# 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					: Tuple			)							->      int:







		"""simple docstring"""





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



def 			__SCREAMING_SNAKE_CASE						(      __UpperCamelCase					: List[Any]				,  __UpperCamelCase					: Union[str, Any]			)							->      Optional[Any]:







		"""simple docstring"""





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



def 			__SCREAMING_SNAKE_CASE						(      __UpperCamelCase					: List[Any]				,  __UpperCamelCase					: List[Any]				,  __UpperCamelCase					: Union[str, Any]			)							->      int:







		"""simple docstring"""





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



def 			__SCREAMING_SNAKE_CASE						(      __UpperCamelCase					: Tuple				,  __UpperCamelCase					: Optional[int]				,  __UpperCamelCase					: Tuple				,  __UpperCamelCase					: Any=7_00_00			)							->      Optional[int]:







		"""simple docstring"""





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

		for iterations in range(__UpperCamelCase			):
				SCREAMING_SNAKE_CASE__    							=   np.dot(__UpperCamelCase				,  __UpperCamelCase			)
				SCREAMING_SNAKE_CASE__    							=   sigmoid_function(__UpperCamelCase			)
				SCREAMING_SNAKE_CASE__    							=   np.dot(x.T				,  h - y			) / y.size
				SCREAMING_SNAKE_CASE__    							=   theta - alpha * gradient  # updating the weights
				SCREAMING_SNAKE_CASE__    							=   np.dot(__UpperCamelCase				,  __UpperCamelCase			)
				SCREAMING_SNAKE_CASE__    							=   sigmoid_function(__UpperCamelCase			)
				SCREAMING_SNAKE_CASE__    							=   cost_function(__UpperCamelCase				,  __UpperCamelCase			)
				if iterations % 1_00 == 0:
						print(f"""loss: {j} \t"""			)  # printing the loss after every 100 iterations
		return theta


# In[68]:

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

			__lowerCamelCase    :				List[str]							     =	0.1
			__lowerCamelCase    :				str							     =	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					: List[str]			)							->      Tuple:







					"""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)    :				int							     =	(x[:, 0].min(), x[:, 0].max())
			(__lowerCamelCase)    :				List[Any]							     =	(x[:, 1].min(), x[:, 1].max())
			(__lowerCamelCase)    :				List[Any]							     =	np.meshgrid(np.linspace(xa_min, xa_max), np.linspace(xa_min, xa_max))
			__lowerCamelCase    :				Optional[int]							     =	np.c_[xxa.ravel(), xxa.ravel()]
			__lowerCamelCase    :				Optional[int]							     =	predict_prob(grid).reshape(xxa.shape)
			plt.contour(xxa, xxa, probs, [0.5], linewidths=1, colors='''black''')

			plt.legend()






			plt.show()

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


__lowerCamelCase    :				Optional[int]							     =	logging.get_logger(__name__)

__lowerCamelCase    :				str							     =	{
    '''facebook/nllb-moe-54B''': '''https://huggingface.co/facebook/nllb-moe-54b/resolve/main/config.json''',
}







class __snake_case       (		lowerCamelCase_	):
   lowerCAmelCase_														=							"nllb-moe"
   lowerCAmelCase_														=							["past_key_values"]
   lowerCAmelCase_														=							{"num_attention_heads": "encoder_attention_heads", "hidden_size": "d_model"}




