





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






def 	_SCREAMING_SNAKE_CASE		(      SCREAMING_SNAKE_CASE		:ndarray   )      ->   str:
 return np.dot(_a   ,   _a   )






class        snake_case_    :






  def __init__(							self			:					Optional[int]       ,  *,
      _snake_case			:					Union[str, Any] = np.inf       ,  _snake_case			:					Dict = "linear"       ,  _snake_case			:					List[Any] = 0.0       ,  )->None:






   '''simple docstring'''

   __lowerCAmelCase					:	Optional[int]      =       regularization
   __lowerCAmelCase					:	List[Any]      =       gamma
   if kernel == "linear":
    __lowerCAmelCase					:	int      =       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"""      )
    __lowerCAmelCase					:	Optional[Any]      =       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:
    __lowerCAmelCase					:	List[Any]      =       F'''Unknown kernel: {kernel}'''
    raise ValueError(_SCREAMING_SNAKE_CASE      )






  def 				UpperCAmelCase__						(							self			:					int       ,  _snake_case			:					List[Any]       ,  _snake_case			:					Tuple      )->float:






   '''simple docstring'''

   return np.dot(_SCREAMING_SNAKE_CASE       ,  _SCREAMING_SNAKE_CASE      )






  def 				UpperCAmelCase__						(							self			:					Dict       ,  _snake_case			:					List[Any]       ,  _snake_case			:					Any      )->float:






   '''simple docstring'''

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






  def 				UpperCAmelCase__						(							self			:					List[Any]       ,  _snake_case			:					str       ,  _snake_case			:					List[Any]      )->None:






   '''simple docstring'''

   __lowerCAmelCase					:	int      =       observations
   __lowerCAmelCase					:	str      =       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

   (__lowerCAmelCase    )					:	Any      =       np.shape(_SCREAMING_SNAKE_CASE      )

   def to_minimize(_snake_case			:					int      ) -> float:
    __lowerCAmelCase					:	Dict      =       0
    (__lowerCAmelCase    )					:	str      =       np.shape(_SCREAMING_SNAKE_CASE      )
    for i in range(_SCREAMING_SNAKE_CASE      ):
     for j in range(_SCREAMING_SNAKE_CASE      ):
      s += (
          candidate[i]
          * candidate[j]
          * classes[i]
          * classes[j]
          * self.kernel(observations[i]       ,  observations[j]      )
      )
    return 1 / 2 * s - sum(_SCREAMING_SNAKE_CASE      )

   __lowerCAmelCase					:	List[Any]      =       LinearConstraint(_SCREAMING_SNAKE_CASE       ,  0       ,  0      )
   __lowerCAmelCase					:	Union[str, Any]      =       Bounds(0       ,  self.regularization      )

   __lowerCAmelCase					:	Tuple      =       minimize(
       _SCREAMING_SNAKE_CASE       ,  np.ones(_SCREAMING_SNAKE_CASE      )       ,  bounds=_SCREAMING_SNAKE_CASE       ,  constraints=[ly_contraint]      ).x
   __lowerCAmelCase					:	Optional[int]      =       l_star

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






  def 				UpperCAmelCase__						(							self			:					Any       ,  _snake_case			:					Optional[int]      )->int:






   '''simple docstring'''

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


if __name__ == "__main__":
      import doctest

      doctest.testmod()












from collections import OrderedDict
from typing import Any, List, Mapping, Optional

from ... import PreTrainedTokenizer, TensorType, is_torch_available
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfigWithPast, PatchingSpec
from ...utils import logging


_UpperCAmelCase             =							logging.get_logger(__name__)

_UpperCAmelCase             =							{
    'EleutherAI/gpt-j-6B': 'https://huggingface.co/EleutherAI/gpt-j-6B/resolve/main/config.json',
    # See all GPT-J models at https://huggingface.co/models?filter=gpt_j
}






class        snake_case_    (		__lowercase						):
  A_      							=   'gptj'
  A_      							=   {
      'max_position_embeddings': 'n_positions',
      'hidden_size': 'n_embd',
      'num_attention_heads': 'n_head',
      'num_hidden_layers': 'n_layer',
  }






  def __init__(							self			:					Tuple       ,  _snake_case			:					Optional[int]=50400       ,  _snake_case			:					Union[str, Any]=2048       ,  _snake_case			:					Tuple=4096       ,  _snake_case			:					Optional[int]=28       ,  _snake_case			:					Tuple=16       ,  _snake_case			:					Optional[Any]=64       ,  _snake_case			:					Optional[int]=None       ,  _snake_case			:					str="gelu_new"       ,  _snake_case			:					str=0.0       ,  _snake_case			:					Optional[int]=0.0       ,  _snake_case			:					List[str]=0.0       ,  _snake_case			:					Tuple=1E-5       ,  _snake_case			:					List[str]=0.02       ,  _snake_case			:					Optional[int]=True       ,  _snake_case			:					Optional[Any]=50256       ,  _snake_case			:					List[str]=50256       ,  _snake_case			:					str=False       ,  **_snake_case			:					Tuple       ,  )->List[Any]:






   '''simple docstring'''

   __lowerCAmelCase					:	str      =       vocab_size
   __lowerCAmelCase					:	Any      =       n_positions
   __lowerCAmelCase					:	Optional[Any]      =       n_embd
   __lowerCAmelCase					:	Optional[int]      =       n_layer
   __lowerCAmelCase					:	Optional[int]      =       n_head
   __lowerCAmelCase					:	List[Any]      =       n_inner
   __lowerCAmelCase					:	List[str]      =       rotary_dim
   __lowerCAmelCase					:	int      =       activation_function
   __lowerCAmelCase					:	str      =       resid_pdrop
   __lowerCAmelCase					:	Union[str, Any]      =       embd_pdrop
   __lowerCAmelCase					:	Dict      =       attn_pdrop
   __lowerCAmelCase					:	Optional[Any]      =       layer_norm_epsilon
   __lowerCAmelCase					:	Optional[Any]      =       initializer_range
   __lowerCAmelCase					:	Dict      =       use_cache

   __lowerCAmelCase					:	List[str]      =       bos_token_id
   __lowerCAmelCase					:	List[Any]      =       eos_token_id

   super().__init__(
       bos_token_id=_snake_case       ,  eos_token_id=_snake_case       ,  tie_word_embeddings=_snake_case       ,  **_snake_case      )






class        snake_case_    (		__lowercase						):






  def __init__(							self			:					Union[str, Any]       ,  _snake_case			:					PretrainedConfig       ,  _snake_case			:					str = "default"       ,  _snake_case			:					List[PatchingSpec] = None       ,  _snake_case			:					bool = False       ,  )->str:






   '''simple docstring'''

   super().__init__(_snake_case       ,  task=_snake_case       ,  patching_specs=_snake_case       ,  use_past=_snake_case      )
   if not getattr(self._config       ,  """pad_token_id"""       ,  _snake_case      ):
    # TODO: how to do that better?
    __lowerCAmelCase					:	Dict      =       0






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






   '''simple docstring'''

   __lowerCAmelCase					:	Any      =       OrderedDict({"""input_ids""": {0: """batch""", 1: """sequence"""}}      )
   if self.use_past:
    self.fill_with_past_key_values_(_snake_case       ,  direction="""inputs"""      )
    __lowerCAmelCase					:	Dict      =       {0: """batch""", 1: """past_sequence + sequence"""}
   else:
    __lowerCAmelCase					:	Optional[Any]      =       {0: """batch""", 1: """sequence"""}

   return common_inputs






  @property
  def 				UpperCAmelCase__						(							self			:					Dict      )->int:






   '''simple docstring'''

   return self._config.n_layer






  @property
  def 				UpperCAmelCase__						(							self			:					str      )->int:






   '''simple docstring'''

   return self._config.n_head






  def 				UpperCAmelCase__						(							self			:					str       ,  _snake_case			:					PreTrainedTokenizer       ,  _snake_case			:					int = -1       ,  _snake_case			:					int = -1       ,  _snake_case			:					bool = False       ,  _snake_case			:					Optional[TensorType] = None       ,  )->Mapping[str, Any]:






   '''simple docstring'''

   __lowerCAmelCase					:	Optional[Any]      =       super(_snake_case       ,  self      ).generate_dummy_inputs(
       _snake_case       ,  batch_size=_snake_case       ,  seq_length=_snake_case       ,  is_pair=_snake_case       ,  framework=_snake_case      )

   # We need to order the input in the way they appears in the forward()
   __lowerCAmelCase					:	Union[str, Any]      =       OrderedDict({"""input_ids""": common_inputs["""input_ids"""]}      )

   # Need to add the past_keys
   if self.use_past:
    if not is_torch_available():
     raise ValueError("""Cannot generate dummy past_keys inputs without PyTorch installed."""      )
    else:
     import torch

     __lowerCAmelCase ,					__lowerCAmelCase					:	List[Any]      =       common_inputs["""input_ids"""].shape
     # Not using the same length for past_key_values
     __lowerCAmelCase					:	Optional[Any]      =       seqlen + 2
     __lowerCAmelCase					:	Dict      =       (
         batch,
         self.num_attention_heads,
         past_key_values_length,
         self._config.hidden_size // self.num_attention_heads,
     )
     __lowerCAmelCase					:	Tuple      =       [
         (torch.zeros(_snake_case      ), torch.zeros(_snake_case      )) for _ in range(self.num_layers      )
     ]

   __lowerCAmelCase					:	int      =       common_inputs["""attention_mask"""]
   if self.use_past:
    __lowerCAmelCase					:	Union[str, Any]      =       ordered_inputs["""attention_mask"""].dtype
    __lowerCAmelCase					:	List[str]      =       torch.cat(
        [ordered_inputs["""attention_mask"""], torch.ones(_snake_case       ,  _snake_case       ,  dtype=_snake_case      )]       ,  dim=1      )

   return ordered_inputs






  @property
  def 				UpperCAmelCase__						(							self			:					str      )->int:






   '''simple docstring'''

   return 13