

'''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
#
########################################################################


lowerCAmelCase_     =      1_6
lowerCAmelCase_     =      3_2

def      __magic_name__							(			A				)      ->		Union[str, Any]:
	return int(x / 2**2_0				)







class lowerCamelCase   :

				def __enter__(       self )    ->							Union[str, Any]:
					gc.collect()
					torch.cuda.empty_cache()
					torch.cuda.reset_max_memory_allocated()  # reset the peak gauge to zero
					snake_case							=					torch.cuda.memory_allocated()
					snake_case							=					psutil.Process()

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

				def 					_lowerCamelCase    (       self )    ->							Optional[int]:
					return self.process.memory_info().rss

				def 					_lowerCamelCase    (       self )    ->							Optional[int]:
					snake_case							=					-1

					while True:
						snake_case							=					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_ )    ->							str:
					snake_case							=					False

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

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

						lowerCAmelCase_     =      mocked_dataloaders  # noqa: F811

def      __magic_name__							(			A       , A				)      ->		Any:
	# For testing only
	if os.environ.get('TESTING_MOCKED_DATALOADERS'       , A				) == "1":
		snake_case							=					2

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

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

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

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

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

	# If the batch size is too big we use gradient accumulation
	snake_case							=					1
	if batch_size > MAX_GPU_BATCH_SIZE and accelerator.distributed_type != DistributedType.TPU:
		snake_case							=					batch_size // MAX_GPU_BATCH_SIZE
		snake_case							=					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.
		snake_case							=					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":
			snake_case							=					1_6
		elif accelerator.mixed_precision != "no":
			snake_case							=					8
		else:
			snake_case							=					None

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

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

	# Instantiate scheduler
	snake_case							=					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.
	snake_case			,			snake_case			,			snake_case			,			snake_case							=					accelerator.prepare(
	    A       , A       , A       , A				)

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

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

			snake_case							=					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":
				snake_case							=					F'''epoch_{epoch}'''
				if args.output_dir is not None:
					snake_case							=					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      __magic_name__							(			)      ->		Optional[int]:
	snake_case							=					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       , )
	snake_case							=					parser.parse_args()
	snake_case							=					{'lr': 2E-5, 'num_epochs': 3, 'seed': 4_2, 'batch_size': 1_6}
	training_function(A       , A				)


if __name__ == "__main__":
						main()











from __future__ import annotations

from collections.abc import Iterable





class        lowerCamelCase__:
      def __init__(							self:					Dict       ,			UpperCamelCase_:					int       ,			UpperCamelCase_:					str  ):
             __lowerCamelCase,  __lowerCamelCase  =		float(UpperCamelCase_  ), float(UpperCamelCase_  )
      def __eq__(							self:					List[Any]       ,			UpperCamelCase_:					List[str]  ):
             return self.x == other.x and self.y == other.y
      def __ne__(							self:					Tuple       ,			UpperCamelCase_:					Dict  ):
             return not self == other
      def __gt__(							self:					List[Any]       ,			UpperCamelCase_:					List[Any]  ):
             if self.x > other.x:
                    return True
             elif self.x == other.x:
                    return self.y > other.y
             return False
      def __lt__(							self:					str       ,			UpperCamelCase_:					int  ):
             return not self > other
      def __ge__(							self:					Dict       ,			UpperCamelCase_:					Any  ):
             if self.x > other.x:
                    return True
             elif self.x == other.x:
                    return self.y >= other.y
             return False
      def __le__(							self:					Union[str, Any]       ,			UpperCamelCase_:					Optional[Any]  ):
             if self.x < other.x:
                    return True
             elif self.x == other.x:
                    return self.y <= other.y
             return False
      def __repr__(							self:					Union[str, Any]  ):
             return F'({self.x}, {self.y})'




      def __hash__(							self:					List[Any]  ):
             return hash(self.x  )




def    lowerCamelCase__			(		A__			:   list[Point] | list[list[float]] | Iterable[list[float]]    ,     ):





       '''simple docstring'''



       __lowerCamelCase  =		[]
       if list_of_tuples:
              for p in list_of_tuples:
                     if isinstance(A__    ,     A__      ):
                            points.append(A__      )
                     else:
                            try:
                                   points.append(Point(p[0]    ,     p[1]      )      )
                            except (IndexError, TypeError):
                                   print(
                                       f'Ignoring deformed point {p}. All points'
                                       """ must have at least 2 coordinates."""      )
       return points




def    lowerCamelCase__			(		A__			:   list[Point] | list[list[float]]      ):





       '''simple docstring'''



       if not hasattr(A__    ,     """__iter__"""      ):
              __lowerCamelCase  =		f'Expecting an iterable object but got an non-iterable type {points}'
              raise ValueError(A__      )

       if not points:
              __lowerCamelCase  =		f'Expecting a list of points but got {points}'
              raise ValueError(A__      )

       return _construct_points(A__      )




def    lowerCamelCase__			(		A__			:   Point    ,     A__			:   Point    ,     A__			:   Point      ):





       '''simple docstring'''



       __lowerCamelCase  =		(a.x * b.y + b.x * c.y + c.x * a.y) - (a.y * b.x + b.y * c.x + c.y * a.x)
       return det




def    lowerCamelCase__			(		A__			:   list[Point]      ):





       '''simple docstring'''



