



"""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


_lowerCAmelCase					:Optional[Any]  =      1_000
class 							_UpperCAmelCase							:





     '''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 ,			)							->      str:
         if isinstance(A ,			torch.Tensor	):
             _UpperCAmelCase							:						Any       							=					img.numpy().astype('''np.uint8'''	)
         if isinstance(A ,			A	):
             _UpperCAmelCase							:						Tuple       							=					img_tensorize(A	)
         assert isinstance(A ,			np.ndarray	)

         _UpperCAmelCase							,       _UpperCAmelCase							:						Tuple       							=					img.shape[1], img.shape[0]
         _UpperCAmelCase							:						int       							=					mplfigure.Figure(frameon=A	)
         _UpperCAmelCase							:						Tuple       							=					fig.get_dpi()
         _UpperCAmelCase							:						Any       							=					(width * scale + 1E-2) / dpi
         _UpperCAmelCase							:						Tuple       							=					(height * scale + 1E-2) / dpi
         fig.set_size_inches(A ,			A	)
         _UpperCAmelCase							:						List[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	)

         _UpperCAmelCase							:						int       							=					saveas
         _UpperCAmelCase							:						Optional[int]       							=					rgb
         _UpperCAmelCase							:						Optional[int]       							=					pynb
         _UpperCAmelCase							:						int       							=					img
         _UpperCAmelCase							:						Optional[Any]       							=					edgecolor
         _UpperCAmelCase							:						Tuple       							=					0.5
         _UpperCAmelCase							:						Optional[Any]       							=					linestyle
         _UpperCAmelCase							:						str       							=					int(np.sqrt(min(A ,			A	)	) * scale // 3	)
         _UpperCAmelCase							:						Optional[Any]       							=					width
         _UpperCAmelCase							:						Dict       							=					height
         _UpperCAmelCase							:						Any       							=					scale
         _UpperCAmelCase							:						Dict       							=					fig
         _UpperCAmelCase							:						str       							=					ax
         _UpperCAmelCase							:						Tuple       							=					pad
         _UpperCAmelCase							:						Optional[int]       							=					idaobj
         _UpperCAmelCase							:						str       							=					idaattr
         _UpperCAmelCase							:						Tuple       							=					FigureCanvasAgg(A	)





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

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





     def 				__lowerCAmelCase			(      self ,			A ,			A ,			A	)							->      Any:
         _UpperCAmelCase							,       _UpperCAmelCase							,       _UpperCAmelCase							,       _UpperCAmelCase							:						List[str]       							=					box
         _UpperCAmelCase							:						Optional[Any]       							=					(xa, ya)
         _UpperCAmelCase							:						List[Any]       							=					(ya - ya) * (xa - xa)
         _UpperCAmelCase							:						List[Any]       							=					_SMALL_OBJ * self.scale
         if instance_area < small or ya - ya < 4_0 * self.scale:
             if ya >= self.height - 5:
                 _UpperCAmelCase							:						Tuple       							=					(xa, ya)
             else:
                 _UpperCAmelCase							:						Tuple       							=					(xa, ya)

         _UpperCAmelCase							:						Any       							=					(ya - ya) / np.sqrt(self.height * self.width	)
         _UpperCAmelCase							:						List[Any]       							=					self._change_color_brightness(A ,			brightness_factor=0.7	)
         _UpperCAmelCase							:						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 				__lowerCAmelCase			(      self ,			A ,			A ,			A="g" ,			A="left" ,			)							->      Dict:
         _UpperCAmelCase							:						Optional[int]       							=					0
         _UpperCAmelCase							:						str       							=					self.font_size
         _UpperCAmelCase							:						int       							=					np.maximum(list(mplc.to_rgb(A	)	) ,			0.2	)
         _UpperCAmelCase							:						Dict       							=					max(0.8 ,			np.max(A	)	)
         _UpperCAmelCase							:						int       							=					{
             '''facecolor''': '''black''',
             '''alpha''': self.alpha,
             '''pad''': self.pad,
             '''edgecolor''': '''none''',
         }
         _UpperCAmelCase							,       _UpperCAmelCase							:						Optional[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=1_0 ,			rotation=A ,			)





