

from typing import Dict

import numpy as np
import torch

from . import residue_constants as rc
from .tensor_utils import tensor_tree_map, tree_map
def 							__SCREAMING_SNAKE_CASE     (   a__    :   Dict[str, torch.Tensor]       )					->				Dict[str, torch.Tensor]:
	__A :			int		    =			[]
	__A :			str		    =			[]
	__A :			List[Any]		    =			[]

	for rt in rc.restypes:
		__A :			str		    =			rc.restype_name_to_atomaa_names[rc.restype_atoa[rt]]
		restype_atomaa_to_atomaa_list.append([(rc.atom_order[name] if name else 0) for name in atom_names]       )
		__A :			int		    =			{name: i for i, name in enumerate(a__       )}
		restype_atomaa_to_atomaa_list.append(
		    [(atom_name_to_idxaa[name] if name in atom_name_to_idxaa else 0) for name in rc.atom_types]       )

		restype_atomaa_mask_list.append([(1.0 if name else 0.0) for name in atom_names]       )

	# Add dummy mapping for restype 'UNK'
	restype_atomaa_to_atomaa_list.append([0] * 14       )
	restype_atomaa_to_atomaa_list.append([0] * 37       )
	restype_atomaa_mask_list.append([0.0] * 14       )

	__A :			Tuple		    =			torch.tensor(
	    a__			,dtype=torch.intaa			,device=protein["""aatype"""].device			,)
	__A :			Dict		    =			torch.tensor(
	    a__			,dtype=torch.intaa			,device=protein["""aatype"""].device			,)
	__A :			Tuple		    =			torch.tensor(
	    a__			,dtype=torch.floataa			,device=protein["""aatype"""].device			,)
	__A :			Tuple		    =			protein["""aatype"""].to(torch.long       )

	# create the mapping for (residx, atom14) --> atom37, i.e. an array
	# with shape (num_res, 14) containing the atom37 indices for this protein
	__A :			Tuple		    =			restype_atomaa_to_atomaa[protein_aatype]
	__A :			Tuple		    =			restype_atomaa_mask[protein_aatype]

	__A :			int		    =			residx_atomaa_mask
	__A :			List[Any]		    =			residx_atomaa_to_atomaa.long()

	# create the gather indices for mapping back
	__A :			List[str]		    =			restype_atomaa_to_atomaa[protein_aatype]
	__A :			Optional[Any]		    =			residx_atomaa_to_atomaa.long()

	# create the corresponding mask
	__A :			int		    =			torch.zeros([21, 37]			,dtype=torch.floataa			,device=protein["""aatype"""].device       )
	for restype, restype_letter in enumerate(rc.restypes       ):
		__A :			List[Any]		    =			rc.restype_atoa[restype_letter]
		__A :			Optional[Any]		    =			rc.residue_atoms[restype_name]
		for atom_name in atom_names:
			__A :			Optional[Any]		    =			rc.atom_order[atom_name]
			__A :			Optional[Any]		    =			1

	__A :			int		    =			restype_atomaa_mask[protein_aatype]
	__A :			Tuple		    =			residx_atomaa_mask

	return protein







def 							__SCREAMING_SNAKE_CASE     (   a__    :   Dict[str, torch.Tensor]       )					->				Dict[str, np.ndarray]:
	__A :			int		    =			tree_map(lambda a__       : torch.tensor(a__			,device=batch["""aatype"""].device       )			,a__			,np.ndarray       )
	__A :			int		    =			tensor_tree_map(lambda a__       : np.array(a__       )			,make_atomaa_masks(a__       )       )
	return out











'''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

lowercase      :   Any      =       imread(R"""digital_image_processing/image_data/lena_small.jpg""")
lowercase      :   List[str]      =       cvtColor(img, COLOR_BGR2GRAY)



def        lowerCamelCase__				(      ):
     snake_case			:      Optional[int]        =			cn.convert_to_negative(__lowercase				)
     # 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(__lowercase     ,   110				)				).startswith(
              """<PIL.Image.Image image mode=RGB size=100x100 at"""				)



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



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



def        lowerCamelCase__				(      ):
     assert gg.gaussian_filter(__lowercase     ,   5     ,   sigma=0.9				).all()



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



def        lowerCamelCase__				(      ):
     assert med.median_filter(__lowercase     ,   3				).any()



def        lowerCamelCase__				(      ):
     snake_case ,		snake_case			:      Dict        =			sob.sobel_filter(__lowercase				)
     assert grad.any() and theta.any()



def        lowerCamelCase__				(      ):
     snake_case			:      int        =			sp.make_sepia(__lowercase     ,   20				)
     assert sepia.all()



def        lowerCamelCase__				(      __lowercase = "digital_image_processing/image_data/lena_small.jpg"				):
     snake_case			:      int        =			bs.Burkes(imread(__lowercase     ,   1				)     ,   120				)
     burkes.process()
     assert burkes.output_img.any()



def        lowerCamelCase__				(      __lowercase = "digital_image_processing/image_data/lena_small.jpg"     ,   ):
     snake_case			:      Dict        =			rs.NearestNeighbour(imread(__lowercase     ,   1				)     ,   400     ,   200				)
     nn.process()
     assert nn.output.any()







def        lowerCamelCase__				(      ):
     snake_case			:      int        =			"""digital_image_processing/image_data/lena.jpg"""

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

     # Test for get_neighbors_pixel function() return not None
     snake_case			:      Optional[int]        =			0
     snake_case			:      Tuple        =			0
     snake_case			:      Any        =			image[x_coordinate][y_coordinate]

     snake_case			:      Any        =			lbp.get_neighbors_pixel(
         __lowercase     ,   __lowercase     ,   __lowercase     ,   __lowercase				)

     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
     snake_case			:      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]				):
               snake_case			:      Optional[Any]        =			lbp.local_binary_value(__lowercase     ,   __lowercase     ,   __lowercase				)

     assert lbp_image.any()



