




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
#
########################################################################


lowercase_     :    int      =			16
lowercase_     :    Optional[Any]      =			32


def 							__SCREAMING_SNAKE_CASE  (      snake_case_     ,   snake_case_						):

						'''simple docstring'''


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

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

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

						_UpperCAmelCase													=					AutoTokenizer.from_pretrained("bert-base-cased"						)
						_UpperCAmelCase													=					load_dataset("glue"     ,   "mrpc"						)
						_UpperCAmelCase													=					evaluate.load("glue"     ,   "mrpc"						)

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

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

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

						# Instantiate dataloaders.
						_UpperCAmelCase													=					DataLoader(
						    tokenized_datasets["train"]     ,   shuffle=snake_case_     ,   collate_fn=snake_case_     ,   batch_size=snake_case_						)
						_UpperCAmelCase													=					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													=					AutoModelForSequenceClassification.from_pretrained("bert-base-cased"     ,   return_dict=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).
						_UpperCAmelCase													=					model.to(accelerator.device						)

						# Instantiate optimizer
						_UpperCAmelCase													=					AdamW(params=model.parameters()     ,   lr=snake_case_						)

						# Instantiate scheduler
						_UpperCAmelCase													=					get_linear_schedule_with_warmup(
						    optimizer=snake_case_     ,   num_warmup_steps=100     ,   num_training_steps=(len(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.
						_UpperCAmelCase							,			_UpperCAmelCase							,			_UpperCAmelCase							,			_UpperCAmelCase							,			_UpperCAmelCase													=					accelerator.prepare(
						    snake_case_     ,   snake_case_     ,   snake_case_     ,   snake_case_     ,   snake_case_						)

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

												if "epoch" in training_difference:
																		_UpperCAmelCase													=					int(training_difference.replace("epoch_"     ,   ""						)						) + 1
																		_UpperCAmelCase													=					None
												else:
																		_UpperCAmelCase													=					int(training_difference.replace("step_"     ,   ""						)						)
																		_UpperCAmelCase													=					resume_step // len(snake_case_						)
																		resume_step -= starting_epoch * len(snake_case_						)

    # Now we train the model
						for epoch in range(snake_case_     ,   snake_case_						):
												model.train()
												if args.with_tracking:
																		_UpperCAmelCase													=					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
																		_UpperCAmelCase													=					accelerator.skip_first_batches(snake_case_     ,   snake_case_						)
																		overall_step += resume_step
												else:
																		# After the first iteration though, we need to go back to the original dataloader
																		_UpperCAmelCase													=					train_dataloader
												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						)
																		_UpperCAmelCase													=					model(**snake_case_						)
																		_UpperCAmelCase													=					outputs.loss
																		_UpperCAmelCase													=					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()

																		overall_step += 1

																		if isinstance(snake_case_     ,   snake_case_						):
																								_UpperCAmelCase													=					f"""step_{overall_step}"""
																								if overall_step % checkpointing_steps == 0:
																														if args.output_dir is not None:
																																				_UpperCAmelCase													=					os.path.join(args.output_dir     ,   snake_case_						)
																														accelerator.save_state(snake_case_						)

												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													=					model(**snake_case_						)
																		_UpperCAmelCase													=					outputs.logits.argmax(dim=-1						)
																		_UpperCAmelCase							,			_UpperCAmelCase													=					accelerator.gather_for_metrics((predictions, batch["labels"])						)
																		metric.add_batch(
																		    predictions=snake_case_     ,   references=snake_case_     ,   )

												_UpperCAmelCase													=					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_						),
																		        "epoch": epoch,
																		    }     ,   step=snake_case_     ,   )

												if checkpointing_steps == "epoch":
																		_UpperCAmelCase													=					f"""epoch_{epoch}"""
																		if args.output_dir is not None:
																								_UpperCAmelCase													=					os.path.join(args.output_dir     ,   snake_case_						)
																		accelerator.save_state(snake_case_						)

						if args.with_tracking:
												accelerator.end_training()


def 							__SCREAMING_SNAKE_CASE  (      ):

						'''simple docstring'''


						_UpperCAmelCase													=					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(
						    "--project_dir"     ,   type=snake_case_     ,   default="logs"     ,   help="Location on where to store experiment tracking logs` and relevent project information"     ,   )
						_UpperCAmelCase													=					parser.parse_args()
						_UpperCAmelCase													=					{"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 abc import abstractmethod
from collections.abc import Iterable
from typing import Generic, Protocol, TypeVar






class    __lowerCAmelCase			(	UpperCAmelCase__			):







			@abstractmethod
			def __lt__(	self   :      T   ,   snake_case__   :      T				):




									"""simple docstring"""


									pass







			@abstractmethod
			def __gt__(	self   :      T   ,   snake_case__   :      T				):




									"""simple docstring"""


									pass







			@abstractmethod
			def __eq__(	self   :      T   ,   snake_case__   :      object				):




