"""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       :  str        = 1_0_0_0

class     __a   :

			"""simple docstring"""

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

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

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






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






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

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






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

								UpperCamelCase__		:    List[str]         =(ya - ya) / np.sqrt(self.height * self.width						)
								UpperCamelCase__		:    int         =self._change_color_brightness(lowercase_						,  brightness_factor=0.7						)
								UpperCamelCase__		:    Tuple         =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=lowercase_						,  position=lowercase_						,  color=lowercase_						,  )






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






			def    _lowerCAmelCase			(							self     :					Tuple						,  lowercase_     :					Tuple=None						):
								if saveas is None:
													UpperCamelCase__		:    Optional[int]         =self.saveas
								if saveas.lower().endswith('''.jpg'''						) or saveas.lower().endswith('''.png'''						):
													cva.imwrite(
													    lowercase_						,  self._get_buffer()[:, :, ::-1]						,  )
								else:
													self.fig.savefig(lowercase_						)






			def    _lowerCAmelCase			(							self     :					List[Any]						,  lowercase_     :					Any						,  lowercase_     :					Optional[Any]						,  lowercase_     :					List[Any]						,  lowercase_     :					List[str]						):
								UpperCamelCase__		:    int         =[self.idaobj[i] for i in classes]
								UpperCamelCase__		:    Tuple         =[self.idaattr[i] for i in attr_classes]
								UpperCamelCase__		:    Tuple         =[
								    f'''{label} {score:.2f} {attr} {attr_score:.2f}'''
								    for label, score, attr, attr_score in zip(lowercase_						,  lowercase_						,  lowercase_						,  lowercase_						)
								]
								return labels






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






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






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

								UpperCamelCase__		:    int         =np.frombuffer(lowercase_						,  dtype='''uint8'''						)
								UpperCamelCase__		:    List[str]         =buffer.reshape(lowercase_						,  lowercase_						,  4						)
								UpperCamelCase__		:    List[Any]         =np.split(lowercase_						,  [3]						,  axis=2						)

								try:
													import numexpr as ne  # fuse them with numexpr

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

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






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



_SCREAMING_SNAKE_CASE       :  Tuple        = (
    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_sentencepiece_available, is_torch_available


_SCREAMING_SNAKE_CASE       :  Union[str, Any]        = {
    """configuration_xlm_prophetnet""": ["""XLM_PROPHETNET_PRETRAINED_CONFIG_ARCHIVE_MAP""", """XLMProphetNetConfig"""],
}

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

try:
				if not is_torch_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				_SCREAMING_SNAKE_CASE       :  Optional[Any]        = [
				    """XLM_PROPHETNET_PRETRAINED_MODEL_ARCHIVE_LIST""",
				    """XLMProphetNetDecoder""",
				    """XLMProphetNetEncoder""",
				    """XLMProphetNetForCausalLM""",
				    """XLMProphetNetForConditionalGeneration""",
				    """XLMProphetNetModel""",
				    """XLMProphetNetPreTrainedModel""",
				]


if TYPE_CHECKING:
				from .configuration_xlm_prophetnet import XLM_PROPHETNET_PRETRAINED_CONFIG_ARCHIVE_MAP, XLMProphetNetConfig

				try:
								if not is_sentencepiece_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .tokenization_xlm_prophetnet import XLMProphetNetTokenizer

				try:
								if not is_torch_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .modeling_xlm_prophetnet import (
								    XLM_PROPHETNET_PRETRAINED_MODEL_ARCHIVE_LIST,
								    XLMProphetNetDecoder,
								    XLMProphetNetEncoder,
								    XLMProphetNetForCausalLM,
								    XLMProphetNetForConditionalGeneration,
								    XLMProphetNetModel,
								    XLMProphetNetPreTrainedModel,
								)

else:
				import sys

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


