
import matplotlib.pyplot as plt
import numpy as np

def 					SCREAMING_SNAKE_CASE  (   __UpperCamelCase			:    np.ndarray				,    __UpperCamelCase			:    np.ndarray				,    __UpperCamelCase			:    float							)			->							np.ndarray:

				"""simple docstring"""







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

				return weights

def 					SCREAMING_SNAKE_CASE  (   __UpperCamelCase			:    np.ndarray				,    __UpperCamelCase			:    np.ndarray				,    __UpperCamelCase			:    np.ndarray				,    __UpperCamelCase			:    float							)			->							np.ndarray:

				"""simple docstring"""







				A__	:						Tuple								=				weight_matrix(__UpperCamelCase				,    __UpperCamelCase				,    __UpperCamelCase							)
				A__	:						Optional[int]								=				np.linalg.inv(x_train.T @ weight_mat @ x_train							) @ (
				    x_train.T @ weight_mat @ y_train.T
				)

				return weight

def 					SCREAMING_SNAKE_CASE  (   __UpperCamelCase			:    np.ndarray				,    __UpperCamelCase			:    np.ndarray				,    __UpperCamelCase			:    float							)			->							np.ndarray:

				"""simple docstring"""







				A__	:						List[Any]								=				np.zeros(len(__UpperCamelCase							)							)  # Initialize array of predictions
				for i, item in enumerate(__UpperCamelCase							):
								A__	:						Union[str, Any]								=				item @ local_weight(__UpperCamelCase				,    __UpperCamelCase				,    __UpperCamelCase				,    __UpperCamelCase							)

				return y_pred

def 					SCREAMING_SNAKE_CASE  (   __UpperCamelCase			:    str				,    __UpperCamelCase			:    str				,    __UpperCamelCase			:    str							)			->							tuple[np.ndarray, np.ndarray, np.ndarray]:

				"""simple docstring"""







				import seaborn as sns

				A__	:						Tuple								=				sns.load_dataset(__UpperCamelCase							)
				A__	:						Any								=				np.array(data[x_name]							)
				A__	:						str								=				np.array(data[y_name]							)

				A__	:						List[Any]								=				np.ones(len(__UpperCamelCase							)							)

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

				return x_train, x_data, y_data

def 					SCREAMING_SNAKE_CASE  (   __UpperCamelCase			:    np.ndarray				,    __UpperCamelCase			:    np.ndarray				,    __UpperCamelCase			:    np.ndarray				,    __UpperCamelCase			:    np.ndarray				,    __UpperCamelCase			:    str				,    __UpperCamelCase			:    str				,    )			->							None:

				"""simple docstring"""







				A__	:						List[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
					_SCREAMING_SNAKE_CASE ,  _SCREAMING_SNAKE_CASE ,  _SCREAMING_SNAKE_CASE     :   int							 =      load_data('tips', 'total_bill', 'tip')
					_SCREAMING_SNAKE_CASE     :   Union[str, Any]							 =      local_weight_regression(training_data_x, tip, 5)
					plot_preds(training_data_x, predictions, total_bill, tip, 'total_bill', 'tip')

from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tf_available, is_torch_available


_SCREAMING_SNAKE_CASE     :   Optional[Any]							 =      {
    'configuration_flaubert': ['FLAUBERT_PRETRAINED_CONFIG_ARCHIVE_MAP', 'FlaubertConfig', 'FlaubertOnnxConfig'],
    'tokenization_flaubert': ['FlaubertTokenizer'],
}

try:
					if not is_torch_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					_SCREAMING_SNAKE_CASE     :   Tuple							 =      [
					    'FLAUBERT_PRETRAINED_MODEL_ARCHIVE_LIST',
					    'FlaubertForMultipleChoice',
					    'FlaubertForQuestionAnswering',
					    'FlaubertForQuestionAnsweringSimple',
					    'FlaubertForSequenceClassification',
					    'FlaubertForTokenClassification',
					    'FlaubertModel',
					    'FlaubertWithLMHeadModel',
					    'FlaubertPreTrainedModel',
					]

try:
					if not is_tf_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					_SCREAMING_SNAKE_CASE     :   Dict							 =      [
					    'TF_FLAUBERT_PRETRAINED_MODEL_ARCHIVE_LIST',
					    'TFFlaubertForMultipleChoice',
					    'TFFlaubertForQuestionAnsweringSimple',
					    'TFFlaubertForSequenceClassification',
					    'TFFlaubertForTokenClassification',
					    'TFFlaubertModel',
					    'TFFlaubertPreTrainedModel',
					    'TFFlaubertWithLMHeadModel',
					]


if TYPE_CHECKING:
					from .configuration_flaubert import FLAUBERT_PRETRAINED_CONFIG_ARCHIVE_MAP, FlaubertConfig, FlaubertOnnxConfig
					from .tokenization_flaubert import FlaubertTokenizer

					try:
										if not is_torch_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_flaubert import (
										    FLAUBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
										    FlaubertForMultipleChoice,
										    FlaubertForQuestionAnswering,
										    FlaubertForQuestionAnsweringSimple,
										    FlaubertForSequenceClassification,
										    FlaubertForTokenClassification,
										    FlaubertModel,
										    FlaubertPreTrainedModel,
										    FlaubertWithLMHeadModel,
										)

					try:
										if not is_tf_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_tf_flaubert import (
										    TF_FLAUBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
										    TFFlaubertForMultipleChoice,
										    TFFlaubertForQuestionAnsweringSimple,
										    TFFlaubertForSequenceClassification,
										    TFFlaubertForTokenClassification,
										    TFFlaubertModel,
										    TFFlaubertPreTrainedModel,
										    TFFlaubertWithLMHeadModel,
										)

else:
					import sys

					_SCREAMING_SNAKE_CASE     :   List[Any]							 =      _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)