




"""simple docstring"""

import inspect
import unittest
import warnings
from typing import List

from transformers import EfficientFormerConfig
from transformers.models.auto import get_values
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_vision_available

from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
							import torch

							from transformers import (
							    MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING,
							    MODEL_MAPPING,
							    EfficientFormerForImageClassification,
							    EfficientFormerForImageClassificationWithTeacher,
							    EfficientFormerModel,
							)
							from transformers.models.efficientformer.modeling_efficientformer import (
							    EFFICIENTFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
							)


if is_vision_available():
							from PIL import Image

							from transformers import EfficientFormerImageProcessor
class      lowerCamelCase__      :


			def __init__(       self			,	SCREAMING_SNAKE_CASE			,	SCREAMING_SNAKE_CASE = 13			,	SCREAMING_SNAKE_CASE = 64			,	SCREAMING_SNAKE_CASE = 2			,	SCREAMING_SNAKE_CASE = 3			,	SCREAMING_SNAKE_CASE = 3			,	SCREAMING_SNAKE_CASE = True			,	SCREAMING_SNAKE_CASE = True			,	SCREAMING_SNAKE_CASE = 128			,	SCREAMING_SNAKE_CASE=[16, 32, 64, 128]			,	SCREAMING_SNAKE_CASE = 7			,	SCREAMING_SNAKE_CASE = 4			,	SCREAMING_SNAKE_CASE = 37			,	SCREAMING_SNAKE_CASE = "gelu"			,	SCREAMING_SNAKE_CASE = 0.1			,	SCREAMING_SNAKE_CASE = 0.1			,	SCREAMING_SNAKE_CASE = 10			,	SCREAMING_SNAKE_CASE = 0.02			,	SCREAMING_SNAKE_CASE = 2			,	SCREAMING_SNAKE_CASE = 1			,	SCREAMING_SNAKE_CASE = 128			,	SCREAMING_SNAKE_CASE = [2, 2, 2, 2]			,	SCREAMING_SNAKE_CASE = 2			,	SCREAMING_SNAKE_CASE = 2			,	):



					"""simple docstring"""







					snake_case						:  Union[str, Any]        =			parent
					snake_case						:  Dict        =			batch_size
					snake_case						:  int        =			image_size
					snake_case						:  str        =			patch_size
					snake_case						:  Optional[int]        =			num_channels
					snake_case						:  Optional[Any]        =			is_training
					snake_case						:  Tuple        =			use_labels
					snake_case						:  Optional[Any]        =			hidden_size
					snake_case						:  List[Any]        =			num_hidden_layers
					snake_case						:  Any        =			num_attention_heads
					snake_case						:  Union[str, Any]        =			intermediate_size
					snake_case						:  Dict        =			hidden_act
					snake_case						:  Union[str, Any]        =			hidden_dropout_prob
					snake_case						:  Dict        =			attention_probs_dropout_prob
					snake_case						:  Optional[int]        =			type_sequence_label_size
					snake_case						:  str        =			initializer_range
					snake_case						:  Optional[Any]        =			encoder_stride
					snake_case						:  Dict        =			num_attention_outputs
					snake_case						:  Any        =			embed_dim
					snake_case						:  int        =			embed_dim + 1
					snake_case						:  int        =			resolution
					snake_case						:  Optional[int]        =			depths
					snake_case						:  Optional[int]        =			hidden_sizes
					snake_case						:  Optional[Any]        =			dim
					snake_case						:  Union[str, Any]        =			mlp_expansion_ratio


			def 							lowerCamelCase_			(       self							):



					"""simple docstring"""







					snake_case						:  Any        =			floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]							)

					snake_case						:  Dict        =			None
					if self.use_labels:
							snake_case						:  Union[str, Any]        =			ids_tensor([self.batch_size]			,	self.type_sequence_label_size							)

					snake_case						:  Optional[int]        =			self.get_config()
					return config, pixel_values, labels


			def 							lowerCamelCase_			(       self							):



