

"""simple docstring"""





from .integrations import (
    is_optuna_available,
    is_ray_available,
    is_sigopt_available,
    is_wandb_available,
    run_hp_search_optuna,
    run_hp_search_ray,
    run_hp_search_sigopt,
    run_hp_search_wandb,
)
from .trainer_utils import (
    HPSearchBackend,
    default_hp_space_optuna,
    default_hp_space_ray,
    default_hp_space_sigopt,
    default_hp_space_wandb,
)
from .utils import logging


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







class 				SCREAMING_SNAKE_CASE					:






					"""simple docstring"""



					lowercase__				 =       42
					lowercase__				 =       None







					@staticmethod
					def        __lowerCAmelCase (       ):
						raise NotImplementedError







					def        __lowerCAmelCase (       self			:				List[Any]   ,lowercase_			:				List[Any]   ,lowercase_			:				int   ,lowercase_			:				str   ,**lowercase_			:				List[str]		):
						raise NotImplementedError







					def        __lowerCAmelCase (       self			:				int   ,lowercase_			:				int		):
						raise NotImplementedError







					def        __lowerCAmelCase (       self			:				List[str]		):
						if not self.is_available():
							raise RuntimeError(
							    F'You picked the {self.name} backend, but it is not installed. Run {self.pip_install()}.'		)







					@classmethod
					def        __lowerCAmelCase (       cls			:				str		):
						return F'`pip install {cls.pip_package or cls.name}`'







class 				SCREAMING_SNAKE_CASE					(		a_       ):






					"""simple docstring"""



					lowercase__				 =       "optuna"







					@staticmethod
					def        __lowerCAmelCase (       ):
						return is_optuna_available()







					def        __lowerCAmelCase (       self			:				Any   ,lowercase_			:				List[str]   ,lowercase_			:				int   ,lowercase_			:				str   ,**lowercase_			:				Any		):
						return run_hp_search_optuna(lowercase_   ,lowercase_   ,lowercase_   ,**lowercase_		)







					def        __lowerCAmelCase (       self			:				int   ,lowercase_			:				List[Any]		):
						return default_hp_space_optuna(lowercase_		)







class 				SCREAMING_SNAKE_CASE					(		a_       ):






					"""simple docstring"""



					lowercase__				 =       "ray"
					lowercase__				 =       "'ray[tune]'"







					@staticmethod
					def        __lowerCAmelCase (       ):
						return is_ray_available()







					def        __lowerCAmelCase (       self			:				str   ,lowercase_			:				int   ,lowercase_			:				int   ,lowercase_			:				str   ,**lowercase_			:				str		):
						return run_hp_search_ray(lowercase_   ,lowercase_   ,lowercase_   ,**lowercase_		)







					def        __lowerCAmelCase (       self			:				Dict   ,lowercase_			:				Tuple		):
						return default_hp_space_ray(lowercase_		)







class 				SCREAMING_SNAKE_CASE					(		a_       ):






					"""simple docstring"""



					lowercase__				 =       "sigopt"







					@staticmethod
					def        __lowerCAmelCase (       ):
						return is_sigopt_available()







					def        __lowerCAmelCase (       self			:				Tuple   ,lowercase_			:				List[str]   ,lowercase_			:				int   ,lowercase_			:				str   ,**lowercase_			:				List[Any]		):
						return run_hp_search_sigopt(lowercase_   ,lowercase_   ,lowercase_   ,**lowercase_		)







					def        __lowerCAmelCase (       self			:				str   ,lowercase_			:				Union[str, Any]		):
						return default_hp_space_sigopt(lowercase_		)







class 				SCREAMING_SNAKE_CASE					(		a_       ):






					"""simple docstring"""



					lowercase__				 =       "wandb"







					@staticmethod
					def        __lowerCAmelCase (       ):
						return is_wandb_available()







					def        __lowerCAmelCase (       self			:				str   ,lowercase_			:				Optional[int]   ,lowercase_			:				int   ,lowercase_			:				str   ,**lowercase_			:				List[Any]		):
						return run_hp_search_wandb(lowercase_   ,lowercase_   ,lowercase_   ,**lowercase_		)







					def        __lowerCAmelCase (       self			:				Any   ,lowercase_			:				Optional[int]		):
						return default_hp_space_wandb(lowercase_		)

