
import argparse
import os

# New Code #
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
from accelerate.utils import find_executable_batch_size


########################################################################
# This is a fully working simple example to use Accelerate,
# specifically showcasing how to combine both the gradient accumulation
# and automatic batch size finder utilities of Accelerate to perfrom
# automatic 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)
#
# 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
#
########################################################################

UpperCamelCase_	:    Optional[Any]  					=       32



def 		UpperCamelCase    (					_UpperCAmelCase     :      Accelerator   , _UpperCAmelCase     :      int = 16     )      -> Union[str, Any]:






						'''simple docstring'''

						_lowercase					:  Optional[int]    			=      AutoTokenizer.from_pretrained("bert-base-cased"     )
						_lowercase					:  int    			=      load_dataset("glue"   , "mrpc"     )

						def tokenize_function(_UpperCAmelCase     :      str     ):
												# max_length=None => use the model max length (it's actually the default)
												_lowercase					:  Optional[int]    			=      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():
												_lowercase					:  Union[str, Any]    			=      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
						_lowercase					:  Dict    			=      tokenized_datasets.rename_column("label"   , "labels"     )

						def collate_fn(_UpperCAmelCase     :      Optional[Any]     ):
												# When using mixed precision we want round multiples of 8/16
												if accelerator.mixed_precision == "fp8":
																		_lowercase					:  Any    			=      16
												elif accelerator.mixed_precision != "no":
																		_lowercase					:  Union[str, Any]    			=      8
												else:
																		_lowercase					:  Optional[int]    			=      None

												return tokenizer.pad(
												    _UpperCAmelCase   , padding="longest"   , pad_to_multiple_of=_UpperCAmelCase   , return_tensors="pt"   , )

						# Instantiate dataloaders.
						_lowercase					:  Dict    			=      DataLoader(
						    tokenized_datasets["train"]   , shuffle=_UpperCAmelCase   , collate_fn=_UpperCAmelCase   , batch_size=_UpperCAmelCase     )
						_lowercase					:  Tuple    			=      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

							UpperCamelCase_	:    Union[str, Any]  					=       mocked_dataloaders  # noqa: F811



def 		UpperCamelCase    (					_UpperCAmelCase     :      Tuple   , _UpperCAmelCase     :      Union[str, Any]     )      -> Dict:






						'''simple docstring'''

						if os.environ.get("TESTING_MOCKED_DATALOADERS"   , _UpperCAmelCase     ) == "1":
												_lowercase					:  Optional[Any]    			=      2
						# Initialize accelerator
						_lowercase					:  Any    			=      Accelerator(cpu=args.cpu   , mixed_precision=args.mixed_precision     )
						# Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
						_lowercase					:  Any    			=      config["lr"]
						_lowercase					:  Dict    			=      int(config["num_epochs"]     )
						_lowercase					:  List[str]    			=      int(config["seed"]     )
						_lowercase					:  List[Any]    			=      int(config["batch_size"]     )

						_lowercase					:  int    			=      evaluate.load("glue"   , "mrpc"     )

						# New Code #
						# We use the `find_executable_batch_size` decorator, passing in the desired observed batch size
						# to train on. If a CUDA OOM error occurs, it will retry this loop cutting the batch size in
						# half each time. From this, we can calculate the number of gradient accumulation steps needed
						# and modify the Accelerator object as a result
						@find_executable_batch_size(starting_batch_size=int(_UpperCAmelCase     )     )
						def inner_training_loop(_UpperCAmelCase     :      Any     ):
												# Since we need to modify the outside accelerator object, we need to bring it
												# to the local scope
												nonlocal accelerator

												# We can calculate the number of gradient accumulation steps based on the current
												# batch size vs the starting batch size
												_lowercase					:  Dict    			=      observed_batch_size // batch_size

												# And then set it in the Accelerator directly:
												_lowercase					:  Union[str, Any]    			=      num_gradient_accumulation_steps

												# Next we need to free all of the stored model references in the Accelerator each time
												accelerator.free_memory()

												# And set the seed so our results are reproducable each reset
												set_seed(_UpperCAmelCase     )

												# Instantiate the model (we build the model here so that the seed also control new weights initialization)
												_lowercase					:  Dict    			=      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).
												_lowercase					:  Dict    			=      model.to(accelerator.device     )

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

												# Instantiate scheduler
												_lowercase					:  Optional[int]    			=      get_linear_schedule_with_warmup(
												    optimizer=_UpperCAmelCase   , num_warmup_steps=100   , num_training_steps=(len(_UpperCAmelCase     ) * 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					:  Dict    			=      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     ):
																								# And perform gradient accumulation
																								with accelerator.accumulate(_UpperCAmelCase     ):
																														# We could avoid this line since we set the accelerator with `device_placement=True`.
																														batch.to(accelerator.device     )
																														_lowercase					:  List[Any]    			=      model(**_UpperCAmelCase     )
																														_lowercase					:  Optional[int]    			=      outputs.loss
																														accelerator.backward(_UpperCAmelCase     )
																														optimizer.step()
																														lr_scheduler.step()
																														optimizer.zero_grad()

																		model.eval()
																		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():
																														_lowercase					:  str    			=      model(**_UpperCAmelCase     )
																								_lowercase					:  Optional[int]    			=      outputs.logits.argmax(dim=-1     )
																								_lowercase			,						_lowercase					:  Optional[int]    			=      accelerator.gather_for_metrics((predictions, batch["labels"])     )
																								metric.add_batch(
																								    predictions=_UpperCAmelCase   , references=_UpperCAmelCase   , )

																		_lowercase					:  List[str]    			=      metric.compute()
																		# Use accelerator.print to print only on the main process.
																		accelerator.print(f"""epoch {epoch}:"""   , _UpperCAmelCase     )

    # New Code #
    # And call it at the end with no arguments
    # Note: You could also refactor this outside of your training loop function
						inner_training_loop()



def 		UpperCamelCase    (					)      -> Any:






						'''simple docstring'''

						_lowercase					:  Union[str, Any]    			=      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."     )
						_lowercase					:  Union[str, Any]    			=      parser.parse_args()
						# New Code #
						# We modify the starting batch size to be an observed batch size of 256, to guarentee an initial CUDA OOM
						_lowercase					:  Union[str, Any]    			=      {"lr": 2e-5, "num_epochs": 3, "seed": 42, "batch_size": 256}
						training_function(_UpperCAmelCase   , _UpperCAmelCase     )


if __name__ == "__main__":
							main()






def 		UpperCamelCase    (					_UpperCAmelCase     :      int     )      -> bool:






						'''simple docstring'''

						if not isinstance(_UpperCAmelCase   , _UpperCAmelCase     ):
												_lowercase					:  Optional[int]    			=      f"""Input value of [number={number}] must be an integer"""
												raise TypeError(_UpperCAmelCase     )
						if number < 0 or number % 2 == 1:
												return False
						_lowercase					:  List[str]    			=      int(number**0.5     )
						return number == number_sqrt * (number_sqrt + 1)


if __name__ == "__main__":
							import doctest

							doctest.testmod()




