





"""simple docstring"""




from typing import Any

import numpy as np





def 		UpperCAmelCase__						(	_UpperCAmelCase   ):






  """simple docstring"""





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

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

  # Get eigenvalues and eignevectors for a symmetric matrix.
  A_		,			A_    :			Any									=    np.linalg.eigh(_UpperCAmelCase   )

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





def 		UpperCAmelCase__						(	_UpperCAmelCase   ):






  """simple docstring"""





  A_    :			str									=    np.random.randn(_UpperCAmelCase					,   _UpperCAmelCase   )
  A_    :			str									=    np.dot(_UpperCAmelCase					,   random_matrix.T   )
  assert _is_matrix_spd(_UpperCAmelCase   )
  return spd_matrix





def 		UpperCAmelCase__						(	_UpperCAmelCase					,   _UpperCAmelCase					,   _UpperCAmelCase = 1000					,   _UpperCAmelCase = 1E-8					,   ):






  """simple docstring"""





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

  # Initialize solution guess, residual, search direction.
  A_    :			Optional[int]									=    np.zeros((np.shape(_UpperCAmelCase   )[0], 1)   )
  A_    :			Dict									=    np.copy(_UpperCAmelCase   )
  A_    :			List[str]									=    np.copy(_UpperCAmelCase   )

  # Set initial errors in solution guess and residual.
  A_    :			Tuple									=    1E9
  A_    :			List[Any]									=    1E9
  A_    :			Optional[Any]									=    1E9

  # Set iteration counter to threshold number of iterations.
  A_    :			str									=    0

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

    # The main algorithm.

    # Update search direction magnitude.
    A_    :			Dict									=    np.dot(ra.T					,   _UpperCAmelCase   ) / np.dot(pa.T					,   _UpperCAmelCase   )
    # Update solution guess.
    A_    :			Union[str, Any]									=    xa + alpha * pa
    # Calculate new residual.
    A_    :			Dict									=    ra - alpha * w
    # Calculate new Krylov subspace scale.
    A_    :			Any									=    np.dot(r.T					,   _UpperCAmelCase   ) / np.dot(ra.T					,   _UpperCAmelCase   )
    # Calculate new A conjuage search direction.
    A_    :			Optional[Any]									=    r + beta * pa

    # Calculate errors.
    A_    :			Union[str, Any]									=    np.linalg.norm(r - ra   )
    A_    :			str									=    np.linalg.norm(x - xa   )
    A_    :			str									=    np.maximum(_UpperCAmelCase					,   _UpperCAmelCase   )

    # Update variables.
    A_    :			str									=    np.copy(_UpperCAmelCase   )
    A_    :			Union[str, Any]									=    np.copy(_UpperCAmelCase   )
    A_    :			Union[str, Any]									=    np.copy(_UpperCAmelCase   )

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

  return x





def 		UpperCAmelCase__						(	):






  """simple docstring"""





  A_    :			Dict									=    3
  A_    :			Optional[int]									=    _create_spd_matrix(_UpperCAmelCase   )
  A_    :			str									=    np.random.randn(_UpperCAmelCase					,   1   )
  A_    :			str									=    np.dot(_UpperCAmelCase					,   _UpperCAmelCase   )

  # Numpy solution.
  A_    :			Optional[Any]									=    np.linalg.solve(_UpperCAmelCase					,   _UpperCAmelCase   )

  # Our implementation.
  A_    :			str									=    conjugate_gradient(_UpperCAmelCase					,   _UpperCAmelCase   )

  # 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()







"""simple docstring"""


import argparse

import torch

from transformers import RoFormerConfig, RoFormerForMaskedLM, load_tf_weights_in_roformer
from transformers.utils import logging


logging.set_verbosity_info()



def    lowerCAmelCase							(       __UpperCamelCase						,			__UpperCamelCase						,			__UpperCamelCase					):




  """simple docstring"""







  __A		     =					RoFormerConfig.from_json_file(__UpperCamelCase					)
  print(f'Building PyTorch model from configuration: {config}'					)
  __A		     =					RoFormerForMaskedLM(__UpperCamelCase					)

  # Load weights from tf checkpoint
  load_tf_weights_in_roformer(__UpperCamelCase						,			__UpperCamelCase						,			__UpperCamelCase					)

  # Save pytorch-model
  print(f'Save PyTorch model to {pytorch_dump_path}'					)
  torch.save(model.state_dict()						,			__UpperCamelCase						,			_use_new_zipfile_serialization=__UpperCamelCase					)


if __name__ == "__main__":
       lowercase_								=	argparse.ArgumentParser()
       # Required parameters
       parser.add_argument(
           '--tf_checkpoint_path', default=None, type=str, required=True, help='Path to the TensorFlow checkpoint path.'
       )
       parser.add_argument(
           '--bert_config_file',
           default=None,
           type=str,
           required=True,
           help=(
               'The config json file corresponding to the pre-trained BERT model. \n'
               'This specifies the model architecture.'
           ),
       )
       parser.add_argument(
           '--pytorch_dump_path', default=None, type=str, required=True, help='Path to the output PyTorch model.'
       )
       lowercase_								=	parser.parse_args()
       convert_tf_checkpoint_to_pytorch(args.tf_checkpoint_path, args.bert_config_file, args.pytorch_dump_path)




