






"""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_rembert import RemBertTokenizer
else:
							_lowerCAmelCase         =							None

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

_lowerCAmelCase         =							{
    "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",
    },
}

_lowerCAmelCase         =							{
    "google/rembert": 256,
}

_lowerCAmelCase         =							"▁"



class      SCREAMING_SNAKE_CASE  (				_SCREAMING_SNAKE_CASE						):





	"""simple docstring"""




	_A       :				Optional[int]    =				VOCAB_FILES_NAMES
	_A       :				Union[str, Any]    =				PRETRAINED_VOCAB_FILES_MAP
	_A       :				str    =				PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
	_A       :				Tuple    =				RemBertTokenizer





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

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

		A_		:						List[Any]	    =   do_lower_case
		A_		:						Tuple	    =   remove_space
		A_		:						Optional[Any]	    =   keep_accents
		A_		:						List[str]	    =   vocab_file
		A_		:						Union[str, Any]	    =   False if not self.vocab_file else True





	def    lowerCamelCase(self     ,     lowerCAmelCase_     ,     lowerCAmelCase_ = None							):
		A_		:						Any	    =   [self.sep_token_id]
		A_		:						List[str]	    =   [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     ,     lowerCAmelCase_     ,     lowerCAmelCase_ = None     ,     lowerCAmelCase_ = False							):

		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(lowerCAmelCase_							)) + [1] + ([0] * len(lowerCAmelCase_							)) + [1]
		return [1] + ([0] * len(lowerCAmelCase_							)) + [1]





	def    lowerCamelCase(self     ,     lowerCAmelCase_     ,     lowerCAmelCase_ = None							):
		A_		:						Any	    =   [self.sep_token_id]
		A_		:						Dict	    =   [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     ,     lowerCAmelCase_     ,     lowerCAmelCase_ = None							):
		if not os.path.isdir(lowerCAmelCase_							):
			logger.error("""Vocabulary path ({}) should be a directory""".format(lowerCAmelCase_							)							)
			return
		A_		:						str	    =   os.path.join(
		    lowerCAmelCase_     ,     (filename_prefix + """-""" if filename_prefix else """""") + VOCAB_FILES_NAMES["""vocab_file"""]							)

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

		return (out_vocab_file,)









"""simple docstring"""
# 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      SCREAMING_SNAKE_CASE  (				_SCREAMING_SNAKE_CASE						):





	"""simple docstring"""




	_A       :				Optional[int]    =				"""facebook/bart-large-mnli"""
	_A       :				str    =				(
	    """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       :				List[str]    =				"""text_classifier"""
	_A       :				Optional[int]    =				AutoTokenizer
	_A       :				Optional[Any]    =				AutoModelForSequenceClassification

	_A       :				List[str]    =				["""text""", ["""text"""]]
	_A       :				Dict    =				["""text"""]





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





	def    lowerCamelCase(self     ,     lowerCAmelCase_							):
		A_		:						str	    =   outputs.logits
		A_		:						Optional[int]	    =   torch.argmax(logits[:, 2]							).item()
		return self._labels[label_id]

