





'''simple docstring'''


import torch

from diffusers import CMStochasticIterativeScheduler

from .test_schedulers import SchedulerCommonTest
class       a_   (				_lowerCAmelCase							):
     __A      =			(CMStochasticIterativeScheduler,)
     __A      =			10





     def 							lowercase__      ( self		:					Optional[Any]	,      **lowercase		:					Tuple						):





        """simple docstring"""




        lowercase_			:Optional[Any]    			= {
            "num_train_timesteps": 201,
            "sigma_min": 0.0_02,
            "sigma_max": 80.0,
        }

        config.update(**lowercase						)
        return config





     def 							lowercase__      ( self		:					List[Any]						):





        """simple docstring"""




        lowercase_			:Optional[Any]    			= 10

        lowercase_			:Dict    			= self.get_scheduler_config()
        lowercase_			:Any    			= self.scheduler_classes[0](**lowercase						)

        scheduler.set_timesteps(lowercase						)

        lowercase_			:Any    			= scheduler.timesteps[0]
        lowercase_			:str    			= scheduler.timesteps[1]

        lowercase_			:Optional[Any]    			= self.dummy_sample
        lowercase_			:str    			= 0.1 * sample

        lowercase_			:Tuple    			= scheduler.step(lowercase	,      lowercase	,      lowercase						).prev_sample
        lowercase_			:str    			= scheduler.step(lowercase	,      lowercase	,      lowercase						).prev_sample

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





     def 							lowercase__      ( self		:					str						):





        """simple docstring"""




        for timesteps in [10, 50, 100, 1_000]:
           self.check_over_configs(num_train_timesteps=lowercase						)





     def 							lowercase__      ( self		:					Any						):





        """simple docstring"""




        for clip_denoised in [True, False]:
           self.check_over_configs(clip_denoised=lowercase						)





     def 							lowercase__      ( self		:					List[str]						):





        """simple docstring"""




        lowercase_			:Optional[int]    			= self.scheduler_classes[0]
        lowercase_			:Any    			= self.get_scheduler_config()
        lowercase_			:Optional[int]    			= scheduler_class(**lowercase						)

        lowercase_			:int    			= 1
        scheduler.set_timesteps(lowercase						)
        lowercase_			:List[Any]    			= scheduler.timesteps

        lowercase_			:List[Any]    			= torch.manual_seed(0						)

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

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

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

           # 3. predict previous sample x_t-1
           lowercase_			:List[Any]    			= scheduler.step(lowercase	,      lowercase	,      lowercase	,      generator=lowercase						).prev_sample

           lowercase_			:Any    			= pred_prev_sample

        lowercase_			:str    			= torch.sum(torch.abs(lowercase						)						)
        lowercase_			:str    			= torch.mean(torch.abs(lowercase						)						)

        assert abs(result_sum.item() - 1_92.76_14						) < 1e-2
        assert abs(result_mean.item() - 0.25_10						) < 1e-3





     def 							lowercase__      ( self		:					Optional[Any]						):





        """simple docstring"""




        lowercase_			:int    			= self.scheduler_classes[0]
        lowercase_			:Dict    			= self.get_scheduler_config()
        lowercase_			:Union[str, Any]    			= scheduler_class(**lowercase						)

        lowercase_			:List[Any]    			= [106, 0]
        scheduler.set_timesteps(timesteps=lowercase						)
        lowercase_			:List[Any]    			= scheduler.timesteps

        lowercase_			:Dict    			= torch.manual_seed(0						)

        lowercase_			:Dict    			= self.dummy_model()
        lowercase_			:Tuple    			= self.dummy_sample_deter * scheduler.init_noise_sigma

        for t in timesteps:
           # 1. scale model input
           lowercase_			:Optional[int]    			= scheduler.scale_model_input(lowercase	,      lowercase						)

           # 2. predict noise residual
           lowercase_			:int    			= model(lowercase	,      lowercase						)

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

           lowercase_			:str    			= pred_prev_sample

        lowercase_			:Any    			= torch.sum(torch.abs(lowercase						)						)
        lowercase_			:str    			= torch.mean(torch.abs(lowercase						)						)

        assert abs(result_sum.item() - 3_47.63_57						) < 1e-2
        assert abs(result_mean.item() - 0.45_27						) < 1e-3





     def 							lowercase__      ( self		:					Any						):





        """simple docstring"""




        lowercase_			:Any    			= self.scheduler_classes[0]
        lowercase_			:Union[str, Any]    			= self.get_scheduler_config()
        lowercase_			:Optional[Any]    			= scheduler_class(**lowercase						)

        lowercase_			:List[Any]    			= [39, 30, 12, 15, 0]

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





     def 							lowercase__      ( self		:					Dict						):





        """simple docstring"""




        lowercase_			:Dict    			= self.scheduler_classes[0]
        lowercase_			:int    			= self.get_scheduler_config()
        lowercase_			:Union[str, Any]    			= scheduler_class(**lowercase						)

        lowercase_			:str    			= [39, 30, 12, 1, 0]
        lowercase_			:int    			= len(lowercase						)

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







     def 							lowercase__      ( self		:					Any						):





        """simple docstring"""




        lowercase_			:Dict    			= self.scheduler_classes[0]
        lowercase_			:List[str]    			= self.get_scheduler_config()
        lowercase_			:List[str]    			= scheduler_class(**lowercase						)

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

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









'''simple docstring'''


from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


lowerCAmelCase					:     Optional[Any]												={
    '''configuration_x_clip''': [
        '''XCLIP_PRETRAINED_CONFIG_ARCHIVE_MAP''',
        '''XCLIPConfig''',
        '''XCLIPTextConfig''',
        '''XCLIPVisionConfig''',
    ],
    '''processing_x_clip''': ['''XCLIPProcessor'''],
}

try:
 if not is_torch_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 lowerCAmelCase					:     Tuple												=[
     '''XCLIP_PRETRAINED_MODEL_ARCHIVE_LIST''',
     '''XCLIPModel''',
     '''XCLIPPreTrainedModel''',
     '''XCLIPTextModel''',
     '''XCLIPVisionModel''',
 ]

if TYPE_CHECKING:
 from .configuration_x_clip import (
     XCLIP_PRETRAINED_CONFIG_ARCHIVE_MAP,
     XCLIPConfig,
     XCLIPTextConfig,
     XCLIPVisionConfig,
 )
 from .processing_x_clip import XCLIPProcessor

 try:
  if not is_torch_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_x_clip import (
      XCLIP_PRETRAINED_MODEL_ARCHIVE_LIST,
      XCLIPModel,
      XCLIPPreTrainedModel,
      XCLIPTextModel,
      XCLIPVisionModel,
  )

else:
 import sys

 lowerCAmelCase					:     str												=_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)


