": "HuggingFace is in ", "text_pair": "Paris is in France"}
        a__     : Optional[Any]  =text_classifier(lowerCAmelCase__							)
        self.assertEqual(
            nested_simplify(lowerCAmelCase__							)		,				{"label": ANY(lowerCAmelCase__							), "score": ANY(lowerCAmelCase__							)}		,				)
        self.assertTrue(outputs["label"] in model.config.idalabel.values()							)

        # This might be used a text pair, but tokenizer + pipe interaction
        # makes it hard to understand that it's not using the pair properly
        # https://github.com/huggingface/transformers/issues/17305
        # We disabled this usage instead as it was outputting wrong outputs.
        a__     : Any  =[["HuggingFace is in ", "Paris is in France"]]
        with self.assertRaises(lowerCAmelCase__							):
          text_classifier(lowerCAmelCase__							)

        # This used to be valid for doing text pairs
        # We're keeping it working because of backward compatibility
        a__     : Optional[int]  =text_classifier([[["HuggingFace is in ", "Paris is in France"]]]							)
        self.assertEqual(
            nested_simplify(lowerCAmelCase__							)		,				[{"label": ANY(lowerCAmelCase__							), "score": ANY(lowerCAmelCase__							)}]		,				)
        self.assertTrue(outputs[0]["label"] in model.config.idalabel.values()							)






