


from typing import Mapping

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


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

SCREAMING_SNAKE_CASE		:Any											=       {
    '''google/long-t5-local-base''': '''https://huggingface.co/google/long-t5-local-base/blob/main/config.json''',
    '''google/long-t5-local-large''': '''https://huggingface.co/google/long-t5-local-large/blob/main/config.json''',
    '''google/long-t5-tglobal-base''': '''https://huggingface.co/google/long-t5-tglobal-base/blob/main/config.json''',
    '''google/long-t5-tglobal-large''': '''https://huggingface.co/google/long-t5-tglobal-large/blob/main/config.json''',
}


class       __lowerCAmelCase       (	a   ):






  """simple docstring"""





  _SCREAMING_SNAKE_CASE						      =    'longt5'
  _SCREAMING_SNAKE_CASE						      =    ['past_key_values']
  _SCREAMING_SNAKE_CASE						      =    {'hidden_size': 'd_model', 'num_attention_heads': 'num_heads', 'num_hidden_layers': 'num_layers'}



  def __init__(       self    :    Optional[Any]      ,			_lowerCAmelCase    :    Optional[int]=3_2_1_2_8      ,			_lowerCAmelCase    :    int=5_1_2      ,			_lowerCAmelCase    :    Any=6_4      ,			_lowerCAmelCase    :    Any=2_0_4_8      ,			_lowerCAmelCase    :    Tuple=6      ,			_lowerCAmelCase    :    Optional[int]=None      ,			_lowerCAmelCase    :    Optional[Any]=8      ,			_lowerCAmelCase    :    Union[str, Any]=1_2_7      ,			_lowerCAmelCase    :    List[str]=1_6      ,			_lowerCAmelCase    :    str=3_2      ,			_lowerCAmelCase    :    Union[str, Any]=1_2_8      ,			_lowerCAmelCase    :    str=0.1      ,			_lowerCAmelCase    :    int=1e-6      ,			_lowerCAmelCase    :    str=1.0      ,			_lowerCAmelCase    :    Dict="relu"      ,			_lowerCAmelCase    :    Optional[int]=True      ,			_lowerCAmelCase    :    List[Any]="local"      ,			_lowerCAmelCase    :    List[Any]=True      ,			_lowerCAmelCase    :    Any=0      ,			_lowerCAmelCase    :    List[Any]=1      ,			**_lowerCAmelCase    :    Optional[int]      ,			)				->	Any:




     """simple docstring"""
     snake_case_         =   vocab_size
     snake_case_         =   d_model
     snake_case_         =   d_kv
     snake_case_         =   d_ff
     snake_case_         =   num_layers
     # default = symmetry
     snake_case_         =   num_decoder_layers if num_decoder_layers is not None else self.num_layers
     snake_case_         =   num_heads
     snake_case_         =   local_radius
     snake_case_         =   global_block_size
     snake_case_         =   relative_attention_num_buckets
     snake_case_         =   relative_attention_max_distance
     snake_case_         =   dropout_rate
     snake_case_         =   layer_norm_epsilon
     snake_case_         =   initializer_factor
     snake_case_         =   feed_forward_proj
     snake_case_         =   encoder_attention_type
     snake_case_         =   use_cache

     snake_case_         =   self.feed_forward_proj.split("-"					)
     snake_case_         =   act_info[-1]
     snake_case_         =   act_info[0] == "gated"

     if len(_lowerCAmelCase					) > 1 and act_info[0] != "gated" or len(_lowerCAmelCase					) > 2:
        raise ValueError(
            F'''`feed_forward_proj`: {feed_forward_proj} is not a valid activation function of the dense layer.'''
            "Please make sure `feed_forward_proj` is of the format `gated-{ACT_FN}` or `{ACT_FN}`, e.g. "
            "'gated-gelu' or 'relu'"					)

     # for backwards compatibility
     if feed_forward_proj == "gated-gelu":
        snake_case_         =   "gelu_new"

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






class       __lowerCAmelCase       (	a   ):






  """simple docstring"""





  @property
  def    lowerCAmelCase__					(       self    :    Any					)				->	Mapping[str, Mapping[int, str]]:




     """simple docstring"""
     snake_case_         =   {
         "input_ids": {0: "batch", 1: "encoder_sequence"},
         "attention_mask": {0: "batch", 1: "encoder_sequence"},
     }
     if self.use_past:
        snake_case_         =   "past_encoder_sequence + sequence"
        snake_case_         =   {0: "batch"}
        snake_case_         =   {0: "batch", 1: "past_decoder_sequence + sequence"}
     else:
        snake_case_         =   {0: "batch", 1: "decoder_sequence"}
        snake_case_         =   {0: "batch", 1: "decoder_sequence"}

     if self.use_past:
        self.fill_with_past_key_values_(_lowerCAmelCase      ,			direction="inputs"					)

     return common_inputs



  @property
  def    lowerCAmelCase__					(       self    :    Union[str, Any]					)				->	int:




     """simple docstring"""
     return 1_3








from typing import Any

import numpy as np



def      _lowerCAmelCase  (							lowerCAmelCase_							:np.ndarray							)->bool:
   '''simple docstring'''






   assert np.shape(lowerCAmelCase_							)[0] == np.shape(lowerCAmelCase_							)[1]

   # If matrix not symmetric, exit right away.
   if np.allclose(lowerCAmelCase_						,  matrix.T							) is False:
      return False

   # Get eigenvalues and eignevectors for a symmetric matrix.
   snake_case_      ,      snake_case_         =   np.linalg.eigh(lowerCAmelCase_							)

   # Check sign of all eigenvalues.
   # np.all returns a value of type np.bool_
   return bool(np.all(eigen_values > 0							)							)



def      _lowerCAmelCase  (							lowerCAmelCase_							:int							)->Any:
   '''simple docstring'''






   snake_case_         =   np.random.randn(lowerCAmelCase_						,  lowerCAmelCase_							)
   snake_case_         =   np.dot(lowerCAmelCase_						,  random_matrix.T							)
   assert _is_matrix_spd(lowerCAmelCase_							)
   return spd_matrix



def      _lowerCAmelCase  (							lowerCAmelCase_							:np.ndarray						,  lowerCAmelCase_							:np.ndarray						,  lowerCAmelCase_							:int = 1_000						,  lowerCAmelCase_							:float = 1e-8						,  )->Any:
   '''simple docstring'''






   assert np.shape(lowerCAmelCase_							)[0] == np.shape(lowerCAmelCase_							)[1]
   assert np.shape(lowerCAmelCase_							)[0] == np.shape(lowerCAmelCase_							)[0]
   assert _is_matrix_spd(lowerCAmelCase_							)

   # Initialize solution guess, residual, search direction.
   snake_case_         =   np.zeros((np.shape(lowerCAmelCase_							)[0], 1)							)
   snake_case_         =   np.copy(lowerCAmelCase_							)
   snake_case_         =   np.copy(lowerCAmelCase_							)

   # Set initial errors in solution guess and residual.
   snake_case_         =   1e9
   snake_case_         =   1e9
   snake_case_         =   1e9

   # Set iteration counter to threshold number of iterations.
   snake_case_         =   0

   while error > tol:
      # Save this value so we only calculate the matrix-vector product once.
      snake_case_         =   np.dot(lowerCAmelCase_						,  lowerCAmelCase_							)

      # The main algorithm.

      # Update search direction magnitude.
      snake_case_         =   np.dot(ra.T						,  lowerCAmelCase_							) / np.dot(pa.T						,  lowerCAmelCase_							)
      # Update solution guess.
      snake_case_         =   xa + alpha * pa
      # Calculate new residual.
      snake_case_         =   ra - alpha * w
      # Calculate new Krylov subspace scale.
      snake_case_         =   np.dot(r.T						,  lowerCAmelCase_							) / np.dot(ra.T						,  lowerCAmelCase_							)
      # Calculate new A conjuage search direction.
      snake_case_         =   r + beta * pa

      # Calculate errors.
      snake_case_         =   np.linalg.norm(r - ra							)
      snake_case_         =   np.linalg.norm(x - xa							)
      snake_case_         =   np.maximum(lowerCAmelCase_						,  lowerCAmelCase_							)

      # Update variables.
      snake_case_         =   np.copy(lowerCAmelCase_							)
      snake_case_         =   np.copy(lowerCAmelCase_							)
      snake_case_         =   np.copy(lowerCAmelCase_							)

      # Update number of iterations.
      iterations += 1
      if iterations > max_iterations:
         break

   return x



def      _lowerCAmelCase  (							)->None:
   '''simple docstring'''






   snake_case_         =   3
   snake_case_         =   _create_spd_matrix(lowerCAmelCase_							)
   snake_case_         =   np.random.randn(lowerCAmelCase_						,  1							)
   snake_case_         =   np.dot(lowerCAmelCase_						,  lowerCAmelCase_							)

   # Numpy solution.
   snake_case_         =   np.linalg.solve(lowerCAmelCase_						,  lowerCAmelCase_							)

   # Our implementation.
   snake_case_         =   conjugate_gradient(lowerCAmelCase_						,  lowerCAmelCase_							)

   # Ensure both solutions are close to x_true (and therefore one another).
   assert np.linalg.norm(x_numpy - x_true							) <= 1e-6
   assert np.linalg.norm(x_conjugate_gradient - x_true							) <= 1e-6


if __name__ == "__main__":
   import doctest

   doctest.testmod()
   test_conjugate_gradient()




