"""simple docstring"""



from typing import Dict

from transformers import EvalPrediction, HfArgumentParser, TrainingArguments, is_torch_available
from transformers.testing_utils import (
    TestCasePlus,
    execute_subprocess_async,
    get_torch_dist_unique_port,
    require_torch_multi_gpu,
    require_torch_neuroncore,
)
from transformers.training_args import ParallelMode
from transformers.utils import logging


_UpperCamelCase								=		logging.get_logger(__name__)


if is_torch_available():
				import torch
				from torch import nn
				from torch.utils.data import Dataset

				from transformers import Trainer
				class 						__UpperCAmelCase    (__A     ):







							'''simple docstring'''





							def __init__(			self   ,      snake_case_ = 101							):







														'''simple docstring'''


														A__       :     Any								=			length





							def __len__(			self							):







														'''simple docstring'''


														return self.length





							def __getitem__(			self   ,      snake_case_							):







														'''simple docstring'''


														return i





				class 						__UpperCAmelCase    :







							'''simple docstring'''





							def __call__(			self   ,      snake_case_							):







														'''simple docstring'''


														return {"input_ids": torch.tensor(snake_case_							), "labels": torch.tensor(snake_case_							)}





				class 						__UpperCAmelCase    (nn.Module     ):







							'''simple docstring'''





							def __init__(			self							):







														'''simple docstring'''


														super().__init__()
														# Add some (unused) params otherwise DDP will complain.
														A__       :     str								=			nn.Linear(120   ,      80							)





							def 		lowerCamelCase					(			self   ,      snake_case_   ,      snake_case_=None							):







														'''simple docstring'''


														if labels is not None:
																					return torch.tensor(0.0   ,      device=input_ids.device							), input_ids
														else:
																					return input_ids
class 						__UpperCAmelCase    (__A     ):







			'''simple docstring'''





			@require_torch_neuroncore
			def 		lowerCamelCase					(			self							):







										'''simple docstring'''


										A__       :     str								=			F'''--nproc_per_node=2
            --master_port={get_torch_dist_unique_port()}
            {self.test_file_dir}/test_trainer_distributed.py
        '''.split()
										A__       :     int								=			self.get_auto_remove_tmp_dir()
										A__       :     int								=			F'''--output_dir {output_dir}'''.split()
										A__       :     Optional[int]								=			["""torchrun"""] + distributed_args + args
										execute_subprocess_async(snake_case_   ,      env=self.get_env()							)
										# successful return here == success - any errors would have caused an error in the sub-call





class 						__UpperCAmelCase    (__A     ):







			'''simple docstring'''





			@require_torch_multi_gpu
			def 		lowerCamelCase					(			self							):







										'''simple docstring'''


										A__       :     str								=			F'''--nproc_per_node={torch.cuda.device_count()}
            --master_port={get_torch_dist_unique_port()}
            {self.test_file_dir}/test_trainer_distributed.py
        '''.split()
										A__       :     Tuple								=			self.get_auto_remove_tmp_dir()
										A__       :     List[str]								=			F'''--output_dir {output_dir}'''.split()
										A__       :     int								=			["""torchrun"""] + distributed_args + args
										execute_subprocess_async(snake_case_   ,      env=self.get_env()							)
										# successful return here == success - any errors would have caused an error in the sub-call


if __name__ == "__main__":
				# The script below is meant to be run under torch.distributed, on a machine with multiple GPUs:
				#
				# PYTHONPATH="src" python -m torch.distributed.run --nproc_per_node 2 --output_dir output_dir ./tests/test_trainer_distributed.py

				_UpperCamelCase								=		HfArgumentParser((TrainingArguments,))
				_UpperCamelCase								=		parser.parse_args_into_dataclasses()[0]

				logger.warning(
				    F'Process rank: {training_args.local_rank}, device: {training_args.device}, n_gpu: {training_args.n_gpu}, '
				    F'distributed training: {training_args.parallel_mode != ParallelMode.NOT_DISTRIBUTED}'
				)

				# Essentially, what we want to verify in the distributed case is that we get all samples back,
				# in the right order. (this is crucial for prediction for instance)
				for dataset_length in [1_01, 40, 7]:
								_UpperCamelCase								=		DummyDataset(dataset_length)

								def  _A(		lowerCAmelCase							):
															A__       :     Union[str, Any]								=			list(range(len(lowerCAmelCase							)							)							)
															A__       :     Tuple								=			p.predictions.tolist() == sequential and p.label_ids.tolist() == sequential
															if not success and training_args.local_rank == 0:
																						logger.warning(
																						    """Predictions and/or labels do not match expected results:\n  - predictions: """
																						    F'''{p.predictions.tolist()}\n  - labels: {p.label_ids.tolist()}\n  - expected: {sequential}'''							)
															return {"success": success}

								_UpperCamelCase								=		Trainer(
								    model=DummyModel(),
								    args=training_args,
								    data_collator=DummyDataCollator(),
								    eval_dataset=dataset,
								    compute_metrics=compute_metrics,
								)
								_UpperCamelCase								=		trainer.evaluate()
								logger.info(metrics)
								if metrics["eval_success"] is not True:
												logger.error(metrics)
												exit(1)

								_UpperCamelCase								=		trainer.predict(dataset)
								logger.info(p.metrics)
								if p.metrics["test_success"] is not True:
												logger.error(p.metrics)
												exit(1)

								_UpperCamelCase								=		2

								_UpperCamelCase								=		trainer.evaluate()
								logger.info(metrics)
								if metrics["eval_success"] is not True:
												logger.error(metrics)
												exit(1)

								_UpperCamelCase								=		trainer.predict(dataset)
								logger.info(p.metrics)
								if p.metrics["test_success"] is not True:
												logger.error(p.metrics)
												exit(1)



								_UpperCamelCase								=		None






"""simple docstring"""



import argparse
import shutil
from pathlib import Path

from tqdm import tqdm

from transformers import AutoTokenizer

def  _A(		lowerCAmelCase			,  lowerCAmelCase			,  lowerCAmelCase			,  lowerCAmelCase=1024							):
							A__				,							A__       :     Any								=			[], []

							A__       :     List[Any]								=			list(zip(lowerCAmelCase			,  lowerCAmelCase							)							)
							A__				,							A__       :     Optional[int]								=			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:]							):
														A__       :     List[Any]								=			new_src + """ """ + src
														A__       :     List[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							)
																					A__				,							A__       :     str								=			src, tgt
														else:  # can fit, keep adding
																					A__				,							A__       :     Union[str, Any]								=			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  _A(		lowerCAmelCase			,  lowerCAmelCase			,  lowerCAmelCase			,  lowerCAmelCase							):
							A__       :     Optional[int]								=			Path(lowerCAmelCase							)
							save_path.mkdir(exist_ok=lowerCAmelCase							)
							for split in ["train"]:
														A__				,							A__       :     List[Any]								=			data_dir / F'''{split}.source''', data_dir / F'''{split}.target'''
														A__       :     Tuple								=			[x.rstrip() for x in Path(lowerCAmelCase							).open().readlines()]
														A__       :     Union[str, Any]								=			[x.rstrip() for x in Path(lowerCAmelCase							).open().readlines()]
														A__				,							A__       :     Tuple								=			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"]:
														A__				,							A__       :     Dict								=			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  _A(		):
							A__       :     str								=			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=128							)
							parser.add_argument("""--data_dir"""			,  type=lowerCAmelCase							)
							parser.add_argument("""--save_path"""			,  type=lowerCAmelCase							)
							A__       :     str								=			parser.parse_args()
							A__       :     Optional[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()





