


import json
import os
import tempfile

import transformers

import datasets
from utils import generate_example_dataset, get_duration


UpperCamelCase			    =     500_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__		)							->  List[str]:






						"""simple docstring"""





						_SCREAMING_SNAKE_CASE     				=  dataset.map(**snake_case__		)

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






						"""simple docstring"""





						_SCREAMING_SNAKE_CASE     				=  dataset.filter(**snake_case__		)

def 	__lowerCamelCase      (		)							->  Optional[int]:






						"""simple docstring"""





						_SCREAMING_SNAKE_CASE     				=  {"""num examples""": SPEED_TEST_N_EXAMPLES}
						with tempfile.TemporaryDirectory() as tmp_dir:
												_SCREAMING_SNAKE_CASE     				=  datasets.Features({"""text""": datasets.Value("""string"""		), """numbers""": datasets.Value("""float32"""		)}		)
												_SCREAMING_SNAKE_CASE     				=  generate_example_dataset(
												    os.path.join(snake_case__	,"""dataset.arrow"""		)	,snake_case__	,num_examples=snake_case__		)

												_SCREAMING_SNAKE_CASE     				=  transformers.AutoTokenizer.from_pretrained("""bert-base-cased"""	,use_fast=snake_case__		)

												def tokenize(snake_case__		):
																		return tokenizer(examples["""text"""]		)

												_SCREAMING_SNAKE_CASE     				=  map(snake_case__		)

												_SCREAMING_SNAKE_CASE     				=  map(snake_case__	,batched=snake_case__		)

												_SCREAMING_SNAKE_CASE     				=  map(snake_case__	,function=lambda snake_case__		: None	,batched=snake_case__		)

												with dataset.formatted_as(type="""numpy"""		):
																		_SCREAMING_SNAKE_CASE     				=  map(snake_case__	,function=lambda snake_case__		: None	,batched=snake_case__		)

												with dataset.formatted_as(type="""pandas"""		):
																		_SCREAMING_SNAKE_CASE     				=  map(snake_case__	,function=lambda snake_case__		: None	,batched=snake_case__		)

												with dataset.formatted_as(type="""torch"""	,columns="""numbers"""		):
																		_SCREAMING_SNAKE_CASE     				=  map(snake_case__	,function=lambda snake_case__		: None	,batched=snake_case__		)

												with dataset.formatted_as(type="""tensorflow"""	,columns="""numbers"""		):
																		_SCREAMING_SNAKE_CASE     				=  map(snake_case__	,function=lambda snake_case__		: None	,batched=snake_case__		)

												_SCREAMING_SNAKE_CASE     				=  map(snake_case__	,function=snake_case__	,batched=snake_case__		)

												_SCREAMING_SNAKE_CASE     				=  filter(snake_case__		)

												# Activate later when tokenizer support batched inputs
												# with dataset.formatted_as(type='numpy'):
												#     times[func.__name__ + " fast-tokenizer batched numpy"] = func(dataset, function=tokenize, batched=True)

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


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









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))

