


'''simple docstring'''

import math
import os
from copy import deepcopy

import datasets
import evaluate
import torch
import transformers
from datasets import load_dataset
from torch.utils.data import DataLoader
from transformers import AutoModelForSequenceClassification, AutoTokenizer

from accelerate import Accelerator
from accelerate.test_utils import RegressionDataset, RegressionModel
from accelerate.utils import is_tpu_available, set_seed


lowerCamelCase				:  Optional[Any]    		=      'true'

def _SCREAMING_SNAKE_CASE (A    ,					A=82    ,					A=16			)			->			Tuple:






       """simple docstring"""







       set_seed(42			)
       lowercase__      =					RegressionModel()
       lowercase__      =					deepcopy(A			)
       lowercase__      =					RegressionDataset(length=A			)
       lowercase__      =					DataLoader(A    ,					batch_size=A			)
       model.to(accelerator.device			)
       lowercase__     ,lowercase__      =					accelerator.prepare(A    ,					A			)
       return model, ddp_model, dataloader

def _SCREAMING_SNAKE_CASE (A    ,					A=False			)			->			Tuple:






       """simple docstring"""







       lowercase__      =					AutoTokenizer.from_pretrained('''hf-internal-testing/mrpc-bert-base-cased'''			)
       lowercase__      =					load_dataset('''glue'''    ,					'''mrpc'''    ,					split='''validation'''			)

       def tokenize_function(A			):
              lowercase__      =					tokenizer(examples['''sentence1''']    ,					examples['''sentence2''']    ,					truncation=A    ,					max_length=A			)
              return outputs

       with accelerator.main_process_first():
              lowercase__      =					dataset.map(
                  A    ,					batched=A    ,					remove_columns=['''idx''', '''sentence1''', '''sentence2''']    ,					)

       lowercase__      =					tokenized_datasets.rename_column('''label'''    ,					'''labels'''			)

       def collate_fn(A			):
              if use_longest:
                     return tokenizer.pad(A    ,					padding='''longest'''    ,					return_tensors='''pt'''			)
              return tokenizer.pad(A    ,					padding='''max_length'''    ,					max_length=128    ,					return_tensors='''pt'''			)

       return DataLoader(A    ,					shuffle=A    ,					collate_fn=A    ,					batch_size=16			)

def _SCREAMING_SNAKE_CASE (A    ,					A			)			->			List[Any]:






       """simple docstring"""







       lowercase__      =					Accelerator(dispatch_batches=A    ,					split_batches=A			)
       lowercase__      =					get_dataloader(A    ,					not dispatch_batches			)
       lowercase__      =					AutoModelForSequenceClassification.from_pretrained(
           '''hf-internal-testing/mrpc-bert-base-cased'''    ,					return_dict=A			)
       lowercase__     ,lowercase__      =					accelerator.prepare(A    ,					A			)
       return {"ddp": [ddp_model, ddp_dataloader, "cuda:0"], "no": [model, dataloader, accelerator.device]}, accelerator

def _SCREAMING_SNAKE_CASE (A    ,					A    ,					A			)			->			Union[str, Any]:






       """simple docstring"""







       lowercase__      =					[]
       for batch in dataloader:
              lowercase__     ,lowercase__      =					batch.values()
              with torch.no_grad():
                     lowercase__      =					model(A			)
                     lowercase__     ,lowercase__      =					accelerator.gather_for_metrics((logit, target)			)
                     logits_and_targets.append((logit, target)			)
       lowercase__     ,lowercase__      =					[], []
       for logit, targ in logits_and_targets:
              logits.append(A			)
              targs.append(A			)
       lowercase__     ,lowercase__      =					torch.cat(A			), torch.cat(A			)
       return logits, targs

def _SCREAMING_SNAKE_CASE (A    ,					A=82    ,					A=False    ,					A=False    ,					A=16			)			->			Tuple:






       """simple docstring"""







       lowercase__     ,lowercase__     ,lowercase__      =					get_basic_setup(A    ,					A    ,					A			)
       lowercase__     ,lowercase__      =					generate_predictions(A    ,					A    ,					A			)
       assert (
           len(A			) == num_samples
       ), f"Unexpected number of inputs:\n    Expected: {num_samples}\n    Actual: {len(A			)}"

def _SCREAMING_SNAKE_CASE (A = False    ,					A = False			)			->			Union[str, Any]:






       """simple docstring"""







       lowercase__      =					evaluate.load('''glue'''    ,					'''mrpc'''			)
       lowercase__     ,lowercase__      =					get_mrpc_setup(A    ,					A			)
       # First do baseline
       lowercase__     ,lowercase__     ,lowercase__      =					setup['''no''']
       model.to(A			)
       model.eval()
       for batch in dataloader:
              batch.to(A			)
              with torch.inference_mode():
                     lowercase__      =					model(**A			)
              lowercase__      =					outputs.logits.argmax(dim=-1			)
              metric.add_batch(predictions=A    ,					references=batch['''labels''']			)
       lowercase__      =					metric.compute()

       # Then do distributed
       lowercase__     ,lowercase__     ,lowercase__      =					setup['''ddp''']
       model.eval()
       for batch in dataloader:
              with torch.inference_mode():
                     lowercase__      =					model(**A			)
              lowercase__      =					outputs.logits.argmax(dim=-1			)
              lowercase__      =					batch['''labels''']
              lowercase__     ,lowercase__      =					accelerator.gather_for_metrics((preds, references)			)
              metric.add_batch(predictions=A    ,					references=A			)
       lowercase__      =					metric.compute()

       for key in "accuracy f1".split():
              assert math.isclose(
                  baseline[key]    ,					distributed[key]			), f"Baseline and Distributed are not the same for key {key}:\n\tBaseline: {baseline[key]}\n\tDistributed: {distributed[key]}\n"

def _SCREAMING_SNAKE_CASE ()			->			Dict:






       """simple docstring"""







       lowercase__      =					Accelerator(split_batches=A    ,					dispatch_batches=A			)
       if accelerator.is_local_main_process:
              datasets.utils.logging.set_verbosity_warning()
              transformers.utils.logging.set_verbosity_warning()
       else:
              datasets.utils.logging.set_verbosity_error()
              transformers.utils.logging.set_verbosity_error()
       # These are a bit slower so they should only be ran on the GPU or TPU
       if torch.cuda.is_available() or is_tpu_available():
              if accelerator.is_local_main_process:
                     print('''**Testing gather_for_metrics**'''			)
              for split_batches in [True, False]:
                     for dispatch_batches in [True, False]:
                            if accelerator.is_local_main_process:
                                   print(f"With: `split_batches={split_batches}`, `dispatch_batches={dispatch_batches}`"			)
                            test_mrpc(A    ,					A			)
                            accelerator.state._reset_state()
       if accelerator.is_local_main_process:
              print('''**Test torch metrics**'''			)
       for split_batches in [True, False]:
              for dispatch_batches in [True, False]:
                     lowercase__      =					Accelerator(split_batches=A    ,					dispatch_batches=A			)
                     if accelerator.is_local_main_process:
                            print(f"With: `split_batches={split_batches}`, `dispatch_batches={dispatch_batches}`, length=99"			)
                     test_torch_metrics(A    ,					99			)
                     accelerator.state._reset_state()
       if accelerator.is_local_main_process:
              print('''**Test last batch is not dropped when perfectly divisible**'''			)
       lowercase__      =					Accelerator()
       test_torch_metrics(A    ,					512			)
       accelerator.state._reset_state()

def _SCREAMING_SNAKE_CASE (A			)			->			Optional[Any]:






       """simple docstring"""







       main()


if __name__ == "__main__":
 main()








'''simple docstring'''

import unittest

import numpy as np

def _SCREAMING_SNAKE_CASE (A    ,					A    ,					A    ,					A = None    ,					)			->			np.ndarray:






       """simple docstring"""







       lowercase__      =					np.shape(A			)
       lowercase__      =					np.shape(A			)
       lowercase__      =					np.shape(A			)

       if shape_a[0] != shape_b[0]:
              lowercase__      =					(
                  '''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(A			)

       if shape_b[1] != shape_c[1]:
              lowercase__      =					(
                  '''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(A			)

       lowercase__      =					pseudo_inv
       if a_inv is None:
              try:
                     lowercase__      =					np.linalg.inv(A			)
              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     __lowerCAmelCase		(unittest.TestCase	):





      '''simple docstring'''






      def  UpperCamelCase__						(self       :       Dict					):

             '''simple docstring'''







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

             lowercase__      =					schur_complement(UpperCamelCase  ,		UpperCamelCase  ,		UpperCamelCase					)

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

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

             self.assertAlmostEqual(UpperCamelCase  ,		det_a * det_s					)



      def  UpperCamelCase__						(self       :       Optional[int]					):

             '''simple docstring'''







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

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






      def  UpperCamelCase__						(self       :       Union[str, Any]					):

             '''simple docstring'''







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

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


if __name__ == "__main__":
 import doctest

 doctest.testmod()
 unittest.main()




