"""simple docstring"""




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   UpperCAmelCase					(	UpperCamelCase__       ,    UpperCamelCase__ = 16				):






							"""simple docstring"""





							A__	     =							AutoTokenizer.from_pretrained('bert-base-cased'				)
							A__	     =							load_dataset('glue'       ,    'mrpc'				)

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

							def collate_fn(UpperCamelCase__				):
														# On TPU it's best to pad everything to the same length or training will be very slow.
														A__	     =							128 if accelerator.distributed_type == DistributedType.TPU else None
														# When using mixed precision we want round multiples of 8/16
														if accelerator.mixed_precision == "fp8":
																					A__	     =							16
														elif accelerator.mixed_precision != "no":
																					A__	     =							8
														else:
																					A__	     =							None

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

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

							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   UpperCAmelCase					(	UpperCamelCase__       ,    UpperCamelCase__				):






							"""simple docstring"""





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

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

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

							set_seed(UpperCamelCase__				)
							A__     ,  A__	     =							get_dataloaders(UpperCamelCase__       ,    UpperCamelCase__				)
							# Instantiate the model (we build the model here so that the seed also control new weights initialization)
							A__	     =							AutoModelForSequenceClassification.from_pretrained('bert-base-cased'       ,    return_dict=UpperCamelCase__				)

							# 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).
							A__	     =							model.to(accelerator.device				)

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

							# Instantiate scheduler
							A__	     =							get_linear_schedule_with_warmup(
							    optimizer=UpperCamelCase__       ,    num_warmup_steps=100       ,    num_training_steps=(len(UpperCamelCase__				) * 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.
							A__     ,  A__     ,  A__     ,  A__     ,  A__	     =							accelerator.prepare(
							    UpperCamelCase__       ,    UpperCamelCase__       ,    UpperCamelCase__       ,    UpperCamelCase__       ,    UpperCamelCase__				)

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

														model.eval()
														A__	     =							0
														for step, batch in enumerate(UpperCamelCase__				):
																					# We could avoid this line since we set the accelerator with `device_placement=True`.
																					batch.to(accelerator.device				)
																					with torch.no_grad():
																												A__	     =							model(**UpperCamelCase__				)
																					A__	     =							outputs.logits.argmax(dim=-1				)
																					A__     ,  A__	     =							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(UpperCamelCase__				) - 1:
																																			# Last batch needs to be truncated on distributed systems as it contains additional samples
																																			A__	     =							predictions[: len(eval_dataloader.dataset				) - samples_seen]
																																			A__	     =							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=UpperCamelCase__       ,    references=UpperCamelCase__       ,    )

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






def   UpperCAmelCase					(	):






							"""simple docstring"""





							A__	     =							argparse.ArgumentParser(description='Simple example of training script.'				)
							parser.add_argument(
							    '--mixed_precision'       ,    type=UpperCamelCase__       ,    default=UpperCamelCase__       ,    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.'				)
							A__	     =							parser.parse_args()
							A__	     =							{'lr': 2E-5, 'num_epochs': 3, 'seed': 42, 'batch_size': 16}
							training_function(UpperCamelCase__       ,    UpperCamelCase__				)


if __name__ == "__main__":
				main()






"""simple docstring"""




import numpy as np
from PIL import Image






def   UpperCAmelCase					(	UpperCamelCase__       ,    UpperCamelCase__       ,    UpperCamelCase__				):






							"""simple docstring"""





							A__	     =							np.array(UpperCamelCase__				)
							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   UpperCAmelCase					(	UpperCamelCase__       ,    UpperCamelCase__       ,    UpperCamelCase__				):






							"""simple docstring"""





							A__	     =							np.array(UpperCamelCase__				)
							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
				__lowerCamelCase											=  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()





