


import torch

from diffusers import DDIMParallelScheduler

from .test_schedulers import SchedulerCommonTest

class     a		(_lowerCAmelCase				):




							"""simple docstring"""


							__UpperCAmelCase   :							Union[str, Any]					  =  (DDIMParallelScheduler,)
							__UpperCAmelCase   :							str					  =  (("eta", 0.0), ("num_inference_steps", 50))

							def    __snake_case (							self	:  List[Any]   ,     **lowerCamelCase	:  Union[str, Any]    )     ->   Tuple:
														__snake_case		:      Optional[Any]										=       {
														    "num_train_timesteps": 1000,
														    "beta_start": 0.00_01,
														    "beta_end": 0.02,
														    "beta_schedule": "linear",
														    "clip_sample": True,
														}

														config.update(**lowerCamelCase    )
														return config

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

														__snake_case			,      __snake_case		:      Dict										=       10, 0.0

														__snake_case		:      Tuple										=       self.dummy_model()
														__snake_case		:      Optional[Any]										=       self.dummy_sample_deter

														scheduler.set_timesteps(lowerCamelCase    )

														for t in scheduler.timesteps:
																					__snake_case		:      Dict										=       model(lowerCamelCase   ,     lowerCamelCase    )
																					__snake_case		:      List[str]										=       scheduler.step(lowerCamelCase   ,     lowerCamelCase   ,     lowerCamelCase   ,     lowerCamelCase    ).prev_sample

														return sample

							def    __snake_case (							self	:  str    )     ->   Tuple:
														for timesteps in [100, 500, 1000]:
																					self.check_over_configs(num_train_timesteps=lowerCamelCase    )

							def    __snake_case (							self	:  List[Any]    )     ->   Any:
														for steps_offset in [0, 1]:
																					self.check_over_configs(steps_offset=lowerCamelCase    )

														__snake_case		:      Optional[Any]										=       self.scheduler_classes[0]
														__snake_case		:      List[Any]										=       self.get_scheduler_config(steps_offset=1    )
														__snake_case		:      List[str]										=       scheduler_class(**lowerCamelCase    )
														scheduler.set_timesteps(5    )
														assert torch.equal(scheduler.timesteps   ,     torch.LongTensor([801, 601, 401, 201, 1]    )    )

							def    __snake_case (							self	:  Dict    )     ->   Union[str, Any]:
														for beta_start, beta_end in zip([0.00_01, 0.0_01, 0.01, 0.1]   ,     [0.0_02, 0.02, 0.2, 2]    ):
																					self.check_over_configs(beta_start=lowerCamelCase   ,     beta_end=lowerCamelCase    )

							def    __snake_case (							self	:  List[Any]    )     ->   Tuple:
														for schedule in ["linear", "squaredcos_cap_v2"]:
																					self.check_over_configs(beta_schedule=lowerCamelCase    )

							def    __snake_case (							self	:  int    )     ->   int:
														for prediction_type in ["epsilon", "v_prediction"]:
																					self.check_over_configs(prediction_type=lowerCamelCase    )

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

							def    __snake_case (							self	:  Any    )     ->   List[str]:
														for timestep_spacing in ["trailing", "leading"]:
																					self.check_over_configs(timestep_spacing=lowerCamelCase    )

							def    __snake_case (							self	:  Any    )     ->   List[str]:
														for rescale_betas_zero_snr in [True, False]:
																					self.check_over_configs(rescale_betas_zero_snr=lowerCamelCase    )

							def    __snake_case (							self	:  List[Any]    )     ->   List[Any]:
														self.check_over_configs(thresholding=lowerCamelCase    )
														for threshold in [0.5, 1.0, 2.0]:
																					for prediction_type in ["epsilon", "v_prediction"]:
																												self.check_over_configs(
																												    thresholding=lowerCamelCase   ,     prediction_type=lowerCamelCase   ,     sample_max_value=lowerCamelCase   ,     )

							def    __snake_case (							self	:  List[Any]    )     ->   str:
														for t in [1, 10, 49]:
																					self.check_over_forward(time_step=lowerCamelCase    )

							def    __snake_case (							self	:  Optional[Any]    )     ->   Optional[Any]:
														for t, num_inference_steps in zip([1, 10, 50]   ,     [10, 50, 500]    ):
																					self.check_over_forward(time_step=lowerCamelCase   ,     num_inference_steps=lowerCamelCase    )

							def    __snake_case (							self	:  Tuple    )     ->   Union[str, Any]:
														for t, eta in zip([1, 10, 49]   ,     [0.0, 0.5, 1.0]    ):
																					self.check_over_forward(time_step=lowerCamelCase   ,     eta=lowerCamelCase    )

