


import argparse
import logging
import os
from datetime import datetime

import numpy as np
import torch
from torch import nn
from torch.utils.data import DataLoader, RandomSampler, TensorDataset
from tqdm import tqdm

from transformers import GPTaLMHeadModel


A							:					Optional[Any]     						=     logging.getLogger(__name__)





def 	_lowerCAmelCase       (    _lowerCAmelCase      ,	_lowerCAmelCase )						->	List[str]:


						'''simple docstring'''






						if os.path.exists(_lowerCAmelCase ):
												if os.path.exists(os.path.join(_lowerCAmelCase      ,	"config.json" ) ) and os.path.isfile(
												    os.path.join(_lowerCAmelCase      ,	"config.json" ) ):
																		os.remove(os.path.join(_lowerCAmelCase      ,	"config.json" ) )
												if os.path.exists(os.path.join(_lowerCAmelCase      ,	"pytorch_model.bin" ) ) and os.path.isfile(
												    os.path.join(_lowerCAmelCase      ,	"pytorch_model.bin" ) ):
																		os.remove(os.path.join(_lowerCAmelCase      ,	"pytorch_model.bin" ) )
						else:
												os.makedirs(_lowerCAmelCase )
						model.save_pretrained(_lowerCAmelCase )





def 	_lowerCAmelCase       (    _lowerCAmelCase      ,	_lowerCAmelCase=False )						->	Optional[Any]:


						'''simple docstring'''






						__snake_case				=		2
						if unlogit:
												__snake_case				=		torch.pow(_lowerCAmelCase      ,	_lowerCAmelCase )
						__snake_case				=		p * torch.log(_lowerCAmelCase )
						__snake_case				=		0
						return -plogp.sum(dim=-1 )





def 	_lowerCAmelCase       (    _lowerCAmelCase )						->	int:


						'''simple docstring'''






						logger.info("lv, h >\t" + "\t".join(F'''{x + 1}''' for x in range(len(_lowerCAmelCase ) ) ) )
						for row in range(len(_lowerCAmelCase ) ):
												if tensor.dtype != torch.long:
																		logger.info(F'''layer {row + 1}:\t''' + "\t".join(F'''{x:.5f}''' for x in tensor[row].cpu().data ) )
												else:
																		logger.info(F'''layer {row + 1}:\t''' + "\t".join(F'''{x:d}''' for x in tensor[row].cpu().data ) )





def 	_lowerCAmelCase       (    _lowerCAmelCase      ,	_lowerCAmelCase      ,	_lowerCAmelCase      ,	_lowerCAmelCase=True      ,	_lowerCAmelCase=True      ,	_lowerCAmelCase=None      ,	_lowerCAmelCase=False )						->	Union[str, Any]:


						'''simple docstring'''






						__snake_case		,					__snake_case				=		model.config.num_hidden_layers, model.config.num_attention_heads
						__snake_case				=		torch.zeros(_lowerCAmelCase      ,	_lowerCAmelCase ).to(args.device )
						__snake_case				=		torch.zeros(_lowerCAmelCase      ,	_lowerCAmelCase ).to(args.device )

						if head_mask is None:
												__snake_case				=		torch.ones(_lowerCAmelCase      ,	_lowerCAmelCase ).to(args.device )

						head_mask.requires_grad_(requires_grad=_lowerCAmelCase )
						# If actually pruned attention multi-head, set head mask to None to avoid shape mismatch
						if actually_pruned:
												__snake_case				=		None

						__snake_case				=		0.0
						__snake_case				=		0.0
						for step, inputs in enumerate(tqdm(_lowerCAmelCase      ,	desc="Iteration"      ,	disable=args.local_rank not in [-1, 0] ) ):
												__snake_case				=		tuple(t.to(args.device ) for t in inputs )
												((__snake_case)		,					)				=		inputs

