
import os
from argparse import ArgumentParser, Namespace

from ..data import SingleSentenceClassificationProcessor as Processor
from ..pipelines import TextClassificationPipeline
from ..utils import is_tf_available, is_torch_available, logging
from . import BaseTransformersCLICommand


if not is_tf_available() and not is_torch_available():
	raise RuntimeError('''At least one of PyTorch or TensorFlow 2.0+ should be installed to use CLI training''')

# TF training parameters
UpperCAmelCase	      = False
UpperCAmelCase	      = False






def 							UpperCAmelCase_		(							__SCREAMING_SNAKE_CASE							):
	return TrainCommand(__SCREAMING_SNAKE_CASE							)







class        A_   (			__lowerCamelCase   ):




	'''simple docstring'''


	@staticmethod
	def 	SCREAMING_SNAKE_CASE__						(       snake_case       ):
		lowercase       =    parser.add_parser('train'		,    help='CLI tool to train a model on a task.'       )

		train_parser.add_argument(
		    '--train_data'		,    type=snake_case		,    required=snake_case		,    help='path to train (and optionally evaluation) dataset as a csv with tab separated labels and sentences.'		,    )
		train_parser.add_argument(
		    '--column_label'		,    type=snake_case		,    default=0		,    help='Column of the dataset csv file with example labels.'       )
		train_parser.add_argument(
		    '--column_text'		,    type=snake_case		,    default=1		,    help='Column of the dataset csv file with example texts.'       )
		train_parser.add_argument(
		    '--column_id'		,    type=snake_case		,    default=2		,    help='Column of the dataset csv file with example ids.'       )
		train_parser.add_argument(
		    '--skip_first_row'		,    action='store_true'		,    help='Skip the first row of the csv file (headers).'       )

		train_parser.add_argument('--validation_data'		,    type=snake_case		,    default=''		,    help='path to validation dataset.'       )
		train_parser.add_argument(
		    '--validation_split'		,    type=snake_case		,    default=0.1		,    help='if validation dataset is not provided, fraction of train dataset to use as validation dataset.'		,    )

		train_parser.add_argument('--output'		,    type=snake_case		,    default='./'		,    help='path to saved the trained model.'       )

		train_parser.add_argument(
		    '--task'		,    type=snake_case		,    default='text_classification'		,    help='Task to train the model on.'       )
		train_parser.add_argument(
		    '--model'		,    type=snake_case		,    default='bert-base-uncased'		,    help='Model\'s name or path to stored model.'       )
		train_parser.add_argument('--train_batch_size'		,    type=snake_case		,    default=32		,    help='Batch size for training.'       )
		train_parser.add_argument('--valid_batch_size'		,    type=snake_case		,    default=64		,    help='Batch size for validation.'       )
		train_parser.add_argument('--learning_rate'		,    type=snake_case		,    default=3E-5		,    help='Learning rate.'       )
		train_parser.add_argument('--adam_epsilon'		,    type=snake_case		,    default=1E-08		,    help='Epsilon for Adam optimizer.'       )
		train_parser.set_defaults(func=snake_case       )





	def __init__(       self		,    snake_case       ):
		lowercase       =    logging.get_logger('transformers-cli/training'       )

		lowercase       =    'tf' if is_tf_available() else 'torch'

		os.makedirs(args.output		,    exist_ok=snake_case       )
		lowercase       =    args.output

		lowercase       =    args.column_label
		lowercase       =    args.column_text
		lowercase       =    args.column_id

		self.logger.info(F'''Loading {args.task} pipeline for {args.model}'''       )
		if args.task == "text_classification":
			lowercase       =    TextClassificationPipeline.from_pretrained(args.model       )
		elif args.task == "token_classification":
			raise NotImplementedError
		elif args.task == "question_answering":
			raise NotImplementedError

		self.logger.info(F'''Loading dataset from {args.train_data}'''       )
		lowercase       =    Processor.create_from_csv(
		    args.train_data		,    column_label=args.column_label		,    column_text=args.column_text		,    column_id=args.column_id		,    skip_first_row=args.skip_first_row		,    )
		lowercase       =    None
		if args.validation_data:
			self.logger.info(F'''Loading validation dataset from {args.validation_data}'''       )
			lowercase       =    Processor.create_from_csv(
			    args.validation_data		,    column_label=args.column_label		,    column_text=args.column_text		,    column_id=args.column_id		,    skip_first_row=args.skip_first_row		,    )

		lowercase       =    args.validation_split
		lowercase       =    args.train_batch_size
		lowercase       =    args.valid_batch_size
		lowercase       =    args.learning_rate
		lowercase       =    args.adam_epsilon





	def 	SCREAMING_SNAKE_CASE__						(       self       ):
		if self.framework == "tf":
			return self.run_tf()
		return self.run_torch()





	def 	SCREAMING_SNAKE_CASE__						(       self       ):
		raise NotImplementedError





	def 	SCREAMING_SNAKE_CASE__						(       self       ):
		self.pipeline.fit(
		    self.train_dataset		,    validation_data=self.valid_dataset		,    validation_split=self.validation_split		,    learning_rate=self.learning_rate		,    adam_epsilon=self.adam_epsilon		,    train_batch_size=self.train_batch_size		,    valid_batch_size=self.valid_batch_size		,    )

		# Save trained pipeline
		self.pipeline.save_pretrained(self.output       )



import sys
from collections import defaultdict







class        A_   :




