'''simple docstring'''




import os

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

import datasets


_lowercase		:				Optional[Any]	       =		datasets.logging.get_logger(__name__)


_lowercase		:				Union[str, Any]	       =		"\\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"

_lowercase		:				Optional[Any]	       =		"\\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"

_lowercase		:				int	       =		"\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"

_lowercase		:				Dict	       =		{
    "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):







			def 			SCREAMING_SNAKE_CASE_  (			self       :						Optional[Any]					):
										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 			SCREAMING_SNAKE_CASE_  (			self       :						str       ,    lowercase_       :						Any					):
										# 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')."""					)
																	lowercase_      :    int       		= """bleurt-base-128"""

										if self.config_name.lower() in CHECKPOINT_URLS:
																	lowercase_      :    Dict       		= self.config_name.lower()

										elif self.config_name.upper() in CHECKPOINT_URLS:
																	lowercase_      :    Dict       		= 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
										lowercase_      :    List[Any]       		= dl_manager.download_and_extract(CHECKPOINT_URLS[checkpoint_name]					)
										lowercase_      :    Optional[Any]       		= score.BleurtScorer(os.path.join(lowercase_       ,    lowercase_					)					)







			def 			SCREAMING_SNAKE_CASE_  (			self       :						Tuple       ,    lowercase_       :						List[Any]       ,    lowercase_       :						Dict					):
										lowercase_      :    int       		= self.scorer.score(references=lowercase_       ,    candidates=lowercase_					)
										return {"scores": scores}






'''simple docstring'''




from collections.abc import Callable

import numpy as np




def 							lowerCamelCase			(   UpperCAmelCase__ :      Callable   ,   UpperCAmelCase__ :      float   ,   UpperCAmelCase__ :      float   ,   UpperCAmelCase__ :      float   ,   UpperCAmelCase__ :      float				)    ->   np.array:
							lowercase_      :    Any       		= int(np.ceil((x_end - xa) / step_size				)				)
							lowercase_      :    List[Any]       		= np.zeros((n + 1,)				)
							lowercase_      :    List[Any]       		= ya
							lowercase_      :    List[str]       		= xa

							for k in range(UpperCAmelCase__				):
														lowercase_      :    Optional[Any]       		= y[k] + step_size * ode_func(UpperCAmelCase__   ,   y[k]				)
														lowercase_      :    List[Any]       		= y[k] + (
														    (step_size / 2) * (ode_func(UpperCAmelCase__   ,   y[k]				) + ode_func(x + step_size   ,   UpperCAmelCase__				))
														)
														x += step_size

							return y


if __name__ == "__main__":
						import doctest

						doctest.testmod()





