


import matplotlib.pyplot as plt
import numpy as np
def        __lowerCamelCase		(				UpperCAmelCase_		:	np.ndarray				,						UpperCAmelCase_		:	np.ndarray				,						UpperCAmelCase_		:	float		):




						"""simple docstring"""





						a		:Union[str, Any]											= len(UpperCAmelCase_		)  # Number of training samples
						a		:Optional[Any]											= np.eye(UpperCAmelCase_		)  # Initialize weights as identity matrix
						for j in range(UpperCAmelCase_		):
												a		:Optional[Any]											= point - x_train[j]
												a		:Optional[int]											= np.exp(diff @ diff.T / (-2.0 * tau**2)		)

						return weights
def        __lowerCamelCase		(				UpperCAmelCase_		:	np.ndarray				,						UpperCAmelCase_		:	np.ndarray				,						UpperCAmelCase_		:	np.ndarray				,						UpperCAmelCase_		:	float		):




						"""simple docstring"""





						a		:Dict											= weight_matrix(UpperCAmelCase_				,						UpperCAmelCase_				,						UpperCAmelCase_		)
						a		:Dict											= np.linalg.inv(x_train.T @ weight_mat @ x_train		) @ (
						    x_train.T @ weight_mat @ y_train.T
						)

						return weight
def        __lowerCamelCase		(				UpperCAmelCase_		:	np.ndarray				,						UpperCAmelCase_		:	np.ndarray				,						UpperCAmelCase_		:	float		):




						"""simple docstring"""





						a		:Tuple											= np.zeros(len(UpperCAmelCase_		)		)  # Initialize array of predictions
						for i, item in enumerate(UpperCAmelCase_		):
												a		:Optional[Any]											= item @ local_weight(UpperCAmelCase_				,						UpperCAmelCase_				,						UpperCAmelCase_				,						UpperCAmelCase_		)

						return y_pred
def        __lowerCamelCase		(				UpperCAmelCase_		:	str				,						UpperCAmelCase_		:	str				,						UpperCAmelCase_		:	str		):




						"""simple docstring"""





						import seaborn as sns

						a		:Optional[int]											= sns.load_dataset(UpperCAmelCase_		)
						a		:Any											= np.array(data[x_name]		)
						a		:List[Any]											= np.array(data[y_name]		)

						a		:List[str]											= np.ones(len(UpperCAmelCase_		)		)

						# pairing elements of one and x_data
						a		:Tuple											= np.column_stack((one, x_data)		)

						return x_train, x_data, y_data
def        __lowerCamelCase		(				UpperCAmelCase_		:	np.ndarray				,						UpperCAmelCase_		:	np.ndarray				,						UpperCAmelCase_		:	np.ndarray				,						UpperCAmelCase_		:	np.ndarray				,						UpperCAmelCase_		:	str				,						UpperCAmelCase_		:	str				,						):




						"""simple docstring"""





						a		:str											= np.sort(UpperCAmelCase_				,						axis=0		)
						plt.scatter(UpperCAmelCase_				,						UpperCAmelCase_				,						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(UpperCAmelCase_		)
						plt.ylabel(UpperCAmelCase_		)
						plt.show()


if __name__ == "__main__":
					import doctest

					doctest.testmod()

					# Demo with a dataset from the seaborn module
					snake_case       :					Union[str, Any]        =  load_data('''tips''', '''total_bill''', '''tip''')
					snake_case       :					Tuple        =  local_weight_regression(training_data_x, tip, 5)
					plot_preds(training_data_x, predictions, total_bill, tip, '''total_bill''', '''tip''')









import copy
from typing import Dict

from ...generation.configuration_utils import GenerationConfig
from ...utils import logging


snake_case       :					Dict        =  logging.get_logger(__name__)







class 	_snake_case						(   _snake_case							):
 SCREAMING_SNAKE_CASE__   = 'semantic'




 def __init__(	self      ,						_lowerCamelCase=1_0000      ,						_lowerCamelCase=True      ,						_lowerCamelCase=768      ,						_lowerCamelCase=False      ,						_lowerCamelCase=False      ,						_lowerCamelCase=False      ,						_lowerCamelCase=False      ,						_lowerCamelCase=0.7      ,						_lowerCamelCase=True      ,						_lowerCamelCase=1_0048      ,						_lowerCamelCase=12_9595      ,						_lowerCamelCase=12_9599      ,						_lowerCamelCase=1_0000      ,						_lowerCamelCase=256      ,						_lowerCamelCase=49.9      ,						**_lowerCamelCase      ,						):
       super().__init__(
           temperature=_lowerCamelCase      ,						do_sample=_lowerCamelCase      ,						eos_token_id=_lowerCamelCase      ,						renormalize_logits=_lowerCamelCase      ,						max_new_tokens=_lowerCamelCase      ,						output_scores=_lowerCamelCase      ,						return_dict_in_generate=_lowerCamelCase      ,						output_hidden_states=_lowerCamelCase      ,						output_attentions=_lowerCamelCase      ,						**_lowerCamelCase      ,						)

