

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					:  Tuple             =      16
_SCREAMING_SNAKE_CASE					:  List[Any]             =      32
def     UpperCAmelCase_       (						_A				,		_A = 16    ):





					'''simple docstring'''




					SCREAMING_SNAKE_CASE__   =   AutoTokenizer.from_pretrained('''bert-base-cased'''    )
					SCREAMING_SNAKE_CASE__   =   load_dataset('''glue'''				,		'''mrpc'''    )

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

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

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

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

					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					:  Any             =      mocked_dataloaders  # noqa: F811
def     UpperCAmelCase_       (						_A				,		_A    ):





					'''simple docstring'''




					if os.environ.get('''TESTING_MOCKED_DATALOADERS'''				,		_A    ) == "1":
										SCREAMING_SNAKE_CASE__   =   2
					# New Code #
					SCREAMING_SNAKE_CASE__   =   int(args.gradient_accumulation_steps    )
					SCREAMING_SNAKE_CASE__   =   int(args.local_sgd_steps    )
					# Initialize accelerator
					SCREAMING_SNAKE_CASE__   =   Accelerator(
					    cpu=args.cpu				,		mixed_precision=args.mixed_precision				,		gradient_accumulation_steps=_A    )
					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
					SCREAMING_SNAKE_CASE__   =   config['''lr''']
					SCREAMING_SNAKE_CASE__   =   int(config['''num_epochs''']    )
					SCREAMING_SNAKE_CASE__   =   int(config['''seed''']    )
					SCREAMING_SNAKE_CASE__   =   int(config['''batch_size''']    )

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

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

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

					# Instantiate optimizer
					SCREAMING_SNAKE_CASE__   =   AdamW(params=model.parameters()				,		lr=_A    )

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

					# Now we train the model
					for epoch in range(_A    ):
										model.train()
										with LocalSGD(
										    accelerator=_A				,		model=_A				,		local_sgd_steps=_A				,		enabled=local_sgd_steps is not None    ) as local_sgd:
															for step, batch in enumerate(_A    ):
																				# 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(_A    ):
																									SCREAMING_SNAKE_CASE__   =   model(**_A    )
																									SCREAMING_SNAKE_CASE__   =   output.loss
																									accelerator.backward(_A    )
																									optimizer.step()
																									lr_scheduler.step()
																									optimizer.zero_grad()
																									# LocalSGD-specific line
																									local_sgd.step()

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

										SCREAMING_SNAKE_CASE__   =   metric.compute()
										# Use accelerator.print to print only on the main process.
										accelerator.print(F'''epoch {epoch}:'''				,		_A    )
def     UpperCAmelCase_       (						):





					'''simple docstring'''




					SCREAMING_SNAKE_CASE__   =   argparse.ArgumentParser(description='''Simple example of training script.'''    )
					parser.add_argument(
					    '''--mixed_precision'''				,		type=_A				,		default=_A				,		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=_A				,		default=1				,		help='''The number of minibatches to be ran before gradients are accumulated.'''				,		)
					parser.add_argument(
					    '''--local_sgd_steps'''				,		type=_A				,		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.'''    )
					SCREAMING_SNAKE_CASE__   =   parser.parse_args()
					SCREAMING_SNAKE_CASE__   =   {'''lr''': 2e-5, '''num_epochs''': 3, '''seed''': 42, '''batch_size''': 16}
					training_function(_A				,		_A    )


if __name__ == "__main__":
							main()





def     UpperCAmelCase_       (						_A    ):





     '''simple docstring'''




     if not isinstance(_A				,		_A    ):
          raise ValueError('''multiplicative_persistence() only accepts integral values'''    )
     if num < 0:
          raise ValueError('''multiplicative_persistence() does not accept negative values'''    )

     SCREAMING_SNAKE_CASE__   =   0
     SCREAMING_SNAKE_CASE__   =   str(_A    )

     while len(_A    ) != 1:
          SCREAMING_SNAKE_CASE__   =   [int(_A    ) for i in num_string]

          SCREAMING_SNAKE_CASE__   =   1
          for i in range(0				,		len(_A    )    ):
               total *= numbers[i]

          SCREAMING_SNAKE_CASE__   =   str(_A    )

          steps += 1
     return steps
def     UpperCAmelCase_       (						_A    ):





     '''simple docstring'''




     if not isinstance(_A				,		_A    ):
          raise ValueError('''additive_persistence() only accepts integral values'''    )
     if num < 0:
          raise ValueError('''additive_persistence() does not accept negative values'''    )

     SCREAMING_SNAKE_CASE__   =   0
     SCREAMING_SNAKE_CASE__   =   str(_A    )

     while len(_A    ) != 1:
          SCREAMING_SNAKE_CASE__   =   [int(_A    ) for i in num_string]

          SCREAMING_SNAKE_CASE__   =   0
          for i in range(0				,		len(_A    )    ):
               total += numbers[i]

          SCREAMING_SNAKE_CASE__   =   str(_A    )

          steps += 1
     return steps


if __name__ == "__main__":
       import doctest

       doctest.testmod()


