


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
#
########################################################################


UpperCAmelCase__														=      1_6
UpperCAmelCase__														=      3_2







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







					'''simple docstring'''



					return int(x / 2**20      )

class 						snake_case			:
			'''simple docstring'''

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




								"""simple docstring"""






								gc.collect()
								torch.cuda.empty_cache()
								torch.cuda.reset_max_memory_allocated()  # reset the peak gauge to zero
								SCREAMING_SNAKE_CASE_	      =       torch.cuda.memory_allocated()
								SCREAMING_SNAKE_CASE_	      =       psutil.Process()

								SCREAMING_SNAKE_CASE_	      =       self.cpu_mem_used()
								SCREAMING_SNAKE_CASE_	      =       True
								SCREAMING_SNAKE_CASE_	      =       threading.Thread(target=self.peak_monitor_func					)
								SCREAMING_SNAKE_CASE_	      =       True
								peak_monitor_thread.start()
								return self

			def _lowercase (	self     :     Any					)					->					str:




								"""simple docstring"""






								return self.process.memory_info().rss

			def _lowercase (	self     :     Tuple					)					->					Optional[Any]:




								"""simple docstring"""






								SCREAMING_SNAKE_CASE_	      =       -1

								while True:
													SCREAMING_SNAKE_CASE_	      =       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     :     Optional[Any]							,							*lowerCAmelCase_     :     Tuple					)					->					str:




								"""simple docstring"""






								SCREAMING_SNAKE_CASE_	      =       False

								gc.collect()
								torch.cuda.empty_cache()
								SCREAMING_SNAKE_CASE_	      =       torch.cuda.memory_allocated()
								SCREAMING_SNAKE_CASE_	      =       torch.cuda.max_memory_allocated()
								SCREAMING_SNAKE_CASE_	      =       bamb(self.end - self.begin					)
								SCREAMING_SNAKE_CASE_	      =       bamb(self.peak - self.begin					)

								SCREAMING_SNAKE_CASE_	      =       self.cpu_mem_used()
								SCREAMING_SNAKE_CASE_	      =       bamb(self.cpu_end - self.cpu_begin					)
								SCREAMING_SNAKE_CASE_	      =       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

		UpperCAmelCase__														=      mocked_dataloaders  # noqa: F811







def 							UpperCAmelCase				(     UpperCAmelCase				,UpperCAmelCase      )->		str:







					'''simple docstring'''



					if os.environ.get('''TESTING_MOCKED_DATALOADERS'''				,UpperCAmelCase      ) == "1":
										SCREAMING_SNAKE_CASE_	      =       2

					# New Code #
					# Pass the advanced FSDP settings not part of the accelerate config by creating fsdp_plugin
					SCREAMING_SNAKE_CASE_	      =       FullyShardedDataParallelPlugin(
					    state_dict_config=FullStateDictConfig(offload_to_cpu=UpperCAmelCase				,ranka_only=UpperCAmelCase      )				,optim_state_dict_config=FullOptimStateDictConfig(offload_to_cpu=UpperCAmelCase				,ranka_only=UpperCAmelCase      )				,)

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

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

					# We need to initialize the trackers we use, and also store our configuration
					if args.with_tracking:
										SCREAMING_SNAKE_CASE_	      =       vars(UpperCAmelCase      )
										accelerator.init_trackers('''fsdp_glue_no_trainer'''				,UpperCAmelCase      )

					SCREAMING_SNAKE_CASE_	      =       AutoTokenizer.from_pretrained(args.model_name_or_path      )
					SCREAMING_SNAKE_CASE_	      =       load_dataset('''glue'''				,'''mrpc'''      )
					SCREAMING_SNAKE_CASE_	      =       evaluate.load('''glue'''				,'''mrpc'''      )

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

					# If the batch size is too big we use gradient accumulation
					SCREAMING_SNAKE_CASE_	      =       1
					if batch_size > MAX_GPU_BATCH_SIZE and accelerator.distributed_type != DistributedType.TPU:
										SCREAMING_SNAKE_CASE_	      =       batch_size // MAX_GPU_BATCH_SIZE
										SCREAMING_SNAKE_CASE_	      =       MAX_GPU_BATCH_SIZE

					def collate_fn(UpperCAmelCase      ):
										# On TPU it's best to pad everything to the same length or training will be very slow.
										SCREAMING_SNAKE_CASE_	      =       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":
															SCREAMING_SNAKE_CASE_	      =       16
										elif accelerator.mixed_precision != "no":
															SCREAMING_SNAKE_CASE_	      =       8
										else:
															SCREAMING_SNAKE_CASE_	      =       None

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

					# Instantiate dataloaders.
					SCREAMING_SNAKE_CASE_	      =       DataLoader(
					    tokenized_datasets['''train''']				,shuffle=UpperCAmelCase				,collate_fn=UpperCAmelCase				,batch_size=UpperCAmelCase      )
					SCREAMING_SNAKE_CASE_	      =       DataLoader(
					    tokenized_datasets['''validation''']				,shuffle=UpperCAmelCase				,collate_fn=UpperCAmelCase				,batch_size=UpperCAmelCase      )

					set_seed(UpperCAmelCase      )

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

					# New Code #
					# For FSDP feature, it is highly recommended and efficient to prepare the model before creating optimizer
					SCREAMING_SNAKE_CASE_	      =       accelerator.prepare(UpperCAmelCase      )
					accelerator.print(UpperCAmelCase      )

