





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
from accelerate.local_sgd import LocalSGD


########################################################################
# This is a fully working simple example to use Accelerate
# with LocalSGD, which is a method to synchronize model
# parameters every K batches. It is different, but complementary
# to gradient accumulation.
#
# 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 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_   :      Any			=   16
SCREAMING_SNAKE_CASE_   :      Dict			=   32



def  SCREAMING_SNAKE_CASE				(      snake_case						,   snake_case = 16							)		->    Tuple:
		__lowercase											=     AutoTokenizer.from_pretrained('bert-base-cased'							)
		__lowercase											=     load_dataset('glue'						,   'mrpc'							)

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

		def collate_fn(snake_case							):
				# On TPU it's best to pad everything to the same length or training will be very slow.
				__lowercase											=     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":
						__lowercase											=     16
				elif accelerator.mixed_precision != "no":
						__lowercase											=     8
				else:
						__lowercase											=     None

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

		# Instantiate dataloaders.
		__lowercase											=     DataLoader(
		    tokenized_datasets['train']						,   shuffle=snake_case						,   collate_fn=snake_case						,   batch_size=snake_case							)
		__lowercase											=     DataLoader(
		    tokenized_datasets['validation']						,   shuffle=snake_case						,   collate_fn=snake_case						,   batch_size=snake_case							)

		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

		SCREAMING_SNAKE_CASE_   :      int			=   mocked_dataloaders  # noqa: F811



def  SCREAMING_SNAKE_CASE				(      snake_case						,   snake_case							)		->    List[Any]:
		# For testing only
		if os.environ.get('TESTING_MOCKED_DATALOADERS'						,   snake_case							) == "1":
				__lowercase											=     2
		# New Code #
		__lowercase											=     int(args.gradient_accumulation_steps							)
		__lowercase											=     int(args.local_sgd_steps							)
		# Initialize accelerator
		__lowercase											=     Accelerator(
		    cpu=args.cpu						,   mixed_precision=args.mixed_precision						,   gradient_accumulation_steps=snake_case							)
		if accelerator.distributed_type not in [DistributedType.NO, DistributedType.MULTI_CPU, DistributedType.MULTI_GPU]:
				raise NotImplementedError('LocalSGD is supported only for CPUs and GPUs (no DeepSpeed or MegatronLM)'							)
		# Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
		__lowercase											=     config['lr']
		__lowercase											=     int(config['num_epochs']							)
		__lowercase											=     int(config['seed']							)
		__lowercase											=     int(config['batch_size']							)

		__lowercase											=     evaluate.load('glue'						,   'mrpc'							)

		set_seed(snake_case							)
		__lowercase						,      __lowercase											=     get_dataloaders(snake_case						,   snake_case							)
		# Instantiate the model (we build the model here so that the seed also control new weights initialization)
		__lowercase											=     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).
		__lowercase											=     model.to(accelerator.device							)

		# Instantiate optimizer
		__lowercase											=     AdamW(params=model.parameters()						,   lr=snake_case							)

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

		# Now we train the model
		for epoch in range(snake_case							):
				model.train()
				with LocalSGD(
				    accelerator=snake_case						,   model=snake_case						,   local_sgd_steps=snake_case						,   enabled=local_sgd_steps is not None							) as local_sgd:
						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							)
								# New code #
								# We use the new `accumulate` context manager to perform gradient accumulation
								# We also currently do not support TPUs nor advise it as bugs were found on the XLA side when running our tests.
								with accelerator.accumulate(snake_case							):
										__lowercase											=     model(**snake_case							)
										__lowercase											=     output.loss
										accelerator.backward(snake_case							)
										optimizer.step()
										lr_scheduler.step()
										optimizer.zero_grad()
										# LocalSGD-specific line
										local_sgd.step()

				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():
								__lowercase											=     model(**snake_case							)
						__lowercase											=     outputs.logits.argmax(dim=-1							)
						__lowercase						,      __lowercase											=     accelerator.gather_for_metrics((predictions, batch['labels'])							)
						metric.add_batch(
						    predictions=snake_case						,   references=snake_case						,   )

				__lowercase											=     metric.compute()
				# Use accelerator.print to print only on the main process.
				accelerator.print(F"epoch {epoch}:"						,   snake_case							)



def  SCREAMING_SNAKE_CASE				(      )		->    List[Any]:
		__lowercase											=     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.'						,   )
		# New Code #
		parser.add_argument(
		    '--gradient_accumulation_steps'						,   type=snake_case						,   default=1						,   help='The number of minibatches to be ran before gradients are accumulated.'						,   )
		parser.add_argument(
		    '--local_sgd_steps'						,   type=snake_case						,   default=8						,   help='Number of local SGD steps or None to disable local SGD'							)
		parser.add_argument('--cpu'						,   action='store_true'						,   help='If passed, will train on the CPU.'							)
		__lowercase											=     parser.parse_args()
		__lowercase											=     {'lr': 2E-5, 'num_epochs': 3, 'seed': 42, 'batch_size': 16}
		training_function(snake_case						,   snake_case							)


if __name__ == "__main__":
		main()











def  SCREAMING_SNAKE_CASE				(      snake_case						,   snake_case = False							)		->    str:

		if not isinstance(snake_case						,   snake_case							):
				__lowercase											=     F"Expected string as input, found {type(snake_case							)}"
				raise ValueError(snake_case							)
		if not isinstance(snake_case						,   snake_case							):
				__lowercase											=     F"Expected boolean as use_pascal parameter, found {type(snake_case							)}"
				raise ValueError(snake_case							)

		__lowercase											=     input_str.split('_'							)

		__lowercase											=     0 if use_pascal else 1

		__lowercase											=     words[start_index:]

		__lowercase											=     [word[0].upper() + word[1:] for word in words_to_capitalize]

		__lowercase											=     '' if use_pascal else words[0]

		return "".join([initial_word, *capitalized_words]							)


if __name__ == "__main__":
		from doctest import testmod

		testmod()




