





"""simple docstring"""




import argparse
import gc
import json
import os

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


a		:					Optional[int]	      =     16
a		:					Optional[Any]	      =     32






def  _SCREAMING_SNAKE_CASE						(      _lowercase		:       Tuple   )					->List[str]:






	'''simple docstring'''






	return int(x / 2**20   )


class 			__UpperCamelCase							:






							def __enter__(  self )      ->					Tuple:
								gc.collect()
								torch.cuda.empty_cache()
								torch.cuda.reset_max_memory_allocated()  # reset the peak gauge to zero
								a :						Union[str, Any]      =							torch.cuda.memory_allocated()
								return self






							def __exit__(  self ,   *lowerCAmelCase__ )      ->					Tuple:
								gc.collect()
								torch.cuda.empty_cache()
								a :						Optional[int]      =							torch.cuda.memory_allocated()
								a :						List[Any]      =							torch.cuda.max_memory_allocated()
								a :						str      =							bamb(self.end - self.begin )
								a :						Tuple      =							bamb(self.peak - self.begin )
								# print(f"delta used/peak {self.used:4d}/{self.peaked:4d}")






def  _SCREAMING_SNAKE_CASE						(      _lowercase		:       Accelerator       ,			_lowercase		:       int = 16       ,			_lowercase		:       str = "bert-base-cased"       ,			_lowercase		:       int = 320       ,			_lowercase		:       int = 160       ,			)					->Any:






	'''simple docstring'''






	a :						Optional[Any]      =							AutoTokenizer.from_pretrained(_lowercase   )
	a :						int      =							load_dataset(
	    "glue"       ,			"mrpc"       ,			split={"train": F"""train[:{n_train}]""", "validation": F"""validation[:{n_val}]"""}   )

	def tokenize_function(_lowercase		:       Optional[Any]   ):
		# max_length=None => use the model max length (it's actually the default)
		a :						int      =							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
	a :						Dict      =							datasets.map(
	    _lowercase       ,			batched=_lowercase       ,			remove_columns=["idx", "sentence1", "sentence2"]       ,			load_from_cache_file=_lowercase   )

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

	def collate_fn(_lowercase		:       str   ):
		# 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(_lowercase       ,			padding="max_length"       ,			max_length=128       ,			return_tensors="pt"   )
		return tokenizer.pad(_lowercase       ,			padding="longest"       ,			return_tensors="pt"   )

	# Instantiate dataloaders.
	a :						int      =							DataLoader(
	    tokenized_datasets["train"]       ,			shuffle=_lowercase       ,			collate_fn=_lowercase       ,			batch_size=_lowercase   )
	a :						int      =							DataLoader(
	    tokenized_datasets["validation"]       ,			shuffle=_lowercase       ,			collate_fn=_lowercase       ,			batch_size=_lowercase   )

	return train_dataloader, eval_dataloader






def  _SCREAMING_SNAKE_CASE						(      _lowercase		:       int       ,			_lowercase		:       List[Any]   )					->Optional[Any]:






	'''simple docstring'''






	a :						List[str]      =							Accelerator()

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

	set_seed(_lowercase   )
	a,			a :						List[Any]      =							get_dataloaders(_lowercase       ,			_lowercase       ,			_lowercase       ,			args.n_train       ,			args.n_val   )

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

	# Instantiate optimizer
	a :						int      =							(
	    AdamW
	    if accelerator.state.deepspeed_plugin is None
	    or "optimizer" not in accelerator.state.deepspeed_plugin.deepspeed_config
	    else DummyOptim
	)
	a :						Union[str, Any]      =							optimizer_cls(params=model.parameters()       ,			lr=_lowercase   )

	if accelerator.state.deepspeed_plugin is not None:
		a :						str      =							accelerator.state.deepspeed_plugin.deepspeed_config[
		    "gradient_accumulation_steps"
		]
	else:
		a :						Optional[Any]      =							1
	a :						List[str]      =							(len(_lowercase   ) * num_epochs) // gradient_accumulation_steps

