



"""simple docstring"""






from typing import Dict, Tuple, overload

import torch
import torch.types
from torch import nn

from . import residue_constants as rc
from .rigid_utils import Rigid, Rotation
from .tensor_utils import batched_gather






@overload
def 	snake_case__  (						__lowerCamelCase							:       torch.Tensor				,						__lowerCamelCase							:       torch.Tensor				,						__lowerCamelCase							:       None      ):
			"""simple docstring"""
			...






@overload
def 	snake_case__  (						__lowerCamelCase							:       torch.Tensor				,						__lowerCamelCase							:       torch.Tensor				,						__lowerCamelCase							:       torch.Tensor      ):
			"""simple docstring"""
			...






def 	snake_case__  (						__lowerCamelCase							:       List[str]				,						__lowerCamelCase							:       Any				,						__lowerCamelCase							:       List[str]      ):
			"""simple docstring"""
			lowerCamelCase__						:						Union[str, Any]						      =aatype == rc.restype_order['''G''']
			lowerCamelCase__						:						Any						      =rc.atom_order['''CA''']
			lowerCamelCase__						:						Any						      =rc.atom_order['''CB''']
			lowerCamelCase__						:						Optional[int]						      =torch.where(
			    is_gly[..., None].expand(*((-1,) * len(is_gly.shape      ))				,						3      )				,						all_atom_positions[..., ca_idx, :]				,						all_atom_positions[..., cb_idx, :]				,						)

			if all_atom_masks is not None:
						lowerCamelCase__						:						Union[str, Any]						      =torch.where(
						    __lowerCamelCase				,						all_atom_masks[..., ca_idx]				,						all_atom_masks[..., cb_idx]				,						)
						return pseudo_beta, pseudo_beta_mask
			else:
						return pseudo_beta






def 	snake_case__  (						__lowerCamelCase							:       torch.Tensor				,						__lowerCamelCase							:       Dict[str, torch.Tensor]      ):
			"""simple docstring"""
			lowerCamelCase__						:						Any						      =batched_gather(
			    __lowerCamelCase				,						batch['''residx_atom37_to_atom14''']				,						dim=-2				,						no_batch_dims=len(atomaa.shape[:-2]      )				,						)

			lowerCamelCase__						:						Union[str, Any]						      =atomaa_data * batch['''atom37_atom_exists'''][..., None]

			return atomaa_data






def 	snake_case__  (						__lowerCamelCase							:       Dict[str, torch.Tensor]      ):
			"""simple docstring"""
			lowerCamelCase__						:						Optional[int]						      =template_feats['''template_aatype''']
			lowerCamelCase__						:						List[str]						      =template_feats['''template_torsion_angles_sin_cos''']
			lowerCamelCase__						:						Optional[int]						      =template_feats['''template_alt_torsion_angles_sin_cos''']
			lowerCamelCase__						:						Optional[Any]						      =template_feats['''template_torsion_angles_mask''']
			lowerCamelCase__						:						Union[str, Any]						      =torch.cat(
			    [
			        nn.functional.one_hot(__lowerCamelCase				,						22      ),
			        torsion_angles_sin_cos.reshape(*torsion_angles_sin_cos.shape[:-2]				,						14      ),
			        alt_torsion_angles_sin_cos.reshape(*alt_torsion_angles_sin_cos.shape[:-2]				,						14      ),
			        torsion_angles_mask,
			    ]				,						dim=-1				,						)

			return template_angle_feat






def 	snake_case__  (						__lowerCamelCase							:       Dict[str, torch.Tensor]				,						__lowerCamelCase							:       torch.types.Number				,						__lowerCamelCase							:       torch.types.Number				,						__lowerCamelCase							:       int				,						__lowerCamelCase							:       bool = False				,						__lowerCamelCase							:       float = 1e-2_0				,						__lowerCamelCase							:       float = 1e8				,						):
			"""simple docstring"""
			lowerCamelCase__						:						Union[str, Any]						      =batch['''template_pseudo_beta_mask''']
			lowerCamelCase__						:						Any						      =template_mask[..., None] * template_mask[..., None, :]