												# Do a forward pass (not with torch.no_grad() since we need gradients for importance score - see below)
												__snake_case				=		model(_lowerCAmelCase      ,	labels=_lowerCAmelCase      ,	head_mask=_lowerCAmelCase )
												#  (loss), lm_logits, presents, (all hidden_states), (attentions)
												__snake_case		,					__snake_case		,					__snake_case				=		(
												    outputs[0],
												    outputs[1],
												    outputs[-1],
												)  # Loss and logits are the first, attention the last
												loss.backward()  # Backpropagate to populate the gradients in the head mask
												total_loss += loss.detach().cpu().numpy()
												if compute_entropy:
																		for layer, attn in enumerate(_lowerCAmelCase ):
																								__snake_case				=		entropy(attn.detach()      ,	_lowerCAmelCase )
																								attn_entropy[layer] += masked_entropy.sum(-1 ).sum(0 ).sum(0 ).detach()

												if compute_importance:
																		head_importance += head_mask.grad.abs().detach()
												tot_tokens += torch.ones_like(_lowerCAmelCase ).float().detach().sum().data

						# Normalize
						attn_entropy /= tot_tokens
						head_importance /= tot_tokens
						# Layerwise importance normalization
						if not args.dont_normalize_importance_by_layer:
												__snake_case				=		2
												__snake_case				=		torch.pow(torch.pow(_lowerCAmelCase      ,	_lowerCAmelCase ).sum(-1 )      ,	1 / exponent )
												head_importance /= norm_by_layer.unsqueeze(-1 ) + 1E-20

						if not args.dont_normalize_global_importance:
												__snake_case				=		(head_importance - head_importance.min()) / (head_importance.max() - head_importance.min())

						# Print matrices
						if compute_entropy:
												logger.info("Attention entropies" )
												print_ad_tensor(_lowerCAmelCase )
						if compute_importance:
												logger.info("Head importance scores" )
												print_ad_tensor(_lowerCAmelCase )
						logger.info("Head ranked by importance scores" )
						__snake_case				=		torch.zeros(head_importance.numel()      ,	dtype=torch.long      ,	device=args.device )
						__snake_case				=		torch.arange(
						    head_importance.numel()      ,	device=args.device )
						__snake_case				=		head_ranks.view_as(_lowerCAmelCase )
						print_ad_tensor(_lowerCAmelCase )
						return attn_entropy, head_importance, total_loss





def 	_lowerCAmelCase       (    _lowerCAmelCase      ,	_lowerCAmelCase      ,	_lowerCAmelCase )						->	Union[str, Any]:


						'''simple docstring'''






						__snake_case		,					__snake_case		,					__snake_case				=		compute_heads_importance(_lowerCAmelCase      ,	_lowerCAmelCase      ,	_lowerCAmelCase      ,	compute_entropy=_lowerCAmelCase )
						__snake_case				=		1 / loss  # instead of downsteam score use the LM loss
						logger.info("Pruning: original score: %f, threshold: %f"      ,	_lowerCAmelCase      ,	original_score * args.masking_threshold )

						__snake_case				=		torch.ones_like(_lowerCAmelCase )
						__snake_case				=		max(1      ,	int(new_head_mask.numel() * args.masking_amount ) )

						__snake_case				=		original_score
						while current_score >= original_score * args.masking_threshold:
												__snake_case				=		new_head_mask.clone().detach()  # save current head mask
												# heads from least important to most - keep only not-masked heads
												__snake_case				=		float("Inf" )
												__snake_case				=		head_importance.view(-1 ).sort()[1]

												if len(_lowerCAmelCase ) <= num_to_mask:
																		print("BREAK BY num_to_mask" )
																		break

												# mask heads
												__snake_case				=		current_heads_to_mask[:num_to_mask]
												logger.info("Heads to mask: %s"      ,	str(current_heads_to_mask.tolist() ) )
												__snake_case				=		new_head_mask.view(-1 )
												__snake_case				=		0.0
												__snake_case				=		new_head_mask.view_as(_lowerCAmelCase )
												__snake_case				=		new_head_mask.clone().detach()
												print_ad_tensor(_lowerCAmelCase )

												# Compute metric and head importance again
												__snake_case		,					__snake_case		,					__snake_case				=		compute_heads_importance(
												    _lowerCAmelCase      ,	_lowerCAmelCase      ,	_lowerCAmelCase      ,	compute_entropy=_lowerCAmelCase      ,	head_mask=_lowerCAmelCase )
												__snake_case				=		1 / loss
												logger.info(
												    "Masking: current score: %f, remaining heads %d (%.1f percents)"      ,	_lowerCAmelCase      ,	new_head_mask.sum()      ,	new_head_mask.sum() / new_head_mask.numel() * 100      ,	)

						logger.info("Final head mask" )
						print_ad_tensor(_lowerCAmelCase )
						np.save(os.path.join(args.output_dir      ,	"head_mask.npy" )      ,	head_mask.detach().cpu().numpy() )

						return head_mask





def 	_lowerCAmelCase       (    _lowerCAmelCase      ,	_lowerCAmelCase      ,	_lowerCAmelCase      ,	_lowerCAmelCase )						->	List[str]:


						'''simple docstring'''






						__snake_case				=		datetime.now()
						__snake_case		,					__snake_case		,					__snake_case				=		compute_heads_importance(
						    _lowerCAmelCase      ,	_lowerCAmelCase      ,	_lowerCAmelCase      ,	compute_entropy=_lowerCAmelCase      ,	compute_importance=_lowerCAmelCase      ,	head_mask=_lowerCAmelCase )
						__snake_case				=		1 / loss
						__snake_case				=		datetime.now() - before_time

						__snake_case				=		sum(p.numel() for p in model.parameters() )
						__snake_case				=		{
						    layer: (1 - head_mask[layer].long()).nonzero().squeeze().tolist() for layer in range(len(_lowerCAmelCase ) )
						}

						for k, v in heads_to_prune.items():
												if isinstance(_lowerCAmelCase      ,	_lowerCAmelCase ):
																		__snake_case				=		[
																		    v,
																		]

						assert sum(len(_lowerCAmelCase ) for h in heads_to_prune.values() ) == (1 - head_mask.long()).sum().item()
						model.prune_heads(_lowerCAmelCase )
						__snake_case				=		sum(p.numel() for p in model.parameters() )

						__snake_case				=		datetime.now()
						__snake_case		,					__snake_case		,					__snake_case				=		compute_heads_importance(
						    _lowerCAmelCase      ,	_lowerCAmelCase      ,	_lowerCAmelCase      ,	compute_entropy=_lowerCAmelCase      ,	compute_importance=_lowerCAmelCase      ,	head_mask=_lowerCAmelCase      ,	actually_pruned=_lowerCAmelCase      ,	)

						__snake_case				=		1 / loss
						__snake_case				=		datetime.now() - before_time

						logger.info(
						    "Pruning: original num of params: %.2e, after pruning %.2e (%.1f percents)"      ,	_lowerCAmelCase      ,	_lowerCAmelCase      ,	pruned_num_params / original_num_params * 100      ,	)
						logger.info("Pruning: score with masking: %f score with pruning: %f"      ,	_lowerCAmelCase      ,	_lowerCAmelCase )
						logger.info("Pruning: speed ratio (original timing / new timing): %f percents"      ,	original_time / new_time * 100 )
						save_model(_lowerCAmelCase      ,	args.output_dir )





def 	_lowerCAmelCase       (    )						->	Tuple:


						'''simple docstring'''






						__snake_case				=		argparse.ArgumentParser()
						# Required parameters
						parser.add_argument(
						    "--data_dir"      ,	default=_lowerCAmelCase      ,	type=_lowerCAmelCase      ,	required=_lowerCAmelCase      ,	help="The input data dir. Should contain the .tsv files (or other data files) for the task."      ,	)
						parser.add_argument(
						    "--model_name_or_path"      ,	default=_lowerCAmelCase      ,	type=_lowerCAmelCase      ,	required=_lowerCAmelCase      ,	help="Path to pretrained model or model identifier from huggingface.co/models"      ,	)
						parser.add_argument(
						    "--output_dir"      ,	default=_lowerCAmelCase      ,	type=_lowerCAmelCase      ,	required=_lowerCAmelCase      ,	help="The output directory where the model predictions and checkpoints will be written."      ,	)

						# Other parameters
						parser.add_argument(
						    "--config_name"      ,	default=""      ,	type=_lowerCAmelCase      ,	help="Pretrained config name or path if not the same as model_name_or_path"      ,	)
						parser.add_argument(
						    "--tokenizer_name"      ,	default=""      ,	type=_lowerCAmelCase      ,	help="Pretrained tokenizer name or path if not the same as model_name_or_path"      ,	)
						parser.add_argument(
						    "--cache_dir"      ,	default=_lowerCAmelCase      ,	type=_lowerCAmelCase      ,	help="Where do you want to store the pre-trained models downloaded from s3"      ,	)
						parser.add_argument(
						    "--data_subset"      ,	type=_lowerCAmelCase      ,	default=-1      ,	help="If > 0: limit the data to a subset of data_subset instances." )
						parser.add_argument(
						    "--overwrite_output_dir"      ,	action="store_true"      ,	help="Whether to overwrite data in output directory" )
						parser.add_argument(
						    "--overwrite_cache"      ,	action="store_true"      ,	help="Overwrite the cached training and evaluation sets" )

