
'''simple docstring'''





import argparse
import os

import evaluate
import torch
from datasets import load_dataset
from torch.optim import AdamW
from torch.utils.data import DataLoader
from transformers import AutoModelForSequenceClassification, AutoTokenizer, get_linear_schedule_with_warmup, set_seed

from accelerate import Accelerator, DistributedType


########################################################################
# This is a fully working simple example to use Accelerate,
# specifically showcasing the checkpointing capability,
# and builds off the `nlp_example.py` script.
#
# This example trains a Bert base model on GLUE MRPC
# in any of the following settings (with the same script):
#   - single CPU or single GPU
#   - multi GPUS (using PyTorch distributed mode)
#   - (multi) TPUs
#   - fp16 (mixed-precision) or fp32 (normal precision)
#
# To help focus on the differences in the code, building `DataLoaders`
# was refactored into its own function.
# New additions from the base script can be found quickly by
# looking for the # New Code # tags
#
# To run it in each of these various modes, follow the instructions
# in the readme for examples:
# https://github.com/huggingface/accelerate/tree/main/examples
#
########################################################################

UpperCAmelCase            = 16
UpperCAmelCase            = 32

def   A			(							A_				:       Accelerator     ,				A_				:       int = 16							):
						snake_case							:  Tuple			=     AutoTokenizer.from_pretrained('''bert-base-cased'''							)
						snake_case							:  List[str]			=     load_dataset('''glue'''     ,				'''mrpc'''							)

						def tokenize_function(A_				:       Dict							):
												# max_length=None => use the model max length (it's actually the default)
												snake_case							:  Optional[Any]			=     tokenizer(examples['''sentence1''']     ,				examples['''sentence2''']     ,				truncation=A_     ,				max_length=A_							)
												return outputs

						# Apply the method we just defined to all the examples in all the splits of the dataset
						# starting with the main process first:
						with accelerator.main_process_first():
												snake_case							:  Any			=     datasets.map(
												    A_     ,				batched=A_     ,				remove_columns=['''idx''', '''sentence1''', '''sentence2''']     ,				)

						# We also rename the 'label' column to 'labels' which is the expected name for labels by the models of the
						# transformers library
						snake_case							:  int			=     tokenized_datasets.rename_column('''label'''     ,				'''labels'''							)

						def collate_fn(A_				:       Optional[int]							):
												# On TPU it's best to pad everything to the same length or training will be very slow.
												snake_case							:  int			=     128 if accelerator.distributed_type == DistributedType.TPU else None
												# When using mixed precision we want round multiples of 8/16
												if accelerator.mixed_precision == "fp8":
																		snake_case							:  Tuple			=     16
												elif accelerator.mixed_precision != "no":
																		snake_case							:  Optional[int]			=     8
												else:
																		snake_case							:  Union[str, Any]			=     None

												return tokenizer.pad(
												    A_     ,				padding='''longest'''     ,				max_length=A_     ,				pad_to_multiple_of=A_     ,				return_tensors='''pt'''     ,				)

						# Instantiate dataloaders.
						snake_case							:  Any			=     DataLoader(
						    tokenized_datasets['''train''']     ,				shuffle=A_     ,				collate_fn=A_     ,				batch_size=A_							)
						snake_case							:  Tuple			=     DataLoader(
						    tokenized_datasets['''validation''']     ,				shuffle=A_     ,				collate_fn=A_     ,				batch_size=A_							)

						return train_dataloader, eval_dataloader


# For testing only
if os.environ.get("TESTING_MOCKED_DATALOADERS", None) == "1":
						from accelerate.test_utils.training import mocked_dataloaders

						UpperCAmelCase            = mocked_dataloaders  # noqa: F811

def   A			(							A_				:       Optional[Any]     ,				A_				:       int							):
						# For testing only
						if os.environ.get('''TESTING_MOCKED_DATALOADERS'''     ,				A_							) == "1":
												snake_case							:  List[str]			=     2
						# Initialize accelerator
						snake_case							:  Optional[int]			=     Accelerator(cpu=args.cpu     ,				mixed_precision=args.mixed_precision							)
						# Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
						snake_case							:  Dict			=     config['''lr''']
						snake_case							:  Dict			=     int(config['''num_epochs''']							)
						snake_case							:  str			=     int(config['''seed''']							)
						snake_case							:  Optional[Any]			=     int(config['''batch_size''']							)

