




"""simple docstring"""
import os

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

import datasets


a_				:    Any										=     datasets.logging.get_logger(__name__)


a_				:    Dict										=     '''\\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_				:    Optional[int]										=     '''\\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_				:    Union[str, Any]										=     '''\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_				:    Union[str, Any]										=     {
    '''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 __lowercase(      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						,	__a     ):
								# 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					:	List[Any]	       =				'bleurt-base-128'

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

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





					def     snake_case_				(    self						,	__a						,	__a     ):
								__lowerCamelCase					:	Tuple	       =				self.scorer.score(references=UpperCamelCase__						,	candidates=UpperCamelCase__     )
								return {"scores": scores}









"""simple docstring"""
def 						UpperCAmelCase				(			A__:    int							,				A__:    list[int]							,				A__:    int     )  ->     int:

			def count_of_possible_combinations(A__:    int     ) -> int:
						if target < 0:
									return 0
						if target == 0:
									return 1
						return sum(count_of_possible_combinations(target - item     ) for item in array     )

			return count_of_possible_combinations(A__     )





def 						UpperCAmelCase				(			A__:    int							,				A__:    list[int]							,				A__:    int     )  ->     int:

			def count_of_possible_combinations_with_dp_array(
			    A__:    int							,				A__:    list[int]     ) -> int:
						if target < 0:
									return 0
						if target == 0:
									return 1
						if dp_array[target] != -1:
									return dp_array[target]
						__lowerCamelCase					:	Dict	       =				sum(
						    count_of_possible_combinations_with_dp_array(target - item							,				A__     )
						    for item in array     )
						__lowerCamelCase					:	str	       =				answer
						return answer

			__lowerCamelCase					:	Union[str, Any]	       =				[-1] * (target + 1)
			return count_of_possible_combinations_with_dp_array(A__							,				A__     )





def 						UpperCAmelCase				(			A__:    int							,				A__:    list[int]							,				A__:    int     )  ->     int:
			__lowerCamelCase					:	int	       =				[0] * (target + 1)
			__lowerCamelCase					:	str	       =				1

			for i in range(1							,				target + 1     ):
						for j in range(A__     ):
									if i - array[j] >= 0:
												dp_array[i] += dp_array[i - array[j]]

			return dp_array[target]


if __name__ == "__main__":
					import doctest

					doctest.testmod()
					a_				:    Any										=     3
					a_				:    str										=     5
					a_				:    List[Any]										=     [1, 2, 5]
					print(combination_sum_iv(n, array, target))



