'''simple docstring'''







from importlib import import_module

from .logging import get_logger


UpperCamelCase_      	=	get_logger(__name__)




class _SCREAMING_SNAKE_CASE:




						def __init__(    self :       Tuple							,							UpperCamelCase_ :       Optional[int]							,							UpperCamelCase_ :       Optional[Any]=None      )     ->				int:
													SCREAMING_SNAKE_CASE__     :Union[str, Any]						    = attrs or []
													if module is not None:
																				for key in module.__dict__:
																											if key in attrs or not key.startswith('__'      ):
																																		setattr(self							,							UpperCamelCase_							,							getattr(UpperCamelCase_							,							UpperCamelCase_      )      )
													SCREAMING_SNAKE_CASE__     :Tuple						    = module._original_module if isinstance(UpperCamelCase_							,							_PatchedModuleObj      ) else module




class _SCREAMING_SNAKE_CASE:
						A_			:							Union[str, Any]     		= []




						def __init__(    self :       str							,							UpperCamelCase_ :       Tuple							,							UpperCamelCase_ :       str							,							UpperCamelCase_ :       List[Any]							,							UpperCamelCase_ :       Optional[Any]=None      )     ->				Tuple:
													SCREAMING_SNAKE_CASE__     :int						    = obj
													SCREAMING_SNAKE_CASE__     :Optional[Any]						    = target
													SCREAMING_SNAKE_CASE__     :int						    = new
													SCREAMING_SNAKE_CASE__     :List[Any]						    = target.split('.'      )[0]
													SCREAMING_SNAKE_CASE__     :Tuple						    = {}
													SCREAMING_SNAKE_CASE__     :int						    = attrs or []




						def __enter__(    self :       Optional[int]      )     ->				List[Any]:
													*SCREAMING_SNAKE_CASE__    ,      SCREAMING_SNAKE_CASE__     :Optional[Any]						    = self.target.split('.'      )

													# Patch modules:
													# it's used to patch attributes of submodules like "os.path.join";
													# in this case we need to patch "os" and "os.path"

													for i in range(len(UpperCamelCase_      )      ):
																				try:
																											SCREAMING_SNAKE_CASE__     :str						    = import_module('.'.join(submodules[: i + 1]      )      )
																				except ModuleNotFoundError:
																											continue
																				# We iterate over all the globals in self.obj in case we find "os" or "os.path"
																				for attr in self.obj.__dir__():
																											SCREAMING_SNAKE_CASE__     :Dict						    = getattr(self.obj							,							UpperCamelCase_      )
																											# We don't check for the name of the global, but rather if its value *is* "os" or "os.path".
																											# This allows to patch renamed modules like "from os import path as ospath".
																											if obj_attr is submodule or (
																											    (isinstance(UpperCamelCase_							,							_PatchedModuleObj      ) and obj_attr._original_module is submodule)
																											):
																																		SCREAMING_SNAKE_CASE__     :Dict						    = obj_attr
																																		# patch at top level
																																		setattr(self.obj							,							UpperCamelCase_							,							_PatchedModuleObj(UpperCamelCase_							,							attrs=self.attrs      )      )
																																		SCREAMING_SNAKE_CASE__     :Union[str, Any]						    = getattr(self.obj							,							UpperCamelCase_      )
																																		# construct lower levels patches
																																		for key in submodules[i + 1 :]:
																																									setattr(UpperCamelCase_							,							UpperCamelCase_							,							_PatchedModuleObj(getattr(UpperCamelCase_							,							UpperCamelCase_							,							UpperCamelCase_      )							,							attrs=self.attrs      )      )
																																									SCREAMING_SNAKE_CASE__     :Optional[int]						    = getattr(UpperCamelCase_							,							UpperCamelCase_      )
																																		# finally set the target attribute
																																		setattr(UpperCamelCase_							,							UpperCamelCase_							,							self.new      )

        # Patch attribute itself:
        # it's used for builtins like "open",
        # and also to patch "os.path.join" we may also need to patch "join"
        # itself if it was imported as "from os.path import join".

													if submodules:  # if it's an attribute of a submodule like "os.path.join"
																				try:
																											SCREAMING_SNAKE_CASE__     :int						    = getattr(import_module('.'.join(UpperCamelCase_      )      )							,							UpperCamelCase_      )
																				except (AttributeError, ModuleNotFoundError):
																											return
																				# We iterate over all the globals in self.obj in case we find "os.path.join"
																				for attr in self.obj.__dir__():
																											# We don't check for the name of the global, but rather if its value *is* "os.path.join".
																											# This allows to patch renamed attributes like "from os.path import join as pjoin".
																											if getattr(self.obj							,							UpperCamelCase_      ) is attr_value:
																																		SCREAMING_SNAKE_CASE__     :Union[str, Any]						    = getattr(self.obj							,							UpperCamelCase_      )
																																		setattr(self.obj							,							UpperCamelCase_							,							self.new      )
													elif target_attr in globals()["__builtins__"]:  # if it'a s builtin like "open"
																				SCREAMING_SNAKE_CASE__     :Optional[Any]						    = globals()['__builtins__'][target_attr]
																				setattr(self.obj							,							UpperCamelCase_							,							self.new      )
													else:
																				raise RuntimeError(f'''Tried to patch attribute {target_attr} instead of a submodule.'''      )




						def __exit__(    self :       int							,							*UpperCamelCase_ :       Dict      )     ->				Any:
													for attr in list(self.original      ):
																				setattr(self.obj							,							UpperCamelCase_							,							self.original.pop(UpperCamelCase_      )      )




						def      __lowerCamelCase       (    self :       int      )     ->				int:
													self.__enter__()
													self._active_patches.append(self      )




						def      __lowerCamelCase       (    self :       str      )     ->				int:
													try:
																				self._active_patches.remove(self      )
													except ValueError:
																				# If the patch hasn't been started this will fail
																				return None

													return self.__exit__()







