





import torch

from diffusers import UnCLIPScheduler

from .test_schedulers import SchedulerCommonTest

class     snake_case_		(   a							):






 '''simple docstring'''





 __UpperCamelCase       =      (UnCLIPScheduler,)

 def  __UpperCAmelCase						(  self,		**A_		)	->       Union[str, Any]:
    UpperCAmelCase__						={
        "num_train_timesteps": 1000,
        "variance_type": "fixed_small_log",
        "clip_sample": True,
        "clip_sample_range": 1.0,
        "prediction_type": "epsilon",
    }

    config.update(**A_		)
    return config

 def  __UpperCAmelCase						(  self		)	->       Optional[Any]:
    for timesteps in [1, 5, 100, 1000]:
       self.check_over_configs(num_train_timesteps=A_		)

 def  __UpperCAmelCase						(  self		)	->       Any:
    for variance in ["fixed_small_log", "learned_range"]:
       self.check_over_configs(variance_type=A_		)

 def  __UpperCAmelCase						(  self		)	->       str:
    for clip_sample in [True, False]:
       self.check_over_configs(clip_sample=A_		)

 def  __UpperCAmelCase						(  self		)	->       List[str]:
    for clip_sample_range in [1, 5, 10, 20]:
       self.check_over_configs(clip_sample_range=A_		)

 def  __UpperCAmelCase						(  self		)	->       Union[str, Any]:
    for prediction_type in ["epsilon", "sample"]:
       self.check_over_configs(prediction_type=A_		)

 def  __UpperCAmelCase						(  self		)	->       Union[str, Any]:
    for time_step in [0, 500, 999]:
       for prev_timestep in [None, 5, 100, 250, 500, 750]:
          if prev_timestep is not None and prev_timestep >= time_step:
             continue

          self.check_over_forward(time_step=A_,		prev_timestep=A_		)

 def  __UpperCAmelCase						(  self		)	->       Any:
    UpperCAmelCase__						=self.scheduler_classes[0]
    UpperCAmelCase__						=self.get_scheduler_config(variance_type="fixed_small_log"		)
    UpperCAmelCase__						=scheduler_class(**A_		)

    assert torch.sum(torch.abs(scheduler._get_variance(0		) - 1.0_000E-10		)		) < 1E-5
    assert torch.sum(torch.abs(scheduler._get_variance(487		) - 0.0_54_96_25		)		) < 1E-5
    assert torch.sum(torch.abs(scheduler._get_variance(999		) - 0.9_99_49_87		)		) < 1E-5

 def  __UpperCAmelCase						(  self		)	->       Optional[Any]:
    UpperCAmelCase__						=self.scheduler_classes[0]
    UpperCAmelCase__						=self.get_scheduler_config(variance_type="learned_range"		)
    UpperCAmelCase__						=scheduler_class(**A_		)

    UpperCAmelCase__						=0.5

    assert scheduler._get_variance(1,		predicted_variance=A_		) - -10.1_71_27_90 < 1E-5
    assert scheduler._get_variance(487,		predicted_variance=A_		) - -5.7_99_80_52 < 1E-5
    assert scheduler._get_variance(999,		predicted_variance=A_		) - -0.0_01_00_11 < 1E-5

 def  __UpperCAmelCase						(  self		)	->       Optional[Any]:
    UpperCAmelCase__						=self.scheduler_classes[0]
    UpperCAmelCase__						=self.get_scheduler_config()
    UpperCAmelCase__						=scheduler_class(**A_		)

    UpperCAmelCase__						=scheduler.timesteps

    UpperCAmelCase__						=self.dummy_model()
    UpperCAmelCase__						=self.dummy_sample_deter
    UpperCAmelCase__						=torch.manual_seed(0		)

    for i, t in enumerate(A_		):
       # 1. predict noise residual
       UpperCAmelCase__						=model(A_,		A_		)

