


"""simple docstring"""







import argparse
import os

import torch

from transformers import FlavaImageCodebook, FlavaImageCodebookConfig


def 					lowercase      (    A_  ,       A_  ,       A_  ,       A_	)->				List[Any]:
  '''simple docstring'''







  a						:		Optional[int]											=      s.rsplit(A_  ,       A_	)
  return new.join(A_	)


def 					lowercase      (    A_	)->				str:
  '''simple docstring'''







  return sum(param.float().sum() if "encoder.embeddings" not in key else 0 for key, param in state_dict.items()	)


def 					lowercase      (    A_	)->				Tuple:
  '''simple docstring'''







  a						:		Any											=      {}

  a						:		Optional[int]											=      ["group_1", "group_2", "group_3", "group_4"]
  for key, value in state_dict.items():
    for group_key in group_keys:
      if group_key in key:
        a						:		List[Any]											=      key.replace(F'''{group_key}.'''  ,       F'''{group_key}.group.'''	)

    if "res_path" in key:
      a						:		int											=      key.replace("res_path."  ,       "res_path.path."	)

    if key.endswith(".w"	):
      a						:		Dict											=      rreplace(A_  ,       ".w"  ,       ".weight"  ,       1	)
    if key.endswith(".b"	):
      a						:		Dict											=      rreplace(A_  ,       ".b"  ,       ".bias"  ,       1	)

    a						:		Tuple											=      value.float()

  return upgrade


@torch.no_grad()
def 					lowercase      (    A_  ,       A_  ,       A_=None  ,       A_=True	)->				Any:
  '''simple docstring'''







  from dall_e import Encoder

  a						:		Optional[int]											=      Encoder()
  if os.path.exists(A_	):
    a						:		Any											=      torch.load(A_	)
  else:
    a						:		Union[str, Any]											=      torch.hub.load_state_dict_from_url(A_	)

  if isinstance(A_  ,       A_	):
    a						:		Union[str, Any]											=      ckpt.state_dict()
  encoder.load_state_dict(A_	)

  if config_path is not None:
    a						:		int											=      FlavaImageCodebookConfig.from_pretrained(A_	)
  else:
    a						:		Tuple											=      FlavaImageCodebookConfig()

  a						:		Any											=      FlavaImageCodebook(A_	).eval()
  a						:		Union[str, Any]											=      encoder.state_dict()

  a						:		Optional[Any]											=      upgrade_state_dict(A_	)
  hf_model.load_state_dict(A_	)
  a						:		List[Any]											=      hf_model.state_dict()
  a						:		Optional[int]											=      count_parameters(A_	)
  a						:		Tuple											=      count_parameters(A_	)

  assert torch.allclose(A_  ,       A_  ,       atol=1e-3	)

  if save_checkpoint:
    hf_model.save_pretrained(A_	)
  else:
    return hf_state_dict


if __name__ == "__main__":
 __lowercase    =				argparse.ArgumentParser()
 parser.add_argument("""--pytorch_dump_folder_path""", default=None, type=str, help="""Path to the output PyTorch model.""")
 parser.add_argument("""--checkpoint_path""", default=None, type=str, help="""Path to flava checkpoint""")
 parser.add_argument("""--config_path""", default=None, type=str, help="""Path to hf config.json of model to convert""")
 __lowercase    =				parser.parse_args()

 convert_dalle_checkpoint(args.checkpoint_path, args.pytorch_dump_folder_path, args.config_path)










"""simple docstring"""







import datasets


__lowercase    =				"""\
@InProceedings{conneau2018xnli,
  author = \"Conneau, Alexis
                 and Rinott, Ruty
                 and Lample, Guillaume
                 and Williams, Adina
                 and Bowman, Samuel R.
                 and Schwenk, Holger
                 and Stoyanov, Veselin\",
  title = \"XNLI: Evaluating Cross-lingual Sentence Representations\",
  booktitle = \"Proceedings of the 2018 Conference on Empirical Methods
               in Natural Language Processing\",
  year = \"2018\",
  publisher = \"Association for Computational Linguistics\",
  location = \"Brussels, Belgium\",
}
"""

__lowercase    =				"""\
XNLI is a subset of a few thousand examples from MNLI which has been translated
into a 14 different languages (some low-ish resource). As with MNLI, the goal is
to predict textual entailment (does sentence A imply/contradict/neither sentence
B) and is a classification task (given two sentences, predict one of three
labels).
"""

__lowercase    =				"""
Computes XNLI score which is just simple accuracy.
Args:
    predictions: Predicted labels.
    references: Ground truth labels.
Returns:
    'accuracy': accuracy
Examples:

    >>> predictions = [0, 1]
    >>> references = [0, 1]
    >>> xnli_metric = datasets.load_metric(\"xnli\")
    >>> results = xnli_metric.compute(predictions=predictions, references=references)
    >>> print(results)
    {'accuracy': 1.0}
"""


def 					lowercase      (    A_  ,       A_	)->				List[str]:
  '''simple docstring'''







  return (preds == labels).mean()







@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION   ,_KWARGS_DESCRIPTION  )
class 						_A  (  datasets.Metric  ):







    """simple docstring"""




    def      __snake_case							( self    :   List[str]):
      return datasets.MetricInfo(
          description=_DESCRIPTION    ,			citation=_CITATION    ,			inputs_description=_KWARGS_DESCRIPTION    ,			features=datasets.Features(
              {
                  "predictions": datasets.Value("int64" if self.config_name != "sts-b" else "float32"),
                  "references": datasets.Value("int64" if self.config_name != "sts-b" else "float32"),
              })    ,			codebase_urls=[]    ,			reference_urls=[]    ,			format="numpy"    ,			)






    def      __snake_case							( self    :   str    ,			__UpperCAmelCase    :   List[Any]    ,			__UpperCAmelCase    :   Tuple):
      return {"accuracy": simple_accuracy(__UpperCAmelCase    ,			__UpperCAmelCase)}






