
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


snake_case__  							=							10_00







class 							UpperCAmelCase	:

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

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

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

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

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

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

											lowercase       :    List[Any]											=  (ya - ya) / np.sqrt(self.height * self.width					)
											lowercase       :    str											=  self._change_color_brightness(lowerCAmelCase ,      brightness_factor=0.7					)
											lowercase       :    Optional[int]											=  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        _lowerCAmelCase	(			self      :					str ,      lowerCAmelCase      :					Dict ,      lowerCAmelCase      :					List[Any] ,      lowerCAmelCase      :					List[Any]="g" ,      lowerCAmelCase      :					int="left" ,      ):
											lowercase       :    Dict											=  0
											lowercase       :    List[Any]											=  self.font_size
											lowercase       :    List[str]											=  np.maximum(list(mplc.to_rgb(lowerCAmelCase					)					) ,      0.2					)
											lowercase       :    Any											=  max(0.8 ,      np.max(lowerCAmelCase					)					)
											lowercase       :    List[str]											=  {
											    '''facecolor''': '''black''',
											    '''alpha''': self.alpha,
											    '''pad''': self.pad,
											    '''edgecolor''': '''none''',
											}
											lowercase				,				lowercase       :    Union[str, Any]											=  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        _lowerCAmelCase	(			self      :					Tuple ,      lowerCAmelCase      :					Optional[int]=None					):
											if saveas is None:
																	lowercase       :    int											=  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        _lowerCAmelCase	(			self      :					List[str] ,      lowerCAmelCase      :					str ,      lowerCAmelCase      :					Tuple ,      lowerCAmelCase      :					Tuple ,      lowerCAmelCase      :					int					):
											lowercase       :    Optional[Any]											=  [self.idaobj[i] for i in classes]
											lowercase       :    Tuple											=  [self.idaattr[i] for i in attr_classes]
											lowercase       :    List[Any]											=  [
											    f'''{label} {score:.2f} {attr} {attr_score:.2f}'''
											    for label, score, attr, attr_score in zip(lowerCAmelCase ,      lowerCAmelCase ,      lowerCAmelCase ,      lowerCAmelCase					)
											]
											return labels

					def        _lowerCAmelCase	(			self      :					Optional[Any] ,      lowerCAmelCase      :					Optional[int] ,      lowerCAmelCase      :					Dict					):
											lowercase       :    int											=  [self.idaobj[i] for i in classes]
											if scores is not None:
																	if labels is None:
																							lowercase       :    Optional[Any]											=  ['''{:.0f}%'''.format(s * 100					) for s in scores]
																	else:
																							lowercase       :    List[Any]											=  ['''{} {:.0f}%'''.format(lowerCAmelCase ,      s * 100					) for li, s in zip(lowerCAmelCase ,      lowerCAmelCase					)]
											return labels

					def        _lowerCAmelCase	(			self      :					str ,      lowerCAmelCase      :					Dict=255					):
											lowercase       :    Optional[int]											=  np.random.randint(0 ,      len(_COLORS					)					)
											lowercase       :    int											=  _COLORS[idx] * maximum
											if not self.rgb:
																	lowercase       :    int											=  ret[::-1]
											return ret

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

											lowercase       :    int											=  np.frombuffer(lowerCAmelCase ,      dtype='''uint8'''					)
											lowercase       :    List[str]											=  buffer.reshape(lowerCAmelCase ,      lowerCAmelCase ,      4					)
											lowercase				,				lowercase       :    Dict											=  np.split(lowerCAmelCase ,      [3] ,      axis=2					)

											try:
																	import numexpr as ne  # fuse them with numexpr

																	lowercase       :    List[str]											=  ne.evaluate('''img * (1 - alpha / 255.0) + rgb * (alpha / 255.0)'''					)
											except ImportError:
																	lowercase       :    List[str]											=  alpha.astype('''float32'''					) / 2_55.0
																	lowercase       :    Optional[int]											=  img * (1 - alpha) + rgb * alpha

