"""simple docstring"""







import collections
import os
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple

from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging


__SCREAMING_SNAKE_CASE								=logging.get_logger(__name__)

__SCREAMING_SNAKE_CASE								="▁"

__SCREAMING_SNAKE_CASE								={"vocab_file": "prophetnet.tokenizer"}

__SCREAMING_SNAKE_CASE								={
    "vocab_file": {
        "microsoft/xprophetnet-large-wiki100-cased": (
            "https://huggingface.co/microsoft/xprophetnet-large-wiki100-cased/resolve/main/prophetnet.tokenizer"
        ),
    }
}

__SCREAMING_SNAKE_CASE								={
    "microsoft/xprophetnet-large-wiki100-cased": {"do_lower_case": False},
}

__SCREAMING_SNAKE_CASE								={
    "microsoft/xprophetnet-large-wiki100-cased": 512,
}



def     lowercase__( __SCREAMING_SNAKE_CASE							:  Dict   ):
     lowercase_			:       str									=			collections.OrderedDict()
     with open(__SCREAMING_SNAKE_CASE  ,    'r'  ,    encoding='utf-8'   ) as reader:
          lowercase_			:       Union[str, Any]									=			reader.readlines()
     for index, token in enumerate(__SCREAMING_SNAKE_CASE   ):
          lowercase_			:       List[str]									=			token.rstrip('\n'   )
          lowercase_			:       List[str]									=			index
     return vocab







class  UpperCamelCase	(    lowercase_						):
   lowercase							=						VOCAB_FILES_NAMES
   lowercase							=						PRETRAINED_VOCAB_FILES_MAP
   lowercase							=						PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
   lowercase							=						['input_ids', 'attention_mask']
   def __init__(    self  ,__UpperCamelCase  ,__UpperCamelCase="[SEP]"  ,__UpperCamelCase="[SEP]"  ,__UpperCamelCase="[SEP]"  ,__UpperCamelCase="[UNK]"  ,__UpperCamelCase="[PAD]"  ,__UpperCamelCase="[CLS]"  ,__UpperCamelCase="[MASK]"  ,__UpperCamelCase = None  ,**__UpperCamelCase  ,)			->   None:


        '''simple docstring'''


        lowercase_			:       Tuple									=			{} if sp_model_kwargs is None else sp_model_kwargs

        super().__init__(
            bos_token=__UpperCamelCase  ,eos_token=__UpperCamelCase  ,sep_token=__UpperCamelCase  ,unk_token=__UpperCamelCase  ,pad_token=__UpperCamelCase  ,cls_token=__UpperCamelCase  ,mask_token=__UpperCamelCase  ,sp_model_kwargs=self.sp_model_kwargs  ,**__UpperCamelCase  ,)

        try:
             import sentencepiece as spm
        except ImportError:
             logger.warning(
                 'You need to install SentencePiece to use XLMRobertaTokenizer: https://github.com/google/sentencepiece'
                 ' pip install sentencepiece'    )
             raise

        lowercase_			:       int									=			spm.SentencePieceProcessor(**self.sp_model_kwargs    )
        self.sp_model.Load(str(__UpperCamelCase    )    )
        lowercase_			:       List[Any]									=			vocab_file

        # Original fairseq vocab and spm vocab must be "aligned":
        # Vocab    |    0    |    1    |   2    |    3    |  4  |  5  |  6  |   7   |   8   |  9
        # -------- | ------- | ------- | ------ | ------- | --- | --- | --- | ----- | ----- | ----
        # fairseq  | '<s>'   | '<pad>' | '</s>' | '<unk>' | ',' | '.' | '▁' | 's'   | '▁de' | '-'
        # spm      | '<unk>' | '<s>'   | '</s>' | ','     | '.' | '▁' | 's' | '▁de' | '-'   | '▁a'

        # put special tokens and [unused] tokens into the vocab
        lowercase_			:       Dict									=			{'[PAD]': 0, '[CLS]': 1, '[SEP]': 2, '[UNK]': 3, '[MASK]': 4}

        for i in range(10    ):
             lowercase_			:       Optional[Any]									=			f'''[unused{i}]'''
             lowercase_			:       Dict									=			5 + i

        # The first "real" token "," has position 15 in the embedding vocab and position 3 in the spm vocab
        lowercase_			:       Union[str, Any]									=			12
        lowercase_			:       Any									=			{v: k for k, v in self.fairseq_tokens_to_ids.items()}
        for k in self.fairseq_tokens_to_ids.keys():
             self.unique_no_split_tokens.append(__UpperCamelCase    )
   def __getstate__(    self    )			->   Any:


        '''simple docstring'''


        lowercase_			:       Tuple									=			self.__dict__.copy()
        lowercase_			:       Tuple									=			None
        return state
   def __setstate__(    self  ,__UpperCamelCase    )			->   Dict:


        '''simple docstring'''


        lowercase_			:       List[Any]									=			d
        try:
             import sentencepiece as spm
        except ImportError:
             logger.warning(
                 'You need to install SentencePiece to use XLMRobertaTokenizer: https://github.com/google/sentencepiece'
                 ' pip install sentencepiece'    )
             raise

