


import json
import os
from dataclasses import dataclass

import numpy as np
import pyarrow as pa

import datasets
from utils import get_duration


__lowerCamelCase   	=  100000000000
__lowerCamelCase   	=  10000

__lowerCamelCase	,							__lowerCamelCase   	=  os.path.split(__file__)
__lowerCamelCase   	=  os.path.join(RESULTS_BASEPATH, '''results''', RESULTS_FILENAME.replace('''.py''', '''.json'''))



def 			_a      (    __UpperCamelCase	,	__UpperCamelCase			):
     a_     :  Dict				      =     pa.Table.from_pydict({"""col""": [0] * chunk_size}			)
     a_     :  Tuple				      =     pa.concat_tables([table] * (num_examples // chunk_size)			)
     return datasets.Dataset(__UpperCamelCase	,	fingerprint="""table_100B"""			)


@dataclass
class 	a__						:
       lowerCamelCase__:  int
       lowerCamelCase__:  int
       lowerCamelCase__:  int
       lowerCamelCase__:  int

       def     UpperCAmelCase( self   :  List[Any]     ):
            a_     :  Any				      =     np.random.default_rng(self.seed     )
            a_     :  List[str]				      =     rng.integers(low=self.low   ,				high=self.high   ,				size=self.size     ).tolist()

       def __iter__( self   :  Union[str, Any]     ):
            return iter(self._sampled_values     )

       def __len__( self   :  Optional[Any]     ):
            return self.size



@get_duration
def 			_a      (    __UpperCamelCase			):
     a_     :  int				      =     dataset[0]



@get_duration
def 			_a      (    __UpperCamelCase			):
     a_     :  Optional[int]				      =     dataset[-1]



@get_duration
def 			_a      (    __UpperCamelCase			):
     a_     :  Optional[Any]				      =     dataset[range(len(__UpperCamelCase			) // 2	,	len(__UpperCamelCase			) // 2 + 1_0_2_4			)]



@get_duration
def 			_a      (    __UpperCamelCase			):
     a_     :  Tuple				      =     dataset[RandIter(0	,	len(__UpperCamelCase			)	,	1_0_2_4	,	seed=4_2			)]



def 			_a      (    ):
     a_     :  Optional[Any]				      =     {"""num examples""": SPEED_TEST_N_EXAMPLES}
     a_     :  int				      =     (get_first_row, get_last_row, get_batch_of_aaaa_rows, get_batch_of_aaaa_random_rows)
     print("""generating dataset"""			)
     a_     :  List[str]				      =     generate_100B_dataset(num_examples=__UpperCamelCase	,	chunk_size=__UpperCamelCase			)
     print("""Functions"""			)
     for func in functions:
          print(func.__name__			)
          a_     :  Any				      =     func(__UpperCamelCase			)

     with open(__UpperCamelCase	,	"""wb"""			) as f:
          f.write(json.dumps(__UpperCamelCase			).encode("""utf-8"""			)			)


if __name__ == "__main__":  # useful to run the profiler
       benchmark_table_100B()




"""simple docstring"""
from sklearn.datasets import load_iris
from sklearn.model_selection import train_test_split
from sklearn.neighbors import KNeighborsClassifier

# Load iris file
lowercase__   			=      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
lowercase__			,   lowercase__			,   lowercase__			,   lowercase__   			=      train_test_split(
    iris['data'], iris['target'], random_state=4
)

# KNN

lowercase__   			=      KNeighborsClassifier(n_neighbors=1)
knn.fit(X_train, y_train)

# new array to test
lowercase__   			=      [[1, 2, 1, 4], [2, 3, 4, 5]]

lowercase__   			=      knn.predict(X_new)

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


