






'''simple docstring'''
from collections import OrderedDict
from typing import Mapping

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


UpperCamelCase__ =			logging.get_logger(__name__)

UpperCamelCase__ =			{
    'andreasmadsen/efficient_mlm_m0.40': (
        'https://huggingface.co/andreasmadsen/efficient_mlm_m0.40/resolve/main/config.json'
    ),
}


class        _UpperCAmelCase						(		snake_case			):
    __lowerCamelCase:   List[str]		       =    'roberta-prelayernorm'




    def __init__(			self  :       List[Any]  ,     a  :       str=5_0_2_6_5  ,     a  :       Tuple=7_6_8  ,     a  :       Optional[int]=1_2  ,     a  :       Tuple=1_2  ,     a  :       List[str]=3_0_7_2  ,     a  :       Optional[int]="gelu"  ,     a  :       str=0.1  ,     a  :       Tuple=0.1  ,     a  :       int=5_1_2  ,     a  :       Union[str, Any]=2  ,     a  :       List[Any]=0.02  ,     a  :       Dict=1e-12  ,     a  :       int=1  ,     a  :       str=0  ,     a  :       List[Any]=2  ,     a  :       List[Any]="absolute"  ,     a  :       str=True  ,     a  :       Dict=None  ,     **a  :       Optional[int]  ,     ):






       '''simple docstring'''






       super().__init__(pad_token_id=a  ,     bos_token_id=a  ,     eos_token_id=a  ,     **a			)

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


class        _UpperCAmelCase						(		snake_case			):




    @property
    def 				lowerCAmelCase__				(			self  :       List[Any]			):






       '''simple docstring'''






       if self.task == "multiple-choice":
          lowercase_	:    str           =     {0: "batch", 1: "choice", 2: "sequence"}
       else:
          lowercase_	:    Tuple           =     {0: "batch", 1: "sequence"}
       return OrderedDict(
           [
               ("input_ids", dynamic_axis),
               ("attention_mask", dynamic_axis),
           ]			)












'''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 		__SCREAMING_SNAKE_CASE					(  _UpperCamelCase						,		_UpperCamelCase							):


   """simple docstring"""




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





@pytest.mark.integration
def 		__SCREAMING_SNAKE_CASE					(  _UpperCamelCase							):


   """simple docstring"""




   lowercase_	:    Optional[int]           =     _TestCommandArgs(dataset=_UpperCamelCase						,		all_configs=_UpperCamelCase						,		save_infos=_UpperCamelCase							)
   lowercase_	:    int           =     TestCommand(*_UpperCamelCase							)
   test_command.run()
   lowercase_	:    List[str]           =     os.path.join(_UpperCamelCase						,		"README.md"							)
   assert os.path.exists(_UpperCamelCase							)
   lowercase_	:    Any           =     DatasetInfosDict.from_directory(_UpperCamelCase							)
   lowercase_	:    Optional[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": 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:
      lowercase_    ,		lowercase_	:    Optional[int]           =     getattr(dataset_infos["default"]						,		_UpperCamelCase							), getattr(expected_dataset_infos["default"]						,		_UpperCamelCase							)
      if key == "num_bytes":
         assert is_apercent_close(_UpperCamelCase						,		_UpperCamelCase							)
      elif key == "splits":
         assert list(_UpperCamelCase							) == list(_UpperCamelCase							)
         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




