

'''simple docstring'''



import comet  # From: unbabel-comet
import torch

import datasets


__snake_case					:      str 		=     datasets.logging.get_logger(__name__)

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

__snake_case					:      Any 		=     '\\nCrosslingual 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).\nWith 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.\n\nSee the [README.md] file at https://unbabel.github.io/COMET/html/models.html for more information.\n'

__snake_case					:      List[str] 		=     '\nCOMET score.\n\nArgs:\n\n`sources` (list of str): Source sentences\n`predictions` (list of str): candidate translations\n`references` (list of str): reference translations\n`cuda` (bool): If set to True, runs COMET using GPU\n`show_progress` (bool): Shows progress\n`model`: COMET model to be used. Will default to `wmt-large-da-estimator-1719` if None.\n\nReturns:\n    `samples`: List of dictionaries with `src`, `mt`, `ref` and `score`.\n    `scores`: List of scores.\n\nExamples:\n\n    >>> comet_metric = datasets.load_metric(\'comet\')\n    >>> # comet_metric = load_metric(\'comet\', \'wmt20-comet-da\')  # you can also choose which model to use\n    >>> source = ["Dem Feuer konnte Einhalt geboten werden", "Schulen und Kindergärten wurden eröffnet."]\n    >>> hypothesis = ["The fire could be stopped", "Schools and kindergartens were open"]\n    >>> reference = ["They were able to control the fire.", "Schools and kindergartens opened"]\n    >>> results = comet_metric.compute(predictions=hypothesis, references=reference, sources=source)\n    >>> print([round(v, 2) for v in results["scores"]])\n    [0.19, 0.92]\n'



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



			'''simple docstring'''






			def lowercase__		( self			:		Dict					)     ->						str:





									'''simple docstring'''
									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 lowercase__		( self			:		List[str]						,     lowerCAmelCase_			:		List[Any]					)     ->						Dict:





									'''simple docstring'''
									if self.config_name == "default":
															A__	:  Union[str, Any]           =comet.load_from_checkpoint(comet.download_model("""wmt20-comet-da"""					)					)
									else:
															A__	:  Tuple           =comet.load_from_checkpoint(comet.download_model(self.config_name					)					)



			def lowercase__		( self			:		str						,     lowerCAmelCase_			:		str						,     lowerCAmelCase_			:		Optional[int]						,     lowerCAmelCase_			:		Any						,     lowerCAmelCase_			:		Union[str, Any]=None						,     lowerCAmelCase_			:		Optional[Any]=False					)     ->						List[Any]:





									'''simple docstring'''
									if gpus is None:
															A__	:  Any           =1 if torch.cuda.is_available() else 0
									A__	:  str           ={"""src""": sources, """mt""": predictions, """ref""": references}
									A__	:  Any           =[dict(zip(__UpperCamelCase						,     __UpperCamelCase					)					) for t in zip(*data.values()					)]
									A__       ,		A__	:  Dict           =self.scorer.predict(__UpperCamelCase						,     gpus=__UpperCamelCase						,     progress_bar=__UpperCamelCase					)
									return {"mean_score": mean_score, "scores": scores}



import importlib.metadata
import operator
import re
import sys
from typing import Optional

from packaging import version


lowercase   =	{
    '''<''': operator.lt,
    '''<=''': operator.le,
    '''==''': operator.eq,
    '''!=''': operator.ne,
    '''>=''': operator.ge,
    '''>''': operator.gt,
}



def   __lowerCAmelCase    (				UpperCAmelCase__					:   Tuple      ,					UpperCAmelCase__					:   int      ,					UpperCAmelCase__					:   Optional[int]      ,					UpperCAmelCase__					:   Optional[int]      ,					UpperCAmelCase__					:   Optional[int]      ,					UpperCAmelCase__					:   int							)	->     Union[str, Any]:
							if got_ver is None or want_ver is None:
														raise ValueError(
														    F'''Unable to compare versions for {requirement}: need={want_ver} found={got_ver}. This is unusual. Consider'''
														    F''' reinstalling {pkg}.'''							)
							if not ops[op](version.parse(UpperCAmelCase__							)      ,					version.parse(UpperCAmelCase__							)							):
														raise ImportError(
														    F'''{requirement} is required for a normal functioning of this module, but found {pkg}=={got_ver}.{hint}'''							)



def   __lowerCAmelCase    (				UpperCAmelCase__					:   str      ,					UpperCAmelCase__					:   Optional[str] = None							)	->     None:
							lowerCamelCase_					=       F'''\n{hint}''' if hint is not None else """"""

							# non-versioned check
							if re.match(R"""^[\w_\-\d]+$"""      ,					UpperCAmelCase__							):
														lowerCamelCase_	, lowerCamelCase_	, lowerCamelCase_					=       requirement, None, None
							else:
														lowerCamelCase_					=       re.findall(R"""^([^!=<>\s]+)([\s!=<>]{1,2}.+)"""      ,					UpperCAmelCase__							)
														if not match:
																					raise ValueError(
																					    """requirement needs to be in the pip package format, .e.g., package_a==1.23, or package_b>=1.23, but"""
																					    F''' got {requirement}'''							)
														lowerCamelCase_	, lowerCamelCase_					=       match[0]
														lowerCamelCase_					=       want_full.split(""","""							)  # there could be multiple requirements
														lowerCamelCase_					=       {}
														for w in want_range:
																					lowerCamelCase_					=       re.findall(R"""^([\s!=<>]{1,2})(.+)"""      ,					UpperCAmelCase__							)
																					if not match:
																												raise ValueError(
																												    """requirement needs to be in the pip package format, .e.g., package_a==1.23, or package_b>=1.23,"""
																												    F''' but got {requirement}'''							)
																					lowerCamelCase_	, lowerCamelCase_					=       match[0]
																					lowerCamelCase_					=       want_ver
																					if op not in ops:
																												raise ValueError(F'''{requirement}: need one of {list(ops.keys()							)}, but got {op}'''							)

    # special case
							if pkg == "python":
														lowerCamelCase_					=       """.""".join([str(UpperCAmelCase__							) for x in sys.version_info[:3]]							)
														for op, want_ver in wanted.items():
																					_compare_versions(UpperCAmelCase__      ,					UpperCAmelCase__      ,					UpperCAmelCase__      ,					UpperCAmelCase__      ,					UpperCAmelCase__      ,					UpperCAmelCase__							)
														return

							# check if any version is installed
							try:
														lowerCamelCase_					=       importlib.metadata.version(UpperCAmelCase__							)
							except importlib.metadata.PackageNotFoundError:
														raise importlib.metadata.PackageNotFoundError(
														    F'''The \'{requirement}\' distribution was not found and is required by this application. {hint}'''							)

							# check that the right version is installed if version number or a range was provided
							if want_ver is not None:
														for op, want_ver in wanted.items():
																					_compare_versions(UpperCAmelCase__      ,					UpperCAmelCase__      ,					UpperCAmelCase__      ,					UpperCAmelCase__      ,					UpperCAmelCase__      ,					UpperCAmelCase__							)



def   __lowerCAmelCase    (				UpperCAmelCase__					:   List[str]							)	->     Dict:
							lowerCamelCase_					=       """Try: pip install transformers -U or pip install -e '.[dev]' if you're working with git main"""
							return require_version(UpperCAmelCase__      ,					UpperCAmelCase__							)





