


"""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    (					_lowercase   :      int ,      _lowercase   :      bool = False  )	->   Optional[int]:


	"""simple docstring"""




	if device_specific:
		seed += AcceleratorState().process_index
	random.seed(_lowercase  )
	np.random.seed(_lowercase  )
	torch.manual_seed(_lowercase  )
	if is_xpu_available():
		torch.xpu.manual_seed_all(_lowercase  )
	elif is_npu_available():
		torch.npu.manual_seed_all(_lowercase  )
	else:
		torch.cuda.manual_seed_all(_lowercase  )
	# ^^ safe to call this function even if cuda is not available
	if is_tpu_available():
		xm.set_rng_state(_lowercase  )
def     UpperCAmelCase    (					_lowercase   :      Optional[RNGType] = None ,      _lowercase   :      Optional[torch.Generator] = None  )	->   List[Any]:


	"""simple docstring"""




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

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

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





def     UpperCAmelCase    (					_lowercase   :      List[Union[str, RNGType]] ,      _lowercase   :      Optional[torch.Generator] = None  )	->   str:


	"""simple docstring"""




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



"""simple docstring"""



from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


lowercase_							 =       {
    'configuration_prophetnet': ['PROPHETNET_PRETRAINED_CONFIG_ARCHIVE_MAP', 'ProphetNetConfig'],
    'tokenization_prophetnet': ['ProphetNetTokenizer'],
}

try:
		if not is_torch_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		lowercase_							 =       [
		    'PROPHETNET_PRETRAINED_MODEL_ARCHIVE_LIST',
		    'ProphetNetDecoder',
		    'ProphetNetEncoder',
		    'ProphetNetForCausalLM',
		    'ProphetNetForConditionalGeneration',
		    'ProphetNetModel',
		    'ProphetNetPreTrainedModel',
		]


if TYPE_CHECKING:
		from .configuration_prophetnet import PROPHETNET_PRETRAINED_CONFIG_ARCHIVE_MAP, ProphetNetConfig
		from .tokenization_prophetnet import ProphetNetTokenizer

		try:
				if not is_torch_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_prophetnet import (
				    PROPHETNET_PRETRAINED_MODEL_ARCHIVE_LIST,
				    ProphetNetDecoder,
				    ProphetNetEncoder,
				    ProphetNetForCausalLM,
				    ProphetNetForConditionalGeneration,
				    ProphetNetModel,
				    ProphetNetPreTrainedModel,
				)

else:
		import sys

		lowercase_							 =       _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)