'''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				   =  1_6
__lowercase				   =  3_2






def     snake_case__							(      _A: Union[str, Any]			)   ->     Tuple:

		'''simple docstring'''


		return int(x / 2**20			)






class     a__:


			'''simple docstring'''






			def __enter__(       self):




					"""simple docstring"""


					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 							a_	(       self):




					"""simple docstring"""


					return self.process.memory_info().rss



			def 							a_	(       self):




					"""simple docstring"""


					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	,   *__lowerCAmelCase):




					"""simple docstring"""


					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

						__lowercase				   =  mocked_dataloaders  # noqa: F811






def     snake_case__							(      _A: List[str]				,       _A: Optional[int]			)   ->     Tuple:

		'''simple docstring'''


		if os.environ.get("""TESTING_MOCKED_DATALOADERS"""				,       _A			) == "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=_A				,       ranka_only=_A			)				,       optim_state_dict_config=FullOptimStateDictConfig(offload_to_cpu=_A				,       ranka_only=_A			)				,       )

		# 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=_A				,       )
		else:
				lowerCAmelCase        =  Accelerator(fsdp_plugin=_A			)
		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(_A			)
				accelerator.init_trackers("""fsdp_glue_no_trainer"""				,       _A			)

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

		def tokenize_function(_A: Tuple			):
				# max_length=None => use the model max length (it's actually the default)
				lowerCAmelCase        =  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():
				lowerCAmelCase        =  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
		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(_A: str			):
				# 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(
				    _A				,       padding="""longest"""				,       max_length=_A				,       pad_to_multiple_of=_A				,       return_tensors="""pt"""				,       )

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

		# Instantiate scheduler
		lowerCAmelCase        =  get_linear_schedule_with_warmup(
		    optimizer=_A				,       num_warmup_steps=10				,       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.
		lowerCAmelCase ,					lowerCAmelCase ,					lowerCAmelCase ,					lowerCAmelCase        =  accelerator.prepare(
		    _A				,       _A				,       _A				,       _A			)

		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(_A			)[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(_A			)
						# If resuming by step, we also need to know exactly how far into the DataLoader we went
						lowerCAmelCase        =  (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:
								lowerCAmelCase        =  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			)
								lowerCAmelCase        =  model(**_A			)
								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(_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			):
										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				,       _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():
										lowerCAmelCase        =  model(**_A			)
								lowerCAmelCase        =  outputs.logits.argmax(dim=-1			)
								lowerCAmelCase ,					lowerCAmelCase        =  accelerator.gather_for_metrics((predictions, batch["""labels"""])			)
								metric.add_batch(
								    predictions=_A				,       references=_A				,       )

						lowerCAmelCase        =  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":
								lowerCAmelCase        =  f"epoch_{epoch}"
								if args.output_dir is not None:
										lowerCAmelCase        =  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     snake_case__							(      )   ->     Optional[Any]:

		'''simple docstring'''


		lowerCAmelCase        =  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				,       )
		lowerCAmelCase        =  parser.parse_args()
		lowerCAmelCase        =  {"""lr""": 2e-5, """num_epochs""": 3, """seed""": 42, """batch_size""": 16}
		training_function(_A				,       _A			)


if __name__ == "__main__":
						main()





'''simple docstring'''




from __future__ import annotations

__lowercase				   =  [2, 1, 4, 5, 6, 0, 8, 9, 1, 2, 0, 6, 4, 2, 0, 6, 5, 3, 2, 7]






class     a__:


			'''simple docstring'''






			def __init__(       self	,   __lowerCAmelCase):




					"""simple docstring"""


					lowerCAmelCase        =  -1
					lowerCAmelCase        =  -1
					lowerCAmelCase        =  [-1] * length
					lowerCAmelCase        =  None
					lowerCAmelCase        =  None







			def __repr__(       self):




					"""simple docstring"""


					return f"Node(min_value={self.minn} max_value={self.maxx})"







def     snake_case__							(      _A: list[int]			)   ->     Node | None:

		'''simple docstring'''


		lowerCAmelCase        =  Node(len(_A			)			)
		lowerCAmelCase ,					lowerCAmelCase        =  min(_A			), max(_A			)
		# Leaf node case where the node contains only one unique value
		if root.minn == root.maxx:
				return root
		lowerCAmelCase        =  (root.minn + root.maxx) // 2

		lowerCAmelCase        =  []
		lowerCAmelCase        =  []

		for index, num in enumerate(_A			):
				if num <= pivot:
						left_arr.append(_A			)
				else:
						right_arr.append(_A			)
				lowerCAmelCase        =  len(_A			)
		lowerCAmelCase        =  build_tree(_A			)
		lowerCAmelCase        =  build_tree(_A			)
		return root






def     snake_case__							(      _A: Node | None				,       _A: int				,       _A: int			)   ->     int:

		'''simple docstring'''


		if index < 0 or node is None:
				return 0
		# Leaf node cases
		if node.minn == node.maxx:
				return index + 1 if node.minn == num else 0
		lowerCAmelCase        =  (node.minn + node.maxx) // 2
		if num <= pivot:
				# go the left subtree and map index to the left subtree
				return rank_till_index(node.left				,       _A				,       node.map_left[index] - 1			)
		else:
				# go to the right subtree and map index to the right subtree
				return rank_till_index(node.right				,       _A				,       index - node.map_left[index]			)






def     snake_case__							(      _A: Node | None				,       _A: int				,       _A: int				,       _A: int			)   ->     int:

		'''simple docstring'''


		if start > end:
				return 0
		lowerCAmelCase        =  rank_till_index(_A				,       _A				,       _A			)
		lowerCAmelCase        =  rank_till_index(_A				,       _A				,       start - 1			)
		return rank_till_end - rank_before_start






def     snake_case__							(      _A: Node | None				,       _A: int				,       _A: int				,       _A: int			)   ->     int:

		'''simple docstring'''


		if index > (end - start) or start > end or node is None:
				return -1
		# Leaf node case
		if node.minn == node.maxx:
				return node.minn
		# Number of elements in the left subtree in interval [start, end]
		lowerCAmelCase        =  node.map_left[end] - (
		    node.map_left[start - 1] if start else 0
		)
		if num_elements_in_left_tree > index:
				return quantile(
				    node.left				,       _A				,       (node.map_left[start - 1] if start else 0)				,       node.map_left[end] - 1				,       )
		else:
				return quantile(
				    node.right				,       index - num_elements_in_left_tree				,       start - (node.map_left[start - 1] if start else 0)				,       end - node.map_left[end]				,       )






def     snake_case__							(      _A: Node | None				,       _A: int				,       _A: int				,       _A: int				,       _A: int			)   ->     int:

		'''simple docstring'''


		if (
		    start > end
		    or node is None
		    or start_num > end_num
		    or node.minn > end_num
		    or node.maxx < start_num
		):
				return 0
		if start_num <= node.minn and node.maxx <= end_num:
				return end - start + 1
		lowerCAmelCase        =  range_counting(
		    node.left				,       (node.map_left[start - 1] if start else 0)				,       node.map_left[end] - 1				,       _A				,       _A				,       )
		lowerCAmelCase        =  range_counting(
		    node.right				,       start - (node.map_left[start - 1] if start else 0)				,       end - node.map_left[end]				,       _A				,       _A				,       )
		return left + right


if __name__ == "__main__":
						import doctest

						doctest.testmod()




