




"""simple docstring"""







import argparse
import json

import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from timm.models import create_model

from transformers import (
    BeitImageProcessor,
    DataaVecVisionConfig,
    DataaVecVisionForImageClassification,
    DataaVecVisionModel,
)





def 							lowercase   (snake_case__				:	Dict ,    snake_case__				:	str=False ,    snake_case__				:	str=False ,    snake_case__				:	Optional[Any]="data2vec."	)       ->					Tuple:


				'''simple docstring'''






				lowerCAmelCase		=			"""backbone.""" if is_semantic else """"""

				lowerCAmelCase		=			[]
				for i in range(config.num_hidden_layers	):
								# encoder layers: output projection, 2 feedforward neural networks and 2 layernorms
								rename_keys.append(
								    (f'''{prefix}blocks.{i}.norm1.weight''', f'''{hf_prefix}encoder.layer.{i}.layernorm_before.weight''')	)
								rename_keys.append((f'''{prefix}blocks.{i}.norm1.bias''', f'''{hf_prefix}encoder.layer.{i}.layernorm_before.bias''')	)
								rename_keys.append(
								    (f'''{prefix}blocks.{i}.attn.proj.weight''', f'''{hf_prefix}encoder.layer.{i}.attention.output.dense.weight''')	)
								rename_keys.append(
								    (f'''{prefix}blocks.{i}.attn.proj.bias''', f'''{hf_prefix}encoder.layer.{i}.attention.output.dense.bias''')	)
								rename_keys.append(
								    (f'''{prefix}blocks.{i}.norm2.weight''', f'''{hf_prefix}encoder.layer.{i}.layernorm_after.weight''')	)
								rename_keys.append((f'''{prefix}blocks.{i}.norm2.bias''', f'''{hf_prefix}encoder.layer.{i}.layernorm_after.bias''')	)
								rename_keys.append(
								    (f'''{prefix}blocks.{i}.mlp.fc1.weight''', f'''{hf_prefix}encoder.layer.{i}.intermediate.dense.weight''')	)
								rename_keys.append(
								    (f'''{prefix}blocks.{i}.mlp.fc1.bias''', f'''{hf_prefix}encoder.layer.{i}.intermediate.dense.bias''')	)
								rename_keys.append((f'''{prefix}blocks.{i}.mlp.fc2.weight''', f'''{hf_prefix}encoder.layer.{i}.output.dense.weight''')	)
								rename_keys.append((f'''{prefix}blocks.{i}.mlp.fc2.bias''', f'''{hf_prefix}encoder.layer.{i}.output.dense.bias''')	)

				# projection layer + position embeddings
				rename_keys.extend(
				    [
				        (f'''{prefix}cls_token''', f'''{hf_prefix}embeddings.cls_token'''),
				        (f'''{prefix}patch_embed.proj.weight''', f'''{hf_prefix}embeddings.patch_embeddings.projection.weight'''),
				        (f'''{prefix}patch_embed.proj.bias''', f'''{hf_prefix}embeddings.patch_embeddings.projection.bias'''),
				    ]	)

				if has_lm_head:
								# mask token + shared relative position bias + layernorm
								rename_keys.extend(
								    [
								        ("""mask_token""", f'''{hf_prefix}embeddings.mask_token'''),
								        (
								            """rel_pos_bias.relative_position_bias_table""",
								            f'''{hf_prefix}encoder.relative_position_bias.relative_position_bias_table''',
								        ),
								        (
								            """rel_pos_bias.relative_position_index""",
								            f'''{hf_prefix}encoder.relative_position_bias.relative_position_index''',
								        ),
								        ("""norm.weight""", """layernorm.weight"""),
								        ("""norm.bias""", """layernorm.bias"""),
								    ]	)
				elif is_semantic:
								# semantic segmentation classification heads
								rename_keys.extend(
								    [
								        ("""decode_head.conv_seg.weight""", """decode_head.classifier.weight"""),
								        ("""decode_head.conv_seg.bias""", """decode_head.classifier.bias"""),
								        ("""auxiliary_head.conv_seg.weight""", """auxiliary_head.classifier.weight"""),
								        ("""auxiliary_head.conv_seg.bias""", """auxiliary_head.classifier.bias"""),
								    ]	)
				else:
								# layernorm + classification head
								rename_keys.extend(
								    [
								        ("""fc_norm.weight""", f'''{hf_prefix}pooler.layernorm.weight'''),
								        ("""fc_norm.bias""", f'''{hf_prefix}pooler.layernorm.bias'''),
								        ("""head.weight""", """classifier.weight"""),
								        ("""head.bias""", """classifier.bias"""),
								    ]	)

