





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, DistributedType
from accelerate.utils import find_executable_batch_size


########################################################################
# This is a fully working simple example to use Accelerate,
# specifically showcasing how to ensure out-of-memory errors never
# interrupt training, 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)
#
# 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
#
########################################################################


__lowercase       :Tuple       =  16
__lowercase       :Dict       =  32







def       UpperCAmelCase							(     _lowerCamelCase      :  Accelerator			,   _lowerCamelCase      :  int = 16     ):





						'''simple docstring'''


						SCREAMING_SNAKE_CASE__     :				List[Any]   =					AutoTokenizer.from_pretrained("bert-base-cased"     )
						SCREAMING_SNAKE_CASE__     :				Tuple   =					load_dataset("glue"			,   "mrpc"     )

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

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

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

						# Instantiate dataloaders.
						SCREAMING_SNAKE_CASE__     :				List[str]   =					DataLoader(
						    tokenized_datasets["train"]			,   shuffle=_lowerCamelCase			,   collate_fn=_lowerCamelCase			,   batch_size=_lowerCamelCase     )
						SCREAMING_SNAKE_CASE__     :				List[Any]   =					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[Any]       =  mocked_dataloaders  # noqa: F811







def       UpperCAmelCase							(     _lowerCamelCase      :  str			,   _lowerCamelCase      :  Any     ):





						'''simple docstring'''


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

						SCREAMING_SNAKE_CASE__     :				Any   =					evaluate.load("glue"			,   "mrpc"     )

						# New Code #
						# We now can define an inner training loop function. It should take a batch size as the only parameter,
						# and build the dataloaders in there.
						# It also gets our decorator
						@find_executable_batch_size(starting_batch_size=_lowerCamelCase     )
						def inner_training_loop(_lowerCamelCase      :  Any     ):
												# And now just move everything below under this function
												# We need to bring in the Accelerator object from earlier
												nonlocal accelerator
												# And reset all of its attributes that could hold onto any memory:
												accelerator.free_memory()

												# Then we can declare the model, optimizer, and everything else:
												set_seed(_lowerCamelCase     )

												# Instantiate the model (we build the model here so that the seed also control new weights initialization)
												SCREAMING_SNAKE_CASE__     :				Optional[int]   =					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).
												SCREAMING_SNAKE_CASE__     :				Optional[int]   =					model.to(accelerator.device     )

												# Instantiate optimizer
												SCREAMING_SNAKE_CASE__     :				Optional[Any]   =					AdamW(params=model.parameters()			,   lr=_lowerCamelCase     )
												SCREAMING_SNAKE_CASE__,							SCREAMING_SNAKE_CASE__     :				Union[str, Any]   =					get_dataloaders(_lowerCamelCase			,   _lowerCamelCase     )

												# Instantiate scheduler
												SCREAMING_SNAKE_CASE__     :				Union[str, Any]   =					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.
												SCREAMING_SNAKE_CASE__,							SCREAMING_SNAKE_CASE__,							SCREAMING_SNAKE_CASE__,							SCREAMING_SNAKE_CASE__,							SCREAMING_SNAKE_CASE__     :				Optional[int]   =					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     )
																								SCREAMING_SNAKE_CASE__     :				Union[str, Any]   =					model(**_lowerCamelCase     )
																								SCREAMING_SNAKE_CASE__     :				Dict   =					outputs.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():
																														SCREAMING_SNAKE_CASE__     :				Optional[Any]   =					model(**_lowerCamelCase     )
																								SCREAMING_SNAKE_CASE__     :				Any   =					outputs.logits.argmax(dim=-1     )
																								SCREAMING_SNAKE_CASE__,							SCREAMING_SNAKE_CASE__     :				List[Any]   =					accelerator.gather_for_metrics((predictions, batch["labels"])     )
																								metric.add_batch(
																								    predictions=_lowerCamelCase			,   references=_lowerCamelCase			,   )

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

    # 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							(     ):





						'''simple docstring'''


						SCREAMING_SNAKE_CASE__     :				List[str]   =					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."			,   )
						parser.add_argument("--cpu"			,   action="store_true"			,   help="If passed, will train on the CPU."     )
						SCREAMING_SNAKE_CASE__     :				str   =					parser.parse_args()
						SCREAMING_SNAKE_CASE__     :				Optional[int]   =					{"lr": 2E-5, "num_epochs": 3, "seed": 42, "batch_size": 16}
						training_function(_lowerCamelCase			,   _lowerCamelCase     )


if __name__ == "__main__":
				main()






import sys

from .dependency_versions_table import deps
from .utils.versions import require_version, require_version_core


# define which module versions we always want to check at run time
# (usually the ones defined in `install_requires` in setup.py)
#
# order specific notes:
# - tqdm must be checked before tokenizers

__lowercase       :List[Any]       =  "python tqdm regex requests packaging filelock numpy tokenizers".split()
if sys.version_info < (3, 7):
				pkgs_to_check_at_runtime.append("dataclasses")
if sys.version_info < (3, 8):
				pkgs_to_check_at_runtime.append("importlib_metadata")

for pkg in pkgs_to_check_at_runtime:
				if pkg in deps:
								if pkg == "tokenizers":
												# must be loaded here, or else tqdm check may fail
												from .utils import is_tokenizers_available

												if not is_tokenizers_available():
																continue  # not required, check version only if installed

								require_version_core(deps[pkg])
				else:
								raise ValueError(f"can't find {pkg} in {deps.keys()}, check dependency_versions_table.py")







def       UpperCAmelCase							(     _lowerCamelCase      :  Optional[Any]			,   _lowerCamelCase      :  Optional[Any]=None     ):





						'''simple docstring'''


						require_version(deps[pkg]			,   _lowerCamelCase     )