






"""simple docstring"""







import argparse
import os

import evaluate
import torch
from datasets import load_dataset
from torch.optim import AdamW
from torch.utils.data import DataLoader
from transformers import AutoModelForSequenceClassification, AutoTokenizer, get_linear_schedule_with_warmup, set_seed

from accelerate import Accelerator, DistributedType


########################################################################
# 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)
#
# 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 :				Optional[int]      					=		16
a :				Tuple      					=		32





def 						lowercase__(A					,		A						)				->Dict:




						"""simple docstring"""



						if args.with_tracking:
												lowercase__				:      int=       Accelerator(
												    cpu=args.cpu					,		mixed_precision=args.mixed_precision					,		log_with="all"					,		project_dir=args.project_dir						)
						else:
												lowercase__				:      int=       Accelerator(cpu=args.cpu					,		mixed_precision=args.mixed_precision						)

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

						# We need to initialize the trackers we use, and also store our configuration
						if args.with_tracking:
												lowercase__				:      Tuple=       os.path.split(_SCREAMING_SNAKE_CASE						)[-1].split("."						)[0]
												accelerator.init_trackers(_SCREAMING_SNAKE_CASE					,		_SCREAMING_SNAKE_CASE						)

						lowercase__				:      List[str]=       AutoTokenizer.from_pretrained("bert-base-cased"						)
						lowercase__				:      Optional[Any]=       load_dataset("glue"					,		"mrpc"						)
						lowercase__				:      Dict=       evaluate.load("glue"					,		"mrpc"						)

						def tokenize_function(A						):
												# max_length=None => use the model max length (it's actually the default)
												lowercase__				:      List[Any]=       tokenizer(examples["sentence1"]					,		examples["sentence2"]					,		truncation=_SCREAMING_SNAKE_CASE					,		max_length=_SCREAMING_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():
												lowercase__				:      Dict=       datasets.map(
												    _SCREAMING_SNAKE_CASE					,		batched=_SCREAMING_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
						lowercase__				:      Optional[int]=       tokenized_datasets.rename_column("label"					,		"labels"						)

						# If the batch size is too big we use gradient accumulation
						lowercase__				:      Tuple=       1
						if batch_size > MAX_GPU_BATCH_SIZE and accelerator.distributed_type != DistributedType.TPU:
												lowercase__				:      Optional[int]=       batch_size // MAX_GPU_BATCH_SIZE
												lowercase__				:      Tuple=       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.
												lowercase__				:      Tuple=       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":
																		lowercase__				:      Optional[int]=       16
												elif accelerator.mixed_precision != "no":
																		lowercase__				:      Tuple=       8
												else:
																		lowercase__				:      Optional[Any]=       None

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

						# Instantiate dataloaders.
						lowercase__				:      str=       DataLoader(
						    tokenized_datasets["train"]					,		shuffle=_SCREAMING_SNAKE_CASE					,		collate_fn=_SCREAMING_SNAKE_CASE					,		batch_size=_SCREAMING_SNAKE_CASE						)
						lowercase__				:      Tuple=       DataLoader(
						    tokenized_datasets["validation"]					,		shuffle=_SCREAMING_SNAKE_CASE					,		collate_fn=_SCREAMING_SNAKE_CASE					,		batch_size=_SCREAMING_SNAKE_CASE						)

						set_seed(_SCREAMING_SNAKE_CASE						)

						# Instantiate the model (we build the model here so that the seed also control new weights initialization)
						lowercase__				:      Optional[int]=       AutoModelForSequenceClassification.from_pretrained("bert-base-cased"					,		return_dict=_SCREAMING_SNAKE_CASE						)

						# We could avoid this line since the accelerator is set with `device_placement=True` (default value).
						# Note that if you are placing tensors on devices manually, this line absolutely needs to be before the optimizer
						# creation otherwise training will not work on TPU (`accelerate` will kindly throw an error to make us aware of that).
						lowercase__				:      List[Any]=       model.to(accelerator.device						)

						# Instantiate optimizer
						lowercase__				:      str=       AdamW(params=model.parameters()					,		lr=_SCREAMING_SNAKE_CASE						)

