"""simple docstring"""




import contextlib
from multiprocessing import Pool, RLock

from tqdm.auto import tqdm

from ..utils import experimental, logging


__lowerCAmelCase     :				Union[str, Any]    				=    logging.get_logger(__name__)

class    a_   :
					UpperCamelCase_			:						Tuple      =     None







@experimental
def 				_UpperCAmelCase		(							lowerCamelCase__      ,	lowerCamelCase__      ,	lowerCamelCase__      ,	lowerCamelCase__      ,	lowerCamelCase__      ,	lowerCamelCase__      ,	lowerCamelCase__  ):





		"""simple docstring"""




		if ParallelBackendConfig.backend_name is None:
				return _map_with_multiprocessing_pool(
				    lowerCAmelCase__      ,	lowerCAmelCase__      ,	lowerCAmelCase__      ,	lowerCAmelCase__      ,	lowerCAmelCase__      ,	lowerCAmelCase__      ,	lowerCAmelCase__  )

		return _map_with_joblib(lowerCAmelCase__      ,	lowerCAmelCase__      ,	lowerCAmelCase__      ,	lowerCAmelCase__      ,	lowerCAmelCase__      ,	lowerCAmelCase__      ,	lowerCAmelCase__  )







def 				_UpperCAmelCase		(							lowerCamelCase__      ,	lowerCamelCase__      ,	lowerCamelCase__      ,	lowerCamelCase__      ,	lowerCamelCase__      ,	lowerCamelCase__      ,	lowerCamelCase__  ):





		"""simple docstring"""




		lowerCAmelCase__   = num_proc if num_proc <= len(lowerCAmelCase__  ) else len(lowerCAmelCase__  )
		lowerCAmelCase__   = []  # We organize the splits ourselve (contiguous splits)
		for index in range(lowerCAmelCase__  ):
				lowerCAmelCase__   = len(lowerCAmelCase__  ) // num_proc
				lowerCAmelCase__   = len(lowerCAmelCase__  ) % num_proc
				lowerCAmelCase__   = div * index + min(lowerCAmelCase__      ,	lowerCAmelCase__  )
				lowerCAmelCase__   = start + div + (1 if index < mod else 0)
				split_kwds.append((function, iterable[start:end], types, index, disable_tqdm, desc)  )

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

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

		return mapped







def 				_UpperCAmelCase		(							lowerCamelCase__      ,	lowerCamelCase__      ,	lowerCamelCase__      ,	lowerCamelCase__      ,	lowerCamelCase__      ,	lowerCamelCase__      ,	lowerCamelCase__  ):





		"""simple docstring"""




		import joblib

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







@experimental
@contextlib.contextmanager
def 				_UpperCAmelCase		(							lowerCamelCase__  ):





		"""simple docstring"""




		lowerCAmelCase__   = 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:
				lowerCAmelCase__   = None






"""simple docstring"""


import timeit

import numpy as np

import datasets
from datasets.arrow_writer import ArrowWriter
from datasets.features.features import _ArrayXD






def  lowercase						(	lowerCAmelCase__						):
			def wrapper(*lowerCAmelCase__	,**lowerCAmelCase__						):
						lowerCamelCase_					=  timeit.default_timer()
						lowerCamelCase_					=  func(*lowerCAmelCase__	,**lowerCAmelCase__						)
						lowerCamelCase_					=  timeit.default_timer() - starttime
						return delta

			lowerCamelCase_					=  func.__name__

			return wrapper






def  lowercase						(	lowerCAmelCase__	,lowerCAmelCase__=100	,lowerCAmelCase__=None						):
			lowerCamelCase_					=  []
			lowerCamelCase_					=  seq_shapes or {}
			for i in range(lowerCAmelCase__						):
						lowerCamelCase_					=  {}
						for col_id, (k, v) in enumerate(features.items()						):
									if isinstance(lowerCAmelCase__	,_ArrayXD						):
												lowerCamelCase_					=  np.random.rand(*v.shape						).astype(v.dtype						)
									elif isinstance(lowerCAmelCase__	,datasets.Value						):
												if v.dtype == "string":
															lowerCamelCase_					=  '''The small grey turtle was surprisingly fast when challenged.'''
												else:
															lowerCamelCase_					=  np.random.randint(10	,size=1						).astype(v.dtype						).item()
									elif isinstance(lowerCAmelCase__	,datasets.Sequence						):
												while isinstance(lowerCAmelCase__	,datasets.Sequence						):
															lowerCamelCase_					=  v.feature
												lowerCamelCase_					=  seq_shapes[k]
												lowerCamelCase_					=  np.random.rand(*lowerCAmelCase__						).astype(v.dtype						)
									lowerCamelCase_					=  data

						dummy_data.append((i, example)						)

			return dummy_data






def  lowercase						(	lowerCAmelCase__	,lowerCAmelCase__	,lowerCAmelCase__=100	,lowerCAmelCase__=None						):
			lowerCamelCase_					=  generate_examples(lowerCAmelCase__	,num_examples=lowerCAmelCase__	,seq_shapes=lowerCAmelCase__						)

			with ArrowWriter(features=lowerCAmelCase__	,path=lowerCAmelCase__						) as writer:
						for key, record in dummy_data:
									lowerCamelCase_					=  features.encode_example(lowerCAmelCase__						)
									writer.write(lowerCAmelCase__						)

						lowerCamelCase_		, lowerCamelCase_					=  writer.finalize()

			if not num_final_examples == num_examples:
						raise ValueError(
						    f"Error writing the dataset, wrote {num_final_examples} examples but should have written {num_examples}."						)

			lowerCamelCase_					=  datasets.Dataset.from_file(filename=lowerCAmelCase__	,info=datasets.DatasetInfo(features=lowerCAmelCase__						)						)

			return dataset



