

"""simple docstring"""





import tempfile

import torch

from diffusers import (
    DEISMultistepScheduler,
    DPMSolverMultistepScheduler,
    DPMSolverSinglestepScheduler,
    UniPCMultistepScheduler,
)

from .test_schedulers import SchedulerCommonTest







class 				SCREAMING_SNAKE_CASE					(		a_       ):






					"""simple docstring"""



					lowercase__				 =       (DEISMultistepScheduler,)
					lowercase__				 =       (("num_inference_steps", 25),)







					def        __lowerCAmelCase (       self			:				Optional[Any]   ,**lowercase_			:				List[str]		):
						lowerCAmelCase__	:						List[Any]  				=	{
						    '''num_train_timesteps''': 1_0_0_0,
						    '''beta_start''': 0.0001,
						    '''beta_end''': 0.02,
						    '''beta_schedule''': '''linear''',
						    '''solver_order''': 2,
						}

						config.update(**lowercase_		)
						return config







					def        __lowerCAmelCase (       self			:				Union[str, Any]   ,lowercase_			:				Optional[int]=0   ,**lowercase_			:				Any		):
						lowerCAmelCase__	:						List[Any]  				=	dict(self.forward_default_kwargs		)
						lowerCAmelCase__	:						Union[str, Any]  				=	kwargs.pop('''num_inference_steps'''   ,lowercase_		)
						lowerCAmelCase__	:						Union[str, Any]  				=	self.dummy_sample
						lowerCAmelCase__	:						str  				=	0.1 * sample
						lowerCAmelCase__	:						Tuple  				=	[residual + 0.2, residual + 0.15, residual + 0.10]

						for scheduler_class in self.scheduler_classes:
							lowerCAmelCase__	:						List[Any]  				=	self.get_scheduler_config(**lowercase_		)
							lowerCAmelCase__	:						Optional[Any]  				=	scheduler_class(**lowercase_		)
							scheduler.set_timesteps(lowercase_		)
							# copy over dummy past residuals
							lowerCAmelCase__	:						str  				=	dummy_past_residuals[: scheduler.config.solver_order]

							with tempfile.TemporaryDirectory() as tmpdirname:
								scheduler.save_config(lowercase_		)
								lowerCAmelCase__	:						Optional[Any]  				=	scheduler_class.from_pretrained(lowercase_		)
								new_scheduler.set_timesteps(lowercase_		)
								# copy over dummy past residuals
								lowerCAmelCase__	:						Optional[int]  				=	dummy_past_residuals[: new_scheduler.config.solver_order]

							lowerCAmelCase__				,lowerCAmelCase__	:						int  				=	sample, sample
							for t in range(lowercase_   ,time_step + scheduler.config.solver_order + 1		):
								lowerCAmelCase__	:						Tuple  				=	scheduler.step(lowercase_   ,lowercase_   ,lowercase_   ,**lowercase_		).prev_sample
								lowerCAmelCase__	:						int  				=	new_scheduler.step(lowercase_   ,lowercase_   ,lowercase_   ,**lowercase_		).prev_sample

								assert torch.sum(torch.abs(output - new_output		)		) < 1E-5, "Scheduler outputs are not identical"







					def        __lowerCAmelCase (       self			:				List[str]		):
						pass







					def        __lowerCAmelCase (       self			:				Union[str, Any]   ,lowercase_			:				str=0   ,**lowercase_			:				List[Any]		):
						lowerCAmelCase__	:						List[Any]  				=	dict(self.forward_default_kwargs		)
						lowerCAmelCase__	:						Union[str, Any]  				=	kwargs.pop('''num_inference_steps'''   ,lowercase_		)
						lowerCAmelCase__	:						Optional[int]  				=	self.dummy_sample
						lowerCAmelCase__	:						Any  				=	0.1 * sample
						lowerCAmelCase__	:						Tuple  				=	[residual + 0.2, residual + 0.15, residual + 0.10]

						for scheduler_class in self.scheduler_classes:
							lowerCAmelCase__	:						str  				=	self.get_scheduler_config()
							lowerCAmelCase__	:						str  				=	scheduler_class(**lowercase_		)
							scheduler.set_timesteps(lowercase_		)

