
'''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

_snake_case				:   Tuple     =				imread(R"""digital_image_processing/image_data/lena_small.jpg""")
_snake_case				:   str     =				cvtColor(img, COLOR_BGR2GRAY)



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



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



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



def 			_a		(	):
						assert gg.gaussian_filter(_SCREAMING_SNAKE_CASE	,			5	,			sigma=0.9  ).all()



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



def 			_a		(	):
						assert med.median_filter(_SCREAMING_SNAKE_CASE	,			3  ).any()



def 			_a		(	):
						_SCREAMING_SNAKE_CASE,							_SCREAMING_SNAKE_CASE				= sob.sobel_filter(_SCREAMING_SNAKE_CASE  )
						assert grad.any() and theta.any()



def 			_a		(	):
						_SCREAMING_SNAKE_CASE				= sp.make_sepia(_SCREAMING_SNAKE_CASE	,			20  )
						assert sepia.all()



def 			_a		(	_SCREAMING_SNAKE_CASE		:   str = "digital_image_processing/image_data/lena_small.jpg"  ):
						_SCREAMING_SNAKE_CASE				= 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"	,			):
						_SCREAMING_SNAKE_CASE				= rs.NearestNeighbour(imread(_SCREAMING_SNAKE_CASE	,			1  )	,			400	,			200  )
						nn.process()
						assert nn.output.any()




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

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

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

						_SCREAMING_SNAKE_CASE				= 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
						_SCREAMING_SNAKE_CASE				= 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]  ):
																		_SCREAMING_SNAKE_CASE				= lbp.local_binary_value(_SCREAMING_SNAKE_CASE	,			_SCREAMING_SNAKE_CASE	,			_SCREAMING_SNAKE_CASE  )

						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,
)


_snake_case				:   int     =				{
    """configuration_longformer""": [
        """LONGFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP""",
        """LongformerConfig""",
        """LongformerOnnxConfig""",
    ],
    """tokenization_longformer""": ["""LongformerTokenizer"""],
}

try:
						if not is_tokenizers_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						_snake_case				:   Optional[int]     =				["""LongformerTokenizerFast"""]

try:
						if not is_torch_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						_snake_case				:   int     =				[
						    """LONGFORMER_PRETRAINED_MODEL_ARCHIVE_LIST""",
						    """LongformerForMaskedLM""",
						    """LongformerForMultipleChoice""",
						    """LongformerForQuestionAnswering""",
						    """LongformerForSequenceClassification""",
						    """LongformerForTokenClassification""",
						    """LongformerModel""",
						    """LongformerPreTrainedModel""",
						    """LongformerSelfAttention""",
						]

try:
						if not is_tf_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						_snake_case				:   Tuple     =				[
						    """TF_LONGFORMER_PRETRAINED_MODEL_ARCHIVE_LIST""",
						    """TFLongformerForMaskedLM""",
						    """TFLongformerForMultipleChoice""",
						    """TFLongformerForQuestionAnswering""",
						    """TFLongformerForSequenceClassification""",
						    """TFLongformerForTokenClassification""",
						    """TFLongformerModel""",
						    """TFLongformerPreTrainedModel""",
						    """TFLongformerSelfAttention""",
						]


if TYPE_CHECKING:
						from .configuration_longformer import (
						    LONGFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP,
						    LongformerConfig,
						    LongformerOnnxConfig,
						)
						from .tokenization_longformer import LongformerTokenizer

						try:
												if not is_tokenizers_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .tokenization_longformer_fast import LongformerTokenizerFast

						try:
												if not is_torch_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .modeling_longformer import (
												    LONGFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
												    LongformerForMaskedLM,
												    LongformerForMultipleChoice,
												    LongformerForQuestionAnswering,
												    LongformerForSequenceClassification,
												    LongformerForTokenClassification,
												    LongformerModel,
												    LongformerPreTrainedModel,
												    LongformerSelfAttention,
												)

						try:
												if not is_tf_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .modeling_tf_longformer import (
												    TF_LONGFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
												    TFLongformerForMaskedLM,
												    TFLongformerForMultipleChoice,
												    TFLongformerForQuestionAnswering,
												    TFLongformerForSequenceClassification,
												    TFLongformerForTokenClassification,
												    TFLongformerModel,
												    TFLongformerPreTrainedModel,
												    TFLongformerSelfAttention,
												)

else:
						import sys

						_snake_case				:   Dict     =				_LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)