






'''simple docstring'''







import torch

from diffusers import CMStochasticIterativeScheduler

from .test_schedulers import SchedulerCommonTest

class _lowercase			(       _lowercase	):
							a          = (CMStochasticIterativeScheduler,)
							a          = 10




							def 	lowerCamelCase_       (							self:       List[Any]    ,							**UpperCamelCase__:       int  ):
														lowerCamelCase__      :			Optional[int] 						= {
														    """num_train_timesteps""": 201,
														    """sigma_min""": 0.002,
														    """sigma_max""": 80.0,
														}

														config.update(**UpperCamelCase__  )
														return config




							def 	lowerCamelCase_       (							self:       Optional[int]  ):
														lowerCamelCase__      :			List[str] 						= 10

														lowerCamelCase__      :			int 						= self.get_scheduler_config()
														lowerCamelCase__      :			Optional[int] 						= self.scheduler_classes[0](**UpperCamelCase__  )

														scheduler.set_timesteps(UpperCamelCase__  )

														lowerCamelCase__      :			Tuple 						= scheduler.timesteps[0]
														lowerCamelCase__      :			Optional[Any] 						= scheduler.timesteps[1]

														lowerCamelCase__      :			str 						= self.dummy_sample
														lowerCamelCase__      :			Dict 						= 0.1 * sample

														lowerCamelCase__      :			Tuple 						= scheduler.step(UpperCamelCase__    ,							UpperCamelCase__    ,							UpperCamelCase__  ).prev_sample
														lowerCamelCase__      :			Union[str, Any] 						= scheduler.step(UpperCamelCase__    ,							UpperCamelCase__    ,							UpperCamelCase__  ).prev_sample

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




							def 	lowerCamelCase_       (							self:       Optional[int]  ):
														for timesteps in [10, 50, 100, 1_000]:
																					self.check_over_configs(num_train_timesteps=UpperCamelCase__  )




							def 	lowerCamelCase_       (							self:       str  ):
														for clip_denoised in [True, False]:
																					self.check_over_configs(clip_denoised=UpperCamelCase__  )




							def 	lowerCamelCase_       (							self:       Union[str, Any]  ):
														lowerCamelCase__      :			Any 						= self.scheduler_classes[0]
														lowerCamelCase__      :			Optional[Any] 						= self.get_scheduler_config()
														lowerCamelCase__      :			int 						= scheduler_class(**UpperCamelCase__  )

														lowerCamelCase__      :			int 						= 1
														scheduler.set_timesteps(UpperCamelCase__  )
														lowerCamelCase__      :			Dict 						= scheduler.timesteps

														lowerCamelCase__      :			Any 						= torch.manual_seed(0  )

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

														for i, t in enumerate(UpperCamelCase__  ):
																					# 1. scale model input
																					lowerCamelCase__      :			int 						= scheduler.scale_model_input(UpperCamelCase__    ,							UpperCamelCase__  )

																					# 2. predict noise residual
																					lowerCamelCase__      :			str 						= model(UpperCamelCase__    ,							UpperCamelCase__  )

																					# 3. predict previous sample x_t-1
																					lowerCamelCase__      :			Union[str, Any] 						= scheduler.step(UpperCamelCase__    ,							UpperCamelCase__    ,							UpperCamelCase__    ,							generator=UpperCamelCase__  ).prev_sample

																					lowerCamelCase__      :			Optional[Any] 						= pred_prev_sample

														lowerCamelCase__      :			Optional[int] 						= torch.sum(torch.abs(UpperCamelCase__  )  )
														lowerCamelCase__      :			Any 						= torch.mean(torch.abs(UpperCamelCase__  )  )

														assert abs(result_sum.item() - 192.7_614  ) < 1e-2
														assert abs(result_mean.item() - 0.2_510  ) < 1e-3




							def 	lowerCamelCase_       (							self:       Optional[int]  ):
														lowerCamelCase__      :			str 						= self.scheduler_classes[0]
														lowerCamelCase__      :			Optional[Any] 						= self.get_scheduler_config()
														lowerCamelCase__      :			Optional[Any] 						= scheduler_class(**UpperCamelCase__  )

														lowerCamelCase__      :			Union[str, Any] 						= [106, 0]
														scheduler.set_timesteps(timesteps=UpperCamelCase__  )
														lowerCamelCase__      :			List[Any] 						= scheduler.timesteps

														lowerCamelCase__      :			Any 						= torch.manual_seed(0  )

														lowerCamelCase__      :			int 						= self.dummy_model()
														lowerCamelCase__      :			List[Any] 						= self.dummy_sample_deter * scheduler.init_noise_sigma

														for t in timesteps:
																					# 1. scale model input
																					lowerCamelCase__      :			str 						= scheduler.scale_model_input(UpperCamelCase__    ,							UpperCamelCase__  )

