


"""simple docstring"""
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


UpperCAmelCase				=						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 				_snake_case      (   __snake_case							:      int						, __snake_case							:      str   ):




   """simple docstring"""







   return (abs(source - target   ) / target) < 0.01





@pytest.mark.integration
def 				_snake_case      (   __snake_case							:      Tuple   ):




   """simple docstring"""







   _lowerCamelCase	:					Tuple   =		_TestCommandArgs(dataset=__snake_case						, all_configs=__snake_case						, save_infos=__snake_case   )
   _lowerCamelCase	:					Union[str, Any]   =		TestCommand(*__snake_case   )
   test_command.run()
   _lowerCamelCase	:					List[str]   =		os.path.join(__snake_case						, """README.md"""   )
   assert os.path.exists(__snake_case   )
   _lowerCamelCase	:					Dict   =		DatasetInfosDict.from_directory(__snake_case   )
   _lowerCamelCase	:					int   =		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:
      _lowerCamelCase   ,    _lowerCamelCase	:					Any   =		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





"""simple docstring"""
import warnings

from ...utils import logging
from .image_processing_imagegpt import ImageGPTImageProcessor


UpperCAmelCase				=						logging.get_logger(__name__)




class 	lowercase__				(					A_				):





   def __init__(							self      ,   *SCREAMING_SNAKE_CASE      ,   **SCREAMING_SNAKE_CASE)  ->  None:
      warnings.warn(
          """The class ImageGPTFeatureExtractor is deprecated and will be removed in version 5 of Transformers."""
          """ Please use ImageGPTImageProcessor instead."""      ,   SCREAMING_SNAKE_CASE      ,   )
      super().__init__(*SCREAMING_SNAKE_CASE      ,   **SCREAMING_SNAKE_CASE)

