






'''simple docstring'''
import copy
from dataclasses import dataclass, field
from typing import ClassVar, Dict

from ..features import Audio, ClassLabel, Features
from .base import TaskTemplate






@dataclass(frozen=_snake_case			)
class        __SCREAMING_SNAKE_CASE (     _snake_case			):
 snake_case_												=							field(default="""audio-classification"""		,			metadata={"""include_in_asdict_even_if_is_default""": True}			)
 snake_case_												=							Features({"""audio""": Audio()}			)
 snake_case_												=							Features({"""labels""": ClassLabel}			)
 snake_case_												=							"""audio"""
 snake_case_												=							"""labels"""




 def        __magic_name__		( self		:							Tuple   , __lowercase		:							Any )		->  Tuple:
       if self.label_column not in features:
             raise ValueError(F"Column {self.label_column} is not present in features." )
       if not isinstance(features[self.label_column]   , UpperCamelCase__ ):
             raise ValueError(F"Column {self.label_column} is not a ClassLabel." )
       SCREAMING_SNAKE_CASE__       :			str													=copy.deepcopy(self )
       SCREAMING_SNAKE_CASE__       :			Tuple													=self.label_schema.copy()
       SCREAMING_SNAKE_CASE__       :			Dict													=features[self.label_column]
       SCREAMING_SNAKE_CASE__       :			Optional[Any]													=label_schema
       return task_template




 @property
 def        __magic_name__		( self		:							List[str] )		->  Dict[str, str]:
       return {
           self.audio_column: "audio",
           self.label_column: "labels",
       }







'''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 (     lowerCamelCase			):
 snake_case_												=							"""facebook/bart-large-mnli"""
 snake_case_												=							(
     """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."""
 )
 snake_case_												=							"""text_classifier"""
 snake_case_												=							AutoTokenizer
 snake_case_												=							AutoModelForSequenceClassification

 snake_case_												=							["""text""", ["""text"""]]
 snake_case_												=							["""text"""]




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




 def        __magic_name__		( self		:							List[Any]   , __lowercase		:							str )		->  List[Any]:
       SCREAMING_SNAKE_CASE__       :			int													=outputs.logits
       SCREAMING_SNAKE_CASE__       :			Any													=torch.argmax(logits[:, 2] ).item()
       return self._labels[label_id]