



# XXX: we want transformers master here - in the absense of conftest manipulating sys.path:
# hack it in for now:
import sys
from pathlib import Path


UpperCAmelCase_		 =					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(4_2)

UpperCAmelCase_		 =					{"""base""": """patrickvonplaten/wav2vec2_tiny_random""", """robust""": """patrickvonplaten/wav2vec2_tiny_random_robust"""}

UpperCAmelCase_		 =					"""zero2"""
UpperCAmelCase_		 =					"""zero3"""
UpperCAmelCase_		 =					[ZEROa, ZEROa]

def 	SCREAMING_SNAKE_CASE_							(	_snake_case	:Tuple	,   _snake_case	:Dict	,   _snake_case	:Union[str, Any]				)  ->     Tuple:
   # 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
   _A      =       parameterized.to_safe_name('''_'''.join(str(_snake_case				) for x in param.args				)				)
   return F'''{func.__name__}_{param_based_name}'''


# Cartesian-product of zero stages with models to test
UpperCAmelCase_		 =					list(itertools.product(stages, models.keys()))


@slow
@require_deepspeed
@require_torch_gpu
class 	lowerCamelCase__	(       _A):



 """simple docstring"""

 @parameterized.expand(__lowerCAmelCase			,						name_func=__lowerCAmelCase      )
 def     snake_case_      (    self					:					Union[str, Any]			,						__lowerCAmelCase					:					str			,						__lowerCAmelCase					:					str      )   ->	Dict:
    self.run_and_check(
        stage=__lowerCAmelCase			,						model=__lowerCAmelCase			,						distributed=__lowerCAmelCase			,						fpaa=__lowerCAmelCase			,						)

 @require_torch_multi_gpu
 @parameterized.expand(__lowerCAmelCase			,						name_func=__lowerCAmelCase      )
 def     snake_case_      (    self					:					Optional[int]			,						__lowerCAmelCase					:					Optional[Any]			,						__lowerCAmelCase					:					Any      )   ->	Dict:
    self.run_and_check(
        stage=__lowerCAmelCase			,						model=__lowerCAmelCase			,						distributed=__lowerCAmelCase			,						fpaa=__lowerCAmelCase			,						)

 @parameterized.expand(__lowerCAmelCase			,						name_func=__lowerCAmelCase      )
 def     snake_case_      (    self					:					Dict			,						__lowerCAmelCase					:					Dict			,						__lowerCAmelCase					:					Union[str, Any]      )   ->	str:
    self.run_and_check(
        stage=__lowerCAmelCase			,						model=__lowerCAmelCase			,						distributed=__lowerCAmelCase			,						fpaa=__lowerCAmelCase			,						)

 @require_torch_multi_gpu
 @parameterized.expand(__lowerCAmelCase			,						name_func=__lowerCAmelCase      )
 def     snake_case_      (    self					:					Union[str, Any]			,						__lowerCAmelCase					:					List[Any]			,						__lowerCAmelCase					:					Union[str, Any]      )   ->	List[str]:
    self.run_and_check(
        stage=__lowerCAmelCase			,						model=__lowerCAmelCase			,						distributed=__lowerCAmelCase			,						fpaa=__lowerCAmelCase			,						)

 def     snake_case_      (    self					:					Union[str, Any]			,						__lowerCAmelCase					:					Optional[int]      )   ->	int:
    # XXX: run_asr is premature and doesn't save any results
    # so all we check for now is that the process didn't fail
    pass

 def     snake_case_      (    self					:					Union[str, Any]			,						__lowerCAmelCase					:					str			,						__lowerCAmelCase					:					str			,						__lowerCAmelCase					:					int = 10			,						__lowerCAmelCase					:					bool = True			,						__lowerCAmelCase					:					bool = True			,						__lowerCAmelCase					:					bool = True			,						)   ->	Any:
    _A      =       models[model]

