
'''simple docstring'''

from statistics import mean

import numpy as np






def 	_snake_case						(      A_       :    list     ,		A_       :    list     ,		A_       :    list     ,		A_       :    int   ):
  """simple docstring"""







  a_      :	Optional[int]      =							0
  # Number of processes finished
  a_      :	Tuple      =							0
  # Displays the finished process.
  # If it is 0, the performance is completed if it is 1, before the performance.
  a_      :	Optional[int]      =							[0] * no_of_process
  # List to include calculation results
  a_      :	int      =							[0] * no_of_process

  # Sort by arrival time.
  a_      :	str      =							[burst_time[i] for i in np.argsort(A_   )]
  a_      :	Optional[int]      =							[process_name[i] for i in np.argsort(A_   )]
  arrival_time.sort()

  while no_of_process > finished_process_count:
    a_      :	Optional[Any]      =							0
    while finished_process[i] == 1:
      i += 1
    if current_time < arrival_time[i]:
      a_      :	Optional[Any]      =							arrival_time[i]

    a_      :	List[str]      =							0
    # Index showing the location of the process being performed
    a_      :	Any      =							0
    # Saves the current response ratio.
    a_      :	List[Any]      =							0
    for i in range(0     ,		A_   ):
      if finished_process[i] == 0 and arrival_time[i] <= current_time:
        a_      :	Tuple      =							(burst_time[i] + (current_time - arrival_time[i])) / burst_time[
            i
        ]
      if response_ratio < temp:
        a_      :	List[str]      =							temp
        a_      :	int      =							i

        # Calculate the turn around time
    a_      :	List[str]      =							current_time + burst_time[loc] - arrival_time[loc]
    current_time += burst_time[loc]
    # Indicates that the process has been performed.
    a_      :	int      =							1
    # Increase finished_process_count by 1
    finished_process_count += 1

  return turn_around_time






def 	_snake_case						(      A_       :    list     ,		A_       :    list     ,		A_       :    list     ,		A_       :    int   ):
  """simple docstring"""







  a_      :	str      =							[0] * no_of_process
  for i in range(0     ,		A_   ):
    a_      :	Any      =							turn_around_time[i] - burst_time[i]
  return waiting_time


if __name__ == "__main__":
    __snake_case:  Dict								=			5
    __snake_case:  List[Any]								=			["A", "B", "C", "D", "E"]
    __snake_case:  Optional[Any]								=			[1, 2, 3, 4, 5]
    __snake_case:  Optional[int]								=			[1, 2, 3, 4, 5]

    __snake_case:  Any								=			calculate_turn_around_time(
        process_name, arrival_time, burst_time, no_of_process
    )
    __snake_case:  str								=			calculate_waiting_time(
        process_name, turn_around_time, burst_time, no_of_process
    )

    print("Process name \tArrival time \tBurst time \tTurn around time \tWaiting time")
    for i in range(0, no_of_process):
        print(
            F"""{process_name[i]}\t\t{arrival_time[i]}\t\t{burst_time[i]}\t\t"""
            F"""{turn_around_time[i]}\t\t\t{waiting_time[i]}"""
        )

    print(F"""average waiting time : {mean(waiting_time):.5f}""")
    print(F"""average turn around time : {mean(turn_around_time):.5f}""")




'''simple docstring'''

import os
import tempfile
import unittest
from pathlib import Path

from transformers import AutoConfig, is_torch_available
from transformers.testing_utils import require_torch, torch_device


if is_torch_available():
    from transformers import PyTorchBenchmark, PyTorchBenchmarkArguments


@require_torch
class  _UpperCAmelCase ( unittest.TestCase    ):

     """simple docstring"""

     def 							_lowerCAmelCase  (      self							,		lowerCAmelCase_ ):

       '''simple docstring'''

       for model_result in results.values():
         for batch_size, sequence_length in zip(model_result["""bs"""]							,		model_result["""ss"""] ):
           a_      :	str      =							model_result["""result"""][batch_size][sequence_length]
           self.assertIsNotNone(lowerCAmelCase_ )


     def 							_lowerCAmelCase  (      self ):

