



"""simple docstring"""
import logging
import os
import sys
from pathlib import Path
from unittest.mock import patch

from parameterized import parameterized
from run_eval import run_generate
from run_eval_search import run_search

from transformers.testing_utils import CaptureStdout, TestCasePlus, slow
from utils import ROUGE_KEYS


logging.basicConfig(level=logging.DEBUG)
A:   Dict								=		logging.getLogger()

def       _snake_case						(						UpperCamelCase						:		Path			,		UpperCamelCase						:		list						):
 UpperCAmelCase	:					str	=	"""\n""".join(UpperCamelCase						)
 Path(UpperCamelCase						).open("""w"""						).writelines(UpperCamelCase						)


A:   int								=		"patrickvonplaten/t5-tiny-random"
A:   Dict								=		"sshleifer/bart-tiny-random"
A:   Tuple								=		"sshleifer/tiny-mbart"

A:   Tuple								=		logging.StreamHandler(sys.stdout)
logger.addHandler(stream_handler)
logging.disable(logging.CRITICAL)  # remove noisy download output from tracebacks



class       SCREAMING_SNAKE_CASE__  (					UpperCAmelCase__ ):

   def 	SCREAMING_SNAKE_CASE     (   self      ,     _SCREAMING_SNAKE_CASE						)  ->      int:





    '''simple docstring'''




    UpperCAmelCase	:					Dict	=	Path(self.get_auto_remove_tmp_dir()						) / """utest_input.source"""
    UpperCAmelCase	:					Dict	=	input_file_name.parent / """utest_output.txt"""
    assert not output_file_name.exists()
    UpperCAmelCase	:					Dict	=	[""" New York (CNN)When Liana Barrientos was 23 years old, she got married in Westchester County."""]
    _dump_articles(_SCREAMING_SNAKE_CASE      ,     _SCREAMING_SNAKE_CASE						)

    UpperCAmelCase	:					Union[str, Any]	=	str(Path(self.get_auto_remove_tmp_dir()						) / """scores.json"""						)
    UpperCAmelCase	:					str	=	"""translation_en_to_de""" if model == T5_TINY else """summarization"""
    UpperCAmelCase	:					Dict	=	F"\n            run_eval_search.py\n            {model}\n            {input_file_name}\n            {output_file_name}\n            --score_path {score_path}\n            --task {task}\n            --num_beams 2\n            --length_penalty 2.0\n            ".split()

    with patch.object(_SCREAMING_SNAKE_CASE      ,     """argv"""      ,     _SCREAMING_SNAKE_CASE						):
     run_generate()
     assert Path(_SCREAMING_SNAKE_CASE						).exists()
     # os.remove(Path(output_file_name))

   def 	SCREAMING_SNAKE_CASE     (   self						)  ->      List[str]:





    '''simple docstring'''




    self.run_eval_tester(_SCREAMING_SNAKE_CASE						)

   @parameterized.expand([BART_TINY, MBART_TINY]						)
   @slow
   def 	SCREAMING_SNAKE_CASE     (   self      ,     _SCREAMING_SNAKE_CASE						)  ->      Optional[Any]:





    '''simple docstring'''




    self.run_eval_tester(_SCREAMING_SNAKE_CASE						)




   @parameterized.expand([T5_TINY, MBART_TINY]						)
   @slow
   def 	SCREAMING_SNAKE_CASE     (   self      ,     _SCREAMING_SNAKE_CASE						)  ->      Any:





    '''simple docstring'''




    UpperCAmelCase	:					Optional[Any]	=	Path(self.get_auto_remove_tmp_dir()						) / """utest_input.source"""
    UpperCAmelCase	:					int	=	input_file_name.parent / """utest_output.txt"""
    assert not output_file_name.exists()

    UpperCAmelCase	:					int	=	{
        """en""": ["""Machine learning is great, isn't it?""", """I like to eat bananas""", """Tomorrow is another great day!"""],
        """de""": [
            """Maschinelles Lernen ist großartig, oder?""",
            """Ich esse gerne Bananen""",
            """Morgen ist wieder ein toller Tag!""",
        ],
    }

    UpperCAmelCase	:					Tuple	=	Path(self.get_auto_remove_tmp_dir()						)
    UpperCAmelCase	:					Optional[int]	=	str(tmp_dir / """scores.json"""						)
    UpperCAmelCase	:					Dict	=	str(tmp_dir / """val.target"""						)
    _dump_articles(_SCREAMING_SNAKE_CASE      ,     text["""en"""]						)
    _dump_articles(_SCREAMING_SNAKE_CASE      ,     text["""de"""]						)
    UpperCAmelCase	:					Tuple	=	"""translation_en_to_de""" if model == T5_TINY else """summarization"""
    UpperCAmelCase	:					Union[str, Any]	=	F"\n            run_eval_search.py\n            {model}\n            {str(_SCREAMING_SNAKE_CASE						)}\n            {str(_SCREAMING_SNAKE_CASE						)}\n            --score_path {score_path}\n            --reference_path {reference_path}\n            --task {task}\n            ".split()
    testargs.extend(["""--search""", """num_beams=1:2 length_penalty=0.9:1.0"""]						)

    with patch.object(_SCREAMING_SNAKE_CASE      ,     """argv"""      ,     _SCREAMING_SNAKE_CASE						):
     with CaptureStdout() as cs:
      run_search()
     UpperCAmelCase	:					str	=	[""" num_beams | length_penalty""", model, """Best score args"""]
     UpperCAmelCase	:					Union[str, Any]	=	["""Info"""]
     if "translation" in task:
      expected_strings.append("""bleu"""						)
     else:
      expected_strings.extend(_SCREAMING_SNAKE_CASE						)
     for w in expected_strings:
      assert w in cs.out
     for w in un_expected_strings:
      assert w not in cs.out
     assert Path(_SCREAMING_SNAKE_CASE						).exists()
     os.remove(Path(_SCREAMING_SNAKE_CASE						)						)




import comet  # From: unbabel-comet
import torch

import datasets


UpperCAmelCase__   	=							datasets.logging.get_logger(__name__)

UpperCAmelCase__   	=							'''\
@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",
}
'''

UpperCAmelCase__   	=							'''\
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.
'''

UpperCAmelCase__   	=							'''
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       lowerCamelCase__   (       datasets.Metric):





     def      __A					(self       )     ->   Optional[int]:
      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		,     UpperCAmelCase       )     ->   Dict:
      if self.config_name == "default":
       _lowercase							      =comet.load_from_checkpoint(comet.download_model('''wmt20-comet-da'''       )       )
      else:
       _lowercase							      =comet.load_from_checkpoint(comet.download_model(self.config_name       )       )





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

