

'''simple docstring'''
from ...configuration_utils import PretrainedConfig
from ...utils import logging


_lowerCamelCase	 =			logging.get_logger(__name__)

_lowerCamelCase	 =			{
    """ZinengTang/tvlt-base""": """https://huggingface.co/ZinengTang/tvlt-base/blob/main/config.json""",
}
class  _snake_case						(__SCREAMING_SNAKE_CASE):
			__A   :     List[Any]          ="tvlt"






			def __init__(   self       ,_snake_case=2_24       ,_snake_case=20_48       ,_snake_case=1_28       ,_snake_case=[16, 16]       ,_snake_case=[16, 16]       ,_snake_case=3       ,_snake_case=1       ,_snake_case=8       ,_snake_case=7_68       ,_snake_case=12       ,_snake_case=12       ,_snake_case=30_72       ,_snake_case="gelu"       ,_snake_case=0.0       ,_snake_case=0.0       ,_snake_case=0.02       ,_snake_case=1E-6       ,_snake_case=True       ,_snake_case=False       ,_snake_case=16       ,_snake_case=5_12       ,_snake_case=8       ,_snake_case=20_48       ,_snake_case=0.75       ,_snake_case=0.15       ,_snake_case="frame-level"       ,_snake_case=True       ,_snake_case=True       ,_snake_case="classification"       ,**_snake_case       ,):
				super().__init__(**_snake_case			)

				if audio_mask_type not in ("frame-level", "patch_level"):
					raise ValueError(
					    "audio_mask_type must be one of two acceptable strategies - {'frame_level', 'patch-level') "
					    f'''got {audio_mask_type}'''			)

				UpperCAmelCase_			:	List[str]      =   image_size
				UpperCAmelCase_			:	List[str]      =   spectrogram_length
				UpperCAmelCase_			:	Any      =   frequency_length
				UpperCAmelCase_			:	Optional[int]      =   image_patch_size
				UpperCAmelCase_			:	Tuple      =   audio_patch_size
				UpperCAmelCase_			:	Dict      =   num_image_channels
				UpperCAmelCase_			:	int      =   num_audio_channels
				UpperCAmelCase_			:	Any      =   num_frames

				UpperCAmelCase_			:	Tuple      =   hidden_size
				UpperCAmelCase_			:	str      =   num_hidden_layers
				UpperCAmelCase_			:	Union[str, Any]      =   num_attention_heads
				UpperCAmelCase_			:	List[str]      =   intermediate_size
				UpperCAmelCase_			:	List[str]      =   hidden_act
				UpperCAmelCase_			:	Any      =   hidden_dropout_prob
				UpperCAmelCase_			:	List[Any]      =   attention_probs_dropout_prob
				UpperCAmelCase_			:	Tuple      =   initializer_range
				UpperCAmelCase_			:	Optional[int]      =   layer_norm_eps
				UpperCAmelCase_			:	Optional[Any]      =   qkv_bias
				UpperCAmelCase_			:	Tuple      =   use_mean_pooling

				UpperCAmelCase_			:	List[Any]      =   decoder_num_attention_heads
				UpperCAmelCase_			:	List[str]      =   decoder_hidden_size
				UpperCAmelCase_			:	List[Any]      =   decoder_num_hidden_layers
				UpperCAmelCase_			:	Dict      =   decoder_intermediate_size
				UpperCAmelCase_			:	Optional[int]      =   pixel_mask_ratio
				UpperCAmelCase_			:	List[Any]      =   audio_mask_ratio
				UpperCAmelCase_			:	str      =   audio_mask_type

				UpperCAmelCase_			:	Optional[int]      =   task_matching
				UpperCAmelCase_			:	List[Any]      =   task_mae
				UpperCAmelCase_			:	Any      =   loss_type







'''simple docstring'''
import torch

from diffusers import KDPMaAncestralDiscreteScheduler
from diffusers.utils import torch_device

from .test_schedulers import SchedulerCommonTest
class  _snake_case						(__SCREAMING_SNAKE_CASE):
			__A   :     Optional[int]          =(KDPMaAncestralDiscreteScheduler,)
			__A   :     List[Any]          =10






			def 						UpperCamelCase__				(   self       ,**_snake_case			):
				UpperCAmelCase_			:	List[Any]      =   {
				    "num_train_timesteps": 11_00,
				    "beta_start": 0.0001,
				    "beta_end": 0.02,
				    "beta_schedule": "linear",
				}

				config.update(**_snake_case			)
				return config






			def 						UpperCamelCase__				(   self			):
				for timesteps in [10, 50, 1_00, 10_00]:
					self.check_over_configs(num_train_timesteps=_snake_case			)






			def 						UpperCamelCase__				(   self			):
				for beta_start, beta_end in zip([0.00001, 0.0001, 0.001]       ,[0.0002, 0.002, 0.02]			):
					self.check_over_configs(beta_start=_snake_case       ,beta_end=_snake_case			)






			def 						UpperCamelCase__				(   self			):
				for schedule in ["linear", "scaled_linear"]:
					self.check_over_configs(beta_schedule=_snake_case			)






			def 						UpperCamelCase__				(   self			):
				if torch_device == "mps":
					return
				UpperCAmelCase_			:	str      =   self.scheduler_classes[0]
				UpperCAmelCase_			:	str      =   self.get_scheduler_config()
				UpperCAmelCase_			:	str      =   scheduler_class(**_snake_case			)

				scheduler.set_timesteps(self.num_inference_steps			)