				return rename_keys





def 							lowercase   (snake_case__				:	int ,    snake_case__				:	int ,    snake_case__				:	int=False ,    snake_case__				:	Union[str, Any]=False ,    snake_case__				:	int="data2vec_vision."	)       ->					Any:


				'''simple docstring'''






				for i in range(config.num_hidden_layers	):
								lowerCAmelCase		=			"""backbone.""" if is_semantic else """"""
								# queries, keys and values
								lowerCAmelCase		=			state_dict.pop(f'''{prefix}blocks.{i}.attn.qkv.weight'''	)
								lowerCAmelCase		=			state_dict.pop(f'''{prefix}blocks.{i}.attn.q_bias'''	)
								lowerCAmelCase		=			state_dict.pop(f'''{prefix}blocks.{i}.attn.v_bias'''	)

								lowerCAmelCase		=			in_proj_weight[
								    : config.hidden_size, :
								]
								lowerCAmelCase		=			q_bias
								lowerCAmelCase		=			in_proj_weight[
								    config.hidden_size : config.hidden_size * 2, :
								]
								lowerCAmelCase		=			in_proj_weight[
								    -config.hidden_size :, :
								]
								lowerCAmelCase		=			v_bias

								# gamma_1 and gamma_2
								# we call them lambda because otherwise they are renamed when using .from_pretrained
								lowerCAmelCase		=			state_dict.pop(f'''{prefix}blocks.{i}.gamma_1'''	)
								lowerCAmelCase		=			state_dict.pop(f'''{prefix}blocks.{i}.gamma_2'''	)

								lowerCAmelCase		=			gamma_a
								lowerCAmelCase		=			gamma_a

								# relative_position bias table + index
								if not has_lm_head:
												# each layer has its own relative position bias
												lowerCAmelCase		=			state_dict.pop(f'''{prefix}blocks.{i}.attn.relative_position_bias_table'''	)
												lowerCAmelCase		=			state_dict.pop(f'''{prefix}blocks.{i}.attn.relative_position_index'''	)

												lowerCAmelCase		=			table
												lowerCAmelCase		=			index





def 							lowercase   ()       ->					List[str]:


				'''simple docstring'''






				lowerCAmelCase		=			argparse.ArgumentParser(
				    """Convert Data2VecVision to HF for image classification and pretraining""" ,    add_help=snake_case__	)
				parser.add_argument("""--hf_checkpoint_name""" ,    type=snake_case__	)
				parser.add_argument("""--input_size""" ,    default=224 ,    type=snake_case__ ,    help="""images input size"""	)
				parser.add_argument("""--beit_checkpoint""" ,    default="""""" ,    help="""beit checkpoint"""	)

				return parser.parse_args()





def 							lowercase   (snake_case__				:	Dict ,    snake_case__				:	int ,    snake_case__				:	Optional[Any]	)       ->					Tuple:


				'''simple docstring'''






				def load_state_dict(snake_case__				:	List[Any] ,    snake_case__				:	Union[str, Any] ,    snake_case__				:	Tuple="" ,    snake_case__				:	Union[str, Any]="relative_position_index"	):
								lowerCAmelCase		=			[]
								lowerCAmelCase		=			[]
								lowerCAmelCase		=			[]
								# copy state_dict so _load_from_state_dict can modify it
								lowerCAmelCase		=			getattr(snake_case__ ,    """_metadata""" ,    snake_case__	)
								lowerCAmelCase		=			state_dict.copy()
								if metadata is not None:
												lowerCAmelCase		=			metadata

								def load(snake_case__				:	List[str] ,    snake_case__				:	Any=""	):
												lowerCAmelCase		=			{} if metadata is None else metadata.get(prefix[:-1] ,    {}	)
												module._load_from_state_dict(
												    snake_case__ ,    snake_case__ ,    snake_case__ ,    snake_case__ ,    snake_case__ ,    snake_case__ ,    snake_case__	)
												for name, child in module._modules.items():
																if child is not None:
																				load(snake_case__ ,    prefix + name + """."""	)

								load(snake_case__ ,    prefix=snake_case__	)

								lowerCAmelCase		=			[]
								lowerCAmelCase		=			[]
								for key in missing_keys:
												lowerCAmelCase		=			True
												for ignore_key in ignore_missing.split("""|"""	):
																if ignore_key in key:
																				lowerCAmelCase		=			False
																				break
												if keep_flag:
																warn_missing_keys.append(snake_case__	)
												else:
																ignore_missing_keys.append(snake_case__	)

								lowerCAmelCase		=			warn_missing_keys

								if len(snake_case__	) > 0:
												print(
												    """Weights of {} not initialized from pretrained model: {}""".format(
												        model.__class__.__name__ ,    snake_case__	)	)
								if len(snake_case__	) > 0:
												print("""Weights from pretrained model not used in {}: {}""".format(model.__class__.__name__ ,    snake_case__	)	)
								if len(snake_case__	) > 0:
												print(
												    """Ignored weights of {} not initialized from pretrained model: {}""".format(
												        model.__class__.__name__ ,    snake_case__	)	)
								if len(snake_case__	) > 0:
												print("""\n""".join(snake_case__	)	)

				lowerCAmelCase		=			{
				    """pretrained""": False,
				    """use_shared_rel_pos_bias""": True,
				    """use_abs_pos_emb""": False,
				    """init_values""": 0.1,
				}

				if is_finetuned:
								model_kwargs.update(
								    {
								        """num_classes""": 1_000,
								        """use_mean_pooling""": True,
								        """init_scale""": 0.001,
								        """use_rel_pos_bias""": True,
								    }	)

				lowerCAmelCase		=			create_model(
				    """beit_large_patch16_224""" if is_large else """beit_base_patch16_224""" ,    **snake_case__ ,    )
				lowerCAmelCase		=			model.patch_embed.patch_size
				lowerCAmelCase		=			(args.input_size // patch_size[0], args.input_size // patch_size[1])
				lowerCAmelCase		=			torch.load(args.beit_checkpoint ,    map_location="""cpu"""	)

				print(f'''Load ckpt from {args.beit_checkpoint}'''	)
				lowerCAmelCase		=			None
				for model_key in ("model", "module"):
								if model_key in checkpoint:
												lowerCAmelCase		=			checkpoint[model_key]
												print(f'''Load state_dict by model_key = {model_key}'''	)
												break

				lowerCAmelCase		=			list(checkpoint_model.keys()	)
				for key in all_keys:
								if "relative_position_index" in key:
												checkpoint_model.pop(snake_case__	)

								if "relative_position_bias_table" in key:
												lowerCAmelCase		=			checkpoint_model[key]
												lowerCAmelCase	,  lowerCAmelCase		=			rel_pos_bias.size()
												lowerCAmelCase	,  lowerCAmelCase		=			model.state_dict()[key].size()
												lowerCAmelCase		=			model.patch_embed.patch_shape
												if dst_patch_shape[0] != dst_patch_shape[1]:
																raise NotImplementedError()

				load_state_dict(snake_case__ ,    snake_case__ ,    prefix=""""""	)

				return model





def 							lowercase   ()       ->					List[str]:


				'''simple docstring'''






				lowerCAmelCase		=			get_args()

				lowerCAmelCase		=			"""ft1k""" in args.hf_checkpoint_name
				lowerCAmelCase		=			"""large""" in args.hf_checkpoint_name

				if is_finetuned:
								# To convert Beit's data2vec_vision to HF you need to copy
								# https://github.com/facebookresearch/data2vec_vision/blob/main/beit/modeling_finetune.py
								# into this folder.
								import modeling_finetune  # noqa: F401
				else:
								# To convert Beit's data2vec_vision to HF you need to copy
								# https://github.com/facebookresearch/data2vec_vision/blob/main/beit/modeling_cyclical.py
								# into this folder
								# IMPORTANT: Note that for now we've only converted the down-stream
								# model and not the full pretrained model. This means for the integration
								# test you need to add a `return x` after the following line:
								# https://github.com/facebookresearch/data2vec_vision/blob/af9a36349aaed59ae66e69b5dabeef2d62fdc5da/beit/modeling_cyclical.py#L197
								# to make the integration test pass.
								import modeling_cyclical  # noqa: F401

				# 1. Create model config
				lowerCAmelCase		=			DataaVecVisionConfig()
				if is_finetuned:
								lowerCAmelCase		=			True
								lowerCAmelCase		=			False
								lowerCAmelCase		=			True
								lowerCAmelCase		=			1_000

								lowerCAmelCase		=			"""huggingface/label-files"""
								lowerCAmelCase		=			"""imagenet-1k-id2label.json"""
								lowerCAmelCase		=			json.load(open(hf_hub_download(snake_case__ ,    snake_case__ ,    repo_type="""dataset"""	) ,    """r"""	)	)
								lowerCAmelCase		=			{int(snake_case__	): v for k, v in idalabel.items()}
								lowerCAmelCase		=			idalabel
								lowerCAmelCase		=			{v: k for k, v in idalabel.items()}
				else:
								lowerCAmelCase		=			False
								lowerCAmelCase		=			True
								lowerCAmelCase		=			False

				if is_large:
								lowerCAmelCase		=			1_024
								lowerCAmelCase		=			4_096
								lowerCAmelCase		=			24
								lowerCAmelCase		=			16

				# 2. Load Beit model
				lowerCAmelCase		=			load_beit_model(snake_case__ ,    snake_case__ ,    snake_case__	)
				orig_model.eval()

				# 3. Forward Beit model
				lowerCAmelCase		=			BeitImageProcessor(size=config.image_size ,    do_center_crop=snake_case__	)
				lowerCAmelCase		=			Image.open("""../../../../tests/fixtures/tests_samples/COCO/000000039769.png"""	)
				lowerCAmelCase		=			image_processor(images=snake_case__ ,    return_tensors="""pt"""	)
				lowerCAmelCase		=			encoding["""pixel_values"""]

				lowerCAmelCase		=			(pixel_values,) if is_finetuned else (pixel_values, None)
				with torch.no_grad():
								lowerCAmelCase		=			orig_model(*snake_case__	)

				# 4. Load HF Data2VecVision model
				if is_finetuned:
								lowerCAmelCase		=			DataaVecVisionForImageClassification(snake_case__	)
								hf_model.eval()
								lowerCAmelCase		=			False
								lowerCAmelCase		=			"""data2vec_vision."""
				else:
								lowerCAmelCase		=			DataaVecVisionModel(snake_case__	)
								hf_model.eval()
								lowerCAmelCase		=			True
								lowerCAmelCase		=			""""""

				lowerCAmelCase		=			create_rename_keys(snake_case__ ,    hf_prefix=snake_case__ ,    has_lm_head=snake_case__	)
				lowerCAmelCase		=			orig_model.state_dict()
				for src, dest in rename_keys:
								lowerCAmelCase		=			state_dict.pop(snake_case__	)
								lowerCAmelCase		=			val

				read_in_q_k_v(snake_case__ ,    snake_case__ ,    hf_prefix=snake_case__ ,    has_lm_head=snake_case__	)
				lowerCAmelCase	,  lowerCAmelCase		=			hf_model.load_state_dict(snake_case__ ,    strict=snake_case__	)
				print("""HF missing""" ,    snake_case__	)
				print("""HF unexpected_keys""" ,    snake_case__	)

