

'''simple docstring'''



import warnings

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


UpperCAmelCase :       Optional[Any]			   =     logging.get_logger(__name__)

UpperCAmelCase :       Dict			   =     {
    'RUCAIBox/mvp': 'https://huggingface.co/RUCAIBox/mvp/resolve/main/config.json',
}







class 						lowerCAmelCase__   (      a					):



 """simple docstring"""



 lowerCAmelCase__  					=      "mvp"
 lowerCAmelCase__  					=      ["past_key_values"]
 lowerCAmelCase__  					=      {"num_attention_heads": "encoder_attention_heads", "hidden_size": "d_model"}
 def __init__(		self					:       List[str]					,							__SCREAMING_SNAKE_CASE					:       Any=50_267					,							__SCREAMING_SNAKE_CASE					:       List[str]=1_024					,							__SCREAMING_SNAKE_CASE					:       str=12					,							__SCREAMING_SNAKE_CASE					:       Tuple=4_096					,							__SCREAMING_SNAKE_CASE					:       int=16					,							__SCREAMING_SNAKE_CASE					:       int=12					,							__SCREAMING_SNAKE_CASE					:       Union[str, Any]=4_096					,							__SCREAMING_SNAKE_CASE					:       Any=16					,							__SCREAMING_SNAKE_CASE					:       str=0.0					,							__SCREAMING_SNAKE_CASE					:       Tuple=0.0					,							__SCREAMING_SNAKE_CASE					:       Optional[Any]="gelu"					,							__SCREAMING_SNAKE_CASE					:       int=1_024					,							__SCREAMING_SNAKE_CASE					:       Any=0.1					,							__SCREAMING_SNAKE_CASE					:       Optional[Any]=0.0					,							__SCREAMING_SNAKE_CASE					:       int=0.0					,							__SCREAMING_SNAKE_CASE					:       Dict=0.02					,							__SCREAMING_SNAKE_CASE					:       Optional[int]=0.0					,							__SCREAMING_SNAKE_CASE					:       int=False					,							__SCREAMING_SNAKE_CASE					:       List[Any]=True					,							__SCREAMING_SNAKE_CASE					:       int=1					,							__SCREAMING_SNAKE_CASE					:       Optional[int]=0					,							__SCREAMING_SNAKE_CASE					:       Any=2					,							__SCREAMING_SNAKE_CASE					:       int=True					,							__SCREAMING_SNAKE_CASE					:       Tuple=2					,							__SCREAMING_SNAKE_CASE					:       List[str]=2					,							__SCREAMING_SNAKE_CASE					:       List[Any]=False					,							__SCREAMING_SNAKE_CASE					:       int=100					,							__SCREAMING_SNAKE_CASE					:       Any=800					,							**__SCREAMING_SNAKE_CASE					:       List[Any]					,							)  -> Union[str, Any]:


      """simple docstring"""




      __SCREAMING_SNAKE_CASE     =      vocab_size
      __SCREAMING_SNAKE_CASE     =      max_position_embeddings
      __SCREAMING_SNAKE_CASE     =      d_model
      __SCREAMING_SNAKE_CASE     =      encoder_ffn_dim
      __SCREAMING_SNAKE_CASE     =      encoder_layers
      __SCREAMING_SNAKE_CASE     =      encoder_attention_heads
      __SCREAMING_SNAKE_CASE     =      decoder_ffn_dim
      __SCREAMING_SNAKE_CASE     =      decoder_layers
      __SCREAMING_SNAKE_CASE     =      decoder_attention_heads
      __SCREAMING_SNAKE_CASE     =      dropout
      __SCREAMING_SNAKE_CASE     =      attention_dropout
      __SCREAMING_SNAKE_CASE     =      activation_dropout
      __SCREAMING_SNAKE_CASE     =      activation_function
      __SCREAMING_SNAKE_CASE     =      init_std
      __SCREAMING_SNAKE_CASE     =      encoder_layerdrop
      __SCREAMING_SNAKE_CASE     =      decoder_layerdrop
      __SCREAMING_SNAKE_CASE     =      classifier_dropout
      __SCREAMING_SNAKE_CASE     =      use_cache
      __SCREAMING_SNAKE_CASE     =      encoder_layers
      __SCREAMING_SNAKE_CASE     =      scale_embedding  # scale factor will be sqrt(d_model) if True
      __SCREAMING_SNAKE_CASE     =      use_prompt
      __SCREAMING_SNAKE_CASE     =      prompt_length
      __SCREAMING_SNAKE_CASE     =      prompt_mid_dim