			# Compute distogram (this seems to differ slightly from Alg. 5)
			lowerCamelCase__						:						str						      =batch['''template_pseudo_beta''']
			lowerCamelCase__						:						Optional[Any]						      =torch.sum((tpb[..., None, :] - tpb[..., None, :, :]) ** 2				,						dim=-1				,						keepdim=__lowerCamelCase      )
			lowerCamelCase__						:						Any						      =torch.linspace(__lowerCamelCase				,						__lowerCamelCase				,						__lowerCamelCase				,						device=tpb.device      ) ** 2
			lowerCamelCase__						:						List[Any]						      =torch.cat([lower[1:], lower.new_tensor([inf]      )]				,						dim=-1      )
			lowerCamelCase__						:						List[Any]						      =((dgram > lower) * (dgram < upper)).type(dgram.dtype      )

			lowerCamelCase__						:						List[str]						      =[dgram, template_mask_ad[..., None]]

			lowerCamelCase__						:						torch.LongTensor						      =nn.functional.one_hot(
			    batch['''template_aatype''']				,						rc.restype_num + 2				,						)

			lowerCamelCase__						:						Union[str, Any]						      =batch['''template_aatype'''].shape[-1]
			to_concat.append(aatype_one_hot[..., None, :, :].expand(*aatype_one_hot.shape[:-2]				,						__lowerCamelCase				,						-1				,						-1      )      )
			to_concat.append(aatype_one_hot[..., None, :].expand(*aatype_one_hot.shape[:-2]				,						-1				,						__lowerCamelCase				,						-1      )      )

			lowerCamelCase__      ,  lowerCamelCase__      ,  lowerCamelCase__						:						Tuple						      =[rc.atom_order[a] for a in ['''N''', '''CA''', '''C''']]
			lowerCamelCase__						:						Dict						      =Rigid.make_transform_from_reference(
			    n_xyz=batch['''template_all_atom_positions'''][..., n, :]				,						ca_xyz=batch['''template_all_atom_positions'''][..., ca, :]				,						c_xyz=batch['''template_all_atom_positions'''][..., c, :]				,						eps=__lowerCamelCase				,						)
			lowerCamelCase__						:						List[str]						      =rigids.get_trans()[..., None, :, :]
			lowerCamelCase__						:						Any						      =rigids[..., None].invert_apply(__lowerCamelCase      )

			lowerCamelCase__						:						List[str]						      =torch.rsqrt(eps + torch.sum(rigid_vec**2				,						dim=-1      )      )

			lowerCamelCase__						:						str						      =batch['''template_all_atom_mask''']
			lowerCamelCase__						:						Optional[Any]						      =t_aa_masks[..., n] * t_aa_masks[..., ca] * t_aa_masks[..., c]
			lowerCamelCase__						:						Dict						      =template_mask[..., None] * template_mask[..., None, :]

			lowerCamelCase__						:						Tuple						      =inv_distance_scalar * template_mask_ad
			lowerCamelCase__						:						Dict						      =rigid_vec * inv_distance_scalar[..., None]

			if not use_unit_vector:
						lowerCamelCase__						:						List[str]						      =unit_vector * 0.0

			to_concat.extend(torch.unbind(unit_vector[..., None, :]				,						dim=-1      )      )
			to_concat.append(template_mask_ad[..., None]      )

			lowerCamelCase__						:						int						      =torch.cat(__lowerCamelCase				,						dim=-1      )
			lowerCamelCase__						:						int						      =act * template_mask_ad[..., None]

			return act






def 	snake_case__  (						__lowerCamelCase							:       Dict[str, torch.Tensor]      ):
			"""simple docstring"""
			lowerCamelCase__						:						torch.LongTensor						      =nn.functional.one_hot(batch['''extra_msa''']				,						23      )
			lowerCamelCase__						:						Optional[int]						      =[
			    msa_ahot,
			    batch['''extra_has_deletion'''].unsqueeze(-1      ),
			    batch['''extra_deletion_value'''].unsqueeze(-1      ),
			]
			return torch.cat(__lowerCamelCase				,						dim=-1      )






def 	snake_case__  (						__lowerCamelCase							:       Rigid				,						__lowerCamelCase							:       torch.Tensor				,						__lowerCamelCase							:       torch.Tensor				,						__lowerCamelCase							:       torch.Tensor				,						):
			"""simple docstring"""
			# [*, N, 8, 4, 4]
			lowerCamelCase__						:						str						      =rrgdf[aatype, ...]

