

"""simple docstring"""





import json
import os
import tempfile

import transformers

import datasets
from utils import generate_example_dataset, get_duration


_a       =							50_00_00

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




@get_duration
def   _A  (  UpperCamelCase_     :       datasets.Dataset,			**UpperCamelCase_     :       Tuple)	->						Optional[Any]:
   '''simple docstring'''


   __lowercase								=						dataset.map(**UpperCamelCase_)




@get_duration
def   _A  (  UpperCamelCase_     :       datasets.Dataset,			**UpperCamelCase_     :       str)	->						Tuple:
   '''simple docstring'''


   __lowercase								=						dataset.filter(**UpperCamelCase_)




def   _A  (  )	->						Optional[int]:
   '''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(UpperCamelCase_,			"dataset.arrow"),			UpperCamelCase_,			num_examples=UpperCamelCase_)

      __lowercase								=						transformers.AutoTokenizer.from_pretrained("bert-base-cased",			use_fast=UpperCamelCase_)

      def tokenize(UpperCamelCase_     :       int):
         return tokenizer(examples["text"])

      __lowercase								=						map(UpperCamelCase_)

      __lowercase								=						map(UpperCamelCase_,			batched=UpperCamelCase_)

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

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

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

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

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

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

      __lowercase								=						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 collections.abc import Sequence




def   _A  (  UpperCamelCase_     :       Sequence[float],			UpperCamelCase_     :       float)	->						float:
   '''simple docstring'''


   return sum(c * (x**i) for i, c in enumerate(UpperCamelCase_))




def   _A  (  UpperCamelCase_     :       Sequence[float],			UpperCamelCase_     :       float)	->						float:
   '''simple docstring'''


   __lowercase								=						0.0
   for coeff in reversed(UpperCamelCase_):
      __lowercase								=						result * x + coeff
   return result


if __name__ == "__main__":
   _a       =							(0.0, 0.0, 5.0, 9.3, 7.0)
   _a       =							10.0
   print(evaluate_poly(poly, x))
   print(horner(poly, x))




