

"""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    (A__      ):
			lowerCamelCase__ :		Optional[Any]       		=				'facebook/bart-large-mnli'
			lowerCamelCase__ :		List[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.'
			)
			lowerCamelCase__ :		List[str]       		=				'text_classifier'
			lowerCamelCase__ :		List[Any]       		=				AutoTokenizer
			lowerCamelCase__ :		List[Any]       		=				AutoModelForSequenceClassification

			lowerCamelCase__ :		str       		=				['text', ['text']]
			lowerCamelCase__ :		Tuple       		=				['text']

			def        SCREAMING_SNAKE_CASE					(       self					:    Any							) ->     List[Any]:
						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(__UpperCAmelCase							)
						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					:    str     ,					__UpperCAmelCase					:    Union[str, Any]     ,					__UpperCAmelCase					:    Tuple							) ->     List[str]:
						SCREAMING_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        SCREAMING_SNAKE_CASE					(       self					:    Union[str, Any]     ,					__UpperCAmelCase					:    Union[str, Any]							) ->     Any:
						SCREAMING_SNAKE_CASE__										=   outputs.logits
						SCREAMING_SNAKE_CASE__										=   torch.argmax(logits[:, 2]							).item()
						return self._labels[label_id]





"""simple docstring"""




def 						A							(  snake_case__		,					snake_case__     ):




			'''simple docstring'''


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

def 						A							(  snake_case__     ):




			'''simple docstring'''


			return binomial_coefficient(2 * node_count		,					snake_case__     ) // (node_count + 1)

def 						A							(  snake_case__     ):




			'''simple docstring'''


			if n < 0:
						raise ValueError("""factorial() not defined for negative values"""     )
			SCREAMING_SNAKE_CASE__										=   1
			for i in range(1		,					n + 1     ):
						result *= i
			return result

def 						A							(  snake_case__     ):




			'''simple docstring'''


			return catalan_number(snake_case__     ) * factorial(snake_case__     )


if __name__ == "__main__":
						A_				:      Optional[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.'
						)