			# [*, N, 8] transformations, i.e.
			#   One [*, N, 8, 3, 3] rotation matrix and
			#   One [*, N, 8, 3]    translation matrix
			lowerCamelCase__						:						Tuple						      =r.from_tensor_axa(__lowerCamelCase      )

			lowerCamelCase__						:						Union[str, Any]						      =alpha.new_zeros((*((1,) * len(alpha.shape[:-1]      )), 2)      )
			lowerCamelCase__						:						Tuple						      =1

			# [*, N, 8, 2]
			lowerCamelCase__						:						Optional[Any]						      =torch.cat([bb_rot.expand(*alpha.shape[:-2]				,						-1				,						-1      ), alpha]				,						dim=-2      )

			# [*, N, 8, 3, 3]
			# Produces rotation matrices of the form:
			# [
			#   [1, 0  , 0  ],
			#   [0, a_2,-a_1],
			#   [0, a_1, a_2]
			# ]
			# This follows the original code rather than the supplement, which uses
			# different indices.

			lowerCamelCase__						:						int						      =alpha.new_zeros(default_r.get_rots().get_rot_mats().shape      )
			lowerCamelCase__						:						Any						      =1
			lowerCamelCase__						:						int						      =alpha[..., 1]
			lowerCamelCase__						:						Optional[int]						      =-alpha[..., 0]
			lowerCamelCase__						:						Union[str, Any]						      =alpha

			lowerCamelCase__						:						Optional[int]						      =default_r.compose(Rigid(Rotation(rot_mats=__lowerCamelCase      )				,						__lowerCamelCase      )      )

			lowerCamelCase__						:						Dict						      =all_frames[..., 5]
			lowerCamelCase__						:						int						      =all_frames[..., 6]
			lowerCamelCase__						:						Optional[int]						      =all_frames[..., 7]

			lowerCamelCase__						:						Optional[int]						      =all_frames[..., 4]
			lowerCamelCase__						:						Union[str, Any]						      =chia_frame_to_bb.compose(__lowerCamelCase      )
			lowerCamelCase__						:						Tuple						      =chia_frame_to_bb.compose(__lowerCamelCase      )
			lowerCamelCase__						:						Any						      =chia_frame_to_bb.compose(__lowerCamelCase      )

			lowerCamelCase__						:						Tuple						      =Rigid.cat(
			    [
			        all_frames[..., :5],
			        chia_frame_to_bb.unsqueeze(-1      ),
			        chia_frame_to_bb.unsqueeze(-1      ),
			        chia_frame_to_bb.unsqueeze(-1      ),
			    ]				,						dim=-1				,						)

			lowerCamelCase__						:						Union[str, Any]						      =r[..., None].compose(__lowerCamelCase      )

			return all_frames_to_global






def 	snake_case__  (						__lowerCamelCase							:       Rigid				,						__lowerCamelCase							:       torch.Tensor				,						__lowerCamelCase							:       torch.Tensor				,						__lowerCamelCase							:       torch.Tensor				,						__lowerCamelCase							:       torch.Tensor				,						__lowerCamelCase							:       torch.Tensor				,						):
			"""simple docstring"""
			# [*, N, 14]
			lowerCamelCase__						:						Union[str, Any]						      =group_idx[aatype, ...]