					"""simple docstring"""







					return EfficientFormerConfig(
					    image_size=self.image_size			,	patch_size=self.patch_size			,	num_channels=self.num_channels			,	hidden_size=self.hidden_size			,	num_hidden_layers=self.num_hidden_layers			,	num_attention_heads=self.num_attention_heads			,	intermediate_size=self.intermediate_size			,	hidden_act=self.hidden_act			,	hidden_dropout_prob=self.hidden_dropout_prob			,	attention_probs_dropout_prob=self.attention_probs_dropout_prob			,	is_decoder=SCREAMING_SNAKE_CASE			,	initializer_range=self.initializer_range			,	encoder_stride=self.encoder_stride			,	resolution=self.resolution			,	depths=self.depths			,	hidden_sizes=self.hidden_sizes			,	dim=self.dim			,	mlp_expansion_ratio=self.mlp_expansion_ratio			,	)


			def 							lowerCamelCase_			(       self			,	SCREAMING_SNAKE_CASE			,	SCREAMING_SNAKE_CASE			,	SCREAMING_SNAKE_CASE							):



					"""simple docstring"""







					snake_case						:  Dict        =			EfficientFormerModel(config=SCREAMING_SNAKE_CASE							)
					model.to(SCREAMING_SNAKE_CASE							)
					model.eval()
					snake_case						:  str        =			model(SCREAMING_SNAKE_CASE							)
					self.parent.assertEqual(result.last_hidden_state.shape			,	(self.batch_size, self.seq_length, self.hidden_size)							)


			def 							lowerCamelCase_			(       self			,	SCREAMING_SNAKE_CASE			,	SCREAMING_SNAKE_CASE			,	SCREAMING_SNAKE_CASE							):



					"""simple docstring"""







					snake_case						:  List[Any]        =			self.type_sequence_label_size
					snake_case						:  str        =			EfficientFormerForImageClassification(SCREAMING_SNAKE_CASE							)
					model.to(SCREAMING_SNAKE_CASE							)
					model.eval()
					snake_case						:  str        =			model(SCREAMING_SNAKE_CASE			,	labels=SCREAMING_SNAKE_CASE							)
					self.parent.assertEqual(result.logits.shape			,	(self.batch_size, self.type_sequence_label_size)							)

					# test greyscale images
					snake_case						:  List[str]        =			1
					snake_case						:  Optional[Any]        =			EfficientFormerForImageClassification(SCREAMING_SNAKE_CASE							)
					model.to(SCREAMING_SNAKE_CASE							)
					model.eval()

					snake_case						:  int        =			floats_tensor([self.batch_size, 1, self.image_size, self.image_size]							)
					snake_case						:  Optional[Any]        =			model(SCREAMING_SNAKE_CASE							)
					self.parent.assertEqual(result.logits.shape			,	(self.batch_size, self.type_sequence_label_size)							)



			def 							lowerCamelCase_			(       self							):



					"""simple docstring"""







					snake_case						:  Optional[Any]        =			self.prepare_config_and_inputs()
					(
					    (
					    snake_case
					)     ,			(
					    snake_case
					)     ,			(
					    snake_case
					)     ,			
					)						:  Tuple        =			config_and_inputs
					snake_case						:  str        =			{"pixel_values": pixel_values}
					return config, inputs_dict







@require_torch
class      lowerCamelCase__      (					lowerCamelCase_			,					lowerCamelCase_			,					unittest.TestCase     ):
			a__	: Any        =       (
			    (
			        EfficientFormerModel,
			        EfficientFormerForImageClassificationWithTeacher,
			        EfficientFormerForImageClassification,
			    )
			    if is_torch_available()
			    else ()
			)
			a__	: str        =       (
			    {
			        """feature-extraction""": EfficientFormerModel,
			        """image-classification""": (
			            EfficientFormerForImageClassification,
			            EfficientFormerForImageClassificationWithTeacher,
			        ),
			    }
			    if is_torch_available()
			    else {}
			)
			a__	: Union[str, Any]        =       False

			a__	: Dict        =       False
			a__	: Any        =       False
			a__	: List[str]        =       False


			def 							lowerCamelCase_			(       self							):



