
'''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


UpperCAmelCase__   : Tuple					=		10_00






class    A     :





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


				"""simple docstring"""

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

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

				lowerCAmelCase__								=							saveas
				lowerCAmelCase__								=							rgb
				lowerCAmelCase__								=							pynb
				lowerCAmelCase__								=							img
				lowerCAmelCase__								=							edgecolor
				lowerCAmelCase__								=							0.5
				lowerCAmelCase__								=							linestyle
				lowerCAmelCase__								=							int(np.sqrt(min(__magic_name__      ,							__magic_name__							)							) * scale // 3							)
				lowerCAmelCase__								=							width
				lowerCAmelCase__								=							height
				lowerCAmelCase__								=							scale
				lowerCAmelCase__								=							fig
				lowerCAmelCase__								=							ax
				lowerCAmelCase__								=							pad
				lowerCAmelCase__								=							idaobj
				lowerCAmelCase__								=							idaattr
				lowerCAmelCase__								=							FigureCanvasAgg(__magic_name__							)





	def 							__SCREAMING_SNAKE_CASE  (					self			:       List[str]      ,							__magic_name__			:       Tuple      ,							__magic_name__			:       List[Any]=None							):


				"""simple docstring"""

				if color is None:
							lowerCAmelCase__								=							self.edgecolor
				((lowerCAmelCase__)	,(lowerCAmelCase__)	,(lowerCAmelCase__)	,(lowerCAmelCase__))								=							box
				lowerCAmelCase__								=							xa - xa
				lowerCAmelCase__								=							ya - ya
				self.ax.add_patch(
				    mpl.patches.Rectangle(
				        (xa, ya)      ,							__magic_name__      ,							__magic_name__      ,							fill=__magic_name__      ,							edgecolor=__magic_name__      ,							linewidth=self.font_size // 3      ,							alpha=self.alpha      ,							linestyle=self.linestyle      ,							)							)





	def 							__SCREAMING_SNAKE_CASE  (					self			:       Optional[int]      ,							__magic_name__			:       Any      ,							__magic_name__			:       int=None      ,							__magic_name__			:       Optional[int]=None      ,							__magic_name__			:       Union[str, Any]=None      ,							__magic_name__			:       Tuple=None							):


				"""simple docstring"""

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

				lowerCAmelCase__								=							[self._random_color(maximum=1							) for _ in range(len(__magic_name__							)							)]
				lowerCAmelCase__								=							[assigned_colors[idx] for idx in sorted_idxs]
				if obj_ids is not None:
							lowerCAmelCase__								=							self._create_text_labels_attr(__magic_name__      ,							__magic_name__      ,							__magic_name__      ,							__magic_name__							)
							for i in range(len(__magic_name__							)							):
										lowerCAmelCase__								=							assigned_colors[i]
										self.add_box(boxes[i]      ,							__magic_name__							)
										self.draw_labels(labels[i]      ,							boxes[i]      ,							__magic_name__							)





	def 							__SCREAMING_SNAKE_CASE  (					self			:       Optional[Any]      ,							__magic_name__			:       str      ,							__magic_name__			:       Union[str, Any]      ,							__magic_name__			:       Optional[Any]							):


				"""simple docstring"""

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

				lowerCAmelCase__								=							(ya - ya) / np.sqrt(self.height * self.width							)
				lowerCAmelCase__								=							self._change_color_brightness(__magic_name__      ,							brightness_factor=0.7							)
				lowerCAmelCase__								=							np.clip((height_ratio - 0.02) / 0.08 + 1      ,							1.2      ,							2							)
				font_size *= 0.75 * self.font_size

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





	def 							__SCREAMING_SNAKE_CASE  (					self			:       Optional[Any]      ,							__magic_name__			:       Optional[int]      ,							__magic_name__			:       Tuple      ,							__magic_name__			:       str="g"      ,							__magic_name__			:       Optional[int]="left"      ,							):


				"""simple docstring"""

				lowerCAmelCase__								=							0
				lowerCAmelCase__								=							self.font_size
				lowerCAmelCase__								=							np.maximum(list(mplc.to_rgb(__magic_name__							)							)      ,							0.2							)
				lowerCAmelCase__								=							max(0.8      ,							np.max(__magic_name__							)							)
				lowerCAmelCase__								=							{
				    "facecolor": "black",
				    "alpha": self.alpha,
				    "pad": self.pad,
				    "edgecolor": "none",
				}
				lowerCAmelCase__	,lowerCAmelCase__								=							position
				self.ax.text(
				    __magic_name__      ,							__magic_name__      ,							__magic_name__      ,							size=font_size * self.scale      ,							family="sans-serif"      ,							bbox=__magic_name__      ,							verticalalignment="top"      ,							horizontalalignment=__magic_name__      ,							color=__magic_name__      ,							zorder=10      ,							rotation=__magic_name__      ,							)





	def 							__SCREAMING_SNAKE_CASE  (					self			:       int      ,							__magic_name__			:       Tuple=None							):


