


'''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
#
########################################################################


UpperCAmelCase__  			=				1_6
UpperCAmelCase__  			=				3_2






def 						UpperCAmelCase__(				_SCREAMING_SNAKE_CASE					: Accelerator,_SCREAMING_SNAKE_CASE					: int = 16 ):






			"""simple docstring"""




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

			def tokenize_function(_SCREAMING_SNAKE_CASE					: Tuple ):
						# max_length=None => use the model max length (it's actually the default)
						__A=	tokenizer(examples['sentence1'],examples['sentence2'],truncation=SCREAMING_SNAKE_CASE_,max_length=SCREAMING_SNAKE_CASE_ )
						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(
						    SCREAMING_SNAKE_CASE_,batched=SCREAMING_SNAKE_CASE_,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(_SCREAMING_SNAKE_CASE					: List[str] ):
						# 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(
						    SCREAMING_SNAKE_CASE_,padding='longest',max_length=SCREAMING_SNAKE_CASE_,pad_to_multiple_of=SCREAMING_SNAKE_CASE_,return_tensors='pt',)

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

			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

					UpperCAmelCase__  			=				mocked_dataloaders  # noqa: F811






def 						UpperCAmelCase__(				_SCREAMING_SNAKE_CASE					: List[Any],_SCREAMING_SNAKE_CASE					: List[str] ):






			"""simple docstring"""




			if os.environ.get('TESTING_MOCKED_DATALOADERS',SCREAMING_SNAKE_CASE_ ) == "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(SCREAMING_SNAKE_CASE_ )
			__A,	__A=	get_dataloaders(SCREAMING_SNAKE_CASE_,SCREAMING_SNAKE_CASE_ )
			# 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=SCREAMING_SNAKE_CASE_ )

			# 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=SCREAMING_SNAKE_CASE_ )

			# Instantiate scheduler
			__A=	get_linear_schedule_with_warmup(
			    optimizer=SCREAMING_SNAKE_CASE_,num_warmup_steps=100,num_training_steps=(len(SCREAMING_SNAKE_CASE_ ) * 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(
			    SCREAMING_SNAKE_CASE_,SCREAMING_SNAKE_CASE_,SCREAMING_SNAKE_CASE_,SCREAMING_SNAKE_CASE_,SCREAMING_SNAKE_CASE_ )

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

						model.eval()
						__A=	0
						for step, batch in enumerate(SCREAMING_SNAKE_CASE_ ):
									# We could avoid this line since we set the accelerator with `device_placement=True`.
									batch.to(accelerator.device )
									with torch.no_grad():
												__A=	model(**SCREAMING_SNAKE_CASE_ )
									__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(SCREAMING_SNAKE_CASE_ ) - 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=SCREAMING_SNAKE_CASE_,references=SCREAMING_SNAKE_CASE_,)

						__A=	metric.compute()
						# Use accelerator.print to print only on the main process.
						accelerator.print(f"""epoch {epoch}:""",SCREAMING_SNAKE_CASE_ )






def 						UpperCAmelCase__(				):






			"""simple docstring"""




			__A=	argparse.ArgumentParser(description='Simple example of training script.' )
			parser.add_argument(
			    '--mixed_precision',type=SCREAMING_SNAKE_CASE_,default=SCREAMING_SNAKE_CASE_,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(SCREAMING_SNAKE_CASE_,SCREAMING_SNAKE_CASE_ )


if __name__ == "__main__":
					main()





import numpy as np
from cva import COLOR_BGR2GRAY, CV_8UC3, cvtColor, filteraD, imread, imshow, waitKey




def 	UpperCamelCase__  (     SCREAMING_SNAKE_CASE_	:	int		,   SCREAMING_SNAKE_CASE_	:	int		,   SCREAMING_SNAKE_CASE_	:	int		,   SCREAMING_SNAKE_CASE_	:	int		,   SCREAMING_SNAKE_CASE_	:	int		,   SCREAMING_SNAKE_CASE_	:	int       )  ->     np.ndarray:

						# prepare kernel
						# the kernel size have to be odd
						if (ksize % 2) == 0:
												_lowercase					       =					ksize + 1
						_lowercase					       =					np.zeros((ksize, ksize)		,   dtype=np.floataa       )

						# each value
						for y in range(SCREAMING_SNAKE_CASE_       ):
												for x in range(SCREAMING_SNAKE_CASE_       ):
																		# distance from center
																		_lowercase					       =					x - ksize // 2
																		_lowercase					       =					y - ksize // 2

																		# degree to radiant
																		_lowercase					       =					theta / 1_80 * np.pi
																		_lowercase					       =					np.cos(_theta       )
																		_lowercase					       =					np.sin(_theta       )

																		# get kernel x
																		_lowercase					       =					cos_theta * px + sin_theta * py

																		# get kernel y
																		_lowercase					       =					-sin_theta * px + cos_theta * py

																		# fill kernel
																		_lowercase					       =					np.exp(
																		    -(_x**2 + gamma**2 * _y**2) / (2 * sigma**2)       ) * np.cos(2 * np.pi * _x / lambd + psi       )

						return gabor


if __name__ == "__main__":
		import doctest

		doctest.testmod()
		# read original image
		A		: Optional[Any]								=     imread('''../image_data/lena.jpg''')
		# turn image in gray scale value
		A		: Optional[int]								=     cvtColor(img, COLOR_BGR2GRAY)

		# Apply multiple Kernel to detect edges
		A		: Optional[int]								=     np.zeros(gray.shape[:2])
		for theta in [0, 30, 60, 90, 120, 150]:
				A		: Optional[int]								=     gabor_filter_kernel(10, 8, theta, 10, 0, 0)
				out += filteraD(gray, CV_8UC3, kernel_aa)
		A		: int								=     out / out.max() * 255
		A		: str								=     out.astype(np.uinta)

		imshow('''Original''', gray)
		imshow('''Gabor filter with 20x20 mask and 6 directions''', out)

		waitKey(0)