




'''simple docstring'''
import torch

from diffusers import KDPMaAncestralDiscreteScheduler
from diffusers.utils import torch_device

from .test_schedulers import SchedulerCommonTest






class 	SCREAMING_SNAKE_CASE(    SCREAMING_SNAKE_CASE__					):
							snake_case_			:					Tuple   = (KDPMaAncestralDiscreteScheduler,)
							snake_case_			:					Any   = 10

							def    snake_case__      (   self ,		**lowerCamelCase__					)   -> List[Any]:

											"""simple docstring"""



											__lowercase          =						{
											    """num_train_timesteps""": 1100,
											    """beta_start""": 0.00_01,
											    """beta_end""": 0.02,
											    """beta_schedule""": """linear""",
											}

											config.update(**lowerCamelCase__					)
											return config

							def    snake_case__      (   self					)   -> List[str]:

											"""simple docstring"""



											for timesteps in [10, 50, 100, 1000]:
															self.check_over_configs(num_train_timesteps=lowerCamelCase__					)

							def    snake_case__      (   self					)   -> Optional[int]:

											"""simple docstring"""



											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    snake_case__      (   self					)   -> List[str]:

											"""simple docstring"""



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

							def    snake_case__      (   self					)   -> List[str]:

											"""simple docstring"""



											if torch_device == "mps":
															return
											__lowercase          =						self.scheduler_classes[0]
											__lowercase          =						self.get_scheduler_config()
											__lowercase          =						scheduler_class(**lowerCamelCase__					)

											scheduler.set_timesteps(self.num_inference_steps					)

											__lowercase          =						torch.manual_seed(0					)

											__lowercase          =						self.dummy_model()
											__lowercase          =						self.dummy_sample_deter * scheduler.init_noise_sigma
											__lowercase          =						sample.to(lowerCamelCase__					)

											for i, t in enumerate(scheduler.timesteps					):
															__lowercase          =						scheduler.scale_model_input(lowerCamelCase__ ,		lowerCamelCase__					)

															__lowercase          =						model(lowerCamelCase__ ,		lowerCamelCase__					)

															__lowercase          =						scheduler.step(lowerCamelCase__ ,		lowerCamelCase__ ,		lowerCamelCase__ ,		generator=lowerCamelCase__					)
															__lowercase          =						output.prev_sample

											__lowercase          =						torch.sum(torch.abs(lowerCamelCase__					)					)
											__lowercase          =						torch.mean(torch.abs(lowerCamelCase__					)					)

											assert abs(result_sum.item() - 1_3849.3877					) < 1E-2
											assert abs(result_mean.item() - 18.03_31					) < 5E-3

							def    snake_case__      (   self					)   -> Optional[Any]:

											"""simple docstring"""



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

							def    snake_case__      (   self					)   -> Optional[Any]:

											"""simple docstring"""



											if torch_device == "mps":
															return
											__lowercase          =						self.scheduler_classes[0]
											__lowercase          =						self.get_scheduler_config(prediction_type="""v_prediction"""					)
											__lowercase          =						scheduler_class(**lowerCamelCase__					)

											scheduler.set_timesteps(self.num_inference_steps					)

											__lowercase          =						self.dummy_model()
											__lowercase          =						self.dummy_sample_deter * scheduler.init_noise_sigma
											__lowercase          =						sample.to(lowerCamelCase__					)

											__lowercase          =						torch.manual_seed(0					)

											for i, t in enumerate(scheduler.timesteps					):
															__lowercase          =						scheduler.scale_model_input(lowerCamelCase__ ,		lowerCamelCase__					)

															__lowercase          =						model(lowerCamelCase__ ,		lowerCamelCase__					)

															__lowercase          =						scheduler.step(lowerCamelCase__ ,		lowerCamelCase__ ,		lowerCamelCase__ ,		generator=lowerCamelCase__					)
															__lowercase          =						output.prev_sample

											__lowercase          =						torch.sum(torch.abs(lowerCamelCase__					)					)
											__lowercase          =						torch.mean(torch.abs(lowerCamelCase__					)					)

											assert abs(result_sum.item() - 3_28.99_70					) < 1E-2
											assert abs(result_mean.item() - 0.42_84					) < 1E-3




							def    snake_case__      (   self					)   -> Tuple:

											"""simple docstring"""



											if torch_device == "mps":
															return
											__lowercase          =						self.scheduler_classes[0]
											__lowercase          =						self.get_scheduler_config()
											__lowercase          =						scheduler_class(**lowerCamelCase__					)

											scheduler.set_timesteps(self.num_inference_steps ,		device=lowerCamelCase__					)
											__lowercase          =						torch.manual_seed(0					)

											__lowercase          =						self.dummy_model()
											__lowercase          =						self.dummy_sample_deter.to(lowerCamelCase__					) * scheduler.init_noise_sigma

											for t in scheduler.timesteps:
															__lowercase          =						scheduler.scale_model_input(lowerCamelCase__ ,		lowerCamelCase__					)

															__lowercase          =						model(lowerCamelCase__ ,		lowerCamelCase__					)

															__lowercase          =						scheduler.step(lowerCamelCase__ ,		lowerCamelCase__ ,		lowerCamelCase__ ,		generator=lowerCamelCase__					)
															__lowercase          =						output.prev_sample

											__lowercase          =						torch.sum(torch.abs(lowerCamelCase__					)					)
											__lowercase          =						torch.mean(torch.abs(lowerCamelCase__					)					)

											assert abs(result_sum.item() - 1_3849.3818					) < 1E-1
											assert abs(result_mean.item() - 18.03_31					) < 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     snake_case_  (       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     snake_case_  (       a__		:     Optional[RNGType] = None   ,a__		:     Optional[torch.Generator] = None      ):



    """simple docstring"""




    if rng_type == RNGType.TORCH:
        __lowercase          =						torch.get_rng_state()
    elif rng_type == RNGType.CUDA:
        __lowercase          =						torch.cuda.get_rng_state()
    elif rng_type == RNGType.XLA:
        assert is_tpu_available(), "Can't synchronize XLA seeds on an environment without TPUs."
        __lowercase          =						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."
        __lowercase          =						torch.npu.get_rng_state()
    elif rng_type == RNGType.XPU:
        assert is_xpu_available(), "Can't synchronize XPU seeds on an environment without XPUs."
        __lowercase          =						torch.xpu.get_rng_state()
    elif rng_type == RNGType.GENERATOR:
        assert generator is not None, "Need a generator to synchronize its seed."
        __lowercase          =						generator.get_state()

    # Broadcast the rng state from device 0 to other devices
    __lowercase          =						AcceleratorState()
    if state.distributed_type == DistributedType.TPU:
        __lowercase          =						rng_state.to(xm.xla_device()      )
        xm.collective_broadcast([rng_state]      )
        xm.mark_step()
        __lowercase          =						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
    ):
        __lowercase          =						rng_state.to(state.device      )
        torch.distributed.broadcast(a__   ,0      )
        __lowercase          =						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     snake_case_  (       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__      )