				"""simple docstring"""

				if saveas is None:
							lowerCAmelCase__								=							self.saveas
				if saveas.lower().endswith(".jpg"							) or saveas.lower().endswith(".png"							):
							cva.imwrite(
							    __magic_name__      ,							self._get_buffer()[:, :, ::-1]      ,							)
				else:
							self.fig.savefig(__magic_name__							)





	def 							__SCREAMING_SNAKE_CASE  (					self			:       Any      ,							__magic_name__			:       Dict      ,							__magic_name__			:       Union[str, Any]      ,							__magic_name__			:       Optional[Any]      ,							__magic_name__			:       Any							):


				"""simple docstring"""

				lowerCAmelCase__								=							[self.idaobj[i] for i in classes]
				lowerCAmelCase__								=							[self.idaattr[i] for i in attr_classes]
				lowerCAmelCase__								=							[
				    f"""{label} {score:.2f} {attr} {attr_score:.2f}"""
				    for label, score, attr, attr_score in zip(__magic_name__      ,							__magic_name__      ,							__magic_name__      ,							__magic_name__							)
				]
				return labels





	def 							__SCREAMING_SNAKE_CASE  (					self			:       Any      ,							__magic_name__			:       Dict      ,							__magic_name__			:       Any							):


				"""simple docstring"""

				lowerCAmelCase__								=							[self.idaobj[i] for i in classes]
				if scores is not None:
							if labels is None:
										lowerCAmelCase__								=							["{:.0f}%".format(s * 100							) for s in scores]
							else:
										lowerCAmelCase__								=							["{} {:.0f}%".format(__magic_name__      ,							s * 100							) for li, s in zip(__magic_name__      ,							__magic_name__							)]
				return labels





	def 							__SCREAMING_SNAKE_CASE  (					self			:       Dict      ,							__magic_name__			:       Optional[Any]=255							):


				"""simple docstring"""

				lowerCAmelCase__								=							np.random.randint(0      ,							len(_COLORS							)							)
				lowerCAmelCase__								=							_COLORS[idx] * maximum
				if not self.rgb:
							lowerCAmelCase__								=							ret[::-1]
				return ret





	def 							__SCREAMING_SNAKE_CASE  (					self			:       Optional[Any]							):


				"""simple docstring"""

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

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

				try:
							import numexpr as ne  # fuse them with numexpr

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

				return visualized_image.astype("uint8"							)







	def 							__SCREAMING_SNAKE_CASE  (					self			:       Optional[int]      ,							__magic_name__			:       List[Any]      ,							__magic_name__			:       int							):


				"""simple docstring"""

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

UpperCAmelCase__   : Dict					=		(
    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)
)







'''simple docstring'''

import argparse

from transformers import TaConfig, TaForConditionalGeneration, load_tf_weights_in_ta
from transformers.utils import logging


logging.set_verbosity_info()







def   A     (      UpperCamelCase_					:     List[str]						,	UpperCamelCase_					:     Union[str, Any]						,	UpperCamelCase_					:     Optional[Any]      )					->       List[str]:







			'''simple docstring'''



			lowerCAmelCase__								=							TaConfig.from_json_file(UpperCamelCase_      )
			print(F"""Building PyTorch model from configuration: {config}"""      )
			lowerCAmelCase__								=							TaForConditionalGeneration(UpperCamelCase_      )

			# Load weights from tf checkpoint
			load_tf_weights_in_ta(UpperCamelCase_						,	UpperCamelCase_						,	UpperCamelCase_      )

			# Save pytorch-model
			print(F"""Save PyTorch model to {pytorch_dump_path}"""      )
			model.save_pretrained(UpperCamelCase_      )


if __name__ == "__main__":
		UpperCAmelCase__   : List[str]					=		argparse.ArgumentParser()
		# Required parameters
		parser.add_argument(
		    "--tf_checkpoint_path", default=None, type=str, required=True, help="Path to the TensorFlow checkpoint path."
		)
		parser.add_argument(
		    "--config_file",
		    default=None,
		    type=str,
		    required=True,
		    help=(
		        "The config json file corresponding to the pre-trained T5 model. \nThis specifies the model architecture."
		    ),
		)
		parser.add_argument(
		    "--pytorch_dump_path", default=None, type=str, required=True, help="Path to the output PyTorch model."
		)
		UpperCAmelCase__   : Tuple					=		parser.parse_args()
		convert_tf_checkpoint_to_pytorch(args.tf_checkpoint_path, args.config_file, args.pytorch_dump_path)





