




'''simple docstring'''





from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


_SCREAMING_SNAKE_CASE							:     Any   	=				{'configuration_visual_bert': ['VISUAL_BERT_PRETRAINED_CONFIG_ARCHIVE_MAP', 'VisualBertConfig']}

try:
		if not is_torch_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		_SCREAMING_SNAKE_CASE							:     int   	=				[
		    'VISUAL_BERT_PRETRAINED_MODEL_ARCHIVE_LIST',
		    'VisualBertForMultipleChoice',
		    'VisualBertForPreTraining',
		    'VisualBertForQuestionAnswering',
		    'VisualBertForRegionToPhraseAlignment',
		    'VisualBertForVisualReasoning',
		    'VisualBertLayer',
		    'VisualBertModel',
		    'VisualBertPreTrainedModel',
		]


if TYPE_CHECKING:
		from .configuration_visual_bert import VISUAL_BERT_PRETRAINED_CONFIG_ARCHIVE_MAP, VisualBertConfig

		try:
				if not is_torch_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_visual_bert import (
				    VISUAL_BERT_PRETRAINED_MODEL_ARCHIVE_LIST,
				    VisualBertForMultipleChoice,
				    VisualBertForPreTraining,
				    VisualBertForQuestionAnswering,
				    VisualBertForRegionToPhraseAlignment,
				    VisualBertForVisualReasoning,
				    VisualBertLayer,
				    VisualBertModel,
				    VisualBertPreTrainedModel,
				)


else:
		import sys

		_SCREAMING_SNAKE_CASE							:     int   	=				_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   UpperCamelCase_(			snake_case							: int							,       snake_case							: bool = False  ):

							'''simple docstring'''






							if device_specific:
														seed += AcceleratorState().process_index
							random.seed(snake_case  )
							np.random.seed(snake_case  )
							torch.manual_seed(snake_case  )
							if is_xpu_available():
														torch.xpu.manual_seed_all(snake_case  )
							elif is_npu_available():
														torch.npu.manual_seed_all(snake_case  )
							else:
														torch.cuda.manual_seed_all(snake_case  )
							# ^^ safe to call this function even if cuda is not available
							if is_tpu_available():
														xm.set_rng_state(snake_case  )


def   UpperCamelCase_(			snake_case							: Optional[RNGType] = None							,       snake_case							: Optional[torch.Generator] = None  ):

							'''simple docstring'''






							if rng_type == RNGType.TORCH:
														snake_case_			      =   torch.get_rng_state()
							elif rng_type == RNGType.CUDA:
														snake_case_			      =   torch.cuda.get_rng_state()
							elif rng_type == RNGType.XLA:
														assert is_tpu_available(), "Can't synchronize XLA seeds on an environment without TPUs."
														snake_case_			      =   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."
														snake_case_			      =   torch.npu.get_rng_state()
							elif rng_type == RNGType.XPU:
														assert is_xpu_available(), "Can't synchronize XPU seeds on an environment without XPUs."
														snake_case_			      =   torch.xpu.get_rng_state()
							elif rng_type == RNGType.GENERATOR:
														assert generator is not None, "Need a generator to synchronize its seed."
														snake_case_			      =   generator.get_state()

							# Broadcast the rng state from device 0 to other devices
							snake_case_			      =   AcceleratorState()
							if state.distributed_type == DistributedType.TPU:
														snake_case_			      =   rng_state.to(xm.xla_device()  )
														xm.collective_broadcast([rng_state]  )
														xm.mark_step()
														snake_case_			      =   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
							):
														snake_case_			      =   rng_state.to(state.device  )
														torch.distributed.broadcast(snake_case							,       0  )
														snake_case_			      =   rng_state.cpu()
							elif state.distributed_type == DistributedType.MULTI_CPU:
														torch.distributed.broadcast(snake_case							,       0  )

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




def   UpperCamelCase_(			snake_case							: List[Union[str, RNGType]]							,       snake_case							: Optional[torch.Generator] = None  ):

							'''simple docstring'''






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




