





import torch

from diffusers import DDIMParallelScheduler

from .test_schedulers import SchedulerCommonTest



class 				_UpperCAmelCase    (    _UpperCamelCase    ):

  """simple docstring"""


  a_        = (DDIMParallelScheduler,)
  a_        = (("""eta""", 0.0), ("""num_inference_steps""", 50))






  def 				lowercase		(				self :		str					,  **lowerCAmelCase_ :		Any     )							->						Union[str, Any]:
     __lowerCAmelCase								=	{
         'num_train_timesteps': 1_0_0_0,
         'beta_start': 0.00_01,
         'beta_end': 0.02,
         'beta_schedule': 'linear',
         'clip_sample': True,
     }

     config.update(**lowerCAmelCase_     )
     return config






  def 				lowercase		(				self :		Tuple					,  **lowerCAmelCase_ :		Tuple     )							->						Optional[Any]:
     __lowerCAmelCase								=	self.scheduler_classes[0]
     __lowerCAmelCase								=	self.get_scheduler_config(**lowerCAmelCase_     )
     __lowerCAmelCase								=	scheduler_class(**lowerCAmelCase_     )

     __lowerCAmelCase	,	__lowerCAmelCase								=	1_0, 0.0

     __lowerCAmelCase								=	self.dummy_model()
     __lowerCAmelCase								=	self.dummy_sample_deter

     scheduler.set_timesteps(lowerCAmelCase_     )

     for t in scheduler.timesteps:
        __lowerCAmelCase								=	model(lowerCAmelCase_					,  lowerCAmelCase_     )
        __lowerCAmelCase								=	scheduler.step(lowerCAmelCase_					,  lowerCAmelCase_					,  lowerCAmelCase_					,  lowerCAmelCase_     ).prev_sample

     return sample






  def 				lowercase		(				self :		int     )							->						int:
     for timesteps in [1_0_0, 5_0_0, 1_0_0_0]:
        self.check_over_configs(num_train_timesteps=lowerCAmelCase_     )






  def 				lowercase		(				self :		Dict     )							->						Optional[int]:
     for steps_offset in [0, 1]:
        self.check_over_configs(steps_offset=lowerCAmelCase_     )

     __lowerCAmelCase								=	self.scheduler_classes[0]
     __lowerCAmelCase								=	self.get_scheduler_config(steps_offset=1     )
     __lowerCAmelCase								=	scheduler_class(**lowerCAmelCase_     )
     scheduler.set_timesteps(5     )
     assert torch.equal(scheduler.timesteps					,  torch.LongTensor([8_0_1, 6_0_1, 4_0_1, 2_0_1, 1]     )     )






  def 				lowercase		(				self :		str     )							->						int:
     for beta_start, beta_end in zip([0.00_01, 0.0_01, 0.01, 0.1]					,  [0.0_02, 0.02, 0.2, 2]     ):
        self.check_over_configs(beta_start=lowerCAmelCase_					,  beta_end=lowerCAmelCase_     )






  def 				lowercase		(				self :		str     )							->						Optional[int]:
     for schedule in ["linear", "squaredcos_cap_v2"]:
        self.check_over_configs(beta_schedule=lowerCAmelCase_     )






  def 				lowercase		(				self :		Any     )							->						Optional[Any]:
     for prediction_type in ["epsilon", "v_prediction"]:
        self.check_over_configs(prediction_type=lowerCAmelCase_     )






  def 				lowercase		(				self :		Optional[Any]     )							->						Optional[int]:
     for clip_sample in [True, False]:
        self.check_over_configs(clip_sample=lowerCAmelCase_     )






  def 				lowercase		(				self :		Optional[Any]     )							->						Tuple:
     for timestep_spacing in ["trailing", "leading"]:
        self.check_over_configs(timestep_spacing=lowerCAmelCase_     )






  def 				lowercase		(				self :		Tuple     )							->						str:
     for rescale_betas_zero_snr in [True, False]:
        self.check_over_configs(rescale_betas_zero_snr=lowerCAmelCase_     )






  def 				lowercase		(				self :		Optional[Any]     )							->						Any:
     self.check_over_configs(thresholding=lowerCAmelCase_     )
     for threshold in [0.5, 1.0, 2.0]:
        for prediction_type in ["epsilon", "v_prediction"]:
           self.check_over_configs(
               thresholding=lowerCAmelCase_					,  prediction_type=lowerCAmelCase_					,  sample_max_value=lowerCAmelCase_					,  )






  def 				lowercase		(				self :		Optional[int]     )							->						List[str]:
     for t in [1, 1_0, 4_9]:
        self.check_over_forward(time_step=lowerCAmelCase_     )