					"""simple docstring"""







					snake_case						:  Union[str, Any]        =			EfficientFormerModelTester(self							)
					snake_case						:  Optional[int]        =			ConfigTester(
					    self			,	config_class=SCREAMING_SNAKE_CASE			,	has_text_modality=SCREAMING_SNAKE_CASE			,	hidden_size=37							)


			def 							lowerCamelCase_			(       self							):



					"""simple docstring"""







					self.config_tester.run_common_tests()


			@unittest.skip(reason="EfficientFormer does not use inputs_embeds"							)
			def 							lowerCamelCase_			(       self							):



					"""simple docstring"""







					pass


			@unittest.skip(reason="EfficientFormer does not support input and output embeddings"							)
			def 							lowerCamelCase_			(       self							):



					"""simple docstring"""







					pass


			def 							lowerCamelCase_			(       self							):



					"""simple docstring"""







					snake_case     ,			snake_case						:  Optional[int]        =			self.model_tester.prepare_config_and_inputs_for_common()

					for model_class in self.all_model_classes:
							snake_case						:  Optional[int]        =			model_class(SCREAMING_SNAKE_CASE							)
							snake_case						:  Dict        =			inspect.signature(model.forward							)
							# signature.parameters is an OrderedDict => so arg_names order is deterministic
							snake_case						:  Optional[int]        =			[*signature.parameters.keys()]

							snake_case						:  List[Any]        =			["pixel_values"]
							self.assertListEqual(arg_names[:1]			,	SCREAMING_SNAKE_CASE							)


			def 							lowerCamelCase_			(       self							):



					"""simple docstring"""







					def check_hidden_states_output(SCREAMING_SNAKE_CASE			,	SCREAMING_SNAKE_CASE			,	SCREAMING_SNAKE_CASE							):
							snake_case						:  List[str]        =			model_class(SCREAMING_SNAKE_CASE							)
							model.to(SCREAMING_SNAKE_CASE							)
							model.eval()

							with torch.no_grad():
									snake_case						:  Any        =			model(**self._prepare_for_class(SCREAMING_SNAKE_CASE			,	SCREAMING_SNAKE_CASE							)							)

							snake_case						:  Union[str, Any]        =			outputs.encoder_hidden_states if config.is_encoder_decoder else outputs.hidden_states

							snake_case						:  Dict        =			getattr(
							    self.model_tester			,	"expected_num_hidden_layers"			,	self.model_tester.num_hidden_layers + 1							)
							self.assertEqual(len(SCREAMING_SNAKE_CASE							)			,	SCREAMING_SNAKE_CASE							)

							if hasattr(self.model_tester			,	"encoder_seq_length"							):
									snake_case						:  List[Any]        =			self.model_tester.encoder_seq_length
									if hasattr(self.model_tester			,	"chunk_length"							) and self.model_tester.chunk_length > 1:
											snake_case						:  str        =			seq_length * self.model_tester.chunk_length
							else:
									snake_case						:  Any        =			self.model_tester.seq_length

							self.assertListEqual(
							    list(hidden_states[-1].shape[-2:]							)			,	[seq_length, self.model_tester.hidden_size]			,	)

							if config.is_encoder_decoder:
									snake_case						:  Union[str, Any]        =			outputs.decoder_hidden_states

									self.assertIsInstance(SCREAMING_SNAKE_CASE			,	(list, tuple)							)
									self.assertEqual(len(SCREAMING_SNAKE_CASE							)			,	SCREAMING_SNAKE_CASE							)
									snake_case						:  List[str]        =			getattr(self.model_tester			,	"seq_length"			,	SCREAMING_SNAKE_CASE							)
									snake_case						:  List[Any]        =			getattr(self.model_tester			,	"decoder_seq_length"			,	SCREAMING_SNAKE_CASE							)

									self.assertListEqual(
									    list(hidden_states[-1].shape[-2:]							)			,	[decoder_seq_length, self.model_tester.hidden_size]			,	)

					snake_case     ,			snake_case						:  List[str]        =			self.model_tester.prepare_config_and_inputs_for_common()

					for model_class in self.all_model_classes:
							snake_case						:  Optional[int]        =			True
							check_hidden_states_output(SCREAMING_SNAKE_CASE			,	SCREAMING_SNAKE_CASE			,	SCREAMING_SNAKE_CASE							)

