'''simple docstring'''


import torch

from diffusers import EulerDiscreteScheduler
from diffusers.utils import torch_device

from .test_schedulers import SchedulerCommonTest






class 					__SCREAMING_SNAKE_CASE						(	lowerCamelCase_	):







  '''simple docstring'''






  lowerCamelCase_		:Union[str, Any]						   =		(EulerDiscreteScheduler,)
  lowerCamelCase_		:str						   =		10


  def _UpperCamelCase					(			self				,		**snake_case_						):




       '''simple docstring'''

       UpperCAmelCase_							:       str       =				{
           'num_train_timesteps': 1_1_0_0,
           'beta_start': 0.00_01,
           'beta_end': 0.02,
           'beta_schedule': 'linear',
       }

       config.update(**snake_case_						)
       return config


  def _UpperCamelCase					(			self						):




       '''simple docstring'''

       for timesteps in [1_0, 5_0, 1_0_0, 1_0_0_0]:
            self.check_over_configs(num_train_timesteps=snake_case_						)


  def _UpperCamelCase					(			self						):




       '''simple docstring'''

       for beta_start, beta_end in zip([0.0_00_01, 0.00_01, 0.0_01]				,		[0.00_02, 0.0_02, 0.02]						):
            self.check_over_configs(beta_start=snake_case_				,		beta_end=snake_case_						)


  def _UpperCamelCase					(			self						):




       '''simple docstring'''

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


  def _UpperCamelCase					(			self						):




       '''simple docstring'''

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


  def _UpperCamelCase					(			self						):




       '''simple docstring'''

       UpperCAmelCase_							:       Dict       =				self.scheduler_classes[0]
       UpperCAmelCase_							:       Dict       =				self.get_scheduler_config()
       UpperCAmelCase_							:       Any       =				scheduler_class(**snake_case_						)

       scheduler.set_timesteps(self.num_inference_steps						)

       UpperCAmelCase_							:       List[str]       =				torch.manual_seed(0						)

       UpperCAmelCase_							:       str       =				self.dummy_model()
       UpperCAmelCase_							:       Optional[Any]       =				self.dummy_sample_deter * scheduler.init_noise_sigma
       UpperCAmelCase_							:       str       =				sample.to(snake_case_						)

       for i, t in enumerate(scheduler.timesteps						):
            UpperCAmelCase_							:       Dict       =				scheduler.scale_model_input(snake_case_				,		snake_case_						)

            UpperCAmelCase_							:       str       =				model(snake_case_				,		snake_case_						)

            UpperCAmelCase_							:       int       =				scheduler.step(snake_case_				,		snake_case_				,		snake_case_				,		generator=snake_case_						)
            UpperCAmelCase_							:       Any       =				output.prev_sample

       UpperCAmelCase_							:       List[str]       =				torch.sum(torch.abs(snake_case_						)						)
       UpperCAmelCase_							:       str       =				torch.mean(torch.abs(snake_case_						)						)

       assert abs(result_sum.item() - 10.08_07						) < 1E-2
       assert abs(result_mean.item() - 0.01_31						) < 1E-3


  def _UpperCamelCase					(			self						):




       '''simple docstring'''

       UpperCAmelCase_							:       List[str]       =				self.scheduler_classes[0]
       UpperCAmelCase_							:       List[Any]       =				self.get_scheduler_config(prediction_type='v_prediction'						)
       UpperCAmelCase_							:       List[str]       =				scheduler_class(**snake_case_						)

       scheduler.set_timesteps(self.num_inference_steps						)

       UpperCAmelCase_							:       Tuple       =				torch.manual_seed(0						)

       UpperCAmelCase_							:       Union[str, Any]       =				self.dummy_model()
       UpperCAmelCase_							:       Optional[int]       =				self.dummy_sample_deter * scheduler.init_noise_sigma
       UpperCAmelCase_							:       str       =				sample.to(snake_case_						)

       for i, t in enumerate(scheduler.timesteps						):
            UpperCAmelCase_							:       Tuple       =				scheduler.scale_model_input(snake_case_				,		snake_case_						)

            UpperCAmelCase_							:       Dict       =				model(snake_case_				,		snake_case_						)

            UpperCAmelCase_							:       Any       =				scheduler.step(snake_case_				,		snake_case_				,		snake_case_				,		generator=snake_case_						)
            UpperCAmelCase_							:       List[Any]       =				output.prev_sample

       UpperCAmelCase_							:       str       =				torch.sum(torch.abs(snake_case_						)						)
       UpperCAmelCase_							:       Dict       =				torch.mean(torch.abs(snake_case_						)						)

       assert abs(result_sum.item() - 0.00_02						) < 1E-2
       assert abs(result_mean.item() - 2.2_676E-06						) < 1E-3


  def _UpperCamelCase					(			self						):




       '''simple docstring'''

       UpperCAmelCase_							:       str       =				self.scheduler_classes[0]
       UpperCAmelCase_							:       str       =				self.get_scheduler_config()
       UpperCAmelCase_							:       str       =				scheduler_class(**snake_case_						)

       scheduler.set_timesteps(self.num_inference_steps				,		device=snake_case_						)

       UpperCAmelCase_							:       Any       =				torch.manual_seed(0						)

