
"""simple docstring"""


from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_sentencepiece_available,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


A_    							=					{"""configuration_xlnet""": ["""XLNET_PRETRAINED_CONFIG_ARCHIVE_MAP""", """XLNetConfig"""]}

try:
						if not is_sentencepiece_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						A_    							=					["""XLNetTokenizer"""]

try:
						if not is_tokenizers_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						A_    							=					["""XLNetTokenizerFast"""]

try:
						if not is_torch_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						A_    							=					[
						    """XLNET_PRETRAINED_MODEL_ARCHIVE_LIST""",
						    """XLNetForMultipleChoice""",
						    """XLNetForQuestionAnswering""",
						    """XLNetForQuestionAnsweringSimple""",
						    """XLNetForSequenceClassification""",
						    """XLNetForTokenClassification""",
						    """XLNetLMHeadModel""",
						    """XLNetModel""",
						    """XLNetPreTrainedModel""",
						    """load_tf_weights_in_xlnet""",
						]

try:
						if not is_tf_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						A_    							=					[
						    """TF_XLNET_PRETRAINED_MODEL_ARCHIVE_LIST""",
						    """TFXLNetForMultipleChoice""",
						    """TFXLNetForQuestionAnsweringSimple""",
						    """TFXLNetForSequenceClassification""",
						    """TFXLNetForTokenClassification""",
						    """TFXLNetLMHeadModel""",
						    """TFXLNetMainLayer""",
						    """TFXLNetModel""",
						    """TFXLNetPreTrainedModel""",
						]


if TYPE_CHECKING:
						from .configuration_xlnet import XLNET_PRETRAINED_CONFIG_ARCHIVE_MAP, XLNetConfig

						try:
												if not is_sentencepiece_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .tokenization_xlnet import XLNetTokenizer

						try:
												if not is_tokenizers_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .tokenization_xlnet_fast import XLNetTokenizerFast

						try:
												if not is_torch_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .modeling_xlnet import (
												    XLNET_PRETRAINED_MODEL_ARCHIVE_LIST,
												    XLNetForMultipleChoice,
												    XLNetForQuestionAnswering,
												    XLNetForQuestionAnsweringSimple,
												    XLNetForSequenceClassification,
												    XLNetForTokenClassification,
												    XLNetLMHeadModel,
												    XLNetModel,
												    XLNetPreTrainedModel,
												    load_tf_weights_in_xlnet,
												)

						try:
												if not is_tf_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .modeling_tf_xlnet import (
												    TF_XLNET_PRETRAINED_MODEL_ARCHIVE_LIST,
												    TFXLNetForMultipleChoice,
												    TFXLNetForQuestionAnsweringSimple,
												    TFXLNetForSequenceClassification,
												    TFXLNetForTokenClassification,
												    TFXLNetLMHeadModel,
												    TFXLNetMainLayer,
												    TFXLNetModel,
												    TFXLNetPreTrainedModel,
												)

else:
						import sys

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





"""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  lowercase						(	lowerCAmelCase__	,lowerCAmelCase__						):
			return (abs(source - target						) / target) < 0.01






@pytest.mark.integration
def  lowercase						(	lowerCAmelCase__						):
			lowerCamelCase_					=  _TestCommandArgs(dataset=lowerCAmelCase__	,all_configs=lowerCAmelCase__	,save_infos=lowerCAmelCase__						)
			lowerCamelCase_					=  TestCommand(*lowerCAmelCase__						)
			test_command.run()
			lowerCamelCase_					=  os.path.join(lowerCAmelCase__	,'''README.md'''						)
			assert os.path.exists(lowerCAmelCase__						)
			lowerCamelCase_					=  DatasetInfosDict.from_directory(lowerCAmelCase__						)
			lowerCamelCase_					=  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:
						lowerCamelCase_		, lowerCamelCase_					=  getattr(dataset_infos['''default''']	,lowerCAmelCase__						), getattr(expected_dataset_infos['''default''']	,lowerCAmelCase__						)
						if key == "num_bytes":
									assert is_apercent_close(lowerCAmelCase__	,lowerCAmelCase__						)
						elif key == "splits":
									assert list(lowerCAmelCase__						) == list(lowerCAmelCase__						)
									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