						# Instantiate scheduler
						lowercase__				:      Optional[int]=       get_linear_schedule_with_warmup(
						    optimizer=_SCREAMING_SNAKE_CASE					,		num_warmup_steps=100					,		num_training_steps=(len(_SCREAMING_SNAKE_CASE						) * num_epochs) // gradient_accumulation_steps					,		)

						# Prepare everything
						# 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.
						lowercase__,	lowercase__,	lowercase__,	lowercase__,	lowercase__				:      List[Any]=       accelerator.prepare(
						    _SCREAMING_SNAKE_CASE					,		_SCREAMING_SNAKE_CASE					,		_SCREAMING_SNAKE_CASE					,		_SCREAMING_SNAKE_CASE					,		_SCREAMING_SNAKE_CASE						)

						# We need to keep track of how many total steps we have iterated over
						lowercase__				:      Optional[int]=       0
						# We also need to keep track of the stating epoch so files are named properly
						lowercase__				:      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						)
																		lowercase__				:      Optional[Any]=       os.path.basename(args.resume_from_checkpoint						)
												else:
																		# Get the most recent checkpoint
																		lowercase__				:      Optional[int]=       [f.name for f in os.scandir(os.getcwd()						) if f.is_dir()]
																		dirs.sort(key=os.path.getctime						)
																		lowercase__				:      List[str]=       dirs[-1]  # Sorts folders by date modified, most recent checkpoint is the last
												# Extract `epoch_{i}` or `step_{i}`
												lowercase__				:      List[Any]=       os.path.splitext(_SCREAMING_SNAKE_CASE						)[0]

												if "epoch" in training_difference:
																		lowercase__				:      int=       int(training_difference.replace("epoch_"					,		""						)						) + 1
																		lowercase__				:      Any=       None
												else:
																		lowercase__				:      Any=       int(training_difference.replace("step_"					,		""						)						)
																		lowercase__				:      Optional[Any]=       resume_step // len(_SCREAMING_SNAKE_CASE						)
																		resume_step -= starting_epoch * len(_SCREAMING_SNAKE_CASE						)

    # Now we train the model
						for epoch in range(_SCREAMING_SNAKE_CASE					,		_SCREAMING_SNAKE_CASE						):
												model.train()
												if args.with_tracking:
																		lowercase__				:      str=       0
												if args.resume_from_checkpoint and epoch == starting_epoch and resume_step is not None:
																		# We need to skip steps until we reach the resumed step
																		lowercase__				:      Dict=       accelerator.skip_first_batches(_SCREAMING_SNAKE_CASE					,		_SCREAMING_SNAKE_CASE						)
																		overall_step += resume_step
												else:
																		# After the first iteration though, we need to go back to the original dataloader
																		lowercase__				:      Dict=       train_dataloader
												for step, batch in enumerate(_SCREAMING_SNAKE_CASE						):
																		# We could avoid this line since we set the accelerator with `device_placement=True`.
																		batch.to(accelerator.device						)
																		lowercase__				:      Any=       model(**_SCREAMING_SNAKE_CASE						)
																		lowercase__				:      Dict=       outputs.loss
																		lowercase__				:      Optional[Any]=       loss / gradient_accumulation_steps
																		# We keep track of the loss at each epoch
																		if args.with_tracking:
																								total_loss += loss.detach().float()
																		accelerator.backward(_SCREAMING_SNAKE_CASE						)
																		if step % gradient_accumulation_steps == 0:
																								optimizer.step()
																								lr_scheduler.step()
																								optimizer.zero_grad()

																		overall_step += 1

																		if isinstance(_SCREAMING_SNAKE_CASE					,		_SCREAMING_SNAKE_CASE						):
																								lowercase__				:      Any=       f'''step_{overall_step}'''
																								if overall_step % checkpointing_steps == 0:
																														if args.output_dir is not None:
																																				lowercase__				:      List[Any]=       os.path.join(args.output_dir					,		_SCREAMING_SNAKE_CASE						)
																														accelerator.save_state(_SCREAMING_SNAKE_CASE						)

