






import logging

import numpy as np
import pytest
from scipy.linalg import eigh

logging.basicConfig(level=logging.INFO, format='%(message)s')

def      _lowerCamelCase	(						__A    :							np.ndarray )      ->					np.ndarray:
    return input_array.reshape((input_array.size, 1) )

def      _lowerCamelCase	(						__A    :							np.ndarray		,		__A    :							np.ndarray		,		__A    :							int )      ->					np.ndarray:
    _UpperCAmelCase				:      Any			  =				np.nan
    for i in range(__A ):
        _UpperCAmelCase				:      Union[str, Any]			  =				features[:, labels == i]
        _UpperCAmelCase				:      Any			  =				data.mean(1 )
        # Centralize the data of class i
        _UpperCAmelCase				:      List[Any]			  =				data - column_reshape(__A )
        if i > 0:
            # If covariance_sum is not None
            covariance_sum += np.dot(__A		,		centered_data.T )
        else:
            # If covariance_sum is np.nan (i.e. first loop)
            _UpperCAmelCase				:      Any			  =				np.dot(__A		,		centered_data.T )

    return covariance_sum / features.shape[1]

def      _lowerCamelCase	(						__A    :							np.ndarray		,		__A    :							np.ndarray		,		__A    :							int )      ->					np.ndarray:
    _UpperCAmelCase				:      Any			  =				features.mean(1 )
    _UpperCAmelCase				:      Optional[Any]			  =				np.nan
    for i in range(__A ):
        _UpperCAmelCase				:      Union[str, Any]			  =				features[:, labels == i]
        _UpperCAmelCase				:      int			  =				data.shape[1]
        _UpperCAmelCase				:      List[Any]			  =				data.mean(1 )
        if i > 0:
            # If covariance_sum is not None
            covariance_sum += device_data * np.dot(
                column_reshape(__A ) - column_reshape(__A )		,		(column_reshape(__A ) - column_reshape(__A )).T		,		)
        else:
            # If covariance_sum is np.nan (i.e. first loop)
            _UpperCAmelCase				:      Dict			  =				device_data * np.dot(
                column_reshape(__A ) - column_reshape(__A )		,		(column_reshape(__A ) - column_reshape(__A )).T		,		)

    return covariance_sum / features.shape[1]

def      _lowerCamelCase	(						__A    :							np.ndarray		,		__A    :							int )      ->					np.ndarray:

    # Check if the features have been loaded
    if features.any():
        _UpperCAmelCase				:      Any			  =				features.mean(1 )
        # Center the dataset
        _UpperCAmelCase				:      int			  =				features - np.reshape(__A		,		(data_mean.size, 1) )
        _UpperCAmelCase				:      int			  =				np.dot(__A		,		centered_data.T ) / features.shape[1]
        _UpperCAmelCase  , _UpperCAmelCase				:      Any			  =				np.linalg.eigh(__A )
        # Take all the columns in the reverse order (-1), and then takes only the first
        _UpperCAmelCase				:      List[Any]			  =				eigenvectors[:, ::-1][:, 0:dimensions]
        # Project the database on the new space
        _UpperCAmelCase				:      List[Any]			  =				np.dot(filtered_eigenvectors.T		,		__A )
        logging.info('''Principal Component Analysis computed''' )

        return projected_data
    else:
        logging.basicConfig(level=logging.ERROR		,		format='''%(message)s'''		,		force=__A )
        logging.error('''Dataset empty''' )
        raise AssertionError

def      _lowerCamelCase	(						__A    :							np.ndarray		,		__A    :							np.ndarray		,		__A    :							int		,		__A    :							int )      ->					np.ndarray:
    assert classes > dimensions

    # Check if features have been already loaded
    if features.any:
        _UpperCAmelCase  , _UpperCAmelCase				:      Optional[Any]			  =				eigh(
            covariance_between_classes(__A		,		__A		,		__A )		,		covariance_within_classes(__A		,		__A		,		__A )		,		)
        _UpperCAmelCase				:      Optional[int]			  =				eigenvectors[:, ::-1][:, :dimensions]
        _UpperCAmelCase  , _UpperCAmelCase  , _UpperCAmelCase				:      List[str]			  =				np.linalg.svd(__A )
        _UpperCAmelCase				:      Union[str, Any]			  =				svd_matrix[:, 0:dimensions]
        _UpperCAmelCase				:      Tuple			  =				np.dot(filtered_svd_matrix.T		,		__A )
        logging.info('''Linear Discriminant Analysis computed''' )

        return projected_data
    else:
        logging.basicConfig(level=logging.ERROR		,		format='''%(message)s'''		,		force=__A )
        logging.error('''Dataset empty''' )
        raise AssertionError

def      _lowerCamelCase	(						)      ->					None:
    # Create dummy dataset with 2 classes and 3 features
    _UpperCAmelCase				:      List[str]			  =				np.array([[1, 2, 3, 4, 5], [2, 3, 4, 5, 6], [3, 4, 5, 6, 7]] )
    _UpperCAmelCase				:      Tuple			  =				np.array([0, 0, 0, 1, 1] )
    _UpperCAmelCase				:      Dict			  =				2
    _UpperCAmelCase				:      List[str]			  =				2

    # Assert that the function raises an AssertionError if dimensions > classes
    with pytest.raises(__A ) as error_info:
        _UpperCAmelCase				:      Dict			  =				linear_discriminant_analysis(
            __A		,		__A		,		__A		,		__A )
        if isinstance(__A		,		np.ndarray ):
            raise AssertionError(
                '''Did not raise AssertionError for dimensions > classes''' )
        assert error_info.type is AssertionError

def      _lowerCamelCase	(						)      ->					None:
    _UpperCAmelCase				:      List[str]			  =				np.array([[1, 2, 3], [4, 5, 6], [7, 8, 9]] )
    _UpperCAmelCase				:      List[str]			  =				2
    _UpperCAmelCase				:      str			  =				np.array([[6.92820323, 8.66025404, 10.39230485], [3.0, 3.0, 3.0]] )

    with pytest.raises(__A ) as error_info:
        _UpperCAmelCase				:      str			  =				principal_component_analysis(__A		,		__A )
        if not np.allclose(__A		,		__A ):
            raise AssertionError
        assert error_info.type is AssertionError


if __name__ == "__main__":
  import doctest

  doctest.testmod()












import argparse

import torch

from transformers import RemBertConfig, RemBertModel, load_tf_weights_in_rembert
from transformers.utils import logging


logging.set_verbosity_info()

def      _lowerCamelCase	(						__A    :							int		,		__A    :							Optional[Any]		,		__A    :							int )      ->					int:
    # Initialise PyTorch model
    _UpperCAmelCase				:      Dict			  =				RemBertConfig.from_json_file(__A )
    print('''Building PyTorch model from configuration: {}'''.format(str(__A ) ) )
    _UpperCAmelCase				:      int			  =				RemBertModel(__A )

    # Load weights from tf checkpoint
    load_tf_weights_in_rembert(__A		,		__A		,		__A )

    # Save pytorch-model
    print('''Save PyTorch model to {}'''.format(__A ) )
    torch.save(model.state_dict()		,		__A )


if __name__ == "__main__":
  SCREAMING_SNAKE_CASE 							=						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(
      '--rembert_config_file',
      default=None,
      type=str,
      required=True,
      help=(
          'The config json file corresponding to the pre-trained RemBERT 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.'
  )
  SCREAMING_SNAKE_CASE 							=						parser.parse_args()
  convert_rembert_tf_checkpoint_to_pytorch(args.tf_checkpoint_path, args.rembert_config_file, args.pytorch_dump_path)




