

import argparse

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
#
# 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      )			->      Any:




				'''simple docstring'''





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

				def tokenize_function(SCREAMING_SNAKE_CASE_:							str      ):
								# 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_	,       drop_last=SCREAMING_SNAKE_CASE_      )
				A__        =			DataLoader(
				    tokenized_datasets["validation"]	,       shuffle=SCREAMING_SNAKE_CASE_	,       collate_fn=SCREAMING_SNAKE_CASE_	,       batch_size=SCREAMING_SNAKE_CASE_	,       drop_last=(accelerator.mixed_precision == "fp8")	,       )

				return train_dataloader, eval_dataloader



def 					lowerCAmelCase__							(	SCREAMING_SNAKE_CASE_:							List[str]	,       SCREAMING_SNAKE_CASE_:							Union[str, Any]      )			->      str:




				'''simple docstring'''





				A__        =			Accelerator(cpu=args.cpu	,       mixed_precision=args.mixed_precision      )
				# 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"      )

				# If the batch size is too big we use gradient accumulation
				A__        =			1
				if batch_size > MAX_GPU_BATCH_SIZE and accelerator.distributed_type != DistributedType.TPU:
								A__        =			batch_size // MAX_GPU_BATCH_SIZE
								A__        =			MAX_GPU_BATCH_SIZE

				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) // 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.

				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      )
												A__        =			model(**SCREAMING_SNAKE_CASE_      )
												A__        =			outputs.loss
												A__        =			loss / gradient_accumulation_steps
												accelerator.backward(SCREAMING_SNAKE_CASE_      )
												if step % gradient_accumulation_steps == 0:
																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__							(	)			->      List[str]:




				'''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."	,       )
				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()




def 					lowerCAmelCase__							(	SCREAMING_SNAKE_CASE_:							int      )			->      int:




				'''simple docstring'''





				if divisor % 5 == 0 or divisor % 2 == 0:
								return 0
				A__        =			1
				A__        =			1
				while repunit:
								A__        =			(1_0 * repunit + 1) % divisor
								repunit_index += 1
				return repunit_index



def 					lowerCAmelCase__							(	SCREAMING_SNAKE_CASE_:							int = 1_0_0_0_0_0_0      )			->      int:




				'''simple docstring'''





				A__        =			limit - 1
				if divisor % 2 == 0:
								divisor += 1
				while least_divisible_repunit(SCREAMING_SNAKE_CASE_      ) <= limit:
								divisor += 2
				return divisor


if __name__ == "__main__":
	print(f"""{solution() = }""")