	# Instantiate scheduler
	if (
	    accelerator.state.deepspeed_plugin is None
	    or "scheduler" not in accelerator.state.deepspeed_plugin.deepspeed_config
	):
		a :						List[Any]      =							get_linear_schedule_with_warmup(
		    optimizer=_lowercase       ,			num_warmup_steps=0       ,			num_training_steps=_lowercase       ,			)
	else:
		a :						int      =							DummyScheduler(_lowercase       ,			total_num_steps=_lowercase       ,			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.
	a,			a,			a,			a,			a :						Tuple      =							accelerator.prepare(
	    _lowercase       ,			_lowercase       ,			_lowercase       ,			_lowercase       ,			_lowercase   )

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

	# Now we train the model
	a :						List[str]      =							{}
	for epoch in range(_lowercase       ,			_lowercase   ):
		with TorchTracemalloc() as tracemalloc:
			model.train()
			for step, batch in enumerate(_lowercase   ):
				a :						Union[str, Any]      =							model(**_lowercase   )
				a :						List[str]      =							outputs.loss
				a :						Dict      =							loss / gradient_accumulation_steps
				accelerator.backward(_lowercase   )
				if step % gradient_accumulation_steps == 0:
					optimizer.step()
					lr_scheduler.step()
					optimizer.zero_grad()

				overall_step += 1

        # 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   )   )   )
		a :						List[str]      =							tracemalloc.peaked + bamb(tracemalloc.begin   )
		if args.peak_memory_upper_bound is not None:
			assert (
			    train_total_peak_memory[F"""epoch-{epoch}"""] <= args.peak_memory_upper_bound
			), "Peak memory usage exceeded the upper bound"

	accelerator.wait_for_everyone()
	if accelerator.is_main_process:
		with open(os.path.join(args.output_dir       ,			"peak_memory_utilization.json"   )       ,			"w"   ) as f:
			json.dump(_lowercase       ,			_lowercase   )






def  _SCREAMING_SNAKE_CASE						(      )					->List[Any]:






	'''simple docstring'''






	a :						str      =							argparse.ArgumentParser(description="Simple example of training script tracking peak GPU memory usage."   )
	parser.add_argument(
	    "--model_name_or_path"       ,			type=_lowercase       ,			default="bert-base-cased"       ,			help="Path to pretrained model or model identifier from huggingface.co/models."       ,			required=_lowercase       ,			)
	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(
	    "--peak_memory_upper_bound"       ,			type=_lowercase       ,			default=_lowercase       ,			help="The upper bound of peak memory usage in MB. If set, the training will throw an error if the peak memory usage exceeds this value."       ,			)
	parser.add_argument(
	    "--n_train"       ,			type=_lowercase       ,			default=320       ,			help="Number of training examples to use."       ,			)
	parser.add_argument(
	    "--n_val"       ,			type=_lowercase       ,			default=160       ,			help="Number of validation examples to use."       ,			)
	parser.add_argument(
	    "--num_epochs"       ,			type=_lowercase       ,			default=1       ,			help="Number of train epochs."       ,			)
	a :						List[str]      =							parser.parse_args()
	a :						Any      =							{"lr": 2E-5, "num_epochs": args.num_epochs, "seed": 42, "batch_size": 16}
	training_function(_lowercase       ,			_lowercase   )


if __name__ == "__main__":
		main()









"""simple docstring"""




import unittest

from transformers import DonutProcessor


a		:					Optional[int]	      =     '''naver-clova-ix/donut-base'''


class 			__UpperCamelCase							(		unittest.TestCase		):






							def        __a  (  self )      ->					Dict:
								a :						Optional[Any]      =							DonutProcessor.from_pretrained(lowerCAmelCase__ )






							def        __a  (  self )      ->					List[Any]:
								a :						Optional[Any]      =							{
								    "name": "John Doe",
								    "age": "99",
								    "city": "Atlanta",
								    "state": "GA",
								    "zip": "30301",
								    "phone": "123-4567",
								    "nicknames": [{"nickname": "Johnny"}, {"nickname": "JD"}],
								}

								a :						Tuple      =							(
								    "<s_name>John Doe</s_name><s_age>99</s_age><s_city>Atlanta</s_city>"
								    "<s_state>GA</s_state><s_zip>30301</s_zip><s_phone>123-4567</s_phone>"
								    "<s_nicknames><s_nickname>Johnny</s_nickname>"
								    "<sep/><s_nickname>JD</s_nickname></s_nicknames>"
								)
								a :						List[Any]      =							self.processor.tokenajson(lowerCAmelCase__ )

								self.assertDictEqual(lowerCAmelCase__ ,   lowerCAmelCase__ )


