






"""simple docstring"""






# 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 							lowerCAmelCase__				(			UpperCAmelCase__ ):






      '''simple docstring'''

      __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 _SCREAMING_SNAKE_CASE (						self     :  Optional[int]):




          '''simple docstring'''


          super().setup()
          SCREAMING_SNAKE_CASE_      :  Optional[int]    						=		self.model.config
          SCREAMING_SNAKE_CASE_      :  List[Any]    						=		-1
          for idx, label in config.idalabel.items():
              if label.lower().startswith('''entail'''):
                  SCREAMING_SNAKE_CASE_      :  List[str]    						=		int(lowercase_)
          if self.entailment_id == -1:
              raise ValueError('''Could not determine the entailment ID from the model config, please pass it at init.''')
      def _SCREAMING_SNAKE_CASE (						self     :  Any						,	lowercase_     :  str						,	lowercase_     :  int):




          '''simple docstring'''


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



      def _SCREAMING_SNAKE_CASE (						self     :  Optional[int]						,	lowercase_     :  Optional[Any]):




          '''simple docstring'''


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










"""simple docstring"""





def      lowerCamelCase__       (							_lowerCamelCase       :		int		,				_lowerCamelCase       :		int )      ->				int:
  lowerCamelCase_         =						1  # To kept the Calculated Value
  # Since C(n, k) = C(n, n-k)
  if k > (n - k):
    lowerCamelCase_         =						n - k
  # Calculate C(n,k)
  for i in range(_lowerCamelCase ):
    result *= n - i
    result //= i + 1
  return result







def      lowerCamelCase__       (							_lowerCamelCase       :		int )      ->				int:
  return binomial_coefficient(2 * node_count		,				_lowerCamelCase ) // (node_count + 1)







def      lowerCamelCase__       (							_lowerCamelCase       :		int )      ->				int:
  if n < 0:
    raise ValueError('factorial() not defined for negative values' )
  lowerCamelCase_         =						1
  for i in range(1		,				n + 1 ):
    result *= i
  return result







def      lowerCamelCase__       (							_lowerCamelCase       :		int )      ->				int:
  return catalan_number(_lowerCamelCase ) * factorial(_lowerCamelCase )


if __name__ == "__main__":
  _SCREAMING_SNAKE_CASE					:				int					    =						int(input('''Enter the number of nodes: ''').strip() or 0)
  if node_count <= 0:
    raise ValueError('''We need some nodes to work with.''')
  print(
      F'''Given {node_count} nodes, there are {binary_tree_count(node_count)} '''
      F'''binary trees and {catalan_number(node_count)} binary search trees.'''
  )




