


"""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   a__			(					__lowercase )						->						Optional[int]:
				return int(x / 2**20 )
class snake_case   :







		def __enter__(       self   :							List[Any]		)							->    Any:





						'''simple docstring'''







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

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







		def 			a_			(       self   :							Optional[int]		)							->    str:





						'''simple docstring'''







						return self.process.memory_info().rss







		def 			a_			(       self   :							Tuple		)							->    Any:





						'''simple docstring'''







						_A       	=      -1

						while True:
										_A       	=      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   :							List[Any]     , *a__   :							Optional[int]		)							->    Tuple:





						'''simple docstring'''







						_A       	=      False

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

						_A       	=      self.cpu_mem_used()
						_A       	=      bamb(self.cpu_end - self.cpu_begin		)
						_A       	=      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   a__			(					__lowercase  ,      __lowercase )						->						Union[str, Any]:
				# For testing only
				if os.environ.get("TESTING_MOCKED_DATALOADERS"  ,      __lowercase ) == "1":
								_A       	=      2

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

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

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

				_A       	=      AutoTokenizer.from_pretrained(args.model_name_or_path )
				_A       	=      load_dataset("glue"  ,      "mrpc" )
				_A       	=      evaluate.load("glue"  ,      "mrpc" )

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

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

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

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

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

				# Instantiate scheduler
				_A       	=      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.
				_A	,		_A	,		_A	,		_A       	=      accelerator.prepare(
				    __lowercase  ,      __lowercase  ,      __lowercase  ,      __lowercase )

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

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

												_A       	=      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":
																_A       	=      f"""epoch_{epoch}"""
																if args.output_dir is not None:
																				_A       	=      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__			(					)						->						Union[str, Any]:
				_A       	=      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  ,      )
				_A       	=      parser.parse_args()
				_A       	=      {"lr": 2E-5, "num_epochs": 3, "seed": 42, "batch_size": 16}
				training_function(__lowercase  ,      __lowercase )


if __name__ == "__main__":
		main()



"""simple docstring"""


def   a__			(					__lowercase  ,      __lowercase )						->						Tuple:
				_A       	=      1
				while b > 0:
								if b & 1:
												res *= a

								a *= a
								b >>= 1

				return res






def   a__			(					__lowercase  ,      __lowercase  ,      __lowercase )						->						Optional[Any]:
				_A       	=      1
				while b > 0:
								if b & 1:
												_A       	=      ((res % c) * (a % c)) % c

								a *= a
								b >>= 1

				return res