

import argparse
import shutil
from pathlib import Path

from tqdm import tqdm

from transformers import AutoTokenizer






def    SCREAMING_SNAKE_CASE__       (  lowerCamelCase__	, lowerCamelCase__	, lowerCamelCase__	, lowerCamelCase__=1_0_2_4			)  ->     Any:
	__lowerCamelCase       , __lowerCamelCase				:				Any					       = [], []

	__lowerCamelCase				:				List[Any]					       = list(zip(lowerCamelCase__	, lowerCamelCase__			)			)
	__lowerCamelCase       , __lowerCamelCase				:				Dict					       = sorted_examples[0]

	def is_too_big(lowerCamelCase__			):
		return tok(lowerCamelCase__	, return_tensors='pt'			).input_ids.shape[1] > max_tokens

	for src, tgt in tqdm(sorted_examples[1:]			):
		__lowerCamelCase				:				Optional[Any]					       = new_src + ' ' + src
		__lowerCamelCase				:				str					       = new_tgt + ' ' + tgt
		if is_too_big(lowerCamelCase__			) or is_too_big(lowerCamelCase__			):  # cant fit, finalize example
			finished_src.append(lowerCamelCase__			)
			finished_tgt.append(lowerCamelCase__			)
			__lowerCamelCase       , __lowerCamelCase				:				List[str]					       = src, tgt
		else:  # can fit, keep adding
			__lowerCamelCase       , __lowerCamelCase				:				Dict					       = cand_src, cand_tgt

    # cleanup
	if new_src:
		assert new_tgt
		finished_src.append(lowerCamelCase__			)
		finished_tgt.append(lowerCamelCase__			)
	return finished_src, finished_tgt






def    SCREAMING_SNAKE_CASE__       (  lowerCamelCase__	, lowerCamelCase__	, lowerCamelCase__	, lowerCamelCase__			)  ->     Optional[int]:
	__lowerCamelCase				:				int					       = Path(lowerCamelCase__			)
	save_path.mkdir(exist_ok=lowerCamelCase__			)
	for split in ["train"]:
		__lowerCamelCase       , __lowerCamelCase				:				str					       = data_dir / F"{split}.source", data_dir / F"{split}.target"
		__lowerCamelCase				:				str					       = [x.rstrip() for x in Path(lowerCamelCase__			).open().readlines()]
		__lowerCamelCase				:				int					       = [x.rstrip() for x in Path(lowerCamelCase__			).open().readlines()]
		__lowerCamelCase       , __lowerCamelCase				:				int					       = pack_examples(lowerCamelCase__	, lowerCamelCase__	, lowerCamelCase__	, lowerCamelCase__			)
		print(F"packed {split} split from {len(lowerCamelCase__			)} examples -> {len(lowerCamelCase__			)}."			)
		Path(save_path / F"{split}.source"			).open('w'			).write('\n'.join(lowerCamelCase__			)			)
		Path(save_path / F"{split}.target"			).open('w'			).write('\n'.join(lowerCamelCase__			)			)
	for split in ["val", "test"]:
		__lowerCamelCase       , __lowerCamelCase				:				Optional[Any]					       = data_dir / F"{split}.source", data_dir / F"{split}.target"
		shutil.copyfile(lowerCamelCase__	, save_path / F"{split}.source"			)
		shutil.copyfile(lowerCamelCase__	, save_path / F"{split}.target"			)






def    SCREAMING_SNAKE_CASE__       (  )  ->     List[str]:
	__lowerCamelCase				:				Optional[int]					       = argparse.ArgumentParser()
	parser.add_argument('--tok_name'	, type=lowerCamelCase__	, help='like facebook/bart-large-cnn,t5-base, etc.'			)
	parser.add_argument('--max_seq_len'	, type=lowerCamelCase__	, default=1_2_8			)
	parser.add_argument('--data_dir'	, type=lowerCamelCase__			)
	parser.add_argument('--save_path'	, type=lowerCamelCase__			)
	__lowerCamelCase				:				List[str]					       = parser.parse_args()
	__lowerCamelCase				:				Any					       = AutoTokenizer.from_pretrained(args.tok_name			)
	return pack_data_dir(lowerCamelCase__	, Path(args.data_dir			)	, args.max_seq_len	, args.save_path			)


if __name__ == "__main__":
					packer_cli()





import gc
import threading
import time

import psutil
import torch




class    _UpperCamelCase      :


			"""simple docstring"""






			def __init__(   self						)     ->					str:

							'''simple docstring'''


							__lowercase 			=							psutil.Process()
							__lowercase 			=							False





			def        _SCREAMING_SNAKE_CASE			(   self						)     ->					str:

							'''simple docstring'''


							__lowercase 			=							-1

							while True:
											__lowercase 			=							max(self.process.memory_info().rss      ,   self.cpu_memory_peak						)

											# can't sleep or will not catch the peak right (this comment is here on purpose)
											if not self.peak_monitoring:
															break





			def        _SCREAMING_SNAKE_CASE			(   self						)     ->					int:

							'''simple docstring'''


							__lowercase 			=							True
							__lowercase 			=							threading.Thread(target=self.peak_monitor						)
							__lowercase 			=							True
							self.thread.start()





			def        _SCREAMING_SNAKE_CASE			(   self						)     ->					str:

							'''simple docstring'''


							__lowercase 			=							False
							self.thread.join()
							return self.cpu_memory_peak


__a  :     List[str]       =  PeakCPUMemory()





def 				UpperCAmelCase						(    ):







				"""simple docstring"""



				__lowercase 			=							{'''time''': time.time()}

				gc.collect()
				torch.cuda.empty_cache()

				# CPU mem
				__lowercase 			=							psutil.Process().memory_info().rss
				cpu_peak_tracker.start()

				# GPU mem
				for i in range(torch.cuda.device_count()							):
								__lowercase 			=							torch.cuda.memory_allocated(lowercase							)
				torch.cuda.reset_peak_memory_stats()

				return measures





def 				UpperCAmelCase						(    lowercase							):







				"""simple docstring"""



				__lowercase 			=							{'''time''': time.time() - start_measures['''time''']}

				gc.collect()
				torch.cuda.empty_cache()

				# CPU mem
				__lowercase 			=							(psutil.Process().memory_info().rss - start_measures['''cpu''']) / 2**20
				__lowercase 			=							(cpu_peak_tracker.stop() - start_measures['''cpu''']) / 2**20

				# GPU mem
				for i in range(torch.cuda.device_count()							):
								__lowercase 			=							(torch.cuda.memory_allocated(lowercase							) - start_measures[str(lowercase							)]) / 2**20
								__lowercase 			=							(torch.cuda.max_memory_allocated(lowercase							) - start_measures[str(lowercase							)]) / 2**20

				return measures





def 				UpperCAmelCase						(    lowercase							,			lowercase							):







				"""simple docstring"""



				print(F"{description}:"							)
				print(F"- Time: {measures['time']:.2f}s"							)
				for i in range(torch.cuda.device_count()							):
								print(F"- GPU {i} allocated: {measures[str(lowercase							)]:.2f}MiB"							)
								__lowercase 			=							measures[F"{i}-peak"]
								print(F"- GPU {i} peak: {peak:.2f}MiB"							)
				print(F"- CPU RAM allocated: {measures['cpu']:.2f}MiB"							)
				print(F"- CPU RAM peak: {measures['cpu-peak']:.2f}MiB"							)