												model.eval()
												for step, batch in enumerate(_SCREAMING_SNAKE_CASE						):
																		# We could avoid this line since we set the accelerator with `device_placement=True`.
																		batch.to(accelerator.device						)
																		with torch.no_grad():
																								lowercase__				:      str=       model(**_SCREAMING_SNAKE_CASE						)
																		lowercase__				:      Dict=       outputs.logits.argmax(dim=-1						)
																		lowercase__,	lowercase__				:      Tuple=       accelerator.gather_for_metrics((predictions, batch["labels"])						)
																		metric.add_batch(
																		    predictions=_SCREAMING_SNAKE_CASE					,		references=_SCREAMING_SNAKE_CASE					,		)

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

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

						if args.with_tracking:
												accelerator.end_training()





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




						"""simple docstring"""



						lowercase__				:      Optional[Any]=       argparse.ArgumentParser(description="Simple example of training script."						)
						parser.add_argument(
						    "--mixed_precision"					,		type=_SCREAMING_SNAKE_CASE					,		default=_SCREAMING_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=_SCREAMING_SNAKE_CASE					,		default=_SCREAMING_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=_SCREAMING_SNAKE_CASE					,		default=_SCREAMING_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=_SCREAMING_SNAKE_CASE					,		default="."					,		help="Optional save directory where all checkpoint folders will be stored. Default is the current working directory."					,		)
						parser.add_argument(
						    "--project_dir"					,		type=_SCREAMING_SNAKE_CASE					,		default="logs"					,		help="Location on where to store experiment tracking logs` and relevent project information"					,		)
						lowercase__				:      Dict=       parser.parse_args()
						lowercase__				:      str=       {"lr": 2e-5, "num_epochs": 3, "seed": 42, "batch_size": 16}
						training_function(_SCREAMING_SNAKE_CASE					,		_SCREAMING_SNAKE_CASE						)


if __name__ == "__main__":
			main()











'''simple docstring'''

from __future__ import annotations

import random
from collections.abc import Iterable
from typing import Any, Generic, TypeVar

UpperCAmelCase			=    TypeVar('T', bound=bool)

class __snake_case(      Generic[T]					):


						'''simple docstring'''



						def __init__(					self	,       A_      )	->	None:
							lowerCAmelCase    =	value
							lowerCAmelCase    =	None
							lowerCAmelCase    =	None


						@property
						def 				__snake_case    (					self      )	->	T:
							return self._value




						@staticmethod
						def 				__snake_case    (					A_	,       A_      )	->	RandomizedHeapNode[T] | None:
							if not roota:
								return roota

							if not roota:
								return roota

							if roota.value > roota.value:
								lowerCAmelCase,   lowerCAmelCase    =	roota, roota

							if random.choice([True, False]      ):
								lowerCAmelCase,   lowerCAmelCase    =	roota.right, roota.left

							lowerCAmelCase    =	RandomizedHeapNode.merge(roota.left	,       A_      )

							return roota







class __snake_case(      Generic[T]					):


						'''simple docstring'''



						def __init__(					self	,       A_ = ()      )	->	None:
							lowerCAmelCase    =	None

							if data:
								for item in data:
									self.insert(A_      )


						def 				__snake_case    (					self	,       A_      )	->	None:
							lowerCAmelCase    =	RandomizedHeapNode.merge(self._root	,       RandomizedHeapNode(A_      )      )


						def 				__snake_case    (					self      )	->	T | None:
							lowerCAmelCase    =	self.top()

							if self._root is None:
								return None

							lowerCAmelCase    =	RandomizedHeapNode.merge(self._root.left	,       self._root.right      )

							return result


						def 				__snake_case    (					self      )	->	T:
							if not self._root:
								raise IndexError("""Can't get top element for the empty heap."""      )
							return self._root.value


						def 				__snake_case    (					self      )	->	None:
							lowerCAmelCase    =	None


						def 				__snake_case    (					self      )	->	list[Any]:
							lowerCAmelCase    =	[]
							while self:
								result.append(self.pop()      )

							return result




						def __bool__(					self      )	->	bool:
							return self._root is not None


if __name__ == "__main__":
				import doctest

				doctest.testmod()