



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

SCREAMING_SNAKE_CASE__       =      imread(r'''digital_image_processing/image_data/lena_small.jpg''')
SCREAMING_SNAKE_CASE__       =      cvtColor(img, COLOR_BGR2GRAY)


def A						(       )						->      List[str]:
			A__       =    cn.convert_to_negative(__UpperCamelCase					)
			# assert negative_img array for at least one True
			assert negative_img.any()


def A						(       )						->      Tuple:



			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 A						(       )						->      Union[str, Any]:
			A__       =    canny.gen_gaussian_kernel(9 ,	sigma=1.4					)
			# Assert ambiguous array
			assert resp.all()


def A						(       )						->      str:
			A__       =    imread('digital_image_processing/image_data/lena_small.jpg' ,	0					)
			# assert ambiguous array for all == True
			assert canny_img.all()
			A__       =    canny.canny(__UpperCamelCase					)
			# assert canny array for at least one True
			assert canny_array.any()


def A						(       )						->      Optional[Any]:
			assert gg.gaussian_filter(__UpperCamelCase ,	5 ,	sigma=0.9					).all()


def A						(       )						->      Optional[int]:
			A__       =    array([[0.25, 0.5, 0.25], [0.5, -3, 0.5], [0.25, 0.5, 0.25]]					)
			A__       =    conv.img_convolve(__UpperCamelCase ,	__UpperCamelCase					).astype(__UpperCamelCase					)
			assert res.any()


def A						(       )						->      str:
			assert med.median_filter(__UpperCamelCase ,	3					).any()


def A						(       )						->      Any:
			A__       =    sob.sobel_filter(__UpperCamelCase					)
			assert grad.any() and theta.any()


def A						(       )						->      List[Any]:
			A__       =    sp.make_sepia(__UpperCamelCase ,	20					)
			assert sepia.all()


def A						(       __UpperCamelCase = "digital_image_processing/image_data/lena_small.jpg"					)						->      Tuple:
			A__       =    bs.Burkes(imread(__UpperCamelCase ,	1					) ,	120					)
			burkes.process()
			assert burkes.output_img.any()


def A						(       __UpperCamelCase = "digital_image_processing/image_data/lena_small.jpg" ,	)						->      Any:
			A__       =    rs.NearestNeighbour(imread(__UpperCamelCase ,	1					) ,	400 ,	200					)
			nn.process()
			assert nn.output.any()






def A						(       )						->      str:
			A__       =    "digital_image_processing/image_data/lena.jpg"

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

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

			A__       =    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
			A__       =    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]					):
									A__       =    lbp.local_binary_value(__UpperCamelCase ,	__UpperCamelCase ,	__UpperCamelCase					)

			assert lbp_image.any()









from typing import List, Optional, Union

from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding, PaddingStrategy, PreTokenizedInput, TextInput, TruncationStrategy
from ...utils import TensorType


class      __lowerCAmelCase     (		UpperCAmelCase_      ):
					"""simple docstring"""






					A__				:		Union[str, Any]			       =    ["image_processor", "tokenizer"]
					A__				:		Optional[Any]			       =    "BridgeTowerImageProcessor"
					A__				:		List[Any]			       =    ("RobertaTokenizer", "RobertaTokenizerFast")



					def __init__(     self					:       List[Any]				,					_snake_case					:       Optional[Any]				,					_snake_case					:       Optional[int]					):






								"""simple docstring"""


								super().__init__(_snake_case				,					_snake_case					)



					def __call__(     self					:       List[Any]				,					_snake_case					:       int				,					_snake_case					:       Union[TextInput, PreTokenizedInput, List[TextInput], List[PreTokenizedInput]] = None				,					_snake_case					:       bool = True				,					_snake_case					:       Union[bool, str, PaddingStrategy] = False				,					_snake_case					:       Union[bool, str, TruncationStrategy] = None				,					_snake_case					:       Optional[int] = None				,					_snake_case					:       int = 0				,					_snake_case					:       Optional[int] = None				,					_snake_case					:       Optional[bool] = None				,					_snake_case					:       Optional[bool] = None				,					_snake_case					:       bool = False				,					_snake_case					:       bool = False				,					_snake_case					:       bool = False				,					_snake_case					:       bool = False				,					_snake_case					:       bool = True				,					_snake_case					:       Optional[Union[str, TensorType]] = None				,					**_snake_case					:       Optional[int]				,					):






								"""simple docstring"""


								A__       =    self.tokenizer(
								    text=_snake_case				,					add_special_tokens=_snake_case				,					padding=_snake_case				,					truncation=_snake_case				,					max_length=_snake_case				,					stride=_snake_case				,					pad_to_multiple_of=_snake_case				,					return_token_type_ids=_snake_case				,					return_attention_mask=_snake_case				,					return_overflowing_tokens=_snake_case				,					return_special_tokens_mask=_snake_case				,					return_offsets_mapping=_snake_case				,					return_length=_snake_case				,					verbose=_snake_case				,					return_tensors=_snake_case				,					**_snake_case				,					)
								# add pixel_values + pixel_mask
								A__       =    self.image_processor(
								    _snake_case				,					return_tensors=_snake_case				,					do_normalize=_snake_case				,					do_center_crop=_snake_case				,					**_snake_case					)
								encoding.update(_snake_case					)

								return encoding



					def     _a							(     self					:       Any				,					*_snake_case					:       Tuple				,					**_snake_case					:       List[Any]					):






								"""simple docstring"""


								return self.tokenizer.batch_decode(*_snake_case				,					**_snake_case					)



					def     _a							(     self					:       Dict				,					*_snake_case					:       Dict				,					**_snake_case					:       List[str]					):






								"""simple docstring"""


								return self.tokenizer.decode(*_snake_case				,					**_snake_case					)



					@property
					def     _a							(     self					:       Tuple					):






								"""simple docstring"""


								A__       =    self.tokenizer.model_input_names
								A__       =    self.image_processor.model_input_names
								return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names					)					)




