





import os

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

import datasets


UpperCamelCase__     =	datasets.logging.get_logger(__name__)


UpperCamelCase__     =	"\\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"

UpperCamelCase__     =	"\\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"

UpperCamelCase__     =	"\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"

UpperCamelCase__     =	{
    "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     __SCREAMING_SNAKE_CASE   (       datasets.Metric    ):






							def 			_lowerCamelCase						(  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 			_lowerCamelCase						(  self					,	__lowerCAmelCase					):
														# 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')."""					)
																					UpperCamelCase__			       =     """bleurt-base-128"""

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

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







							def 			_lowerCamelCase						(  self					,	__lowerCAmelCase					,	__lowerCAmelCase					):
														UpperCamelCase__			       =     self.scorer.score(references=__lowerCAmelCase					,	candidates=__lowerCAmelCase					)
														return {"scores": scores}











from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_sentencepiece_available


UpperCamelCase__     =	{}

try:
					if not is_sentencepiece_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					UpperCamelCase__     =	["GPTSw3Tokenizer"]


if TYPE_CHECKING:
					try:
										if not is_sentencepiece_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .tokenization_gpt_swa import GPTSwaTokenizer

else:
					import sys

					UpperCamelCase__     =	_LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)




