




"""simple docstring"""

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


__A							=     logging.get_logger(__name__)

__A							=     {
    "ZinengTang/tvlt-base": "https://huggingface.co/ZinengTang/tvlt-base/blob/main/config.json",
}
class      lowerCamelCase__      (					lowerCamelCase_     ):
			a__	: Optional[int]        =       """tvlt"""


			def __init__(       self			,	SCREAMING_SNAKE_CASE=224			,	SCREAMING_SNAKE_CASE=2_048			,	SCREAMING_SNAKE_CASE=128			,	SCREAMING_SNAKE_CASE=[16, 16]			,	SCREAMING_SNAKE_CASE=[16, 16]			,	SCREAMING_SNAKE_CASE=3			,	SCREAMING_SNAKE_CASE=1			,	SCREAMING_SNAKE_CASE=8			,	SCREAMING_SNAKE_CASE=768			,	SCREAMING_SNAKE_CASE=12			,	SCREAMING_SNAKE_CASE=12			,	SCREAMING_SNAKE_CASE=3_072			,	SCREAMING_SNAKE_CASE="gelu"			,	SCREAMING_SNAKE_CASE=0.0			,	SCREAMING_SNAKE_CASE=0.0			,	SCREAMING_SNAKE_CASE=0.02			,	SCREAMING_SNAKE_CASE=1E-6			,	SCREAMING_SNAKE_CASE=True			,	SCREAMING_SNAKE_CASE=False			,	SCREAMING_SNAKE_CASE=16			,	SCREAMING_SNAKE_CASE=512			,	SCREAMING_SNAKE_CASE=8			,	SCREAMING_SNAKE_CASE=2_048			,	SCREAMING_SNAKE_CASE=0.75			,	SCREAMING_SNAKE_CASE=0.15			,	SCREAMING_SNAKE_CASE="frame-level"			,	SCREAMING_SNAKE_CASE=True			,	SCREAMING_SNAKE_CASE=True			,	SCREAMING_SNAKE_CASE="classification"			,	**SCREAMING_SNAKE_CASE			,	):



					"""simple docstring"""







					super().__init__(**SCREAMING_SNAKE_CASE							)

					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}'''							)

					snake_case						:  Union[str, Any]        =			image_size
					snake_case						:  str        =			spectrogram_length
					snake_case						:  Tuple        =			frequency_length
					snake_case						:  Tuple        =			image_patch_size
					snake_case						:  List[str]        =			audio_patch_size
					snake_case						:  str        =			num_image_channels
					snake_case						:  Tuple        =			num_audio_channels
					snake_case						:  Optional[int]        =			num_frames

					snake_case						:  Union[str, Any]        =			hidden_size
					snake_case						:  str        =			num_hidden_layers
					snake_case						:  List[Any]        =			num_attention_heads
					snake_case						:  Optional[int]        =			intermediate_size
					snake_case						:  Optional[int]        =			hidden_act
					snake_case						:  List[str]        =			hidden_dropout_prob
					snake_case						:  str        =			attention_probs_dropout_prob
					snake_case						:  int        =			initializer_range
					snake_case						:  List[Any]        =			layer_norm_eps
					snake_case						:  Union[str, Any]        =			qkv_bias
					snake_case						:  List[Any]        =			use_mean_pooling

					snake_case						:  Optional[Any]        =			decoder_num_attention_heads
					snake_case						:  Tuple        =			decoder_hidden_size
					snake_case						:  Dict        =			decoder_num_hidden_layers
					snake_case						:  Union[str, Any]        =			decoder_intermediate_size
					snake_case						:  Dict        =			pixel_mask_ratio
					snake_case						:  Tuple        =			audio_mask_ratio
					snake_case						:  Optional[Any]        =			audio_mask_type

					snake_case						:  Dict        =			task_matching
					snake_case						:  Dict        =			task_mae
					snake_case						:  Any        =			loss_type







"""simple docstring"""

import matplotlib.pyplot as plt
import numpy as np



def 					UpperCamelCase__       (						lowercase__					:       np.ndarray    ,       lowercase__					:       np.ndarray    ,       lowercase__					:       float				):
		snake_case						:  Any        =			len(lowercase__				)  # Number of training samples
		snake_case						:  str        =			np.eye(lowercase__				)  # Initialize weights as identity matrix
		for j in range(lowercase__				):
				snake_case						:  Any        =			point - x_train[j]
				snake_case						:  Dict        =			np.exp(diff @ diff.T / (-2.0 * tau**2)				)

		return weights



def 					UpperCamelCase__       (						lowercase__					:       np.ndarray    ,       lowercase__					:       np.ndarray    ,       lowercase__					:       np.ndarray    ,       lowercase__					:       float				):
		snake_case						:  str        =			weight_matrix(lowercase__    ,       lowercase__    ,       lowercase__				)
		snake_case						:  str        =			np.linalg.inv(x_train.T @ weight_mat @ x_train				) @ (
		    x_train.T @ weight_mat @ y_train.T
		)

		return weight



def 					UpperCamelCase__       (						lowercase__					:       np.ndarray    ,       lowercase__					:       np.ndarray    ,       lowercase__					:       float				):
		snake_case						:  Optional[int]        =			np.zeros(len(lowercase__				)				)  # Initialize array of predictions
		for i, item in enumerate(lowercase__				):
				snake_case						:  Dict        =			item @ local_weight(lowercase__    ,       lowercase__    ,       lowercase__    ,       lowercase__				)

		return y_pred



def 					UpperCamelCase__       (						lowercase__					:       str    ,       lowercase__					:       str    ,       lowercase__					:       str				):
		import seaborn as sns

		snake_case						:  Union[str, Any]        =			sns.load_dataset(lowercase__				)
		snake_case						:  Any        =			np.array(data[x_name]				)
		snake_case						:  str        =			np.array(data[y_name]				)

		snake_case						:  Any        =			np.ones(len(lowercase__				)				)

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

		return x_train, x_data, y_data



def 					UpperCamelCase__       (						lowercase__					:       np.ndarray    ,       lowercase__					:       np.ndarray    ,       lowercase__					:       np.ndarray    ,       lowercase__					:       np.ndarray    ,       lowercase__					:       str    ,       lowercase__					:       str    ,       ):
		snake_case						:  List[str]        =			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
							__A						,    __A						,    __A							=     load_data("tips", "total_bill", "tip")
							__A							=     local_weight_regression(training_data_x, tip, 5)
							plot_preds(training_data_x, predictions, total_bill, tip, "total_bill", "tip")

