"""simple docstring"""







import json
from typing import TYPE_CHECKING, List, Optional, Tuple

from tokenizers import pre_tokenizers

from ...tokenization_utils_base import BatchEncoding
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_gpta import GPTaTokenizer


if TYPE_CHECKING:
       from transformers.pipelines.conversational import Conversation


__SCREAMING_SNAKE_CASE								=logging.get_logger(__name__)

__SCREAMING_SNAKE_CASE								={'vocab_file': 'vocab.json', 'merges_file': 'merges.txt', 'tokenizer_file': 'tokenizer.json'}

__SCREAMING_SNAKE_CASE								={
    'vocab_file': {
        'gpt2': 'https://huggingface.co/gpt2/resolve/main/vocab.json',
        'gpt2-medium': 'https://huggingface.co/gpt2-medium/resolve/main/vocab.json',
        'gpt2-large': 'https://huggingface.co/gpt2-large/resolve/main/vocab.json',
        'gpt2-xl': 'https://huggingface.co/gpt2-xl/resolve/main/vocab.json',
        'distilgpt2': 'https://huggingface.co/distilgpt2/resolve/main/vocab.json',
    },
    'merges_file': {
        'gpt2': 'https://huggingface.co/gpt2/resolve/main/merges.txt',
        'gpt2-medium': 'https://huggingface.co/gpt2-medium/resolve/main/merges.txt',
        'gpt2-large': 'https://huggingface.co/gpt2-large/resolve/main/merges.txt',
        'gpt2-xl': 'https://huggingface.co/gpt2-xl/resolve/main/merges.txt',
        'distilgpt2': 'https://huggingface.co/distilgpt2/resolve/main/merges.txt',
    },
    'tokenizer_file': {
        'gpt2': 'https://huggingface.co/gpt2/resolve/main/tokenizer.json',
        'gpt2-medium': 'https://huggingface.co/gpt2-medium/resolve/main/tokenizer.json',
        'gpt2-large': 'https://huggingface.co/gpt2-large/resolve/main/tokenizer.json',
        'gpt2-xl': 'https://huggingface.co/gpt2-xl/resolve/main/tokenizer.json',
        'distilgpt2': 'https://huggingface.co/distilgpt2/resolve/main/tokenizer.json',
    },
}

__SCREAMING_SNAKE_CASE								={
    'gpt2': 1024,
    'gpt2-medium': 1024,
    'gpt2-large': 1024,
    'gpt2-xl': 1024,
    'distilgpt2': 1024,
}







class  UpperCamelCase	(    lowercase_						):
   lowercase							=						VOCAB_FILES_NAMES
   lowercase							=						PRETRAINED_VOCAB_FILES_MAP
   lowercase							=						PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
   lowercase							=						['''input_ids''', '''attention_mask''']
   lowercase							=						GPTaTokenizer
   def __init__(    self  ,__UpperCamelCase=None  ,__UpperCamelCase=None  ,__UpperCamelCase=None  ,__UpperCamelCase="<|endoftext|>"  ,__UpperCamelCase="<|endoftext|>"  ,__UpperCamelCase="<|endoftext|>"  ,__UpperCamelCase=False  ,**__UpperCamelCase  ,)			->   List[str]:


          '''simple docstring'''


          super().__init__(
              A__  ,A__  ,tokenizer_file=A__  ,unk_token=A__  ,bos_token=A__  ,eos_token=A__  ,add_prefix_space=A__  ,**A__  ,)

          lowercase_			:       Union[str, Any]									=			kwargs.pop('add_bos_token'  ,A__    )

          lowercase_			:       Union[str, Any]									=			json.loads(self.backend_tokenizer.pre_tokenizer.__getstate__()    )
          if pre_tok_state.get('add_prefix_space'  ,A__    ) != add_prefix_space:
                 lowercase_			:       Union[str, Any]									=			getattr(A__  ,pre_tok_state.pop('type'    )    )
                 lowercase_			:       Tuple									=			add_prefix_space
                 lowercase_			:       Dict									=			pre_tok_class(**A__    )

          lowercase_			:       Dict									=			add_prefix_space
   def     _UpperCAmelCase     (    self  ,*__UpperCamelCase  ,**__UpperCamelCase    )			->   Optional[Any]:


          '''simple docstring'''


          lowercase_			:       Any									=			kwargs.get('is_split_into_words'  ,A__    )
          assert self.add_prefix_space or not is_split_into_words, (
              f'''You need to instantiate {self.__class__.__name__} with add_prefix_space=True '''
              "to use it with pretokenized inputs."
          )

          return super()._batch_encode_plus(*A__  ,**A__    )
   def     _UpperCAmelCase     (    self  ,*__UpperCamelCase  ,**__UpperCamelCase    )			->   Any:


          '''simple docstring'''


          lowercase_			:       Optional[Any]									=			kwargs.get('is_split_into_words'  ,A__    )

          assert self.add_prefix_space or not is_split_into_words, (
              f'''You need to instantiate {self.__class__.__name__} with add_prefix_space=True '''
              "to use it with pretokenized inputs."
          )

          return super()._encode_plus(*A__  ,**A__    )
   def     _UpperCAmelCase     (    self  ,__UpperCamelCase  ,__UpperCamelCase = None    )			->   Any:


          '''simple docstring'''


          lowercase_			:       Optional[int]									=			self._tokenizer.model.save(A__  ,name=A__    )
          return tuple(A__    )







   def     _UpperCAmelCase     (    self  ,__UpperCamelCase    )			->   Dict:


          '''simple docstring'''


          lowercase_			:       Optional[Any]									=			[]
          for is_user, text in conversation.iter_texts():
                 input_ids.extend(self.encode(A__  ,add_special_tokens=A__    ) + [self.eos_token_id]    )

          if len(A__    ) > self.model_max_length:
                 lowercase_			:       str									=			input_ids[-self.model_max_length :]
          return input_ids


"""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

