
"""simple docstring"""




from ..utils import DummyObject, requires_backends







class     lowercase		( metaclass=SCREAMING_SNAKE_CASE__		):
							lowercase_	:	Optional[Any]	 =['''note_seq''']




							def __init__(    self       ,*A__       ,**A__):
												requires_backends(self       ,['''note_seq'''])




							@classmethod
							def 			A__				(    cls       ,*A__       ,**A__):
												requires_backends(cls       ,['''note_seq'''])





							@classmethod
							def 			A__				(    cls       ,*A__       ,**A__):
												requires_backends(cls       ,['''note_seq'''])






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
# and perform 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
#
########################################################################


lowercase__		:Union[str, Any]			  =   16
lowercase__		:Optional[Any]			  =   32



def 	UpperCamelCase   (							lowerCAmelCase__			,      lowerCAmelCase__ = 16		):
					'''simple docstring'''
					lowercase						     =						AutoTokenizer.from_pretrained('''bert-base-cased'''		)
					lowercase						     =						load_dataset('''glue'''			,      '''mrpc'''		)

					def tokenize_function(lowerCAmelCase__		):
										# max_length=None => use the model max length (it's actually the default)
										lowercase						     =						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						     =						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						     =						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						     =						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(
										    lowerCAmelCase__			,      padding='''longest'''			,      max_length=lowerCAmelCase__			,      pad_to_multiple_of=lowerCAmelCase__			,      return_tensors='''pt'''			,      )

					# Instantiate dataloaders.
					lowercase						     =						DataLoader(
					    tokenized_datasets['''train''']			,      shuffle=lowerCAmelCase__			,      collate_fn=lowerCAmelCase__			,      batch_size=lowerCAmelCase__		)
					lowercase						     =						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

							lowercase__		:List[str]			  =   mocked_dataloaders  # noqa: F811



def 	UpperCamelCase   (							lowerCAmelCase__			,      lowerCAmelCase__		):
					'''simple docstring'''
					# For testing only
					if os.environ.get('''TESTING_MOCKED_DATALOADERS'''			,      lowerCAmelCase__		) == "1":
										lowercase						     =						2
					# New Code #
					lowercase						     =						int(args.gradient_accumulation_steps		)
					# Initialize accelerator
					lowercase						     =						Accelerator(
					    cpu=args.cpu			,      mixed_precision=args.mixed_precision			,      gradient_accumulation_steps=lowerCAmelCase__		)
					if accelerator.distributed_type == DistributedType.TPU and gradient_accumulation_steps > 1:
										raise NotImplementedError(
										    '''Gradient accumulation on TPUs is currently not supported. Pass `gradient_accumulation_steps=1`'''		)
					# 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(lowerCAmelCase__		)
					lowercase     ,						lowercase						     =						get_dataloaders(lowerCAmelCase__			,      lowerCAmelCase__		)
					# 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=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						     =						model.to(accelerator.device		)

					# Instantiate optimizer
					lowercase						     =						AdamW(params=model.parameters()			,      lr=lowerCAmelCase__		)

					# Instantiate scheduler
					lowercase						     =						get_linear_schedule_with_warmup(
					    optimizer=lowerCAmelCase__			,      num_warmup_steps=100			,      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						     =						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		)
															# 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(lowerCAmelCase__		):
																				lowercase						     =						model(**lowerCAmelCase__		)
																				lowercase						     =						output.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						     =						model(**lowerCAmelCase__		)
															lowercase						     =						outputs.logits.argmax(dim=-1		)
															lowercase     ,						lowercase						     =						accelerator.gather_for_metrics((predictions, batch['''labels'''])		)
															metric.add_batch(
															    predictions=lowerCAmelCase__			,      references=lowerCAmelCase__			,      )

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



def 	UpperCamelCase   (							):
					'''simple docstring'''
					lowercase						     =						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.'''			,      )
					# New Code #
					parser.add_argument(
					    '''--gradient_accumulation_steps'''			,      type=lowerCAmelCase__			,      default=1			,      help='''The number of minibatches to be ran before gradients are accumulated.'''			,      )
					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(lowerCAmelCase__			,      lowerCAmelCase__		)


if __name__ == "__main__":
							main()