																					# 2. predict noise residual
																					lowerCamelCase__      :			Tuple 						= model(UpperCamelCase__    ,							UpperCamelCase__  )

																					# 3. predict previous sample x_t-1
																					lowerCamelCase__      :			Tuple 						= scheduler.step(UpperCamelCase__    ,							UpperCamelCase__    ,							UpperCamelCase__    ,							generator=UpperCamelCase__  ).prev_sample

																					lowerCamelCase__      :			Dict 						= pred_prev_sample

														lowerCamelCase__      :			int 						= torch.sum(torch.abs(UpperCamelCase__  )  )
														lowerCamelCase__      :			Union[str, Any] 						= torch.mean(torch.abs(UpperCamelCase__  )  )

														assert abs(result_sum.item() - 347.6_357  ) < 1e-2
														assert abs(result_mean.item() - 0.4_527  ) < 1e-3




							def 	lowerCamelCase_       (							self:       int  ):
														lowerCamelCase__      :			Dict 						= self.scheduler_classes[0]
														lowerCamelCase__      :			int 						= self.get_scheduler_config()
														lowerCamelCase__      :			Any 						= scheduler_class(**UpperCamelCase__  )

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

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




							def 	lowerCamelCase_       (							self:       str  ):
														lowerCamelCase__      :			Optional[Any] 						= self.scheduler_classes[0]
														lowerCamelCase__      :			str 						= self.get_scheduler_config()
														lowerCamelCase__      :			str 						= scheduler_class(**UpperCamelCase__  )

														lowerCamelCase__      :			str 						= [39, 30, 12, 1, 0]
														lowerCamelCase__      :			Optional[Any] 						= len(UpperCamelCase__  )

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




							def 	lowerCamelCase_       (							self:       int  ):
														lowerCamelCase__      :			Dict 						= self.scheduler_classes[0]
														lowerCamelCase__      :			Optional[int] 						= self.get_scheduler_config()
														lowerCamelCase__      :			int 						= scheduler_class(**UpperCamelCase__  )

														lowerCamelCase__      :			int 						= [scheduler.config.num_train_timesteps]

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









'''simple docstring'''







from ...configuration_utils import PretrainedConfig
from ...utils import logging


_A			:    Union[str, Any]					  =logging.get_logger(__name__)

_A			:    List[str]					  ={
    '''MIT/ast-finetuned-audioset-10-10-0.4593''': (
        '''https://huggingface.co/MIT/ast-finetuned-audioset-10-10-0.4593/resolve/main/config.json'''
    ),
}

class _lowercase			(       _lowercase	):
							a          = """audio-spectrogram-transformer"""




							def __init__(							self:       str    ,							UpperCamelCase__:       Any=768    ,							UpperCamelCase__:       Union[str, Any]=12    ,							UpperCamelCase__:       List[Any]=12    ,							UpperCamelCase__:       int=3_072    ,							UpperCamelCase__:       Optional[Any]="gelu"    ,							UpperCamelCase__:       Optional[int]=0.0    ,							UpperCamelCase__:       Tuple=0.0    ,							UpperCamelCase__:       Union[str, Any]=0.02    ,							UpperCamelCase__:       Dict=1e-12    ,							UpperCamelCase__:       List[str]=16    ,							UpperCamelCase__:       List[str]=True    ,							UpperCamelCase__:       Any=10    ,							UpperCamelCase__:       List[str]=10    ,							UpperCamelCase__:       Any=1_024    ,							UpperCamelCase__:       Optional[Any]=128    ,							**UpperCamelCase__:       Union[str, Any]    ,							):
														super().__init__(**UpperCamelCase__  )

														lowerCamelCase__      :			List[Any] 						= hidden_size
														lowerCamelCase__      :			int 						= num_hidden_layers
														lowerCamelCase__      :			List[str] 						= num_attention_heads
														lowerCamelCase__      :			Optional[int] 						= intermediate_size
														lowerCamelCase__      :			List[Any] 						= hidden_act
														lowerCamelCase__      :			List[Any] 						= hidden_dropout_prob
														lowerCamelCase__      :			str 						= attention_probs_dropout_prob
														lowerCamelCase__      :			Dict 						= initializer_range
														lowerCamelCase__      :			List[str] 						= layer_norm_eps
														lowerCamelCase__      :			List[Any] 						= patch_size
														lowerCamelCase__      :			List[str] 						= qkv_bias
														lowerCamelCase__      :			Dict 						= frequency_stride
														lowerCamelCase__      :			List[Any] 						= time_stride
														lowerCamelCase__      :			str 						= max_length
														lowerCamelCase__      :			Dict 						= num_mel_bins

