



import argparse
from typing import List

import evaluate
import numpy as np
import torch
from datasets import DatasetDict, load_dataset

# New Code #
# We'll be using StratifiedKFold for this example
from sklearn.model_selection import StratifiedKFold
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,
# specifically showcasing how to perform Cross Validation,
# 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)
#
# To help focus on the differences in the code, building `DataLoaders`
# was refactored into its own function.
# 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
#
########################################################################


lowerCAmelCase__					  =  1_6
lowerCAmelCase__					  =  3_2


def  __lowerCamelCase (       lowerCamelCase__					,			lowerCamelCase__					,			lowerCamelCase__					,			lowerCamelCase__					,			lowerCamelCase__ = 16						):

				"""simple docstring"""


				lowercase__					:   Tuple													=					AutoTokenizer.from_pretrained("bert-base-cased"						)
				lowercase__					:   Optional[Any]													=					DatasetDict(
				    {
				        "train": dataset["train"].select(lowerCamelCase__						),
				        "validation": dataset["train"].select(lowerCamelCase__						),
				        "test": dataset["validation"],
				    }						)

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

				def collate_fn(lowerCamelCase__						):
								# On TPU it's best to pad everything to the same length or training will be very slow.
								lowercase__					:   List[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":
												lowercase__					:   int													=					16
								elif accelerator.mixed_precision != "no":
												lowercase__					:   Dict													=					8
								else:
												lowercase__					:   str													=					None

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

				# Instantiate dataloaders.
				lowercase__					:   int													=					DataLoader(
				    tokenized_datasets["train"]					,			shuffle=lowerCamelCase__					,			collate_fn=lowerCamelCase__					,			batch_size=lowerCamelCase__						)
				lowercase__					:   Any													=					DataLoader(
				    tokenized_datasets["validation"]					,			shuffle=lowerCamelCase__					,			collate_fn=lowerCamelCase__					,			batch_size=lowerCamelCase__						)

				lowercase__					:   Dict													=					DataLoader(
				    tokenized_datasets["test"]					,			shuffle=lowerCamelCase__					,			collate_fn=lowerCamelCase__					,			batch_size=lowerCamelCase__						)

				return train_dataloader, eval_dataloader, test_dataloader


def  __lowerCamelCase (       lowerCamelCase__					,			lowerCamelCase__						):

				"""simple docstring"""


				lowercase__					:   Union[str, Any]													=					[]
				# Download the dataset
				lowercase__					:   str													=					load_dataset("glue"					,			"mrpc"						)
				# Create our splits
				lowercase__					:   Any													=					StratifiedKFold(n_splits=int(args.num_folds						)						)
				# Initialize accelerator
				lowercase__					:   Union[str, Any]													=					Accelerator(cpu=args.cpu					,			mixed_precision=args.mixed_precision						)
				# Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
				lowercase__					:   Dict													=					config["lr"]
				lowercase__					:   Union[str, Any]													=					int(config["num_epochs"]						)
				lowercase__					:   List[Any]													=					int(config["seed"]						)
				lowercase__					:   Any													=					int(config["batch_size"]						)

				lowercase__					:   Tuple													=					evaluate.load("glue"					,			"mrpc"						)

				# If the batch size is too big we use gradient accumulation
				lowercase__					:   Optional[int]													=					1
				if batch_size > MAX_GPU_BATCH_SIZE and accelerator.distributed_type != DistributedType.TPU:
								lowercase__					:   Optional[Any]													=					batch_size // MAX_GPU_BATCH_SIZE
								lowercase__					:   Union[str, Any]													=					MAX_GPU_BATCH_SIZE

				set_seed(lowerCamelCase__						)

				# New Code #
				# Create our folds:
				lowercase__					:   Union[str, Any]													=					kfold.split(np.zeros(datasets["train"].num_rows						)					,			datasets["train"]["label"]						)
				lowercase__					:   Optional[int]													=					[]
				# Iterate over them
				for i, (train_idxs, valid_idxs) in enumerate(lowerCamelCase__						):
								lowercase__							,							lowercase__							,							lowercase__					:   Dict													=					get_fold_dataloaders(
								    lowerCamelCase__					,			lowerCamelCase__					,			lowerCamelCase__					,			lowerCamelCase__					,			)
								# Instantiate the model (we build the model here so that the seed also control new weights initialization)
								lowercase__					:   Any													=					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).
								lowercase__					:   Optional[int]													=					model.to(accelerator.device						)

								# Instantiate optimizer
								lowercase__					:   List[Any]													=					AdamW(params=model.parameters()					,			lr=lowerCamelCase__						)

