'''simple docstring'''
import torch

from diffusers import KDPMaAncestralDiscreteScheduler
from diffusers.utils import torch_device

from .test_schedulers import SchedulerCommonTest






class       __lowercase		(    _lowerCamelCase    ):







     """simple docstring"""






     SCREAMING_SNAKE_CASE__		 =    (KDPMaAncestralDiscreteScheduler,)
     SCREAMING_SNAKE_CASE__		 =    10







     def lowerCAmelCase  (		self  ,      **_lowerCamelCase     ):
         __UpperCamelCase						:						Union[str, Any]		     =	{
             'num_train_timesteps': 1_1_0_0,
             'beta_start': 0.0_0_0_1,
             'beta_end': 0.0_2,
             'beta_schedule': 'linear',
         }

         config.update(**_lowerCamelCase     )
         return config







     def lowerCAmelCase  (		self     ):
         for timesteps in [1_0, 5_0, 1_0_0, 1_0_0_0]:
             self.check_over_configs(num_train_timesteps=_lowerCamelCase     )







     def lowerCAmelCase  (		self     ):
         for beta_start, beta_end in zip([0.0_0_0_0_1, 0.0_0_0_1, 0.0_0_1]  ,      [0.0_0_0_2, 0.0_0_2, 0.0_2]     ):
             self.check_over_configs(beta_start=_lowerCamelCase  ,      beta_end=_lowerCamelCase     )







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







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

         scheduler.set_timesteps(self.num_inference_steps     )

         __UpperCamelCase						:						int		     =	torch.manual_seed(0     )

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

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

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

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

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

         assert abs(result_sum.item() - 1_3_8_4_9.3_8_7_7     ) < 1E-2
         assert abs(result_mean.item() - 1_8.0_3_3_1     ) < 5E-3







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







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

         scheduler.set_timesteps(self.num_inference_steps     )

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

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

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

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

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

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

         assert abs(result_sum.item() - 3_2_8.9_9_7_0     ) < 1E-2
         assert abs(result_mean.item() - 0.4_2_8_4     ) < 1E-3







     def lowerCAmelCase  (		self     ):
         if torch_device == "mps":
             return
         __UpperCamelCase						:						Optional[Any]		     =	self.scheduler_classes[0]
         __UpperCamelCase						:						Optional[Any]		     =	self.get_scheduler_config()
         __UpperCamelCase						:						Any		     =	scheduler_class(**_lowerCamelCase     )

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

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

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

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

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

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

         assert abs(result_sum.item() - 1_3_8_4_9.3_8_1_8     ) < 1E-1
         assert abs(result_mean.item() - 1_8.0_3_3_1     ) < 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      )



