
import importlib
import sys
from argparse import REMAINDER, ArgumentParser
from pathlib import Path

import torch_xla.distributed.xla_multiprocessing as xmp





def 				_lowerCamelCase      (       ):



				'''simple docstring'''







				A  :	List[str]				=					ArgumentParser(
				    description=(
				        '''PyTorch TPU distributed training launch '''
				        '''helper utility that will spawn up '''
				        '''multiple distributed processes'''
				    )		)

				# Optional arguments for the launch helper
				parser.add_argument('''--num_cores'''					,						type=_SCREAMING_SNAKE_CASE					,						default=1					,						help='''Number of TPU cores to use (1 or 8).'''		)

				# positional
				parser.add_argument(
				    '''training_script'''					,						type=_SCREAMING_SNAKE_CASE					,						help=(
				        '''The full path to the single TPU training '''
				        '''program/script to be launched in parallel, '''
				        '''followed by all the arguments for the '''
				        '''training script'''
				    )					,						)

				# rest from the training program
				parser.add_argument('''training_script_args'''					,						nargs=_SCREAMING_SNAKE_CASE		)

				return parser.parse_args()





def 				_lowerCamelCase      (       ):



				'''simple docstring'''







				A  :	List[Any]				=					parse_args()

				# Import training_script as a module.
				A  :	Dict				=					Path(args.training_script		)
				sys.path.append(str(script_fpath.parent.resolve()		)		)
				A  :	Dict				=					script_fpath.stem
				A  :	str				=					importlib.import_module(_SCREAMING_SNAKE_CASE		)

				# Patch sys.argv
				A  :	Any				=					[args.training_script] + args.training_script_args + ["""--tpu_num_cores""", str(args.num_cores		)]
				xmp.spawn(mod._mp_fn					,						args=()					,						nprocs=args.num_cores		)


if __name__ == "__main__":
					main()
import unittest

from transformers import load_tool
from transformers.utils import is_torch_available


if is_torch_available():
	import torch

from transformers.testing_utils import require_torch

from .test_tools_common import ToolTesterMixin

@require_torch
class        UpperCamelCase						(				unittest.TestCase	,	__a				):





						def        A_							(self						)				->				Tuple:
												UpperCamelCase_		:							Any  =		load_tool("""text-to-speech"""						)
												self.tool.setup()





						def        A_							(self						)				->				Dict:
												# SpeechT5 isn't deterministic
												torch.manual_seed(0						)
												UpperCamelCase_		:							Optional[Any]  =		self.tool("""hey"""						)
												UpperCamelCase_		:							Optional[Any]  =		result.to_raw()
												self.assertTrue(
												    torch.allclose(
												        resulting_tensor[:3]			,     torch.tensor([-0.0_005_966_668_832_115_829, -0.0_003_657_640_190_795_064, -0.00_013_439_502_799_883_485]						)			,     )						)





						def        A_							(self						)				->				Dict:
												# SpeechT5 isn't deterministic
												torch.manual_seed(0						)
												UpperCamelCase_		:							Any  =		self.tool("""hey"""						)
												UpperCamelCase_		:							Tuple  =		result.to_raw()
												self.assertTrue(
												    torch.allclose(
												        resulting_tensor[:3]			,     torch.tensor([-0.0_005_966_668_832_115_829, -0.0_003_657_640_190_795_064, -0.00_013_439_502_799_883_485]						)			,     )						)






