





'''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


snake_case_     =	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__    : Dict    ,       UpperCamelCase__    : Any		)		->  List[Any]:
    return (abs(source - target		) / target) < 0.0_1





@pytest.mark.integration
def 							_lowerCamelCase(  UpperCamelCase__    : Optional[Any]		)		->  Union[str, Any]:
    A       :       List[Any]							=						_TestCommandArgs(dataset=UpperCamelCase__    ,       all_configs=UpperCamelCase__    ,       save_infos=UpperCamelCase__		)
    A       :       str							=						TestCommand(*UpperCamelCase__		)
    test_command.run()
    A       :       Optional[Any]							=						os.path.join(UpperCamelCase__    ,       '''README.md'''		)
    assert os.path.exists(UpperCamelCase__		)
    A       :       int							=						DatasetInfosDict.from_directory(UpperCamelCase__		)
    A       :       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_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       :       Union[str, 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


snake_case_     =	{
    """configuration_biogpt""": ["""BIOGPT_PRETRAINED_CONFIG_ARCHIVE_MAP""", """BioGptConfig"""],
    """tokenization_biogpt""": ["""BioGptTokenizer"""],
}

try:
     if not is_torch_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     snake_case_     =	[
         """BIOGPT_PRETRAINED_MODEL_ARCHIVE_LIST""",
         """BioGptForCausalLM""",
         """BioGptForTokenClassification""",
         """BioGptForSequenceClassification""",
         """BioGptModel""",
         """BioGptPreTrainedModel""",
     ]


if TYPE_CHECKING:
     from .configuration_biogpt import BIOGPT_PRETRAINED_CONFIG_ARCHIVE_MAP, BioGptConfig
     from .tokenization_biogpt import BioGptTokenizer

     try:
          if not is_torch_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .modeling_biogpt import (
              BIOGPT_PRETRAINED_MODEL_ARCHIVE_LIST,
              BioGptForCausalLM,
              BioGptForSequenceClassification,
              BioGptForTokenClassification,
              BioGptModel,
              BioGptPreTrainedModel,
          )


else:
     import sys

     snake_case_     =	_LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)