'''simple docstring'''







import comet  # From: unbabel-comet
import torch

import datasets


UpperCamelCase_      	=	datasets.logging.get_logger(__name__)

UpperCamelCase_      	=	'''\
@inproceedings{rei-EtAl:2020:WMT,
   author    = {Rei, Ricardo  and  Stewart, Craig  and  Farinha, Ana C  and  Lavie, Alon},
   title     = {Unbabel\'s Participation in the WMT20 Metrics Shared Task},
   booktitle      = {Proceedings of the Fifth Conference on Machine Translation},
   month          = {November},
   year           = {2020},
   address        = {Online},
   publisher      = {Association for Computational Linguistics},
   pages     = {909--918},
}
@inproceedings{rei-etal-2020-comet,
   title = "{COMET}: A Neural Framework for {MT} Evaluation",
   author = "Rei, Ricardo  and
      Stewart, Craig  and
      Farinha, Ana C  and
      Lavie, Alon",
   booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)",
   month = nov,
   year = "2020",
   address = "Online",
   publisher = "Association for Computational Linguistics",
   url = "https://www.aclweb.org/anthology/2020.emnlp-main.213",
   pages = "2685--2702",
}
'''

UpperCamelCase_      	=	'''\
Crosslingual Optimized Metric for Evaluation of Translation (COMET) is an open-source framework used to train Machine Translation metrics that achieve high levels of correlation with different types of human judgments (HTER, DA\'s or MQM).
With the release of the framework the authors also released fully trained models that were used to compete in the WMT20 Metrics Shared Task achieving SOTA in that years competition.

See the [README.md] file at https://unbabel.github.io/COMET/html/models.html for more information.
'''

UpperCamelCase_      	=	'''
COMET score.

Args:

`sources` (list of str): Source sentences
`predictions` (list of str): candidate translations
`references` (list of str): reference translations
`cuda` (bool): If set to True, runs COMET using GPU
`show_progress` (bool): Shows progress
`model`: COMET model to be used. Will default to `wmt-large-da-estimator-1719` if None.

Returns:
    `samples`: List of dictionaries with `src`, `mt`, `ref` and `score`.
    `scores`: List of scores.

Examples:

    >>> comet_metric = datasets.load_metric(\'comet\')
    >>> # comet_metric = load_metric(\'comet\', \'wmt20-comet-da\')  # you can also choose which model to use
    >>> source = ["Dem Feuer konnte Einhalt geboten werden", "Schulen und Kindergärten wurden eröffnet."]
    >>> hypothesis = ["The fire could be stopped", "Schools and kindergartens were open"]
    >>> reference = ["They were able to control the fire.", "Schools and kindergartens opened"]
    >>> results = comet_metric.compute(predictions=hypothesis, references=reference, sources=source)
    >>> print([round(v, 2) for v in results["scores"]])
    [0.19, 0.92]
'''




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




						def      __lowerCamelCase       (    self :       Tuple      )     ->				Any:
													return datasets.MetricInfo(
													    description=_DESCRIPTION							,							citation=_CITATION							,							homepage='https://unbabel.github.io/COMET/html/index.html'							,							inputs_description=_KWARGS_DESCRIPTION							,							features=datasets.Features(
													        {
													            'sources': datasets.Value('string'							,							id='sequence'      ),
													            'predictions': datasets.Value('string'							,							id='sequence'      ),
													            'references': datasets.Value('string'							,							id='sequence'      ),
													        }      )							,							codebase_urls=['https://github.com/Unbabel/COMET']							,							reference_urls=[
													        'https://github.com/Unbabel/COMET',
													        'https://www.aclweb.org/anthology/2020.emnlp-main.213/',
													        'http://www.statmt.org/wmt20/pdf/2020.wmt-1.101.pdf6',
													    ]							,							)




						def      __lowerCamelCase       (    self :       List[str]							,							UpperCamelCase_ :       List[str]      )     ->				Dict:
													if self.config_name == "default":
																				SCREAMING_SNAKE_CASE__     :List[Any]						    = comet.load_from_checkpoint(comet.download_model('wmt20-comet-da'      )      )
													else:
																				SCREAMING_SNAKE_CASE__     :List[Any]						    = comet.load_from_checkpoint(comet.download_model(self.config_name      )      )




						def      __lowerCamelCase       (    self :       str							,							UpperCamelCase_ :       List[str]							,							UpperCamelCase_ :       Dict							,							UpperCamelCase_ :       Any							,							UpperCamelCase_ :       str=None							,							UpperCamelCase_ :       Optional[Any]=False      )     ->				Any:
													if gpus is None:
																				SCREAMING_SNAKE_CASE__     :Dict						    = 1 if torch.cuda.is_available() else 0
													SCREAMING_SNAKE_CASE__     :Dict						    = {'src': sources, 'mt': predictions, 'ref': references}
													SCREAMING_SNAKE_CASE__     :List[str]						    = [dict(zip(UpperCamelCase_							,							UpperCamelCase_      )      ) for t in zip(*data.values()      )]
													SCREAMING_SNAKE_CASE__    ,      SCREAMING_SNAKE_CASE__     :Union[str, Any]						    = self.scorer.predict(UpperCamelCase_							,							gpus=UpperCamelCase_							,							progress_bar=UpperCamelCase_      )
													return {"mean_score": mean_score, "scores": scores}






