





"""simple docstring"""






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


lowerCAmelCase_      =				logging.get_logger(__name__)

lowerCAmelCase_      =				{
    '''uw-madison/yoso-4096''': '''https://huggingface.co/uw-madison/yoso-4096/resolve/main/config.json''',
    # See all YOSO models at https://huggingface.co/models?filter=yoso
}



class 						lowerCAmelCase       (   snake_case				):
       lowerCAmelCase__    			=	"""yoso"""






       def __init__( self     ,	a__=5_02_65     ,	a__=7_68     ,	a__=12     ,	a__=12     ,	a__=30_72     ,	a__="gelu"     ,	a__=0.1     ,	a__=0.1     ,	a__=40_96     ,	a__=1     ,	a__=0.02     ,	a__=1E-12     ,	a__="absolute"     ,	a__=True     ,	a__=9     ,	a__=64     ,	a__=None     ,	a__=True     ,	a__=True     ,	a__=1     ,	a__=0     ,	a__=2     ,	**a__     ,	):
              super().__init__(pad_token_id=a__     ,	bos_token_id=a__     ,	eos_token_id=a__     ,	**a__  )

              _UpperCAmelCase			     =  vocab_size
              _UpperCAmelCase			     =  max_position_embeddings
              _UpperCAmelCase			     =  hidden_size
              _UpperCAmelCase			     =  num_hidden_layers
              _UpperCAmelCase			     =  num_attention_heads
              _UpperCAmelCase			     =  intermediate_size
              _UpperCAmelCase			     =  hidden_act
              _UpperCAmelCase			     =  hidden_dropout_prob
              _UpperCAmelCase			     =  attention_probs_dropout_prob
              _UpperCAmelCase			     =  initializer_range
              _UpperCAmelCase			     =  type_vocab_size
              _UpperCAmelCase			     =  layer_norm_eps
              _UpperCAmelCase			     =  position_embedding_type
              _UpperCAmelCase			     =  use_expectation
              _UpperCAmelCase			     =  hash_code_len
              _UpperCAmelCase			     =  num_hash
              _UpperCAmelCase			     =  conv_window
              _UpperCAmelCase			     =  use_fast_hash
              _UpperCAmelCase			     =  lsh_backward













"""simple docstring"""






import torch

from diffusers import LMSDiscreteScheduler
from diffusers.utils import torch_device

from .test_schedulers import SchedulerCommonTest



class 						lowerCAmelCase       (   snake_case				):
       lowerCAmelCase__    			=	(LMSDiscreteScheduler,)
       lowerCAmelCase__    			=	10






       def 				__A			( self     ,	**a__  ):
              _UpperCAmelCase			     =  {
                  'num_train_timesteps': 11_00,
                  'beta_start': 0.0_001,
                  'beta_end': 0.02,
                  'beta_schedule': 'linear',
              }

              config.update(**a__  )
              return config






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






       def 				__A			( self  ):
              for beta_start, beta_end in zip([0.00_001, 0.0_001, 0.001]     ,	[0.0_002, 0.002, 0.02]  ):
                     self.check_over_configs(beta_start=a__     ,	beta_end=a__  )






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






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






       def 				__A			( self  ):
              for t in [0, 5_00, 8_00]:
                     self.check_over_forward(time_step=a__  )






       def 				__A			( self  ):
              _UpperCAmelCase			     =  self.scheduler_classes[0]
              _UpperCAmelCase			     =  self.get_scheduler_config()
              _UpperCAmelCase			     =  scheduler_class(**a__  )

              scheduler.set_timesteps(self.num_inference_steps  )

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

              for i, t in enumerate(scheduler.timesteps  ):
                     _UpperCAmelCase			     =  scheduler.scale_model_input(a__     ,	a__  )

                     _UpperCAmelCase			     =  model(a__     ,	a__  )

                     _UpperCAmelCase			     =  scheduler.step(a__     ,	a__     ,	a__  )
                     _UpperCAmelCase			     =  output.prev_sample

