


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}





'''simple docstring'''



import importlib
import os
import sys


# This is required to make the module import works (when the python process is running from the root of the repo)
sys.path.append('.')






def   lowerCamelCase							(_SCREAMING_SNAKE_CASE      :   List[Any]   ):
					__a			:       Any					    =  test_file.split(os.path.sep   )
					if components[0:2] != ["tests", "models"]:
										raise ValueError(
										    '`test_file` should start with `tests/models/` (with `/` being the OS specific path separator). Got '
										    F"""{test_file} instead."""   )
					__a			:       Tuple					    =  components[-1]
					if not test_fn.endswith('py'   ):
										raise ValueError(F"""`test_file` should be a python file. Got {test_fn} instead."""   )
					if not test_fn.startswith('test_modeling_'   ):
										raise ValueError(
										    F"""`test_file` should point to a file name of the form `test_modeling_*.py`. Got {test_fn} instead."""   )

					__a			:       List[str]					    =  components[:-1] + [test_fn.replace('.py'						,      ''   )]
					__a			:       Optional[Any]					    =  '.'.join(_SCREAMING_SNAKE_CASE   )

					return test_module_path






def   lowerCamelCase							(_SCREAMING_SNAKE_CASE      :   Tuple   ):
					__a			:       List[str]					    =  get_module_path(_SCREAMING_SNAKE_CASE   )
					__a			:       Dict					    =  importlib.import_module(_SCREAMING_SNAKE_CASE   )

					return test_module






def   lowerCamelCase							(_SCREAMING_SNAKE_CASE      :   Tuple   ):
					__a			:       List[str]					    =  []
					__a			:       List[str]					    =  get_test_module(_SCREAMING_SNAKE_CASE   )
					for attr in dir(_SCREAMING_SNAKE_CASE   ):
										if attr.endswith('ModelTester'   ):
															tester_classes.append(getattr(_SCREAMING_SNAKE_CASE						,      _SCREAMING_SNAKE_CASE   )   )

    # sort with class names
					return sorted(_SCREAMING_SNAKE_CASE						,      key=lambda _SCREAMING_SNAKE_CASE   : x.__name__   )






def   lowerCamelCase							(_SCREAMING_SNAKE_CASE      :   Tuple   ):
					__a			:       Any					    =  []
					__a			:       str					    =  get_test_module(_SCREAMING_SNAKE_CASE   )
					for attr in dir(_SCREAMING_SNAKE_CASE   ):
										__a			:       int					    =  getattr(_SCREAMING_SNAKE_CASE						,      _SCREAMING_SNAKE_CASE   )
										# (TF/Flax)ModelTesterMixin is also an attribute in specific model test module. Let's exclude them by checking
										# `all_model_classes` is not empty (which also excludes other special classes).
										__a			:       Optional[Any]					    =  getattr(_SCREAMING_SNAKE_CASE						,      'all_model_classes'						,      []   )
										if len(_SCREAMING_SNAKE_CASE   ) > 0:
															test_classes.append(_SCREAMING_SNAKE_CASE   )

    # sort with class names
					return sorted(_SCREAMING_SNAKE_CASE						,      key=lambda _SCREAMING_SNAKE_CASE   : x.__name__   )






def   lowerCamelCase							(_SCREAMING_SNAKE_CASE      :   int   ):
					__a			:       str					    =  get_test_classes(_SCREAMING_SNAKE_CASE   )
					__a			:       Any					    =  set()
					for test_class in test_classes:
										model_classes.update(test_class.all_model_classes   )

					# sort with class names
					return sorted(_SCREAMING_SNAKE_CASE						,      key=lambda _SCREAMING_SNAKE_CASE   : x.__name__   )






def   lowerCamelCase							(_SCREAMING_SNAKE_CASE      :   Optional[Any]   ):
					__a			:       Tuple					    =  test_class()
					if hasattr(_SCREAMING_SNAKE_CASE						,      'setUp'   ):
										test.setUp()

