





'''simple docstring'''







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_					:    Optional[int]      	=			16
lowerCAmelCase_					:    Any      	=			32






def  _lowerCamelCase				( lowercase						:      Accelerator    ,							lowercase						:      int = 16						)	->	List[Any]:
				_a								= AutoTokenizer.from_pretrained("bert-base-cased"						)
				_a								= load_dataset("glue"    ,							"mrpc"						)

				def tokenize_function(lowercase						:      Union[str, Any]						):
								# max_length=None => use the model max length (it's actually the default)
								_a								= tokenizer(examples["sentence1"]    ,							examples["sentence2"]    ,							truncation=lowercase    ,							max_length=lowercase						)
								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(
								    lowercase    ,							batched=lowercase    ,							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(lowercase						:      Dict						):
								# On TPU it's best to pad everything to the same length or training will be very slow.
								_a								= 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":
												_a								= 16
								elif accelerator.mixed_precision != "no":
												_a								= 8
								else:
												_a								= None

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

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

				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_					:    str      	=			mocked_dataloaders  # noqa: F811






def  _lowerCamelCase				( lowercase						:      List[Any]    ,							lowercase						:      List[Any]						)	->	str:





				if os.environ.get("TESTING_MOCKED_DATALOADERS"    ,							lowercase						) == "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=lowercase						)
				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(lowercase						)
				_a								= get_dataloaders(lowercase    ,							lowercase						)
				# 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=lowercase						)

				# 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=lowercase						)

				# Instantiate scheduler
				_a								= get_linear_schedule_with_warmup(
				    optimizer=lowercase    ,							num_warmup_steps=100    ,							num_training_steps=(len(lowercase						) * 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								= accelerator.prepare(
				    lowercase    ,							lowercase    ,							lowercase    ,							lowercase    ,							lowercase						)

				# Now we train the model
				for epoch in range(lowercase						):
								model.train()
								for step, batch in enumerate(lowercase						):
												# 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(lowercase						):
																_a								= model(**lowercase						)
																_a								= output.loss
																accelerator.backward(lowercase						)
																optimizer.step()
																lr_scheduler.step()
																optimizer.zero_grad()

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

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






def  _lowerCamelCase				( )	->	Tuple:
				_a								= argparse.ArgumentParser(description="Simple example of training script."						)
				parser.add_argument(
				    "--mixed_precision"    ,							type=lowercase    ,							default=lowercase    ,							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=lowercase    ,							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": 42, "batch_size": 16}
				training_function(lowercase    ,							lowercase						)


if __name__ == "__main__":
					main()




"""simple docstring"""



def   __lowerCAmelCase						(						lowercase  :  int )	->						bool:


    """simple docstring"""





    if p < 2:
        raise ValueError("p should not be less than 2!" )
    elif p == 2:
        return True

    snake_case    :      Dict      					=    4
    snake_case    :      str      					=    (1 << p) - 1
    for _ in range(p - 2 ):
        snake_case    :      Any      					=    ((s * s) - 2) % m
    return s == 0


if __name__ == "__main__":
  print(lucas_lehmer_test(7))
  print(lucas_lehmer_test(11))





