




"""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 				snake_case_			(					datasets.Metric			):







   """simple docstring"""




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



'''simple docstring'''
from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available


UpperCAmelCase_			:  Any										=   {
    'configuration_maskformer': ['MASKFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP', 'MaskFormerConfig'],
    'configuration_maskformer_swin': ['MaskFormerSwinConfig'],
}

try:
 if not is_vision_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 UpperCAmelCase_			:  Tuple										=   ['MaskFormerFeatureExtractor']
 UpperCAmelCase_			:  List[str]										=   ['MaskFormerImageProcessor']


try:
 if not is_torch_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 UpperCAmelCase_			:  Optional[Any]										=   [
     'MASKFORMER_PRETRAINED_MODEL_ARCHIVE_LIST',
     'MaskFormerForInstanceSegmentation',
     'MaskFormerModel',
     'MaskFormerPreTrainedModel',
 ]
 UpperCAmelCase_			:  Optional[int]										=   [
     'MaskFormerSwinBackbone',
     'MaskFormerSwinModel',
     'MaskFormerSwinPreTrainedModel',
 ]

if TYPE_CHECKING:
 from .configuration_maskformer import MASKFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP, MaskFormerConfig
 from .configuration_maskformer_swin import MaskFormerSwinConfig

 try:
  if not is_vision_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .feature_extraction_maskformer import MaskFormerFeatureExtractor
  from .image_processing_maskformer import MaskFormerImageProcessor
 try:
  if not is_torch_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_maskformer import (
      MASKFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
      MaskFormerForInstanceSegmentation,
      MaskFormerModel,
      MaskFormerPreTrainedModel,
  )
  from .modeling_maskformer_swin import (
      MaskFormerSwinBackbone,
      MaskFormerSwinModel,
      MaskFormerSwinPreTrainedModel,
  )


else:
 import sys

 UpperCAmelCase_			:  Union[str, Any]										=   _LazyModule(__name__, globals()['__file__'], _import_structure)






