

def        __lowerCAmelCase    (       _UpperCamelCase		:  int		,							_UpperCamelCase		:  list[int]		,							_UpperCamelCase		:  int	)		->							int:
 '''simple docstring'''


 def count_of_possible_combinations(_UpperCamelCase		:  int	) -> int:
  if target < 0:
   return 0
  if target == 0:
   return 1
  return sum(count_of_possible_combinations(target - item	) for item in array	)

 return count_of_possible_combinations(A__	)


def        __lowerCAmelCase    (       _UpperCamelCase		:  int		,							_UpperCamelCase		:  list[int]		,							_UpperCamelCase		:  int	)		->							Optional[Any]:
 '''simple docstring'''


 def count_of_possible_combinations_with_dp_array(
     _UpperCamelCase		:  int		,							_UpperCamelCase		:  list[int]	) -> int:
  if target < 0:
   return 0
  if target == 0:
   return 1
  if dp_array[target] != -1:
   return dp_array[target]
  SCREAMING_SNAKE_CASE								=			sum(
      count_of_possible_combinations_with_dp_array(target - item		,							A__	)
      for item in array	)
  SCREAMING_SNAKE_CASE								=			answer
  return answer

 SCREAMING_SNAKE_CASE								=			[-1] * (target + 1)
 return count_of_possible_combinations_with_dp_array(A__		,							A__	)


def        __lowerCAmelCase    (       _UpperCamelCase		:  int		,							_UpperCamelCase		:  list[int]		,							_UpperCamelCase		:  int	)		->							Union[str, Any]:
 '''simple docstring'''


 SCREAMING_SNAKE_CASE								=			[0] * (target + 1)
 SCREAMING_SNAKE_CASE								=			1

 for i in range(1		,							target + 1	):
  for j in range(A__	):
   if i - array[j] >= 0:
    dp_array[i] += dp_array[i - array[j]]

 return dp_array[target]


if __name__ == "__main__":
      import doctest

      doctest.testmod()
      a_    :						List[str]    		=      3
      a_    :						str    		=      5
      a_    :						List[str]    		=      [1, 2, 5]
      print(combination_sum_iv(n, array, target))




# 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]

