



"""simple docstring"""






import argparse
import json
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
from accelerate.utils.deepspeed import DummyOptim, DummyScheduler


_lowercase				:				Any   		=		1_6
_lowercase				:				Union[str, Any]   		=		3_2







def   snake_case__     (	__lowerCamelCase       :					Optional[Any]							,	__lowerCamelCase       :					str = 16							,	__lowerCamelCase       :					List[Any] = "bert-base-cased"			):
			"""simple docstring"""
			lowerCamelCase__	:	Tuple				=AutoTokenizer.from_pretrained(__snake_case			)
			lowerCamelCase__	:	Optional[Any]				=load_dataset('''glue'''							,	'''mrpc'''			)

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

			# Apply the method we just defined to all the examples in all the splits of the dataset
			lowerCamelCase__	:	Optional[int]				=datasets.map(
			    __snake_case							,	batched=__snake_case							,	remove_columns=['''idx''', '''sentence1''', '''sentence2''']							,	load_from_cache_file=__snake_case			)

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

			def collate_fn(__lowerCamelCase       :					Any			):
						# On TPU it's best to pad everything to the same length or training will be very slow.
						if accelerator.distributed_type == DistributedType.TPU:
									return tokenizer.pad(__snake_case							,	padding='''max_length'''							,	max_length=128							,	return_tensors='''pt'''			)
						return tokenizer.pad(__snake_case							,	padding='''longest'''							,	return_tensors='''pt'''			)

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

			return train_dataloader, eval_dataloader







def   snake_case__     (	__lowerCamelCase       :					List[str]							,	__lowerCamelCase       :					Any			):
			"""simple docstring"""
			lowerCamelCase__	:	Tuple				=Accelerator()

			# Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
			lowerCamelCase__	:	str				=config['''lr''']
			lowerCamelCase__	:	Dict				=int(config['''num_epochs''']			)
			lowerCamelCase__	:	Optional[int]				=int(config['''seed''']			)
			lowerCamelCase__	:	Dict				=int(config['''batch_size''']			)
			lowerCamelCase__	:	Optional[int]				=args.model_name_or_path

			set_seed(__snake_case			)
			lowerCamelCase__			,     lowerCamelCase__	:	List[str]				=get_dataloaders(__snake_case							,	__snake_case							,	__snake_case			)

			# Instantiate the model (we build the model here so that the seed also control new weights initialization)
			lowerCamelCase__	:	List[str]				=AutoModelForSequenceClassification.from_pretrained(__snake_case							,	return_dict=__snake_case			)

			# Instantiate optimizer
			lowerCamelCase__	:	List[str]				=(
			    AdamW
			    if accelerator.state.deepspeed_plugin is None
			    or '''optimizer''' not in accelerator.state.deepspeed_plugin.deepspeed_config
			    else DummyOptim
			)
			lowerCamelCase__	:	Any				=optimizer_cls(params=model.parameters()							,	lr=__snake_case			)

			if accelerator.state.deepspeed_plugin is not None:
						lowerCamelCase__	:	List[str]				=accelerator.state.deepspeed_plugin.deepspeed_config[
						    '''gradient_accumulation_steps'''
						]
			else:
						lowerCamelCase__	:	Union[str, Any]				=1
			lowerCamelCase__	:	Union[str, Any]				=(len(__snake_case			) * num_epochs) // gradient_accumulation_steps

			# Instantiate scheduler
			if (
			    accelerator.state.deepspeed_plugin is None
			    or "scheduler" not in accelerator.state.deepspeed_plugin.deepspeed_config
			):
						lowerCamelCase__	:	Any				=get_linear_schedule_with_warmup(
						    optimizer=__snake_case							,	num_warmup_steps=0							,	num_training_steps=__snake_case							,	)
			else:
						lowerCamelCase__	:	Dict				=DummyScheduler(__snake_case							,	total_num_steps=__snake_case							,	warmup_num_steps=0			)

