

'''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
#
########################################################################


lowerCAmelCase_ 			=			16
lowerCAmelCase_ 			=			32


def 						__SCREAMING_SNAKE_CASE      (SCREAMING_SNAKE_CASE__ ):
						return int(x / 2**20 )





class 			snake_case_     :







			'''simple docstring'''

			def __enter__(  self				:       Optional[int]							)   ->Optional[int]:
									gc.collect()
									torch.cuda.empty_cache()
									torch.cuda.reset_max_memory_allocated()  # reset the peak gauge to zero
									snake_case_     =			torch.cuda.memory_allocated()
									snake_case_     =			psutil.Process()

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







			def        snake_case__(  self				:       List[str]							)   ->Union[str, Any]:
									return self.process.memory_info().rss







			def        snake_case__(  self				:       int							)   ->Union[str, Any]:
									snake_case_     =			-1

									while True:
															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				:       Tuple							,     *_UpperCamelCase				:       str							)   ->str:
									snake_case_     =			False

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

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

					lowerCAmelCase_ 			=			mocked_dataloaders  # noqa: F811


def 						__SCREAMING_SNAKE_CASE      (SCREAMING_SNAKE_CASE__						,	SCREAMING_SNAKE_CASE__ ):


						if os.environ.get('''TESTING_MOCKED_DATALOADERS'''						,	SCREAMING_SNAKE_CASE__ ) == "1":
												snake_case_     =			2

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

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

						if hasattr(args.checkpointing_steps						,	'''isdigit''' ):
												if args.checkpointing_steps == "epoch":
																		snake_case_     =			args.checkpointing_steps
												elif args.checkpointing_steps.isdigit():
																		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:
												snake_case_     =			None
						# Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
						snake_case_     =			config['''lr''']
						snake_case_     =			int(config['''num_epochs'''] )
						snake_case_     =			int(config['''seed'''] )
						snake_case_     =			int(config['''batch_size'''] )

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

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

						def tokenize_function(SCREAMING_SNAKE_CASE__ ):
												# max_length=None => use the model max length (it's actually the default)
												snake_case_     =			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():
												snake_case_     =			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
						snake_case_     =			tokenized_datasets.rename_column('''label'''						,	'''labels''' )

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

						def collate_fn(SCREAMING_SNAKE_CASE__ ):
												# On TPU it's best to pad everything to the same length or training will be very slow.
												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":
																		snake_case_     =			16
												elif accelerator.mixed_precision != "no":
																		snake_case_     =			8
												else:
																		snake_case_     =			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.
						snake_case_     =			DataLoader(
						    tokenized_datasets['''train''']						,	shuffle=SCREAMING_SNAKE_CASE__						,	collate_fn=SCREAMING_SNAKE_CASE__						,	batch_size=SCREAMING_SNAKE_CASE__ )
						snake_case_     =			DataLoader(
						    tokenized_datasets['''validation''']						,	shuffle=SCREAMING_SNAKE_CASE__						,	collate_fn=SCREAMING_SNAKE_CASE__						,	batch_size=SCREAMING_SNAKE_CASE__ )

						set_seed(SCREAMING_SNAKE_CASE__ )

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

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

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

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

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

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

    # Now we train the model
						for epoch in range(SCREAMING_SNAKE_CASE__ ):
												# New Code #
												# context manager to track the peak memory usage during the training epoch
												with TorchTracemalloc() as tracemalloc:
																		model.train()
																		if args.with_tracking:
																								snake_case_     =			0
																		for step, batch in enumerate(SCREAMING_SNAKE_CASE__ ):
																								# 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 )
																								snake_case_     =			model(**SCREAMING_SNAKE_CASE__ )
																								snake_case_     =			outputs.loss
																								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(SCREAMING_SNAKE_CASE__ )
																								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(SCREAMING_SNAKE_CASE__						,	SCREAMING_SNAKE_CASE__ ):
																														snake_case_     =			F'''step_{overall_step}'''
																														if overall_step % checkpointing_steps == 0:
																																				if args.output_dir is not None:
																																										snake_case_     =			os.path.join(args.output_dir						,	SCREAMING_SNAKE_CASE__ )
																																				accelerator.save_state(SCREAMING_SNAKE_CASE__ )
        # 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=SCREAMING_SNAKE_CASE__						,	)

												# New Code #
												# context manager to track the peak memory usage during the evaluation
												with TorchTracemalloc() as tracemalloc:
																		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():
																														snake_case_     =			model(**SCREAMING_SNAKE_CASE__ )
																								snake_case_     =			outputs.logits.argmax(dim=-1 )
																								snake_case_     =			accelerator.gather_for_metrics((predictions, batch['''labels''']) )
																								metric.add_batch(
																								    predictions=SCREAMING_SNAKE_CASE__						,	references=SCREAMING_SNAKE_CASE__						,	)

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

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

						if args.with_tracking:
												accelerator.end_training()


def 						__SCREAMING_SNAKE_CASE      ():
						snake_case_     =			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.'''						,	)
						parser.add_argument('''--cpu'''						,	action='''store_true'''						,	help='''If passed, will train on the CPU.''' )
						parser.add_argument(
						    '''--checkpointing_steps'''						,	type=SCREAMING_SNAKE_CASE__						,	default=SCREAMING_SNAKE_CASE__						,	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=SCREAMING_SNAKE_CASE__						,	default=SCREAMING_SNAKE_CASE__						,	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=SCREAMING_SNAKE_CASE__						,	default='''.'''						,	help='''Optional save directory where all checkpoint folders will be stored. Default is the current working directory.'''						,	)
						parser.add_argument(
						    '''--logging_dir'''						,	type=SCREAMING_SNAKE_CASE__						,	default='''logs'''						,	help='''Location on where to store experiment tracking logs`'''						,	)
						parser.add_argument(
						    '''--model_name_or_path'''						,	type=SCREAMING_SNAKE_CASE__						,	help='''Path to pretrained model or model identifier from huggingface.co/models.'''						,	required=SCREAMING_SNAKE_CASE__						,	)
						snake_case_     =			parser.parse_args()
						snake_case_     =			{'''lr''': 2E-5, '''num_epochs''': 3, '''seed''': 42, '''batch_size''': 16}
						training_function(SCREAMING_SNAKE_CASE__						,	SCREAMING_SNAKE_CASE__ )


if __name__ == "__main__":
					main()


import warnings

from .generation import GenerationMixin





class 			snake_case_     (	__A      ):







			'''simple docstring'''

			warnings.warn(
			    "Importing `GenerationMixin` from `src/transformers/generation_utils.py` is deprecated and will "
			    "be removed in Transformers v5. Import as `from transformers import GenerationMixin` instead."			, __A			, )