



'''simple docstring'''



import torch

from diffusers import CMStochasticIterativeScheduler

from .test_schedulers import SchedulerCommonTest





class _UpperCamelCase		(     __SCREAMING_SNAKE_CASE		):





    '''simple docstring'''





    lowerCAmelCase__					    =    (CMStochasticIterativeScheduler,)
    lowerCAmelCase__					    =    10

    def 				__lowerCamelCase     (							self  :  Dict  ,       **_lowerCAmelCase  :  int):







           '''simple docstring'''

           __lowercase       	={
               """num_train_timesteps""": 2_0_1,
               """sigma_min""": 0.002,
               """sigma_max""": 80.0,
           }

           config.update(**_a)
           return config

    def 				__lowerCamelCase     (							self  :  Union[str, Any]):







           '''simple docstring'''

           __lowercase       	=1_0

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

           scheduler.set_timesteps(_a)

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

           __lowercase       	=self.dummy_sample
           __lowercase       	=0.1 * sample

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

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

    def 				__lowerCamelCase     (							self  :  Any):







           '''simple docstring'''

           for timesteps in [1_0, 5_0, 1_0_0, 1_0_0_0]:
                  self.check_over_configs(num_train_timesteps=_a)

    def 				__lowerCamelCase     (							self  :  int):







           '''simple docstring'''

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

    def 				__lowerCamelCase     (							self  :  Optional[Any]):







           '''simple docstring'''

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

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

           __lowercase       	=torch.manual_seed(0)

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

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

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

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

                  __lowercase       	=pred_prev_sample

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

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

    def 				__lowerCamelCase     (							self  :  int):







           '''simple docstring'''

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

           __lowercase       	=[1_0_6, 0]
           scheduler.set_timesteps(timesteps=_a)
           __lowercase       	=scheduler.timesteps

           __lowercase       	=torch.manual_seed(0)

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

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

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

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

                  __lowercase       	=pred_prev_sample

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

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

    def 				__lowerCamelCase     (							self  :  Optional[int]):







           '''simple docstring'''

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

           __lowercase       	=[3_9, 3_0, 1_2, 1_5, 0]

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

    def 				__lowerCamelCase     (							self  :  Union[str, Any]):







           '''simple docstring'''

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

           __lowercase       	=[3_9, 3_0, 1_2, 1, 0]
           __lowercase       	=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 				__lowerCamelCase     (							self  :  Optional[Any]):







           '''simple docstring'''

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

           __lowercase       	=[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)








'''simple docstring'''



from typing import TYPE_CHECKING

from ...utils import _LazyModule


lowerCamelCase	 =       {"""tokenization_bertweet""": ["""BertweetTokenizer"""]}


if TYPE_CHECKING:
		from .tokenization_bertweet import BertweetTokenizer

else:
		import sys

		lowerCamelCase	 =       _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)



