
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


lowercase__						:Dict					    =       1000







class 							lowercase						:




							def __init__(							self  ,A__  ,A__=1.2  ,A__="g"  ,A__=0.5  ,A__="-"  ,A__="test_out.jpg"  ,A__=True  ,A__=False  ,A__=None  ,A__=None  ,A__=0.7  ,):
												if isinstance(A__  ,torch.Tensor):
																	lowercase		      =		img.numpy().astype('''np.uint8''')
												if isinstance(A__  ,A__):
																	lowercase		      =		img_tensorize(A__)
												assert isinstance(A__  ,np.ndarray)

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

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




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

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




							def 					A__ (							self  ,A__  ,A__  ,A__):
												lowercase      ,		lowercase      ,		lowercase      ,		lowercase		      =		box
												lowercase		      =		(xa, ya)
												lowercase		      =		(ya - ya) * (xa - xa)
												lowercase		      =		_SMALL_OBJ * self.scale
												if instance_area < small or ya - ya < 4_0 * self.scale:
																	if ya >= self.height - 5:
																						lowercase		      =		(xa, ya)
																	else:
																						lowercase		      =		(xa, ya)

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

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




							def 					A__ (							self  ,A__  ,A__  ,A__="g"  ,A__="left"  ,):
												lowercase		      =		0
												lowercase		      =		self.font_size
												lowercase		      =		np.maximum(list(mplc.to_rgb(A__))  ,0.2)
												lowercase		      =		max(0.8  ,np.max(A__))
												lowercase		      =		{
												    '''facecolor''': '''black''',
												    '''alpha''': self.alpha,
												    '''pad''': self.pad,
												    '''edgecolor''': '''none''',
												}
												lowercase      ,		lowercase		      =		position
												self.ax.text(
												    A__  ,A__  ,A__  ,size=font_size * self.scale  ,family='''sans-serif'''  ,bbox=A__  ,verticalalignment='''top'''  ,horizontalalignment=A__  ,color=A__  ,zorder=1_0  ,rotation=A__  ,)




							def 					A__ (							self  ,A__=None):
												if saveas is None:
																	lowercase		      =		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 					A__ (							self  ,A__  ,A__  ,A__  ,A__):
												lowercase		      =		[self.idaobj[i] for i in classes]
												lowercase		      =		[self.idaattr[i] for i in attr_classes]
												lowercase		      =		[
												    f'{label} {score:.2f} {attr} {attr_score:.2f}'
												    for label, score, attr, attr_score in zip(A__  ,A__  ,A__  ,A__)
												]
												return labels




							def 					A__ (							self  ,A__  ,A__):
												lowercase		      =		[self.idaobj[i] for i in classes]
												if scores is not None:
																	if labels is None:
																						lowercase		      =		['''{:.0f}%'''.format(s * 1_0_0) for s in scores]
																	else:
																						lowercase		      =		['''{} {:.0f}%'''.format(A__  ,s * 1_0_0) for li, s in zip(A__  ,A__)]
												return labels




							def 					A__ (							self  ,A__=2_5_5):
												lowercase		      =		np.random.randint(0  ,len(_COLORS))
												lowercase		      =		_COLORS[idx] * maximum
												if not self.rgb:
																	lowercase		      =		ret[::-1]
												return ret




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

												lowercase		      =		np.frombuffer(A__  ,dtype='''uint8''')
												lowercase		      =		buffer.reshape(A__  ,A__  ,4)
												lowercase      ,		lowercase		      =		np.split(A__  ,[3]  ,axis=2)

												try:
																	import numexpr as ne  # fuse them with numexpr

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

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





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


lowercase__						: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)
)






import os
import random
from pathlib import Path
from typing import List

import numpy as np
import torch
from torch.cuda.amp import GradScaler

from .utils import (
    MODEL_NAME,
    OPTIMIZER_NAME,
    RNG_STATE_NAME,
    SCALER_NAME,
    SCHEDULER_NAME,
    get_pretty_name,
    is_tpu_available,
    is_xpu_available,
    save,
)


if is_tpu_available(check_device=False):
							import torch_xla.core.xla_model as xm

from .logging import get_logger
from .state import PartialState


lowercase__						:Optional[Any]					    =       get_logger(__name__)



def 			UpperCamelCase       (				lowerCAmelCase__					,     lowerCAmelCase__					,     lowerCAmelCase__					,     lowerCAmelCase__					,     lowerCAmelCase__					,     lowerCAmelCase__ = None					,     ):
					'''simple docstring'''
					# Model states
					for i, state in enumerate(lowerCAmelCase__						):
										lowercase		      =		f'{MODEL_NAME}.bin' if i == 0 else f'{MODEL_NAME}_{i}.bin'
										lowercase		      =		os.path.join(lowerCAmelCase__					,     lowerCAmelCase__						)
										save(lowerCAmelCase__					,     lowerCAmelCase__						)
										logger.info(f'Model weights saved in {output_model_file}'						)
					# Optimizer states
					for i, opt in enumerate(lowerCAmelCase__						):
										lowercase		      =		opt.state_dict()
										lowercase		      =		f'{OPTIMIZER_NAME}.bin' if i == 0 else f'{OPTIMIZER_NAME}_{i}.bin'
										lowercase		      =		os.path.join(lowerCAmelCase__					,     lowerCAmelCase__						)
										save(lowerCAmelCase__					,     lowerCAmelCase__						)
										logger.info(f'Optimizer state saved in {output_optimizer_file}'						)
					# Scheduler states
					for i, scheduler in enumerate(lowerCAmelCase__						):
										lowercase		      =		scheduler.state_dict()
										lowercase		      =		f'{SCHEDULER_NAME}.bin' if i == 0 else f'{SCHEDULER_NAME}_{i}.bin'
										lowercase		      =		os.path.join(lowerCAmelCase__					,     lowerCAmelCase__						)
										save(lowerCAmelCase__					,     lowerCAmelCase__						)
										logger.info(f'Scheduler state saved in {output_scheduler_file}'						)
					# GradScaler state
					if scaler is not None:
										lowercase		      =		scaler.state_dict()
										lowercase		      =		os.path.join(lowerCAmelCase__					,     lowerCAmelCase__						)
										torch.save(lowerCAmelCase__					,     lowerCAmelCase__						)
										logger.info(f'Gradient scaler state saved in {output_scaler_file}'						)
					# Random number generator states
					lowercase		      =		{}
					lowercase		      =		f'{RNG_STATE_NAME}_{process_index}.pkl'
					lowercase		      =		random.getstate()
					lowercase		      =		np.random.get_state()
					lowercase		      =		torch.get_rng_state()
					if is_xpu_available():
										lowercase		      =		torch.xpu.get_rng_state_all()
					else:
										lowercase		      =		torch.cuda.get_rng_state_all()
					if is_tpu_available():
										lowercase		      =		xm.get_rng_state()
					lowercase		      =		os.path.join(lowerCAmelCase__					,     lowerCAmelCase__						)
					torch.save(lowerCAmelCase__					,     lowerCAmelCase__						)
					logger.info(f'Random states saved in {output_states_file}'						)
					return output_dir



