






"""simple docstring"""
import json
import os
import tempfile

import datasets
from utils import generate_example_dataset, get_duration


_lowerCAmelCase         =							50_000
_lowerCAmelCase         =							5_000

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






@get_duration
def 			__UpperCamelCase	(		snake_case__   ,   snake_case__					):
	for i in range(snake_case__					):
		A_		:						List[str]	    =   dataset[i]






@get_duration
def 			__UpperCamelCase	(		snake_case__   ,   snake_case__   ,   snake_case__					):
	for i in range(0   ,   len(snake_case__					)   ,   snake_case__					):
		A_		:						Optional[int]	    =   dataset[i : i + batch_size]






@get_duration
def 			__UpperCamelCase	(		snake_case__   ,   snake_case__   ,   snake_case__					):
	with dataset.formatted_as(type=snake_case__					):
		for i in range(snake_case__					):
			A_		:						Tuple	    =   dataset[i]






@get_duration
def 			__UpperCamelCase	(		snake_case__   ,   snake_case__   ,   snake_case__   ,   snake_case__					):
	with dataset.formatted_as(type=snake_case__					):
		for i in range(0   ,   snake_case__   ,   snake_case__					):
			A_		:						Union[str, Any]	    =   dataset[i : i + batch_size]






def 			__UpperCamelCase	(		):
	A_		:						str	    =   {"""num examples""": SPEED_TEST_N_EXAMPLES}
	A_		:						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""": 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}),
	]

	A_		:						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_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"""					)
		A_		:						int	    =   datasets.Features(
		    {"""list""": datasets.Sequence(datasets.Value("""float32"""					)					), """numbers""": datasets.Value("""float32"""					)}					)
		A_		:						List[str]	    =   generate_example_dataset(
		    os.path.join(snake_case__   ,   """dataset.arrow"""					)   ,   snake_case__   ,   num_examples=snake_case__   ,   seq_shapes={"""list""": (100,)}   ,   )
		print("""first set of iterations"""					)
		for func, kwargs in functions:
			print(func.__name__   ,   str(snake_case__					)					)
			A_		:						Union[str, Any]	    =   func(snake_case__   ,   **snake_case__					)

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

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


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









"""simple docstring"""
_lowerCAmelCase         =							{"a": ["c", "b"], "b": ["d", "e"], "c": [], "d": [], "e": []}
_lowerCAmelCase         =							["a", "b", "c", "d", "e"]






def 			__UpperCamelCase	(		snake_case__   ,   snake_case__   ,   snake_case__					):
	A_		:						int	    =   start
	# add current to visited
	visited.append(snake_case__					)
	A_		:						Any	    =   edges[current]
	for neighbor in neighbors:
		# if neighbor not in visited, visit
		if neighbor not in visited:
			A_		:						Optional[Any]	    =   topological_sort(snake_case__   ,   snake_case__   ,   snake_case__					)
    # if all neighbors visited add current to sort
	sort.append(snake_case__					)
	# if all vertices haven't been visited select a new one to visit
	if len(snake_case__					) != len(snake_case__					):
		for vertice in vertices:
			if vertice not in visited:
				A_		:						Optional[Any]	    =   topological_sort(snake_case__   ,   snake_case__   ,   snake_case__					)
    # return sort
	return sort


if __name__ == "__main__":
							_lowerCAmelCase         =							topological_sort("a", [], [])
							print(sort)

