"""simple docstring"""


import torch

from diffusers import DDIMScheduler

from .test_schedulers import SchedulerCommonTest




class 					UpperCamelCase   (							lowerCAmelCase__  ):
       SCREAMING_SNAKE_CASE_						      = (DDIMScheduler,)
       SCREAMING_SNAKE_CASE_						      = (("eta", 0.0), ("num_inference_steps", 5_0))







       def 						a_		(					self,				**lowerCAmelCase__) -> str:
            snake_case_   			=   {
                'num_train_timesteps': 1000,
                'beta_start': 0.0001,
                'beta_end': 0.02,
                'beta_schedule': 'linear',
                'clip_sample': True,
            }

            config.update(**lowerCAmelCase__)
            return config







       def 						a_		(					self,				**lowerCAmelCase__) -> Tuple:
            snake_case_   			=   self.scheduler_classes[0]
            snake_case_   			=   self.get_scheduler_config(**lowerCAmelCase__)
            snake_case_   			=   scheduler_class(**lowerCAmelCase__)

            snake_case_			,      snake_case_   			=   10, 0.0

            snake_case_   			=   self.dummy_model()
            snake_case_   			=   self.dummy_sample_deter

            scheduler.set_timesteps(lowerCAmelCase__)

            for t in scheduler.timesteps:
                 snake_case_   			=   model(lowerCAmelCase__,				lowerCAmelCase__)
                 snake_case_   			=   scheduler.step(lowerCAmelCase__,				lowerCAmelCase__,				lowerCAmelCase__,				lowerCAmelCase__).prev_sample

            return sample







       def 						a_		(					self) -> Union[str, Any]:
            for timesteps in [100, 500, 1000]:
                 self.check_over_configs(num_train_timesteps=lowerCAmelCase__)







       def 						a_		(					self) -> List[Any]:
            for steps_offset in [0, 1]:
                 self.check_over_configs(steps_offset=lowerCAmelCase__)

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







       def 						a_		(					self) -> Optional[Any]:
            for beta_start, beta_end in zip([0.0001, 0.001, 0.01, 0.1],				[0.002, 0.02, 0.2, 2]):
                 self.check_over_configs(beta_start=lowerCAmelCase__,				beta_end=lowerCAmelCase__)







       def 						a_		(					self) -> Any:
            for schedule in ["linear", "squaredcos_cap_v2"]:
                 self.check_over_configs(beta_schedule=lowerCAmelCase__)







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







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







       def 						a_		(					self) -> int:
            for timestep_spacing in ["trailing", "leading"]:
                 self.check_over_configs(timestep_spacing=lowerCAmelCase__)







       def 						a_		(					self) -> Optional[int]:
            for rescale_betas_zero_snr in [True, False]:
                 self.check_over_configs(rescale_betas_zero_snr=lowerCAmelCase__)







       def 						a_		(					self) -> Optional[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 						a_		(					self) -> str:
            for t in [1, 10, 49]:
                 self.check_over_forward(time_step=lowerCAmelCase__)







       def 						a_		(					self) -> Tuple:
            for t, num_inference_steps in zip([1, 10, 50],				[10, 50, 500]):
                 self.check_over_forward(time_step=lowerCAmelCase__,				num_inference_steps=lowerCAmelCase__)







       def 						a_		(					self) -> Optional[int]:
            for t, eta in zip([1, 10, 49],				[0.0, 0.5, 1.0]):
                 self.check_over_forward(time_step=lowerCAmelCase__,				eta=lowerCAmelCase__)







       def 						a_		(					self) -> Optional[Any]:
            snake_case_   			=   self.scheduler_classes[0]
            snake_case_   			=   self.get_scheduler_config()
            snake_case_   			=   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(420,				400) - 0.14771)) < 1e-5
            assert torch.sum(torch.abs(scheduler._get_variance(980,				960) - 0.32460)) < 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) - 0.00979)) < 1e-5
            assert torch.sum(torch.abs(scheduler._get_variance(999,				998) - 0.02)) < 1e-5







       def 						a_		(					self) -> Tuple:
            snake_case_   			=   self.full_loop()

            snake_case_   			=   torch.sum(torch.abs(lowerCAmelCase__))
            snake_case_   			=   torch.mean(torch.abs(lowerCAmelCase__))

            assert abs(result_sum.item() - 172.0067) < 1e-2
            assert abs(result_mean.item() - 0.223967) < 1e-3







       def 						a_		(					self) -> Dict:
            snake_case_   			=   self.full_loop(prediction_type='v_prediction')

            snake_case_   			=   torch.sum(torch.abs(lowerCAmelCase__))
            snake_case_   			=   torch.mean(torch.abs(lowerCAmelCase__))

            assert abs(result_sum.item() - 52.5302) < 1e-2
            assert abs(result_mean.item() - 0.0684) < 1e-3







       def 						a_		(					self) -> Any:
            # We specify different beta, so that the first alpha is 0.99
            snake_case_   			=   self.full_loop(set_alpha_to_one=lowerCAmelCase__,				beta_start=0.01)
            snake_case_   			=   torch.sum(torch.abs(lowerCAmelCase__))
            snake_case_   			=   torch.mean(torch.abs(lowerCAmelCase__))

            assert abs(result_sum.item() - 149.8295) < 1e-2
            assert abs(result_mean.item() - 0.1951) < 1e-3







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

            assert abs(result_sum.item() - 149.0784) < 1e-2
            assert abs(result_mean.item() - 0.1941) < 1e-3


"""simple docstring"""


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


__UpperCamelCase										=     logging.get_logger(__name__)

__UpperCamelCase										=     {
    '''tau/splinter-base''': '''https://huggingface.co/tau/splinter-base/resolve/main/config.json''',
    '''tau/splinter-base-qass''': '''https://huggingface.co/tau/splinter-base-qass/resolve/main/config.json''',
    '''tau/splinter-large''': '''https://huggingface.co/tau/splinter-large/resolve/main/config.json''',
    '''tau/splinter-large-qass''': '''https://huggingface.co/tau/splinter-large-qass/resolve/main/config.json''',
    # See all Splinter models at https://huggingface.co/models?filter=splinter
}




class 					UpperCamelCase   (							lowerCAmelCase__  ):
       SCREAMING_SNAKE_CASE_						      = "splinter"







       def __init__(					self,				lowerCAmelCase__=3_0522,				lowerCAmelCase__=768,				lowerCAmelCase__=12,				lowerCAmelCase__=12,				lowerCAmelCase__=3072,				lowerCAmelCase__="gelu",				lowerCAmelCase__=0.1,				lowerCAmelCase__=0.1,				lowerCAmelCase__=512,				lowerCAmelCase__=2,				lowerCAmelCase__=0.02,				lowerCAmelCase__=1e-12,				lowerCAmelCase__=True,				lowerCAmelCase__=0,				lowerCAmelCase__=104,				**lowerCAmelCase__,				) -> Dict:
            super().__init__(pad_token_id=lowerCAmelCase__,				**lowerCAmelCase__)

            snake_case_   			=   vocab_size
            snake_case_   			=   max_position_embeddings
            snake_case_   			=   hidden_size
            snake_case_   			=   num_hidden_layers
            snake_case_   			=   num_attention_heads
            snake_case_   			=   intermediate_size
            snake_case_   			=   hidden_act
            snake_case_   			=   hidden_dropout_prob
            snake_case_   			=   attention_probs_dropout_prob
            snake_case_   			=   initializer_range
            snake_case_   			=   type_vocab_size
            snake_case_   			=   layer_norm_eps
            snake_case_   			=   use_cache
            snake_case_   			=   question_token_id