								# Instantiate scheduler
								lowercase__					:   Any													=					get_linear_schedule_with_warmup(
								    optimizer=lowerCamelCase__					,			num_warmup_steps=100					,			num_training_steps=(len(lowerCamelCase__						) * 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.
								lowercase__							,							lowercase__							,							lowercase__							,							lowercase__							,							lowercase__					:   List[Any]													=					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						)
																lowercase__					:   Dict													=					model(**lowerCamelCase__						)
																lowercase__					:   List[str]													=					outputs.loss
																lowercase__					:   Optional[int]													=					loss / gradient_accumulation_steps
																accelerator.backward(lowerCamelCase__						)
																if step % gradient_accumulation_steps == 0:
																				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():
																				lowercase__					:   Optional[Any]													=					model(**lowerCamelCase__						)
																lowercase__					:   Dict													=					outputs.logits.argmax(dim=-1						)
																lowercase__							,							lowercase__					:   Any													=					accelerator.gather_for_metrics((predictions, batch["labels"])						)
																metric.add_batch(
																    predictions=lowerCamelCase__					,			references=lowerCamelCase__					,			)

												lowercase__					:   List[Any]													=					metric.compute()
												# Use accelerator.print to print only on the main process.
												accelerator.print(F"""epoch {epoch}:"""					,			lowerCamelCase__						)

								# New Code #
								# We also run predictions on the test set at the very end
								lowercase__					:   Tuple													=					[]
								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():
																lowercase__					:   Optional[int]													=					model(**lowerCamelCase__						)
												lowercase__					:   Tuple													=					outputs.logits
												lowercase__							,							lowercase__					:   Optional[int]													=					accelerator.gather_for_metrics((predictions, batch["labels"])						)
												fold_predictions.append(predictions.cpu()						)
												if i == 0:
																# We need all of the test predictions
																test_references.append(references.cpu()						)
																# Use accelerator.print to print only on the main process.
								test_predictions.append(torch.cat(lowerCamelCase__					,			dim=0						)						)
								# We now need to release all our memory and get rid of the current model, optimizer, etc
								accelerator.free_memory()
				# New Code #
				# Finally we check the accuracy of our folded results:
				lowercase__					:   Optional[Any]													=					torch.cat(lowerCamelCase__					,			dim=0						)
				lowercase__					:   Optional[Any]													=					torch.stack(lowerCamelCase__					,			dim=0						).sum(dim=0						).div(int(args.num_folds						)						).argmax(dim=-1						)
				lowercase__					:   Dict													=					metric.compute(predictions=lowerCamelCase__					,			references=lowerCamelCase__						)
				accelerator.print("Average test metrics from all folds:"					,			lowerCamelCase__						)


def  __lowerCamelCase (       ):

				"""simple docstring"""


				lowercase__					:   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."						)
				# New Code #
				parser.add_argument("--num_folds"					,			type=lowerCamelCase__					,			default=3					,			help="The number of splits to perform across the dataset"						)
				lowercase__					:   List[Any]													=					parser.parse_args()
				lowercase__					:   Any													=					{"lr": 2e-5, "num_epochs": 3, "seed": 42, "batch_size": 16}
				training_function(lowerCamelCase__					,			lowerCamelCase__						)


if __name__ == "__main__":
				main()









from __future__ import annotations

from math import pi

# Define the Reduced Planck Constant ℏ (H bar), speed of light C, value of
# Pi and the function
lowerCAmelCase__					  =  1.054571817e-34  # unit of ℏ : J * s

lowerCAmelCase__					  =  3e8  # unit of c : m * s^-1


def  __lowerCamelCase (       lowerCamelCase__					,			lowerCamelCase__					,			lowerCamelCase__						):

				"""simple docstring"""


				if (force, area, distance).count(0						) != 1:
								raise ValueError("One and only one argument must be 0"						)
				if force < 0:
								raise ValueError("Magnitude of force can not be negative"						)
				if distance < 0:
								raise ValueError("Distance can not be negative"						)
				if area < 0:
								raise ValueError("Area can not be negative"						)
				if force == 0:
								lowercase__					:   Optional[Any]													=					(REDUCED_PLANCK_CONSTANT * SPEED_OF_LIGHT * pi**2 * area) / (
								    240 * (distance) ** 4
								)
								return {"force": force}
				elif area == 0:
								lowercase__					:   str													=					(240 * force * (distance) ** 4) / (
								    REDUCED_PLANCK_CONSTANT * SPEED_OF_LIGHT * pi**2
								)
								return {"area": area}
				elif distance == 0:
								lowercase__					:   Tuple													=					(
								    (REDUCED_PLANCK_CONSTANT * SPEED_OF_LIGHT * pi**2 * area) / (240 * force)
								) ** (1 / 4)
								return {"distance": distance}
				raise ValueError("One and only one argument must be 0"						)


# Run doctest
if __name__ == "__main__":
				import doctest

				doctest.testmod()




