'''simple docstring'''






import torch

from ..models.auto import AutoModelForSequenceClassification, AutoTokenizer
from .base import PipelineTool

class  UpperCAmelCase__						(       UpperCamelCase__ ):
    lowerCAmelCase_				    =		"""facebook/bart-large-mnli"""
    lowerCAmelCase_				    =		(
        """This is a tool that classifies an English text using provided labels. It takes two inputs: `text`, which """
        """should be the text to classify, and `labels`, which should be the list of labels to use for classification. """
        """It returns the most likely label in the list of provided `labels` for the input text."""
    )
    lowerCAmelCase_				    =		"""text_classifier"""
    lowerCAmelCase_				    =		AutoTokenizer
    lowerCAmelCase_				    =		AutoModelForSequenceClassification

    lowerCAmelCase_				    =		["""text""", ["""text"""]]
    lowerCAmelCase_				    =		["""text"""]



    def   lowerCamelCase_  (	self      :       List[Any]     ):
          super().setup()
          _lowerCamelCase				:   Dict								=   self.model.config
          _lowerCamelCase				:   Tuple								=   -1
          for idx, label in config.idalabel.items():
                if label.lower().startswith("entail"     ):
                      _lowerCamelCase				:   List[Any]								=   int(__A     )
          if self.entailment_id == -1:
                raise ValueError("Could not determine the entailment ID from the model config, please pass it at init."     )



    def   lowerCamelCase_  (	self      :       str,__A      :       Union[str, Any],__A      :       Dict     ):
          _lowerCamelCase				:   List[Any]								=   labels
          return self.pre_processor(
              [text] * len(__A     ),[f'This example is {label}' for label in labels],return_tensors="pt",padding="max_length",)



    def   lowerCamelCase_  (	self      :       Tuple,__A      :       Dict     ):
          _lowerCamelCase				:   Tuple								=   outputs.logits
          _lowerCamelCase				:   Optional[Any]								=   torch.argmax(logits[:, 2]     ).item()
          return self._labels[label_id]

'''simple docstring'''






import os
from shutil import copyfile
from typing import List, Optional, Tuple

from ...tokenization_utils import AddedToken
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging


if is_sentencepiece_available():
 from .tokenization_albert import AlbertTokenizer
else:
 UpperCAmelCase_       :   Any   =       None

UpperCAmelCase_       :   Any   =       logging.get_logger(__name__)
UpperCAmelCase_       :   Optional[Any]   =       {'vocab_file': 'spiece.model', 'tokenizer_file': 'tokenizer.json'}

UpperCAmelCase_       :   List[Any]   =       {
    'vocab_file': {
        'albert-base-v1': 'https://huggingface.co/albert-base-v1/resolve/main/spiece.model',
        'albert-large-v1': 'https://huggingface.co/albert-large-v1/resolve/main/spiece.model',
        'albert-xlarge-v1': 'https://huggingface.co/albert-xlarge-v1/resolve/main/spiece.model',
        'albert-xxlarge-v1': 'https://huggingface.co/albert-xxlarge-v1/resolve/main/spiece.model',
        'albert-base-v2': 'https://huggingface.co/albert-base-v2/resolve/main/spiece.model',
        'albert-large-v2': 'https://huggingface.co/albert-large-v2/resolve/main/spiece.model',
        'albert-xlarge-v2': 'https://huggingface.co/albert-xlarge-v2/resolve/main/spiece.model',
        'albert-xxlarge-v2': 'https://huggingface.co/albert-xxlarge-v2/resolve/main/spiece.model',
    },
    'tokenizer_file': {
        'albert-base-v1': 'https://huggingface.co/albert-base-v1/resolve/main/tokenizer.json',
        'albert-large-v1': 'https://huggingface.co/albert-large-v1/resolve/main/tokenizer.json',
        'albert-xlarge-v1': 'https://huggingface.co/albert-xlarge-v1/resolve/main/tokenizer.json',
        'albert-xxlarge-v1': 'https://huggingface.co/albert-xxlarge-v1/resolve/main/tokenizer.json',
        'albert-base-v2': 'https://huggingface.co/albert-base-v2/resolve/main/tokenizer.json',
        'albert-large-v2': 'https://huggingface.co/albert-large-v2/resolve/main/tokenizer.json',
        'albert-xlarge-v2': 'https://huggingface.co/albert-xlarge-v2/resolve/main/tokenizer.json',
        'albert-xxlarge-v2': 'https://huggingface.co/albert-xxlarge-v2/resolve/main/tokenizer.json',
    },
}

