
"""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


lowerCamelCase__ :       Union[str, Any]						     =			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 __A						(   a_ :							Optional[Any]		,		a_ :							int     )->							Tuple:




		'''simple docstring'''






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





@pytest.mark.integration
def __A						(   a_ :							List[Any]     )->							int:




		'''simple docstring'''






		SCREAMING_SNAKE_CASE       :     Optional[Any]				= _TestCommandArgs(dataset=a_		,		all_configs=a_		,		save_infos=a_     )
		SCREAMING_SNAKE_CASE       :     int				= TestCommand(*a_     )
		test_command.run()
		SCREAMING_SNAKE_CASE       :     Optional[int]				= os.path.join(a_		,		'''README.md'''     )
		assert os.path.exists(a_     )
		SCREAMING_SNAKE_CASE       :     Dict				= DatasetInfosDict.from_directory(a_     )
		SCREAMING_SNAKE_CASE       :     Any				= 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:
				SCREAMING_SNAKE_CASE,					SCREAMING_SNAKE_CASE       :     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







"""simple docstring"""






import os
import sys

lowerCamelCase__ :       List[Any]						     =			os.path.join(os.path.dirname(__file__), "src")
sys.path.append(SRC_DIR)


from transformers import (
    AutoConfig,
    AutoModel,
    AutoModelForCausalLM,
    AutoModelForMaskedLM,
    AutoModelForQuestionAnswering,
    AutoModelForSequenceClassification,
    AutoTokenizer,
    add_start_docstrings,
)


lowerCamelCase__ :       str						     =			[
    "torch",
    "numpy",
    "tokenizers",
    "filelock",
    "requests",
    "tqdm",
    "regex",
    "sentencepiece",
    "sacremoses",
    "importlib_metadata",
    "huggingface_hub",
]





@add_start_docstrings(AutoConfig.__doc__     )
def __A						(   *a_ :							Any		,		**a_ :							Union[str, Any]     )->							Dict:




		'''simple docstring'''






		return AutoConfig.from_pretrained(*a_		,		**a_     )





@add_start_docstrings(AutoTokenizer.__doc__     )
def __A						(   *a_ :							str		,		**a_ :							Union[str, Any]     )->							Union[str, Any]:




		'''simple docstring'''






		return AutoTokenizer.from_pretrained(*a_		,		**a_     )





@add_start_docstrings(AutoModel.__doc__     )
def __A						(   *a_ :							List[str]		,		**a_ :							int     )->							Dict:




		'''simple docstring'''






		return AutoModel.from_pretrained(*a_		,		**a_     )





@add_start_docstrings(AutoModelForCausalLM.__doc__     )
def __A						(   *a_ :							Any		,		**a_ :							Tuple     )->							Dict:




		'''simple docstring'''






		return AutoModelForCausalLM.from_pretrained(*a_		,		**a_     )





@add_start_docstrings(AutoModelForMaskedLM.__doc__     )
def __A						(   *a_ :							Dict		,		**a_ :							Optional[Any]     )->							Optional[int]:




		'''simple docstring'''






		return AutoModelForMaskedLM.from_pretrained(*a_		,		**a_     )





@add_start_docstrings(AutoModelForSequenceClassification.__doc__     )
def __A						(   *a_ :							Optional[int]		,		**a_ :							str     )->							Optional[int]:




		'''simple docstring'''






		return AutoModelForSequenceClassification.from_pretrained(*a_		,		**a_     )





@add_start_docstrings(AutoModelForQuestionAnswering.__doc__     )
def __A						(   *a_ :							List[str]		,		**a_ :							int     )->							List[Any]:




		'''simple docstring'''






		return AutoModelForQuestionAnswering.from_pretrained(*a_		,		**a_     )





