



from typing import List, Union

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


UpperCAmelCase_          =				logging.get_logger(__name__)

UpperCAmelCase_          =				{
    "allenai/led-base-16384": "https://huggingface.co/allenai/led-base-16384/resolve/main/config.json",
    # See all LED models at https://huggingface.co/models?filter=led
}





class 		UpperCAmelCase    (    snake_case_					):
   SCREAMING_SNAKE_CASE__					      =  '''led'''
   SCREAMING_SNAKE_CASE__					      =  {
       '''num_attention_heads''': '''encoder_attention_heads''',
       '''hidden_size''': '''d_model''',
       '''attention_probs_dropout_prob''': '''attention_dropout''',
       '''initializer_range''': '''init_std''',
   }





   def __init__(						self							,  _lowerCAmelCase=50_265							,  _lowerCAmelCase=16_384							,  _lowerCAmelCase=1_024							,  _lowerCAmelCase=12							,  _lowerCAmelCase=4_096							,  _lowerCAmelCase=16							,  _lowerCAmelCase=12							,  _lowerCAmelCase=4_096							,  _lowerCAmelCase=16							,  _lowerCAmelCase=0.0							,  _lowerCAmelCase=0.0							,  _lowerCAmelCase=True							,  _lowerCAmelCase=True							,  _lowerCAmelCase="gelu"							,  _lowerCAmelCase=1_024							,  _lowerCAmelCase=0.1							,  _lowerCAmelCase=0.0							,  _lowerCAmelCase=0.0							,  _lowerCAmelCase=0.02							,  _lowerCAmelCase=2							,  _lowerCAmelCase=0.0							,  _lowerCAmelCase=1							,  _lowerCAmelCase=0							,  _lowerCAmelCase=2							,  _lowerCAmelCase = 512							,  **_lowerCAmelCase							,  ):
         _lowerCAmelCase			      =	vocab_size
         _lowerCAmelCase			      =	max_encoder_position_embeddings
         _lowerCAmelCase			      =	max_decoder_position_embeddings
         _lowerCAmelCase			      =	d_model
         _lowerCAmelCase			      =	encoder_ffn_dim
         _lowerCAmelCase			      =	encoder_layers
         _lowerCAmelCase			      =	encoder_attention_heads
         _lowerCAmelCase			      =	decoder_ffn_dim
         _lowerCAmelCase			      =	decoder_layers
         _lowerCAmelCase			      =	decoder_attention_heads
         _lowerCAmelCase			      =	dropout
         _lowerCAmelCase			      =	attention_dropout
         _lowerCAmelCase			      =	activation_dropout
         _lowerCAmelCase			      =	activation_function
         _lowerCAmelCase			      =	init_std
         _lowerCAmelCase			      =	encoder_layerdrop
         _lowerCAmelCase			      =	decoder_layerdrop
         _lowerCAmelCase			      =	classifier_dropout
         _lowerCAmelCase			      =	use_cache
         _lowerCAmelCase			      =	encoder_layers
         _lowerCAmelCase			      =	attention_window

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




import numpy as np
def 				UpperCAmelCase__		(					_SCREAMING_SNAKE_CASE :						Tuple					)->Union[str, Any]:
      _lowerCAmelCase      , _lowerCAmelCase			      =	a.shape
      _lowerCAmelCase			      =	min(_SCREAMING_SNAKE_CASE  ,					_SCREAMING_SNAKE_CASE					)
      _lowerCAmelCase			      =	np.eye(_SCREAMING_SNAKE_CASE					)
      _lowerCAmelCase			      =	a.copy()

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

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

            _lowerCAmelCase			      =	q @ q_k.T
            _lowerCAmelCase			      =	q_k @ r

      return q, r


if __name__ == "__main__":
    import doctest

    doctest.testmod()