


import torch

from diffusers import CMStochasticIterativeScheduler

from .test_schedulers import SchedulerCommonTest

class 							a						(_lowerCAmelCase ):




							"""simple docstring"""


							__UpperCAmelCase       :				Optional[int]	    =    (CMStochasticIterativeScheduler,)
							__UpperCAmelCase       :				List[Any]	    =    10

							def        __snake_case   (				self			:    Any       ,      **lowerCamelCase			:    Union[str, Any]							)      ->       Optional[Any]:
														__snake_case						:     Union[str, Any]									=  {
														    "num_train_timesteps": 201,
														    "sigma_min": 0.0_02,
														    "sigma_max": 80.0,
														}

														config.update(**lowerCamelCase							)
														return config

							def        __snake_case   (				self			:    Optional[int]							)      ->       List[str]:
														__snake_case						:     Optional[int]									=  10

														__snake_case						:     Any									=  self.get_scheduler_config()
														__snake_case						:     List[str]									=  self.scheduler_classes[0](**lowerCamelCase							)

														scheduler.set_timesteps(lowerCamelCase							)

														__snake_case						:     List[Any]									=  scheduler.timesteps[0]
														__snake_case						:     List[Any]									=  scheduler.timesteps[1]

														__snake_case						:     int									=  self.dummy_sample
														__snake_case						:     Optional[int]									=  0.1 * sample

														__snake_case						:     str									=  scheduler.step(lowerCamelCase       ,      lowerCamelCase       ,      lowerCamelCase							).prev_sample
														__snake_case						:     str									=  scheduler.step(lowerCamelCase       ,      lowerCamelCase       ,      lowerCamelCase							).prev_sample

														self.assertEqual(output_a.shape       ,      sample.shape							)
														self.assertEqual(output_a.shape       ,      output_a.shape							)

							def        __snake_case   (				self			:    Dict							)      ->       str:
														for timesteps in [10, 50, 100, 1000]:
																					self.check_over_configs(num_train_timesteps=lowerCamelCase							)

							def        __snake_case   (				self			:    Optional[int]							)      ->       Dict:
														for clip_denoised in [True, False]:
																					self.check_over_configs(clip_denoised=lowerCamelCase							)

							def        __snake_case   (				self			:    Dict							)      ->       Tuple:
														__snake_case						:     Tuple									=  self.scheduler_classes[0]
														__snake_case						:     Union[str, Any]									=  self.get_scheduler_config()
														__snake_case						:     Any									=  scheduler_class(**lowerCamelCase							)

														__snake_case						:     Union[str, Any]									=  1
														scheduler.set_timesteps(lowerCamelCase							)
														__snake_case						:     Union[str, Any]									=  scheduler.timesteps

														__snake_case						:     Optional[Any]									=  torch.manual_seed(0							)

														__snake_case						:     Tuple									=  self.dummy_model()
														__snake_case						:     Any									=  self.dummy_sample_deter * scheduler.init_noise_sigma

														for i, t in enumerate(lowerCamelCase							):
																					# 1. scale model input
																					__snake_case						:     List[str]									=  scheduler.scale_model_input(lowerCamelCase       ,      lowerCamelCase							)

																					# 2. predict noise residual
																					__snake_case						:     Union[str, Any]									=  model(lowerCamelCase       ,      lowerCamelCase							)

																					# 3. predict previous sample x_t-1
																					__snake_case						:     Any									=  scheduler.step(lowerCamelCase       ,      lowerCamelCase       ,      lowerCamelCase       ,      generator=lowerCamelCase							).prev_sample

																					__snake_case						:     Tuple									=  pred_prev_sample

														__snake_case						:     Union[str, Any]									=  torch.sum(torch.abs(lowerCamelCase							)							)
														__snake_case						:     List[str]									=  torch.mean(torch.abs(lowerCamelCase							)							)

														assert abs(result_sum.item() - 1_92.76_14							) < 1E-2
														assert abs(result_mean.item() - 0.25_10							) < 1E-3

							def        __snake_case   (				self			:    str							)      ->       Optional[Any]:
														__snake_case						:     Dict									=  self.scheduler_classes[0]
														__snake_case						:     List[Any]									=  self.get_scheduler_config()
														__snake_case						:     Union[str, Any]									=  scheduler_class(**lowerCamelCase							)

														__snake_case						:     Tuple									=  [106, 0]
														scheduler.set_timesteps(timesteps=lowerCamelCase							)
														__snake_case						:     Union[str, Any]									=  scheduler.timesteps