									"""simple docstring"""


									pass




lowercase_     :    Tuple      =			TypeVar('T', bound=Comparable)






class    __lowerCAmelCase			(	Generic[T]			):







			def __init__(	self   :      Optional[int]				):




									"""simple docstring"""


									_UpperCAmelCase													=					[]
									_UpperCAmelCase													=					0







			def __repr__(	self   :      Dict				):




									"""simple docstring"""


									return str(self.h				)







			def UpperCamelCase      (	self   :      Optional[Any]   ,   snake_case__   :      int				):




									"""simple docstring"""


									if child_idx > 0:
															return (child_idx - 1) // 2
									return None







			def UpperCamelCase      (	self   :      Tuple   ,   snake_case__   :      int				):




									"""simple docstring"""


									_UpperCAmelCase													=					2 * parent_idx + 1
									if left_child_index < self.heap_size:
															return left_child_index
									return None







			def UpperCamelCase      (	self   :      Union[str, Any]   ,   snake_case__   :      int				):




									"""simple docstring"""


									_UpperCAmelCase													=					2 * parent_idx + 2
									if right_child_index < self.heap_size:
															return right_child_index
									return None







			def UpperCamelCase      (	self   :      Dict   ,   snake_case__   :      int				):




									"""simple docstring"""


									if index < self.heap_size:
															_UpperCAmelCase													=					index
															_UpperCAmelCase													=					self.left_child_idx(snake_case__				)
															_UpperCAmelCase													=					self.right_child_idx(snake_case__				)
															# check which child is larger than its parent
															if left_child is not None and self.h[left_child] > self.h[violation]:
																					_UpperCAmelCase													=					left_child
															if right_child is not None and self.h[right_child] > self.h[violation]:
																					_UpperCAmelCase													=					right_child
															# if violation indeed exists
															if violation != index:
																					# swap to fix the violation
																					_UpperCAmelCase							,			_UpperCAmelCase													=					self.h[index], self.h[violation]
																					# fix the subsequent violation recursively if any
																					self.max_heapify(snake_case__				)







			def UpperCamelCase      (	self   :      Tuple   ,   snake_case__   :      Iterable[T]				):




									"""simple docstring"""


									_UpperCAmelCase													=					list(snake_case__				)
									_UpperCAmelCase													=					len(self.h				)
									if self.heap_size > 1:
															# max_heapify from right to left but exclude leaves (last level)
															for i in range(self.heap_size // 2 - 1   ,   -1   ,   -1				):
																					self.max_heapify(snake_case__				)







			def UpperCamelCase      (	self   :      Tuple				):




									"""simple docstring"""


									if self.heap_size >= 2:
															_UpperCAmelCase													=					self.h[0]
															_UpperCAmelCase													=					self.h.pop(-1				)
															self.heap_size -= 1
															self.max_heapify(0				)
															return me
									elif self.heap_size == 1:
															self.heap_size -= 1
															return self.h.pop(-1				)
									else:
															raise Exception("Empty heap"				)







			def UpperCamelCase      (	self   :      str   ,   snake_case__   :      T				):




									"""simple docstring"""


									self.h.append(snake_case__				)
									_UpperCAmelCase													=					(self.heap_size - 1) // 2
									self.heap_size += 1
									while idx >= 0:
															self.max_heapify(snake_case__				)
															_UpperCAmelCase													=					(idx - 1) // 2







			def UpperCamelCase      (	self   :      Any				):




									"""simple docstring"""


									_UpperCAmelCase													=					self.heap_size
									for j in range(size - 1   ,   0   ,   -1				):
															_UpperCAmelCase							,			_UpperCAmelCase													=					self.h[j], self.h[0]
															self.heap_size -= 1
															self.max_heapify(0				)
									_UpperCAmelCase													=					size


if __name__ == "__main__":
				import doctest

				# run doc test
				doctest.testmod()

				# demo
				for unsorted in [
				    [0],
				    [2],
				    [3, 5],
				    [5, 3],
				    [5, 5],
				    [0, 0, 0, 0],
				    [1, 1, 1, 1],
				    [2, 2, 3, 5],
				    [0, 2, 2, 3, 5],
				    [2, 5, 3, 0, 2, 3, 0, 3],
				    [6, 1, 2, 7, 9, 3, 4, 5, 10, 8],
				    [1_03, 9, 1, 7, 11, 15, 25, 2_01, 2_09, 1_07, 5],
				    [-45, -2, -5],
				]:
								print(f"""unsorted array: {unsorted}""")

								lowercase_     :    Heap[int]      =			Heap()
								heap.build_max_heap(unsorted)
								print(f"""after build heap: {heap}""")

								print(f"""max value: {heap.extract_max()}""")
								print(f"""after max value removed: {heap}""")

								heap.insert(1_00)
								print(f"""after new value 100 inserted: {heap}""")

								heap.heap_sort()
								print(f"""heap-sorted array: {heap}\n""")




