
'''simple docstring'''


import inspect
import os
import unittest

import torch

import accelerate
from accelerate import Accelerator
from accelerate.test_utils import execute_subprocess_async, require_multi_gpu
from accelerate.utils import patch_environment

class 	__lowercase				(  unittest.TestCase    ):

			def        UpperCAmelCase__ (self      ):
								lowerCamelCase_					:      Union[str, Any]     =		inspect.getfile(accelerate.test_utils      )
								lowerCamelCase_					:      List[str]     =		os.path.sep.join(mod_file.split(os.path.sep      )[:-1] + ['''scripts''', '''test_script.py''']      )
								lowerCamelCase_					:      List[str]     =		os.path.sep.join(
								    mod_file.split(os.path.sep      )[:-1] + ['''scripts''', '''test_distributed_data_loop.py''']      )
								lowerCamelCase_					:      List[Any]     =		os.path.sep.join(mod_file.split(os.path.sep      )[:-1] + ['''scripts''', '''test_ops.py''']      )

			@require_multi_gpu
			def        UpperCAmelCase__ (self      ):
								print(F"""Found {torch.cuda.device_count()} devices."""      )
								lowerCamelCase_					:      Tuple     =		['''torchrun''', F"""--nproc_per_node={torch.cuda.device_count()}""", self.test_file_path]
								with patch_environment(omp_num_threads=1      ):
													execute_subprocess_async(UpperCamelCase__			,   env=os.environ.copy()      )

			@require_multi_gpu
			def        UpperCAmelCase__ (self      ):
								print(F"""Found {torch.cuda.device_count()} devices."""      )
								lowerCamelCase_					:      str     =		['''torchrun''', F"""--nproc_per_node={torch.cuda.device_count()}""", self.operation_file_path]
								print(F"""Command: {cmd}"""      )
								with patch_environment(omp_num_threads=1      ):
													execute_subprocess_async(UpperCamelCase__			,   env=os.environ.copy()      )

			@require_multi_gpu
			def        UpperCAmelCase__ (self      ):
								lowerCamelCase_					:      Tuple     =		['''torchrun''', F"""--nproc_per_node={torch.cuda.device_count()}""", inspect.getfile(self.__class__      )]
								with patch_environment(omp_num_threads=1      ):
													execute_subprocess_async(UpperCamelCase__			,   env=os.environ.copy()      )






			@require_multi_gpu
			def        UpperCAmelCase__ (self      ):
								print(F"""Found {torch.cuda.device_count()} devices, using 2 devices only"""      )
								lowerCamelCase_					:      Optional[Any]     =		['''torchrun''', F"""--nproc_per_node={torch.cuda.device_count()}""", self.data_loop_file_path]
								with patch_environment(omp_num_threads=1			,   cuda_visible_devices='''0,1'''      ):
													execute_subprocess_async(UpperCamelCase__			,   env=os.environ.copy()      )


if __name__ == "__main__":
					__lowercase							:   Dict					=     Accelerator()
					__lowercase							:   str					=     (accelerator.state.process_index + 2, 10)
					__lowercase							:   List[str]					=     torch.randint(0, 10, shape).to(accelerator.device)

					__lowercase							:   Any					=     ''''''

					__lowercase							:   Dict					=     accelerator.pad_across_processes(tensor)
					if tensora.shape[0] != accelerator.state.num_processes + 1:
										error_msg += f"Found shape {tensora.shape} but should have {accelerator.state.num_processes + 1} at dim 0."
					if not torch.equal(tensora[: accelerator.state.process_index + 2], tensor):
										error_msg += "Tensors have different values."
					if not torch.all(tensora[accelerator.state.process_index + 2 :] == 0):
										error_msg += "Padding was not done with the right value (0)."

					__lowercase							:   int					=     accelerator.pad_across_processes(tensor, pad_first=True)
					if tensora.shape[0] != accelerator.state.num_processes + 1:
										error_msg += f"Found shape {tensora.shape} but should have {accelerator.state.num_processes + 1} at dim 0."
					__lowercase							:   Union[str, Any]					=     accelerator.state.num_processes - accelerator.state.process_index - 1
					if not torch.equal(tensora[index:], tensor):
										error_msg += "Tensors have different values."
					if not torch.all(tensora[:index] == 0):
										error_msg += "Padding was not done with the right value (0)."

					# Raise error at the end to make sure we don't stop at the first failure.
					if len(error_msg) > 0:
										raise ValueError(error_msg)




'''simple docstring'''


from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_sentencepiece_available


__lowercase							:   int					=     {}

try:
					if not is_sentencepiece_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					__lowercase							:   Any					=     ['''BartphoTokenizer''']

if TYPE_CHECKING:
					try:
										if not is_sentencepiece_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .tokenization_bartpho import BartphoTokenizer

else:
					import sys

					__lowercase							:   Union[str, Any]					=     _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)