UpperCAmelCase_       :   str   =       {
    'albert-base-v1': 512,
    'albert-large-v1': 512,
    'albert-xlarge-v1': 512,
    'albert-xxlarge-v1': 512,
    'albert-base-v2': 512,
    'albert-large-v2': 512,
    'albert-xlarge-v2': 512,
    'albert-xxlarge-v2': 512,
}

UpperCAmelCase_       :   str   =       '▁'

class  UpperCAmelCase__						(       A ):
    lowerCAmelCase_				    =		VOCAB_FILES_NAMES
    lowerCAmelCase_				    =		PRETRAINED_VOCAB_FILES_MAP
    lowerCAmelCase_				    =		PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
    lowerCAmelCase_				    =		AlbertTokenizer



    def __init__(	self      :       Dict,__A      :       str=None,__A      :       Union[str, Any]=None,__A      :       Union[str, Any]=True,__A      :       int=True,__A      :       List[Any]=False,__A      :       Any="[CLS]",__A      :       List[str]="[SEP]",__A      :       Tuple="<unk>",__A      :       Dict="[SEP]",__A      :       Dict="<pad>",__A      :       List[str]="[CLS]",__A      :       Any="[MASK]",**__A      :       Tuple,):
          # Mask token behave like a normal word, i.e. include the space before it and
          # is included in the raw text, there should be a match in a non-normalized sentence.
          _lowerCamelCase				:   Union[str, Any]								=   (
              AddedToken(__A,lstrip=__A,rstrip=__A,normalized=__A     )
              if isinstance(__A,__A     )
              else mask_token
          )

          super().__init__(
              __A,tokenizer_file=__A,do_lower_case=__A,remove_space=__A,keep_accents=__A,bos_token=__A,eos_token=__A,unk_token=__A,sep_token=__A,pad_token=__A,cls_token=__A,mask_token=__A,**__A,)

          _lowerCamelCase				:   Dict								=   do_lower_case
          _lowerCamelCase				:   List[str]								=   remove_space
          _lowerCamelCase				:   Any								=   keep_accents
          _lowerCamelCase				:   Tuple								=   vocab_file
          _lowerCamelCase				:   Optional[int]								=   False if not self.vocab_file else True



    def   lowerCamelCase_  (	self      :       List[str],__A      :       List[int],__A      :       Optional[List[int]] = None     ):
          _lowerCamelCase				:   List[Any]								=   [self.sep_token_id]
          _lowerCamelCase				:   Any								=   [self.cls_token_id]
          if token_ids_a is None:
                return cls + token_ids_a + sep
          return cls + token_ids_a + sep + token_ids_a + sep



    def   lowerCamelCase_  (	self      :       List[str],__A      :       List[int],__A      :       Optional[List[int]] = None     ):
          _lowerCamelCase				:   Optional[Any]								=   [self.sep_token_id]
          _lowerCamelCase				:   Union[str, Any]								=   [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      :       str,__A      :       str,__A      :       Optional[str] = None     ):
          if not self.can_save_slow_tokenizer:
                raise ValueError(
                    "Your fast tokenizer does not have the necessary information to save the vocabulary for a slow "
                    "tokenizer."     )

          if not os.path.isdir(__A     ):
                logger.error(f'Vocabulary path ({save_directory}) should be a directory'     )
                return
          _lowerCamelCase				:   Union[str, Any]								=   os.path.join(
              __A,(filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"]     )

          if os.path.abspath(self.vocab_file     ) != os.path.abspath(__A     ):
                copyfile(self.vocab_file,__A     )

          return (out_vocab_file,)