





"""simple docstring"""

import torch

from diffusers import DPMSolverSDEScheduler
from diffusers.utils import torch_device
from diffusers.utils.testing_utils import require_torchsde

from .test_schedulers import SchedulerCommonTest


@require_torchsde
class       lowerCAmelCase__				(   __a  ):
 lowercase__     :  str				=   (DPMSolverSDEScheduler,)
 lowercase__     :  Union[str, Any]				=   10







 def  lowercase_       (   self		,	**UpperCamelCase__					):

    '''simple docstring'''


    A__											=					{
        "num_train_timesteps": 11_00,
        "beta_start": 0.0001,
        "beta_end": 0.02,
        "beta_schedule": "linear",
        "noise_sampler_seed": 0,
    }

    config.update(**_lowerCamelCase					)
    return config







 def  lowercase_       (   self					):

    '''simple docstring'''


    for timesteps in [10, 50, 1_00, 10_00]:
       self.check_over_configs(num_train_timesteps=_lowerCamelCase					)







 def  lowercase_       (   self					):

    '''simple docstring'''


    for beta_start, beta_end in zip([0.0_0001, 0.0001, 0.001]		,	[0.0002, 0.002, 0.02]					):
       self.check_over_configs(beta_start=_lowerCamelCase		,	beta_end=_lowerCamelCase					)







 def  lowercase_       (   self					):

    '''simple docstring'''


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







 def  lowercase_       (   self					):

    '''simple docstring'''


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







 def  lowercase_       (   self					):

    '''simple docstring'''


    A__											=					self.scheduler_classes[0]
    A__											=					self.get_scheduler_config()
    A__											=					scheduler_class(**_lowerCamelCase					)

    scheduler.set_timesteps(self.num_inference_steps					)

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

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

       A__											=					model(_lowerCamelCase		,	_lowerCamelCase					)

       A__											=					scheduler.step(_lowerCamelCase		,	_lowerCamelCase		,	_lowerCamelCase					)
       A__											=					output.prev_sample

    A__											=					torch.sum(torch.abs(_lowerCamelCase					)					)
    A__											=					torch.mean(torch.abs(_lowerCamelCase					)					)

    if torch_device in ["mps"]:
       assert abs(result_sum.item() - 167.47_8210_4492_1875					) < 1e-2
       assert abs(result_mean.item() - 0.2178_7059_6456_5277					) < 1e-3
    elif torch_device in ["cuda"]:
       assert abs(result_sum.item() - 171.59_3521_1181_6406					) < 1e-2
       assert abs(result_mean.item() - 0.2_2342_9068_9229_9652					) < 1e-3
    else:
       assert abs(result_sum.item() - 162.52_3834_2285_1562					) < 1e-2
       assert abs(result_mean.item() - 0.211_6195_7085_1326					) < 1e-3







 def  lowercase_       (   self					):

    '''simple docstring'''


    A__											=					self.scheduler_classes[0]
    A__											=					self.get_scheduler_config(prediction_type="v_prediction"					)
    A__											=					scheduler_class(**_lowerCamelCase					)

    scheduler.set_timesteps(self.num_inference_steps					)

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

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

       A__											=					model(_lowerCamelCase		,	_lowerCamelCase					)

       A__											=					scheduler.step(_lowerCamelCase		,	_lowerCamelCase		,	_lowerCamelCase					)
       A__											=					output.prev_sample

    A__											=					torch.sum(torch.abs(_lowerCamelCase					)					)
    A__											=					torch.mean(torch.abs(_lowerCamelCase					)					)

    if torch_device in ["mps"]:
       assert abs(result_sum.item() - 124.77_1492_0043_9453					) < 1e-2
       assert abs(result_mean.item() - 0.1_6226_2890_1481_6284					) < 1e-3
    elif torch_device in ["cuda"]:
       assert abs(result_sum.item() - 128.1_6633_6059_5703					) < 1e-2
       assert abs(result_mean.item() - 0.1_6688_3260_0116_7297					) < 1e-3
    else:
       assert abs(result_sum.item() - 119.8_4875_4882_8125					) < 1e-2
       assert abs(result_mean.item() - 0.1560_5306_6253_6621					) < 1e-3







 def  lowercase_       (   self					):