			# [*, N, 14, 8]
			lowerCamelCase__						:						torch.LongTensor						      =nn.functional.one_hot(
			    __lowerCamelCase				,						num_classes=default_frames.shape[-3]				,						)

			# [*, N, 14, 8]
			lowerCamelCase__						:						str						      =r[..., None, :] * group_mask_one_hot

			# [*, N, 14]
			lowerCamelCase__						:						Union[str, Any]						      =t_atoms_to_global.map_tensor_fn(lambda __lowerCamelCase      : torch.sum(__lowerCamelCase				,						dim=-1      )      )

			# [*, N, 14, 1]
			lowerCamelCase__						:						Any						      =atom_mask[aatype, ...].unsqueeze(-1      )

			# [*, N, 14, 3]
			lowerCamelCase__						:						str						      =lit_positions[aatype, ...]
			lowerCamelCase__						:						str						      =t_atoms_to_global.apply(__lowerCamelCase      )
			lowerCamelCase__						:						Any						      =pred_positions * atom_mask

			return pred_positions






'''simple docstring'''





import colorsys
import io

import cva
import matplotlib as mpl
import matplotlib.colors as mplc
import matplotlib.figure as mplfigure
import numpy as np
import torch
from matplotlib.backends.backend_agg import FigureCanvasAgg

from utils import img_tensorize


UpperCAmelCase            = 1_000

class     a :




				def __init__(				self						:					Optional[int],     SCREAMING_SNAKE_CASE_						:					Tuple,     SCREAMING_SNAKE_CASE_						:					int=1.2,     SCREAMING_SNAKE_CASE_						:					Optional[Any]="g",     SCREAMING_SNAKE_CASE_						:					Any=0.5,     SCREAMING_SNAKE_CASE_						:					int="-",     SCREAMING_SNAKE_CASE_						:					Dict="test_out.jpg",     SCREAMING_SNAKE_CASE_						:					Any=True,     SCREAMING_SNAKE_CASE_						:					Tuple=False,     SCREAMING_SNAKE_CASE_						:					Any=None,     SCREAMING_SNAKE_CASE_						:					str=None,     SCREAMING_SNAKE_CASE_						:					str=0.7,     ):
										if isinstance(SCREAMING_SNAKE_CASE_,     torch.Tensor   ):
																snake_case							:  Dict			=     img.numpy().astype('''np.uint8'''   )
										if isinstance(SCREAMING_SNAKE_CASE_,     SCREAMING_SNAKE_CASE_   ):
																snake_case							:  List[Any]			=     img_tensorize(SCREAMING_SNAKE_CASE_   )
										assert isinstance(SCREAMING_SNAKE_CASE_,     np.ndarray   )

										snake_case,       snake_case							:  Optional[Any]			=     img.shape[1], img.shape[0]
										snake_case							:  int			=     mplfigure.Figure(frameon=SCREAMING_SNAKE_CASE_   )
										snake_case							:  int			=     fig.get_dpi()
										snake_case							:  Optional[int]			=     (width * scale + 1e-2) / dpi
										snake_case							:  List[Any]			=     (height * scale + 1e-2) / dpi
										fig.set_size_inches(SCREAMING_SNAKE_CASE_,     SCREAMING_SNAKE_CASE_   )
										snake_case							:  Any			=     fig.add_axes([0.0, 0.0, 1.0, 1.0]   )
										ax.axis('''off'''   )
										ax.set_xlim(0.0,     SCREAMING_SNAKE_CASE_   )
										ax.set_ylim(SCREAMING_SNAKE_CASE_   )

										snake_case							:  Union[str, Any]			=     saveas
										snake_case							:  str			=     rgb
										snake_case							:  Tuple			=     pynb
										snake_case							:  Optional[int]			=     img
										snake_case							:  List[str]			=     edgecolor
										snake_case							:  str			=     0.5
										snake_case							:  Optional[Any]			=     linestyle
										snake_case							:  Optional[Any]			=     int(np.sqrt(min(SCREAMING_SNAKE_CASE_,     SCREAMING_SNAKE_CASE_   )   ) * scale // 3   )
										snake_case							:  Any			=     width
										snake_case							:  List[str]			=     height
										snake_case							:  Optional[Any]			=     scale
										snake_case							:  int			=     fig
										snake_case							:  Optional[int]			=     ax
										snake_case							:  List[str]			=     pad
										snake_case							:  Tuple			=     idaobj
										snake_case							:  Tuple			=     idaattr
										snake_case							:  Dict			=     FigureCanvasAgg(SCREAMING_SNAKE_CASE_   )