						# New Code #
						# Parse out whether we are saving every epoch or after a certain number of batches
						if hasattr(args.checkpointing_steps     ,				'''isdigit'''							):
												if args.checkpointing_steps == "epoch":
																		snake_case							:  int			=     args.checkpointing_steps
												elif args.checkpointing_steps.isdigit():
																		snake_case							:  Tuple			=     int(args.checkpointing_steps							)
												else:
																		raise ValueError(
																		    F"""Argument `checkpointing_steps` must be either a number or `epoch`. `{args.checkpointing_steps}` passed."""							)
						else:
												snake_case							:  Optional[int]			=     None

						set_seed(A_							)

						snake_case,       snake_case							:  str			=     get_dataloaders(A_     ,				A_							)
						snake_case							:  Any			=     evaluate.load('''glue'''     ,				'''mrpc'''							)

						# If the batch size is too big we use gradient accumulation
						snake_case							:  Optional[Any]			=     1
						if batch_size > MAX_GPU_BATCH_SIZE and accelerator.distributed_type != DistributedType.TPU:
												snake_case							:  Dict			=     batch_size // MAX_GPU_BATCH_SIZE
												snake_case							:  List[str]			=     MAX_GPU_BATCH_SIZE

						# Instantiate the model (we build the model here so that the seed also control new weights initialization)
						snake_case							:  int			=     AutoModelForSequenceClassification.from_pretrained('''bert-base-cased'''     ,				return_dict=A_							)

						# We could avoid this line since the accelerator is set with `device_placement=True` (default value).
						# Note that if you are placing tensors on devices manually, this line absolutely needs to be before the optimizer
						# creation otherwise training will not work on TPU (`accelerate` will kindly throw an error to make us aware of that).
						snake_case							:  List[str]			=     model.to(accelerator.device							)

						# Instantiate optimizer
						snake_case							:  Tuple			=     AdamW(params=model.parameters()     ,				lr=A_							)

						# Instantiate scheduler
						snake_case							:  Any			=     get_linear_schedule_with_warmup(
						    optimizer=A_     ,				num_warmup_steps=100     ,				num_training_steps=(len(A_							) * num_epochs) // gradient_accumulation_steps     ,				)

						# Prepare everything
						# There is no specific order to remember, we just need to unpack the objects in the same order we gave them to the
						# prepare method.
						snake_case,       snake_case,       snake_case,       snake_case,       snake_case							:  str			=     accelerator.prepare(
						    A_     ,				A_     ,				A_     ,				A_     ,				A_							)

						# New Code #
						# We need to keep track of how many total steps we have iterated over
						snake_case							:  List[Any]			=     0
						# We also need to keep track of the stating epoch so files are named properly
						snake_case							:  List[str]			=     0

						# We need to load the checkpoint back in before training here with `load_state`
						# The total number of epochs is adjusted based on where the state is being loaded from,
						# as we assume continuation of the same training script
						if args.resume_from_checkpoint:
												if args.resume_from_checkpoint is not None or args.resume_from_checkpoint != "":
																		accelerator.print(F"""Resumed from checkpoint: {args.resume_from_checkpoint}"""							)
																		accelerator.load_state(args.resume_from_checkpoint							)
																		snake_case							:  str			=     os.path.basename(args.resume_from_checkpoint							)
												else:
																		# Get the most recent checkpoint
																		snake_case							:  Optional[int]			=     [f.name for f in os.scandir(os.getcwd()							) if f.is_dir()]
																		dirs.sort(key=os.path.getctime							)
																		snake_case							:  Optional[Any]			=     dirs[-1]  # Sorts folders by date modified, most recent checkpoint is the last
												# Extract `epoch_{i}` or `step_{i}`
												snake_case							:  List[Any]			=     os.path.splitext(A_							)[0]

												if "epoch" in training_difference:
																		snake_case							:  str			=     int(training_difference.replace('''epoch_'''     ,				''''''							)							) + 1
																		snake_case							:  Union[str, Any]			=     None
												else:
																		snake_case							:  int			=     int(training_difference.replace('''step_'''     ,				''''''							)							)
																		snake_case							:  Any			=     resume_step // len(A_							)
																		resume_step -= starting_epoch * len(A_							)

