

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


__snake_case							:     int 						=	16
__snake_case							:     int 						=	32


def 			_UpperCAmelCase							(		a__) ->    int:

			'''simple docstring'''



			return int(x / 2**2_0)



class    A__:
						"""simple docstring"""






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

									a_		:		Tuple				    =				self.cpu_mem_used()
									a_		:		List[str]				    =				True
									a_		:		Optional[Any]				    =				threading.Thread(target=self.peak_monitor_func  )
									a_		:		int				    =				True
									peak_monitor_thread.start()
									return self





						def 	UpperCamelCase__			(  self  )      ->				Dict:
									return self.process.memory_info().rss





						def 	UpperCamelCase__			(  self  )      ->				Optional[Any]:
									a_		:		Any				    =				-1

									while True:
												a_		:		List[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       ,     *_lowercase  )      ->				List[Any]:
									a_		:		Dict				    =				False

									gc.collect()
									torch.cuda.empty_cache()
									a_		:		Dict				    =				torch.cuda.memory_allocated()
									a_		:		Any				    =				torch.cuda.max_memory_allocated()
									a_		:		Any				    =				bamb(self.end - self.begin  )
									a_		:		Dict				    =				bamb(self.peak - self.begin  )

									a_		:		Optional[Any]				    =				self.cpu_mem_used()
									a_		:		Union[str, Any]				    =				bamb(self.cpu_end - self.cpu_begin  )
									a_		:		List[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

	__snake_case							:     List[str] 						=	mocked_dataloaders  # noqa: F811


def 			_UpperCAmelCase							(		a__       ,					a__) ->    Any:

			'''simple docstring'''



			if os.environ.get("""TESTING_MOCKED_DATALOADERS"""       ,					a__) == "1":
						a_		:		int				    =				2

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

			# Initialize accelerator
			if args.with_tracking:
						a_		:		Dict				    =				Accelerator(
						    cpu=args.cpu       ,					mixed_precision=args.mixed_precision       ,					log_with="""wandb"""       ,					project_dir=args.logging_dir       ,					fsdp_plugin=a__       ,					)
			else:
						a_		:		str				    =				Accelerator(fsdp_plugin=a__)
			accelerator.print(accelerator.distributed_type)

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

			# We need to initialize the trackers we use, and also store our configuration
			if args.with_tracking:
						a_		:		List[str]				    =				vars(a__)
						accelerator.init_trackers("""fsdp_glue_no_trainer"""       ,					a__)

			a_		:		Tuple				    =				AutoTokenizer.from_pretrained(args.model_name_or_path)
			a_		:		Optional[Any]				    =				load_dataset("""glue"""       ,					"""mrpc""")
			a_		:		int				    =				evaluate.load("""glue"""       ,					"""mrpc""")

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

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

			def collate_fn(a__):
						# On TPU it's best to pad everything to the same length or training will be very slow.
						a_		:		Tuple				    =				1_2_8 if accelerator.distributed_type == DistributedType.TPU else None
						# When using mixed precision we want round multiples of 8/16
						if accelerator.mixed_precision == "fp8":
									a_		:		str				    =				1_6
						elif accelerator.mixed_precision != "no":
									a_		:		Any				    =				8
						else:
									a_		:		List[Any]				    =				None

						return tokenizer.pad(
						    a__       ,					padding="""longest"""       ,					max_length=a__       ,					pad_to_multiple_of=a__       ,					return_tensors="""pt"""       ,					)

			# Instantiate dataloaders.
			a_		:		str				    =				DataLoader(
			    tokenized_datasets["""train"""]       ,					shuffle=a__       ,					collate_fn=a__       ,					batch_size=a__)
			a_		:		int				    =				DataLoader(
			    tokenized_datasets["""validation"""]       ,					shuffle=a__       ,					collate_fn=a__       ,					batch_size=a__)

			set_seed(a__)

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

			# New Code #
			# For FSDP feature, it is highly recommended and efficient to prepare the model before creating optimizer
			a_		:		Optional[int]				    =				accelerator.prepare(a__)
			accelerator.print(a__)

			# 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
			a_		:		Tuple				    =				torch.optim.AdamW(params=model.parameters()       ,					lr=a__       ,					weight_decay=2e-4)

			# Instantiate scheduler
			a_		:		str				    =				get_linear_schedule_with_warmup(
			    optimizer=a__       ,					num_warmup_steps=1_0       ,					num_training_steps=(len(a__) * 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.
			a_		:		Dict				    =				accelerator.prepare(
			    a__       ,					a__       ,					a__       ,					a__)

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

						if "epoch" in training_difference:
									num_epochs -= int(training_difference.replace("""epoch_"""       ,					""""""))
									a_		:		Any				    =				None
						else:
									a_		:		Optional[int]				    =				int(training_difference.replace("""step_"""       ,					""""""))
									num_epochs -= resume_step // len(a__)
									# If resuming by step, we also need to know exactly how far into the DataLoader we went
									a_		:		Union[str, Any]				    =				(num_epochs * len(a__)) - resume_step

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

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

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

									if checkpointing_steps == "epoch":
												a_		:		Dict				    =				f'''epoch_{epoch}'''
												if args.output_dir is not None:
															a_		:		Optional[Any]				    =				os.path.join(args.output_dir       ,					a__)
												accelerator.save_state(a__)
        # 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=a__       ,					)

			if args.with_tracking:
						accelerator.end_training()


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

			'''simple docstring'''



			a_		:		Optional[Any]				    =				argparse.ArgumentParser(description="""Simple example of training script.""")
			parser.add_argument(
			    """--mixed_precision"""       ,					type=a__       ,					default=a__       ,					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=a__       ,					default=a__       ,					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=a__       ,					default=a__       ,					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=a__       ,					default="""."""       ,					help="""Optional save directory where all checkpoint folders will be stored. Default is the current working directory."""       ,					)
			parser.add_argument(
			    """--logging_dir"""       ,					type=a__       ,					default="""logs"""       ,					help="""Location on where to store experiment tracking logs`"""       ,					)
			parser.add_argument(
			    """--model_name_or_path"""       ,					type=a__       ,					help="""Path to pretrained model or model identifier from huggingface.co/models."""       ,					required=a__       ,					)
			a_		:		str				    =				parser.parse_args()
			a_		:		Tuple				    =				{"""lr""": 2e-5, """num_epochs""": 3, """seed""": 4_2, """batch_size""": 1_6}
			training_function(a__       ,					a__)


if __name__ == "__main__":
	main()





import sys

from transformers import AutoTokenizer


__snake_case							:     int 						=	sys.argv[1]
__snake_case							:     Dict 						=	sys.argv[2]
__snake_case							:     Dict 						=	int(sys.argv[3])

__snake_case							:     Union[str, Any] 						=	0

__snake_case							:     Any 						=	AutoTokenizer.from_pretrained(model_name_or_path)
max_len -= tokenizer.num_special_tokens_to_add()

with open(dataset, """rt""") as f_p:
	for line in f_p:
		__snake_case							:     str 						=	line.rstrip()

		if not line:
			print(line)
			__snake_case							:     Optional[int] 						=	0
			continue

		__snake_case							:     Dict 						=	line.split()[0]

		__snake_case							:     str 						=	len(tokenizer.tokenize(token))

		# Token contains strange control characters like \x96 or \x95
		# Just filter out the complete line
		if current_subwords_len == 0:
			continue

		if (subword_len_counter + current_subwords_len) > max_len:
			print("""""")
			print(line)
			__snake_case							:     Optional[int] 						=	current_subwords_len
			continue

		subword_len_counter += current_subwords_len

		print(line)


