




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   :						Optional[int]				=			imread(R'''digital_image_processing/image_data/lena_small.jpg''')
__lowerCamelCase   :						str				=			cvtColor(img, COLOR_BGR2GRAY)







def 		lowercase__   (		):





					'''simple docstring'''






					__magic_name__							:	Optional[int]    							=							cn.convert_to_negative(__A				)
					# assert negative_img array for at least one True
					assert negative_img.any()







def 		lowercase__   (		):





					'''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(__A							,1_1_0				)				).startswith(
										    '''<PIL.Image.Image image mode=RGB size=100x100 at'''				)







def 		lowercase__   (		):





					'''simple docstring'''






					__magic_name__							:	List[Any]    							=							canny.gen_gaussian_kernel(9							,sigma=1.4				)
					# Assert ambiguous array
					assert resp.all()







def 		lowercase__   (		):





					'''simple docstring'''






					__magic_name__							:	Tuple    							=							imread('''digital_image_processing/image_data/lena_small.jpg'''							,0				)
					# assert ambiguous array for all == True
					assert canny_img.all()
					__magic_name__							:	Optional[Any]    							=							canny.canny(__A				)
					# assert canny array for at least one True
					assert canny_array.any()







def 		lowercase__   (		):





					'''simple docstring'''






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







def 		lowercase__   (		):





					'''simple docstring'''






					__magic_name__							:	Tuple    							=							array([[0.25, 0.5, 0.25], [0.5, -3, 0.5], [0.25, 0.5, 0.25]]				)
					__magic_name__							:	str    							=							conv.img_convolve(__A							,__A				).astype(__A				)
					assert res.any()







def 		lowercase__   (		):





					'''simple docstring'''






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







def 		lowercase__   (		):





					'''simple docstring'''






					__magic_name__							,    __magic_name__							:	Optional[int]    							=							sob.sobel_filter(__A				)
					assert grad.any() and theta.any()







def 		lowercase__   (		):





					'''simple docstring'''






					__magic_name__							:	Dict    							=							sp.make_sepia(__A							,2_0				)
					assert sepia.all()







def 		lowercase__   (		__A:	str = "digital_image_processing/image_data/lena_small.jpg"				):





					'''simple docstring'''






					__magic_name__							:	Optional[int]    							=							bs.Burkes(imread(__A							,1				)							,1_2_0				)
					burkes.process()
					assert burkes.output_img.any()







def 		lowercase__   (		__A:	str = "digital_image_processing/image_data/lena_small.jpg"							,):





					'''simple docstring'''






					__magic_name__							:	Any    							=							rs.NearestNeighbour(imread(__A							,1				)							,4_0_0							,2_0_0				)
					nn.process()
					assert nn.output.any()







def 		lowercase__   (		):





					'''simple docstring'''






					__magic_name__							:	List[Any]    							=							'''digital_image_processing/image_data/lena.jpg'''

					# Reading the image and converting it to grayscale.
					__magic_name__							:	Tuple    							=							imread(__A							,0				)

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

					__magic_name__							:	str    							=							lbp.get_neighbors_pixel(
					    __A							,__A							,__A							,__A				)

					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
					__magic_name__							:	Optional[Any]    							=							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]				):
															__magic_name__							:	Tuple    							=							lbp.local_binary_value(__A							,__A							,__A				)

					assert lbp_image.any()









# flake8: noqa
# Lint as: python3

from typing import Dict, List, Optional, Type

from .. import config
from ..utils import logging
from .formatting import (
    ArrowFormatter,
    CustomFormatter,
    Formatter,
    PandasFormatter,
    PythonFormatter,
    TensorFormatter,
    format_table,
    query_table,
)
from .np_formatter import NumpyFormatter


