



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, DistributedType
from accelerate.utils import find_executable_batch_size


########################################################################
# This is a fully working simple example to use Accelerate,
# specifically showcasing how to ensure out-of-memory errors never
# interrupt training, 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)
#
# 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
#
########################################################################


__A   			=1_6
__A   			=3_2

def        a						(      _UpperCAmelCase							:					Accelerator ,   _UpperCAmelCase							:					int = 16						):







		'''simple docstring'''





		__UpperCAmelCase    :     Union[str, Any]													=	AutoTokenizer.from_pretrained('''bert-base-cased'''						)
		__UpperCAmelCase    :     Any													=	load_dataset('''glue''' ,   '''mrpc'''						)

		def tokenize_function(_UpperCAmelCase							:					int						):
				# max_length=None => use the model max length (it's actually the default)
				__UpperCAmelCase    :     Optional[Any]													=	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():
				__UpperCAmelCase    :     Tuple													=	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
		__UpperCAmelCase    :     Optional[Any]													=	tokenized_datasets.rename_column('''label''' ,   '''labels'''						)

		def collate_fn(_UpperCAmelCase							:					str						):
				# On TPU it's best to pad everything to the same length or training will be very slow.
				__UpperCAmelCase    :     Optional[Any]													=	1_28 if accelerator.distributed_type == DistributedType.TPU else None
				# When using mixed precision we want round multiples of 8/16
				if accelerator.mixed_precision == "fp8":
						__UpperCAmelCase    :     Union[str, Any]													=	16
				elif accelerator.mixed_precision != "no":
						__UpperCAmelCase    :     Dict													=	8
				else:
						__UpperCAmelCase    :     int													=	None

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

		# Instantiate dataloaders.
		__UpperCAmelCase    :     int													=	DataLoader(
		    tokenized_datasets['''train'''] ,   shuffle=_UpperCAmelCase ,   collate_fn=_UpperCAmelCase ,   batch_size=_UpperCAmelCase						)
		__UpperCAmelCase    :     List[Any]													=	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

				__A   			=mocked_dataloaders  # noqa: F811

def        a						(      _UpperCAmelCase							:					int ,   _UpperCAmelCase							:					List[Any]						):







		'''simple docstring'''





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

		__UpperCAmelCase    :     Optional[int]													=	evaluate.load('''glue''' ,   '''mrpc'''						)

		# New Code #
		# We now can define an inner training loop function. It should take a batch size as the only parameter,
		# and build the dataloaders in there.
		# It also gets our decorator
		@find_executable_batch_size(starting_batch_size=_UpperCAmelCase						)
		def inner_training_loop(_UpperCAmelCase							:					Optional[int]						):
				# And now just move everything below under this function
				# We need to bring in the Accelerator object from earlier
				nonlocal accelerator
				# And reset all of its attributes that could hold onto any memory:
				accelerator.free_memory()

				# Then we can declare the model, optimizer, and everything else:
				set_seed(_UpperCAmelCase						)

				# Instantiate the model (we build the model here so that the seed also control new weights initialization)
				__UpperCAmelCase    :     Dict													=	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).
				__UpperCAmelCase    :     Optional[int]													=	model.to(accelerator.device						)

				# Instantiate optimizer
				__UpperCAmelCase    :     Optional[Any]													=	AdamW(params=model.parameters() ,   lr=_UpperCAmelCase						)
				__UpperCAmelCase							,					__UpperCAmelCase    :     Optional[int]													=	get_dataloaders(_UpperCAmelCase ,   _UpperCAmelCase						)

				# Instantiate scheduler
				__UpperCAmelCase    :     Any													=	get_linear_schedule_with_warmup(
				    optimizer=_UpperCAmelCase ,   num_warmup_steps=1_00 ,   num_training_steps=(len(_UpperCAmelCase						) * 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    :     Optional[Any]													=	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						)
								__UpperCAmelCase    :     Any													=	model(**_UpperCAmelCase						)
								__UpperCAmelCase    :     Dict													=	outputs.loss
								accelerator.backward(_UpperCAmelCase						)
								optimizer.step()
								lr_scheduler.step()
								optimizer.zero_grad()

						model.eval()
						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():
										__UpperCAmelCase    :     Tuple													=	model(**_UpperCAmelCase						)
								__UpperCAmelCase    :     str													=	outputs.logits.argmax(dim=-1						)
								__UpperCAmelCase							,					__UpperCAmelCase    :     str													=	accelerator.gather_for_metrics((predictions, batch['''labels'''])						)
								metric.add_batch(
								    predictions=_UpperCAmelCase ,   references=_UpperCAmelCase ,   )

