


from typing import TYPE_CHECKING

from ...utils import _LazyModule


lowerCamelCase__							:						Dict							     =   {"""processing_wav2vec2_with_lm""": ["""Wav2Vec2ProcessorWithLM"""]}


if TYPE_CHECKING:
   from .processing_wavaveca_with_lm import WavaVecaProcessorWithLM
else:
   import sys

   lowerCamelCase__							:						Optional[int]							     =   _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)





import torch

from diffusers import CMStochasticIterativeScheduler

from .test_schedulers import SchedulerCommonTest
class 		__magic_name__						(snake_case_				):





     '''simple docstring'''



     __lowercase				:							str	   =       (CMStochasticIterativeScheduler,)
     __lowercase				:							List[str]	   =       10

     def 	SCREAMING_SNAKE_CASE__						(					self:int     ,		**_a:Optional[int]   ):
       snake_case__						= {
           '''num_train_timesteps''': 2_01,
           '''sigma_min''': 0.002,
           '''sigma_max''': 80.0,
       }

       config.update(**_a   )
       return config

     def 	SCREAMING_SNAKE_CASE__						(					self:Optional[int]   ):
       snake_case__						= 10

       snake_case__						= self.get_scheduler_config()
       snake_case__						= self.scheduler_classes[0](**_a   )

       scheduler.set_timesteps(_a   )

       snake_case__						= scheduler.timesteps[0]
       snake_case__						= scheduler.timesteps[1]

       snake_case__						= self.dummy_sample
       snake_case__						= 0.1 * sample

       snake_case__						= scheduler.step(_a     ,		_a     ,		_a   ).prev_sample
       snake_case__						= scheduler.step(_a     ,		_a     ,		_a   ).prev_sample

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

     def 	SCREAMING_SNAKE_CASE__						(					self:Any   ):
       for timesteps in [10, 50, 1_00, 10_00]:
         self.check_over_configs(num_train_timesteps=_a   )

     def 	SCREAMING_SNAKE_CASE__						(					self:List[str]   ):
       for clip_denoised in [True, False]:
         self.check_over_configs(clip_denoised=_a   )

     def 	SCREAMING_SNAKE_CASE__						(					self:int   ):
       snake_case__						= self.scheduler_classes[0]
       snake_case__						= self.get_scheduler_config()
       snake_case__						= scheduler_class(**_a   )

       snake_case__						= 1
       scheduler.set_timesteps(_a   )
       snake_case__						= scheduler.timesteps

       snake_case__						= torch.manual_seed(0   )

       snake_case__						= self.dummy_model()
       snake_case__						= self.dummy_sample_deter * scheduler.init_noise_sigma

       for i, t in enumerate(_a   ):
         # 1. scale model input
         snake_case__						= scheduler.scale_model_input(_a     ,		_a   )

         # 2. predict noise residual
         snake_case__						= model(_a     ,		_a   )

         # 3. predict previous sample x_t-1
         snake_case__						= scheduler.step(_a     ,		_a     ,		_a     ,		generator=_a   ).prev_sample

         snake_case__						= pred_prev_sample

       snake_case__						= torch.sum(torch.abs(_a   )   )
       snake_case__						= torch.mean(torch.abs(_a   )   )

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

     def 	SCREAMING_SNAKE_CASE__						(					self:Tuple   ):
       snake_case__						= self.scheduler_classes[0]
       snake_case__						= self.get_scheduler_config()
       snake_case__						= scheduler_class(**_a   )

       snake_case__						= [1_06, 0]
       scheduler.set_timesteps(timesteps=_a   )
       snake_case__						= scheduler.timesteps

       snake_case__						= torch.manual_seed(0   )

       snake_case__						= self.dummy_model()
       snake_case__						= self.dummy_sample_deter * scheduler.init_noise_sigma

       for t in timesteps:
         # 1. scale model input
         snake_case__						= scheduler.scale_model_input(_a     ,		_a   )

         # 2. predict noise residual
         snake_case__						= model(_a     ,		_a   )

         # 3. predict previous sample x_t-1
         snake_case__						= scheduler.step(_a     ,		_a     ,		_a     ,		generator=_a   ).prev_sample

         snake_case__						= pred_prev_sample

       snake_case__						= torch.sum(torch.abs(_a   )   )
       snake_case__						= torch.mean(torch.abs(_a   )   )

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

     def 	SCREAMING_SNAKE_CASE__						(					self:Any   ):
       snake_case__						= self.scheduler_classes[0]
       snake_case__						= self.get_scheduler_config()
       snake_case__						= scheduler_class(**_a   )

       snake_case__						= [39, 30, 12, 15, 0]

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

     def 	SCREAMING_SNAKE_CASE__						(					self:Any   ):
       snake_case__						= self.scheduler_classes[0]
       snake_case__						= self.get_scheduler_config()
       snake_case__						= scheduler_class(**_a   )

       snake_case__						= [39, 30, 12, 1, 0]
       snake_case__						= len(_a   )

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







     def 	SCREAMING_SNAKE_CASE__						(					self:Tuple   ):
       snake_case__						= self.scheduler_classes[0]
       snake_case__						= self.get_scheduler_config()
       snake_case__						= scheduler_class(**_a   )

       snake_case__						= [scheduler.config.num_train_timesteps]

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

