


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							:			Optional[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							:			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							:			Any     =						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							:			Dict     =						torch.xpu.get_rng_state()
  elif rng_type == RNGType.GENERATOR:
    assert generator is not None, "Need a generator to synchronize its seed."
    __UpperCAmelCase							:			Tuple     =						generator.get_state()

  # Broadcast the rng state from device 0 to other devices
  __UpperCAmelCase							:			Tuple     =						AcceleratorState()
  if state.distributed_type == DistributedType.TPU:
    __UpperCAmelCase							:			Dict     =						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							:			Dict     =						rng_state.to(state.device	)
    torch.distributed.broadcast(__lowerCamelCase						,	0	)
    __UpperCAmelCase							:			List[str]     =						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	)










import enum
import inspect
import warnings
from functools import wraps
from typing import Callable, Optional

from .logging import get_logger


a	: Any      =				set()
a	: List[Any]      =				get_logger(__name__)






def        lowerCamelCase__   (					__lowerCamelCase    :  Optional[str] = None	):

  def decorator(__lowerCamelCase    :  Callable	):
    global _emitted_deprecation_warnings

    if inspect.isclass(__lowerCamelCase	):
      __UpperCAmelCase							:			Optional[Any]     =						deprecated_class_or_function.__init__
      __UpperCAmelCase							:			int     =						deprecated_class_or_function.__name__
    else:
      __UpperCAmelCase							:			Any     =						deprecated_class_or_function
      __UpperCAmelCase							:			Any     =						deprecated_function.__name__
      # Support deprecating __init__ class method: class name instead
      __UpperCAmelCase							:			List[str]     =						name if name != """__init__""" else deprecated_function.__qualname__.split("""."""	)[-2]

    __UpperCAmelCase							:			Union[str, Any]     =						(
        f"""{name} is deprecated and will be removed in the next major version of datasets.""" + f""" {help_message}"""
        if help_message
        else """"""
    )

    @wraps(__lowerCamelCase	)
    def wrapper(*__lowerCamelCase    :  List[str]						,	**__lowerCamelCase    :  Tuple	):
      __UpperCAmelCase							:			Optional[Any]     =						hash(__lowerCamelCase	)
      if func_hash not in _emitted_deprecation_warnings:
        warnings.warn(__lowerCamelCase						,	category=__lowerCamelCase						,	stacklevel=2	)
        _emitted_deprecation_warnings.add(__lowerCamelCase	)
      return deprecated_function(*__lowerCamelCase						,	**__lowerCamelCase	)

    __UpperCAmelCase							:			List[str]     =						"""deprecated"""

    if inspect.isclass(__lowerCamelCase	):
      __UpperCAmelCase							:			int     =						wrapper
      return deprecated_class_or_function
    else:
      return wrapper

  return decorator
class 					a					(				enum.EnumMeta     ):



      """simple docstring"""


      def __getattribute__(   cls      :    Optional[int]	,   __lowercase      :    List[Any]      )							->   str:
        __UpperCAmelCase							:			List[Any]     =						super().__getattribute__(__lowercase      )
        if isinstance(__lowercase	,   enum.Enum      ) and obj._on_access:
          obj._on_access()
        return obj
      def __getitem__(   cls      :    Union[str, Any]	,   __lowercase      :    Optional[int]      )							->   Optional[int]:
        __UpperCAmelCase							:			Union[str, Any]     =						super().__getitem__(__lowercase      )
        if member._on_access:
          member._on_access()
        return member




      def __call__(   cls      :    List[str]	,   __lowercase      :    Dict	,   __lowercase      :    List[Any]=None	,   *, __lowercase      :    Dict=None	,   __lowercase      :    Any=None	,   __lowercase      :    Optional[Any]=None	,   __lowercase      :    Dict=1      )							->   List[Any]:
        __UpperCAmelCase							:			Union[str, Any]     =						super().__call__(__lowercase	,   __lowercase	,   module=__lowercase	,   qualname=__lowercase	,   type=__lowercase	,   start=__lowercase      )
        if isinstance(__lowercase	,   enum.Enum      ) and obj._on_access:
          obj._on_access()
        return obj


class 					a					(				enum.Enum							, metaclass=lowercase__     ):



      """simple docstring"""


      def __new__(   cls      :    Dict	,   __lowercase      :    Tuple      )							->   Optional[Any]:
        __UpperCAmelCase							:			List[Any]     =						object.__new__(cls      )
        __UpperCAmelCase							:			Any     =						value
        __UpperCAmelCase							:			int     =						member.deprecate
        return member
      @property
      def 			UpperCAmelCase   (   self      :    Optional[Any]      )							->   Dict:
        return ""




      def 			UpperCAmelCase   (   self      :    str      )							->   Dict:
        __UpperCAmelCase							:			Optional[int]     =						f""" {self.help_message}""" if self.help_message else """"""
        warnings.warn(
            f"""'{self.__objclass__.__name__}' is deprecated and will be removed in the next major version of datasets."""
            + help_message	,   __lowercase	,   stacklevel=3	,   )


