

import contextlib
from multiprocessing import Pool, RLock

from tqdm.auto import tqdm

from ..utils import experimental, logging


a_      =     logging.get_logger(__name__)






class _UpperCamelCase				:
			'''simple docstring'''







			lowerCamelCase__							     =None

@experimental
def 			lowerCamelCase__			(       _a				,      _a				,      _a				,      _a				,      _a				,      _a				,      _a):
	if ParallelBackendConfig.backend_name is None:
		return _map_with_multiprocessing_pool(
		    _a				,      _a				,      _a				,      _a				,      _a				,      _a				,      _a)

	return _map_with_joblib(_a				,      _a				,      _a				,      _a				,      _a				,      _a				,      _a)

def 			lowerCamelCase__			(       _a				,      _a				,      _a				,      _a				,      _a				,      _a				,      _a):
	SCREAMING_SNAKE_CASE :   Dict					  =							num_proc if num_proc <= len(_a) else len(_a)
	SCREAMING_SNAKE_CASE :   Optional[int]					  =							[]  # We organize the splits ourselve (contiguous splits)
	for index in range(_a):
		SCREAMING_SNAKE_CASE :   Any					  =							len(_a) // num_proc
		SCREAMING_SNAKE_CASE :   int					  =							len(_a) % num_proc
		SCREAMING_SNAKE_CASE :   List[Any]					  =							div * index + min(_a				,      _a)
		SCREAMING_SNAKE_CASE :   List[str]					  =							start + div + (1 if index < mod else 0)
		split_kwds.append((function, iterable[start:end], types, index, disable_tqdm, desc))

	if len(_a) != sum(len(i[1]) for i in split_kwds):
		raise ValueError(
		    f"Error dividing inputs iterable among processes. "
		    f"Total number of objects {len(_a)}, "
		    f"length: {sum(len(i[1]) for i in split_kwds)}")

	logger.info(
	    f"Spawning {num_proc} processes for {len(_a)} objects in slices of {[len(i[1]) for i in split_kwds]}")
	SCREAMING_SNAKE_CASE  ,SCREAMING_SNAKE_CASE :   Union[str, Any]					  =							None, None
	if not disable_tqdm:
		SCREAMING_SNAKE_CASE  ,SCREAMING_SNAKE_CASE :   Optional[int]					  =							(RLock(),), tqdm.set_lock
	with Pool(_a				,      initargs=_a				,      initializer=_a) as pool:
		SCREAMING_SNAKE_CASE :   List[str]					  =							pool.map(_a				,      _a)
	logger.info(f"Finished {num_proc} processes")
	SCREAMING_SNAKE_CASE :   str					  =							[obj for proc_res in mapped for obj in proc_res]
	logger.info(f"Unpacked {len(_a)} objects")

	return mapped

def 			lowerCamelCase__			(       _a				,      _a				,      _a				,      _a				,      _a				,      _a				,      _a):
	# progress bar is not yet supported for _map_with_joblib, because tqdm couldn't accurately be applied to joblib,
	# and it requires monkey-patching joblib internal classes which is subject to change
	import joblib

	with joblib.parallel_backend(ParallelBackendConfig.backend_name				,      n_jobs=_a):
		return joblib.Parallel()(
		    joblib.delayed(_a)((function, obj, types, None, True, None)) for obj in iterable)


@experimental
@contextlib.contextmanager
def 			lowerCamelCase__			(       _a):
	SCREAMING_SNAKE_CASE :   int					  =							backend_name

	if backend_name == "spark":
		from joblibspark import register_spark

		register_spark()

		# TODO: call create_cache_and_write_probe if "download" in steps
		# TODO: raise NotImplementedError when Dataset.map etc is called

	try:
		yield
	finally:
		SCREAMING_SNAKE_CASE :   Optional[int]					  =							None


from math import factorial, pi

def 			lowerCamelCase__			(       _a				,      _a = 30):

	if not isinstance(_a				,      (int, float)):
		raise ValueError("maclaurin_sin() requires either an int or float for theta")

	if not isinstance(_a				,      _a) or accuracy <= 0:
		raise ValueError("maclaurin_sin() requires a positive int for accuracy")

	SCREAMING_SNAKE_CASE :   int					  =							float(_a)
	SCREAMING_SNAKE_CASE :   Dict					  =							theta // (2 * pi)
	theta -= 2 * div * pi
	return sum(
	    (-1) ** r * theta ** (2 * r + 1) / factorial(2 * r + 1) for r in range(_a))

def 			lowerCamelCase__			(       _a				,      _a = 30):

	if not isinstance(_a				,      (int, float)):
		raise ValueError("maclaurin_cos() requires either an int or float for theta")

	if not isinstance(_a				,      _a) or accuracy <= 0:
		raise ValueError("maclaurin_cos() requires a positive int for accuracy")

	SCREAMING_SNAKE_CASE :   str					  =							float(_a)
	SCREAMING_SNAKE_CASE :   Any					  =							theta // (2 * pi)
	theta -= 2 * div * pi
	return sum((-1) ** r * theta ** (2 * r) / factorial(2 * r) for r in range(_a))


if __name__ == "__main__":
			import doctest

			doctest.testmod()

			print(maclaurin_sin(10))
			print(maclaurin_sin(-10))
			print(maclaurin_sin(10, 15))
			print(maclaurin_sin(-10, 15))

			print(maclaurin_cos(5))
			print(maclaurin_cos(-5))
			print(maclaurin_cos(10, 15))
			print(maclaurin_cos(-10, 15))