"""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_		(   lowerCamelCase   ,     lowerCamelCase = 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     a_		(   lowerCamelCase = None   ,     lowerCamelCase = None			):
				# Get the proper rng state
				if rng_type == RNGType.TORCH:
								UpperCAmelCase__			 =				torch.get_rng_state()
				elif rng_type == RNGType.CUDA:
								UpperCAmelCase__			 =				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__			 =				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__			 =				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__			 =				torch.xpu.get_rng_state()
				elif rng_type == RNGType.GENERATOR:
								assert generator is not None, "Need a generator to synchronize its seed."
								UpperCAmelCase__			 =				generator.get_state()

				# Broadcast the rng state from device 0 to other devices
				UpperCAmelCase__			 =				AcceleratorState()
				if state.distributed_type == DistributedType.TPU:
								UpperCAmelCase__			 =				rng_state.to(xm.xla_device()			)
								xm.collective_broadcast([rng_state]			)
								xm.mark_step()
								UpperCAmelCase__			 =				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__			 =				rng_state.to(state.device			)
								torch.distributed.broadcast(lowerCamelCase   ,     0			)
								UpperCAmelCase__			 =				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     a_		(   lowerCamelCase   ,     lowerCamelCase = None			):
				for rng_type in rng_types:
								synchronize_rng_state(RNGType(lowerCamelCase			)   ,     generator=lowerCamelCase			)




"""simple docstring"""






import os
import unittest

import pytest
from parameterized import parameterized

from transformers.testing_utils import require_torch, require_torch_gpu, slow, torch_device


# skipping in unittest tests

lowerCAmelCase__    :    Union[str, Any]			=      [(1,)]



def     a_		(   ):
				UpperCAmelCase__			 =				bool(os.getenv('RUN_SLOW'   ,     0			)			)
				if run_slow:
								assert True
				else:
								assert False, "should have been skipped"



def     a_		(   ):
				UpperCAmelCase__			 =				bool(os.getenv('RUN_SLOW'   ,     0			)			)
				if run_slow and torch_device == "cuda":
								assert True
				else:
								assert False, "should have been skipped"
@require_torch
class 							snake_case			(					unittest.TestCase							):




					"""simple docstring"""

					@slow
					@require_torch_gpu
					def 						__lowerCAmelCase		(							self  :      Optional[Any]     ):
									check_slow_torch_cuda()







					@require_torch_gpu
					@slow
					def 						__lowerCAmelCase		(							self  :      Any     ):
									check_slow_torch_cuda()







					@parameterized.expand(lowerCamelCase__     )
					@slow
					def 						__lowerCAmelCase		(							self  :      Union[str, Any]       ,lowerCamelCase__  :      str=None     ):
									check_slow()






@slow
@require_torch_gpu
def     a_		(   ):
				check_slow_torch_cuda()



@require_torch_gpu
@slow
def     a_		(   ):
				check_slow_torch_cuda()



@slow
@pytest.mark.parametrize('param'   ,     [1]			)
def     a_		(   lowerCamelCase			):
				check_slow()







@pytest.mark.parametrize('param'   ,     [1]			)
@slow
def     a_		(   lowerCamelCase			):
				check_slow()



