






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






def     lowerCamelCase__				(   __lowerCamelCase      :					Optional[RNGType] = None  ,		__lowerCamelCase      :					Optional[torch.Generator] = None		):
  # Get the proper rng state
  if rng_type == RNGType.TORCH:
    __UpperCAmelCase							:     Union[str, Any]									=  torch.get_rng_state()
  elif rng_type == RNGType.CUDA:
    __UpperCAmelCase							:     Any									=  torch.cuda.get_rng_state()
  elif rng_type == RNGType.XLA:
    assert is_tpu_available(), "Can't synchronize XLA seeds on an environment without TPUs."
    __UpperCAmelCase							:     List[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."
    __UpperCAmelCase							:     str									=  torch.npu.get_rng_state()
  elif rng_type == RNGType.XPU:
    assert is_xpu_available(), "Can't synchronize XPU seeds on an environment without XPUs."
    __UpperCAmelCase							:     int									=  torch.xpu.get_rng_state()
  elif rng_type == RNGType.GENERATOR:
    assert generator is not None, "Need a generator to synchronize its seed."
    __UpperCAmelCase							:     Any									=  generator.get_state()

  # Broadcast the rng state from device 0 to other devices
  __UpperCAmelCase							:     Dict									=  AcceleratorState()
  if state.distributed_type == DistributedType.TPU:
    __UpperCAmelCase							:     str									=  rng_state.to(xm.xla_device()		)
    xm.collective_broadcast([rng_state]		)
    xm.mark_step()
    __UpperCAmelCase							:     List[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
  ):
    __UpperCAmelCase							:     Union[str, Any]									=  rng_state.to(state.device		)
    torch.distributed.broadcast(__lowerCamelCase  ,		0		)
    __UpperCAmelCase							:     Tuple									=  rng_state.cpu()
  elif state.distributed_type == DistributedType.MULTI_CPU:
    torch.distributed.broadcast(__lowerCamelCase  ,		0		)

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







def     lowerCamelCase__				(   __lowerCamelCase      :					List[Union[str, RNGType]]  ,		__lowerCamelCase      :					Optional[torch.Generator] = None		):
  for rng_type in rng_types:
    synchronize_rng_state(RNGType(__lowerCamelCase		)  ,		generator=__lowerCamelCase		)










def     lowerCamelCase__				(   __lowerCamelCase      :					list  ,		__lowerCamelCase      :					int		):
  for index, item in enumerate(__lowerCamelCase		):
    if item == target:
      return index
  return -1






def     lowerCamelCase__				(   __lowerCamelCase      :					list  ,		__lowerCamelCase      :					int  ,		__lowerCamelCase      :					int  ,		__lowerCamelCase      :					int		):
  if not (0 <= high < len(__lowerCamelCase		) and 0 <= low < len(__lowerCamelCase		)):
    raise Exception("""Invalid upper or lower bound!"""		)
  if high < low:
    return -1
  if sequence[low] == target:
    return low
  if sequence[high] == target:
    return high
  return rec_linear_search(__lowerCamelCase  ,		low + 1  ,		high - 1  ,		__lowerCamelCase		)


if __name__ == "__main__":
       a		:	List[str]      					=			input("Enter numbers separated by comma:\n").strip()
       a		:	Tuple      					=			[int(item.strip()) for item in user_input.split(",")]

       a		:	List[str]      					=			int(input("Enter a single number to be found in the list:\n").strip())
       a		:	Optional[Any]      					=			linear_search(sequence, target)
       if result != -1:
              print(f"""linear_search({sequence}, {target}) = {result}""")
       else:
              print(f"""{target} was not found in {sequence}""")


