

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,
# specifically showcasing how to properly calculate the metrics on the
# validation dataset when in a distributed system, 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)
#
# To help focus on the differences in the code, building `DataLoaders`
# was refactored into its own function.
# 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    :       Union[str, Any]        =							1_6
_lowerCamelCase    :       int        =							3_2

def       a__					(		UpperCAmelCase							:      Accelerator    ,			UpperCAmelCase							:      int = 16      )  ->						str:
		UpperCAmelCase	:							int						=			AutoTokenizer.from_pretrained('''bert-base-cased'''      )
		UpperCAmelCase	:							List[str]						=			load_dataset('''glue'''    ,			'''mrpc'''      )

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

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

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

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

		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    :       Any        =							mocked_dataloaders  # noqa: F811

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

		UpperCAmelCase	:							int						=			evaluate.load('''glue'''    ,			'''mrpc'''      )

		# If the batch size is too big we use gradient accumulation
		UpperCAmelCase	:							List[str]						=			1
		if batch_size > MAX_GPU_BATCH_SIZE and accelerator.distributed_type != DistributedType.TPU:
				UpperCAmelCase	:							Any						=			batch_size // MAX_GPU_BATCH_SIZE
				UpperCAmelCase	:							Dict						=			MAX_GPU_BATCH_SIZE

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

		# 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).
		UpperCAmelCase	:							Dict						=			model.to(accelerator.device      )

		# Instantiate optimizer
		UpperCAmelCase	:							List[str]						=			AdamW(params=model.parameters()    ,			lr=UpperCAmelCase      )

		# Instantiate scheduler
		UpperCAmelCase	:							int						=			get_linear_schedule_with_warmup(
		    optimizer=UpperCAmelCase    ,			num_warmup_steps=100    ,			num_training_steps=(len(UpperCAmelCase      ) * num_epochs) // gradient_accumulation_steps    ,			)

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

		# Now we train the model
		for epoch in range(UpperCAmelCase      ):
				model.train()
				for step, batch in enumerate(UpperCAmelCase      ):
						# We could avoid this line since we set the accelerator with `device_placement=True`.
						batch.to(accelerator.device      )
						UpperCAmelCase	:							int						=			model(**UpperCAmelCase      )
						UpperCAmelCase	:							List[Any]						=			outputs.loss
						UpperCAmelCase	:							Any						=			loss / gradient_accumulation_steps
						accelerator.backward(UpperCAmelCase      )
						if step % gradient_accumulation_steps == 0:
								optimizer.step()
								lr_scheduler.step()
								optimizer.zero_grad()

				model.eval()
				UpperCAmelCase	:							List[str]						=			0
				for step, batch in enumerate(UpperCAmelCase      ):
						# We could avoid this line since we set the accelerator with `device_placement=True`.
						batch.to(accelerator.device      )
						with torch.no_grad():
								UpperCAmelCase	:							List[str]						=			model(**UpperCAmelCase      )
						UpperCAmelCase	:							Union[str, Any]						=			outputs.logits.argmax(dim=-1      )
						UpperCAmelCase	:							Dict						=			accelerator.gather((predictions, batch['''labels'''])      )
						# New Code #
						# First we check if it's a distributed system
						if accelerator.use_distributed:
								# Then see if we're on the last batch of our eval dataloader
								if step == len(UpperCAmelCase      ) - 1:
										# Last batch needs to be truncated on distributed systems as it contains additional samples
										UpperCAmelCase	:							str						=			predictions[: len(eval_dataloader.dataset      ) - samples_seen]
										UpperCAmelCase	:							Union[str, Any]						=			references[: len(eval_dataloader.dataset      ) - samples_seen]
								else:
										# Otherwise we add the number of samples seen
										samples_seen += references.shape[0]
										  # All of this can be avoided if you use `Accelerator.gather_for_metrics` instead of `Accelerator.gather`:
										  # accelerator.gather_for_metrics((predictions, batch["labels"]))
						metric.add_batch(
						    predictions=UpperCAmelCase    ,			references=UpperCAmelCase    ,			)

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

def       a__					(		)  ->						List[Any]:
		UpperCAmelCase	:							str						=			argparse.ArgumentParser(description='''Simple example of training script.'''      )
		parser.add_argument(
		    '''--mixed_precision'''    ,			type=UpperCAmelCase    ,			default=UpperCAmelCase    ,			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.'''      )
		UpperCAmelCase	:							str						=			parser.parse_args()
		UpperCAmelCase	:							Optional[int]						=			{'''lr''': 2E-5, '''num_epochs''': 3, '''seed''': 42, '''batch_size''': 16}
		training_function(UpperCAmelCase    ,			UpperCAmelCase      )


if __name__ == "__main__":
					main()








def       a__					(		UpperCAmelCase							:      int      )  ->						bool:
  if not isinstance(UpperCAmelCase    ,			UpperCAmelCase      ):
    UpperCAmelCase	:							List[str]						=			f'''Input value of [number={number}] must be an integer'''
    raise TypeError(UpperCAmelCase      )
  if number < 0:
    return False
  UpperCAmelCase	:							List[str]						=			number * number
  while number > 0:
    if number % 10 != number_square % 10:
      return False
    number //= 10
    number_square //= 10
  return True


if __name__ == "__main__":
     import doctest

     doctest.testmod()





