
"""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
#
########################################################################


SCREAMING_SNAKE_CASE_			      =					16
SCREAMING_SNAKE_CASE_			      =					32

def 				lowercase      (_lowerCAmelCase				,  _lowerCAmelCase		):
						# Initialize accelerator
						if args.with_tracking:
												__lowerCAmelCase        =       Accelerator(
												    cpu=args.cpu				,  mixed_precision=args.mixed_precision				,  log_with="""all"""				,  project_dir=args.project_dir		)
						else:
												__lowerCAmelCase        =       Accelerator(cpu=args.cpu				,  mixed_precision=args.mixed_precision		)

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

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

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

						def tokenize_function(_lowerCAmelCase		):
												# max_length=None => use the model max length (it's actually the default)
												__lowerCAmelCase        =       tokenizer(examples["""sentence1"""]				,  examples["""sentence2"""]				,  truncation=_lowerCAmelCase				,  max_length=_lowerCAmelCase		)
												return outputs

						# Apply the method we just defined to all the examples in all the splits of the dataset
						# starting with the main process first:
						with accelerator.main_process_first():
												__lowerCAmelCase        =       datasets.map(
												    _lowerCAmelCase				,  batched=_lowerCAmelCase				,  remove_columns=["""idx""", """sentence1""", """sentence2"""]				,  )

						# We also rename the 'label' column to 'labels' which is the expected name for labels by the models of the
						# transformers library
						__lowerCAmelCase        =       tokenized_datasets.rename_column("""label"""				,  """labels"""		)

						# If the batch size is too big we use gradient accumulation
						__lowerCAmelCase        =       1
						if batch_size > MAX_GPU_BATCH_SIZE and accelerator.distributed_type != DistributedType.TPU:
												__lowerCAmelCase        =       batch_size // MAX_GPU_BATCH_SIZE
												__lowerCAmelCase        =       MAX_GPU_BATCH_SIZE

						def collate_fn(_lowerCAmelCase		):
												# On TPU it's best to pad everything to the same length or training will be very slow.
												__lowerCAmelCase        =       128 if accelerator.distributed_type == DistributedType.TPU else None
												# When using mixed precision we want round multiples of 8/16
												if accelerator.mixed_precision == "fp8":
																		__lowerCAmelCase        =       16
												elif accelerator.mixed_precision != "no":
																		__lowerCAmelCase        =       8
												else:
																		__lowerCAmelCase        =       None

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

						# Instantiate dataloaders.
						__lowerCAmelCase        =       DataLoader(
						    tokenized_datasets["""train"""]				,  shuffle=_lowerCAmelCase				,  collate_fn=_lowerCAmelCase				,  batch_size=_lowerCAmelCase		)
						__lowerCAmelCase        =       DataLoader(
						    tokenized_datasets["""validation"""]				,  shuffle=_lowerCAmelCase				,  collate_fn=_lowerCAmelCase				,  batch_size=_lowerCAmelCase		)

						set_seed(_lowerCAmelCase		)

						# Instantiate the model (we build the model here so that the seed also control new weights initialization)
						__lowerCAmelCase        =       AutoModelForSequenceClassification.from_pretrained("""bert-base-cased"""				,  return_dict=_lowerCAmelCase		)

						# 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).
						__lowerCAmelCase        =       model.to(accelerator.device		)

						# Instantiate optimizer
						__lowerCAmelCase        =       AdamW(params=model.parameters()				,  lr=_lowerCAmelCase		)

						# Instantiate scheduler
						__lowerCAmelCase        =       get_linear_schedule_with_warmup(
						    optimizer=_lowerCAmelCase				,  num_warmup_steps=100				,  num_training_steps=(len(_lowerCAmelCase		) * 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.
						__lowerCAmelCase ,      __lowerCAmelCase ,      __lowerCAmelCase ,      __lowerCAmelCase ,      __lowerCAmelCase        =       accelerator.prepare(
						    _lowerCAmelCase				,  _lowerCAmelCase				,  _lowerCAmelCase				,  _lowerCAmelCase				,  _lowerCAmelCase		)

						# We need to keep track of how many total steps we have iterated over
						__lowerCAmelCase        =       0
						# We also need to keep track of the stating epoch so files are named properly
						__lowerCAmelCase        =       0

