



import argparse
import shutil
from pathlib import Path

from tqdm import tqdm

from transformers import AutoTokenizer



def 					_lowerCamelCase(			lowerCAmelCase__				:		Optional[Any]    ,    lowerCAmelCase__				:		List[Any]    ,    lowerCAmelCase__				:		Optional[Any]    ,    lowerCAmelCase__				:		Tuple=1024      ):





				'''simple docstring'''







				SCREAMING_SNAKE_CASE_   :	List[Any]													=   [], []

				SCREAMING_SNAKE_CASE_   :	Optional[int]													=   list(zip(lowerCAmelCase__    ,    lowerCAmelCase__      )      )
				SCREAMING_SNAKE_CASE_   :	List[str]													=   sorted_examples[0]

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

				for src, tgt in tqdm(sorted_examples[1:]      ):
								SCREAMING_SNAKE_CASE_   :	Dict													=   new_src + ' ' + src
								SCREAMING_SNAKE_CASE_   :	Optional[int]													=   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__      )
												SCREAMING_SNAKE_CASE_   :	int													=   src, tgt
								else:  # can fit, keep adding
												SCREAMING_SNAKE_CASE_   :	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 					_lowerCamelCase(			lowerCAmelCase__				:		Dict    ,    lowerCAmelCase__				:		Path    ,    lowerCAmelCase__				:		List[Any]    ,    lowerCAmelCase__				:		Optional[int]      ):





				'''simple docstring'''







				SCREAMING_SNAKE_CASE_   :	int													=   Path(lowerCAmelCase__      )
				save_path.mkdir(exist_ok=lowerCAmelCase__      )
				for split in ["train"]:
								SCREAMING_SNAKE_CASE_   :	Dict													=   data_dir / F'''{split}.source''', data_dir / F'''{split}.target'''
								SCREAMING_SNAKE_CASE_   :	List[Any]													=   [x.rstrip() for x in Path(lowerCAmelCase__      ).open().readlines()]
								SCREAMING_SNAKE_CASE_   :	Optional[Any]													=   [x.rstrip() for x in Path(lowerCAmelCase__      ).open().readlines()]
								SCREAMING_SNAKE_CASE_   :	List[Any]													=   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"]:
								SCREAMING_SNAKE_CASE_   :	str													=   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 					_lowerCamelCase(			):





				'''simple docstring'''







				SCREAMING_SNAKE_CASE_   :	Dict													=   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__      )
				SCREAMING_SNAKE_CASE_   :	List[str]													=   parser.parse_args()
				SCREAMING_SNAKE_CASE_   :	List[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 os

from bleurt import score  # From: git+https://github.com/google-research/bleurt.git

import datasets


A        =			datasets.logging.get_logger(__name__)


A        =			'\\n@inproceedings{bleurt,\n  title={BLEURT: Learning Robust Metrics for Text Generation},\n  author={Thibault Sellam and Dipanjan Das and Ankur P. Parikh},\n  booktitle={ACL},\n  year={2020},\n  url={https://arxiv.org/abs/2004.04696}\n}\n'

A        =			'\\nBLEURT a learnt evaluation metric for Natural Language Generation. It is built using multiple phases of transfer learning starting from a pretrained BERT model (Devlin et al. 2018)\nand then employing another pre-training phrase using synthetic data. Finally it is trained on WMT human annotations. You may run BLEURT out-of-the-box or fine-tune\nit for your specific application (the latter is expected to perform better).\n\nSee the project\'s README at https://github.com/google-research/bleurt#readme for more information.\n'

A        =			'\nBLEURT score.\n\nArgs:\n    `predictions` (list of str): prediction/candidate sentences\n    `references` (list of str): reference sentences\n    `checkpoint` BLEURT checkpoint. Will default to BLEURT-tiny if None.\n\nReturns:\n    \'scores\': List of scores.\nExamples:\n\n    >>> predictions = ["hello there", "general kenobi"]\n    >>> references = ["hello there", "general kenobi"]\n    >>> bleurt = datasets.load_metric("bleurt")\n    >>> results = bleurt.compute(predictions=predictions, references=references)\n    >>> print([round(v, 2) for v in results["scores"]])\n    [1.03, 1.04]\n'

