






"""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, 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
#
########################################################################


lowerCamelCase_       			=   16
lowerCamelCase_       			=   32

def        snake_case   (     A__			,A__ = 16		):
			UpperCAmelCase_  :				List[Any]      =      AutoTokenizer.from_pretrained("bert-base-cased"		)
			UpperCAmelCase_  :				int      =      load_dataset("glue"			,"mrpc"		)

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

			def collate_fn(A__		):
						# On TPU it's best to pad everything to the same length or training will be very slow.
						UpperCAmelCase_  :				List[str]      =      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_  :				Tuple      =      16
						elif accelerator.mixed_precision != "no":
									UpperCAmelCase_  :				Optional[int]      =      8
						else:
									UpperCAmelCase_  :				List[str]      =      None

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

			# Instantiate dataloaders.
			UpperCAmelCase_  :				Any      =      DataLoader(
			    tokenized_datasets["train"]			,shuffle=A__			,collate_fn=A__			,batch_size=A__		)
			UpperCAmelCase_  :				Union[str, Any]      =      DataLoader(
			    tokenized_datasets["validation"]			,shuffle=A__			,collate_fn=A__			,batch_size=A__		)

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

			UpperCAmelCase_  :				List[Any]      =      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=A__		)
			def inner_training_loop(A__		):
						# 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(A__		)

						# Instantiate the model (we build the model here so that the seed also control new weights initialization)
						UpperCAmelCase_  :				List[str]      =      AutoModelForSequenceClassification.from_pretrained("bert-base-cased"			,return_dict=A__		)

						# 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_  :				int      =      model.to(accelerator.device		)

						# Instantiate optimizer
						UpperCAmelCase_  :				Union[str, Any]      =      AdamW(params=model.parameters()			,lr=A__		)
						UpperCAmelCase_  ,			UpperCAmelCase_  :				Dict      =      get_dataloaders(A__			,A__		)

						# Instantiate scheduler
						UpperCAmelCase_  :				Optional[Any]      =      get_linear_schedule_with_warmup(
						    optimizer=A__			,num_warmup_steps=1_00			,num_training_steps=(len(A__		) * 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_  :				int      =      accelerator.prepare(
						    A__			,A__			,A__			,A__			,A__		)

						# Now we train the model
						for epoch in range(A__		):
									model.train()
									for step, batch in enumerate(A__		):
												# We could avoid this line since we set the accelerator with `device_placement=True`.
												batch.to(accelerator.device		)
												UpperCAmelCase_  :				int      =      model(**A__		)
												UpperCAmelCase_  :				List[Any]      =      outputs.loss
												accelerator.backward(A__		)
												optimizer.step()
												lr_scheduler.step()
												optimizer.zero_grad()

									model.eval()
									for step, batch in enumerate(A__		):
												# We could avoid this line since we set the accelerator with `device_placement=True`.
												batch.to(accelerator.device		)
												with torch.no_grad():
															UpperCAmelCase_  :				int      =      model(**A__		)
												UpperCAmelCase_  :				List[Any]      =      outputs.logits.argmax(dim=-1		)
												UpperCAmelCase_  ,			UpperCAmelCase_  :				str      =      accelerator.gather_for_metrics((predictions, batch["labels"])		)
												metric.add_batch(
												    predictions=A__			,references=A__			,)

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

    # 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        snake_case   (     ):
			UpperCAmelCase_  :				int      =      argparse.ArgumentParser(description="Simple example of training script."		)
			parser.add_argument(
			    "--mixed_precision"			,type=A__			,default=A__			,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_  :				Dict      =      parser.parse_args()
			UpperCAmelCase_  :				Optional[Any]      =      {"lr": 2e-5, "num_epochs": 3, "seed": 42, "batch_size": 16}
			training_function(A__			,A__		)


if __name__ == "__main__":
			main()











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

def        snake_case   (     A__			,A__			,A__			,A__			,A__			,A__		):

			# prepare kernel
			# the kernel size have to be odd
			if (ksize % 2) == 0:
						UpperCAmelCase_  :				List[Any]      =      ksize + 1
			UpperCAmelCase_  :				Optional[Any]      =      np.zeros((ksize, ksize)			,dtype=np.floataa		)

			# each value
			for y in range(A__		):
						for x in range(A__		):
									# distance from center
									UpperCAmelCase_  :				Tuple      =      x - ksize // 2
									UpperCAmelCase_  :				Any      =      y - ksize // 2

									# degree to radiant
									UpperCAmelCase_  :				int      =      theta / 1_80 * np.pi
									UpperCAmelCase_  :				Optional[int]      =      np.cos(_theta		)
									UpperCAmelCase_  :				Union[str, Any]      =      np.sin(_theta		)

									# get kernel x
									UpperCAmelCase_  :				Tuple      =      cos_theta * px + sin_theta * py

									# get kernel y
									UpperCAmelCase_  :				List[str]      =      -sin_theta * px + cos_theta * py

									# fill kernel
									UpperCAmelCase_  :				Dict      =      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
			lowerCamelCase_       			=   imread('''../image_data/lena.jpg''')
			# turn image in gray scale value
			lowerCamelCase_       			=   cvtColor(img, COLOR_BGR2GRAY)

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

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

			waitKey(0)



