


"""simple docstring"""


import argparse
import shutil
from pathlib import Path

from tqdm import tqdm

from transformers import AutoTokenizer


def _lowerCamelCase			(	_UpperCamelCase					,				_UpperCamelCase					,				_UpperCamelCase					,				_UpperCamelCase=1024   ):

			'''simple docstring'''




			__lowerCAmelCase					,			__lowerCAmelCase       					=    [], []

			__lowerCAmelCase       					=    list(zip(_UpperCamelCase					,				_UpperCamelCase   )   )
			__lowerCAmelCase					,			__lowerCAmelCase       					=    sorted_examples[0]

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

			for src, tgt in tqdm(sorted_examples[1:]   ):
						__lowerCAmelCase       					=    new_src + " " + src
						__lowerCAmelCase       					=    new_tgt + " " + tgt
						if is_too_big(_UpperCamelCase   ) or is_too_big(_UpperCamelCase   ):  # cant fit, finalize example
									finished_src.append(_UpperCamelCase   )
									finished_tgt.append(_UpperCamelCase   )
									__lowerCAmelCase					,			__lowerCAmelCase       					=    src, tgt
						else:  # can fit, keep adding
									__lowerCAmelCase					,			__lowerCAmelCase       					=    cand_src, cand_tgt

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


def _lowerCamelCase			(	_UpperCamelCase					,				_UpperCamelCase					,				_UpperCamelCase					,				_UpperCamelCase   ):

			'''simple docstring'''




			__lowerCAmelCase       					=    Path(_UpperCamelCase   )
			save_path.mkdir(exist_ok=_UpperCamelCase   )
			for split in ["train"]:
						__lowerCAmelCase					,			__lowerCAmelCase       					=    data_dir / f"{split}.source", data_dir / f"{split}.target"
						__lowerCAmelCase       					=    [x.rstrip() for x in Path(_UpperCamelCase   ).open().readlines()]
						__lowerCAmelCase       					=    [x.rstrip() for x in Path(_UpperCamelCase   ).open().readlines()]
						__lowerCAmelCase					,			__lowerCAmelCase       					=    pack_examples(_UpperCamelCase					,				_UpperCamelCase					,				_UpperCamelCase					,				_UpperCamelCase   )
						print(f"packed {split} split from {len(_UpperCamelCase   )} examples -> {len(_UpperCamelCase   )}."   )
						Path(save_path / f"{split}.source"   ).open("w"   ).write("\n".join(_UpperCamelCase   )   )
						Path(save_path / f"{split}.target"   ).open("w"   ).write("\n".join(_UpperCamelCase   )   )
			for split in ["val", "test"]:
						__lowerCAmelCase					,			__lowerCAmelCase       					=    data_dir / f"{split}.source", data_dir / f"{split}.target"
						shutil.copyfile(_UpperCamelCase					,				save_path / f"{split}.source"   )
						shutil.copyfile(_UpperCamelCase					,				save_path / f"{split}.target"   )


def _lowerCamelCase			(	):

			'''simple docstring'''




			__lowerCAmelCase       					=    argparse.ArgumentParser()
			parser.add_argument("--tok_name"					,				type=_UpperCamelCase					,				help="like facebook/bart-large-cnn,t5-base, etc."   )
			parser.add_argument("--max_seq_len"					,				type=_UpperCamelCase					,				default=128   )
			parser.add_argument("--data_dir"					,				type=_UpperCamelCase   )
			parser.add_argument("--save_path"					,				type=_UpperCamelCase   )
			__lowerCAmelCase       					=    parser.parse_args()
			__lowerCAmelCase       					=    AutoTokenizer.from_pretrained(args.tok_name   )
			return pack_data_dir(_UpperCamelCase					,				Path(args.data_dir   )					,				args.max_seq_len					,				args.save_path   )


if __name__ == "__main__":
	packer_cli()








"""simple docstring"""


import gc
import threading
import time

import psutil
import torch
class 						_UpperCamelCase						:
							'''simple docstring'''







							def __init__(  self			):
										__lowerCAmelCase       					=    psutil.Process()
										__lowerCAmelCase       					=    False

							def     snake_case       (  self			):
										__lowerCAmelCase       					=    -1

										while True:
													__lowerCAmelCase       					=    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     snake_case       (  self			):
										__lowerCAmelCase       					=    True
										__lowerCAmelCase       					=    threading.Thread(target=self.peak_monitor			)
										__lowerCAmelCase       					=    True
										self.thread.start()



							def     snake_case       (  self			):
										__lowerCAmelCase       					=    False
										self.thread.join()
										return self.cpu_memory_peak
A  :     Any    					=    PeakCPUMemory()


def _lowerCamelCase			(	):

			'''simple docstring'''




			__lowerCAmelCase       					=    {"time": time.time()}

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

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

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

			return measures


def _lowerCamelCase			(	_UpperCamelCase   ):

			'''simple docstring'''




			__lowerCAmelCase       					=    {"time": time.time() - start_measures["time"]}

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

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

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

			return measures


def _lowerCamelCase			(	_UpperCamelCase					,				_UpperCamelCase   ):

			'''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(_UpperCamelCase   )]:.2f}MiB"   )
						__lowerCAmelCase       					=    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"   )