A        =			{
    'bleurt-tiny-128': 'https://storage.googleapis.com/bleurt-oss/bleurt-tiny-128.zip',
    'bleurt-tiny-512': 'https://storage.googleapis.com/bleurt-oss/bleurt-tiny-512.zip',
    'bleurt-base-128': 'https://storage.googleapis.com/bleurt-oss/bleurt-base-128.zip',
    'bleurt-base-512': 'https://storage.googleapis.com/bleurt-oss/bleurt-base-512.zip',
    'bleurt-large-128': 'https://storage.googleapis.com/bleurt-oss/bleurt-large-128.zip',
    'bleurt-large-512': 'https://storage.googleapis.com/bleurt-oss/bleurt-large-512.zip',
    'BLEURT-20-D3': 'https://storage.googleapis.com/bleurt-oss-21/BLEURT-20-D3.zip',
    'BLEURT-20-D6': 'https://storage.googleapis.com/bleurt-oss-21/BLEURT-20-D6.zip',
    'BLEURT-20-D12': 'https://storage.googleapis.com/bleurt-oss-21/BLEURT-20-D12.zip',
    'BLEURT-20': 'https://storage.googleapis.com/bleurt-oss-21/BLEURT-20.zip',
}


@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION ,	_KWARGS_DESCRIPTION						)
class 							__a							(  datasets.Metric						):





		'''simple docstring'''



		def   __snake_case			(						self       ):
						return datasets.MetricInfo(
						    description=_DESCRIPTION ,    citation=_CITATION ,    homepage='https://github.com/google-research/bleurt' ,    inputs_description=_KWARGS_DESCRIPTION ,    features=datasets.Features(
						        {
						            'predictions': datasets.Value('string' ,    id='sequence'       ),
						            'references': datasets.Value('string' ,    id='sequence'       ),
						        }       ) ,    codebase_urls=['https://github.com/google-research/bleurt'] ,    reference_urls=['https://github.com/google-research/bleurt', 'https://arxiv.org/abs/2004.04696'] ,    )


		def   __snake_case			(						self ,    UpperCamelCase__       ):
						# check that config name specifies a valid BLEURT model
						if self.config_name == "default":
										logger.warning(
										    'Using default BLEURT-Base checkpoint for sequence maximum length 128. '
										    'You can use a bigger model for better results with e.g.: datasets.load_metric(\'bleurt\', \'bleurt-large-512\').'       )
										SCREAMING_SNAKE_CASE_   :	List[Any]													=   'bleurt-base-128'

						if self.config_name.lower() in CHECKPOINT_URLS:
										SCREAMING_SNAKE_CASE_   :	Tuple													=   self.config_name.lower()

						elif self.config_name.upper() in CHECKPOINT_URLS:
										SCREAMING_SNAKE_CASE_   :	Any													=   self.config_name.upper()

						else:
										raise KeyError(
										    F'''{self.config_name} model not found. You should supply the name of a model checkpoint for bleurt in {CHECKPOINT_URLS.keys()}'''       )

						# download the model checkpoint specified by self.config_name and set up the scorer
						SCREAMING_SNAKE_CASE_   :	List[Any]													=   dl_manager.download_and_extract(CHECKPOINT_URLS[checkpoint_name]       )
						SCREAMING_SNAKE_CASE_   :	int													=   score.BleurtScorer(os.path.join(UpperCamelCase__ ,    UpperCamelCase__       )       )






		def   __snake_case			(						self ,    UpperCamelCase__ ,    UpperCamelCase__       ):
						SCREAMING_SNAKE_CASE_   :	Tuple													=   self.scorer.score(references=UpperCamelCase__ ,    candidates=UpperCamelCase__       )
						return {"scores": scores}