

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

import sentencepiece as spm

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


a_    :						Dict    		=      logging.get_logger(__name__)


a_    :						Optional[int]    		=      "▁"

a_    :						Optional[Any]    		=      {"vocab_file": "spiece.model"}

a_    :						Dict    		=      {
    "vocab_file": {
        "google/reformer-crime-and-punishment": (
            "https://huggingface.co/google/reformer-crime-and-punishment/resolve/main/spiece.model"
        )
    }
}

a_    :						Optional[int]    		=      {
    "google/reformer-crime-and-punishment": 52_4288,
}




class      UpperCamelCase (     SCREAMING_SNAKE_CASE   ):
    __UpperCamelCase         =VOCAB_FILES_NAMES
    __UpperCamelCase         =PRETRAINED_VOCAB_FILES_MAP
    __UpperCamelCase         =PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
    __UpperCamelCase         =["input_ids", "attention_mask"]

    def __init__(			self :      List[str]							,    snake_case__ :      List[Any]							,    snake_case__ :      Optional[int]="</s>"							,    snake_case__ :      Dict="<unk>"							,    snake_case__ :      List[Any]=[]							,    snake_case__ :      Optional[Dict[str, Any]] = None							,    **snake_case__ :      List[Any]							,    ):

     """simple docstring"""






     SCREAMING_SNAKE_CASE								=			{} if sp_model_kwargs is None else sp_model_kwargs

     super().__init__(
         eos_token=snake_case__							,    unk_token=snake_case__							,    additional_special_tokens=snake_case__							,    sp_model_kwargs=self.sp_model_kwargs							,    **snake_case__							,    )

     SCREAMING_SNAKE_CASE								=			vocab_file
     SCREAMING_SNAKE_CASE								=			spm.SentencePieceProcessor(**self.sp_model_kwargs    )
     self.sp_model.Load(snake_case__    )

    @property
    def UpperCamelCase			(			self :      Tuple    ):

     """simple docstring"""






     return self.sp_model.get_piece_size()

    def UpperCamelCase			(			self :      Optional[Any]    ):

     """simple docstring"""






     SCREAMING_SNAKE_CASE								=			{self.convert_ids_to_tokens(snake_case__    ): i for i in range(self.vocab_size    )}
     vocab.update(self.added_tokens_encoder    )
     return vocab

    def __getstate__(			self :      Dict    ):

     """simple docstring"""






     SCREAMING_SNAKE_CASE								=			self.__dict__.copy()
     SCREAMING_SNAKE_CASE								=			None
     return state

    def __setstate__(			self :      int							,    snake_case__ :      str    ):

     """simple docstring"""






     SCREAMING_SNAKE_CASE								=			d

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

     SCREAMING_SNAKE_CASE								=			spm.SentencePieceProcessor(**self.sp_model_kwargs    )
     self.sp_model.Load(self.vocab_file    )

    def UpperCamelCase			(			self :      Union[str, Any]							,    snake_case__ :      str    ):

     """simple docstring"""






     return self.sp_model.encode(snake_case__							,    out_type=snake_case__    )

    def UpperCamelCase			(			self :      List[str]							,    snake_case__ :      Tuple    ):

     """simple docstring"""






     return self.sp_model.piece_to_id(snake_case__    )

    def UpperCamelCase			(			self :      Any							,    snake_case__ :      str    ):

     """simple docstring"""






     if index < self.sp_model.get_piece_size():
      SCREAMING_SNAKE_CASE								=			self.sp_model.IdToPiece(snake_case__    )
     return token

    def UpperCamelCase			(			self :      int							,    snake_case__ :      Optional[int]    ):

     """simple docstring"""






     SCREAMING_SNAKE_CASE								=			[]
     SCREAMING_SNAKE_CASE								=			''
     for token in tokens:
      # make sure that special tokens are not decoded using sentencepiece model
      if token in self.all_special_tokens:
       out_string += self.sp_model.decode(snake_case__    ) + token
       SCREAMING_SNAKE_CASE								=			[]
      else:
       current_sub_tokens.append(snake_case__    )
     out_string += self.sp_model.decode(snake_case__    )
     return out_string.strip()




    def UpperCamelCase			(			self :      List[Any]							,    snake_case__ :      str							,    snake_case__ :      Optional[str] = None    ):

     """simple docstring"""






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

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

     return (out_vocab_file,)




# Copyright 2023 The HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import torch

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




class      UpperCamelCase (     SCREAMING_SNAKE_CASE   ):
    __UpperCamelCase         ="facebook/bart-large-mnli"
    __UpperCamelCase         =(
        "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."
    )
    __UpperCamelCase         ="text_classifier"
    __UpperCamelCase         =AutoTokenizer
    __UpperCamelCase         =AutoModelForSequenceClassification

    __UpperCamelCase         =["text", ["text"]]
    __UpperCamelCase         =["text"]

    def UpperCamelCase			(			self :      Optional[Any]    ):

     """simple docstring"""






     super().setup()
     SCREAMING_SNAKE_CASE								=			self.model.config
     SCREAMING_SNAKE_CASE								=			-1
     for idx, label in config.idalabel.items():
      if label.lower().startswith('entail'    ):
       SCREAMING_SNAKE_CASE								=			int(snake_case__    )
     if self.entailment_id == -1:
      raise ValueError('Could not determine the entailment ID from the model config, please pass it at init.'    )

    def UpperCamelCase			(			self :      Optional[Any]							,    snake_case__ :      List[str]							,    snake_case__ :      Dict    ):

     """simple docstring"""






     SCREAMING_SNAKE_CASE								=			labels
     return self.pre_processor(
         [text] * len(snake_case__    )							,    [F"""This example is {label}""" for label in labels]							,    return_tensors='pt'							,    padding='max_length'							,    )




    def UpperCamelCase			(			self :      Dict							,    snake_case__ :      Dict    ):

     """simple docstring"""






     SCREAMING_SNAKE_CASE								=			outputs.logits
     SCREAMING_SNAKE_CASE								=			torch.argmax(logits[:, 2]    ).item()
     return self._labels[label_id]