						# Potentially load in the weights and states from a previous save
						if args.resume_from_checkpoint:
												if args.resume_from_checkpoint is not None or args.resume_from_checkpoint != "":
																		accelerator.print(f"""Resumed from checkpoint: {args.resume_from_checkpoint}"""		)
																		accelerator.load_state(args.resume_from_checkpoint		)
																		__lowerCAmelCase        =       os.path.basename(args.resume_from_checkpoint		)
												else:
																		# Get the most recent checkpoint
																		__lowerCAmelCase        =       [f.name for f in os.scandir(os.getcwd()		) if f.is_dir()]
																		dirs.sort(key=os.path.getctime		)
																		__lowerCAmelCase        =       dirs[-1]  # Sorts folders by date modified, most recent checkpoint is the last
												# Extract `epoch_{i}` or `step_{i}`
												__lowerCAmelCase        =       os.path.splitext(_lowerCAmelCase		)[0]

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

    # Now we train the model
						for epoch in range(_lowerCAmelCase				,  _lowerCAmelCase		):
												model.train()
												if args.with_tracking:
																		__lowerCAmelCase        =       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
																		__lowerCAmelCase        =       accelerator.skip_first_batches(_lowerCAmelCase				,  _lowerCAmelCase		)
																		overall_step += resume_step
												else:
																		# After the first iteration though, we need to go back to the original dataloader
																		__lowerCAmelCase        =       train_dataloader
												for step, batch in enumerate(_lowerCAmelCase		):
																		# We could avoid this line since we set the accelerator with `device_placement=True`.
																		batch.to(accelerator.device		)
																		__lowerCAmelCase        =       model(**_lowerCAmelCase		)
																		__lowerCAmelCase        =       outputs.loss
																		__lowerCAmelCase        =       loss / gradient_accumulation_steps
																		# We keep track of the loss at each epoch
																		if args.with_tracking:
																								total_loss += loss.detach().float()
																		accelerator.backward(_lowerCAmelCase		)
																		if step % gradient_accumulation_steps == 0:
																								optimizer.step()
																								lr_scheduler.step()
																								optimizer.zero_grad()

																		overall_step += 1

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

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

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

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

						if args.with_tracking:
												accelerator.end_training()

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


if __name__ == "__main__":
			main()



"""simple docstring"""
from __future__ import annotations

from typing import Generic, TypeVar

SCREAMING_SNAKE_CASE_			      =					TypeVar('''T''')
class      lowerCAmelCase_						(							A__				):



							'''simple docstring'''





							pass
class      lowerCAmelCase_						(							A__				):



							'''simple docstring'''





							pass
class      lowerCAmelCase_						(							Generic[T]				):



							'''simple docstring'''





							def __init__(				self  ,							snake_case_ = 10							)   ->       Tuple:
													__lowerCAmelCase        =       []
													__lowerCAmelCase        =       limit




							def __bool__(				self							)   ->       bool:
													return bool(self.stack							)




							def __str__(				self							)   ->       str:
													return str(self.stack							)




							def  A__		(				self  ,							snake_case_							)   ->       None:
													if len(self.stack							) >= self.limit:
																			raise StackOverflowError
													self.stack.append(snake_case_							)




							def  A__		(				self							)   ->       T:
													if not self.stack:
																			raise StackUnderflowError
													return self.stack.pop()




							def  A__		(				self							)   ->       T:
													if not self.stack:
																			raise StackUnderflowError
													return self.stack[-1]




							def  A__		(				self							)   ->       bool:
													return not bool(self.stack							)




							def  A__		(				self							)   ->       bool:
													return self.size() == self.limit




							def  A__		(				self							)   ->       int:
													return len(self.stack							)




							def __contains__(				self  ,							snake_case_							)   ->       bool:
													return item in self.stack

def 				lowercase      ():
						__lowerCAmelCase        =       Stack(10		)
						assert bool(_lowerCAmelCase		) is False
						assert stack.is_empty() is True
						assert stack.is_full() is False
						assert str(_lowerCAmelCase		) == "[]"

						try:
												__lowerCAmelCase        =       stack.pop()
												raise AssertionError  # This should not happen
						except StackUnderflowError:
												assert True  # This should happen

						try:
												__lowerCAmelCase        =       stack.peek()
												raise AssertionError  # This should not happen
						except StackUnderflowError:
												assert True  # This should happen

						for i in range(10		):
												assert stack.size() == i
												stack.push(_lowerCAmelCase		)

						assert bool(_lowerCAmelCase		)
						assert not stack.is_empty()
						assert stack.is_full()
						assert str(_lowerCAmelCase		) == str(list(range(10		)		)		)
						assert stack.pop() == 9
						assert stack.peek() == 8

						stack.push(100		)
						assert str(_lowerCAmelCase		) == str([0, 1, 2, 3, 4, 5, 6, 7, 8, 100]		)

						try:
												stack.push(200		)
												raise AssertionError  # This should not happen
						except StackOverflowError:
												assert True  # This should happen

						assert not stack.is_empty()
						assert stack.size() == 10

						assert 5 in stack
						assert 55 not in stack


if __name__ == "__main__":
			test_stack()
