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 					snake_case						(__lowercase					,			__lowercase = False						)       ->       Any:

						'''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 					snake_case						(__lowercase = None					,			__lowercase = None						)       ->       Any:

						'''simple docstring'''



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

						# Broadcast the rng state from device 0 to other devices
						_snake_case						:     Optional[int]				=    AcceleratorState()
						if state.distributed_type == DistributedType.TPU:
												_snake_case						:     List[str]				=    rng_state.to(xm.xla_device()						)
												xm.collective_broadcast([rng_state]						)
												xm.mark_step()
												_snake_case						:     Tuple				=    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						:     Optional[Any]				=    rng_state.to(state.device						)
												torch.distributed.broadcast(__lowercase					,			0						)
												_snake_case						:     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 					snake_case						(__lowercase					,			__lowercase = None						)       ->       str:

						'''simple docstring'''



						for rng_type in rng_types:
												synchronize_rng_state(RNGType(__lowercase						)					,			generator=__lowercase						)
from dataclasses import dataclass
from typing import Callable, Optional

import datasets


@dataclass
class 						a__							(   datasets.BuilderConfig		):
			A__				:   Optional[Callable]								=			None
			A__				:   Optional[dict]								=			None
			A__				:   Optional[datasets.Features]								=			None



			def __SCREAMING_SNAKE_CASE							(						self    ) ->    Any:
								assert self.generator is not None, "generator must be specified"

								if self.gen_kwargs is None:
													__a							=   {}






class 						a__							(   datasets.GeneratorBasedBuilder		):
			A__				:   List[Any]								=			GeneratorConfig



			def __SCREAMING_SNAKE_CASE							(						self    ) ->    Tuple:
								return datasets.DatasetInfo(features=self.config.features    )



			def __SCREAMING_SNAKE_CASE							(						self      ,						UpperCAmelCase    ) ->    Optional[int]:
								return [datasets.SplitGenerator(name=datasets.Split.TRAIN      ,						gen_kwargs=self.config.gen_kwargs    )]







			def __SCREAMING_SNAKE_CASE							(						self      ,						**UpperCAmelCase    ) ->    Tuple:
								for idx, ex in enumerate(self.config.generator(**UpperCAmelCase    )    ):
													yield idx, ex




