



import torch

from diffusers import DDIMParallelScheduler

from .test_schedulers import SchedulerCommonTest



class 		_lowercase		(       __snake_case	):
   _UpperCAmelCase     	=			(DDIMParallelScheduler,)
   _UpperCAmelCase     	=			(('''eta''', 0.0), ('''num_inference_steps''', 50))

   def 		A							(      self			:       str							,   **__lowerCAmelCase			:       str							)       ->				str:


    """simple docstring"""






    a						=		{
        "num_train_timesteps": 1000,
        "beta_start": 0.0_0_0_1,
        "beta_end": 0.0_2,
        "beta_schedule": "linear",
        "clip_sample": True,
    }

    config.update(**__UpperCamelCase							)
    return config

   def 		A							(      self			:       Union[str, Any]							,   **__lowerCAmelCase			:       Tuple							)       ->				List[str]:


    """simple docstring"""






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

    a		,  a						=		10, 0.0

    a						=		self.dummy_model()
    a						=		self.dummy_sample_deter

    scheduler.set_timesteps(__UpperCamelCase							)

    for t in scheduler.timesteps:
     a						=		model(__UpperCamelCase							,   __UpperCamelCase							)
     a						=		scheduler.step(__UpperCamelCase							,   __UpperCamelCase							,   __UpperCamelCase							,   __UpperCamelCase							).prev_sample

    return sample

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


    """simple docstring"""






    for timesteps in [100, 500, 1000]:
     self.check_over_configs(num_train_timesteps=__UpperCamelCase							)

   def 		A							(      self			:       Any							)       ->				int:


    """simple docstring"""






    for steps_offset in [0, 1]:
     self.check_over_configs(steps_offset=__UpperCamelCase							)

    a						=		self.scheduler_classes[0]
    a						=		self.get_scheduler_config(steps_offset=1							)
    a						=		scheduler_class(**__UpperCamelCase							)
    scheduler.set_timesteps(5							)
    assert torch.equal(scheduler.timesteps							,   torch.LongTensor([801, 601, 401, 201, 1]							)							)

   def 		A							(      self			:       List[Any]							)       ->				Optional[int]:


    """simple docstring"""






    for beta_start, beta_end in zip([0.0_0_0_1, 0.0_0_1, 0.0_1, 0.1]							,   [0.0_0_2, 0.0_2, 0.2, 2]							):
     self.check_over_configs(beta_start=__UpperCamelCase							,   beta_end=__UpperCamelCase							)

   def 		A							(      self			:       Union[str, Any]							)       ->				List[Any]:


    """simple docstring"""






    for schedule in ["linear", "squaredcos_cap_v2"]:
     self.check_over_configs(beta_schedule=__UpperCamelCase							)

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


    """simple docstring"""






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

   def 		A							(      self			:       Any							)       ->				Union[str, Any]:


    """simple docstring"""






    for clip_sample in [True, False]:
     self.check_over_configs(clip_sample=__UpperCamelCase							)

   def 		A							(      self			:       Dict							)       ->				List[Any]:


    """simple docstring"""






    for timestep_spacing in ["trailing", "leading"]:
     self.check_over_configs(timestep_spacing=__UpperCamelCase							)

   def 		A							(      self			:       Union[str, Any]							)       ->				Dict:


    """simple docstring"""






    for rescale_betas_zero_snr in [True, False]:
     self.check_over_configs(rescale_betas_zero_snr=__UpperCamelCase							)

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


    """simple docstring"""






    self.check_over_configs(thresholding=__UpperCamelCase							)
    for threshold in [0.5, 1.0, 2.0]:
     for prediction_type in ["epsilon", "v_prediction"]:
      self.check_over_configs(
          thresholding=__UpperCamelCase							,   prediction_type=__UpperCamelCase							,   sample_max_value=__UpperCamelCase							,   )

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


    """simple docstring"""






    for t in [1, 10, 49]:
     self.check_over_forward(time_step=__UpperCamelCase							)

   def 		A							(      self			:       Union[str, Any]							)       ->				Optional[int]:


    """simple docstring"""






    for t, num_inference_steps in zip([1, 10, 50]							,   [10, 50, 500]							):
     self.check_over_forward(time_step=__UpperCamelCase							,   num_inference_steps=__UpperCamelCase							)

   def 		A							(      self			:       Optional[int]							)       ->				Any:


    """simple docstring"""






    for t, eta in zip([1, 10, 49]							,   [0.0, 0.5, 1.0]							):
     self.check_over_forward(time_step=__UpperCamelCase							,   eta=__UpperCamelCase							)

   def 		A							(      self			:       int							)       ->				Optional[Any]:


    """simple docstring"""






