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_rembert import RemBertTokenizer
else:
    __lowercase							 =					None

__lowercase							 =					logging.get_logger(__name__)
__lowercase							 =					{'''vocab_file''': '''sentencepiece.model''', '''tokenizer_file''': '''tokenizer.json'''}

__lowercase							 =					{
    '''vocab_file''': {
        '''google/rembert''': '''https://huggingface.co/google/rembert/resolve/main/sentencepiece.model''',
    },
    '''tokenizer_file''': {
        '''google/rembert''': '''https://huggingface.co/google/rembert/resolve/main/tokenizer.json''',
    },
}

__lowercase							 =					{
    '''google/rembert''': 256,
}

__lowercase							 =					'''▁'''


class 	lowerCamelCase_     (   UpperCAmelCase_ ):






    '''simple docstring'''






    a__	:						Union[str, Any]						=      VOCAB_FILES_NAMES
    a__	:						Union[str, Any]						=      PRETRAINED_VOCAB_FILES_MAP
    a__	:						str						=      PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
    a__	:						Optional[Any]						=      RemBertTokenizer







    def __init__(	self , __lowercase=None , __lowercase=None , __lowercase=True , __lowercase=True , __lowercase=False , __lowercase="[CLS]" , __lowercase="[SEP]" , __lowercase="<unk>" , __lowercase="[SEP]" , __lowercase="<pad>" , __lowercase="[CLS]" , __lowercase="[MASK]" , **__lowercase , )       ->	Dict:
        # Mask token behave like a normal word, i.e. include the space before it
        __UpperCamelCase     :int			  =		AddedToken(__lowercase , lstrip=__lowercase , rstrip=__lowercase) if isinstance(__lowercase , __lowercase) else mask_token

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

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







    def 			UpperCamelCase__   (	self , __lowercase , __lowercase = None)       ->	List[int]:
        __UpperCamelCase     :Dict			  =		[self.sep_token_id]
        __UpperCamelCase     :int			  =		[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 			UpperCamelCase__   (	self , __lowercase , __lowercase = None , __lowercase = False)       ->	List[int]:

        if already_has_special_tokens:
            if token_ids_a is not None:
                raise ValueError(
                    '''You should not supply a second sequence if the provided sequence of '''
                    '''ids is already formatted with special tokens for the model.''')
            return [1 if x in [self.sep_token_id, self.cls_token_id] else 0 for x in token_ids_a]

        if token_ids_a is not None:
            return [1] + ([0] * len(__lowercase)) + [1] + ([0] * len(__lowercase)) + [1]
        return [1] + ([0] * len(__lowercase)) + [1]







    def 			UpperCamelCase__   (	self , __lowercase , __lowercase = None)       ->	List[int]:
        __UpperCamelCase     :Optional[Any]			  =		[self.sep_token_id]
        __UpperCamelCase     :List[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 			UpperCamelCase__   (	self , __lowercase , __lowercase = None)       ->	Tuple[str]:
        if not os.path.isdir(__lowercase):
            logger.error('''Vocabulary path ({}) should be a directory'''.format(__lowercase))
            return
        __UpperCamelCase     :List[str]			  =		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):
            copyfile(self.vocab_file , __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 	lowerCamelCase_     (   UpperCAmelCase_ ):






    '''simple docstring'''






    a__	:						Optional[Any]						=      """facebook/bart-large-mnli"""
    a__	:						int						=      (
        """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."""
    )
    a__	:						Optional[Any]						=      """text_classifier"""
    a__	:						Any						=      AutoTokenizer
    a__	:						str						=      AutoModelForSequenceClassification

    a__	:						str						=      ["""text""", ["""text"""]]
    a__	:						Optional[int]						=      ["""text"""]







    def 			UpperCamelCase__   (	self)       ->	Union[str, Any]:
        super().setup()
        __UpperCamelCase     :int			  =		self.model.config
        __UpperCamelCase     :Optional[Any]			  =		-1
        for idx, label in config.idalabel.items():
            if label.lower().startswith('''entail'''):
                __UpperCamelCase     :List[Any]			  =		int(__lowercase)
        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 , __lowercase , __lowercase)       ->	Union[str, Any]:
        __UpperCamelCase     :Any			  =		labels
        return self.pre_processor(
            [text] * len(__lowercase) , [f"""This example is {label}""" for label in labels] , return_tensors='''pt''' , padding='''max_length''' , )







    def 			UpperCamelCase__   (	self , __lowercase)       ->	Optional[Any]:
        __UpperCamelCase     :List[Any]			  =		outputs.logits
        __UpperCamelCase     :Any			  =		torch.argmax(logits[:, 2]).item()
        return self._labels[label_id]





