




import json
import os
import tempfile

import datasets
from datasets.arrow_writer import ArrowWriter
from datasets.features import ArrayaD
from utils import generate_examples, get_duration


lowercase__      :			int           =					(30, 487)
lowercase__      :			List[str]           =					(36, 1_024)
lowercase__      :			List[str]           =					(100, 100)
lowercase__      :			Optional[Any]           =					100

lowercase__      :			Dict           =					datasets.Features(
    {"text": ArrayaD(SHAPE_TEST_1, dtype="float32"), "image": ArrayaD(SHAPE_TEST_2, dtype="float32")}
)

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

@get_duration
def 					SCREAMING_SNAKE_CASE							(   __UpperCamelCase		,					__UpperCamelCase		,					__UpperCamelCase)			->     Tuple:
     with ArrowWriter(features=__UpperCamelCase		,					path=os.path.join(__UpperCamelCase		,					"beta.arrow")) as writer:
          for key, record in dummy_data:
               a      =      my_features.encode_example(__UpperCamelCase)
               writer.write(__UpperCamelCase)
          a  ,      a      =      writer.finalize()

@get_duration
def 					SCREAMING_SNAKE_CASE							(   __UpperCamelCase		,					__UpperCamelCase)			->     Tuple:
     a      =      datasets.Dataset.from_file(
         filename=os.path.join(__UpperCamelCase		,					"beta.arrow")		,					info=datasets.DatasetInfo(features=__UpperCamelCase))
     for _ in dataset:
          pass

@get_duration
def 					SCREAMING_SNAKE_CASE							(   __UpperCamelCase		,					__UpperCamelCase)			->     List[str]:
     a      =      datasets.Dataset.from_file(
         filename=os.path.join(__UpperCamelCase		,					"beta.arrow")		,					info=datasets.DatasetInfo(features=__UpperCamelCase))
     dataset.set_format("numpy")
     for _ in dataset:
          pass

@get_duration
def 					SCREAMING_SNAKE_CASE							(   __UpperCamelCase		,					__UpperCamelCase)			->     Tuple:
     a      =      10
     a      =      datasets.Dataset.from_file(
         filename=os.path.join(__UpperCamelCase		,					"beta.arrow")		,					info=datasets.DatasetInfo(features=__UpperCamelCase))
     for i in range(0		,					len(__UpperCamelCase)		,					__UpperCamelCase):
          a      =      dataset[i : i + batch_size]

@get_duration
def 					SCREAMING_SNAKE_CASE							(   __UpperCamelCase		,					__UpperCamelCase)			->     Any:
     a      =      10
     a      =      datasets.Dataset.from_file(
         filename=os.path.join(__UpperCamelCase		,					"beta.arrow")		,					info=datasets.DatasetInfo(features=__UpperCamelCase))
     dataset.set_format("numpy")
     for i in range(0		,					len(__UpperCamelCase)		,					__UpperCamelCase):
          a      =      dataset[i : i + batch_size]

@get_duration
def 					SCREAMING_SNAKE_CASE							(   __UpperCamelCase		,					__UpperCamelCase)			->     Optional[Any]:
     a      =      datasets.Dataset.from_file(
         filename=os.path.join(__UpperCamelCase		,					"beta.arrow")		,					info=datasets.DatasetInfo(features=__UpperCamelCase))
     for col in feats:
          a      =      dataset[col]

@get_duration
def 					SCREAMING_SNAKE_CASE							(   __UpperCamelCase		,					__UpperCamelCase)			->     int:
     a      =      datasets.Dataset.from_file(
         filename=os.path.join(__UpperCamelCase		,					"beta.arrow")		,					info=datasets.DatasetInfo(features=__UpperCamelCase))
     dataset.set_format("numpy")
     for col in feats:
          a      =      dataset[col]

def 					SCREAMING_SNAKE_CASE							(   )			->     str:
     a      =      {}
     a      =      (
         read_unformated,
         read_formatted_as_numpy,
         read_batch_unformated,
         read_batch_formatted_as_numpy,
         read_col_unformated,
         read_col_formatted_as_numpy,
     )
     with tempfile.TemporaryDirectory() as tmp_dir:
          a      =      datasets.Features({"image": ArrayaD(__UpperCamelCase		,					dtype="float32")})
          a      =      generate_examples(features=__UpperCamelCase		,					num_examples=__UpperCamelCase)
          a      =      write(__UpperCamelCase		,					__UpperCamelCase		,					__UpperCamelCase)
          for read_func in read_functions:
               a      =      read_func(__UpperCamelCase		,					__UpperCamelCase)

     with tempfile.TemporaryDirectory() as tmp_dir:
          # don't use fixed length for fair comparison
          # feats = datasets.Features(
          #     {"image": datasets.Sequence(datasets.Sequence(datasets.Value("float32"), SPEED_TEST_SHAPE[1]), SPEED_TEST_SHAPE[0])}
          # )
          a      =      datasets.Features({"image": datasets.Sequence(datasets.Sequence(datasets.Value("float32")))})
          a      =      generate_examples(
              features=__UpperCamelCase		,					num_examples=__UpperCamelCase		,					seq_shapes={"image": SPEED_TEST_SHAPE})
          a      =      write(__UpperCamelCase		,					__UpperCamelCase		,					__UpperCamelCase)
          for read_func in read_functions:
               a      =      read_func(__UpperCamelCase		,					__UpperCamelCase)

     with tempfile.TemporaryDirectory() as tmp_dir:
          # don't use fixed length for fair comparison
          # feats = datasets.Features(
          #     {"image": datasets.Sequence(datasets.Value("float32"), SPEED_TEST_SHAPE[0] * SPEED_TEST_SHAPE[1])}
          # )
          a      =      datasets.Features({"image": datasets.Sequence(datasets.Value("float32"))})
          a      =      generate_examples(
              features=__UpperCamelCase		,					num_examples=__UpperCamelCase		,					seq_shapes={"image": [SPEED_TEST_SHAPE[0] * SPEED_TEST_SHAPE[1]]}		,					)
          a      =      write(__UpperCamelCase		,					__UpperCamelCase		,					__UpperCamelCase)
          for read_func in read_functions:
               a      =      read_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_array_xd()







import doctest

import projectq
from projectq.ops import H, Measure

def 					SCREAMING_SNAKE_CASE							(   __UpperCamelCase)			->     int:
     a      =      quantum_engine.allocate_qubit()
     H | qubit
     Measure | qubit
     return int(__UpperCamelCase)


if __name__ == "__main__":
   doctest.testmod()

   # initialises a new quantum backend
   lowercase__      :			Optional[int]           =					projectq.MainEngine()

   # Generate a list of 10 random numbers
   lowercase__      :			List[str]           =					[get_random_number(quantum_engine) for _ in range(10)]

   # Flushes the quantum engine from memory
   quantum_engine.flush()

   print("Random numbers", random_numbers_list)

