


'''simple docstring'''



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


A__	:      List[str]				=						logging.get_logger(__name__)

A__	:      List[Any]				=						{
    """MCG-NJU/videomae-base""": """https://huggingface.co/MCG-NJU/videomae-base/resolve/main/config.json""",
}





class      UpperCAmelCase_						(_UpperCAmelCase						):





    """simple docstring"""






    lowerCamelCase						:      Any							  =    'videomae'







    def __init__(							self    ,  SCREAMING_SNAKE_CASE_=2_24    ,  SCREAMING_SNAKE_CASE_=16    ,  SCREAMING_SNAKE_CASE_=3    ,  SCREAMING_SNAKE_CASE_=16    ,  SCREAMING_SNAKE_CASE_=2    ,  SCREAMING_SNAKE_CASE_=7_68    ,  SCREAMING_SNAKE_CASE_=12    ,  SCREAMING_SNAKE_CASE_=12    ,  SCREAMING_SNAKE_CASE_=30_72    ,  SCREAMING_SNAKE_CASE_="gelu"    ,  SCREAMING_SNAKE_CASE_=0.0    ,  SCREAMING_SNAKE_CASE_=0.0    ,  SCREAMING_SNAKE_CASE_=0.0_2    ,  SCREAMING_SNAKE_CASE_=1E-12    ,  SCREAMING_SNAKE_CASE_=True    ,  SCREAMING_SNAKE_CASE_=True    ,  SCREAMING_SNAKE_CASE_=6    ,  SCREAMING_SNAKE_CASE_=3_84    ,  SCREAMING_SNAKE_CASE_=4    ,  SCREAMING_SNAKE_CASE_=15_36    ,  SCREAMING_SNAKE_CASE_=True    ,  **SCREAMING_SNAKE_CASE_    ,  )       ->  Union[str, Any]:
         super().__init__(**SCREAMING_SNAKE_CASE_			)

         __lowerCamelCase     :			Tuple              =	image_size
         __lowerCamelCase     :			Optional[Any]              =	patch_size
         __lowerCamelCase     :			Optional[int]              =	num_channels
         __lowerCamelCase     :			Union[str, Any]              =	num_frames
         __lowerCamelCase     :			Union[str, Any]              =	tubelet_size

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

         __lowerCamelCase     :			Union[str, Any]              =	decoder_num_attention_heads
         __lowerCamelCase     :			Tuple              =	decoder_hidden_size
         __lowerCamelCase     :			List[Any]              =	decoder_num_hidden_layers
         __lowerCamelCase     :			List[Any]              =	decoder_intermediate_size
         __lowerCamelCase     :			List[Any]              =	norm_pix_loss










'''simple docstring'''



import torch

from diffusers import KDPMaAncestralDiscreteScheduler
from diffusers.utils import torch_device

from .test_schedulers import SchedulerCommonTest





class      UpperCAmelCase_						(_UpperCAmelCase						):





    """simple docstring"""






    lowerCamelCase						:      Optional[Any]							  =    (KDPMaAncestralDiscreteScheduler,)
    lowerCamelCase						:      Tuple							  =    1_0







    def    lowercase_       (							self    ,  **SCREAMING_SNAKE_CASE_			)       ->  Tuple:
         __lowerCamelCase     :			Union[str, Any]              =	{
             'num_train_timesteps': 11_00,
             'beta_start': 0.0_0_0_1,
             'beta_end': 0.0_2,
             'beta_schedule': 'linear',
         }

         config.update(**SCREAMING_SNAKE_CASE_			)
         return config







    def    lowercase_       (							self			)       ->  Union[str, Any]:
         for timesteps in [10, 50, 1_00, 10_00]:
              self.check_over_configs(num_train_timesteps=SCREAMING_SNAKE_CASE_			)







    def    lowercase_       (							self			)       ->  Dict:
         for beta_start, beta_end in zip([0.0_0_0_0_1, 0.0_0_0_1, 0.0_0_1]    ,  [0.0_0_0_2, 0.0_0_2, 0.0_2]			):
              self.check_over_configs(beta_start=SCREAMING_SNAKE_CASE_    ,  beta_end=SCREAMING_SNAKE_CASE_			)







    def    lowercase_       (							self			)       ->  List[str]:
         for schedule in ["linear", "scaled_linear"]:
              self.check_over_configs(beta_schedule=SCREAMING_SNAKE_CASE_			)







    def    lowercase_       (							self			)       ->  int:
         if torch_device == "mps":
              return
         __lowerCamelCase     :			Union[str, Any]              =	self.scheduler_classes[0]
         __lowerCamelCase     :			Optional[Any]              =	self.get_scheduler_config()
         __lowerCamelCase     :			Optional[int]              =	scheduler_class(**SCREAMING_SNAKE_CASE_			)

         scheduler.set_timesteps(self.num_inference_steps			)

         __lowerCamelCase     :			int              =	torch.manual_seed(0			)

         __lowerCamelCase     :			int              =	self.dummy_model()
         __lowerCamelCase     :			List[Any]              =	self.dummy_sample_deter * scheduler.init_noise_sigma
         __lowerCamelCase     :			Any              =	sample.to(SCREAMING_SNAKE_CASE_			)

         for i, t in enumerate(scheduler.timesteps			):
              __lowerCamelCase     :			Dict              =	scheduler.scale_model_input(SCREAMING_SNAKE_CASE_    ,  SCREAMING_SNAKE_CASE_			)

