

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


lowerCamelCase__				:  List[str]													=  16
lowerCamelCase__				:  int													=  32
def       UpperCamelCase					(			_lowerCAmelCase     :						List[str],  _lowerCAmelCase     :						Optional[Any] )				->			Tuple:
	# Initialize accelerator
	if args.with_tracking:
		_UpperCAmelCase    :							str									=					Accelerator(
		    cpu=args.cpu,  mixed_precision=args.mixed_precision,  log_with="""all""",  project_dir=args.project_dir )
	else:
		_UpperCAmelCase    :							int									=					Accelerator(cpu=args.cpu,  mixed_precision=args.mixed_precision )

	if hasattr(args.checkpointing_steps,  """isdigit""" ):
		if args.checkpointing_steps == "epoch":
			_UpperCAmelCase    :							str									=					args.checkpointing_steps
		elif args.checkpointing_steps.isdigit():
			_UpperCAmelCase    :							List[str]									=					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    :							List[Any]									=					config["""lr"""]
	_UpperCAmelCase    :							Optional[int]									=					int(config["""num_epochs"""] )
	_UpperCAmelCase    :							List[str]									=					int(config["""seed"""] )
	_UpperCAmelCase    :							List[str]									=					int(config["""batch_size"""] )

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

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

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

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

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

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

	# Instantiate dataloaders.
	_UpperCAmelCase    :							Optional[Any]									=					DataLoader(
	    tokenized_datasets["""train"""],  shuffle=lowercase__,  collate_fn=lowercase__,  batch_size=lowercase__ )
	_UpperCAmelCase    :							Union[str, Any]									=					DataLoader(
	    tokenized_datasets["""validation"""],  shuffle=lowercase__,  collate_fn=lowercase__,  batch_size=lowercase__ )

	set_seed(lowercase__ )

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

	# 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    :							Optional[Any]									=					model.to(accelerator.device )

	# Instantiate optimizer
	_UpperCAmelCase    :							List[str]									=					AdamW(params=model.parameters(),  lr=lowercase__ )

	# Instantiate scheduler
	_UpperCAmelCase    :							Dict									=					get_linear_schedule_with_warmup(
	    optimizer=lowercase__,  num_warmup_steps=100,  num_training_steps=(len(lowercase__ ) * 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    :							int									=					accelerator.prepare(
	    lowercase__,  lowercase__,  lowercase__,  lowercase__,  lowercase__ )

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

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

    # Now we train the model
	for epoch in range(lowercase__,  lowercase__ ):
		model.train()
		if args.with_tracking:
			_UpperCAmelCase    :							List[Any]									=					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    :							int									=					accelerator.skip_first_batches(lowercase__,  lowercase__ )
			overall_step += resume_step
		else:
			# After the first iteration though, we need to go back to the original dataloader
			_UpperCAmelCase    :							List[Any]									=					train_dataloader
		for step, batch in enumerate(lowercase__ ):
			# We could avoid this line since we set the accelerator with `device_placement=True`.
			batch.to(accelerator.device )
			_UpperCAmelCase    :							str									=					model(**lowercase__ )
			_UpperCAmelCase    :							Any									=					outputs.loss
			_UpperCAmelCase    :							Union[str, 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(lowercase__ )
			if step % gradient_accumulation_steps == 0:
				optimizer.step()
				lr_scheduler.step()
				optimizer.zero_grad()

			overall_step += 1

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

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

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

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

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


if __name__ == "__main__":
						main()








"""simple docstring"""






def       UpperCamelCase					(			_lowerCAmelCase     :						int )				->			str:
	_UpperCAmelCase    :							Any									=					1
	_UpperCAmelCase    :							List[str]									=					0
	_UpperCAmelCase    :							str									=					1
	_UpperCAmelCase    :							Union[str, Any]									=					1
	_UpperCAmelCase    :							int									=					3
	_UpperCAmelCase    :							int									=					3
	_UpperCAmelCase    :							List[str]									=					limit
	_UpperCAmelCase    :							Optional[Any]									=					0

	_UpperCAmelCase    :							Any									=					""""""
	while counter != decimal + 1:
		if 4 * q + r - t < n * t:
			result += str(_lowerCAmelCase )
			if counter == 0:
				result += "."

			if decimal == counter:
				break

			counter += 1
			_UpperCAmelCase    :							str									=					10 * (r - n * t)
			_UpperCAmelCase    :							List[Any]									=					((10 * (3 * q + r)) // t) - 10 * n
			q *= 10
			_UpperCAmelCase    :							int									=					nr
		else:
			_UpperCAmelCase    :							Any									=					(2 * q + r) * l
			_UpperCAmelCase    :							str									=					(q * (7 * k) + 2 + (r * l)) // (t * l)
			q *= k
			t *= l
			l += 2
			k += 1
			_UpperCAmelCase    :							List[Any]									=					nn
			_UpperCAmelCase    :							Optional[int]									=					nr
	return result
def       UpperCamelCase					(			)				->			None:
	print(f'''{calculate_pi(50 ) = }''' )
	import doctest

	doctest.testmod()


if __name__ == "__main__":
						main()





