

"""simple docstring"""


from __future__ import annotations

import math





class    snake_case     :

				def __init__(     self							:    Union[str, Any]					,  UpperCamelCase__							:    int)->       None:



						'''simple docstring'''


						__lowerCAmelCase:	Tuple   				=      size
						# approximate the overall size of segment tree with given value
						__lowerCAmelCase:	List[Any]   				=      [0 for i in range(0					,  4 * size)]
						# create array to store lazy update
						__lowerCAmelCase:	str   				=      [0 for i in range(0					,  4 * size)]
						__lowerCAmelCase:	Dict   				=      [0 for i in range(0					,  4 * size)]  # flag for lazy update

				def 							lowercase_					(     self							:    Any					,  UpperCamelCase__							:    int)->       int:



						'''simple docstring'''


						return idx * 2

				def 							lowercase_					(     self							:    List[Any]					,  UpperCamelCase__							:    int)->       int:



						'''simple docstring'''


						return idx * 2 + 1

				def 							lowercase_					(     self							:    List[Any]					,  UpperCamelCase__							:    int					,  UpperCamelCase__							:    int					,  UpperCamelCase__							:    int					,  UpperCamelCase__							:    list[int])->       None:



						'''simple docstring'''


						if left_element == right_element:
								__lowerCAmelCase:	str   				=      a[left_element - 1]
						else:
								__lowerCAmelCase:	Dict   				=      (left_element + right_element) // 2
								self.build(self.left(UpperCamelCase__)					,  UpperCamelCase__					,  UpperCamelCase__					,  UpperCamelCase__)
								self.build(self.right(UpperCamelCase__)					,  mid + 1					,  UpperCamelCase__					,  UpperCamelCase__)
								__lowerCAmelCase:	List[str]   				=      max(
								    self.segment_tree[self.left(UpperCamelCase__)]					,  self.segment_tree[self.right(UpperCamelCase__)])

				def 							lowercase_					(     self							:    List[Any]					,  UpperCamelCase__							:    int					,  UpperCamelCase__							:    int					,  UpperCamelCase__							:    int					,  UpperCamelCase__							:    int					,  UpperCamelCase__							:    int					,  UpperCamelCase__							:    int)->       bool:



						'''simple docstring'''


						if self.flag[idx] is True:
								__lowerCAmelCase:	Optional[int]   				=      self.lazy[idx]
								__lowerCAmelCase:	List[Any]   				=      False
								if left_element != right_element:
										__lowerCAmelCase:	List[Any]   				=      self.lazy[idx]
										__lowerCAmelCase:	List[str]   				=      self.lazy[idx]
										__lowerCAmelCase:	str   				=      True
										__lowerCAmelCase:	Union[str, Any]   				=      True

						if right_element < a or left_element > b:
								return True
						if left_element >= a and right_element <= b:
								__lowerCAmelCase:	Tuple   				=      val
								if left_element != right_element:
										__lowerCAmelCase:	Any   				=      val
										__lowerCAmelCase:	Tuple   				=      val
										__lowerCAmelCase:	Any   				=      True
										__lowerCAmelCase:	List[str]   				=      True
								return True
						__lowerCAmelCase:	List[Any]   				=      (left_element + right_element) // 2
						self.update(self.left(UpperCamelCase__)					,  UpperCamelCase__					,  UpperCamelCase__					,  UpperCamelCase__					,  UpperCamelCase__					,  UpperCamelCase__)
						self.update(self.right(UpperCamelCase__)					,  mid + 1					,  UpperCamelCase__					,  UpperCamelCase__					,  UpperCamelCase__					,  UpperCamelCase__)
						__lowerCAmelCase:	Dict   				=      max(
						    self.segment_tree[self.left(UpperCamelCase__)]					,  self.segment_tree[self.right(UpperCamelCase__)])
						return True

				def 							lowercase_					(     self							:    str					,  UpperCamelCase__							:    int					,  UpperCamelCase__							:    int					,  UpperCamelCase__							:    int					,  UpperCamelCase__							:    int					,  UpperCamelCase__							:    int)->       int | float:



						'''simple docstring'''


						if self.flag[idx] is True:
								__lowerCAmelCase:	Tuple   				=      self.lazy[idx]
								__lowerCAmelCase:	List[str]   				=      False
								if left_element != right_element:
										__lowerCAmelCase:	List[Any]   				=      self.lazy[idx]
										__lowerCAmelCase:	Optional[Any]   				=      self.lazy[idx]
										__lowerCAmelCase:	int   				=      True
										__lowerCAmelCase:	Union[str, Any]   				=      True
						if right_element < a or left_element > b:
								return -math.inf
						if left_element >= a and right_element <= b:
								return self.segment_tree[idx]
						__lowerCAmelCase:	List[Any]   				=      (left_element + right_element) // 2
						__lowerCAmelCase:	Any   				=      self.query(self.left(UpperCamelCase__)					,  UpperCamelCase__					,  UpperCamelCase__					,  UpperCamelCase__					,  UpperCamelCase__)
						__lowerCAmelCase:	int   				=      self.query(self.right(UpperCamelCase__)					,  mid + 1					,  UpperCamelCase__					,  UpperCamelCase__					,  UpperCamelCase__)
						return max(UpperCamelCase__					,  UpperCamelCase__)




				def __str__(     self							:    Optional[Any])->       str:



						'''simple docstring'''


						return str([self.query(1					,  1					,  self.size					,  UpperCamelCase__					,  UpperCamelCase__) for i in range(1					,  self.size + 1)])


if __name__ == "__main__":
			__A						       =      [1, 2, -4, 7, 3, -5, 6, 11, -20, 9, 14, 15, 5, 2, -8]
			__A						       =      15
			__A						       =      SegmentTree(size)
			segt.build(1, 1, size, A)
			print(segt.query(1, 1, size, 4, 6))
			print(segt.query(1, 1, size, 7, 11))
			print(segt.query(1, 1, size, 7, 12))
			segt.update(1, 1, size, 1, 3, 111)
			print(segt.query(1, 1, size, 1, 15))
			segt.update(1, 1, size, 7, 8, 235)
			print(segt)





"""simple docstring"""


import comet  # From: unbabel-comet
import torch

import datasets


__A						       =      datasets.logging.get_logger(__name__)

__A						       =      "\\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"

__A						       =      "\\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"

__A						       =      "\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    snake_case     (    datasets.Metric  ):

				def 							lowercase_					(     self							:    List[Any])->       Dict:



						'''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							:    Tuple					,  UpperCamelCase__							:    Any)->       Dict:



						'''simple docstring'''


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




				def 							lowercase_					(     self							:    Optional[int]					,  UpperCamelCase__							:    Optional[Any]					,  UpperCamelCase__							:    Optional[Any]					,  UpperCamelCase__							:    int					,  UpperCamelCase__							:    Optional[int]=None					,  UpperCamelCase__							:    List[str]=False)->       str:



						'''simple docstring'''


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


