

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	:     str		       = 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       __lowerCamelCase			(				UpperCamelCase__     ,							UpperCamelCase__   ):







							'''simple docstring'''






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





@pytest.mark.integration
def       __lowerCamelCase			(				UpperCamelCase__   ):







							'''simple docstring'''






							snake_case_           =					_TestCommandArgs(dataset=UpperCamelCase__     ,							all_configs=UpperCamelCase__     ,							save_infos=UpperCamelCase__   )
							snake_case_           =					TestCommand(*UpperCamelCase__   )
							test_command.run()
							snake_case_           =					os.path.join(UpperCamelCase__     ,							'README.md'   )
							assert os.path.exists(UpperCamelCase__   )
							snake_case_           =					DatasetInfosDict.from_directory(UpperCamelCase__   )
							snake_case_           =					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:
														snake_case_       ,					snake_case_           =					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







from dataclasses import dataclass
from typing import List, Optional, Union

import numpy as np
import PIL

from ...utils import BaseOutput, OptionalDependencyNotAvailable, is_torch_available, is_transformers_available
from .timesteps import (
    fastaa_timesteps,
    smartaa_timesteps,
    smartaa_timesteps,
    smartaaa_timesteps,
    smartaaa_timesteps,
    superaa_timesteps,
    superaa_timesteps,
    superaaa_timesteps,
)







@dataclass
class 				lowercase       ( lowercase_    ):
				__SCREAMING_SNAKE_CASE      :				Union[List[PIL.Image.Image], np.ndarray]
				__SCREAMING_SNAKE_CASE      :				Optional[List[bool]]
				__SCREAMING_SNAKE_CASE      :				Optional[List[bool]]


try:
					if not (is_transformers_available() and is_torch_available()):
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					from ...utils.dummy_torch_and_transformers_objects import *  # noqa F403
else:
					from .pipeline_if import IFPipeline
					from .pipeline_if_imgaimg import IFImgaImgPipeline
					from .pipeline_if_imgaimg_superresolution import IFImgaImgSuperResolutionPipeline
					from .pipeline_if_inpainting import IFInpaintingPipeline
					from .pipeline_if_inpainting_superresolution import IFInpaintingSuperResolutionPipeline
					from .pipeline_if_superresolution import IFSuperResolutionPipeline
					from .safety_checker import IFSafetyChecker
					from .watermark import IFWatermarker