        # for backward compatibility
        if not hasattr(self  ,'sp_model_kwargs'    ):
             lowercase_			:       str									=			{}

        lowercase_			:       Tuple									=			spm.SentencePieceProcessor(**self.sp_model_kwargs    )
        self.sp_model.Load(self.vocab_file    )
   def     _UpperCAmelCase     (    self  ,__UpperCamelCase  ,__UpperCamelCase = None  ,__UpperCamelCase = False    )			->   List[int]:


        '''simple docstring'''


        if already_has_special_tokens:
             return super().get_special_tokens_mask(
                 token_ids_a=__UpperCamelCase  ,token_ids_a=__UpperCamelCase  ,already_has_special_tokens=__UpperCamelCase    )

        if token_ids_a is None:
             return ([0] * len(__UpperCamelCase    )) + [1]
        return ([0] * len(__UpperCamelCase    )) + [1] + ([0] * len(__UpperCamelCase    )) + [1]
   def     _UpperCAmelCase     (    self  ,__UpperCamelCase  ,__UpperCamelCase = None    )			->   List[int]:


        '''simple docstring'''


        lowercase_			:       Dict									=			[self.sep_token_id]

        if token_ids_a is None:
             return len(token_ids_a + sep    ) * [0]
        return len(token_ids_a + sep + sep + token_ids_a + sep    ) * [0]
   @property
   def     _UpperCAmelCase     (    self    )			->   str:


        '''simple docstring'''


        return len(self.sp_model    ) + self.fairseq_offset
   def     _UpperCAmelCase     (    self    )			->   Optional[Any]:


        '''simple docstring'''


        lowercase_			:       Union[str, Any]									=			{self.convert_ids_to_tokens(__UpperCamelCase    ): i for i in range(self.vocab_size    )}
        vocab.update(self.added_tokens_encoder    )
        return vocab
   def     _UpperCAmelCase     (    self  ,__UpperCamelCase    )			->   str:


        '''simple docstring'''


        return self.sp_model.encode(__UpperCamelCase  ,out_type=__UpperCamelCase    )
   def     _UpperCAmelCase     (    self  ,__UpperCamelCase    )			->   Optional[Any]:


        '''simple docstring'''


        if token in self.fairseq_tokens_to_ids:
             return self.fairseq_tokens_to_ids[token]
        lowercase_			:       str									=			self.sp_model.PieceToId(__UpperCamelCase    )

        # Need to return unknown token if the SP model returned 0
        return spm_id + self.fairseq_offset if spm_id else self.unk_token_id
   def     _UpperCAmelCase     (    self  ,__UpperCamelCase    )			->   Optional[Any]:


        '''simple docstring'''


        if index in self.fairseq_ids_to_tokens:
             return self.fairseq_ids_to_tokens[index]
        return self.sp_model.IdToPiece(index - self.fairseq_offset    )
   def     _UpperCAmelCase     (    self  ,__UpperCamelCase    )			->   List[Any]:


        '''simple docstring'''


        lowercase_			:       List[str]									=			''.join(__UpperCamelCase    ).replace(__UpperCamelCase  ,' '    ).strip()
        return out_string
   def     _UpperCAmelCase     (    self  ,__UpperCamelCase  ,__UpperCamelCase = None    )			->   Tuple[str]:


        '''simple docstring'''


        if not os.path.isdir(__UpperCamelCase    ):
             logger.error(f'''Vocabulary path ({save_directory}) should be a directory'''    )
             return
        lowercase_			:       Tuple									=			os.path.join(
            __UpperCamelCase  ,(filename_prefix + '-' if filename_prefix else '') + VOCAB_FILES_NAMES['vocab_file']    )

        if os.path.abspath(self.vocab_file    ) != os.path.abspath(__UpperCamelCase    ) and os.path.isfile(self.vocab_file    ):
             copyfile(self.vocab_file  ,__UpperCamelCase    )
        elif not os.path.isfile(self.vocab_file    ):
             with open(__UpperCamelCase  ,'wb'    ) as fi:
                  lowercase_			:       Any									=			self.sp_model.serialized_model_proto()
                  fi.write(__UpperCamelCase    )

        return (out_vocab_file,)







   def     _UpperCAmelCase     (    self  ,__UpperCamelCase  ,__UpperCamelCase = None    )			->   List[int]:


        '''simple docstring'''


        if token_ids_a is None:
             return token_ids_a + [self.sep_token_id]
        lowercase_			:       Optional[int]									=			[self.sep_token_id]
        return token_ids_a + sep + token_ids_a + sep


"""simple docstring"""







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

__SCREAMING_SNAKE_CASE								="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

__SCREAMING_SNAKE_CASE								=concatenate_datasets
__SCREAMING_SNAKE_CASE								=DownloadConfig
__SCREAMING_SNAKE_CASE								=DownloadManager
__SCREAMING_SNAKE_CASE								=DownloadMode
__SCREAMING_SNAKE_CASE								=DownloadConfig
__SCREAMING_SNAKE_CASE								=DownloadMode
__SCREAMING_SNAKE_CASE								=DownloadManager

del _arrow_dataset, _utils, _deprecated_download_manager

