


'''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


__lowercase			:		List[str] =			10_00





class 							__UpperCamelCase       :




	def __init__(     self		,       __a		,       __a=1.2		,       __a="g"		,       __a=0.5		,       __a="-"		,       __a="test_out.jpg"		,       __a=True		,       __a=False		,       __a=None		,       __a=None		,       __a=0.7		,       ):

						'''simple docstring'''





						if isinstance(__a		,       torch.Tensor							):
											__a    : Dict      					=						img.numpy().astype('np.uint8'							)
						if isinstance(__a		,       __a							):
											__a    : Tuple      					=						img_tensorize(__a							)
						assert isinstance(__a		,       np.ndarray							)

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

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




	def    __UpperCAmelCase							(     self		,       __a		,       __a=None							):

						'''simple docstring'''





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




	def    __UpperCAmelCase							(     self		,       __a		,       __a=None		,       __a=None		,       __a=None		,       __a=None							):

						'''simple docstring'''





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

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




	def    __UpperCAmelCase							(     self		,       __a		,       __a		,       __a							):

						'''simple docstring'''





						__a    : Any      					=						box
						__a    : Union[str, Any]      					=						(xa, ya)
						__a    : str      					=						(ya - ya) * (xa - xa)
						__a    : Tuple      					=						_SMALL_OBJ * self.scale
						if instance_area < small or ya - ya < 40 * self.scale:
											if ya >= self.height - 5:
																__a    : int      					=						(xa, ya)
											else:
																__a    : List[str]      					=						(xa, ya)

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

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




	def    __UpperCAmelCase							(     self		,       __a		,       __a		,       __a="g"		,       __a="left"		,       ):

						'''simple docstring'''





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




	def    __UpperCAmelCase							(     self		,       __a=None							):

						'''simple docstring'''





						if saveas is None:
											__a    : Optional[int]      					=						self.saveas
						if saveas.lower().endswith('.jpg'							) or saveas.lower().endswith('.png'							):
											cva.imwrite(
											    __a		,       self._get_buffer()[:, :, ::-1]		,       )
						else:
											self.fig.savefig(__a							)




	def    __UpperCAmelCase							(     self		,       __a		,       __a		,       __a		,       __a							):

						'''simple docstring'''





						__a    : Any      					=						[self.idaobj[i] for i in classes]
						__a    : Optional[int]      					=						[self.idaattr[i] for i in attr_classes]
						__a    : int      					=						[
						    f"""{label} {score:.2f} {attr} {attr_score:.2f}"""
						    for label, score, attr, attr_score in zip(__a		,       __a		,       __a		,       __a							)
						]
						return labels




	def    __UpperCAmelCase							(     self		,       __a		,       __a							):

						'''simple docstring'''





						__a    : Union[str, Any]      					=						[self.idaobj[i] for i in classes]
						if scores is not None:
											if labels is None:
																__a    : List[str]      					=						['{:.0f}%'.format(s * 100							) for s in scores]
											else:
																__a    : Union[str, Any]      					=						['{} {:.0f}%'.format(__a		,       s * 100							) for li, s in zip(__a		,       __a							)]
						return labels




	def    __UpperCAmelCase							(     self		,       __a=255							):

						'''simple docstring'''





						__a    : Dict      					=						np.random.randint(0		,       len(_COLORS							)							)
						__a    : Tuple      					=						_COLORS[idx] * maximum
						if not self.rgb:
											__a    : Optional[Any]      					=						ret[::-1]
						return ret




	def    __UpperCAmelCase							(     self							):

						'''simple docstring'''





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

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

						try:
											import numexpr as ne  # fuse them with numexpr

											__a    : List[str]      					=						ne.evaluate('img * (1 - alpha / 255.0) + rgb * (alpha / 255.0)'							)
						except ImportError:
											__a    : List[Any]      					=						alpha.astype('float32'							) / 255.0
											__a    : Any      					=						img * (1 - alpha) + rgb * alpha

						return visualized_image.astype('uint8'							)





	def    __UpperCAmelCase							(     self		,       __a		,       __a							):

						'''simple docstring'''