__UpperCamelCase  :      Dict							      =		{
    HPSearchBackend(backend.name): backend for backend in [OptunaBackend, RayTuneBackend, SigOptBackend, WandbBackend]
}

def   __SCREAMING_SNAKE_CASE  (      ):
	lowerCAmelCase__	:						Tuple  				=	[backend for backend in ALL_HYPERPARAMETER_SEARCH_BACKENDS.values() if backend.is_available()]
	if len(A_ ) > 0:
		lowerCAmelCase__	:						Optional[int]  				=	available_backends[0].name
		if len(A_ ) > 1:
			logger.info(
			    f'{len(A_ )} hyperparameter search backends available. Using {name} as the default.' )
		return name
	raise RuntimeError(
	    '''No hyperparameter search backend available.\n'''
	    + '''\n'''.join(
	        f' - To install {backend.name} run {backend.pip_install()}'
	        for backend in ALL_HYPERPARAMETER_SEARCH_BACKENDS.values() ) )






import sacrebleu as scb
from packaging import version
from sacrebleu import CHRF

import datasets


SCREAMING_SNAKE_CASE__   :			Union[str, Any]												=    "\\n@inproceedings{popovic-2015-chrf,\n    title = \"chr{F}: character n-gram {F}-score for automatic {MT} evaluation\",\n    author = \"Popovi{\'c}, Maja\",\n    booktitle = \"Proceedings of the Tenth Workshop on Statistical Machine Translation\",\n    month = sep,\n    year = \"2015\",\n    address = \"Lisbon, Portugal\",\n    publisher = \"Association for Computational Linguistics\",\n    url = \"https://aclanthology.org/W15-3049\",\n    doi = \"10.18653/v1/W15-3049\",\n    pages = \"392--395\",\n}\n@inproceedings{popovic-2017-chrf,\n    title = \"chr{F}++: words helping character n-grams\",\n    author = \"Popovi{\'c}, Maja\",\n    booktitle = \"Proceedings of the Second Conference on Machine Translation\",\n    month = sep,\n    year = \"2017\",\n    address = \"Copenhagen, Denmark\",\n    publisher = \"Association for Computational Linguistics\",\n    url = \"https://aclanthology.org/W17-4770\",\n    doi = \"10.18653/v1/W17-4770\",\n    pages = \"612--618\",\n}\n@inproceedings{post-2018-call,\n    title = \"A Call for Clarity in Reporting {BLEU} Scores\",\n    author = \"Post, Matt\",\n    booktitle = \"Proceedings of the Third Conference on Machine Translation: Research Papers\",\n    month = oct,\n    year = \"2018\",\n    address = \"Belgium, Brussels\",\n    publisher = \"Association for Computational Linguistics\",\n    url = \"https://www.aclweb.org/anthology/W18-6319\",\n    pages = \"186--191\",\n}\n"

SCREAMING_SNAKE_CASE__   :			int												=    "\\nChrF and ChrF++ are two MT evaluation metrics. They both use the F-score statistic for character n-gram matches,\nand ChrF++ adds word n-grams as well which correlates more strongly with direct assessment. We use the implementation\nthat is already present in sacrebleu.\n\nThe implementation here is slightly different from sacrebleu in terms of the required input format. The length of\nthe references and hypotheses lists need to be the same, so you may need to transpose your references compared to\nsacrebleu's required input format. See https://github.com/huggingface/datasets/issues/3154#issuecomment-950746534\n\nSee the README.md file at https://github.com/mjpost/sacreBLEU#chrf--chrf for more information.\n"

