


'''simple docstring'''


import math

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






def        __lowerCAmelCase       (__lowerCAmelCase					,       __lowerCAmelCase					,       __lowerCAmelCase = 1					,       __lowerCAmelCase = 1					,       __lowerCAmelCase = 1.0e4					,       __lowerCAmelCase = False					,       __lowerCAmelCase = 1.0					,       ):
		assert timesteps.ndim == 1, "Timesteps should be a 1d-array"
		assert embedding_dim % 2 == 0, F"""Embedding dimension {embedding_dim} should be even"""
		_UpperCAmelCase						:				Any	    =					float(embedding_dim // 2      )
		_UpperCAmelCase						:				List[Any]	    =					math.log(max_timescale / min_timescale      ) / (num_timescales - freq_shift)
		_UpperCAmelCase						:				int	    =					min_timescale * jnp.exp(jnp.arange(__lowerCAmelCase					,       dtype=jnp.floataa      ) * -log_timescale_increment      )
		_UpperCAmelCase						:				Dict	    =					jnp.expand_dims(__lowerCAmelCase					,       1      ) * jnp.expand_dims(__lowerCAmelCase					,       0      )

		# scale embeddings
		_UpperCAmelCase						:				List[Any]	    =					scale * emb

		if flip_sin_to_cos:
				_UpperCAmelCase						:				Union[str, Any]	    =					jnp.concatenate([jnp.cos(__lowerCAmelCase      ), jnp.sin(__lowerCAmelCase      )]					,       axis=1      )
		else:
				_UpperCAmelCase						:				Dict	    =					jnp.concatenate([jnp.sin(__lowerCAmelCase      ), jnp.cos(__lowerCAmelCase      )]					,       axis=1      )
		_UpperCAmelCase						:				str	    =					jnp.reshape(__lowerCAmelCase					,       [jnp.shape(__lowerCAmelCase      )[0], embedding_dim]      )
		return signal






class 		lowerCAmelCase__	(	nn.Module		):
			lowerCAmelCase       :		int				      =   32
			lowerCAmelCase       :		jnp.dtype				      =   jnp.floataa




			@nn.compact
			def __call__(						self     :   Any		,     lowerCamelCase__     :   Any       )      ->Dict:



					'''simple docstring'''







					_UpperCAmelCase						:				int	    =					nn.Dense(self.time_embed_dim		,     dtype=self.dtype		,     name="linear_1"       )(lowerCAmelCase__       )
					_UpperCAmelCase						:				Optional[int]	    =					nn.silu(lowerCAmelCase__       )
					_UpperCAmelCase						:				Optional[Any]	    =					nn.Dense(self.time_embed_dim		,     dtype=self.dtype		,     name="linear_2"       )(lowerCAmelCase__       )
					return temb






class 		lowerCAmelCase__	(	nn.Module		):
			lowerCAmelCase       :		int				      =   32
			lowerCAmelCase       :		bool				      =   False
			lowerCAmelCase       :		float				      =   1




			@nn.compact
			def __call__(						self     :   str		,     lowerCamelCase__     :   List[Any]       )      ->Optional[int]:



					'''simple docstring'''







					return get_sinusoidal_embeddings(
					    lowerCAmelCase__		,     embedding_dim=self.dim		,     flip_sin_to_cos=self.flip_sin_to_cos		,     freq_shift=self.freq_shift       )




import torch

from diffusers import KDPMaDiscreteScheduler
from diffusers.utils import torch_device

from .test_schedulers import SchedulerCommonTest




class    _UpperCamelCase      (						_UpperCAmelCase ):


			"""simple docstring"""






			__a  : Optional[Any]			 =						(KDPMaDiscreteScheduler,)
			__a  : Dict			 =						10





			def        _SCREAMING_SNAKE_CASE			(   self      ,   **lowerCAmelCase__						)     ->					int:

							'''simple docstring'''


							__lowercase 			=							{
							    '''num_train_timesteps''': 11_00,
							    '''beta_start''': 0.0001,
							    '''beta_end''': 0.02,
							    '''beta_schedule''': '''linear''',
							}

							config.update(**lowerCAmelCase__						)
							return config





			def        _SCREAMING_SNAKE_CASE			(   self						)     ->					List[str]:

							'''simple docstring'''


							for timesteps in [10, 50, 1_00, 10_00]:
											self.check_over_configs(num_train_timesteps=lowerCAmelCase__						)





			def        _SCREAMING_SNAKE_CASE			(   self						)     ->					Any:

							'''simple docstring'''


							for beta_start, beta_end in zip([0.0_0001, 0.0001, 0.001]      ,   [0.0002, 0.002, 0.02]						):
											self.check_over_configs(beta_start=lowerCAmelCase__      ,   beta_end=lowerCAmelCase__						)





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

							'''simple docstring'''


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





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

							'''simple docstring'''


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





			def        _SCREAMING_SNAKE_CASE			(   self						)     ->					Tuple:

							'''simple docstring'''


							__lowercase 			=							self.scheduler_classes[0]
							__lowercase 			=							self.get_scheduler_config(prediction_type='''v_prediction'''						)
							__lowercase 			=							scheduler_class(**lowerCAmelCase__						)

							scheduler.set_timesteps(self.num_inference_steps						)

							__lowercase 			=							self.dummy_model()
							__lowercase 			=							self.dummy_sample_deter * scheduler.init_noise_sigma
							__lowercase 			=							sample.to(lowerCAmelCase__						)

							for i, t in enumerate(scheduler.timesteps						):
											__lowercase 			=							scheduler.scale_model_input(lowerCAmelCase__      ,   lowerCAmelCase__						)

											__lowercase 			=							model(lowerCAmelCase__      ,   lowerCAmelCase__						)

											__lowercase 			=							scheduler.step(lowerCAmelCase__      ,   lowerCAmelCase__      ,   lowerCAmelCase__						)
											__lowercase 			=							output.prev_sample

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

							if torch_device in ["cpu", "mps"]:
											assert abs(result_sum.item() - 4.6934E-07						) < 1E-2
											assert abs(result_mean.item() - 6.1112E-10						) < 1E-3
							else:
											# CUDA
											assert abs(result_sum.item() - 4.693_4286_5017_0972E-07						) < 1E-2
											assert abs(result_mean.item() - 0.0002						) < 1E-3





			def        _SCREAMING_SNAKE_CASE			(   self						)     ->					str:

							'''simple docstring'''


							if torch_device == "mps":
											return
							__lowercase 			=							self.scheduler_classes[0]
							__lowercase 			=							self.get_scheduler_config()
							__lowercase 			=							scheduler_class(**lowerCAmelCase__						)

							scheduler.set_timesteps(self.num_inference_steps						)

							__lowercase 			=							self.dummy_model()
							__lowercase 			=							self.dummy_sample_deter * scheduler.init_noise_sigma
							__lowercase 			=							sample.to(lowerCAmelCase__						)

							for i, t in enumerate(scheduler.timesteps						):
											__lowercase 			=							scheduler.scale_model_input(lowerCAmelCase__      ,   lowerCAmelCase__						)

											__lowercase 			=							model(lowerCAmelCase__      ,   lowerCAmelCase__						)

											__lowercase 			=							scheduler.step(lowerCAmelCase__      ,   lowerCAmelCase__      ,   lowerCAmelCase__						)
											__lowercase 			=							output.prev_sample

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

							if torch_device in ["cpu", "mps"]:
											assert abs(result_sum.item() - 20.4125						) < 1E-2
											assert abs(result_mean.item() - 0.0266						) < 1E-3
							else:
											# CUDA
											assert abs(result_sum.item() - 20.4125						) < 1E-2
											assert abs(result_mean.item() - 0.0266						) < 1E-3





			def        _SCREAMING_SNAKE_CASE			(   self						)     ->					Dict:

							'''simple docstring'''


							if torch_device == "mps":
											return
							__lowercase 			=							self.scheduler_classes[0]
							__lowercase 			=							self.get_scheduler_config()
							__lowercase 			=							scheduler_class(**lowerCAmelCase__						)

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

							__lowercase 			=							self.dummy_model()
							__lowercase 			=							self.dummy_sample_deter.to(lowerCAmelCase__						) * scheduler.init_noise_sigma

							for t in scheduler.timesteps:
											__lowercase 			=							scheduler.scale_model_input(lowerCAmelCase__      ,   lowerCAmelCase__						)

											__lowercase 			=							model(lowerCAmelCase__      ,   lowerCAmelCase__						)

											__lowercase 			=							scheduler.step(lowerCAmelCase__      ,   lowerCAmelCase__      ,   lowerCAmelCase__						)
											__lowercase 			=							output.prev_sample

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

							if str(lowerCAmelCase__						).startswith('''cpu'''						):
											# The following sum varies between 148 and 156 on mps. Why?
											assert abs(result_sum.item() - 20.4125						) < 1E-2
											assert abs(result_mean.item() - 0.0266						) < 1E-3
							else:
											# CUDA
											assert abs(result_sum.item() - 20.4125						) < 1E-2
											assert abs(result_mean.item() - 0.0266						) < 1E-3