						parser.add_argument(
						    "--dont_normalize_importance_by_layer"      ,	action="store_true"      ,	help="Don't normalize importance score by layers" )
						parser.add_argument(
						    "--dont_normalize_global_importance"      ,	action="store_true"      ,	help="Don't normalize all importance scores between 0 and 1"      ,	)

						parser.add_argument(
						    "--try_masking"      ,	action="store_true"      ,	help="Whether to try to mask head until a threshold of accuracy." )
						parser.add_argument(
						    "--masking_threshold"      ,	default=0.9      ,	type=_lowerCAmelCase      ,	help="masking threshold in term of metrics (stop masking when metric < threshold * original metric value)."      ,	)
						parser.add_argument(
						    "--masking_amount"      ,	default=0.1      ,	type=_lowerCAmelCase      ,	help="Amount to heads to masking at each masking step." )
						parser.add_argument("--metric_name"      ,	default="acc"      ,	type=_lowerCAmelCase      ,	help="Metric to use for head masking." )

						parser.add_argument(
						    "--max_seq_length"      ,	default=128      ,	type=_lowerCAmelCase      ,	help=(
						        "The maximum total input sequence length after WordPiece tokenization. \n"
						        "Sequences longer than this will be truncated, sequences shorter padded."
						    )      ,	)
						parser.add_argument("--batch_size"      ,	default=1      ,	type=_lowerCAmelCase      ,	help="Batch size." )

						parser.add_argument("--seed"      ,	type=_lowerCAmelCase      ,	default=42 )
						parser.add_argument("--local_rank"      ,	type=_lowerCAmelCase      ,	default=-1      ,	help="local_rank for distributed training on gpus" )
						parser.add_argument("--no_cuda"      ,	action="store_true"      ,	help="Whether not to use CUDA when available" )
						parser.add_argument("--server_ip"      ,	type=_lowerCAmelCase      ,	default=""      ,	help="Can be used for distant debugging." )
						parser.add_argument("--server_port"      ,	type=_lowerCAmelCase      ,	default=""      ,	help="Can be used for distant debugging." )
						__snake_case				=		parser.parse_args()

						if args.server_ip and args.server_port:
												# Distant debugging - see https://code.visualstudio.com/docs/python/debugging#_attach-to-a-local-script
												import ptvsd

												print("Waiting for debugger attach" )
												ptvsd.enable_attach(address=(args.server_ip, args.server_port)      ,	redirect_output=_lowerCAmelCase )
												ptvsd.wait_for_attach()

						# Setup devices and distributed training
						if args.local_rank == -1 or args.no_cuda:
												__snake_case				=		torch.device("cuda" if torch.cuda.is_available() and not args.no_cuda else "cpu" )
												__snake_case				=		0 if args.no_cuda else torch.cuda.device_count()
						else:
												torch.cuda.set_device(args.local_rank )
												__snake_case				=		torch.device("cuda"      ,	args.local_rank )
												__snake_case				=		1
												torch.distributed.init_process_group(backend="nccl" )  # Initializes the distributed backend

						# Setup logging
						logging.basicConfig(level=logging.INFO if args.local_rank in [-1, 0] else logging.WARN )
						logger.info("device: {} n_gpu: {}, distributed: {}".format(args.device      ,	args.n_gpu      ,	bool(args.local_rank != -1 ) ) )

						__snake_case				=		GPTaLMHeadModel.from_pretrained(args.model_name_or_path )

						# Distributed and parallel training
						model.to(args.device )
						if args.local_rank != -1:
												__snake_case				=		nn.parallel.DistributedDataParallel(
												    _lowerCAmelCase      ,	device_ids=[args.local_rank]      ,	output_device=args.local_rank      ,	find_unused_parameters=_lowerCAmelCase )
						elif args.n_gpu > 1:
												__snake_case				=		nn.DataParallel(_lowerCAmelCase )

