






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

def        snake_case   (     ):
			UpperCAmelCase_  :				int      =      cn.convert_to_negative(A__		)
			# assert negative_img array for at least one True
			assert negative_img.any()

def        snake_case   (     ):
			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_10		)		).startswith(
						    "<PIL.Image.Image image mode=RGB size=100x100 at"		)

def        snake_case   (     ):
			UpperCAmelCase_  :				Optional[int]      =      canny.gen_gaussian_kernel(9			,sigma=1.4		)
			# Assert ambiguous array
			assert resp.all()

def        snake_case   (     ):
			UpperCAmelCase_  :				Optional[Any]      =      imread("digital_image_processing/image_data/lena_small.jpg"			,0		)
			# assert ambiguous array for all == True
			assert canny_img.all()
			UpperCAmelCase_  :				Union[str, Any]      =      canny.canny(A__		)
			# assert canny array for at least one True
			assert canny_array.any()

def        snake_case   (     ):
			assert gg.gaussian_filter(A__			,5			,sigma=0.9		).all()

def        snake_case   (     ):
			# laplace diagonals
			UpperCAmelCase_  :				Tuple      =      array([[0.25, 0.5, 0.25], [0.5, -3, 0.5], [0.25, 0.5, 0.25]]		)
			UpperCAmelCase_  :				int      =      conv.img_convolve(A__			,A__		).astype(A__		)
			assert res.any()

def        snake_case   (     ):
			assert med.median_filter(A__			,3		).any()

def        snake_case   (     ):
			UpperCAmelCase_  ,			UpperCAmelCase_  :				List[str]      =      sob.sobel_filter(A__		)
			assert grad.any() and theta.any()

def        snake_case   (     ):
			UpperCAmelCase_  :				Tuple      =      sp.make_sepia(A__			,20		)
			assert sepia.all()

def        snake_case   (     A__ = "digital_image_processing/image_data/lena_small.jpg"		):
			UpperCAmelCase_  :				int      =      bs.Burkes(imread(A__			,1		)			,1_20		)
			burkes.process()
			assert burkes.output_img.any()

def        snake_case   (     A__ = "digital_image_processing/image_data/lena_small.jpg"			,):
			UpperCAmelCase_  :				int      =      rs.NearestNeighbour(imread(A__			,1		)			,4_00			,2_00		)
			nn.process()
			assert nn.output.any()




def        snake_case   (     ):
			UpperCAmelCase_  :				Tuple      =      "digital_image_processing/image_data/lena.jpg"

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

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

			UpperCAmelCase_  :				Union[str, Any]      =      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
			UpperCAmelCase_  :				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(A__			,A__			,A__		)

			assert lbp_image.any()











"""simple docstring"""
from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


lowerCamelCase_       			=   {
    '''configuration_funnel''': ['''FUNNEL_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''FunnelConfig'''],
    '''convert_funnel_original_tf_checkpoint_to_pytorch''': [],
    '''tokenization_funnel''': ['''FunnelTokenizer'''],
}

try:
			if not is_tokenizers_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			lowerCamelCase_       			=   ['''FunnelTokenizerFast''']

try:
			if not is_torch_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			lowerCamelCase_       			=   [
			    '''FUNNEL_PRETRAINED_MODEL_ARCHIVE_LIST''',
			    '''FunnelBaseModel''',
			    '''FunnelForMaskedLM''',
			    '''FunnelForMultipleChoice''',
			    '''FunnelForPreTraining''',
			    '''FunnelForQuestionAnswering''',
			    '''FunnelForSequenceClassification''',
			    '''FunnelForTokenClassification''',
			    '''FunnelModel''',
			    '''FunnelPreTrainedModel''',
			    '''load_tf_weights_in_funnel''',
			]

try:
			if not is_tf_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			lowerCamelCase_       			=   [
			    '''TF_FUNNEL_PRETRAINED_MODEL_ARCHIVE_LIST''',
			    '''TFFunnelBaseModel''',
			    '''TFFunnelForMaskedLM''',
			    '''TFFunnelForMultipleChoice''',
			    '''TFFunnelForPreTraining''',
			    '''TFFunnelForQuestionAnswering''',
			    '''TFFunnelForSequenceClassification''',
			    '''TFFunnelForTokenClassification''',
			    '''TFFunnelModel''',
			    '''TFFunnelPreTrainedModel''',
			]


if TYPE_CHECKING:
			from .configuration_funnel import FUNNEL_PRETRAINED_CONFIG_ARCHIVE_MAP, FunnelConfig
			from .tokenization_funnel import FunnelTokenizer

			try:
						if not is_tokenizers_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .tokenization_funnel_fast import FunnelTokenizerFast

			try:
						if not is_torch_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .modeling_funnel import (
						    FUNNEL_PRETRAINED_MODEL_ARCHIVE_LIST,
						    FunnelBaseModel,
						    FunnelForMaskedLM,
						    FunnelForMultipleChoice,
						    FunnelForPreTraining,
						    FunnelForQuestionAnswering,
						    FunnelForSequenceClassification,
						    FunnelForTokenClassification,
						    FunnelModel,
						    FunnelPreTrainedModel,
						    load_tf_weights_in_funnel,
						)

			try:
						if not is_tf_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .modeling_tf_funnel import (
						    TF_FUNNEL_PRETRAINED_MODEL_ARCHIVE_LIST,
						    TFFunnelBaseModel,
						    TFFunnelForMaskedLM,
						    TFFunnelForMultipleChoice,
						    TFFunnelForPreTraining,
						    TFFunnelForQuestionAnswering,
						    TFFunnelForSequenceClassification,
						    TFFunnelForTokenClassification,
						    TFFunnelModel,
						    TFFunnelPreTrainedModel,
						)

else:
			import sys

			lowerCamelCase_       			=   _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)