    # Now we train the model
						for epoch in range(A_     ,				A_							):
												model.train()
												# New Code #
												if args.resume_from_checkpoint and epoch == starting_epoch and resume_step is not None:
																		# We need to skip steps until we reach the resumed step
																		snake_case							:  Optional[int]			=     accelerator.skip_first_batches(A_     ,				A_							)
																		overall_step += resume_step
												else:
																		# After the first iteration though, we need to go back to the original dataloader
																		snake_case							:  Optional[int]			=     train_dataloader
												for step, batch in enumerate(A_							):
																		# We could avoid this line since we set the accelerator with `device_placement=True`.
																		batch.to(accelerator.device							)
																		snake_case							:  Union[str, Any]			=     model(**A_							)
																		snake_case							:  Dict			=     outputs.loss
																		snake_case							:  Dict			=     loss / gradient_accumulation_steps
																		accelerator.backward(A_							)
																		if step % gradient_accumulation_steps == 0:
																								optimizer.step()
																								lr_scheduler.step()
																								optimizer.zero_grad()
																		# New Code #
																		overall_step += 1

																		# New Code #
																		# We save the model, optimizer, lr_scheduler, and seed states by calling `save_state`
																		# These are saved to folders named `step_{overall_step}`
																		# Will contain files: "pytorch_model.bin", "optimizer.bin", "scheduler.bin", and "random_states.pkl"
																		# If mixed precision was used, will also save a "scalar.bin" file
																		if isinstance(A_     ,				A_							):
																								snake_case							:  Any			=     F"""step_{overall_step}"""
																								if overall_step % checkpointing_steps == 0:
																														if args.output_dir is not None:
																																				snake_case							:  List[str]			=     os.path.join(args.output_dir     ,				A_							)
																														accelerator.save_state(A_							)

												model.eval()
												for step, batch in enumerate(A_							):
																		# We could avoid this line since we set the accelerator with `device_placement=True` (the default).
																		batch.to(accelerator.device							)
																		with torch.no_grad():
																								snake_case							:  Union[str, Any]			=     model(**A_							)
																		snake_case							:  Union[str, Any]			=     outputs.logits.argmax(dim=-1							)
																		snake_case,       snake_case							:  Dict			=     accelerator.gather_for_metrics((predictions, batch['''labels'''])							)
																		metric.add_batch(
																		    predictions=A_     ,				references=A_     ,				)

												snake_case							:  Dict			=     metric.compute()
												# Use accelerator.print to print only on the main process.
												accelerator.print(F"""epoch {epoch}:"""     ,				A_							)

												# New Code #
												# We save the model, optimizer, lr_scheduler, and seed states by calling `save_state`
												# These are saved to folders named `epoch_{epoch}`
												# Will contain files: "pytorch_model.bin", "optimizer.bin", "scheduler.bin", and "random_states.pkl"
												# If mixed precision was used, will also save a "scalar.bin" file
												if checkpointing_steps == "epoch":
																		snake_case							:  Any			=     F"""epoch_{epoch}"""
																		if args.output_dir is not None:
																								snake_case							:  Dict			=     os.path.join(args.output_dir     ,				A_							)
																		accelerator.save_state(A_							)

def   A			(							):
						snake_case							:  str			=     argparse.ArgumentParser(description='''Simple example of training script.'''							)
						parser.add_argument(
						    '''--mixed_precision'''     ,				type=A_     ,				default=A_     ,				choices=['''no''', '''fp16''', '''bf16''', '''fp8''']     ,				help='''Whether to use mixed precision. Choose'''
						    '''between fp16 and bf16 (bfloat16). Bf16 requires PyTorch >= 1.10.'''
						    '''and an Nvidia Ampere GPU.'''     ,				)
						parser.add_argument('''--cpu'''     ,				action='''store_true'''     ,				help='''If passed, will train on the CPU.'''							)
						parser.add_argument(
						    '''--checkpointing_steps'''     ,				type=A_     ,				default=A_     ,				help='''Whether the various states should be saved at the end of every n steps, or \'epoch\' for each epoch.'''     ,				)
						parser.add_argument(
						    '''--output_dir'''     ,				type=A_     ,				default='''.'''     ,				help='''Optional save directory where all checkpoint folders will be stored. Default is the current working directory.'''     ,				)
						parser.add_argument(
						    '''--resume_from_checkpoint'''     ,				type=A_     ,				default=A_     ,				help='''If the training should continue from a checkpoint folder.'''     ,				)
						snake_case							:  Optional[int]			=     parser.parse_args()
						snake_case							:  Tuple			=     {'''lr''': 2e-5, '''num_epochs''': 3, '''seed''': 42, '''batch_size''': 16}
						training_function(A_     ,				A_							)