							# check that output_hidden_states also work using config
							del inputs_dict["output_hidden_states"]
							snake_case						:  Union[str, Any]        =			True

							check_hidden_states_output(SCREAMING_SNAKE_CASE			,	SCREAMING_SNAKE_CASE			,	SCREAMING_SNAKE_CASE							)


			def 							lowerCamelCase_			(       self			,	SCREAMING_SNAKE_CASE			,	SCREAMING_SNAKE_CASE			,	SCREAMING_SNAKE_CASE=False							):



					"""simple docstring"""







					snake_case						:  int        =			super()._prepare_for_class(SCREAMING_SNAKE_CASE			,	SCREAMING_SNAKE_CASE			,	return_labels=SCREAMING_SNAKE_CASE							)

					if return_labels:
							if model_class.__name__ == "EfficientFormerForImageClassificationWithTeacher":
									del inputs_dict["labels"]

					return inputs_dict


			def 							lowerCamelCase_			(       self							):



					"""simple docstring"""







					snake_case						:  Optional[int]        =			self.model_tester.prepare_config_and_inputs()
					self.model_tester.create_and_check_model(*SCREAMING_SNAKE_CASE							)


			@unittest.skip(reason="EfficientFormer does not implement masked image modeling yet"							)
			def 							lowerCamelCase_			(       self							):



					"""simple docstring"""







					snake_case						:  Tuple        =			self.model_tester.prepare_config_and_inputs()
					self.model_tester.create_and_check_for_masked_image_modeling(*SCREAMING_SNAKE_CASE							)


			def 							lowerCamelCase_			(       self							):



					"""simple docstring"""







					snake_case						:  Optional[Any]        =			self.model_tester.prepare_config_and_inputs()
					self.model_tester.create_and_check_for_image_classification(*SCREAMING_SNAKE_CASE							)


			def 							lowerCamelCase_			(       self							):



					"""simple docstring"""







					if not self.model_tester.is_training:
							return

					snake_case     ,			snake_case						:  Optional[int]        =			self.model_tester.prepare_config_and_inputs_for_common()
					snake_case						:  str        =			True

					for model_class in self.all_model_classes:
							# EfficientFormerForImageClassificationWithTeacher supports inference-only
							if (
							    model_class in get_values(SCREAMING_SNAKE_CASE							)
							    or model_class.__name__ == "EfficientFormerForImageClassificationWithTeacher"
							):
									continue
							snake_case						:  Any        =			model_class(SCREAMING_SNAKE_CASE							)
							model.to(SCREAMING_SNAKE_CASE							)
							model.train()
							snake_case						:  Tuple        =			self._prepare_for_class(SCREAMING_SNAKE_CASE			,	SCREAMING_SNAKE_CASE			,	return_labels=SCREAMING_SNAKE_CASE							)
							snake_case						:  int        =			model(**SCREAMING_SNAKE_CASE							).loss
							loss.backward()


			def 							lowerCamelCase_			(       self							):



