

"""simple docstring"""




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

from tokenizers import pre_tokenizers, processors

from ...tokenization_utils_base import AddedToken, BatchEncoding
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_blenderbot import BlenderbotTokenizer


if TYPE_CHECKING:
						from transformers.pipelines.conversational import Conversation

A_							:			Optional[Any]											= logging.get_logger(__name__)


A_							:			List[str]											= {
    "vocab_file": "vocab.json",
    "merges_file": "merges.txt",
    "tokenizer_config_file": "tokenizer_config.json",
}

A_							:			Union[str, Any]											= {
    "vocab_file": {"facebook/blenderbot-3B": "https://huggingface.co/facebook/blenderbot-3B/resolve/main/vocab.json"},
    "merges_file": {"facebook/blenderbot-3B": "https://huggingface.co/facebook/blenderbot-3B/resolve/main/merges.txt"},
    "tokenizer_config_file": {
        "facebook/blenderbot-3B": "https://huggingface.co/facebook/blenderbot-3B/resolve/main/tokenizer_config.json"
    },
}

A_							:			Tuple											= {"facebook/blenderbot-3B": 128}





class    lowerCamelCase						(A__			):
			lowerCamelCase__   :		Tuple							=							VOCAB_FILES_NAMES
			lowerCamelCase__   :		List[str]							=							PRETRAINED_VOCAB_FILES_MAP
			lowerCamelCase__   :		int							=							PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
			lowerCamelCase__   :		Optional[Any]							=							['input_ids', 'attention_mask']
			lowerCamelCase__   :		str							=							BlenderbotTokenizer

			def __init__(					self				:						Optional[int]    ,				__UpperCAmelCase				:						Any=None    ,				__UpperCAmelCase				:						Union[str, Any]=None    ,				__UpperCAmelCase				:						Dict=None    ,				__UpperCAmelCase				:						str="replace"    ,				__UpperCAmelCase				:						Dict="<s>"    ,				__UpperCAmelCase				:						Tuple="</s>"    ,				__UpperCAmelCase				:						Dict="</s>"    ,				__UpperCAmelCase				:						List[Any]="<s>"    ,				__UpperCAmelCase				:						List[str]="<unk>"    ,				__UpperCAmelCase				:						Any="<pad>"    ,				__UpperCAmelCase				:						Optional[Any]="<mask>"    ,				__UpperCAmelCase				:						List[Any]=False    ,				__UpperCAmelCase				:						str=True    ,				**__UpperCAmelCase				:						List[str]    ,				)   ->    List[str]:
						super().__init__(
						    __UpperCAmelCase    ,				__UpperCAmelCase    ,				tokenizer_file=__UpperCAmelCase    ,				errors=__UpperCAmelCase    ,				bos_token=__UpperCAmelCase    ,				eos_token=__UpperCAmelCase    ,				sep_token=__UpperCAmelCase    ,				cls_token=__UpperCAmelCase    ,				unk_token=__UpperCAmelCase    ,				pad_token=__UpperCAmelCase    ,				mask_token=__UpperCAmelCase    ,				add_prefix_space=__UpperCAmelCase    ,				trim_offsets=__UpperCAmelCase    ,				**__UpperCAmelCase    ,				)

						SCREAMING_SNAKE_CASE__								=      json.loads(self.backend_tokenizer.pre_tokenizer.__getstate__()       )
						if pre_tok_state.get("""add_prefix_space"""    ,				__UpperCAmelCase       ) != add_prefix_space:
									SCREAMING_SNAKE_CASE__								=      getattr(__UpperCAmelCase    ,				pre_tok_state.pop("""type"""       )       )
									SCREAMING_SNAKE_CASE__								=      add_prefix_space
									SCREAMING_SNAKE_CASE__								=      pre_tok_class(**__UpperCAmelCase       )

						SCREAMING_SNAKE_CASE__								=      add_prefix_space

						SCREAMING_SNAKE_CASE__								=      """post_processor"""
						SCREAMING_SNAKE_CASE__								=      getattr(self.backend_tokenizer    ,				__UpperCAmelCase    ,				__UpperCAmelCase       )
						if tokenizer_component_instance:
									SCREAMING_SNAKE_CASE__								=      json.loads(tokenizer_component_instance.__getstate__()       )

									# The lists 'sep' and 'cls' must be cased in tuples for the object `post_processor_class`
									if "sep" in state:
												SCREAMING_SNAKE_CASE__								=      tuple(state["""sep"""]       )
									if "cls" in state:
												SCREAMING_SNAKE_CASE__								=      tuple(state["""cls"""]       )

