


'''simple docstring'''
import numpy as np
import torch
from torch.utils.data import DataLoader

from accelerate.utils.dataclasses import DistributedType
class    lowercase	:

		"""simple docstring"""

		def __init__(		self    ,   UpperCamelCase_=2    ,   UpperCamelCase_=3    ,   UpperCamelCase_=64    ,   UpperCamelCase_=None							):

							'''simple docstring'''


							UpperCamelCase__   :Optional[int]												= np.random.default_rng(UpperCamelCase_							)
							UpperCamelCase__   :Tuple												= length
							UpperCamelCase__   :Optional[int]												= rng.normal(size=(length,)							).astype(np.floataa							)
							UpperCamelCase__   :str												= a * self.x + b + rng.normal(scale=0.1    ,   size=(length,)							).astype(np.floataa							)




		def __len__(		self							):

							'''simple docstring'''


							return self.length




		def __getitem__(		self    ,   UpperCamelCase_							):

							'''simple docstring'''


							return {"x": self.x[i], "y": self.y[i]}



class    lowercase	(							torch.nn.Module					):

		"""simple docstring"""

		def __init__(		self    ,   UpperCamelCase_=0    ,   UpperCamelCase_=0    ,   UpperCamelCase_=False							):

							'''simple docstring'''


							super().__init__()
							UpperCamelCase__   :Tuple												= torch.nn.Parameter(torch.tensor([2, 3]							).float()							)
							UpperCamelCase__   :List[str]												= torch.nn.Parameter(torch.tensor([2, 3]							).float()							)
							UpperCamelCase__   :Tuple												= True




		def     lowerCAmelCase__				(		self    ,   UpperCamelCase_=None							):

							'''simple docstring'''


							if self.first_batch:
												print(F'''Model dtype: {self.a.dtype}, {self.b.dtype}. Input dtype: {x.dtype}'''							)
												UpperCamelCase__   :Tuple												= False
							return x * self.a[0] + self.b[0]



class    lowercase	(							torch.nn.Module					):

		"""simple docstring"""

		def __init__(		self    ,   UpperCamelCase_=0    ,   UpperCamelCase_=0    ,   UpperCamelCase_=False							):

							'''simple docstring'''


							super().__init__()
							UpperCamelCase__   :List[str]												= torch.nn.Parameter(torch.tensor(UpperCamelCase_							).float()							)
							UpperCamelCase__   :List[Any]												= torch.nn.Parameter(torch.tensor(UpperCamelCase_							).float()							)
							UpperCamelCase__   :str												= True




		def     lowerCAmelCase__				(		self    ,   UpperCamelCase_=None							):

							'''simple docstring'''


							if self.first_batch:
												print(F'''Model dtype: {self.a.dtype}, {self.b.dtype}. Input dtype: {x.dtype}'''							)
												UpperCamelCase__   :Optional[int]												= False
							return x * self.a + self.b



def 			a      (			__a ,					__a = 16   )  ->	Union[str, Any]:



					'''simple docstring'''






					from datasets import load_dataset
					from transformers import AutoTokenizer

					UpperCamelCase__   :Any												= AutoTokenizer.from_pretrained('''bert-base-cased'''   )
					UpperCamelCase__   :Tuple												= {'''train''': '''tests/test_samples/MRPC/train.csv''', '''validation''': '''tests/test_samples/MRPC/dev.csv'''}
					UpperCamelCase__   :Dict												= load_dataset('''csv''' ,					data_files=__a   )
					UpperCamelCase__   :int												= datasets['''train'''].unique('''label'''   )

					UpperCamelCase__   :List[str]												= {v: i for i, v in enumerate(__a   )}

					def tokenize_function(__a   ):
										# max_length=None => use the model max length (it's actually the default)
										UpperCamelCase__   :Any												= tokenizer(
										    examples['''sentence1'''] ,					examples['''sentence2'''] ,					truncation=__a ,					max_length=__a ,					padding='''max_length'''   )
										if "label" in examples:
															UpperCamelCase__   :str												= [label_to_id[l] for l in examples['''label''']]
										return outputs

					# Apply the method we just defined to all the examples in all the splits of the dataset
					UpperCamelCase__   :str												= datasets.map(
					    __a ,					batched=__a ,					remove_columns=['''sentence1''', '''sentence2''', '''label'''] ,					)

					def collate_fn(__a   ):
										# On TPU it's best to pad everything to the same length or training will be very slow.
										if accelerator.distributed_type == DistributedType.TPU:
															return tokenizer.pad(__a ,					padding='''max_length''' ,					max_length=128 ,					return_tensors='''pt'''   )
										return tokenizer.pad(__a ,					padding='''longest''' ,					return_tensors='''pt'''   )

