


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


__lowerCAmelCase			=							16
__lowerCAmelCase			=							32




def        snake_case_					(  snake_case      ,							snake_case = 16					)  ->    List[str]:
							lowercase__:       Union[str, Any]        =       AutoTokenizer.from_pretrained('bert-base-cased'					)
							lowercase__:       Optional[int]        =       load_dataset('glue'      ,							'mrpc'					)

							def tokenize_function(snake_case					):
														# max_length=None => use the model max length (it's actually the default)
														lowercase__:       str        =       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__:       Union[str, Any]        =       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__:       List[str]        =       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__:       Tuple        =       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__:       str        =       16
														elif accelerator.mixed_precision != "no":
																					lowercase__:       Dict        =       8
														else:
																					lowercase__:       int        =       None

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

							# Instantiate dataloaders.
							lowercase__:       Optional[int]        =       DataLoader(
							    tokenized_datasets['train']      ,							shuffle=snake_case      ,							collate_fn=snake_case      ,							batch_size=snake_case					)
							lowercase__:       Dict        =       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

	__lowerCAmelCase			=							mocked_dataloaders  # noqa: F811




def        snake_case_					(  snake_case      ,							snake_case					)  ->    List[str]:
							# For testing only
							if os.environ.get('TESTING_MOCKED_DATALOADERS'      ,							snake_case					) == "1":
														lowercase__:       Any        =       2
							# New Code #
							lowercase__:       str        =       int(args.gradient_accumulation_steps					)
							lowercase__:       str        =       int(args.local_sgd_steps					)
							# Initialize accelerator
							lowercase__:       List[Any]        =       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__:       List[str]        =       config['lr']
							lowercase__:       str        =       int(config['num_epochs']					)
							lowercase__:       Any        =       int(config['seed']					)
							lowercase__:       int        =       int(config['batch_size']					)

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

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

							# Instantiate optimizer
							lowercase__:       int        =       AdamW(params=model.parameters()      ,							lr=snake_case					)

							# Instantiate scheduler
							lowercase__:       Optional[int]        =       get_linear_schedule_with_warmup(
							    optimizer=snake_case      ,							num_warmup_steps=1_00      ,							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__:       Tuple        =       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__:       List[str]        =       model(**snake_case					)
																																			lowercase__:       Union[str, Any]        =       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__:       Union[str, Any]        =       model(**snake_case					)
																					lowercase__:       Tuple        =       outputs.logits.argmax(dim=-1					)
																					lowercase__:       Optional[Any]        =       accelerator.gather_for_metrics((predictions, batch['labels'])					)
																					metric.add_batch(
																					    predictions=snake_case      ,							references=snake_case      ,							)

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




def        snake_case_					(  )  ->    List[Any]:
							lowercase__:       Any        =       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__:       Dict        =       parser.parse_args()
							lowercase__:       Dict        =       {'lr': 2e-5, 'num_epochs': 3, 'seed': 42, 'batch_size': 16}
							training_function(snake_case      ,							snake_case					)


if __name__ == "__main__":
	main()






def        snake_case_					(  snake_case					)  ->    list[int]:
       lowercase__:       Dict        =       [0 for i in range(len(snake_case					)					)]

       # initialize interval's left pointer and right pointer
       lowercase__      ,						lowercase__:       Union[str, Any]        =       0, 0

       for i in range(1      ,							len(snake_case					)					):
              # case when current index is inside the interval
              if i <= right_pointer:
                     lowercase__:       List[Any]        =       min(right_pointer - i + 1      ,							z_result[i - left_pointer]					)
                     lowercase__:       List[str]        =       min_edge

              while go_next(snake_case      ,							snake_case      ,							snake_case					):
                     z_result[i] += 1

              # if new index's result gives us more right interval,
              # we've to update left_pointer and right_pointer
              if i + z_result[i] - 1 > right_pointer:
                     lowercase__      ,						lowercase__:       List[Any]        =       i, i + z_result[i] - 1

       return z_result




def        snake_case_					(  snake_case      ,							snake_case      ,							snake_case					)  ->    bool:
       return i + z_result[i] < len(snake_case					) and s[z_result[i]] == s[i + z_result[i]]




def        snake_case_					(  snake_case      ,							snake_case					)  ->    int:
       lowercase__:       Tuple        =       0
       # concatenate 'pattern' and 'input_str' and call z_function
       # with concatenated string
       lowercase__:       Any        =       z_function(pattern + input_str					)

       for val in z_result:
              # if value is greater then length of the pattern string
              # that means this index is starting position of substring
              # which is equal to pattern string
              if val >= len(snake_case					):
                     answer += 1

       return answer


if __name__ == "__main__":
 import doctest

 doctest.testmod()


