


'''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


__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__   ):
			UpperCAmelCase__					:   List[str]       				=       _TestCommandArgs(dataset=UpperCamelCase__	, all_configs=UpperCamelCase__	, save_infos=UpperCamelCase__   )
			UpperCAmelCase__					:   Optional[Any]       				=       TestCommand(*UpperCamelCase__   )
			test_command.run()
			UpperCAmelCase__					:   Tuple       				=       os.path.join(UpperCamelCase__	, """README.md"""   )
			assert os.path.exists(UpperCamelCase__   )
			UpperCAmelCase__					:   int       				=       DatasetInfosDict.from_directory(UpperCamelCase__   )
			UpperCAmelCase__					:   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""": 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:
						UpperCAmelCase__				,    UpperCAmelCase__					:   Any       				=       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



'''simple docstring'''



from typing import TYPE_CHECKING

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


__A		={
    'configuration_m2m_100': ['M2M_100_PRETRAINED_CONFIG_ARCHIVE_MAP', 'M2M100Config', 'M2M100OnnxConfig'],
    'tokenization_m2m_100': ['M2M100Tokenizer'],
}


try:
		if not is_torch_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		__A		=[
		    'M2M_100_PRETRAINED_MODEL_ARCHIVE_LIST',
		    'M2M100ForConditionalGeneration',
		    'M2M100Model',
		    'M2M100PreTrainedModel',
		]


if TYPE_CHECKING:
		from .configuration_mam_aaa import M2M_100_PRETRAINED_CONFIG_ARCHIVE_MAP, MaMaaaConfig, MaMaaaOnnxConfig
		from .tokenization_mam_aaa import MaMaaaTokenizer

		try:
				if not is_torch_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_mam_aaa import (
				    M2M_100_PRETRAINED_MODEL_ARCHIVE_LIST,
				    MaMaaaForConditionalGeneration,
				    MaMaaaModel,
				    MaMaaaPreTrainedModel,
				)


else:
		import sys

		__A		=_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)