

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
#
########################################################################


lowerCAmelCase__	   = 1_6
lowerCAmelCase__	   = 3_2



def 					lowerCAmelCase__							(	SCREAMING_SNAKE_CASE_:							Accelerator	,       SCREAMING_SNAKE_CASE_:							int = 1_6      )			->      List[Any]:




				'''simple docstring'''





				A__        =			AutoTokenizer.from_pretrained("bert-base-cased"      )
				A__        =			load_dataset("glue"	,       "mrpc"      )

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

				def collate_fn(SCREAMING_SNAKE_CASE_:							Optional[int]      ):
								# On TPU it's best to pad everything to the same length or training will be very slow.
								A__        =			1_2_8 if accelerator.distributed_type == DistributedType.TPU else None
								# When using mixed precision we want round multiples of 8/16
								if accelerator.mixed_precision == "fp8":
												A__        =			1_6
								elif accelerator.mixed_precision != "no":
												A__        =			8
								else:
												A__        =			None

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

				# Instantiate dataloaders.
				A__        =			DataLoader(
				    tokenized_datasets["train"]	,       shuffle=SCREAMING_SNAKE_CASE_	,       collate_fn=SCREAMING_SNAKE_CASE_	,       batch_size=SCREAMING_SNAKE_CASE_      )
				A__        =			DataLoader(
				    tokenized_datasets["validation"]	,       shuffle=SCREAMING_SNAKE_CASE_	,       collate_fn=SCREAMING_SNAKE_CASE_	,       batch_size=SCREAMING_SNAKE_CASE_      )

				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__	   = mocked_dataloaders  # noqa: F811



def 					lowerCAmelCase__							(	SCREAMING_SNAKE_CASE_:							Tuple	,       SCREAMING_SNAKE_CASE_:							Optional[int]      )			->      Optional[Any]:




				'''simple docstring'''





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

				A__        =			evaluate.load("glue"	,       "mrpc"      )

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

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

				# Instantiate optimizer
				A__        =			AdamW(params=model.parameters()	,       lr=SCREAMING_SNAKE_CASE_      )

				# Instantiate scheduler
				A__        =			get_linear_schedule_with_warmup(
				    optimizer=SCREAMING_SNAKE_CASE_	,       num_warmup_steps=1_0_0	,       num_training_steps=(len(SCREAMING_SNAKE_CASE_      ) * 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.
				A__    ,				A__    ,				A__    ,				A__    ,				A__        =			accelerator.prepare(
				    SCREAMING_SNAKE_CASE_	,       SCREAMING_SNAKE_CASE_	,       SCREAMING_SNAKE_CASE_	,       SCREAMING_SNAKE_CASE_	,       SCREAMING_SNAKE_CASE_      )

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

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

								A__        =			metric.compute()
								# Use accelerator.print to print only on the main process.
								accelerator.print(F'epoch {epoch}:'	,       SCREAMING_SNAKE_CASE_      )



def 					lowerCAmelCase__							(	)			->      List[Any]:




				'''simple docstring'''





				A__        =			argparse.ArgumentParser(description="Simple example of training script."      )
				parser.add_argument(
				    "--mixed_precision"	,       type=SCREAMING_SNAKE_CASE_	,       default=SCREAMING_SNAKE_CASE_	,       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=SCREAMING_SNAKE_CASE_	,       default=1	,       help="The number of minibatches to be ran before gradients are accumulated."	,       )
				parser.add_argument(
				    "--local_sgd_steps"	,       type=SCREAMING_SNAKE_CASE_	,       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."      )
				A__        =			parser.parse_args()
				A__        =			{"lr": 2e-5, "num_epochs": 3, "seed": 4_2, "batch_size": 1_6}
				training_function(SCREAMING_SNAKE_CASE_	,       SCREAMING_SNAKE_CASE_      )


if __name__ == "__main__":
	main()




from typing import Union

import fire
import torch
from tqdm import tqdm



def 					lowerCAmelCase__							(	SCREAMING_SNAKE_CASE_:							str	,       SCREAMING_SNAKE_CASE_:							str = "cpu"	,       SCREAMING_SNAKE_CASE_:							Union[str, None] = None      )			->      None:




				'''simple docstring'''





				A__        =			torch.load(SCREAMING_SNAKE_CASE_	,       map_location=SCREAMING_SNAKE_CASE_      )
				for k, v in tqdm(state_dict.items()      ):
								if not isinstance(SCREAMING_SNAKE_CASE_	,       torch.Tensor      ):
												raise TypeError("FP16 conversion only works on paths that are saved state dicts, like pytorch_model.bin"      )
								A__        =			v.half()
				if save_path is None:  # overwrite src_path
								A__        =			src_path
				torch.save(SCREAMING_SNAKE_CASE_	,       SCREAMING_SNAKE_CASE_      )


if __name__ == "__main__":
	fire.Fire(convert)

