

'''simple docstring'''



import sys
from pathlib import Path


lowerCAmelCase_     :       int								=						Path(__file__).resolve().parents[3] / 'src'
sys.path.insert(1, str(git_repo_path))

import dataclasses  # noqa
import io  # noqa
import itertools  # noqa
import json  # noqa
import os  # noqa
import unittest  # noqa
from copy import deepcopy  # noqa

from parameterized import parameterized  # noqa
from transformers import TrainingArguments, is_torch_available  # noqa
from transformers.deepspeed import is_deepspeed_available  # noqa
from transformers.file_utils import WEIGHTS_NAME  # noqa
from transformers.testing_utils import (  # noqa
    CaptureLogger,
    ExtendSysPath,
    TestCasePlus,
    execute_subprocess_async,
    get_gpu_count,
    mockenv_context,
    require_deepspeed,
    require_torch_gpu,
    require_torch_multi_gpu,
    slow,
)
from transformers.trainer_utils import set_seed  # noqa


set_seed(42)

lowerCAmelCase_     :       List[str]								=						{'base': 'patrickvonplaten/wav2vec2_tiny_random', 'robust': 'patrickvonplaten/wav2vec2_tiny_random_robust'}

lowerCAmelCase_     :       List[Any]								=						'zero2'
lowerCAmelCase_     :       Dict								=						'zero3'
lowerCAmelCase_     :       List[str]								=						[ZEROa, ZEROa]
def 					UpperCAmelCase   (						A  :				Optional[Any]					,		A  :				Optional[int]					,		A  :				Optional[int]				):
							# customize the test name generator function as we want both params to appear in the sub-test
							# name, as by default it shows only the first param
							SCREAMING_SNAKE_CASE					:      str     					=			parameterized.to_safe_name('''_'''.join(str(A				) for x in param.args				)				)
							return F"""{func.__name__}_{param_based_name}"""


# Cartesian-product of zero stages with models to test
lowerCAmelCase_     :       Union[str, Any]								=						list(itertools.product(stages, models.keys()))






@slow
@require_deepspeed
@require_torch_gpu
class 	lowerCamelCase_							(					snake_case_			):







						@parameterized.expand(lowerCAmelCase__				,    name_func=lowerCAmelCase__				)
						def 					__lowercase    (  self   :      Optional[int]				,    lowerCAmelCase__   :      Optional[int]				,    lowerCAmelCase__   :      Tuple				):

													"""simple docstring"""
													self.run_and_check(
													    stage=lowerCAmelCase__				,    model=lowerCAmelCase__				,    distributed=lowerCAmelCase__				,    fpaa=lowerCAmelCase__				,    )







						@require_torch_multi_gpu
						@parameterized.expand(lowerCAmelCase__				,    name_func=lowerCAmelCase__				)
						def 					__lowercase    (  self   :      Any				,    lowerCAmelCase__   :      List[str]				,    lowerCAmelCase__   :      int				):

													"""simple docstring"""
													self.run_and_check(
													    stage=lowerCAmelCase__				,    model=lowerCAmelCase__				,    distributed=lowerCAmelCase__				,    fpaa=lowerCAmelCase__				,    )







						@parameterized.expand(lowerCAmelCase__				,    name_func=lowerCAmelCase__				)
						def 					__lowercase    (  self   :      Union[str, Any]				,    lowerCAmelCase__   :      Union[str, Any]				,    lowerCAmelCase__   :      List[str]				):

													"""simple docstring"""
													self.run_and_check(
													    stage=lowerCAmelCase__				,    model=lowerCAmelCase__				,    distributed=lowerCAmelCase__				,    fpaa=lowerCAmelCase__				,    )







						@require_torch_multi_gpu
						@parameterized.expand(lowerCAmelCase__				,    name_func=lowerCAmelCase__				)
						def 					__lowercase    (  self   :      List[str]				,    lowerCAmelCase__   :      Optional[int]				,    lowerCAmelCase__   :      Any				):

													"""simple docstring"""
													self.run_and_check(
													    stage=lowerCAmelCase__				,    model=lowerCAmelCase__				,    distributed=lowerCAmelCase__				,    fpaa=lowerCAmelCase__				,    )







						def 					__lowercase    (  self   :      Any				,    lowerCAmelCase__   :      int				):

													"""simple docstring"""
													pass







						def 					__lowercase    (  self   :      List[str]				,    lowerCAmelCase__   :      str				,    lowerCAmelCase__   :      str				,    lowerCAmelCase__   :      int = 10				,    lowerCAmelCase__   :      bool = True				,    lowerCAmelCase__   :      bool = True				,    lowerCAmelCase__   :      bool = True				,    ):

