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

import sentencepiece as spm

from ...tokenization_utils import PreTrainedTokenizer


if TYPE_CHECKING:
		from ...tokenization_utils_base import TextInput
from ...utils import logging


lowerCamelCase      				=     logging.get_logger(__name__)

lowerCamelCase      				=     {'vocab_file': 'spiece.model'}

lowerCamelCase      				=     {
    'vocab_file': {
        't5-small': 'https://huggingface.co/t5-small/resolve/main/spiece.model',
        't5-base': 'https://huggingface.co/t5-base/resolve/main/spiece.model',
        't5-large': 'https://huggingface.co/t5-large/resolve/main/spiece.model',
        't5-3b': 'https://huggingface.co/t5-3b/resolve/main/spiece.model',
        't5-11b': 'https://huggingface.co/t5-11b/resolve/main/spiece.model',
    }
}


# TODO(PVP) - this should be removed in Transformers v5
lowerCamelCase      				=     {
    't5-small': 5_1_2,
    't5-base': 5_1_2,
    't5-large': 5_1_2,
    't5-3b': 5_1_2,
    't5-11b': 5_1_2,
}

lowerCamelCase      				=     '▁'





class     snake_case_			(				a__							):


							"""simple docstring"""





							__UpperCAmelCase       =VOCAB_FILES_NAMES
							__UpperCAmelCase       =PRETRAINED_VOCAB_FILES_MAP
							__UpperCAmelCase       =PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
							__UpperCAmelCase       =["""input_ids""", """attention_mask"""]




							def __init__( self					,						_A					,						_A="</s>"					,						_A="<unk>"					,						_A="<pad>"					,						_A=1_0_0					,						_A=None					,						_A = None					,						_A=True					,						**_A					,						):





												if extra_ids > 0 and additional_special_tokens is None:
																	__lowerCAmelCase					=							[F"""<extra_id_{i}>""" for i in range(lowercase__							)]
												elif extra_ids > 0 and additional_special_tokens is not None:
																	# Check that we have the right number of extra_id special tokens
																	__lowerCAmelCase					=							len(set(filter(lambda _A							: bool('extra_id' in str(lowercase__							)							)					,						lowercase__							)							)							)
																	if extra_tokens != extra_ids:
																						raise ValueError(
																						    F"""Both extra_ids ({extra_ids}) and additional_special_tokens ({additional_special_tokens}) are"""
																						    ' provided to T5Tokenizer. In this case the additional_special_tokens must include the extra_ids'
																						    ' tokens'							)
												if legacy:
																	logger.warning_once(
																	    F"""You are using the legacy behaviour of the {self.__class__}. This means that tokens that come after special tokens will not be properly handled. We recommend you to"""
																	    ' read the related pull request available at https://github.com/huggingface/transformers/pull/24565'							)

												__lowerCAmelCase					=							legacy
												__lowerCAmelCase					=							{} if sp_model_kwargs is None else sp_model_kwargs

												super().__init__(
												    eos_token=lowercase__					,						unk_token=lowercase__					,						pad_token=lowercase__					,						extra_ids=lowercase__					,						additional_special_tokens=lowercase__					,						sp_model_kwargs=self.sp_model_kwargs					,						legacy=lowercase__					,						**lowercase__					,						)

												__lowerCAmelCase					=							vocab_file
												__lowerCAmelCase					=							extra_ids

												__lowerCAmelCase					=							spm.SentencePieceProcessor(**self.sp_model_kwargs							)
												self.sp_model.Load(lowercase__							)




							@staticmethod
							def        A__  ( _A					,						_A					,						_A							):





												if pretrained_model_name_or_path in TaTokenizer.max_model_input_sizes:
																	__lowerCAmelCase					=							TaTokenizer.max_model_input_sizes[pretrained_model_name_or_path]
																	if init_max_model_length is not None and init_max_model_length != max_model_length:
																						return init_max_model_length
																	elif init_max_model_length is None:
																						warnings.warn(
																						    'This tokenizer was incorrectly instantiated with a model max length of'
																						    F""" {deprecated_max_model_length} which will be corrected in Transformers v5.\nFor now, this"""
																						    ' behavior is kept to avoid breaking backwards compatibility when padding/encoding with'
																						    ' `truncation is True`.\n- Be aware that you SHOULD NOT rely on'
																						    F""" {pretrained_model_name_or_path} automatically truncating your input to"""
																						    F""" {deprecated_max_model_length} when padding/encoding.\n- If you want to encode/pad to sequences"""
																						    F""" longer than {deprecated_max_model_length} you can either instantiate this tokenizer with"""
																						    ' `model_max_length` or pass `max_length` when encoding/padding.\n- To avoid this warning, please'
																						    ' instantiate this tokenizer with `model_max_length` set to your preferred value.'					,						lowercase__					,						)

												return max_model_length




							@property
							def        A__  ( self							):
												return self.sp_model.get_piece_size() + self._extra_ids




							def        A__  ( self							):
												__lowerCAmelCase					=							{self.convert_ids_to_tokens(lowercase__							): i for i in range(self.vocab_size							)}
												vocab.update(self.added_tokens_encoder							)
												return vocab




							def        A__  ( self					,						_A					,						_A = None					,						_A = False							):





