





"""simple docstring"""






import tempfile

import torch

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

from .test_schedulers import SchedulerCommonTest




class  __lowercase	(	lowercase__							):







					'''simple docstring'''




					__lowerCAmelCase     =     (DEISMultistepScheduler,)
					__lowerCAmelCase     =     (('''num_inference_steps''', 25),)







					def        _lowerCamelCase						(  self		,	**_UpperCAmelCase   ):
									__a       :  Optional[Any]					=     {
									    'num_train_timesteps': 1000,
									    'beta_start': 0.0_0_0_1,
									    'beta_end': 0.0_2,
									    'beta_schedule': 'linear',
									    'solver_order': 2,
									}

									config.update(**SCREAMING_SNAKE_CASE__   )
									return config







					def        _lowerCamelCase						(  self		,	_UpperCAmelCase=0		,	**_UpperCAmelCase   ):
									__a       :  Optional[Any]					=     dict(self.forward_default_kwargs   )
									__a       :  Dict					=     kwargs.pop('''num_inference_steps'''		,	SCREAMING_SNAKE_CASE__   )
									__a       :  Tuple					=     self.dummy_sample
									__a       :  str					=     0.1 * sample
									__a       :  Tuple					=     [residual + 0.2, residual + 0.1_5, residual + 0.1_0]

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

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

													__a       :  Union[str, Any]					=     sample, sample
													for t in range(SCREAMING_SNAKE_CASE__		,	time_step + scheduler.config.solver_order + 1   ):
																	__a       :  Tuple					=     scheduler.step(SCREAMING_SNAKE_CASE__		,	SCREAMING_SNAKE_CASE__		,	SCREAMING_SNAKE_CASE__		,	**SCREAMING_SNAKE_CASE__   ).prev_sample
																	__a       :  Tuple					=     new_scheduler.step(SCREAMING_SNAKE_CASE__		,	SCREAMING_SNAKE_CASE__		,	SCREAMING_SNAKE_CASE__		,	**SCREAMING_SNAKE_CASE__   ).prev_sample

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







					def        _lowerCamelCase						(  self   ):
									pass







					def        _lowerCamelCase						(  self		,	_UpperCAmelCase=0		,	**_UpperCAmelCase   ):
									__a       :  int					=     dict(self.forward_default_kwargs   )
									__a       :  Tuple					=     kwargs.pop('''num_inference_steps'''		,	SCREAMING_SNAKE_CASE__   )
									__a       :  int					=     self.dummy_sample
									__a       :  Any					=     0.1 * sample
									__a       :  Union[str, Any]					=     [residual + 0.2, residual + 0.1_5, residual + 0.1_0]

									for scheduler_class in self.scheduler_classes:
													__a       :  Tuple					=     self.get_scheduler_config()
													__a       :  Optional[int]					=     scheduler_class(**SCREAMING_SNAKE_CASE__   )
													scheduler.set_timesteps(SCREAMING_SNAKE_CASE__   )

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

													with tempfile.TemporaryDirectory() as tmpdirname:
																	scheduler.save_config(SCREAMING_SNAKE_CASE__   )
																	__a       :  Tuple					=     scheduler_class.from_pretrained(SCREAMING_SNAKE_CASE__   )
																	# copy over dummy past residuals
																	new_scheduler.set_timesteps(SCREAMING_SNAKE_CASE__   )

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

													__a       :  Any					=     scheduler.step(SCREAMING_SNAKE_CASE__		,	SCREAMING_SNAKE_CASE__		,	SCREAMING_SNAKE_CASE__		,	**SCREAMING_SNAKE_CASE__   ).prev_sample
													__a       :  Tuple					=     new_scheduler.step(SCREAMING_SNAKE_CASE__		,	SCREAMING_SNAKE_CASE__		,	SCREAMING_SNAKE_CASE__		,	**SCREAMING_SNAKE_CASE__   ).prev_sample

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







					def        _lowerCamelCase						(  self		,	_UpperCAmelCase=None		,	**_UpperCAmelCase   ):
									if scheduler is None:
													__a       :  Dict					=     self.scheduler_classes[0]
													__a       :  Union[str, Any]					=     self.get_scheduler_config(**SCREAMING_SNAKE_CASE__   )
													__a       :  Tuple					=     scheduler_class(**SCREAMING_SNAKE_CASE__   )

									__a       :  int					=     self.scheduler_classes[0]
									__a       :  str					=     self.get_scheduler_config(**SCREAMING_SNAKE_CASE__   )
									__a       :  str					=     scheduler_class(**SCREAMING_SNAKE_CASE__   )

