
import torch

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





class       __a  (   _lowercase	):
 _lowerCAmelCase	:	Tuple				=			'''facebook/bart-large-mnli'''
 _lowerCAmelCase	:	Union[str, 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.'''
 )
 _lowerCAmelCase	:	int				=			'''text_classifier'''
 _lowerCAmelCase	:	Union[str, Any]				=			AutoTokenizer
 _lowerCAmelCase	:	List[str]				=			AutoModelForSequenceClassification

 _lowerCAmelCase	:	Tuple				=			['''text''', ['''text''']]
 _lowerCAmelCase	:	List[str]				=			['''text''']







 def  __lowercase (		self :       Dict						):


      '''simple docstring'''






      super().setup()
      UpperCamelCase__      :       Optional[int]	    =  self.model.config
      UpperCamelCase__      :       str	    =  -1
      for idx, label in config.idalabel.items():
           if label.lower().startswith("entail"						):
                UpperCamelCase__      :       Optional[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  __lowercase (		self :       Optional[int]							,       SCREAMING_SNAKE_CASE :       Any							,       SCREAMING_SNAKE_CASE :       str						):


      '''simple docstring'''






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







 def  __lowercase (		self :       Union[str, Any]							,       SCREAMING_SNAKE_CASE :       Tuple						):


      '''simple docstring'''






      UpperCamelCase__      :       List[Any]	    =  outputs.logits
      UpperCamelCase__      :       int	    =  torch.argmax(logits[:, 2]						).item()
      return self._labels[label_id]







from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ...utils.backbone_utils import BackboneConfigMixin, get_aligned_output_features_output_indices


lowerCamelCase     :							List[Any]    =logging.get_logger(__name__)

lowerCamelCase     :							Optional[Any]    ={
    '''google/bit-50''': '''https://huggingface.co/google/bit-50/resolve/main/config.json''',
}





class       __a  (   A__	,       A__	):
 _lowerCAmelCase	:	Union[str, Any]				=			'''bit'''
 _lowerCAmelCase	:	List[str]				=			['''preactivation''', '''bottleneck''']
 _lowerCAmelCase	:	Any				=			['''SAME''', '''VALID''']







 def __init__(		self :       str							,       SCREAMING_SNAKE_CASE :       Tuple=3							,       SCREAMING_SNAKE_CASE :       str=64							,       SCREAMING_SNAKE_CASE :       List[Any]=[2_56, 5_12, 10_24, 20_48]							,       SCREAMING_SNAKE_CASE :       Union[str, Any]=[3, 4, 6, 3]							,       SCREAMING_SNAKE_CASE :       str="preactivation"							,       SCREAMING_SNAKE_CASE :       Any="relu"							,       SCREAMING_SNAKE_CASE :       Dict=None							,       SCREAMING_SNAKE_CASE :       List[Any]=32							,       SCREAMING_SNAKE_CASE :       Tuple=0.0							,       SCREAMING_SNAKE_CASE :       Dict=False							,       SCREAMING_SNAKE_CASE :       int=32							,       SCREAMING_SNAKE_CASE :       str=1							,       SCREAMING_SNAKE_CASE :       List[str]=None							,       SCREAMING_SNAKE_CASE :       Optional[int]=None							,       **SCREAMING_SNAKE_CASE :       int							,       ):


      '''simple docstring'''






      super().__init__(**SCREAMING_SNAKE_CASE						)
      if layer_type not in self.layer_types:
           raise ValueError(F'layer_type={layer_type} is not one of {",".join(self.layer_types						)}'						)
      if global_padding is not None:
           if global_padding.upper() in self.supported_padding:
                UpperCamelCase__      :       Any	    =  global_padding.upper()
           else:
                raise ValueError(F'Padding strategy {global_padding} not supported'						)
      UpperCamelCase__      :       Dict	    =  num_channels
      UpperCamelCase__      :       Dict	    =  embedding_size
      UpperCamelCase__      :       Tuple	    =  hidden_sizes
      UpperCamelCase__      :       Any	    =  depths
      UpperCamelCase__      :       Optional[int]	    =  layer_type
      UpperCamelCase__      :       int	    =  hidden_act
      UpperCamelCase__      :       str	    =  global_padding
      UpperCamelCase__      :       Any	    =  num_groups
      UpperCamelCase__      :       str	    =  drop_path_rate
      UpperCamelCase__      :       Optional[Any]	    =  embedding_dynamic_padding
      UpperCamelCase__      :       Tuple	    =  output_stride
      UpperCamelCase__      :       List[str]	    =  width_factor

      UpperCamelCase__      :       Any	    =  ["stem"] + [F'stage{idx}' for idx in range(1							,       len(SCREAMING_SNAKE_CASE						) + 1						)]
      UpperCamelCase__    ,					UpperCamelCase__      :       Union[str, Any]	    =  get_aligned_output_features_output_indices(
          out_features=SCREAMING_SNAKE_CASE							,       out_indices=SCREAMING_SNAKE_CASE							,       stage_names=self.stage_names						)