					__a			:       List[Any]					    =  None
					if hasattr(_SCREAMING_SNAKE_CASE						,      'model_tester'   ):
										# `(TF/Flax)ModelTesterMixin` has this attribute default to `None`. Let's skip this case.
										if test.model_tester is not None:
															__a			:       List[str]					    =  test.model_tester.__class__

					return model_tester






def   lowerCamelCase							(_SCREAMING_SNAKE_CASE      :   int						,      _SCREAMING_SNAKE_CASE      :   Optional[Any]   ):
					__a			:       str					    =  get_test_classes(_SCREAMING_SNAKE_CASE   )

					__a			:       int					    =  []
					for test_class in test_classes:
										if model_class in test_class.all_model_classes:
															target_test_classes.append(_SCREAMING_SNAKE_CASE   )

    # sort with class names
					return sorted(_SCREAMING_SNAKE_CASE						,      key=lambda _SCREAMING_SNAKE_CASE   : x.__name__   )






def   lowerCamelCase							(_SCREAMING_SNAKE_CASE      :   Tuple						,      _SCREAMING_SNAKE_CASE      :   List[str]   ):
					__a			:       List[Any]					    =  get_test_classes_for_model(_SCREAMING_SNAKE_CASE						,      _SCREAMING_SNAKE_CASE   )

					__a			:       Any					    =  []
					for test_class in test_classes:
										__a			:       Any					    =  get_model_tester_from_test_class(_SCREAMING_SNAKE_CASE   )
										if tester_class is not None:
															tester_classes.append(_SCREAMING_SNAKE_CASE   )

    # sort with class names
					return sorted(_SCREAMING_SNAKE_CASE						,      key=lambda _SCREAMING_SNAKE_CASE   : x.__name__   )






def   lowerCamelCase							(_SCREAMING_SNAKE_CASE      :   Optional[int]   ):
					__a			:       str					    =  get_test_classes(_SCREAMING_SNAKE_CASE   )
					__a			:       int					    =  {test_class: get_model_tester_from_test_class(_SCREAMING_SNAKE_CASE   ) for test_class in test_classes}
					return test_tester_mapping






def   lowerCamelCase							(_SCREAMING_SNAKE_CASE      :   List[str]   ):
					__a			:       Optional[Any]					    =  get_model_classes(_SCREAMING_SNAKE_CASE   )
					__a			:       Optional[int]					    =  {
					    model_class: get_test_classes_for_model(_SCREAMING_SNAKE_CASE						,      _SCREAMING_SNAKE_CASE   ) for model_class in model_classes
					}
					return model_test_mapping






def   lowerCamelCase							(_SCREAMING_SNAKE_CASE      :   List[str]   ):
					__a			:       Optional[Any]					    =  get_model_classes(_SCREAMING_SNAKE_CASE   )
					__a			:       str					    =  {
					    model_class: get_tester_classes_for_model(_SCREAMING_SNAKE_CASE						,      _SCREAMING_SNAKE_CASE   ) for model_class in model_classes
					}
					return model_to_tester_mapping






def   lowerCamelCase							(_SCREAMING_SNAKE_CASE      :   str   ):
					if isinstance(_SCREAMING_SNAKE_CASE						,      _SCREAMING_SNAKE_CASE   ):
										return o
					elif isinstance(_SCREAMING_SNAKE_CASE						,      _SCREAMING_SNAKE_CASE   ):
										return o.__name__
					elif isinstance(_SCREAMING_SNAKE_CASE						,      (list, tuple)   ):
										return [to_json(_SCREAMING_SNAKE_CASE   ) for x in o]
					elif isinstance(_SCREAMING_SNAKE_CASE						,      _SCREAMING_SNAKE_CASE   ):
										return {to_json(_SCREAMING_SNAKE_CASE   ): to_json(_SCREAMING_SNAKE_CASE   ) for k, v in o.items()}
					else:
										return o



