





"""simple docstring"""


import torch

from diffusers import DDIMScheduler

from .test_schedulers import SchedulerCommonTest



class   lowercase_					(  __lowerCAmelCase				):
       snake_case__					:				Optional[int]									=  (DDIMScheduler,)
       snake_case__					:				str									=  (("""eta""", 0.0), ("""num_inference_steps""", 50))

       def 		_SCREAMING_SNAKE_CASE(      self ,       **__SCREAMING_SNAKE_CASE  ):






        '''simple docstring'''

        snake_case__     				=					{
            "num_train_timesteps": 1000,
            "beta_start": 0.0001,
            "beta_end": 0.02,
            "beta_schedule": "linear",
            "clip_sample": True,
        }

        config.update(**__SCREAMING_SNAKE_CASE  )
        return config

       def 		_SCREAMING_SNAKE_CASE(      self ,       **__SCREAMING_SNAKE_CASE  ):






        '''simple docstring'''

        snake_case__     				=					self.scheduler_classes[0]
        snake_case__     				=					self.get_scheduler_config(**__SCREAMING_SNAKE_CASE  )
        snake_case__     				=					scheduler_class(**__SCREAMING_SNAKE_CASE  )

        snake_case__				, snake_case__     				=					10, 0.0

        snake_case__     				=					self.dummy_model()
        snake_case__     				=					self.dummy_sample_deter

        scheduler.set_timesteps(__SCREAMING_SNAKE_CASE  )

        for t in scheduler.timesteps:
         snake_case__     				=					model(__SCREAMING_SNAKE_CASE ,       __SCREAMING_SNAKE_CASE  )
         snake_case__     				=					scheduler.step(__SCREAMING_SNAKE_CASE ,       __SCREAMING_SNAKE_CASE ,       __SCREAMING_SNAKE_CASE ,       __SCREAMING_SNAKE_CASE  ).prev_sample

        return sample

       def 		_SCREAMING_SNAKE_CASE(      self  ):






        '''simple docstring'''

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

       def 		_SCREAMING_SNAKE_CASE(      self  ):






        '''simple docstring'''

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

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

       def 		_SCREAMING_SNAKE_CASE(      self  ):






        '''simple docstring'''

        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=__SCREAMING_SNAKE_CASE ,       beta_end=__SCREAMING_SNAKE_CASE  )

       def 		_SCREAMING_SNAKE_CASE(      self  ):






        '''simple docstring'''

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

       def 		_SCREAMING_SNAKE_CASE(      self  ):






        '''simple docstring'''

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

       def 		_SCREAMING_SNAKE_CASE(      self  ):






        '''simple docstring'''

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

       def 		_SCREAMING_SNAKE_CASE(      self  ):






        '''simple docstring'''

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

       def 		_SCREAMING_SNAKE_CASE(      self  ):






        '''simple docstring'''

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

       def 		_SCREAMING_SNAKE_CASE(      self  ):






        '''simple docstring'''

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

       def 		_SCREAMING_SNAKE_CASE(      self  ):






        '''simple docstring'''

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

       def 		_SCREAMING_SNAKE_CASE(      self  ):






        '''simple docstring'''

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

       def 		_SCREAMING_SNAKE_CASE(      self  ):






        '''simple docstring'''

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

       def 		_SCREAMING_SNAKE_CASE(      self  ):






        '''simple docstring'''

        snake_case__     				=					self.scheduler_classes[0]
        snake_case__     				=					self.get_scheduler_config()
        snake_case__     				=					scheduler_class(**__SCREAMING_SNAKE_CASE  )

        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 		_SCREAMING_SNAKE_CASE(      self  ):






        '''simple docstring'''

        snake_case__     				=					self.full_loop()

        snake_case__     				=					torch.sum(torch.abs(__SCREAMING_SNAKE_CASE  )  )
        snake_case__     				=					torch.mean(torch.abs(__SCREAMING_SNAKE_CASE  )  )

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

       def 		_SCREAMING_SNAKE_CASE(      self  ):






        '''simple docstring'''

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

        snake_case__     				=					torch.sum(torch.abs(__SCREAMING_SNAKE_CASE  )  )
        snake_case__     				=					torch.mean(torch.abs(__SCREAMING_SNAKE_CASE  )  )

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

       def 		_SCREAMING_SNAKE_CASE(      self  ):






        '''simple docstring'''

        snake_case__     				=					self.full_loop(set_alpha_to_one=__SCREAMING_SNAKE_CASE ,       beta_start=0.01  )
        snake_case__     				=					torch.sum(torch.abs(__SCREAMING_SNAKE_CASE  )  )
        snake_case__     				=					torch.mean(torch.abs(__SCREAMING_SNAKE_CASE  )  )

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

       def 		_SCREAMING_SNAKE_CASE(      self  ):






        '''simple docstring'''

        snake_case__     				=					self.full_loop(set_alpha_to_one=__SCREAMING_SNAKE_CASE ,       beta_start=0.01  )
        snake_case__     				=					torch.sum(torch.abs(__SCREAMING_SNAKE_CASE  )  )
        snake_case__     				=					torch.mean(torch.abs(__SCREAMING_SNAKE_CASE  )  )

        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


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

a__       :  int       		=					{
    """microsoft/mpnet-base""": """https://huggingface.co/microsoft/mpnet-base/resolve/main/config.json""",
}



class   lowercase_					(  __lowerCAmelCase				):
       snake_case__					:				int									=  """mpnet"""

       def __init__(      self ,       __SCREAMING_SNAKE_CASE=3_0527 ,       __SCREAMING_SNAKE_CASE=768 ,       __SCREAMING_SNAKE_CASE=12 ,       __SCREAMING_SNAKE_CASE=12 ,       __SCREAMING_SNAKE_CASE=3072 ,       __SCREAMING_SNAKE_CASE="gelu" ,       __SCREAMING_SNAKE_CASE=0.1 ,       __SCREAMING_SNAKE_CASE=0.1 ,       __SCREAMING_SNAKE_CASE=512 ,       __SCREAMING_SNAKE_CASE=0.02 ,       __SCREAMING_SNAKE_CASE=1e-12 ,       __SCREAMING_SNAKE_CASE=32 ,       __SCREAMING_SNAKE_CASE=1 ,       __SCREAMING_SNAKE_CASE=0 ,       __SCREAMING_SNAKE_CASE=2 ,       **__SCREAMING_SNAKE_CASE ,       ):






        '''simple docstring'''

        super().__init__(pad_token_id=__SCREAMING_SNAKE_CASE ,       bos_token_id=__SCREAMING_SNAKE_CASE ,       eos_token_id=__SCREAMING_SNAKE_CASE ,       **__SCREAMING_SNAKE_CASE  )

        snake_case__     				=					vocab_size
        snake_case__     				=					hidden_size
        snake_case__     				=					num_hidden_layers
        snake_case__     				=					num_attention_heads
        snake_case__     				=					hidden_act
        snake_case__     				=					intermediate_size
        snake_case__     				=					hidden_dropout_prob
        snake_case__     				=					attention_probs_dropout_prob
        snake_case__     				=					max_position_embeddings
        snake_case__     				=					initializer_range
        snake_case__     				=					layer_norm_eps
        snake_case__     				=					relative_attention_num_buckets

