


import json
import os
import tempfile

import datasets
from utils import generate_example_dataset, get_duration


UpperCamelCase			    =     50_000
UpperCamelCase			    =     5_000

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

@get_duration
def 	__lowerCamelCase      (		snake_case__	,snake_case__		)							->  Dict:






						"""simple docstring"""





						for i in range(__a		):
												_SCREAMING_SNAKE_CASE     				=  dataset[i]

@get_duration
def 	__lowerCamelCase      (		snake_case__	,snake_case__	,snake_case__		)							->  Optional[Any]:






						"""simple docstring"""





						for i in range(0	,len(__a		)	,__a		):
												_SCREAMING_SNAKE_CASE     				=  dataset[i : i + batch_size]

@get_duration
def 	__lowerCamelCase      (		snake_case__	,snake_case__	,snake_case__		)							->  Optional[Any]:






						"""simple docstring"""





						with dataset.formatted_as(type=__a		):
												for i in range(__a		):
																		_SCREAMING_SNAKE_CASE     				=  dataset[i]

@get_duration
def 	__lowerCamelCase      (		snake_case__	,snake_case__	,snake_case__	,snake_case__		)							->  Optional[int]:






						"""simple docstring"""





						with dataset.formatted_as(type=__a		):
												for i in range(0	,__a	,__a		):
																		_SCREAMING_SNAKE_CASE     				=  dataset[i : i + batch_size]

def 	__lowerCamelCase      (		)							->  Tuple:






						"""simple docstring"""





						_SCREAMING_SNAKE_CASE     				=  {"""num examples""": SPEED_TEST_N_EXAMPLES}
						_SCREAMING_SNAKE_CASE     				=  [
						    (read, {"""length""": SMALL_TEST}),
						    (read, {"""length""": SPEED_TEST_N_EXAMPLES}),
						    (read_batch, {"""length""": SPEED_TEST_N_EXAMPLES, """batch_size""": 10}),
						    (read_batch, {"""length""": SPEED_TEST_N_EXAMPLES, """batch_size""": 1_00}),
						    (read_batch, {"""length""": SPEED_TEST_N_EXAMPLES, """batch_size""": 10_00}),
						    (read_formatted, {"""type""": """numpy""", """length""": SMALL_TEST}),
						    (read_formatted, {"""type""": """pandas""", """length""": SMALL_TEST}),
						    (read_formatted, {"""type""": """torch""", """length""": SMALL_TEST}),
						    (read_formatted, {"""type""": """tensorflow""", """length""": SMALL_TEST}),
						    (read_formatted_batch, {"""type""": """numpy""", """length""": SMALL_TEST, """batch_size""": 10}),
						    (read_formatted_batch, {"""type""": """numpy""", """length""": SMALL_TEST, """batch_size""": 10_00}),
						]

						_SCREAMING_SNAKE_CASE     				=  [
						    (read, {"""length""": SMALL_TEST}),
						    (read, {"""length""": SPEED_TEST_N_EXAMPLES}),
						    (read_batch, {"""length""": SPEED_TEST_N_EXAMPLES, """batch_size""": 10}),
						    (read_batch, {"""length""": SPEED_TEST_N_EXAMPLES, """batch_size""": 1_00}),
						    (read_batch, {"""length""": SPEED_TEST_N_EXAMPLES, """batch_size""": 10_00}),
						    (read_formatted, {"""type""": """numpy""", """length""": SMALL_TEST}),
						    (read_formatted_batch, {"""type""": """numpy""", """length""": SMALL_TEST, """batch_size""": 10}),
						    (read_formatted_batch, {"""type""": """numpy""", """length""": SMALL_TEST, """batch_size""": 10_00}),
						]
						with tempfile.TemporaryDirectory() as tmp_dir:
												print("""generating dataset"""		)
												_SCREAMING_SNAKE_CASE     				=  datasets.Features(
												    {"""list""": datasets.Sequence(datasets.Value("""float32"""		)		), """numbers""": datasets.Value("""float32"""		)}		)
												_SCREAMING_SNAKE_CASE     				=  generate_example_dataset(
												    os.path.join(__a	,"""dataset.arrow"""		)	,__a	,num_examples=__a	,seq_shapes={"""list""": (1_00,)}	,)
												print("""first set of iterations"""		)
												for func, kwargs in functions:
																		print(func.__name__	,str(__a		)		)
																		_SCREAMING_SNAKE_CASE     				=  func(__a	,**__a		)

												print("""shuffling dataset"""		)
												_SCREAMING_SNAKE_CASE     				=  dataset.shuffle()
												print("""Second set of iterations (after shuffling"""		)
												for func, kwargs in functions_shuffled:
																		print("""shuffled """	,func.__name__	,str(__a		)		)
																		_SCREAMING_SNAKE_CASE     				=  func(
																		    __a	,**__a		)

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


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









def 	__lowerCamelCase      (		snake_case__		)							->  List[Any]:






						"""simple docstring"""





						_SCREAMING_SNAKE_CASE     				=  len(snake_case__		)
						for i in range(length - 1		):
												_SCREAMING_SNAKE_CASE     				=  i
												for k in range(i + 1	,snake_case__		):
																		if collection[k] < collection[least]:
																								_SCREAMING_SNAKE_CASE     				=  k
												if least != i:
																		_SCREAMING_SNAKE_CASE				,					_SCREAMING_SNAKE_CASE     				=  (collection[i], collection[least])
						return collection


if __name__ == "__main__":
		UpperCamelCase			    =     input('''Enter numbers separated by a comma:\n''').strip()
		UpperCamelCase			    =     [int(item) for item in user_input.split(''',''')]
		print(selection_sort(unsorted))