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



__lowercase			:		Any =			(
    np.array(
        [
            0.0_00,
            0.4_47,
            0.7_41,
            0.8_50,
            0.3_25,
            0.0_98,
            0.9_29,
            0.6_94,
            0.1_25,
            0.4_94,
            0.1_84,
            0.5_56,
            0.4_66,
            0.6_74,
            0.1_88,
            0.3_01,
            0.7_45,
            0.9_33,
            0.6_35,
            0.0_78,
            0.1_84,
            0.3_00,
            0.3_00,
            0.3_00,
            0.6_00,
            0.6_00,
            0.6_00,
            1.0_00,
            0.0_00,
            0.0_00,
            1.0_00,
            0.5_00,
            0.0_00,
            0.7_49,
            0.7_49,
            0.0_00,
            0.0_00,
            1.0_00,
            0.0_00,
            0.0_00,
            0.0_00,
            1.0_00,
            0.6_67,
            0.0_00,
            1.0_00,
            0.3_33,
            0.3_33,
            0.0_00,
            0.3_33,
            0.6_67,
            0.0_00,
            0.3_33,
            1.0_00,
            0.0_00,
            0.6_67,
            0.3_33,
            0.0_00,
            0.6_67,
            0.6_67,
            0.0_00,
            0.6_67,
            1.0_00,
            0.0_00,
            1.0_00,
            0.3_33,
            0.0_00,
            1.0_00,
            0.6_67,
            0.0_00,
            1.0_00,
            1.0_00,
            0.0_00,
            0.0_00,
            0.3_33,
            0.5_00,
            0.0_00,
            0.6_67,
            0.5_00,
            0.0_00,
            1.0_00,
            0.5_00,
            0.3_33,
            0.0_00,
            0.5_00,
            0.3_33,
            0.3_33,
            0.5_00,
            0.3_33,
            0.6_67,
            0.5_00,
            0.3_33,
            1.0_00,
            0.5_00,
            0.6_67,
            0.0_00,
            0.5_00,
            0.6_67,
            0.3_33,
            0.5_00,
            0.6_67,
            0.6_67,
            0.5_00,
            0.6_67,
            1.0_00,
            0.5_00,
            1.0_00,
            0.0_00,
            0.5_00,
            1.0_00,
            0.3_33,
            0.5_00,
            1.0_00,
            0.6_67,
            0.5_00,
            1.0_00,
            1.0_00,
            0.5_00,
            0.0_00,
            0.3_33,
            1.0_00,
            0.0_00,
            0.6_67,
            1.0_00,
            0.0_00,
            1.0_00,
            1.0_00,
            0.3_33,
            0.0_00,
            1.0_00,
            0.3_33,
            0.3_33,
            1.0_00,
            0.3_33,
            0.6_67,
            1.0_00,
            0.3_33,
            1.0_00,
            1.0_00,
            0.6_67,
            0.0_00,
            1.0_00,
            0.6_67,
            0.3_33,
            1.0_00,
            0.6_67,
            0.6_67,
            1.0_00,
            0.6_67,
            1.0_00,
            1.0_00,
            1.0_00,
            0.0_00,
            1.0_00,
            1.0_00,
            0.3_33,
            1.0_00,
            1.0_00,
            0.6_67,
            1.0_00,
            0.3_33,
            0.0_00,
            0.0_00,
            0.5_00,
            0.0_00,
            0.0_00,
            0.6_67,
            0.0_00,
            0.0_00,
            0.8_33,
            0.0_00,
            0.0_00,
            1.0_00,
            0.0_00,
            0.0_00,
            0.0_00,
            0.1_67,
            0.0_00,
            0.0_00,
            0.3_33,
            0.0_00,
            0.0_00,
            0.5_00,
            0.0_00,
            0.0_00,
            0.6_67,
            0.0_00,
            0.0_00,
            0.8_33,
            0.0_00,
            0.0_00,
            1.0_00,
            0.0_00,
            0.0_00,
            0.0_00,
            0.1_67,
            0.0_00,
            0.0_00,
            0.3_33,
            0.0_00,
            0.0_00,
            0.5_00,
            0.0_00,
            0.0_00,
            0.6_67,
            0.0_00,
            0.0_00,
            0.8_33,
            0.0_00,
            0.0_00,
            1.0_00,
            0.0_00,
            0.0_00,
            0.0_00,
            0.1_43,
            0.1_43,
            0.1_43,
            0.8_57,
            0.8_57,
            0.8_57,
            1.0_00,
            1.0_00,
            1.0_00,
        ]
    )
    .astype(np.floataa)
    .reshape(-1, 3)
)







'''simple docstring'''



import torch

from diffusers import KDPMaAncestralDiscreteScheduler
from diffusers.utils import torch_device

from .test_schedulers import SchedulerCommonTest





class 							__UpperCamelCase       (	lowerCAmelCase_      ):
	A_			    =					(KDPMaAncestralDiscreteScheduler,)
	A_			    =					10




	def    __UpperCAmelCase							(     self		,       **__a							):

						'''simple docstring'''





						__a    : Dict      					=						{
						    'num_train_timesteps': 1100,
						    'beta_start': 0.0001,
						    'beta_end': 0.02,
						    'beta_schedule': 'linear',
						}

						config.update(**__a							)
						return config




	def    __UpperCAmelCase							(     self							):

