



from collections import OrderedDict
from typing import Mapping

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfigWithPast
from ...utils import logging


UpperCAmelCase_          =				logging.get_logger(__name__)

UpperCAmelCase_          =				{
    "uclanlp/plbart-base": "https://huggingface.co/uclanlp/plbart-base/resolve/main/config.json",
    # See all PLBART models at https://huggingface.co/models?filter=plbart
}





class 		UpperCAmelCase    (    snake_case_					):
   SCREAMING_SNAKE_CASE__					      =  '''plbart'''
   SCREAMING_SNAKE_CASE__					      =  ['''past_key_values''']
   SCREAMING_SNAKE_CASE__					      =  {'''num_attention_heads''': '''encoder_attention_heads''', '''hidden_size''': '''d_model'''}





   def __init__(						self							,  _lowerCAmelCase=50_005							,  _lowerCAmelCase=1_024							,  _lowerCAmelCase=6							,  _lowerCAmelCase=3_072							,  _lowerCAmelCase=12							,  _lowerCAmelCase=6							,  _lowerCAmelCase=3_072							,  _lowerCAmelCase=12							,  _lowerCAmelCase=0.0							,  _lowerCAmelCase=0.0							,  _lowerCAmelCase=True							,  _lowerCAmelCase=True							,  _lowerCAmelCase="gelu"							,  _lowerCAmelCase=768							,  _lowerCAmelCase=0.1							,  _lowerCAmelCase=0.1							,  _lowerCAmelCase=0.0							,  _lowerCAmelCase=0.02							,  _lowerCAmelCase=0.0							,  _lowerCAmelCase=True							,  _lowerCAmelCase=1							,  _lowerCAmelCase=0							,  _lowerCAmelCase=2							,  _lowerCAmelCase=2							,  **_lowerCAmelCase							,  ):
         _lowerCAmelCase			      =	vocab_size
         _lowerCAmelCase			      =	max_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			      =	scale_embedding  # scale factor will be sqrt(d_model) if True
         super().__init__(
             pad_token_id=_lowerCAmelCase							,  bos_token_id=_lowerCAmelCase							,  eos_token_id=_lowerCAmelCase							,  is_encoder_decoder=_lowerCAmelCase							,  forced_eos_token_id=_lowerCAmelCase							,  **_lowerCAmelCase							,  )






class 		UpperCAmelCase    (    snake_case_					):





   @property
   def 						__lowerCAmelCase				(						self      ):
         return OrderedDict(
             [
                 ('''input_ids''', {0: '''batch''', 1: '''sequence'''}),
                 ('''attention_mask''', {0: '''batch''', 1: '''sequence'''}),
             ]      )





   @property
   def 						__lowerCAmelCase				(						self      ):
         if self.use_past:
               return OrderedDict(
                   [
                       ('''last_hidden_state''', {0: '''batch''', 1: '''sequence'''}),
                       ('''past_keys''', {0: '''batch''', 2: '''sequence'''}),
                       ('''encoder_last_hidden_state''', {0: '''batch''', 1: '''sequence'''}),
                   ]      )
         else:
               return OrderedDict(
                   [
                       ('''last_hidden_state''', {0: '''batch''', 1: '''sequence'''}),
                       ('''encoder_last_hidden_state''', {0: '''batch''', 1: '''sequence'''}),
                   ]      )




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()