						# Print/save training arguments
						os.makedirs(args.output_dir      ,	exist_ok=_lowerCAmelCase )
						torch.save(_lowerCAmelCase      ,	os.path.join(args.output_dir      ,	"run_args.bin" ) )
						logger.info("Training/evaluation parameters %s"      ,	_lowerCAmelCase )

						# Prepare dataset
						__snake_case				=		np.concatenate(
						    [
						        np.loadtxt(args.data_dir      ,	dtype=np.intaa ),
						    ] )
						__snake_case				=		(torch.from_numpy(_lowerCAmelCase ),)
						__snake_case				=		TensorDataset(*_lowerCAmelCase )
						__snake_case				=		RandomSampler(_lowerCAmelCase )
						__snake_case				=		DataLoader(_lowerCAmelCase      ,	sampler=_lowerCAmelCase      ,	batch_size=args.batch_size )

						# Compute head entropy and importance score
						compute_heads_importance(_lowerCAmelCase      ,	_lowerCAmelCase      ,	_lowerCAmelCase )

						# Try head masking (set heads to zero until the score goes under a threshole)
						# and head pruning (remove masked heads and see the effect on the network)
						if args.try_masking and args.masking_threshold > 0.0 and args.masking_threshold < 1.0:
												__snake_case				=		mask_heads(_lowerCAmelCase      ,	_lowerCAmelCase      ,	_lowerCAmelCase )
												prune_heads(_lowerCAmelCase      ,	_lowerCAmelCase      ,	_lowerCAmelCase      ,	_lowerCAmelCase )


if __name__ == "__main__":
	main()









class     UpperCamelCase:


					def __init__(						self	:		Any						)			->	Any:




											'''simple docstring'''

											__snake_case				=		0
											__snake_case				=		0
											__snake_case				=		{}


					def 							SCREAMING_SNAKE_CASE_  (						self	:		Dict     ,    SCREAMING_SNAKE_CASE	:		Optional[int]						)			->	Optional[Any]:




											'''simple docstring'''

											if vertex not in self.adjacency:
																	__snake_case				=		{}
																	self.num_vertices += 1


					def 							SCREAMING_SNAKE_CASE_  (						self	:		Union[str, Any]     ,    SCREAMING_SNAKE_CASE	:		Optional[int]     ,    SCREAMING_SNAKE_CASE	:		Optional[int]     ,    SCREAMING_SNAKE_CASE	:		str						)			->	Optional[Any]:




											'''simple docstring'''

											self.add_vertex(SCREAMING_SNAKE_CASE						)
											self.add_vertex(SCREAMING_SNAKE_CASE						)

											if head == tail:
																	return

											__snake_case				=		weight
											__snake_case				=		weight


					def 							SCREAMING_SNAKE_CASE_  (						self	:		Optional[int]						)			->	Tuple:




											'''simple docstring'''

											__snake_case				=		self.get_edges()
											for edge in edges:
																	__snake_case		,					__snake_case		,					__snake_case				=		edge
																	edges.remove((tail, head, weight)						)
											for i in range(len(SCREAMING_SNAKE_CASE						)						):
																	__snake_case				=		list(edges[i]						)

											edges.sort(key=lambda SCREAMING_SNAKE_CASE						: e[2]						)
											for i in range(len(SCREAMING_SNAKE_CASE						) - 1						):
																	if edges[i][2] >= edges[i + 1][2]:
																							__snake_case				=		edges[i][2] + 1
											for edge in edges:
																	__snake_case		,					__snake_case		,					__snake_case				=		edge
																	__snake_case				=		weight
																	__snake_case				=		weight


					def __str__(						self	:		Tuple						)			->	List[Any]:




											'''simple docstring'''

											__snake_case				=		""
											for tail in self.adjacency:
																	for head in self.adjacency[tail]:
																							__snake_case				=		self.adjacency[head][tail]
																							string += f'''{head} -> {tail} == {weight}\n'''
											return string.rstrip("\n"						)


					def 							SCREAMING_SNAKE_CASE_  (						self	:		int						)			->	Optional[Any]:




											'''simple docstring'''

											__snake_case				=		[]
											for tail in self.adjacency:
																	for head in self.adjacency[tail]:
																							output.append((tail, head, self.adjacency[head][tail])						)
											return output


					def 							SCREAMING_SNAKE_CASE_  (						self	:		Any						)			->	List[Any]:




											'''simple docstring'''

											return self.adjacency.keys()


