

"""simple docstring"""



import json
import os
import tempfile

import transformers

import datasets
from utils import generate_example_dataset, get_duration


SCREAMING_SNAKE_CASE_					:	Any							  =						5_0_0_0_0_0

SCREAMING_SNAKE_CASE_  ,SCREAMING_SNAKE_CASE_					:	List[Any]							  =						os.path.split(__file__)
SCREAMING_SNAKE_CASE_					:	Optional[int]							  =						os.path.join(RESULTS_BASEPATH, 'results', RESULTS_FILENAME.replace('.py', '.json'))





@get_duration
def  _snake_case     (    UpperCAmelCase_  :					datasets.Dataset					,   **UpperCAmelCase_  :					Optional[int]			):
     A__										=  dataset.map(**UpperCAmelCase_			)





@get_duration
def  _snake_case     (    UpperCAmelCase_  :					datasets.Dataset					,   **UpperCAmelCase_  :					Tuple			):
     A__										=  dataset.filter(**UpperCAmelCase_			)





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

          A__										=  transformers.AutoTokenizer.from_pretrained("""bert-base-cased"""					,   use_fast=UpperCAmelCase_			)

          def tokenize(UpperCAmelCase_  :					Dict			):
               return tokenizer(examples["""text"""]			)

          A__										=  map(UpperCAmelCase_			)

          A__										=  map(UpperCAmelCase_					,   batched=UpperCAmelCase_			)

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

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

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

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

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

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

          A__										=  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"""



import baseaa





def  _snake_case     (    UpperCAmelCase_  :					str			):
     return baseaa.aaaencode(string.encode("""utf-8"""			)			)





def  _snake_case     (    UpperCAmelCase_  :					bytes			):
     return baseaa.aaadecode(UpperCAmelCase_			).decode("""utf-8"""			)


if __name__ == "__main__":
 import doctest

 doctest.testmod()