					"""simple docstring"""







					snake_case     ,			snake_case						:  Any        =			self.model_tester.prepare_config_and_inputs_for_common()

					snake_case						:  Any        =			[
					    {"title": "multi_label_classification", "num_labels": 2, "dtype": torch.float},
					    {"title": "single_label_classification", "num_labels": 1, "dtype": torch.long},
					    {"title": "regression", "num_labels": 1, "dtype": torch.float},
					]

					for model_class in self.all_model_classes:
							if (
							    model_class
							    not in [
							        *get_values(SCREAMING_SNAKE_CASE							),
							    ]
							    or model_class.__name__ == "EfficientFormerForImageClassificationWithTeacher"
							):
									continue

							for problem_type in problem_types:
									with self.subTest(msg=F'''Testing {model_class} with {problem_type['title']}'''							):
											snake_case						:  Dict        =			problem_type["title"]
											snake_case						:  Dict        =			problem_type["num_labels"]

											snake_case						:  Tuple        =			model_class(SCREAMING_SNAKE_CASE							)
											model.to(SCREAMING_SNAKE_CASE							)
											model.train()

											snake_case						:  int        =			self._prepare_for_class(SCREAMING_SNAKE_CASE			,	SCREAMING_SNAKE_CASE			,	return_labels=SCREAMING_SNAKE_CASE							)

											if problem_type["num_labels"] > 1:
													snake_case						:  List[str]        =			inputs["labels"].unsqueeze(1							).repeat(1			,	problem_type["num_labels"]							)

											snake_case						:  Optional[int]        =			inputs["labels"].to(problem_type["dtype"]							)

											# This tests that we do not trigger the warning form PyTorch "Using a target size that is different
											# to the input size. This will likely lead to incorrect results due to broadcasting. Please ensure
											# they have the same size." which is a symptom something in wrong for the regression problem.
											# See https://github.com/huggingface/transformers/issues/11780
											with warnings.catch_warnings(record=SCREAMING_SNAKE_CASE							) as warning_list:
													snake_case						:  Optional[int]        =			model(**SCREAMING_SNAKE_CASE							).loss
											for w in warning_list:
													if "Using a target size that is different to the input size" in str(w.message							):
															raise ValueError(
															    F'''Something is going wrong in the regression problem: intercepted {w.message}'''							)

											loss.backward()


			@slow
			def 							lowerCamelCase_			(       self							):



					"""simple docstring"""







					for model_name in EFFICIENTFORMER_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
							snake_case						:  List[str]        =			EfficientFormerModel.from_pretrained(SCREAMING_SNAKE_CASE							)
							self.assertIsNotNone(SCREAMING_SNAKE_CASE							)



			def 							lowerCamelCase_			(       self							):



					"""simple docstring"""







					snake_case     ,			snake_case						:  List[Any]        =			self.model_tester.prepare_config_and_inputs_for_common()
					snake_case						:  List[Any]        =			True

					snake_case						:  List[Any]        =			getattr(self.model_tester			,	"seq_length"			,	SCREAMING_SNAKE_CASE							)
					snake_case						:  str        =			getattr(self.model_tester			,	"encoder_seq_length"			,	SCREAMING_SNAKE_CASE							)
					snake_case						:  Union[str, Any]        =			getattr(self.model_tester			,	"key_length"			,	SCREAMING_SNAKE_CASE							)
					snake_case						:  List[str]        =			getattr(self.model_tester			,	"chunk_length"			,	SCREAMING_SNAKE_CASE							)

					if chunk_length is not None and hasattr(self.model_tester			,	"num_hashes"							):
							snake_case						:  Optional[Any]        =			encoder_seq_length * self.model_tester.num_hashes

					for model_class in self.all_model_classes:
							snake_case						:  int        =			True
							snake_case						:  str        =			False
							snake_case						:  int        =			True
							snake_case						:  List[Any]        =			model_class(SCREAMING_SNAKE_CASE							)
							model.to(SCREAMING_SNAKE_CASE							)
							model.eval()
							with torch.no_grad():
									snake_case						:  List[str]        =			model(**self._prepare_for_class(SCREAMING_SNAKE_CASE			,	SCREAMING_SNAKE_CASE							)							)
							snake_case						:  Dict        =			outputs.encoder_attentions if config.is_encoder_decoder else outputs.attentions
							self.assertEqual(len(SCREAMING_SNAKE_CASE							)			,	self.model_tester.num_attention_outputs							)

