
"""simple docstring"""







import json
import os
import tempfile

import transformers

import datasets
from utils import generate_example_dataset, get_duration


lowerCamelCase__        =		500_000

lowerCamelCase__ ,						lowerCamelCase__        =		os.path.split(__file__)
lowerCamelCase__        =		os.path.join(RESULTS_BASEPATH, """results""", RESULTS_FILENAME.replace(""".py""", """.json"""))
@get_duration
def    __lowerCAmelCase    (_UpperCamelCase	,  **_UpperCamelCase		):
 __lowerCAmelCase      :						Optional[int]						      =				dataset.map(**_UpperCamelCase		)
@get_duration
def    __lowerCAmelCase    (_UpperCamelCase	,  **_UpperCamelCase		):
 __lowerCAmelCase      :						Optional[int]						      =				dataset.filter(**_UpperCamelCase		)
def    __lowerCAmelCase    ():
 __lowerCAmelCase      :						str						      =				{'num examples': SPEED_TEST_N_EXAMPLES}
 with tempfile.TemporaryDirectory() as tmp_dir:
  __lowerCAmelCase      :						Optional[int]						      =				datasets.Features({'text': datasets.Value('string'		), 'numbers': datasets.Value('float32'		)}		)
  __lowerCAmelCase      :						str						      =				generate_example_dataset(
      os.path.join(_UpperCamelCase	,  'dataset.arrow'		)	,  _UpperCamelCase	,  num_examples=_UpperCamelCase		)

  __lowerCAmelCase      :						int						      =				transformers.AutoTokenizer.from_pretrained('bert-base-cased'	,  use_fast=_UpperCamelCase		)

  def tokenize(_UpperCamelCase		):
   return tokenizer(examples['text']		)

  __lowerCAmelCase      :						List[str]						      =				map(_UpperCamelCase		)

  __lowerCAmelCase      :						Union[str, Any]						      =				map(_UpperCamelCase	,  batched=_UpperCamelCase		)

  __lowerCAmelCase      :						Dict						      =				map(_UpperCamelCase	,  function=lambda _UpperCamelCase		: None	,  batched=_UpperCamelCase		)

  with dataset.formatted_as(type='numpy'		):
   __lowerCAmelCase      :						Union[str, Any]						      =				map(_UpperCamelCase	,  function=lambda _UpperCamelCase		: None	,  batched=_UpperCamelCase		)

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

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

  with dataset.formatted_as(type='tensorflow'	,  columns='numbers'		):
   __lowerCAmelCase      :						Optional[int]						      =				map(_UpperCamelCase	,  function=lambda _UpperCamelCase		: None	,  batched=_UpperCamelCase		)

  __lowerCAmelCase      :						List[str]						      =				map(_UpperCamelCase	,  function=_UpperCamelCase	,  batched=_UpperCamelCase		)

  __lowerCAmelCase      :						Optional[int]						      =				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()

"""simple docstring"""







def    __lowerCAmelCase    (_UpperCamelCase		):
	if len(_UpperCamelCase		) <= 1:
		return lst

	__lowerCAmelCase      :						str						      =				1

	while i < len(_UpperCamelCase		):
		if lst[i - 1] <= lst[i]:
			i += 1
		else:
			__lowerCAmelCase      ,       __lowerCAmelCase      :						List[Any]						      =				lst[i], lst[i - 1]
			i -= 1
			if i == 0:
				__lowerCAmelCase      :						int						      =				1

	return lst


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