__lowerCamelCase   :						int				=			logging.get_logger(__name__)

__lowerCamelCase   :						Dict[Optional[str], Type[Formatter]]				=			{}
__lowerCamelCase   :						Dict[Optional[str], str]				=			{}
__lowerCamelCase   :						Dict[Optional[str], Exception]				=			{}







def 		lowercase__   (		__A:	type							,__A:	Optional[str]							,__A:	Optional[List[str]] = None							,):





					'''simple docstring'''






					__magic_name__							:	Union[str, Any]    							=							aliases if aliases is not None else []
					if format_type in _FORMAT_TYPES:
										logger.warning(
										    F'''Overwriting format type \'{format_type}\' ({_FORMAT_TYPES[format_type].__name__} -> {formatter_cls.__name__})'''				)
					__magic_name__							:	Tuple    							=							formatter_cls
					for alias in set(aliases + [format_type]				):
										if alias in _FORMAT_TYPES_ALIASES:
															logger.warning(
															    F'''Overwriting format type alias \'{alias}\' ({_FORMAT_TYPES_ALIASES[alias]} -> {format_type})'''				)
										__magic_name__							:	Optional[int]    							=							format_type







def 		lowercase__   (		__A:	Exception							,__A:	Optional[str]							,__A:	Optional[List[str]] = None				):





					'''simple docstring'''






					__magic_name__							:	Union[str, Any]    							=							aliases if aliases is not None else []
					for alias in set(aliases + [format_type]				):
										__magic_name__							:	List[str]    							=							unavailable_error


# Here we define all the available formatting functions that can be used by `Dataset.set_format`
_register_formatter(PythonFormatter, None, aliases=['''python'''])
_register_formatter(ArrowFormatter, '''arrow''', aliases=['''pa''', '''pyarrow'''])
_register_formatter(NumpyFormatter, '''numpy''', aliases=['''np'''])
_register_formatter(PandasFormatter, '''pandas''', aliases=['''pd'''])
_register_formatter(CustomFormatter, '''custom''')

if config.TORCH_AVAILABLE:
	from .torch_formatter import TorchFormatter

	_register_formatter(TorchFormatter, '''torch''', aliases=['''pt''', '''pytorch'''])
else:
	__lowerCamelCase   :						str				=			ValueError('''PyTorch needs to be installed to be able to return PyTorch tensors.''')
	_register_unavailable_formatter(_torch_error, '''torch''', aliases=['''pt''', '''pytorch'''])

if config.TF_AVAILABLE:
	from .tf_formatter import TFFormatter

	_register_formatter(TFFormatter, '''tensorflow''', aliases=['''tf'''])
else:
	__lowerCamelCase   :						int				=			ValueError('''Tensorflow needs to be installed to be able to return Tensorflow tensors.''')
	_register_unavailable_formatter(_tf_error, '''tensorflow''', aliases=['''tf'''])

if config.JAX_AVAILABLE:
	from .jax_formatter import JaxFormatter

	_register_formatter(JaxFormatter, '''jax''', aliases=[])
else:
	__lowerCamelCase   :						str				=			ValueError('''JAX needs to be installed to be able to return JAX arrays.''')
	_register_unavailable_formatter(_jax_error, '''jax''', aliases=[])







def 		lowercase__   (		__A:	Optional[str]				):





					'''simple docstring'''






					if format_type in _FORMAT_TYPES_ALIASES:
										return _FORMAT_TYPES_ALIASES[format_type]
					else:
										return format_type







def 		lowercase__   (		__A:	Optional[str]							,**__A:	Any				):





					'''simple docstring'''






					__magic_name__							:	Tuple    							=							get_format_type_from_alias(__A				)
					if format_type in _FORMAT_TYPES:
										return _FORMAT_TYPES[format_type](**__A				)
					if format_type in _FORMAT_TYPES_ALIASES_UNAVAILABLE:
										raise _FORMAT_TYPES_ALIASES_UNAVAILABLE[format_type]
					else:
										raise ValueError(
										    F'''Return type should be None or selected in {list(type for type in _FORMAT_TYPES.keys() if type != None				)}, but got \'{format_type}\''''				)



