



'''simple docstring'''





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


_lowerCAmelCase										=     logging.get_logger(__name__)

_lowerCAmelCase										=     {
    '''EleutherAI/gpt-neox-20b''': '''https://huggingface.co/EleutherAI/gpt-neox-20b/resolve/main/config.json''',
    # See all GPTNeoX models at https://huggingface.co/models?filter=gpt_neox
}






class 	lowerCAmelCase_(    SCREAMING_SNAKE_CASE_					):
  '''simple docstring'''




  __lowercase							:				Optional[int]           =				'''gpt_neox'''




  def __init__(   self  ,__UpperCAmelCase=5_0432  ,__UpperCAmelCase=6144  ,__UpperCAmelCase=44  ,__UpperCAmelCase=64  ,__UpperCAmelCase=2_4576  ,__UpperCAmelCase="gelu"  ,__UpperCAmelCase=0.2_5  ,__UpperCAmelCase=1_0000  ,__UpperCAmelCase=0.0  ,__UpperCAmelCase=0.0  ,__UpperCAmelCase=0.1  ,__UpperCAmelCase=2048  ,__UpperCAmelCase=0.0_2  ,__UpperCAmelCase=1E-5  ,__UpperCAmelCase=True  ,__UpperCAmelCase=0  ,__UpperCAmelCase=2  ,__UpperCAmelCase=False  ,__UpperCAmelCase=True  ,__UpperCAmelCase=None  ,**__UpperCAmelCase  ,)   ->		int:
      super().__init__(bos_token_id=__UpperCAmelCase  ,eos_token_id=__UpperCAmelCase  ,**__UpperCAmelCase				)
      lowerCAmelCase__			:  int											= vocab_size
      lowerCAmelCase__			:  int											= max_position_embeddings
      lowerCAmelCase__			:  Dict											= hidden_size
      lowerCAmelCase__			:  str											= num_hidden_layers
      lowerCAmelCase__			:  Dict											= num_attention_heads
      lowerCAmelCase__			:  List[str]											= intermediate_size
      lowerCAmelCase__			:  Tuple											= hidden_act
      lowerCAmelCase__			:  int											= rotary_pct
      lowerCAmelCase__			:  List[Any]											= rotary_emb_base
      lowerCAmelCase__			:  int											= attention_dropout
      lowerCAmelCase__			:  List[str]											= hidden_dropout
      lowerCAmelCase__			:  Optional[Any]											= classifier_dropout
      lowerCAmelCase__			:  Optional[int]											= initializer_range
      lowerCAmelCase__			:  Tuple											= layer_norm_eps
      lowerCAmelCase__			:  List[str]											= use_cache
      lowerCAmelCase__			:  int											= tie_word_embeddings
      lowerCAmelCase__			:  List[Any]											= use_parallel_residual
      lowerCAmelCase__			:  Union[str, Any]											= rope_scaling
      self._rope_scaling_validation()

      if self.hidden_size % self.num_attention_heads != 0:
          raise ValueError(
              """The hidden size is not divisble by the number of attention heads! Make sure to update them!"""				)






  def 							UpperCAmelCase_     (   self				)   ->		int:
      if self.rope_scaling is None:
          return

      if not isinstance(self.rope_scaling  ,__UpperCAmelCase				) or len(self.rope_scaling				) != 2:
          raise ValueError(
              """`rope_scaling` must be a dictionary with with two fields, `name` and `factor`, """
              F"""got {self.rope_scaling}"""				)
      lowerCAmelCase__			:  List[str]											= self.rope_scaling.get("""type"""  ,__UpperCAmelCase				)
      lowerCAmelCase__			:  List[Any]											= self.rope_scaling.get("""factor"""  ,__UpperCAmelCase				)
      if rope_scaling_type is None or rope_scaling_type not in ["linear", "dynamic"]:
          raise ValueError(
              F"""`rope_scaling`'s name field must be one of ['linear', 'dynamic'], got {rope_scaling_type}"""				)
      if rope_scaling_factor is None or not isinstance(__UpperCAmelCase  ,__UpperCAmelCase				) or rope_scaling_factor <= 1.0:
          raise ValueError(F"""`rope_scaling`'s factor field must be an float > 1, got {rope_scaling_factor}"""				)











