





'''simple docstring'''

import torch

from diffusers import DDIMParallelScheduler

from .test_schedulers import SchedulerCommonTest
class     __lowerCAmelCase					(   __magic_name__			):






       """simple docstring"""
       _snake_case     :					Tuple						    =   (DDIMParallelScheduler,)
       _snake_case     :					str						    =   (('eta', 0.0), ('num_inference_steps', 5_0))





       def snake_case__						(							self       :		Tuple	,			**lowerCAmelCase__       :		str					)   ->					Any:

            '''simple docstring'''




            _UpperCamelCase       =	{
                '''num_train_timesteps''': 1000,
                '''beta_start''': 0.0001,
                '''beta_end''': 0.02,
                '''beta_schedule''': '''linear''',
                '''clip_sample''': True,
            }

            config.update(**lowerCAmelCase__					)
            return config





       def snake_case__						(							self       :		Tuple	,			**lowerCAmelCase__       :		Any					)   ->					List[str]:

            '''simple docstring'''




            _UpperCamelCase       =	self.scheduler_classes[0]
            _UpperCamelCase       =	self.get_scheduler_config(**lowerCAmelCase__					)
            _UpperCamelCase       =	scheduler_class(**lowerCAmelCase__					)

            _UpperCamelCase   ,    _UpperCamelCase       =	10, 0.0

            _UpperCamelCase       =	self.dummy_model()
            _UpperCamelCase       =	self.dummy_sample_deter

            scheduler.set_timesteps(lowerCAmelCase__					)

            for t in scheduler.timesteps:
                 _UpperCamelCase       =	model(lowerCAmelCase__	,			lowerCAmelCase__					)
                 _UpperCamelCase       =	scheduler.step(lowerCAmelCase__	,			lowerCAmelCase__	,			lowerCAmelCase__	,			lowerCAmelCase__					).prev_sample

            return sample





       def snake_case__						(							self       :		Dict					)   ->					Any:

            '''simple docstring'''




            for timesteps in [100, 500, 1000]:
                 self.check_over_configs(num_train_timesteps=lowerCAmelCase__					)





       def snake_case__						(							self       :		Optional[int]					)   ->					Union[str, Any]:

            '''simple docstring'''




            for steps_offset in [0, 1]:
                 self.check_over_configs(steps_offset=lowerCAmelCase__					)

            _UpperCamelCase       =	self.scheduler_classes[0]
            _UpperCamelCase       =	self.get_scheduler_config(steps_offset=1					)
            _UpperCamelCase       =	scheduler_class(**lowerCAmelCase__					)
            scheduler.set_timesteps(5					)
            assert torch.equal(scheduler.timesteps	,			torch.LongTensor([801, 601, 401, 201, 1]					)					)





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

            '''simple docstring'''




            for beta_start, beta_end in zip([0.0001, 0.001, 0.01, 0.1]	,			[0.002, 0.02, 0.2, 2]					):
                 self.check_over_configs(beta_start=lowerCAmelCase__	,			beta_end=lowerCAmelCase__					)





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

            '''simple docstring'''




            for schedule in ["linear", "squaredcos_cap_v2"]:
                 self.check_over_configs(beta_schedule=lowerCAmelCase__					)





       def snake_case__						(							self       :		Union[str, Any]					)   ->					Dict:

            '''simple docstring'''




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





       def snake_case__						(							self       :		int					)   ->					List[Any]:

            '''simple docstring'''




            for clip_sample in [True, False]:
                 self.check_over_configs(clip_sample=lowerCAmelCase__					)





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

            '''simple docstring'''




            for timestep_spacing in ["trailing", "leading"]:
                 self.check_over_configs(timestep_spacing=lowerCAmelCase__					)





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

            '''simple docstring'''




            for rescale_betas_zero_snr in [True, False]:
                 self.check_over_configs(rescale_betas_zero_snr=lowerCAmelCase__					)





       def snake_case__						(							self       :		Any					)   ->					Union[str, Any]:

            '''simple docstring'''




            self.check_over_configs(thresholding=lowerCAmelCase__					)
            for threshold in [0.5, 1.0, 2.0]:
                 for prediction_type in ["epsilon", "v_prediction"]:
                      self.check_over_configs(
                          thresholding=lowerCAmelCase__	,			prediction_type=lowerCAmelCase__	,			sample_max_value=lowerCAmelCase__	,			)





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

            '''simple docstring'''




            for t in [1, 10, 49]:
                 self.check_over_forward(time_step=lowerCAmelCase__					)





       def snake_case__						(							self       :		Tuple					)   ->					List[Any]:

            '''simple docstring'''




            for t, num_inference_steps in zip([1, 10, 50]	,			[10, 50, 500]					):
                 self.check_over_forward(time_step=lowerCAmelCase__	,			num_inference_steps=lowerCAmelCase__					)





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

            '''simple docstring'''




            for t, eta in zip([1, 10, 49]	,			[0.0, 0.5, 1.0]					):
                 self.check_over_forward(time_step=lowerCAmelCase__	,			eta=lowerCAmelCase__					)





       def snake_case__						(							self       :		Dict					)   ->					Any:

            '''simple docstring'''