       '''simple docstring'''

       a_      :	Optional[int]      =							"""sshleifer/tiny-gpt2"""
       a_      :	Any      =							PyTorchBenchmarkArguments(
           models=[MODEL_ID]							,		training=lowerCAmelCase_							,		inference=lowerCAmelCase_							,		sequence_lengths=[8]							,		batch_sizes=[1]							,		multi_process=lowerCAmelCase_							,		)
       a_      :	List[Any]      =							PyTorchBenchmark(lowerCAmelCase_ )
       a_      :	List[Any]      =							benchmark.run()
       self.check_results_dict_not_empty(results.time_inference_result )
       self.check_results_dict_not_empty(results.memory_inference_result )


     def 							_lowerCAmelCase  (      self ):

       '''simple docstring'''

       a_      :	Optional[int]      =							"""sgugger/tiny-distilbert-classification"""
       a_      :	str      =							PyTorchBenchmarkArguments(
           models=[MODEL_ID]							,		training=lowerCAmelCase_							,		inference=lowerCAmelCase_							,		sequence_lengths=[8]							,		batch_sizes=[1]							,		multi_process=lowerCAmelCase_							,		only_pretrain_model=lowerCAmelCase_							,		)
       a_      :	Optional[Any]      =							PyTorchBenchmark(lowerCAmelCase_ )
       a_      :	Optional[Any]      =							benchmark.run()
       self.check_results_dict_not_empty(results.time_inference_result )
       self.check_results_dict_not_empty(results.memory_inference_result )


     def 							_lowerCAmelCase  (      self ):

       '''simple docstring'''

       a_      :	List[str]      =							"""sshleifer/tiny-gpt2"""
       a_      :	Any      =							PyTorchBenchmarkArguments(
           models=[MODEL_ID]							,		training=lowerCAmelCase_							,		inference=lowerCAmelCase_							,		torchscript=lowerCAmelCase_							,		sequence_lengths=[8]							,		batch_sizes=[1]							,		multi_process=lowerCAmelCase_							,		)
       a_      :	Optional[Any]      =							PyTorchBenchmark(lowerCAmelCase_ )
       a_      :	Optional[Any]      =							benchmark.run()
       self.check_results_dict_not_empty(results.time_inference_result )
       self.check_results_dict_not_empty(results.memory_inference_result )


     @unittest.skipIf(torch_device == """cpu"""							,		"""Cant do half precision""" )
     def 							_lowerCAmelCase  (      self ):

       '''simple docstring'''

       a_      :	int      =							"""sshleifer/tiny-gpt2"""
       a_      :	Tuple      =							PyTorchBenchmarkArguments(
           models=[MODEL_ID]							,		training=lowerCAmelCase_							,		inference=lowerCAmelCase_							,		fpaa=lowerCAmelCase_							,		sequence_lengths=[8]							,		batch_sizes=[1]							,		multi_process=lowerCAmelCase_							,		)
       a_      :	List[Any]      =							PyTorchBenchmark(lowerCAmelCase_ )
       a_      :	Dict      =							benchmark.run()
       self.check_results_dict_not_empty(results.time_inference_result )
       self.check_results_dict_not_empty(results.memory_inference_result )


     def 							_lowerCAmelCase  (      self ):

       '''simple docstring'''

       a_      :	Dict      =							"""sshleifer/tiny-gpt2"""
       a_      :	Dict      =							AutoConfig.from_pretrained(lowerCAmelCase_ )
       # set architectures equal to `None`
       a_      :	Any      =							None
       a_      :	List[Any]      =							PyTorchBenchmarkArguments(
           models=[MODEL_ID]							,		training=lowerCAmelCase_							,		inference=lowerCAmelCase_							,		sequence_lengths=[8]							,		batch_sizes=[1]							,		multi_process=lowerCAmelCase_							,		)
       a_      :	Optional[Any]      =							PyTorchBenchmark(lowerCAmelCase_							,		configs=[config] )
       a_      :	Any      =							benchmark.run()
       self.check_results_dict_not_empty(results.time_inference_result )
       self.check_results_dict_not_empty(results.memory_inference_result )


     def 							_lowerCAmelCase  (      self ):