   def __init__(					self    :       int ,			_lowercase    :       Tuple=12_81_12 ,			_lowercase    :       List[Any]=10_24 ,			_lowercase    :       Any=12 ,			_lowercase    :       List[Any]=40_96 ,			_lowercase    :       str=16 ,			_lowercase    :       str=12 ,			_lowercase    :       Optional[int]=40_96 ,			_lowercase    :       List[Any]=16 ,			_lowercase    :       str=0.05 ,			_lowercase    :       Tuple=0.05 ,			_lowercase    :       str=True ,			_lowercase    :       List[str]=True ,			_lowercase    :       Optional[Any]="relu" ,			_lowercase    :       str=10_24 ,			_lowercase    :       Tuple=0.1 ,			_lowercase    :       int=0.1 ,			_lowercase    :       Dict=0.0 ,			_lowercase    :       List[str]=0.02 ,			_lowercase    :       int=2 ,			_lowercase    :       Optional[Any]=True ,			_lowercase    :       List[Any]=False ,			_lowercase    :       List[str]="float32" ,			_lowercase    :       Optional[Any]=False ,			_lowercase    :       str=1_28 ,			_lowercase    :       int=64 ,			_lowercase    :       Optional[int]=4 ,			_lowercase    :       List[str]=4 ,			_lowercase    :       Union[str, Any]=0.0_01 ,			_lowercase    :       List[Any]=0.0_01 ,			_lowercase    :       List[str]="all" ,			_lowercase    :       Optional[Any]=False ,			_lowercase    :       int=False ,			_lowercase    :       Tuple=1.0 ,			_lowercase    :       Optional[int]=0.2 ,			_lowercase    :       Optional[int]=1 ,			_lowercase    :       List[Any]=0 ,			_lowercase    :       List[Any]=2 ,			_lowercase    :       int=False ,			**_lowercase    :       Union[str, Any] ,			):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__    							=   vocab_size
     SCREAMING_SNAKE_CASE__    							=   max_position_embeddings
     SCREAMING_SNAKE_CASE__    							=   d_model
     SCREAMING_SNAKE_CASE__    							=   encoder_ffn_dim
     SCREAMING_SNAKE_CASE__    							=   encoder_layers
     SCREAMING_SNAKE_CASE__    							=   encoder_attention_heads
     SCREAMING_SNAKE_CASE__    							=   decoder_ffn_dim
     SCREAMING_SNAKE_CASE__    							=   decoder_layers
     SCREAMING_SNAKE_CASE__    							=   decoder_attention_heads
     SCREAMING_SNAKE_CASE__    							=   dropout
     SCREAMING_SNAKE_CASE__    							=   attention_dropout
     SCREAMING_SNAKE_CASE__    							=   activation_dropout
     SCREAMING_SNAKE_CASE__    							=   activation_function
     SCREAMING_SNAKE_CASE__    							=   init_std
     SCREAMING_SNAKE_CASE__    							=   encoder_layerdrop
     SCREAMING_SNAKE_CASE__    							=   decoder_layerdrop
     SCREAMING_SNAKE_CASE__    							=   use_cache
     SCREAMING_SNAKE_CASE__    							=   encoder_layers
     SCREAMING_SNAKE_CASE__    							=   scale_embedding  # scale factor will be sqrt(d_model) if True
     SCREAMING_SNAKE_CASE__    							=   router_z_loss_coef
     SCREAMING_SNAKE_CASE__    							=   router_aux_loss_coef
     SCREAMING_SNAKE_CASE__    							=   decoder_sparse_step
     SCREAMING_SNAKE_CASE__    							=   encoder_sparse_step
     SCREAMING_SNAKE_CASE__    							=   num_experts
     SCREAMING_SNAKE_CASE__    							=   expert_capacity
     SCREAMING_SNAKE_CASE__    							=   router_bias
     if router_dtype not in ["float32", "float16", "bfloat16"]:
       raise ValueError(f"""`router_dtype` must be one of 'float32', 'float16' or 'bfloat16', got {router_dtype}"""   )
     SCREAMING_SNAKE_CASE__    							=   router_dtype

     SCREAMING_SNAKE_CASE__    							=   router_ignore_padding_tokens
     SCREAMING_SNAKE_CASE__    							=   batch_prioritized_routing
     SCREAMING_SNAKE_CASE__    							=   second_expert_policy
     SCREAMING_SNAKE_CASE__    							=   normalize_router_prob_before_dropping
     SCREAMING_SNAKE_CASE__    							=   moe_eval_capacity_token_fraction
     SCREAMING_SNAKE_CASE__    							=   moe_token_dropout
     SCREAMING_SNAKE_CASE__    							=   output_router_logits
     super().__init__(
         pad_token_id=_lowercase ,			bos_token_id=_lowercase ,			eos_token_id=_lowercase ,			is_encoder_decoder=_lowercase ,			decoder_start_token_id=_lowercase ,			**_lowercase ,			)
