'''simple docstring'''




import itertools
from dataclasses import dataclass
from io import StringIO
from typing import Optional

import pyarrow as pa

import datasets
from datasets.features.features import require_storage_cast
from datasets.table import table_cast


_lowercase		:					Any													=		datasets.utils.logging.get_logger(__name__)





@dataclass
class        __magic_name__		(       datasets.BuilderConfig):
			UpperCamelCase__							=			None
			UpperCamelCase__							=			"utf-8"
			UpperCamelCase__							=			None
			UpperCamelCase__							=			10 << 20  # 10MB
			UpperCamelCase__							=			False
			UpperCamelCase__							=			"line"





class        __magic_name__		(       datasets.ArrowBasedBuilder):
			UpperCamelCase__							=			TextConfig







			def       SCREAMING_SNAKE_CASE_	(      self							:    int       ):
										return datasets.DatasetInfo(features=self.config.features       )







			def       SCREAMING_SNAKE_CASE_	(      self							:    str							,     lowercase_							:    List[Any]       ):







										if not self.config.data_files:
																	raise ValueError(f'''At least one data file must be specified, but got data_files={self.config.data_files}'''       )
										lowercase_   :     List[Any]									=			dl_manager.download_and_extract(self.config.data_files       )
										if isinstance(__lowercase							,     (str, list, tuple)       ):
																	lowercase_   :     Optional[int]									=			data_files
																	if isinstance(__lowercase							,     __lowercase       ):
																								lowercase_   :     str									=			[files]
																	lowercase_   :     Tuple									=			[dl_manager.iter_files(__lowercase       ) for file in files]
																	return [datasets.SplitGenerator(name=datasets.Split.TRAIN							,     gen_kwargs={"""files""": files}       )]
										lowercase_   :     Tuple									=			[]
										for split_name, files in data_files.items():
																	if isinstance(__lowercase							,     __lowercase       ):
																								lowercase_   :     Dict									=			[files]
																	lowercase_   :     Dict									=			[dl_manager.iter_files(__lowercase       ) for file in files]
																	splits.append(datasets.SplitGenerator(name=__lowercase							,     gen_kwargs={"""files""": files}       )       )
										return splits







			def       SCREAMING_SNAKE_CASE_	(      self							:    Optional[Any]							,     lowercase_							:    pa.Table       ):







										if self.config.features is not None:
																	lowercase_   :     Optional[Any]									=			self.config.features.arrow_schema
																	if all(not require_storage_cast(__lowercase       ) for feature in self.config.features.values()       ):
																								# cheaper cast
																								lowercase_   :     Any									=			pa_table.cast(__lowercase       )
																	else:
																								# more expensive cast; allows str <-> int/float or str to Audio for example
																								lowercase_   :     Optional[Any]									=			table_cast(__lowercase							,     __lowercase       )
																	return pa_table
										else:
																	return pa_table.cast(pa.schema({"""text""": pa.string()}       )       )







