


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					  =     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_				(      UpperCAmelCase__						,							UpperCAmelCase__  ):
					return (abs(source - target  ) / target) < 0.01



@pytest.mark.integration
def 			UpperCAmelCase_				(      UpperCAmelCase__  ):
					lowercase_  							=			_TestCommandArgs(dataset=UpperCAmelCase__						,							all_configs=UpperCAmelCase__						,							save_infos=UpperCAmelCase__  )
					lowercase_  							=			TestCommand(*UpperCAmelCase__  )
					test_command.run()
					lowercase_  							=			os.path.join(UpperCAmelCase__						,							"""README.md"""  )
					assert os.path.exists(UpperCAmelCase__  )
					lowercase_  							=			DatasetInfosDict.from_directory(UpperCAmelCase__  )
					lowercase_  							=			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:
										lowercase_							,					lowercase_  							=			getattr(dataset_infos["""default"""]						,							UpperCAmelCase__  ), getattr(expected_dataset_infos["""default"""]						,							UpperCAmelCase__  )
										if key == "num_bytes":
															assert is_apercent_close(UpperCAmelCase__						,							UpperCAmelCase__  )
										elif key == "splits":
															assert list(UpperCAmelCase__  ) == list(UpperCAmelCase__  )
															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 PIL.Image
import PIL.ImageOps
from packaging import version
from PIL import Image


if version.parse(version.parse(PIL.__version__).base_version) >= version.parse('9.1.0'):
							a					  =     {
							    'linear': PIL.Image.Resampling.BILINEAR,
							    'bilinear': PIL.Image.Resampling.BILINEAR,
							    'bicubic': PIL.Image.Resampling.BICUBIC,
							    'lanczos': PIL.Image.Resampling.LANCZOS,
							    'nearest': PIL.Image.Resampling.NEAREST,
							}
else:
							a					  =     {
							    'linear': PIL.Image.LINEAR,
							    'bilinear': PIL.Image.BILINEAR,
							    'bicubic': PIL.Image.BICUBIC,
							    'lanczos': PIL.Image.LANCZOS,
							    'nearest': PIL.Image.NEAREST,
							}


def 			UpperCAmelCase_				(      UpperCAmelCase__  ):
					lowercase_  							=			(images / 2 + 0.5).clamp(0						,							1  )
					lowercase_  							=			images.cpu().permute(0						,							2						,							3						,							1  ).float().numpy()
					lowercase_  							=			numpy_to_pil(UpperCAmelCase__  )
					return images



def 			UpperCAmelCase_				(      UpperCAmelCase__  ):
					if images.ndim == 3:
										lowercase_  							=			images[None, ...]
					lowercase_  							=			(images * 2_5_5).round().astype("""uint8"""  )
					if images.shape[-1] == 1:
										# special case for grayscale (single channel) images
										lowercase_  							=			[Image.fromarray(image.squeeze()						,							mode="""L"""  ) for image in images]
					else:
										lowercase_  							=			[Image.fromarray(UpperCAmelCase__  ) for image in images]

					return pil_images