    '''simple docstring'''


    A__											=					self.scheduler_classes[0]
    A__											=					self.get_scheduler_config()
    A__											=					scheduler_class(**_lowerCamelCase					)

    scheduler.set_timesteps(self.num_inference_steps		,	device=_lowerCamelCase					)

    A__											=					self.dummy_model()
    A__											=					self.dummy_sample_deter.to(_lowerCamelCase					) * scheduler.init_noise_sigma

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

       A__											=					model(_lowerCamelCase		,	_lowerCamelCase					)

       A__											=					scheduler.step(_lowerCamelCase		,	_lowerCamelCase		,	_lowerCamelCase					)
       A__											=					output.prev_sample

    A__											=					torch.sum(torch.abs(_lowerCamelCase					)					)
    A__											=					torch.mean(torch.abs(_lowerCamelCase					)					)

    if torch_device in ["mps"]:
       assert abs(result_sum.item() - 167.46_9573_9746_0938					) < 1e-2
       assert abs(result_mean.item() - 0.2_1805_9346_0798_2635					) < 1e-3
    elif torch_device in ["cuda"]:
       assert abs(result_sum.item() - 171.59_3536_3769_5312					) < 1e-2
       assert abs(result_mean.item() - 0.2_2342_9083_8241_5771					) < 1e-3
    else:
       assert abs(result_sum.item() - 162.52_3834_2285_1562					) < 1e-2
       assert abs(result_mean.item() - 0.211_6195_7085_1326					) < 1e-3







 def  lowercase_       (   self					):

    '''simple docstring'''


    A__											=					self.scheduler_classes[0]
    A__											=					self.get_scheduler_config()
    A__											=					scheduler_class(**_lowerCamelCase		,	use_karras_sigmas=_lowerCamelCase					)

    scheduler.set_timesteps(self.num_inference_steps		,	device=_lowerCamelCase					)

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

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

       A__											=					model(_lowerCamelCase		,	_lowerCamelCase					)

       A__											=					scheduler.step(_lowerCamelCase		,	_lowerCamelCase		,	_lowerCamelCase					)
       A__											=					output.prev_sample

    A__											=					torch.sum(torch.abs(_lowerCamelCase					)					)
    A__											=					torch.mean(torch.abs(_lowerCamelCase					)					)

    if torch_device in ["mps"]:
       assert abs(result_sum.item() - 176.66_9741_3574_2188					) < 1e-2
       assert abs(result_mean.item() - 0.2_3003_8727_3098_1811					) < 1e-2
    elif torch_device in ["cuda"]:
       assert abs(result_sum.item() - 177.63_6535_6445_3125					) < 1e-2
       assert abs(result_mean.item() - 0.2_3003_8727_3098_1811					) < 1e-2
    else:
       assert abs(result_sum.item() - 170.3_1352_2338_8672					) < 1e-2
       assert abs(result_mean.item() - 0.2_3003_8727_3098_1811					) < 1e-2

"""simple docstring"""






from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available


_lowercase							=				{
    '''configuration_mask2former''': [
        '''MASK2FORMER_PRETRAINED_CONFIG_ARCHIVE_MAP''',
        '''Mask2FormerConfig''',
    ],
}

try:
					if not is_vision_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					_lowercase							=				['''Mask2FormerImageProcessor''']

try:
					if not is_torch_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					_lowercase							=				[
					    '''MASK2FORMER_PRETRAINED_MODEL_ARCHIVE_LIST''',
					    '''Mask2FormerForUniversalSegmentation''',
					    '''Mask2FormerModel''',
					    '''Mask2FormerPreTrainedModel''',
					]

if TYPE_CHECKING:
					from .configuration_maskaformer import MASK2FORMER_PRETRAINED_CONFIG_ARCHIVE_MAP, MaskaFormerConfig

					try:
										if not is_vision_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .image_processing_maskaformer import MaskaFormerImageProcessor

					try:
										if not is_torch_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_maskaformer import (
										    MASK2FORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
										    MaskaFormerForUniversalSegmentation,
										    MaskaFormerModel,
										    MaskaFormerPreTrainedModel,
										)


else:
					import sys

					_lowercase							=				_LazyModule(__name__, globals()['''__file__'''], _import_structure)





