



"""simple docstring"""
import inspect
import unittest

from transformers import DataaVecVisionConfig
from transformers.models.auto import get_values
from transformers.testing_utils import require_torch, require_torch_multi_gpu, require_vision, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_vision_available

from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, _config_zero_init, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
						import torch
						from torch import nn

						from transformers import (
						    MODEL_MAPPING,
						    DataaVecVisionForImageClassification,
						    DataaVecVisionForSemanticSegmentation,
						    DataaVecVisionModel,
						)
						from transformers.models.dataavec.modeling_dataavec_vision import DATA2VEC_VISION_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
						from PIL import Image

						from transformers import BeitImageProcessor
class 							lowerCamelCase    :






					'''simple docstring'''





					def __init__(			self:				str		,	snake_case:				Union[str, Any]		,	snake_case:				Optional[int]=100		,	snake_case:				Tuple=13		,	snake_case:				Optional[Any]=30		,	snake_case:				Dict=2		,	snake_case:				int=3		,	snake_case:				Union[str, Any]=True		,	snake_case:				List[str]=True		,	snake_case:				List[Any]=32		,	snake_case:				Optional[Any]=4		,	snake_case:				str=4		,	snake_case:				Any=37		,	snake_case:				str="gelu"		,	snake_case:				Union[str, Any]=0.1		,	snake_case:				int=0.1		,	snake_case:				Optional[int]=10		,	snake_case:				Any=0.0_2		,	snake_case:				str=3		,	snake_case:				List[str]=None		,	snake_case:				int=[0, 1, 2, 3]		,	)  ->      Union[str, Any]:
											snake_case_					:Tuple		=     parent
											snake_case_					:Union[str, Any]		=     100
											snake_case_					:Union[str, Any]		=     batch_size
											snake_case_					:Union[str, Any]		=     image_size
											snake_case_					:Tuple		=     patch_size
											snake_case_					:Any		=     num_channels
											snake_case_					:str		=     is_training
											snake_case_					:Dict		=     use_labels
											snake_case_					:Any		=     hidden_size
											snake_case_					:Optional[Any]		=     num_hidden_layers
											snake_case_					:Optional[Any]		=     num_attention_heads
											snake_case_					:str		=     intermediate_size
											snake_case_					:List[str]		=     hidden_act
											snake_case_					:Any		=     hidden_dropout_prob
											snake_case_					:List[str]		=     attention_probs_dropout_prob
											snake_case_					:int		=     type_sequence_label_size
											snake_case_					:Optional[int]		=     initializer_range
											snake_case_					:Optional[Any]		=     scope
											snake_case_					:Any		=     out_indices
											snake_case_					:Dict		=     num_labels

											# in BeiT, the seq length equals the number of patches + 1 (we add 1 for the [CLS] token)
											snake_case_					:Tuple		=     (image_size // patch_size) ** 2
											snake_case_					:Tuple		=     num_patches + 1






					def   lowerCAmelCase_							(			self:				str		)  ->      List[Any]:
											snake_case_					:str		=     floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]		)

											snake_case_					:Union[str, Any]		=     None
											snake_case_					:Optional[Any]		=     None
											if self.use_labels:
																	snake_case_					:Tuple		=     ids_tensor([self.batch_size]		,	self.type_sequence_label_size		)
																	snake_case_					:List[Any]		=     ids_tensor([self.batch_size, self.image_size, self.image_size]		,	self.num_labels		)

											snake_case_					:Tuple		=     self.get_config()

											return config, pixel_values, labels, pixel_labels






					def   lowerCAmelCase_							(			self:				Union[str, Any]		)  ->      Dict:
											return DataaVecVisionConfig(
											    vocab_size=self.vocab_size		,	image_size=self.image_size		,	patch_size=self.patch_size		,	num_channels=self.num_channels		,	hidden_size=self.hidden_size		,	num_hidden_layers=self.num_hidden_layers		,	num_attention_heads=self.num_attention_heads		,	intermediate_size=self.intermediate_size		,	hidden_act=self.hidden_act		,	hidden_dropout_prob=self.hidden_dropout_prob		,	attention_probs_dropout_prob=self.attention_probs_dropout_prob		,	is_decoder=snake_case		,	initializer_range=self.initializer_range		,	out_indices=self.out_indices		,	)






