

"""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
#
########################################################################


_SCREAMING_SNAKE_CASE       :  List[str]        = 1_6
_SCREAMING_SNAKE_CASE       :  Any        = 3_2



def        _lowerCAmelCase      (						UpperCAmelCase     :							Tuple				):


					'''simple docstring'''






					return int(x / 2**20				)

class     __a   :

			"""simple docstring"""

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

								UpperCamelCase__		:    Union[str, Any]         =self.cpu_mem_used()
								UpperCamelCase__		:    Dict         =True
								UpperCamelCase__		:    str         =threading.Thread(target=self.peak_monitor_func						)
								UpperCamelCase__		:    int         =True
								peak_monitor_thread.start()
								return self






			def    _lowerCAmelCase			(							self     :					str						):
								return self.process.memory_info().rss






			def    _lowerCAmelCase			(							self     :					str						):
								UpperCamelCase__		:    int         =-1

								while True:
													UpperCamelCase__		:    str         =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     :					Any						,  *lowercase_     :					Tuple						):
								UpperCamelCase__		:    List[Any]         =False

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

								UpperCamelCase__		:    Optional[int]         =self.cpu_mem_used()
								UpperCamelCase__		:    Dict         =bamb(self.cpu_end - self.cpu_begin						)
								UpperCamelCase__		:    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

				_SCREAMING_SNAKE_CASE       :  Any        = mocked_dataloaders  # noqa: F811



def        _lowerCAmelCase      (						UpperCAmelCase     :							List[Any]      ,							UpperCAmelCase     :							List[str]				):


					'''simple docstring'''






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

					# New Code #
					# Pass the advanced FSDP settings not part of the accelerate config by creating fsdp_plugin
					UpperCamelCase__		:    List[str]         =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:
										UpperCamelCase__		:    Optional[int]         =Accelerator(
										    cpu=args.cpu      ,							mixed_precision=args.mixed_precision      ,							log_with='''wandb'''      ,							project_dir=args.logging_dir      ,							fsdp_plugin=UpperCAmelCase      ,							)
					else:
										UpperCamelCase__		:    Union[str, Any]         =Accelerator(fsdp_plugin=UpperCAmelCase				)
					accelerator.print(accelerator.distributed_type				)

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

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

					UpperCamelCase__		:    List[Any]         =AutoTokenizer.from_pretrained(args.model_name_or_path				)
					UpperCamelCase__		:    Tuple         =load_dataset('''glue'''      ,							'''mrpc'''				)
					UpperCamelCase__		:    List[Any]         =evaluate.load('''glue'''      ,							'''mrpc'''				)

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

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

					def collate_fn(UpperCAmelCase     :							Union[str, Any]				):
										# On TPU it's best to pad everything to the same length or training will be very slow.
										UpperCamelCase__		:    Tuple         =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__		:    Tuple         =16
										elif accelerator.mixed_precision != "no":
															UpperCamelCase__		:    List[Any]         =8
										else:
															UpperCamelCase__		:    List[str]         =None

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

					# Instantiate dataloaders.
					UpperCamelCase__		:    str         =DataLoader(
					    tokenized_datasets['''train''']      ,							shuffle=UpperCAmelCase      ,							collate_fn=UpperCAmelCase      ,							batch_size=UpperCAmelCase				)
					UpperCamelCase__		:    Any         =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)
					UpperCamelCase__		:    List[Any]         =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
					UpperCamelCase__		:    Dict         =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
					UpperCamelCase__		:    int         =torch.optim.AdamW(params=model.parameters()      ,							lr=UpperCAmelCase      ,							weight_decay=2E-4				)

					# Instantiate scheduler
					UpperCamelCase__		:    Optional[int]         =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.
					UpperCamelCase__  ,			UpperCamelCase__  ,			UpperCamelCase__  ,			UpperCamelCase__		:    Union[str, Any]         =accelerator.prepare(
					    UpperCAmelCase      ,							UpperCAmelCase      ,							UpperCAmelCase      ,							UpperCAmelCase				)

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

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

															UpperCamelCase__		:    List[Any]         =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":
																				UpperCamelCase__		:    Tuple         =F'''epoch_{epoch}'''
																				if args.output_dir is not None:
																									UpperCamelCase__		:    List[str]         =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        _lowerCAmelCase      (						):


					'''simple docstring'''






					UpperCamelCase__		:    List[str]         =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      ,							)
					UpperCamelCase__		:    Dict         =parser.parse_args()
					UpperCamelCase__		:    Any         ={'''lr''': 2E-5, '''num_epochs''': 3, '''seed''': 42, '''batch_size''': 16}
					training_function(UpperCAmelCase      ,							UpperCAmelCase				)


if __name__ == "__main__":
				main()





"""simple docstring"""





from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available


_SCREAMING_SNAKE_CASE       :  List[Any]        = {
    """configuration_deta""": ["""DETA_PRETRAINED_CONFIG_ARCHIVE_MAP""", """DetaConfig"""],
}

try:
				if not is_vision_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				_SCREAMING_SNAKE_CASE       :  int        = ["""DetaImageProcessor"""]

try:
				if not is_torch_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				_SCREAMING_SNAKE_CASE       :  Dict        = [
				    """DETA_PRETRAINED_MODEL_ARCHIVE_LIST""",
				    """DetaForObjectDetection""",
				    """DetaModel""",
				    """DetaPreTrainedModel""",
				]


if TYPE_CHECKING:
				from .configuration_deta import DETA_PRETRAINED_CONFIG_ARCHIVE_MAP, DetaConfig

				try:
								if not is_vision_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .image_processing_deta import DetaImageProcessor

				try:
								if not is_torch_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .modeling_deta import (
								    DETA_PRETRAINED_MODEL_ARCHIVE_LIST,
								    DetaForObjectDetection,
								    DetaModel,
								    DetaPreTrainedModel,
								)

else:
				import sys

				_SCREAMING_SNAKE_CASE       :  Optional[int]        = _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)


