
'''simple docstring'''







import argparse
import gc
import os
import threading

import evaluate
import psutil
import torch
from datasets import load_dataset
from torch.distributed.fsdp.fully_sharded_data_parallel import FullOptimStateDictConfig, FullStateDictConfig
from torch.utils.data import DataLoader
from transformers import (
    AutoModelForSequenceClassification,
    AutoTokenizer,
    get_linear_schedule_with_warmup,
    set_seed,
)

from accelerate import Accelerator, DistributedType, FullyShardedDataParallelPlugin


########################################################################
# This is a fully working simple example to use Accelerate
#
# 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)
#   - FSDP
#
# This example also demonstrates the checkpointing and sharding capabilities
#
# 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				:						List[Any]												= 1_6
_lowercase				:						Union[str, Any]												= 3_2
def       snake_case_	(       __SCREAMING_SNAKE_CASE      :				int							):



						"""simple docstring"""





						return int(x / 2**20							)






class 				lowerCAmelCase__     :






					def __enter__(     self	):


											"""simple docstring"""





											gc.collect()
											torch.cuda.empty_cache()
											torch.cuda.reset_max_memory_allocated()  # reset the peak gauge to zero
											lowercase_			:  List[Any]									= torch.cuda.memory_allocated()
											lowercase_			:  Tuple									= psutil.Process()

											lowercase_			:  List[str]									= self.cpu_mem_used()
											lowercase_			:  Optional[Any]									= True
											lowercase_			:  List[str]									= threading.Thread(target=self.peak_monitor_func	)
											lowercase_			:  Dict									= True
											peak_monitor_thread.start()
											return self






					def 			_snake_case					(     self	):


											"""simple docstring"""





											return self.process.memory_info().rss






					def 			_snake_case					(     self	):


											"""simple docstring"""





											lowercase_			:  Tuple									= -1

											while True:
																	lowercase_			:  Optional[int]									= max(self.cpu_mem_used()       ,							self.cpu_peak	)

																	# can't sleep or will not catch the peak right (this comment is here on purpose)
																	# time.sleep(0.001) # 1msec

																	if not self.peak_monitoring:
																							break






					def __exit__(     self       ,							*__SCREAMING_SNAKE_CASE	):


											"""simple docstring"""





											lowercase_			:  List[str]									= False

											gc.collect()
											torch.cuda.empty_cache()
											lowercase_			:  Optional[int]									= torch.cuda.memory_allocated()
											lowercase_			:  str									= torch.cuda.max_memory_allocated()
											lowercase_			:  Union[str, Any]									= bamb(self.end - self.begin	)
											lowercase_			:  Union[str, Any]									= bamb(self.peak - self.begin	)

											lowercase_			:  Optional[Any]									= self.cpu_mem_used()
											lowercase_			:  Optional[int]									= bamb(self.cpu_end - self.cpu_begin	)
											lowercase_			:  Optional[Any]									= bamb(self.cpu_peak - self.cpu_begin	)
											# print(f"delta used/peak {self.used:4d}/{self.peaked:4d}")


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

					_lowercase				:						Dict												= mocked_dataloaders  # noqa: F811
def       snake_case_	(       __SCREAMING_SNAKE_CASE      :				List[Any]   ,   __SCREAMING_SNAKE_CASE      :				List[str]							):



						"""simple docstring"""





						if os.environ.get('''TESTING_MOCKED_DATALOADERS'''   ,   lowercase__							) == "1":
												lowercase_			:  Union[str, Any]									= 2

						# New Code #
						# Pass the advanced FSDP settings not part of the accelerate config by creating fsdp_plugin
						lowercase_			:  Optional[int]									= FullyShardedDataParallelPlugin(
						    state_dict_config=FullStateDictConfig(offload_to_cpu=lowercase__   ,   ranka_only=lowercase__							)   ,   optim_state_dict_config=FullOptimStateDictConfig(offload_to_cpu=lowercase__   ,   ranka_only=lowercase__							)   ,   )