									SCREAMING_SNAKE_CASE__								=      False

									if state.get("""add_prefix_space"""    ,				__UpperCAmelCase       ) != add_prefix_space:
												SCREAMING_SNAKE_CASE__								=      add_prefix_space
												SCREAMING_SNAKE_CASE__								=      True

									if state.get("""trim_offsets"""    ,				__UpperCAmelCase       ) != trim_offsets:
												SCREAMING_SNAKE_CASE__								=      trim_offsets
												SCREAMING_SNAKE_CASE__								=      True

									if changes_to_apply:
												SCREAMING_SNAKE_CASE__								=      getattr(__UpperCAmelCase    ,				state.pop("""type"""       )       )
												SCREAMING_SNAKE_CASE__								=      component_class(**__UpperCAmelCase       )
												setattr(self.backend_tokenizer    ,				__UpperCAmelCase    ,				__UpperCAmelCase       )

			@property
			# Copied from transformers.models.roberta.tokenization_roberta_fast.RobertaTokenizerFast.mask_token with Roberta->Blenderbot, RoBERTa->Blenderbot
			def 					SCREAMING_SNAKE_CASE				(					self				:						Any       )   ->    str:
						if self._mask_token is None:
									if self.verbose:
												logger.error("""Using mask_token, but it is not set yet."""       )
									return None
						return str(self._mask_token       )

			@mask_token.setter
			def 					SCREAMING_SNAKE_CASE				(					self				:						List[str]    ,				__UpperCAmelCase				:						Optional[Any]       )   ->    List[Any]:
						SCREAMING_SNAKE_CASE__								=      AddedToken(__UpperCAmelCase    ,				lstrip=__UpperCAmelCase    ,				rstrip=__UpperCAmelCase       ) if isinstance(__UpperCAmelCase    ,				__UpperCAmelCase       ) else value
						SCREAMING_SNAKE_CASE__								=      value

			def 					SCREAMING_SNAKE_CASE				(					self				:						int    ,				*__UpperCAmelCase				:						Tuple    ,				**__UpperCAmelCase				:						Tuple       )   ->    BatchEncoding:
						SCREAMING_SNAKE_CASE__								=      kwargs.get("""is_split_into_words"""    ,				__UpperCAmelCase       )
						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(*__UpperCAmelCase    ,				**__UpperCAmelCase       )

			def 					SCREAMING_SNAKE_CASE				(					self				:						Optional[int]    ,				*__UpperCAmelCase				:						int    ,				**__UpperCAmelCase				:						Optional[Any]       )   ->    BatchEncoding:
						SCREAMING_SNAKE_CASE__								=      kwargs.get("""is_split_into_words"""    ,				__UpperCAmelCase       )

						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(*__UpperCAmelCase    ,				**__UpperCAmelCase       )

			def 					SCREAMING_SNAKE_CASE				(					self				:						Optional[Any]    ,				__UpperCAmelCase				:						str    ,				__UpperCAmelCase				:						Optional[str] = None       )   ->    Tuple[str]:
						SCREAMING_SNAKE_CASE__								=      self._tokenizer.model.save(__UpperCAmelCase    ,				name=__UpperCAmelCase       )
						return tuple(__UpperCAmelCase       )

			def 					SCREAMING_SNAKE_CASE				(					self				:						Tuple    ,				__UpperCAmelCase				:						List[int]    ,				__UpperCAmelCase				:						Optional[List[int]] = None       )   ->    List[int]:
						SCREAMING_SNAKE_CASE__								=      [self.sep_token_id]
						SCREAMING_SNAKE_CASE__								=      [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 + sep + token_ids_a + sep       ) * [0]

			def 					SCREAMING_SNAKE_CASE				(					self				:						List[Any]    ,				__UpperCAmelCase				:						List[int]    ,				__UpperCAmelCase				:						Optional[List[int]] = None       )   ->    str:
						return token_ids_a + [self.eos_token_id]





			def 					SCREAMING_SNAKE_CASE				(					self				:						List[str]    ,				__UpperCAmelCase				:						"Conversation"       )   ->    List[int]:
						SCREAMING_SNAKE_CASE__								=      []
						for is_user, text in conversation.iter_texts():
									if is_user:
												# We need to space prefix as it's being done within blenderbot
												inputs.append(""" """ + text       )
									else:
												# Generated responses should contain them already.
												inputs.append(__UpperCAmelCase       )

						SCREAMING_SNAKE_CASE__								=      """  """.join(__UpperCAmelCase       )
						SCREAMING_SNAKE_CASE__								=      self.encode(__UpperCAmelCase       )
						if len(__UpperCAmelCase       ) > self.model_max_length:
									SCREAMING_SNAKE_CASE__								=      input_ids[-self.model_max_length :]
									logger.warning(F"""Trimmed input from conversation as it was longer than {self.model_max_length} tokens."""       )
						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

A_							:			Tuple											= "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

A_							:			Tuple											= concatenate_datasets
A_							:			List[str]											= DownloadConfig
A_							:			int											= DownloadManager
A_							:			Optional[Any]											= DownloadMode
A_							:			Optional[int]											= DownloadConfig
A_							:			List[Any]											= DownloadMode
A_							:			Any											= DownloadManager

del _arrow_dataset, _utils, _deprecated_download_manager