							# copy over dummy past residuals (must be after setting timesteps)
							lowerCAmelCase__	:						Tuple  				=	dummy_past_residuals[: scheduler.config.solver_order]

							with tempfile.TemporaryDirectory() as tmpdirname:
								scheduler.save_config(lowercase_		)
								lowerCAmelCase__	:						Any  				=	scheduler_class.from_pretrained(lowercase_		)
								# copy over dummy past residuals
								new_scheduler.set_timesteps(lowercase_		)

								# copy over dummy past residual (must be after setting timesteps)
								lowerCAmelCase__	:						Optional[Any]  				=	dummy_past_residuals[: new_scheduler.config.solver_order]

							lowerCAmelCase__	:						Tuple  				=	scheduler.step(lowercase_   ,lowercase_   ,lowercase_   ,**lowercase_		).prev_sample
							lowerCAmelCase__	:						Any  				=	new_scheduler.step(lowercase_   ,lowercase_   ,lowercase_   ,**lowercase_		).prev_sample

							assert torch.sum(torch.abs(output - new_output		)		) < 1E-5, "Scheduler outputs are not identical"







					def        __lowerCAmelCase (       self			:				Dict   ,lowercase_			:				str=None   ,**lowercase_			:				str		):
						if scheduler is None:
							lowerCAmelCase__	:						Tuple  				=	self.scheduler_classes[0]
							lowerCAmelCase__	:						List[Any]  				=	self.get_scheduler_config(**lowercase_		)
							lowerCAmelCase__	:						Tuple  				=	scheduler_class(**lowercase_		)

						lowerCAmelCase__	:						Tuple  				=	self.scheduler_classes[0]
						lowerCAmelCase__	:						Optional[int]  				=	self.get_scheduler_config(**lowercase_		)
						lowerCAmelCase__	:						List[str]  				=	scheduler_class(**lowercase_		)

						lowerCAmelCase__	:						Union[str, Any]  				=	1_0
						lowerCAmelCase__	:						Dict  				=	self.dummy_model()
						lowerCAmelCase__	:						int  				=	self.dummy_sample_deter
						scheduler.set_timesteps(lowercase_		)

						for i, t in enumerate(scheduler.timesteps		):
							lowerCAmelCase__	:						Optional[int]  				=	model(lowercase_   ,lowercase_		)
							lowerCAmelCase__	:						int  				=	scheduler.step(lowercase_   ,lowercase_   ,lowercase_		).prev_sample

						return sample







					def        __lowerCAmelCase (       self			:				Dict		):
						lowerCAmelCase__	:						Union[str, Any]  				=	dict(self.forward_default_kwargs		)

						lowerCAmelCase__	:						Dict  				=	kwargs.pop('''num_inference_steps'''   ,lowercase_		)

						for scheduler_class in self.scheduler_classes:
							lowerCAmelCase__	:						str  				=	self.get_scheduler_config()
							lowerCAmelCase__	:						Optional[int]  				=	scheduler_class(**lowercase_		)

							lowerCAmelCase__	:						int  				=	self.dummy_sample
							lowerCAmelCase__	:						int  				=	0.1 * sample

							if num_inference_steps is not None and hasattr(lowercase_   ,'''set_timesteps'''		):
								scheduler.set_timesteps(lowercase_		)
							elif num_inference_steps is not None and not hasattr(lowercase_   ,'''set_timesteps'''		):
								lowerCAmelCase__	:						Tuple  				=	num_inference_steps

							# copy over dummy past residuals (must be done after set_timesteps)
							lowerCAmelCase__	:						Dict  				=	[residual + 0.2, residual + 0.15, residual + 0.10]
							lowerCAmelCase__	:						int  				=	dummy_past_residuals[: scheduler.config.solver_order]

							lowerCAmelCase__	:						str  				=	scheduler.timesteps[5]
							lowerCAmelCase__	:						Optional[Any]  				=	scheduler.timesteps[6]

							lowerCAmelCase__	:						Dict  				=	scheduler.step(lowercase_   ,lowercase_   ,lowercase_   ,**lowercase_		).prev_sample
							lowerCAmelCase__	:						List[str]  				=	scheduler.step(lowercase_   ,lowercase_   ,lowercase_   ,**lowercase_		).prev_sample