       __lowerCamelCase  =		sorted(_validate_input(A__      )      )
       __lowerCamelCase  =		len(A__      )
       __lowerCamelCase  =		set()

       for i in range(n - 1      ):
              for j in range(i + 1    ,     A__      ):
                     __lowerCamelCase  =		__lowerCamelCase  =		False
                     __lowerCamelCase  =		True
                     for k in range(A__      ):
                            if k != i and k != j:
                                   __lowerCamelCase  =		_det(points[i]    ,     points[j]    ,     points[k]      )

                                   if det_k > 0:
                                          __lowerCamelCase  =		True
                                   elif det_k < 0:
                                          __lowerCamelCase  =		True
                                   else:
                                          # point[i], point[j], point[k] all lie on a straight line
                                          # if point[k] is to the left of point[i] or it's to the
                                          # right of point[j], then point[i], point[j] cannot be
                                          # part of the convex hull of A
                                          if points[k] < points[i] or points[k] > points[j]:
                                                 __lowerCamelCase  =		False
                                                 break

                            if points_left_of_ij and points_right_of_ij:
                                   __lowerCamelCase  =		False
                                   break

                     if ij_part_of_convex_hull:
                            convex_set.update([points[i], points[j]]      )

       return sorted(A__      )




def    lowerCamelCase__			(		A__			:   list[Point]      ):





       '''simple docstring'''



       __lowerCamelCase  =		sorted(_validate_input(A__      )      )
       __lowerCamelCase  =		len(A__      )

       # divide all the points into an upper hull and a lower hull
       # the left most point and the right most point are definitely
       # members of the convex hull by definition.
       # use these two anchors to divide all the points into two hulls,
       # an upper hull and a lower hull.

       # all points to the left (above) the line joining the extreme points belong to the
       # upper hull
       # all points to the right (below) the line joining the extreme points below to the
       # lower hull
       # ignore all points on the line joining the extreme points since they cannot be
       # part of the convex hull

       __lowerCamelCase  =		points[0]
       __lowerCamelCase  =		points[n - 1]

       __lowerCamelCase  =		{left_most_point, right_most_point}
       __lowerCamelCase  =		[]
       __lowerCamelCase  =		[]

       for i in range(1    ,     n - 1      ):
              __lowerCamelCase  =		_det(A__    ,     A__    ,     points[i]      )

              if det > 0:
                     upper_hull.append(points[i]      )
              elif det < 0:
                     lower_hull.append(points[i]      )

       _construct_hull(A__    ,     A__    ,     A__    ,     A__      )
       _construct_hull(A__    ,     A__    ,     A__    ,     A__      )

       return sorted(A__      )




def    lowerCamelCase__			(		A__			:   list[Point]    ,     A__			:   Point    ,     A__			:   Point    ,     A__			:   set[Point]      ):





       '''simple docstring'''



       if points:
              __lowerCamelCase  =		None
              __lowerCamelCase  =		float("""-inf"""      )
              __lowerCamelCase  =		[]

              for p in points:
                     __lowerCamelCase  =		_det(A__    ,     A__    ,     A__      )

                     if det > 0:
                            candidate_points.append(A__      )

                            if det > extreme_point_distance:
                                   __lowerCamelCase  =		det
                                   __lowerCamelCase  =		p

              if extreme_point:
                     _construct_hull(A__    ,     A__    ,     A__    ,     A__      )
                     convex_set.add(A__      )
                     _construct_hull(A__    ,     A__    ,     A__    ,     A__      )




def    lowerCamelCase__			(		A__			:   list[Point]      ):





       '''simple docstring'''



       __lowerCamelCase  =		sorted(_validate_input(A__      )      )
       __lowerCamelCase  =		len(A__      )

       __lowerCamelCase  =		points[:2]
       for i in range(2    ,     A__      ):
              __lowerCamelCase  =		_det(convex_hull[1]    ,     convex_hull[0]    ,     points[i]      )
              if det > 0:
                     convex_hull.insert(0    ,     points[i]      )
                     break
              elif det < 0:
                     convex_hull.append(points[i]      )
                     break
              else:
                     __lowerCamelCase  =		points[i]
       i += 1

       for j in range(A__    ,     A__      ):
              if (
                  _det(convex_hull[0]    ,     convex_hull[-1]    ,     points[j]      ) > 0
                  and _det(convex_hull[-1]    ,     convex_hull[0]    ,     points[1]      ) < 0
              ):
                     # The point lies within the convex hull
                     continue

              convex_hull.insert(0    ,     points[j]      )
              convex_hull.append(points[j]      )
              while _det(convex_hull[0]    ,     convex_hull[1]    ,     convex_hull[2]      ) >= 0:
                     del convex_hull[1]
              while _det(convex_hull[-1]    ,     convex_hull[-2]    ,     convex_hull[-3]      ) <= 0:
                     del convex_hull[-2]

    # `convex_hull` is contains the convex hull in circular order
       return sorted(convex_hull[1:] if len(A__      ) > 3 else convex_hull      )




def    lowerCamelCase__			(		):





       '''simple docstring'''



       __lowerCamelCase  =		[
           (0, 3),
           (2, 2),
           (1, 1),
           (2, 1),
           (3, 0),
           (0, 0),
           (3, 3),
           (2, -1),
           (2, -4),
           (1, -3),
       ]
       # the convex set of points is
       # [(0, 0), (0, 3), (1, -3), (2, -4), (3, 0), (3, 3)]
       __lowerCamelCase  =		convex_hull_bf(A__      )

       __lowerCamelCase  =		convex_hull_recursive(A__      )
       assert results_bf == results_recursive

       __lowerCamelCase  =		convex_hull_melkman(A__      )
       assert results_bf == results_melkman

       print(A__      )


if __name__ == "__main__":
       main()