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

														__snake_case						:     Any									=  self.dummy_model()
														__snake_case						:     List[Any]									=  self.dummy_sample_deter * scheduler.init_noise_sigma

														for t in timesteps:
																					# 1. scale model input
																					__snake_case						:     Tuple									=  scheduler.scale_model_input(lowerCamelCase       ,      lowerCamelCase							)

																					# 2. predict noise residual
																					__snake_case						:     List[str]									=  model(lowerCamelCase       ,      lowerCamelCase							)

																					# 3. predict previous sample x_t-1
																					__snake_case						:     str									=  scheduler.step(lowerCamelCase       ,      lowerCamelCase       ,      lowerCamelCase       ,      generator=lowerCamelCase							).prev_sample

																					__snake_case						:     List[Any]									=  pred_prev_sample

														__snake_case						:     Any									=  torch.sum(torch.abs(lowerCamelCase							)							)
														__snake_case						:     Optional[int]									=  torch.mean(torch.abs(lowerCamelCase							)							)

														assert abs(result_sum.item() - 3_47.63_57							) < 1E-2
														assert abs(result_mean.item() - 0.45_27							) < 1E-3

							def        __snake_case   (				self			:    List[str]							)      ->       List[Any]:
														__snake_case						:     Optional[int]									=  self.scheduler_classes[0]
														__snake_case						:     str									=  self.get_scheduler_config()
														__snake_case						:     int									=  scheduler_class(**lowerCamelCase							)

														__snake_case						:     Any									=  [39, 30, 12, 15, 0]

														with self.assertRaises(lowerCamelCase       ,      msg="`timesteps` must be in descending order."							):
																					scheduler.set_timesteps(timesteps=lowerCamelCase							)

							def        __snake_case   (				self			:    Tuple							)      ->       Union[str, Any]:
														__snake_case						:     Optional[int]									=  self.scheduler_classes[0]
														__snake_case						:     int									=  self.get_scheduler_config()
														__snake_case						:     Any									=  scheduler_class(**lowerCamelCase							)

														__snake_case						:     Tuple									=  [39, 30, 12, 1, 0]
														__snake_case						:     Union[str, Any]									=  len(lowerCamelCase							)

														with self.assertRaises(lowerCamelCase       ,      msg="Can only pass one of `num_inference_steps` or `timesteps`."							):
																					scheduler.set_timesteps(num_inference_steps=lowerCamelCase       ,      timesteps=lowerCamelCase							)






							def        __snake_case   (				self			:    List[Any]							)      ->       Optional[int]:
														__snake_case						:     str									=  self.scheduler_classes[0]
														__snake_case						:     Dict									=  self.get_scheduler_config()
														__snake_case						:     Optional[int]									=  scheduler_class(**lowerCamelCase							)

														__snake_case						:     int									=  [scheduler.config.num_train_timesteps]

														with self.assertRaises(
														    lowerCamelCase       ,      msg="`timesteps` must start before `self.config.train_timesteps`: {scheduler.config.num_train_timesteps}}"       ,      ):
																					scheduler.set_timesteps(timesteps=lowerCamelCase							)



"""simple docstring"""





import warnings

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






class        _UpperCAmelCase(       lowerCamelCase      ):
		lowercase__         = ['image_processor', 'tokenizer']
		lowercase__         = 'ViTImageProcessor'
		lowercase__         = ('CLIPTokenizer', 'CLIPTokenizerFast')



		def __init__(   self							,						__a=None							,						__a=None							,						**__a)			->			Union[str, Any]:






							'''simple docstring'''
							_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.'''							,						__a							,						)
												_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__(__a							,						__a)



		def __call__(   self							,						__a=None							,						__a=None							,						__a=None							,						__a=None							,						**__a)			->			Tuple:






							'''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									=    self.tokenizer(__a							,						return_tensors=__a							,						**__a)

							if visual_prompt is not None:
												_UpperCamelCase									=    self.image_processor(__a							,						return_tensors=__a							,						**__a)

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

							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(**__a)							,						tensor_type=__a)



		def 		UpperCAmelCase				(   self							,						*__a							,						**__a)			->			Any:






							'''simple docstring'''
							return self.tokenizer.batch_decode(*__a							,						**__a)



		def 		UpperCAmelCase				(   self							,						*__a							,						**__a)			->			List[str]:






							'''simple docstring'''
							return self.tokenizer.decode(*__a							,						**__a)



		@property
		def 		UpperCAmelCase				(   self)			->			List[str]:






							'''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 		UpperCAmelCase				(   self)			->			List[str]:






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






