


'''simple docstring'''



import argparse

import torch

from transformers import MraConfig, MraForMaskedLM



def       UpperCAmelCase__ (     UpperCAmelCase_   :    Tuple							)					->		Tuple:
     if "model" in orig_key:
          __lowerCamelCase       :     Any        =		orig_key.replace('model.'				,   ''							)
     if "norm1" in orig_key:
          __lowerCamelCase       :     Optional[int]        =		orig_key.replace('norm1'				,   'attention.output.LayerNorm'							)
     if "norm2" in orig_key:
          __lowerCamelCase       :     Any        =		orig_key.replace('norm2'				,   'output.LayerNorm'							)
     if "norm" in orig_key:
          __lowerCamelCase       :     str        =		orig_key.replace('norm'				,   'LayerNorm'							)
     if "transformer" in orig_key:
          __lowerCamelCase       :     int        =		orig_key.split('.'							)[0].split('_'							)[-1]
          __lowerCamelCase       :     Union[str, Any]        =		orig_key.replace(F'transformer_{layer_num}'				,   F'encoder.layer.{layer_num}'							)
     if "mha.attn" in orig_key:
          __lowerCamelCase       :     List[str]        =		orig_key.replace('mha.attn'				,   'attention.self'							)
     if "mha" in orig_key:
          __lowerCamelCase       :     List[Any]        =		orig_key.replace('mha'				,   'attention'							)
     if "W_q" in orig_key:
          __lowerCamelCase       :     Union[str, Any]        =		orig_key.replace('W_q'				,   'self.query'							)
     if "W_k" in orig_key:
          __lowerCamelCase       :     int        =		orig_key.replace('W_k'				,   'self.key'							)
     if "W_v" in orig_key:
          __lowerCamelCase       :     str        =		orig_key.replace('W_v'				,   'self.value'							)
     if "ff.0" in orig_key:
          __lowerCamelCase       :     List[str]        =		orig_key.replace('ff.0'				,   'intermediate.dense'							)
     if "ff.2" in orig_key:
          __lowerCamelCase       :     str        =		orig_key.replace('ff.2'				,   'output.dense'							)
     if "ff" in orig_key:
          __lowerCamelCase       :     str        =		orig_key.replace('ff'				,   'output.dense'							)
     if "mlm_class" in orig_key:
          __lowerCamelCase       :     Union[str, Any]        =		orig_key.replace('mlm.mlm_class'				,   'cls.predictions.decoder'							)
     if "mlm" in orig_key:
          __lowerCamelCase       :     int        =		orig_key.replace('mlm'				,   'cls.predictions.transform'							)
     if "backbone.backbone.encoders" in orig_key:
          __lowerCamelCase       :     Optional[int]        =		orig_key.replace('backbone.backbone.encoders'				,   'encoder.layer'							)
     if "cls" not in orig_key:
          __lowerCamelCase       :     Optional[Any]        =		'mra.' + orig_key

     return orig_key



def       UpperCAmelCase__ (     UpperCAmelCase_   :    Tuple				,   UpperCAmelCase_   :    Any							)					->		Tuple:
     for key in orig_state_dict.copy().keys():
          __lowerCamelCase       :     int        =		orig_state_dict.pop(UpperCAmelCase_							)

          if ("pooler" in key) or ("sen_class" in key):
               continue
          else:
               __lowerCamelCase       :     Any        =		val

     __lowerCamelCase       :     List[str]        =		orig_state_dict['cls.predictions.decoder.bias']
     __lowerCamelCase       :     Optional[Any]        =		torch.arange(UpperCAmelCase_							).expand((1, -1)							) + 2

     return orig_state_dict



def       UpperCAmelCase__ (     UpperCAmelCase_   :    Optional[Any]				,   UpperCAmelCase_   :    Any				,   UpperCAmelCase_   :    Union[str, Any]							)					->		Union[str, Any]:
     __lowerCamelCase       :     int        =		torch.load(UpperCAmelCase_				,   map_location='cpu'							)['model_state_dict']
     __lowerCamelCase       :     Optional[int]        =		MraConfig.from_json_file(UpperCAmelCase_							)
     __lowerCamelCase       :     Dict        =		MraForMaskedLM(UpperCAmelCase_							)

     __lowerCamelCase       :     List[str]        =		convert_checkpoint_helper(config.max_position_embeddings				,   UpperCAmelCase_							)

     print(model.load_state_dict(UpperCAmelCase_							)							)
     model.eval()
     model.save_pretrained(UpperCAmelCase_							)

     print(F'Checkpoint successfuly converted. Model saved at {pytorch_dump_path}'							)


if __name__ == "__main__":
      A__		: List[Any]												=  argparse.ArgumentParser()
      # Required parameters
      parser.add_argument(
          """--pytorch_model_path""", default=None, type=str, required=True, help="""Path to Mra pytorch checkpoint."""
      )
      parser.add_argument(
          """--config_file""",
          default=None,
          type=str,
          required=True,
          help="""The json file for Mra model config.""",
      )
      parser.add_argument(
          """--pytorch_dump_path""", default=None, type=str, required=True, help="""Path to the output PyTorch model."""
      )
      A__		: str												=  parser.parse_args()
      convert_mra_checkpoint(args.pytorch_model_path, args.config_file, args.pytorch_dump_path)










'''simple docstring'''



import glob
import os

from get_test_info import get_tester_classes


if __name__ == "__main__":
      A__		: Optional[int]												=  []

      A__		: List[Any]												=  os.path.join("""tests""", """models""", """**""", """test_modeling_*.py""")
      A__		: Optional[int]												=  glob.glob(pattern)
      # TODO: deal with TF/Flax too
      A__		: List[str]												=  [
          x for x in test_files if not (x.startswith("""test_modeling_tf_""") or x.startswith("""test_modeling_flax_"""))
      ]

      for test_file in test_files:
            A__		: Tuple												=  get_tester_classes(test_file)
            for tester_class in tester_classes:
                  # A few tester classes don't have `parent` parameter in `__init__`.
                  # TODO: deal this better
                  try:
                        A__		: Tuple												=  tester_class(parent=None)
                  except Exception:
                        continue
                  if hasattr(tester, """get_config"""):
                        A__		: Any												=  tester.get_config()
                        for k, v in config.to_dict().items():
                              if isinstance(v, int):
                                    A__		: List[Any]												=  None
                                    if k in ["vocab_size"]:
                                          A__		: Any												=  100
                                    elif k in ["max_position_embeddings"]:
                                          A__		: List[Any]												=  128
                                    elif k in ["hidden_size", "d_model"]:
                                          A__		: List[str]												=  40
                                    elif k == ["num_layers", "num_hidden_layers", "num_encoder_layers", "num_decoder_layers"]:
                                          A__		: str												=  5
                                    if target is not None and v > target:
                                          failures.append(
                                              f'''{tester_class.__name__} will produce a `config` of type `{config.__class__.__name__}`'''
                                              f''' with config["{k}"] = {v} which is too large for testing! Set its value to be smaller'''
                                              f''' than {target}.'''
                                          )

      if len(failures) > 0:
            raise Exception(f'''There were {len(failures)} failures:\n''' + """\n""".join(failures))






