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#
# 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      (a_      ):
      snake_case_	=				"""facebook/bart-large-mnli"""
      snake_case_	=				(
          """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_	=				"""text_classifier"""
      snake_case_	=				AutoTokenizer
      snake_case_	=				AutoModelForSequenceClassification

      snake_case_	=				["""text""", ["""text"""]]
      snake_case_	=				["""text"""]


      def 			__UpperCAmelCase		(						self						)->						List[Any]:
           super().setup()
           __lowerCAmelCase             =       self.model.config
           __lowerCAmelCase             =       -1
           for idx, label in config.idalabel.items():
                if label.lower().startswith("entail"						):
                     __lowerCAmelCase             =       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 			__UpperCAmelCase		(						self       ,    __UpperCamelCase       ,    __UpperCamelCase						)->						List[str]:
           __lowerCAmelCase             =       labels
           return self.pre_processor(
               [text] * len(__UpperCamelCase						)       ,    [F"""This example is {label}""" for label in labels]       ,    return_tensors="pt"       ,    padding="max_length"       ,    )


      def 			__UpperCAmelCase		(						self       ,    __UpperCamelCase						)->						str:
           __lowerCAmelCase             =       outputs.logits
           __lowerCAmelCase             =       torch.argmax(logits[:, 2]						).item()
           return self._labels[label_id]














import os

# Precomputes a list of the 100 first triangular numbers
lowerCamelCase      :       str    =						[int(0.5 * n * (n + 1)) for n in range(1, 101)]





def    __lowerCAmelCase   (    ):
     __lowerCAmelCase             =       os.path.dirname(os.path.realpath(__snake_case							)							)
     __lowerCAmelCase             =       os.path.join(__snake_case       ,			"words.txt"							)

     __lowerCAmelCase             =       ""
     with open(__snake_case							) as f:
          __lowerCAmelCase             =       f.readline()

     __lowerCAmelCase             =       [word.strip("\""							) for word in words.strip("\r\n"							).split(","							)]
     __lowerCAmelCase             =       [
         word
         for word in [sum(ord(__snake_case							) - 64 for x in word							) for word in words]
         if word in TRIANGULAR_NUMBERS
     ]
     return len(__snake_case							)


if __name__ == "__main__":
 print(solution())