													"""simple docstring"""
													SCREAMING_SNAKE_CASE					:      str     					=			models[model]

													SCREAMING_SNAKE_CASE					:      Dict     					=			self.run_trainer(
													    stage=lowerCAmelCase__				,    model_name=lowerCAmelCase__				,    eval_steps=lowerCAmelCase__				,    num_train_epochs=1				,    distributed=lowerCAmelCase__				,    fpaa=lowerCAmelCase__				,    )

													self.do_checks(lowerCAmelCase__				)

													return output_dir







						def 					__lowercase    (  self   :      str				,    lowerCAmelCase__   :      str				,    lowerCAmelCase__   :      str				,    lowerCAmelCase__   :      int = 10				,    lowerCAmelCase__   :      int = 1				,    lowerCAmelCase__   :      bool = True				,    lowerCAmelCase__   :      bool = True				,    ):

													"""simple docstring"""
													SCREAMING_SNAKE_CASE					:      Tuple     					=			self.get_auto_remove_tmp_dir('''./xxx'''				,    after=lowerCAmelCase__				)
													SCREAMING_SNAKE_CASE					:      Optional[int]     					=			F"""
            --model_name_or_path {model_name}
            --dataset_name hf-internal-testing/librispeech_asr_dummy
            --dataset_config_name clean
            --train_split_name validation
            --validation_split_name validation
            --output_dir {output_dir}
            --num_train_epochs {str(lowerCAmelCase__				)}
            --per_device_train_batch_size 2
            --per_device_eval_batch_size 2
            --evaluation_strategy steps
            --learning_rate 5e-4
            --warmup_steps 8
            --orthography timit
            --preprocessing_num_workers 1
            --group_by_length
            --freeze_feature_extractor
            --report_to none
            --save_steps 0
            --eval_steps {eval_steps}
            --report_to none
        """.split()

													if fpaa:
																				args.extend(['''--fp16''']				)

													# currently ds_config_wav2vec2_zero.json requires "zero_optimization.find_unused_parameters": true,
													# hence the separate config files
													SCREAMING_SNAKE_CASE					:      str     					=			F"""--deepspeed {self.test_file_dir_str}/ds_config_wav2vec2_{stage}.json""".split()
													SCREAMING_SNAKE_CASE					:      Tuple     					=			[F"""{self.examples_dir_str}/research_projects/wav2vec2/run_asr.py"""]
													SCREAMING_SNAKE_CASE					:      str     					=			self.get_launcher(lowerCAmelCase__				)

													SCREAMING_SNAKE_CASE					:      Any     					=			launcher + script + args + ds_args
													# keep for quick debug
													# print(" ".join([f"\nPYTHONPATH={self.src_dir_str}"] +cmd)); die
													execute_subprocess_async(lowerCAmelCase__				,    env=self.get_env()				)

													return output_dir







						def 					__lowercase    (  self   :      List[str]				,    lowerCAmelCase__   :      List[Any]=False				):

													"""simple docstring"""
													SCREAMING_SNAKE_CASE					:      Optional[Any]     					=			min(2				,    get_gpu_count()				) if distributed else 1
													return F"""deepspeed --num_nodes 1 --num_gpus {num_gpus}""".split()









'''simple docstring'''



from collections import deque
class 	lowerCamelCase_							:







						def __init__(  self   :      Tuple				,    lowerCAmelCase__   :      str				,    lowerCAmelCase__   :      int				,    lowerCAmelCase__   :      int				):

													"""simple docstring"""
													SCREAMING_SNAKE_CASE					:      Optional[Any]     					=			process_name  # process name
													SCREAMING_SNAKE_CASE					:      int     					=			arrival_time  # arrival time of the process
													# completion time of finished process or last interrupted time
													SCREAMING_SNAKE_CASE					:      List[str]     					=			arrival_time
													SCREAMING_SNAKE_CASE					:      List[Any]     					=			burst_time  # remaining burst time
													SCREAMING_SNAKE_CASE					:      List[str]     					=			0  # total time of the process wait in ready queue
													SCREAMING_SNAKE_CASE					:      Optional[int]     					=			0  # time from arrival time to completion time


class 	lowerCamelCase_							:







						def __init__(  self   :      str				,    lowerCAmelCase__   :      int				,    lowerCAmelCase__   :      list[int]				,    lowerCAmelCase__   :      deque[Process]				,    lowerCAmelCase__   :      int				,    ):

													"""simple docstring"""
													# total number of mlfq's queues
													SCREAMING_SNAKE_CASE					:      str     					=			number_of_queues
													# time slice of queues that round robin algorithm applied
													SCREAMING_SNAKE_CASE					:      int     					=			time_slices
													# unfinished process is in this ready_queue
													SCREAMING_SNAKE_CASE					:      str     					=			queue
													# current time
													SCREAMING_SNAKE_CASE					:      Any     					=			current_time
													# finished process is in this sequence queue
													SCREAMING_SNAKE_CASE					:      deque[Process]     					=			deque()







						def 					__lowercase    (  self   :      Optional[int]				):