						'''simple docstring'''





						for timesteps in [10, 50, 100, 1000]:
											self.check_over_configs(num_train_timesteps=__a							)




	def    __UpperCAmelCase							(     self							):

						'''simple docstring'''





						for beta_start, beta_end in zip([0.00001, 0.0001, 0.001]		,       [0.0002, 0.002, 0.02]							):
											self.check_over_configs(beta_start=__a		,       beta_end=__a							)




	def    __UpperCAmelCase							(     self							):

						'''simple docstring'''





						for schedule in ["linear", "scaled_linear"]:
											self.check_over_configs(beta_schedule=__a							)




	def    __UpperCAmelCase							(     self							):

						'''simple docstring'''





						if torch_device == "mps":
											return
						__a    : Dict      					=						self.scheduler_classes[0]
						__a    : Tuple      					=						self.get_scheduler_config()
						__a    : Optional[int]      					=						scheduler_class(**__a							)

						scheduler.set_timesteps(self.num_inference_steps							)

						__a    : int      					=						torch.manual_seed(0							)

						__a    : Dict      					=						self.dummy_model()
						__a    : List[Any]      					=						self.dummy_sample_deter * scheduler.init_noise_sigma
						__a    : int      					=						sample.to(__a							)

						for i, t in enumerate(scheduler.timesteps							):
											__a    : Any      					=						scheduler.scale_model_input(__a		,       __a							)

											__a    : int      					=						model(__a		,       __a							)

											__a    : Dict      					=						scheduler.step(__a		,       __a		,       __a		,       generator=__a							)
											__a    : int      					=						output.prev_sample

						__a    : Tuple      					=						torch.sum(torch.abs(__a							)							)
						__a    : List[str]      					=						torch.mean(torch.abs(__a							)							)

						assert abs(result_sum.item() - 13849.3877							) < 1E-2
						assert abs(result_mean.item() - 18.0331							) < 5E-3




	def    __UpperCAmelCase							(     self							):

						'''simple docstring'''





						for prediction_type in ["epsilon", "v_prediction"]:
											self.check_over_configs(prediction_type=__a							)




	def    __UpperCAmelCase							(     self							):

						'''simple docstring'''





						if torch_device == "mps":
											return
						__a    : Dict      					=						self.scheduler_classes[0]
						__a    : Dict      					=						self.get_scheduler_config(prediction_type='v_prediction'							)
						__a    : List[str]      					=						scheduler_class(**__a							)

						scheduler.set_timesteps(self.num_inference_steps							)

						__a    : Dict      					=						self.dummy_model()
						__a    : List[str]      					=						self.dummy_sample_deter * scheduler.init_noise_sigma
						__a    : List[str]      					=						sample.to(__a							)

						__a    : Tuple      					=						torch.manual_seed(0							)

						for i, t in enumerate(scheduler.timesteps							):
											__a    : List[Any]      					=						scheduler.scale_model_input(__a		,       __a							)

											__a    : Optional[int]      					=						model(__a		,       __a							)

											__a    : Union[str, Any]      					=						scheduler.step(__a		,       __a		,       __a		,       generator=__a							)
											__a    : Union[str, Any]      					=						output.prev_sample

						__a    : Any      					=						torch.sum(torch.abs(__a							)							)
						__a    : Optional[int]      					=						torch.mean(torch.abs(__a							)							)

						assert abs(result_sum.item() - 328.9970							) < 1E-2
						assert abs(result_mean.item() - 0.4284							) < 1E-3





	def    __UpperCAmelCase							(     self							):

						'''simple docstring'''





						if torch_device == "mps":
											return
						__a    : List[str]      					=						self.scheduler_classes[0]
						__a    : Union[str, Any]      					=						self.get_scheduler_config()
						__a    : List[Any]      					=						scheduler_class(**__a							)

						scheduler.set_timesteps(self.num_inference_steps		,       device=__a							)
						__a    : Union[str, Any]      					=						torch.manual_seed(0							)

						__a    : List[str]      					=						self.dummy_model()
						__a    : Any      					=						self.dummy_sample_deter.to(__a							) * scheduler.init_noise_sigma

						for t in scheduler.timesteps:
											__a    : List[Any]      					=						scheduler.scale_model_input(__a		,       __a							)

											__a    : str      					=						model(__a		,       __a							)

											__a    : str      					=						scheduler.step(__a		,       __a		,       __a		,       generator=__a							)
											__a    : Any      					=						output.prev_sample

						__a    : Union[str, Any]      					=						torch.sum(torch.abs(__a							)							)
						__a    : str      					=						torch.mean(torch.abs(__a							)							)

						assert abs(result_sum.item() - 13849.3818							) < 1E-1
						assert abs(result_mean.item() - 18.0331							) < 1E-3



