

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


lowerCAmelCase__	   = 1_6
lowerCAmelCase__	   = 3_2



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




				'''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_:							Any      ):
								# 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_:							str	,       SCREAMING_SNAKE_CASE_:							int      )			->      Union[str, Any]:




				'''simple docstring'''





				if os.environ.get("TESTING_MOCKED_DATALOADERS"	,       SCREAMING_SNAKE_CASE_      ) == "1":
								A__        =			2
				# New Code #
				A__        =			int(args.gradient_accumulation_steps      )
				# Initialize accelerator
				A__        =			Accelerator(
				    cpu=args.cpu	,       mixed_precision=args.mixed_precision	,       gradient_accumulation_steps=SCREAMING_SNAKE_CASE_      )
				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
				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()
								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()

								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__							(	)			->      Tuple:




				'''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("--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()




import argparse
import shutil
from pathlib import Path

from tqdm import tqdm

from transformers import AutoTokenizer



def 					lowerCAmelCase__							(	SCREAMING_SNAKE_CASE_:							List[str]	,       SCREAMING_SNAKE_CASE_:							List[str]	,       SCREAMING_SNAKE_CASE_:							List[str]	,       SCREAMING_SNAKE_CASE_:							str=1_0_2_4      )			->      Any:




				'''simple docstring'''





				A__    ,				A__        =			[], []

				A__        =			list(zip(SCREAMING_SNAKE_CASE_	,       SCREAMING_SNAKE_CASE_      )      )
				A__    ,				A__        =			sorted_examples[0]

				def is_too_big(SCREAMING_SNAKE_CASE_:							List[str]      ):
								return tok(SCREAMING_SNAKE_CASE_	,       return_tensors="pt"      ).input_ids.shape[1] > max_tokens

				for src, tgt in tqdm(sorted_examples[1:]      ):
								A__        =			new_src + " " + src
								A__        =			new_tgt + " " + tgt
								if is_too_big(SCREAMING_SNAKE_CASE_      ) or is_too_big(SCREAMING_SNAKE_CASE_      ):  # cant fit, finalize example
												finished_src.append(SCREAMING_SNAKE_CASE_      )
												finished_tgt.append(SCREAMING_SNAKE_CASE_      )
												A__    ,				A__        =			src, tgt
								else:  # can fit, keep adding
												A__    ,				A__        =			cand_src, cand_tgt

    # cleanup
				if new_src:
								assert new_tgt
								finished_src.append(SCREAMING_SNAKE_CASE_      )
								finished_tgt.append(SCREAMING_SNAKE_CASE_      )
				return finished_src, finished_tgt



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




				'''simple docstring'''





				A__        =			Path(SCREAMING_SNAKE_CASE_      )
				save_path.mkdir(exist_ok=SCREAMING_SNAKE_CASE_      )
				for split in ["train"]:
								A__    ,				A__        =			data_dir / F'{split}.source', data_dir / F'{split}.target'
								A__        =			[x.rstrip() for x in Path(SCREAMING_SNAKE_CASE_      ).open().readlines()]
								A__        =			[x.rstrip() for x in Path(SCREAMING_SNAKE_CASE_      ).open().readlines()]
								A__    ,				A__        =			pack_examples(SCREAMING_SNAKE_CASE_	,       SCREAMING_SNAKE_CASE_	,       SCREAMING_SNAKE_CASE_	,       SCREAMING_SNAKE_CASE_      )
								print(F'packed {split} split from {len(SCREAMING_SNAKE_CASE_      )} examples -> {len(SCREAMING_SNAKE_CASE_      )}.'      )
								Path(save_path / F'{split}.source'      ).open("w"      ).write("\n".join(SCREAMING_SNAKE_CASE_      )      )
								Path(save_path / F'{split}.target'      ).open("w"      ).write("\n".join(SCREAMING_SNAKE_CASE_      )      )
				for split in ["val", "test"]:
								A__    ,				A__        =			data_dir / F'{split}.source', data_dir / F'{split}.target'
								shutil.copyfile(SCREAMING_SNAKE_CASE_	,       save_path / F'{split}.source'      )
								shutil.copyfile(SCREAMING_SNAKE_CASE_	,       save_path / F'{split}.target'      )



def 					lowerCAmelCase__							(	)			->      Optional[int]:




				'''simple docstring'''





				A__        =			argparse.ArgumentParser()
				parser.add_argument("--tok_name"	,       type=SCREAMING_SNAKE_CASE_	,       help="like facebook/bart-large-cnn,t5-base, etc."      )
				parser.add_argument("--max_seq_len"	,       type=SCREAMING_SNAKE_CASE_	,       default=1_2_8      )
				parser.add_argument("--data_dir"	,       type=SCREAMING_SNAKE_CASE_      )
				parser.add_argument("--save_path"	,       type=SCREAMING_SNAKE_CASE_      )
				A__        =			parser.parse_args()
				A__        =			AutoTokenizer.from_pretrained(args.tok_name      )
				return pack_data_dir(SCREAMING_SNAKE_CASE_	,       Path(args.data_dir      )	,       args.max_seq_len	,       args.save_path      )


if __name__ == "__main__":
	packer_cli()

