




'''simple docstring'''
import torch
from transformers import AutoModelForSequenceClassification, AutoTokenizer, Trainer, TrainingArguments

import datasets


snake_case_				:			int						 =						'\\n@article{eddine2021frugalscore,\n  title={FrugalScore: Learning Cheaper, Lighter and Faster Evaluation Metrics for Automatic Text Generation},\n  author={Eddine, Moussa Kamal and Shang, Guokan and Tixier, Antoine J-P and Vazirgiannis, Michalis},\n  journal={arXiv preprint arXiv:2110.08559},\n  year={2021}\n}\n'

snake_case_				:			Optional[Any]						 =						'\\nFrugalScore is a reference-based metric for NLG models evaluation. It is based on a distillation approach that allows to learn a fixed, low cost version of any expensive NLG metric, while retaining most of its original performance.\n'


snake_case_				:			Optional[Any]						 =						'\nCalculates how good are predictions given some references, using certain scores.\nArgs:\n    predictions (list of str): list of predictions to score. Each predictions\n        should be a string.\n    references (list of str): list of reference for each prediction. Each\n        reference should be a string.\n    batch_size (int): the batch size for predictions.\n    max_length (int): maximum sequence length.\n    device (str): either gpu or cpu\nReturns:\n    scores (list of int): list of scores.\nExamples:\n    >>> frugalscore = datasets.load_metric("frugalscore")\n    >>> results = frugalscore.compute(predictions=[\'hello there\', \'huggingface\'], references=[\'hello world\', \'hugging face\'])\n    >>> print([round(s, 3) for s in results["scores"]])\n    [0.631, 0.645]\n'






@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION		,      _KWARGS_DESCRIPTION     )
class 							lowercase__			(							datasets.Metric     ):





	def 			UpperCamelCase_						(				self      :       List[str]				):







							'''simple docstring'''
							return datasets.MetricInfo(
							    description=_DESCRIPTION				,citation=_CITATION				,inputs_description=_KWARGS_DESCRIPTION				,features=datasets.Features(
							        {
							            'predictions': datasets.Value('string'				),
							            'references': datasets.Value('string'				),
							        }				)				,homepage='https://github.com/moussaKam/FrugalScore'				,)





	def 			UpperCamelCase_						(				self      :       List[str]				,lowerCamelCase__      :       Optional[Any]				):







							'''simple docstring'''






							if self.config_name == "default":
													_UpperCamelCase      :	Optional[Any]				       =     'moussaKam/frugalscore_tiny_bert-base_bert-score'
							else:
													_UpperCamelCase      :	Optional[Any]				       =     self.config_name
							_UpperCamelCase      :	Dict				       =     AutoModelForSequenceClassification.from_pretrained(lowercase_				)
							_UpperCamelCase      :	List[Any]				       =     AutoTokenizer.from_pretrained(lowercase_				)





	def 			UpperCamelCase_						(				self      :       List[str]				,lowerCamelCase__      :       Optional[Any]				,lowerCamelCase__      :       Any				,lowerCamelCase__      :       int=32				,lowerCamelCase__      :       Tuple=128				,lowerCamelCase__      :       List[Any]=None				,):







							'''simple docstring'''
							assert len(lowercase_				) == len(
							    lowercase_				), "predictions and references should have the same number of sentences."
							if device is not None:
													assert device in ["gpu", "cpu"], "device should be either gpu or cpu."
							else:
													_UpperCamelCase      :	Optional[int]				       =     'gpu' if torch.cuda.is_available() else 'cpu'
							_UpperCamelCase      :	Union[str, Any]				       =     TrainingArguments(
							    'trainer'				,fpaa=(device == 'gpu')				,per_device_eval_batch_size=lowercase_				,report_to='all'				,no_cuda=(device == 'cpu')				,log_level='warning'				,)
							_UpperCamelCase      :	Optional[Any]				       =     {'sentence1': predictions, 'sentence2': references}
							_UpperCamelCase      :	int				       =     datasets.Dataset.from_dict(lowercase_				)

							def tokenize_function(lowerCamelCase__      :       List[str]				):
													return self.tokenizer(
													    data['sentence1']				,data['sentence2']				,max_length=lowercase_				,truncation=lowercase_				,padding=lowercase_				)

							_UpperCamelCase      :	Optional[Any]				       =     raw_datasets.map(lowercase_				,batched=lowercase_				)
							tokenized_datasets.remove_columns(['sentence1', 'sentence2']				)
							_UpperCamelCase      :	Union[str, Any]				       =     Trainer(self.model				,lowercase_				,tokenizer=self.tokenizer				)
							_UpperCamelCase      :	Tuple				       =     trainer.predict(lowercase_				)
							return {"scores": list(predictions.predictions.squeeze(-1				)				)}







'''simple docstring'''
from __future__ import annotations

from queue import Queue






class 							lowercase__			:





	def __init__(				self      :       int				):







							'''simple docstring'''
							_UpperCamelCase      :	dict[int, list[int]]				       =     {}





	def 			UpperCamelCase_						(				self      :       int				):







							'''simple docstring'''
							for i in self.vertices:
													print(lowerCamelCase__				,' : '				,' -> '.join([str(lowerCamelCase__				) for j in self.vertices[i]]				)				)





	def 			UpperCamelCase_						(				self      :       Optional[Any]				,lowerCamelCase__      :       int				,lowerCamelCase__      :       int				):







							'''simple docstring'''
							if from_vertex in self.vertices:
													self.vertices[from_vertex].append(lowerCamelCase__				)
							else:
													_UpperCamelCase      :	Any				       =     [to_vertex]





	def 			UpperCamelCase_						(				self      :       int				,lowerCamelCase__      :       int				):







							'''simple docstring'''
							_UpperCamelCase      :	Dict				       =     set()

							# create a first in first out queue to store all the vertices for BFS
							_UpperCamelCase      :	Queue				       =     Queue()

							# mark the source node as visited and enqueue it
							visited.add(lowerCamelCase__				)
							queue.put(lowerCamelCase__				)

							while not queue.empty():
													_UpperCamelCase      :	Optional[Any]				       =     queue.get()

													# loop through all adjacent vertex and enqueue it if not yet visited
													for adjacent_vertex in self.vertices[vertex]:
																			if adjacent_vertex not in visited:
																									queue.put(lowerCamelCase__				)
																									visited.add(lowerCamelCase__				)
							return visited


if __name__ == "__main__":
					from doctest import testmod

					testmod(verbose=True)

					snake_case_				:			int						 =						Graph()
					g.add_edge(0, 1)
					g.add_edge(0, 2)
					g.add_edge(1, 2)
					g.add_edge(2, 0)
					g.add_edge(2, 3)
					g.add_edge(3, 3)

					g.print_graph()
					# 0  :  1 -> 2
					# 1  :  2
					# 2  :  0 -> 3
					# 3  :  3

					assert sorted(g.bfs(2)) == [0, 1, 2, 3]


