

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:      str								=     1000

class   __UpperCamelCase      :




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


							'''simple docstring'''







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

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

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




						def  __A   (						self		:  Optional[int]						,		lowerCAmelCase		:  List[Any]						,		lowerCAmelCase		:  Dict=None							):


							'''simple docstring'''







							if color is None:
								UpperCAmelCase_    			=     self.edgecolor
							((UpperCAmelCase_)			,				(UpperCAmelCase_)			,				(UpperCAmelCase_)			,				(UpperCAmelCase_))    			=     box
							UpperCAmelCase_    			=     xa - xa
							UpperCAmelCase_    			=     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  __A   (						self		:  Union[str, Any]						,		lowerCAmelCase		:  Dict						,		lowerCAmelCase		:  int=None						,		lowerCAmelCase		:  Union[str, Any]=None						,		lowerCAmelCase		:  List[Any]=None						,		lowerCAmelCase		:  int=None							):


							'''simple docstring'''







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

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




						def  __A   (						self		:  Any						,		lowerCAmelCase		:  Optional[Any]						,		lowerCAmelCase		:  Any						,		lowerCAmelCase		:  str							):


							'''simple docstring'''







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

							UpperCAmelCase_    			=     (ya - ya) / np.sqrt(self.height * self.width							)
							UpperCAmelCase_    			=     self._change_color_brightness(lowerCAmelCase						,		brightness_factor=0.7							)
							UpperCAmelCase_    			=     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  __A   (						self		:  int						,		lowerCAmelCase		:  List[Any]						,		lowerCAmelCase		:  List[Any]						,		lowerCAmelCase		:  Union[str, Any]="g"						,		lowerCAmelCase		:  int="left"						,		):


							'''simple docstring'''







							UpperCAmelCase_    			=     0
							UpperCAmelCase_    			=     self.font_size
							UpperCAmelCase_    			=     np.maximum(list(mplc.to_rgb(lowerCAmelCase							)							)						,		0.2							)
							UpperCAmelCase_    			=     max(0.8						,		np.max(lowerCAmelCase							)							)
							UpperCAmelCase_    			=     {
							    "facecolor": "black",
							    "alpha": self.alpha,
							    "pad": self.pad,
							    "edgecolor": "none",
							}
							UpperCAmelCase_			,				UpperCAmelCase_    			=     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  __A   (						self		:  List[Any]						,		lowerCAmelCase		:  Union[str, Any]=None							):


							'''simple docstring'''







							if saveas is None:
								UpperCAmelCase_    			=     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  __A   (						self		:  int						,		lowerCAmelCase		:  Optional[Any]						,		lowerCAmelCase		:  List[Any]						,		lowerCAmelCase		:  Any						,		lowerCAmelCase		:  int							):


							'''simple docstring'''







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




						def  __A   (						self		:  Optional[int]						,		lowerCAmelCase		:  Any						,		lowerCAmelCase		:  List[Any]							):


							'''simple docstring'''







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




						def  __A   (						self		:  Any						,		lowerCAmelCase		:  Optional[int]=255							):


							'''simple docstring'''







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




						def  __A   (						self		:  str							):


							'''simple docstring'''







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

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

							try:
								import numexpr as ne  # fuse them with numexpr

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

							return visualized_image.astype("uint8"							)




						def  __A   (						self		:  Optional[int]						,		lowerCAmelCase		:  Optional[Any]						,		lowerCAmelCase		:  Optional[Any]							):


							'''simple docstring'''







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


_a:      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)
)


from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available


_a:      Dict								=     {
    """configuration_nystromformer""": ["""NYSTROMFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP""", """NystromformerConfig"""],
}

try:
				if not is_torch_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				_a:      Any								=     [
				    """NYSTROMFORMER_PRETRAINED_MODEL_ARCHIVE_LIST""",
				    """NystromformerForMaskedLM""",
				    """NystromformerForMultipleChoice""",
				    """NystromformerForQuestionAnswering""",
				    """NystromformerForSequenceClassification""",
				    """NystromformerForTokenClassification""",
				    """NystromformerLayer""",
				    """NystromformerModel""",
				    """NystromformerPreTrainedModel""",
				]


if TYPE_CHECKING:
				from .configuration_nystromformer import NYSTROMFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP, NystromformerConfig

				try:
								if not is_torch_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .modeling_nystromformer import (
								    NYSTROMFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
								    NystromformerForMaskedLM,
								    NystromformerForMultipleChoice,
								    NystromformerForQuestionAnswering,
								    NystromformerForSequenceClassification,
								    NystromformerForTokenClassification,
								    NystromformerLayer,
								    NystromformerModel,
								    NystromformerPreTrainedModel,
								)


else:
				import sys

				_a:      Optional[Any]								=     _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)