				def 						__snake_case				(				self						:					Tuple,     SCREAMING_SNAKE_CASE_						:					Union[str, Any],     SCREAMING_SNAKE_CASE_						:					int=None   ):
										if color is None:
																snake_case							:  int			=     self.edgecolor
										((snake_case),       (snake_case),       (snake_case),       (snake_case))							:  Optional[int]			=     box
										snake_case							:  List[Any]			=     xa - xa
										snake_case							:  Optional[int]			=     ya - ya
										self.ax.add_patch(
										    mpl.patches.Rectangle(
										        (xa, ya),     SCREAMING_SNAKE_CASE_,     SCREAMING_SNAKE_CASE_,     fill=SCREAMING_SNAKE_CASE_,     edgecolor=SCREAMING_SNAKE_CASE_,     linewidth=self.font_size // 3,     alpha=self.alpha,     linestyle=self.linestyle,     )   )




				def 						__snake_case				(				self						:					Tuple,     SCREAMING_SNAKE_CASE_						:					Optional[Any],     SCREAMING_SNAKE_CASE_						:					str=None,     SCREAMING_SNAKE_CASE_						:					Union[str, Any]=None,     SCREAMING_SNAKE_CASE_						:					Optional[int]=None,     SCREAMING_SNAKE_CASE_						:					Tuple=None   ):
										if len(boxes.shape   ) > 2:
																snake_case							:  Dict			=     boxes[0]
										if len(obj_ids.shape   ) > 1:
																snake_case							:  Optional[int]			=     obj_ids[0]
										if len(obj_scores.shape   ) > 1:
																snake_case							:  str			=     obj_scores[0]
										if len(attr_ids.shape   ) > 1:
																snake_case							:  int			=     attr_ids[0]
										if len(attr_scores.shape   ) > 1:
																snake_case							:  Union[str, Any]			=     attr_scores[0]
										if isinstance(SCREAMING_SNAKE_CASE_,     torch.Tensor   ):
																snake_case							:  List[Any]			=     boxes.numpy()
										if isinstance(SCREAMING_SNAKE_CASE_,     SCREAMING_SNAKE_CASE_   ):
																snake_case							:  str			=     np.array(SCREAMING_SNAKE_CASE_   )
										assert isinstance(SCREAMING_SNAKE_CASE_,     np.ndarray   )
										snake_case							:  Optional[int]			=     np.prod(boxes[:, 2:] - boxes[:, :2],     axis=1   )
										snake_case							:  str			=     np.argsort(-areas   ).tolist()
										snake_case							:  Union[str, Any]			=     boxes[sorted_idxs] if boxes is not None else None
										snake_case							:  Dict			=     obj_ids[sorted_idxs] if obj_ids is not None else None
										snake_case							:  Tuple			=     obj_scores[sorted_idxs] if obj_scores is not None else None
										snake_case							:  List[str]			=     attr_ids[sorted_idxs] if attr_ids is not None else None
										snake_case							:  Optional[int]			=     attr_scores[sorted_idxs] if attr_scores is not None else None

										snake_case							:  Union[str, Any]			=     [self._random_color(maximum=1   ) for _ in range(len(SCREAMING_SNAKE_CASE_   )   )]
										snake_case							:  List[Any]			=     [assigned_colors[idx] for idx in sorted_idxs]
										if obj_ids is not None:
																snake_case							:  Dict			=     self._create_text_labels_attr(SCREAMING_SNAKE_CASE_,     SCREAMING_SNAKE_CASE_,     SCREAMING_SNAKE_CASE_,     SCREAMING_SNAKE_CASE_   )
																for i in range(len(SCREAMING_SNAKE_CASE_   )   ):
																						snake_case							:  Any			=     assigned_colors[i]
																						self.add_box(boxes[i],     SCREAMING_SNAKE_CASE_   )
																						self.draw_labels(labels[i],     boxes[i],     SCREAMING_SNAKE_CASE_   )