					# 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
					SCREAMING_SNAKE_CASE_	      =       torch.optim.AdamW(params=model.parameters()				,lr=UpperCAmelCase				,weight_decay=2E-4      )

					# Instantiate scheduler
					SCREAMING_SNAKE_CASE_	      =       get_linear_schedule_with_warmup(
					    optimizer=UpperCAmelCase				,num_warmup_steps=10				,num_training_steps=(len(UpperCAmelCase      ) * 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.
					SCREAMING_SNAKE_CASE_      ,SCREAMING_SNAKE_CASE_      ,SCREAMING_SNAKE_CASE_      ,SCREAMING_SNAKE_CASE_	      =       accelerator.prepare(
					    UpperCAmelCase				,UpperCAmelCase				,UpperCAmelCase				,UpperCAmelCase      )

					SCREAMING_SNAKE_CASE_	      =       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      )
															SCREAMING_SNAKE_CASE_	      =       os.path.basename(args.resume_from_checkpoint      )
										else:
															# Get the most recent checkpoint
															SCREAMING_SNAKE_CASE_	      =       [f.name for f in os.scandir(os.getcwd()      ) if f.is_dir()]
															dirs.sort(key=os.path.getctime      )
															SCREAMING_SNAKE_CASE_	      =       dirs[-1]  # Sorts folders by date modified, most recent checkpoint is the last
										# Extract `epoch_{i}` or `step_{i}`
										SCREAMING_SNAKE_CASE_	      =       os.path.splitext(UpperCAmelCase      )[0]

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

    # Now we train the model
					for epoch in range(UpperCAmelCase      ):
										# New Code #
										# context manager to track the peak memory usage during the training epoch
										with TorchTracemalloc() as tracemalloc:
															model.train()
															if args.with_tracking:
																				SCREAMING_SNAKE_CASE_	      =       0
															for step, batch in enumerate(UpperCAmelCase      ):
																				# 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      )
																				SCREAMING_SNAKE_CASE_	      =       model(**UpperCAmelCase      )
																				SCREAMING_SNAKE_CASE_	      =       outputs.loss
																				SCREAMING_SNAKE_CASE_	      =       loss / gradient_accumulation_steps
																				# We keep track of the loss at each epoch
																				if args.with_tracking:
																									total_loss += loss.detach().float()
																				accelerator.backward(UpperCAmelCase      )
																				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(UpperCAmelCase				,UpperCAmelCase      ):
																									SCREAMING_SNAKE_CASE_	      =       f'''step_{overall_step}'''
																									if overall_step % checkpointing_steps == 0:
																														if args.output_dir is not None:
																																			SCREAMING_SNAKE_CASE_	      =       os.path.join(args.output_dir				,UpperCAmelCase      )
																														accelerator.save_state(UpperCAmelCase      )
        # 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=UpperCAmelCase				,)

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

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

															if checkpointing_steps == "epoch":
																				SCREAMING_SNAKE_CASE_	      =       f'''epoch_{epoch}'''
																				if args.output_dir is not None:
																									SCREAMING_SNAKE_CASE_	      =       os.path.join(args.output_dir				,UpperCAmelCase      )
																				accelerator.save_state(UpperCAmelCase      )
        # 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=UpperCAmelCase				,)

					if args.with_tracking:
										accelerator.end_training()







def 							UpperCAmelCase				(     )->		List[Any]:







					'''simple docstring'''



					SCREAMING_SNAKE_CASE_	      =       argparse.ArgumentParser(description='''Simple example of training script.'''      )
					parser.add_argument(
					    '''--mixed_precision'''				,type=UpperCAmelCase				,default=UpperCAmelCase				,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=UpperCAmelCase				,default=UpperCAmelCase				,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=UpperCAmelCase				,default=UpperCAmelCase				,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=UpperCAmelCase				,default='''.'''				,help='''Optional save directory where all checkpoint folders will be stored. Default is the current working directory.'''				,)
					parser.add_argument(
					    '''--logging_dir'''				,type=UpperCAmelCase				,default='''logs'''				,help='''Location on where to store experiment tracking logs`'''				,)
					parser.add_argument(
					    '''--model_name_or_path'''				,type=UpperCAmelCase				,help='''Path to pretrained model or model identifier from huggingface.co/models.'''				,required=UpperCAmelCase				,)
					SCREAMING_SNAKE_CASE_	      =       parser.parse_args()
					SCREAMING_SNAKE_CASE_	      =       {'''lr''': 2E-5, '''num_epochs''': 3, '''seed''': 42, '''batch_size''': 16}
					training_function(UpperCAmelCase				,UpperCAmelCase      )


if __name__ == "__main__":
		main()






def 							UpperCAmelCase				(     UpperCAmelCase				,UpperCAmelCase      )->		int:







					'''simple docstring'''



					return (input_a, input_a).count(0      ) % 2







def 							UpperCAmelCase				(     )->		None:







					'''simple docstring'''



					assert xor_gate(0				,0      ) == 0
					assert xor_gate(0				,1      ) == 1
					assert xor_gate(1				,0      ) == 1
					assert xor_gate(1				,1      ) == 0


if __name__ == "__main__":
		print(xor_gate(0, 0))
		print(xor_gate(0, 1))


