




import warnings

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


_lowerCamelCase			:	Dict							 =     logging.get_logger(__name__)

_lowerCamelCase			:	str							 =     {
    """RUCAIBox/mvp""": """https://huggingface.co/RUCAIBox/mvp/resolve/main/config.json""",
}



class        UpperCamelCase_					(				__a  ):

    '''simple docstring'''






    UpperCAmelCase__													=   "mvp"
    UpperCAmelCase__													=   ["past_key_values"]
    UpperCAmelCase__													=   {"num_attention_heads": "encoder_attention_heads", "hidden_size": "d_model"}


    def __init__(    self   :			Tuple      ,				UpperCAmelCase__   :			List[str]=50_267      ,				UpperCAmelCase__   :			str=1_024      ,				UpperCAmelCase__   :			List[Any]=12      ,				UpperCAmelCase__   :			int=4_096      ,				UpperCAmelCase__   :			str=16      ,				UpperCAmelCase__   :			Optional[Any]=12      ,				UpperCAmelCase__   :			List[str]=4_096      ,				UpperCAmelCase__   :			Dict=16      ,				UpperCAmelCase__   :			List[Any]=0.0      ,				UpperCAmelCase__   :			Optional[int]=0.0      ,				UpperCAmelCase__   :			int="gelu"      ,				UpperCAmelCase__   :			Any=1_024      ,				UpperCAmelCase__   :			Dict=0.1      ,				UpperCAmelCase__   :			Optional[int]=0.0      ,				UpperCAmelCase__   :			Tuple=0.0      ,				UpperCAmelCase__   :			List[str]=0.02      ,				UpperCAmelCase__   :			str=0.0      ,				UpperCAmelCase__   :			Tuple=False      ,				UpperCAmelCase__   :			Optional[Any]=True      ,				UpperCAmelCase__   :			Optional[Any]=1      ,				UpperCAmelCase__   :			Tuple=0      ,				UpperCAmelCase__   :			List[Any]=2      ,				UpperCAmelCase__   :			Optional[Any]=True      ,				UpperCAmelCase__   :			Dict=2      ,				UpperCAmelCase__   :			Any=2      ,				UpperCAmelCase__   :			List[str]=False      ,				UpperCAmelCase__   :			str=100      ,				UpperCAmelCase__   :			List[str]=800      ,				**UpperCAmelCase__   :			Dict      ,				)		->Any:





           '''simple docstring'''

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

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

           if self.forced_bos_token_id is None and kwargs.get('''force_bos_token_to_be_generated'''      ,				lowerCAmelCase_):
                  A__   				=					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.''')








# 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       ( lowercase_		)     -> Dict:



       """simple docstring"""

       return 1 / (1 + np.exp(-z		))







def SCREAMING_SNAKE_CASE       ( lowercase_				,       lowercase_		)     -> Optional[int]:



       """simple docstring"""

       return (-y * np.log(lowercase_		) - (1 - y) * np.log(1 - h		)).mean()







def SCREAMING_SNAKE_CASE       ( lowercase_				,       lowercase_				,       lowercase_		)     -> int:



       """simple docstring"""

       A__   				=					np.dot(lowercase_				,       lowercase_		)
       return np.sum(y * scores - np.log(1 + np.exp(lowercase_		)		)		)







def SCREAMING_SNAKE_CASE       ( lowercase_				,       lowercase_				,       lowercase_				,       lowercase_=70_000		)     -> Dict:



       """simple docstring"""

       A__   				=					np.zeros(x.shape[1]		)

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


# In[68]:

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

  _lowerCamelCase			:	Union[str, Any]							 =     0.1
  _lowerCamelCase			:	Tuple							 =     logistic_reg(alpha, x, y, max_iterations=70000)
  print("""theta: """, theta)  # printing the theta i.e our weights vector







  def SCREAMING_SNAKE_CASE       ( lowercase_		)     -> List[Any]:



         """simple docstring"""

         return sigmoid_function(
             np.dot(lowercase_				,       lowercase_		)		)  # 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))			:	int							 =     (x[:, 0].min(), x[:, 0].max())
  ((_lowerCamelCase) ,    (_lowerCamelCase))			:	Union[str, Any]							 =     (x[:, 1].min(), x[:, 1].max())
  ((_lowerCamelCase) ,    (_lowerCamelCase))			:	List[Any]							 =     np.meshgrid(np.linspace(xa_min, xa_max), np.linspace(xa_min, xa_max))
  _lowerCamelCase			:	str							 =     np.c_[xxa.ravel(), xxa.ravel()]
  _lowerCamelCase			:	Tuple							 =     predict_prob(grid).reshape(xxa.shape)
  plt.contour(xxa, xxa, probs, [0.5], linewidths=1, colors="""black""")

  plt.legend()
  plt.show()


