






"""simple docstring"""



import torch

from ..models.auto import AutoModelForSequenceClassification, AutoTokenizer
from .base import PipelineTool




class 			lowerCAmelCase__			(    A__   ):
   SCREAMING_SNAKE_CASE_    ='facebook/bart-large-mnli'
   SCREAMING_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.'
   )
   SCREAMING_SNAKE_CASE_    ='text_classifier'
   SCREAMING_SNAKE_CASE_    =AutoTokenizer
   SCREAMING_SNAKE_CASE_    =AutoModelForSequenceClassification

   SCREAMING_SNAKE_CASE_    =['text', ['text']]
   SCREAMING_SNAKE_CASE_    =['text']
   def       __a							(		self      :	Any							):


        '''simple docstring'''
        super().setup()
        UpperCAmelCase__						:    Any							=							self.model.config
        UpperCAmelCase__						:    List[Any]							=							-1
        for idx, label in config.idalabel.items():
             if label.lower().startswith("entail"							):
                  UpperCAmelCase__						:    Optional[int]							=							int(__UpperCAmelCase							)
        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      :	str							,			snake_case__      :	Union[str, Any]							,			snake_case__      :	Tuple							):


        '''simple docstring'''
        UpperCAmelCase__						:    Dict							=							labels
        return self.pre_processor(
            [text] * len(__UpperCAmelCase							)							,			[f'This example is {label}' for label in labels]							,			return_tensors="pt"							,			padding="max_length"							,			)




   def       __a							(		self      :	Union[str, Any]							,			snake_case__      :	Union[str, Any]							):


        '''simple docstring'''
        UpperCAmelCase__						:    Optional[int]							=							outputs.logits
        UpperCAmelCase__						:    Dict							=							torch.argmax(logits[:, 2]							).item()
        return self._labels[label_id]





"""simple docstring"""




from __future__ import annotations

from typing import Dict

from ...configuration_utils import PretrainedConfig


A_				:      Union[str, Any]           =			{
    "susnato/ernie-m-base_pytorch": "https://huggingface.co/susnato/ernie-m-base_pytorch/blob/main/config.json",
    "susnato/ernie-m-large_pytorch": "https://huggingface.co/susnato/ernie-m-large_pytorch/blob/main/config.json",
}





class  lowerCamelCase    (A__      ):
			lowerCamelCase__ :		int       		=				'ernie_m'
			lowerCamelCase__ :		Dict[str, str]       		=				{"dropout": "classifier_dropout", "num_classes": "num_labels"}

			def __init__(       self					:    List[str]     ,					__UpperCAmelCase					:    int = 2_5_0_0_0_2     ,					__UpperCAmelCase					:    int = 7_6_8     ,					__UpperCAmelCase					:    int = 1_2     ,					__UpperCAmelCase					:    int = 1_2     ,					__UpperCAmelCase					:    int = 3_0_7_2     ,					__UpperCAmelCase					:    str = "gelu"     ,					__UpperCAmelCase					:    float = 0.1     ,					__UpperCAmelCase					:    float = 0.1     ,					__UpperCAmelCase					:    int = 5_1_4     ,					__UpperCAmelCase					:    float = 0.02     ,					__UpperCAmelCase					:    int = 1     ,					__UpperCAmelCase					:    float = 1e-05     ,					__UpperCAmelCase					:    Union[str, Any]=None     ,					__UpperCAmelCase					:    List[Any]=False     ,					__UpperCAmelCase					:    Union[str, Any]=0.0     ,					**__UpperCAmelCase					:    Any     ,					) ->     Optional[Any]:
						super().__init__(pad_token_id=__UpperCAmelCase     ,					**__UpperCAmelCase							)
						SCREAMING_SNAKE_CASE__										=   vocab_size
						SCREAMING_SNAKE_CASE__										=   hidden_size
						SCREAMING_SNAKE_CASE__										=   num_hidden_layers
						SCREAMING_SNAKE_CASE__										=   num_attention_heads
						SCREAMING_SNAKE_CASE__										=   intermediate_size
						SCREAMING_SNAKE_CASE__										=   hidden_act
						SCREAMING_SNAKE_CASE__										=   hidden_dropout_prob
						SCREAMING_SNAKE_CASE__										=   attention_probs_dropout_prob
						SCREAMING_SNAKE_CASE__										=   max_position_embeddings
						SCREAMING_SNAKE_CASE__										=   initializer_range
						SCREAMING_SNAKE_CASE__										=   layer_norm_eps
						SCREAMING_SNAKE_CASE__										=   classifier_dropout
						SCREAMING_SNAKE_CASE__										=   is_decoder
						SCREAMING_SNAKE_CASE__										=   act_dropout


