
"""simple docstring"""
import comet  # From: unbabel-comet
import torch

import datasets


lowercase_							:    Dict         =						datasets.logging.get_logger(__name__)

lowercase_							:    List[str]         =						'\\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'

lowercase_							:    Optional[int]         =						'\\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'

lowercase_							:    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       UpperCamelCase   (				datasets.Metric	):
     def 					__SCREAMING_SNAKE_CASE				(  self					):




       """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	,  snake_case__					):




       """simple docstring"""


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




     def 					__SCREAMING_SNAKE_CASE				(  self	,  snake_case__	,  snake_case__	,  snake_case__	,  snake_case__=None	,  snake_case__=False					):




       """simple docstring"""


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






'''simple docstring'''



import importlib
import inspect
import os
import re


# All paths are set with the intent you should run this script from the root of the repo with the command
# python utils/check_config_docstrings.py
UpperCAmelCase					:			Any       =	'src/transformers'


# This is to make sure the transformers module imported is the one in the repo.
UpperCAmelCase					:			Optional[int]       =	importlib.util.spec_from_file_location(
    'transformers',
    os.path.join(PATH_TO_TRANSFORMERS, '__init__.py'),
    submodule_search_locations=[PATH_TO_TRANSFORMERS],
)
UpperCAmelCase					:			Dict       =	spec.loader.load_module()

UpperCAmelCase					:			Dict       =	transformers.models.auto.configuration_auto.CONFIG_MAPPING

# Regex pattern used to find the checkpoint mentioned in the docstring of `config_class`.
# For example, `[bert-base-uncased](https://huggingface.co/bert-base-uncased)`
UpperCAmelCase					:			Dict       =	re.compile('\[(.+?)\]\((https://huggingface\.co/.+?)\)')


UpperCAmelCase					:			Any       =	{
    'CLIPConfigMixin',
    'DecisionTransformerConfigMixin',
    'EncoderDecoderConfigMixin',
    'RagConfigMixin',
    'SpeechEncoderDecoderConfigMixin',
    'VisionEncoderDecoderConfigMixin',
    'VisionTextDualEncoderConfigMixin',
}







def   a__			(    ):




     """simple docstring"""


     __SCREAMING_SNAKE_CASE      = []

     for config_class in list(CONFIG_MAPPING.values()		):
          __SCREAMING_SNAKE_CASE      = False

          # source code of `config_class`
          __SCREAMING_SNAKE_CASE      = inspect.getsource(a__		)
          __SCREAMING_SNAKE_CASE      = _re_checkpoint.findall(a__		)

          for checkpoint in checkpoints:
               # Each `checkpoint` is a tuple of a checkpoint name and a checkpoint link.
               # For example, `('bert-base-uncased', 'https://huggingface.co/bert-base-uncased')`
               __SCREAMING_SNAKE_CASE  ,     __SCREAMING_SNAKE_CASE      = checkpoint

               # verify the checkpoint name corresponds to the checkpoint link
               __SCREAMING_SNAKE_CASE      = F'https://huggingface.co/{ckpt_name}'
               if ckpt_link == ckpt_link_from_name:
                    __SCREAMING_SNAKE_CASE      = True
                    break

          __SCREAMING_SNAKE_CASE      = config_class.__name__
          if not checkpoint_found and name not in CONFIG_CLASSES_TO_IGNORE_FOR_DOCSTRING_CHECKPOINT_CHECK:
               configs_without_checkpoint.append(a__		)

     if len(a__		) > 0:
          __SCREAMING_SNAKE_CASE      = """\n""".join(sorted(a__		)		)
          raise ValueError(F'The following configurations don\'t contain any valid checkpoint:\n{message}'		)


if __name__ == "__main__":
  check_config_docstrings_have_checkpoints()






