
'''simple docstring'''







import argparse
import os

import evaluate
import torch
from datasets import load_dataset
from torch.optim import AdamW
from torch.utils.data import DataLoader
from transformers import AutoModelForSequenceClassification, AutoTokenizer, get_linear_schedule_with_warmup, set_seed

from accelerate import Accelerator, DistributedType
from accelerate.local_sgd import LocalSGD


########################################################################
# This is a fully working simple example to use Accelerate
# with LocalSGD, which is a method to synchronize model
# parameters every K batches. It is different, but complementary
# to gradient accumulation.
#
# This example trains a Bert base model on GLUE MRPC
# in any of the following settings (with the same script):
#   - single CPU or single GPU
#   - multi GPUS (using PyTorch distributed mode)
#   - (multi) TPUs
#   - fp16 (mixed-precision) or fp32 (normal precision)
#
# To run it in each of these various modes, follow the instructions
# in the readme for examples:
# https://github.com/huggingface/accelerate/tree/main/examples
#
########################################################################


_lowercase			:  str   							=	1_6
_lowercase			:  List[Any]   							=	3_2
def  snake_case_		(    __SCREAMING_SNAKE_CASE :			Accelerator				,				__SCREAMING_SNAKE_CASE :			int = 16							):



						"""simple docstring"""





						lowercase_     :					Union[str, Any]													=	AutoTokenizer.from_pretrained('''bert-base-cased'''							)
						lowercase_     :					List[str]													=	load_dataset('''glue'''				,				'''mrpc'''							)

						def tokenize_function(__SCREAMING_SNAKE_CASE :			Dict							):
												# max_length=None => use the model max length (it's actually the default)
												lowercase_     :					Optional[Any]													=	tokenizer(examples['''sentence1''']				,				examples['''sentence2''']				,				truncation=__SCREAMING_SNAKE_CASE				,				max_length=__SCREAMING_SNAKE_CASE							)
												return outputs

						# Apply the method we just defined to all the examples in all the splits of the dataset
						# starting with the main process first:
						with accelerator.main_process_first():
												lowercase_     :					str													=	datasets.map(
												    __SCREAMING_SNAKE_CASE				,				batched=__SCREAMING_SNAKE_CASE				,				remove_columns=['''idx''', '''sentence1''', '''sentence2''']				,				)

						# We also rename the 'label' column to 'labels' which is the expected name for labels by the models of the
						# transformers library
						lowercase_     :					Optional[Any]													=	tokenized_datasets.rename_column('''label'''				,				'''labels'''							)

						def collate_fn(__SCREAMING_SNAKE_CASE :			Dict							):
												# On TPU it's best to pad everything to the same length or training will be very slow.
												lowercase_     :					Optional[Any]													=	128 if accelerator.distributed_type == DistributedType.TPU else None
												# When using mixed precision we want round multiples of 8/16
												if accelerator.mixed_precision == "fp8":
																		lowercase_     :					Optional[int]													=	16
												elif accelerator.mixed_precision != "no":
																		lowercase_     :					Union[str, Any]													=	8
												else:
																		lowercase_     :					int													=	None

												return tokenizer.pad(
												    __SCREAMING_SNAKE_CASE				,				padding='''longest'''				,				max_length=__SCREAMING_SNAKE_CASE				,				pad_to_multiple_of=__SCREAMING_SNAKE_CASE				,				return_tensors='''pt'''				,				)

						# Instantiate dataloaders.
						lowercase_     :					str													=	DataLoader(
						    tokenized_datasets['''train''']				,				shuffle=__SCREAMING_SNAKE_CASE				,				collate_fn=__SCREAMING_SNAKE_CASE				,				batch_size=__SCREAMING_SNAKE_CASE							)
						lowercase_     :					Tuple													=	DataLoader(
						    tokenized_datasets['''validation''']				,				shuffle=__SCREAMING_SNAKE_CASE				,				collate_fn=__SCREAMING_SNAKE_CASE				,				batch_size=__SCREAMING_SNAKE_CASE							)

						return train_dataloader, eval_dataloader


# For testing only
if os.environ.get("TESTING_MOCKED_DATALOADERS", None) == "1":
							from accelerate.test_utils.training import mocked_dataloaders

