


import json
import os
import tempfile

import datasets
from utils import generate_example_dataset, get_duration


snake_case__				:	Tuple					       =					5_0000
snake_case__				:	Any					       =					5000

snake_case__       ,   snake_case__				:	Dict					       =					os.path.split(__file__)
snake_case__				:	List[str]					       =					os.path.join(RESULTS_BASEPATH, 'results', RESULTS_FILENAME.replace('.py', '.json'))


@get_duration
def    __lowerCamelCase   (      A__						: datasets.Dataset						,						A__						: Dict					)     ->    Dict:
   for i in range(A__					):
      lowerCamelCase_						:   str		= dataset[i]


@get_duration
def    __lowerCamelCase   (      A__						: datasets.Dataset						,						A__						: Any						,						A__						: Optional[Any]					)     ->    Tuple:
   for i in range(0						,						len(A__					)						,						A__					):
      lowerCamelCase_						:   List[Any]		= dataset[i : i + batch_size]


@get_duration
def    __lowerCamelCase   (      A__						: datasets.Dataset						,						A__						: List[Any]						,						A__						: Any					)     ->    Dict:
   with dataset.formatted_as(type=A__					):
      for i in range(A__					):
         lowerCamelCase_						:   Tuple		= dataset[i]


@get_duration
def    __lowerCamelCase   (      A__						: datasets.Dataset						,						A__						: Union[str, Any]						,						A__						: Union[str, Any]						,						A__						: Optional[int]					)     ->    str:
   with dataset.formatted_as(type=A__					):
      for i in range(0						,						A__						,						A__					):
         lowerCamelCase_						:   int		= dataset[i : i + batch_size]


def    __lowerCamelCase   (      )     ->    Any:
   lowerCamelCase_						:   int		= {"""num examples""": SPEED_TEST_N_EXAMPLES}
   lowerCamelCase_						:   Dict		= [
       (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""": 100}),
       (read_batch, {"""length""": SPEED_TEST_N_EXAMPLES, """batch_size""": 1000}),
       (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""": 1000}),
   ]

   lowerCamelCase_						:   str		= [
       (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""": 100}),
       (read_batch, {"""length""": SPEED_TEST_N_EXAMPLES, """batch_size""": 1000}),
       (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""": 1000}),
   ]
   with tempfile.TemporaryDirectory() as tmp_dir:
      print("""generating dataset"""					)
      lowerCamelCase_						:   Union[str, Any]		= datasets.Features(
          {"""list""": datasets.Sequence(datasets.Value("""float32"""					)					), """numbers""": datasets.Value("""float32"""					)}					)
      lowerCamelCase_						:   Optional[int]		= generate_example_dataset(
          os.path.join(A__						,						"""dataset.arrow"""					)						,						A__						,						num_examples=A__						,						seq_shapes={"""list""": (100,)}						,						)
      print("""first set of iterations"""					)
      for func, kwargs in functions:
         print(func.__name__						,						str(A__					)					)
         lowerCamelCase_						:   Optional[int]		= func(A__						,						**A__					)

      print("""shuffling dataset"""					)
      lowerCamelCase_						:   Optional[int]		= dataset.shuffle()
      print("""Second set of iterations (after shuffling"""					)
      for func, kwargs in functions_shuffled:
         print("""shuffled """						,						func.__name__						,						str(A__					)					)
         lowerCamelCase_						:   List[str]		= func(
             A__						,						**A__					)

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


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









from __future__ import annotations

from collections import namedtuple


def    __lowerCamelCase   (      A__						: float						,						A__						: float						,						A__						: float					)     ->    tuple:
   lowerCamelCase_						:   Dict		= namedtuple("""result"""						,						"""name value"""					)
   if (voltage, current, power).count(0					) != 1:
      raise ValueError("""Only one argument must be 0"""					)
   elif power < 0:
      raise ValueError(
          """Power cannot be negative in any electrical/electronics system"""					)
   elif voltage == 0:
      return result("""voltage"""						,						power / current					)
   elif current == 0:
      return result("""current"""						,						power / voltage					)
   elif power == 0:
      return result("""power"""						,						float(round(abs(voltage * current					)						,						2					)					)					)
   else:
      raise ValueError("""Exactly one argument must be 0"""					)


if __name__ == "__main__":
      import doctest

      doctest.testmod()