def 			UpperCamelCase       (				lowerCAmelCase__					,     lowerCAmelCase__					,     lowerCAmelCase__					,     lowerCAmelCase__					,     lowerCAmelCase__					,     lowerCAmelCase__=None					,     lowerCAmelCase__=None					,     **lowerCAmelCase__					,     ):
					'''simple docstring'''
					if map_location not in [None, "cpu", "on_device"]:
										raise TypeError(
										    '''Unsupported optimizer map location passed, please choose one of `None`, `\'cpu\'`, or `\'on_device\'`'''						)
					if map_location is None:
										lowercase		      =		'''cpu'''
					elif map_location == "on_device":
										lowercase		      =		PartialState().device
					# Model states
					for i, model in enumerate(lowerCAmelCase__						):
										lowercase		      =		f'{MODEL_NAME}.bin' if i == 0 else f'{MODEL_NAME}_{i}.bin'
										lowercase		      =		os.path.join(lowerCAmelCase__					,     lowerCAmelCase__						)
										models[i].load_state_dict(torch.load(lowerCAmelCase__					,     map_location=lowerCAmelCase__						)					,     **lowerCAmelCase__						)
					logger.info('''All model weights loaded successfully'''						)

					# Optimizer states
					for i, opt in enumerate(lowerCAmelCase__						):
										lowercase		      =		f'{OPTIMIZER_NAME}.bin' if i == 0 else f'{OPTIMIZER_NAME}_{i}.bin'
										lowercase		      =		os.path.join(lowerCAmelCase__					,     lowerCAmelCase__						)
										lowercase		      =		torch.load(lowerCAmelCase__					,     map_location=lowerCAmelCase__						)
										optimizers[i].load_state_dict(lowerCAmelCase__						)
					logger.info('''All optimizer states loaded successfully'''						)

					# Scheduler states
					for i, scheduler in enumerate(lowerCAmelCase__						):
										lowercase		      =		f'{SCHEDULER_NAME}.bin' if i == 0 else f'{SCHEDULER_NAME}_{i}.bin'
										lowercase		      =		os.path.join(lowerCAmelCase__					,     lowerCAmelCase__						)
										scheduler.load_state_dict(torch.load(lowerCAmelCase__						)						)
					logger.info('''All scheduler states loaded successfully'''						)

					# GradScaler state
					if scaler is not None:
										lowercase		      =		os.path.join(lowerCAmelCase__					,     lowerCAmelCase__						)
										scaler.load_state_dict(torch.load(lowerCAmelCase__						)						)
										logger.info('''GradScaler state loaded successfully'''						)

					# Random states
					try:
										lowercase		      =		torch.load(os.path.join(lowerCAmelCase__					,     f'{RNG_STATE_NAME}_{process_index}.pkl'						)						)
										random.setstate(states['''random_state''']						)
										np.random.set_state(states['''numpy_random_seed''']						)
										torch.set_rng_state(states['''torch_manual_seed''']						)
										if is_xpu_available():
															torch.xpu.set_rng_state_all(states['''torch_xpu_manual_seed''']						)
										else:
															torch.cuda.set_rng_state_all(states['''torch_cuda_manual_seed''']						)
										if is_tpu_available():
															xm.set_rng_state(states['''xm_seed''']						)
										logger.info('''All random states loaded successfully'''						)
					except Exception:
										logger.info('''Could not load random states'''						)



def 			UpperCamelCase       (				lowerCAmelCase__					,     lowerCAmelCase__					,     lowerCAmelCase__ = 0						):
					'''simple docstring'''
					lowercase		      =		Path(lowerCAmelCase__						) / f'custom_checkpoint_{index}.pkl'
					logger.info(f'Saving the state of {get_pretty_name(lowerCAmelCase__						)} to {save_location}'						)
					torch.save(obj.state_dict()					,     lowerCAmelCase__						)



def 			UpperCamelCase       (				lowerCAmelCase__					,     lowerCAmelCase__					,     lowerCAmelCase__ = 0						):
					'''simple docstring'''
					lowercase		      =		f'{path}/custom_checkpoint_{index}.pkl'
					logger.info(f'Loading the state of {get_pretty_name(lowerCAmelCase__						)} from {load_location}'						)
					obj.load_state_dict(torch.load(lowerCAmelCase__					,     map_location='''cpu'''						)						)