       '''simple docstring'''

       a_      :	Dict      =							"""sshleifer/tiny-gpt2"""
       a_      :	Tuple      =							PyTorchBenchmarkArguments(
           models=[MODEL_ID]							,		training=lowerCAmelCase_							,		inference=lowerCAmelCase_							,		sequence_lengths=[8]							,		batch_sizes=[1]							,		multi_process=lowerCAmelCase_							,		)
       a_      :	int      =							PyTorchBenchmark(lowerCAmelCase_ )
       a_      :	Tuple      =							benchmark.run()
       self.check_results_dict_not_empty(results.time_train_result )
       self.check_results_dict_not_empty(results.memory_train_result )


     @unittest.skipIf(torch_device == """cpu"""							,		"""Can't do half precision""" )
     def 							_lowerCAmelCase  (      self ):

       '''simple docstring'''

       a_      :	List[str]      =							"""sshleifer/tiny-gpt2"""
       a_      :	List[Any]      =							PyTorchBenchmarkArguments(
           models=[MODEL_ID]							,		training=lowerCAmelCase_							,		inference=lowerCAmelCase_							,		sequence_lengths=[8]							,		batch_sizes=[1]							,		fpaa=lowerCAmelCase_							,		multi_process=lowerCAmelCase_							,		)
       a_      :	List[str]      =							PyTorchBenchmark(lowerCAmelCase_ )
       a_      :	Optional[int]      =							benchmark.run()
       self.check_results_dict_not_empty(results.time_train_result )
       self.check_results_dict_not_empty(results.memory_train_result )


     def 							_lowerCAmelCase  (      self ):

       '''simple docstring'''

       a_      :	List[Any]      =							"""sshleifer/tiny-gpt2"""
       a_      :	List[str]      =							AutoConfig.from_pretrained(lowerCAmelCase_ )
       a_      :	Union[str, Any]      =							PyTorchBenchmarkArguments(
           models=[MODEL_ID]							,		training=lowerCAmelCase_							,		inference=lowerCAmelCase_							,		sequence_lengths=[8]							,		batch_sizes=[1]							,		multi_process=lowerCAmelCase_							,		)
       a_      :	Tuple      =							PyTorchBenchmark(lowerCAmelCase_							,		configs=[config] )
       a_      :	List[str]      =							benchmark.run()
       self.check_results_dict_not_empty(results.time_inference_result )
       self.check_results_dict_not_empty(results.memory_inference_result )


     def 							_lowerCAmelCase  (      self ):

       '''simple docstring'''

       a_      :	Dict      =							"""sshleifer/tinier_bart"""
       a_      :	Union[str, Any]      =							AutoConfig.from_pretrained(lowerCAmelCase_ )
       a_      :	Dict      =							PyTorchBenchmarkArguments(
           models=[MODEL_ID]							,		training=lowerCAmelCase_							,		inference=lowerCAmelCase_							,		sequence_lengths=[8]							,		batch_sizes=[1]							,		multi_process=lowerCAmelCase_							,		)
       a_      :	Union[str, Any]      =							PyTorchBenchmark(lowerCAmelCase_							,		configs=[config] )
       a_      :	Optional[Any]      =							benchmark.run()
       self.check_results_dict_not_empty(results.time_inference_result )
       self.check_results_dict_not_empty(results.memory_inference_result )


     def 							_lowerCAmelCase  (      self ):

       '''simple docstring'''

       a_      :	Union[str, Any]      =							"""sshleifer/tiny-gpt2"""
       a_      :	Tuple      =							AutoConfig.from_pretrained(lowerCAmelCase_ )
       a_      :	Tuple      =							PyTorchBenchmarkArguments(
           models=[MODEL_ID]							,		training=lowerCAmelCase_							,		inference=lowerCAmelCase_							,		sequence_lengths=[8]							,		batch_sizes=[1]							,		multi_process=lowerCAmelCase_							,		)
       a_      :	Union[str, Any]      =							PyTorchBenchmark(lowerCAmelCase_							,		configs=[config] )
       a_      :	Tuple      =							benchmark.run()
       self.check_results_dict_not_empty(results.time_train_result )
       self.check_results_dict_not_empty(results.memory_train_result )


     def 							_lowerCAmelCase  (      self ):