									__a       :  List[Any]					=     10
									__a       :  str					=     self.dummy_model()
									__a       :  Optional[Any]					=     self.dummy_sample_deter
									scheduler.set_timesteps(SCREAMING_SNAKE_CASE__   )

									for i, t in enumerate(scheduler.timesteps   ):
													__a       :  Union[str, Any]					=     model(SCREAMING_SNAKE_CASE__		,	SCREAMING_SNAKE_CASE__   )
													__a       :  Optional[Any]					=     scheduler.step(SCREAMING_SNAKE_CASE__		,	SCREAMING_SNAKE_CASE__		,	SCREAMING_SNAKE_CASE__   ).prev_sample

									return sample







					def        _lowerCamelCase						(  self   ):
									__a       :  int					=     dict(self.forward_default_kwargs   )

									__a       :  str					=     kwargs.pop('''num_inference_steps'''		,	SCREAMING_SNAKE_CASE__   )

									for scheduler_class in self.scheduler_classes:
													__a       :  Union[str, Any]					=     self.get_scheduler_config()
													__a       :  Optional[int]					=     scheduler_class(**SCREAMING_SNAKE_CASE__   )

													__a       :  Any					=     self.dummy_sample
													__a       :  List[Any]					=     0.1 * sample

													if num_inference_steps is not None and hasattr(SCREAMING_SNAKE_CASE__		,	'''set_timesteps'''   ):
																	scheduler.set_timesteps(SCREAMING_SNAKE_CASE__   )
													elif num_inference_steps is not None and not hasattr(SCREAMING_SNAKE_CASE__		,	'''set_timesteps'''   ):
																	__a       :  Optional[Any]					=     num_inference_steps

													# copy over dummy past residuals (must be done after set_timesteps)
													__a       :  Union[str, Any]					=     [residual + 0.2, residual + 0.1_5, residual + 0.1_0]
													__a       :  str					=     dummy_past_residuals[: scheduler.config.solver_order]

													__a       :  Any					=     scheduler.timesteps[5]
													__a       :  Dict					=     scheduler.timesteps[6]

													__a       :  int					=     scheduler.step(SCREAMING_SNAKE_CASE__		,	SCREAMING_SNAKE_CASE__		,	SCREAMING_SNAKE_CASE__		,	**SCREAMING_SNAKE_CASE__   ).prev_sample
													__a       :  int					=     scheduler.step(SCREAMING_SNAKE_CASE__		,	SCREAMING_SNAKE_CASE__		,	SCREAMING_SNAKE_CASE__		,	**SCREAMING_SNAKE_CASE__   ).prev_sample

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







					def        _lowerCamelCase						(  self   ):
									# make sure that iterating over schedulers with same config names gives same results
									# for defaults
									__a       :  int					=     DEISMultistepScheduler(**self.get_scheduler_config()   )
									__a       :  List[Any]					=     self.full_loop(scheduler=SCREAMING_SNAKE_CASE__   )
									__a       :  Any					=     torch.mean(torch.abs(SCREAMING_SNAKE_CASE__   )   )

									assert abs(result_mean.item() - 0.2_3_9_1_6   ) < 1e-3

									__a       :  List[Any]					=     DPMSolverSinglestepScheduler.from_config(scheduler.config   )
									__a       :  Union[str, Any]					=     DPMSolverMultistepScheduler.from_config(scheduler.config   )
									__a       :  Optional[Any]					=     UniPCMultistepScheduler.from_config(scheduler.config   )
									__a       :  Optional[Any]					=     DEISMultistepScheduler.from_config(scheduler.config   )

									__a       :  Dict					=     self.full_loop(scheduler=SCREAMING_SNAKE_CASE__   )
									__a       :  Optional[int]					=     torch.mean(torch.abs(SCREAMING_SNAKE_CASE__   )   )

									assert abs(result_mean.item() - 0.2_3_9_1_6   ) < 1e-3







					def        _lowerCamelCase						(  self   ):
									for timesteps in [25, 50, 100, 999, 1000]:
													self.check_over_configs(num_train_timesteps=SCREAMING_SNAKE_CASE__   )







					def        _lowerCamelCase						(  self   ):
									self.check_over_configs(thresholding=SCREAMING_SNAKE_CASE__   )
									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=SCREAMING_SNAKE_CASE__		,	prediction_type=SCREAMING_SNAKE_CASE__		,	sample_max_value=SCREAMING_SNAKE_CASE__		,	algorithm_type='''deis'''		,	solver_order=SCREAMING_SNAKE_CASE__		,	solver_type=SCREAMING_SNAKE_CASE__		,	)







