





from typing import Mapping

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


lowercase      :       int									=  logging.get_logger(__name__)

lowercase      :       Dict									=  {
    """t5-small""": """https://huggingface.co/t5-small/resolve/main/config.json""",
    """t5-base""": """https://huggingface.co/t5-base/resolve/main/config.json""",
    """t5-large""": """https://huggingface.co/t5-large/resolve/main/config.json""",
    """t5-3b""": """https://huggingface.co/t5-3b/resolve/main/config.json""",
    """t5-11b""": """https://huggingface.co/t5-11b/resolve/main/config.json""",
}





class  A__    (							__UpperCAmelCase	):




   """simple docstring"""



   __A   :				Union[str, Any]         =  '''t5'''
   __A   :				Dict         =  ['''past_key_values''']
   __A   :				int         =  {'''hidden_size''': '''d_model''', '''num_attention_heads''': '''num_heads''', '''num_hidden_layers''': '''num_layers'''}

   def __init__(					self					,  lowercase=3_2128					,  lowercase=512					,  lowercase=64					,  lowercase=2048					,  lowercase=6					,  lowercase=None					,  lowercase=8					,  lowercase=32					,  lowercase=128					,  lowercase=0.1					,  lowercase=1e-6					,  lowercase=1.0					,  lowercase="relu"					,  lowercase=True					,  lowercase=True					,  lowercase=0					,  lowercase=1					,  **lowercase					,  )			->						Optional[int]:







          '''simple docstring'''

          a__						:	List[Any] =				vocab_size
          a__						:	str =				d_model
          a__						:	str =				d_kv
          a__						:	Any =				d_ff
          a__						:	Optional[Any] =				num_layers
          a__						:	Tuple =				(
              num_decoder_layers if num_decoder_layers is not None else self.num_layers
          )  # default = symmetry
          a__						:	Any =				num_heads
          a__						:	Any =				relative_attention_num_buckets
          a__						:	List[str] =				relative_attention_max_distance
          a__						:	str =				dropout_rate
          a__						:	str =				layer_norm_epsilon
          a__						:	Union[str, Any] =				initializer_factor
          a__						:	List[Any] =				feed_forward_proj
          a__						:	str =				use_cache

          a__						:	Optional[int] =				self.feed_forward_proj.split('-')
          a__						:	Optional[int] =				act_info[-1]
          a__						:	Tuple =				act_info[0] == 'gated'

          if len(lowercase) > 1 and act_info[0] != "gated" or len(lowercase) > 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":
                 a__						:	int =				'gelu_new'

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





class  A__    (							__UpperCAmelCase	):




   """simple docstring"""



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







          '''simple docstring'''

          a__						:	str =				{
              'input_ids': {0: 'batch', 1: 'encoder_sequence'},
              'attention_mask': {0: 'batch', 1: 'encoder_sequence'},
          }
          if self.use_past:
                 a__						:	int =				'past_encoder_sequence + sequence'
                 a__						:	int =				{0: 'batch'}
                 a__						:	Dict =				{0: 'batch', 1: 'past_decoder_sequence + sequence'}
          else:
                 a__						:	Any =				{0: 'batch', 1: 'decoder_sequence'}
                 a__						:	Optional[Any] =				{0: 'batch', 1: 'decoder_sequence'}

          if self.use_past:
                 self.fill_with_past_key_values_(lowercase					,  direction='inputs')

          return common_inputs

   @property
   def 							__lowercase		(					self)			->						int:







          '''simple docstring'''

          return 13









import numpy as np
from numpy import ndarray
from scipy.optimize import Bounds, LinearConstraint, minimize



def A_	(		A__				)  ->		float:
       return np.dot(A__		,      A__				)





class  A__    :




   """simple docstring"""



   def __init__(					self					,  *,
       lowercase = np.inf					,  lowercase = "linear"					,  lowercase = 0.0					,  )			->						None:







          '''simple docstring'''

          a__						:	Tuple =				regularization
          a__						:	Optional[Any] =				gamma
          if kernel == "linear":
                 a__						:	Optional[Any] =				self.__linear
          elif kernel == "rbf":
                 if self.gamma == 0:
                        raise ValueError('rbf kernel requires gamma')
                 if not isinstance(self.gamma					,  (float, int)):
                        raise ValueError('gamma must be float or int')
                 if not self.gamma > 0:
                        raise ValueError('gamma must be > 0')
                 a__						:	str =				self.__rbf
                 # in the future, there could be a default value like in sklearn
                 # sklear: def_gamma = 1/(n_features * X.var()) (wiki)
                 # previously it was 1/(n_features)
          else:
                 a__						:	Optional[int] =				F'Unknown kernel: {kernel}'
                 raise ValueError(lowercase)

   def 							__lowercase		(					self					,  lowercase					,  lowercase)			->						float:







          '''simple docstring'''

          return np.dot(lowercase					,  lowercase)

   def 							__lowercase		(					self					,  lowercase					,  lowercase)			->						float:







          '''simple docstring'''

          return np.exp(-(self.gamma * norm_squared(vectora - vectora)))

   def 							__lowercase		(					self					,  lowercase					,  lowercase)			->						None:







          '''simple docstring'''

          a__						:	List[str] =				observations
          a__						:	Dict =				classes

          # using Wolfe's Dual to calculate w.
          # Primal problem: minimize 1/2*norm_squared(w)
          #   constraint: yn(w . xn + b) >= 1
          #
          # With l a vector
          # Dual problem: maximize sum_n(ln) -
          #       1/2 * sum_n(sum_m(ln*lm*yn*ym*xn . xm))
          #   constraint: self.C >= ln >= 0
          #           and sum_n(ln*yn) = 0
          # Then we get w using w = sum_n(ln*yn*xn)
          # At the end we can get b ~= mean(yn - w . xn)
          #
          # Since we use kernels, we only need l_star to calculate b
          # and to classify observations

          ((a__) ,							)						:	Optional[int] =				np.shape(lowercase)

          def to_minimize(lowercase) -> float:
                 a__						:	Tuple =				0
                 ((a__) ,							)						:	Optional[int] =				np.shape(lowercase)
                 for i in range(lowercase):
                        for j in range(lowercase):
                               s += (
                                   candidate[i]
                                   * candidate[j]
                                   * classes[i]
                                   * classes[j]
                                   * self.kernel(observations[i]					,  observations[j])
                               )
                 return 1 / 2 * s - sum(lowercase)

          a__						:	Optional[Any] =				LinearConstraint(lowercase					,  0					,  0)
          a__						:	str =				Bounds(0					,  self.regularization)

          a__						:	List[str] =				minimize(
              lowercase					,  np.ones(lowercase)					,  bounds=lowercase					,  constraints=[ly_contraint]).x
          a__						:	Dict =				l_star

          # calculating mean offset of separation plane to points
          a__						:	int =				0
          for i in range(lowercase):
                 for j in range(lowercase):
                        s += classes[i] - classes[i] * self.optimum[i] * self.kernel(
                            observations[i]					,  observations[j])
          a__						:	List[str] =				s / n

   def 							__lowercase		(					self					,  lowercase)			->						int:







          '''simple docstring'''

          a__						:	int =				sum(
              self.optimum[n]
              * self.classes[n]
              * self.kernel(self.observations[n]					,  lowercase)
              for n in range(len(self.classes)))
          return 1 if s + self.offset >= 0 else -1


if __name__ == "__main__":
    import doctest

    doctest.testmod()