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

												# normal case: some special tokens
												if token_ids_a is None:
																	return ([0] * len(lowercase__							)) + [1]
												return ([0] * len(lowercase__							)) + [1] + ([0] * len(lowercase__							)) + [1]




							def        A__  ( self							):
												return list(
												    set(filter(lambda _A							: bool(re.search(R'<extra_id_\d+>'					,						lowercase__							)							) is not None					,						self.additional_special_tokens							)							)							)




							def        A__  ( self							):
												return [self._convert_token_to_id(lowercase__							) for token in self.get_sentinel_tokens()]




							def        A__  ( self					,						_A							):





												if len(lowercase__							) > 0 and token_ids[-1] == self.eos_token_id:
																	warnings.warn(
																	    F"""This sequence already has {self.eos_token}. In future versions this behavior may lead to duplicated"""
																	    ' eos tokens being added.'							)
																	return token_ids
												else:
																	return token_ids + [self.eos_token_id]




							def        A__  ( self					,						_A					,						_A = None							):
												__lowerCAmelCase					=							[self.eos_token_id]

												if token_ids_a is None:
																	return len(token_ids_a + eos							) * [0]
												return len(token_ids_a + eos + token_ids_a + eos							) * [0]




							def        A__  ( self					,						_A					,						_A = None							):
												__lowerCAmelCase					=							self._add_eos_if_not_present(lowercase__							)
												if token_ids_a is None:
																	return token_ids_a
												else:
																	__lowerCAmelCase					=							self._add_eos_if_not_present(lowercase__							)
																	return token_ids_a + token_ids_a




							def __getstate__( self							):
												__lowerCAmelCase					=							self.__dict__.copy()
												__lowerCAmelCase					=							None
												return state




							def __setstate__( self					,						_A							):
												__lowerCAmelCase					=							d

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

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




							def        A__  ( self					,						_A					,						**_A							):





												if not self.legacy:
																	__lowerCAmelCase					=							SPIECE_UNDERLINE + text.replace(lowercase__					,						' '							)
												return super().tokenize(lowercase__					,						**lowercase__							)




							def        A__  ( self					,						_A					,						**_A							):





												if not self.legacy:
																	__lowerCAmelCase					=							text.startswith(lowercase__							)
																	if is_first:
																						__lowerCAmelCase					=							text[1:]

												__lowerCAmelCase					=							self.sp_model.encode(lowercase__					,						out_type=lowercase__							)

												if not self.legacy and not is_first and not text.startswith(' '							) and tokens[0].startswith(lowercase__							):
																	__lowerCAmelCase					=							([tokens[0][1:]] if len(tokens[0]							) > 1 else []) + tokens[1:]
												return tokens




							def        A__  ( self					,						_A							):





												if token.startswith('<extra_id_'							):
																	__lowerCAmelCase					=							re.match(R'<extra_id_(\d+)>'					,						lowercase__							)
																	__lowerCAmelCase					=							int(match.group(1							)							)
																	return self.vocab_size - num - 1
												return self.sp_model.piece_to_id(lowercase__							)




							def        A__  ( self					,						_A							):





												if index < self.sp_model.get_piece_size():
																	__lowerCAmelCase					=							self.sp_model.IdToPiece(lowercase__							)
												else:
																	__lowerCAmelCase					=							F"""<extra_id_{self.vocab_size - 1 - index}>"""
												return token




							def        A__  ( self					,						_A							):
												__lowerCAmelCase					=							[]
												__lowerCAmelCase					=							''''''
												__lowerCAmelCase					=							False
												for token in tokens:
																	# make sure that special tokens are not decoded using sentencepiece model
																	if token in self.all_special_tokens:
																						if not prev_is_special:
																											out_string += " "
																						out_string += self.sp_model.decode(lowercase__							) + token
																						__lowerCAmelCase					=							True
																						__lowerCAmelCase					=							[]
																	else:
																						current_sub_tokens.append(lowercase__							)
																						__lowerCAmelCase					=							False
												out_string += self.sp_model.decode(lowercase__							)
												return out_string.strip()




							def        A__  ( self					,						_A					,						_A = None							):





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

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

												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     snake_case_			(				_a							):


							"""simple docstring"""





							__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        A__  ( self							):
												super().setup()
												__lowerCAmelCase					=							self.model.config
												__lowerCAmelCase					=							-1
												for idx, label in config.idalabel.items():
																	if label.lower().startswith('entail'							):
																						__lowerCAmelCase					=							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        A__  ( self					,						_A					,						_A							):
												__lowerCAmelCase					=							labels
												return self.pre_processor(
												    [text] * len(_A							)					,						[F"""This example is {label}""" for label in labels]					,						return_tensors='pt'					,						padding='max_length'					,						)




							def        A__  ( self					,						_A							):
												__lowerCAmelCase					=							outputs.logits
												__lowerCAmelCase					=							torch.argmax(logits[:, 2]							).item()
												return self._labels[label_id]






