





'''simple docstring'''




import torch

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


class 			SCREAMING_SNAKE_CASE   (a__							):
     lowerCAmelCase		      =	'''facebook/bart-large-mnli'''
     lowerCAmelCase		      =	(
         '''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.'''
     )
     lowerCAmelCase		      =	'''text_classifier'''
     lowerCAmelCase		      =	AutoTokenizer
     lowerCAmelCase		      =	AutoModelForSequenceClassification

     lowerCAmelCase		      =	['''text''', ['''text''']]
     lowerCAmelCase		      =	['''text''']


     def 							SCREAMING_SNAKE_CASE  (						self):

          '''simple docstring'''






          super().setup()
          __A						:			Union[str, Any]								=	self.model.config
          __A						:			Any								=	-1
          for idx, label in config.idalabel.items():
               if label.lower().startswith('entail'):
                    __A						:			Any								=	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 							SCREAMING_SNAKE_CASE  (						self						,     _UpperCAmelCase						,     _UpperCAmelCase):

          '''simple docstring'''






          __A						:			str								=	labels
          return self.pre_processor(
              [text] * len(_UpperCAmelCase)						,     [F'This example is {label}' for label in labels]						,     return_tensors='pt'						,     padding='max_length'						,     )


     def 							SCREAMING_SNAKE_CASE  (						self						,     _UpperCAmelCase):

          '''simple docstring'''






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






'''simple docstring'''




from arguments import InitializationArguments

from transformers import AutoConfig, AutoModelForCausalLM, AutoTokenizer, HfArgumentParser


# Configuration
lowercase__   :				List[Any]      =   HfArgumentParser(InitializationArguments)
lowercase__   :				List[Any]      =   parser.parse_args()

# Load codeparrot tokenizer trained for Python code tokenization
lowercase__   :				Union[str, Any]      =   AutoTokenizer.from_pretrained(args.tokenizer_name)

# Config: "scale_attn_by_layer_idx" and "reorder_and_upcast_attn" are Mistral stability tweaks
lowercase__   :				List[str]      =   {
    '''vocab_size''': len(tokenizer),
    '''scale_attn_by_inverse_layer_idx''': True,
    '''reorder_and_upcast_attn''': True,
}

# Load model config (GPT-2 large in this case)
lowercase__   :				Optional[int]      =   AutoConfig.from_pretrained(args.config_name, **config_kwargs)

# Initialize new model with config
lowercase__   :				Optional[int]      =   AutoModelForCausalLM.from_config(config)

# Save model to the hub
model.save_pretrained(args.model_name, push_to_hub=args.push_to_hub)