






'''simple docstring'''
from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available


__lowerCAmelCase							={
    "configuration_realm": ["REALM_PRETRAINED_CONFIG_ARCHIVE_MAP", "RealmConfig"],
    "tokenization_realm": ["RealmTokenizer"],
}

try:
						if not is_tokenizers_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						__lowerCAmelCase							=["RealmTokenizerFast"]

try:
						if not is_torch_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						__lowerCAmelCase							=[
						    "REALM_PRETRAINED_MODEL_ARCHIVE_LIST",
						    "RealmEmbedder",
						    "RealmForOpenQA",
						    "RealmKnowledgeAugEncoder",
						    "RealmPreTrainedModel",
						    "RealmReader",
						    "RealmScorer",
						    "load_tf_weights_in_realm",
						]
						__lowerCAmelCase							=["RealmRetriever"]


if TYPE_CHECKING:
						from .configuration_realm import REALM_PRETRAINED_CONFIG_ARCHIVE_MAP, RealmConfig
						from .tokenization_realm import RealmTokenizer

						try:
												if not is_tokenizers_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .tokenization_realm import RealmTokenizerFast

						try:
												if not is_torch_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .modeling_realm import (
												    REALM_PRETRAINED_MODEL_ARCHIVE_LIST,
												    RealmEmbedder,
												    RealmForOpenQA,
												    RealmKnowledgeAugEncoder,
												    RealmPreTrainedModel,
												    RealmReader,
												    RealmScorer,
												    load_tf_weights_in_realm,
												)
												from .retrieval_realm import RealmRetriever


else:
						import sys

						__lowerCAmelCase							=_LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)









'''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 							a	(   _UpperCAmelCase					,  _UpperCAmelCase = False )				->				List[str]:






					"""simple docstring"""







					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 							a	(   _UpperCAmelCase = None					,  _UpperCAmelCase = None )				->				Tuple:






					"""simple docstring"""







					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 							a	(   _UpperCAmelCase					,  _UpperCAmelCase = None )				->				Any:






					"""simple docstring"""







					for rng_type in rng_types:
										synchronize_rng_state(RNGType(_UpperCAmelCase )					,  generator=_UpperCAmelCase )

