import json
import os
import tempfile

import transformers

import datasets
from utils import generate_example_dataset, get_duration


__UpperCAmelCase												=5_0_0_0_0_0

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






@get_duration
def __lowerCAmelCase			(							UpperCamelCase__    ,  **UpperCamelCase__    )     ->   Tuple:
    __lowerCamelCase          =  dataset.map(**UpperCamelCase__    )






@get_duration
def __lowerCAmelCase			(							UpperCamelCase__    ,  **UpperCamelCase__    )     ->   Optional[int]:
    __lowerCamelCase          =  dataset.filter(**UpperCamelCase__    )






def __lowerCAmelCase			(							)     ->   List[str]:
    __lowerCamelCase          =  {'''num examples''': SPEED_TEST_N_EXAMPLES}
    with tempfile.TemporaryDirectory() as tmp_dir:
        __lowerCamelCase          =  datasets.Features({'''text''': datasets.Value('''string'''    ), '''numbers''': datasets.Value('''float32'''    )}    )
        __lowerCamelCase          =  generate_example_dataset(
            os.path.join(UpperCamelCase__    ,  '''dataset.arrow'''    )    ,  UpperCamelCase__    ,  num_examples=UpperCamelCase__    )

        __lowerCamelCase          =  transformers.AutoTokenizer.from_pretrained('''bert-base-cased'''    ,  use_fast=UpperCamelCase__    )

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

        __lowerCamelCase          =  map(UpperCamelCase__    )

        __lowerCamelCase          =  map(UpperCamelCase__    ,  batched=UpperCamelCase__    )

        __lowerCamelCase          =  map(UpperCamelCase__    ,  function=lambda UpperCamelCase__    : None    ,  batched=UpperCamelCase__    )

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

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

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

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

        __lowerCamelCase          =  map(UpperCamelCase__    ,  function=UpperCamelCase__    ,  batched=UpperCamelCase__    )

        __lowerCamelCase          =  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'''




from bisect import bisect
from itertools import accumulate






def __lowerCAmelCase			(							UpperCamelCase__    ,  UpperCamelCase__    ,  UpperCamelCase__    ,  UpperCamelCase__    )     ->   str:
    __lowerCamelCase          =  sorted(zip(UpperCamelCase__    ,  UpperCamelCase__    )    ,  key=lambda UpperCamelCase__    : x[0] / x[1]    ,  reverse=UpperCamelCase__    )
    __lowerCamelCase     ,  __lowerCamelCase          =  [i[0] for i in r], [i[1] for i in r]
    __lowerCamelCase          =  list(accumulate(UpperCamelCase__    )    )
    __lowerCamelCase          =  bisect(UpperCamelCase__    ,  UpperCamelCase__    )
    return (
        0
        if k == 0
        else sum(vl[:k]    ) + (w - acc[k - 1]) * (vl[k]) / (wt[k])
        if k != n
        else sum(vl[:k]    )
    )


if __name__ == "__main__":
  import doctest

  doctest.testmod()
