




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







def 				lowerCAmelCase_	(     ):
      _A							:     Optional[int]			=						cn.convert_to_negative(__SCREAMING_SNAKE_CASE     )
      # assert negative_img array for at least one True
      assert negative_img.any()







def 				lowerCAmelCase_	(     ):





      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 				lowerCAmelCase_	(     ):
      _A							:     Union[str, Any]			=						canny.gen_gaussian_kernel(9,sigma=1.4     )
      # Assert ambiguous array
      assert resp.all()







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







def 				lowerCAmelCase_	(     ):
      assert gg.gaussian_filter(__SCREAMING_SNAKE_CASE,5,sigma=0.9     ).all()







def 				lowerCAmelCase_	(     ):
      _A							:     Tuple			=						array([[0.25, 0.5, 0.25], [0.5, -3, 0.5], [0.25, 0.5, 0.25]]     )
      _A							:     Optional[Any]			=						conv.img_convolve(__SCREAMING_SNAKE_CASE,__SCREAMING_SNAKE_CASE     ).astype(__SCREAMING_SNAKE_CASE     )
      assert res.any()







def 				lowerCAmelCase_	(     ):
      assert med.median_filter(__SCREAMING_SNAKE_CASE,3     ).any()







def 				lowerCAmelCase_	(     ):
      _A							:     List[str]			=						sob.sobel_filter(__SCREAMING_SNAKE_CASE     )
      assert grad.any() and theta.any()







def 				lowerCAmelCase_	(     ):
      _A							:     int			=						sp.make_sepia(__SCREAMING_SNAKE_CASE,20     )
      assert sepia.all()







def 				lowerCAmelCase_	(     snake_case_ = "digital_image_processing/image_data/lena_small.jpg"     ):
      _A							:     int			=						bs.Burkes(imread(__SCREAMING_SNAKE_CASE,1     ),120     )
      burkes.process()
      assert burkes.output_img.any()







def 				lowerCAmelCase_	(     snake_case_ = "digital_image_processing/image_data/lena_small.jpg",):
      _A							:     Any			=						rs.NearestNeighbour(imread(__SCREAMING_SNAKE_CASE,1     ),400,200     )
      nn.process()
      assert nn.output.any()







def 				lowerCAmelCase_	(     ):
      _A							:     Optional[Any]			=						'''digital_image_processing/image_data/lena.jpg'''

      # Reading the image and converting it to grayscale.
      _A							:     List[Any]			=						imread(__SCREAMING_SNAKE_CASE,0     )

      # Test for get_neighbors_pixel function() return not None
      _A							:     Tuple			=						0
      _A							:     List[Any]			=						0
      _A							:     Dict			=						image[x_coordinate][y_coordinate]

      _A							:     Any			=						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
      _A							:     int			=						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							:     Optional[Any]			=						lbp.local_binary_value(__SCREAMING_SNAKE_CASE,__SCREAMING_SNAKE_CASE,__SCREAMING_SNAKE_CASE     )

      assert lbp_image.any()



'''simple docstring'''







from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ...utils.backbone_utils import BackboneConfigMixin, get_aligned_output_features_output_indices


_lowercase :  int    							=       logging.get_logger(__name__)

_lowercase :  List[Any]    							=       {
    "shi-labs/nat-mini-in1k-224": "https://huggingface.co/shi-labs/nat-mini-in1k-224/resolve/main/config.json",
    # See all Nat models at https://huggingface.co/models?filter=nat
}






class  lowerCAmelCase__   (  lowerCamelCase_    ,						lowerCamelCase_     ):
					lowerCAmelCase_        =     '''nat'''

					lowerCAmelCase_        =     {
					    '''num_attention_heads''': '''num_heads''',
					    '''num_hidden_layers''': '''num_layers''',
					}






					def __init__(   self				,							__SCREAMING_SNAKE_CASE=4				,							__SCREAMING_SNAKE_CASE=3				,							__SCREAMING_SNAKE_CASE=64				,							__SCREAMING_SNAKE_CASE=[3, 4, 6, 5]				,							__SCREAMING_SNAKE_CASE=[2, 4, 8, 16]				,							__SCREAMING_SNAKE_CASE=7				,							__SCREAMING_SNAKE_CASE=3.0				,							__SCREAMING_SNAKE_CASE=True				,							__SCREAMING_SNAKE_CASE=0.0				,							__SCREAMING_SNAKE_CASE=0.0				,							__SCREAMING_SNAKE_CASE=0.1				,							__SCREAMING_SNAKE_CASE="gelu"				,							__SCREAMING_SNAKE_CASE=0.02				,							__SCREAMING_SNAKE_CASE=1E-5				,							__SCREAMING_SNAKE_CASE=0.0				,							__SCREAMING_SNAKE_CASE=None				,							__SCREAMING_SNAKE_CASE=None				,							**__SCREAMING_SNAKE_CASE				,							):


											"""simple docstring"""





											super().__init__(**__SCREAMING_SNAKE_CASE		)

											lowercase_	:     Optional[int]													=       patch_size
											lowercase_	:     List[Any]													=       num_channels
											lowercase_	:     str													=       embed_dim
											lowercase_	:     List[str]													=       depths
											lowercase_	:     str													=       len(__SCREAMING_SNAKE_CASE		)
											lowercase_	:     Tuple													=       num_heads
											lowercase_	:     int													=       kernel_size
											lowercase_	:     Union[str, Any]													=       mlp_ratio
											lowercase_	:     Optional[int]													=       qkv_bias
											lowercase_	:     List[Any]													=       hidden_dropout_prob
											lowercase_	:     Optional[int]													=       attention_probs_dropout_prob
											lowercase_	:     List[Any]													=       drop_path_rate
											lowercase_	:     List[Any]													=       hidden_act
											lowercase_	:     int													=       layer_norm_eps
											lowercase_	:     int													=       initializer_range
											# we set the hidden_size attribute in order to make Nat work with VisionEncoderDecoderModel
											# this indicates the channel dimension after the last stage of the model
											lowercase_	:     Dict													=       int(embed_dim * 2 ** (len(__SCREAMING_SNAKE_CASE		) - 1)		)
											lowercase_	:     Tuple													=       layer_scale_init_value
											lowercase_	:     Union[str, Any]													=       ['''stem'''] + [F'''stage{idx}''' for idx in range(1				,							len(__SCREAMING_SNAKE_CASE		) + 1		)]
											lowercase_						, lowercase_	:     int													=       get_aligned_output_features_output_indices(
											    out_features=__SCREAMING_SNAKE_CASE				,							out_indices=__SCREAMING_SNAKE_CASE				,							stage_names=self.stage_names		)