							_lowercase			:  Optional[int]   							=	mocked_dataloaders  # noqa: F811
def  snake_case_		(    __SCREAMING_SNAKE_CASE :			Optional[int]				,				__SCREAMING_SNAKE_CASE :			Any							):



						"""simple docstring"""





						if os.environ.get('''TESTING_MOCKED_DATALOADERS'''				,				__SCREAMING_SNAKE_CASE							) == "1":
												lowercase_     :					List[Any]													=	2
						# New Code #
						lowercase_     :					str													=	int(args.gradient_accumulation_steps							)
						lowercase_     :					Dict													=	int(args.local_sgd_steps							)
						# Initialize accelerator
						lowercase_     :					Dict													=	Accelerator(
						    cpu=args.cpu				,				mixed_precision=args.mixed_precision				,				gradient_accumulation_steps=__SCREAMING_SNAKE_CASE							)
						if accelerator.distributed_type not in [DistributedType.NO, DistributedType.MULTI_CPU, DistributedType.MULTI_GPU]:
												raise NotImplementedError('''LocalSGD is supported only for CPUs and GPUs (no DeepSpeed or MegatronLM)'''							)
						# Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
						lowercase_     :					Optional[int]													=	config['''lr''']
						lowercase_     :					Tuple													=	int(config['''num_epochs''']							)
						lowercase_     :					Optional[Any]													=	int(config['''seed''']							)
						lowercase_     :					Optional[Any]													=	int(config['''batch_size''']							)

						lowercase_     :					str													=	evaluate.load('''glue'''				,				'''mrpc'''							)

						set_seed(__SCREAMING_SNAKE_CASE							)
						lowercase_						,			lowercase_     :					str													=	get_dataloaders(__SCREAMING_SNAKE_CASE				,				__SCREAMING_SNAKE_CASE							)
						# Instantiate the model (we build the model here so that the seed also control new weights initialization)
						lowercase_     :					Union[str, Any]													=	AutoModelForSequenceClassification.from_pretrained('''bert-base-cased'''				,				return_dict=__SCREAMING_SNAKE_CASE							)

						# We could avoid this line since the accelerator is set with `device_placement=True` (default value).
						# Note that if you are placing tensors on devices manually, this line absolutely needs to be before the optimizer
						# creation otherwise training will not work on TPU (`accelerate` will kindly throw an error to make us aware of that).
						lowercase_     :					List[str]													=	model.to(accelerator.device							)

						# Instantiate optimizer
						lowercase_     :					Optional[int]													=	AdamW(params=model.parameters()				,				lr=__SCREAMING_SNAKE_CASE							)

						# Instantiate scheduler
						lowercase_     :					List[str]													=	get_linear_schedule_with_warmup(
						    optimizer=__SCREAMING_SNAKE_CASE				,				num_warmup_steps=100				,				num_training_steps=(len(__SCREAMING_SNAKE_CASE							) * num_epochs)				,				)

						# Prepare everything
						# There is no specific order to remember, we just need to unpack the objects in the same order we gave them to the
						# prepare method.
						lowercase_						,			lowercase_						,			lowercase_						,			lowercase_						,			lowercase_     :					Optional[Any]													=	accelerator.prepare(
						    __SCREAMING_SNAKE_CASE				,				__SCREAMING_SNAKE_CASE				,				__SCREAMING_SNAKE_CASE				,				__SCREAMING_SNAKE_CASE				,				__SCREAMING_SNAKE_CASE							)

