



import matplotlib.pyplot as plt
import numpy as np


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

		return weights


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

		return weight


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

		return y_pred


def       UpperCamelCase(  __UpperCamelCase							:		str					,__UpperCamelCase							:		str					,__UpperCamelCase							:		str			):
		import seaborn as sns

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

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

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

		return x_train, x_data, y_data


def       UpperCamelCase(  __UpperCamelCase							:		np.ndarray					,__UpperCamelCase							:		np.ndarray					,__UpperCamelCase							:		np.ndarray					,__UpperCamelCase							:		np.ndarray					,__UpperCamelCase							:		str					,__UpperCamelCase							:		str					,):
		lowerCAmelCase_      :					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__  :  Union[str, Any]       = load_data('''tips''', '''total_bill''', '''tip''')
						A__  :  Optional[Any]       = local_weight_regression(training_data_x, tip, 5)
						plot_preds(training_data_x, predictions, total_bill, tip, '''total_bill''', '''tip''')




import random
import unittest

import torch

from diffusers import IFImgaImgPipeline
from diffusers.utils import floats_tensor
from diffusers.utils.import_utils import is_xformers_available
from diffusers.utils.testing_utils import skip_mps, torch_device

from ..pipeline_params import (
    TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS,
    TEXT_GUIDED_IMAGE_VARIATION_PARAMS,
)
from ..test_pipelines_common import PipelineTesterMixin
from . import IFPipelineTesterMixin



@skip_mps
class     __snake_case						(					UpperCamelCase_						,UpperCamelCase_						,unittest.TestCase						):
							_a      				=			IFImgaImgPipeline
							_a      				=			TEXT_GUIDED_IMAGE_VARIATION_PARAMS - {'''width''', '''height'''}
							_a      				=			TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS
							_a      				=			PipelineTesterMixin.required_optional_params - {'''latents'''}




							def        UpperCAmelCase__      (	self					:	Any):
									return self._get_dummy_components()




							def        UpperCAmelCase__      (	self					:	str    ,    A_					:	Optional[int]    ,    A_					:	List[Any]=0):
									if str(A_).startswith('''mps'''):
											lowerCAmelCase_      :					Dict     =			torch.manual_seed(A_)
									else:
											lowerCAmelCase_      :					List[Any]     =			torch.Generator(device=A_).manual_seed(A_)

									lowerCAmelCase_      :					List[Any]     =			floats_tensor((1, 3, 3_2, 3_2)    ,    rng=random.Random(A_)).to(A_)

									lowerCAmelCase_      :					Any     =			{
									    '''prompt''': '''A painting of a squirrel eating a burger''',
									    '''image''': image,
									    '''generator''': generator,
									    '''num_inference_steps''': 2,
									    '''output_type''': '''numpy''',
									}

									return inputs




							def        UpperCAmelCase__      (	self					:	Union[str, Any]):
									self._test_save_load_optional_components()




							@unittest.skipIf(
							    torch_device != '''cuda''' or not is_xformers_available()    ,    reason='''XFormers attention is only available with CUDA and `xformers` installed'''    ,    )
							def        UpperCAmelCase__      (	self					:	List[str]):
									self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=1e-3)




							@unittest.skipIf(torch_device != '''cuda'''    ,    reason='''float16 requires CUDA''')
							def        UpperCAmelCase__      (	self					:	Dict):
									# Due to non-determinism in save load of the hf-internal-testing/tiny-random-t5 text encoder
									super().test_save_load_floataa(expected_max_diff=1e-1)




							@unittest.skipIf(torch_device != '''cuda'''    ,    reason='''float16 requires CUDA''')
							def        UpperCAmelCase__      (	self					:	Tuple):
									super().test_floataa_inference(expected_max_diff=1e-1)




							def        UpperCAmelCase__      (	self					:	str):
									self._test_attention_slicing_forward_pass(expected_max_diff=1e-2)




							def        UpperCAmelCase__      (	self					:	int):
									self._test_save_load_local()







							def        UpperCAmelCase__      (	self					:	Union[str, Any]):
									self._test_inference_batch_single_identical(
									    expected_max_diff=1e-2    ,    )