  def 				lowercase		(				self :		List[Any]     )							->						Any:
     for t, num_inference_steps in zip([1, 1_0, 5_0]					,  [1_0, 5_0, 5_0_0]     ):
        self.check_over_forward(time_step=lowerCAmelCase_					,  num_inference_steps=lowerCAmelCase_     )






  def 				lowercase		(				self :		str     )							->						List[Any]:
     for t, eta in zip([1, 1_0, 4_9]					,  [0.0, 0.5, 1.0]     ):
        self.check_over_forward(time_step=lowerCAmelCase_					,  eta=lowerCAmelCase_     )






  def 				lowercase		(				self :		str     )							->						Optional[Any]:
     __lowerCAmelCase								=	self.scheduler_classes[0]
     __lowerCAmelCase								=	self.get_scheduler_config()
     __lowerCAmelCase								=	scheduler_class(**lowerCAmelCase_     )

     assert torch.sum(torch.abs(scheduler._get_variance(0					,  0     ) - 0.0     )     ) < 1e-5
     assert torch.sum(torch.abs(scheduler._get_variance(4_2_0					,  4_0_0     ) - 0.1_47_71     )     ) < 1e-5
     assert torch.sum(torch.abs(scheduler._get_variance(9_8_0					,  9_6_0     ) - 0.3_24_60     )     ) < 1e-5
     assert torch.sum(torch.abs(scheduler._get_variance(0					,  0     ) - 0.0     )     ) < 1e-5
     assert torch.sum(torch.abs(scheduler._get_variance(4_8_7					,  4_8_6     ) - 0.0_09_79     )     ) < 1e-5
     assert torch.sum(torch.abs(scheduler._get_variance(9_9_9					,  9_9_8     ) - 0.02     )     ) < 1e-5






  def 				lowercase		(				self :		Optional[int]     )							->						Tuple:
     __lowerCAmelCase								=	self.scheduler_classes[0]
     __lowerCAmelCase								=	self.get_scheduler_config()
     __lowerCAmelCase								=	scheduler_class(**lowerCAmelCase_     )

     __lowerCAmelCase	,	__lowerCAmelCase								=	1_0, 0.0
     scheduler.set_timesteps(lowerCAmelCase_     )

     __lowerCAmelCase								=	self.dummy_model()
     __lowerCAmelCase								=	self.dummy_sample_deter
     __lowerCAmelCase								=	self.dummy_sample_deter + 0.1
     __lowerCAmelCase								=	self.dummy_sample_deter - 0.1

     __lowerCAmelCase								=	samplea.shape[0]
     __lowerCAmelCase								=	torch.stack([samplea, samplea, samplea]					,  dim=0     )
     __lowerCAmelCase								=	torch.arange(lowerCAmelCase_     )[0:3, None].repeat(1					,  lowerCAmelCase_     )

     __lowerCAmelCase								=	model(samples.flatten(0					,  1     )					,  timesteps.flatten(0					,  1     )     )
     __lowerCAmelCase								=	scheduler.batch_step_no_noise(lowerCAmelCase_					,  timesteps.flatten(0					,  1     )					,  samples.flatten(0					,  1     )					,  lowerCAmelCase_     )

     __lowerCAmelCase								=	torch.sum(torch.abs(lowerCAmelCase_     )     )
     __lowerCAmelCase								=	torch.mean(torch.abs(lowerCAmelCase_     )     )

     assert abs(result_sum.item() - 11_47.79_04     ) < 1e-2
     assert abs(result_mean.item() - 0.49_82     ) < 1e-3






  def 				lowercase		(				self :		List[Any]     )							->						int:
     __lowerCAmelCase								=	self.full_loop()

     __lowerCAmelCase								=	torch.sum(torch.abs(lowerCAmelCase_     )     )
     __lowerCAmelCase								=	torch.mean(torch.abs(lowerCAmelCase_     )     )

     assert abs(result_sum.item() - 1_72.00_67     ) < 1e-2
     assert abs(result_mean.item() - 0.22_39_67     ) < 1e-3






  def 				lowercase		(				self :		Any     )							->						int:
     __lowerCAmelCase								=	self.full_loop(prediction_type='v_prediction'     )

