





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
__lowercase       :List[Any]       =  False
__lowercase       :Union[str, Any]       =  False







def       UpperCAmelCase							(     _lowerCamelCase      :  Namespace     ):





      '''simple docstring'''


      return TrainCommand(_lowerCamelCase     )



class    _a     (				lowercase__							):






       """simple docstring"""

       @staticmethod
       def      A_  (      a				:     ArgumentParser    )	->List[Any]:
             SCREAMING_SNAKE_CASE__     :				List[str]   =					parser.add_parser("train"							,	help="CLI tool to train a model on a task."    )

             train_parser.add_argument(
                 "--train_data"							,	type=a							,	required=a							,	help="path to train (and optionally evaluation) dataset as a csv with tab separated labels and sentences."							,	)
             train_parser.add_argument(
                 "--column_label"							,	type=a							,	default=0							,	help="Column of the dataset csv file with example labels."    )
             train_parser.add_argument(
                 "--column_text"							,	type=a							,	default=1							,	help="Column of the dataset csv file with example texts."    )
             train_parser.add_argument(
                 "--column_id"							,	type=a							,	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=a							,	default=""							,	help="path to validation dataset."    )
             train_parser.add_argument(
                 "--validation_split"							,	type=a							,	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=a							,	default="./"							,	help="path to saved the trained model."    )

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

       def __init__(      self				:     Union[str, Any]							,	a				:     Namespace    )	->Tuple:
             SCREAMING_SNAKE_CASE__     :				List[str]   =					logging.get_logger("transformers-cli/training"    )

             SCREAMING_SNAKE_CASE__     :				Dict   =					"tf" if is_tf_available() else "torch"

             os.makedirs(args.output							,	exist_ok=a    )
             SCREAMING_SNAKE_CASE__     :				Tuple   =					args.output

             SCREAMING_SNAKE_CASE__     :				Optional[Any]   =					args.column_label
             SCREAMING_SNAKE_CASE__     :				int   =					args.column_text
             SCREAMING_SNAKE_CASE__     :				int   =					args.column_id

             self.logger.info(f"""Loading {args.task} pipeline for {args.model}"""    )
             if args.task == "text_classification":
                   SCREAMING_SNAKE_CASE__     :				List[Any]   =					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}"""    )
             SCREAMING_SNAKE_CASE__     :				Tuple   =					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							,	)
             SCREAMING_SNAKE_CASE__     :				str   =					None
             if args.validation_data:
                   self.logger.info(f"""Loading validation dataset from {args.validation_data}"""    )
                   SCREAMING_SNAKE_CASE__     :				Dict   =					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							,	)

             SCREAMING_SNAKE_CASE__     :				Tuple   =					args.validation_split
             SCREAMING_SNAKE_CASE__     :				str   =					args.train_batch_size
             SCREAMING_SNAKE_CASE__     :				Tuple   =					args.valid_batch_size
             SCREAMING_SNAKE_CASE__     :				Optional[Any]   =					args.learning_rate
             SCREAMING_SNAKE_CASE__     :				List[str]   =					args.adam_epsilon

       def      A_  (      self				:     Dict    )	->Dict:
             if self.framework == "tf":
                   return self.run_tf()
             return self.run_torch()

       def      A_  (      self				:     int    )	->Union[str, Any]:
             raise NotImplementedError






       def      A_  (      self				:     Tuple    )	->str:
             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				:     Any    )	->Dict:
													SCREAMING_SNAKE_CASE__     :				Tuple   =					[]

							def      A_  (      self				:     int							,	a				:     List[str]    )	->Dict:
													return self.node_position[vertex]

							def      A_  (      self				:     Optional[Any]							,	a				:     Any							,	a				:     List[str]    )	->Optional[Any]:
													SCREAMING_SNAKE_CASE__     :				str   =					pos

							def      A_  (      self				:     List[Any]							,	a				:     List[str]							,	a				:     Dict							,	a				:     Dict							,	a				:     List[Any]    )	->Optional[int]:
													if start > size // 2 - 1:
																			return
													else:
																			if 2 * start + 2 >= size:
																									SCREAMING_SNAKE_CASE__     :				Optional[Any]   =					2 * start + 1
																			else:
																									if heap[2 * start + 1] < heap[2 * start + 2]:
																															SCREAMING_SNAKE_CASE__     :				Dict   =					2 * start + 1
																									else:
																															SCREAMING_SNAKE_CASE__     :				Tuple   =					2 * start + 2
																			if heap[smallest_child] < heap[start]:
																									SCREAMING_SNAKE_CASE__,							SCREAMING_SNAKE_CASE__     :				int   =					heap[smallest_child], positions[smallest_child]
																									SCREAMING_SNAKE_CASE__,							SCREAMING_SNAKE_CASE__     :				Optional[int]   =					(
																									    heap[start],
																									    positions[start],
																									)
																									SCREAMING_SNAKE_CASE__,							SCREAMING_SNAKE_CASE__     :				Tuple   =					temp, tempa