'''simple docstring'''





# Logistic Regression from scratch

# In[62]:

# In[63]:

# importing all the required libraries

import numpy as np
from matplotlib import pyplot as plt
from sklearn import datasets
def        _SCREAMING_SNAKE_CASE  (	UpperCamelCase      ):



    """simple docstring"""





    return 1 / (1 + np.exp(-z      ))
def        _SCREAMING_SNAKE_CASE  (	UpperCamelCase							,   UpperCamelCase      ):



    """simple docstring"""





    return (-y * np.log(UpperCamelCase      ) - (1 - y) * np.log(1 - h      )).mean()
def        _SCREAMING_SNAKE_CASE  (	UpperCamelCase							,   UpperCamelCase							,   UpperCamelCase      ):



    """simple docstring"""





    lowerCAmelCase__			:  Union[str, Any]											= np.dot(UpperCamelCase							,   UpperCamelCase      )
    return np.sum(y * scores - np.log(1 + np.exp(UpperCamelCase      )      )      )
def        _SCREAMING_SNAKE_CASE  (	UpperCamelCase							,   UpperCamelCase							,   UpperCamelCase							,   UpperCamelCase=70000      ):



    """simple docstring"""





    lowerCAmelCase__			:  Union[str, Any]											= np.zeros(x.shape[1]      )

    for iterations in range(UpperCamelCase      ):
        lowerCAmelCase__			:  str											= np.dot(UpperCamelCase							,   UpperCamelCase      )
        lowerCAmelCase__			:  Tuple											= sigmoid_function(UpperCamelCase      )
        lowerCAmelCase__			:  List[str]											= np.dot(x.T							,   h - y      ) / y.size
        lowerCAmelCase__			:  List[Any]											= theta - alpha * gradient  # updating the weights
        lowerCAmelCase__			:  List[Any]											= np.dot(UpperCamelCase							,   UpperCamelCase      )
        lowerCAmelCase__			:  List[Any]											= sigmoid_function(UpperCamelCase      )
        lowerCAmelCase__			:  List[Any]											= cost_function(UpperCamelCase							,   UpperCamelCase      )
        if iterations % 100 == 0:
            print(f"""loss: {j} \t"""      )  # printing the loss after every 100 iterations
    return theta


# In[68]:

if __name__ == "__main__":
 _lowerCAmelCase										=     datasets.load_iris()
 _lowerCAmelCase										=     iris.data[:, :2]
 _lowerCAmelCase										=     (iris.target != 0) * 1

 _lowerCAmelCase										=     0.1
 _lowerCAmelCase										=     logistic_reg(alpha, x, y, max_iterations=7_0000)
 print('''theta: ''', theta)  # printing the theta i.e our weights vector
 def        _SCREAMING_SNAKE_CASE  (	UpperCamelCase      ):



     """simple docstring"""





     return sigmoid_function(
         np.dot(UpperCamelCase							,   UpperCamelCase      )      )  # predicting the value of probability from the logistic regression algorithm

 plt.figure(figsize=(10, 6))
 plt.scatter(x[y == 0][:, 0], x[y == 0][:, 1], color='''b''', label='''0''')
 plt.scatter(x[y == 1][:, 0], x[y == 1][:, 1], color='''r''', label='''1''')
 ((_lowerCAmelCase)						,      (_lowerCAmelCase))										=     (x[:, 0].min(), x[:, 0].max())
 ((_lowerCAmelCase)						,      (_lowerCAmelCase))										=     (x[:, 1].min(), x[:, 1].max())
 ((_lowerCAmelCase)						,      (_lowerCAmelCase))										=     np.meshgrid(np.linspace(xa_min, xa_max), np.linspace(xa_min, xa_max))
 _lowerCAmelCase										=     np.c_[xxa.ravel(), xxa.ravel()]
 _lowerCAmelCase										=     predict_prob(grid).reshape(xxa.shape)
 plt.contour(xxa, xxa, probs, [0.5], linewidths=1, colors='''black''')

 plt.legend()




 plt.show()


