
import json
import os
import tempfile

import datasets
from utils import generate_example_dataset, get_duration


lowercase  :					Optional[Any]													=       50000
lowercase  :					Dict													=       5000

lowercase  :					Optional[Any]													=       os.path.split(__file__)
lowercase  :					Dict													=       os.path.join(RESULTS_BASEPATH, 'results', RESULTS_FILENAME.replace('.py', '.json'))




@get_duration
def 	_SCREAMING_SNAKE_CASE					(		_lowerCamelCase :			datasets.Dataset						,  _lowerCamelCase :			Tuple)					->    Union[str, Any]:






							'''simple docstring'''





							for i in range(_lowerCamelCase):
														__UpperCamelCase	:   List[str]			   =					dataset[i]




@get_duration
def 	_SCREAMING_SNAKE_CASE					(		_lowerCamelCase :			datasets.Dataset						,  _lowerCamelCase :			Any						,  _lowerCamelCase :			Dict)					->    str:






							'''simple docstring'''





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




@get_duration
def 	_SCREAMING_SNAKE_CASE					(		_lowerCamelCase :			datasets.Dataset						,  _lowerCamelCase :			Tuple						,  _lowerCamelCase :			List[str])					->    Dict:






							'''simple docstring'''





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




@get_duration
def 	_SCREAMING_SNAKE_CASE					(		_lowerCamelCase :			datasets.Dataset						,  _lowerCamelCase :			Any						,  _lowerCamelCase :			Optional[Any]						,  _lowerCamelCase :			List[Any])					->    List[Any]:






							'''simple docstring'''





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




def 	_SCREAMING_SNAKE_CASE					(		)					->    Optional[Any]:






							'''simple docstring'''





							__UpperCamelCase	:   int			   =					{"num examples": SPEED_TEST_N_EXAMPLES}
							__UpperCamelCase	:   Tuple			   =					[
							    (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": 1_000}),
							    (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": 1_000}),
							]

							__UpperCamelCase	:   int			   =					[
							    (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": 1_000}),
							    (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": 1_000}),
							]
							with tempfile.TemporaryDirectory() as tmp_dir:
														print("generating dataset")
														__UpperCamelCase	:   List[Any]			   =					datasets.Features(
														    {"list": datasets.Sequence(datasets.Value("float32")), "numbers": datasets.Value("float32")})
														__UpperCamelCase	:   Optional[int]			   =					generate_example_dataset(
														    os.path.join(_lowerCamelCase						,  "dataset.arrow")						,  _lowerCamelCase						,  num_examples=_lowerCamelCase						,  seq_shapes={"list": (100,)}						,  )
														print("first set of iterations")
														for func, kwargs in functions:
																					print(func.__name__						,  str(_lowerCamelCase))
																					__UpperCamelCase	:   Optional[int]			   =					func(_lowerCamelCase						,  **_lowerCamelCase)

														print("shuffling dataset")
														__UpperCamelCase	:   Union[str, Any]			   =					dataset.shuffle()
														print("Second set of iterations (after shuffling")
														for func, kwargs in functions_shuffled:
																					print("shuffled "						,  func.__name__						,  str(_lowerCamelCase))
																					__UpperCamelCase	:   int			   =					func(
																					    _lowerCamelCase						,  **_lowerCamelCase)

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


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





from collections.abc import Callable

import numpy as np

def 			_SCREAMING_SNAKE_CASE			(							lowercase	:       Callable	,							lowercase	:       float	,							lowercase	:       float	,							lowercase	:       float	,							lowercase	:       float     ):


      '''simple docstring'''



      lowerCamelCase_		     =    int(np.ceil((x_end - xa) / step_size     )     )
      lowerCamelCase_		     =    np.zeros((n + 1,)     )
      lowerCamelCase_		     =    ya
      lowerCamelCase_		     =    xa

      for k in range(lowercase     ):
            lowerCamelCase_		     =    y[k] + step_size * ode_func(lowercase	,							y[k]     )
            lowerCamelCase_		     =    y[k] + (
                (step_size / 2) * (ode_func(lowercase	,							y[k]     ) + ode_func(x + step_size	,							lowercase     ))
            )
            x += step_size

      return y


if __name__ == "__main__":
 import doctest

 doctest.testmod()



