





"""simple docstring"""




import torch

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

class 		UpperCamelCase__			(				_a       ):



     '''simple docstring'''

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

     __snake_case   :     Optional[Any]		     =      ["text", ["text"]]
     __snake_case   :     List[str]		     =      ["text"]






     def     SCREAMING_SNAKE_CASE__  (							self						:   Union[str, Any]	)					->		List[str]:




           '''simple docstring'''


           super().setup()
           SCREAMING_SNAKE_CASE		=						self.model.config
           SCREAMING_SNAKE_CASE		=						-1
           for idx, label in config.idalabel.items():
                 if label.lower().startswith("""entail"""	):
                       SCREAMING_SNAKE_CASE		=						int(snake_case_	)
           if self.entailment_id == -1:
                 raise ValueError("""Could not determine the entailment ID from the model config, please pass it at init."""	)






     def     SCREAMING_SNAKE_CASE__  (							self						:   Tuple						,lowerCamelCase__						:   int						,lowerCamelCase__						:   Tuple	)					->		Union[str, Any]:




           '''simple docstring'''


           SCREAMING_SNAKE_CASE		=						labels
           return self.pre_processor(
               [text] * len(snake_case_	)						,[F"""This example is {label}""" for label in labels]						,return_tensors="""pt"""						,padding="""max_length"""						,)






     def     SCREAMING_SNAKE_CASE__  (							self						:   int						,lowerCamelCase__						:   Tuple	)					->		List[str]:




           '''simple docstring'''


           SCREAMING_SNAKE_CASE		=						outputs.logits
           SCREAMING_SNAKE_CASE		=						torch.argmax(logits[:, 2]	).item()
           return self._labels[label_id]









import unittest
from dataclasses import dataclass

import pytest

from accelerate.commands.config.config_args import SageMakerConfig
from accelerate.utils import ComputeEnvironment
from accelerate.utils.launch import _convert_nargs_to_dict

@dataclass
class 		UpperCamelCase__			(				lowerCAmelCase_       ):



     '''simple docstring'''

     __snake_case   :     int		     =      ComputeEnvironment.AMAZON_SAGEMAKER
     __snake_case   :     List[Any]		     =      True
     __snake_case   :     Optional[int]		     =      "ml.p3.2xlarge"
     __snake_case   :     List[str]		     =      "accelerate_sagemaker_execution_role"
     __snake_case   :     Tuple		     =      "hf-sm"
     __snake_case   :     Any		     =      "us-east-1"
     __snake_case   :     Union[str, Any]		     =      1
     __snake_case   :     Dict		     =      "accelerate-sagemaker-1"
     __snake_case   :     Tuple		     =      "1.6"
     __snake_case   :     List[str]		     =      "4.4"
     __snake_case   :     str		     =      "train.py"
     __snake_case   :     List[str]		     =      [
         "--model_name_or_path",
         "bert",
         "--do_train",
         "False",
         "--epochs",
         "3",
         "--learning_rate",
         "5e-5",
         "--max_steps",
         "50.5",
     ]
     __snake_case   :     Optional[int]		     =      [
         "--model_name_or_path",
         "bert",
         "--do_train",
         "--do_test",
         "False",
         "--do_predict",
         "--epochs",
         "3",
         "--learning_rate",
         "5e-5",
         "--max_steps",
         "50.5",
     ]

class 		UpperCamelCase__			(				unittest.TestCase       ):



     '''simple docstring'''

     def     SCREAMING_SNAKE_CASE__  (							self						:   Union[str, Any]	)					->		int:




           '''simple docstring'''


           SCREAMING_SNAKE_CASE		=						_convert_nargs_to_dict(MockLaunchConfig.success_training_script_args	)
           assert isinstance(converted_args["""model_name_or_path"""]						,lowerCamelCase__	)
           assert isinstance(converted_args["""do_train"""]						,lowerCamelCase__	)
           assert isinstance(converted_args["""epochs"""]						,lowerCamelCase__	)
           assert isinstance(converted_args["""learning_rate"""]						,lowerCamelCase__	)
           assert isinstance(converted_args["""max_steps"""]						,lowerCamelCase__	)

           with pytest.raises(lowerCamelCase__	):
                 _convert_nargs_to_dict(MockLaunchConfig.fail_training_script_args	)


