



import copy

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


__lowerCamelCase								=   logging.get_logger(__name__)

__lowerCamelCase								=   {}



class 						_snake_case (     lowerCamelCase			):





 """simple docstring"""



 lowerCamelCase_       =     '''fsmt_decoder'''
 def __init__(  self		,      a=0		,      a=0   )		->							Any:




   """simple docstring"""







   super().__init__()
   _A          =      vocab_size
   _A          =      bos_token_id







class 						_snake_case (     lowerCamelCase			):





 """simple docstring"""



 lowerCamelCase_       =     '''fsmt'''
 lowerCamelCase_       =     {'''num_attention_heads''': '''encoder_attention_heads''', '''hidden_size''': '''d_model'''}
 def __init__(  self		,      a=["en", "de"]		,      a=4_2_0_2_4		,      a=4_2_0_2_4		,      a="relu"		,      a=1_0_2_4		,      a=2_0_0		,      a=1_0_2_4		,      a=4_0_9_6		,      a=1_2		,      a=1_6		,      a=0.0		,      a=4_0_9_6		,      a=1_2		,      a=1_6		,      a=0.0		,      a=0.0		,      a=0.1		,      a=0.0		,      a=0.02		,      a=2		,      a=True		,      a=True		,      a=False		,      a=5		,      a=1.0		,      a=False		,      a=True		,      a=1		,      a=0		,      a=2		,      a=2		,      **a		,      )		->							Optional[int]:




   """simple docstring"""







   _A          =      langs
   _A          =      src_vocab_size
   _A          =      tgt_vocab_size
   _A          =      d_model  # encoder_embed_dim and decoder_embed_dim

   _A          =      encoder_ffn_dim
   _A          =      _A          =      encoder_layers
   _A          =      encoder_attention_heads
   _A          =      encoder_layerdrop
   _A          =      decoder_layerdrop
   _A          =      decoder_ffn_dim
   _A          =      decoder_layers
   _A          =      decoder_attention_heads
   _A          =      max_position_embeddings
   _A          =      init_std  # Normal(0, this parameter)
   _A          =      activation_function

   _A          =      DecoderConfig(vocab_size=a		,      bos_token_id=a   )
   if "decoder" in common_kwargs:
     del common_kwargs["decoder"]

   _A          =      scale_embedding  # scale factor will be sqrt(d_model) if True

   # 3 Types of Dropout
   _A          =      attention_dropout
   _A          =      activation_dropout
   _A          =      dropout

   _A          =      use_cache
   super().__init__(
       pad_token_id=a		,      bos_token_id=a		,      eos_token_id=a		,      decoder_start_token_id=a		,      is_encoder_decoder=a		,      tie_word_embeddings=a		,      forced_eos_token_id=a		,      max_length=a		,      num_beams=a		,      length_penalty=a		,      early_stopping=a		,      **a		,      )



 def lowercase_						(  self   )		->							List[Any]:




   """simple docstring"""







   _A          =      copy.deepcopy(self.__dict__   )
   _A          =      self.decoder.to_dict()
   _A          =      self.__class__.model_type
   return output




import torch

from diffusers import EulerAncestralDiscreteScheduler
from diffusers.utils import torch_device

from .test_schedulers import SchedulerCommonTest



class 						_snake_case (     lowerCamelCase			):





 """simple docstring"""



 lowerCamelCase_       =     (EulerAncestralDiscreteScheduler,)
 lowerCamelCase_       =     1_0
 def lowercase_						(  self		,      **a   )		->							Tuple:




   """simple docstring"""







   _A          =      {
       '''num_train_timesteps''': 1_1_0_0,
       '''beta_start''': 0.0_001,
       '''beta_end''': 0.02,
       '''beta_schedule''': '''linear''',
   }

   config.update(**a   )
   return config
 def lowercase_						(  self   )		->							Dict:




   """simple docstring"""







   for timesteps in [1_0, 5_0, 1_0_0, 1_0_0_0]:
     self.check_over_configs(num_train_timesteps=a   )
 def lowercase_						(  self   )		->							List[str]:




   """simple docstring"""







   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 lowercase_						(  self   )		->							Optional[Any]:




   """simple docstring"""







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




   """simple docstring"""







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




   """simple docstring"""







   _A          =      self.scheduler_classes[0]
   _A          =      self.get_scheduler_config()
   _A          =      scheduler_class(**a   )

   scheduler.set_timesteps(self.num_inference_steps   )

   _A          =      torch.manual_seed(0   )

   _A          =      self.dummy_model()
   _A          =      self.dummy_sample_deter * scheduler.init_noise_sigma.cpu()
   _A          =      sample.to(a   )

   for i, t in enumerate(scheduler.timesteps   ):
     _A          =      scheduler.scale_model_input(a		,      a   )

     _A          =      model(a		,      a   )

     _A          =      scheduler.step(a		,      a		,      a		,      generator=a   )
     _A          =      output.prev_sample

   _A          =      torch.sum(torch.abs(a   )   )
   _A          =      torch.mean(torch.abs(a   )   )

   assert abs(result_sum.item() - 152.3_192   ) < 1e-2
   assert abs(result_mean.item() - 0.1_983   ) < 1e-3
 def lowercase_						(  self   )		->							int:




   """simple docstring"""







   _A          =      self.scheduler_classes[0]
   _A          =      self.get_scheduler_config(prediction_type='''v_prediction'''   )
   _A          =      scheduler_class(**a   )

   scheduler.set_timesteps(self.num_inference_steps   )

   _A          =      torch.manual_seed(0   )

   _A          =      self.dummy_model()
   _A          =      self.dummy_sample_deter * scheduler.init_noise_sigma
   _A          =      sample.to(a   )

   for i, t in enumerate(scheduler.timesteps   ):
     _A          =      scheduler.scale_model_input(a		,      a   )

     _A          =      model(a		,      a   )

     _A          =      scheduler.step(a		,      a		,      a		,      generator=a   )
     _A          =      output.prev_sample

   _A          =      torch.sum(torch.abs(a   )   )
   _A          =      torch.mean(torch.abs(a   )   )

   assert abs(result_sum.item() - 108.4_439   ) < 1e-2
   assert abs(result_mean.item() - 0.1_412   ) < 1e-3



 def lowercase_						(  self   )		->							Optional[Any]:




   """simple docstring"""







   _A          =      self.scheduler_classes[0]
   _A          =      self.get_scheduler_config()
   _A          =      scheduler_class(**a   )

   scheduler.set_timesteps(self.num_inference_steps		,      device=a   )
   _A          =      torch.manual_seed(0   )

   _A          =      self.dummy_model()
   _A          =      self.dummy_sample_deter * scheduler.init_noise_sigma.cpu()
   _A          =      sample.to(a   )

   for t in scheduler.timesteps:
     _A          =      scheduler.scale_model_input(a		,      a   )

     _A          =      model(a		,      a   )

     _A          =      scheduler.step(a		,      a		,      a		,      generator=a   )
     _A          =      output.prev_sample

   _A          =      torch.sum(torch.abs(a   )   )
   _A          =      torch.mean(torch.abs(a   )   )

   assert abs(result_sum.item() - 152.3_192   ) < 1e-2
   assert abs(result_mean.item() - 0.1_983   ) < 1e-3