






import os
from collections import namedtuple

import pytest

from datasets import ClassLabel, Features, Sequence, Value
from datasets.commands.test import TestCommand
from datasets.info import DatasetInfo, DatasetInfosDict


__a:     Optional[Any]   					=					namedtuple(
    '''_TestCommandArgs''',
    [
        '''dataset''',
        '''name''',
        '''cache_dir''',
        '''data_dir''',
        '''all_configs''',
        '''save_infos''',
        '''ignore_verifications''',
        '''force_redownload''',
        '''clear_cache''',
    ],
    defaults=[None, None, None, False, False, False, False, False],
)




def       _SCREAMING_SNAKE_CASE (							__snake_case   ,      __snake_case )				->						str:
       return (abs(source - target ) / target) < 0.01





@pytest.mark.integration
def       _SCREAMING_SNAKE_CASE (							__snake_case )				->						List[Any]:
       _UpperCAmelCase							=    _TestCommandArgs(dataset=__snake_case   ,      all_configs=__snake_case   ,      save_infos=__snake_case )
       _UpperCAmelCase							=    TestCommand(*__snake_case )
       test_command.run()
       _UpperCAmelCase							=    os.path.join(__snake_case   ,      """README.md""" )
       assert os.path.exists(__snake_case )
       _UpperCAmelCase							=    DatasetInfosDict.from_directory(__snake_case )
       _UpperCAmelCase							=    DatasetInfosDict(
           {
               """default""": DatasetInfo(
                   features=Features(
                       {
                           """tokens""": Sequence(Value("""string""" ) ),
                           """ner_tags""": Sequence(
                               ClassLabel(names=["""O""", """B-PER""", """I-PER""", """B-ORG""", """I-ORG""", """B-LOC""", """I-LOC"""] ) ),
                           """langs""": Sequence(Value("""string""" ) ),
                           """spans""": Sequence(Value("""string""" ) ),
                       } )   ,      splits=[
                       {
                           """name""": """train""",
                           """num_bytes""": 2_3_5_1_5_6_3,
                           """num_examples""": 1_0_0_0_0,
                       },
                       {
                           """name""": """validation""",
                           """num_bytes""": 2_3_8_4_1_8,
                           """num_examples""": 1_0_0_0,
                       },
                   ]   ,      download_size=3_9_4_0_6_8_0   ,      dataset_size=2_5_8_9_9_8_1   ,      )
           } )
       assert dataset_infos.keys() == expected_dataset_infos.keys()
       for key in DatasetInfo._INCLUDED_INFO_IN_YAML:
              _UpperCAmelCase	,			_UpperCAmelCase							=    getattr(dataset_infos["""default"""]   ,      __snake_case ), getattr(expected_dataset_infos["""default"""]   ,      __snake_case )
              if key == "num_bytes":
                     assert is_apercent_close(__snake_case   ,      __snake_case )
              elif key == "splits":
                     assert list(__snake_case ) == list(__snake_case )
                     for split in result:
                            assert result[split].name == expected[split].name
                            assert result[split].num_examples == expected[split].num_examples
                            assert is_apercent_close(result[split].num_bytes   ,      expected[split].num_bytes )
              else:
                     result == expected







import gc
import threading
import time

import psutil
import torch
class 		SCREAMING_SNAKE_CASE__	:

  '''simple docstring'''







  def __init__(      self		:			Union[str, Any]							)						->	str:


         """simple docstring"""
         _UpperCAmelCase							=    psutil.Process()
         _UpperCAmelCase							=    False


  def  lowerCamelCase       (      self		:			List[Any]							)						->	int:


         """simple docstring"""
         _UpperCAmelCase							=    -1

         while True:
                _UpperCAmelCase							=    max(self.process.memory_info().rss       ,				self.cpu_memory_peak							)

                # can't sleep or will not catch the peak right (this comment is here on purpose)
                if not self.peak_monitoring:
                       break


  def  lowerCamelCase       (      self		:			Optional[Any]							)						->	List[Any]:


         """simple docstring"""
         _UpperCAmelCase							=    True
         _UpperCAmelCase							=    threading.Thread(target=self.peak_monitor							)
         _UpperCAmelCase							=    True
         self.thread.start()





  def  lowerCamelCase       (      self		:			Optional[int]							)						->	List[str]:


         """simple docstring"""
         _UpperCAmelCase							=    False
         self.thread.join()
         return self.cpu_memory_peak



__a:     Union[str, Any]   					=					PeakCPUMemory()




def       _SCREAMING_SNAKE_CASE (							)				->						List[Any]:
       # Time
       _UpperCAmelCase							=    {"""time""": time.time()}

       gc.collect()
       torch.cuda.empty_cache()

       # CPU mem
       _UpperCAmelCase							=    psutil.Process().memory_info().rss
       cpu_peak_tracker.start()

       # GPU mem
       for i in range(torch.cuda.device_count() ):
              _UpperCAmelCase							=    torch.cuda.memory_allocated(__snake_case )
       torch.cuda.reset_peak_memory_stats()

       return measures




def       _SCREAMING_SNAKE_CASE (							__snake_case )				->						Union[str, Any]:
       # Time
       _UpperCAmelCase							=    {"""time""": time.time() - start_measures["""time"""]}

       gc.collect()
       torch.cuda.empty_cache()

       # CPU mem
       _UpperCAmelCase							=    (psutil.Process().memory_info().rss - start_measures["""cpu"""]) / 2**2_0
       _UpperCAmelCase							=    (cpu_peak_tracker.stop() - start_measures["""cpu"""]) / 2**2_0

       # GPU mem
       for i in range(torch.cuda.device_count() ):
              _UpperCAmelCase							=    (torch.cuda.memory_allocated(__snake_case ) - start_measures[str(__snake_case )]) / 2**2_0
              _UpperCAmelCase							=    (torch.cuda.max_memory_allocated(__snake_case ) - start_measures[str(__snake_case )]) / 2**2_0

       return measures





def       _SCREAMING_SNAKE_CASE (							__snake_case   ,      __snake_case )				->						str:
       print(f"""{description}:""" )
       print(f"""- Time: {measures["time"]:.2f}s""" )
       for i in range(torch.cuda.device_count() ):
              print(f"""- GPU {i} allocated: {measures[str(__snake_case )]:.2f}MiB""" )
              _UpperCAmelCase							=    measures[f"""{i}-peak"""]
              print(f"""- GPU {i} peak: {peak:.2f}MiB""" )
       print(f"""- CPU RAM allocated: {measures["cpu"]:.2f}MiB""" )
       print(f"""- CPU RAM peak: {measures["cpu-peak"]:.2f}MiB""" )