
from typing import Callable, List, Optional, Union

import torch

from ...models import UNetaDModel
from ...schedulers import CMStochasticIterativeScheduler
from ...utils import (
    is_accelerate_available,
    is_accelerate_version,
    logging,
    randn_tensor,
    replace_example_docstring,
)
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput


__snake_case					:							Union[str, Any]           =							logging.get_logger(__name__)  # pylint: disable=invalid-name


__snake_case					:							Tuple           =							'\n    Examples:\n        ```py\n        >>> import torch\n\n        >>> from diffusers import ConsistencyModelPipeline\n\n        >>> device = \"cuda\"\n        >>> # Load the cd_imagenet64_l2 checkpoint.\n        >>> model_id_or_path = \"openai/diffusers-cd_imagenet64_l2\"\n        >>> pipe = ConsistencyModelPipeline.from_pretrained(model_id_or_path, torch_dtype=torch.float16)\n        >>> pipe.to(device)\n\n        >>> # Onestep Sampling\n        >>> image = pipe(num_inference_steps=1).images[0]\n        >>> image.save(\"cd_imagenet64_l2_onestep_sample.png\")\n\n        >>> # Onestep sampling, class-conditional image generation\n        >>> # ImageNet-64 class label 145 corresponds to king penguins\n        >>> image = pipe(num_inference_steps=1, class_labels=145).images[0]\n        >>> image.save(\"cd_imagenet64_l2_onestep_sample_penguin.png\")\n\n        >>> # Multistep sampling, class-conditional image generation\n        >>> # Timesteps can be explicitly specified; the particular timesteps below are from the original Github repo:\n        >>> # https://github.com/openai/consistency_models/blob/main/scripts/launch.sh#L77\n        >>> image = pipe(num_inference_steps=None, timesteps=[22, 0], class_labels=145).images[0]\n        >>> image.save(\"cd_imagenet64_l2_multistep_sample_penguin.png\")\n        ```\n'


class 	UpperCamelCase__       (  UpperCAmelCase__):

						'''simple docstring'''



						def __init__(  self  , A  , A	)  ->List[Any]:
									super().__init__()

									self.register_modules(
									    unet=SCREAMING_SNAKE_CASE_  , scheduler=SCREAMING_SNAKE_CASE_  , )

									UpperCAmelCase__		:Optional[int] 		=    None


						def    A__       (  self  , A=0	)  ->Tuple:
									if is_accelerate_available() and is_accelerate_version('>='  , '0.14.0'	):
												from accelerate import cpu_offload
									else:
												raise ImportError('`enable_sequential_cpu_offload` requires `accelerate v0.14.0` or higher'	)

									UpperCAmelCase__		:List[str] 		=    torch.device(F"""cuda:{gpu_id}"""	)

									if self.device.type != "cpu":
												self.to('cpu'  , silence_dtype_warnings=SCREAMING_SNAKE_CASE_	)
												torch.cuda.empty_cache()  # otherwise we don't see the memory savings (but they probably exist)

									for cpu_offloaded_model in [self.unet]:
												cpu_offload(SCREAMING_SNAKE_CASE_  , SCREAMING_SNAKE_CASE_	)

									if self.safety_checker is not None:
												cpu_offload(self.safety_checker  , execution_device=SCREAMING_SNAKE_CASE_  , offload_buffers=SCREAMING_SNAKE_CASE_	)


						def    A__       (  self  , A=0	)  ->Any:
									if is_accelerate_available() and is_accelerate_version('>='  , '0.17.0.dev0'	):
												from accelerate import cpu_offload_with_hook
									else:
												raise ImportError('`enable_model_cpu_offload` requires `accelerate v0.17.0` or higher.'	)

									UpperCAmelCase__		:Union[str, Any] 		=    torch.device(F"""cuda:{gpu_id}"""	)

									if self.device.type != "cpu":
												self.to('cpu'  , silence_dtype_warnings=SCREAMING_SNAKE_CASE_	)
												torch.cuda.empty_cache()  # otherwise we don't see the memory savings (but they probably exist)

									UpperCAmelCase__		:Any 		=    None
									for cpu_offloaded_model in [self.unet]:
												UpperCAmelCase__		:List[str] 		=    cpu_offload_with_hook(SCREAMING_SNAKE_CASE_  , SCREAMING_SNAKE_CASE_  , prev_module_hook=SCREAMING_SNAKE_CASE_	)

									if self.safety_checker is not None:
												UpperCAmelCase__		:Optional[int] 		=    cpu_offload_with_hook(self.safety_checker  , SCREAMING_SNAKE_CASE_  , prev_module_hook=SCREAMING_SNAKE_CASE_	)

