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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       snake_case_  (						__A   ):







			'''simple docstring'''

			SCREAMING_SNAKE_CASE	:  Optional[int]		     =							"facebook/bart-large-mnli"
			SCREAMING_SNAKE_CASE	:  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."
			)
			SCREAMING_SNAKE_CASE	:  List[str]		     =							"text_classifier"
			SCREAMING_SNAKE_CASE	:  Dict		     =							AutoTokenizer
			SCREAMING_SNAKE_CASE	:  int		     =							AutoModelForSequenceClassification

			SCREAMING_SNAKE_CASE	:  Any		     =							["text", ["text"]]
			SCREAMING_SNAKE_CASE	:  Tuple		     =							["text"]







			def 							snake_case__(	self					:							Any				) ->Tuple:
									super().setup()
									snake_case_  =      self.model.config
									snake_case_  =      -1
									for idx, label in config.idalabel.items():
															if label.lower().startswith('''entail'''				):
																					snake_case_  =      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 							snake_case__(	self					:							int				,  _UpperCamelCase					:							Dict				,  _UpperCamelCase					:							str				) ->Union[str, Any]:
									snake_case_  =      labels
									return self.pre_processor(
									    [text] * len(_UpperCamelCase				)				,  [f'''This example is {label}''' for label in labels]				,  return_tensors='''pt'''				,  padding='''max_length'''				,  )







			def 							snake_case__(	self					:							int				,  _UpperCamelCase					:							str				) ->Dict:
									snake_case_  =      outputs.logits
									snake_case_  =      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())






