import unittest

import numpy as np






def 				__lowerCamelCase		(						lowerCAmelCase__     ,  lowerCAmelCase__     ,  lowerCAmelCase__     ,  lowerCAmelCase__ = None     ,  ):
       lowerCAmelCase__					     = np.shape(lowerCAmelCase__     )
       lowerCAmelCase__					     = np.shape(lowerCAmelCase__     )
       lowerCAmelCase__					     = np.shape(lowerCAmelCase__     )

       if shape_a[0] != shape_b[0]:
              lowerCAmelCase__					     = (
                  'Expected the same number of rows for A and B. '
                  F"""Instead found A of size {shape_a} and B of size {shape_b}"""
              )
              raise ValueError(lowerCAmelCase__     )

       if shape_b[1] != shape_c[1]:
              lowerCAmelCase__					     = (
                  'Expected the same number of columns for B and C. '
                  F"""Instead found B of size {shape_b} and C of size {shape_c}"""
              )
              raise ValueError(lowerCAmelCase__     )

       lowerCAmelCase__					     = pseudo_inv
       if a_inv is None:
              try:
                     lowerCAmelCase__					     = np.linalg.inv(lowerCAmelCase__     )
              except np.linalg.LinAlgError:
                     raise ValueError(
                         'Input matrix A is not invertible. Cannot compute Schur complement.'     )

       return mat_c - mat_b.T @ a_inv @ mat_b
class     a_			(						unittest.TestCase						):






 '''simple docstring'''






 def 		__snake_case						(      self :  str):







        '''simple docstring'''



        lowerCAmelCase__					     = np.array([[1, 2, 1], [2, 1, 2], [3, 2, 4]])
        lowerCAmelCase__					     = np.array([[0, 3], [3, 0], [2, 3]])
        lowerCAmelCase__					     = np.array([[2, 1], [6, 3]])

        lowerCAmelCase__					     = schur_complement(lowercase__      ,   lowercase__      ,   lowercase__)

        lowerCAmelCase__					     = np.block([[a, b], [b.T, c]])

        lowerCAmelCase__					     = np.linalg.det(lowercase__)
        lowerCAmelCase__					     = np.linalg.det(lowercase__)
        lowerCAmelCase__					     = np.linalg.det(lowercase__)

        self.assertAlmostEqual(lowercase__      ,   det_a * det_s)




 def 		__snake_case						(      self :  Tuple):







        '''simple docstring'''



        lowerCAmelCase__					     = np.array([[1, 2, 1], [2, 1, 2], [3, 2, 4]])
        lowerCAmelCase__					     = np.array([[0, 3], [3, 0], [2, 3]])
        lowerCAmelCase__					     = np.array([[2, 1], [6, 3]])

        with self.assertRaises(lowercase__):
               schur_complement(lowercase__      ,   lowercase__      ,   lowercase__)




 def 		__snake_case						(      self :  List[Any]):







        '''simple docstring'''



        lowerCAmelCase__					     = np.array([[1, 2, 1], [2, 1, 2], [3, 2, 4]])
        lowerCAmelCase__					     = np.array([[0, 3], [3, 0], [2, 3]])
        lowerCAmelCase__					     = np.array([[2, 1, 3], [6, 3, 5]])

        with self.assertRaises(lowercase__):
               schur_complement(lowercase__      ,   lowercase__      ,   lowercase__)


if __name__ == "__main__":
    import doctest

    doctest.testmod()
    unittest.main()

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,
# specifically showcasing how to properly calculate the metrics on the
# validation dataset when in a distributed system, 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 				__lowerCamelCase		(						lowerCAmelCase__     ,  lowerCAmelCase__ = 1_6     ):
       lowerCAmelCase__					     = AutoTokenizer.from_pretrained('bert-base-cased'     )
       lowerCAmelCase__					     = load_dataset('glue'     ,  'mrpc'     )

       def tokenize_function(lowerCAmelCase__     ):
              # max_length=None => use the model max length (it's actually the default)
              lowerCAmelCase__					     = 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():
              lowerCAmelCase__					     = 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
       lowerCAmelCase__					     = 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.
              lowerCAmelCase__					     = 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":
                     lowerCAmelCase__					     = 1_6
              elif accelerator.mixed_precision != "no":
                     lowerCAmelCase__					     = 8
              else:
                     lowerCAmelCase__					     = None

