





"""simple docstring"""


import sys
from dataclasses import dataclass
from typing import TYPE_CHECKING, Dict, List, Optional, Tuple, Union

import pandas as pd
import pyarrow as pa

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


if TYPE_CHECKING:
      import sqlitea

      import sqlalchemy


A_     :      str								=datasets.utils.logging.get_logger(__name__)







@dataclass
class 	__a   (  datasets.BuilderConfig		):
     SCREAMING_SNAKE_CASE__ :      Union[str, "sqlalchemy.sql.Selectable"]       		=			None
     SCREAMING_SNAKE_CASE__ :      Union[str, "sqlalchemy.engine.Connection", "sqlalchemy.engine.Engine", "sqlite3.Connection"]       		=			None
     SCREAMING_SNAKE_CASE__ :      Optional[Union[str, List[str]]]       		=			None
     SCREAMING_SNAKE_CASE__ :      bool       		=			True
     SCREAMING_SNAKE_CASE__ :      Optional[Union[List, Tuple, Dict]]       		=			None
     SCREAMING_SNAKE_CASE__ :      Optional[Union[List, Dict]]       		=			None
     SCREAMING_SNAKE_CASE__ :      Optional[List[str]]       		=			None
     SCREAMING_SNAKE_CASE__ :      Optional[int]       		=			10000
     SCREAMING_SNAKE_CASE__ :      Optional[datasets.Features]       		=			None



     def 						snake_case_			(       self   ):
            if self.sql is None:
                   raise ValueError('sql must be specified'   )
            if self.con is None:
                   raise ValueError('con must be specified'   )



     def 						snake_case_			(       self					,     a__					,     a__ = None					,     ):
            _lowerCamelCase    	=					config_kwargs.copy()
            # We need to stringify the Selectable object to make its hash deterministic

            # The process of stringifying is explained here: http://docs.sqlalchemy.org/en/latest/faq/sqlexpressions.html
            _lowerCamelCase    	=					config_kwargs['sql']
            if not isinstance(a__					,     a__   ):
                   if datasets.config.SQLALCHEMY_AVAILABLE and "sqlalchemy" in sys.modules:
                          import sqlalchemy

                          if isinstance(a__					,     sqlalchemy.sql.Selectable   ):
                                 _lowerCamelCase    	=					sqlalchemy.create_engine(config_kwargs['con'].split('://'   )[0] + '://'   )
                                 _lowerCamelCase    	=					str(sql.compile(dialect=engine.dialect   )   )
                                 _lowerCamelCase    	=					sql_str
                          else:
                                 raise TypeError(
                                     F'Supported types for \'sql\' are string and sqlalchemy.sql.Selectable but got {type(a__   )}: {sql}'   )
                   else:
                          raise TypeError(
                              F'Supported types for \'sql\' are string and sqlalchemy.sql.Selectable but got {type(a__   )}: {sql}'   )
            _lowerCamelCase    	=					config_kwargs['con']
            if not isinstance(a__					,     a__   ):
                   _lowerCamelCase    	=					id(a__   )
                   logger.info(
                       F'SQL connection \'con\' of type {type(a__   )} couldn\'t be hashed properly. To enable hashing, specify \'con\' as URI string instead.'   )

            return super().create_config_id(a__					,     custom_features=a__   )



     @property
     def 						snake_case_			(       self   ):
            _lowerCamelCase    	=					{
                'index_col': self.index_col,
                'columns': self.columns,
                'params': self.params,
                'coerce_float': self.coerce_float,
                'parse_dates': self.parse_dates,
            }
            return pd_read_sql_kwargs







class 	__a   (  datasets.ArrowBasedBuilder		):
     SCREAMING_SNAKE_CASE__ :      Any       		=			SqlConfig



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



     def 						snake_case_			(       self					,     a__   ):
            return [datasets.SplitGenerator(name=datasets.Split.TRAIN					,     gen_kwargs={}   )]



     def 						snake_case_			(       self					,     a__   ):
            if self.config.features is not None:
                   _lowerCamelCase    	=					self.config.features.arrow_schema
                   if all(not require_storage_cast(a__   ) for feature in self.config.features.values()   ):
                          # cheaper cast
                          _lowerCamelCase    	=					pa.Table.from_arrays([pa_table[field.name] for field in schema]					,     schema=a__   )
                   else:
                          # more expensive cast; allows str <-> int/float or str to Audio for example
                          _lowerCamelCase    	=					table_cast(a__					,     a__   )
            return pa_table



     def 						snake_case_			(       self   ):
            _lowerCamelCase    	=					self.config.chunksize
            _lowerCamelCase    	=					pd.read_sql(
                self.config.sql					,     self.config.con					,     chunksize=a__					,     **self.config.pd_read_sql_kwargs   )
            _lowerCamelCase    	=					[sql_reader] if chunksize is None else sql_reader
            for chunk_idx, df in enumerate(a__   ):
                   _lowerCamelCase    	=					pa.Table.from_pandas(a__   )
                   yield chunk_idx, self._cast_table(a__   )













"""simple docstring"""


from sklearn.datasets import load_iris
from sklearn.model_selection import train_test_split
from sklearn.neighbors import KNeighborsClassifier

# Load iris file
A_     :      List[str]								=load_iris()
iris.keys()


print(f'Target names: \n {iris.target_names} ')
print(f'\n Features: \n {iris.feature_names}')

# Train set e Test set
A_							,	A_							,	A_							,	A_     :      List[Any]								=train_test_split(
    iris["""data"""], iris["""target"""], random_state=4
)

# KNN

A_     :      str								=KNeighborsClassifier(n_neighbors=1)
knn.fit(X_train, y_train)

# new array to test
A_     :      Any								=[[1, 2, 1, 4], [2, 3, 4, 5]]

A_     :      int								=knn.predict(X_new)

print(
    f'\nNew array: \n {X_new}\n\nTarget Names Prediction: \n'
    f' {iris["target_names"][prediction]}'
)






