





'''simple docstring'''

import torch

from diffusers import UnCLIPScheduler

from .test_schedulers import SchedulerCommonTest
class 							__lowerCAmelCase	(       __magic_name__					):






       """simple docstring"""
       _snake_case      :	int									=       (UnCLIPScheduler,)





       def snake_case__		(				self  :						Dict			,					**lowerCAmelCase__  :						Any	)       ->	int:

            '''simple docstring'''




            _UpperCamelCase							       =			{
                '''num_train_timesteps''': 1000,
                '''variance_type''': '''fixed_small_log''',
                '''clip_sample''': True,
                '''clip_sample_range''': 1.0,
                '''prediction_type''': '''epsilon''',
            }

            config.update(**lowerCAmelCase__	)
            return config





       def snake_case__		(				self  :						str	)       ->	Optional[Any]:

            '''simple docstring'''




            for timesteps in [1, 5, 100, 1000]:
                 self.check_over_configs(num_train_timesteps=lowerCAmelCase__	)





       def snake_case__		(				self  :						int	)       ->	List[str]:

            '''simple docstring'''




            for variance in ["fixed_small_log", "learned_range"]:
                 self.check_over_configs(variance_type=lowerCAmelCase__	)





       def snake_case__		(				self  :						List[str]	)       ->	Union[str, Any]:

            '''simple docstring'''




            for clip_sample in [True, False]:
                 self.check_over_configs(clip_sample=lowerCAmelCase__	)





       def snake_case__		(				self  :						Any	)       ->	Union[str, Any]:

            '''simple docstring'''




            for clip_sample_range in [1, 5, 10, 20]:
                 self.check_over_configs(clip_sample_range=lowerCAmelCase__	)





       def snake_case__		(				self  :						List[Any]	)       ->	Any:

            '''simple docstring'''




            for prediction_type in ["epsilon", "sample"]:
                 self.check_over_configs(prediction_type=lowerCAmelCase__	)





       def snake_case__		(				self  :						List[Any]	)       ->	Optional[int]:

            '''simple docstring'''




            for time_step in [0, 500, 999]:
                 for prev_timestep in [None, 5, 100, 250, 500, 750]:
                      if prev_timestep is not None and prev_timestep >= time_step:
                           continue

                      self.check_over_forward(time_step=lowerCAmelCase__			,					prev_timestep=lowerCAmelCase__	)





       def snake_case__		(				self  :						List[str]	)       ->	int:

            '''simple docstring'''




            _UpperCamelCase							       =			self.scheduler_classes[0]
            _UpperCamelCase							       =			self.get_scheduler_config(variance_type='''fixed_small_log'''	)
            _UpperCamelCase							       =			scheduler_class(**lowerCAmelCase__	)

            assert torch.sum(torch.abs(scheduler._get_variance(0	) - 1.0_0_0_0e-1_0	)	) < 1e-5
            assert torch.sum(torch.abs(scheduler._get_variance(487	) - 0.0549625	)	) < 1e-5
            assert torch.sum(torch.abs(scheduler._get_variance(999	) - 0.9994987	)	) < 1e-5





       def snake_case__		(				self  :						List[str]	)       ->	Optional[int]:

            '''simple docstring'''




            _UpperCamelCase							       =			self.scheduler_classes[0]
            _UpperCamelCase							       =			self.get_scheduler_config(variance_type='''learned_range'''	)
            _UpperCamelCase							       =			scheduler_class(**lowerCAmelCase__	)

            _UpperCamelCase							       =			0.5

            assert scheduler._get_variance(1			,					predicted_variance=lowerCAmelCase__	) - -10.1712790 < 1e-5
            assert scheduler._get_variance(487			,					predicted_variance=lowerCAmelCase__	) - -5.7998052 < 1e-5
            assert scheduler._get_variance(999			,					predicted_variance=lowerCAmelCase__	) - -0.0010011 < 1e-5





       def snake_case__		(				self  :						Tuple	)       ->	int:

            '''simple docstring'''




            _UpperCamelCase							       =			self.scheduler_classes[0]
            _UpperCamelCase							       =			self.get_scheduler_config()
            _UpperCamelCase							       =			scheduler_class(**lowerCAmelCase__	)

            _UpperCamelCase							       =			scheduler.timesteps

            _UpperCamelCase							       =			self.dummy_model()
            _UpperCamelCase							       =			self.dummy_sample_deter
            _UpperCamelCase							       =			torch.manual_seed(0	)

            for i, t in enumerate(lowerCAmelCase__	):
                 # 1. predict noise residual
                 _UpperCamelCase							       =			model(lowerCAmelCase__			,					lowerCAmelCase__	)