				# 5. Forward HF Data2VecVision model
				with torch.no_grad():
								lowerCAmelCase		=			hf_model(snake_case__	)

				lowerCAmelCase		=			hf_model_output.logits if is_finetuned else hf_model_output.last_hidden_state

				# 6. Compare
				lowerCAmelCase		=			torch.max(torch.abs(hf_output - orig_model_output	)	).item()

				print(f'''max_absolute_diff = {max_absolute_diff}'''	)
				lowerCAmelCase		=			torch.allclose(snake_case__ ,    snake_case__ ,    atol=1e-3	)
				print("""Do both models output the same tensors?""" ,    """🔥""" if success else """💩"""	)
				if not success:
								raise Exception("""Something went wRoNg"""	)

				# 7. Save
				print(f'''Saving to {args.hf_checkpoint_name}'''	)
				hf_model.save_pretrained(args.hf_checkpoint_name	)
				image_processor.save_pretrained(args.hf_checkpoint_name	)


if __name__ == "__main__":
		main()
		# Run the following to convert checkpoints
		#  python ./convert_data2vec_vision_original_pytorch_checkpoint_to_pytorch.py \
		#          --beit_checkpoint ./pretrained_base.pt \
		#          --hf_checkpoint_name "./data2vec-vision-base"
		#  python ./convert_data2vec_vision_original_pytorch_checkpoint_to_pytorch.py \
		#          --beit_checkpoint ./finetuned_base.pt \
		#          --hf_checkpoint_name "./data2vec-vision-base-ft1k"
		#  python ./convert_data2vec_vision_original_pytorch_checkpoint_to_pytorch.py \
		#          --beit_checkpoint ./pretrained_large.pt \
		#          --hf_checkpoint_name "./data2vec-vision-large"
		#  python ./convert_data2vec_vision_original_pytorch_checkpoint_to_pytorch.py \
		#          --beit_checkpoint ./finetuned_large.pt \
		#          --hf_checkpoint_name "./data2vec-vision-large-ft1k"











"""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


a								=							1_0_0_0






class  SCREAMING_SNAKE_CASE__   :


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

										lowerCAmelCase	,  lowerCAmelCase		=			img.shape[1], img.shape[0]
										lowerCAmelCase		=			mplfigure.Figure(frameon=lowerCAmelCase   )
										lowerCAmelCase		=			fig.get_dpi()
										lowerCAmelCase		=			(width * scale + 1e-2) / dpi
										lowerCAmelCase		=			(height * scale + 1e-2) / dpi
										fig.set_size_inches(lowerCAmelCase  ,  lowerCAmelCase   )
										lowerCAmelCase		=			fig.add_axes([0.0, 0.0, 1.0, 1.0]   )
										ax.axis("""off"""   )
										ax.set_xlim(0.0  ,  lowerCAmelCase   )
										ax.set_ylim(lowerCAmelCase   )

										lowerCAmelCase		=			saveas
										lowerCAmelCase		=			rgb
										lowerCAmelCase		=			pynb
										lowerCAmelCase		=			img
										lowerCAmelCase		=			edgecolor
										lowerCAmelCase		=			0.5
										lowerCAmelCase		=			linestyle
										lowerCAmelCase		=			int(np.sqrt(min(lowerCAmelCase  ,  lowerCAmelCase   )   ) * scale // 3   )
										lowerCAmelCase		=			width
										lowerCAmelCase		=			height
										lowerCAmelCase		=			scale
										lowerCAmelCase		=			fig
										lowerCAmelCase		=			ax
										lowerCAmelCase		=			pad
										lowerCAmelCase		=			idaobj
										lowerCAmelCase		=			idaattr
										lowerCAmelCase		=			FigureCanvasAgg(lowerCAmelCase   )