						# Initialize accelerator
						if args.with_tracking:
												lowercase_			:  Dict									= Accelerator(
												    cpu=args.cpu   ,   mixed_precision=args.mixed_precision   ,   log_with='''wandb'''   ,   project_dir=args.logging_dir   ,   fsdp_plugin=lowercase__   ,   )
						else:
												lowercase_			:  Dict									= Accelerator(fsdp_plugin=lowercase__							)
						accelerator.print(accelerator.distributed_type							)

						if hasattr(args.checkpointing_steps   ,   '''isdigit'''							):
												if args.checkpointing_steps == "epoch":
																		lowercase_			:  List[str]									= args.checkpointing_steps
												elif args.checkpointing_steps.isdigit():
																		lowercase_			:  int									= int(args.checkpointing_steps							)
												else:
																		raise ValueError(
																		    F'''Argument `checkpointing_steps` must be either a number or `epoch`. `{args.checkpointing_steps}` passed.'''							)
						else:
												lowercase_			:  List[str]									= None
						# Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
						lowercase_			:  Optional[Any]									= config['''lr''']
						lowercase_			:  Union[str, Any]									= int(config['''num_epochs''']							)
						lowercase_			:  Optional[Any]									= int(config['''seed''']							)
						lowercase_			:  Dict									= int(config['''batch_size''']							)

						# We need to initialize the trackers we use, and also store our configuration
						if args.with_tracking:
												lowercase_			:  Optional[int]									= vars(lowercase__							)
												accelerator.init_trackers('''fsdp_glue_no_trainer'''   ,   lowercase__							)

						lowercase_			:  Tuple									= AutoTokenizer.from_pretrained(args.model_name_or_path							)
						lowercase_			:  int									= load_dataset('''glue'''   ,   '''mrpc'''							)
						lowercase_			:  Union[str, Any]									= evaluate.load('''glue'''   ,   '''mrpc'''							)

						def tokenize_function(__SCREAMING_SNAKE_CASE      :				Any							):
												# max_length=None => use the model max length (it's actually the default)
												lowercase_			:  List[str]									= tokenizer(examples['''sentence1''']   ,   examples['''sentence2''']   ,   truncation=lowercase__   ,   max_length=lowercase__							)
												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_			:  List[str]									= datasets.map(
												    lowercase__   ,   batched=lowercase__   ,   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_			:  List[Any]									= tokenized_datasets.rename_column('''label'''   ,   '''labels'''							)

						# If the batch size is too big we use gradient accumulation
						lowercase_			:  Optional[Any]									= 1
						if batch_size > MAX_GPU_BATCH_SIZE and accelerator.distributed_type != DistributedType.TPU:
												lowercase_			:  Tuple									= batch_size // MAX_GPU_BATCH_SIZE
												lowercase_			:  Union[str, Any]									= MAX_GPU_BATCH_SIZE

						def collate_fn(__SCREAMING_SNAKE_CASE      :				Optional[int]							):
												# On TPU it's best to pad everything to the same length or training will be very slow.
												lowercase_			:  List[str]									= 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_			:  Dict									= 16
												elif accelerator.mixed_precision != "no":
																		lowercase_			:  Optional[int]									= 8
												else:
																		lowercase_			:  str									= None

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

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

						set_seed(lowercase__							)

						# Instantiate the model (we build the model here so that the seed also control new weights initialization)
						lowercase_			:  int									= AutoModelForSequenceClassification.from_pretrained(
						    args.model_name_or_path   ,   return_dict=lowercase__   ,   low_cpu_mem_usage=lowercase__							)

						# New Code #
						# For FSDP feature, it is highly recommended and efficient to prepare the model before creating optimizer
						lowercase_			:  List[Any]									= accelerator.prepare(lowercase__							)
						accelerator.print(lowercase__							)