					def   lowerCAmelCase_							(			self:				Optional[int]		,	snake_case:				Optional[int]		,	snake_case:				int		,	snake_case:				str		,	snake_case:				Dict		)  ->      int:
											snake_case_					:Union[str, Any]		=     DataaVecVisionModel(config=snake_case		)
											model.to(snake_case		)
											model.eval()
											snake_case_					:Optional[int]		=     model(snake_case		)
											# expected sequence length = num_patches + 1 (we add 1 for the [CLS] token)
											snake_case_					:Dict		=     (self.image_size // self.patch_size) ** 2
											self.parent.assertEqual(result.last_hidden_state.shape		,	(self.batch_size, num_patches + 1, self.hidden_size)		)






					def   lowerCAmelCase_							(			self:				Optional[Any]		,	snake_case:				Tuple		,	snake_case:				Union[str, Any]		,	snake_case:				Tuple		,	snake_case:				List[Any]		)  ->      Tuple:
											snake_case_					:Union[str, Any]		=     self.type_sequence_label_size
											snake_case_					:int		=     DataaVecVisionForImageClassification(snake_case		)
											model.to(snake_case		)
											model.eval()
											snake_case_					:Any		=     model(snake_case		,	labels=snake_case		)
											self.parent.assertEqual(result.logits.shape		,	(self.batch_size, self.type_sequence_label_size)		)






					def   lowerCAmelCase_							(			self:				List[Any]		,	snake_case:				Any		,	snake_case:				Tuple		,	snake_case:				List[Any]		,	snake_case:				Optional[int]		)  ->      Tuple:
											snake_case_					:Tuple		=     self.num_labels
											snake_case_					:List[str]		=     DataaVecVisionForSemanticSegmentation(snake_case		)
											model.to(snake_case		)
											model.eval()
											snake_case_					:Tuple		=     model(snake_case		)
											self.parent.assertEqual(
											    result.logits.shape		,	(self.batch_size, self.num_labels, self.image_size * 2, self.image_size * 2)		)
											snake_case_					:List[str]		=     model(snake_case		,	labels=snake_case		)
											self.parent.assertEqual(
											    result.logits.shape		,	(self.batch_size, self.num_labels, self.image_size * 2, self.image_size * 2)		)






					def   lowerCAmelCase_							(			self:				List[str]		)  ->      Union[str, Any]:
											snake_case_					:List[str]		=     self.prepare_config_and_inputs()
											snake_case_,			snake_case_,			snake_case_,			snake_case_					:List[str]		=     config_and_inputs
											snake_case_					:List[str]		=     {"""pixel_values""": pixel_values}
											return config, inputs_dict

@require_torch
class 							lowerCamelCase    (     _lowerCAmelCase       ,		_lowerCAmelCase       ,		unittest.TestCase							):






					'''simple docstring'''





					_A						:     List[str]		=	(
					    (DataaVecVisionModel, DataaVecVisionForImageClassification, DataaVecVisionForSemanticSegmentation)
					    if is_torch_available()
					    else ()
					)
					_A						:     Union[str, Any]		=	(
					    {
					        """feature-extraction""": DataaVecVisionModel,
					        """image-classification""": DataaVecVisionForImageClassification,
					        """image-segmentation""": DataaVecVisionForSemanticSegmentation,
					    }
					    if is_torch_available()
					    else {}
					)

					_A						:     List[Any]		=	False
					_A						:     int		=	False
					_A						:     List[str]		=	False






					def   lowerCAmelCase_							(			self:				Any		)  ->      Any:
											snake_case_					:Union[str, Any]		=     DataaVecVisionModelTester(self		)
											snake_case_					:Union[str, Any]		=     ConfigTester(
											    self		,	config_class=snake_case		,	has_text_modality=snake_case		,	hidden_size=37		)






					def   lowerCAmelCase_							(			self:				Union[str, Any]		)  ->      Dict:
											self.config_tester.run_common_tests()






					def   lowerCAmelCase_							(			self:				int		)  ->      Union[str, Any]:
											# Data2VecVision does not use inputs_embeds
											pass