					@staticmethod
					def 							SCREAMING_SNAKE_CASE_  (						SCREAMING_SNAKE_CASE	:		Any=None     ,    SCREAMING_SNAKE_CASE	:		List[Any]=None						)			->	int:




											'''simple docstring'''

											__snake_case				=		Graph()
											if vertices is None:
																	__snake_case				=		[]
											if edges is None:
																	__snake_case				=		[]
											for vertex in vertices:
																	g.add_vertex(SCREAMING_SNAKE_CASE						)
											for edge in edges:
																	g.add_edge(*SCREAMING_SNAKE_CASE						)
											return g






					class     UpperCamelCase:


										def __init__(						self	:		Union[str, Any]						)			->	Union[str, Any]:




																'''simple docstring'''

																__snake_case				=		{}
																__snake_case				=		{}


										def __len__(						self	:		List[str]						)			->	Dict:




																'''simple docstring'''

																return len(self.parent						)


										def 							SCREAMING_SNAKE_CASE_  (						self	:		List[str]     ,    SCREAMING_SNAKE_CASE	:		int						)			->	List[str]:




																'''simple docstring'''

																if item in self.parent:
																						return self.find(SCREAMING_SNAKE_CASE						)

																__snake_case				=		item
																__snake_case				=		0
																return item


										def 							SCREAMING_SNAKE_CASE_  (						self	:		Optional[Any]     ,    SCREAMING_SNAKE_CASE	:		List[str]						)			->	Any:




																'''simple docstring'''

																if item not in self.parent:
																						return self.make_set(SCREAMING_SNAKE_CASE						)
																if item != self.parent[item]:
																						__snake_case				=		self.find(self.parent[item]						)
																return self.parent[item]






										def 							SCREAMING_SNAKE_CASE_  (						self	:		Optional[Any]     ,    SCREAMING_SNAKE_CASE	:		int     ,    SCREAMING_SNAKE_CASE	:		Union[str, Any]						)			->	Dict:




																'''simple docstring'''

																__snake_case				=		self.find(SCREAMING_SNAKE_CASE						)
																__snake_case				=		self.find(SCREAMING_SNAKE_CASE						)

																if roota == roota:
																						return roota

																if self.rank[roota] > self.rank[roota]:
																						__snake_case				=		roota
																						return roota

																if self.rank[roota] < self.rank[roota]:
																						__snake_case				=		roota
																						return roota

																if self.rank[roota] == self.rank[roota]:
																						self.rank[roota] += 1
																						__snake_case				=		roota
																						return roota
																return None




					@staticmethod
					def 							SCREAMING_SNAKE_CASE_  (						SCREAMING_SNAKE_CASE	:		str						)			->	Any:




											'''simple docstring'''

											__snake_case				=		graph.num_vertices

											__snake_case				=		Graph.UnionFind()
											__snake_case				=		[]
											while num_components > 1:
																	__snake_case				=		{}
																	for vertex in graph.get_vertices():
																							__snake_case				=		-1

																	__snake_case				=		graph.get_edges()
																	for edge in edges:
																							__snake_case		,					__snake_case		,					__snake_case				=		edge
																							edges.remove((tail, head, weight)						)
																	for edge in edges:
																							__snake_case		,					__snake_case		,					__snake_case				=		edge
																							__snake_case				=		union_find.find(SCREAMING_SNAKE_CASE						)
																							__snake_case				=		union_find.find(SCREAMING_SNAKE_CASE						)
																							if seta != seta:
																													if cheap_edge[seta] == -1 or cheap_edge[seta][2] > weight:
																																			__snake_case				=		[head, tail, weight]

																													if cheap_edge[seta] == -1 or cheap_edge[seta][2] > weight:
																																			__snake_case				=		[head, tail, weight]
																	for vertex in cheap_edge:
																							if cheap_edge[vertex] != -1:
																													__snake_case		,					__snake_case		,					__snake_case				=		cheap_edge[vertex]
																													if union_find.find(SCREAMING_SNAKE_CASE						) != union_find.find(SCREAMING_SNAKE_CASE						):
																																			union_find.union(SCREAMING_SNAKE_CASE     ,    SCREAMING_SNAKE_CASE						)
																																			mst_edges.append(cheap_edge[vertex]						)
																																			__snake_case				=		num_components - 1
											__snake_case				=		Graph.build(edges=SCREAMING_SNAKE_CASE						)
											return mst