				def 						__snake_case				(				self						:					List[str],     SCREAMING_SNAKE_CASE_						:					int,     SCREAMING_SNAKE_CASE_						:					Optional[int],     SCREAMING_SNAKE_CASE_						:					Union[str, Any]   ):
										snake_case,       snake_case,       snake_case,       snake_case							:  Dict			=     box
										snake_case							:  Union[str, Any]			=     (xa, ya)
										snake_case							:  Any			=     (ya - ya) * (xa - xa)
										snake_case							:  Any			=     _SMALL_OBJ * self.scale
										if instance_area < small or ya - ya < 40 * self.scale:
																if ya >= self.height - 5:
																						snake_case							:  Union[str, Any]			=     (xa, ya)
																else:
																						snake_case							:  Dict			=     (xa, ya)

										snake_case							:  List[str]			=     (ya - ya) / np.sqrt(self.height * self.width   )
										snake_case							:  Union[str, Any]			=     self._change_color_brightness(SCREAMING_SNAKE_CASE_,     brightness_factor=0.7   )
										snake_case							:  int			=     np.clip((height_ratio - 0.02) / 0.08 + 1,     1.2,     2   )
										font_size *= 0.75 * self.font_size

										self.draw_text(
										    text=SCREAMING_SNAKE_CASE_,     position=SCREAMING_SNAKE_CASE_,     color=SCREAMING_SNAKE_CASE_,     )




				def 						__snake_case				(				self						:					Optional[Any],     SCREAMING_SNAKE_CASE_						:					Union[str, Any],     SCREAMING_SNAKE_CASE_						:					Tuple,     SCREAMING_SNAKE_CASE_						:					List[str]="g",     SCREAMING_SNAKE_CASE_						:					List[str]="left",     ):
										snake_case							:  Optional[int]			=     0
										snake_case							:  Tuple			=     self.font_size
										snake_case							:  Tuple			=     np.maximum(list(mplc.to_rgb(SCREAMING_SNAKE_CASE_   )   ),     0.2   )
										snake_case							:  Dict			=     max(0.8,     np.max(SCREAMING_SNAKE_CASE_   )   )
										snake_case							:  List[Any]			=     {
										    '''facecolor''': '''black''',
										    '''alpha''': self.alpha,
										    '''pad''': self.pad,
										    '''edgecolor''': '''none''',
										}
										snake_case,       snake_case							:  str			=     position
										self.ax.text(
										    SCREAMING_SNAKE_CASE_,     SCREAMING_SNAKE_CASE_,     SCREAMING_SNAKE_CASE_,     size=font_size * self.scale,     family='''sans-serif''',     bbox=SCREAMING_SNAKE_CASE_,     verticalalignment='''top''',     horizontalalignment=SCREAMING_SNAKE_CASE_,     color=SCREAMING_SNAKE_CASE_,     zorder=10,     rotation=SCREAMING_SNAKE_CASE_,     )




				def 						__snake_case				(				self						:					Any,     SCREAMING_SNAKE_CASE_						:					Any=None   ):
										if saveas is None:
																snake_case							:  List[str]			=     self.saveas
										if saveas.lower().endswith('''.jpg'''   ) or saveas.lower().endswith('''.png'''   ):
																cva.imwrite(
																    SCREAMING_SNAKE_CASE_,     self._get_buffer()[:, :, ::-1],     )
										else:
																self.fig.savefig(SCREAMING_SNAKE_CASE_   )




