


'''simple docstring'''



import contextlib
from multiprocessing import Pool, RLock

from tqdm.auto import tqdm

from ..utils import experimental, logging


__lowercase	:						List[Any]       =	logging.get_logger(__name__)





class 							__UpperCamelCase				:
	A_				=    None






@experimental
def   lowerCamelCase							(_SCREAMING_SNAKE_CASE      :   str						,      _SCREAMING_SNAKE_CASE      :   Any						,      _SCREAMING_SNAKE_CASE      :   int						,      _SCREAMING_SNAKE_CASE      :   Dict						,      _SCREAMING_SNAKE_CASE      :   Dict						,      _SCREAMING_SNAKE_CASE      :   Tuple						,      _SCREAMING_SNAKE_CASE      :   Union[str, Any]   ):
					if ParallelBackendConfig.backend_name is None:
										return _map_with_multiprocessing_pool(
										    _SCREAMING_SNAKE_CASE						,      _SCREAMING_SNAKE_CASE						,      _SCREAMING_SNAKE_CASE						,      _SCREAMING_SNAKE_CASE						,      _SCREAMING_SNAKE_CASE						,      _SCREAMING_SNAKE_CASE						,      _SCREAMING_SNAKE_CASE   )

					return _map_with_joblib(_SCREAMING_SNAKE_CASE						,      _SCREAMING_SNAKE_CASE						,      _SCREAMING_SNAKE_CASE						,      _SCREAMING_SNAKE_CASE						,      _SCREAMING_SNAKE_CASE						,      _SCREAMING_SNAKE_CASE						,      _SCREAMING_SNAKE_CASE   )






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

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

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

					return mapped






def   lowerCamelCase							(_SCREAMING_SNAKE_CASE      :   Union[str, Any]						,      _SCREAMING_SNAKE_CASE      :   Tuple						,      _SCREAMING_SNAKE_CASE      :   List[Any]						,      _SCREAMING_SNAKE_CASE      :   Tuple						,      _SCREAMING_SNAKE_CASE      :   str						,      _SCREAMING_SNAKE_CASE      :   Optional[int]						,      _SCREAMING_SNAKE_CASE      :   Optional[int]   ):
					# 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=_SCREAMING_SNAKE_CASE   ):
										return joblib.Parallel()(
										    joblib.delayed(_SCREAMING_SNAKE_CASE   )((function, obj, types, None, True, None)   ) for obj in iterable   )






@experimental
@contextlib.contextmanager
def   lowerCamelCase							(_SCREAMING_SNAKE_CASE      :   str   ):
					__a			:       Union[str, Any]					    =  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:
										__a			:       Optional[int]					    =  None







'''simple docstring'''



import inspect
import os
import unittest

import torch

import accelerate
from accelerate import debug_launcher
from accelerate.test_utils import (
    execute_subprocess_async,
    require_cpu,
    require_huggingface_suite,
    require_multi_gpu,
    require_single_gpu,
)
from accelerate.utils import patch_environment





@require_huggingface_suite
class 							__UpperCamelCase				(		unittest.TestCase					):




	def       __UpperCAmelCase							(   self							):

						'''simple docstring'''





						__a			:       List[Any]					    =  inspect.getfile(accelerate.test_utils							)
						__a			:       List[str]					    =  os.path.sep.join(
						    mod_file.split(os.path.sep							)[:-1] + ['scripts', 'external_deps', 'test_metrics.py']							)

						from accelerate.test_utils.scripts.external_deps import test_metrics  # noqa: F401

						__a			:       Union[str, Any]					    =  test_metrics




	@require_cpu
	def       __UpperCAmelCase							(   self							):

						'''simple docstring'''





						debug_launcher(self.test_metrics.main						,				num_processes=1							)




	@require_cpu
	def       __UpperCAmelCase							(   self							):

						'''simple docstring'''





						debug_launcher(self.test_metrics.main							)




	@require_single_gpu
	def       __UpperCAmelCase							(   self							):

						'''simple docstring'''





						self.test_metrics.main()





	@require_multi_gpu
	def       __UpperCAmelCase							(   self							):

						'''simple docstring'''





						print(f"""Found {torch.cuda.device_count()} devices."""							)
						__a			:       List[Any]					    =  ['torchrun', f"""--nproc_per_node={torch.cuda.device_count()}""", self.test_file_path]
						with patch_environment(omp_num_threads=1							):
											execute_subprocess_async(__a						,				env=os.environ.copy()							)