							# check that output_attentions also work using config
							del inputs_dict["output_attentions"]
							snake_case						:  Union[str, Any]        =			True
							snake_case						:  List[str]        =			model_class(SCREAMING_SNAKE_CASE							)
							model.to(SCREAMING_SNAKE_CASE							)
							model.eval()
							with torch.no_grad():
									snake_case						:  int        =			model(**self._prepare_for_class(SCREAMING_SNAKE_CASE			,	SCREAMING_SNAKE_CASE							)							)
							snake_case						:  Any        =			outputs.encoder_attentions if config.is_encoder_decoder else outputs.attentions
							self.assertEqual(len(SCREAMING_SNAKE_CASE							)			,	self.model_tester.num_attention_outputs							)

							if chunk_length is not None:
									self.assertListEqual(
									    list(attentions[0].shape[-4:]							)			,	[self.model_tester.num_attention_heads, encoder_seq_length, chunk_length, encoder_key_length]			,	)
							else:
									self.assertListEqual(
									    list(attentions[0].shape[-3:]							)			,	[self.model_tester.num_attention_heads, encoder_seq_length, encoder_key_length]			,	)







def 					UpperCamelCase__       (						):
		snake_case						:  Optional[int]        =			Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png"				)
		return image
@require_torch
@require_vision
class      lowerCamelCase__      (					unittest.TestCase     ):


			@cached_property
			def 							lowerCamelCase_			(       self							):



					"""simple docstring"""







					return (
					    EfficientFormerImageProcessor.from_pretrained("snap-research/efficientformer-l1-300"							)
					    if is_vision_available()
					    else None
					)


			@slow
			def 							lowerCamelCase_			(       self							):



					"""simple docstring"""







					snake_case						:  str        =			EfficientFormerForImageClassification.from_pretrained("snap-research/efficientformer-l1-300"							).to(
					    SCREAMING_SNAKE_CASE							)

					snake_case						:  int        =			self.default_image_processor
					snake_case						:  Union[str, Any]        =			prepare_img()
					snake_case						:  Optional[int]        =			image_processor(images=SCREAMING_SNAKE_CASE			,	return_tensors="pt"							).to(SCREAMING_SNAKE_CASE							)

					# forward pass
					with torch.no_grad():
							snake_case						:  int        =			model(**SCREAMING_SNAKE_CASE							)

					# verify the logits
					snake_case						:  Dict        =			(1, 1_000)
					self.assertEqual(outputs.logits.shape			,	SCREAMING_SNAKE_CASE							)

					snake_case						:  List[str]        =			torch.tensor([-0.05_55, 0.48_25, -0.08_52]							).to(SCREAMING_SNAKE_CASE							)
					self.assertTrue(torch.allclose(outputs.logits[0][:3]			,	SCREAMING_SNAKE_CASE			,	atol=1E-4							)							)



			@slow
			def 							lowerCamelCase_			(       self							):



					"""simple docstring"""







					snake_case						:  Any        =			EfficientFormerForImageClassificationWithTeacher.from_pretrained(
					    "snap-research/efficientformer-l1-300"							).to(SCREAMING_SNAKE_CASE							)

					snake_case						:  Tuple        =			self.default_image_processor
					snake_case						:  Tuple        =			prepare_img()
					snake_case						:  List[str]        =			image_processor(images=SCREAMING_SNAKE_CASE			,	return_tensors="pt"							).to(SCREAMING_SNAKE_CASE							)

					# forward pass
					with torch.no_grad():
							snake_case						:  Any        =			model(**SCREAMING_SNAKE_CASE							)

					# verify the logits
					snake_case						:  List[str]        =			(1, 1_000)
					self.assertEqual(outputs.logits.shape			,	SCREAMING_SNAKE_CASE							)

					snake_case						:  int        =			torch.tensor([-0.13_12, 0.43_53, -1.04_99]							).to(SCREAMING_SNAKE_CASE							)
					self.assertTrue(torch.allclose(outputs.logits[0][:3]			,	SCREAMING_SNAKE_CASE			,	atol=1E-4							)							)







"""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")

