




"""simple docstring"""


import json
import os
import tempfile

import transformers

import datasets
from utils import generate_example_dataset, get_duration


A_      =    50_00_00

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


@get_duration
def  UpperCAmelCase__	(snake_case__							: datasets.Dataset		,       **snake_case__							: Union[str, Any]   ):
  """simple docstring"""

  _snake_case							:		List[Any]								=  dataset.map(**snake_case__   )


@get_duration
def  UpperCAmelCase__	(snake_case__							: datasets.Dataset		,       **snake_case__							: Union[str, Any]   ):
  """simple docstring"""

  _snake_case							:		int								=  dataset.filter(**snake_case__   )


def  UpperCAmelCase__	():
  """simple docstring"""

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

    _snake_case							:		List[Any]								=  transformers.AutoTokenizer.from_pretrained("""bert-base-cased"""		,       use_fast=snake_case__   )

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

    _snake_case							:		List[Any]								=  map(snake_case__   )

    _snake_case							:		List[Any]								=  map(snake_case__		,       batched=snake_case__   )

    _snake_case							:		Any								=  map(snake_case__		,       function=lambda snake_case__   : None		,       batched=snake_case__   )

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

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

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

    with dataset.formatted_as(type="""tensorflow"""		,       columns="""numbers"""   ):
      _snake_case							:		Optional[Any]								=  map(snake_case__		,       function=lambda snake_case__   : None		,       batched=snake_case__   )

    _snake_case							:		str								=  map(snake_case__		,       function=snake_case__		,       batched=snake_case__   )

    _snake_case							:		List[Any]								=  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()








"""simple docstring"""


def  UpperCAmelCase__	(snake_case__							: Optional[Any]   ):
  """simple docstring"""

  _snake_case							:		Union[str, Any]								=  []
  _snake_case							:		Dict								=  set({"""(""", """[""", """{"""}   )
  _snake_case							:		Union[str, Any]								=  set({""")""", """]""", """}"""}   )
  _snake_case							:		Tuple								=  {"""{""": """}""", """[""": """]""", """(""": """)"""}

  for i in range(len(snake_case__   )   ):
    if s[i] in open_brackets:
      stack.append(s[i]   )

    elif s[i] in closed_brackets and (
        len(snake_case__   ) == 0 or (len(snake_case__   ) > 0 and open_to_closed[stack.pop()] != s[i])
    ):
      return False

  return len(snake_case__   ) == 0


def  UpperCAmelCase__	():
  """simple docstring"""

  _snake_case							:		Any								=  input("""Enter sequence of brackets: """   )
  if is_balanced(snake_case__   ):
    print(snake_case__		,       """is balanced"""   )
  else:
    print(snake_case__		,       """is not balanced"""   )


if __name__ == "__main__":
     main()


