





'''simple docstring'''

import json
import os
import tempfile

import transformers

import datasets
from utils import generate_example_dataset, get_duration


lowercase__		:     Optional[int]			=							50_00_00

lowercase__			,  lowercase__		:     List[str]			=							os.path.split(__file__)
lowercase__		:     str			=							os.path.join(RESULTS_BASEPATH, 'results', RESULTS_FILENAME.replace('.py', '.json'))







@get_duration
def    a__							(							lowercase   :	datasets.Dataset,   **lowercase   :	Union[str, Any] )						->						Union[str, Any]:





     """simple docstring"""

     _UpperCamelCase							       =			dataset.map(**lowercase )







@get_duration
def    a__							(							lowercase   :	datasets.Dataset,   **lowercase   :	Optional[Any] )						->						str:





     """simple docstring"""

     _UpperCamelCase							       =			dataset.filter(**lowercase )







def    a__							(							)						->						Any:





     """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(lowercase,   '''dataset.arrow''' ),   lowercase,   num_examples=lowercase )

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

          def tokenize(lowercase   :	List[Any] ):
               return tokenizer(examples['''text'''] )

          _UpperCamelCase							       =			map(lowercase )

          _UpperCamelCase							       =			map(lowercase,   batched=lowercase )

          _UpperCamelCase							       =			map(lowercase,   function=lambda lowercase : None,   batched=lowercase )

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

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

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

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

          _UpperCamelCase							       =			map(lowercase,   function=lowercase,   batched=lowercase )

          _UpperCamelCase							       =			filter(lowercase )

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


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








'''simple docstring'''





from __future__ import annotations




def lowerCAmelCase    (__A):



     """simple docstring"""



     _a           =    2
     _a           =    []
     while i * i <= n:
          if n % i:
               i += 1
          else:
               n //= i
               factors.append(__A)
     if n > 1:
          factors.append(__A)
     return factors


if __name__ == "__main__":
  import doctest

  doctest.testmod()



