






# 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














import json
from typing import List, Optional, Tuple

from tokenizers import normalizers

from ...tokenization_utils_fast import PreTrainedTokenizerFast
from .tokenization_electra import ElectraTokenizer


UpperCAmelCase__				   =		{"vocab_file": "vocab.txt", "tokenizer_file": "tokenizer.json"}

UpperCAmelCase__				   =		{
    "vocab_file": {
        "google/electra-small-generator": (
            "https://huggingface.co/google/electra-small-generator/resolve/main/vocab.txt"
        ),
        "google/electra-base-generator": "https://huggingface.co/google/electra-base-generator/resolve/main/vocab.txt",
        "google/electra-large-generator": (
            "https://huggingface.co/google/electra-large-generator/resolve/main/vocab.txt"
        ),
        "google/electra-small-discriminator": (
            "https://huggingface.co/google/electra-small-discriminator/resolve/main/vocab.txt"
        ),
        "google/electra-base-discriminator": (
            "https://huggingface.co/google/electra-base-discriminator/resolve/main/vocab.txt"
        ),
        "google/electra-large-discriminator": (
            "https://huggingface.co/google/electra-large-discriminator/resolve/main/vocab.txt"
        ),
    },
    "tokenizer_file": {
        "google/electra-small-generator": (
            "https://huggingface.co/google/electra-small-generator/resolve/main/tokenizer.json"
        ),
        "google/electra-base-generator": (
            "https://huggingface.co/google/electra-base-generator/resolve/main/tokenizer.json"
        ),
        "google/electra-large-generator": (
            "https://huggingface.co/google/electra-large-generator/resolve/main/tokenizer.json"
        ),
        "google/electra-small-discriminator": (
            "https://huggingface.co/google/electra-small-discriminator/resolve/main/tokenizer.json"
        ),
        "google/electra-base-discriminator": (
            "https://huggingface.co/google/electra-base-discriminator/resolve/main/tokenizer.json"
        ),
        "google/electra-large-discriminator": (
            "https://huggingface.co/google/electra-large-discriminator/resolve/main/tokenizer.json"
        ),
    },
}

UpperCAmelCase__				   =		{
    "google/electra-small-generator": 512,
    "google/electra-base-generator": 512,
    "google/electra-large-generator": 512,
    "google/electra-small-discriminator": 512,
    "google/electra-base-discriminator": 512,
    "google/electra-large-discriminator": 512,
}

UpperCAmelCase__				   =		{
    "google/electra-small-generator": {"do_lower_case": True},
    "google/electra-base-generator": {"do_lower_case": True},
    "google/electra-large-generator": {"do_lower_case": True},
    "google/electra-small-discriminator": {"do_lower_case": True},
    "google/electra-base-discriminator": {"do_lower_case": True},
    "google/electra-large-discriminator": {"do_lower_case": True},
}






class 		lowercase_					(	lowercase   ):






      '''simple docstring'''




      __snake_case       =  VOCAB_FILES_NAMES
      __snake_case       =  PRETRAINED_VOCAB_FILES_MAP
      __snake_case       =  PRETRAINED_INIT_CONFIGURATION
      __snake_case       =  PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
      __snake_case       =  ElectraTokenizer







      def __init__(      self      :						Dict ,					__UpperCAmelCase      :						int=None ,					__UpperCAmelCase      :						str=None ,					__UpperCAmelCase      :						Optional[int]=True ,					__UpperCAmelCase      :						str="[UNK]" ,					__UpperCAmelCase      :						Any="[SEP]" ,					__UpperCAmelCase      :						str="[PAD]" ,					__UpperCAmelCase      :						Optional[Any]="[CLS]" ,					__UpperCAmelCase      :						Union[str, Any]="[MASK]" ,					__UpperCAmelCase      :						List[str]=True ,					__UpperCAmelCase      :						Tuple=None ,					**__UpperCAmelCase      :						Optional[int] ,					)    ->str:




       """simple docstring"""


       super().__init__(
           __UpperCAmelCase ,					tokenizer_file=__UpperCAmelCase ,					do_lower_case=__UpperCAmelCase ,					unk_token=__UpperCAmelCase ,					sep_token=__UpperCAmelCase ,					pad_token=__UpperCAmelCase ,					cls_token=__UpperCAmelCase ,					mask_token=__UpperCAmelCase ,					tokenize_chinese_chars=__UpperCAmelCase ,					strip_accents=__UpperCAmelCase ,					**__UpperCAmelCase ,					)

       a		  =      json.loads(self.backend_tokenizer.normalizer.__getstate__()  )
       if (
           normalizer_state.get('''lowercase''' ,					__UpperCAmelCase  ) != do_lower_case
           or normalizer_state.get('''strip_accents''' ,					__UpperCAmelCase  ) != strip_accents
           or normalizer_state.get('''handle_chinese_chars''' ,					__UpperCAmelCase  ) != tokenize_chinese_chars
       ):
        a		  =      getattr(__UpperCAmelCase ,					normalizer_state.pop('''type'''  )  )
        a		  =      do_lower_case
        a		  =      strip_accents
        a		  =      tokenize_chinese_chars
        a		  =      normalizer_class(**__UpperCAmelCase  )

       a		  =      do_lower_case







      def       __lowerCAmelCase					(      self      :						List[Any] ,					__UpperCAmelCase      :						Optional[int] ,					__UpperCAmelCase      :						Tuple=None  )    ->str:




       """simple docstring"""


       a		  =      [self.cls_token_id] + token_ids_a + [self.sep_token_id]

       if token_ids_a:
        output += token_ids_a + [self.sep_token_id]

       return output







      def       __lowerCAmelCase					(      self      :						Optional[Any] ,					__UpperCAmelCase      :						List[int] ,					__UpperCAmelCase      :						Optional[List[int]] = None  )    ->List[int]:




       """simple docstring"""


       a		  =      [self.sep_token_id]
       a		  =      [self.cls_token_id]
       if token_ids_a is None:
        return len(cls + token_ids_a + sep  ) * [0]
       return len(cls + token_ids_a + sep  ) * [0] + len(token_ids_a + sep  ) * [1]







      def       __lowerCAmelCase					(      self      :						List[str] ,					__UpperCAmelCase      :						str ,					__UpperCAmelCase      :						Optional[str] = None  )    ->Tuple[str]:




       """simple docstring"""


       a		  =      self._tokenizer.model.save(__UpperCAmelCase ,					name=__UpperCAmelCase  )
       return tuple(__UpperCAmelCase  )


