code: 
  
  
  '''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)
  )
  
  
  
  
style_context: 
  
  
  '''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
  
  
  
  