			# 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.
			lowerCamelCase__			,     lowerCamelCase__			,     lowerCamelCase__			,     lowerCamelCase__			,     lowerCamelCase__	:	List[str]				=accelerator.prepare(
			    __snake_case							,	__snake_case							,	__snake_case							,	__snake_case							,	__snake_case			)

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

			# Now we train the model
			lowerCamelCase__	:	Union[str, Any]				=evaluate.load('''glue'''							,	'''mrpc'''			)
			lowerCamelCase__	:	Optional[int]				=0
			lowerCamelCase__	:	Union[str, Any]				={}
			for epoch in range(__snake_case							,	__snake_case			):
						model.train()
						for step, batch in enumerate(__snake_case			):
									lowerCamelCase__	:	Tuple				=model(**__snake_case			)
									lowerCamelCase__	:	Union[str, Any]				=outputs.loss
									lowerCamelCase__	:	Any				=loss / gradient_accumulation_steps
									accelerator.backward(__snake_case			)
									if step % gradient_accumulation_steps == 0:
												optimizer.step()
												lr_scheduler.step()
												optimizer.zero_grad()

									overall_step += 1

						model.eval()
						lowerCamelCase__	:	Tuple				=0
						for step, batch in enumerate(__snake_case			):
									# We could avoid this line since we set the accelerator with `device_placement=True`.
									batch.to(accelerator.device			)
									with torch.no_grad():
												lowerCamelCase__	:	int				=model(**__snake_case			)
									lowerCamelCase__	:	List[str]				=outputs.logits.argmax(dim=-1			)
									# It is slightly faster to call this once, than multiple times
									lowerCamelCase__			,     lowerCamelCase__	:	List[Any]				=accelerator.gather(
									    (predictions, batch['''labels'''])			)  # If we are in a multiprocess environment, the last batch has duplicates
									if accelerator.use_distributed:
												if step == len(__snake_case			) - 1:
															lowerCamelCase__	:	List[Any]				=predictions[: len(eval_dataloader.dataset			) - samples_seen]
															lowerCamelCase__	:	Any				=references[: len(eval_dataloader.dataset			) - samples_seen]
												else:
															samples_seen += references.shape[0]
									metric.add_batch(
									    predictions=__snake_case							,	references=__snake_case							,	)

						lowerCamelCase__	:	Union[str, Any]				=metric.compute()
						# Use accelerator.print to print only on the main process.
						accelerator.print(f'''epoch {epoch}:'''							,	__snake_case			)
						lowerCamelCase__	:	Tuple				=eval_metric['''accuracy''']

						if best_performance < eval_metric["accuracy"]:
									lowerCamelCase__	:	int				=eval_metric['''accuracy''']

			if args.performance_lower_bound is not None:
						assert (
						    args.performance_lower_bound <= best_performance
						), f'''Best performance metric {best_performance} is lower than the lower bound {args.performance_lower_bound}'''

			accelerator.wait_for_everyone()
			if accelerator.is_main_process:
						with open(os.path.join(args.output_dir							,	'''all_results.json'''			)							,	'''w'''			) as f:
									json.dump(__snake_case							,	__snake_case			)







def   snake_case__     (	):
			"""simple docstring"""
			lowerCamelCase__	:	Any				=argparse.ArgumentParser(description='''Simple example of training script tracking peak GPU memory usage.'''			)
			parser.add_argument(
			    '''--model_name_or_path'''							,	type=__snake_case							,	default='''bert-base-cased'''							,	help='''Path to pretrained model or model identifier from huggingface.co/models.'''							,	required=__snake_case							,	)
			parser.add_argument(
			    '''--output_dir'''							,	type=__snake_case							,	default='''.'''							,	help='''Optional save directory where all checkpoint folders will be stored. Default is the current working directory.'''							,	)
			parser.add_argument(
			    '''--performance_lower_bound'''							,	type=__snake_case							,	default=__snake_case							,	help='''Optional lower bound for the performance metric. If set, the training will throw error when the performance metric drops below this value.'''							,	)
			parser.add_argument(
			    '''--num_epochs'''							,	type=__snake_case							,	default=3							,	help='''Number of train epochs.'''							,	)
			lowerCamelCase__	:	List[str]				=parser.parse_args()
			lowerCamelCase__	:	Dict				={'''lr''': 2e-5, '''num_epochs''': args.num_epochs, '''seed''': 42, '''batch_size''': 16}
			training_function(__snake_case							,	__snake_case			)


if __name__ == "__main__":
		main()