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




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





snake_case__  							=							(
    np.array(
        [
            0.0_0_0,
            0.4_4_7,
            0.7_4_1,
            0.8_5_0,
            0.3_2_5,
            0.0_9_8,
            0.9_2_9,
            0.6_9_4,
            0.1_2_5,
            0.4_9_4,
            0.1_8_4,
            0.5_5_6,
            0.4_6_6,
            0.6_7_4,
            0.1_8_8,
            0.3_0_1,
            0.7_4_5,
            0.9_3_3,
            0.6_3_5,
            0.0_7_8,
            0.1_8_4,
            0.3_0_0,
            0.3_0_0,
            0.3_0_0,
            0.6_0_0,
            0.6_0_0,
            0.6_0_0,
            1.0_0_0,
            0.0_0_0,
            0.0_0_0,
            1.0_0_0,
            0.5_0_0,
            0.0_0_0,
            0.7_4_9,
            0.7_4_9,
            0.0_0_0,
            0.0_0_0,
            1.0_0_0,
            0.0_0_0,
            0.0_0_0,
            0.0_0_0,
            1.0_0_0,
            0.6_6_7,
            0.0_0_0,
            1.0_0_0,
            0.3_3_3,
            0.3_3_3,
            0.0_0_0,
            0.3_3_3,
            0.6_6_7,
            0.0_0_0,
            0.3_3_3,
            1.0_0_0,
            0.0_0_0,
            0.6_6_7,
            0.3_3_3,
            0.0_0_0,
            0.6_6_7,
            0.6_6_7,
            0.0_0_0,
            0.6_6_7,
            1.0_0_0,
            0.0_0_0,
            1.0_0_0,
            0.3_3_3,
            0.0_0_0,
            1.0_0_0,
            0.6_6_7,
            0.0_0_0,
            1.0_0_0,
            1.0_0_0,
            0.0_0_0,
            0.0_0_0,
            0.3_3_3,
            0.5_0_0,
            0.0_0_0,
            0.6_6_7,
            0.5_0_0,
            0.0_0_0,
            1.0_0_0,
            0.5_0_0,
            0.3_3_3,
            0.0_0_0,
            0.5_0_0,
            0.3_3_3,
            0.3_3_3,
            0.5_0_0,
            0.3_3_3,
            0.6_6_7,
            0.5_0_0,
            0.3_3_3,
            1.0_0_0,
            0.5_0_0,
            0.6_6_7,
            0.0_0_0,
            0.5_0_0,
            0.6_6_7,
            0.3_3_3,
            0.5_0_0,
            0.6_6_7,
            0.6_6_7,
            0.5_0_0,
            0.6_6_7,
            1.0_0_0,
            0.5_0_0,
            1.0_0_0,
            0.0_0_0,
            0.5_0_0,
            1.0_0_0,
            0.3_3_3,
            0.5_0_0,
            1.0_0_0,
            0.6_6_7,
            0.5_0_0,
            1.0_0_0,
            1.0_0_0,
            0.5_0_0,
            0.0_0_0,
            0.3_3_3,
            1.0_0_0,
            0.0_0_0,
            0.6_6_7,
            1.0_0_0,
            0.0_0_0,
            1.0_0_0,
            1.0_0_0,
            0.3_3_3,
            0.0_0_0,
            1.0_0_0,
            0.3_3_3,
            0.3_3_3,
            1.0_0_0,
            0.3_3_3,
            0.6_6_7,
            1.0_0_0,
            0.3_3_3,
            1.0_0_0,
            1.0_0_0,
            0.6_6_7,
            0.0_0_0,
            1.0_0_0,
            0.6_6_7,
            0.3_3_3,
            1.0_0_0,
            0.6_6_7,
            0.6_6_7,
            1.0_0_0,
            0.6_6_7,
            1.0_0_0,
            1.0_0_0,
            1.0_0_0,
            0.0_0_0,
            1.0_0_0,
            1.0_0_0,
            0.3_3_3,
            1.0_0_0,
            1.0_0_0,
            0.6_6_7,
            1.0_0_0,
            0.3_3_3,
            0.0_0_0,
            0.0_0_0,
            0.5_0_0,
            0.0_0_0,
            0.0_0_0,
            0.6_6_7,
            0.0_0_0,
            0.0_0_0,
            0.8_3_3,
            0.0_0_0,
            0.0_0_0,
            1.0_0_0,
            0.0_0_0,
            0.0_0_0,
            0.0_0_0,
            0.1_6_7,
            0.0_0_0,
            0.0_0_0,
            0.3_3_3,
            0.0_0_0,
            0.0_0_0,
            0.5_0_0,
            0.0_0_0,
            0.0_0_0,
            0.6_6_7,
            0.0_0_0,
            0.0_0_0,
            0.8_3_3,
            0.0_0_0,
            0.0_0_0,
            1.0_0_0,
            0.0_0_0,
            0.0_0_0,
            0.0_0_0,
            0.1_6_7,
            0.0_0_0,
            0.0_0_0,
            0.3_3_3,
            0.0_0_0,
            0.0_0_0,
            0.5_0_0,
            0.0_0_0,
            0.0_0_0,
            0.6_6_7,
            0.0_0_0,
            0.0_0_0,
            0.8_3_3,
            0.0_0_0,
            0.0_0_0,
            1.0_0_0,
            0.0_0_0,
            0.0_0_0,
            0.0_0_0,
            0.1_4_3,
            0.1_4_3,
            0.1_4_3,
            0.8_5_7,
            0.8_5_7,
            0.8_5_7,
            1.0_0_0,
            1.0_0_0,
            1.0_0_0,
        ]
    )
    .astype(np.floataa)
    .reshape(-1, 3)
)




