


'''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


_lowercase    :    List[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__   ( A     :    Dict   ,     A     :    str  ):




						'''simple docstring'''






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




@pytest.mark.integration
def    lowerCamelCase__   ( A     :    Optional[int]  ):




						'''simple docstring'''






						UpperCAmelCase         =			_TestCommandArgs(dataset=A   ,     all_configs=A   ,     save_infos=A  )
						UpperCAmelCase         =			TestCommand(*A  )
						test_command.run()
						UpperCAmelCase         =			os.path.join(A   ,     '''README.md'''  )
						assert os.path.exists(A  )
						UpperCAmelCase         =			DatasetInfosDict.from_directory(A  )
						UpperCAmelCase         =			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_35_15_63,
						                    '''num_examples''': 1_00_00,
						                },
						                {
						                    '''name''': '''validation''',
						                    '''num_bytes''': 23_84_18,
						                    '''num_examples''': 10_00,
						                },
						            ]   ,     download_size=3_94_06_80   ,     dataset_size=2_58_99_81   ,     )
						    }  )
						assert dataset_infos.keys() == expected_dataset_infos.keys()
						for key in DatasetInfo._INCLUDED_INFO_IN_YAML:
												UpperCAmelCase       ,       UpperCAmelCase         =			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'''



# Copyright 2023 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.

from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


_lowercase    :    int			     =  {
    """configuration_xmod""": [
        """XMOD_PRETRAINED_CONFIG_ARCHIVE_MAP""",
        """XmodConfig""",
        """XmodOnnxConfig""",
    ],
}

try:
	if not is_torch_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	_lowercase    :    str			     =  [
	    """XMOD_PRETRAINED_MODEL_ARCHIVE_LIST""",
	    """XmodForCausalLM""",
	    """XmodForMaskedLM""",
	    """XmodForMultipleChoice""",
	    """XmodForQuestionAnswering""",
	    """XmodForSequenceClassification""",
	    """XmodForTokenClassification""",
	    """XmodModel""",
	    """XmodPreTrainedModel""",
	]

if TYPE_CHECKING:
	from .configuration_xmod import XMOD_PRETRAINED_CONFIG_ARCHIVE_MAP, XmodConfig, XmodOnnxConfig

	try:
		if not is_torch_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_xmod import (
		    XMOD_PRETRAINED_MODEL_ARCHIVE_LIST,
		    XmodForCausalLM,
		    XmodForMaskedLM,
		    XmodForMultipleChoice,
		    XmodForQuestionAnswering,
		    XmodForSequenceClassification,
		    XmodForTokenClassification,
		    XmodModel,
		    XmodPreTrainedModel,
		)

else:
	import sys

	_lowercase    :    List[Any]			     =  _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)





