
import comet  # From: unbabel-comet
import torch

import datasets


_lowerCamelCase       :				Union[str, Any]       =			datasets.logging.get_logger(__name__)

_lowerCamelCase       :				List[Any]       =			'''\
@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",
}
'''

_lowerCamelCase       :				Tuple       =			'''\
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.
'''

_lowerCamelCase       :				Any       =			'''
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					):





	'''simple docstring'''



	def  A							(  self	:    Optional[int]	):


				'''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  A							(  self	:    Optional[int]			,  lowercase	:    List[str]	):


				'''simple docstring'''


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





	def  A							(  self	:    Any			,  lowercase	:    Tuple			,  lowercase	:    Union[str, Any]			,  lowercase	:    Optional[Any]			,  lowercase	:    str=None			,  lowercase	:    str=False	):


				'''simple docstring'''


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

from __future__ import annotations

import time
from collections.abc import Sequence
from random import randint

from matplotlib import pyplot as plt






def        a_		(       __lowercase   :	Sequence[float]       ,					__lowercase   :	int       ,					__lowercase   :	int ) ->  tuple[int | None, int | None, float]:
			if not arr:
						return None, None, 0
			if low == high:
						return low, high, arr[low]

			_snake_case		=			(low + high) // 2
			_snake_case	,					_snake_case	,					_snake_case		=			max_subarray(__lowercase       ,					__lowercase       ,					__lowercase )
			_snake_case	,					_snake_case	,					_snake_case		=			max_subarray(__lowercase       ,					mid + 1       ,					__lowercase )
			_snake_case	,					_snake_case	,					_snake_case		=			max_cross_sum(__lowercase       ,					__lowercase       ,					__lowercase       ,					__lowercase )
			if left_sum >= right_sum and left_sum >= cross_sum:
						return left_low, left_high, left_sum
			elif right_sum >= left_sum and right_sum >= cross_sum:
						return right_low, right_high, right_sum
			return cross_left, cross_right, cross_sum






def        a_		(       __lowercase   :	Sequence[float]       ,					__lowercase   :	int       ,					__lowercase   :	int       ,					__lowercase   :	int ) ->  tuple[int, int, float]:
			_snake_case	,					_snake_case		=			float('-inf' ), -1
			_snake_case	,					_snake_case		=			float('-inf' ), -1

			_snake_case		=			0
			for i in range(__lowercase       ,					low - 1       ,					-1 ):
						summ += arr[i]
						if summ > left_sum:
									_snake_case		=			summ
									_snake_case		=			i

			_snake_case		=			0
			for i in range(mid + 1       ,					high + 1 ):
						summ += arr[i]
						if summ > right_sum:
									_snake_case		=			summ
									_snake_case		=			i

			return max_left, max_right, (left_sum + right_sum)






def        a_		(       __lowercase   :	int ) ->  float:
			_snake_case		=			[randint(1       ,					__lowercase ) for _ in range(__lowercase )]
			_snake_case		=			time.time()
			max_subarray(__lowercase       ,					0       ,					input_size - 1 )
			_snake_case		=			time.time()
			return end - start






def        a_		(       ) ->  None:
			_snake_case		=			[10, 100, 1_000, 10_000, 50_000, 100_000, 200_000, 300_000, 400_000, 500_000]
			_snake_case		=			[time_max_subarray(__lowercase ) for input_size in input_sizes]
			print('No of Inputs\t\tTime Taken' )
			for input_size, runtime in zip(__lowercase       ,					__lowercase ):
						print(__lowercase       ,					'\t\t'       ,					__lowercase )
			plt.plot(__lowercase       ,					__lowercase )
			plt.xlabel('Number of Inputs' )
			plt.ylabel('Time taken in seconds' )
			plt.show()


if __name__ == "__main__":
			from doctest import testmod

			testmod()