





import torch

from diffusers import CMStochasticIterativeScheduler

from .test_schedulers import SchedulerCommonTest





class 			lowerCAmelCase__( __lowercase    ):






       '''simple docstring'''


       __snake_case 			=    (CMStochasticIterativeScheduler,)
       __snake_case 			=    1_0




       def     UpperCamelCase_			(      self    ,						**__lowerCamelCase							)			->     str:
            _SCREAMING_SNAKE_CASE       :	Optional[int]						    =							{
                "num_train_timesteps": 2_0_1,
                "sigma_min": 0.002,
                "sigma_max": 80.0,
            }

            config.update(**__lowerCamelCase							)
            return config




       def     UpperCamelCase_			(      self							)			->     Optional[Any]:
            _SCREAMING_SNAKE_CASE       :	Optional[Any]						    =							1_0

            _SCREAMING_SNAKE_CASE       :	int						    =							self.get_scheduler_config()
            _SCREAMING_SNAKE_CASE       :	int						    =							self.scheduler_classes[0](**__lowerCamelCase							)

            scheduler.set_timesteps(__lowerCamelCase							)

            _SCREAMING_SNAKE_CASE       :	Union[str, Any]						    =							scheduler.timesteps[0]
            _SCREAMING_SNAKE_CASE       :	int						    =							scheduler.timesteps[1]

            _SCREAMING_SNAKE_CASE       :	Union[str, Any]						    =							self.dummy_sample
            _SCREAMING_SNAKE_CASE       :	int						    =							0.1 * sample

            _SCREAMING_SNAKE_CASE       :	List[str]						    =							scheduler.step(__lowerCamelCase    ,						__lowerCamelCase    ,						__lowerCamelCase							).prev_sample
            _SCREAMING_SNAKE_CASE       :	str						    =							scheduler.step(__lowerCamelCase    ,						__lowerCamelCase    ,						__lowerCamelCase							).prev_sample

            self.assertEqual(output_a.shape    ,						sample.shape							)
            self.assertEqual(output_a.shape    ,						output_a.shape							)




       def     UpperCamelCase_			(      self							)			->     List[str]:
            for timesteps in [1_0, 5_0, 1_0_0, 1_0_0_0]:
                 self.check_over_configs(num_train_timesteps=__lowerCamelCase							)




       def     UpperCamelCase_			(      self							)			->     List[str]:
            for clip_denoised in [True, False]:
                 self.check_over_configs(clip_denoised=__lowerCamelCase							)




       def     UpperCamelCase_			(      self							)			->     List[Any]:
            _SCREAMING_SNAKE_CASE       :	Union[str, Any]						    =							self.scheduler_classes[0]
            _SCREAMING_SNAKE_CASE       :	Union[str, Any]						    =							self.get_scheduler_config()
            _SCREAMING_SNAKE_CASE       :	List[Any]						    =							scheduler_class(**__lowerCamelCase							)

            _SCREAMING_SNAKE_CASE       :	Optional[int]						    =							1
            scheduler.set_timesteps(__lowerCamelCase							)
            _SCREAMING_SNAKE_CASE       :	Union[str, Any]						    =							scheduler.timesteps

            _SCREAMING_SNAKE_CASE       :	int						    =							torch.manual_seed(0							)

            _SCREAMING_SNAKE_CASE       :	Union[str, Any]						    =							self.dummy_model()
            _SCREAMING_SNAKE_CASE       :	List[str]						    =							self.dummy_sample_deter * scheduler.init_noise_sigma

            for i, t in enumerate(__lowerCamelCase							):
                 # 1. scale model input
                 _SCREAMING_SNAKE_CASE       :	Tuple						    =							scheduler.scale_model_input(__lowerCamelCase    ,						__lowerCamelCase							)

                 # 2. predict noise residual
                 _SCREAMING_SNAKE_CASE       :	List[str]						    =							model(__lowerCamelCase    ,						__lowerCamelCase							)

                 # 3. predict previous sample x_t-1
                 _SCREAMING_SNAKE_CASE       :	int						    =							scheduler.step(__lowerCamelCase    ,						__lowerCamelCase    ,						__lowerCamelCase    ,						generator=__lowerCamelCase							).prev_sample

                 _SCREAMING_SNAKE_CASE       :	Dict						    =							pred_prev_sample

            _SCREAMING_SNAKE_CASE       :	List[str]						    =							torch.sum(torch.abs(__lowerCamelCase							)							)
            _SCREAMING_SNAKE_CASE       :	List[str]						    =							torch.mean(torch.abs(__lowerCamelCase							)							)

            assert abs(result_sum.item() - 192.7614							) < 1E-2
            assert abs(result_mean.item() - 0.2510							) < 1E-3




       def     UpperCamelCase_			(      self							)			->     int:
            _SCREAMING_SNAKE_CASE       :	Union[str, Any]						    =							self.scheduler_classes[0]
            _SCREAMING_SNAKE_CASE       :	Tuple						    =							self.get_scheduler_config()
            _SCREAMING_SNAKE_CASE       :	int						    =							scheduler_class(**__lowerCamelCase							)

            _SCREAMING_SNAKE_CASE       :	List[str]						    =							[1_0_6, 0]
            scheduler.set_timesteps(timesteps=__lowerCamelCase							)
            _SCREAMING_SNAKE_CASE       :	Optional[int]						    =							scheduler.timesteps