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







					def        _lowerCamelCase						(  self   ):
									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=SCREAMING_SNAKE_CASE__		,	solver_type=SCREAMING_SNAKE_CASE__		,	prediction_type=SCREAMING_SNAKE_CASE__		,	algorithm_type=SCREAMING_SNAKE_CASE__		,	)
																									__a       :  Optional[Any]					=     self.full_loop(
																									    solver_order=SCREAMING_SNAKE_CASE__		,	solver_type=SCREAMING_SNAKE_CASE__		,	prediction_type=SCREAMING_SNAKE_CASE__		,	algorithm_type=SCREAMING_SNAKE_CASE__		,	)
																									assert not torch.isnan(SCREAMING_SNAKE_CASE__   ).any(), "Samples have nan numbers"







					def        _lowerCamelCase						(  self   ):
									self.check_over_configs(lower_order_final=SCREAMING_SNAKE_CASE__   )
									self.check_over_configs(lower_order_final=SCREAMING_SNAKE_CASE__   )







					def        _lowerCamelCase						(  self   ):
									for num_inference_steps in [1, 2, 3, 5, 10, 50, 100, 999, 1000]:
													self.check_over_forward(num_inference_steps=SCREAMING_SNAKE_CASE__		,	time_step=0   )







					def        _lowerCamelCase						(  self   ):
									__a       :  List[str]					=     self.full_loop()
									__a       :  List[Any]					=     torch.mean(torch.abs(SCREAMING_SNAKE_CASE__   )   )

									assert abs(result_mean.item() - 0.2_3_9_1_6   ) < 1e-3







					def        _lowerCamelCase						(  self   ):
									__a       :  Dict					=     self.full_loop(prediction_type='''v_prediction'''   )
									__a       :  Union[str, Any]					=     torch.mean(torch.abs(SCREAMING_SNAKE_CASE__   )   )

									assert abs(result_mean.item() - 0.0_9_1   ) < 1e-3







					def        _lowerCamelCase						(  self   ):
									__a       :  int					=     self.scheduler_classes[0]
									__a       :  Union[str, Any]					=     self.get_scheduler_config(thresholding=SCREAMING_SNAKE_CASE__		,	dynamic_thresholding_ratio=0   )
									__a       :  List[str]					=     scheduler_class(**SCREAMING_SNAKE_CASE__   )

									__a       :  Union[str, Any]					=     10
									__a       :  List[str]					=     self.dummy_model()
									__a       :  Optional[int]					=     self.dummy_sample_deter.half()
									scheduler.set_timesteps(SCREAMING_SNAKE_CASE__   )

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

									assert sample.dtype == torch.floataa



import math

import flax.linen as nn
import jax.numpy as jnp







def        SCREAMING_SNAKE_CASE_						(			__A :			jnp.ndarray  ,    __A :			int  ,    __A :			float = 1  ,    __A :			float = 1  ,    __A :			float = 1.0e4  ,    __A :			bool = False  ,    __A :			float = 1.0  ,    )	->		jnp.ndarray:

       """simple docstring"""

       assert timesteps.ndim == 1, "Timesteps should be a 1d-array"
       assert embedding_dim % 2 == 0, F"""Embedding dimension {embedding_dim} should be even"""
       a_					:		int							=  float(embedding_dim // 2				)
       a_					:		str							=  math.log(max_timescale / min_timescale				) / (num_timescales - freq_shift)
       a_					:		Optional[int]							=  min_timescale * jnp.exp(jnp.arange(__A  ,    dtype=jnp.floataa				) * -log_timescale_increment				)
       a_					:		Optional[int]							=  jnp.expand_dims(__A  ,    1				) * jnp.expand_dims(__A  ,    0				)

       # scale embeddings
       a_					:		str							=  scale * emb

       if flip_sin_to_cos:
              a_					:		str							=  jnp.concatenate([jnp.cos(__A				), jnp.sin(__A				)]  ,    axis=1				)
       else:
              a_					:		Any							=  jnp.concatenate([jnp.sin(__A				), jnp.cos(__A				)]  ,    axis=1				)
       a_					:		Optional[int]							=  jnp.reshape(__A  ,    [jnp.shape(__A				)[0], embedding_dim]				)
       return signal
class  SCREAMING_SNAKE_CASE__  (				nn.Module			):
      snake_case__				:     int        =						32
      snake_case__				:     jnp.dtype        =						jnp.floataa
      @nn.compact
      def __call__(       self				:       Tuple     ,							SCREAMING_SNAKE_CASE__				:       str     )		->			List[str]:
             a_					:		Optional[Any]							=  nn.Dense(self.time_embed_dim     ,							dtype=self.dtype     ,							name='linear_1'     )(SCREAMING_SNAKE_CASE__     )
             a_					:		Tuple							=  nn.silu(SCREAMING_SNAKE_CASE__     )
             a_					:		str							=  nn.Dense(self.time_embed_dim     ,							dtype=self.dtype     ,							name='linear_2'     )(SCREAMING_SNAKE_CASE__     )
             return temb
class  SCREAMING_SNAKE_CASE__  (				nn.Module			):
      snake_case__				:     int        =						32
      snake_case__				:     bool        =						False
      snake_case__				:     float        =						1
      @nn.compact
      def __call__(       self				:       str     ,							SCREAMING_SNAKE_CASE__				:       int     )		->			Tuple:
             return get_sinusoidal_embeddings(
                 SCREAMING_SNAKE_CASE__     ,							embedding_dim=self.dim     ,							flip_sin_to_cos=self.flip_sin_to_cos     ,							freq_shift=self.freq_shift     )