						# Instantiate optimizer
						# New Code #
						# For FSDP feature, at present it doesn't support multiple parameter groups,
						# so we need to create a single parameter group for the whole model
						lowercase_			:  Dict									= torch.optim.AdamW(params=model.parameters()   ,   lr=lowercase__   ,   weight_decay=2E-4							)

						# Instantiate scheduler
						lowercase_			:  int									= get_linear_schedule_with_warmup(
						    optimizer=lowercase__   ,   num_warmup_steps=10   ,   num_training_steps=(len(lowercase__							) * num_epochs) // gradient_accumulation_steps   ,   )

						# New Code #
						# For FSDP feature, prepare everything except the model as we have already prepared the model
						# before creating the optimizer
						# 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_			:  List[str]									= accelerator.prepare(
						    lowercase__   ,   lowercase__   ,   lowercase__   ,   lowercase__							)

						lowercase_			:  Optional[Any]									= 0

						# Potentially load in the weights and states from a previous save
						if args.resume_from_checkpoint:
												if args.resume_from_checkpoint is not None or args.resume_from_checkpoint != "":
																		accelerator.print(F'''Resumed from checkpoint: {args.resume_from_checkpoint}'''							)
																		accelerator.load_state(args.resume_from_checkpoint							)
																		lowercase_			:  List[str]									= os.path.basename(args.resume_from_checkpoint							)
												else:
																		# Get the most recent checkpoint
																		lowercase_			:  Union[str, Any]									= [f.name for f in os.scandir(os.getcwd()							) if f.is_dir()]
																		dirs.sort(key=os.path.getctime							)
																		lowercase_			:  List[str]									= dirs[-1]  # Sorts folders by date modified, most recent checkpoint is the last
												# Extract `epoch_{i}` or `step_{i}`
												lowercase_			:  Union[str, Any]									= os.path.splitext(lowercase__							)[0]

												if "epoch" in training_difference:
																		num_epochs -= int(training_difference.replace('''epoch_'''   ,   ''''''							)							)
																		lowercase_			:  int									= None
												else:
																		lowercase_			:  Any									= int(training_difference.replace('''step_'''   ,   ''''''							)							)
																		num_epochs -= resume_step // len(lowercase__							)
																		# If resuming by step, we also need to know exactly how far into the DataLoader we went
																		lowercase_			:  int									= (num_epochs * len(lowercase__							)) - resume_step

    # Now we train the model
						for epoch in range(lowercase__							):
												# New Code #
												# context manager to track the peak memory usage during the training epoch
												with TorchTracemalloc() as tracemalloc:
																		model.train()
																		if args.with_tracking:
																								lowercase_			:  List[str]									= 0
																		for step, batch in enumerate(lowercase__							):
																								# We need to skip steps until we reach the resumed step
																								if args.resume_from_checkpoint and epoch == 0:
																														if resume_step is not None and step < resume_step:
																																				pass
                # We could avoid this line since we set the accelerator with `device_placement=True`.
																								batch.to(accelerator.device							)
																								lowercase_			:  str									= model(**lowercase__							)
																								lowercase_			:  str									= outputs.loss
																								lowercase_			:  List[Any]									= loss / gradient_accumulation_steps
																								# We keep track of the loss at each epoch
																								if args.with_tracking:
																														total_loss += loss.detach().float()
																								accelerator.backward(lowercase__							)
																								if step % gradient_accumulation_steps == 0:
																														optimizer.step()
																														lr_scheduler.step()
																														optimizer.zero_grad()
																														# accelerator.print(lr_scheduler.get_lr())