       # 2. predict previous mean of sample x_t-1
       UpperCAmelCase__						=scheduler.step(A_,		A_,		A_,		generator=A_		).prev_sample

       UpperCAmelCase__						=pred_prev_sample

    UpperCAmelCase__						=torch.sum(torch.abs(A_		)		)
    UpperCAmelCase__						=torch.mean(torch.abs(A_		)		)

    assert abs(result_sum.item() - 2_52.2_68_24_95		) < 1E-2
    assert abs(result_mean.item() - 0.3_28_47_43		) < 1E-3

 def  __UpperCAmelCase						(  self		)	->       Any:
    UpperCAmelCase__						=self.scheduler_classes[0]
    UpperCAmelCase__						=self.get_scheduler_config()
    UpperCAmelCase__						=scheduler_class(**A_		)

    scheduler.set_timesteps(25		)

    UpperCAmelCase__						=scheduler.timesteps

    UpperCAmelCase__						=self.dummy_model()
    UpperCAmelCase__						=self.dummy_sample_deter
    UpperCAmelCase__						=torch.manual_seed(0		)

    for i, t in enumerate(A_		):
       # 1. predict noise residual
       UpperCAmelCase__						=model(A_,		A_		)

       if i + 1 == timesteps.shape[0]:
          UpperCAmelCase__						=None
       else:
          UpperCAmelCase__						=timesteps[i + 1]

       # 2. predict previous mean of sample x_t-1
       UpperCAmelCase__						=scheduler.step(
           A_,		A_,		A_,		prev_timestep=A_,		generator=A_		).prev_sample

       UpperCAmelCase__						=pred_prev_sample

    UpperCAmelCase__						=torch.sum(torch.abs(A_		)		)
    UpperCAmelCase__						=torch.mean(torch.abs(A_		)		)

    assert abs(result_sum.item() - 2_58.2_04_49_83		) < 1E-2
    assert abs(result_mean.item() - 0.3_36_20_38		) < 1E-3

 def  __UpperCAmelCase						(  self		)	->       Tuple:
    pass






 def  __UpperCAmelCase						(  self		)	->       List[Any]:
    pass










from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_flax_available,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
    is_vision_available,
)


UpperCamelCase_												=	{
    'configuration_owlvit': [
        'OWLVIT_PRETRAINED_CONFIG_ARCHIVE_MAP',
        'OwlViTConfig',
        'OwlViTOnnxConfig',
        'OwlViTTextConfig',
        'OwlViTVisionConfig',
    ],
    'processing_owlvit': ['OwlViTProcessor'],
}


try:
    if not is_vision_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    UpperCamelCase_												=	['OwlViTFeatureExtractor']
    UpperCamelCase_												=	['OwlViTImageProcessor']

try:
    if not is_torch_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    UpperCamelCase_												=	[
        'OWLVIT_PRETRAINED_MODEL_ARCHIVE_LIST',
        'OwlViTModel',
        'OwlViTPreTrainedModel',
        'OwlViTTextModel',
        'OwlViTVisionModel',
        'OwlViTForObjectDetection',
    ]

if TYPE_CHECKING:
    from .configuration_owlvit import (
        OWLVIT_PRETRAINED_CONFIG_ARCHIVE_MAP,
        OwlViTConfig,
        OwlViTOnnxConfig,
        OwlViTTextConfig,
        OwlViTVisionConfig,
    )
    from .processing_owlvit import OwlViTProcessor

    try:
        if not is_vision_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .feature_extraction_owlvit import OwlViTFeatureExtractor
        from .image_processing_owlvit import OwlViTImageProcessor

    try:
        if not is_torch_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .modeling_owlvit import (
            OWLVIT_PRETRAINED_MODEL_ARCHIVE_LIST,
            OwlViTForObjectDetection,
            OwlViTModel,
            OwlViTPreTrainedModel,
            OwlViTTextModel,
            OwlViTVisionModel,
        )

else:
    import sys

    UpperCamelCase_												=	_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)



