





"""simple docstring"""


import torch

from diffusers import DDIMScheduler

from .test_schedulers import SchedulerCommonTest



class    lowercase_ (   __lowerCAmelCase							):
       snake_case__ :							Dict 							=   (DDIMScheduler,)
       snake_case__ :							Optional[int] 							=   (("""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"""


import warnings

from ...utils import logging
from .image_processing_vilt import ViltImageProcessor


a__    :   str    						= logging.get_logger(__name__)



class    lowercase_ (   __lowerCAmelCase							):

       def __init__(	self      ,    *__SCREAMING_SNAKE_CASE      ,    **__SCREAMING_SNAKE_CASE   ):






        '''simple docstring'''

        warnings.warn(
            "The class ViltFeatureExtractor is deprecated and will be removed in version 5 of Transformers. Please"
            " use ViltImageProcessor instead."      ,    __SCREAMING_SNAKE_CASE      ,    )
        super().__init__(*__SCREAMING_SNAKE_CASE      ,    **__SCREAMING_SNAKE_CASE   )

