

'''simple docstring'''







from typing import Callable, Optional, Union

from ...configuration_utils import PretrainedConfig
from ...utils import logging


__lowerCamelCase									=     logging.get_logger(__name__)

__lowerCamelCase									=     {
    '''microsoft/xprophetnet-large-wiki100-cased''': (
        '''https://huggingface.co/microsoft/xprophetnet-large-wiki100-cased/resolve/main/config.json'''
    ),
}


class        A__   (   _snake_case      ):
    lowercase						       =		"xlm-prophetnet"
    lowercase						       =		["past_key_values"]
    lowercase						       =		{
        "num_attention_heads": "num_encoder_attention_heads",
    }



    def __init__(       self		,					UpperCamelCase__ = 0.1		,					UpperCamelCase__ = "gelu"		,					UpperCamelCase__ = 30522		,					UpperCamelCase__ = 1024		,					UpperCamelCase__ = 4096		,					UpperCamelCase__ = 12		,					UpperCamelCase__ = 16		,					UpperCamelCase__ = 4096		,					UpperCamelCase__ = 12		,					UpperCamelCase__ = 16		,					UpperCamelCase__ = 0.1		,					UpperCamelCase__ = 0.1		,					UpperCamelCase__ = 512		,					UpperCamelCase__ = 0.02		,					UpperCamelCase__ = True		,					UpperCamelCase__ = True		,					UpperCamelCase__ = 0		,					UpperCamelCase__ = 2		,					UpperCamelCase__ = 32		,					UpperCamelCase__ = 128		,					UpperCamelCase__ = False		,					UpperCamelCase__ = 0.0		,					UpperCamelCase__ = True		,					UpperCamelCase__ = 0		,					UpperCamelCase__ = 1		,					UpperCamelCase__ = 2		,					**UpperCamelCase__		,					)    ->   str:





           '''simple docstring'''
           A_						       =   vocab_size
           A_						       =   hidden_size
           A_						       =   encoder_ffn_dim
           A_						       =   num_encoder_layers
           A_						       =   num_encoder_attention_heads
           A_						       =   decoder_ffn_dim
           A_						       =   num_decoder_layers
           A_						       =   num_decoder_attention_heads
           A_						       =   max_position_embeddings
           A_						       =   init_std  # Normal(0, this parameter)
           A_						       =   activation_function

           # parameters for xlmprophetnet
           A_						       =   ngram
           A_						       =   num_buckets
           A_						       =   relative_max_distance
           A_						       =   disable_ngram_loss
           A_						       =   eps

           # 3 Types of Dropout
           A_						       =   attention_dropout
           A_						       =   activation_dropout
           A_						       =   dropout

           A_						       =   use_cache

           super().__init__(
               pad_token_id=UpperCamelCase__		,					bos_token_id=UpperCamelCase__		,					eos_token_id=UpperCamelCase__		,					is_encoder_decoder=UpperCamelCase__		,					add_cross_attention=UpperCamelCase__		,					decoder_start_token_id=UpperCamelCase__		,					**UpperCamelCase__		,					)



    @property
    def        snake_case_      (       self							)    ->   int:





           '''simple docstring'''
           return self.num_encoder_layers + self.num_decoder_layers







    @num_hidden_layers.setter
    def        snake_case_      (       self		,					UpperCamelCase__							)    ->   str:





           '''simple docstring'''
           raise NotImplementedError(
               """This model does not support the setting of `num_hidden_layers`. Please set `num_encoder_layers` and"""
               """ `num_decoder_layers`."""							)



'''simple docstring'''







import numpy as np
def    UpperCAmelCase__     (   UpperCAmelCase__,							UpperCAmelCase__,							UpperCAmelCase__ = 1e-12,							UpperCAmelCase__ = 1_00,							)      ->      tuple[float, np.ndarray]:
       assert np.shape(UpperCAmelCase__					)[0] == np.shape(UpperCAmelCase__					)[1]
       # Ensure proper dimensionality.
       assert np.shape(UpperCAmelCase__					)[0] == np.shape(UpperCAmelCase__					)[0]
       # Ensure inputs are either both complex or both real
       assert np.iscomplexobj(UpperCAmelCase__					) == np.iscomplexobj(UpperCAmelCase__					)
       A_						       =   np.iscomplexobj(UpperCAmelCase__					)
       if is_complex:
              # Ensure complex input_matrix is Hermitian
              assert np.array_equal(UpperCAmelCase__,							input_matrix.conj().T					)

       # Set convergence to False. Will define convergence when we exceed max_iterations
       # or when we have small changes from one iteration to next.

       A_						       =   False
       A_						       =   0
       A_						       =   0
       A_						       =   1e12

       while not convergence:
              # Multiple matrix by the vector.
              A_						       =   np.dot(UpperCAmelCase__,							UpperCAmelCase__					)
              # Normalize the resulting output vector.
              A_						       =   w / np.linalg.norm(UpperCAmelCase__					)
              # Find rayleigh quotient
              # (faster than usual b/c we know vector is normalized already)
              A_						       =   vector.conj().T if is_complex else vector.T
              A_						       =   np.dot(UpperCAmelCase__,							np.dot(UpperCAmelCase__,							UpperCAmelCase__					)					)

              # Check convergence.
              A_						       =   np.abs(lambda_ - lambda_previous					) / lambda_
              iterations += 1

              if error <= error_tol or iterations >= max_iterations:
                     A_						       =   True

              A_						       =   lambda_

       if is_complex:
              A_						       =   np.real(lambda_					)

       return lambda_, vector
def    UpperCAmelCase__     (   )      ->      None:
       A_						       =   np.array([[41, 4, 20], [4, 26, 30], [20, 30, 50]]					)
       A_						       =   np.array([41, 4, 20]					)
       A_						       =   real_input_matrix.astype(np.complexaaa					)
       A_						       =   np.triu(1j * complex_input_matrix,							1					)
       complex_input_matrix += imag_matrix
       complex_input_matrix += -1 * imag_matrix.T
       A_						       =   np.array([41, 4, 20]					).astype(np.complexaaa					)

       for problem_type in ["real", "complex"]:
              if problem_type == "real":
                     A_						       =   real_input_matrix
                     A_						       =   real_vector
              elif problem_type == "complex":
                     A_						       =   complex_input_matrix
                     A_						       =   complex_vector

              # Our implementation.
              A_       ,      A_						       =   power_iteration(UpperCAmelCase__,							UpperCAmelCase__					)

              # Numpy implementation.

              # Get eigenvalues and eigenvectors using built-in numpy
              # eigh (eigh used for symmetric or hermetian matrices).
              A_       ,      A_						       =   np.linalg.eigh(UpperCAmelCase__					)
              # Last eigenvalue is the maximum one.
              A_						       =   eigen_values[-1]
              # Last column in this matrix is eigenvector corresponding to largest eigenvalue.
              A_						       =   eigen_vectors[:, -1]

              # Check our implementation and numpy gives close answers.
              assert np.abs(eigen_value - eigen_value_max					) <= 1e-6
              # Take absolute values element wise of each eigenvector.
              # as they are only unique to a minus sign.
              assert np.linalg.norm(np.abs(UpperCAmelCase__					) - np.abs(UpperCAmelCase__					)					) <= 1e-6


if __name__ == "__main__":
      import doctest

      doctest.testmod()
      test_power_iteration()
