





import comet  # From: unbabel-comet
import torch

import datasets


lowercase_          =		datasets.logging.get_logger(__name__)

lowercase_          =		'''\
@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",
}
'''

lowercase_          =		'''\
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.
'''

lowercase_          =		'''
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     SCREAMING_SNAKE_CASE       (datasets.Metric				):


   def 	SCREAMING_SNAKE_CASE_							(   self      :    Optional[Any]	)->       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 	SCREAMING_SNAKE_CASE_							(   self      :    List[str]      ,					a      :    List[str]	)->       Optional[int]:





     """simple docstring"""







     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 	SCREAMING_SNAKE_CASE_							(   self      :    Any      ,					a      :    List[str]      ,					a      :    Any      ,					a      :    Optional[Any]      ,					a      :    List[Any]=None      ,					a      :    int=False	)->       List[str]:





     """simple docstring"""







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









import os
import socket
from contextlib import contextmanager

import torch

from ..commands.config.default import write_basic_config  # noqa: F401
from ..state import PartialState
from .dataclasses import DistributedType
from .imports import is_deepspeed_available, is_tpu_available
from .transformer_engine import convert_model
from .versions import is_torch_version


if is_deepspeed_available():
      from deepspeed import DeepSpeedEngine

if is_tpu_available(check_device=False):
      import torch_xla.core.xla_model as xm




def       __UpperCamelCase  (_SCREAMING_SNAKE_CASE		)    ->       List[str]:
  if is_torch_version('<'		,						'2.0.0'		) or not hasattr(_SCREAMING_SNAKE_CASE		,						'_dynamo'		):
    return False
  return isinstance(_SCREAMING_SNAKE_CASE		,						torch._dynamo.eval_frame.OptimizedModule		)




def       __UpperCamelCase  (_SCREAMING_SNAKE_CASE		,						_SCREAMING_SNAKE_CASE = True		)    ->       Dict:
  lowercase__           =  (torch.nn.parallel.DistributedDataParallel, torch.nn.DataParallel)

  lowercase__           =  is_compiled_module(_SCREAMING_SNAKE_CASE		)
  if is_compiled:
    lowercase__           =  model
    lowercase__           =  model._orig_mod

  if is_deepspeed_available():
    options += (DeepSpeedEngine,)

  while isinstance(_SCREAMING_SNAKE_CASE		,						_SCREAMING_SNAKE_CASE		):
    lowercase__           =  model.module

  if not keep_fpaa_wrapper:
    lowercase__           =  getattr(_SCREAMING_SNAKE_CASE		,						'forward'		)
    lowercase__           =  model.__dict__.pop('_original_forward'		,						_SCREAMING_SNAKE_CASE		)
    if original_forward is not None:
      while hasattr(_SCREAMING_SNAKE_CASE		,						'__wrapped__'		):
        lowercase__           =  forward.__wrapped__
        if forward == original_forward:
          break
      lowercase__           =  forward
    if getattr(_SCREAMING_SNAKE_CASE		,						'_converted_to_transformer_engine'		,						_SCREAMING_SNAKE_CASE		):
      convert_model(_SCREAMING_SNAKE_CASE		,						to_transformer_engine=_SCREAMING_SNAKE_CASE		)

  if is_compiled:
    lowercase__           =  model
    lowercase__           =  compiled_model

  return model




def       __UpperCamelCase  ()    ->       Tuple:
  PartialState().wait_for_everyone()




def       __UpperCamelCase  (_SCREAMING_SNAKE_CASE		,						_SCREAMING_SNAKE_CASE		)    ->       List[Any]:
  if PartialState().distributed_type == DistributedType.TPU:
    xm.save(_SCREAMING_SNAKE_CASE		,						_SCREAMING_SNAKE_CASE		)
  elif PartialState().local_process_index == 0:
    torch.save(_SCREAMING_SNAKE_CASE		,						_SCREAMING_SNAKE_CASE		)




@contextmanager
def       __UpperCamelCase  (**_SCREAMING_SNAKE_CASE		)    ->       Optional[Any]:
  for key, value in kwargs.items():
    lowercase__           =  str(_SCREAMING_SNAKE_CASE		)

  yield

  for key in kwargs:
    if key.upper() in os.environ:
      del os.environ[key.upper()]




def       __UpperCamelCase  (_SCREAMING_SNAKE_CASE		)    ->       List[str]:
  if not hasattr(_SCREAMING_SNAKE_CASE		,						'__qualname__'		) and not hasattr(_SCREAMING_SNAKE_CASE		,						'__name__'		):
    lowercase__           =  getattr(_SCREAMING_SNAKE_CASE		,						'__class__'		,						_SCREAMING_SNAKE_CASE		)
  if hasattr(_SCREAMING_SNAKE_CASE		,						'__qualname__'		):
    return obj.__qualname__
  if hasattr(_SCREAMING_SNAKE_CASE		,						'__name__'		):
    return obj.__name__
  return str(_SCREAMING_SNAKE_CASE		)




def       __UpperCamelCase  (_SCREAMING_SNAKE_CASE		,						_SCREAMING_SNAKE_CASE		)    ->       Dict:
  for key, value in source.items():
    if isinstance(_SCREAMING_SNAKE_CASE		,						_SCREAMING_SNAKE_CASE		):
      lowercase__           =  destination.setdefault(_SCREAMING_SNAKE_CASE		,						{}		)
      merge_dicts(_SCREAMING_SNAKE_CASE		,						_SCREAMING_SNAKE_CASE		)
    else:
      lowercase__           =  value

  return destination




def       __UpperCamelCase  (_SCREAMING_SNAKE_CASE = None		)    ->       bool:
  if port is None:
    lowercase__           =  29500
  with socket.socket(socket.AF_INET		,						socket.SOCK_STREAM		) as s:
    return s.connect_ex(('localhost', port)		) == 0