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

														assert torch.sum(torch.abs(scheduler._get_variance(0   ,     0    ) - 0.0    )    ) < 1E-5
														assert torch.sum(torch.abs(scheduler._get_variance(420   ,     400    ) - 0.1_47_71    )    ) < 1E-5
														assert torch.sum(torch.abs(scheduler._get_variance(980   ,     960    ) - 0.3_24_60    )    ) < 1E-5
														assert torch.sum(torch.abs(scheduler._get_variance(0   ,     0    ) - 0.0    )    ) < 1E-5
														assert torch.sum(torch.abs(scheduler._get_variance(487   ,     486    ) - 0.0_09_79    )    ) < 1E-5
														assert torch.sum(torch.abs(scheduler._get_variance(999   ,     998    ) - 0.02    )    ) < 1E-5

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

														__snake_case			,      __snake_case		:      Any										=       10, 0.0
														scheduler.set_timesteps(lowerCamelCase    )

														__snake_case		:      Optional[int]										=       self.dummy_model()
														__snake_case		:      List[str]										=       self.dummy_sample_deter
														__snake_case		:      str										=       self.dummy_sample_deter + 0.1
														__snake_case		:      Tuple										=       self.dummy_sample_deter - 0.1

														__snake_case		:      Optional[Any]										=       samplea.shape[0]
														__snake_case		:      Any										=       torch.stack([samplea, samplea, samplea]   ,     dim=0    )
														__snake_case		:      Dict										=       torch.arange(lowerCamelCase    )[0:3, None].repeat(1   ,     lowerCamelCase    )

														__snake_case		:      int										=       model(samples.flatten(0   ,     1    )   ,     timesteps.flatten(0   ,     1    )    )
														__snake_case		:      Tuple										=       scheduler.batch_step_no_noise(lowerCamelCase   ,     timesteps.flatten(0   ,     1    )   ,     samples.flatten(0   ,     1    )   ,     lowerCamelCase    )

														__snake_case		:      Tuple										=       torch.sum(torch.abs(lowerCamelCase    )    )
														__snake_case		:      List[str]										=       torch.mean(torch.abs(lowerCamelCase    )    )

														assert abs(result_sum.item() - 11_47.79_04    ) < 1E-2
														assert abs(result_mean.item() - 0.49_82    ) < 1E-3

							def    __snake_case (							self	:  Tuple    )     ->   Optional[Any]:
														__snake_case		:      Optional[int]										=       self.full_loop()

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

														assert abs(result_sum.item() - 1_72.00_67    ) < 1E-2
														assert abs(result_mean.item() - 0.22_39_67    ) < 1E-3

							def    __snake_case (							self	:  Optional[int]    )     ->   Any:
														__snake_case		:      int										=       self.full_loop(prediction_type="v_prediction"    )

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

														assert abs(result_sum.item() - 52.53_02    ) < 1E-2
														assert abs(result_mean.item() - 0.06_84    ) < 1E-3

							def    __snake_case (							self	:  List[Any]    )     ->   Union[str, Any]:
														# We specify different beta, so that the first alpha is 0.99
														__snake_case		:      List[str]										=       self.full_loop(set_alpha_to_one=lowerCamelCase   ,     beta_start=0.01    )
														__snake_case		:      List[str]										=       torch.sum(torch.abs(lowerCamelCase    )    )
														__snake_case		:      Union[str, Any]										=       torch.mean(torch.abs(lowerCamelCase    )    )

														assert abs(result_sum.item() - 1_49.82_95    ) < 1E-2
														assert abs(result_mean.item() - 0.19_51    ) < 1E-3






							def    __snake_case (							self	:  List[Any]    )     ->   List[Any]:
														# We specify different beta, so that the first alpha is 0.99
														__snake_case		:      Dict										=       self.full_loop(set_alpha_to_one=lowerCamelCase   ,     beta_start=0.01    )
														__snake_case		:      List[Any]										=       torch.sum(torch.abs(lowerCamelCase    )    )
														__snake_case		:      Union[str, Any]										=       torch.mean(torch.abs(lowerCamelCase    )    )

														assert abs(result_sum.item() - 1_49.07_84    ) < 1E-2
														assert abs(result_mean.item() - 0.19_41    ) < 1E-3





import importlib
import math
import os
from dataclasses import dataclass
from enum import Enum
from typing import Any, Dict, Optional, Tuple, Union

import flax
import jax.numpy as jnp

from ..utils import BaseOutput


_snake_case					:		int      =    "scheduler_config.json"

class     a		(_lowerCAmelCase				):