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

       # Instantiate dataloaders.
       lowerCAmelCase__					     = DataLoader(
           tokenized_datasets['train']     ,  shuffle=lowerCAmelCase__     ,  collate_fn=lowerCAmelCase__     ,  batch_size=lowerCAmelCase__     )
       lowerCAmelCase__					     = DataLoader(
           tokenized_datasets['validation']     ,  shuffle=lowerCAmelCase__     ,  collate_fn=lowerCAmelCase__     ,  batch_size=lowerCAmelCase__     )

       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__     						=			mocked_dataloaders  # noqa: F811






def 				__lowerCamelCase		(						lowerCAmelCase__     ,  lowerCAmelCase__     ):
       # For testing only
       if os.environ.get('TESTING_MOCKED_DATALOADERS'     ,  lowerCAmelCase__     ) == "1":
              lowerCAmelCase__					     = 2
       # Initialize accelerator
       lowerCAmelCase__					     = Accelerator(cpu=args.cpu     ,  mixed_precision=args.mixed_precision     )
       # Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
       lowerCAmelCase__					     = config['lr']
       lowerCAmelCase__					     = int(config['num_epochs']     )
       lowerCAmelCase__					     = int(config['seed']     )
       lowerCAmelCase__					     = int(config['batch_size']     )

       lowerCAmelCase__					     = evaluate.load('glue'     ,  'mrpc'     )

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

       set_seed(lowerCAmelCase__     )
       lowerCAmelCase__     , lowerCAmelCase__					     = get_dataloaders(lowerCAmelCase__     ,  lowerCAmelCase__     )
       # Instantiate the model (we build the model here so that the seed also control new weights initialization)
       lowerCAmelCase__					     = 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).
       lowerCAmelCase__					     = model.to(accelerator.device     )

       # Instantiate optimizer
       lowerCAmelCase__					     = AdamW(params=model.parameters()     ,  lr=lowerCAmelCase__     )

       # Instantiate scheduler
       lowerCAmelCase__					     = get_linear_schedule_with_warmup(
           optimizer=lowerCAmelCase__     ,  num_warmup_steps=1_0_0     ,  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.
       lowerCAmelCase__     , lowerCAmelCase__     , lowerCAmelCase__     , lowerCAmelCase__     , lowerCAmelCase__					     = 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     )
                     lowerCAmelCase__					     = model(**lowerCAmelCase__     )
                     lowerCAmelCase__					     = outputs.loss
                     lowerCAmelCase__					     = loss / gradient_accumulation_steps
                     accelerator.backward(lowerCAmelCase__     )
                     if step % gradient_accumulation_steps == 0:
                            optimizer.step()
                            lr_scheduler.step()
                            optimizer.zero_grad()

              model.eval()
              lowerCAmelCase__					     = 0
              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():
                            lowerCAmelCase__					     = model(**lowerCAmelCase__     )
                     lowerCAmelCase__					     = outputs.logits.argmax(dim=-1     )
                     lowerCAmelCase__     , lowerCAmelCase__					     = accelerator.gather((predictions, batch['labels'])     )
                     # New Code #
                     # First we check if it's a distributed system
                     if accelerator.use_distributed:
                            # Then see if we're on the last batch of our eval dataloader
                            if step == len(lowerCAmelCase__     ) - 1:
                                   # Last batch needs to be truncated on distributed systems as it contains additional samples
                                   lowerCAmelCase__					     = predictions[: len(eval_dataloader.dataset     ) - samples_seen]
                                   lowerCAmelCase__					     = references[: len(eval_dataloader.dataset     ) - samples_seen]
                            else:
                                   # Otherwise we add the number of samples seen
                                   samples_seen += references.shape[0]
            # All of this can be avoided if you use `Accelerator.gather_for_metrics` instead of `Accelerator.gather`:
            # accelerator.gather_for_metrics((predictions, batch["labels"]))
                     metric.add_batch(
                         predictions=lowerCAmelCase__     ,  references=lowerCAmelCase__     ,  )

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






def 				__lowerCamelCase		(						):
       lowerCAmelCase__					     = 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.'     )
       lowerCAmelCase__					     = parser.parse_args()
       lowerCAmelCase__					     = {'lr': 2e-5, 'num_epochs': 3, 'seed': 4_2, 'batch_size': 1_6}
       training_function(lowerCAmelCase__     ,  lowerCAmelCase__     )


if __name__ == "__main__":
    main()
