
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      :					Any   	=       1000




class 	UpperCAmelCase_      :


		'''simple docstring'''







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







				"""simple docstring"""






				if isinstance(UpperCamelCase__      ,							torch.Tensor   ):
						__magic_name__    					=	img.numpy().astype("""np.uint8"""   )
				if isinstance(UpperCamelCase__      ,							UpperCamelCase__   ):
						__magic_name__    					=	img_tensorize(UpperCamelCase__   )
				assert isinstance(UpperCamelCase__      ,							np.ndarray   )

				__magic_name__					,   __magic_name__    					=	img.shape[1], img.shape[0]
				__magic_name__    					=	mplfigure.Figure(frameon=UpperCamelCase__   )
				__magic_name__    					=	fig.get_dpi()
				__magic_name__    					=	(width * scale + 1E-2) / dpi
				__magic_name__    					=	(height * scale + 1E-2) / dpi
				fig.set_size_inches(UpperCamelCase__      ,							UpperCamelCase__   )
				__magic_name__    					=	fig.add_axes([0.0, 0.0, 1.0, 1.0]   )
				ax.axis("""off"""   )
				ax.set_xlim(0.0      ,							UpperCamelCase__   )
				ax.set_ylim(UpperCamelCase__   )

				__magic_name__    					=	saveas
				__magic_name__    					=	rgb
				__magic_name__    					=	pynb
				__magic_name__    					=	img
				__magic_name__    					=	edgecolor
				__magic_name__    					=	0.5
				__magic_name__    					=	linestyle
				__magic_name__    					=	int(np.sqrt(min(UpperCamelCase__      ,							UpperCamelCase__   )   ) * scale // 3   )
				__magic_name__    					=	width
				__magic_name__    					=	height
				__magic_name__    					=	scale
				__magic_name__    					=	fig
				__magic_name__    					=	ax
				__magic_name__    					=	pad
				__magic_name__    					=	idaobj
				__magic_name__    					=	idaattr
				__magic_name__    					=	FigureCanvasAgg(UpperCamelCase__   )
		def 							_lowercase							(			self      :			Tuple      ,							UpperCamelCase__      :			Any      ,							UpperCamelCase__      :			int=None   )      ->     Any:







				"""simple docstring"""






				if color is None:
						__magic_name__    					=	self.edgecolor
				((__magic_name__)					,   (__magic_name__)					,   (__magic_name__)					,   (__magic_name__))    					=	box
				__magic_name__    					=	xa - xa
				__magic_name__    					=	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      :			str      ,							UpperCamelCase__      :			List[str]      ,							UpperCamelCase__      :			Optional[Any]=None      ,							UpperCamelCase__      :			Dict=None      ,							UpperCamelCase__      :			Any=None      ,							UpperCamelCase__      :			int=None   )      ->     int:







				"""simple docstring"""






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

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







				"""simple docstring"""






				__magic_name__					,   __magic_name__					,   __magic_name__					,   __magic_name__    					=	box
				__magic_name__    					=	(xa, ya)
				__magic_name__    					=	(ya - ya) * (xa - xa)
				__magic_name__    					=	_SMALL_OBJ * self.scale
				if instance_area < small or ya - ya < 40 * self.scale:
						if ya >= self.height - 5:
								__magic_name__    					=	(xa, ya)
						else:
								__magic_name__    					=	(xa, ya)

				__magic_name__    					=	(ya - ya) / np.sqrt(self.height * self.width   )
				__magic_name__    					=	self._change_color_brightness(UpperCamelCase__      ,							brightness_factor=0.7   )
				__magic_name__    					=	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      :			List[str]      ,							UpperCamelCase__      :			Dict      ,							UpperCamelCase__      :			Any      ,							UpperCamelCase__      :			str="g"      ,							UpperCamelCase__      :			List[Any]="left"      ,							)      ->     str:







				"""simple docstring"""






				__magic_name__    					=	0
				__magic_name__    					=	self.font_size
				__magic_name__    					=	np.maximum(list(mplc.to_rgb(UpperCamelCase__   )   )      ,							0.2   )
				__magic_name__    					=	max(0.8      ,							np.max(UpperCamelCase__   )   )
				__magic_name__    					=	{
				    """facecolor""": """black""",
				    """alpha""": self.alpha,
				    """pad""": self.pad,
				    """edgecolor""": """none""",
				}
				__magic_name__					,   __magic_name__    					=	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      :			str      ,							UpperCamelCase__      :			Union[str, Any]=None   )      ->     Any:







				"""simple docstring"""






				if saveas is None:
						__magic_name__    					=	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      :			List[str]      ,							UpperCamelCase__      :			List[Any]      ,							UpperCamelCase__      :			Dict      ,							UpperCamelCase__      :			List[str]      ,							UpperCamelCase__      :			Dict   )      ->     Union[str, Any]:







				"""simple docstring"""