				def 						__snake_case				(				self						:					Tuple,     SCREAMING_SNAKE_CASE_						:					Optional[int],     SCREAMING_SNAKE_CASE_						:					str,     SCREAMING_SNAKE_CASE_						:					str,     SCREAMING_SNAKE_CASE_						:					int   ):
										snake_case							:  int			=     [self.idaobj[i] for i in classes]
										snake_case							:  str			=     [self.idaattr[i] for i in attr_classes]
										snake_case							:  Optional[Any]			=     [
										    F"""{label} {score:.2f} {attr} {attr_score:.2f}"""
										    for label, score, attr, attr_score in zip(SCREAMING_SNAKE_CASE_,     SCREAMING_SNAKE_CASE_,     SCREAMING_SNAKE_CASE_,     SCREAMING_SNAKE_CASE_   )
										]
										return labels




				def 						__snake_case				(				self						:					Any,     SCREAMING_SNAKE_CASE_						:					List[Any],     SCREAMING_SNAKE_CASE_						:					List[str]   ):
										snake_case							:  Union[str, Any]			=     [self.idaobj[i] for i in classes]
										if scores is not None:
																if labels is None:
																						snake_case							:  Optional[Any]			=     ['''{:.0f}%'''.format(s * 1_00   ) for s in scores]
																else:
																						snake_case							:  str			=     ['''{} {:.0f}%'''.format(SCREAMING_SNAKE_CASE_,     s * 1_00   ) for li, s in zip(SCREAMING_SNAKE_CASE_,     SCREAMING_SNAKE_CASE_   )]
										return labels




				def 						__snake_case				(				self						:					Union[str, Any],     SCREAMING_SNAKE_CASE_						:					int=2_55   ):
										snake_case							:  Union[str, Any]			=     np.random.randint(0,     len(_COLORS   )   )
										snake_case							:  Optional[int]			=     _COLORS[idx] * maximum
										if not self.rgb:
																snake_case							:  str			=     ret[::-1]
										return ret




				def 						__snake_case				(				self						:					List[Any]   ):
										if not self.pynb:
																snake_case,       snake_case							:  int			=     self.canvas.print_to_buffer()
																if (width, height) != (self.width, self.height):
																						snake_case							:  List[Any]			=     cva.resize(self.img,     (width, height)   )
																else:
																						snake_case							:  Tuple			=     self.img
										else:
																snake_case							:  List[str]			=     io.BytesIO()  # works for cairo backend
																self.canvas.print_rgba(SCREAMING_SNAKE_CASE_   )
																snake_case,       snake_case							:  Union[str, Any]			=     self.width, self.height
																snake_case							:  Optional[Any]			=     buf.getvalue()
																snake_case							:  List[Any]			=     self.img

										snake_case							:  Union[str, Any]			=     np.frombuffer(SCREAMING_SNAKE_CASE_,     dtype='''uint8'''   )
										snake_case							:  Union[str, Any]			=     buffer.reshape(SCREAMING_SNAKE_CASE_,     SCREAMING_SNAKE_CASE_,     4   )
										snake_case,       snake_case							:  Any			=     np.split(SCREAMING_SNAKE_CASE_,     [3],     axis=2   )

										try:
																import numexpr as ne  # fuse them with numexpr

																snake_case							:  str			=     ne.evaluate('''img * (1 - alpha / 255.0) + rgb * (alpha / 255.0)'''   )
										except ImportError:
																snake_case							:  Dict			=     alpha.astype('''float32'''   ) / 255.0
																snake_case							:  Tuple			=     img * (1 - alpha) + rgb * alpha

										return visualized_image.astype('''uint8'''   )




				def 						__snake_case				(				self						:					List[str],     SCREAMING_SNAKE_CASE_						:					Optional[int],     SCREAMING_SNAKE_CASE_						:					Any   ):
										assert brightness_factor >= -1.0 and brightness_factor <= 1.0
										snake_case							:  Any			=     mplc.to_rgb(SCREAMING_SNAKE_CASE_   )
										snake_case							:  List[str]			=     colorsys.rgb_to_hls(*mplc.to_rgb(SCREAMING_SNAKE_CASE_   )   )
										snake_case							:  Union[str, Any]			=     polygon_color[1] + (brightness_factor * polygon_color[1])
										snake_case							:  List[Any]			=     0.0 if modified_lightness < 0.0 else modified_lightness
										snake_case							:  Dict			=     1.0 if modified_lightness > 1.0 else modified_lightness
										snake_case							:  Dict			=     colorsys.hls_to_rgb(polygon_color[0],     SCREAMING_SNAKE_CASE_,     polygon_color[2]   )
										return modified_color




