





from collections import OrderedDict
from typing import Mapping

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


_lowerCamelCase :    Optional[int]  							= logging.get_logger(__name__)

_lowerCamelCase :    str  							= {
    '''facebook/xlm-roberta-xl''': '''https://huggingface.co/facebook/xlm-roberta-xl/resolve/main/config.json''',
    '''facebook/xlm-roberta-xxl''': '''https://huggingface.co/facebook/xlm-roberta-xxl/resolve/main/config.json''',
    # See all XLM-RoBERTa-XL models at https://huggingface.co/models?filter=xlm-roberta-xl
}


class 						lowerCamelCase       (__lowerCamelCase      ):







							"""simple docstring"""




							UpperCAmelCase_      						=   "xlm-roberta-xl"






							def __init__( self     :					Union[str, Any],			_UpperCAmelCase     :					Optional[int]=2_5_0_8_8_0,			_UpperCAmelCase     :					Tuple=2_5_6_0,			_UpperCAmelCase     :					str=3_6,			_UpperCAmelCase     :					str=3_2,			_UpperCAmelCase     :					List[Any]=1_0_2_4_0,			_UpperCAmelCase     :					Optional[int]="gelu",			_UpperCAmelCase     :					Optional[Any]=0.1,			_UpperCAmelCase     :					Any=0.1,			_UpperCAmelCase     :					str=5_1_4,			_UpperCAmelCase     :					Optional[int]=1,			_UpperCAmelCase     :					Tuple=0.02,			_UpperCAmelCase     :					Optional[Any]=1E-05,			_UpperCAmelCase     :					str=1,			_UpperCAmelCase     :					Optional[int]=0,			_UpperCAmelCase     :					int=2,			_UpperCAmelCase     :					Optional[int]="absolute",			_UpperCAmelCase     :					Optional[int]=True,			_UpperCAmelCase     :					Optional[Any]=None,			**_UpperCAmelCase     :					Optional[Any],			)				->			Optional[int]:





														"""simple docstring"""
														super().__init__(pad_token_id=_UpperCAmelCase,			bos_token_id=_UpperCAmelCase,			eos_token_id=_UpperCAmelCase,			**_UpperCAmelCase						)
														SCREAMING_SNAKE_CASE__       :      Optional[Any]     =						vocab_size
														SCREAMING_SNAKE_CASE__       :      Tuple     =						hidden_size
														SCREAMING_SNAKE_CASE__       :      Any     =						num_hidden_layers
														SCREAMING_SNAKE_CASE__       :      List[Any]     =						num_attention_heads
														SCREAMING_SNAKE_CASE__       :      str     =						hidden_act
														SCREAMING_SNAKE_CASE__       :      List[Any]     =						intermediate_size
														SCREAMING_SNAKE_CASE__       :      str     =						hidden_dropout_prob
														SCREAMING_SNAKE_CASE__       :      Optional[int]     =						attention_probs_dropout_prob
														SCREAMING_SNAKE_CASE__       :      Optional[int]     =						max_position_embeddings
														SCREAMING_SNAKE_CASE__       :      Dict     =						type_vocab_size
														SCREAMING_SNAKE_CASE__       :      Dict     =						initializer_range
														SCREAMING_SNAKE_CASE__       :      Any     =						layer_norm_eps
														SCREAMING_SNAKE_CASE__       :      int     =						position_embedding_type
														SCREAMING_SNAKE_CASE__       :      Any     =						use_cache
														SCREAMING_SNAKE_CASE__       :      List[str]     =						classifier_dropout


class 						lowerCamelCase       (__lowerCamelCase      ):







							"""simple docstring"""




							@property
							def  A_     ( self     :					Any						)				->			Mapping[str, Mapping[int, str]]:





														"""simple docstring"""
														if self.task == "multiple-choice":
																					SCREAMING_SNAKE_CASE__       :      Union[str, Any]     =						{0: "batch", 1: "choice", 2: "sequence"}
														else:
																					SCREAMING_SNAKE_CASE__       :      List[str]     =						{0: "batch", 1: "sequence"}
														return OrderedDict(
														    [
														        ("input_ids", dynamic_axis),
														        ("attention_mask", dynamic_axis),
														    ]						)













import pytest

from datasets.splits import SplitDict, SplitInfo
from datasets.utils.py_utils import asdict




@pytest.mark.parametrize(
    "split_dict"			,				[
        SplitDict(),
        SplitDict({"train": SplitInfo(name="train"			,				num_bytes=13_37			,				num_examples=42			,				dataset_name="my_dataset"       )}       ),
        SplitDict({"train": SplitInfo(name="train"			,				num_bytes=13_37			,				num_examples=42       )}       ),
        SplitDict({"train": SplitInfo()}       ),
    ]			,				)
def _a (  SCREAMING_SNAKE_CASE__      :  SplitDict       )	->    str:

							'''simple docstring'''






							SCREAMING_SNAKE_CASE__       :      Dict     =						split_dict._to_yaml_list()
							assert len(SCREAMING_SNAKE_CASE__       ) == len(SCREAMING_SNAKE_CASE__       )
							SCREAMING_SNAKE_CASE__       :      List[Any]     =						SplitDict._from_yaml_list(SCREAMING_SNAKE_CASE__       )
							for split_name, split_info in split_dict.items():
														# dataset_name field is deprecated, and is therefore not part of the YAML dump
														SCREAMING_SNAKE_CASE__       :      str     =						None
														# the split name of split_dict takes over the name of the split info object
														SCREAMING_SNAKE_CASE__       :      Union[str, Any]     =						split_name
							assert split_dict == reloaded







@pytest.mark.parametrize(
    "split_info"			,				[SplitInfo(), SplitInfo(dataset_name=SCREAMING_SNAKE_CASE__       ), SplitInfo(dataset_name="my_dataset"       )]       )
def _a (  SCREAMING_SNAKE_CASE__      :  Any       )	->    Dict:

							'''simple docstring'''






							SCREAMING_SNAKE_CASE__       :      str     =						asdict(SplitDict({"train": split_info}       )       )
							assert "dataset_name" in split_dict_asdict["train"]
							assert split_dict_asdict["train"]["dataset_name"] == split_info.dataset_name






