from ...utils import is_torch_available, is_transformers_available


if is_transformers_available() and is_torch_available():
		from .pipeline_vq_diffusion import LearnedClassifierFreeSamplingEmbeddings, VQDiffusionPipeline









# 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]