            _SCREAMING_SNAKE_CASE       :	int						    =							torch.manual_seed(0							)

            _SCREAMING_SNAKE_CASE       :	Optional[int]						    =							self.dummy_model()
            _SCREAMING_SNAKE_CASE       :	Tuple						    =							self.dummy_sample_deter * scheduler.init_noise_sigma

            for t in timesteps:
                 # 1. scale model input
                 _SCREAMING_SNAKE_CASE       :	Dict						    =							scheduler.scale_model_input(__lowerCamelCase    ,						__lowerCamelCase							)

                 # 2. predict noise residual
                 _SCREAMING_SNAKE_CASE       :	Union[str, Any]						    =							model(__lowerCamelCase    ,						__lowerCamelCase							)

                 # 3. predict previous sample x_t-1
                 _SCREAMING_SNAKE_CASE       :	Optional[int]						    =							scheduler.step(__lowerCamelCase    ,						__lowerCamelCase    ,						__lowerCamelCase    ,						generator=__lowerCamelCase							).prev_sample

                 _SCREAMING_SNAKE_CASE       :	Any						    =							pred_prev_sample

            _SCREAMING_SNAKE_CASE       :	List[str]						    =							torch.sum(torch.abs(__lowerCamelCase							)							)
            _SCREAMING_SNAKE_CASE       :	str						    =							torch.mean(torch.abs(__lowerCamelCase							)							)

            assert abs(result_sum.item() - 347.6357							) < 1E-2
            assert abs(result_mean.item() - 0.4527							) < 1E-3




       def     UpperCamelCase_			(      self							)			->     List[str]:
            _SCREAMING_SNAKE_CASE       :	List[str]						    =							self.scheduler_classes[0]
            _SCREAMING_SNAKE_CASE       :	Optional[Any]						    =							self.get_scheduler_config()
            _SCREAMING_SNAKE_CASE       :	int						    =							scheduler_class(**__lowerCamelCase							)

            _SCREAMING_SNAKE_CASE       :	List[str]						    =							[3_9, 3_0, 1_2, 1_5, 0]

            with self.assertRaises(__lowerCamelCase    ,						msg="`timesteps` must be in descending order."							):
                 scheduler.set_timesteps(timesteps=__lowerCamelCase							)




       def     UpperCamelCase_			(      self							)			->     List[str]:
            _SCREAMING_SNAKE_CASE       :	Tuple						    =							self.scheduler_classes[0]
            _SCREAMING_SNAKE_CASE       :	List[Any]						    =							self.get_scheduler_config()
            _SCREAMING_SNAKE_CASE       :	Any						    =							scheduler_class(**__lowerCamelCase							)

            _SCREAMING_SNAKE_CASE       :	Dict						    =							[3_9, 3_0, 1_2, 1, 0]
            _SCREAMING_SNAKE_CASE       :	Any						    =							len(__lowerCamelCase							)

            with self.assertRaises(__lowerCamelCase    ,						msg="Can only pass one of `num_inference_steps` or `timesteps`."							):
                 scheduler.set_timesteps(num_inference_steps=__lowerCamelCase    ,						timesteps=__lowerCamelCase							)







       def     UpperCamelCase_			(      self							)			->     str:
            _SCREAMING_SNAKE_CASE       :	Dict						    =							self.scheduler_classes[0]
            _SCREAMING_SNAKE_CASE       :	Optional[Any]						    =							self.get_scheduler_config()
            _SCREAMING_SNAKE_CASE       :	Optional[int]						    =							scheduler_class(**__lowerCamelCase							)

            _SCREAMING_SNAKE_CASE       :	Optional[int]						    =							[scheduler.config.num_train_timesteps]

            with self.assertRaises(
                __lowerCamelCase    ,						msg="`timesteps` must start before `self.config.train_timesteps`: {scheduler.config.num_train_timesteps}}"    ,						):
                 scheduler.set_timesteps(timesteps=__lowerCamelCase							)






from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


UpperCamelCase__      ={
    'configuration_clipseg': [
        'CLIPSEG_PRETRAINED_CONFIG_ARCHIVE_MAP',
        'CLIPSegConfig',
        'CLIPSegTextConfig',
        'CLIPSegVisionConfig',
    ],
    'processing_clipseg': ['CLIPSegProcessor'],
}

try:
       if not is_torch_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       UpperCamelCase__      =[
           'CLIPSEG_PRETRAINED_MODEL_ARCHIVE_LIST',
           'CLIPSegModel',
           'CLIPSegPreTrainedModel',
           'CLIPSegTextModel',
           'CLIPSegVisionModel',
           'CLIPSegForImageSegmentation',
       ]

if TYPE_CHECKING:
       from .configuration_clipseg import (
           CLIPSEG_PRETRAINED_CONFIG_ARCHIVE_MAP,
           CLIPSegConfig,
           CLIPSegTextConfig,
           CLIPSegVisionConfig,
       )
       from .processing_clipseg import CLIPSegProcessor

       try:
              if not is_torch_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .modeling_clipseg import (
                  CLIPSEG_PRETRAINED_MODEL_ARCHIVE_LIST,
                  CLIPSegForImageSegmentation,
                  CLIPSegModel,
                  CLIPSegPreTrainedModel,
                  CLIPSegTextModel,
                  CLIPSegVisionModel,
              )

else:
       import sys

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