	'''simple docstring'''


	def __init__(       self       ):
		lowercase       =    []





	def 	SCREAMING_SNAKE_CASE__						(       self		,    snake_case       ):
		return self.node_position[vertex]





	def 	SCREAMING_SNAKE_CASE__						(       self		,    snake_case		,    snake_case       ):
		lowercase       =    pos





	def 	SCREAMING_SNAKE_CASE__						(       self		,    snake_case		,    snake_case		,    snake_case		,    snake_case       ):
		if start > size // 2 - 1:
			return
		else:
			if 2 * start + 2 >= size:
				lowercase       =    2 * start + 1
			else:
				if heap[2 * start + 1] < heap[2 * start + 2]:
					lowercase       =    2 * start + 1
				else:
					lowercase       =    2 * start + 2
			if heap[smallest_child] < heap[start]:
				lowercase  ,					lowercase       =    heap[smallest_child], positions[smallest_child]
				lowercase  ,					lowercase       =    (
				    heap[start],
				    positions[start],
				)
				lowercase  ,					lowercase       =    temp, tempa

				lowercase       =    self.get_position(positions[smallest_child]       )
				self.set_position(
				    positions[smallest_child]		,    self.get_position(positions[start]       )       )
				self.set_position(positions[start]		,    snake_case       )

				self.top_to_bottom(snake_case		,    snake_case		,    snake_case		,    snake_case       )





	def 	SCREAMING_SNAKE_CASE__						(       self		,    snake_case		,    snake_case		,    snake_case		,    snake_case       ):
		lowercase       =    position[index]

		while index != 0:
			lowercase       =    int((index - 2) / 2       ) if index % 2 == 0 else int((index - 1) / 2       )

			if val < heap[parent]:
				lowercase       =    heap[parent]
				lowercase       =    position[parent]
				self.set_position(position[parent]		,    snake_case       )
			else:
				lowercase       =    val
				lowercase       =    temp
				self.set_position(snake_case		,    snake_case       )
				break
			lowercase       =    parent
		else:
			lowercase       =    val
			lowercase       =    temp
			self.set_position(snake_case		,    0       )





	def 	SCREAMING_SNAKE_CASE__						(       self		,    snake_case		,    snake_case       ):
		lowercase       =    len(snake_case       ) // 2 - 1
		for i in range(snake_case		,    -1		,    -1       ):
			self.top_to_bottom(snake_case		,    snake_case		,    len(snake_case       )		,    snake_case       )





	def 	SCREAMING_SNAKE_CASE__						(       self		,    snake_case		,    snake_case       ):
		lowercase       =    positions[0]
		lowercase       =    sys.maxsize
		self.top_to_bottom(snake_case		,    0		,    len(snake_case       )		,    snake_case       )
		return temp






def 							UpperCAmelCase_		(							__SCREAMING_SNAKE_CASE							):
	lowercase       =    Heap()

	lowercase       =    [0] * len(__SCREAMING_SNAKE_CASE							)
	lowercase       =    [-1] * len(__SCREAMING_SNAKE_CASE							)  # Neighboring Tree Vertex of selected vertex
	# Minimum Distance of explored vertex with neighboring vertex of partial tree
	# formed in graph
	lowercase       =    []  # Heap of Distance of vertices from their neighboring vertex
	lowercase       =    []

	for vertex in range(len(__SCREAMING_SNAKE_CASE							)							):
		distance_tv.append(sys.maxsize							)
		positions.append(__SCREAMING_SNAKE_CASE							)
		heap.node_position.append(__SCREAMING_SNAKE_CASE							)

	lowercase       =    []
	lowercase       =    1
	lowercase       =    sys.maxsize
	for neighbor, distance in adjacency_list[0]:
		lowercase       =    0
		lowercase       =    distance
	heap.heapify(__SCREAMING_SNAKE_CASE ,							__SCREAMING_SNAKE_CASE							)

	for _ in range(1 ,							len(__SCREAMING_SNAKE_CASE							)							):
		lowercase       =    heap.delete_minimum(__SCREAMING_SNAKE_CASE ,							__SCREAMING_SNAKE_CASE							)
		if visited[vertex] == 0:
			tree_edges.append((nbr_tv[vertex], vertex)							)
			lowercase       =    1
			for neighbor, distance in adjacency_list[vertex]:
				if (
				    visited[neighbor] == 0
				    and distance < distance_tv[heap.get_position(__SCREAMING_SNAKE_CASE							)]
				):
					lowercase       =    distance
					heap.bottom_to_top(
					    __SCREAMING_SNAKE_CASE ,							heap.get_position(__SCREAMING_SNAKE_CASE							) ,							__SCREAMING_SNAKE_CASE ,							__SCREAMING_SNAKE_CASE							)
					lowercase       =    vertex
	return tree_edges


if __name__ == "__main__":  # pragma: no cover
	# < --------- Prims Algorithm --------- >
	UpperCAmelCase	      = int(input('''Enter number of edges: ''').strip())
	UpperCAmelCase	      = defaultdict(list)
	for _ in range(edges_number):
		UpperCAmelCase	      = [int(x) for x in input().strip().split()]
		adjacency_list[edge[0]].append([edge[1], edge[2]])
		adjacency_list[edge[1]].append([edge[0], edge[2]])
	print(prisms_algorithm(adjacency_list))

