





'''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


__UpperCamelCase					    =	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						(       _lowerCamelCase    ,	_lowerCamelCase ) ->				List[Any]:



			"""simple docstring"""


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







@pytest.mark.integration
def 					_a						(       _lowerCamelCase ) ->				int:



			"""simple docstring"""


			__snake_case					:      Optional[int]							      =     _TestCommandArgs(dataset=_lowerCamelCase    ,	all_configs=_lowerCamelCase    ,	save_infos=_lowerCamelCase )
			__snake_case					:      Any							      =     TestCommand(*_lowerCamelCase )
			test_command.run()
			__snake_case					:      Tuple							      =     os.path.join(_lowerCamelCase    ,	"""README.md""" )
			assert os.path.exists(_lowerCamelCase )
			__snake_case					:      Optional[int]							      =     DatasetInfosDict.from_directory(_lowerCamelCase )
			__snake_case					:      Dict							      =     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""": 235_1563,
			                    """num_examples""": 1_0000,
			                },
			                {
			                    """name""": """validation""",
			                    """num_bytes""": 23_8418,
			                    """num_examples""": 1000,
			                },
			            ]    ,	download_size=394_0680    ,	dataset_size=258_9981    ,	)
			    } )
			assert dataset_infos.keys() == expected_dataset_infos.keys()
			for key in DatasetInfo._INCLUDED_INFO_IN_YAML:
						__snake_case      ,     __snake_case					:      Dict							      =     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









'''simple docstring'''




import logging

from transformers.configuration_utils import PretrainedConfig


_SCREAMING_SNAKE_CASE							     =			logging.getLogger(__name__)
class 			_lowerCAmelCase		(			A__	):






     """simple docstring"""






     snake_case_						      =   "masked_bert"



     def __init__(   self							:			Any   ,       __snake_case							:			Optional[Any]=3_05_22   ,       __snake_case							:			List[Any]=7_68   ,       __snake_case							:			int=12   ,       __snake_case							:			List[str]=12   ,       __snake_case							:			int=30_72   ,       __snake_case							:			Optional[int]="gelu"   ,       __snake_case							:			Any=0.1   ,       __snake_case							:			Tuple=0.1   ,       __snake_case							:			List[str]=5_12   ,       __snake_case							:			Tuple=2   ,       __snake_case							:			Tuple=0.02   ,       __snake_case							:			Tuple=1e-12   ,       __snake_case							:			Union[str, Any]=0   ,       __snake_case							:			int="topK"   ,       __snake_case							:			int="constant"   ,       __snake_case							:			List[str]=0.0   ,       **__snake_case							:			Any   ,       )->	Union[str, Any]:
        super().__init__(pad_token_id=__snake_case   ,       **__snake_case						)

        snake_case 						=   vocab_size
        snake_case 						=   hidden_size
        snake_case 						=   num_hidden_layers
        snake_case 						=   num_attention_heads
        snake_case 						=   hidden_act
        snake_case 						=   intermediate_size
        snake_case 						=   hidden_dropout_prob
        snake_case 						=   attention_probs_dropout_prob
        snake_case 						=   max_position_embeddings
        snake_case 						=   type_vocab_size
        snake_case 						=   initializer_range
        snake_case 						=   layer_norm_eps
        snake_case 						=   pruning_method
        snake_case 						=   mask_init
        snake_case 						=   mask_scale