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


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

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


						def 					__lowercase	(							self			:     int  ,  lowerCAmelCase			:     Optional[Any]  ,  lowerCAmelCase			:     Any  ,  lowerCAmelCase			:     Dict   ):
										lowerCAmelCase	,  lowerCAmelCase	,  lowerCAmelCase	,  lowerCAmelCase		=			box
										lowerCAmelCase		=			(xa, ya)
										lowerCAmelCase		=			(ya - ya) * (xa - xa)
										lowerCAmelCase		=			_SMALL_OBJ * self.scale
										if instance_area < small or ya - ya < 40 * self.scale:
														if ya >= self.height - 5:
																		lowerCAmelCase		=			(xa, ya)
														else:
																		lowerCAmelCase		=			(xa, ya)

										lowerCAmelCase		=			(ya - ya) / np.sqrt(self.height * self.width   )
										lowerCAmelCase		=			self._change_color_brightness(lowerCAmelCase  ,  brightness_factor=0.7   )
										lowerCAmelCase		=			np.clip((height_ratio - 0.02) / 0.08 + 1  ,  1.2  ,  2   )
										font_size *= 0.75 * self.font_size

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


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


						def 					__lowercase	(							self			:     str  ,  lowerCAmelCase			:     Tuple=None   ):
										if saveas is None:
														lowerCAmelCase		=			self.saveas
										if saveas.lower().endswith(""".jpg"""   ) or saveas.lower().endswith(""".png"""   ):
														cva.imwrite(
														    lowerCAmelCase  ,  self._get_buffer()[:, :, ::-1]  ,  )
										else:
														self.fig.savefig(lowerCAmelCase   )


						def 					__lowercase	(							self			:     str  ,  lowerCAmelCase			:     int  ,  lowerCAmelCase			:     List[str]  ,  lowerCAmelCase			:     Any  ,  lowerCAmelCase			:     Optional[Any]   ):
										lowerCAmelCase		=			[self.idaobj[i] for i in classes]
										lowerCAmelCase		=			[self.idaattr[i] for i in attr_classes]
										lowerCAmelCase		=			[
										    f'''{label} {score:.2f} {attr} {attr_score:.2f}'''
										    for label, score, attr, attr_score in zip(lowerCAmelCase  ,  lowerCAmelCase  ,  lowerCAmelCase  ,  lowerCAmelCase   )
										]
										return labels


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


						def 					__lowercase	(							self			:     Any  ,  lowerCAmelCase			:     str=255   ):
										lowerCAmelCase		=			np.random.randint(0  ,  len(_COLORS   )   )
										lowerCAmelCase		=			_COLORS[idx] * maximum
										if not self.rgb:
														lowerCAmelCase		=			ret[::-1]
										return ret


						def 					__lowercase	(							self			:     List[str]   ):
										if not self.pynb:
														lowerCAmelCase	,  lowerCAmelCase		=			self.canvas.print_to_buffer()
														if (width, height) != (self.width, self.height):
																		lowerCAmelCase		=			cva.resize(self.img  ,  (width, height)   )
														else:
																		lowerCAmelCase		=			self.img
										else:
														lowerCAmelCase		=			io.BytesIO()  # works for cairo backend
														self.canvas.print_rgba(lowerCAmelCase   )
														lowerCAmelCase	,  lowerCAmelCase		=			self.width, self.height
														lowerCAmelCase		=			buf.getvalue()
														lowerCAmelCase		=			self.img

										lowerCAmelCase		=			np.frombuffer(lowerCAmelCase  ,  dtype="""uint8"""   )
										lowerCAmelCase		=			buffer.reshape(lowerCAmelCase  ,  lowerCAmelCase  ,  4   )
										lowerCAmelCase	,  lowerCAmelCase		=			np.split(lowerCAmelCase  ,  [3]  ,  axis=2   )

										try:
														import numexpr as ne  # fuse them with numexpr

														lowerCAmelCase		=			ne.evaluate("""img * (1 - alpha / 255.0) + rgb * (alpha / 255.0)"""   )
										except ImportError:
														lowerCAmelCase		=			alpha.astype("""float32"""   ) / 255.0
														lowerCAmelCase		=			img * (1 - alpha) + rgb * alpha

										return visualized_image.astype("""uint8"""   )




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





a								=							(
    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)
)