              _UpperCAmelCase			     =  torch.sum(torch.abs(a__  )  )
              _UpperCAmelCase			     =  torch.mean(torch.abs(a__  )  )

              assert abs(result_sum.item() - 1_006.388  ) < 1E-2
              assert abs(result_mean.item() - 1.31  ) < 1E-3






       def 				__A			( self  ):
              _UpperCAmelCase			     =  self.scheduler_classes[0]
              _UpperCAmelCase			     =  self.get_scheduler_config(prediction_type='v_prediction'  )
              _UpperCAmelCase			     =  scheduler_class(**a__  )

              scheduler.set_timesteps(self.num_inference_steps  )

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

              for i, t in enumerate(scheduler.timesteps  ):
                     _UpperCAmelCase			     =  scheduler.scale_model_input(a__     ,	a__  )

                     _UpperCAmelCase			     =  model(a__     ,	a__  )

                     _UpperCAmelCase			     =  scheduler.step(a__     ,	a__     ,	a__  )
                     _UpperCAmelCase			     =  output.prev_sample

              _UpperCAmelCase			     =  torch.sum(torch.abs(a__  )  )
              _UpperCAmelCase			     =  torch.mean(torch.abs(a__  )  )

              assert abs(result_sum.item() - 0.0_017  ) < 1E-2
              assert abs(result_mean.item() - 2.2676E-06  ) < 1E-3






       def 				__A			( self  ):
              _UpperCAmelCase			     =  self.scheduler_classes[0]
              _UpperCAmelCase			     =  self.get_scheduler_config()
              _UpperCAmelCase			     =  scheduler_class(**a__  )

              scheduler.set_timesteps(self.num_inference_steps     ,	device=a__  )

              _UpperCAmelCase			     =  self.dummy_model()
              _UpperCAmelCase			     =  self.dummy_sample_deter * scheduler.init_noise_sigma.cpu()
              _UpperCAmelCase			     =  sample.to(a__  )

              for i, t in enumerate(scheduler.timesteps  ):
                     _UpperCAmelCase			     =  scheduler.scale_model_input(a__     ,	a__  )

                     _UpperCAmelCase			     =  model(a__     ,	a__  )

                     _UpperCAmelCase			     =  scheduler.step(a__     ,	a__     ,	a__  )
                     _UpperCAmelCase			     =  output.prev_sample

              _UpperCAmelCase			     =  torch.sum(torch.abs(a__  )  )
              _UpperCAmelCase			     =  torch.mean(torch.abs(a__  )  )

              assert abs(result_sum.item() - 1_006.388  ) < 1E-2
              assert abs(result_mean.item() - 1.31  ) < 1E-3






       def 				__A			( self  ):
              _UpperCAmelCase			     =  self.scheduler_classes[0]
              _UpperCAmelCase			     =  self.get_scheduler_config()
              _UpperCAmelCase			     =  scheduler_class(**a__     ,	use_karras_sigmas=a__  )

              scheduler.set_timesteps(self.num_inference_steps     ,	device=a__  )

              _UpperCAmelCase			     =  self.dummy_model()
              _UpperCAmelCase			     =  self.dummy_sample_deter.to(a__  ) * scheduler.init_noise_sigma
              _UpperCAmelCase			     =  sample.to(a__  )

              for t in scheduler.timesteps:
                     _UpperCAmelCase			     =  scheduler.scale_model_input(a__     ,	a__  )

                     _UpperCAmelCase			     =  model(a__     ,	a__  )

                     _UpperCAmelCase			     =  scheduler.step(a__     ,	a__     ,	a__  )
                     _UpperCAmelCase			     =  output.prev_sample

              _UpperCAmelCase			     =  torch.sum(torch.abs(a__  )  )
              _UpperCAmelCase			     =  torch.mean(torch.abs(a__  )  )

              assert abs(result_sum.item() - 3_812.9_927  ) < 2E-2
              assert abs(result_mean.item() - 4.9_648  ) < 1E-3