UpperCAmelCase            = (
    np.array(
        [
            0.000,
            0.447,
            0.741,
            0.850,
            0.325,
            0.098,
            0.929,
            0.694,
            0.125,
            0.494,
            0.184,
            0.556,
            0.466,
            0.674,
            0.188,
            0.301,
            0.745,
            0.933,
            0.635,
            0.078,
            0.184,
            0.300,
            0.300,
            0.300,
            0.600,
            0.600,
            0.600,
            1.000,
            0.000,
            0.000,
            1.000,
            0.500,
            0.000,
            0.749,
            0.749,
            0.000,
            0.000,
            1.000,
            0.000,
            0.000,
            0.000,
            1.000,
            0.667,
            0.000,
            1.000,
            0.333,
            0.333,
            0.000,
            0.333,
            0.667,
            0.000,
            0.333,
            1.000,
            0.000,
            0.667,
            0.333,
            0.000,
            0.667,
            0.667,
            0.000,
            0.667,
            1.000,
            0.000,
            1.000,
            0.333,
            0.000,
            1.000,
            0.667,
            0.000,
            1.000,
            1.000,
            0.000,
            0.000,
            0.333,
            0.500,
            0.000,
            0.667,
            0.500,
            0.000,
            1.000,
            0.500,
            0.333,
            0.000,
            0.500,
            0.333,
            0.333,
            0.500,
            0.333,
            0.667,
            0.500,
            0.333,
            1.000,
            0.500,
            0.667,
            0.000,
            0.500,
            0.667,
            0.333,
            0.500,
            0.667,
            0.667,
            0.500,
            0.667,
            1.000,
            0.500,
            1.000,
            0.000,
            0.500,
            1.000,
            0.333,
            0.500,
            1.000,
            0.667,
            0.500,
            1.000,
            1.000,
            0.500,
            0.000,
            0.333,
            1.000,
            0.000,
            0.667,
            1.000,
            0.000,
            1.000,
            1.000,
            0.333,
            0.000,
            1.000,
            0.333,
            0.333,
            1.000,
            0.333,
            0.667,
            1.000,
            0.333,
            1.000,
            1.000,
            0.667,
            0.000,
            1.000,
            0.667,
            0.333,
            1.000,
            0.667,
            0.667,
            1.000,
            0.667,
            1.000,
            1.000,
            1.000,
            0.000,
            1.000,
            1.000,
            0.333,
            1.000,
            1.000,
            0.667,
            1.000,
            0.333,
            0.000,
            0.000,
            0.500,
            0.000,
            0.000,
            0.667,
            0.000,
            0.000,
            0.833,
            0.000,
            0.000,
            1.000,
            0.000,
            0.000,
            0.000,
            0.167,
            0.000,
            0.000,
            0.333,
            0.000,
            0.000,
            0.500,
            0.000,
            0.000,
            0.667,
            0.000,
            0.000,
            0.833,
            0.000,
            0.000,
            1.000,
            0.000,
            0.000,
            0.000,
            0.167,
            0.000,
            0.000,
            0.333,
            0.000,
            0.000,
            0.500,
            0.000,
            0.000,
            0.667,
            0.000,
            0.000,
            0.833,
            0.000,
            0.000,
            1.000,
            0.000,
            0.000,
            0.000,
            0.143,
            0.143,
            0.143,
            0.857,
            0.857,
            0.857,
            1.000,
            1.000,
            1.000,
        ]
    )
    .astype(np.floataa)
    .reshape(-1, 3)
)