					@require_torch_multi_gpu
					@unittest.skip(
					    reason="""Data2VecVision has some layers using `add_module` which doesn't work well with `nn.DataParallel`"""		)
					def   lowerCAmelCase_							(			self:				Any		)  ->      Optional[Any]:
											pass






					def   lowerCAmelCase_							(			self:				List[Any]		)  ->      Optional[int]:
											snake_case_,			snake_case_					:Optional[Any]		=     self.model_tester.prepare_config_and_inputs_for_common()

											for model_class in self.all_model_classes:
																	snake_case_					:Dict		=     model_class(snake_case		)
																	self.assertIsInstance(model.get_input_embeddings()		,	(nn.Module)		)
																	snake_case_					:Optional[int]		=     model.get_output_embeddings()
																	self.assertTrue(x is None or isinstance(snake_case		,	nn.Linear		)		)






					def   lowerCAmelCase_							(			self:				List[str]		)  ->      Dict:
											snake_case_,			snake_case_					:str		=     self.model_tester.prepare_config_and_inputs_for_common()

											for model_class in self.all_model_classes:
																	snake_case_					:int		=     model_class(snake_case		)
																	snake_case_					:List[str]		=     inspect.signature(model.forward		)
																	# signature.parameters is an OrderedDict => so arg_names order is deterministic
																	snake_case_					:Optional[int]		=     [*signature.parameters.keys()]

																	snake_case_					:Union[str, Any]		=     ["""pixel_values"""]
																	self.assertListEqual(arg_names[:1]		,	snake_case		)






					def   lowerCAmelCase_							(			self:				int		)  ->      List[Any]:
											snake_case_					:int		=     self.model_tester.prepare_config_and_inputs()
											self.model_tester.create_and_check_model(*snake_case		)






					def   lowerCAmelCase_							(			self:				Optional[Any]		)  ->      Any:
											snake_case_					:Tuple		=     self.model_tester.prepare_config_and_inputs()
											self.model_tester.create_and_check_for_image_segmentation(*snake_case		)






					def   lowerCAmelCase_							(			self:				Optional[int]		)  ->      List[str]:
											if not self.model_tester.is_training:
																	return

											snake_case_,			snake_case_					:List[str]		=     self.model_tester.prepare_config_and_inputs_for_common()
											snake_case_					:Optional[Any]		=     True

											for model_class in self.all_model_classes:
																	if model_class in [*get_values(snake_case		)]:
																							continue

																	snake_case_					:List[str]		=     model_class(snake_case		)
																	model.to(snake_case		)
																	model.train()
																	snake_case_					:Optional[Any]		=     self._prepare_for_class(snake_case		,	snake_case		,	return_labels=snake_case		)
																	snake_case_					:int		=     model(**snake_case		).loss
																	loss.backward()






					def   lowerCAmelCase_							(			self:				Tuple		)  ->      Tuple:
											snake_case_,			snake_case_					:List[str]		=     self.model_tester.prepare_config_and_inputs_for_common()
											if not self.model_tester.is_training:
																	return

											snake_case_					:List[Any]		=     False
											snake_case_					:Optional[int]		=     True

											for model_class in self.all_model_classes:
																	if model_class in [*get_values(snake_case		)] or not model_class.supports_gradient_checkpointing:
																							continue
																	# TODO: remove the following 3 lines once we have a MODEL_FOR_SEMANTIC_SEGMENTATION_MAPPING
																	# this can then be incorporated into _prepare_for_class in test_modeling_common.py
																	elif model_class.__name__ == "Data2VecVisionForSemanticSegmentation":
																							snake_case_,			snake_case_,			snake_case_,			snake_case_					:Tuple		=     inputs_dict["""pixel_values"""].shape
																							snake_case_					:List[Any]		=     torch.zeros(
																							    [self.model_tester.batch_size, height, width]		,	device=snake_case		).long()
																	snake_case_					:Dict		=     model_class(snake_case		)
																	model.gradient_checkpointing_enable()
																	model.to(snake_case		)
																	model.train()
																	snake_case_					:Any		=     self._prepare_for_class(snake_case		,	snake_case		,	return_labels=snake_case		)
																	snake_case_					:List[str]		=     model(**snake_case		).loss
																	loss.backward()






