


import torch
import torch.nn as nn
from transformers.modeling_utils import ModuleUtilsMixin
from transformers.models.ta.modeling_ta import (
    TaBlock,
    TaConfig,
    TaLayerNorm,
)

from ...configuration_utils import ConfigMixin, register_to_config
from ...models import ModelMixin



class 					__lowerCamelCase	(A__							,     A__							,     A__		):







    @register_to_config
    def __init__(	self:				Any,A_:				int,A_:				int,A_:				int,A_:				float,A_:				int,A_:				int,A_:				int,A_:				int,A_:				str,A_:				bool = False,):






         '''simple docstring'''




         super().__init__()

         __UpperCamelCase				=	nn.Linear(__snake_case,__snake_case,bias=__snake_case			)

         __UpperCamelCase				=	nn.Embedding(__snake_case,__snake_case			)
         __UpperCamelCase				=	False

         __UpperCamelCase				=	nn.Dropout(p=__snake_case			)

         __UpperCamelCase				=	TaConfig(
             d_model=__snake_case,num_heads=__snake_case,d_kv=__snake_case,d_ff=__snake_case,feed_forward_proj=__snake_case,dropout_rate=__snake_case,is_decoder=__snake_case,is_encoder_decoder=__snake_case,)
         __UpperCamelCase				=	nn.ModuleList()
         for lyr_num in range(__snake_case			):
              __UpperCamelCase				=	TaBlock(__snake_case			)
              self.encoders.append(__snake_case			)

         __UpperCamelCase				=	TaLayerNorm(__snake_case			)
         __UpperCamelCase				=	nn.Dropout(p=__snake_case			)







    def 						snake_case_					(	self:				List[Any],A_:				int,A_:				Optional[Any]			):






         '''simple docstring'''




         __UpperCamelCase				=	self.input_proj(__snake_case			)

         # terminal relative positional encodings
         __UpperCamelCase				=	encoder_inputs.shape[1]
         __UpperCamelCase				=	torch.arange(__snake_case,device=encoder_inputs.device			)

         __UpperCamelCase				=	encoder_inputs_mask.sum(-1			)
         __UpperCamelCase				=	torch.roll(input_positions.unsqueeze(0			),tuple(seq_lens.tolist()			),dims=0			)
         x += self.position_encoding(__snake_case			)

         __UpperCamelCase				=	self.dropout_pre(__snake_case			)

         # inverted the attention mask
         __UpperCamelCase				=	encoder_inputs.size()
         __UpperCamelCase				=	self.get_extended_attention_mask(__snake_case,__snake_case			)

         for lyr in self.encoders:
              __UpperCamelCase				=	lyr(__snake_case,__snake_case			)[0]
         __UpperCamelCase				=	self.layer_norm(__snake_case			)

         return self.dropout_post(__snake_case			), encoder_inputs_mask






import matplotlib.pyplot as plt
import numpy as np






def 						_A						(						_lowercase			,       _lowercase			,       _lowercase				)			->				np.ndarray:


     """simple docstring"""


     __UpperCamelCase				=	len(_lowercase				)  # Number of training samples
     __UpperCamelCase				=	np.eye(_lowercase				)  # Initialize weights as identity matrix
     for j in range(_lowercase				):
          __UpperCamelCase				=	point - x_train[j]
          __UpperCamelCase				=	np.exp(diff @ diff.T / (-2.0 * tau**2)				)

     return weights






def 						_A						(						_lowercase			,       _lowercase			,       _lowercase			,       _lowercase				)			->				np.ndarray:


     """simple docstring"""


     __UpperCamelCase				=	weight_matrix(_lowercase			,       _lowercase			,       _lowercase				)
     __UpperCamelCase				=	np.linalg.inv(x_train.T @ weight_mat @ x_train				) @ (
         x_train.T @ weight_mat @ y_train.T
     )

     return weight






def 						_A						(						_lowercase			,       _lowercase			,       _lowercase				)			->				np.ndarray:


     """simple docstring"""


     __UpperCamelCase				=	np.zeros(len(_lowercase				)				)  # Initialize array of predictions
     for i, item in enumerate(_lowercase				):
          __UpperCamelCase				=	item @ local_weight(_lowercase			,       _lowercase			,       _lowercase			,       _lowercase				)

     return y_pred






def 						_A						(						_lowercase			,       _lowercase			,       _lowercase				)			->				tuple[np.ndarray, np.ndarray, np.ndarray]:


     """simple docstring"""


     import seaborn as sns

     __UpperCamelCase				=	sns.load_dataset(_lowercase				)
     __UpperCamelCase				=	np.array(data[x_name]				)
     __UpperCamelCase				=	np.array(data[y_name]				)

     __UpperCamelCase				=	np.ones(len(_lowercase				)				)

     # pairing elements of one and x_data
     __UpperCamelCase				=	np.column_stack((one, x_data)				)

     return x_train, x_data, y_data






def 						_A						(						_lowercase			,       _lowercase			,       _lowercase			,       _lowercase			,       _lowercase			,       _lowercase			,       )			->				None:


     """simple docstring"""


     __UpperCamelCase				=	np.sort(_lowercase			,       axis=0				)
     plt.scatter(_lowercase			,       _lowercase			,       color='blue'				)
     plt.plot(
         x_train_sorted[:, 1]			,       preds[x_train[:, 1].argsort(0				)]			,       color='yellow'			,       linewidth=5			,       )
     plt.title('Local Weighted Regression'				)
     plt.xlabel(_lowercase				)
     plt.ylabel(_lowercase				)
     plt.show()


if __name__ == "__main__":
    import doctest

    doctest.testmod()

    # Demo with a dataset from the seaborn module
    __snake_case    ,					__snake_case    ,					__snake_case     =		load_data('''tips''', '''total_bill''', '''tip''')
    __snake_case     =		local_weight_regression(training_data_x, tip, 5)
    plot_preds(training_data_x, predictions, total_bill, tip, '''total_bill''', '''tip''')


