




"""simple docstring"""




import json
import os
import tempfile

import datasets
from utils import generate_example_dataset, get_duration


lowercase__				:      Tuple	   = 50_000
lowercase__				:      int	   = 5_000

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

@get_duration
def  SCREAMING_SNAKE_CASE							(       __UpperCamelCase		, __UpperCamelCase)      ->					Optional[Any]:



     for i in range(__UpperCamelCase):
          a     =				dataset[i]

@get_duration
def  SCREAMING_SNAKE_CASE							(       __UpperCamelCase		, __UpperCamelCase		, __UpperCamelCase)      ->					Any:



     for i in range(0		, len(__UpperCamelCase)		, __UpperCamelCase):
          a     =				dataset[i : i + batch_size]

@get_duration
def  SCREAMING_SNAKE_CASE							(       __UpperCamelCase		, __UpperCamelCase		, __UpperCamelCase)      ->					List[str]:



     with dataset.formatted_as(type=__UpperCamelCase):
          for i in range(__UpperCamelCase):
               a     =				dataset[i]

@get_duration
def  SCREAMING_SNAKE_CASE							(       __UpperCamelCase		, __UpperCamelCase		, __UpperCamelCase		, __UpperCamelCase)      ->					int:



     with dataset.formatted_as(type=__UpperCamelCase):
          for i in range(0		, __UpperCamelCase		, __UpperCamelCase):
               a     =				dataset[i : i + batch_size]

def  SCREAMING_SNAKE_CASE							(       )      ->					Union[str, Any]:
     a     =				{"""num examples""": SPEED_TEST_N_EXAMPLES}
     a     =				[
         (read, {"""length""": SMALL_TEST}),
         (read, {"""length""": SPEED_TEST_N_EXAMPLES}),
         (read_batch, {"""length""": SPEED_TEST_N_EXAMPLES, """batch_size""": 10}),
         (read_batch, {"""length""": SPEED_TEST_N_EXAMPLES, """batch_size""": 1_00}),
         (read_batch, {"""length""": SPEED_TEST_N_EXAMPLES, """batch_size""": 10_00}),
         (read_formatted, {"""type""": """numpy""", """length""": SMALL_TEST}),
         (read_formatted, {"""type""": """pandas""", """length""": SMALL_TEST}),
         (read_formatted, {"""type""": """torch""", """length""": SMALL_TEST}),
         (read_formatted, {"""type""": """tensorflow""", """length""": SMALL_TEST}),
         (read_formatted_batch, {"""type""": """numpy""", """length""": SMALL_TEST, """batch_size""": 10}),
         (read_formatted_batch, {"""type""": """numpy""", """length""": SMALL_TEST, """batch_size""": 10_00}),
     ]

     a     =				[
         (read, {"""length""": SMALL_TEST}),
         (read, {"""length""": SPEED_TEST_N_EXAMPLES}),
         (read_batch, {"""length""": SPEED_TEST_N_EXAMPLES, """batch_size""": 10}),
         (read_batch, {"""length""": SPEED_TEST_N_EXAMPLES, """batch_size""": 1_00}),
         (read_batch, {"""length""": SPEED_TEST_N_EXAMPLES, """batch_size""": 10_00}),
         (read_formatted, {"""type""": """numpy""", """length""": SMALL_TEST}),
         (read_formatted_batch, {"""type""": """numpy""", """length""": SMALL_TEST, """batch_size""": 10}),
         (read_formatted_batch, {"""type""": """numpy""", """length""": SMALL_TEST, """batch_size""": 10_00}),
     ]
     with tempfile.TemporaryDirectory() as tmp_dir:
          print("generating dataset")
          a     =				datasets.Features(
              {"list": datasets.Sequence(datasets.Value("float32")), "numbers": datasets.Value("float32")})
          a     =				generate_example_dataset(
              os.path.join(__UpperCamelCase		, "dataset.arrow")		, __UpperCamelCase		, num_examples=__UpperCamelCase		, seq_shapes={"list": (1_00,)}		, )
          print("first set of iterations")
          for func, kwargs in functions:
               print(func.__name__		, str(__UpperCamelCase))
               a     =				func(__UpperCamelCase		, **__UpperCamelCase)

          print("shuffling dataset")
          a     =				dataset.shuffle()
          print("Second set of iterations (after shuffling")
          for func, kwargs in functions_shuffled:
               print("shuffled "		, func.__name__		, str(__UpperCamelCase))
               a     =				func(
                   __UpperCamelCase		, **__UpperCamelCase)

     with open(__UpperCamelCase		, "wb") as f:
          f.write(json.dumps(__UpperCamelCase).encode("utf-8"))


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







from __future__ import annotations

def  SCREAMING_SNAKE_CASE							(       __UpperCamelCase)      ->					list[int]:
     a     =				2
     a     =				[]
     while i * i <= n:
          if n % i:
               i += 1
          else:
               n //= i
               factors.append(__UpperCamelCase)
     if n > 1:
          factors.append(__UpperCamelCase)
     return factors


if __name__ == "__main__":
   import doctest

   doctest.testmod()

