
"""simple docstring"""





import torch

from diffusers import DDIMParallelScheduler

from .test_schedulers import SchedulerCommonTest





class 				a ( SCREAMING_SNAKE_CASE_							):





       """simple docstring"""







       A__       :							Optional[int]    				=				(DDIMParallelScheduler,)
       A__       :							int    				=				(("eta", 0.0), ("num_inference_steps", 50))






       def 				__A						(			self							,	**snake_case_			)    ->				List[str]:
          _UpperCAmelCase         =      {
              'num_train_timesteps': 1000,
              'beta_start': 0.00_01,
              'beta_end': 0.02,
              'beta_schedule': 'linear',
              'clip_sample': True,
          }

          config.update(**__a			)
          return config






       def 				__A						(			self							,	**snake_case_			)    ->				List[str]:
          _UpperCAmelCase         =      self.scheduler_classes[0]
          _UpperCAmelCase         =      self.get_scheduler_config(**__a			)
          _UpperCAmelCase         =      scheduler_class(**__a			)

          _UpperCAmelCase         =      10, 0.0

          _UpperCAmelCase         =      self.dummy_model()
          _UpperCAmelCase         =      self.dummy_sample_deter

          scheduler.set_timesteps(__a			)

          for t in scheduler.timesteps:
             _UpperCAmelCase         =      model(__a							,	__a			)
             _UpperCAmelCase         =      scheduler.step(__a							,	__a							,	__a							,	__a			).prev_sample

          return sample






       def 				__A						(			self			)    ->				Optional[Any]:
          for timesteps in [100, 500, 1000]:
             self.check_over_configs(num_train_timesteps=__a			)






       def 				__A						(			self			)    ->				Tuple:
          for steps_offset in [0, 1]:
             self.check_over_configs(steps_offset=__a			)

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






       def 				__A						(			self			)    ->				int:
          for beta_start, beta_end in zip([0.00_01, 0.0_01, 0.01, 0.1]							,	[0.0_02, 0.02, 0.2, 2]			):
             self.check_over_configs(beta_start=__a							,	beta_end=__a			)






       def 				__A						(			self			)    ->				Optional[int]:
          for schedule in ["linear", "squaredcos_cap_v2"]:
             self.check_over_configs(beta_schedule=__a			)






       def 				__A						(			self			)    ->				Any:
          for prediction_type in ["epsilon", "v_prediction"]:
             self.check_over_configs(prediction_type=__a			)






       def 				__A						(			self			)    ->				List[str]:
          for clip_sample in [True, False]:
             self.check_over_configs(clip_sample=__a			)






       def 				__A						(			self			)    ->				Dict:
          for timestep_spacing in ["trailing", "leading"]:
             self.check_over_configs(timestep_spacing=__a			)






       def 				__A						(			self			)    ->				str:
          for rescale_betas_zero_snr in [True, False]:
             self.check_over_configs(rescale_betas_zero_snr=__a			)






       def 				__A						(			self			)    ->				Any:
          self.check_over_configs(thresholding=__a			)
          for threshold in [0.5, 1.0, 2.0]:
             for prediction_type in ["epsilon", "v_prediction"]:
                self.check_over_configs(
                    thresholding=__a							,	prediction_type=__a							,	sample_max_value=__a							,	)






       def 				__A						(			self			)    ->				int:
          for t in [1, 10, 49]:
             self.check_over_forward(time_step=__a			)






       def 				__A						(			self			)    ->				int:
          for t, num_inference_steps in zip([1, 10, 50]							,	[10, 50, 500]			):
             self.check_over_forward(time_step=__a							,	num_inference_steps=__a			)






       def 				__A						(			self			)    ->				List[Any]:
          for t, eta in zip([1, 10, 49]							,	[0.0, 0.5, 1.0]			):
             self.check_over_forward(time_step=__a							,	eta=__a			)






       def 				__A						(			self			)    ->				str:
          _UpperCAmelCase         =      self.scheduler_classes[0]
          _UpperCAmelCase         =      self.get_scheduler_config()
          _UpperCAmelCase         =      scheduler_class(**__a			)

          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_47_71			)			) < 1e-5
          assert torch.sum(torch.abs(scheduler._get_variance(980							,	960			) - 0.3_24_60			)			) < 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_09_79			)			) < 1e-5
          assert torch.sum(torch.abs(scheduler._get_variance(999							,	998			) - 0.02			)			) < 1e-5






       def 				__A						(			self			)    ->				Tuple:
          _UpperCAmelCase         =      self.scheduler_classes[0]
          _UpperCAmelCase         =      self.get_scheduler_config()
          _UpperCAmelCase         =      scheduler_class(**__a			)

          _UpperCAmelCase         =      10, 0.0
          scheduler.set_timesteps(__a			)

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

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

          _UpperCAmelCase         =      model(samples.flatten(0							,	1			)							,	timesteps.flatten(0							,	1			)			)
          _UpperCAmelCase         =      scheduler.batch_step_no_noise(__a							,	timesteps.flatten(0							,	1			)							,	samples.flatten(0							,	1			)							,	__a			)