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

    assert torch.sum(torch.abs(scheduler._get_variance(0							,   0							) - 0.0							)							) < 1E-5
    assert torch.sum(torch.abs(scheduler._get_variance(420							,   400							) - 0.1_4_7_7_1							)							) < 1E-5
    assert torch.sum(torch.abs(scheduler._get_variance(980							,   960							) - 0.3_2_4_6_0							)							) < 1E-5
    assert torch.sum(torch.abs(scheduler._get_variance(0							,   0							) - 0.0							)							) < 1E-5
    assert torch.sum(torch.abs(scheduler._get_variance(487							,   486							) - 0.0_0_9_7_9							)							) < 1E-5
    assert torch.sum(torch.abs(scheduler._get_variance(999							,   998							) - 0.0_2							)							) < 1E-5

   def 		A							(      self			:       Union[str, Any]							)       ->				Dict:


    """simple docstring"""






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

    a		,  a						=		10, 0.0
    scheduler.set_timesteps(__UpperCamelCase							)

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

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

    a						=		model(samples.flatten(0							,   1							)							,   timesteps.flatten(0							,   1							)							)
    a						=		scheduler.batch_step_no_noise(__UpperCamelCase							,   timesteps.flatten(0							,   1							)							,   samples.flatten(0							,   1							)							,   __UpperCamelCase							)

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

    assert abs(result_sum.item() - 1_1_4_7.7_9_0_4							) < 1E-2
    assert abs(result_mean.item() - 0.4_9_8_2							) < 1E-3

   def 		A							(      self			:       Tuple							)       ->				List[str]:


    """simple docstring"""






    a						=		self.full_loop()

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

    assert abs(result_sum.item() - 1_7_2.0_0_6_7							) < 1E-2
    assert abs(result_mean.item() - 0.2_2_3_9_6_7							) < 1E-3

   def 		A							(      self			:       Optional[Any]							)       ->				Dict:


    """simple docstring"""






    a						=		self.full_loop(prediction_type="v_prediction"							)

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

    assert abs(result_sum.item() - 5_2.5_3_0_2							) < 1E-2
    assert abs(result_mean.item() - 0.0_6_8_4							) < 1E-3

   def 		A							(      self			:       Union[str, Any]							)       ->				Tuple:


    """simple docstring"""






    a						=		self.full_loop(set_alpha_to_one=__UpperCamelCase							,   beta_start=0.0_1							)
    a						=		torch.sum(torch.abs(__UpperCamelCase							)							)
    a						=		torch.mean(torch.abs(__UpperCamelCase							)							)

    assert abs(result_sum.item() - 1_4_9.8_2_9_5							) < 1E-2
    assert abs(result_mean.item() - 0.1_9_5_1							) < 1E-3




   def 		A							(      self			:       str							)       ->				Any:


    """simple docstring"""






    a						=		self.full_loop(set_alpha_to_one=__UpperCamelCase							,   beta_start=0.0_1							)
    a						=		torch.sum(torch.abs(__UpperCamelCase							)							)
    a						=		torch.mean(torch.abs(__UpperCamelCase							)							)

    assert abs(result_sum.item() - 1_4_9.0_7_8_4							) < 1E-2
    assert abs(result_mean.item() - 0.1_9_4_1							) < 1E-3









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


A_		:     Any        =     logging.get_logger(__name__)

A_		:     Optional[int]        =     {
    '''SCUT-DLVCLab/lilt-roberta-en-base''': (
        '''https://huggingface.co/SCUT-DLVCLab/lilt-roberta-en-base/resolve/main/config.json'''
    ),
}



class 		_lowercase		(       UpperCAmelCase__	):
   _UpperCAmelCase     	=			'''lilt'''

   def __init__(      self			:       Union[str, Any]							,   __lowerCAmelCase			:       Optional[Any]=3_0522							,   __lowerCAmelCase			:       str=768							,   __lowerCAmelCase			:       Any=12							,   __lowerCAmelCase			:       Optional[Any]=12							,   __lowerCAmelCase			:       List[Any]=3072							,   __lowerCAmelCase			:       Dict="gelu"							,   __lowerCAmelCase			:       int=0.1							,   __lowerCAmelCase			:       Union[str, Any]=0.1							,   __lowerCAmelCase			:       List[str]=512							,   __lowerCAmelCase			:       Optional[int]=2							,   __lowerCAmelCase			:       int=0.0_2							,   __lowerCAmelCase			:       Union[str, Any]=1E-12							,   __lowerCAmelCase			:       Tuple=0							,   __lowerCAmelCase			:       List[Any]="absolute"							,   __lowerCAmelCase			:       int=None							,   __lowerCAmelCase			:       Dict=4							,   __lowerCAmelCase			:       Dict=1024							,   **__lowerCAmelCase			:       Dict							,   )       ->				int:


    """simple docstring"""






    super().__init__(pad_token_id=__lowerCAmelCase							,   **__lowerCAmelCase							)

    a						=		vocab_size
    a						=		hidden_size
    a						=		num_hidden_layers
    a						=		num_attention_heads
    a						=		hidden_act
    a						=		intermediate_size
    a						=		hidden_dropout_prob
    a						=		attention_probs_dropout_prob
    a						=		max_position_embeddings
    a						=		type_vocab_size
    a						=		initializer_range
    a						=		layer_norm_eps
    a						=		position_embedding_type
    a						=		classifier_dropout
    a						=		channel_shrink_ratio
    a						=		max_ad_position_embeddings