			def       SCREAMING_SNAKE_CASE_	(      self							:    Any							,     lowercase_							:    Any       ):
										lowercase_   :     Optional[Any]									=			list(self.config.features       ) if self.config.features is not None else ["""text"""]
										for file_idx, file in enumerate(itertools.chain.from_iterable(__lowercase       )       ):
																	# open in text mode, by default translates universal newlines ("\n", "\r\n" and "\r") into "\n"
																	with open(__lowercase							,     encoding=self.config.encoding							,     errors=self.config.errors       ) as f:
																								if self.config.sample_by == "line":
																															lowercase_   :     Optional[int]									=			0
																															while True:
																																						lowercase_   :     Tuple									=			f.read(self.config.chunksize       )
																																						if not batch:
																																													break
																																						batch += f.readline()  # finish current line
																																						# StringIO.readlines, by default splits only on "\n" (and keeps line breaks)
																																						lowercase_   :     Tuple									=			StringIO(__lowercase       ).readlines()
																																						if not self.config.keep_linebreaks:
																																													lowercase_   :     List[Any]									=			[line.rstrip("""\n"""       ) for line in batch]
																																						lowercase_   :     Union[str, Any]									=			pa.Table.from_arrays([pa.array(__lowercase       )]							,     names=__lowercase       )
																																						# Uncomment for debugging (will print the Arrow table size and elements)
																																						# logger.warning(f"pa_table: {pa_table} num rows: {pa_table.num_rows}")
																																						# logger.warning('\n'.join(str(pa_table.slice(i, 1).to_pydict()) for i in range(pa_table.num_rows)))
																																						yield (file_idx, batch_idx), self._cast_table(__lowercase       )
																																						batch_idx += 1
																								elif self.config.sample_by == "paragraph":
																															lowercase_   :     List[Any]									=			0
																															lowercase_   :     int									=			""""""
																															while True:
																																						lowercase_   :     Tuple									=			f.read(self.config.chunksize       )
																																						if not new_batch:
																																													break
																																						batch += new_batch
																																						batch += f.readline()  # finish current line
																																						lowercase_   :     List[Any]									=			batch.split("""\n\n"""       )
																																						lowercase_   :     Tuple									=			pa.Table.from_arrays(
																																						    [pa.array([example for example in batch[:-1] if example]       )]							,     names=__lowercase       )
																																						# Uncomment for debugging (will print the Arrow table size and elements)
																																						# logger.warning(f"pa_table: {pa_table} num rows: {pa_table.num_rows}")
																																						# logger.warning('\n'.join(str(pa_table.slice(i, 1).to_pydict()) for i in range(pa_table.num_rows)))
																																						yield (file_idx, batch_idx), self._cast_table(__lowercase       )
																																						batch_idx += 1
																																						lowercase_   :     str									=			batch[-1]
																															if batch:
																																						lowercase_   :     Optional[Any]									=			pa.Table.from_arrays([pa.array([batch]       )]							,     names=__lowercase       )
																																						yield (file_idx, batch_idx), self._cast_table(__lowercase       )
																								elif self.config.sample_by == "document":
																															lowercase_   :     Any									=			f.read()
																															lowercase_   :     Tuple									=			pa.Table.from_arrays([pa.array([text]       )]							,     names=__lowercase       )
																															yield file_idx, self._cast_table(__lowercase       )












import matplotlib.pyplot as plt
import numpy as np




def  A_(      A					,   A					,   A ):
	UpperCAmelCase_		    =  len(A )  # Number of training samples
	UpperCAmelCase_		    =  np.eye(A )  # Initialize weights as identity matrix
	for j in range(A ):
		UpperCAmelCase_		    =  point - x_train[j]
		UpperCAmelCase_		    =  np.exp(diff @ diff.T / (-2.0 * tau**2) )

	return weights




def  A_(      A					,   A					,   A					,   A ):
	UpperCAmelCase_		    =  weight_matrix(A					,   A					,   A )
	UpperCAmelCase_		    =  np.linalg.inv(x_train.T @ weight_mat @ x_train ) @ (
	    x_train.T @ weight_mat @ y_train.T
	)

	return weight




def  A_(      A					,   A					,   A ):
	UpperCAmelCase_		    =  np.zeros(len(A ) )  # Initialize array of predictions
	for i, item in enumerate(A ):
		UpperCAmelCase_		    =  item @ local_weight(A					,   A					,   A					,   A )

	return y_pred




def  A_(      A					,   A					,   A ):
	import seaborn as sns

	UpperCAmelCase_		    =  sns.load_dataset(A )
	UpperCAmelCase_		    =  np.array(data[x_name] )
	UpperCAmelCase_		    =  np.array(data[y_name] )

	UpperCAmelCase_		    =  np.ones(len(A ) )

	# pairing elements of one and x_data
	UpperCAmelCase_		    =  np.column_stack((one, x_data) )

	return x_train, x_data, y_data




def  A_(      A					,   A					,   A					,   A					,   A					,   A					,   ):
	UpperCAmelCase_		    =  np.sort(A					,   axis=0 )
	plt.scatter(A					,   A					,   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(A )
	plt.ylabel(A )
	plt.show()


if __name__ == "__main__":
				import doctest

				doctest.testmod()

				# Demo with a dataset from the seaborn module
				UpperCamelCase__	,      UpperCamelCase__	,      UpperCamelCase__ :			Tuple       	=			load_data("""tips""", """total_bill""", """tip""")
				UpperCamelCase__ :			Tuple       	=			local_weight_regression(training_data_x, tip, 5)
				plot_preds(training_data_x, predictions, total_bill, tip, """total_bill""", """tip""")


