



"""simple docstring"""







import random
from typing import List, Optional, Union

import numpy as np
import torch

from ..state import AcceleratorState
from .constants import CUDA_DISTRIBUTED_TYPES
from .dataclasses import DistributedType, RNGType
from .imports import is_npu_available, is_tpu_available, is_xpu_available


if is_tpu_available(check_device=False):
							import torch_xla.core.xla_model as xm

def 	lowerCAmelCase__      (  lowerCamelCase__					,   lowerCamelCase__ = False      )			->				int:
							if device_specific:
														seed += AcceleratorState().process_index
							random.seed(UpperCamelCase__      )
							np.random.seed(UpperCamelCase__      )
							torch.manual_seed(UpperCamelCase__      )
							if is_xpu_available():
														torch.xpu.manual_seed_all(UpperCamelCase__      )
							elif is_npu_available():
														torch.npu.manual_seed_all(UpperCamelCase__      )
							else:
														torch.cuda.manual_seed_all(UpperCamelCase__      )
							# ^^ safe to call this function even if cuda is not available
							if is_tpu_available():
														xm.set_rng_state(UpperCamelCase__      )

def 	lowerCAmelCase__      (  lowerCamelCase__ = None					,   lowerCamelCase__ = None      )			->				Tuple:
							# Get the proper rng state
							if rng_type == RNGType.TORCH:
														A    =       torch.get_rng_state()
							elif rng_type == RNGType.CUDA:
														A    =       torch.cuda.get_rng_state()
							elif rng_type == RNGType.XLA:
														assert is_tpu_available(), "Can't synchronize XLA seeds on an environment without TPUs."
														A    =       torch.tensor(xm.get_rng_state()      )
							elif rng_type == RNGType.NPU:
														assert is_npu_available(), "Can't synchronize NPU seeds on an environment without NPUs."
														A    =       torch.npu.get_rng_state()
							elif rng_type == RNGType.XPU:
														assert is_xpu_available(), "Can't synchronize XPU seeds on an environment without XPUs."
														A    =       torch.xpu.get_rng_state()
							elif rng_type == RNGType.GENERATOR:
														assert generator is not None, "Need a generator to synchronize its seed."
														A    =       generator.get_state()

							# Broadcast the rng state from device 0 to other devices
							A    =       AcceleratorState()
							if state.distributed_type == DistributedType.TPU:
														A    =       rng_state.to(xm.xla_device()      )
														xm.collective_broadcast([rng_state]      )
														xm.mark_step()
														A    =       rng_state.cpu()
							elif (
							    state.distributed_type in CUDA_DISTRIBUTED_TYPES
							    or state.distributed_type == DistributedType.MULTI_NPU
							    or state.distributed_type == DistributedType.MULTI_XPU
							):
														A    =       rng_state.to(state.device      )
														torch.distributed.broadcast(UpperCamelCase__					,   0      )
														A    =       rng_state.cpu()
							elif state.distributed_type == DistributedType.MULTI_CPU:
														torch.distributed.broadcast(UpperCamelCase__					,   0      )

							# Set the broadcast rng state
							if rng_type == RNGType.TORCH:
														torch.set_rng_state(UpperCamelCase__      )
							elif rng_type == RNGType.CUDA:
														torch.cuda.set_rng_state(UpperCamelCase__      )
							elif rng_type == RNGType.NPU:
														torch.npu.set_rng_state(UpperCamelCase__      )
							elif rng_type == RNGType.XPU:
														torch.xpu.set_rng_state(UpperCamelCase__      )
							elif rng_type == RNGType.XLA:
														xm.set_rng_state(rng_state.item()      )
							elif rng_type == RNGType.GENERATOR:
														generator.set_state(UpperCamelCase__      )

def 	lowerCAmelCase__      (  lowerCamelCase__					,   lowerCamelCase__ = None      )			->				Union[str, Any]:
							for rng_type in rng_types:
														synchronize_rng_state(RNGType(UpperCamelCase__      )					,   generator=UpperCamelCase__      )








'''simple docstring'''



from typing import TYPE_CHECKING

from ...utils import _LazyModule


_lowercase       :						List[str]						={"tokenization_bert_japanese": ["BertJapaneseTokenizer", "CharacterTokenizer", "MecabTokenizer"]}


if TYPE_CHECKING:
				from .tokenization_bert_japanese import BertJapaneseTokenizer, CharacterTokenizer, MecabTokenizer

else:
				import sys

				_lowercase       :						Dict						=_LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)




