
import json
import os
import tempfile

import transformers

import datasets
from utils import generate_example_dataset, get_duration


UpperCAmelCase__     = 50_0000

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





@get_duration
def 							A			(  _UpperCAmelCase       :     datasets.Dataset							,     **_UpperCAmelCase       :     Union[str, Any]					)       ->				List[Any]:







			'''simple docstring'''
			_UpperCAmelCase	 =     dataset.map(**_UpperCAmelCase					)





@get_duration
def 							A			(  _UpperCAmelCase       :     datasets.Dataset							,     **_UpperCAmelCase       :     List[Any]					)       ->				Optional[Any]:







			'''simple docstring'''
			_UpperCAmelCase	 =     dataset.filter(**_UpperCAmelCase					)





def 							A			(  )       ->				Dict:







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

						_UpperCAmelCase	 =     transformers.AutoTokenizer.from_pretrained('bert-base-cased'							,     use_fast=_UpperCAmelCase					)

						def tokenize(_UpperCAmelCase       :     List[str]					):
									return tokenizer(examples['text']					)

						_UpperCAmelCase	 =     map(_UpperCAmelCase					)

						_UpperCAmelCase	 =     map(_UpperCAmelCase							,     batched=_UpperCAmelCase					)

						_UpperCAmelCase	 =     map(_UpperCAmelCase							,     function=lambda _UpperCAmelCase					: None							,     batched=_UpperCAmelCase					)

						with dataset.formatted_as(type='numpy'					):
									_UpperCAmelCase	 =     map(_UpperCAmelCase							,     function=lambda _UpperCAmelCase					: None							,     batched=_UpperCAmelCase					)

						with dataset.formatted_as(type='pandas'					):
									_UpperCAmelCase	 =     map(_UpperCAmelCase							,     function=lambda _UpperCAmelCase					: None							,     batched=_UpperCAmelCase					)

						with dataset.formatted_as(type='torch'							,     columns='numbers'					):
									_UpperCAmelCase	 =     map(_UpperCAmelCase							,     function=lambda _UpperCAmelCase					: None							,     batched=_UpperCAmelCase					)

						with dataset.formatted_as(type='tensorflow'							,     columns='numbers'					):
									_UpperCAmelCase	 =     map(_UpperCAmelCase							,     function=lambda _UpperCAmelCase					: None							,     batched=_UpperCAmelCase					)

						_UpperCAmelCase	 =     map(_UpperCAmelCase							,     function=_UpperCAmelCase							,     batched=_UpperCAmelCase					)

						_UpperCAmelCase	 =     filter(_UpperCAmelCase					)

						# 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(_UpperCAmelCase							,     'wb'					) as f:
						f.write(json.dumps(_UpperCAmelCase					).encode('utf-8'					)					)


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





def 							A			(  _UpperCAmelCase       :     list					)       ->				list:







			'''simple docstring'''
			if len(_UpperCAmelCase					) <= 1:
						return lst

			_UpperCAmelCase	 =     1

			while i < len(_UpperCAmelCase					):
						if lst[i - 1] <= lst[i]:
									i += 1
						else:
									_UpperCAmelCase ,  _UpperCAmelCase	 =     lst[i], lst[i - 1]
									i -= 1
									if i == 0:
												_UpperCAmelCase	 =     1

			return lst


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