




'''simple docstring'''

import argparse
import gc
import os
import threading

import evaluate
import psutil
import torch
from datasets import load_dataset
from torch.distributed.fsdp.fully_sharded_data_parallel import FullOptimStateDictConfig, FullStateDictConfig
from torch.utils.data import DataLoader
from transformers import (
    AutoModelForSequenceClassification,
    AutoTokenizer,
    get_linear_schedule_with_warmup,
    set_seed,
)

from accelerate import Accelerator, DistributedType, FullyShardedDataParallelPlugin


########################################################################
# This is a fully working simple example to use Accelerate
#
# 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)
#   - FSDP
#
# This example also demonstrates the checkpointing and sharding capabilities
#
# 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
#
########################################################################


a__         =     16
a__         =     32




def    snake_case__     (  a	)			->       Optional[int]:





			'''simple docstring'''






			return int(x / 2**20	)





class __magic_name__:






						def __enter__(   self						:						List[Any]       ):



									'''simple docstring'''






									gc.collect()
									torch.cuda.empty_cache()
									torch.cuda.reset_max_memory_allocated()  # reset the peak gauge to zero
									snake_case__          =						torch.cuda.memory_allocated()
									snake_case__          =						psutil.Process()

									snake_case__          =						self.cpu_mem_used()
									snake_case__          =						True
									snake_case__          =						threading.Thread(target=self.peak_monitor_func       )
									snake_case__          =						True
									peak_monitor_thread.start()
									return self






						def       __lowerCAmelCase(   self						:						Optional[int]       ):



									'''simple docstring'''






									return self.process.memory_info().rss






						def       __lowerCAmelCase(   self						:						Tuple       ):



									'''simple docstring'''






									snake_case__          =						-1

									while True:
												snake_case__          =						max(self.cpu_mem_used()					,   self.cpu_peak       )

												# can't sleep or will not catch the peak right (this comment is here on purpose)
												# time.sleep(0.001) # 1msec

												if not self.peak_monitoring:
															break







						def __exit__(   self						:						List[Any]					,   *__UpperCamelCase						:						Optional[int]       ):



									'''simple docstring'''






									snake_case__          =						False

									gc.collect()
									torch.cuda.empty_cache()
									snake_case__          =						torch.cuda.memory_allocated()
									snake_case__          =						torch.cuda.max_memory_allocated()
									snake_case__          =						bamb(self.end - self.begin       )
									snake_case__          =						bamb(self.peak - self.begin       )

									snake_case__          =						self.cpu_mem_used()
									snake_case__          =						bamb(self.cpu_end - self.cpu_begin       )
									snake_case__          =						bamb(self.cpu_peak - self.cpu_begin       )
									# print(f"delta used/peak {self.used:4d}/{self.peaked:4d}")


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

		a__         =     mocked_dataloaders  # noqa: F811




def    snake_case__     (  a       ,      a	)			->       Union[str, Any]:





			'''simple docstring'''






			if os.environ.get("""TESTING_MOCKED_DATALOADERS"""       ,      __lowercase	) == "1":
						snake_case__          =						2

			# New Code #
			# Pass the advanced FSDP settings not part of the accelerate config by creating fsdp_plugin
			snake_case__          =						FullyShardedDataParallelPlugin(
			    state_dict_config=FullStateDictConfig(offload_to_cpu=__lowercase       ,      ranka_only=__lowercase	)       ,      optim_state_dict_config=FullOptimStateDictConfig(offload_to_cpu=__lowercase       ,      ranka_only=__lowercase	)       ,      )

			# Initialize accelerator
			if args.with_tracking:
						snake_case__          =						Accelerator(
						    cpu=args.cpu       ,      mixed_precision=args.mixed_precision       ,      log_with="""wandb"""       ,      project_dir=args.logging_dir       ,      fsdp_plugin=__lowercase       ,      )
			else:
						snake_case__          =						Accelerator(fsdp_plugin=__lowercase	)
			accelerator.print(accelerator.distributed_type	)

			if hasattr(args.checkpointing_steps       ,      """isdigit"""	):
						if args.checkpointing_steps == "epoch":
									snake_case__          =						args.checkpointing_steps
						elif args.checkpointing_steps.isdigit():
									snake_case__          =						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__          =						None
			# Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
			snake_case__          =						config["""lr"""]
			snake_case__          =						int(config["""num_epochs"""]	)
			snake_case__          =						int(config["""seed"""]	)
			snake_case__          =						int(config["""batch_size"""]	)

