






"""simple docstring"""



import json
import os
import tempfile

import transformers

import datasets
from utils import generate_example_dataset, get_duration


_UpperCamelCase		:  Any						=		5_0_0_0_0_0

_UpperCamelCase		:  List[Any]						=		os.path.split(__file__)
_UpperCamelCase		:  Tuple						=		os.path.join(RESULTS_BASEPATH, 'results', RESULTS_FILENAME.replace('.py', '.json'))
@get_duration
def 						_SCREAMING_SNAKE_CASE (    __snake_case			:  int		,					**__snake_case			:  str    ):





  '''simple docstring'''





  lowercase							    =       dataset.map(**lowercase_    )
@get_duration
def 						_SCREAMING_SNAKE_CASE (    __snake_case			:  List[str]		,					**__snake_case			:  int    ):





  '''simple docstring'''





  lowercase							    =       dataset.filter(**lowercase_    )
def 						_SCREAMING_SNAKE_CASE (    ):





  '''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(lowercase_		,					'dataset.arrow'    )		,					lowercase_		,					num_examples=lowercase_    )

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

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

    lowercase							    =       map(lowercase_    )

    lowercase							    =       map(lowercase_		,					batched=lowercase_    )

    lowercase							    =       map(lowercase_		,					function=lambda __snake_case    : None		,					batched=lowercase_    )

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

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

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

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

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

    lowercase							    =       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()








from __future__ import annotations

import typing
from collections.abc import Iterable

import numpy as np

_lowerCamelCase	:		str							       =   typing.Union[Iterable[float], Iterable[int], np.ndarray]  # noqa: UP007
_lowerCamelCase	:		Tuple							       =   typing.Union[np.floataa, int, float]  # noqa: UP007







def SCREAMING_SNAKE_CASE				(       lowercase_ ,				lowercase_						)   ->       VectorOut:



       """simple docstring"""

       return np.sqrt(np.sum((np.asarray(lowercase_						) - np.asarray(lowercase_						)) ** 2						)						)







def SCREAMING_SNAKE_CASE				(       lowercase_ ,				lowercase_						)   ->       VectorOut:



       """simple docstring"""

       return sum((va - va) ** 2 for va, va in zip(lowercase_ ,				lowercase_						)						) ** (1 / 2)


if __name__ == "__main__":







  def SCREAMING_SNAKE_CASE				(       )   ->       None:



         """simple docstring"""

         from timeit import timeit

         print('''Without Numpy'''						)
         print(
             timeit(
                 '''euclidean_distance_no_np([1, 2, 3], [4, 5, 6])''' ,				number=10_000 ,				globals=globals() ,				)						)
         print('''With Numpy'''						)
         print(
             timeit(
                 '''euclidean_distance([1, 2, 3], [4, 5, 6])''' ,				number=10_000 ,				globals=globals() ,				)						)
  benchmark()


