
"""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        _UpperCAmelCase(       lowerCamelCase      ):
		lowercase__         = 'facebook/bart-large-mnli'
		lowercase__         = (
		    '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.'
		)
		lowercase__         = 'text_classifier'
		lowercase__         = AutoTokenizer
		lowercase__         = AutoModelForSequenceClassification

		lowercase__         = ['text', ['text']]
		lowercase__         = ['text']



		def 		UpperCAmelCase				(   self)			->			int:






							'''simple docstring'''
							super().setup()
							_UpperCamelCase									=    self.model.config
							_UpperCamelCase									=    -1
							for idx, label in config.idalabel.items():
												if label.lower().startswith('''entail'''):
																	_UpperCamelCase									=    int(__a)
							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							,						__a							,						__a)			->			Optional[Any]:






							'''simple docstring'''
							_UpperCamelCase									=    labels
							return self.pre_processor(
							    [text] * len(__a)							,						[F'''This example is {label}''' for label in labels]							,						return_tensors='''pt'''							,						padding='''max_length'''							,						)




		def 		UpperCAmelCase				(   self							,						__a)			->			List[Any]:






							'''simple docstring'''
							_UpperCamelCase									=    outputs.logits
							_UpperCamelCase									=    torch.argmax(logits[:, 2]).item()
							return self._labels[label_id]








"""simple docstring"""





import heapq



def  lowerCamelCase__						(     __snake_case      )				->						set[int]:


					"""simple docstring"""





					_UpperCamelCase									=    []

					# for each node and his adjacency list add them and the rank of the node to queue
					# using heapq module the queue will be filled like a Priority Queue
					# heapq works with a min priority queue, so I used -1*len(v) to build it
					for key, value in graph.items():
										# O(log(n))
										heapq.heappush(__snake_case,	[-1 * len(__snake_case      ), (key, value)]      )

					# chosen_vertices = set of chosen vertices
					_UpperCamelCase									=    set()

					# while queue isn't empty and there are still edges
					#   (queue[0][0] is the rank of the node with max rank)
					while queue and queue[0][0] != 0:
										# extract vertex with max rank from queue and add it to chosen_vertices
										_UpperCamelCase									=    heapq.heappop(__snake_case      )[1][0]
										chosen_vertices.add(__snake_case      )

										# Remove all arcs adjacent to argmax
										for elem in queue:
															# if v haven't adjacent node, skip
															if elem[0] == 0:
																				continue
															# if argmax is reachable from elem
															# remove argmax from elem's adjacent list and update his rank
															if argmax in elem[1][1]:
																				_UpperCamelCase									=    elem[1][1].index(__snake_case      )
																				del elem[1][1][index]
																				elem[0] += 1
        # re-order the queue
										heapq.heapify(__snake_case      )
					return chosen_vertices


if __name__ == "__main__":
				import doctest

				doctest.testmod()

				_a								=						{0: [1, 3], 1: [0, 3], 2: [0, 3, 4], 3: [0, 1, 2], 4: [2, 3]}
				print(F"""Minimum vertex cover:\n{greedy_min_vertex_cover(graph)}""")