SCREAMING_SNAKE_CASE__   :			Optional[Any]												=    "\nProduces ChrF(++) scores for hypotheses given reference translations.\n\nArgs:\n    predictions (list of str): The predicted sentences.\n    references (list of list of str): The references. There should be one reference sub-list for each prediction sentence.\n    char_order (int): Character n-gram order. Defaults to `6`.\n    word_order (int): Word n-gram order. If equals to `2`, the metric is referred to as chrF++. Defaults to `0`.\n    beta (int): Determine the importance of recall w.r.t precision. Defaults to `2`.\n    lowercase (bool): if `True`, enables case-insensitivity. Defaults to `False`.\n    whitespace (bool): If `True`, include whitespaces when extracting character n-grams.\n    eps_smoothing (bool): If `True`, applies epsilon smoothing similar\n    to reference chrF++.py, NLTK and Moses implementations. If `False`,\n    it takes into account effective match order similar to sacreBLEU < 2.0.0. Defaults to `False`.\n\nReturns:\n    'score' (float): The chrF (chrF++) score,\n    'char_order' (int): The character n-gram order,\n    'word_order' (int): The word n-gram order. If equals to 2, the metric is referred to as chrF++,\n    'beta' (int): Determine the importance of recall w.r.t precision\n\nExamples:\n    Example 1--a simple example of calculating chrF:\n        >>> prediction = [\"The relationship between cats and dogs is not exactly friendly.\", \"a good bookshop is just a genteel black hole that knows how to read.\"]\n        >>> reference = [[\"The relationship between dogs and cats is not exactly friendly.\"], [\"A good bookshop is just a genteel Black Hole that knows how to read.\"]]\n        >>> chrf = datasets.load_metric(\"chrf\")\n        >>> results = chrf.compute(predictions=prediction, references=reference)\n        >>> print(results)\n        {'score': 84.64214891738334, 'char_order': 6, 'word_order': 0, 'beta': 2}\n\n    Example 2--the same example, but with the argument word_order=2, to calculate chrF++ instead of chrF:\n        >>> prediction = [\"The relationship between cats and dogs is not exactly friendly.\", \"a good bookshop is just a genteel black hole that knows how to read.\"]\n        >>> reference = [[\"The relationship between dogs and cats is not exactly friendly.\"], [\"A good bookshop is just a genteel Black Hole that knows how to read.\"]]\n        >>> chrf = datasets.load_metric(\"chrf\")\n        >>> results = chrf.compute(predictions=prediction,\n        ...                         references=reference,\n        ...                         word_order=2)\n        >>> print(results)\n        {'score': 82.87263732906315, 'char_order': 6, 'word_order': 2, 'beta': 2}\n\n    Example 3--the same chrF++ example as above, but with `lowercase=True` to normalize all case:\n        >>> prediction = [\"The relationship between cats and dogs is not exactly friendly.\", \"a good bookshop is just a genteel black hole that knows how to read.\"]\n        >>> reference = [[\"The relationship between dogs and cats is not exactly friendly.\"], [\"A good bookshop is just a genteel Black Hole that knows how to read.\"]]\n        >>> chrf = datasets.load_metric(\"chrf\")\n        >>> results = chrf.compute(predictions=prediction,\n        ...                         references=reference,\n        ...                         word_order=2,\n        ...                         lowercase=True)\n        >>> print(results)\n        {'score': 92.12853119829202, 'char_order': 6, 'word_order': 2, 'beta': 2}\n"

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






		def  __A       (		self		:							Dict  )     ->							Tuple:
						if version.parse(scb.__version__  ) < version.parse('''1.4.12'''  ):
										raise ImportWarning(
										    '''To use `sacrebleu`, the module `sacrebleu>=1.4.12` is required, and the current version of `sacrebleu` doesn\'t match this condition.\n'''
										    '''You can install it with `pip install "sacrebleu>=1.4.12"`.'''  )
						return datasets.MetricInfo(
						    description=_DESCRIPTION							,			citation=_CITATION							,			homepage='''https://github.com/mjpost/sacreBLEU#chrf--chrf'''							,			inputs_description=_KWARGS_DESCRIPTION							,			features=datasets.Features(
						        {
						            '''predictions''': datasets.Value('''string'''							,			id='''sequence'''  ),
						            '''references''': datasets.Sequence(datasets.Value('''string'''							,			id='''sequence'''  )							,			id='''references'''  ),
						        }  )							,			codebase_urls=['''https://github.com/mjpost/sacreBLEU#chrf--chrf''']							,			reference_urls=[
						        '''https://github.com/m-popovic/chrF''',
						    ]							,			)







		def  __A       (		self		:							Union[str, Any]							,			SCREAMING_SNAKE_CASE__		:							Optional[Any]							,			SCREAMING_SNAKE_CASE__		:							Optional[Any]							,			SCREAMING_SNAKE_CASE__		:							int = CHRF.CHAR_ORDER							,			SCREAMING_SNAKE_CASE__		:							int = CHRF.WORD_ORDER							,			SCREAMING_SNAKE_CASE__		:							int = CHRF.BETA							,			SCREAMING_SNAKE_CASE__		:							bool = False							,			SCREAMING_SNAKE_CASE__		:							bool = False							,			SCREAMING_SNAKE_CASE__		:							bool = False							,			)     ->							Optional[Any]:
						__lowerCamelCase    					=	len(references[0]  )
						if any(len(SCREAMING_SNAKE_CASE__  ) != references_per_prediction for refs in references  ):
										raise ValueError('''Sacrebleu requires the same number of references for each prediction'''  )
						__lowerCamelCase    					=	[[refs[i] for refs in references] for i in range(SCREAMING_SNAKE_CASE__  )]

						__lowerCamelCase    					=	CHRF(SCREAMING_SNAKE_CASE__							,			SCREAMING_SNAKE_CASE__							,			SCREAMING_SNAKE_CASE__							,			SCREAMING_SNAKE_CASE__							,			SCREAMING_SNAKE_CASE__							,			SCREAMING_SNAKE_CASE__  )
						__lowerCamelCase    					=	sb_chrf.corpus_score(SCREAMING_SNAKE_CASE__							,			SCREAMING_SNAKE_CASE__  )

						return {
						    "score": output.score,
						    "char_order": output.char_order,
						    "word_order": output.word_order,
						    "beta": output.beta,
						}