				UpperCAmelCase_			:	Dict      =   torch.manual_seed(0			)

				UpperCAmelCase_			:	str      =   self.dummy_model()
				UpperCAmelCase_			:	str      =   self.dummy_sample_deter * scheduler.init_noise_sigma
				UpperCAmelCase_			:	Optional[Any]      =   sample.to(_snake_case			)

				for i, t in enumerate(scheduler.timesteps			):
					UpperCAmelCase_			:	Tuple      =   scheduler.scale_model_input(_snake_case       ,_snake_case			)

					UpperCAmelCase_			:	Union[str, Any]      =   model(_snake_case       ,_snake_case			)

					UpperCAmelCase_			:	Optional[Any]      =   scheduler.step(_snake_case       ,_snake_case       ,_snake_case       ,generator=_snake_case			)
					UpperCAmelCase_			:	Optional[Any]      =   output.prev_sample

				UpperCAmelCase_			:	Dict      =   torch.sum(torch.abs(_snake_case			)			)
				UpperCAmelCase_			:	List[Any]      =   torch.mean(torch.abs(_snake_case			)			)

				assert abs(result_sum.item() - 13849.3877			) < 1E-2
				assert abs(result_mean.item() - 18.0331			) < 5E-3






			def 						UpperCamelCase__				(   self			):
				for prediction_type in ["epsilon", "v_prediction"]:
					self.check_over_configs(prediction_type=_snake_case			)






			def 						UpperCamelCase__				(   self			):
				if torch_device == "mps":
					return
				UpperCAmelCase_			:	Any      =   self.scheduler_classes[0]
				UpperCAmelCase_			:	Any      =   self.get_scheduler_config(prediction_type="v_prediction"			)
				UpperCAmelCase_			:	Dict      =   scheduler_class(**_snake_case			)

				scheduler.set_timesteps(self.num_inference_steps			)

				UpperCAmelCase_			:	Union[str, Any]      =   self.dummy_model()
				UpperCAmelCase_			:	List[str]      =   self.dummy_sample_deter * scheduler.init_noise_sigma
				UpperCAmelCase_			:	int      =   sample.to(_snake_case			)

				UpperCAmelCase_			:	str      =   torch.manual_seed(0			)

				for i, t in enumerate(scheduler.timesteps			):
					UpperCAmelCase_			:	Dict      =   scheduler.scale_model_input(_snake_case       ,_snake_case			)

					UpperCAmelCase_			:	str      =   model(_snake_case       ,_snake_case			)

					UpperCAmelCase_			:	Optional[Any]      =   scheduler.step(_snake_case       ,_snake_case       ,_snake_case       ,generator=_snake_case			)
					UpperCAmelCase_			:	Any      =   output.prev_sample

				UpperCAmelCase_			:	Any      =   torch.sum(torch.abs(_snake_case			)			)
				UpperCAmelCase_			:	Tuple      =   torch.mean(torch.abs(_snake_case			)			)

				assert abs(result_sum.item() - 328.9970			) < 1E-2
				assert abs(result_mean.item() - 0.4284			) < 1E-3






			def 						UpperCamelCase__				(   self			):
				if torch_device == "mps":
					return
				UpperCAmelCase_			:	Optional[Any]      =   self.scheduler_classes[0]
				UpperCAmelCase_			:	Union[str, Any]      =   self.get_scheduler_config()
				UpperCAmelCase_			:	str      =   scheduler_class(**_snake_case			)

				scheduler.set_timesteps(self.num_inference_steps       ,device=_snake_case			)
				UpperCAmelCase_			:	Dict      =   torch.manual_seed(0			)

				UpperCAmelCase_			:	List[str]      =   self.dummy_model()
				UpperCAmelCase_			:	str      =   self.dummy_sample_deter.to(_snake_case			) * scheduler.init_noise_sigma

				for t in scheduler.timesteps:
					UpperCAmelCase_			:	Union[str, Any]      =   scheduler.scale_model_input(_snake_case       ,_snake_case			)

					UpperCAmelCase_			:	Tuple      =   model(_snake_case       ,_snake_case			)

					UpperCAmelCase_			:	int      =   scheduler.step(_snake_case       ,_snake_case       ,_snake_case       ,generator=_snake_case			)
					UpperCAmelCase_			:	List[str]      =   output.prev_sample

				UpperCAmelCase_			:	Any      =   torch.sum(torch.abs(_snake_case			)			)
				UpperCAmelCase_			:	Optional[int]      =   torch.mean(torch.abs(_snake_case			)			)

				assert abs(result_sum.item() - 13849.3818			) < 1E-1
				assert abs(result_mean.item() - 18.0331			) < 1E-3




