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     :   Union[str, Any]			=						16
_snake_case     :   Union[str, Any]			=						32




def   _UpperCamelCase   (			snake_case__							)						->   Optional[int]:
							return int(x / 2**20							)




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

												__UpperCAmelCase						:			Optional[Any]         =   self.cpu_mem_used()
												__UpperCAmelCase						:			List[str]         =   True
												__UpperCAmelCase						:			Union[str, Any]         =   threading.Thread(target=self.peak_monitor_func     )
												__UpperCAmelCase						:			Tuple         =   True
												peak_monitor_thread.start()
												return self
					def    _lowerCamelCase (  self:				Dict     )    ->				Tuple:
												return self.process.memory_info().rss
					def    _lowerCamelCase (  self:				str     )    ->				Tuple:
												__UpperCAmelCase						:			int         =   -1

												while True:
																			__UpperCAmelCase						:			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:				Dict	,  *__lowerCamelCase:				Tuple     )    ->				List[Any]:
												__UpperCAmelCase						:			Any         =   False

												gc.collect()
												torch.cuda.empty_cache()
												__UpperCAmelCase						:			List[Any]         =   torch.cuda.memory_allocated()
												__UpperCAmelCase						:			Any         =   torch.cuda.max_memory_allocated()
												__UpperCAmelCase						:			List[Any]         =   bamb(self.end - self.begin     )
												__UpperCAmelCase						:			str         =   bamb(self.peak - self.begin     )

												__UpperCAmelCase						:			str         =   self.cpu_mem_used()
												__UpperCAmelCase						:			List[str]         =   bamb(self.cpu_end - self.cpu_begin     )
												__UpperCAmelCase						:			List[str]         =   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     :   Optional[Any]			=						mocked_dataloaders  # noqa: F811




def   _UpperCamelCase   (			snake_case__,    snake_case__							)						->   Dict:
							# For testing only
							if os.environ.get("TESTING_MOCKED_DATALOADERS",    snake_case__							) == "1":
														__UpperCAmelCase						:			Tuple         =   2

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

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

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

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

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

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

							# If the batch size is too big we use gradient accumulation
							__UpperCAmelCase						:			Any         =   1
							if batch_size > MAX_GPU_BATCH_SIZE and accelerator.distributed_type != DistributedType.TPU:
														__UpperCAmelCase						:			str         =   batch_size // MAX_GPU_BATCH_SIZE
														__UpperCAmelCase						:			Optional[int]         =   MAX_GPU_BATCH_SIZE

							def collate_fn(snake_case__							):
														# On TPU it's best to pad everything to the same length or training will be very slow.
														__UpperCAmelCase						:			List[str]         =   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":
																					__UpperCAmelCase						:			List[str]         =   16
														elif accelerator.mixed_precision != "no":
																					__UpperCAmelCase						:			Optional[int]         =   8
														else:
																					__UpperCAmelCase						:			str         =   None

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

							# Instantiate dataloaders.
							__UpperCAmelCase						:			str         =   DataLoader(
							    tokenized_datasets["train"],    shuffle=snake_case__,    collate_fn=snake_case__,    batch_size=snake_case__							)
							__UpperCAmelCase						:			Any         =   DataLoader(
							    tokenized_datasets["validation"],    shuffle=snake_case__,    collate_fn=snake_case__,    batch_size=snake_case__							)

							set_seed(snake_case__							)

							# Instantiate the model (we build the model here so that the seed also control new weights initialization)
							__UpperCAmelCase						:			List[str]         =   AutoModelForSequenceClassification.from_pretrained(
							    args.model_name_or_path,    return_dict=snake_case__,    low_cpu_mem_usage=snake_case__							)

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

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

							# Instantiate scheduler
							__UpperCAmelCase						:			Optional[Any]         =   get_linear_schedule_with_warmup(
							    optimizer=snake_case__,    num_warmup_steps=10,    num_training_steps=(len(snake_case__							) * 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.
							__UpperCAmelCase						:			Dict         =   accelerator.prepare(
							    snake_case__,    snake_case__,    snake_case__,    snake_case__							)

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

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

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

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

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

																					if checkpointing_steps == "epoch":
																												__UpperCAmelCase						:			Dict         =   f'''epoch_{epoch}'''
																												if args.output_dir is not None:
																																			__UpperCAmelCase						:			str         =   os.path.join(args.output_dir,    snake_case__							)
																												accelerator.save_state(snake_case__							)
        # 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=snake_case__,    )

							if args.with_tracking:
														accelerator.end_training()




def   _UpperCamelCase   (			)						->   Union[str, Any]:
							__UpperCAmelCase						:			Any         =   argparse.ArgumentParser(description="Simple example of training script."							)
							parser.add_argument(
							    "--mixed_precision",    type=snake_case__,    default=snake_case__,    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=snake_case__,    default=snake_case__,    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=snake_case__,    default=snake_case__,    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=snake_case__,    default=".",    help="Optional save directory where all checkpoint folders will be stored. Default is the current working directory.",    )
							parser.add_argument(
							    "--logging_dir",    type=snake_case__,    default="logs",    help="Location on where to store experiment tracking logs`",    )
							parser.add_argument(
							    "--model_name_or_path",    type=snake_case__,    help="Path to pretrained model or model identifier from huggingface.co/models.",    required=snake_case__,    )
							__UpperCAmelCase						:			str         =   parser.parse_args()
							__UpperCAmelCase						:			int         =   {"lr": 2e-5, "num_epochs": 3, "seed": 42, "batch_size": 16}
							training_function(snake_case__,    snake_case__							)