			# We need to initialize the trackers we use, and also store our configuration
			if args.with_tracking:
						snake_case__          =						vars(__lowercase	)
						accelerator.init_trackers("""fsdp_glue_no_trainer"""       ,      __lowercase	)

			snake_case__          =						AutoTokenizer.from_pretrained(args.model_name_or_path	)
			snake_case__          =						load_dataset("""glue"""       ,      """mrpc"""	)
			snake_case__          =						evaluate.load("""glue"""       ,      """mrpc"""	)

			def tokenize_function(a	):
						# max_length=None => use the model max length (it's actually the default)
						snake_case__          =						tokenizer(examples["""sentence1"""]       ,      examples["""sentence2"""]       ,      truncation=__lowercase       ,      max_length=__lowercase	)
						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__          =						datasets.map(
						    __lowercase       ,      batched=__lowercase       ,      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__          =						tokenized_datasets.rename_column("""label"""       ,      """labels"""	)

			# If the batch size is too big we use gradient accumulation
			snake_case__          =						1
			if batch_size > MAX_GPU_BATCH_SIZE and accelerator.distributed_type != DistributedType.TPU:
						snake_case__          =						batch_size // MAX_GPU_BATCH_SIZE
						snake_case__          =						MAX_GPU_BATCH_SIZE

			def collate_fn(a	):
						# On TPU it's best to pad everything to the same length or training will be very slow.
						snake_case__          =						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__          =						16
						elif accelerator.mixed_precision != "no":
									snake_case__          =						8
						else:
									snake_case__          =						None

						return tokenizer.pad(
						    __lowercase       ,      padding="""longest"""       ,      max_length=__lowercase       ,      pad_to_multiple_of=__lowercase       ,      return_tensors="""pt"""       ,      )

			# Instantiate dataloaders.
			snake_case__          =						DataLoader(
			    tokenized_datasets["""train"""]       ,      shuffle=__lowercase       ,      collate_fn=__lowercase       ,      batch_size=__lowercase	)
			snake_case__          =						DataLoader(
			    tokenized_datasets["""validation"""]       ,      shuffle=__lowercase       ,      collate_fn=__lowercase       ,      batch_size=__lowercase	)

			set_seed(__lowercase	)

			# Instantiate the model (we build the model here so that the seed also control new weights initialization)
			snake_case__          =						AutoModelForSequenceClassification.from_pretrained(
			    args.model_name_or_path       ,      return_dict=__lowercase       ,      low_cpu_mem_usage=__lowercase	)

			# New Code #
			# For FSDP feature, it is highly recommended and efficient to prepare the model before creating optimizer
			snake_case__          =						accelerator.prepare(__lowercase	)
			accelerator.print(__lowercase	)

			# Instantiate optimizer
			# New Code #
			# For FSDP feature, at present it doesn't support multiple parameter groups,
			# so we need to create a single parameter group for the whole model
			snake_case__          =						torch.optim.AdamW(params=model.parameters()       ,      lr=__lowercase       ,      weight_decay=2e-4	)

			# Instantiate scheduler
			snake_case__          =						get_linear_schedule_with_warmup(
			    optimizer=__lowercase       ,      num_warmup_steps=10       ,      num_training_steps=(len(__lowercase	) * num_epochs) // gradient_accumulation_steps       ,      )

			# New Code #
			# For FSDP feature, prepare everything except the model as we have already prepared the model
			# before creating the optimizer
			# 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__          =						accelerator.prepare(
			    __lowercase       ,      __lowercase       ,      __lowercase       ,      __lowercase	)

			snake_case__          =						0

			# Potentially load in the weights and states from a previous save
			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__          =						os.path.basename(args.resume_from_checkpoint	)
						else:
									# Get the most recent checkpoint
									snake_case__          =						[f.name for f in os.scandir(os.getcwd()	) if f.is_dir()]
									dirs.sort(key=os.path.getctime	)
									snake_case__          =						dirs[-1]  # Sorts folders by date modified, most recent checkpoint is the last
						# Extract `epoch_{i}` or `step_{i}`
						snake_case__          =						os.path.splitext(__lowercase	)[0]

						if "epoch" in training_difference:
									num_epochs -= int(training_difference.replace("""epoch_"""       ,      """"""	)	)
									snake_case__          =						None
						else:
									snake_case__          =						int(training_difference.replace("""step_"""       ,      """"""	)	)
									num_epochs -= resume_step // len(__lowercase	)
									# If resuming by step, we also need to know exactly how far into the DataLoader we went
									snake_case__          =						(num_epochs * len(__lowercase	)) - resume_step