from typing import TYPE_CHECKING

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


snake_case__  							=							{"""configuration_t5""": ["""T5_PRETRAINED_CONFIG_ARCHIVE_MAP""", """T5Config""", """T5OnnxConfig"""]}

try:
				if not is_sentencepiece_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				snake_case__  							=							["""T5Tokenizer"""]

try:
				if not is_tokenizers_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				snake_case__  							=							["""T5TokenizerFast"""]

try:
				if not is_torch_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				snake_case__  							=							[
				    """T5_PRETRAINED_MODEL_ARCHIVE_LIST""",
				    """T5EncoderModel""",
				    """T5ForConditionalGeneration""",
				    """T5Model""",
				    """T5PreTrainedModel""",
				    """load_tf_weights_in_t5""",
				    """T5ForQuestionAnswering""",
				]

try:
				if not is_tf_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				snake_case__  							=							[
				    """TF_T5_PRETRAINED_MODEL_ARCHIVE_LIST""",
				    """TFT5EncoderModel""",
				    """TFT5ForConditionalGeneration""",
				    """TFT5Model""",
				    """TFT5PreTrainedModel""",
				]

try:
				if not is_flax_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				snake_case__  							=							[
				    """FlaxT5EncoderModel""",
				    """FlaxT5ForConditionalGeneration""",
				    """FlaxT5Model""",
				    """FlaxT5PreTrainedModel""",
				]


if TYPE_CHECKING:
				from .configuration_ta import T5_PRETRAINED_CONFIG_ARCHIVE_MAP, TaConfig, TaOnnxConfig

				try:
								if not is_sentencepiece_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .tokenization_ta import TaTokenizer

				try:
								if not is_tokenizers_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .tokenization_ta_fast import TaTokenizerFast

				try:
								if not is_torch_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .modeling_ta import (
								    T5_PRETRAINED_MODEL_ARCHIVE_LIST,
								    TaEncoderModel,
								    TaForConditionalGeneration,
								    TaForQuestionAnswering,
								    TaModel,
								    TaPreTrainedModel,
								    load_tf_weights_in_ta,
								)

				try:
								if not is_tf_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .modeling_tf_ta import (
								    TF_T5_PRETRAINED_MODEL_ARCHIVE_LIST,
								    TFTaEncoderModel,
								    TFTaForConditionalGeneration,
								    TFTaModel,
								    TFTaPreTrainedModel,
								)

				try:
								if not is_flax_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .modeling_flax_ta import (
								    FlaxTaEncoderModel,
								    FlaxTaForConditionalGeneration,
								    FlaxTaModel,
								    FlaxTaPreTrainedModel,
								)


else:
				import sys

				snake_case__  							=							_LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)


