


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







def     _a					(   _SCREAMING_SNAKE_CASE = None					,			_SCREAMING_SNAKE_CASE = None					)     ->					Optional[Any]:
							# Get the proper rng state
							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(_SCREAMING_SNAKE_CASE					,			0					)
														snake_case_     							=		rng_state.cpu()
							elif state.distributed_type == DistributedType.MULTI_CPU:
														torch.distributed.broadcast(_SCREAMING_SNAKE_CASE					,			0					)

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







def     _a					(   _SCREAMING_SNAKE_CASE					,			_SCREAMING_SNAKE_CASE = None					)     ->					List[str]:
							for rng_type in rng_types:
														synchronize_rng_state(RNGType(_SCREAMING_SNAKE_CASE					)					,			generator=_SCREAMING_SNAKE_CASE					)





'''simple docstring'''






from __future__ import annotations







def 		SCREAMING_SNAKE_CASE__					(							_SCREAMING_SNAKE_CASE  ,					_SCREAMING_SNAKE_CASE  ,					_SCREAMING_SNAKE_CASE  ):
		if len(_SCREAMING_SNAKE_CASE  ) == 0:
				raise ValueError('''find_min() arg is an empty sequence'''  )
		if (
		    left >= len(_SCREAMING_SNAKE_CASE  )
		    or left < -len(_SCREAMING_SNAKE_CASE  )
		    or right >= len(_SCREAMING_SNAKE_CASE  )
		    or right < -len(_SCREAMING_SNAKE_CASE  )
		):
				raise IndexError('''list index out of range'''  )
		if left == right:
				return nums[left]
		lowerCAmelCase_   :       Dict    							=(left + right) >> 1  # the middle
		lowerCAmelCase_   :       Optional[int]    							=find_min(_SCREAMING_SNAKE_CASE  ,					_SCREAMING_SNAKE_CASE  ,					_SCREAMING_SNAKE_CASE  )  # find min in range[left, mid]
		lowerCAmelCase_   :       Any    							=find_min(_SCREAMING_SNAKE_CASE  ,					mid + 1  ,					_SCREAMING_SNAKE_CASE  )  # find min in range[mid + 1, right]

		return left_min if left_min <= right_min else right_min


if __name__ == "__main__":
							import doctest

							doctest.testmod(verbose=True)






