




import torch

from diffusers import DDIMParallelScheduler

from .test_schedulers import SchedulerCommonTest

class  _lowerCamelCase						(			__lowerCamelCase   ):

      """simple docstring"""

      lowerCAmelCase__			: Dict    =					(DDIMParallelScheduler,)
      lowerCAmelCase__			: Dict    =					(("eta", 0.0), ("num_inference_steps", 50))







      def       snake_case	(     self					: Optional[int] ,       **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(**UpperCAmelCase_				)
          return config







      def       snake_case	(     self					: Optional[int] ,       **snake_case					: Optional[Any]				):
          __UpperCamelCase  		=						self.scheduler_classes[0]
          __UpperCamelCase  		=						self.get_scheduler_config(**UpperCAmelCase_				)
          __UpperCamelCase  		=						scheduler_class(**UpperCAmelCase_				)

          __UpperCamelCase  		=						10, 0.0

          __UpperCamelCase  		=						self.dummy_model()
          __UpperCamelCase  		=						self.dummy_sample_deter

          scheduler.set_timesteps(UpperCAmelCase_				)

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

          return sample







      def       snake_case	(     self					: Optional[int]				):
          for timesteps in [100, 500, 1000]:
              self.check_over_configs(num_train_timesteps=UpperCAmelCase_				)







      def       snake_case	(     self					: str				):
          for steps_offset in [0, 1]:
              self.check_over_configs(steps_offset=UpperCAmelCase_				)

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







      def       snake_case	(     self					: List[str]				):
          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=UpperCAmelCase_ ,       beta_end=UpperCAmelCase_				)







      def       snake_case	(     self					: Optional[Any]				):
          for schedule in ["linear", "squaredcos_cap_v2"]:
              self.check_over_configs(beta_schedule=UpperCAmelCase_				)







      def       snake_case	(     self					: List[str]				):
          for prediction_type in ["epsilon", "v_prediction"]:
              self.check_over_configs(prediction_type=UpperCAmelCase_				)







      def       snake_case	(     self					: int				):
          for clip_sample in [True, False]:
              self.check_over_configs(clip_sample=UpperCAmelCase_				)







      def       snake_case	(     self					: int				):
          for timestep_spacing in ["trailing", "leading"]:
              self.check_over_configs(timestep_spacing=UpperCAmelCase_				)







      def       snake_case	(     self					: Optional[int]				):
          for rescale_betas_zero_snr in [True, False]:
              self.check_over_configs(rescale_betas_zero_snr=UpperCAmelCase_				)







      def       snake_case	(     self					: Any				):
          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       snake_case	(     self					: int				):
          for t in [1, 10, 49]:
              self.check_over_forward(time_step=UpperCAmelCase_				)







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







      def       snake_case	(     self					: Optional[Any]				):
          __UpperCamelCase  		=						self.scheduler_classes[0]
          __UpperCamelCase  		=						self.get_scheduler_config()
          __UpperCamelCase  		=						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_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       snake_case	(     self					: Dict				):
          __UpperCamelCase  		=						self.scheduler_classes[0]
          __UpperCamelCase  		=						self.get_scheduler_config()
          __UpperCamelCase  		=						scheduler_class(**UpperCAmelCase_				)

          __UpperCamelCase  		=						10, 0.0
          scheduler.set_timesteps(UpperCAmelCase_				)

          __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(UpperCAmelCase_				)[0:3, None].repeat(1 ,       UpperCAmelCase_				)

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

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

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







      def       snake_case	(     self					: int				):
          __UpperCamelCase  		=						self.full_loop()

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

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







      def       snake_case	(     self					: Optional[int]				):
          __UpperCamelCase  		=						self.full_loop(prediction_type='''v_prediction'''				)

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

          assert abs(result_sum.item() - 52.53_02				) < 1E-2
          assert abs(result_mean.item() - 0.06_84				) < 1E-3







      def       snake_case	(     self					: List[str]				):
          # We specify different beta, so that the first alpha is 0.99
          __UpperCamelCase  		=						self.full_loop(set_alpha_to_one=UpperCAmelCase_ ,       beta_start=0.01				)
          __UpperCamelCase  		=						torch.sum(torch.abs(UpperCAmelCase_				)				)
          __UpperCamelCase  		=						torch.mean(torch.abs(UpperCAmelCase_				)				)

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







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

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












import warnings

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

class  _lowerCamelCase						(			_SCREAMING_SNAKE_CASE   ):

      """simple docstring"""

      lowerCAmelCase__			: Dict    =					["image_processor", "tokenizer"]
      lowerCAmelCase__			: List[Any]    =					"ViTImageProcessor"
      lowerCAmelCase__			: Optional[int]    =					("CLIPTokenizer", "CLIPTokenizerFast")







      def __init__(     self					: Any ,       snake_case					: Any=None ,       snake_case					: Dict=None ,       **snake_case					: Tuple				):
          __UpperCamelCase  		=						None
          if "feature_extractor" in kwargs:
              warnings.warn(
                  '''The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`'''
                  ''' instead.''' ,       snake_case ,       )
              __UpperCamelCase  		=						kwargs.pop('''feature_extractor'''				)

          __UpperCamelCase  		=						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__(snake_case ,       snake_case				)







      def __call__(     self					: Dict ,       snake_case					: Union[str, Any]=None ,       snake_case					: Tuple=None ,       snake_case					: Any=None ,       snake_case					: Any=None ,       **snake_case					: Optional[int]				):
          if text is None and visual_prompt is None and images is None:
              raise ValueError('''You have to specify either text, visual prompt or images.'''				)

          if text is not None and visual_prompt is not None:
              raise ValueError('''You have to specify exactly one type of prompt. Either text or visual prompt.'''				)

          if text is not None:
              __UpperCamelCase  		=						self.tokenizer(snake_case ,       return_tensors=snake_case ,       **snake_case				)

          if visual_prompt is not None:
              __UpperCamelCase  		=						self.image_processor(snake_case ,       return_tensors=snake_case ,       **snake_case				)

          if images is not None:
              __UpperCamelCase  		=						self.image_processor(snake_case ,       return_tensors=snake_case ,       **snake_case				)

          if visual_prompt is not None and images is not None:
              __UpperCamelCase  		=						{
                  '''pixel_values''': image_features.pixel_values,
                  '''conditional_pixel_values''': prompt_features.pixel_values,
              }
              return encoding
          elif text is not None and images is not None:
              __UpperCamelCase  		=						image_features.pixel_values
              return encoding
          elif text is not None:
              return encoding
          elif visual_prompt is not None:
              __UpperCamelCase  		=						{
                  '''conditional_pixel_values''': prompt_features.pixel_values,
              }
              return encoding
          else:
              return BatchEncoding(data=dict(**snake_case				) ,       tensor_type=snake_case				)







      def       snake_case	(     self					: Optional[int] ,       *snake_case					: Tuple ,       **snake_case					: Any				):
          return self.tokenizer.batch_decode(*snake_case ,       **snake_case				)







      def       snake_case	(     self					: Optional[int] ,       *snake_case					: Dict ,       **snake_case					: List[str]				):
          return self.tokenizer.decode(*snake_case ,       **snake_case				)







      @property
      def       snake_case	(     self					: Optional[Any]				):
          warnings.warn(
              '''`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead.''' ,       snake_case ,       )
          return self.image_processor_class







      @property
      def       snake_case	(     self					: Optional[Any]				):
          warnings.warn(
              '''`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead.''' ,       snake_case ,       )
          return self.image_processor