if __name__ == "__main__":
	main()





from __future__ import annotations

from collections import deque
from enum import Enum
from math import atana, degrees
from sys import maxsize




class 					_snake_case						(						_lowercase				):
					lowerCamelCase__:		Tuple  =     1
					lowerCamelCase__:		Dict  =     2
					lowerCamelCase__:		List[str]  =     3
					def __repr__(  self:				List[Any]     )    ->				int:
												return f'''{self.__class__.__name__}.{self.name}'''




def   _UpperCamelCase   (			snake_case__,    snake_case__,    snake_case__							)						->   float:
							__UpperCAmelCase    ,	__UpperCAmelCase						:			Optional[Any]         =   point
							return degrees(atana(y - miny,    x - minx							)							)




def   _UpperCamelCase   (			snake_case__,    snake_case__,    snake_case__							)						->   Direction:
							__UpperCAmelCase    ,	__UpperCAmelCase						:			List[Any]         =   starting
							__UpperCAmelCase    ,	__UpperCAmelCase						:			Optional[int]         =   via
							__UpperCAmelCase    ,	__UpperCAmelCase						:			List[str]         =   target
							__UpperCAmelCase						:			Tuple         =   degrees(atana(ya - ya,    xa - xa							)							)
							via_angle %= 360
							__UpperCAmelCase						:			str         =   degrees(atana(ya - ya,    xa - xa							)							)
							target_angle %= 360
							# t-
							#  \ \
							#   \ v
							#    \|
							#     s
							# via_angle is always lower than target_angle, if direction is left.
							# If they are same, it means they are on a same line of convex hull.
							if target_angle > via_angle:
														return Direction.left
							elif target_angle == via_angle:
														return Direction.straight
							else:
														return Direction.right







def   _UpperCamelCase   (			snake_case__							)						->   list[tuple[int, int]]:

							if len(snake_case__							) <= 2:
														# There is no convex hull
														raise ValueError("graham_scan: argument must contain more than 3 points."							)
							if len(snake_case__							) == 3:
														return points
							# find the lowest and the most left point
							__UpperCAmelCase						:			str         =   0
							__UpperCAmelCase    ,	__UpperCAmelCase						:			Any         =   maxsize, maxsize
							for i, point in enumerate(snake_case__							):
														__UpperCAmelCase						:			str         =   point[0]
														__UpperCAmelCase						:			Any         =   point[1]
														if y < miny:
																					__UpperCAmelCase						:			Tuple         =   y
																					__UpperCAmelCase						:			int         =   x
																					__UpperCAmelCase						:			str         =   i
														if y == miny and x < minx:
																					__UpperCAmelCase						:			Any         =   x
																					__UpperCAmelCase						:			Dict         =   i

    # remove the lowest and the most left point from points for preparing for sort
							points.pop(snake_case__							)

							__UpperCAmelCase						:			Any         =   sorted(snake_case__,    key=lambda snake_case__							: angle_comparer(snake_case__,    snake_case__,    snake_case__							)							)
							# This insert actually costs complexity,
							# and you should instead add (minx, miny) into stack later.
							# I'm using insert just for easy understanding.
							sorted_points.insert(0,    (minx, miny)							)

							__UpperCAmelCase						:			deque[tuple[int, int]]         =   deque()
							stack.append(sorted_points[0]							)
							stack.append(sorted_points[1]							)
							stack.append(sorted_points[2]							)
							# In any ways, the first 3 points line are towards left.
							# Because we sort them the angle from minx, miny.
							__UpperCAmelCase						:			Any         =   Direction.left

							for i in range(3,    len(snake_case__							)							):
														while True:
																					__UpperCAmelCase						:			List[Any]         =   stack[-2]
																					__UpperCAmelCase						:			Tuple         =   stack[-1]
																					__UpperCAmelCase						:			Any         =   sorted_points[i]
																					__UpperCAmelCase						:			Optional[Any]         =   check_direction(snake_case__,    snake_case__,    snake_case__							)

																					if next_direction == Direction.left:
																												__UpperCAmelCase						:			Optional[int]         =   Direction.left
																												break
																					if next_direction == Direction.straight:
																												if current_direction == Direction.left:
																																			# We keep current_direction as left.
																																			# Because if the straight line keeps as straight,
																																			# we want to know if this straight line is towards left.
																																			break
																												elif current_direction == Direction.right:
																																			# If the straight line is towards right,
																																			# every previous points on those straigh line is not convex hull.
																																			stack.pop()
																					if next_direction == Direction.right:
																												stack.pop()
														stack.append(sorted_points[i]							)
							return list(snake_case__							)