					def   lowerCAmelCase_							(			self:				List[str]		)  ->      Any:
											snake_case_,			snake_case_					:Dict		=     self.model_tester.prepare_config_and_inputs_for_common()

											snake_case_					:Union[str, Any]		=     _config_zero_init(snake_case		)
											for model_class in self.all_model_classes:
																	snake_case_					:List[Any]		=     model_class(config=snake_case		)
																	for name, param in model.named_parameters():
																							# we skip lambda parameters as these require special initial values
																							# determined by config.layer_scale_init_value
																							if "lambda" in name:
																													continue
																							if param.requires_grad:
																													self.assertIn(
																													    ((param.data.mean() * 1E9).round() / 1E9).item()		,	[0.0, 1.0]		,	msg=f"""Parameter {name} of model {model_class} seems not properly initialized"""		,	)






					def   lowerCAmelCase_							(			self:				List[str]		,	snake_case:				int		,	snake_case:				int		,	snake_case:				int		,	snake_case:				List[str]=2E-4		,	snake_case:				Any="outputs"		,	snake_case:				Tuple=None		)  ->      int:
											# We override with a slightly higher tol value, as semseg models tend to diverge a bit more
											super().check_pt_tf_outputs(snake_case		,	snake_case		,	snake_case		,	snake_case		,	snake_case		,	snake_case		)






					def   lowerCAmelCase_							(			self:				List[str]		)  ->      Optional[Any]:
											snake_case_					:str		=     self.model_tester.prepare_config_and_inputs()
											self.model_tester.create_and_check_for_image_classification(*snake_case		)






					@slow
					def   lowerCAmelCase_							(			self:				Dict		)  ->      str:
											for model_name in DATA2VEC_VISION_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
																	snake_case_					:Tuple		=     DataaVecVisionModel.from_pretrained(snake_case		)
																	self.assertIsNotNone(snake_case		)

def   A_    (     ):







						'''simple docstring'''


						snake_case_					:Dict		=     Image.open("""./tests/fixtures/tests_samples/COCO/000000039769.png"""      )
						return image

@require_torch
@require_vision
class 							lowerCamelCase    (     unittest.TestCase							):






					'''simple docstring'''





					@cached_property
					def   lowerCAmelCase_							(			self:				List[str]		)  ->      List[Any]:
											return (
											    BeitImageProcessor.from_pretrained("""facebook/data2vec-vision-base-ft1k"""		) if is_vision_available() else None
											)






					@slow
					def   lowerCAmelCase_							(			self:				Tuple		)  ->      Dict:
											snake_case_					:Optional[int]		=     DataaVecVisionForImageClassification.from_pretrained("""facebook/data2vec-vision-base-ft1k"""		).to(
											    snake_case		)

											snake_case_					:Union[str, Any]		=     self.default_image_processor
											snake_case_					:Union[str, Any]		=     prepare_img()
											snake_case_					:Dict		=     image_processor(images=snake_case		,	return_tensors="""pt"""		).to(snake_case		)

											# forward pass
											with torch.no_grad():
																	snake_case_					:str		=     model(**snake_case		)
											snake_case_					:Union[str, Any]		=     outputs.logits

											# verify the logits
											snake_case_					:Union[str, Any]		=     torch.Size((1, 1_000)		)
											self.assertEqual(logits.shape		,	snake_case		)

											snake_case_					:int		=     torch.tensor([0.3_2_7_7, -0.1_3_9_5, 0.0_9_1_1]		).to(snake_case		)

											self.assertTrue(torch.allclose(logits[0, :3]		,	snake_case		,	atol=1E-4		)		)

											snake_case_					:List[str]		=     [model.config.labelaid[i] for i in ["""remote control, remote""", """tabby, tabby cat"""]]
											self.assertEqual(logits[0].topk(2		).indices.cpu().tolist()		,	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


a_											=     10_00
class snake_case   :







		def __init__(       self   :							Optional[int]     , a__   :							int     , a__   :							List[str]=1.2     , a__   :							int="g"     , a__   :							List[Any]=0.5     , a__   :							Tuple="-"     , a__   :							Optional[int]="test_out.jpg"     , a__   :							Tuple=True     , a__   :							List[str]=False     , a__   :							Dict=None     , a__   :							Optional[Any]=None     , a__   :							Optional[int]=0.7     , )							->    Union[str, Any]:





						'''simple docstring'''







						if isinstance(a__     , torch.Tensor		):
										_A       	=      img.numpy().astype("np.uint8"		)
						if isinstance(a__     , a__		):
										_A       	=      img_tensorize(a__		)
						assert isinstance(a__     , np.ndarray		)

						_A	,		_A       	=      img.shape[1], img.shape[0]
						_A       	=      mplfigure.Figure(frameon=a__		)
						_A       	=      fig.get_dpi()
						_A       	=      (width * scale + 1E-2) / dpi
						_A       	=      (height * scale + 1E-2) / dpi
						fig.set_size_inches(a__     , a__		)
						_A       	=      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       	=      saveas
						_A       	=      rgb
						_A       	=      pynb
						_A       	=      img
						_A       	=      edgecolor
						_A       	=      0.5
						_A       	=      linestyle
						_A       	=      int(np.sqrt(min(a__     , a__		)		) * scale // 3		)
						_A       	=      width
						_A       	=      height
						_A       	=      scale
						_A       	=      fig
						_A       	=      ax
						_A       	=      pad
						_A       	=      idaobj
						_A       	=      idaattr
						_A       	=      FigureCanvasAgg(a__		)







		def 			a_			(       self   :							List[Any]     , a__   :							Union[str, Any]     , a__   :							Tuple=None		)							->    Tuple:





						'''simple docstring'''







						if color is None:
										_A       	=      self.edgecolor
						((_A)	,		(_A)	,		(_A)	,		(_A))       	=      box
						_A       	=      xa - xa
						_A       	=      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 			a_			(       self   :							Union[str, Any]     , a__   :							Dict     , a__   :							List[Any]=None     , a__   :							int=None     , a__   :							str=None     , a__   :							Optional[int]=None		)							->    List[str]:





						'''simple docstring'''







						if len(boxes.shape		) > 2:
										_A       	=      boxes[0]
						if len(obj_ids.shape		) > 1:
										_A       	=      obj_ids[0]
						if len(obj_scores.shape		) > 1:
										_A       	=      obj_scores[0]
						if len(attr_ids.shape		) > 1:
										_A       	=      attr_ids[0]
						if len(attr_scores.shape		) > 1:
										_A       	=      attr_scores[0]
						if isinstance(a__     , torch.Tensor		):
										_A       	=      boxes.numpy()
						if isinstance(a__     , a__		):
										_A       	=      np.array(a__		)
						assert isinstance(a__     , np.ndarray		)
						_A       	=      np.prod(boxes[:, 2:] - boxes[:, :2]     , axis=1		)
						_A       	=      np.argsort(-areas		).tolist()
						_A       	=      boxes[sorted_idxs] if boxes is not None else None
						_A       	=      obj_ids[sorted_idxs] if obj_ids is not None else None
						_A       	=      obj_scores[sorted_idxs] if obj_scores is not None else None
						_A       	=      attr_ids[sorted_idxs] if attr_ids is not None else None
						_A       	=      attr_scores[sorted_idxs] if attr_scores is not None else None

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







		def 			a_			(       self   :							List[str]     , a__   :							Any     , a__   :							List[str]     , a__   :							Tuple		)							->    int:





						'''simple docstring'''







						_A	,		_A	,		_A	,		_A       	=      box
						_A       	=      (xa, ya)
						_A       	=      (ya - ya) * (xa - xa)
						_A       	=      _SMALL_OBJ * self.scale
						if instance_area < small or ya - ya < 40 * self.scale:
										if ya >= self.height - 5:
														_A       	=      (xa, ya)
										else:
														_A       	=      (xa, ya)