					# Instantiate dataloaders.
					UpperCamelCase__   :int												= DataLoader(tokenized_datasets['''train'''] ,					shuffle=__a ,					collate_fn=__a ,					batch_size=2   )
					UpperCamelCase__   :Dict												= DataLoader(tokenized_datasets['''validation'''] ,					shuffle=__a ,					collate_fn=__a ,					batch_size=1   )

					return train_dataloader, eval_dataloader



"""simple docstring"""


import numpy as np
from cva import COLOR_BGR2GRAY, cvtColor, imread
from numpy import array, uinta
from PIL import Image

from digital_image_processing import change_contrast as cc
from digital_image_processing import convert_to_negative as cn
from digital_image_processing import sepia as sp
from digital_image_processing.dithering import burkes as bs
from digital_image_processing.edge_detection import canny
from digital_image_processing.filters import convolve as conv
from digital_image_processing.filters import gaussian_filter as gg
from digital_image_processing.filters import local_binary_pattern as lbp
from digital_image_processing.filters import median_filter as med
from digital_image_processing.filters import sobel_filter as sob
from digital_image_processing.resize import resize as rs

A     :   Union[str, Any]			    =			imread(R"digital_image_processing/image_data/lena_small.jpg")
A     :   Optional[Any]			    =			cvtColor(img, COLOR_BGR2GRAY)


def _lowerCamelCase	(		):

			'''simple docstring'''




			__lowerCAmelCase				    =			cn.convert_to_negative(_UpperCamelCase      )
			# assert negative_img array for at least one True
			assert negative_img.any()


def _lowerCamelCase	(		):

			'''simple docstring'''




			with Image.open("digital_image_processing/image_data/lena_small.jpg"      ) as img:
						# Work around assertion for response
						assert str(cc.change_contrast(_UpperCamelCase    , 110      )      ).startswith(
						    "<PIL.Image.Image image mode=RGB size=100x100 at"      )


def _lowerCamelCase	(		):

			'''simple docstring'''




			__lowerCAmelCase				    =			canny.gen_gaussian_kernel(9    , sigma=1.4      )
			# Assert ambiguous array
			assert resp.all()


def _lowerCamelCase	(		):

			'''simple docstring'''




			__lowerCAmelCase				    =			imread("digital_image_processing/image_data/lena_small.jpg"    , 0      )
			# assert ambiguous array for all == True
			assert canny_img.all()
			__lowerCAmelCase				    =			canny.canny(_UpperCamelCase      )
			# assert canny array for at least one True
			assert canny_array.any()


def _lowerCamelCase	(		):

			'''simple docstring'''




			assert gg.gaussian_filter(_UpperCamelCase    , 5    , sigma=0.9      ).all()


def _lowerCamelCase	(		):

			'''simple docstring'''




			__lowerCAmelCase				    =			array([[0.25, 0.5, 0.25], [0.5, -3, 0.5], [0.25, 0.5, 0.25]]      )
			__lowerCAmelCase				    =			conv.img_convolve(_UpperCamelCase    , _UpperCamelCase      ).astype(_UpperCamelCase      )
			assert res.any()


def _lowerCamelCase	(		):

			'''simple docstring'''




			assert med.median_filter(_UpperCamelCase    , 3      ).any()


def _lowerCamelCase	(		):

			'''simple docstring'''




			__lowerCAmelCase    ,	__lowerCAmelCase				    =			sob.sobel_filter(_UpperCamelCase      )
			assert grad.any() and theta.any()


def _lowerCamelCase	(		):

			'''simple docstring'''




			__lowerCAmelCase				    =			sp.make_sepia(_UpperCamelCase    , 20      )
			assert sepia.all()


def _lowerCamelCase	(		_UpperCamelCase = "digital_image_processing/image_data/lena_small.jpg"      ):

			'''simple docstring'''




			__lowerCAmelCase				    =			bs.Burkes(imread(_UpperCamelCase    , 1      )    , 120      )
			burkes.process()
			assert burkes.output_img.any()


def _lowerCamelCase	(		_UpperCamelCase = "digital_image_processing/image_data/lena_small.jpg"    , ):

			'''simple docstring'''




			__lowerCAmelCase				    =			rs.NearestNeighbour(imread(_UpperCamelCase    , 1      )    , 400    , 200      )
			nn.process()
			assert nn.output.any()


def _lowerCamelCase	(		):

			'''simple docstring'''




			__lowerCAmelCase				    =			"digital_image_processing/image_data/lena.jpg"

			# Reading the image and converting it to grayscale.
			__lowerCAmelCase				    =			imread(_UpperCamelCase    , 0      )

			# Test for get_neighbors_pixel function() return not None
			__lowerCAmelCase				    =			0
			__lowerCAmelCase				    =			0
			__lowerCAmelCase				    =			image[x_coordinate][y_coordinate]

			__lowerCAmelCase				    =			lbp.get_neighbors_pixel(
			    _UpperCamelCase    , _UpperCamelCase    , _UpperCamelCase    , _UpperCamelCase      )

			assert neighbors_pixels is not None

			# Test for local_binary_pattern function()
			# Create a numpy array as the same height and width of read image
			__lowerCAmelCase				    =			np.zeros((image.shape[0], image.shape[1])      )

			# Iterating through the image and calculating the local binary pattern value
			# for each pixel.
			for i in range(0    , image.shape[0]      ):
						for j in range(0    , image.shape[1]      ):
									__lowerCAmelCase				    =			lbp.local_binary_value(_UpperCamelCase    , _UpperCamelCase    , _UpperCamelCase      )

			assert lbp_image.any()