									# We'll offload the last model manually.
									UpperCAmelCase__		:str 		=    hook


						@property
						# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline._execution_device
						def    A__       (  self	)  ->str:
									if not hasattr(self.unet  , '_hf_hook'	):
												return self.device
									for module in self.unet.modules():
												if (
												    hasattr(SCREAMING_SNAKE_CASE_  , '_hf_hook'	)
												    and hasattr(module._hf_hook  , 'execution_device'	)
												    and module._hf_hook.execution_device is not None
												):
															return torch.device(module._hf_hook.execution_device	)
									return self.device


						def    A__       (  self  , A  , A  , A  , A  , A  , A  , A  , A=None	)  ->Optional[Any]:
									UpperCAmelCase__		:Optional[Any] 		=    (batch_size, num_channels, height, width)
									if isinstance(SCREAMING_SNAKE_CASE_  , SCREAMING_SNAKE_CASE_	) and len(SCREAMING_SNAKE_CASE_	) != batch_size:
												raise ValueError(
												    F"""You have passed a list of generators of length {len(SCREAMING_SNAKE_CASE_	)}, but requested an effective batch"""
												    F""" size of {batch_size}. Make sure the batch size matches the length of the generators."""	)

									if latents is None:
												UpperCAmelCase__		:Any 		=    randn_tensor(SCREAMING_SNAKE_CASE_  , generator=SCREAMING_SNAKE_CASE_  , device=SCREAMING_SNAKE_CASE_  , dtype=SCREAMING_SNAKE_CASE_	)
									else:
												UpperCAmelCase__		:Union[str, Any] 		=    latents.to(device=SCREAMING_SNAKE_CASE_  , dtype=SCREAMING_SNAKE_CASE_	)

									# scale the initial noise by the standard deviation required by the scheduler
									UpperCAmelCase__		:Optional[Any] 		=    latents * self.scheduler.init_noise_sigma
									return latents


						def    A__       (  self  , A  , A = "pil"	)  ->str:
									if output_type not in ["pt", "np", "pil"]:
												raise ValueError(
												    F"""output_type={output_type} is not supported. Make sure to choose one of ['pt', 'np', or 'pil']"""	)

									# Equivalent to diffusers.VaeImageProcessor.denormalize
									UpperCAmelCase__		:List[Any] 		=    (sample / 2 + 0.5).clamp(0  , 1	)
									if output_type == "pt":
												return sample

									# Equivalent to diffusers.VaeImageProcessor.pt_to_numpy
									UpperCAmelCase__		:Optional[int] 		=    sample.cpu().permute(0  , 2  , 3  , 1	).numpy()
									if output_type == "np":
												return sample

									# Output_type must be 'pil'
									UpperCAmelCase__		:Any 		=    self.numpy_to_pil(SCREAMING_SNAKE_CASE_	)
									return sample


						def    A__       (  self  , A  , A  , A=None	)  ->int:
									if self.unet.config.num_class_embeds is not None:
												if isinstance(SCREAMING_SNAKE_CASE_  , SCREAMING_SNAKE_CASE_	):
															UpperCAmelCase__		:str 		=    torch.tensor(SCREAMING_SNAKE_CASE_  , dtype=torch.int	)
												elif isinstance(SCREAMING_SNAKE_CASE_  , SCREAMING_SNAKE_CASE_	):
															assert batch_size == 1, "Batch size must be 1 if classes is an int"
															UpperCAmelCase__		:Any 		=    torch.tensor([class_labels]  , dtype=torch.int	)
												elif class_labels is None:
															# Randomly generate batch_size class labels
															# TODO: should use generator here? int analogue of randn_tensor is not exposed in ...utils
															UpperCAmelCase__		:str 		=    torch.randint(0  , self.unet.config.num_class_embeds  , size=(batch_size,)	)
												UpperCAmelCase__		:str 		=    class_labels.to(SCREAMING_SNAKE_CASE_	)
									else:
												UpperCAmelCase__		:List[Any] 		=    None
									return class_labels


						def    A__       (  self  , A  , A  , A  , A  , A  , A	)  ->Dict:
									if num_inference_steps is None and timesteps is None:
												raise ValueError('Exactly one of `num_inference_steps` or `timesteps` must be supplied.'	)

									if num_inference_steps is not None and timesteps is not None:
												logger.warning(
												    F"""Both `num_inference_steps`: {num_inference_steps} and `timesteps`: {timesteps} are supplied;"""
												    ' `timesteps` will be used over `num_inference_steps`.'	)

									if latents is not None:
												UpperCAmelCase__		:Dict 		=    (batch_size, 3, img_size, img_size)
												if latents.shape != expected_shape:
															raise ValueError(F"""The shape of latents is {latents.shape} but is expected to be {expected_shape}."""	)

									if (callback_steps is None) or (
									    callback_steps is not None and (not isinstance(SCREAMING_SNAKE_CASE_  , SCREAMING_SNAKE_CASE_	) or callback_steps <= 0)
									):
												raise ValueError(
												    F"""`callback_steps` has to be a positive integer but is {callback_steps} of type"""
												    F""" {type(SCREAMING_SNAKE_CASE_	)}."""	)