            _UpperCamelCase       =	self.scheduler_classes[0]
            _UpperCamelCase       =	self.get_scheduler_config()
            _UpperCamelCase       =	scheduler_class(**lowerCAmelCase__					)

            assert torch.sum(torch.abs(scheduler._get_variance(0	,			0					) - 0.0					)					) < 1e-5
            assert torch.sum(torch.abs(scheduler._get_variance(420	,			400					) - 0.14771					)					) < 1e-5
            assert torch.sum(torch.abs(scheduler._get_variance(980	,			960					) - 0.32460					)					) < 1e-5
            assert torch.sum(torch.abs(scheduler._get_variance(0	,			0					) - 0.0					)					) < 1e-5
            assert torch.sum(torch.abs(scheduler._get_variance(487	,			486					) - 0.00979					)					) < 1e-5
            assert torch.sum(torch.abs(scheduler._get_variance(999	,			998					) - 0.02					)					) < 1e-5





       def snake_case__						(							self       :		Tuple					)   ->					str:

            '''simple docstring'''




            _UpperCamelCase       =	self.scheduler_classes[0]
            _UpperCamelCase       =	self.get_scheduler_config()
            _UpperCamelCase       =	scheduler_class(**lowerCAmelCase__					)

            _UpperCamelCase   ,    _UpperCamelCase       =	10, 0.0
            scheduler.set_timesteps(lowerCAmelCase__					)

            _UpperCamelCase       =	self.dummy_model()
            _UpperCamelCase       =	self.dummy_sample_deter
            _UpperCamelCase       =	self.dummy_sample_deter + 0.1
            _UpperCamelCase       =	self.dummy_sample_deter - 0.1

            _UpperCamelCase       =	samplea.shape[0]
            _UpperCamelCase       =	torch.stack([samplea, samplea, samplea]	,			dim=0					)
            _UpperCamelCase       =	torch.arange(lowerCAmelCase__					)[0:3, None].repeat(1	,			lowerCAmelCase__					)

            _UpperCamelCase       =	model(samples.flatten(0	,			1					)	,			timesteps.flatten(0	,			1					)					)
            _UpperCamelCase       =	scheduler.batch_step_no_noise(lowerCAmelCase__	,			timesteps.flatten(0	,			1					)	,			samples.flatten(0	,			1					)	,			lowerCAmelCase__					)

            _UpperCamelCase       =	torch.sum(torch.abs(lowerCAmelCase__					)					)
            _UpperCamelCase       =	torch.mean(torch.abs(lowerCAmelCase__					)					)

            assert abs(result_sum.item() - 1147.7904					) < 1e-2
            assert abs(result_mean.item() - 0.4982					) < 1e-3





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

            '''simple docstring'''




            _UpperCamelCase       =	self.full_loop()

            _UpperCamelCase       =	torch.sum(torch.abs(lowerCAmelCase__					)					)
            _UpperCamelCase       =	torch.mean(torch.abs(lowerCAmelCase__					)					)

            assert abs(result_sum.item() - 172.0067					) < 1e-2
            assert abs(result_mean.item() - 0.223967					) < 1e-3





       def snake_case__						(							self       :		Dict					)   ->					Dict:

            '''simple docstring'''




            _UpperCamelCase       =	self.full_loop(prediction_type='''v_prediction'''					)

            _UpperCamelCase       =	torch.sum(torch.abs(lowerCAmelCase__					)					)
            _UpperCamelCase       =	torch.mean(torch.abs(lowerCAmelCase__					)					)

            assert abs(result_sum.item() - 52.5302					) < 1e-2
            assert abs(result_mean.item() - 0.0684					) < 1e-3





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

            '''simple docstring'''




            _UpperCamelCase       =	self.full_loop(set_alpha_to_one=lowerCAmelCase__	,			beta_start=0.01					)
            _UpperCamelCase       =	torch.sum(torch.abs(lowerCAmelCase__					)					)
            _UpperCamelCase       =	torch.mean(torch.abs(lowerCAmelCase__					)					)

            assert abs(result_sum.item() - 149.8295					) < 1e-2
            assert abs(result_mean.item() - 0.1951					) < 1e-3







       def snake_case__						(							self       :		Dict					)   ->					Union[str, Any]:

            '''simple docstring'''




            _UpperCamelCase       =	self.full_loop(set_alpha_to_one=lowerCAmelCase__	,			beta_start=0.01					)
            _UpperCamelCase       =	torch.sum(torch.abs(lowerCAmelCase__					)					)
            _UpperCamelCase       =	torch.mean(torch.abs(lowerCAmelCase__					)					)

            assert abs(result_sum.item() - 149.0784					) < 1e-2
            assert abs(result_mean.item() - 0.1941					) < 1e-3










'''simple docstring'''

from typing import TYPE_CHECKING

# rely on isort to merge the imports
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


lowercase__						:      Optional[Any]	=    {
    'configuration_autoformer': [
        'AUTOFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP',
        'AutoformerConfig',
    ],
}

try:
   if not is_torch_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   lowercase__						:      List[Any]	=    [
       'AUTOFORMER_PRETRAINED_MODEL_ARCHIVE_LIST',
       'AutoformerForPrediction',
       'AutoformerModel',
       'AutoformerPreTrainedModel',
   ]


if TYPE_CHECKING:
   from .configuration_autoformer import (
       AUTOFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP,
       AutoformerConfig,
   )

   try:
      if not is_torch_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_autoformer import (
          AUTOFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
          AutoformerForPrediction,
          AutoformerModel,
          AutoformerPreTrainedModel,
      )

else:
   import sys

   lowercase__						:      Tuple	=    _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)