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







					def        __lowerCAmelCase (       self			:				int		):
						# make sure that iterating over schedulers with same config names gives same results
						# for defaults
						lowerCAmelCase__	:						str  				=	DEISMultistepScheduler(**self.get_scheduler_config()		)
						lowerCAmelCase__	:						str  				=	self.full_loop(scheduler=lowercase_		)
						lowerCAmelCase__	:						Optional[int]  				=	torch.mean(torch.abs(lowercase_		)		)

						assert abs(result_mean.item() - 0.2_3916		) < 1E-3

						lowerCAmelCase__	:						Dict  				=	DPMSolverSinglestepScheduler.from_config(scheduler.config		)
						lowerCAmelCase__	:						List[Any]  				=	DPMSolverMultistepScheduler.from_config(scheduler.config		)
						lowerCAmelCase__	:						str  				=	UniPCMultistepScheduler.from_config(scheduler.config		)
						lowerCAmelCase__	:						Dict  				=	DEISMultistepScheduler.from_config(scheduler.config		)

						lowerCAmelCase__	:						List[Any]  				=	self.full_loop(scheduler=lowercase_		)
						lowerCAmelCase__	:						int  				=	torch.mean(torch.abs(lowercase_		)		)

						assert abs(result_mean.item() - 0.2_3916		) < 1E-3







					def        __lowerCAmelCase (       self			:				Optional[int]		):
						for timesteps in [2_5, 5_0, 1_0_0, 9_9_9, 1_0_0_0]:
							self.check_over_configs(num_train_timesteps=lowercase_		)







					def        __lowerCAmelCase (       self			:				Tuple		):
						self.check_over_configs(thresholding=lowercase_		)
						for order in [1, 2, 3]:
							for solver_type in ["logrho"]:
								for threshold in [0.5, 1.0, 2.0]:
									for prediction_type in ["epsilon", "sample"]:
										self.check_over_configs(
										    thresholding=lowercase_   ,prediction_type=lowercase_   ,sample_max_value=lowercase_   ,algorithm_type='''deis'''   ,solver_order=lowercase_   ,solver_type=lowercase_   ,)







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







					def        __lowerCAmelCase (       self			:				List[Any]		):
						for algorithm_type in ["deis"]:
							for solver_type in ["logrho"]:
								for order in [1, 2, 3]:
									for prediction_type in ["epsilon", "sample"]:
										self.check_over_configs(
										    solver_order=lowercase_   ,solver_type=lowercase_   ,prediction_type=lowercase_   ,algorithm_type=lowercase_   ,)
										lowerCAmelCase__	:						str  				=	self.full_loop(
										    solver_order=lowercase_   ,solver_type=lowercase_   ,prediction_type=lowercase_   ,algorithm_type=lowercase_   ,)
										assert not torch.isnan(lowercase_		).any(), "Samples have nan numbers"







					def        __lowerCAmelCase (       self			:				List[Any]		):
						self.check_over_configs(lower_order_final=lowercase_		)
						self.check_over_configs(lower_order_final=lowercase_		)







					def        __lowerCAmelCase (       self			:				Union[str, Any]		):
						for num_inference_steps in [1, 2, 3, 5, 1_0, 5_0, 1_0_0, 9_9_9, 1_0_0_0]:
							self.check_over_forward(num_inference_steps=lowercase_   ,time_step=0		)







					def        __lowerCAmelCase (       self			:				Any		):
						lowerCAmelCase__	:						Optional[Any]  				=	self.full_loop()
						lowerCAmelCase__	:						Any  				=	torch.mean(torch.abs(lowercase_		)		)

						assert abs(result_mean.item() - 0.2_3916		) < 1E-3







					def        __lowerCAmelCase (       self			:				int		):
						lowerCAmelCase__	:						Tuple  				=	self.full_loop(prediction_type='''v_prediction'''		)
						lowerCAmelCase__	:						Optional[int]  				=	torch.mean(torch.abs(lowercase_		)		)

						assert abs(result_mean.item() - 0.091		) < 1E-3







					def        __lowerCAmelCase (       self			:				str		):
						lowerCAmelCase__	:						Union[str, Any]  				=	self.scheduler_classes[0]
						lowerCAmelCase__	:						Union[str, Any]  				=	self.get_scheduler_config(thresholding=lowercase_   ,dynamic_thresholding_ratio=0		)
						lowerCAmelCase__	:						Optional[Any]  				=	scheduler_class(**lowercase_		)

