






"""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_		:    Any						     = dataset[i]






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






def       __UpperCamelCase						(		):
	A_		:    List[Any]						     = {"""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_		:    List[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""": 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_		:    Optional[Any]						     = datasets.Features(
		    {"""list""": datasets.Sequence(datasets.Value("""float32"""       )       ), """numbers""": datasets.Value("""float32"""       )}       )
		A_		:    Optional[int]						     = 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_		:    Optional[Any]						     = func(snake_case__ , **snake_case__       )

		print("""shuffling dataset"""       )
		A_		:    Union[str, 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"""
def       __UpperCamelCase						(		snake_case__ = 200       ):
	A_		:    Union[str, Any]						     = [1, 2, 5, 10, 20, 50, 100, 200]
	A_		:    int						     = [0] * (pence + 1)
	A_		:    Tuple						     = 1  # base case: 1 way to make 0 pence

	for coin in coins:
		for i in range(snake_case__ , pence + 1 , 1       ):
			number_of_ways[i] += number_of_ways[i - coin]
	return number_of_ways[pence]


if __name__ == "__main__":
							assert solution(200) == 73_682