if __name__ == "__main__":
						main()



'''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            = 1_000

class     a :




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

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

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




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




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

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




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

										snake_case							:  List[str]			=     (ya - ya) / np.sqrt(self.height * self.width   )
										snake_case							:  Union[str, Any]			=     self._change_color_brightness(SCREAMING_SNAKE_CASE_,     brightness_factor=0.7   )
										snake_case							:  int			=     np.clip((height_ratio - 0.02) / 0.08 + 1,     1.2,     2   )
										font_size *= 0.75 * self.font_size

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




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




				def 						__snake_case				(				self						:					Any,     SCREAMING_SNAKE_CASE_						:					Any=None   ):
										if saveas is None:
																snake_case							:  List[str]			=     self.saveas
										if saveas.lower().endswith('''.jpg'''   ) or saveas.lower().endswith('''.png'''   ):
																cva.imwrite(
																    SCREAMING_SNAKE_CASE_,     self._get_buffer()[:, :, ::-1],     )
										else:
																self.fig.savefig(SCREAMING_SNAKE_CASE_   )




				def 						__snake_case				(				self						:					Tuple,     SCREAMING_SNAKE_CASE_						:					Optional[int],     SCREAMING_SNAKE_CASE_						:					str,     SCREAMING_SNAKE_CASE_						:					str,     SCREAMING_SNAKE_CASE_						:					int   ):
										snake_case							:  int			=     [self.idaobj[i] for i in classes]
										snake_case							:  str			=     [self.idaattr[i] for i in attr_classes]
										snake_case							:  Optional[Any]			=     [
										    F"""{label} {score:.2f} {attr} {attr_score:.2f}"""
										    for label, score, attr, attr_score in zip(SCREAMING_SNAKE_CASE_,     SCREAMING_SNAKE_CASE_,     SCREAMING_SNAKE_CASE_,     SCREAMING_SNAKE_CASE_   )
										]
										return labels




				def 						__snake_case				(				self						:					Any,     SCREAMING_SNAKE_CASE_						:					List[Any],     SCREAMING_SNAKE_CASE_						:					List[str]   ):
										snake_case							:  Union[str, Any]			=     [self.idaobj[i] for i in classes]
										if scores is not None:
																if labels is None:
																						snake_case							:  Optional[Any]			=     ['''{:.0f}%'''.format(s * 1_00   ) for s in scores]
																else:
																						snake_case							:  str			=     ['''{} {:.0f}%'''.format(SCREAMING_SNAKE_CASE_,     s * 1_00   ) for li, s in zip(SCREAMING_SNAKE_CASE_,     SCREAMING_SNAKE_CASE_   )]
										return labels




				def 						__snake_case				(				self						:					Union[str, Any],     SCREAMING_SNAKE_CASE_						:					int=2_55   ):
										snake_case							:  Union[str, Any]			=     np.random.randint(0,     len(_COLORS   )   )
										snake_case							:  Optional[int]			=     _COLORS[idx] * maximum
										if not self.rgb:
																snake_case							:  str			=     ret[::-1]
										return ret




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

										snake_case							:  Union[str, Any]			=     np.frombuffer(SCREAMING_SNAKE_CASE_,     dtype='''uint8'''   )
										snake_case							:  Union[str, Any]			=     buffer.reshape(SCREAMING_SNAKE_CASE_,     SCREAMING_SNAKE_CASE_,     4   )
										snake_case,       snake_case							:  Any			=     np.split(SCREAMING_SNAKE_CASE_,     [3],     axis=2   )

										try:
																import numexpr as ne  # fuse them with numexpr

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

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




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




UpperCAmelCase            = (
    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)
)

