
import torch

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


class   __lowerCAmelCase		(		lowerCAmelCase_					):



      """simple docstring"""







      A__		:	Tuple         =			'''facebook/bart-large-mnli'''
      A__		:	Optional[int]         =			(
          '''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.'''
      )
      A__		:	int         =			'''text_classifier'''
      A__		:	int         =			AutoTokenizer
      A__		:	Dict         =			AutoModelForSequenceClassification

      A__		:	Union[str, Any]         =			['''text''', ['''text''']]
      A__		:	Tuple         =			['''text''']




      def 						snake_case_	(					self						:				Optional[int] ):
          super().setup()
          __lowercase						:	int					=				self.model.config
          __lowercase						:	int					=				-1
          for idx, label in config.idalabel.items():
              if label.lower().startswith('''entail''' ):
                  __lowercase						:	Optional[int]					=				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 						snake_case_	(					self						:				int ,				_snake_case						:				List[Any] ,				_snake_case						:				int ):
          __lowercase						:	Dict					=				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 						snake_case_	(					self						:				Tuple ,				_snake_case						:				List[str] ):
          __lowercase						:	str					=				outputs.logits
          __lowercase						:	int					=				torch.argmax(logits[:, 2] ).item()
          return self._labels[label_id]







"""simple docstring"""
from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available


_lowerCAmelCase    :str     =					{
    'configuration_squeezebert': [
        'SQUEEZEBERT_PRETRAINED_CONFIG_ARCHIVE_MAP',
        'SqueezeBertConfig',
        'SqueezeBertOnnxConfig',
    ],
    'tokenization_squeezebert': ['SqueezeBertTokenizer'],
}

try:
  if not is_tokenizers_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  _lowerCAmelCase    :Optional[int]     =					['SqueezeBertTokenizerFast']

try:
  if not is_torch_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  _lowerCAmelCase    :str     =					[
      'SQUEEZEBERT_PRETRAINED_MODEL_ARCHIVE_LIST',
      'SqueezeBertForMaskedLM',
      'SqueezeBertForMultipleChoice',
      'SqueezeBertForQuestionAnswering',
      'SqueezeBertForSequenceClassification',
      'SqueezeBertForTokenClassification',
      'SqueezeBertModel',
      'SqueezeBertModule',
      'SqueezeBertPreTrainedModel',
  ]


if TYPE_CHECKING:
  from .configuration_squeezebert import (
      SQUEEZEBERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
      SqueezeBertConfig,
      SqueezeBertOnnxConfig,
  )
  from .tokenization_squeezebert import SqueezeBertTokenizer

  try:
    if not is_tokenizers_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .tokenization_squeezebert_fast import SqueezeBertTokenizerFast

  try:
    if not is_torch_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_squeezebert import (
        SQUEEZEBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
        SqueezeBertForMaskedLM,
        SqueezeBertForMultipleChoice,
        SqueezeBertForQuestionAnswering,
        SqueezeBertForSequenceClassification,
        SqueezeBertForTokenClassification,
        SqueezeBertModel,
        SqueezeBertModule,
        SqueezeBertPreTrainedModel,
    )

else:
  import sys

  _lowerCAmelCase    :Optional[Any]     =					_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)


