

"""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

lowerCamelCase__					:	Dict				    =	imread(r'''digital_image_processing/image_data/lena_small.jpg''')
lowerCamelCase__					:	List[Any]				    =	cvtColor(img, COLOR_BGR2GRAY)
def    UpperCamelCase       (      )					->      Optional[Any]:
	_UpperCAmelCase     :				Optional[Any]          =       cn.convert_to_negative(__UpperCAmelCase			)
	# assert negative_img array for at least one True
	assert negative_img.any()
def    UpperCamelCase       (      )					->      List[Any]:







	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    UpperCamelCase       (      )					->      List[Any]:
	_UpperCAmelCase     :				str          =       canny.gen_gaussian_kernel(9,	sigma=1.4			)
	# Assert ambiguous array
	assert resp.all()
def    UpperCamelCase       (      )					->      Dict:
	_UpperCAmelCase     :				Union[str, Any]          =       imread("""digital_image_processing/image_data/lena_small.jpg""",	0			)
	# assert ambiguous array for all == True
	assert canny_img.all()
	_UpperCAmelCase     :				Tuple          =       canny.canny(__UpperCAmelCase			)
	# assert canny array for at least one True
	assert canny_array.any()
def    UpperCamelCase       (      )					->      Union[str, Any]:
	assert gg.gaussian_filter(__UpperCAmelCase,	5,	sigma=0.9			).all()
def    UpperCamelCase       (      )					->      Tuple:
	_UpperCAmelCase     :				Dict          =       array([[0.25, 0.5, 0.25], [0.5, -3, 0.5], [0.25, 0.5, 0.25]]			)
	_UpperCAmelCase     :				Dict          =       conv.img_convolve(__UpperCAmelCase,	__UpperCAmelCase			).astype(__UpperCAmelCase			)
	assert res.any()
def    UpperCamelCase       (      )					->      Optional[int]:
	assert med.median_filter(__UpperCAmelCase,	3			).any()
def    UpperCamelCase       (      )					->      List[Any]:
	_UpperCAmelCase     :				Tuple          =       sob.sobel_filter(__UpperCAmelCase			)
	assert grad.any() and theta.any()
def    UpperCamelCase       (      )					->      Tuple:
	_UpperCAmelCase     :				int          =       sp.make_sepia(__UpperCAmelCase,	20			)
	assert sepia.all()
def    UpperCamelCase       (      _lowerCAmelCase  :    List[Any] = "digital_image_processing/image_data/lena_small.jpg"			)					->      Dict:
	_UpperCAmelCase     :				Optional[Any]          =       bs.Burkes(imread(__UpperCAmelCase,	1			),	120			)
	burkes.process()
	assert burkes.output_img.any()
def    UpperCamelCase       (      _lowerCAmelCase  :    List[str] = "digital_image_processing/image_data/lena_small.jpg",	)					->      Dict:
	_UpperCAmelCase     :				Optional[int]          =       rs.NearestNeighbour(imread(__UpperCAmelCase,	1			),	400,	200			)
	nn.process()
	assert nn.output.any()





def    UpperCamelCase       (      )					->      Dict:
	_UpperCAmelCase     :				Dict          =       'digital_image_processing/image_data/lena.jpg'

	# Reading the image and converting it to grayscale.
	_UpperCAmelCase     :				Union[str, Any]          =       imread(__UpperCAmelCase,	0			)

	# Test for get_neighbors_pixel function() return not None
	_UpperCAmelCase     :				Union[str, Any]          =       0
	_UpperCAmelCase     :				Dict          =       0
	_UpperCAmelCase     :				Any          =       image[x_coordinate][y_coordinate]

	_UpperCAmelCase     :				int          =       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
	_UpperCAmelCase     :				str          =       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]			):
			_UpperCAmelCase     :				Any          =       lbp.local_binary_value(__UpperCAmelCase,	__UpperCAmelCase,	__UpperCAmelCase			)

	assert lbp_image.any()










'''simple docstring'''

import os
import unittest

from transformers import FunnelTokenizer, FunnelTokenizerFast
from transformers.models.funnel.tokenization_funnel import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers

from ...test_tokenization_common import TokenizerTesterMixin






@require_tokenizers
class 						a_		(     snake_case    ,   unittest.TestCase    ):
							UpperCAmelCase			:		int        =				FunnelTokenizer
							UpperCAmelCase			:		Tuple        =				FunnelTokenizerFast
							UpperCAmelCase			:		List[str]        =				True
							UpperCAmelCase			:		Tuple        =				True


							def    UpperCamelCase     (				self   :						Optional[Any]			)			->					Optional[Any]:
										super().setUp()

										snake_case:    Optional[Any]      							=[
										    '<unk>',
										    '<cls>',
										    '<sep>',
										    'want',
										    '##want',
										    '##ed',
										    'wa',
										    'un',
										    'runn',
										    '##ing',
										    ',',
										    'low',
										    'lowest',
										]
										snake_case:    Any      							=os.path.join(self.tmpdirname      ,						VOCAB_FILES_NAMES['vocab_file']			)
										with open(self.vocab_file      ,						'w'      ,						encoding='utf-8'			) as vocab_writer:
													vocab_writer.write(''.join([x + '\n' for x in vocab_tokens]			)			)


							def    UpperCamelCase     (				self   :						Tuple      ,						**a_   :						str			)			->					Any:
										return FunnelTokenizer.from_pretrained(self.tmpdirname      ,						**a_			)


							def    UpperCamelCase     (				self   :						List[Any]      ,						**a_   :						int			)			->					str:
										return FunnelTokenizerFast.from_pretrained(self.tmpdirname      ,						**a_			)


							def    UpperCamelCase     (				self   :						Any      ,						a_   :						Union[str, Any]			)			->					Union[str, Any]:
										snake_case:    List[str]      							='UNwant\u00E9d,running'
										snake_case:    Dict      							='unwanted, running'
										return input_text, output_text


							def    UpperCamelCase     (				self   :						Optional[int]			)			->					List[Any]:
										snake_case:    Union[str, Any]      							=self.tokenizer_class(self.vocab_file			)

										snake_case:    Dict      							=tokenizer.tokenize('UNwant\u00E9d,running'			)
										self.assertListEqual(a_      ,						['un', '##want', '##ed', ',', 'runn', '##ing']			)
										self.assertListEqual(tokenizer.convert_tokens_to_ids(a_			)      ,						[7, 4, 5, 1_0, 8, 9]			)




							def    UpperCamelCase     (				self   :						List[str]			)			->					Dict:
										snake_case:    Optional[Any]      							=self.get_tokenizers(do_lower_case=a_			)
										for tokenizer in tokenizers:
													snake_case:    str      							=tokenizer('UNwant\u00E9d,running'			)
													snake_case:    Union[str, Any]      							=len(inputs['input_ids']			) - 1
													self.assertListEqual(inputs['token_type_ids']      ,						[2] + [0] * sentence_len			)

													snake_case:    List[str]      							=tokenizer('UNwant\u00E9d,running'      ,						'UNwant\u00E9d,running'			)
													self.assertListEqual(inputs['token_type_ids']      ,						[2] + [0] * sentence_len + [1] * sentence_len			)