							"""simple docstring"""


							__UpperCAmelCase   :							Tuple					  =  1
							__UpperCAmelCase   :							Tuple					  =  2
							__UpperCAmelCase   :							Union[str, Any]					  =  3
							__UpperCAmelCase   :							List[Any]					  =  4
							__UpperCAmelCase   :							Tuple					  =  5

@dataclass
class     a		(_lowerCAmelCase				):




							"""simple docstring"""


							__UpperCAmelCase   :							jnp.ndarray

class     a		:




							"""simple docstring"""


							__UpperCAmelCase   :							Dict					  =  SCHEDULER_CONFIG_NAME
							__UpperCAmelCase   :							Union[str, Any]					  =  ["dtype"]
							__UpperCAmelCase   :							Tuple					  =  []
							__UpperCAmelCase   :							int					  =  True

							@classmethod
							def    __snake_case (							cls	:  List[str]   ,     lowerCamelCase	:  Dict[str, Any] = None   ,     lowerCamelCase	:  Optional[str] = None   ,     lowerCamelCase	:  List[str]=False   ,     **lowerCamelCase	:  Union[str, Any]   ,     )     ->   List[str]:
														__snake_case			,      __snake_case		:      List[str]										=       cls.load_config(
														    pretrained_model_name_or_path=lowerCamelCase   ,     subfolder=lowerCamelCase   ,     return_unused_kwargs=lowerCamelCase   ,     **lowerCamelCase   ,     )
														__snake_case			,      __snake_case		:      Dict										=       cls.from_config(lowerCamelCase   ,     return_unused_kwargs=lowerCamelCase   ,     **lowerCamelCase    )

														if hasattr(lowerCamelCase   ,     "create_state"    ) and getattr(lowerCamelCase   ,     "has_state"   ,     lowerCamelCase    ):
																					__snake_case		:      Tuple										=       scheduler.create_state()

														if return_unused_kwargs:
																					return scheduler, state, unused_kwargs

														return scheduler, state

							def    __snake_case (							self	:  Any   ,     lowerCamelCase	:  Union[str, os.PathLike]   ,     lowerCamelCase	:  bool = False   ,     **lowerCamelCase	:  List[Any]    )     ->   int:
														self.save_config(save_directory=lowerCamelCase   ,     push_to_hub=lowerCamelCase   ,     **lowerCamelCase    )

							@property
							def    __snake_case (							self	:  Tuple    )     ->   List[Any]:
														return self._get_compatibles()






							@classmethod
							def    __snake_case (							cls	:  int    )     ->   Dict:
														__snake_case		:      Tuple										=       list(set([cls.__name__] + cls._compatibles    )    )
														__snake_case		:      int										=       importlib.import_module(__name__.split("."    )[0]    )
														__snake_case		:      Tuple										=       [
														    getattr(lowerCamelCase   ,     lowerCamelCase    ) for c in compatible_classes_str if hasattr(lowerCamelCase   ,     lowerCamelCase    )
														]
														return compatible_classes







def     lowerCAmelCase_					( __lowerCamelCase							,     __lowerCamelCase     ):
							assert len(__lowerCamelCase     ) >= x.ndim
							return jnp.broadcast_to(x.reshape(x.shape + (1,) * (len(__lowerCamelCase     ) - x.ndim)     )							,     __lowerCamelCase     )







def     lowerCAmelCase_					( __lowerCamelCase							,     __lowerCamelCase=0.9_9_9							,     __lowerCamelCase=jnp.floataa     ):

							def alpha_bar(__lowerCamelCase     ):
														return math.cos((time_step + 0.0_0_8) / 1.0_0_8 * math.pi / 2     ) ** 2