																									SCREAMING_SNAKE_CASE__     :				Optional[Any]   =					self.get_position(positions[smallest_child]    )
																									self.set_position(
																									    positions[smallest_child]							,	self.get_position(positions[start]    )    )
																									self.set_position(positions[start]							,	a    )

																									self.top_to_bottom(a							,	a							,	a							,	a    )

							def      A_  (      self				:     Union[str, Any]							,	a				:     Tuple							,	a				:     Tuple							,	a				:     Union[str, Any]							,	a				:     List[Any]    )	->Optional[int]:
													SCREAMING_SNAKE_CASE__     :				List[Any]   =					position[index]

													while index != 0:
																			SCREAMING_SNAKE_CASE__     :				Union[str, Any]   =					int((index - 2) / 2    ) if index % 2 == 0 else int((index - 1) / 2    )

																			if val < heap[parent]:
																									SCREAMING_SNAKE_CASE__     :				List[Any]   =					heap[parent]
																									SCREAMING_SNAKE_CASE__     :				str   =					position[parent]
																									self.set_position(position[parent]							,	a    )
																			else:
																									SCREAMING_SNAKE_CASE__     :				int   =					val
																									SCREAMING_SNAKE_CASE__     :				Optional[Any]   =					temp
																									self.set_position(a							,	a    )
																									break
																			SCREAMING_SNAKE_CASE__     :				Optional[int]   =					parent
													else:
																			SCREAMING_SNAKE_CASE__     :				int   =					val
																			SCREAMING_SNAKE_CASE__     :				List[str]   =					temp
																			self.set_position(a							,	0    )

							def      A_  (      self				:     Union[str, Any]							,	a				:     int							,	a				:     List[str]    )	->Union[str, Any]:
													SCREAMING_SNAKE_CASE__     :				List[str]   =					len(a    ) // 2 - 1
													for i in range(a							,	-1							,	-1    ):
																			self.top_to_bottom(a							,	a							,	len(a    )							,	a    )






							def      A_  (      self				:     Dict							,	a				:     List[Any]							,	a				:     Dict    )	->Optional[int]:
													SCREAMING_SNAKE_CASE__     :				Any   =					positions[0]
													SCREAMING_SNAKE_CASE__     :				Optional[int]   =					sys.maxsize
													self.top_to_bottom(a							,	0							,	len(a    )							,	a    )
													return temp







def       UpperCAmelCase							(     _lowerCamelCase      :  str     ):





						'''simple docstring'''


						SCREAMING_SNAKE_CASE__     :				Optional[int]   =					Heap()

						SCREAMING_SNAKE_CASE__     :				Any   =					[0] * len(_lowerCamelCase     )
						SCREAMING_SNAKE_CASE__     :				Any   =					[-1] * len(_lowerCamelCase     )  # Neighboring Tree Vertex of selected vertex
						# Minimum Distance of explored vertex with neighboring vertex of partial tree
						# formed in graph
						SCREAMING_SNAKE_CASE__     :				Union[str, Any]   =					[]  # Heap of Distance of vertices from their neighboring vertex
						SCREAMING_SNAKE_CASE__     :				str   =					[]

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

						SCREAMING_SNAKE_CASE__     :				List[Any]   =					[]
						SCREAMING_SNAKE_CASE__     :				int   =					1
						SCREAMING_SNAKE_CASE__     :				int   =					sys.maxsize
						for neighbor, distance in adjacency_list[0]:
												SCREAMING_SNAKE_CASE__     :				int   =					0
												SCREAMING_SNAKE_CASE__     :				List[str]   =					distance
						heap.heapify(_lowerCamelCase			,   _lowerCamelCase     )

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


if __name__ == "__main__":  # pragma: no cover
				# < --------- Prims Algorithm --------- >
				__lowercase       :Union[str, Any]       =  int(input("Enter number of edges: ").strip())
				__lowercase       :Dict       =  defaultdict(list)
				for _ in range(edges_number):
								__lowercase       :Any       =  [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))