    # Now we train the model
			for epoch in range(__lowercase	):
						# New Code #
						# context manager to track the peak memory usage during the training epoch
						with TorchTracemalloc() as tracemalloc:
									model.train()
									if args.with_tracking:
												snake_case__          =						0
									for step, batch in enumerate(__lowercase	):
												# We need to skip steps until we reach the resumed step
												if args.resume_from_checkpoint and epoch == 0:
															if resume_step is not None and step < resume_step:
																		pass
                # We could avoid this line since we set the accelerator with `device_placement=True`.
												batch.to(accelerator.device	)
												snake_case__          =						model(**__lowercase	)
												snake_case__          =						outputs.loss
												snake_case__          =						loss / gradient_accumulation_steps
												# We keep track of the loss at each epoch
												if args.with_tracking:
															total_loss += loss.detach().float()
												accelerator.backward(__lowercase	)
												if step % gradient_accumulation_steps == 0:
															optimizer.step()
															lr_scheduler.step()
															optimizer.zero_grad()
															# accelerator.print(lr_scheduler.get_lr())

												overall_step += 1

												if isinstance(__lowercase       ,      __lowercase	):
															snake_case__          =						F"""step_{overall_step}"""
															if overall_step % checkpointing_steps == 0:
																		if args.output_dir is not None:
																					snake_case__          =						os.path.join(args.output_dir       ,      __lowercase	)
																		accelerator.save_state(__lowercase	)
        # New Code #
        # Printing the GPU memory usage details such as allocated memory, peak memory, and total memory usage
						accelerator.print("""Memory before entering the train : {}""".format(bamb(tracemalloc.begin	)	)	)
						accelerator.print("""Memory consumed at the end of the train (end-begin): {}""".format(tracemalloc.used	)	)
						accelerator.print("""Peak Memory consumed during the train (max-begin): {}""".format(tracemalloc.peaked	)	)
						accelerator.print(
						    """Total Peak Memory consumed during the train (max): {}""".format(
						        tracemalloc.peaked + bamb(tracemalloc.begin	)	)	)
						# Logging the peak memory usage of the GPU to the tracker
						if args.with_tracking:
									accelerator.log(
									    {
									        """train_total_peak_memory""": tracemalloc.peaked + bamb(tracemalloc.begin	),
									    }       ,      step=__lowercase       ,      )

						# New Code #
						# context manager to track the peak memory usage during the evaluation
						with TorchTracemalloc() as tracemalloc:
									model.eval()
									for step, batch in enumerate(__lowercase	):
												# We could avoid this line since we set the accelerator with `device_placement=True`.
												batch.to(accelerator.device	)
												with torch.no_grad():
															snake_case__          =						model(**__lowercase	)
												snake_case__          =						outputs.logits.argmax(dim=-1	)
												snake_case__    ,			snake_case__          =						accelerator.gather_for_metrics((predictions, batch["""labels"""])	)
												metric.add_batch(
												    predictions=__lowercase       ,      references=__lowercase       ,      )

									snake_case__          =						metric.compute()
									# Use accelerator.print to print only on the main process.
									accelerator.print(F"""epoch {epoch}:"""       ,      __lowercase	)
									if args.with_tracking:
												accelerator.log(
												    {
												        """accuracy""": eval_metric["""accuracy"""],
												        """f1""": eval_metric["""f1"""],
												        """train_loss""": total_loss.item() / len(__lowercase	),
												    }       ,      step=__lowercase       ,      )

									if checkpointing_steps == "epoch":
												snake_case__          =						F"""epoch_{epoch}"""
												if args.output_dir is not None:
															snake_case__          =						os.path.join(args.output_dir       ,      __lowercase	)
												accelerator.save_state(__lowercase	)
        # New Code #
        # Printing the GPU memory usage details such as allocated memory, peak memory, and total memory usage
						accelerator.print("""Memory before entering the eval : {}""".format(bamb(tracemalloc.begin	)	)	)
						accelerator.print("""Memory consumed at the end of the eval (end-begin): {}""".format(tracemalloc.used	)	)
						accelerator.print("""Peak Memory consumed during the eval (max-begin): {}""".format(tracemalloc.peaked	)	)
						accelerator.print(
						    """Total Peak Memory consumed during the eval (max): {}""".format(tracemalloc.peaked + bamb(tracemalloc.begin	)	)	)
						# Logging the peak memory usage of the GPU to the tracker
						if args.with_tracking:
									accelerator.log(
									    {
									        """eval_total_peak_memory""": tracemalloc.peaked + bamb(tracemalloc.begin	),
									    }       ,      step=__lowercase       ,      )

			if args.with_tracking:
						accelerator.end_training()




def    snake_case__     (  )			->       Union[str, Any]:





			'''simple docstring'''






			snake_case__          =						argparse.ArgumentParser(description="""Simple example of training script."""	)
			parser.add_argument(
			    """--mixed_precision"""       ,      type=__lowercase       ,      default=__lowercase       ,      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=__lowercase       ,      default=__lowercase       ,      help="""Whether the various states should be saved at the end of every n steps, or 'epoch' for each epoch."""       ,      )
			parser.add_argument(
			    """--resume_from_checkpoint"""       ,      type=__lowercase       ,      default=__lowercase       ,      help="""If the training should continue from a checkpoint folder."""       ,      )
			parser.add_argument(
			    """--with_tracking"""       ,      action="""store_true"""       ,      help="""Whether to load in all available experiment trackers from the environment and use them for logging."""       ,      )
			parser.add_argument(
			    """--output_dir"""       ,      type=__lowercase       ,      default="""."""       ,      help="""Optional save directory where all checkpoint folders will be stored. Default is the current working directory."""       ,      )
			parser.add_argument(
			    """--logging_dir"""       ,      type=__lowercase       ,      default="""logs"""       ,      help="""Location on where to store experiment tracking logs`"""       ,      )
			parser.add_argument(
			    """--model_name_or_path"""       ,      type=__lowercase       ,      help="""Path to pretrained model or model identifier from huggingface.co/models."""       ,      required=__lowercase       ,      )
			snake_case__          =						parser.parse_args()
			snake_case__          =						{"""lr""": 2e-5, """num_epochs""": 3, """seed""": 42, """batch_size""": 16}
			training_function(__lowercase       ,      __lowercase	)


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


a_											=     10_00
class snake_case   :







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





						'''simple docstring'''







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

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

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







		def 			a_			(       self   :							List[Any]     , a__   :							Union[str, Any]     , a__   :							Tuple=None		)							->    Tuple:





						'''simple docstring'''







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





						'''simple docstring'''







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

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







		def 			a_			(       self   :							List[str]     , a__   :							Any     , a__   :							List[str]     , a__   :							Tuple		)							->    int:





						'''simple docstring'''







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

						_A       	=      (ya - ya) / np.sqrt(self.height * self.width		)
						_A       	=      self._change_color_brightness(a__     , brightness_factor=0.7		)
						_A       	=      np.clip((height_ratio - 0.0_2) / 0.0_8 + 1     , 1.2     , 2		)
						font_size *= 0.7_5 * self.font_size

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







		def 			a_			(       self   :							str     , a__   :							Optional[int]     , a__   :							Optional[int]     , a__   :							Any="g"     , a__   :							Optional[int]="left"     , )							->    Dict:





						'''simple docstring'''







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







		def 			a_			(       self   :							List[str]     , a__   :							str=None		)							->    Optional[int]:





						'''simple docstring'''







						if saveas is None:
										_A       	=      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   :							Union[str, Any]     , a__   :							Optional[Any]     , a__   :							List[Any]     , a__   :							Tuple     , a__   :							List[str]		)							->    Optional[int]:





						'''simple docstring'''







						_A       	=      [self.idaobj[i] for i in classes]
						_A       	=      [self.idaattr[i] for i in attr_classes]
						_A       	=      [
						    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   :							Any     , a__   :							Dict     , a__   :							Tuple		)							->    Dict:





						'''simple docstring'''







						_A       	=      [self.idaobj[i] for i in classes]
						if scores is not None:
										if labels is None:
														_A       	=      ["{:.0f}%".format(s * 1_00		) for s in scores]
										else:
														_A       	=      ["{} {:.0f}%".format(a__     , s * 1_00		) for li, s in zip(a__     , a__		)]
						return labels







		def 			a_			(       self   :							List[Any]     , a__   :							Optional[Any]=2_55		)							->    str:





						'''simple docstring'''







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







		def 			a_			(       self   :							int		)							->    Dict:





						'''simple docstring'''







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

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

						try:
										import numexpr as ne  # fuse them with numexpr

										_A       	=      ne.evaluate("img * (1 - alpha / 255.0) + rgb * (alpha / 255.0)"		)
						except ImportError:
										_A       	=      alpha.astype("float32"		) / 2_5_5.0
										_A       	=      img * (1 - alpha) + rgb * alpha

						return visualized_image.astype("uint8"		)







		def 			a_			(       self   :							Optional[int]     , a__   :							str     , a__   :							Union[str, Any]		)							->    str:





						'''simple docstring'''







