
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							=imread(R'''digital_image_processing/image_data/lena_small.jpg''')
__A							=cvtColor(img, COLOR_BGR2GRAY)





def      lowerCamelCase_					(							):
				lowerCamelCase_         =     cn.convert_to_negative(lowerCamelCase__       )
				# assert negative_img array for at least one True
				assert negative_img.any()





def      lowerCamelCase_					(							):
				with Image.open("digital_image_processing/image_data/lena_small.jpg"       ) as img:
								# Work around assertion for response
								assert str(cc.change_contrast(lowerCamelCase__      ,					1_1_0       )       ).startswith(
								    "<PIL.Image.Image image mode=RGB size=100x100 at"       )





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





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





def      lowerCamelCase_					(							):
				assert gg.gaussian_filter(lowerCamelCase__      ,					5      ,					sigma=0.9       ).all()





def      lowerCamelCase_					(							):
				# laplace diagonals
				lowerCamelCase_         =     array([[0.25, 0.5, 0.25], [0.5, -3, 0.5], [0.25, 0.5, 0.25]]       )
				lowerCamelCase_         =     conv.img_convolve(lowerCamelCase__      ,					lowerCamelCase__       ).astype(lowerCamelCase__       )
				assert res.any()





def      lowerCamelCase_					(							):
				assert med.median_filter(lowerCamelCase__      ,					3       ).any()





def      lowerCamelCase_					(							):
				lowerCamelCase_      ,       lowerCamelCase_         =     sob.sobel_filter(lowerCamelCase__       )
				assert grad.any() and theta.any()





def      lowerCamelCase_					(							):
				lowerCamelCase_         =     sp.make_sepia(lowerCamelCase__      ,					2_0       )
				assert sepia.all()





def      lowerCamelCase_					(							lowerCamelCase__ = "digital_image_processing/image_data/lena_small.jpg"       ):
				lowerCamelCase_         =     bs.Burkes(imread(lowerCamelCase__      ,					1       )      ,					1_2_0       )
				burkes.process()
				assert burkes.output_img.any()





def      lowerCamelCase_					(							lowerCamelCase__ = "digital_image_processing/image_data/lena_small.jpg"      ,					):
				lowerCamelCase_         =     rs.NearestNeighbour(imread(lowerCamelCase__      ,					1       )      ,					4_0_0      ,					2_0_0       )
				nn.process()
				assert nn.output.any()





def      lowerCamelCase_					(							):
				lowerCamelCase_         =     "digital_image_processing/image_data/lena.jpg"

				# Reading the image and converting it to grayscale.
				lowerCamelCase_         =     imread(lowerCamelCase__      ,					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(
				    lowerCamelCase__      ,					lowerCamelCase__      ,					lowerCamelCase__      ,					lowerCamelCase__       )

				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(lowerCamelCase__      ,					lowerCamelCase__      ,					lowerCamelCase__       )

				assert lbp_image.any()




import unittest

from transformers import AlbertTokenizer, AlbertTokenizerFast
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, slow

from ...test_tokenization_common import TokenizerTesterMixin


__A							=get_tests_dir('''fixtures/spiece.model''')




@require_sentencepiece
@require_tokenizers
class        _SCREAMING_SNAKE_CASE	( snake_case_     ,			unittest.TestCase		):
	lowerCAmelCase__   =					AlbertTokenizer
	lowerCAmelCase__   =					AlbertTokenizerFast
	lowerCAmelCase__   =					True
	lowerCAmelCase__   =					True
	lowerCAmelCase__   =					True



	def        SCREAMING_SNAKE_CASE_(       self					)							->			Any:
					super().setUp()

					# We have a SentencePiece fixture for testing
					lowerCamelCase_         =     AlbertTokenizer(lowercase					)
					tokenizer.save_pretrained(self.tmpdirname					)



	def        SCREAMING_SNAKE_CASE_(       self		,    lowercase					)							->			str:
					lowerCamelCase_         =     "this is a test"
					lowerCamelCase_         =     "this is a test"
					return input_text, output_text



	def        SCREAMING_SNAKE_CASE_(       self					)							->			Dict:
					lowerCamelCase_         =     "<pad>"
					lowerCamelCase_         =     0

					self.assertEqual(self.get_tokenizer()._convert_token_to_id(lowercase					)		,    lowercase					)
					self.assertEqual(self.get_tokenizer()._convert_id_to_token(lowercase					)		,    lowercase					)



	def        SCREAMING_SNAKE_CASE_(       self					)							->			Any:
					lowerCamelCase_         =     list(self.get_tokenizer().get_vocab().keys()					)

					self.assertEqual(vocab_keys[0]		,    "<pad>"					)
					self.assertEqual(vocab_keys[1]		,    "<unk>"					)
					self.assertEqual(vocab_keys[-1]		,    "▁eloquent"					)
					self.assertEqual(len(lowercase					)		,    30000					)



	def        SCREAMING_SNAKE_CASE_(       self					)							->			Union[str, Any]:
					self.assertEqual(self.get_tokenizer().vocab_size		,    30000					)



	def        SCREAMING_SNAKE_CASE_(       self					)							->			Union[str, Any]:
					if not self.test_rust_tokenizer:
									return

					lowerCamelCase_         =     self.get_tokenizer()
					lowerCamelCase_         =     self.get_rust_tokenizer()

					lowerCamelCase_         =     "I was born in 92000, and this is falsé."