"""simple docstring"""






import colorsys

from PIL import Image  # type: ignore






def   snake_case__     (	__lowerCamelCase       :					float							,	__lowerCamelCase       :					float							,	__lowerCamelCase       :					int			):
			"""simple docstring"""
			lowerCamelCase__	:	Optional[Any]				=x
			lowerCamelCase__	:	Any				=y
			for step in range(__lowerCamelCase			):  # noqa: B007
						lowerCamelCase__	:	List[Any]				=a * a - b * b + x
						lowerCamelCase__	:	Optional[int]				=2 * a * b + y
						lowerCamelCase__	:	Union[str, Any]				=a_new

						# divergence happens for all complex number with an absolute value
						# greater than 4
						if a * a + b * b > 4:
									break
			return step / (max_step - 1)






def   snake_case__     (	__lowerCamelCase       :					float			):
			"""simple docstring"""
			if distance == 1:
						return (0, 0, 0)
			else:
						return (255, 255, 255)






def   snake_case__     (	__lowerCamelCase       :					float			):
			"""simple docstring"""
			if distance == 1:
						return (0, 0, 0)
			else:
						return tuple(round(i * 255			) for i in colorsys.hsv_to_rgb(__lowerCamelCase							,	1							,	1			)			)






def   snake_case__     (	__lowerCamelCase       :					int = 800							,	__lowerCamelCase       :					int = 600							,	__lowerCamelCase       :					float = -0.6							,	__lowerCamelCase       :					float = 0							,	__lowerCamelCase       :					float = 3.2							,	__lowerCamelCase       :					int = 50							,	__lowerCamelCase       :					bool = True							,	):
			"""simple docstring"""
			lowerCamelCase__	:	Optional[Any]				=Image.new('''RGB'''							,	(image_width, image_height)			)
			lowerCamelCase__	:	Optional[int]				=img.load()

			# loop through the image-coordinates
			for image_x in range(__lowerCamelCase			):
						for image_y in range(__lowerCamelCase			):
									# determine the figure-coordinates based on the image-coordinates
									lowerCamelCase__	:	Optional[Any]				=figure_width / image_width * image_height
									lowerCamelCase__	:	Dict				=figure_center_x + (image_x / image_width - 0.5) * figure_width
									lowerCamelCase__	:	Optional[int]				=figure_center_y + (image_y / image_height - 0.5) * figure_height

									lowerCamelCase__	:	Any				=get_distance(__lowerCamelCase							,	__lowerCamelCase							,	__lowerCamelCase			)

									# color the corresponding pixel based on the selected coloring-function
									if use_distance_color_coding:
												lowerCamelCase__	:	int				=get_color_coded_rgb(__lowerCamelCase			)
									else:
												lowerCamelCase__	:	Optional[int]				=get_black_and_white_rgb(__lowerCamelCase			)

			return img


if __name__ == "__main__":
		import doctest

		doctest.testmod()

		# colored version, full figure
		_lowercase				:				Optional[Any]   		=		get_image()

		# uncomment for colored version, different section, zoomed in
		# img = get_image(figure_center_x = -0.6, figure_center_y = -0.4,
		# figure_width = 0.8)

		# uncomment for black and white version, full figure
		# img = get_image(use_distance_color_coding = False)

		# uncomment to save the image
		# img.save("mandelbrot.png")

		img.show()




