




import csv
import json
import os

import datasets


# TODO: Add BibTeX citation
# Find for instance the citation on arxiv or on the dataset repo/website
UpperCamelCase__						=		'\\n@InProceedings{huggingface:dataset,\ntitle = {A great new dataset},\nauthor={huggingface, Inc.\n},\nyear={2020}\n}\n'

# TODO: Add description of the dataset here
# You can copy an official description
UpperCamelCase__						=		'\\nThis new dataset is designed to solve this great NLP task and is crafted with a lot of care.\n'

# TODO: Add a link to an official homepage for the dataset here
UpperCamelCase__						=		''

# TODO: Add the licence for the dataset here if you can find it
UpperCamelCase__						=		''

# TODO: Add link to the official dataset URLs here
# The HuggingFace Datasets library doesn't host the datasets but only points to the original files.
# This can be an arbitrary nested dict/list of URLs (see below in `_split_generators` method)
UpperCamelCase__						=		{
    'first_domain': 'https://huggingface.co/great-new-dataset-first_domain.zip',
    'second_domain': 'https://huggingface.co/great-new-dataset-second_domain.zip',
}


class 							__SCREAMING_SNAKE_CASE				(						datasets.GeneratorBasedBuilder    ):
					_lowerCamelCase						    =					datasets.Version("""1.1.0"""    )

					# This is an example of a dataset with multiple configurations.
					# If you don't want/need to define several sub-sets in your dataset,
					# just remove the BUILDER_CONFIG_CLASS and the BUILDER_CONFIGS attributes.

					# If you need to make complex sub-parts in the datasets with configurable options
					# You can create your own builder configuration class to store attribute, inheriting from datasets.BuilderConfig
					# BUILDER_CONFIG_CLASS = MyBuilderConfig

					# You will be able to load one or the other configurations in the following list with
					# data = datasets.load_dataset('my_dataset', 'first_domain')
					# data = datasets.load_dataset('my_dataset', 'second_domain')
					_lowerCamelCase						    =					[
					    datasets.BuilderConfig(name="""first_domain"""  ,	version=lowerCamelCase  ,	description="""This part of my dataset covers a first domain"""    ),
					    datasets.BuilderConfig(name="""second_domain"""  ,	version=lowerCamelCase  ,	description="""This part of my dataset covers a second domain"""    ),
					]

					_lowerCamelCase						    =					"""first_domain"""  # It's not mandatory to have a default configuration. Just use one if it make sense.

					def       lowercase_				(							self   ):







						'''simple docstring'''




						if self.config.name == "first_domain":  # This is the name of the configuration selected in BUILDER_CONFIGS above
							lowerCamelCase_				:						Any				  =				datasets.Features(
							    {
							        'sentence': datasets.Value('string'   ),
							        'option1': datasets.Value('string'   ),
							        'answer': datasets.Value('string'   )
							        # These are the features of your dataset like images, labels ...
							    }   )
						else:  # This is an example to show how to have different features for "first_domain" and "second_domain"
							lowerCamelCase_				:						List[str]				  =				datasets.Features(
							    {
							        'sentence': datasets.Value('string'   ),
							        'option2': datasets.Value('string'   ),
							        'second_domain_answer': datasets.Value('string'   )
							        # These are the features of your dataset like images, labels ...
							    }   )
						return datasets.DatasetInfo(
						    # This is the description that will appear on the datasets page.
						    description=_DESCRIPTION					,			features=__lowercase					,			homepage=_HOMEPAGE					,			license=_LICENSE					,			citation=_CITATION					,			)

					def       lowercase_				(							self					,			__lowercase   ):







						'''simple docstring'''




						lowerCamelCase_				:						str				  =				_URLS[self.config.name]
						lowerCamelCase_				:						int				  =				dl_manager.download_and_extract(__lowercase   )
						return [
						    datasets.SplitGenerator(
						        name=datasets.Split.TRAIN					,			gen_kwargs={
						            'filepath': os.path.join(__lowercase					,			'train.jsonl'   ),
						            'split': 'train',
						        }					,			),
						    datasets.SplitGenerator(
						        name=datasets.Split.VALIDATION					,			gen_kwargs={
						            'filepath': os.path.join(__lowercase					,			'dev.jsonl'   ),
						            'split': 'dev',
						        }					,			),
						    datasets.SplitGenerator(
						        name=datasets.Split.TEST					,			gen_kwargs={
						            'filepath': os.path.join(__lowercase					,			'test.jsonl'   ),
						            'split': 'test'
						        }					,			),
						]





					def       lowercase_				(							self					,			__lowercase					,			__lowercase   ):







						'''simple docstring'''




						with open(__lowercase					,			encoding='utf-8'   ) as f:
							for key, row in enumerate(__lowercase   ):
								lowerCamelCase_				:						Optional[int]				  =				json.loads(__lowercase   )
								if self.config.name == "first_domain":
									# Yields examples as (key, example) tuples
									yield key, {
									    "sentence": data["sentence"],
									    "option1": data["option1"],
									    "answer": "" if split == "test" else data["answer"],
									}
								else:
									yield key, {
									    "sentence": data["sentence"],
									    "option2": data["option2"],
									    "second_domain_answer": "" if split == "test" else data["second_domain_answer"],
									}





'''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""")