              __lowerCamelCase     :			Dict              =	model(SCREAMING_SNAKE_CASE_    ,  SCREAMING_SNAKE_CASE_			)

              __lowerCamelCase     :			Union[str, Any]              =	scheduler.step(SCREAMING_SNAKE_CASE_    ,  SCREAMING_SNAKE_CASE_    ,  SCREAMING_SNAKE_CASE_    ,  generator=SCREAMING_SNAKE_CASE_			)
              __lowerCamelCase     :			Any              =	output.prev_sample

         __lowerCamelCase     :			Tuple              =	torch.sum(torch.abs(SCREAMING_SNAKE_CASE_			)			)
         __lowerCamelCase     :			Optional[int]              =	torch.mean(torch.abs(SCREAMING_SNAKE_CASE_			)			)

         assert abs(result_sum.item() - 1_3_8_4_9.3_8_7_7			) < 1E-2
         assert abs(result_mean.item() - 1_8.0_3_3_1			) < 5E-3







    def    lowercase_       (							self			)       ->  List[str]:
         for prediction_type in ["epsilon", "v_prediction"]:
              self.check_over_configs(prediction_type=SCREAMING_SNAKE_CASE_			)







    def    lowercase_       (							self			)       ->  List[str]:
         if torch_device == "mps":
              return
         __lowerCamelCase     :			Union[str, Any]              =	self.scheduler_classes[0]
         __lowerCamelCase     :			Optional[Any]              =	self.get_scheduler_config(prediction_type='v_prediction'			)
         __lowerCamelCase     :			str              =	scheduler_class(**SCREAMING_SNAKE_CASE_			)

         scheduler.set_timesteps(self.num_inference_steps			)

         __lowerCamelCase     :			int              =	self.dummy_model()
         __lowerCamelCase     :			int              =	self.dummy_sample_deter * scheduler.init_noise_sigma
         __lowerCamelCase     :			Dict              =	sample.to(SCREAMING_SNAKE_CASE_			)

         __lowerCamelCase     :			int              =	torch.manual_seed(0			)

         for i, t in enumerate(scheduler.timesteps			):
              __lowerCamelCase     :			Any              =	scheduler.scale_model_input(SCREAMING_SNAKE_CASE_    ,  SCREAMING_SNAKE_CASE_			)

              __lowerCamelCase     :			List[str]              =	model(SCREAMING_SNAKE_CASE_    ,  SCREAMING_SNAKE_CASE_			)

              __lowerCamelCase     :			str              =	scheduler.step(SCREAMING_SNAKE_CASE_    ,  SCREAMING_SNAKE_CASE_    ,  SCREAMING_SNAKE_CASE_    ,  generator=SCREAMING_SNAKE_CASE_			)
              __lowerCamelCase     :			Tuple              =	output.prev_sample

         __lowerCamelCase     :			int              =	torch.sum(torch.abs(SCREAMING_SNAKE_CASE_			)			)
         __lowerCamelCase     :			int              =	torch.mean(torch.abs(SCREAMING_SNAKE_CASE_			)			)

         assert abs(result_sum.item() - 3_2_8.9_9_7_0			) < 1E-2
         assert abs(result_mean.item() - 0.4_2_8_4			) < 1E-3







    def    lowercase_       (							self			)       ->  Dict:
         if torch_device == "mps":
              return
         __lowerCamelCase     :			Optional[int]              =	self.scheduler_classes[0]
         __lowerCamelCase     :			str              =	self.get_scheduler_config()
         __lowerCamelCase     :			Union[str, Any]              =	scheduler_class(**SCREAMING_SNAKE_CASE_			)

         scheduler.set_timesteps(self.num_inference_steps    ,  device=SCREAMING_SNAKE_CASE_			)
         __lowerCamelCase     :			List[Any]              =	torch.manual_seed(0			)

         __lowerCamelCase     :			Optional[Any]              =	self.dummy_model()
         __lowerCamelCase     :			Dict              =	self.dummy_sample_deter.to(SCREAMING_SNAKE_CASE_			) * scheduler.init_noise_sigma

         for t in scheduler.timesteps:
              __lowerCamelCase     :			Any              =	scheduler.scale_model_input(SCREAMING_SNAKE_CASE_    ,  SCREAMING_SNAKE_CASE_			)

              __lowerCamelCase     :			Optional[Any]              =	model(SCREAMING_SNAKE_CASE_    ,  SCREAMING_SNAKE_CASE_			)

              __lowerCamelCase     :			Dict              =	scheduler.step(SCREAMING_SNAKE_CASE_    ,  SCREAMING_SNAKE_CASE_    ,  SCREAMING_SNAKE_CASE_    ,  generator=SCREAMING_SNAKE_CASE_			)
              __lowerCamelCase     :			Union[str, Any]              =	output.prev_sample

         __lowerCamelCase     :			int              =	torch.sum(torch.abs(SCREAMING_SNAKE_CASE_			)			)
         __lowerCamelCase     :			Dict              =	torch.mean(torch.abs(SCREAMING_SNAKE_CASE_			)			)

         assert abs(result_sum.item() - 1_3_8_4_9.3_8_1_8			) < 1E-1
         assert abs(result_mean.item() - 1_8.0_3_3_1			) < 1E-3