						__UpperCAmelCase    :     int													=	metric.compute()
						# Use accelerator.print to print only on the main process.
						accelerator.print(f'epoch {epoch}:' ,   _UpperCAmelCase						)

    # 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        a						(      ):







		'''simple docstring'''





		__UpperCAmelCase    :     Optional[Any]													=	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.'''						)
		__UpperCAmelCase    :     List[Any]													=	parser.parse_args()
		__UpperCAmelCase    :     List[str]													=	{'''lr''': 2e-5, '''num_epochs''': 3, '''seed''': 42, '''batch_size''': 16}
		training_function(_UpperCAmelCase ,   _UpperCAmelCase						)


if __name__ == "__main__":
				main()










from itertools import product

from cva import COLOR_BGR2GRAY, cvtColor, imread, imshow, waitKey
from numpy import dot, exp, mgrid, pi, ravel, square, uinta, zeros

def        a						(      _UpperCAmelCase							:					Tuple ,   _UpperCAmelCase							:					Optional[int]						):







		'''simple docstring'''





		__UpperCAmelCase    :     Union[str, Any]													=	k_size // 2
		__UpperCAmelCase							,					__UpperCAmelCase    :     Optional[Any]													=	mgrid[0 - center : k_size - center, 0 - center : k_size - center]
		__UpperCAmelCase    :     Any													=	1 / (2 * pi * sigma) * exp(-(square(_UpperCAmelCase						) + square(_UpperCAmelCase						)) / (2 * square(_UpperCAmelCase						))						)
		return g

def        a						(      _UpperCAmelCase							:					Optional[int] ,   _UpperCAmelCase							:					Any ,   _UpperCAmelCase							:					List[Any]						):







		'''simple docstring'''





		__UpperCAmelCase							,					__UpperCAmelCase    :     Union[str, Any]													=	image.shape[0], image.shape[1]
		# dst image height and width
		__UpperCAmelCase    :     str													=	height - k_size + 1
		__UpperCAmelCase    :     Optional[int]													=	width - k_size + 1

		# im2col, turn the k_size*k_size pixels into a row and np.vstack all rows
		__UpperCAmelCase    :     str													=	zeros((dst_height * dst_width, k_size * k_size)						)
		__UpperCAmelCase    :     Optional[Any]													=	0
		for i, j in product(range(_UpperCAmelCase						) ,   range(_UpperCAmelCase						)						):
				__UpperCAmelCase    :     int													=	ravel(image[i : i + k_size, j : j + k_size]						)
				__UpperCAmelCase    :     Union[str, Any]													=	window
				row += 1

		#  turn the kernel into shape(k*k, 1)
		__UpperCAmelCase    :     Tuple													=	gen_gaussian_kernel(_UpperCAmelCase ,   _UpperCAmelCase						)
		__UpperCAmelCase    :     List[Any]													=	ravel(_UpperCAmelCase						)

		# reshape and get the dst image
		__UpperCAmelCase    :     Optional[Any]													=	dot(_UpperCAmelCase ,   _UpperCAmelCase						).reshape(_UpperCAmelCase ,   _UpperCAmelCase						).astype(_UpperCAmelCase						)

		return dst


if __name__ == "__main__":
				# read original image
				__A   			=imread(R"../image_data/lena.jpg")
				# turn image in gray scale value
				__A   			=cvtColor(img, COLOR_BGR2GRAY)

				# get values with two different mask size
				__A   			=gaussian_filter(gray, 3, sigma=1)
				__A   			=gaussian_filter(gray, 5, sigma=0.8)

				# show result images
				imshow("gaussian filter with 3x3 mask", gaussianaxa)
				imshow("gaussian filter with 5x5 mask", gaussianaxa)
				waitKey()