																								overall_step += 1

																								if isinstance(lowercase__   ,   lowercase__							):
																														lowercase_			:  str									= F'''step_{overall_step}'''
																														if overall_step % checkpointing_steps == 0:
																																				if args.output_dir is not None:
																																										lowercase_			:  Tuple									= os.path.join(args.output_dir   ,   lowercase__							)
																																				accelerator.save_state(lowercase__							)
        # New Code #
        # Printing the GPU memory usage details such as allocated memory, peak memory, and total memory usage
												accelerator.print('''Memory before entering the train : {}'''.format(bamb(tracemalloc.begin							)							)							)
												accelerator.print('''Memory consumed at the end of the train (end-begin): {}'''.format(tracemalloc.used							)							)
												accelerator.print('''Peak Memory consumed during the train (max-begin): {}'''.format(tracemalloc.peaked							)							)
												accelerator.print(
												    '''Total Peak Memory consumed during the train (max): {}'''.format(
												        tracemalloc.peaked + bamb(tracemalloc.begin							)							)							)
												# Logging the peak memory usage of the GPU to the tracker
												if args.with_tracking:
																		accelerator.log(
																		    {
																		        '''train_total_peak_memory''': tracemalloc.peaked + bamb(tracemalloc.begin							),
																		    }   ,   step=lowercase__   ,   )

												# New Code #
												# context manager to track the peak memory usage during the evaluation
												with TorchTracemalloc() as tracemalloc:
																		model.eval()
																		for step, batch in enumerate(lowercase__							):
																								# We could avoid this line since we set the accelerator with `device_placement=True`.
																								batch.to(accelerator.device							)
																								with torch.no_grad():
																														lowercase_			:  Dict									= model(**lowercase__							)
																								lowercase_			:  Tuple									= outputs.logits.argmax(dim=-1							)
																								lowercase_		,				lowercase_			:  Union[str, Any]									= accelerator.gather_for_metrics((predictions, batch['''labels'''])							)
																								metric.add_batch(
																								    predictions=lowercase__   ,   references=lowercase__   ,   )

																		lowercase_			:  Optional[int]									= metric.compute()
																		# Use accelerator.print to print only on the main process.
																		accelerator.print(F'''epoch {epoch}:'''   ,   lowercase__							)
																		if args.with_tracking:
																								accelerator.log(
																								    {
																								        '''accuracy''': eval_metric['''accuracy'''],
																								        '''f1''': eval_metric['''f1'''],
																								        '''train_loss''': total_loss.item() / len(lowercase__							),
																								    }   ,   step=lowercase__   ,   )

																		if checkpointing_steps == "epoch":
																								lowercase_			:  List[str]									= F'''epoch_{epoch}'''
																								if args.output_dir is not None:
																														lowercase_			:  Union[str, Any]									= os.path.join(args.output_dir   ,   lowercase__							)
																								accelerator.save_state(lowercase__							)
        # New Code #
        # Printing the GPU memory usage details such as allocated memory, peak memory, and total memory usage
												accelerator.print('''Memory before entering the eval : {}'''.format(bamb(tracemalloc.begin							)							)							)
												accelerator.print('''Memory consumed at the end of the eval (end-begin): {}'''.format(tracemalloc.used							)							)
												accelerator.print('''Peak Memory consumed during the eval (max-begin): {}'''.format(tracemalloc.peaked							)							)
												accelerator.print(
												    '''Total Peak Memory consumed during the eval (max): {}'''.format(tracemalloc.peaked + bamb(tracemalloc.begin							)							)							)
												# Logging the peak memory usage of the GPU to the tracker
												if args.with_tracking:
																		accelerator.log(
																		    {
																		        '''eval_total_peak_memory''': tracemalloc.peaked + bamb(tracemalloc.begin							),
																		    }   ,   step=lowercase__   ,   )

						if args.with_tracking:
												accelerator.end_training()
def       snake_case_	(       ):



