


'''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							:   Dict  						=	'true'

def _SCREAMING_SNAKE_CASE      (A  , A=82  , A=16       )       ->       List[Any]:






       """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       )       ->       Dict:






       """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       )       ->       str:






       """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       )       ->       Union[str, Any]:






       """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       )       ->       List[str]:






       """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      ()       ->       List[Any]:






       """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       )       ->       Any:






       """simple docstring"""







       main()


if __name__ == "__main__":
 main()











import numpy as np
from PIL import Image



def _a				(	lowerCamelCase:   np.ndarray					,				lowerCamelCase:   int					,				lowerCamelCase:   int )	->						np.ndarray:







      '''simple docstring'''






      __A          =				np.array(lowerCamelCase )
      if arr.shape[0] != arr.shape[1]:
            raise ValueError('''The input array is not a square matrix''' )
      __A          =				0
      __A          =				0
      __A          =				0
      __A          =				0

      # compute the shape of the output matrix
      __A          =				(arr.shape[0] - size) // stride + 1
      # initialize the output matrix with zeros of shape maxpool_shape
      __A          =				np.zeros((maxpool_shape, maxpool_shape) )

      while i < arr.shape[0]:
            if i + size > arr.shape[0]:
                  # if the end of the matrix is reached, break
                  break
            while j < arr.shape[1]:
                  # if the end of the matrix is reached, break
                  if j + size > arr.shape[1]:
                        break
                  # compute the maximum of the pooling matrix
                  __A          =				np.max(arr[i : i + size, j : j + size] )
                  # shift the pooling matrix by stride of column pixels
                  j += stride
                  mat_j += 1

            # shift the pooling matrix by stride of row pixels
            i += stride
            mat_i += 1

            # reset the column index to 0
            __A          =				0
            __A          =				0

      return updated_arr



def _a				(	lowerCamelCase:   np.ndarray					,				lowerCamelCase:   int					,				lowerCamelCase:   int )	->						np.ndarray:







      '''simple docstring'''






      __A          =				np.array(lowerCamelCase )
      if arr.shape[0] != arr.shape[1]:
            raise ValueError('''The input array is not a square matrix''' )
      __A          =				0
      __A          =				0
      __A          =				0
      __A          =				0

      # compute the shape of the output matrix
      __A          =				(arr.shape[0] - size) // stride + 1
      # initialize the output matrix with zeros of shape avgpool_shape
      __A          =				np.zeros((avgpool_shape, avgpool_shape) )

      while i < arr.shape[0]:
            # if the end of the matrix is reached, break
            if i + size > arr.shape[0]:
                  break
            while j < arr.shape[1]:
                  # if the end of the matrix is reached, break
                  if j + size > arr.shape[1]:
                        break
                  # compute the average of the pooling matrix
                  __A          =				int(np.average(arr[i : i + size, j : j + size] ) )
                  # shift the pooling matrix by stride of column pixels
                  j += stride
                  mat_j += 1

            # shift the pooling matrix by stride of row pixels
            i += stride
            mat_i += 1
            # reset the column index to 0
            __A          =				0
            __A          =				0

      return updated_arr


# Main Function
if __name__ == "__main__":
       from doctest import testmod

       testmod(name='avgpooling', verbose=True)

       # Loading the image
       snake_case__					:							Optional[Any]													=     Image.open('path_to_image')

       # Converting the image to numpy array and maxpooling, displaying the result
       # Ensure that the image is a square matrix

       Image.fromarray(maxpooling(np.array(image), size=3, stride=2)).show()

       # Converting the image to numpy array and averagepooling, displaying the result
       # Ensure that the image is a square matrix

       Image.fromarray(avgpooling(np.array(image), size=3, stride=2)).show()