				__magic_name__    					=	[self.idaobj[i] for i in classes]
				__magic_name__    					=	[self.idaattr[i] for i in attr_classes]
				__magic_name__    					=	[
				    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      :			Union[str, Any]      ,							UpperCamelCase__      :			Tuple      ,							UpperCamelCase__      :			Tuple   )      ->     Union[str, Any]:







				"""simple docstring"""






				__magic_name__    					=	[self.idaobj[i] for i in classes]
				if scores is not None:
						if labels is None:
								__magic_name__    					=	["""{:.0f}%""".format(s * 100   ) for s in scores]
						else:
								__magic_name__    					=	["""{} {:.0f}%""".format(UpperCamelCase__      ,							s * 100   ) for li, s in zip(UpperCamelCase__      ,							UpperCamelCase__   )]
				return labels
		def 							_lowercase							(			self      :			Any      ,							UpperCamelCase__      :			Optional[Any]=255   )      ->     Optional[Any]:







				"""simple docstring"""






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







				"""simple docstring"""






				if not self.pynb:
						__magic_name__					,   __magic_name__    					=	self.canvas.print_to_buffer()
						if (width, height) != (self.width, self.height):
								__magic_name__    					=	cva.resize(self.img      ,							(width, height)   )
						else:
								__magic_name__    					=	self.img
				else:
						__magic_name__    					=	io.BytesIO()  # works for cairo backend
						self.canvas.print_rgba(UpperCamelCase__   )
						__magic_name__					,   __magic_name__    					=	self.width, self.height
						__magic_name__    					=	buf.getvalue()
						__magic_name__    					=	self.img

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

				try:
						import numexpr as ne  # fuse them with numexpr

						__magic_name__    					=	ne.evaluate("""img * (1 - alpha / 255.0) + rgb * (alpha / 255.0)"""   )
				except ImportError:
						__magic_name__    					=	alpha.astype("""float32"""   ) / 255.0
						__magic_name__    					=	img * (1 - alpha) + rgb * alpha

				return visualized_image.astype("""uint8"""   )





		def 							_lowercase							(			self      :			Any      ,							UpperCamelCase__      :			Optional[int]      ,							UpperCamelCase__      :			Optional[int]   )      ->     str:







				"""simple docstring"""






				assert brightness_factor >= -1.0 and brightness_factor <= 1.0
				__magic_name__    					=	mplc.to_rgb(UpperCamelCase__   )
				__magic_name__    					=	colorsys.rgb_to_hls(*mplc.to_rgb(UpperCamelCase__   )   )
				__magic_name__    					=	polygon_color[1] + (brightness_factor * polygon_color[1])
				__magic_name__    					=	0.0 if modified_lightness < 0.0 else modified_lightness
				__magic_name__    					=	1.0 if modified_lightness > 1.0 else modified_lightness
				__magic_name__    					=	colorsys.hls_to_rgb(polygon_color[0]      ,							UpperCamelCase__      ,							polygon_color[2]   )
				return modified_color







__lowerCAmelCase      :					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)
)



import warnings
from typing import List, Optional, Union

from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding, PaddingStrategy, PreTokenizedInput, TextInput, TruncationStrategy
from ...utils import TensorType




class 	UpperCAmelCase_      (			_A					):


  '''simple docstring'''







  a__   	=		["""image_processor""", """tokenizer"""]
  a__   	=		"""LayoutLMv2ImageProcessor"""
  a__   	=		("""LayoutLMv2Tokenizer""", """LayoutLMv2TokenizerFast""")
  def __init__(			self      :			int      ,							UpperCamelCase__      :			Dict=None      ,							UpperCamelCase__      :			int=None      ,							**UpperCamelCase__      :			str   )      ->     Union[str, Any]:







    """simple docstring"""






    __magic_name__    					=	None
    if "feature_extractor" in kwargs:
      warnings.warn(
          """The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`"""
          """ instead."""      ,							UpperCamelCase__      ,							)
      __magic_name__    					=	kwargs.pop("""feature_extractor"""   )

    __magic_name__    					=	image_processor if image_processor is not None else feature_extractor
    if image_processor is None:
      raise ValueError("""You need to specify an `image_processor`."""   )
    if tokenizer is None:
      raise ValueError("""You need to specify a `tokenizer`."""   )

    super().__init__(UpperCamelCase__      ,							UpperCamelCase__   )
  def __call__(			self      :			List[str]      ,							UpperCamelCase__      :			Union[str, Any]      ,							UpperCamelCase__      :			Union[TextInput, PreTokenizedInput, List[TextInput], List[PreTokenizedInput]] = None      ,							UpperCamelCase__      :			Optional[Union[PreTokenizedInput, List[PreTokenizedInput]]] = None      ,							UpperCamelCase__      :			Union[List[List[int]], List[List[List[int]]]] = None      ,							UpperCamelCase__      :			Optional[Union[List[int], List[List[int]]]] = None      ,							UpperCamelCase__      :			bool = True      ,							UpperCamelCase__      :			Union[bool, str, PaddingStrategy] = False      ,							UpperCamelCase__      :			Union[bool, str, TruncationStrategy] = False      ,							UpperCamelCase__      :			Optional[int] = None      ,							UpperCamelCase__      :			int = 0      ,							UpperCamelCase__      :			Optional[int] = None      ,							UpperCamelCase__      :			Optional[bool] = None      ,							UpperCamelCase__      :			Optional[bool] = None      ,							UpperCamelCase__      :			bool = False      ,							UpperCamelCase__      :			bool = False      ,							UpperCamelCase__      :			bool = False      ,							UpperCamelCase__      :			bool = False      ,							UpperCamelCase__      :			bool = True      ,							UpperCamelCase__      :			Optional[Union[str, TensorType]] = None      ,							**UpperCamelCase__      :			Union[str, Any]      ,							)      ->     BatchEncoding:







    """simple docstring"""






    if self.image_processor.apply_ocr and (boxes is not None):
      raise ValueError(
          """You cannot provide bounding boxes if you initialized the image processor with apply_ocr set to True."""   )

    if self.image_processor.apply_ocr and (word_labels is not None):
      raise ValueError(
          """You cannot provide word labels if you initialized the image processor with apply_ocr set to True."""   )

    if return_overflowing_tokens is True and return_offsets_mapping is False:
      raise ValueError("""You cannot return overflowing tokens without returning the offsets mapping."""   )

    # first, apply the image processor
    __magic_name__    					=	self.image_processor(images=UpperCamelCase__      ,							return_tensors=UpperCamelCase__   )

    # second, apply the tokenizer
    if text is not None and self.image_processor.apply_ocr and text_pair is None:
      if isinstance(UpperCamelCase__      ,							UpperCamelCase__   ):
        __magic_name__    					=	[text]  # add batch dimension (as the image processor always adds a batch dimension)
      __magic_name__    					=	features["""words"""]

    __magic_name__    					=	self.tokenizer(
        text=text if text is not None else features["""words"""]      ,							text_pair=text_pair if text_pair is not None else None      ,							boxes=boxes if boxes is not None else features["""boxes"""]      ,							word_labels=UpperCamelCase__      ,							add_special_tokens=UpperCamelCase__      ,							padding=UpperCamelCase__      ,							truncation=UpperCamelCase__      ,							max_length=UpperCamelCase__      ,							stride=UpperCamelCase__      ,							pad_to_multiple_of=UpperCamelCase__      ,							return_token_type_ids=UpperCamelCase__      ,							return_attention_mask=UpperCamelCase__      ,							return_overflowing_tokens=UpperCamelCase__      ,							return_special_tokens_mask=UpperCamelCase__      ,							return_offsets_mapping=UpperCamelCase__      ,							return_length=UpperCamelCase__      ,							verbose=UpperCamelCase__      ,							return_tensors=UpperCamelCase__      ,							**UpperCamelCase__      ,							)

    # add pixel values
    __magic_name__    					=	features.pop("""pixel_values"""   )
    if return_overflowing_tokens is True:
      __magic_name__    					=	self.get_overflowing_images(UpperCamelCase__      ,							encoded_inputs["""overflow_to_sample_mapping"""]   )
    __magic_name__    					=	images

    return encoded_inputs
  def 							_lowercase							(			self      :			Optional[int]      ,							UpperCamelCase__      :			Tuple      ,							UpperCamelCase__      :			Optional[int]   )      ->     Any:







    """simple docstring"""






    __magic_name__    					=	[]
    for sample_idx in overflow_to_sample_mapping:
      images_with_overflow.append(images[sample_idx]   )

    if len(UpperCamelCase__   ) != len(UpperCamelCase__   ):
      raise ValueError(
          """Expected length of images to be the same as the length of `overflow_to_sample_mapping`, but got"""
          F''' {len(UpperCamelCase__   )} and {len(UpperCamelCase__   )}'''   )

    return images_with_overflow
  def 							_lowercase							(			self      :			Optional[int]      ,							*UpperCamelCase__      :			str      ,							**UpperCamelCase__      :			int   )      ->     Union[str, Any]:







    """simple docstring"""






    return self.tokenizer.batch_decode(*UpperCamelCase__      ,							**UpperCamelCase__   )
  def 							_lowercase							(			self      :			Dict      ,							*UpperCamelCase__      :			Optional[int]      ,							**UpperCamelCase__      :			str   )      ->     str:







    """simple docstring"""






    return self.tokenizer.decode(*UpperCamelCase__      ,							**UpperCamelCase__   )
  @property
  def 							_lowercase							(			self      :			Optional[Any]   )      ->     Optional[int]:







    """simple docstring"""






    return ["input_ids", "bbox", "token_type_ids", "attention_mask", "image"]
  @property
  def 							_lowercase							(			self      :			Union[str, Any]   )      ->     Optional[int]:







    """simple docstring"""






    warnings.warn(
        """`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead."""      ,							UpperCamelCase__      ,							)
    return self.image_processor_class





  @property
  def 							_lowercase							(			self      :			Union[str, Any]   )      ->     Dict:







    """simple docstring"""






    warnings.warn(
        """`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead."""      ,							UpperCamelCase__      ,							)
    return self.image_processor

