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


_lowerCAmelCase        =       logging.get_logger(__name__)

_lowerCAmelCase        =       {
    'google/reformer-crime-and-punishment': (
        'https://huggingface.co/google/reformer-crime-and-punishment/resolve/main/config.json'
    ),
    'google/reformer-enwik8': 'https://huggingface.co/google/reformer-enwik8/resolve/main/config.json',
}






class     __UpperCAmelCase(    snake_case_				):






 """simple docstring"""




 __magic_name__          =   """reformer"""
 __magic_name__          =   ["""past_buckets_states"""]
 __magic_name__          =   {}
 def __init__(    self ,       __magic_name__=64 ,       __magic_name__=["local", "lsh", "local", "lsh", "local", "lsh"] ,       __magic_name__=1.0 ,       __magic_name__=True ,       __magic_name__=[64, 64] ,       __magic_name__=[64, 192] ,       __magic_name__=0 ,       __magic_name__=2 ,       __magic_name__=512 ,       __magic_name__=None ,       __magic_name__="relu" ,       __magic_name__=0.05 ,       __magic_name__=256 ,       __magic_name__=0.02 ,       __magic_name__=False ,       __magic_name__=1e-12 ,       __magic_name__=1 ,       __magic_name__=0 ,       __magic_name__=0.05 ,       __magic_name__=64 ,       __magic_name__=64 ,       __magic_name__=0.0 ,       __magic_name__=1 ,       __magic_name__=0 ,       __magic_name__=4096 ,       __magic_name__=12 ,       __magic_name__=None ,       __magic_name__=1 ,       __magic_name__=0 ,       __magic_name__=320 ,       __magic_name__=False ,       __magic_name__=True ,       __magic_name__=None ,       **__magic_name__ ,       ):

        """simple docstring"""

        A_							:       Dict	      =				hash_seed
        A_							:       int	      =				vocab_size
        A_							:       Union[str, Any]	      =				attention_head_size
        A_							:       Any	      =				hidden_size
        A_							:       str	      =				num_attention_heads
        A_							:       Optional[Any]	      =				num_hashes
        A_							:       Dict	      =				len(_lowerCAmelCase      )
        A_							:       Any	      =				tuple(_lowerCAmelCase      ) if isinstance(_lowerCAmelCase ,       _lowerCAmelCase      ) else num_buckets
        A_							:       Tuple	      =				lsh_attn_chunk_length
        A_							:       Tuple	      =				local_attn_chunk_length
        A_							:       Union[str, Any]	      =				lsh_num_chunks_after
        A_							:       str	      =				lsh_num_chunks_before
        A_							:       Tuple	      =				local_num_chunks_after
        A_							:       List[str]	      =				local_num_chunks_before
        A_							:       str	      =				hidden_act
        A_							:       Tuple	      =				feed_forward_size
        A_							:       Dict	      =				hidden_dropout_prob
        A_							:       Optional[Any]	      =				lsh_attention_probs_dropout_prob
        A_							:       Optional[Any]	      =				local_attention_probs_dropout_prob
        A_							:       str	      =				max_position_embeddings
        A_							:       Optional[Any]	      =				initializer_range
        A_							:       Any	      =				layer_norm_eps
        A_							:       List[Any]	      =				axial_pos_embds
        A_							:       Optional[int]	      =				tuple(_lowerCAmelCase      )
        A_							:       Optional[Any]	      =				tuple(_lowerCAmelCase      )
        A_							:       str	      =				axial_norm_std
        A_							:       Tuple	      =				chunk_size_lm_head
        A_							:       Dict	      =				attn_layers
        A_							:       List[Any]	      =				use_cache
        A_							:       Tuple	      =				classifier_dropout
        super().__init__(
            pad_token_id=_lowerCAmelCase ,       eos_token_id=_lowerCAmelCase ,       is_decoder=_lowerCAmelCase ,       tie_word_embeddings=_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()