






"""simple docstring"""







import argparse
import os

# New Code #
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
from accelerate.utils import find_executable_batch_size


########################################################################
# This is a fully working simple example to use Accelerate,
# specifically showcasing how to combine both the gradient accumulation
# and automatic batch size finder utilities of Accelerate to perfrom
# automatic gradient accumulation
#
# 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)
#
# 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_		:			Optional[Any]				   =					3_2






def 					_lowerCAmelCase				(					lowerCAmelCase		,					lowerCAmelCase = 16						):







       '''simple docstring'''
       UpperCAmelCase					=	AutoTokenizer.from_pretrained("""bert-base-cased"""						)
       UpperCAmelCase					=	load_dataset("""glue"""		,					"""mrpc"""						)

       def tokenize_function(lowerCAmelCase						):
              # max_length=None => use the model max length (it's actually the default)
              UpperCAmelCase					=	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():
              UpperCAmelCase					=	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
       UpperCAmelCase					=	tokenized_datasets.rename_column("""label"""		,					"""labels"""						)

       def collate_fn(lowerCAmelCase						):
              # When using mixed precision we want round multiples of 8/16
              if accelerator.mixed_precision == "fp8":
                     UpperCAmelCase					=	16
              elif accelerator.mixed_precision != "no":
                     UpperCAmelCase					=	8
              else:
                     UpperCAmelCase					=	None

              return tokenizer.pad(
                  lowerCAmelCase		,					padding="""longest"""		,					pad_to_multiple_of=lowerCAmelCase		,					return_tensors="""pt"""		,					)

       # Instantiate dataloaders.
       UpperCAmelCase					=	DataLoader(
           tokenized_datasets["""train"""]		,					shuffle=lowerCAmelCase		,					collate_fn=lowerCAmelCase		,					batch_size=lowerCAmelCase						)
       UpperCAmelCase					=	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_		:			int				   =					mocked_dataloaders  # noqa: F811






def 					_lowerCAmelCase				(					lowerCAmelCase		,					lowerCAmelCase						):







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

       UpperCAmelCase					=	evaluate.load("""glue"""		,					"""mrpc"""						)

       # New Code #
       # We use the `find_executable_batch_size` decorator, passing in the desired observed batch size
       # to train on. If a CUDA OOM error occurs, it will retry this loop cutting the batch size in
       # half each time. From this, we can calculate the number of gradient accumulation steps needed
       # and modify the Accelerator object as a result
       @find_executable_batch_size(starting_batch_size=int(lowerCAmelCase						)						)
       def inner_training_loop(lowerCAmelCase						):
              # Since we need to modify the outside accelerator object, we need to bring it
              # to the local scope
              nonlocal accelerator

              # We can calculate the number of gradient accumulation steps based on the current
              # batch size vs the starting batch size
              UpperCAmelCase					=	observed_batch_size // batch_size

              # And then set it in the Accelerator directly:
              UpperCAmelCase					=	num_gradient_accumulation_steps

              # Next we need to free all of the stored model references in the Accelerator each time
              accelerator.free_memory()

              # And set the seed so our results are reproducable each reset
              set_seed(lowerCAmelCase						)

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

              # Instantiate optimizer
              UpperCAmelCase					=	AdamW(params=model.parameters()		,					lr=lowerCAmelCase						)
              UpperCAmelCase			,				UpperCAmelCase					=	get_dataloaders(lowerCAmelCase		,					lowerCAmelCase						)

              # Instantiate scheduler
              UpperCAmelCase					=	get_linear_schedule_with_warmup(
                  optimizer=lowerCAmelCase		,					num_warmup_steps=100		,					num_training_steps=(len(lowerCAmelCase						) * num_epochs)		,					)

              # 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.
              UpperCAmelCase			,				UpperCAmelCase			,				UpperCAmelCase			,				UpperCAmelCase			,				UpperCAmelCase					=	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						):
                            # And perform gradient accumulation
                            with accelerator.accumulate(lowerCAmelCase						):
                                   # We could avoid this line since we set the accelerator with `device_placement=True`.
                                   batch.to(accelerator.device						)
                                   UpperCAmelCase					=	model(**lowerCAmelCase						)
                                   UpperCAmelCase					=	outputs.loss
                                   accelerator.backward(lowerCAmelCase						)
                                   optimizer.step()
                                   lr_scheduler.step()
                                   optimizer.zero_grad()

                     model.eval()
                     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():
                                   UpperCAmelCase					=	model(**lowerCAmelCase						)
                            UpperCAmelCase					=	outputs.logits.argmax(dim=-1						)
                            UpperCAmelCase			,				UpperCAmelCase					=	accelerator.gather_for_metrics((predictions, batch["""labels"""])						)
                            metric.add_batch(
                                predictions=lowerCAmelCase		,					references=lowerCAmelCase		,					)

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

    # New Code #
    # And call it at the end with no arguments
    # Note: You could also refactor this outside of your training loop function
       inner_training_loop()






def 					_lowerCAmelCase				(					):







       '''simple docstring'''
       UpperCAmelCase					=	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."""						)
       UpperCAmelCase					=	parser.parse_args()
       # New Code #
       # We modify the starting batch size to be an observed batch size of 256, to guarentee an initial CUDA OOM
       UpperCAmelCase					=	{"""lr""": 2e-5, """num_epochs""": 3, """seed""": 42, """batch_size""": 256}
       training_function(lowerCAmelCase		,					lowerCAmelCase						)


if __name__ == "__main__":
 main()








"""simple docstring"""







import cva
import numpy as np

from digital_image_processing.filters.convolve import img_convolve
from digital_image_processing.filters.sobel_filter import sobel_filter

lowerCAmelCase_		:			List[Any]				   =					1_8_0






def 					_lowerCAmelCase				(					lowerCAmelCase		,					lowerCAmelCase						):







