






'''simple docstring'''

import logging
import os

from .state import PartialState






class 				a_						(			logging.LoggerAdapter				):

  @staticmethod
  def   A__						(      _SCREAMING_SNAKE_CASE      )  ->      List[str]:



        """simple docstring"""






        UpperCamelCase				  =					PartialState()
        return not main_process_only or (main_process_only and state.is_main_process)


  def   A__						(      self			,		_SCREAMING_SNAKE_CASE			,		_SCREAMING_SNAKE_CASE			,		*_SCREAMING_SNAKE_CASE			,		**_SCREAMING_SNAKE_CASE      )  ->      Optional[int]:



        """simple docstring"""






        if PartialState._shared_state == {}:
              raise RuntimeError(
                  """You must initialize the accelerate state by calling either `PartialState()` or `Accelerator()` before using the logging utility."""      )
        UpperCamelCase				  =					kwargs.pop("""main_process_only"""			,		A__      )
        UpperCamelCase				  =					kwargs.pop("""in_order"""			,		A__      )

        if self.isEnabledFor(A__      ):
              if self._should_log(A__      ):
                    UpperCamelCase				  =					self.process(A__			,		A__      )
                    self.logger.log(A__			,		A__			,		*A__			,		**A__      )

              elif in_order:
                    UpperCamelCase				  =					PartialState()
                    for i in range(state.num_processes      ):
                          if i == state.process_index:
                                UpperCamelCase				  =					self.process(A__			,		A__      )
                                self.logger.log(A__			,		A__			,		*A__			,		**A__      )
                          state.wait_for_everyone()







def     lowercase__ (      __UpperCamelCase		,						__UpperCamelCase = None    )-> Optional[int]:
      if log_level is None:
            UpperCamelCase				  =					os.environ.get("""ACCELERATE_LOG_LEVEL"""		,						lowercase_    )
      UpperCamelCase				  =					logging.getLogger(lowercase_    )
      if log_level is not None:
            logger.setLevel(log_level.upper()    )
            logger.root.setLevel(log_level.upper()    )
      return MultiProcessAdapter(lowercase_		,						{}    )



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_       : 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 						UpperCamelCase					(lowercase_: Any    ,      lowercase_: List[str]					)     ->      Optional[int]:
	return (abs(source - target					) / target) < 0.01


@pytest.mark.integration
def 						UpperCamelCase					(lowercase_: str					)     ->      str:
	A__							:    List[str]        =		_TestCommandArgs(dataset=lowercase_    ,      all_configs=lowercase_    ,      save_infos=lowercase_					)
	A__							:    int        =		TestCommand(*lowercase_					)
	test_command.run()
	A__							:    Optional[Any]        =		os.path.join(lowercase_    ,      """README.md"""					)
	assert os.path.exists(lowercase_					)
	A__							:    Dict        =		DatasetInfosDict.from_directory(lowercase_					)
	A__							:    str        =		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""": 2351563,
	                    """num_examples""": 10000,
	                },
	                {
	                    """name""": """validation""",
	                    """num_bytes""": 238418,
	                    """num_examples""": 1000,
	                },
	            ]    ,      download_size=3940680    ,      dataset_size=2589981    ,      )
	    }					)
	assert dataset_infos.keys() == expected_dataset_infos.keys()
	for key in DatasetInfo._INCLUDED_INFO_IN_YAML:
		A__   ,   A__							:    Optional[Any]        =		getattr(dataset_infos["""default"""]    ,      lowercase_					), getattr(expected_dataset_infos["""default"""]    ,      lowercase_					)
		if key == "num_bytes":
			assert is_apercent_close(lowercase_    ,      lowercase_					)
		elif key == "splits":
			assert list(lowercase_					) == list(lowercase_					)
			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