					lowerCamelCase_         =     tokenizer.tokenize(lowercase					)
					lowerCamelCase_         =     rust_tokenizer.tokenize(lowercase					)
					self.assertListEqual(lowercase		,    lowercase					)

					lowerCamelCase_         =     tokenizer.encode(lowercase		,    add_special_tokens=lowercase					)
					lowerCamelCase_         =     rust_tokenizer.encode(lowercase		,    add_special_tokens=lowercase					)
					self.assertListEqual(lowercase		,    lowercase					)

					lowerCamelCase_         =     self.get_rust_tokenizer()
					lowerCamelCase_         =     tokenizer.encode(lowercase					)
					lowerCamelCase_         =     rust_tokenizer.encode(lowercase					)
					self.assertListEqual(lowercase		,    lowercase					)



	def        SCREAMING_SNAKE_CASE_(       self					)							->			Optional[Any]:
					lowerCamelCase_         =     AlbertTokenizer(lowercase		,    keep_accents=lowercase					)

					lowerCamelCase_         =     tokenizer.tokenize("This is a test"					)
					self.assertListEqual(lowercase		,    ["▁this", "▁is", "▁a", "▁test"]					)

					self.assertListEqual(tokenizer.convert_tokens_to_ids(lowercase					)		,    [48, 25, 21, 1289]					)

					lowerCamelCase_         =     tokenizer.tokenize("I was born in 92000, and this is falsé."					)
					self.assertListEqual(
					    lowercase		,    ["▁i", "▁was", "▁born", "▁in", "▁9", "2000", ",", "▁and", "▁this", "▁is", "▁fal", "s", "é", "."]					)
					lowerCamelCase_         =     tokenizer.convert_tokens_to_ids(lowercase					)
					self.assertListEqual(lowercase		,    [31, 23, 386, 19, 561, 3050, 15, 17, 48, 25, 8256, 18, 1, 9]					)

					lowerCamelCase_         =     tokenizer.convert_ids_to_tokens(lowercase					)
					self.assertListEqual(
					    lowercase		,    ["▁i", "▁was", "▁born", "▁in", "▁9", "2000", ",", "▁and", "▁this", "▁is", "▁fal", "s", "<unk>", "."]		,    )



	def        SCREAMING_SNAKE_CASE_(       self					)							->			Any:
					lowerCamelCase_         =     AlbertTokenizer(lowercase					)

					lowerCamelCase_         =     tokenizer.encode("sequence builders"					)
					lowerCamelCase_         =     tokenizer.encode("multi-sequence build"					)

					lowerCamelCase_         =     tokenizer.build_inputs_with_special_tokens(lowercase					)
					lowerCamelCase_         =     tokenizer.build_inputs_with_special_tokens(lowercase		,    lowercase					)

					assert encoded_sentence == [tokenizer.cls_token_id] + text + [tokenizer.sep_token_id]
					assert encoded_pair == [tokenizer.cls_token_id] + text + [tokenizer.sep_token_id] + text_a + [
					    tokenizer.sep_token_id
					]



	@slow
	def        SCREAMING_SNAKE_CASE_(       self					)							->			Optional[Any]:
					# fmt: off
					lowerCamelCase_         =     {"attention_mask": [[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]], "input_ids": [[2, 21970, 13, 5, 6092, 167, 28, 7103, 2153, 673, 8, 7028, 12051, 18, 17, 7103, 2153, 673, 8, 3515, 18684, 8, 4461, 6, 1927, 297, 8, 12060, 2607, 18, 13, 5, 4461, 15, 10538, 38, 8, 135, 15, 822, 58, 15, 993, 10363, 15, 1460, 8005, 4461, 15, 993, 255, 2328, 9, 9, 9, 6, 26, 1112, 816, 3260, 13, 5, 103, 2377, 6, 17, 1112, 816, 2782, 13, 5, 103, 10641, 6, 29, 84, 2512, 2430, 782, 18684, 2761, 19, 808, 2430, 2556, 17, 855, 1480, 9477, 4091, 128, 11712, 15, 7103, 2153, 673, 17, 24883, 9990, 9, 3], [2, 11502, 25, 1006, 20, 782, 8, 11809, 855, 1732, 19393, 18667, 37, 367, 21018, 69, 1854, 34, 11860, 19124, 27, 156, 225, 17, 193, 4141, 19, 65, 9124, 9, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [2, 14, 2231, 886, 2385, 17659, 84, 14, 16792, 1952, 9, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]], "token_type_ids": [[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]]}  # noqa: E501
					# fmt: on

					self.tokenizer_integration_test_util(
					    expected_encoding=lowercase		,    model_name="albert-base-v2"		,    revision="6b6560eaf5ff2e250b00c50f380c5389a9c2d82e"		,    )


