

'''simple docstring'''






import json
import os
import tempfile

import transformers

import datasets
from utils import generate_example_dataset, get_duration


lowerCAmelCase__      		=			50_0000

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


@get_duration
def 	_A  (					A__  ,       **A__   ):






 """simple docstring"""



 __lowercase													=				dataset.map(**A__   )


@get_duration
def 	_A  (					A__  ,       **A__   ):






 """simple docstring"""



 __lowercase													=				dataset.filter(**A__   )


def 	_A  (					):






 """simple docstring"""



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

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

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

  __lowercase													=				map(A__   )

  __lowercase													=				map(A__  ,       batched=A__   )

  __lowercase													=				map(A__  ,       function=lambda A__   : None  ,       batched=A__   )

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

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

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

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

  __lowercase													=				map(A__  ,       function=A__  ,       batched=A__   )

  __lowercase													=				filter(A__   )

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


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




'''simple docstring'''






def 	_A  (					A__ = 50   ):






 """simple docstring"""



 __lowercase													=				[1] * (length + 1)

 for row_length in range(3  ,       length + 1   ):
  for block_length in range(3  ,       row_length + 1   ):
   for block_start in range(row_length - block_length   ):
    ways_number[row_length] += ways_number[
        row_length - block_start - block_length - 1
    ]

   ways_number[row_length] += 1

 return ways_number[length]


if __name__ == "__main__":
 print(f'{solution() = }')

