import copy
import os

import cva
import numpy as np
from matplotlib import pyplot as plt


class 	lowerCamelCase_     :






    '''simple docstring'''






    def __init__(	self)       ->	Any:
        __UpperCamelCase     :Tuple			  =		""
        __UpperCamelCase     :str			  =		""
        __UpperCamelCase     :Dict			  =		[]
        __UpperCamelCase     :str			  =		0
        __UpperCamelCase     :Tuple			  =		256
        __UpperCamelCase     :Optional[Any]			  =		0
        __UpperCamelCase     :Union[str, Any]			  =		0
        __UpperCamelCase     :Any			  =		0
        __UpperCamelCase     :Union[str, Any]			  =		0







    def 			UpperCamelCase__   (	self , __lowercase)       ->	List[Any]:
        __UpperCamelCase     :Optional[Any]			  =		cva.imread(snake_case__ , 0)
        __UpperCamelCase     :Union[str, Any]			  =		copy.deepcopy(self.img)
        __UpperCamelCase     :Tuple			  =		plt.hist(self.img.ravel() , 256 , [0, 256] , label='''x''')
        __UpperCamelCase     :Any			  =		np.sum(snake_case__)
        for i in range(len(snake_case__)):
            __UpperCamelCase     :Union[str, Any]			  =		x[i] / self.k
            self.sk += prk
            __UpperCamelCase     :Optional[Any]			  =		(self.L - 1) * self.sk
            if self.rem != 0:
                __UpperCamelCase     :List[str]			  =		int(last % last)
            __UpperCamelCase     :List[str]			  =		int(last + 1 if self.rem >= 0.5 else last)
            self.last_list.append(snake_case__)
            __UpperCamelCase     :int			  =		int(np.ma.count(self.img) / self.img[1].size)
            __UpperCamelCase     :Optional[int]			  =		self.img[1].size
        for i in range(self.number_of_cols):
            for j in range(self.number_of_rows):
                __UpperCamelCase     :Union[str, Any]			  =		self.img[j][i]
                if num != self.last_list[num]:
                    __UpperCamelCase     :Dict			  =		self.last_list[num]
        cva.imwrite('''output_data/output.jpg''' , self.img)







    def 			UpperCamelCase__   (	self)       ->	List[Any]:
        plt.hist(self.img.ravel() , 256 , [0, 256])







    def 			UpperCamelCase__   (	self)       ->	int:
        cva.imshow('''Output-Image''' , self.img)
        cva.imshow('''Input-Image''' , self.original_image)
        cva.waitKey(5_000)
        cva.destroyAllWindows()


if __name__ == "__main__":
    __lowercase							 =					os.path.join(os.path.basename(__file__), '''image_data/input.jpg''')
    __lowercase							 =					ConstantStretch()
    stretcher.stretch(file_path)
    stretcher.plot_histogram()
    stretcher.show_image()







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 			UpperCamelCase					(    __lowerCamelCase     : Dict[str, torch.Tensor]    ):
       snake_case       :   List[str]							=      []
       snake_case       :   Optional[int]							=      []
       snake_case       :   Any							=      []

       for rt in rc.restypes:
              snake_case       :   List[Any]							=      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]    )
              snake_case       :   str							=      {name: i for i, name in enumerate(__lowerCamelCase    )}
              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    )

       snake_case       :   Optional[Any]							=      torch.tensor(
           __lowerCamelCase				,					dtype=torch.intaa				,					device=protein["aatype"].device				,					)
       snake_case       :   List[Any]							=      torch.tensor(
           __lowerCamelCase				,					dtype=torch.intaa				,					device=protein["aatype"].device				,					)
       snake_case       :   int							=      torch.tensor(
           __lowerCamelCase				,					dtype=torch.floataa				,					device=protein["aatype"].device				,					)
       snake_case       :   int							=      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
       snake_case       :   List[Any]							=      restype_atomaa_to_atomaa[protein_aatype]
       snake_case       :   str							=      restype_atomaa_mask[protein_aatype]

       snake_case       :   str							=      residx_atomaa_mask
       snake_case       :   Any							=      residx_atomaa_to_atomaa.long()

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

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

       snake_case       :   List[Any]							=      restype_atomaa_mask[protein_aatype]
       snake_case       :   int							=      residx_atomaa_mask

       return protein




def 			UpperCamelCase					(    __lowerCamelCase     : Dict[str, torch.Tensor]    ):
       snake_case       :   Dict							=      tree_map(lambda __lowerCamelCase    : torch.tensor(__lowerCamelCase				,					device=batch["aatype"].device    )				,					__lowerCamelCase				,					np.ndarray    )
       snake_case       :   List[str]							=      tensor_tree_map(lambda __lowerCamelCase    : np.array(__lowerCamelCase    )				,					make_atomaa_masks(__lowerCamelCase    )    )
       return out





