

from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_torch_available,
)


a_    :						Dict    		=      {
    "configuration_encodec": [
        "ENCODEC_PRETRAINED_CONFIG_ARCHIVE_MAP",
        "EncodecConfig",
    ],
    "feature_extraction_encodec": ["EncodecFeatureExtractor"],
}

try:
      if not is_torch_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      a_    :						Optional[Any]    		=      [
          "ENCODEC_PRETRAINED_MODEL_ARCHIVE_LIST",
          "EncodecModel",
          "EncodecPreTrainedModel",
      ]

if TYPE_CHECKING:
      from .configuration_encodec import (
          ENCODEC_PRETRAINED_CONFIG_ARCHIVE_MAP,
          EncodecConfig,
      )
      from .feature_extraction_encodec import EncodecFeatureExtractor

      try:
            if not is_torch_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_encodec import (
                ENCODEC_PRETRAINED_MODEL_ARCHIVE_LIST,
                EncodecModel,
                EncodecPreTrainedModel,
            )

else:
      import sys

      a_    :						str    		=      _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)




# Copyright 2023 The HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import torch

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




class      UpperCamelCase (     SCREAMING_SNAKE_CASE   ):
    __UpperCamelCase         ="facebook/bart-large-mnli"
    __UpperCamelCase         =(
        "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."
    )
    __UpperCamelCase         ="text_classifier"
    __UpperCamelCase         =AutoTokenizer
    __UpperCamelCase         =AutoModelForSequenceClassification

    __UpperCamelCase         =["text", ["text"]]
    __UpperCamelCase         =["text"]

    def UpperCamelCase			(			self :      Optional[Any]    ):

     """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 UpperCamelCase			(			self :      Optional[Any]							,    snake_case__ :      List[str]							,    snake_case__ :      Dict    ):

     """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 UpperCamelCase			(			self :      Dict							,    snake_case__ :      Dict    ):

     """simple docstring"""






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