          _UpperCAmelCase         =      torch.sum(torch.abs(__a			)			)
          _UpperCAmelCase         =      torch.mean(torch.abs(__a			)			)

          assert abs(result_sum.item() - 1147.7904			) < 1e-2
          assert abs(result_mean.item() - 0.49_82			) < 1e-3






       def 				__A						(			self			)    ->				Optional[Any]:
          _UpperCAmelCase         =      self.full_loop()

          _UpperCAmelCase         =      torch.sum(torch.abs(__a			)			)
          _UpperCAmelCase         =      torch.mean(torch.abs(__a			)			)

          assert abs(result_sum.item() - 172.0067			) < 1e-2
          assert abs(result_mean.item() - 0.22_39_67			) < 1e-3






       def 				__A						(			self			)    ->				Union[str, Any]:
          _UpperCAmelCase         =      self.full_loop(prediction_type="v_prediction"			)

          _UpperCAmelCase         =      torch.sum(torch.abs(__a			)			)
          _UpperCAmelCase         =      torch.mean(torch.abs(__a			)			)

          assert abs(result_sum.item() - 52.5302			) < 1e-2
          assert abs(result_mean.item() - 0.06_84			) < 1e-3






       def 				__A						(			self			)    ->				str:
          _UpperCAmelCase         =      self.full_loop(set_alpha_to_one=__a							,	beta_start=0.01			)
          _UpperCAmelCase         =      torch.sum(torch.abs(__a			)			)
          _UpperCAmelCase         =      torch.mean(torch.abs(__a			)			)

          assert abs(result_sum.item() - 149.8295			) < 1e-2
          assert abs(result_mean.item() - 0.19_51			) < 1e-3






       def 				__A						(			self			)    ->				Any:
          _UpperCAmelCase         =      self.full_loop(set_alpha_to_one=__a							,	beta_start=0.01			)
          _UpperCAmelCase         =      torch.sum(torch.abs(__a			)			)
          _UpperCAmelCase         =      torch.mean(torch.abs(__a			)			)

          assert abs(result_sum.item() - 149.0784			) < 1e-2
          assert abs(result_mean.item() - 0.19_41			) < 1e-3






'''simple docstring'''





import warnings

from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding



class  snake_case__						(    SCREAMING_SNAKE_CASE_				):
 A__				= ['''image_processor''', '''tokenizer''']
 A__				= '''CLIPImageProcessor'''
 A__				= ('''CLIPTokenizer''', '''CLIPTokenizerFast''')
 def __init__(   self						:							Any      ,     __a						:							str=None      ,     __a						:							List[Any]=None      ,     **__a						:							List[str]  )      ->   Optional[Any]:







       '''simple docstring'''
       __snake_case :   Optional[Any]													=    None
       if "feature_extractor" in kwargs:
             warnings.warn(
                 'The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`'
                 ' instead.'      ,     __a      ,     )
             __snake_case :   List[str]													=    kwargs.pop('feature_extractor'  )

       __snake_case :   List[Any]													=    image_processor if image_processor is not None else feature_extractor
       if image_processor is None:
             raise ValueError('You need to specify an `image_processor`.'  )
       if tokenizer is None:
             raise ValueError('You need to specify a `tokenizer`.'  )

       super().__init__(__a      ,     __a  )
 def __call__(   self						:							List[Any]      ,     __a						:							Optional[int]=None      ,     __a						:							Optional[int]=None      ,     __a						:							Union[str, Any]=None      ,     **__a						:							Union[str, Any]  )      ->   List[Any]:







       '''simple docstring'''

       if text is None and images is None:
             raise ValueError('You have to specify either text or images. Both cannot be none.'  )

       if text is not None:
             __snake_case :   Any													=    self.tokenizer(__a      ,     return_tensors=__a      ,     **__a  )

       if images is not None:
             __snake_case :   str													=    self.image_processor(__a      ,     return_tensors=__a      ,     **__a  )

       if text is not None and images is not None:
             __snake_case :   Optional[Any]													=    image_features.pixel_values
             return encoding
       elif text is not None:
             return encoding
       else:
             return BatchEncoding(data=dict(**__a  )      ,     tensor_type=__a  )
 def A_   (   self						:							List[Any]      ,     *__a						:							Dict      ,     **__a						:							Dict  )      ->   Union[str, Any]:







       '''simple docstring'''
       return self.tokenizer.batch_decode(*__a      ,     **__a  )
 def A_   (   self						:							str      ,     *__a						:							Tuple      ,     **__a						:							List[str]  )      ->   int:







       '''simple docstring'''
       return self.tokenizer.decode(*__a      ,     **__a  )
 @property
 def A_   (   self						:							Union[str, Any]  )      ->   Optional[Any]:







       '''simple docstring'''
       __snake_case :   Tuple													=    self.tokenizer.model_input_names
       __snake_case :   Tuple													=    self.image_processor.model_input_names
       return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names  )  )
 @property
 def A_   (   self						:							Any  )      ->   Any:







       '''simple docstring'''
       warnings.warn(
           '`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead.'      ,     __a      ,     )
       return self.image_processor_class





 @property
 def A_   (   self						:							Optional[int]  )      ->   Optional[Any]:







       '''simple docstring'''
       warnings.warn(
           '`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead.'      ,     __a      ,     )
       return self.image_processor



