

import torch
from transformers import AutoModelForSequenceClassification, AutoTokenizer, Trainer, TrainingArguments

import datasets


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

lowerCamelCase   					=  '''\
FrugalScore 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.
'''


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





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


							"""simple docstring"""





							def 							A__	(			self				):
												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 							A__	(			self       ,    _A				):
												if self.config_name == "default":
																	__lowerCAmelCase       =				'moussaKam/frugalscore_tiny_bert-base_bert-score'
												else:
																	__lowerCAmelCase       =				self.config_name
												__lowerCAmelCase       =				AutoModelForSequenceClassification.from_pretrained(_A				)
												__lowerCAmelCase       =				AutoTokenizer.from_pretrained(_A				)




							def 							A__	(			self       ,    _A       ,    _A       ,    _A=3_2       ,    _A=1_2_8       ,    _A=None       ,    ):
												assert len(_A				) == len(
												    _A				), "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:
																	__lowerCAmelCase       =				'gpu' if torch.cuda.is_available() else 'cpu'
												__lowerCAmelCase       =				TrainingArguments(
												    'trainer'       ,    fpaa=(device == 'gpu')       ,    per_device_eval_batch_size=_A       ,    report_to='all'       ,    no_cuda=(device == 'cpu')       ,    log_level='warning'       ,    )
												__lowerCAmelCase       =				{'sentence1': predictions, 'sentence2': references}
												__lowerCAmelCase       =				datasets.Dataset.from_dict(_A				)

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

												__lowerCAmelCase       =				raw_datasets.map(_A       ,    batched=_A				)
												tokenized_datasets.remove_columns(['sentence1', 'sentence2']				)
												__lowerCAmelCase       =				Trainer(self.model       ,    _A       ,    tokenizer=self.tokenizer				)
												__lowerCAmelCase       =				trainer.predict(_A				)
												return {"scores": list(predictions.predictions.squeeze(-1				)				)}









import math
import sys





class      snake_case_      :


							"""simple docstring"""





							def __init__(			self				):
												__lowerCAmelCase       =				0
												__lowerCAmelCase       =				[]
												__lowerCAmelCase       =				{}  # To store the pos of node in array




							def 							A__	(			self				):
												return self.cur_size == 0




							def 							A__	(			self       ,    _A				):
												__lowerCAmelCase       =				self.left(_A				)
												__lowerCAmelCase       =				self.right(_A				)
												if lc < self.cur_size and self.array(_A				)[0] < self.array(_A				)[0]:
																	__lowerCAmelCase       =				lc
												else:
																	__lowerCAmelCase       =				idx
												if rc < self.cur_size and self.array(_A				)[0] < self.array(_A				)[0]:
																	__lowerCAmelCase       =				rc
												if smallest != idx:
																	self.swap(_A       ,    _A				)
																	self.min_heapify(_A				)




							def 							A__	(			self       ,    _A				):
												# Inserts a node into the Priority Queue
												__lowerCAmelCase       =				self.cur_size
												self.cur_size += 1
												self.array.append((sys.maxsize, tup[1])				)
												self.decrease_key((sys.maxsize, tup[1])       ,    tup[0]				)




							def 							A__	(			self				):
												# Removes and returns the min element at top of priority queue
												__lowerCAmelCase       =				self.array[0][1]
												__lowerCAmelCase       =				self.array[self.cur_size - 1]
												self.cur_size -= 1
												self.min_heapify(1				)
												del self.pos[min_node]
												return min_node




							def 							A__	(			self       ,    _A				):
												# returns the index of left child
												return 2 * i + 1




							def 							A__	(			self       ,    _A				):
												# returns the index of right child
												return 2 * i + 2




							def 							A__	(			self       ,    _A				):
												# returns the index of parent
												return math.floor(i / 2				)




							def 							A__	(			self       ,    _A       ,    _A				):
												# swaps array elements at indices i and j
												# update the pos{}
												__lowerCAmelCase       =				j
												__lowerCAmelCase       =				i
												__lowerCAmelCase       =				self.array[i]
												__lowerCAmelCase       =				self.array[j]
												__lowerCAmelCase       =				temp




							def 							A__	(			self       ,    _A       ,    _A				):
												__lowerCAmelCase       =				self.pos[tup[1]]
												# assuming the new_d is atmost old_d
												__lowerCAmelCase       =				(new_d, tup[1])
												while idx > 0 and self.array[self.par(_A				)][0] > self.array[idx][0]:
																	self.swap(_A       ,    self.par(_A				)				)
																	__lowerCAmelCase       =				self.par(_A				)





class      snake_case_      :