						@torch.no_grad()
						@replace_example_docstring(SCREAMING_SNAKE_CASE_	)
						def __call__(  self  , A = 1  , A = None  , A = 1  , A = None  , A = None  , A = None  , A = "pil"  , A = True  , A = None  , A = 1  , )  ->List[str]:
									UpperCAmelCase__		:List[str] 		=    self.unet.config.sample_size
									UpperCAmelCase__		:Optional[Any] 		=    self._execution_device

									# 1. Check inputs
									self.check_inputs(SCREAMING_SNAKE_CASE_  , SCREAMING_SNAKE_CASE_  , SCREAMING_SNAKE_CASE_  , SCREAMING_SNAKE_CASE_  , SCREAMING_SNAKE_CASE_  , SCREAMING_SNAKE_CASE_	)

									# 2. Prepare image latents
									# Sample image latents x_0 ~ N(0, sigma_0^2 * I)
									UpperCAmelCase__		:int 		=    self.prepare_latents(
									    batch_size=SCREAMING_SNAKE_CASE_  , num_channels=self.unet.config.in_channels  , height=SCREAMING_SNAKE_CASE_  , width=SCREAMING_SNAKE_CASE_  , dtype=self.unet.dtype  , device=SCREAMING_SNAKE_CASE_  , generator=SCREAMING_SNAKE_CASE_  , latents=SCREAMING_SNAKE_CASE_  , )

									# 3. Handle class_labels for class-conditional models
									UpperCAmelCase__		:Any 		=    self.prepare_class_labels(SCREAMING_SNAKE_CASE_  , SCREAMING_SNAKE_CASE_  , class_labels=SCREAMING_SNAKE_CASE_	)

									# 4. Prepare timesteps
									if timesteps is not None:
												self.scheduler.set_timesteps(timesteps=SCREAMING_SNAKE_CASE_  , device=SCREAMING_SNAKE_CASE_	)
												UpperCAmelCase__		:Union[str, Any] 		=    self.scheduler.timesteps
												UpperCAmelCase__		:List[Any] 		=    len(SCREAMING_SNAKE_CASE_	)
									else:
												self.scheduler.set_timesteps(SCREAMING_SNAKE_CASE_	)
												UpperCAmelCase__		:List[Any] 		=    self.scheduler.timesteps

									# 5. Denoising loop
									# Multistep sampling: implements Algorithm 1 in the paper
									with self.progress_bar(total=SCREAMING_SNAKE_CASE_	) as progress_bar:
												for i, t in enumerate(SCREAMING_SNAKE_CASE_	):
															UpperCAmelCase__		:Union[str, Any] 		=    self.scheduler.scale_model_input(SCREAMING_SNAKE_CASE_  , SCREAMING_SNAKE_CASE_	)
															UpperCAmelCase__		:List[str] 		=    self.unet(SCREAMING_SNAKE_CASE_  , SCREAMING_SNAKE_CASE_  , class_labels=SCREAMING_SNAKE_CASE_  , return_dict=SCREAMING_SNAKE_CASE_	)[0]

															UpperCAmelCase__		:List[Any] 		=    self.scheduler.step(SCREAMING_SNAKE_CASE_  , SCREAMING_SNAKE_CASE_  , SCREAMING_SNAKE_CASE_  , generator=SCREAMING_SNAKE_CASE_	)[0]

															# call the callback, if provided
															progress_bar.update()
															if callback is not None and i % callback_steps == 0:
																		callback(SCREAMING_SNAKE_CASE_  , SCREAMING_SNAKE_CASE_  , SCREAMING_SNAKE_CASE_	)

        # 6. Post-process image sample
									UpperCAmelCase__		:List[Any] 		=    self.postprocess_image(SCREAMING_SNAKE_CASE_  , output_type=SCREAMING_SNAKE_CASE_	)

									# Offload last model to CPU
									if hasattr(self  , 'final_offload_hook'	) and self.final_offload_hook is not None:
												self.final_offload_hook.offload()

									if not return_dict:
												return (image,)

									return ImagePipelineOutput(images=SCREAMING_SNAKE_CASE_	)







'''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)
)

