

"""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       :  Dict        = 1_0_0_0

class     __a   :

   """simple docstring"""

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







        if isinstance(__A						,  torch.Tensor						):
             UpperCamelCase__		:    int         =img.numpy().astype('''np.uint8'''						)
        if isinstance(__A						,  __A						):
             UpperCamelCase__		:    Tuple         =img_tensorize(__A						)
        assert isinstance(__A						,  np.ndarray						)

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






   def    _lowerCAmelCase			(							self     :					Tuple						,  lowercase_     :					Union[str, Any]						,  lowercase_     :					List[Any]=None						):







        if color is None:
             UpperCamelCase__		:    Any         =self.edgecolor
        (UpperCamelCase__)		:    Union[str, Any]         =box
        UpperCamelCase__		:    Optional[Any]         =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     :					List[Any]						,  lowercase_     :					List[Any]						,  lowercase_     :					List[Any]=None						,  lowercase_     :					Dict=None						,  lowercase_     :					str=None						,  lowercase_     :					Optional[int]=None						):







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

        UpperCamelCase__		:    Any         =[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__		:    Union[str, Any]         =self._create_text_labels_attr(__A						,  __A						,  __A						,  __A						)
             for i in range(len(__A						)						):
                  UpperCamelCase__		:    Optional[int]         =assigned_colors[i]
                  self.add_box(boxes[i]						,  __A						)
                  self.draw_labels(labels[i]						,  boxes[i]						,  __A						)






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

        UpperCamelCase__		:    Union[str, Any]         =(ya - ya) / np.sqrt(self.height * self.width						)
        UpperCamelCase__		:    Optional[Any]         =self._change_color_brightness(__A						,  brightness_factor=0.7						)
        UpperCamelCase__		:    Optional[int]         =np.clip((height_ratio - 0.0_2) / 0.0_8 + 1						,  1.2						,  2						)
        font_size *= 0.7_5 * self.font_size

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






   def    _lowerCAmelCase			(							self     :					Union[str, Any]						,  lowercase_     :					Any						,  lowercase_     :					Optional[int]						,  lowercase_     :					Optional[int]="g"						,  lowercase_     :					Union[str, Any]="left"						,  ):
        UpperCamelCase__		:    Union[str, Any]         =0
        UpperCamelCase__		:    int         =self.font_size
        UpperCamelCase__		:    Union[str, Any]         =np.maximum(list(mplc.to_rgb(__A						)						)						,  0.2						)
        UpperCamelCase__		:    Any         =max(0.8						,  np.max(__A						)						)
        UpperCamelCase__		:    Optional[Any]         ={
            "facecolor": "black",
            "alpha": self.alpha,
            "pad": self.pad,
            "edgecolor": "none",
        }
        UpperCamelCase__		:    Optional[int]         =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    _lowerCAmelCase			(							self     :					Optional[int]						,  lowercase_     :					Union[str, Any]=None						):







        if saveas is None:
             UpperCamelCase__		:    int         =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     :					Union[str, Any]						,  lowercase_     :					Tuple						,  lowercase_     :					int						,  lowercase_     :					List[Any]						,  lowercase_     :					Union[str, Any]						):
        UpperCamelCase__		:    str         =[self.idaobj[i] for i in classes]
        UpperCamelCase__		:    Tuple         =[self.idaattr[i] for i in attr_classes]
        UpperCamelCase__		:    List[str]         =[
            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     :					Optional[Any]						,  lowercase_     :					Tuple						,  lowercase_     :					Tuple						):
        UpperCamelCase__		:    Optional[int]         =[self.idaobj[i] for i in classes]
        if scores is not None:
             if labels is None:
                  UpperCamelCase__		:    str         =["{:.0f}%".format(s * 100						) for s in scores]
             else:
                  UpperCamelCase__		:    List[Any]         =["{} {:.0f}%".format(__A						,  s * 100						) for li, s in zip(__A						,  __A						)]
        return labels






   def    _lowerCAmelCase			(							self     :					Optional[int]						,  lowercase_     :					Union[str, Any]=255						):
        UpperCamelCase__		:    Optional[Any]         =np.random.randint(0						,  len(_COLORS						)						)
        UpperCamelCase__		:    int         =_COLORS[idx] * maximum
        if not self.rgb:
             UpperCamelCase__		:    Tuple         =ret[::-1]
        return ret






   def    _lowerCAmelCase			(							self     :					int						):







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

        UpperCamelCase__		:    Dict         =np.frombuffer(__A						,  dtype='''uint8'''						)
        UpperCamelCase__		:    Any         =buffer.reshape(__A						,  __A						,  4						)
        UpperCamelCase__		:    Optional[Any]         =np.split(__A						,  [3]						,  axis=2						)

        try:
             import numexpr as ne  # fuse them with numexpr

             UpperCamelCase__		:    Any         =ne.evaluate('''img * (1 - alpha / 255.0) + rgb * (alpha / 255.0)'''						)
        except ImportError:
             UpperCamelCase__		:    List[Any]         =alpha.astype('''float32'''						) / 2_5_5.0
             UpperCamelCase__		:    Tuple         =img * (1 - alpha) + rgb * alpha

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






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



_SCREAMING_SNAKE_CASE       :  List[Any]        = (
    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 typing import TYPE_CHECKING

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


_SCREAMING_SNAKE_CASE       :  List[Any]        = {"""configuration_t5""": ["""T5_PRETRAINED_CONFIG_ARCHIVE_MAP""", """T5Config""", """T5OnnxConfig"""]}

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

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

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

try:
				if not is_flax_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				_SCREAMING_SNAKE_CASE       :  Any        = [
				    """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

				_SCREAMING_SNAKE_CASE       :  str        = _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)