                 # 2. predict previous mean of sample x_t-1
                 _UpperCamelCase							       =			scheduler.step(lowerCAmelCase__			,					lowerCAmelCase__			,					lowerCAmelCase__			,					generator=lowerCAmelCase__	).prev_sample

                 _UpperCamelCase							       =			pred_prev_sample

            _UpperCamelCase							       =			torch.sum(torch.abs(lowerCAmelCase__	)	)
            _UpperCamelCase							       =			torch.mean(torch.abs(lowerCAmelCase__	)	)

            assert abs(result_sum.item() - 252.2682495	) < 1e-2
            assert abs(result_mean.item() - 0.3284743	) < 1e-3





       def snake_case__		(				self  :						Optional[Any]	)       ->	Optional[Any]:

            '''simple docstring'''




            _UpperCamelCase							       =			self.scheduler_classes[0]
            _UpperCamelCase							       =			self.get_scheduler_config()
            _UpperCamelCase							       =			scheduler_class(**lowerCAmelCase__	)

            scheduler.set_timesteps(25	)

            _UpperCamelCase							       =			scheduler.timesteps

            _UpperCamelCase							       =			self.dummy_model()
            _UpperCamelCase							       =			self.dummy_sample_deter
            _UpperCamelCase							       =			torch.manual_seed(0	)

            for i, t in enumerate(lowerCAmelCase__	):
                 # 1. predict noise residual
                 _UpperCamelCase							       =			model(lowerCAmelCase__			,					lowerCAmelCase__	)

                 if i + 1 == timesteps.shape[0]:
                      _UpperCamelCase							       =			None
                 else:
                      _UpperCamelCase							       =			timesteps[i + 1]

                 # 2. predict previous mean of sample x_t-1
                 _UpperCamelCase							       =			scheduler.step(
                     lowerCAmelCase__			,					lowerCAmelCase__			,					lowerCAmelCase__			,					prev_timestep=lowerCAmelCase__			,					generator=lowerCAmelCase__	).prev_sample

                 _UpperCamelCase							       =			pred_prev_sample

            _UpperCamelCase							       =			torch.sum(torch.abs(lowerCAmelCase__	)	)
            _UpperCamelCase							       =			torch.mean(torch.abs(lowerCAmelCase__	)	)

            assert abs(result_sum.item() - 258.2044983	) < 1e-2
            assert abs(result_mean.item() - 0.3362038	) < 1e-3





       def snake_case__		(				self  :						Any	)       ->	Tuple:

            '''simple docstring'''




            pass







       def snake_case__		(				self  :						Optional[int]	)       ->	List[str]:

            '''simple docstring'''




            pass







'''simple docstring'''





import numpy as np

from transformers import Pipeline






def 						UpperCAmelCase__(				_SCREAMING_SNAKE_CASE					: Any ):






			"""simple docstring"""




			__A=	np.max(_SCREAMING_SNAKE_CASE,axis=-1,keepdims=_SCREAMING_SNAKE_CASE )
			__A=	np.exp(outputs - maxes )
			return shifted_exp / shifted_exp.sum(axis=-1,keepdims=_SCREAMING_SNAKE_CASE )






class 						a__					(					a_   ):





			'''simple docstring'''

			def 					lowerCAmelCase     (  self							:			Optional[Any]			,    **lowerCAmelCase_							:			List[str]	)      ->		List[str]:
						__A=	{}
						if "second_text" in kwargs:
									__A=	kwargs['second_text']
						return preprocess_kwargs, {}, {}
			def 					lowerCAmelCase     (  self							:			str			,    lowerCAmelCase_							:			Union[str, Any]			,    lowerCAmelCase_							:			Optional[int]=None	)      ->		int:
						return self.tokenizer(lowerCAmelCase_			,    text_pair=lowerCAmelCase_			,    return_tensors=self.framework	)
			def 					lowerCAmelCase     (  self							:			Dict			,    lowerCAmelCase_							:			List[str]	)      ->		Tuple:
						return self.model(**lowerCAmelCase_	)

			def 					lowerCAmelCase     (  self							:			str			,    lowerCAmelCase_							:			Dict	)      ->		Optional[int]:
						__A=	model_outputs.logits[0].numpy()
						__A=	softmax(lowerCAmelCase_	)

						__A=	np.argmax(lowerCAmelCase_	)
						__A=	self.model.config.idalabel[best_class]
						__A=	probabilities[best_class].item()
						__A=	logits.tolist()
						return {"label": label, "score": score, "logits": logits}