       '''simple docstring'''

       a_      :	Optional[int]      =							"""sshleifer/tinier_bart"""
       a_      :	int      =							AutoConfig.from_pretrained(lowerCAmelCase_ )
       a_      :	List[Any]      =							PyTorchBenchmarkArguments(
           models=[MODEL_ID]							,		training=lowerCAmelCase_							,		inference=lowerCAmelCase_							,		sequence_lengths=[8]							,		batch_sizes=[1]							,		multi_process=lowerCAmelCase_							,		)
       a_      :	int      =							PyTorchBenchmark(lowerCAmelCase_							,		configs=[config] )
       a_      :	Dict      =							benchmark.run()
       self.check_results_dict_not_empty(results.time_train_result )
       self.check_results_dict_not_empty(results.memory_train_result )


     def 							_lowerCAmelCase  (      self ):

       '''simple docstring'''

       a_      :	Optional[Any]      =							"""sshleifer/tiny-gpt2"""
       with tempfile.TemporaryDirectory() as tmp_dir:
         a_      :	List[str]      =							PyTorchBenchmarkArguments(
             models=[MODEL_ID]							,		training=lowerCAmelCase_							,		inference=lowerCAmelCase_							,		save_to_csv=lowerCAmelCase_							,		sequence_lengths=[8]							,		batch_sizes=[1]							,		inference_time_csv_file=os.path.join(lowerCAmelCase_							,		"""inf_time.csv""" )							,		train_memory_csv_file=os.path.join(lowerCAmelCase_							,		"""train_mem.csv""" )							,		inference_memory_csv_file=os.path.join(lowerCAmelCase_							,		"""inf_mem.csv""" )							,		train_time_csv_file=os.path.join(lowerCAmelCase_							,		"""train_time.csv""" )							,		env_info_csv_file=os.path.join(lowerCAmelCase_							,		"""env.csv""" )							,		multi_process=lowerCAmelCase_							,		)
         a_      :	Tuple      =							PyTorchBenchmark(lowerCAmelCase_ )
         benchmark.run()
         self.assertTrue(Path(os.path.join(lowerCAmelCase_							,		"""inf_time.csv""" ) ).exists() )
         self.assertTrue(Path(os.path.join(lowerCAmelCase_							,		"""train_time.csv""" ) ).exists() )
         self.assertTrue(Path(os.path.join(lowerCAmelCase_							,		"""inf_mem.csv""" ) ).exists() )
         self.assertTrue(Path(os.path.join(lowerCAmelCase_							,		"""train_mem.csv""" ) ).exists() )
         self.assertTrue(Path(os.path.join(lowerCAmelCase_							,		"""env.csv""" ) ).exists() )






     def 							_lowerCAmelCase  (      self ):

       '''simple docstring'''

       a_      :	Dict      =							"""sshleifer/tiny-gpt2"""

       def _check_summary_is_not_empty(lowerCAmelCase_ ):
         self.assertTrue(hasattr(lowerCAmelCase_							,		"""sequential""" ) )
         self.assertTrue(hasattr(lowerCAmelCase_							,		"""cumulative""" ) )
         self.assertTrue(hasattr(lowerCAmelCase_							,		"""current""" ) )
         self.assertTrue(hasattr(lowerCAmelCase_							,		"""total""" ) )

       with tempfile.TemporaryDirectory() as tmp_dir:
         a_      :	List[Any]      =							PyTorchBenchmarkArguments(
             models=[MODEL_ID]							,		training=lowerCAmelCase_							,		inference=lowerCAmelCase_							,		sequence_lengths=[8]							,		batch_sizes=[1]							,		log_filename=os.path.join(lowerCAmelCase_							,		"""log.txt""" )							,		log_print=lowerCAmelCase_							,		trace_memory_line_by_line=lowerCAmelCase_							,		multi_process=lowerCAmelCase_							,		)
         a_      :	Dict      =							PyTorchBenchmark(lowerCAmelCase_ )
         a_      :	Tuple      =							benchmark.run()
         _check_summary_is_not_empty(result.inference_summary )
         _check_summary_is_not_empty(result.train_summary )
         self.assertTrue(Path(os.path.join(lowerCAmelCase_							,		"""log.txt""" ) ).exists() )


