





'''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__		:    Dict =       16
lowercase__		:    Any =       32







def       a__       (       lowercase      :  str   )	->	Dict:





					"""simple docstring"""

					return int(x / 2**20   )
class        __lowerCAmelCase  :






							"""simple docstring"""





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

												'''simple docstring'''




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

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





							def snake_case__		(							self     :	int  )					->  Dict:

												'''simple docstring'''




												return self.process.memory_info().rss





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

												'''simple docstring'''




												_UpperCamelCase       					= -1

												while True:
																	_UpperCamelCase       					= 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[int] ,				*lowerCAmelCase__     :	Any  )					->  str:

												'''simple docstring'''




												_UpperCamelCase       					= False

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

												_UpperCamelCase       					= self.cpu_mem_used()
												_UpperCamelCase       					= bamb(self.cpu_end - self.cpu_begin  )
												_UpperCamelCase       					= 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__		:    int =       mocked_dataloaders  # noqa: F811







def       a__       (       lowercase      :  Union[str, Any],      lowercase      :  Optional[Any]   )	->	List[str]:





					"""simple docstring"""

					if os.environ.get('''TESTING_MOCKED_DATALOADERS''',      lowercase   ) == "1":
										_UpperCamelCase       					= 2

					# New Code #
					# Pass the advanced FSDP settings not part of the accelerate config by creating fsdp_plugin
					_UpperCamelCase       					= 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:
										_UpperCamelCase       					= Accelerator(
										    cpu=args.cpu,      mixed_precision=args.mixed_precision,      log_with='''wandb''',      project_dir=args.logging_dir,      fsdp_plugin=lowercase,      )
					else:
										_UpperCamelCase       					= Accelerator(fsdp_plugin=lowercase   )
					accelerator.print(accelerator.distributed_type   )

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

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

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

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

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

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

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

					# Instantiate dataloaders.
					_UpperCamelCase       					= DataLoader(
					    tokenized_datasets['''train'''],      shuffle=lowercase,      collate_fn=lowercase,      batch_size=lowercase   )
					_UpperCamelCase       					= 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)
					_UpperCamelCase       					= 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
					_UpperCamelCase       					= 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
					_UpperCamelCase       					= torch.optim.AdamW(params=model.parameters(),      lr=lowercase,      weight_decay=2e-4   )

					# Instantiate scheduler
					_UpperCamelCase       					= 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.
					_UpperCamelCase					,       _UpperCamelCase					,       _UpperCamelCase					,       _UpperCamelCase       					= accelerator.prepare(
					    lowercase,      lowercase,      lowercase,      lowercase   )

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

										if "epoch" in training_difference:
															num_epochs -= int(training_difference.replace('''epoch_''',      ''''''   )   )
															_UpperCamelCase       					= None
										else:
															_UpperCamelCase       					= 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
															_UpperCamelCase       					= (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:
																				_UpperCamelCase       					= 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   )
																				_UpperCamelCase       					= model(**lowercase   )
																				_UpperCamelCase       					= outputs.loss
																				_UpperCamelCase       					= 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   ):
																									_UpperCamelCase       					= F"""step_{overall_step}"""
																									if overall_step % checkpointing_steps == 0:
																														if args.output_dir is not None:
																																			_UpperCamelCase       					= 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():
																									_UpperCamelCase       					= model(**lowercase   )
																				_UpperCamelCase       					= outputs.logits.argmax(dim=-1   )
																				_UpperCamelCase					,       _UpperCamelCase       					= accelerator.gather_for_metrics((predictions, batch['''labels'''])   )
																				metric.add_batch(
																				    predictions=lowercase,      references=lowercase,      )

															_UpperCamelCase       					= 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":
																				_UpperCamelCase       					= F"""epoch_{epoch}"""
																				if args.output_dir is not None:
																									_UpperCamelCase       					= 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       a__       (       )	->	Optional[int]:





					"""simple docstring"""

					_UpperCamelCase       					= 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,      )
					_UpperCamelCase       					= parser.parse_args()
					_UpperCamelCase       					= {'''lr''': 2e-5, '''num_epochs''': 3, '''seed''': 42, '''batch_size''': 16}
					training_function(lowercase,      lowercase   )


if __name__ == "__main__":
			main()










'''simple docstring'''

lowercase__		:    Tuple =       '\n# Transformers installation\n! pip install transformers datasets evaluate\n# To install from source instead of the last release, comment the command above and uncomment the following one.\n# ! pip install git+https://github.com/huggingface/transformers.git\n'

lowercase__		:    Union[str, Any] =       [{'type': 'code', 'content': INSTALL_CONTENT}]
lowercase__		:    str =       {
    '{processor_class}': 'FakeProcessorClass',
    '{model_class}': 'FakeModelClass',
    '{object_class}': 'FakeObjectClass',
}