    _A      =       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     snake_case_      (    self					:					List[str]			,						__lowerCAmelCase					:					str			,						__lowerCAmelCase					:					str			,						__lowerCAmelCase					:					int = 10			,						__lowerCAmelCase					:					int = 1			,						__lowerCAmelCase					:					bool = True			,						__lowerCAmelCase					:					bool = True			,						)   ->	Any:
    _A      =       self.get_auto_remove_tmp_dir('''./xxx'''			,						after=__lowerCAmelCase      )
    _A      =       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
    _A      =       f'''--deepspeed {self.test_file_dir_str}/ds_config_wav2vec2_{stage}.json'''.split()
    _A      =       [f'''{self.examples_dir_str}/research_projects/wav2vec2/run_asr.py''']
    _A      =       self.get_launcher(__lowerCAmelCase      )

    _A      =       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     snake_case_      (    self					:					List[str]			,						__lowerCAmelCase					:					Dict=False      )   ->	Tuple:
    # 1. explicitly set --num_nodes=1 just in case these tests end up run on a multi-node setup
    # - it won't be able to handle that
    # 2. for now testing with just 2 gpus max (since some quality tests may give different
    # results with mode gpus because we use very little data)
    _A      =       min(2			,						get_gpu_count()      ) if distributed else 1
    return f'''deepspeed --num_nodes 1 --num_gpus {num_gpus}'''.split()







from __future__ import annotations

from statistics import mean







def SCREAMING_SNAKE_CASE	(			__lowerCAmelCase							,   __lowerCAmelCase							,   __lowerCAmelCase				)    ->				list[int]:
  snake_case__						= [0] * no_of_processes
  snake_case__						= [0] * no_of_processes

  # Initialize remaining_time to waiting_time.

  for i in range(__lowerCAmelCase				):
    snake_case__						= burst_time[i]
  snake_case__						= []

  snake_case__						= 0
  snake_case__						= 0

  # When processes are not completed,
  # A process whose arrival time has passed \
  # and has remaining execution time is put into the ready_process.
  # The shortest process in the ready_process, target_process is executed.

  while completed != no_of_processes:
    snake_case__						= []
    snake_case__						= -1

    for i in range(__lowerCAmelCase				):
      if (arrival_time[i] <= total_time) and (remaining_time[i] > 0):
        ready_process.append(__lowerCAmelCase				)

    if len(__lowerCAmelCase				) > 0:
      snake_case__						= ready_process[0]
      for i in ready_process:
        if remaining_time[i] < remaining_time[target_process]:
          snake_case__						= i
      total_time += burst_time[target_process]
      completed += 1
      snake_case__						= 0
      snake_case__						= (
          total_time - arrival_time[target_process] - burst_time[target_process]
      )
    else:
      total_time += 1

  return waiting_time







def SCREAMING_SNAKE_CASE	(			__lowerCAmelCase							,   __lowerCAmelCase							,   __lowerCAmelCase				)    ->				list[int]:
  snake_case__						= [0] * no_of_processes
  for i in range(__lowerCAmelCase				):
    snake_case__						= burst_time[i] + waiting_time[i]
  return turn_around_time


if __name__ == "__main__":
   print("""[TEST CASE 01]""")

   lowerCamelCase__							:						Tuple							     =   4
   lowerCamelCase__							:						Union[str, Any]							     =   [2, 5, 3, 7]
   lowerCamelCase__							:						Optional[Any]							     =   [0, 0, 0, 0]
   lowerCamelCase__							:						Dict							     =   calculate_waitingtime(arrival_time, burst_time, no_of_processes)
   lowerCamelCase__							:						Union[str, Any]							     =   calculate_turnaroundtime(
       burst_time, no_of_processes, waiting_time
   )

   # Printing the Result
   print("""PID\tBurst Time\tArrival Time\tWaiting Time\tTurnaround Time""")
   for i, process_id in enumerate(list(range(1, 5))):
      print(
          F"""{process_id}\t{burst_time[i]}\t\t\t{arrival_time[i]}\t\t\t\t"""
          F"""{waiting_time[i]}\t\t\t\t{turn_around_time[i]}"""
      )
   print(F"""\nAverage waiting time = {mean(waiting_time):.5f}""")
   print(F"""Average turnaround time = {mean(turn_around_time):.5f}""")

