"""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


__lowerCamelCase							 =     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__ ):






							"""simple docstring"""





							return (abs(source - target ) / target) < 0.0_1






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






							"""simple docstring"""





							A__		   =							_TestCommandArgs(dataset=A_				,			all_configs=A_				,			save_infos=A_ )
							A__		   =							TestCommand(*A_ )
							test_command.run()
							A__		   =							os.path.join(A_				,			'README.md' )
							assert os.path.exists(A_ )
							A__		   =							DatasetInfosDict.from_directory(A_ )
							A__		   =							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_351_563,
							                    'num_examples': 10_000,
							                },
							                {
							                    'name': 'validation',
							                    'num_bytes': 238_418,
							                    'num_examples': 1_000,
							                },
							            ]				,			download_size=3_940_680				,			dataset_size=2_589_981				,			)
							    } )
							assert dataset_infos.keys() == expected_dataset_infos.keys()
							for key in DatasetInfo._INCLUDED_INFO_IN_YAML:
														A__		   =							getattr(dataset_infos['default']				,			A_ ), getattr(expected_dataset_infos['default']				,			A_ )
														if key == "num_bytes":
																					assert is_apercent_close(A_				,			A_ )
														elif key == "splits":
																					assert list(A_ ) == list(A_ )
																					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"""





from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_flax_available, is_torch_available


__UpperCamelCase  :      str							      =		{
    '''configuration_longt5''': ['''LONGT5_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''LongT5Config''', '''LongT5OnnxConfig'''],
}

try:
							if not is_torch_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							__UpperCamelCase  :      str							      =		[
							    '''LONGT5_PRETRAINED_MODEL_ARCHIVE_LIST''',
							    '''LongT5EncoderModel''',
							    '''LongT5ForConditionalGeneration''',
							    '''LongT5Model''',
							    '''LongT5PreTrainedModel''',
							]

try:
							if not is_flax_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							__UpperCamelCase  :      int							      =		[
							    '''FlaxLongT5ForConditionalGeneration''',
							    '''FlaxLongT5Model''',
							    '''FlaxLongT5PreTrainedModel''',
							]


if TYPE_CHECKING:
							from .configuration_longta import LONGT5_PRETRAINED_CONFIG_ARCHIVE_MAP, LongTaConfig, LongTaOnnxConfig

							try:
														if not is_torch_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_longta import (
														    LONGT5_PRETRAINED_MODEL_ARCHIVE_LIST,
														    LongTaEncoderModel,
														    LongTaForConditionalGeneration,
														    LongTaModel,
														    LongTaPreTrainedModel,
														)

							try:
														if not is_flax_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_flax_longta import (
														    FlaxLongTaForConditionalGeneration,
														    FlaxLongTaModel,
														    FlaxLongTaPreTrainedModel,
														)


else:
							import sys

							__UpperCamelCase  :      int							      =		_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)