						lowerCAmelCase__	:						Optional[Any]  				=	1_0
						lowerCAmelCase__	:						Optional[Any]  				=	self.dummy_model()
						lowerCAmelCase__	:						str  				=	self.dummy_sample_deter.half()
						scheduler.set_timesteps(lowercase_		)

						for i, t in enumerate(scheduler.timesteps		):
							lowerCAmelCase__	:						List[Any]  				=	model(lowercase_   ,lowercase_		)
							lowerCAmelCase__	:						Optional[Any]  				=	scheduler.step(lowercase_   ,lowercase_   ,lowercase_		).prev_sample

						assert sample.dtype == torch.floataa








import warnings

from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding
class  SCREAMING_SNAKE_CASE__  (				lowercase__			):
      snake_case__				:     Tuple        =						['''image_processor''', '''tokenizer''']
      snake_case__				:     Union[str, Any]        =						'''CLIPImageProcessor'''
      snake_case__				:     Dict        =						('''CLIPTokenizer''', '''CLIPTokenizerFast''')
      def __init__(       self				:       Optional[int]     ,							SCREAMING_SNAKE_CASE__				:       Union[str, Any]=None     ,							SCREAMING_SNAKE_CASE__				:       List[str]=None     ,							**SCREAMING_SNAKE_CASE__				:       int     )		->			Any:
             a_					:		List[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.'     ,							SCREAMING_SNAKE_CASE__     ,							)
                    a_					:		Tuple							=  kwargs.pop('feature_extractor'     )

             a_					:		Tuple							=  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__(SCREAMING_SNAKE_CASE__     ,							SCREAMING_SNAKE_CASE__     )
      def __call__(       self				:       List[Any]     ,							SCREAMING_SNAKE_CASE__				:       Optional[Any]=None     ,							SCREAMING_SNAKE_CASE__				:       Union[str, Any]=None     ,							SCREAMING_SNAKE_CASE__				:       Union[str, Any]=None     ,							**SCREAMING_SNAKE_CASE__				:       str     )		->			Optional[Any]:

             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:
                    a_					:		List[str]							=  self.tokenizer(SCREAMING_SNAKE_CASE__     ,							return_tensors=SCREAMING_SNAKE_CASE__     ,							**SCREAMING_SNAKE_CASE__     )

             if images is not None:
                    a_					:		Dict							=  self.image_processor(SCREAMING_SNAKE_CASE__     ,							return_tensors=SCREAMING_SNAKE_CASE__     ,							**SCREAMING_SNAKE_CASE__     )

             if text is not None and images is not None:
                    a_					:		Dict							=  image_features.pixel_values
                    return encoding
             elif text is not None:
                    return encoding
             else:
                    return BatchEncoding(data=dict(**SCREAMING_SNAKE_CASE__     )     ,							tensor_type=SCREAMING_SNAKE_CASE__     )
      def        SCREAMING_SNAKE_CASE							(       self				:       Any     ,							*SCREAMING_SNAKE_CASE__				:       List[Any]     ,							**SCREAMING_SNAKE_CASE__				:       Optional[int]     )		->			List[Any]:
             return self.tokenizer.batch_decode(*SCREAMING_SNAKE_CASE__     ,							**SCREAMING_SNAKE_CASE__     )
      def        SCREAMING_SNAKE_CASE							(       self				:       Optional[Any]     ,							*SCREAMING_SNAKE_CASE__				:       List[Any]     ,							**SCREAMING_SNAKE_CASE__				:       Dict     )		->			Optional[Any]:
             return self.tokenizer.decode(*SCREAMING_SNAKE_CASE__     ,							**SCREAMING_SNAKE_CASE__     )
      @property
      def        SCREAMING_SNAKE_CASE							(       self				:       List[Any]     )		->			Any:
             a_					:		str							=  self.tokenizer.model_input_names
             a_					:		Tuple							=  self.image_processor.model_input_names
             return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names     )     )
      @property
      def        SCREAMING_SNAKE_CASE							(       self				:       str     )		->			str:
             warnings.warn(
                 '`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead.'     ,							SCREAMING_SNAKE_CASE__     ,							)
             return self.image_processor_class

      @property
      def        SCREAMING_SNAKE_CASE							(       self				:       str     )		->			Tuple:
             warnings.warn(
                 '`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead.'     ,							SCREAMING_SNAKE_CASE__     ,							)
             return self.image_processor