						# Now we train the model
						for epoch in range(__SCREAMING_SNAKE_CASE							):
												model.train()
												with LocalSGD(
												    accelerator=__SCREAMING_SNAKE_CASE				,				model=__SCREAMING_SNAKE_CASE				,				local_sgd_steps=__SCREAMING_SNAKE_CASE				,				enabled=local_sgd_steps is not None							) as local_sgd:
																		for step, batch in enumerate(__SCREAMING_SNAKE_CASE							):
																								# We could avoid this line since we set the accelerator with `device_placement=True`.
																								batch.to(accelerator.device							)
																								# New code #
																								# We use the new `accumulate` context manager to perform gradient accumulation
																								# We also currently do not support TPUs nor advise it as bugs were found on the XLA side when running our tests.
																								with accelerator.accumulate(__SCREAMING_SNAKE_CASE							):
																														lowercase_     :					List[Any]													=	model(**__SCREAMING_SNAKE_CASE							)
																														lowercase_     :					Dict													=	output.loss
																														accelerator.backward(__SCREAMING_SNAKE_CASE							)
																														optimizer.step()
																														lr_scheduler.step()
																														optimizer.zero_grad()
																														# LocalSGD-specific line
																														local_sgd.step()

												model.eval()
												for step, batch in enumerate(__SCREAMING_SNAKE_CASE							):
																		# We could avoid this line since we set the accelerator with `device_placement=True`.
																		batch.to(accelerator.device							)
																		with torch.no_grad():
																								lowercase_     :					Optional[int]													=	model(**__SCREAMING_SNAKE_CASE							)
																		lowercase_     :					str													=	outputs.logits.argmax(dim=-1							)
																		lowercase_						,			lowercase_     :					str													=	accelerator.gather_for_metrics((predictions, batch['''labels'''])							)
																		metric.add_batch(
																		    predictions=__SCREAMING_SNAKE_CASE				,				references=__SCREAMING_SNAKE_CASE				,				)

												lowercase_     :					Optional[int]													=	metric.compute()
												# Use accelerator.print to print only on the main process.
												accelerator.print(F'''epoch {epoch}:'''				,				__SCREAMING_SNAKE_CASE							)
def  snake_case_		(    ):



						"""simple docstring"""





						lowercase_     :					Any													=	argparse.ArgumentParser(description='''Simple example of training script.'''							)
						parser.add_argument(
						    '''--mixed_precision'''				,				type=__SCREAMING_SNAKE_CASE				,				default=__SCREAMING_SNAKE_CASE				,				choices=['''no''', '''fp16''', '''bf16''', '''fp8''']				,				help='''Whether to use mixed precision. Choose'''
						    '''between fp16 and bf16 (bfloat16). Bf16 requires PyTorch >= 1.10.'''
						    '''and an Nvidia Ampere GPU.'''				,				)
						# New Code #
						parser.add_argument(
						    '''--gradient_accumulation_steps'''				,				type=__SCREAMING_SNAKE_CASE				,				default=1				,				help='''The number of minibatches to be ran before gradients are accumulated.'''				,				)
						parser.add_argument(
						    '''--local_sgd_steps'''				,				type=__SCREAMING_SNAKE_CASE				,				default=8				,				help='''Number of local SGD steps or None to disable local SGD'''							)
						parser.add_argument('''--cpu'''				,				action='''store_true'''				,				help='''If passed, will train on the CPU.'''							)
						lowercase_     :					List[Any]													=	parser.parse_args()
						lowercase_     :					Tuple													=	{'''lr''': 2E-5, '''num_epochs''': 3, '''seed''': 42, '''batch_size''': 16}
						training_function(__SCREAMING_SNAKE_CASE				,				__SCREAMING_SNAKE_CASE							)


if __name__ == "__main__":
							main()





'''simple docstring'''







from __future__ import annotations

from typing import Any






class 			lowerCAmelCase__       :






					def __init__(       self    ,							__SCREAMING_SNAKE_CASE		):


