
import warnings

from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding


class 			__magic_name__       (       _snake_case ):
       UpperCamelCase_   =		['''image_processor''', '''tokenizer''']
       UpperCamelCase_   =		'''CLIPImageProcessor'''
       UpperCamelCase_   =		('''CLIPTokenizer''', '''CLIPTokenizerFast''')
       def __init__(     self ,				A_=None ,				A_=None ,				**A_       )     ->	str:





         """simple docstring"""




         _lowercase:			Optional[Any]       =     None
         if "feature_extractor" in kwargs:
           warnings.warn(
               '''The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`'''
               ''' instead.''' ,				lowerCAmelCase__ ,				)
           _lowercase:			Union[str, Any]       =     kwargs.pop('''feature_extractor'''       )

         _lowercase:			Optional[Any]       =     image_processor if image_processor is not None else feature_extractor
         if image_processor is None:
           raise ValueError('''You need to specify an `image_processor`.'''       )
         if tokenizer is None:
           raise ValueError('''You need to specify a `tokenizer`.'''       )

         super().__init__(lowerCAmelCase__ ,				lowerCAmelCase__       )
       def __call__(     self ,				A_=None ,				A_=None ,				A_=None ,				**A_       )     ->	Tuple:





         """simple docstring"""




         if text is None and images is None:
           raise ValueError('''You have to specify either text or images. Both cannot be none.'''       )

         if text is not None:
           _lowercase:			Optional[Any]       =     self.tokenizer(lowerCAmelCase__ ,				return_tensors=lowerCAmelCase__ ,				**lowerCAmelCase__       )

         if images is not None:
           _lowercase:			Optional[Any]       =     self.image_processor(lowerCAmelCase__ ,				return_tensors=lowerCAmelCase__ ,				**lowerCAmelCase__       )

         if text is not None and images is not None:
           _lowercase:			Optional[int]       =     image_features.pixel_values
           return encoding
         elif text is not None:
           return encoding
         else:
           return BatchEncoding(data=dict(**lowerCAmelCase__       ) ,				tensor_type=lowerCAmelCase__       )
       def 							lowercase_							(     self ,				*A_ ,				**A_       )     ->	Dict:





         """simple docstring"""




         return self.tokenizer.batch_decode(*lowerCAmelCase__ ,				**lowerCAmelCase__       )
       def 							lowercase_							(     self ,				*A_ ,				**A_       )     ->	int:





         """simple docstring"""




         return self.tokenizer.decode(*lowerCAmelCase__ ,				**lowerCAmelCase__       )
       @property
       def 							lowercase_							(     self       )     ->	str:





         """simple docstring"""




         _lowercase:			int       =     self.tokenizer.model_input_names
         _lowercase:			Dict       =     self.image_processor.model_input_names
         return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names       )       )
       @property
       def 							lowercase_							(     self       )     ->	int:





         """simple docstring"""




         warnings.warn(
             '''`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead.''' ,				lowerCAmelCase__ ,				)
         return self.image_processor_class



       @property
       def 							lowercase_							(     self       )     ->	Optional[int]:





         """simple docstring"""




         warnings.warn(
             '''`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead.''' ,				lowerCAmelCase__ ,				)
         return self.image_processor


"""simple docstring"""




import comet  # From: unbabel-comet
import torch

import datasets


A__   :					int							=   datasets.logging.get_logger(__name__)

A__   :					Optional[Any]							=   '\\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__   :					Union[str, Any]							=   '\\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__   :					int							=   '\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 			__magic_name__       (       datasets.Metric ):
       def 							lowercase_							(     self       )     ->	Tuple:





         """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 ,				A_       )     ->	Dict:





         """simple docstring"""




         if self.config_name == "default":
           _lowercase:			str       =     comet.load_from_checkpoint(comet.download_model('''wmt20-comet-da'''       )       )
         else:
           _lowercase:			Optional[Any]       =     comet.load_from_checkpoint(comet.download_model(self.config_name       )       )



       def 							lowercase_							(     self ,				A_ ,				A_ ,				A_ ,				A_=None ,				A_=False       )     ->	List[str]:





         """simple docstring"""




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