


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

__UpperCamelCase       :       Tuple						       =				imread(r"digital_image_processing/image_data/lena_small.jpg")
__UpperCamelCase       :       List[Any]						       =				cvtColor(img, COLOR_BGR2GRAY)

def 			_a  (				):







	"""simple docstring"""



	UpperCamelCase__					:    Any          =   cn.convert_to_negative(SCREAMING_SNAKE_CASE     )
	# assert negative_img array for at least one True
	assert negative_img.any()

def 			_a  (				):







	"""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(SCREAMING_SNAKE_CASE    , 110     )     ).startswith(
		    '''<PIL.Image.Image image mode=RGB size=100x100 at'''     )

def 			_a  (				):







	"""simple docstring"""



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

def 			_a  (				):







	"""simple docstring"""



	UpperCamelCase__					:    str          =   imread('''digital_image_processing/image_data/lena_small.jpg'''    , 0     )
	# assert ambiguous array for all == True
	assert canny_img.all()
	UpperCamelCase__					:    Any          =   canny.canny(SCREAMING_SNAKE_CASE     )
	# assert canny array for at least one True
	assert canny_array.any()

def 			_a  (				):







	"""simple docstring"""



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

def 			_a  (				):







	"""simple docstring"""



	UpperCamelCase__					:    Union[str, Any]          =   array([[0.25, 0.5, 0.25], [0.5, -3, 0.5], [0.25, 0.5, 0.25]]     )
	UpperCamelCase__					:    Optional[int]          =   conv.img_convolve(SCREAMING_SNAKE_CASE    , SCREAMING_SNAKE_CASE     ).astype(SCREAMING_SNAKE_CASE     )
	assert res.any()

def 			_a  (				):







	"""simple docstring"""



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

def 			_a  (				):







	"""simple docstring"""



	UpperCamelCase__					:    List[str]          =   sob.sobel_filter(SCREAMING_SNAKE_CASE     )
	assert grad.any() and theta.any()

def 			_a  (				):







	"""simple docstring"""



	UpperCamelCase__					:    Dict          =   sp.make_sepia(SCREAMING_SNAKE_CASE    , 20     )
	assert sepia.all()

def 			_a  (				SCREAMING_SNAKE_CASE	:				str = "digital_image_processing/image_data/lena_small.jpg"     ):







	"""simple docstring"""



	UpperCamelCase__					:    int          =   bs.Burkes(imread(SCREAMING_SNAKE_CASE    , 1     )    , 120     )
	burkes.process()
	assert burkes.output_img.any()

def 			_a  (				SCREAMING_SNAKE_CASE	:				str = "digital_image_processing/image_data/lena_small.jpg"    , ):







	"""simple docstring"""



	UpperCamelCase__					:    int          =   rs.NearestNeighbour(imread(SCREAMING_SNAKE_CASE    , 1     )    , 400    , 200     )
	nn.process()
	assert nn.output.any()






def 			_a  (				):







	"""simple docstring"""



	UpperCamelCase__					:    Union[str, Any]          =   '''digital_image_processing/image_data/lena.jpg'''

	# Reading the image and converting it to grayscale.
	UpperCamelCase__					:    int          =   imread(SCREAMING_SNAKE_CASE    , 0     )

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

	UpperCamelCase__					:    Optional[int]          =   lbp.get_neighbors_pixel(
	    SCREAMING_SNAKE_CASE    , SCREAMING_SNAKE_CASE    , SCREAMING_SNAKE_CASE    , SCREAMING_SNAKE_CASE     )

	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__					:    List[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]     ):
			UpperCamelCase__					:    int          =   lbp.local_binary_value(SCREAMING_SNAKE_CASE    , SCREAMING_SNAKE_CASE    , SCREAMING_SNAKE_CASE     )

	assert lbp_image.any()






from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tf_available, is_torch_available


__UpperCamelCase       :       Optional[int]						       =				{
    "configuration_rag": ["RagConfig"],
    "retrieval_rag": ["RagRetriever"],
    "tokenization_rag": ["RagTokenizer"],
}

try:
       if not is_torch_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       __UpperCamelCase       :       Optional[int]						       =				[
           "RagModel",
           "RagPreTrainedModel",
           "RagSequenceForGeneration",
           "RagTokenForGeneration",
       ]

try:
       if not is_tf_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       __UpperCamelCase       :       List[Any]						       =				[
           "TFRagModel",
           "TFRagPreTrainedModel",
           "TFRagSequenceForGeneration",
           "TFRagTokenForGeneration",
       ]


if TYPE_CHECKING:
       from .configuration_rag import RagConfig
       from .retrieval_rag import RagRetriever
       from .tokenization_rag import RagTokenizer

       try:
              if not is_torch_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .modeling_rag import RagModel, RagPreTrainedModel, RagSequenceForGeneration, RagTokenForGeneration

       try:
              if not is_tf_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .modeling_tf_rag import (
                  TFRagModel,
                  TFRagPreTrainedModel,
                  TFRagSequenceForGeneration,
                  TFRagTokenForGeneration,
              )

else:
       import sys

       __UpperCamelCase       :       List[Any]						       =				_LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)


