




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[Any]  =       1000




class 		UpperCamelCase__     :

    '''simple docstring'''



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

           lowerCamelCase			,				lowerCamelCase       :				Union[str, Any]      			= img.shape[1], img.shape[0]
           lowerCamelCase       :				Optional[Any]      			= mplfigure.Figure(frameon=UpperCamelCase__					)
           lowerCamelCase       :				Tuple      			= fig.get_dpi()
           lowerCamelCase       :				Optional[int]      			= (width * scale + 1e-2) / dpi
           lowerCamelCase       :				Optional[Any]      			= (height * scale + 1e-2) / dpi
           fig.set_size_inches(UpperCamelCase__					,  UpperCamelCase__					)
           lowerCamelCase       :				List[str]      			= fig.add_axes([0.0, 0.0, 1.0, 1.0]					)
           ax.axis("off"					)
           ax.set_xlim(0.0					,  UpperCamelCase__					)
           ax.set_ylim(UpperCamelCase__					)

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







    def    _lowercase					(   self					,  UpperCamelCase__					,  UpperCamelCase__=None					)					->				Dict:
           if color is None:
                  lowerCamelCase       :				Tuple      			= self.edgecolor
           ((lowerCamelCase)			,				(lowerCamelCase)			,				(lowerCamelCase)			,				(lowerCamelCase))       :				Optional[Any]      			= box
           lowerCamelCase       :				Tuple      			= xa - xa
           lowerCamelCase       :				List[str]      			= ya - ya
           self.ax.add_patch(
               mpl.patches.Rectangle(
                   (xa, ya)					,  UpperCamelCase__					,  UpperCamelCase__					,  fill=UpperCamelCase__					,  edgecolor=UpperCamelCase__					,  linewidth=self.font_size // 3					,  alpha=self.alpha					,  linestyle=self.linestyle					,  )					)







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

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







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

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

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







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







    def    _lowercase					(   self					,  UpperCamelCase__=None					)					->				Tuple:
           if saveas is None:
                  lowerCamelCase       :				Union[str, Any]      			= self.saveas
           if saveas.lower().endswith(".jpg"					) or saveas.lower().endswith(".png"					):
                  cva.imwrite(
                      UpperCamelCase__					,  self._get_buffer()[:, :, ::-1]					,  )
           else:
                  self.fig.savefig(UpperCamelCase__					)







    def    _lowercase					(   self					,  UpperCamelCase__					,  UpperCamelCase__					,  UpperCamelCase__					,  UpperCamelCase__					)					->				List[str]:
           lowerCamelCase       :				Any      			= [self.idaobj[i] for i in classes]
           lowerCamelCase       :				List[str]      			= [self.idaattr[i] for i in attr_classes]
           lowerCamelCase       :				Union[str, Any]      			= [
               F'''{label} {score:.2f} {attr} {attr_score:.2f}'''
               for label, score, attr, attr_score in zip(UpperCamelCase__					,  UpperCamelCase__					,  UpperCamelCase__					,  UpperCamelCase__					)
           ]
           return labels







    def    _lowercase					(   self					,  UpperCamelCase__					,  UpperCamelCase__					)					->				Dict:
           lowerCamelCase       :				Optional[Any]      			= [self.idaobj[i] for i in classes]
           if scores is not None:
                  if labels is None:
                         lowerCamelCase       :				Any      			= ["{:.0f}%".format(s * 100					) for s in scores]
                  else:
                         lowerCamelCase       :				Dict      			= ["{} {:.0f}%".format(UpperCamelCase__					,  s * 100					) for li, s in zip(UpperCamelCase__					,  UpperCamelCase__					)]
           return labels







    def    _lowercase					(   self					,  UpperCamelCase__=255					)					->				Optional[int]:
           lowerCamelCase       :				Tuple      			= np.random.randint(0					,  len(_COLORS					)					)
           lowerCamelCase       :				List[Any]      			= _COLORS[idx] * maximum
           if not self.rgb:
                  lowerCamelCase       :				Dict      			= ret[::-1]
           return ret







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

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

           try:
                  import numexpr as ne  # fuse them with numexpr

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

           return visualized_image.astype("uint8"					)







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












from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_flax_available,
    is_tf_available,
    is_torch_available,
)


SCREAMING_SNAKE_CASE__	:				Optional[int]  =       {
    'configuration_gptsan_japanese': ['GPTSAN_JAPANESE_PRETRAINED_CONFIG_ARCHIVE_MAP', 'GPTSanJapaneseConfig'],
    'tokenization_gptsan_japanese': ['GPTSanJapaneseTokenizer'],
}

try:
       if not is_torch_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       SCREAMING_SNAKE_CASE__	:				Optional[int]  =       [
           'GPTSAN_JAPANESE_PRETRAINED_MODEL_ARCHIVE_LIST',
           'GPTSanJapaneseForConditionalGeneration',
           'GPTSanJapaneseModel',
           'GPTSanJapanesePreTrainedModel',
       ]
       SCREAMING_SNAKE_CASE__	:				List[str]  =       [
           'GPTSanJapaneseTokenizer',
       ]


if TYPE_CHECKING:
       from .configuration_gptsan_japanese import GPTSAN_JAPANESE_PRETRAINED_CONFIG_ARCHIVE_MAP, GPTSanJapaneseConfig
       from .tokenization_gptsan_japanese import GPTSanJapaneseTokenizer

       try:
              if not is_torch_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .modeling_gptsan_japanese import (
                  GPTSAN_JAPANESE_PRETRAINED_MODEL_ARCHIVE_LIST,
                  GPTSanJapaneseForConditionalGeneration,
                  GPTSanJapaneseModel,
                  GPTSanJapanesePreTrainedModel,
              )
              from .tokenization_gptsan_japanese import GPTSanJapaneseTokenizer


else:
       import sys

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