							"""simple docstring"""





							def __init__(			self       ,    _A				):
												__lowerCAmelCase       =				{}  # To store graph: u -> (v,w)
												__lowerCAmelCase       =				num  # Number of nodes in graph
												# To store the distance from source vertex
												__lowerCAmelCase       =				[0] * self.num_nodes
												__lowerCAmelCase       =				[-1] * self.num_nodes  # To store the path




							def 							A__	(			self       ,    _A       ,    _A       ,    _A				):
												#  Edge going from node u to v and v to u with weight w
												# u (w)-> v, v (w) -> u
												# Check if u already in graph
												if u in self.adjList:
																	self.adjList[u].append((v, w)				)
												else:
																	__lowerCAmelCase       =				[(v, w)]

												# Assuming undirected graph
												if v in self.adjList:
																	self.adjList[v].append((u, w)				)
												else:
																	__lowerCAmelCase       =				[(u, w)]




							def 							A__	(			self				):
												# u -> v(w)
												for u in self.adjList:
																	print(_A       ,    '->'       ,    ' -> '.join(str(F"""{v}({w})"""				) for v, w in self.adjList[u]				)				)




							def 							A__	(			self       ,    _A				):
												# Flush old junk values in par[]
												__lowerCAmelCase       =				[-1] * self.num_nodes
												# src is the source node
												__lowerCAmelCase       =				0
												__lowerCAmelCase       =				PriorityQueue()
												q.insert((0, src)				)  # (dist from src, node)
												for u in self.adjList:
																	if u != src:
																						__lowerCAmelCase       =				sys.maxsize  # Infinity
																						__lowerCAmelCase       =				-1

												while not q.is_empty():
																	__lowerCAmelCase       =				q.extract_min()  # Returns node with the min dist from source
																	# Update the distance of all the neighbours of u and
																	# if their prev dist was INFINITY then push them in Q
																	for v, w in self.adjList[u]:
																						__lowerCAmelCase       =				self.dist[u] + w
																						if self.dist[v] > new_dist:
																											if self.dist[v] == sys.maxsize:
																																q.insert((new_dist, v)				)
																											else:
																																q.decrease_key((self.dist[v], v)       ,    _A				)
																											__lowerCAmelCase       =				new_dist
																											__lowerCAmelCase       =				u

        # Show the shortest distances from src
												self.show_distances(_A				)




							def 							A__	(			self       ,    _A				):
												print(F"""Distance from node: {src}"""				)
												for u in range(self.num_nodes				):
																	print(F"""Node {u} has distance: {self.dist[u]}"""				)




							def 							A__	(			self       ,    _A       ,    _A				):
												# To show the shortest path from src to dest
												# WARNING: Use it *after* calling dijkstra
												__lowerCAmelCase       =				[]
												__lowerCAmelCase       =				0
												__lowerCAmelCase       =				dest
												# Backtracking from dest to src
												while self.par[temp] != -1:
																	path.append(_A				)
																	if temp != src:
																						for v, w in self.adjList[temp]:
																											if v == self.par[temp]:
																																cost += w
																																break
																	__lowerCAmelCase       =				self.par[temp]
												path.append(_A				)
												path.reverse()

												print(F"""----Path to reach {dest} from {src}----"""				)
												for u in path:
																	print(F"""{u}"""       ,    end=' '				)
																	if u != dest:
																						print('-> '       ,    end=''				)

												print('\nTotal cost of path: '       ,    _A				)


if __name__ == "__main__":
		lowerCamelCase   					=  Graph(9)
		graph.add_edge(0, 1, 4)
		graph.add_edge(0, 7, 8)
		graph.add_edge(1, 2, 8)
		graph.add_edge(1, 7, 1_1)
		graph.add_edge(2, 3, 7)
		graph.add_edge(2, 8, 2)
		graph.add_edge(2, 5, 4)
		graph.add_edge(3, 4, 9)
		graph.add_edge(3, 5, 1_4)
		graph.add_edge(4, 5, 1_0)
		graph.add_edge(5, 6, 2)
		graph.add_edge(6, 7, 1)
		graph.add_edge(6, 8, 6)
		graph.add_edge(7, 8, 7)
		graph.show_graph()
		graph.dijkstra(0)
		graph.show_path(0, 4)

# OUTPUT
# 0 -> 1(4) -> 7(8)
# 1 -> 0(4) -> 2(8) -> 7(11)
# 7 -> 0(8) -> 1(11) -> 6(1) -> 8(7)
# 2 -> 1(8) -> 3(7) -> 8(2) -> 5(4)
# 3 -> 2(7) -> 4(9) -> 5(14)
# 8 -> 2(2) -> 6(6) -> 7(7)
# 5 -> 2(4) -> 3(14) -> 4(10) -> 6(2)
# 4 -> 3(9) -> 5(10)
# 6 -> 5(2) -> 7(1) -> 8(6)
# Distance from node: 0
# Node 0 has distance: 0
# Node 1 has distance: 4
# Node 2 has distance: 12
# Node 3 has distance: 19
# Node 4 has distance: 21
# Node 5 has distance: 11
# Node 6 has distance: 9
# Node 7 has distance: 8
# Node 8 has distance: 14
# ----Path to reach 4 from 0----
# 0 -> 7 -> 6 -> 5 -> 4
# Total cost of path:  21






