






"""simple docstring"""
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_					=      16
lowerCamelCase_					=      32

def 				snake_case      (   A__	,A__	,A__	,A__	,A__ = 16						):
			UpperCAmelCase_     : int			     =					AutoTokenizer.from_pretrained("bert-base-cased"						)
			UpperCAmelCase_     : List[str]			     =					DatasetDict(
			    {
			        "train": dataset["train"].select(A__						),
			        "validation": dataset["train"].select(A__						),
			        "test": dataset["validation"],
			    }						)

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

			def collate_fn(A__						):
						# On TPU it's best to pad everything to the same length or training will be very slow.
						UpperCAmelCase_     : Optional[int]			     =					1_28 if accelerator.distributed_type == DistributedType.TPU else None
						# When using mixed precision we want round multiples of 8/16
						if accelerator.mixed_precision == "fp8":
									UpperCAmelCase_     : Tuple			     =					16
						elif accelerator.mixed_precision != "no":
									UpperCAmelCase_     : List[str]			     =					8
						else:
									UpperCAmelCase_     : List[Any]			     =					None

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

			# Instantiate dataloaders.
			UpperCAmelCase_     : List[str]			     =					DataLoader(
			    tokenized_datasets["train"]	,shuffle=A__	,collate_fn=A__	,batch_size=A__						)
			UpperCAmelCase_     : int			     =					DataLoader(
			    tokenized_datasets["validation"]	,shuffle=A__	,collate_fn=A__	,batch_size=A__						)

			UpperCAmelCase_     : int			     =					DataLoader(
			    tokenized_datasets["test"]	,shuffle=A__	,collate_fn=A__	,batch_size=A__						)

			return train_dataloader, eval_dataloader, test_dataloader

def 				snake_case      (   A__	,A__						):
			# New Code #
			UpperCAmelCase_     : int			     =					[]
			# Download the dataset
			UpperCAmelCase_     : Any			     =					load_dataset("glue"	,"mrpc"						)
			# Create our splits
			UpperCAmelCase_     : Optional[Any]			     =					StratifiedKFold(n_splits=int(args.num_folds						)						)
			# Initialize accelerator
			UpperCAmelCase_     : 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
			UpperCAmelCase_     : Optional[Any]			     =					config["lr"]
			UpperCAmelCase_     : Dict			     =					int(config["num_epochs"]						)
			UpperCAmelCase_     : Union[str, Any]			     =					int(config["seed"]						)
			UpperCAmelCase_     : List[Any]			     =					int(config["batch_size"]						)

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

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

			set_seed(A__						)

			# New Code #
			# Create our folds:
			UpperCAmelCase_     : List[str]			     =					kfold.split(np.zeros(datasets["train"].num_rows						)	,datasets["train"]["label"]						)
			UpperCAmelCase_     : Dict			     =					[]
			# Iterate over them
			for i, (train_idxs, valid_idxs) in enumerate(A__						):
						UpperCAmelCase_     ,	UpperCAmelCase_     ,	UpperCAmelCase_     : Optional[Any]			     =					get_fold_dataloaders(
						    A__	,A__	,A__	,A__	,)
						# Instantiate the model (we build the model here so that the seed also control new weights initialization)
						UpperCAmelCase_     : Union[str, Any]			     =					AutoModelForSequenceClassification.from_pretrained("bert-base-cased"	,return_dict=A__						)

						# 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).
						UpperCAmelCase_     : Optional[int]			     =					model.to(accelerator.device						)

						# Instantiate optimizer
						UpperCAmelCase_     : Optional[int]			     =					AdamW(params=model.parameters()	,lr=A__						)

						# Instantiate scheduler
						UpperCAmelCase_     : Optional[Any]			     =					get_linear_schedule_with_warmup(
						    optimizer=A__	,num_warmup_steps=1_00	,num_training_steps=(len(A__						) * 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.
						UpperCAmelCase_     ,	UpperCAmelCase_     ,	UpperCAmelCase_     ,	UpperCAmelCase_     ,	UpperCAmelCase_     : List[Any]			     =					accelerator.prepare(
						    A__	,A__	,A__	,A__	,A__						)

						# Now we train the model
						for epoch in range(A__						):
									model.train()
									for step, batch in enumerate(A__						):
												# We could avoid this line since we set the accelerator with `device_placement=True`.
												batch.to(accelerator.device						)
												UpperCAmelCase_     : List[str]			     =					model(**A__						)
												UpperCAmelCase_     : Optional[Any]			     =					outputs.loss
												UpperCAmelCase_     : Dict			     =					loss / gradient_accumulation_steps
												accelerator.backward(A__						)
												if step % gradient_accumulation_steps == 0:
															optimizer.step()
															lr_scheduler.step()
															optimizer.zero_grad()