							__snake_case		:      List[Any]										=       []
							for i in range(__lowerCamelCase     ):
														__snake_case		:      Dict										=       i / num_diffusion_timesteps
														__snake_case		:      str										=       (i + 1) / num_diffusion_timesteps
														betas.append(min(1 - alpha_bar(__lowerCamelCase     ) / alpha_bar(__lowerCamelCase     )							,     __lowerCamelCase     )     )
							return jnp.array(__lowerCamelCase							,     dtype=__lowerCamelCase     )

@flax.struct.dataclass
class     a		:




							"""simple docstring"""


							__UpperCAmelCase   :							jnp.ndarray
							__UpperCAmelCase   :							jnp.ndarray
							__UpperCAmelCase   :							jnp.ndarray

							@classmethod
							def    __snake_case (							cls	:  Union[str, Any]   ,     lowerCamelCase	:  int    )     ->   List[Any]:
														__snake_case		:      Dict										=       scheduler.config

														if config.trained_betas is not None:
																					__snake_case		:      Dict										=       jnp.asarray(config.trained_betas   ,     dtype=scheduler.dtype    )
														elif config.beta_schedule == "linear":
																					__snake_case		:      Optional[int]										=       jnp.linspace(config.beta_start   ,     config.beta_end   ,     config.num_train_timesteps   ,     dtype=scheduler.dtype    )
														elif config.beta_schedule == "scaled_linear":
																					# this schedule is very specific to the latent diffusion model.
																					__snake_case		:      str										=       (
																					    jnp.linspace(
																					        config.beta_start**0.5   ,     config.beta_end**0.5   ,     config.num_train_timesteps   ,     dtype=scheduler.dtype    )
																					    ** 2
																					)
														elif config.beta_schedule == "squaredcos_cap_v2":
																					# Glide cosine schedule
																					__snake_case		:      Optional[Any]										=       betas_for_alpha_bar(config.num_train_timesteps   ,     dtype=scheduler.dtype    )
														else:
																					raise NotImplementedError(
																					    F'beta_schedule {config.beta_schedule} is not implemented for scheduler {scheduler.__class__.__name__}'    )

														__snake_case		:      Any										=       1.0 - betas

														__snake_case		:      int										=       jnp.cumprod(lowerCamelCase   ,     axis=0    )

														return cls(
														    alphas=lowerCamelCase   ,     betas=lowerCamelCase   ,     alphas_cumprod=lowerCamelCase   ,     )







def     lowerCAmelCase_					( __lowerCamelCase							,     __lowerCamelCase							,     __lowerCamelCase							,     __lowerCamelCase     ):
							__snake_case		:      List[Any]										=       state.alphas_cumprod

							__snake_case		:      str										=       alphas_cumprod[timesteps] ** 0.5
							__snake_case		:      Dict										=       sqrt_alpha_prod.flatten()
							__snake_case		:      str										=       broadcast_to_shape_from_left(__lowerCamelCase							,     original_samples.shape     )

							__snake_case		:      Tuple										=       (1 - alphas_cumprod[timesteps]) ** 0.5
							__snake_case		:      str										=       sqrt_one_minus_alpha_prod.flatten()
							__snake_case		:      Tuple										=       broadcast_to_shape_from_left(__lowerCamelCase							,     original_samples.shape     )

							return sqrt_alpha_prod, sqrt_one_minus_alpha_prod







def     lowerCAmelCase_					( __lowerCamelCase							,     __lowerCamelCase							,     __lowerCamelCase							,     __lowerCamelCase     ):
							__snake_case			,      __snake_case		:      Union[str, Any]										=       get_sqrt_alpha_prod(__lowerCamelCase							,     __lowerCamelCase							,     __lowerCamelCase							,     __lowerCamelCase     )
							__snake_case		:      List[str]										=       sqrt_alpha_prod * original_samples + sqrt_one_minus_alpha_prod * noise
							return noisy_samples







def     lowerCAmelCase_					( __lowerCamelCase							,     __lowerCamelCase							,     __lowerCamelCase							,     __lowerCamelCase     ):
							__snake_case			,      __snake_case		:      Dict										=       get_sqrt_alpha_prod(__lowerCamelCase							,     __lowerCamelCase							,     __lowerCamelCase							,     __lowerCamelCase     )
							__snake_case		:      Optional[int]										=       sqrt_alpha_prod * noise - sqrt_one_minus_alpha_prod * sample
							return velocity

