



'''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


__SCREAMING_SNAKE_CASE :      Optional[int]             =1000


class   SCREAMING_SNAKE_CASE__				:
	"""simple docstring"""


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

				A							,		A:      Tuple														=   img.shape[1], img.shape[0]
				A:      List[str]														=   mplfigure.Figure(frameon=A			)
				A:      Dict														=   fig.get_dpi()
				A:      Tuple														=   (width * scale + 1e-2) / dpi
				A:      List[str]														=   (height * scale + 1e-2) / dpi
				fig.set_size_inches(A					,   A			)
				A:      Union[str, Any]														=   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:      Optional[Any]														=   saveas
				A:      Optional[Any]														=   rgb
				A:      int														=   pynb
				A:      Any														=   img
				A:      Tuple														=   edgecolor
				A:      str														=   0.5
				A:      str														=   linestyle
				A:      Any														=   int(np.sqrt(min(A					,   A			)			) * scale // 3			)
				A:      int														=   width
				A:      int														=   height
				A:      List[str]														=   scale
				A:      str														=   fig
				A:      str														=   ax
				A:      str														=   pad
				A:      str														=   idaobj
				A:      Optional[int]														=   idaattr
				A:      str														=   FigureCanvasAgg(A			)


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

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


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

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

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


	def      a__ (		self					,   A					,   A					,   A="g"					,   A="left"					,   ) ->							Tuple:
				A:      Tuple														=   0
				A:      Dict														=   self.font_size
				A:      Optional[Any]														=   np.maximum(list(mplc.to_rgb(A			)			)					,   0.2			)
				A:      int														=   max(0.8					,   np.max(A			)			)
				A:      List[str]														=   {
				    """facecolor""": """black""",
				    """alpha""": self.alpha,
				    """pad""": self.pad,
				    """edgecolor""": """none""",
				}
				A							,		A:      Union[str, Any]														=   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					,   A=None			) ->							int:
				if saveas is None:
							A:      List[Any]														=   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					,   A					,   A					,   A					,   A			) ->							Optional[int]:
				A:      Optional[int]														=   [self.idaobj[i] for i in classes]
				A:      Tuple														=   [self.idaattr[i] for i in attr_classes]
				A:      Dict														=   [
				    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					,   A					,   A			) ->							str:
				A:      Tuple														=   [self.idaobj[i] for i in classes]
				if scores is not None:
							if labels is None:
										A:      List[str]														=   ["""{:.0f}%""".format(s * 1_00			) for s in scores]
							else:
										A:      List[str]														=   ["""{} {:.0f}%""".format(A					,   s * 1_00			) for li, s in zip(A					,   A			)]
				return labels


	def      a__ (		self					,   A=2_55			) ->							Tuple:
				A:      Optional[int]														=   np.random.randint(0					,   len(_COLORS			)			)
				A:      str														=   _COLORS[idx] * maximum
				if not self.rgb:
							A:      str														=   ret[::-1]
				return ret


	def      a__ (		self			) ->							int:
				if not self.pynb:
							A							,		A:      int														=   self.canvas.print_to_buffer()
							if (width, height) != (self.width, self.height):
										A:      Optional[int]														=   cva.resize(self.img					,   (width, height)			)
							else:
										A:      str														=   self.img
				else:
							A:      Tuple														=   io.BytesIO()  # works for cairo backend
							self.canvas.print_rgba(A			)
							A							,		A:      Optional[Any]														=   self.width, self.height
							A:      str														=   buf.getvalue()
							A:      int														=   self.img

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

				try:
							import numexpr as ne  # fuse them with numexpr

							A:      Union[str, Any]														=   ne.evaluate("""img * (1 - alpha / 255.0) + rgb * (alpha / 255.0)"""			)
				except ImportError:
							A:      Union[str, Any]														=   alpha.astype("""float32"""			) / 255.0
							A:      Union[str, Any]														=   img * (1 - alpha) + rgb * alpha

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





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







__SCREAMING_SNAKE_CASE :      List[str]             =(
    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)
)










'''simple docstring'''
from collections import OrderedDict
from typing import Mapping

from packaging import version

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
from ...utils.backbone_utils import BackboneConfigMixin, get_aligned_output_features_output_indices


__SCREAMING_SNAKE_CASE :      int             =logging.get_logger(__name__)

__SCREAMING_SNAKE_CASE :      Optional[int]             ={
    'facebook/convnext-tiny-224': 'https://huggingface.co/facebook/convnext-tiny-224/resolve/main/config.json',
    # See all ConvNeXT models at https://huggingface.co/models?filter=convnext
}


class   SCREAMING_SNAKE_CASE__				(   snake_case_     ,			snake_case_					):
	"""simple docstring"""


	A__    :    List[str]						=				'''convnext'''


	def __init__(		self					,   A=3					,   A=4					,   A=4					,   A=None					,   A=None					,   A="gelu"					,   A=0.02					,   A=1e-12					,   A=1e-6					,   A=0.0					,   A=2_24					,   A=None					,   A=None					,   **A					,   ) ->							Dict:
				super().__init__(**A			)

				A:      List[str]														=   num_channels
				A:      Optional[Any]														=   patch_size
				A:      List[str]														=   num_stages
				A:      Tuple														=   [96, 1_92, 3_84, 7_68] if hidden_sizes is None else hidden_sizes
				A:      List[Any]														=   [3, 3, 9, 3] if depths is None else depths
				A:      int														=   hidden_act
				A:      int														=   initializer_range
				A:      Optional[int]														=   layer_norm_eps
				A:      Tuple														=   layer_scale_init_value
				A:      int														=   drop_path_rate
				A:      Tuple														=   image_size
				A:      Union[str, Any]														=   ["""stem"""] + [f'stage{idx}' for idx in range(1					,   len(self.depths			) + 1			)]
				A							,		A:      List[str]														=   get_aligned_output_features_output_indices(
				    out_features=A					,   out_indices=A					,   stage_names=self.stage_names			)







class   SCREAMING_SNAKE_CASE__				(   snake_case_					):
	"""simple docstring"""


	A__    :    Any						=				version.parse('''1.11'''					)


	@property
	def      a__ (		self			) ->							Mapping[str, Mapping[int, str]]:
				return OrderedDict(
				    [
				        ("""pixel_values""", {0: """batch""", 1: """num_channels""", 2: """height""", 3: """width"""}),
				    ]			)





	@property
	def      a__ (		self			) ->							float:
				return 1e-5





