
import argparse

import torch

from transformers import MraConfig, MraForMaskedLM


def 		_lowerCamelCase       (   _a  ):
  """simple docstring"""




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

  return orig_key


def 		_lowerCamelCase       (   _a			,   _a  ):
  """simple docstring"""




  for key in orig_state_dict.copy().keys():
    _lowerCamelCase      =						orig_state_dict.pop(a_  )

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

  _lowerCamelCase      =						orig_state_dict["""cls.predictions.decoder.bias"""]
  _lowerCamelCase      =						torch.arange(a_  ).expand((1, -1)  ) + 2

  return orig_state_dict


def 		_lowerCamelCase       (   _a			,   _a			,   _a  ):
  """simple docstring"""




  _lowerCamelCase      =						torch.load(a_			,   map_location='''cpu'''  )["""model_state_dict"""]
  _lowerCamelCase      =						MraConfig.from_json_file(a_  )
  _lowerCamelCase      =						MraForMaskedLM(a_  )

  _lowerCamelCase      =						convert_checkpoint_helper(config.max_position_embeddings			,   a_  )

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

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


if __name__ == "__main__":
  _UpperCAmelCase    			=					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."
  )
  _UpperCAmelCase    			=					parser.parse_args()
  convert_mra_checkpoint(args.pytorch_model_path, args.config_file, args.pytorch_dump_path)









'''simple docstring'''





import torch
import torch.nn.functional as F

from diffusers import VQDiffusionScheduler

from .test_schedulers import SchedulerCommonTest


class 							_lowerCAmelCase	(  __A							):


 """simple docstring"""

 lowerCamelCase									=     (VQDiffusionScheduler,)







 def       UpperCAmelCase_       (      self					,    **_lowerCamelCase  )    ->    Any:
    A_  :		List[Any]						=					{
        """num_vec_classes""": 4097,
        """num_train_timesteps""": 100,
    }

    config.update(**_lowerCamelCase  )
    return config







 def       UpperCAmelCase_       (      self					,    _lowerCamelCase  )    ->    Tuple:
    A_  :		Tuple						=					4
    A_  :		List[str]						=					8
    A_  :		Dict						=					8

    A_  :		int						=					torch.randint(0					,    _lowerCamelCase					,    (batch_size, height * width)  )

    return sample







 @property
 def       UpperCAmelCase_       (      self  )    ->    Union[str, Any]:
    assert False







 def       UpperCAmelCase_       (      self					,    _lowerCamelCase  )    ->    List[str]:
    def model(_lowerCamelCase					,    _lowerCamelCase					,    *_lowerCamelCase  ):
       A_						,  A_  :		List[Any]						=					sample.shape
       A_  :		int						=					torch.rand((batch_size, num_vec_classes - 1, num_latent_pixels)  )
       A_  :		Optional[int]						=					F.log_softmax(logits.double()					,    dim=1  ).float()
       return return_value

    return model







 def       UpperCAmelCase_       (      self  )    ->    Tuple:
    for timesteps in [2, 5, 100, 1000]:
       self.check_over_configs(num_train_timesteps=_lowerCamelCase  )







 def       UpperCAmelCase_       (      self  )    ->    Tuple:
    for num_vec_classes in [5, 100, 1000, 4000]:
       self.check_over_configs(num_vec_classes=_lowerCamelCase  )







 def       UpperCAmelCase_       (      self  )    ->    int:
    for t in [0, 50, 99]:
       self.check_over_forward(time_step=_lowerCamelCase  )







 def       UpperCAmelCase_       (      self  )    ->    Optional[int]:
    pass
