





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


UpperCAmelCase						:   Dict			  =     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  _SCREAMING_SNAKE_CASE				(   a					,						a  )      ->      int:
					return (abs(source - target  ) / target) < 0.01







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











from typing import TYPE_CHECKING

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


UpperCAmelCase						:   Any			  =     {
    '''configuration_mvp''': ['''MVP_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''MvpConfig''', '''MvpOnnxConfig'''],
    '''tokenization_mvp''': ['''MvpTokenizer'''],
}

try:
				if not is_tokenizers_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				UpperCAmelCase						:   int			  =     ['''MvpTokenizerFast''']

try:
				if not is_torch_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				UpperCAmelCase						:   str			  =     [
				    '''MVP_PRETRAINED_MODEL_ARCHIVE_LIST''',
				    '''MvpForCausalLM''',
				    '''MvpForConditionalGeneration''',
				    '''MvpForQuestionAnswering''',
				    '''MvpForSequenceClassification''',
				    '''MvpModel''',
				    '''MvpPreTrainedModel''',
				]

if TYPE_CHECKING:
				from .configuration_mvp import MVP_PRETRAINED_CONFIG_ARCHIVE_MAP, MvpConfig, MvpOnnxConfig
				from .tokenization_mvp import MvpTokenizer

				try:
								if not is_tokenizers_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .tokenization_mvp_fast import MvpTokenizerFast

				try:
								if not is_torch_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .modeling_mvp import (
								    MVP_PRETRAINED_MODEL_ARCHIVE_LIST,
								    MvpForCausalLM,
								    MvpForConditionalGeneration,
								    MvpForQuestionAnswering,
								    MvpForSequenceClassification,
								    MvpModel,
								    MvpPreTrainedModel,
								)

else:
				import sys

				UpperCAmelCase						:   List[str]			  =     _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)




