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


A_						       =				16
A_						       =				32






def    lowercase	(  lowerCAmelCase__	):
			return int(x / 2**20	)




class    __lowerCamelCase     :




				def __enter__(							self      ):
							gc.collect()
							torch.cuda.empty_cache()
							torch.cuda.reset_max_memory_allocated()  # reset the peak gauge to zero
							lowerCamelCase_ 		=     torch.cuda.memory_allocated()
							lowerCamelCase_ 		=     psutil.Process()

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




				def 					UpperCAmelCase__	(							self      ):
							return self.process.memory_info().rss




				def 					UpperCAmelCase__	(							self      ):
							lowerCamelCase_ 		=     -1

							while True:
										lowerCamelCase_ 		=     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       ,	*UpperCAmelCase      ):
							lowerCamelCase_ 		=     False

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

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

						A_						       =				mocked_dataloaders  # noqa: F811






def    lowercase	(  lowerCAmelCase__  ,lowerCAmelCase__	):






			if os.environ.get('''TESTING_MOCKED_DATALOADERS'''  ,_UpperCAmelCase	) == "1":
						lowerCamelCase_ 		=     2

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

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

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

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

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

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

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

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

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

			# Instantiate scheduler
			lowerCamelCase_ 		=     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.
			lowerCamelCase_ 		=     accelerator.prepare(
			    _UpperCAmelCase  ,_UpperCAmelCase  ,_UpperCAmelCase  ,_UpperCAmelCase	)

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

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

									lowerCamelCase_ 		=     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":
												lowerCamelCase_ 		=     f"epoch_{epoch}"
												if args.output_dir is not None:
															lowerCamelCase_ 		=     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    lowercase	(  ):
			lowerCamelCase_ 		=     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  ,)
			lowerCamelCase_ 		=     parser.parse_args()
			lowerCamelCase_ 		=     {'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


A_						       =				{
    """configuration_flava""": [
        """FLAVA_PRETRAINED_CONFIG_ARCHIVE_MAP""",
        """FlavaConfig""",
        """FlavaImageCodebookConfig""",
        """FlavaImageConfig""",
        """FlavaMultimodalConfig""",
        """FlavaTextConfig""",
    ],
}

try:
						if not is_vision_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						A_						       =				["""FlavaFeatureExtractor"""]
						A_						       =				["""FlavaImageProcessor"""]
						A_						       =				["""FlavaProcessor"""]

try:
						if not is_torch_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						A_						       =				[
						    """FLAVA_PRETRAINED_MODEL_ARCHIVE_LIST""",
						    """FlavaForPreTraining""",
						    """FlavaImageCodebook""",
						    """FlavaImageModel""",
						    """FlavaModel""",
						    """FlavaMultimodalModel""",
						    """FlavaPreTrainedModel""",
						    """FlavaTextModel""",
						]

if TYPE_CHECKING:
						from .configuration_flava import (
						    FLAVA_PRETRAINED_CONFIG_ARCHIVE_MAP,
						    FlavaConfig,
						    FlavaImageCodebookConfig,
						    FlavaImageConfig,
						    FlavaMultimodalConfig,
						    FlavaTextConfig,
						)

						try:
												if not is_vision_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .feature_extraction_flava import FlavaFeatureExtractor
												from .image_processing_flava import FlavaImageProcessor
												from .processing_flava import FlavaProcessor

						try:
												if not is_torch_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .modeling_flava import (
												    FLAVA_PRETRAINED_MODEL_ARCHIVE_LIST,
												    FlavaForPreTraining,
												    FlavaImageCodebook,
												    FlavaImageModel,
												    FlavaModel,
												    FlavaMultimodalModel,
												    FlavaPreTrainedModel,
												    FlavaTextModel,
												)

else:
						import sys

						A_						       =				_LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)



