





from typing import Callable, Optional, Union

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


_UpperCamelCase   :							List[str]     =	logging.get_logger(__name__)

_UpperCamelCase   :							Any     =	{
    """microsoft/prophetnet-large-uncased""": (
        """https://huggingface.co/microsoft/prophetnet-large-uncased/resolve/main/config.json"""
    ),
}







class 						_lowerCAmelCase(						_a):

  """simple docstring"""







  lowerCamelCase__      					=   '''prophetnet'''
  lowerCamelCase__      					=   ['''past_key_values''']
  lowerCamelCase__      					=   {
      '''num_attention_heads''': '''num_encoder_attention_heads''',
  }

  def __init__( self  ,    UpperCAmelCase = 0.1  ,    UpperCAmelCase = "gelu"  ,    UpperCAmelCase = 3_05_22  ,    UpperCAmelCase = 10_24  ,    UpperCAmelCase = 40_96  ,    UpperCAmelCase = 12  ,    UpperCAmelCase = 16  ,    UpperCAmelCase = 40_96  ,    UpperCAmelCase = 12  ,    UpperCAmelCase = 16  ,    UpperCAmelCase = 0.1  ,    UpperCAmelCase = 0.1  ,    UpperCAmelCase = 5_12  ,    UpperCAmelCase = 0.02  ,    UpperCAmelCase = True  ,    UpperCAmelCase = True  ,    UpperCAmelCase = 0  ,    UpperCAmelCase = 2  ,    UpperCAmelCase = 32  ,    UpperCAmelCase = 1_28  ,    UpperCAmelCase = False  ,    UpperCAmelCase = 0.0  ,    UpperCAmelCase = True  ,    UpperCAmelCase = 0  ,    UpperCAmelCase = 1  ,    UpperCAmelCase = 2  ,    **UpperCAmelCase  ,    )-> List[str]:
     __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 prophetnet
     __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 		SCREAMING_SNAKE_CASE__	( self     )-> int:
     return self.num_encoder_layers + self.num_decoder_layers





  @num_hidden_layers.setter
  def 		SCREAMING_SNAKE_CASE__	( self  ,    UpperCAmelCase     )-> Any:
     raise NotImplementedError(
         '''This model does not support the setting of `num_hidden_layers`. Please set `num_encoder_layers` and'''
         ''' `num_decoder_layers`.'''     )








import numpy as np
def 				__UpperCamelCase  (		snake_case     )  ->						Any:


   '''simple docstring'''







   __A ,						__A				 =       a.shape
   __A				 =       min(snake_case    ,	snake_case     )
   __A				 =       np.eye(snake_case     )
   __A				 =       a.copy()

   for k in range(t - 1     ):
      # select a column of modified matrix A':
      __A				 =       r[k:, [k]]
      # construct first basis vector
      __A				 =       np.zeros_like(snake_case     )
      __A				 =       1.0
      # determine scaling factor
      __A				 =       np.linalg.norm(snake_case     )
      # construct vector v for Householder reflection
      __A				 =       x + np.sign(x[0]     ) * alpha * ea
      v /= np.linalg.norm(snake_case     )

      # construct the Householder matrix
      __A				 =       np.eye(m - k     ) - 2.0 * v @ v.T
      # pad with ones and zeros as necessary
      __A				 =       np.block([[np.eye(snake_case     ), np.zeros((k, m - k)     )], [np.zeros((m - k, k)     ), q_k]]     )

      __A				 =       q @ q_k.T
      __A				 =       q_k @ r

   return q, r


if __name__ == "__main__":
    import doctest

    doctest.testmod()

