
import comet  # From: unbabel-comet
import torch

import datasets


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

SCREAMING_SNAKE_CASE							:List[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'

SCREAMING_SNAKE_CASE							:Tuple										=    '\\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'

SCREAMING_SNAKE_CASE							:Union[str, Any]										=    '\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 							UpperCAmelCase				(  datasets.Metric  ):




	'''simple docstring'''





	def     UpperCamelCase_  (	self :			List[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     UpperCamelCase_  (	self :			Tuple							,A :			Any			):


					if self.config_name == "default":
									__A             =			comet.load_from_checkpoint(comet.download_model("wmt20-comet-da"			)			)
					else:
									__A             =			comet.load_from_checkpoint(comet.download_model(self.config_name			)			)







	def     UpperCamelCase_  (	self :			Optional[int]							,A :			Optional[Any]							,A :			Optional[Any]							,A :			int							,A :			Optional[int]=None							,A :			List[str]=False			):


					if gpus is None:
									__A             =			1 if torch.cuda.is_available() else 0
					__A             =			{"src": sources, "mt": predictions, "ref": references}
					__A             =			[dict(zip(UpperCamelCase__							,UpperCamelCase__			)			) for t in zip(*data.values()			)]
					__A             =			self.scorer.predict(UpperCamelCase__							,gpus=UpperCamelCase__							,progress_bar=UpperCamelCase__			)
					return {"mean_score": mean_score, "scores": scores}


from collections import Counter

import numpy as np
from sklearn import datasets
from sklearn.model_selection import train_test_split

SCREAMING_SNAKE_CASE							:Tuple										=    datasets.load_iris()

SCREAMING_SNAKE_CASE							:Dict										=    np.array(data['data'])
SCREAMING_SNAKE_CASE							:Optional[int]										=    np.array(data['target'])
SCREAMING_SNAKE_CASE							:List[str]										=    data['target_names']

SCREAMING_SNAKE_CASE			,   SCREAMING_SNAKE_CASE			,   SCREAMING_SNAKE_CASE			,   SCREAMING_SNAKE_CASE							:List[Any]										=    train_test_split(X, y)


def 						UpperCAmelCase      (     a_		,       a_						)		->     Tuple:







				"""simple docstring"""



				return np.linalg.norm(np.array(a_						) - np.array(a_						)						)


def 						UpperCAmelCase      (     a_		,       a_		,       a_		,       a_		,       a_=5						)		->     Dict:







				"""simple docstring"""



				__A             =			zip(a_		,       a_						)
				# List of distances of all points from the point to be classified
				__A             =			[]
				for data_point in data:
								__A             =			euclidean_distance(data_point[0]		,       a_						)
								distances.append((distance, data_point[1])						)
				# Choosing 'k' points with the least distances.
				__A             =			[i[1] for i in sorted(a_						)[:k]]
				# Most commonly occurring class among them
				# is the class into which the point is classified
				__A             =			Counter(a_						).most_common(1						)[0][0]
				return classes[result]


if __name__ == "__main__":
					print(classifier(X_train, y_train, classes, [4.4, 3.1, 1.3, 1.4]))