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

      if self.forced_bos_token_id is None and kwargs.get("""force_bos_token_to_be_generated"""					,							__SCREAMING_SNAKE_CASE			):
           __SCREAMING_SNAKE_CASE     =      self.bos_token_id
           warnings.warn(
               f'Please make sure the config includes `forced_bos_token_id={self.bos_token_id}` in future versions. '
               """The config can simply be saved and uploaded again to be fixed."""			)









'''simple docstring'''



import numpy as np







def    a__       (  a__      ,    a__      ,    a__ = 1E-1_2      ,    a__ = 1_00      ,    ):




     """simple docstring"""


     assert np.shape(a__						)[0] == np.shape(a__						)[1]
     # Ensure proper dimensionality.
     assert np.shape(a__						)[0] == np.shape(a__						)[0]
     # Ensure inputs are either both complex or both real
     assert np.iscomplexobj(a__						) == np.iscomplexobj(a__						)
     __SCREAMING_SNAKE_CASE     =      np.iscomplexobj(a__						)
     if is_complex:
          # Ensure complex input_matrix is Hermitian
          assert np.array_equal(a__      ,    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.

     __SCREAMING_SNAKE_CASE     =      False
     __SCREAMING_SNAKE_CASE     =      0
     __SCREAMING_SNAKE_CASE     =      0
     __SCREAMING_SNAKE_CASE     =      1E1_2

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

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

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

          __SCREAMING_SNAKE_CASE     =      lambda_

     if is_complex:
          __SCREAMING_SNAKE_CASE     =      np.real(lambda_						)

     return lambda_, vector







def    a__       (  ):




     """simple docstring"""


     __SCREAMING_SNAKE_CASE     =      np.array([[41, 4, 20], [4, 26, 30], [20, 30, 50]]						)
     __SCREAMING_SNAKE_CASE     =      np.array([41, 4, 20]						)
     __SCREAMING_SNAKE_CASE     =      real_input_matrix.astype(np.complexaaa						)
     __SCREAMING_SNAKE_CASE     =      np.triu(1J * complex_input_matrix      ,    1						)
     complex_input_matrix += imag_matrix
     complex_input_matrix += -1 * imag_matrix.T
     __SCREAMING_SNAKE_CASE     =      np.array([41, 4, 20]						).astype(np.complexaaa						)

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

          # Our implementation.
          __SCREAMING_SNAKE_CASE   ,			__SCREAMING_SNAKE_CASE     =      power_iteration(a__      ,    a__						)

          # Numpy implementation.

          # Get eigenvalues and eigenvectors using built-in numpy
          # eigh (eigh used for symmetric or hermetian matrices).
          __SCREAMING_SNAKE_CASE   ,			__SCREAMING_SNAKE_CASE     =      np.linalg.eigh(a__						)
          # Last eigenvalue is the maximum one.
          __SCREAMING_SNAKE_CASE     =      eigen_values[-1]
          # Last column in this matrix is eigenvector corresponding to largest eigenvalue.
          __SCREAMING_SNAKE_CASE     =      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(a__						) - np.abs(a__						)						) <= 1E-6


if __name__ == "__main__":
  import doctest

  doctest.testmod()
  test_power_iteration()






