




'''simple docstring'''



import argparse

import torch

from transformers import MraConfig, MraForMaskedLM







def 			_UpperCamelCase (_lowerCamelCase							:     Any	)->   Dict:




     '''simple docstring'''





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

     return orig_key







def 			_UpperCamelCase (_lowerCamelCase							:     Tuple   ,	_lowerCamelCase							:     int	)->   List[str]:




     '''simple docstring'''





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

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

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

     return orig_state_dict







def 			_UpperCamelCase (_lowerCamelCase							:     Union[str, Any]   ,	_lowerCamelCase							:     Any   ,	_lowerCamelCase							:     Dict	)->   Tuple:




     '''simple docstring'''





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

     __snake_case    					=  convert_checkpoint_helper(config.max_position_embeddings   ,	_lowerCamelCase	)

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

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


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












'''simple docstring'''



from typing import Any

import numpy as np







def 			_UpperCamelCase (_lowerCamelCase							:     np.ndarray	)->   bool:




     '''simple docstring'''





     assert np.shape(_lowerCamelCase	)[0] == np.shape(_lowerCamelCase	)[1]

     # If matrix not symmetric, exit right away.
     if np.allclose(_lowerCamelCase   ,	matrix.T	) is False:
          return False

     # Get eigenvalues and eignevectors for a symmetric matrix.
     __snake_case					,    __snake_case    					=  np.linalg.eigh(_lowerCamelCase	)

     # Check sign of all eigenvalues.
     # np.all returns a value of type np.bool_
     return bool(np.all(eigen_values > 0	)	)







def 			_UpperCamelCase (_lowerCamelCase							:     int	)->   Any:




     '''simple docstring'''





     __snake_case    					=  np.random.randn(_lowerCamelCase   ,	_lowerCamelCase	)
     __snake_case    					=  np.dot(_lowerCamelCase   ,	random_matrix.T	)
     assert _is_matrix_spd(_lowerCamelCase	)
     return spd_matrix







def 			_UpperCamelCase (_lowerCamelCase							:     np.ndarray   ,	_lowerCamelCase							:     np.ndarray   ,	_lowerCamelCase							:     int = 10_00   ,	_lowerCamelCase							:     float = 1E-8   ,	)->   Any:




     '''simple docstring'''





     assert np.shape(_lowerCamelCase	)[0] == np.shape(_lowerCamelCase	)[1]
     assert np.shape(_lowerCamelCase	)[0] == np.shape(_lowerCamelCase	)[0]
     assert _is_matrix_spd(_lowerCamelCase	)

     # Initialize solution guess, residual, search direction.
     __snake_case    					=  np.zeros((np.shape(_lowerCamelCase	)[0], 1)	)
     __snake_case    					=  np.copy(_lowerCamelCase	)
     __snake_case    					=  np.copy(_lowerCamelCase	)

     # Set initial errors in solution guess and residual.
     __snake_case    					=  1E9
     __snake_case    					=  1E9
     __snake_case    					=  1E9

     # Set iteration counter to threshold number of iterations.
     __snake_case    					=  0

     while error > tol:
          # Save this value so we only calculate the matrix-vector product once.
          __snake_case    					=  np.dot(_lowerCamelCase   ,	_lowerCamelCase	)

          # The main algorithm.

          # Update search direction magnitude.
          __snake_case    					=  np.dot(ra.T   ,	_lowerCamelCase	) / np.dot(pa.T   ,	_lowerCamelCase	)
          # Update solution guess.
          __snake_case    					=  xa + alpha * pa
          # Calculate new residual.
          __snake_case    					=  ra - alpha * w
          # Calculate new Krylov subspace scale.
          __snake_case    					=  np.dot(r.T   ,	_lowerCamelCase	) / np.dot(ra.T   ,	_lowerCamelCase	)
          # Calculate new A conjuage search direction.
          __snake_case    					=  r + beta * pa

          # Calculate errors.
          __snake_case    					=  np.linalg.norm(r - ra	)
          __snake_case    					=  np.linalg.norm(x - xa	)
          __snake_case    					=  np.maximum(_lowerCamelCase   ,	_lowerCamelCase	)

          # Update variables.
          __snake_case    					=  np.copy(_lowerCamelCase	)
          __snake_case    					=  np.copy(_lowerCamelCase	)
          __snake_case    					=  np.copy(_lowerCamelCase	)

          # Update number of iterations.
          iterations += 1
          if iterations > max_iterations:
               break

     return x







def 			_UpperCamelCase ()->   None:




     '''simple docstring'''





     __snake_case    					=  3
     __snake_case    					=  _create_spd_matrix(_lowerCamelCase	)
     __snake_case    					=  np.random.randn(_lowerCamelCase   ,	1	)
     __snake_case    					=  np.dot(_lowerCamelCase   ,	_lowerCamelCase	)

     # Numpy solution.
     __snake_case    					=  np.linalg.solve(_lowerCamelCase   ,	_lowerCamelCase	)

     # Our implementation.
     __snake_case    					=  conjugate_gradient(_lowerCamelCase   ,	_lowerCamelCase	)

     # Ensure both solutions are close to x_true (and therefore one another).
     assert np.linalg.norm(x_numpy - x_true	) <= 1E-6
     assert np.linalg.norm(x_conjugate_gradient - x_true	) <= 1E-6


if __name__ == "__main__":
     import doctest

     doctest.testmod()
     test_conjugate_gradient()