     def 				__lowerCAmelCase			(      self ,			A=None	)							->      str:
         if saveas is None:
             _UpperCAmelCase							:						str       							=					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 				__lowerCAmelCase			(      self ,			A ,			A ,			A ,			A	)							->      int:
         _UpperCAmelCase							:						List[str]       							=					[self.idaobj[i] for i in classes]
         _UpperCAmelCase							:						Union[str, Any]       							=					[self.idaattr[i] for i in attr_classes]
         _UpperCAmelCase							:						List[Any]       							=					[
             f'{label} {score:.2f} {attr} {attr_score:.2f}'
             for label, score, attr, attr_score in zip(A ,			A ,			A ,			A	)
         ]
         return labels





     def 				__lowerCAmelCase			(      self ,			A ,			A	)							->      List[str]:
         _UpperCAmelCase							:						Tuple       							=					[self.idaobj[i] for i in classes]
         if scores is not None:
             if labels is None:
                 _UpperCAmelCase							:						Tuple       							=					['''{:.0f}%'''.format(s * 1_0_0	) for s in scores]
             else:
                 _UpperCAmelCase							:						Any       							=					['''{} {:.0f}%'''.format(A ,			s * 1_0_0	) for li, s in zip(A ,			A	)]
         return labels





     def 				__lowerCAmelCase			(      self ,			A=2_5_5	)							->      str:
         _UpperCAmelCase							:						Union[str, Any]       							=					np.random.randint(0 ,			len(_COLORS	)	)
         _UpperCAmelCase							:						Any       							=					_COLORS[idx] * maximum
         if not self.rgb:
             _UpperCAmelCase							:						Dict       							=					ret[::-1]
         return ret





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

         _UpperCAmelCase							:						int       							=					np.frombuffer(A ,			dtype='''uint8'''	)
         _UpperCAmelCase							:						List[Any]       							=					buffer.reshape(A ,			A ,			4	)
         _UpperCAmelCase							,       _UpperCAmelCase							:						Tuple       							=					np.split(A ,			[3] ,			axis=2	)

         try:
             import numexpr as ne  # fuse them with numexpr

             _UpperCAmelCase							:						Optional[Any]       							=					ne.evaluate('''img * (1 - alpha / 255.0) + rgb * (alpha / 255.0)'''	)
         except ImportError:
             _UpperCAmelCase							:						str       							=					alpha.astype('''float32'''	) / 255.0
             _UpperCAmelCase							:						List[str]       							=					img * (1 - alpha) + rgb * alpha

         return visualized_image.astype('''uint8'''	)





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



_lowerCAmelCase					:int  =      (
    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"""
# Lint as: python3
import sys
from collections.abc import Mapping
from typing import TYPE_CHECKING

import numpy as np
import pyarrow as pa

from .. import config
from ..utils.py_utils import map_nested
from .formatting import TensorFormatter


if TYPE_CHECKING:
  import tensorflow as tf
class 							_UpperCAmelCase							(				TensorFormatter[Mapping, '''tf.Tensor''', Mapping]   ):





     '''simple docstring'''



     def __init__(      self ,			A=None ,			**A	)							->      List[str]:
         super().__init__(features=A	)
         _UpperCAmelCase							:						Any       							=					tf_tensor_kwargs
         import tensorflow as tf  # noqa: import tf at initialization





     def 				__lowerCAmelCase			(      self ,			A	)							->      Optional[Any]:
         import tensorflow as tf