						_A       	=      (ya - ya) / np.sqrt(self.height * self.width		)
						_A       	=      self._change_color_brightness(a__     , brightness_factor=0.7		)
						_A       	=      np.clip((height_ratio - 0.0_2) / 0.0_8 + 1     , 1.2     , 2		)
						font_size *= 0.7_5 * self.font_size

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







		def 			a_			(       self   :							str     , a__   :							Optional[int]     , a__   :							Optional[int]     , a__   :							Any="g"     , a__   :							Optional[int]="left"     , )							->    Dict:





						'''simple docstring'''







						_A       	=      0
						_A       	=      self.font_size
						_A       	=      np.maximum(list(mplc.to_rgb(a__		)		)     , 0.2		)
						_A       	=      max(0.8     , np.max(a__		)		)
						_A       	=      {
						    "facecolor": "black",
						    "alpha": self.alpha,
						    "pad": self.pad,
						    "edgecolor": "none",
						}
						_A	,		_A       	=      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 			a_			(       self   :							List[str]     , a__   :							str=None		)							->    Optional[int]:





						'''simple docstring'''







						if saveas is None:
										_A       	=      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 			a_			(       self   :							Union[str, Any]     , a__   :							Optional[Any]     , a__   :							List[Any]     , a__   :							Tuple     , a__   :							List[str]		)							->    Optional[int]:





						'''simple docstring'''







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







		def 			a_			(       self   :							Any     , a__   :							Dict     , a__   :							Tuple		)							->    Dict:





						'''simple docstring'''







						_A       	=      [self.idaobj[i] for i in classes]
						if scores is not None:
										if labels is None:
														_A       	=      ["{:.0f}%".format(s * 1_00		) for s in scores]
										else:
														_A       	=      ["{} {:.0f}%".format(a__     , s * 1_00		) for li, s in zip(a__     , a__		)]
						return labels







		def 			a_			(       self   :							List[Any]     , a__   :							Optional[Any]=2_55		)							->    str:





						'''simple docstring'''







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







		def 			a_			(       self   :							int		)							->    Dict:





						'''simple docstring'''







						if not self.pynb:
										_A	,		_A       	=      self.canvas.print_to_buffer()
										if (width, height) != (self.width, self.height):
														_A       	=      cva.resize(self.img     , (width, height)		)
										else:
														_A       	=      self.img
						else:
										_A       	=      io.BytesIO()  # works for cairo backend
										self.canvas.print_rgba(a__		)
										_A	,		_A       	=      self.width, self.height
										_A       	=      buf.getvalue()
										_A       	=      self.img

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

						try:
										import numexpr as ne  # fuse them with numexpr

										_A       	=      ne.evaluate("img * (1 - alpha / 255.0) + rgb * (alpha / 255.0)"		)
						except ImportError:
										_A       	=      alpha.astype("float32"		) / 2_5_5.0
										_A       	=      img * (1 - alpha) + rgb * alpha

						return visualized_image.astype("uint8"		)







		def 			a_			(       self   :							Optional[int]     , a__   :							str     , a__   :							Union[str, Any]		)							->    str:





						'''simple docstring'''







						assert brightness_factor >= -1.0 and brightness_factor <= 1.0
						_A       	=      mplc.to_rgb(a__		)
						_A       	=      colorsys.rgb_to_hls(*mplc.to_rgb(a__		)		)
						_A       	=      polygon_color[1] + (brightness_factor * polygon_color[1])
						_A       	=      0.0 if modified_lightness < 0.0 else modified_lightness
						_A       	=      1.0 if modified_lightness > 1.0 else modified_lightness
						_A       	=      colorsys.hls_to_rgb(polygon_color[0]     , a__     , polygon_color[2]		)
						return modified_color





a_											=     (
    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,
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            0.3_33,
            0.3_33,
            0.5_00,
            0.3_33,
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            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,
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            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,
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            0.0_00,
            0.5_00,
            0.0_00,
            0.0_00,
            0.6_67,
            0.0_00,
            0.0_00,
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            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)
)