


from collections import OrderedDict
from typing import Mapping

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


_snake_case					:		Union[str, Any]      =    logging.get_logger(__name__)

_snake_case					:		str      =    {
    "xlm-roberta-base": "https://huggingface.co/xlm-roberta-base/resolve/main/config.json",
    "xlm-roberta-large": "https://huggingface.co/xlm-roberta-large/resolve/main/config.json",
    "xlm-roberta-large-finetuned-conll02-dutch": (
        "https://huggingface.co/xlm-roberta-large-finetuned-conll02-dutch/resolve/main/config.json"
    ),
    "xlm-roberta-large-finetuned-conll02-spanish": (
        "https://huggingface.co/xlm-roberta-large-finetuned-conll02-spanish/resolve/main/config.json"
    ),
    "xlm-roberta-large-finetuned-conll03-english": (
        "https://huggingface.co/xlm-roberta-large-finetuned-conll03-english/resolve/main/config.json"
    ),
    "xlm-roberta-large-finetuned-conll03-german": (
        "https://huggingface.co/xlm-roberta-large-finetuned-conll03-german/resolve/main/config.json"
    ),
}

class     a		(_lowerCAmelCase				):




							"""simple docstring"""


							__UpperCAmelCase   :							List[str]					  =  "xlm-roberta"

							def __init__(							self	:  Tuple   ,     lowerCamelCase	:  Optional[int]=30522   ,     lowerCamelCase	:  str=768   ,     lowerCamelCase	:  Tuple=12   ,     lowerCamelCase	:  Optional[int]=12   ,     lowerCamelCase	:  List[Any]=3072   ,     lowerCamelCase	:  Tuple="gelu"   ,     lowerCamelCase	:  Optional[int]=0.1   ,     lowerCamelCase	:  List[str]=0.1   ,     lowerCamelCase	:  List[Any]=512   ,     lowerCamelCase	:  Optional[int]=2   ,     lowerCamelCase	:  Optional[Any]=0.02   ,     lowerCamelCase	:  Any=1E-12   ,     lowerCamelCase	:  List[str]=1   ,     lowerCamelCase	:  Dict=0   ,     lowerCamelCase	:  Any=2   ,     lowerCamelCase	:  List[Any]="absolute"   ,     lowerCamelCase	:  Optional[Any]=True   ,     lowerCamelCase	:  Dict=None   ,     **lowerCamelCase	:  Any   ,     )     ->   List[str]:
														super().__init__(pad_token_id=lowerCamelCase   ,     bos_token_id=lowerCamelCase   ,     eos_token_id=lowerCamelCase   ,     **lowerCamelCase    )

														__snake_case		:      Optional[int]										=       vocab_size
														__snake_case		:      str										=       hidden_size
														__snake_case		:      Union[str, Any]										=       num_hidden_layers
														__snake_case		:      int										=       num_attention_heads
														__snake_case		:      Union[str, Any]										=       hidden_act
														__snake_case		:      List[Any]										=       intermediate_size
														__snake_case		:      Optional[int]										=       hidden_dropout_prob
														__snake_case		:      Optional[int]										=       attention_probs_dropout_prob
														__snake_case		:      int										=       max_position_embeddings
														__snake_case		:      List[Any]										=       type_vocab_size
														__snake_case		:      Dict										=       initializer_range
														__snake_case		:      Tuple										=       layer_norm_eps
														__snake_case		:      Any										=       position_embedding_type
														__snake_case		:      List[str]										=       use_cache
														__snake_case		:      int										=       classifier_dropout

class     a		(_lowerCAmelCase				):




							"""simple docstring"""


							@property
							def    __snake_case (							self	:  List[Any]    )     ->   Mapping[str, Mapping[int, str]]:
														if self.task == "multiple-choice":
																					__snake_case		:      List[str]										=       {0: "batch", 1: "choice", 2: "sequence"}
														else:
																					__snake_case		:      Tuple										=       {0: "batch", 1: "sequence"}
														return OrderedDict(
														    [
														        ("input_ids", dynamic_axis),
														        ("attention_mask", dynamic_axis),
														    ]    )





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


_snake_case					:		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_					( __lowerCamelCase							,     __lowerCamelCase     ):
							return (abs(source - target     ) / target) < 0.0_1







@pytest.mark.integration
def     lowerCAmelCase_					( __lowerCamelCase     ):
							__snake_case		:      Tuple										=       _TestCommandArgs(dataset=__lowerCamelCase							,     all_configs=__lowerCamelCase							,     save_infos=__lowerCamelCase     )
							__snake_case		:      List[Any]										=       TestCommand(*__lowerCamelCase     )
							test_command.run()
							__snake_case		:      List[Any]										=       os.path.join(__lowerCamelCase							,     "README.md"     )
							assert os.path.exists(__lowerCamelCase     )
							__snake_case		:      Optional[Any]										=       DatasetInfosDict.from_directory(__lowerCamelCase     )
							__snake_case		:      List[str]										=       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_3_5_1_5_6_3,
							                    "num_examples": 1_0_0_0_0,
							                },
							                {
							                    "name": "validation",
							                    "num_bytes": 2_3_8_4_1_8,
							                    "num_examples": 1_0_0_0,
							                },
							            ]							,     download_size=3_9_4_0_6_8_0							,     dataset_size=2_5_8_9_9_8_1							,     )
							    }     )
							assert dataset_infos.keys() == expected_dataset_infos.keys()
							for key in DatasetInfo._INCLUDED_INFO_IN_YAML:
														__snake_case			,      __snake_case		:      Tuple										=       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

