






'''simple docstring'''

import comet  # From: unbabel-comet
import torch

import datasets


SCREAMING_SNAKE_CASE__     =       datasets.logging.get_logger(__name__)

SCREAMING_SNAKE_CASE__     =       '\\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__     =       '\\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__     =       '\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     a_						(      datasets.Metric    ):

  def   A__						(				self				)  ->				Any:



        """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      ,		_SCREAMING_SNAKE_CASE				)  ->				Dict:



        """simple docstring"""






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


  def   A__						(				self      ,		_SCREAMING_SNAKE_CASE      ,		_SCREAMING_SNAKE_CASE      ,		_SCREAMING_SNAKE_CASE      ,		_SCREAMING_SNAKE_CASE=None      ,		_SCREAMING_SNAKE_CASE=False				)  ->				Any:



        """simple docstring"""






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












'''simple docstring'''

from __future__ import annotations

import collections
import pprint
from pathlib import Path







def    lowercase__			(				__UpperCamelCase   )->			str:
      return "".join(sorted(__UpperCamelCase   )   )







def    lowercase__			(				__UpperCamelCase   )->			list[str]:
      return word_by_signature[signature(__UpperCamelCase   )]


SCREAMING_SNAKE_CASE__     =       Path(__file__).parent.joinpath('words.txt').read_text(encoding='utf-8')
SCREAMING_SNAKE_CASE__     =       sorted({word.strip().lower() for word in data.splitlines()})

SCREAMING_SNAKE_CASE__     =       collections.defaultdict(list)
for word in word_list:
     word_by_signature[signature(word)].append(word)

if __name__ == "__main__":
     SCREAMING_SNAKE_CASE__     =       {word: anagram(word) for word in word_list if len(anagram(word)) > 1}

     with open('anagrams.txt', 'w') as file:
          file.write('all_anagrams = \n ')
          file.write(pprint.pformat(all_anagrams))