											"""simple docstring"""





											lowercase_     :					str													=	num_of_nodes
											lowercase_     :					list[list[int]]													=	[]
											lowercase_     :					dict[int, int]													=	{}






					def      _snake_case   (       self    ,							__SCREAMING_SNAKE_CASE    ,							__SCREAMING_SNAKE_CASE    ,							__SCREAMING_SNAKE_CASE		):


											"""simple docstring"""





											self.m_edges.append([u_node, v_node, weight]		)






					def      _snake_case   (       self    ,							__SCREAMING_SNAKE_CASE		):


											"""simple docstring"""





											if self.m_component[u_node] == u_node:
																	return u_node
											return self.find_component(self.m_component[u_node]		)






					def      _snake_case   (       self    ,							__SCREAMING_SNAKE_CASE		):


											"""simple docstring"""





											if self.m_component[u_node] != u_node:
																	for k in self.m_component:
																							lowercase_     :					Optional[int]													=	self.find_component(__SCREAMING_SNAKE_CASE		)






					def      _snake_case   (       self    ,							__SCREAMING_SNAKE_CASE    ,							__SCREAMING_SNAKE_CASE    ,							__SCREAMING_SNAKE_CASE		):


											"""simple docstring"""





											if component_size[u_node] <= component_size[v_node]:
																	lowercase_     :					Any													=	v_node
																	component_size[v_node] += component_size[u_node]
																	self.set_component(__SCREAMING_SNAKE_CASE		)

											elif component_size[u_node] >= component_size[v_node]:
																	lowercase_     :					int													=	self.find_component(__SCREAMING_SNAKE_CASE		)
																	component_size[u_node] += component_size[v_node]
																	self.set_component(__SCREAMING_SNAKE_CASE		)






					def      _snake_case   (       self		):


											"""simple docstring"""





											lowercase_     :					Dict													=	[]
											lowercase_     :					Optional[Any]													=	0

											lowercase_     :					list[Any]													=	[-1] * self.m_num_of_nodes

											# A list of components (initialized to all of the nodes)
											for node in range(self.m_num_of_nodes		):
																	self.m_component.update({node: node}		)
																	component_size.append(1		)

											lowercase_     :					Union[str, Any]													=	self.m_num_of_nodes

											while num_of_components > 1:
																	for edge in self.m_edges:
																							lowercase_						,			lowercase_						,			lowercase_     :					List[Any]													=	edge

																							lowercase_     :					Dict													=	self.m_component[u]
																							lowercase_     :					Any													=	self.m_component[v]

																							if u_component != v_component:

																													for component in (u_component, v_component):
																																			if (
																																			    minimum_weight_edge[component] == -1
																																			    or minimum_weight_edge[component][2] > w
																																			):
																																									lowercase_     :					Union[str, Any]													=	[u, v, w]

																	for edge in minimum_weight_edge:
																							if isinstance(__SCREAMING_SNAKE_CASE    ,							__SCREAMING_SNAKE_CASE		):
																													lowercase_						,			lowercase_						,			lowercase_     :					str													=	edge

																													lowercase_     :					Tuple													=	self.m_component[u]
																													lowercase_     :					Union[str, Any]													=	self.m_component[v]

																													if u_component != v_component:
																																			mst_weight += w
																																			self.union(__SCREAMING_SNAKE_CASE    ,							__SCREAMING_SNAKE_CASE    ,							__SCREAMING_SNAKE_CASE		)
																																			print(F'''Added edge [{u} - {v}]\nAdded weight: {w}\n'''		)
																																			num_of_components -= 1

																	lowercase_     :					str													=	[-1] * self.m_num_of_nodes
											print(F'''The total weight of the minimal spanning tree is: {mst_weight}'''		)







def  snake_case_		(    ):



						"""simple docstring"""







if __name__ == "__main__":
							import doctest

							doctest.testmod()



