


import os
from typing import Dict, List, Tuple, TypeVar, Union


_A		       =						TypeVar('T')

_A		       =						Union[List[T], Tuple[T, ...]]
_A		       =						Union[T, List[T], Dict[str, T]]
_A		       =						Union[str, bytes, os.PathLike]










"""simple docstring"""







import argparse
import os

# New Code #
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
from accelerate.utils import find_executable_batch_size


########################################################################
# This is a fully working simple example to use Accelerate,
# specifically showcasing how to ensure out-of-memory errors never
# interrupt training, 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)
#
# 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
#
########################################################################


lowerCAmelCase_   						=   16
lowerCAmelCase_   						=   32



def      __UpperCAmelCase      (    __lowerCamelCase						,     __lowerCamelCase = 16	)							->      Optional[Any]:
		lowercase__   :    Optional[Any]				=			AutoTokenizer.from_pretrained('''bert-base-cased'''	)
		lowercase__   :    int				=			load_dataset('''glue'''						,     '''mrpc'''	)

		def tokenize_function(__lowerCamelCase	):
				# max_length=None => use the model max length (it's actually the default)
				lowercase__   :    str				=			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():
				lowercase__   :    str				=			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
		lowercase__   :    Union[str, Any]				=			tokenized_datasets.rename_column('''label'''						,     '''labels'''	)

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

				return tokenizer.pad(
				    __lowerCamelCase						,     padding='''longest'''						,     max_length=__lowerCamelCase						,     pad_to_multiple_of=__lowerCamelCase						,     return_tensors='''pt'''						,     )

		# Instantiate dataloaders.
		lowercase__   :    List[Any]				=			DataLoader(
		    tokenized_datasets['''train''']						,     shuffle=__lowerCamelCase						,     collate_fn=__lowerCamelCase						,     batch_size=__lowerCamelCase	)
		lowercase__   :    str				=			DataLoader(
		    tokenized_datasets['''validation''']						,     shuffle=__lowerCamelCase						,     collate_fn=__lowerCamelCase						,     batch_size=__lowerCamelCase	)

		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

							lowerCAmelCase_   						=   mocked_dataloaders  # noqa: F811



def      __UpperCAmelCase      (    __lowerCamelCase						,     __lowerCamelCase	)							->      str:
		# For testing only
		if os.environ.get('''TESTING_MOCKED_DATALOADERS'''						,     __lowerCamelCase	) == "1":
				lowercase__   :    List[Any]				=			2
		# Initialize accelerator
		lowercase__   :    Optional[int]				=			Accelerator(cpu=args.cpu						,     mixed_precision=args.mixed_precision	)
		# Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
		lowercase__   :    str				=			config['''lr''']
		lowercase__   :    str				=			int(config['''num_epochs''']	)
		lowercase__   :    Optional[int]				=			int(config['''seed''']	)
		lowercase__   :    Tuple				=			int(config['''batch_size''']	)

		lowercase__   :    List[Any]				=			evaluate.load('''glue'''						,     '''mrpc'''	)

		# New Code #
		# We now can define an inner training loop function. It should take a batch size as the only parameter,
		# and build the dataloaders in there.
		# It also gets our decorator
		@find_executable_batch_size(starting_batch_size=__lowerCamelCase	)
		def inner_training_loop(__lowerCamelCase	):
				# And now just move everything below under this function
				# We need to bring in the Accelerator object from earlier
				nonlocal accelerator
				# And reset all of its attributes that could hold onto any memory:
				accelerator.free_memory()

				# Then we can declare the model, optimizer, and everything else:
				set_seed(__lowerCamelCase	)

				# Instantiate the model (we build the model here so that the seed also control new weights initialization)
				lowercase__   :    List[str]				=			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).
				lowercase__   :    Tuple				=			model.to(accelerator.device	)

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

				# Instantiate scheduler
				lowercase__   :    Optional[int]				=			get_linear_schedule_with_warmup(
				    optimizer=__lowerCamelCase						,     num_warmup_steps=1_00						,     num_training_steps=(len(__lowerCamelCase	) * num_epochs)						,     )

				# 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__   :    Optional[int]				=			accelerator.prepare(
				    __lowerCamelCase						,     __lowerCamelCase						,     __lowerCamelCase						,     __lowerCamelCase						,     __lowerCamelCase	)

				# Now we train the model
				for epoch in range(__lowerCamelCase	):
						model.train()
						for step, batch in enumerate(__lowerCamelCase	):
								# We could avoid this line since we set the accelerator with `device_placement=True`.
								batch.to(accelerator.device	)
								lowercase__   :    Dict				=			model(**__lowerCamelCase	)
								lowercase__   :    List[Any]				=			outputs.loss
								accelerator.backward(__lowerCamelCase	)
								optimizer.step()
								lr_scheduler.step()
								optimizer.zero_grad()

						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():
										lowercase__   :    Tuple				=			model(**__lowerCamelCase	)
								lowercase__   :    Any				=			outputs.logits.argmax(dim=-1	)
								lowercase__		,			lowercase__   :    int				=			accelerator.gather_for_metrics((predictions, batch['''labels'''])	)
								metric.add_batch(
								    predictions=__lowerCamelCase						,     references=__lowerCamelCase						,     )

						lowercase__   :    List[Any]				=			metric.compute()
						# Use accelerator.print to print only on the main process.
						accelerator.print(f"""epoch {epoch}:"""						,     __lowerCamelCase	)

    # New Code #
    # And call it at the end with no arguments
    # Note: You could also refactor this outside of your training loop function
		inner_training_loop()



def      __UpperCAmelCase      (    )							->      Dict:
		lowercase__   :    Optional[int]				=			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.'''	)
		lowercase__   :    int				=			parser.parse_args()
		lowercase__   :    Union[str, Any]				=			{'''lr''': 2E-5, '''num_epochs''': 3, '''seed''': 42, '''batch_size''': 16}
		training_function(__lowerCamelCase						,     __lowerCamelCase	)


if __name__ == "__main__":
							main()
