



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


a_   :List[str]        =      16
a_   :Optional[Any]        =      32
def        lowercase_							(A : Accelerator		,					A : DatasetDict		,					A : List[int]		,					A : List[int]		,					A : int = 1_6      ):
       snake_case__							:	int   	=	AutoTokenizer.from_pretrained('bert-base-cased'      )
       snake_case__							:	Any   	=	DatasetDict(
           {
               'train': dataset['train'].select(A      ),
               'validation': dataset['train'].select(A      ),
               'test': dataset['validation'],
           }      )

       def tokenize_function(A : int      ):
              # max_length=None => use the model max length (it's actually the default)
              snake_case__							:	Dict   	=	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():
              snake_case__							:	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
       snake_case__							:	Dict   	=	tokenized_datasets.rename_column('label'		,					'labels'      )

       def collate_fn(A : Dict      ):
              # On TPU it's best to pad everything to the same length or training will be very slow.
              snake_case__							:	Union[str, Any]   	=	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":
                     snake_case__							:	Optional[int]   	=	1_6
              elif accelerator.mixed_precision != "no":
                     snake_case__							:	Optional[int]   	=	8
              else:
                     snake_case__							:	List[str]   	=	None

              return tokenizer.pad(
                  A		,					padding='longest'		,					max_length=A		,					pad_to_multiple_of=A		,					return_tensors='pt'		,					)

       # Instantiate dataloaders.
       snake_case__							:	Union[str, Any]   	=	DataLoader(
           tokenized_datasets['train']		,					shuffle=A		,					collate_fn=A		,					batch_size=A      )
       snake_case__							:	Tuple   	=	DataLoader(
           tokenized_datasets['validation']		,					shuffle=A		,					collate_fn=A		,					batch_size=A      )

       snake_case__							:	Dict   	=	DataLoader(
           tokenized_datasets['test']		,					shuffle=A		,					collate_fn=A		,					batch_size=A      )

       return train_dataloader, eval_dataloader, test_dataloader
def        lowercase_							(A : int		,					A : str      ):
       # New Code #
       snake_case__							:	Any   	=	[]
       # Download the dataset
       snake_case__							:	int   	=	load_dataset('glue'		,					'mrpc'      )
       # Create our splits
       snake_case__							:	List[str]   	=	StratifiedKFold(n_splits=int(args.num_folds      )      )
       # Initialize accelerator
       snake_case__							:	List[str]   	=	Accelerator(cpu=args.cpu		,					mixed_precision=args.mixed_precision      )
       # Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
       snake_case__							:	int   	=	config['lr']
       snake_case__							:	Tuple   	=	int(config['num_epochs']      )
       snake_case__							:	Optional[int]   	=	int(config['seed']      )
       snake_case__							:	Optional[Any]   	=	int(config['batch_size']      )

       snake_case__							:	Union[str, Any]   	=	evaluate.load('glue'		,					'mrpc'      )

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

       set_seed(A      )

       # New Code #
       # Create our folds:
       snake_case__							:	Optional[Any]   	=	kfold.split(np.zeros(datasets['train'].num_rows      )		,					datasets['train']['label']      )
       snake_case__							:	Union[str, Any]   	=	[]
       # Iterate over them
       for i, (train_idxs, valid_idxs) in enumerate(A      ):
              snake_case__	,				snake_case__	,				snake_case__							:	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)
              snake_case__							:	Optional[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).
              snake_case__							:	Dict   	=	model.to(accelerator.device      )

              # Instantiate optimizer
              snake_case__							:	Union[str, Any]   	=	AdamW(params=model.parameters()		,					lr=A      )

              # Instantiate scheduler
              snake_case__							:	List[Any]   	=	get_linear_schedule_with_warmup(
                  optimizer=A		,					num_warmup_steps=1_0_0		,					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.
              snake_case__	,				snake_case__	,				snake_case__	,				snake_case__	,				snake_case__							:	Dict   	=	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      )
                            snake_case__							:	str   	=	model(**A      )
                            snake_case__							:	int   	=	outputs.loss
                            snake_case__							:	List[str]   	=	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():
                                   snake_case__							:	int   	=	model(**A      )
                            snake_case__							:	Union[str, Any]   	=	outputs.logits.argmax(dim=-1      )
                            snake_case__	,				snake_case__							:	Optional[Any]   	=	accelerator.gather_for_metrics((predictions, batch['labels'])      )
                            metric.add_batch(
                                predictions=A		,					references=A		,					)

                     snake_case__							:	Optional[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
              snake_case__							:	Tuple   	=	[]
              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():
                            snake_case__							:	Union[str, Any]   	=	model(**A      )
                     snake_case__							:	Any   	=	outputs.logits
                     snake_case__	,				snake_case__							:	Tuple   	=	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:
       snake_case__							:	Any   	=	torch.cat(A		,					dim=0      )
       snake_case__							:	Optional[int]   	=	torch.stack(A		,					dim=0      ).sum(dim=0      ).div(int(args.num_folds      )      ).argmax(dim=-1      )
       snake_case__							:	Optional[Any]   	=	metric.compute(predictions=A		,					references=A      )
       accelerator.print('Average test metrics from all folds:'		,					A      )
def        lowercase_							():
       snake_case__							:	Union[str, Any]   	=	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'      )
       snake_case__							:	Optional[Any]   	=	parser.parse_args()
       snake_case__							:	Tuple   	=	{'lr': 2e-5, 'num_epochs': 3, 'seed': 4_2, 'batch_size': 1_6}
       training_function(A		,					A      )


if __name__ == "__main__":
 main()











from math import factorial
def        lowercase_							(A : int		,					A : int		,					A : float      ):
       if successes > trials:
              raise ValueError('successes must be lower or equal to trials'      )
       if trials < 0 or successes < 0:
              raise ValueError('the function is defined for non-negative integers'      )
       if not isinstance(A		,					A      ) or not isinstance(A		,					A      ):
              raise ValueError('the function is defined for non-negative integers'      )
       if not 0 < prob < 1:
              raise ValueError('prob has to be in range of 1 - 0'      )
       snake_case__							:	List[Any]   	=	(prob**successes) * ((1 - prob) ** (trials - successes))
       # Calculate the binomial coefficient: n! / k!(n-k)!
       snake_case__							:	List[str]   	=	float(factorial(A      )      )
       coefficient /= factorial(A      ) * factorial(trials - successes      )
       return probability * coefficient


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

 testmod()
 print("Probability of 2 successes out of 4 trails")
 print("with probability of 0.75 is:", end=" ")
 print(binomial_distribution(2, 4, 0.75))






