



'''simple docstring'''







import matplotlib.pyplot as plt
import numpy as np







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

		return weights







def  SCREAMING_SNAKE_CASE__ (				SCREAMING_SNAKE_CASE ,		SCREAMING_SNAKE_CASE ,		SCREAMING_SNAKE_CASE ,		SCREAMING_SNAKE_CASE     ):
		SCREAMING_SNAKE_CASE_						:int										=      weight_matrix(SCREAMING_SNAKE_CASE ,		SCREAMING_SNAKE_CASE ,		SCREAMING_SNAKE_CASE     )
		SCREAMING_SNAKE_CASE_						:str										=      np.linalg.inv(x_train.T @ weight_mat @ x_train     ) @ (
		    x_train.T @ weight_mat @ y_train.T
		)

		return weight







def  SCREAMING_SNAKE_CASE__ (				SCREAMING_SNAKE_CASE ,		SCREAMING_SNAKE_CASE ,		SCREAMING_SNAKE_CASE     ):
		SCREAMING_SNAKE_CASE_						:int										=      np.zeros(len(SCREAMING_SNAKE_CASE     )     )  # Initialize array of predictions
		for i, item in enumerate(SCREAMING_SNAKE_CASE     ):
				SCREAMING_SNAKE_CASE_						:Dict										=      item @ local_weight(SCREAMING_SNAKE_CASE ,		SCREAMING_SNAKE_CASE ,		SCREAMING_SNAKE_CASE ,		SCREAMING_SNAKE_CASE     )

		return y_pred







def  SCREAMING_SNAKE_CASE__ (				SCREAMING_SNAKE_CASE ,		SCREAMING_SNAKE_CASE ,		SCREAMING_SNAKE_CASE     ):
		import seaborn as sns

		SCREAMING_SNAKE_CASE_						:Union[str, Any]										=      sns.load_dataset(SCREAMING_SNAKE_CASE     )
		SCREAMING_SNAKE_CASE_						:Tuple										=      np.array(data[x_name]     )
		SCREAMING_SNAKE_CASE_						:Optional[int]										=      np.array(data[y_name]     )

		SCREAMING_SNAKE_CASE_						:str										=      np.ones(len(SCREAMING_SNAKE_CASE     )     )

		# pairing elements of one and x_data
		SCREAMING_SNAKE_CASE_						:Optional[int]										=      np.column_stack((one, x_data)     )

		return x_train, x_data, y_data







def  SCREAMING_SNAKE_CASE__ (				SCREAMING_SNAKE_CASE ,		SCREAMING_SNAKE_CASE ,		SCREAMING_SNAKE_CASE ,		SCREAMING_SNAKE_CASE ,		SCREAMING_SNAKE_CASE ,		SCREAMING_SNAKE_CASE ,		):
		SCREAMING_SNAKE_CASE_						:Tuple										=      np.sort(SCREAMING_SNAKE_CASE ,		axis=0     )
		plt.scatter(SCREAMING_SNAKE_CASE ,		SCREAMING_SNAKE_CASE ,		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(SCREAMING_SNAKE_CASE     )
		plt.ylabel(SCREAMING_SNAKE_CASE     )
		plt.show()


if __name__ == "__main__":
							import doctest

							doctest.testmod()

							# Demo with a dataset from the seaborn module
							SCREAMING_SNAKE_CASE__	,							SCREAMING_SNAKE_CASE__	,							SCREAMING_SNAKE_CASE__     : str				=   load_data("""tips""", """total_bill""", """tip""")
							SCREAMING_SNAKE_CASE__     : str				=   local_weight_regression(training_data_x, tip, 5)
							plot_preds(training_data_x, predictions, total_bill, tip, """total_bill""", """tip""")








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


_UpperCAmelCase					:						Any         =     logging.get_logger(__name__)

_UpperCAmelCase					:						Dict         =     {
    'ZinengTang/tvlt-base': 'https://huggingface.co/ZinengTang/tvlt-base/blob/main/config.json',
}


class 		lowerCAmelCase_				(  UpperCamelCase ):
     __lowerCamelCase       :    Any 	=     '''tvlt'''





     def __init__(	self							, _UpperCAmelCase=224							, _UpperCAmelCase=2048							, _UpperCAmelCase=128							, _UpperCAmelCase=[16, 16]							, _UpperCAmelCase=[16, 16]							, _UpperCAmelCase=3							, _UpperCAmelCase=1							, _UpperCAmelCase=8							, _UpperCAmelCase=768							, _UpperCAmelCase=12							, _UpperCAmelCase=12							, _UpperCAmelCase=3072							, _UpperCAmelCase="gelu"							, _UpperCAmelCase=0.0							, _UpperCAmelCase=0.0							, _UpperCAmelCase=0.02							, _UpperCAmelCase=1E-6							, _UpperCAmelCase=True							, _UpperCAmelCase=False							, _UpperCAmelCase=16							, _UpperCAmelCase=512							, _UpperCAmelCase=8							, _UpperCAmelCase=2048							, _UpperCAmelCase=0.75							, _UpperCAmelCase=0.15							, _UpperCAmelCase="frame-level"							, _UpperCAmelCase=True							, _UpperCAmelCase=True							, _UpperCAmelCase="classification"							, **_UpperCAmelCase							, )		->				List[Any]:
        super().__init__(**_UpperCAmelCase  )

        if audio_mask_type not in ("frame-level", "patch_level"):
           raise ValueError(
               """audio_mask_type must be one of two acceptable strategies - {'frame_level', 'patch-level') """
               F'''got {audio_mask_type}'''  )

        lowerCamelCase									=image_size
        lowerCamelCase									=spectrogram_length
        lowerCamelCase									=frequency_length
        lowerCamelCase									=image_patch_size
        lowerCamelCase									=audio_patch_size
        lowerCamelCase									=num_image_channels
        lowerCamelCase									=num_audio_channels
        lowerCamelCase									=num_frames

        lowerCamelCase									=hidden_size
        lowerCamelCase									=num_hidden_layers
        lowerCamelCase									=num_attention_heads
        lowerCamelCase									=intermediate_size
        lowerCamelCase									=hidden_act
        lowerCamelCase									=hidden_dropout_prob
        lowerCamelCase									=attention_probs_dropout_prob
        lowerCamelCase									=initializer_range
        lowerCamelCase									=layer_norm_eps
        lowerCamelCase									=qkv_bias
        lowerCamelCase									=use_mean_pooling

        lowerCamelCase									=decoder_num_attention_heads
        lowerCamelCase									=decoder_hidden_size
        lowerCamelCase									=decoder_num_hidden_layers
        lowerCamelCase									=decoder_intermediate_size
        lowerCamelCase									=pixel_mask_ratio
        lowerCamelCase									=audio_mask_ratio
        lowerCamelCase									=audio_mask_type

        lowerCamelCase									=task_matching
        lowerCamelCase									=task_mae
        lowerCamelCase									=loss_type