       UpperCAmelCase_							:       Tuple       =				self.dummy_model()
       UpperCAmelCase_							:       int       =				self.dummy_sample_deter * scheduler.init_noise_sigma.cpu()
       UpperCAmelCase_							:       str       =				sample.to(snake_case_						)

       for t in scheduler.timesteps:
            UpperCAmelCase_							:       List[Any]       =				scheduler.scale_model_input(snake_case_				,		snake_case_						)

            UpperCAmelCase_							:       int       =				model(snake_case_				,		snake_case_						)

            UpperCAmelCase_							:       List[str]       =				scheduler.step(snake_case_				,		snake_case_				,		snake_case_				,		generator=snake_case_						)
            UpperCAmelCase_							:       List[str]       =				output.prev_sample

       UpperCAmelCase_							:       List[str]       =				torch.sum(torch.abs(snake_case_						)						)
       UpperCAmelCase_							:       str       =				torch.mean(torch.abs(snake_case_						)						)

       assert abs(result_sum.item() - 10.08_07						) < 1E-2
       assert abs(result_mean.item() - 0.01_31						) < 1E-3






  def _UpperCamelCase					(			self						):




       '''simple docstring'''

       UpperCAmelCase_							:       Any       =				self.scheduler_classes[0]
       UpperCAmelCase_							:       Union[str, Any]       =				self.get_scheduler_config()
       UpperCAmelCase_							:       List[str]       =				scheduler_class(**snake_case_				,		use_karras_sigmas=snake_case_						)

       scheduler.set_timesteps(self.num_inference_steps				,		device=snake_case_						)

       UpperCAmelCase_							:       Any       =				torch.manual_seed(0						)

       UpperCAmelCase_							:       Tuple       =				self.dummy_model()
       UpperCAmelCase_							:       int       =				self.dummy_sample_deter * scheduler.init_noise_sigma.cpu()
       UpperCAmelCase_							:       str       =				sample.to(snake_case_						)

       for t in scheduler.timesteps:
            UpperCAmelCase_							:       Any       =				scheduler.scale_model_input(snake_case_				,		snake_case_						)

            UpperCAmelCase_							:       List[str]       =				model(snake_case_				,		snake_case_						)

            UpperCAmelCase_							:       List[Any]       =				scheduler.step(snake_case_				,		snake_case_				,		snake_case_				,		generator=snake_case_						)
            UpperCAmelCase_							:       Optional[Any]       =				output.prev_sample

       UpperCAmelCase_							:       Tuple       =				torch.sum(torch.abs(snake_case_						)						)
       UpperCAmelCase_							:       int       =				torch.mean(torch.abs(snake_case_						)						)

       assert abs(result_sum.item() - 1_24.52_29_94_99_51_17_19						) < 1E-2
       assert abs(result_mean.item() - 0.1_62_13_93_26_33_39_99_63						) < 1E-3







'''simple docstring'''


import warnings

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






class 					__SCREAMING_SNAKE_CASE						(	lowerCamelCase_	):







  '''simple docstring'''






  lowerCamelCase_		:Tuple						   =		['''image_processor''', '''tokenizer''']
  lowerCamelCase_		:Optional[Any]						   =		'''ViTImageProcessor'''
  lowerCamelCase_		:int						   =		('''CLIPTokenizer''', '''CLIPTokenizerFast''')


  def __init__(			self				,		snake_case_=None				,		snake_case_=None				,		**snake_case_						):




       '''simple docstring'''

       UpperCAmelCase_							:       Dict       =				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_							:       int       =				kwargs.pop('feature_extractor'						)

       UpperCAmelCase_							:       str       =				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				,		snake_case_=None				,		snake_case_=None				,		snake_case_=None				,		snake_case_=None				,		**snake_case_						):




       '''simple docstring'''

       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_							:       Optional[int]       =				self.tokenizer(snake_case_				,		return_tensors=snake_case_				,		**snake_case_						)

       if visual_prompt is not None:
            UpperCAmelCase_							:       Optional[Any]       =				self.image_processor(snake_case_				,		return_tensors=snake_case_				,		**snake_case_						)

       if images is not None:
            UpperCAmelCase_							:       int       =				self.image_processor(snake_case_				,		return_tensors=snake_case_				,		**snake_case_						)

       if visual_prompt is not None and images is not None:
            UpperCAmelCase_							:       Tuple       =				{
                '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_							:       Optional[int]       =				image_features.pixel_values
            return encoding
       elif text is not None:
            return encoding
       elif visual_prompt is not None:
            UpperCAmelCase_							:       Dict       =				{
                'conditional_pixel_values': prompt_features.pixel_values,
            }
            return encoding
       else:
            return BatchEncoding(data=dict(**snake_case_						)				,		tensor_type=snake_case_						)


  def _UpperCamelCase					(			self				,		*snake_case_				,		**snake_case_						):




       '''simple docstring'''

       return self.tokenizer.batch_decode(*snake_case_				,		**snake_case_						)


  def _UpperCamelCase					(			self				,		*snake_case_				,		**snake_case_						):




       '''simple docstring'''

       return self.tokenizer.decode(*snake_case_				,		**snake_case_						)


  @property
  def _UpperCamelCase					(			self						):




       '''simple docstring'''

       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 _UpperCamelCase					(			self						):




       '''simple docstring'''

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