     __lowerCAmelCase								=	torch.sum(torch.abs(lowerCAmelCase_     )     )
     __lowerCAmelCase								=	torch.mean(torch.abs(lowerCAmelCase_     )     )

     assert abs(result_sum.item() - 52.53_02     ) < 1e-2
     assert abs(result_mean.item() - 0.06_84     ) < 1e-3






  def 				lowercase		(				self :		int     )							->						Dict:
     # We specify different beta, so that the first alpha is 0.99
     __lowerCAmelCase								=	self.full_loop(set_alpha_to_one=lowerCAmelCase_					,  beta_start=0.01     )
     __lowerCAmelCase								=	torch.sum(torch.abs(lowerCAmelCase_     )     )
     __lowerCAmelCase								=	torch.mean(torch.abs(lowerCAmelCase_     )     )

     assert abs(result_sum.item() - 1_49.82_95     ) < 1e-2
     assert abs(result_mean.item() - 0.19_51     ) < 1e-3






  def 				lowercase		(				self :		str     )							->						List[Any]:
     # We specify different beta, so that the first alpha is 0.99
     __lowerCAmelCase								=	self.full_loop(set_alpha_to_one=lowerCAmelCase_					,  beta_start=0.01     )
     __lowerCAmelCase								=	torch.sum(torch.abs(lowerCAmelCase_     )     )
     __lowerCAmelCase								=	torch.mean(torch.abs(lowerCAmelCase_     )     )

     assert abs(result_sum.item() - 1_49.07_84     ) < 1e-2
     assert abs(result_mean.item() - 0.19_41     ) < 1e-3








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


_snake_case  :    List[Any]						    =  logging.get_logger(__name__)

_snake_case  :    Any						    =  {
    'google/pegasus-large': 'https://huggingface.co/google/pegasus-large/resolve/main/config.json',
    # See all PEGASUS models at https://huggingface.co/models?filter=pegasus
}



class 				_UpperCAmelCase    (    _UpperCamelCase    ):

  """simple docstring"""


  a_        = """pegasus"""
  a_        = ["""past_key_values"""]
  a_        = {"""num_attention_heads""": """encoder_attention_heads""", """hidden_size""": """d_model"""}






  def __init__(				self :		List[str]					,  lowerCAmelCase_ :		Union[str, Any]=5_0_2_6_5					,  lowerCAmelCase_ :		Union[str, Any]=1_0_2_4					,  lowerCAmelCase_ :		Union[str, Any]=1_2					,  lowerCAmelCase_ :		Dict=4_0_9_6					,  lowerCAmelCase_ :		str=1_6					,  lowerCAmelCase_ :		List[Any]=1_2					,  lowerCAmelCase_ :		Union[str, Any]=4_0_9_6					,  lowerCAmelCase_ :		Union[str, Any]=1_6					,  lowerCAmelCase_ :		Any=0.0					,  lowerCAmelCase_ :		Optional[int]=0.0					,  lowerCAmelCase_ :		List[Any]=True					,  lowerCAmelCase_ :		Any=True					,  lowerCAmelCase_ :		Dict="gelu"					,  lowerCAmelCase_ :		Optional[int]=1_0_2_4					,  lowerCAmelCase_ :		List[str]=0.1					,  lowerCAmelCase_ :		str=0.0					,  lowerCAmelCase_ :		Union[str, Any]=0.0					,  lowerCAmelCase_ :		Optional[int]=0.02					,  lowerCAmelCase_ :		Tuple=0					,  lowerCAmelCase_ :		Tuple=False					,  lowerCAmelCase_ :		Union[str, Any]=0					,  lowerCAmelCase_ :		int=1					,  lowerCAmelCase_ :		Tuple=1					,  **lowerCAmelCase_ :		Tuple					,  )							->						List[str]:
     __lowerCAmelCase								=	vocab_size
     __lowerCAmelCase								=	max_position_embeddings
     __lowerCAmelCase								=	d_model
     __lowerCAmelCase								=	encoder_ffn_dim
     __lowerCAmelCase								=	encoder_layers
     __lowerCAmelCase								=	encoder_attention_heads
     __lowerCAmelCase								=	decoder_ffn_dim
     __lowerCAmelCase								=	decoder_layers
     __lowerCAmelCase								=	decoder_attention_heads
     __lowerCAmelCase								=	dropout
     __lowerCAmelCase								=	attention_dropout
     __lowerCAmelCase								=	activation_dropout
     __lowerCAmelCase								=	activation_function
     __lowerCAmelCase								=	init_std
     __lowerCAmelCase								=	encoder_layerdrop
     __lowerCAmelCase								=	decoder_layerdrop
     __lowerCAmelCase								=	use_cache
     __lowerCAmelCase								=	encoder_layers
     __lowerCAmelCase								=	scale_embedding  # scale factor will be sqrt(d_model) if True
     super().__init__(
         pad_token_id=lowerCAmelCase_					,  eos_token_id=lowerCAmelCase_					,  is_encoder_decoder=lowerCAmelCase_					,  decoder_start_token_id=lowerCAmelCase_					,  forced_eos_token_id=lowerCAmelCase_					,  **lowerCAmelCase_					,  )






  @property
  def 				lowercase		(				self :		List[Any]     )							->						int:
     return self.encoder_attention_heads






  @property
  def 				lowercase		(				self :		Optional[Any]     )							->						int:
     return self.d_model