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





a_											=     (
    np.array(
        [
            0.0_00,
            0.4_47,
            0.7_41,
            0.8_50,
            0.3_25,
            0.0_98,
            0.9_29,
            0.6_94,
            0.1_25,
            0.4_94,
            0.1_84,
            0.5_56,
            0.4_66,
            0.6_74,
            0.1_88,
            0.3_01,
            0.7_45,
            0.9_33,
            0.6_35,
            0.0_78,
            0.1_84,
            0.3_00,
            0.3_00,
            0.3_00,
            0.6_00,
            0.6_00,
            0.6_00,
            1.0_00,
            0.0_00,
            0.0_00,
            1.0_00,
            0.5_00,
            0.0_00,
            0.7_49,
            0.7_49,
            0.0_00,
            0.0_00,
            1.0_00,
            0.0_00,
            0.0_00,
            0.0_00,
            1.0_00,
            0.6_67,
            0.0_00,
            1.0_00,
            0.3_33,
            0.3_33,
            0.0_00,
            0.3_33,
            0.6_67,
            0.0_00,
            0.3_33,
            1.0_00,
            0.0_00,
            0.6_67,
            0.3_33,
            0.0_00,
            0.6_67,
            0.6_67,
            0.0_00,
            0.6_67,
            1.0_00,
            0.0_00,
            1.0_00,
            0.3_33,
            0.0_00,
            1.0_00,
            0.6_67,
            0.0_00,
            1.0_00,
            1.0_00,
            0.0_00,
            0.0_00,
            0.3_33,
            0.5_00,
            0.0_00,
            0.6_67,
            0.5_00,
            0.0_00,
            1.0_00,
            0.5_00,
            0.3_33,
            0.0_00,
            0.5_00,
            0.3_33,
            0.3_33,
            0.5_00,
            0.3_33,
            0.6_67,
            0.5_00,
            0.3_33,
            1.0_00,
            0.5_00,
            0.6_67,
            0.0_00,
            0.5_00,
            0.6_67,
            0.3_33,
            0.5_00,
            0.6_67,
            0.6_67,
            0.5_00,
            0.6_67,
            1.0_00,
            0.5_00,
            1.0_00,
            0.0_00,
            0.5_00,
            1.0_00,
            0.3_33,
            0.5_00,
            1.0_00,
            0.6_67,
            0.5_00,
            1.0_00,
            1.0_00,
            0.5_00,
            0.0_00,
            0.3_33,
            1.0_00,
            0.0_00,
            0.6_67,
            1.0_00,
            0.0_00,
            1.0_00,
            1.0_00,
            0.3_33,
            0.0_00,
            1.0_00,
            0.3_33,
            0.3_33,
            1.0_00,
            0.3_33,
            0.6_67,
            1.0_00,
            0.3_33,
            1.0_00,
            1.0_00,
            0.6_67,
            0.0_00,
            1.0_00,
            0.6_67,
            0.3_33,
            1.0_00,
            0.6_67,
            0.6_67,
            1.0_00,
            0.6_67,
            1.0_00,
            1.0_00,
            1.0_00,
            0.0_00,
            1.0_00,
            1.0_00,
            0.3_33,
            1.0_00,
            1.0_00,
            0.6_67,
            1.0_00,
            0.3_33,
            0.0_00,
            0.0_00,
            0.5_00,
            0.0_00,
            0.0_00,
            0.6_67,
            0.0_00,
            0.0_00,
            0.8_33,
            0.0_00,
            0.0_00,
            1.0_00,
            0.0_00,
            0.0_00,
            0.0_00,
            0.1_67,
            0.0_00,
            0.0_00,
            0.3_33,
            0.0_00,
            0.0_00,
            0.5_00,
            0.0_00,
            0.0_00,
            0.6_67,
            0.0_00,
            0.0_00,
            0.8_33,
            0.0_00,
            0.0_00,
            1.0_00,
            0.0_00,
            0.0_00,
            0.0_00,
            0.1_67,
            0.0_00,
            0.0_00,
            0.3_33,
            0.0_00,
            0.0_00,
            0.5_00,
            0.0_00,
            0.0_00,
            0.6_67,
            0.0_00,
            0.0_00,
            0.8_33,
            0.0_00,
            0.0_00,
            1.0_00,
            0.0_00,
            0.0_00,
            0.0_00,
            0.1_43,
            0.1_43,
            0.1_43,
            0.8_57,
            0.8_57,
            0.8_57,
            1.0_00,
            1.0_00,
            1.0_00,
        ]
    )
    .astype(np.floataa)
    .reshape(-1, 3)
)