         if isinstance(A ,			A	) and column:
             if all(
                 isinstance(A ,			tf.Tensor	) and x.shape == column[0].shape and x.dtype == column[0].dtype for x in column	):
                 return tf.stack(A	)
             elif all(
                 isinstance(A ,			(tf.Tensor, tf.RaggedTensor)	) and x.ndim == 1 and x.dtype == column[0].dtype
                 for x in column	):
                 # only rag 1-D tensors, otherwise some dimensions become ragged even though they were consolidated
                 return tf.ragged.stack(A	)

         return column





     def 				__lowerCAmelCase			(      self ,			A	)							->      int:
         import tensorflow as tf

         if value is None:
             return value

         _UpperCAmelCase							:						Any       							=					{}

         if isinstance(A ,			(np.number, np.ndarray)	) and np.issubdtype(value.dtype ,			np.integer	):
             _UpperCAmelCase							:						Union[str, Any]       							=					{'''dtype''': tf.intaa}
         elif isinstance(A ,			(np.number, np.ndarray)	) and np.issubdtype(value.dtype ,			np.floating	):
             _UpperCAmelCase							:						Dict       							=					{'''dtype''': tf.floataa}
         elif config.PIL_AVAILABLE and "PIL" in sys.modules:
             import PIL.Image

             if isinstance(A ,			PIL.Image.Image	):
                 _UpperCAmelCase							:						Optional[int]       							=					np.asarray(A	)

         return tf.convert_to_tensor(A ,			**{**default_dtype, **self.tf_tensor_kwargs}	)





     def 				__lowerCAmelCase			(      self ,			A	)							->      Tuple:
         import tensorflow as tf

         # support for torch, tf, jax etc.
         if config.TORCH_AVAILABLE and "torch" in sys.modules:
             import torch

             if isinstance(A ,			torch.Tensor	):
                 return self._tensorize(data_struct.detach().cpu().numpy()[()]	)
         if hasattr(A ,			'''__array__'''	) and not isinstance(A ,			tf.Tensor	):
             _UpperCAmelCase							:						List[str]       							=					data_struct.__array__()
         # support for nested types like struct of list of struct
         if isinstance(A ,			np.ndarray	):
             if data_struct.dtype == object:  # tf tensors cannot be instantied from an array of objects
                 return self._consolidate([self.recursive_tensorize(A	) for substruct in data_struct]	)
         elif isinstance(A ,			(list, tuple)	):
             return self._consolidate([self.recursive_tensorize(A	) for substruct in data_struct]	)
         return self._tensorize(A	)





     def 				__lowerCAmelCase			(      self ,			A	)							->      Optional[int]:
         return map_nested(self._recursive_tensorize ,			A ,			map_list=A	)





     def 				__lowerCAmelCase			(      self ,			A	)							->      Mapping:
         _UpperCAmelCase							:						str       							=					self.numpy_arrow_extractor().extract_row(A	)
         _UpperCAmelCase							:						List[Any]       							=					self.python_features_decoder.decode_row(A	)
         return self.recursive_tensorize(A	)





     def 				__lowerCAmelCase			(      self ,			A	)							->      "tf.Tensor":
         _UpperCAmelCase							:						Union[str, Any]       							=					self.numpy_arrow_extractor().extract_column(A	)
         _UpperCAmelCase							:						Tuple       							=					self.python_features_decoder.decode_column(A ,			pa_table.column_names[0]	)
         _UpperCAmelCase							:						str       							=					self.recursive_tensorize(A	)
         _UpperCAmelCase							:						Optional[Any]       							=					self._consolidate(A	)
         return column





     def 				__lowerCAmelCase			(      self ,			A	)							->      Mapping:
         _UpperCAmelCase							:						Dict       							=					self.numpy_arrow_extractor().extract_batch(A	)
         _UpperCAmelCase							:						Tuple       							=					self.python_features_decoder.decode_batch(A	)
         _UpperCAmelCase							:						Union[str, Any]       							=					self.recursive_tensorize(A	)
         for column_name in batch:
             _UpperCAmelCase							:						str       							=					self._consolidate(batch[column_name]	)
         return batch