													"""simple docstring"""
													SCREAMING_SNAKE_CASE					:      List[str]     					=			[]
													for i in range(len(self.finish_queue				)				):
																				sequence.append(self.finish_queue[i].process_name				)
													return sequence







						def 					__lowercase    (  self   :      int				,    lowerCAmelCase__   :      list[Process]				):

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







						def 					__lowercase    (  self   :      List[Any]				,    lowerCAmelCase__   :      list[Process]				):

													"""simple docstring"""
													SCREAMING_SNAKE_CASE					:      Dict     					=			[]
													for i in range(len(lowerCAmelCase__				)				):
																				turnaround_times.append(queue[i].turnaround_time				)
													return turnaround_times







						def 					__lowercase    (  self   :      Dict				,    lowerCAmelCase__   :      list[Process]				):

													"""simple docstring"""
													SCREAMING_SNAKE_CASE					:      Union[str, Any]     					=			[]
													for i in range(len(lowerCAmelCase__				)				):
																				completion_times.append(queue[i].stop_time				)
													return completion_times







						def 					__lowercase    (  self   :      Union[str, Any]				,    lowerCAmelCase__   :      deque[Process]				):

													"""simple docstring"""
													return [q.burst_time for q in queue]







						def 					__lowercase    (  self   :      Optional[int]				,    lowerCAmelCase__   :      Process				):

													"""simple docstring"""
													process.waiting_time += self.current_time - process.stop_time
													return process.waiting_time







						def 					__lowercase    (  self   :      Dict				,    lowerCAmelCase__   :      deque[Process]				):

													"""simple docstring"""
													SCREAMING_SNAKE_CASE					:      deque[Process]     					=			deque()  # sequence deque of finished process
													while len(lowerCAmelCase__				) != 0:
																				SCREAMING_SNAKE_CASE					:      Tuple     					=			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
																				SCREAMING_SNAKE_CASE					:      Optional[Any]     					=			0
																				# set the process's turnaround time because it is finished
																				SCREAMING_SNAKE_CASE					:      Union[str, Any]     					=			self.current_time - cp.arrival_time
																				# set the completion time
																				SCREAMING_SNAKE_CASE					:      Union[str, Any]     					=			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   :      Optional[int]				,    lowerCAmelCase__   :      deque[Process]				,    lowerCAmelCase__   :      int				):

													"""simple docstring"""
													SCREAMING_SNAKE_CASE					:      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__				)				):
																				SCREAMING_SNAKE_CASE					:      List[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
																											SCREAMING_SNAKE_CASE					:      Union[str, Any]     					=			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
																											SCREAMING_SNAKE_CASE					:      Optional[Any]     					=			0
																											# set the finish time
																											SCREAMING_SNAKE_CASE					:      Tuple     					=			self.current_time
																											# update the process' turnaround time because it is finished
																											SCREAMING_SNAKE_CASE					:      List[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   :      Union[str, Any]				):

													"""simple docstring"""

													#  all queues except last one have round_robin algorithm
													for i in range(self.number_of_queues - 1				):
																				SCREAMING_SNAKE_CASE					,SCREAMING_SNAKE_CASE					:      Optional[int]     					=			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_     :       str								=						Process('P1', 0, 53)
							lowerCAmelCase_     :       Optional[int]								=						Process('P2', 0, 17)
							lowerCAmelCase_     :       Optional[Any]								=						Process('P3', 0, 68)
							lowerCAmelCase_     :       Optional[int]								=						Process('P4', 0, 24)
							lowerCAmelCase_     :       List[str]								=						3
							lowerCAmelCase_     :       str								=						[17, 25]
							lowerCAmelCase_     :       Optional[Any]								=						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_     :       Tuple								=						Process('P1', 0, 53)
							lowerCAmelCase_     :       int								=						Process('P2', 0, 17)
							lowerCAmelCase_     :       Union[str, Any]								=						Process('P3', 0, 68)
							lowerCAmelCase_     :       Any								=						Process('P4', 0, 24)
							lowerCAmelCase_     :       int								=						3
							lowerCAmelCase_     :       Optional[Any]								=						[17, 25]
							lowerCAmelCase_     :       Dict								=						deque([Pa, Pa, Pa, Pa])
							lowerCAmelCase_     :       Union[str, Any]								=						MLFQ(number_of_queues, time_slices, queue, 0)
							lowerCAmelCase_     :       List[Any]								=						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()}'
							)




