"""simple docstring"""


# Lint as: python3
# pylint: enable=line-too-long
# pylint: disable=g-import-not-at-top,g-bad-import-order,wrong-import-position

UpperCAmelCase__     						=  """2.13.1"""

import platform

import pyarrow
from packaging import version


if version.parse(platform.python_version()) < version.parse("""3.7"""):
    raise ImportWarning(
        """To use `datasets`, Python>=3.7 is required, and the current version of Python doesn't match this condition."""
    )

if version.parse(pyarrow.__version__).major < 8:
    raise ImportWarning(
        """To use `datasets`, the module `pyarrow>=8.0.0` is required, and the current version of `pyarrow` doesn't match this condition.\n"""
        """If you are running this in a Google Colab, you should probably just restart the runtime to use the right version of `pyarrow`."""
    )

del platform
del pyarrow
del version

from .arrow_dataset import Dataset
from .arrow_reader import ReadInstruction
from .builder import ArrowBasedBuilder, BeamBasedBuilder, BuilderConfig, DatasetBuilder, GeneratorBasedBuilder
from .combine import concatenate_datasets, interleave_datasets
from .dataset_dict import DatasetDict, IterableDatasetDict
from .download import *
from .features import *
from .fingerprint import disable_caching, enable_caching, is_caching_enabled, set_caching_enabled
from .info import DatasetInfo, MetricInfo
from .inspect import (
    get_dataset_config_info,
    get_dataset_config_names,
    get_dataset_infos,
    get_dataset_split_names,
    inspect_dataset,
    inspect_metric,
    list_datasets,
    list_metrics,
)
from .iterable_dataset import IterableDataset
from .load import load_dataset, load_dataset_builder, load_from_disk, load_metric
from .metric import Metric
from .splits import (
    NamedSplit,
    NamedSplitAll,
    Split,
    SplitBase,
    SplitDict,
    SplitGenerator,
    SplitInfo,
    SubSplitInfo,
    percent,
)
from .tasks import *
from .utils import *
from .utils import logging


# deprecated modules
from datasets import arrow_dataset as _arrow_dataset  # isort:skip
from datasets import utils as _utils  # isort:skip
from datasets.utils import download_manager as _deprecated_download_manager  # isort:skip

UpperCAmelCase__     						=  concatenate_datasets
UpperCAmelCase__     						=  DownloadConfig
UpperCAmelCase__     						=  DownloadManager
UpperCAmelCase__     						=  DownloadMode
UpperCAmelCase__     						=  DownloadConfig
UpperCAmelCase__     						=  DownloadMode
UpperCAmelCase__     						=  DownloadManager

del _arrow_dataset, _utils, _deprecated_download_manager







from collections import OrderedDict
from typing import Mapping

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


__lowerCamelCase       :     List[Any]   =     logging.get_logger(__name__)

__lowerCamelCase       :     str   =     {
    """roberta-base""": """https://huggingface.co/roberta-base/resolve/main/config.json""",
    """roberta-large""": """https://huggingface.co/roberta-large/resolve/main/config.json""",
    """roberta-large-mnli""": """https://huggingface.co/roberta-large-mnli/resolve/main/config.json""",
    """distilroberta-base""": """https://huggingface.co/distilroberta-base/resolve/main/config.json""",
    """roberta-base-openai-detector""": """https://huggingface.co/roberta-base-openai-detector/resolve/main/config.json""",
    """roberta-large-openai-detector""": """https://huggingface.co/roberta-large-openai-detector/resolve/main/config.json""",
}



class 					A__   (       __snake_case			):
      _UpperCAmelCase	:Union[str, Any]						    =   'roberta'





      def __init__( self       ,							A_=5_0265       ,							A_=768       ,							A_=12       ,							A_=12       ,							A_=3072       ,							A_="gelu"       ,							A_=0.1       ,							A_=0.1       ,							A_=512       ,							A_=2       ,							A_=0.02       ,							A_=1e-12       ,							A_=1       ,							A_=0       ,							A_=2       ,							A_="absolute"       ,							A_=True       ,							A_=None       ,							**A_       ,							):
       '''simple docstring'''




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

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



class 					A__   (       __snake_case			):





      @property
      def        __UpperCamelCase( self   ):
       '''simple docstring'''




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