




"""simple docstring"""




import os

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

import datasets


lowerCAmelCase							:			List[str]     							=							datasets.logging.get_logger(__name__)


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

lowerCAmelCase							:			List[str]     							=							"""\
BLEURT 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)
and 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
it for your specific application (the latter is expected to perform better).

See the project's README at https://github.com/google-research/bleurt#readme for more information.
"""

lowerCAmelCase							:			Tuple     							=							"""
BLEURT score.

Args:
    `predictions` (list of str): prediction/candidate sentences
    `references` (list of str): reference sentences
    `checkpoint` BLEURT checkpoint. Will default to BLEURT-tiny if None.

Returns:
    'scores': List of scores.
Examples:

    >>> predictions = [\"hello there\", \"general kenobi\"]
    >>> references = [\"hello there\", \"general kenobi\"]
    >>> bleurt = datasets.load_metric(\"bleurt\")
    >>> results = bleurt.compute(predictions=predictions, references=references)
    >>> print([round(v, 2) for v in results[\"scores\"]])
    [1.03, 1.04]
"""

lowerCAmelCase							:			List[str]     							=							{
    """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     __magic_name__							(       datasets.Metric				):






				'''simple docstring'''





				def    _lowerCAmelCase				(       self ):





							"""simple docstring"""
							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    _lowerCAmelCase				(       self		, _a ):





							"""simple docstring"""
							# 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').""" )
										lowerCamelCase			      =					"""bleurt-base-128"""

							if self.config_name.lower() in CHECKPOINT_URLS:
										lowerCamelCase			      =					self.config_name.lower()

							elif self.config_name.upper() in CHECKPOINT_URLS:
										lowerCamelCase			      =					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
							lowerCamelCase			      =					dl_manager.download_and_extract(CHECKPOINT_URLS[checkpoint_name] )
							lowerCamelCase			      =					score.BleurtScorer(os.path.join(_a		, _a ) )





				def    _lowerCAmelCase				(       self		, _a		, _a ):





							"""simple docstring"""
							lowerCamelCase			      =					self.scorer.score(references=_a		, candidates=_a )
							return {"scores": scores}









"""simple docstring"""




import importlib
import sys
from argparse import REMAINDER, ArgumentParser
from pathlib import Path

import torch_xla.distributed.xla_multiprocessing as xmp



def     a__ (				)						->					Union[str, Any]:
			lowerCamelCase			      =					ArgumentParser(
			    description=(
			        """PyTorch TPU distributed training launch """
			        """helper utility that will spawn up """
			        """multiple distributed processes"""
			    )				)

			# Optional arguments for the launch helper
			parser.add_argument("""--num_cores"""       , type=snake_case__       , default=1       , help="""Number of TPU cores to use (1 or 8)."""				)

			# positional
			parser.add_argument(
			    """training_script"""       , type=snake_case__       , help=(
			        """The full path to the single TPU training """
			        """program/script to be launched in parallel, """
			        """followed by all the arguments for the """
			        """training script"""
			    )       , )

			# rest from the training program
			parser.add_argument("""training_script_args"""       , nargs=snake_case__				)

			return parser.parse_args()



def     a__ (				)						->					List[str]:
			lowerCamelCase			      =					parse_args()

			# Import training_script as a module.
			lowerCamelCase			      =					Path(args.training_script				)
			sys.path.append(str(script_fpath.parent.resolve()				)				)
			lowerCamelCase			      =					script_fpath.stem
			lowerCamelCase			      =					importlib.import_module(snake_case__				)

			# Patch sys.argv
			lowerCamelCase			      =					[args.training_script] + args.training_script_args + ["""--tpu_num_cores""", str(args.num_cores				)]
			xmp.spawn(mod._mp_fn       , args=()       , nprocs=args.num_cores				)


if __name__ == "__main__":
					main()