       a		:Dict											= text_encoding_offset
       a		:Any											= text_pad_token
       a		:List[Any]											= eos_token_id
       a		:str											= semantic_infer_token
       a		:Any											= semantic_vocab_size
       a		:Any											= max_input_semantic_length
       a		:Dict											= semantic_rate_hz







class 	_snake_case						(   _snake_case							):
 SCREAMING_SNAKE_CASE__   = 'coarse_acoustics'




 def __init__(	self      ,						_lowerCamelCase=True      ,						_lowerCamelCase=False      ,						_lowerCamelCase=False      ,						_lowerCamelCase=False      ,						_lowerCamelCase=False      ,						_lowerCamelCase=0.7      ,						_lowerCamelCase=True      ,						_lowerCamelCase=1_2048      ,						_lowerCamelCase=75      ,						_lowerCamelCase=2      ,						_lowerCamelCase=1_2050      ,						_lowerCamelCase=256      ,						_lowerCamelCase = 630      ,						_lowerCamelCase = 60      ,						**_lowerCamelCase      ,						):
       super().__init__(
           temperature=_lowerCamelCase      ,						do_sample=_lowerCamelCase      ,						renormalize_logits=_lowerCamelCase      ,						output_scores=_lowerCamelCase      ,						return_dict_in_generate=_lowerCamelCase      ,						output_hidden_states=_lowerCamelCase      ,						output_attentions=_lowerCamelCase      ,						**_lowerCamelCase      ,						)

       a		:Optional[Any]											= coarse_semantic_pad_token
       a		:Dict											= coarse_rate_hz
       a		:List[str]											= n_coarse_codebooks
       a		:List[Any]											= coarse_infer_token
       a		:Optional[Any]											= max_coarse_input_length
       a		:Any											= max_coarse_history
       a		:Optional[int]											= sliding_window_len







class 	_snake_case						(   _snake_case							):
 SCREAMING_SNAKE_CASE__   = 'fine_acoustics'




 def __init__(	self      ,						_lowerCamelCase=0.5      ,						_lowerCamelCase=512      ,						_lowerCamelCase=1024      ,						_lowerCamelCase=8      ,						**_lowerCamelCase      ,						):
       super().__init__(temperature=_lowerCamelCase					)

       a		:Union[str, Any]											= max_fine_history_length
       a		:int											= max_fine_input_length
       a		:str											= n_fine_codebooks







class 	_snake_case						(   _snake_case							):
 SCREAMING_SNAKE_CASE__   = 'bark'
 SCREAMING_SNAKE_CASE__   = True




 def __init__(	self      ,						_lowerCamelCase = None      ,						_lowerCamelCase = None      ,						_lowerCamelCase = None      ,						_lowerCamelCase=2_4000      ,						_lowerCamelCase=1024      ,						**_lowerCamelCase      ,						):
       if semantic_config is None:
             a		:str											= {}
             logger.info('''semantic_config is None. initializing the semantic model with default values.'''					)

       if coarse_acoustics_config is None:
             a		:List[str]											= {}
             logger.info('''coarse_acoustics_config is None. initializing the coarse model with default values.'''					)

       if fine_acoustics_config is None:
             a		:Any											= {}
             logger.info('''fine_acoustics_config is None. initializing the fine model with default values.'''					)

       a		:Any											= BarkSemanticGenerationConfig(**_lowerCamelCase					)
       a		:int											= BarkCoarseGenerationConfig(**_lowerCamelCase					)
       a		:Optional[int]											= BarkFineGenerationConfig(**_lowerCamelCase					)

       a		:Tuple											= sample_rate
       a		:Dict											= codebook_size




 @classmethod
 def  SCREAMING_SNAKE_CASE__       (	cls      ,						_lowerCamelCase      ,						_lowerCamelCase      ,						_lowerCamelCase      ,						**_lowerCamelCase      ,						):
       return cls(
           semantic_config=semantic_config.to_dict()      ,						coarse_acoustics_config=coarse_acoustics_config.to_dict()      ,						fine_acoustics_config=fine_acoustics_config.to_dict()      ,						**_lowerCamelCase      ,						)




 def  SCREAMING_SNAKE_CASE__       (	self					):
       a		:int											= copy.deepcopy(self.__dict__					)

       a		:List[Any]											= self.semantic_config.to_dict()
       a		:Union[str, Any]											= self.coarse_acoustics_config.to_dict()
       a		:int											= self.fine_acoustics_config.to_dict()

       a		:Tuple											= self.__class__.model_type
       return output