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

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

						# New Code #
						# We also run predictions on the test set at the very end
						UpperCAmelCase_     : List[Any]			     =					[]
						for step, batch in enumerate(A__						):
									# We could avoid this line since we set the accelerator with `device_placement=True`.
									batch.to(accelerator.device						)
									with torch.no_grad():
												UpperCAmelCase_     : List[Any]			     =					model(**A__						)
									UpperCAmelCase_     : Dict			     =					outputs.logits
									UpperCAmelCase_     ,	UpperCAmelCase_     : 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(A__	,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:
			UpperCAmelCase_     : int			     =					torch.cat(A__	,dim=0						)
			UpperCAmelCase_     : List[str]			     =					torch.stack(A__	,dim=0						).sum(dim=0						).div(int(args.num_folds						)						).argmax(dim=-1						)
			UpperCAmelCase_     : Union[str, Any]			     =					metric.compute(predictions=A__	,references=A__						)
			accelerator.print("Average test metrics from all folds:"	,A__						)

def 				snake_case      (   ):
			UpperCAmelCase_     : int			     =					argparse.ArgumentParser(description="Simple example of training script."						)
			parser.add_argument(
			    "--mixed_precision"	,type=A__	,default=A__	,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=A__	,default=3	,help="The number of splits to perform across the dataset"						)
			UpperCAmelCase_     : List[str]			     =					parser.parse_args()
			UpperCAmelCase_     : Dict			     =					{"lr": 2e-5, "num_epochs": 3, "seed": 42, "batch_size": 16}
			training_function(A__	,A__						)


if __name__ == "__main__":
			main()











"""simple docstring"""
from __future__ import annotations

import time
from collections.abc import Sequence
from random import randint

from matplotlib import pyplot as plt

def 				snake_case      (   A__	,A__	,A__						):
			if not arr:
						return None, None, 0
			if low == high:
						return low, high, arr[low]

			UpperCAmelCase_     : Dict			     =					(low + high) // 2
			UpperCAmelCase_     ,	UpperCAmelCase_     ,	UpperCAmelCase_     : Any			     =					max_subarray(A__	,A__	,A__						)
			UpperCAmelCase_     ,	UpperCAmelCase_     ,	UpperCAmelCase_     : Union[str, Any]			     =					max_subarray(A__	,mid + 1	,A__						)
			UpperCAmelCase_     ,	UpperCAmelCase_     ,	UpperCAmelCase_     : Optional[Any]			     =					max_cross_sum(A__	,A__	,A__	,A__						)
			if left_sum >= right_sum and left_sum >= cross_sum:
						return left_low, left_high, left_sum
			elif right_sum >= left_sum and right_sum >= cross_sum:
						return right_low, right_high, right_sum
			return cross_left, cross_right, cross_sum

def 				snake_case      (   A__	,A__	,A__	,A__						):
			UpperCAmelCase_     ,	UpperCAmelCase_     : str			     =					float("-inf"						), -1
			UpperCAmelCase_     ,	UpperCAmelCase_     : Optional[Any]			     =					float("-inf"						), -1

			UpperCAmelCase_     : int | float			     =					0
			for i in range(A__	,low - 1	,-1						):
						summ += arr[i]
						if summ > left_sum:
									UpperCAmelCase_     : str			     =					summ
									UpperCAmelCase_     : Any			     =					i

			UpperCAmelCase_     : Dict			     =					0
			for i in range(mid + 1	,high + 1						):
						summ += arr[i]
						if summ > right_sum:
									UpperCAmelCase_     : List[Any]			     =					summ
									UpperCAmelCase_     : Optional[Any]			     =					i

			return max_left, max_right, (left_sum + right_sum)

def 				snake_case      (   A__						):
			UpperCAmelCase_     : str			     =					[randint(1	,A__						) for _ in range(A__						)]
			UpperCAmelCase_     : str			     =					time.time()
			max_subarray(A__	,0	,input_size - 1						)
			UpperCAmelCase_     : int			     =					time.time()
			return end - start

def 				snake_case      (   ):
			UpperCAmelCase_     : int			     =					[10, 1_00, 10_00, 1_00_00, 5_00_00, 10_00_00, 20_00_00, 30_00_00, 40_00_00, 50_00_00]
			UpperCAmelCase_     : List[str]			     =					[time_max_subarray(A__						) for input_size in input_sizes]
			print("No of Inputs\t\tTime Taken"						)
			for input_size, runtime in zip(A__	,A__						):
						print(A__	,"\t\t"	,A__						)
			plt.plot(A__	,A__						)
			plt.xlabel("Number of Inputs"						)
			plt.ylabel("Time taken in seconds"						)
			plt.show()


if __name__ == "__main__":
			from doctest import testmod

			testmod()



