



import torch

from diffusers import KDPMaAncestralDiscreteScheduler
from diffusers.utils import torch_device

from .test_schedulers import SchedulerCommonTest




class 				lowerCAmelCase__	(						_lowerCamelCase    ):
					__A  :     Optional[int]     =    (KDPMaAncestralDiscreteScheduler,)
					__A  :     Dict     =    10







					def 			_lowercase      (							self   :  int      ,		**_A   :  Dict):
											A__						:				Union[str, Any]         =			{
											    'num_train_timesteps': 1100,
											    'beta_start': 0.00_01,
											    'beta_end': 0.02,
											    'beta_schedule': 'linear',
											}

											config.update(**_lowerCamelCase)
											return config







					def 			_lowercase      (							self   :  Optional[Any]):
											for timesteps in [10, 50, 100, 1000]:
																	self.check_over_configs(num_train_timesteps=_lowerCamelCase)







					def 			_lowercase      (							self   :  Any):
											for beta_start, beta_end in zip([0.0_00_01, 0.00_01, 0.0_01]      ,		[0.00_02, 0.0_02, 0.02]):
																	self.check_over_configs(beta_start=_lowerCamelCase      ,		beta_end=_lowerCamelCase)







					def 			_lowercase      (							self   :  str):
											for schedule in ["linear", "scaled_linear"]:
																	self.check_over_configs(beta_schedule=_lowerCamelCase)







					def 			_lowercase      (							self   :  Optional[int]):
											if torch_device == "mps":
																	return
											A__						:				Any         =			self.scheduler_classes[0]
											A__						:				str         =			self.get_scheduler_config()
											A__						:				Union[str, Any]         =			scheduler_class(**_lowerCamelCase)

											scheduler.set_timesteps(self.num_inference_steps)

											A__						:				int         =			torch.manual_seed(0)

											A__						:				Union[str, Any]         =			self.dummy_model()
											A__						:				Union[str, Any]         =			self.dummy_sample_deter * scheduler.init_noise_sigma
											A__						:				Any         =			sample.to(_lowerCamelCase)

											for i, t in enumerate(scheduler.timesteps):
																	A__						:				Tuple         =			scheduler.scale_model_input(_lowerCamelCase      ,		_lowerCamelCase)

																	A__						:				Union[str, Any]         =			model(_lowerCamelCase      ,		_lowerCamelCase)

																	A__						:				Dict         =			scheduler.step(_lowerCamelCase      ,		_lowerCamelCase      ,		_lowerCamelCase      ,		generator=_lowerCamelCase)
																	A__						:				Optional[Any]         =			output.prev_sample

											A__						:				Any         =			torch.sum(torch.abs(_lowerCamelCase))
											A__						:				List[Any]         =			torch.mean(torch.abs(_lowerCamelCase))

											assert abs(result_sum.item() - 1_3849.3877) < 1e-2
											assert abs(result_mean.item() - 18.0331) < 5e-3







					def 			_lowercase      (							self   :  List[str]):
											for prediction_type in ["epsilon", "v_prediction"]:
																	self.check_over_configs(prediction_type=_lowerCamelCase)







					def 			_lowercase      (							self   :  int):
											if torch_device == "mps":
																	return
											A__						:				Optional[int]         =			self.scheduler_classes[0]
											A__						:				Tuple         =			self.get_scheduler_config(prediction_type="v_prediction")
											A__						:				List[Any]         =			scheduler_class(**_lowerCamelCase)

											scheduler.set_timesteps(self.num_inference_steps)

											A__						:				Dict         =			self.dummy_model()
											A__						:				str         =			self.dummy_sample_deter * scheduler.init_noise_sigma
											A__						:				Dict         =			sample.to(_lowerCamelCase)

											A__						:				Optional[Any]         =			torch.manual_seed(0)

											for i, t in enumerate(scheduler.timesteps):
																	A__						:				Union[str, Any]         =			scheduler.scale_model_input(_lowerCamelCase      ,		_lowerCamelCase)

																	A__						:				Union[str, Any]         =			model(_lowerCamelCase      ,		_lowerCamelCase)

																	A__						:				int         =			scheduler.step(_lowerCamelCase      ,		_lowerCamelCase      ,		_lowerCamelCase      ,		generator=_lowerCamelCase)
																	A__						:				Tuple         =			output.prev_sample

											A__						:				Union[str, Any]         =			torch.sum(torch.abs(_lowerCamelCase))
											A__						:				Union[str, Any]         =			torch.mean(torch.abs(_lowerCamelCase))

											assert abs(result_sum.item() - 328.9970) < 1e-2
											assert abs(result_mean.item() - 0.42_84) < 1e-3







					def 			_lowercase      (							self   :  Union[str, Any]):
											if torch_device == "mps":
																	return
											A__						:				Optional[Any]         =			self.scheduler_classes[0]
											A__						:				Optional[Any]         =			self.get_scheduler_config()
											A__						:				Any         =			scheduler_class(**_lowerCamelCase)

											scheduler.set_timesteps(self.num_inference_steps      ,		device=_lowerCamelCase)
											A__						:				Union[str, Any]         =			torch.manual_seed(0)

											A__						:				List[Any]         =			self.dummy_model()
											A__						:				Dict         =			self.dummy_sample_deter.to(_lowerCamelCase) * scheduler.init_noise_sigma

											for t in scheduler.timesteps:
																	A__						:				Tuple         =			scheduler.scale_model_input(_lowerCamelCase      ,		_lowerCamelCase)

																	A__						:				Optional[int]         =			model(_lowerCamelCase      ,		_lowerCamelCase)

																	A__						:				Any         =			scheduler.step(_lowerCamelCase      ,		_lowerCamelCase      ,		_lowerCamelCase      ,		generator=_lowerCamelCase)
																	A__						:				List[Any]         =			output.prev_sample

											A__						:				Any         =			torch.sum(torch.abs(_lowerCamelCase))
											A__						:				Union[str, Any]         =			torch.mean(torch.abs(_lowerCamelCase))

											assert abs(result_sum.item() - 1_3849.3818) < 1e-1
											assert abs(result_mean.item() - 18.0331) < 1e-3
'''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 			_UpperCamelCase			(  _a						:						int					,  _a						:						bool = False      ):




    """simple docstring"""



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







def 			_UpperCamelCase			(  _a						:						Optional[RNGType] = None					,  _a						:						Optional[torch.Generator] = None      ):




    """simple docstring"""



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

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

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







def 			_UpperCamelCase			(  _a						:						List[Union[str, RNGType]]					,  _a						:						Optional[torch.Generator] = None      ):




    """simple docstring"""



    for rng_type in rng_types:
        synchronize_rng_state(RNGType(_a      )					,  generator=_a      )