						"""simple docstring"""





						lowercase_			:  str									= argparse.ArgumentParser(description='''Simple example of training script.'''							)
						parser.add_argument(
						    '''--mixed_precision'''   ,   type=lowercase__   ,   default=lowercase__   ,   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.'''   ,   )
						parser.add_argument('''--cpu'''   ,   action='''store_true'''   ,   help='''If passed, will train on the CPU.'''							)
						parser.add_argument(
						    '''--checkpointing_steps'''   ,   type=lowercase__   ,   default=lowercase__   ,   help='''Whether the various states should be saved at the end of every n steps, or \'epoch\' for each epoch.'''   ,   )
						parser.add_argument(
						    '''--resume_from_checkpoint'''   ,   type=lowercase__   ,   default=lowercase__   ,   help='''If the training should continue from a checkpoint folder.'''   ,   )
						parser.add_argument(
						    '''--with_tracking'''   ,   action='''store_true'''   ,   help='''Whether to load in all available experiment trackers from the environment and use them for logging.'''   ,   )
						parser.add_argument(
						    '''--output_dir'''   ,   type=lowercase__   ,   default='''.'''   ,   help='''Optional save directory where all checkpoint folders will be stored. Default is the current working directory.'''   ,   )
						parser.add_argument(
						    '''--logging_dir'''   ,   type=lowercase__   ,   default='''logs'''   ,   help='''Location on where to store experiment tracking logs`'''   ,   )
						parser.add_argument(
						    '''--model_name_or_path'''   ,   type=lowercase__   ,   help='''Path to pretrained model or model identifier from huggingface.co/models.'''   ,   required=lowercase__   ,   )
						lowercase_			:  int									= parser.parse_args()
						lowercase_			:  List[str]									= {'''lr''': 2E-5, '''num_epochs''': 3, '''seed''': 42, '''batch_size''': 16}
						training_function(lowercase__   ,   lowercase__							)


if __name__ == "__main__":
					main()





'''simple docstring'''







from __future__ import annotations

from queue import Queue






class 				lowerCAmelCase__     :






					def __init__(     self	):


											"""simple docstring"""





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






					def 			_snake_case					(     self	):


											"""simple docstring"""





											for i in self.vertices:
																	print(__SCREAMING_SNAKE_CASE       ,							''' : '''       ,							''' -> '''.join([str(__SCREAMING_SNAKE_CASE	) for j in self.vertices[i]]	)	)






					def 			_snake_case					(     self       ,							__SCREAMING_SNAKE_CASE       ,							__SCREAMING_SNAKE_CASE	):


											"""simple docstring"""





											if from_vertex in self.vertices:
																	self.vertices[from_vertex].append(__SCREAMING_SNAKE_CASE	)
											else:
																	lowercase_			:  Optional[Any]									= [to_vertex]






					def 			_snake_case					(     self       ,							__SCREAMING_SNAKE_CASE	):


											"""simple docstring"""





											lowercase_			:  List[Any]									= set()

											# create a first in first out queue to store all the vertices for BFS
											lowercase_			:  Queue									= Queue()

											# mark the source node as visited and enqueue it
											visited.add(__SCREAMING_SNAKE_CASE	)
											queue.put(__SCREAMING_SNAKE_CASE	)

											while not queue.empty():
																	lowercase_			:  List[Any]									= queue.get()

																	# loop through all adjacent vertex and enqueue it if not yet visited
																	for adjacent_vertex in self.vertices[vertex]:
																							if adjacent_vertex not in visited:
																													queue.put(__SCREAMING_SNAKE_CASE	)
																													visited.add(__SCREAMING_SNAKE_CASE	)
											return visited


if __name__ == "__main__":
							from doctest import testmod

							testmod(verbose=True)

							_lowercase				:						Optional[int]												= Graph()
							g.add_edge(0, 1)
							g.add_edge(0, 2)
							g.add_edge(1, 2)
							g.add_edge(2, 0)
							g.add_edge(2, 3)
							g.add_edge(3, 3)

							g.print_graph()
							# 0  :  1 -> 2
							# 1  :  2
							# 2  :  0 -> 3
							# 3  :  3

							assert sorted(g.bfs(2)) == [0, 1, 2, 3]