       '''simple docstring'''
       UpperCAmelCase					=	k_size // 2
       UpperCAmelCase			,				UpperCAmelCase					=	np.mgrid[0 - center : k_size - center, 0 - center : k_size - center]
       UpperCAmelCase					=	(
           1
           / (2 * np.pi * sigma)
           * np.exp(-(np.square(lowerCAmelCase						) + np.square(lowerCAmelCase						)) / (2 * np.square(lowerCAmelCase						))						)
       )
       return g






def 					_lowerCAmelCase				(					lowerCAmelCase		,					lowerCAmelCase		,					lowerCAmelCase						):







       '''simple docstring'''
       UpperCAmelCase					=	np.zeros(lowerCAmelCase						)

       for row in range(1		,					image_shape[0] - 1						):
              for col in range(1		,					image_shape[1] - 1						):
                     UpperCAmelCase					=	gradient_direction[row, col]

                     if (
                         0 <= direction < PI / 8
                         or 15 * PI / 8 <= direction <= 2 * PI
                         or 7 * PI / 8 <= direction <= 9 * PI / 8
                     ):
                            UpperCAmelCase					=	sobel_grad[row, col - 1]
                            UpperCAmelCase					=	sobel_grad[row, col + 1]
                            if sobel_grad[row, col] >= w and sobel_grad[row, col] >= e:
                                   UpperCAmelCase					=	sobel_grad[row, col]

                     elif (
                         PI / 8 <= direction < 3 * PI / 8
                         or 9 * PI / 8 <= direction < 11 * PI / 8
                     ):
                            UpperCAmelCase					=	sobel_grad[row + 1, col - 1]
                            UpperCAmelCase					=	sobel_grad[row - 1, col + 1]
                            if sobel_grad[row, col] >= sw and sobel_grad[row, col] >= ne:
                                   UpperCAmelCase					=	sobel_grad[row, col]

                     elif (
                         3 * PI / 8 <= direction < 5 * PI / 8
                         or 11 * PI / 8 <= direction < 13 * PI / 8
                     ):
                            UpperCAmelCase					=	sobel_grad[row - 1, col]
                            UpperCAmelCase					=	sobel_grad[row + 1, col]
                            if sobel_grad[row, col] >= n and sobel_grad[row, col] >= s:
                                   UpperCAmelCase					=	sobel_grad[row, col]

                     elif (
                         5 * PI / 8 <= direction < 7 * PI / 8
                         or 13 * PI / 8 <= direction < 15 * PI / 8
                     ):
                            UpperCAmelCase					=	sobel_grad[row - 1, col - 1]
                            UpperCAmelCase					=	sobel_grad[row + 1, col + 1]
                            if sobel_grad[row, col] >= nw and sobel_grad[row, col] >= se:
                                   UpperCAmelCase					=	sobel_grad[row, col]

       return destination






def 					_lowerCAmelCase				(					lowerCAmelCase		,					lowerCAmelCase		,					lowerCAmelCase		,					lowerCAmelCase		,					lowerCAmelCase		,					lowerCAmelCase						):







       '''simple docstring'''
       for row in range(1		,					image_shape[0] - 1						):
              for col in range(1		,					image_shape[1] - 1						):
                     if destination[row, col] >= threshold_high:
                            UpperCAmelCase					=	strong
                     elif destination[row, col] <= threshold_low:
                            UpperCAmelCase					=	0
                     else:
                            UpperCAmelCase					=	weak






def 					_lowerCAmelCase				(					lowerCAmelCase		,					lowerCAmelCase		,					lowerCAmelCase		,					lowerCAmelCase						):







       '''simple docstring'''
       for row in range(1		,					image_shape[0]						):
              for col in range(1		,					image_shape[1]						):
                     if destination[row, col] == weak:
                            if 255 in (
                                destination[row, col + 1],
                                destination[row, col - 1],
                                destination[row - 1, col],
                                destination[row + 1, col],
                                destination[row - 1, col - 1],
                                destination[row + 1, col - 1],
                                destination[row - 1, col + 1],
                                destination[row + 1, col + 1],
                            ):
                                   UpperCAmelCase					=	strong
                            else:
                                   UpperCAmelCase					=	0






def 					_lowerCAmelCase				(					lowerCAmelCase		,					lowerCAmelCase=15		,					lowerCAmelCase=30		,					lowerCAmelCase=128		,					lowerCAmelCase=255						):







       '''simple docstring'''
       # gaussian_filter
       UpperCAmelCase					=	img_convolve(lowerCAmelCase		,					gen_gaussian_kernel(9		,					sigma=1.4						)						)
       # get the gradient and degree by sobel_filter
       UpperCAmelCase			,				UpperCAmelCase					=	sobel_filter(lowerCAmelCase						)
       UpperCAmelCase					=	PI + np.radadeg(lowerCAmelCase						)

       UpperCAmelCase					=	suppress_non_maximum(image.shape		,					lowerCAmelCase		,					lowerCAmelCase						)

       detect_high_low_threshold(
           image.shape		,					lowerCAmelCase		,					lowerCAmelCase		,					lowerCAmelCase		,					lowerCAmelCase		,					lowerCAmelCase						)

       track_edge(image.shape		,					lowerCAmelCase		,					lowerCAmelCase		,					lowerCAmelCase						)

       return destination


if __name__ == "__main__":
 # read original image in gray mode
 lowerCAmelCase_		:			Optional[Any]				   =					cva.imread(R'''../image_data/lena.jpg''', 0)
 # canny edge detection
 lowerCAmelCase_		:			Dict				   =					canny(lena)
 cva.imshow('''canny''', canny_destination)
 cva.waitKey(0)
