


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 		lowercase_(  SCREAMING_SNAKE_CASE_				,						SCREAMING_SNAKE_CASE_ = False     ):


		'''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 		lowercase_(  SCREAMING_SNAKE_CASE_ = None				,						SCREAMING_SNAKE_CASE_ = None     ):


		'''simple docstring'''

		if rng_type == RNGType.TORCH:
				lowerCamelCase			: Optional[Any]   =    torch.get_rng_state()
		elif rng_type == RNGType.CUDA:
				lowerCamelCase			: Optional[int]   =    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			: Optional[Any]   =    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			: int   =    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			: Union[str, Any]   =    torch.xpu.get_rng_state()
		elif rng_type == RNGType.GENERATOR:
				assert generator is not None, "Need a generator to synchronize its seed."
				lowerCamelCase			: Tuple   =    generator.get_state()

		# Broadcast the rng state from device 0 to other devices
		lowerCamelCase			: Any   =    AcceleratorState()
		if state.distributed_type == DistributedType.TPU:
				lowerCamelCase			: Any   =    rng_state.to(xm.xla_device()     )
				xm.collective_broadcast([rng_state]     )
				xm.mark_step()
				lowerCamelCase			: str   =    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			: List[Any]   =    rng_state.to(state.device     )
				torch.distributed.broadcast(lowercase_				,						0     )
				lowerCamelCase			: List[Any]   =    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 		lowercase_(  SCREAMING_SNAKE_CASE_				,						SCREAMING_SNAKE_CASE_ = None     ):


		'''simple docstring'''

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







from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_torch_available,
)


_snake_case					=					{
    '''configuration_rwkv''': ['''RWKV_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''RwkvConfig''', '''RwkvOnnxConfig'''],
}

try:
   if not is_torch_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   _snake_case					=					[
       '''RWKV_PRETRAINED_MODEL_ARCHIVE_LIST''',
       '''RwkvForCausalLM''',
       '''RwkvModel''',
       '''RwkvPreTrainedModel''',
   ]


if TYPE_CHECKING:
   from .configuration_rwkv import RWKV_PRETRAINED_CONFIG_ARCHIVE_MAP, RwkvConfig, RwkvOnnxConfig

   try:
      if not is_torch_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_rwkv import (
          RWKV_PRETRAINED_MODEL_ARCHIVE_LIST,
          RwkvForCausalLM,
          RwkvModel,
          RwkvPreTrainedModel,
      )
else:
   import sys

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



