


from typing import TYPE_CHECKING

from ....utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available


_lowercase       :      int	=	{"configuration_van": ["VAN_PRETRAINED_CONFIG_ARCHIVE_MAP", "VanConfig"]}


try:
				if not is_torch_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				_lowercase       :      Any	=	[
				    "VAN_PRETRAINED_MODEL_ARCHIVE_LIST",
				    "VanForImageClassification",
				    "VanModel",
				    "VanPreTrainedModel",
				]

if TYPE_CHECKING:
				from .configuration_van import VAN_PRETRAINED_CONFIG_ARCHIVE_MAP, VanConfig

				try:
								if not is_torch_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .modeling_van import (
								    VAN_PRETRAINED_MODEL_ARCHIVE_LIST,
								    VanForImageClassification,
								    VanModel,
								    VanPreTrainedModel,
								)

else:
				import sys

				_lowercase       :      Optional[int]	=	_LazyModule(__name__, globals()["__file__"], _import_structure)






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,
# specifically showcasing how to properly calculate the metrics on the
# validation dataset when in a distributed system, and builds off the
# `nlp_example.py` script.
#
# 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)
#
# To help focus on the differences in the code, building `DataLoaders`
# was refactored into its own function.
# New additions from the base script can be found quickly by
# looking for the # New Code # tags
#
# 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       :      Optional[Any]	=	16
_lowercase       :      List[Any]	=	32





def   _lowerCAmelCase				(   UpperCamelCase__:     Accelerator							,					UpperCamelCase__:     int = 16						) ->   Tuple:





	"""simple docstring"""







	A								=				AutoTokenizer.from_pretrained("""bert-base-cased"""						)
	A								=				load_dataset("""glue"""							,					"""mrpc"""						)

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

	def collate_fn(UpperCamelCase__:     Union[str, Any]						):
		# On TPU it's best to pad everything to the same length or training will be very slow.
		A								=				1_28 if accelerator.distributed_type == DistributedType.TPU else None
		# When using mixed precision we want round multiples of 8/16
		if accelerator.mixed_precision == "fp8":
			A								=				16
		elif accelerator.mixed_precision != "no":
			A								=				8
		else:
			A								=				None

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

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

	return train_dataloader, eval_dataloader


# For testing only
if os.environ.get("TESTING_MOCKED_DATALOADERS", None) == "1":
				from accelerate.test_utils.training import mocked_dataloaders

				_lowercase       :      List[Any]	=	mocked_dataloaders  # noqa: F811





def   _lowerCAmelCase				(   UpperCamelCase__:     Tuple							,					UpperCamelCase__:     Any						) ->   int:





	"""simple docstring"""







	if os.environ.get("""TESTING_MOCKED_DATALOADERS"""							,					UpperCamelCase__						) == "1":
		A								=				2
	# Initialize accelerator
	A								=				Accelerator(cpu=args.cpu							,					mixed_precision=args.mixed_precision						)
	# Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
	A								=				config["""lr"""]
	A								=				int(config["""num_epochs"""]						)
	A								=				int(config["""seed"""]						)
	A								=				int(config["""batch_size"""]						)

	A								=				evaluate.load("""glue"""							,					"""mrpc"""						)

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

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

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

	# Instantiate optimizer
	A								=				AdamW(params=model.parameters()							,					lr=UpperCamelCase__						)

	# Instantiate scheduler
	A								=				get_linear_schedule_with_warmup(
	    optimizer=UpperCamelCase__							,					num_warmup_steps=1_00							,					num_training_steps=(len(UpperCamelCase__						) * 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.
	A					,			A					,			A					,			A					,			A								=				accelerator.prepare(
	    UpperCamelCase__							,					UpperCamelCase__							,					UpperCamelCase__							,					UpperCamelCase__							,					UpperCamelCase__						)

	# Now we train the model
	for epoch in range(UpperCamelCase__						):
		model.train()
		for step, batch in enumerate(UpperCamelCase__						):
			# We could avoid this line since we set the accelerator with `device_placement=True`.
			batch.to(accelerator.device						)
			A								=				model(**UpperCamelCase__						)
			A								=				outputs.loss
			A								=				loss / gradient_accumulation_steps
			accelerator.backward(UpperCamelCase__						)
			if step % gradient_accumulation_steps == 0:
				optimizer.step()
				lr_scheduler.step()
				optimizer.zero_grad()

		model.eval()
		A								=				0
		for step, batch in enumerate(UpperCamelCase__						):
			# We could avoid this line since we set the accelerator with `device_placement=True`.
			batch.to(accelerator.device						)
			with torch.no_grad():
				A								=				model(**UpperCamelCase__						)
			A								=				outputs.logits.argmax(dim=-1						)
			A					,			A								=				accelerator.gather((predictions, batch["""labels"""])						)
			# New Code #
			# First we check if it's a distributed system
			if accelerator.use_distributed:
				# Then see if we're on the last batch of our eval dataloader
				if step == len(UpperCamelCase__						) - 1:
					# Last batch needs to be truncated on distributed systems as it contains additional samples
					A								=				predictions[: len(eval_dataloader.dataset						) - samples_seen]
					A								=				references[: len(eval_dataloader.dataset						) - samples_seen]
				else:
					# Otherwise we add the number of samples seen
					samples_seen += references.shape[0]
            # All of this can be avoided if you use `Accelerator.gather_for_metrics` instead of `Accelerator.gather`:
            # accelerator.gather_for_metrics((predictions, batch["labels"]))
			metric.add_batch(
			    predictions=UpperCamelCase__							,					references=UpperCamelCase__							,					)

		A								=				metric.compute()
		# Use accelerator.print to print only on the main process.
		accelerator.print(f'epoch {epoch}:'							,					UpperCamelCase__						)





def   _lowerCAmelCase				(   ) ->   Dict:





	"""simple docstring"""







	A								=				argparse.ArgumentParser(description="""Simple example of training script."""						)
	parser.add_argument(
	    """--mixed_precision"""							,					type=UpperCamelCase__							,					default=UpperCamelCase__							,					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."""						)
	A								=				parser.parse_args()
	A								=				{"""lr""": 2e-5, """num_epochs""": 3, """seed""": 42, """batch_size""": 16}
	training_function(UpperCamelCase__							,					UpperCamelCase__						)


if __name__ == "__main__":
				main()



