




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


SCREAMING_SNAKE_CASE:     Tuple									= '''scheduler_config.json'''



class    lowercase_		(SCREAMING_SNAKE_CASE__ ):
						lowerCAmelCase__			      =1
						lowerCAmelCase__			      =2
						lowerCAmelCase__			      =3
						lowerCAmelCase__			      =4
						lowerCAmelCase__			      =5



@dataclass
class    lowercase_		(SCREAMING_SNAKE_CASE__ ):
						lowerCAmelCase__			      =42



class    lowercase_		:
						lowerCAmelCase__			      =SCHEDULER_CONFIG_NAME
						lowerCAmelCase__			      =["dtype"]
						lowerCAmelCase__			      =[]
						lowerCAmelCase__			      =True



						@classmethod
						def 	__a					(			cls	:   Optional[int]					,							snake_case__	:   Dict[str, Any] = None					,							snake_case__	:   Optional[str] = None					,							snake_case__	:   Tuple=False					,							**snake_case__	:   str					,							):

								"""simple docstring"""

								SCREAMING_SNAKE_CASE_      ,				SCREAMING_SNAKE_CASE_					      =							cls.load_config(
								    pretrained_model_name_or_path=snake_case__					,							subfolder=snake_case__					,							return_unused_kwargs=snake_case__					,							**snake_case__					,							)
								SCREAMING_SNAKE_CASE_      ,				SCREAMING_SNAKE_CASE_					      =							cls.from_config(snake_case__					,							return_unused_kwargs=snake_case__					,							**snake_case__  )

								if hasattr(snake_case__					,							'create_state'  ) and getattr(snake_case__					,							'has_state'					,							snake_case__  ):
										SCREAMING_SNAKE_CASE_					      =							scheduler.create_state()

								if return_unused_kwargs:
										return scheduler, state, unused_kwargs

								return scheduler, state



						def 	__a					(			self	:   str					,							snake_case__	:   Union[str, os.PathLike]					,							snake_case__	:   bool = False					,							**snake_case__	:   List[str]  ):

								"""simple docstring"""

								self.save_config(save_directory=snake_case__					,							push_to_hub=snake_case__					,							**snake_case__  )



						@property
						def 	__a					(			self	:   str  ):

								"""simple docstring"""

								return self._get_compatibles()






						@classmethod
						def 	__a					(			cls	:   Optional[int]  ):

								"""simple docstring"""

								SCREAMING_SNAKE_CASE_					      =							list(set([cls.__name__] + cls._compatibles  )  )
								SCREAMING_SNAKE_CASE_					      =							importlib.import_module(__name__.split('.'  )[0]  )
								SCREAMING_SNAKE_CASE_					      =							[
								    getattr(snake_case__					,							snake_case__  ) for c in compatible_classes_str if hasattr(snake_case__					,							snake_case__  )
								]
								return compatible_classes


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


def 					_a							(					lowerCAmelCase    ,						lowerCAmelCase=0.9_9_9    ,						lowerCAmelCase=jnp.floataa )->	jnp.ndarray:

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

		SCREAMING_SNAKE_CASE_					      =							[]
		for i in range(lowerCAmelCase ):
				SCREAMING_SNAKE_CASE_					      =							i / num_diffusion_timesteps
				SCREAMING_SNAKE_CASE_					      =							(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    lowercase_		:
						lowerCAmelCase__			      =42
						lowerCAmelCase__			      =42
						lowerCAmelCase__			      =42



						@classmethod
						def 	__a					(			cls	:   Tuple					,							snake_case__	:   str  ):

								"""simple docstring"""

								SCREAMING_SNAKE_CASE_					      =							scheduler.config

								if config.trained_betas is not None:
										SCREAMING_SNAKE_CASE_					      =							jnp.asarray(config.trained_betas					,							dtype=scheduler.dtype  )
								elif config.beta_schedule == "linear":
										SCREAMING_SNAKE_CASE_					      =							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.
										SCREAMING_SNAKE_CASE_					      =							(
										    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
										SCREAMING_SNAKE_CASE_					      =							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__}'''  )

								SCREAMING_SNAKE_CASE_					      =							1.0 - betas

								SCREAMING_SNAKE_CASE_					      =							jnp.cumprod(snake_case__					,							axis=0  )

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


def 					_a							(					lowerCAmelCase    ,						lowerCAmelCase    ,						lowerCAmelCase    ,						lowerCAmelCase )->	Union[str, Any]:
		SCREAMING_SNAKE_CASE_					      =							state.alphas_cumprod

		SCREAMING_SNAKE_CASE_					      =							alphas_cumprod[timesteps] ** 0.5
		SCREAMING_SNAKE_CASE_					      =							sqrt_alpha_prod.flatten()
		SCREAMING_SNAKE_CASE_					      =							broadcast_to_shape_from_left(lowerCAmelCase    ,						original_samples.shape )

		SCREAMING_SNAKE_CASE_					      =							(1 - alphas_cumprod[timesteps]) ** 0.5
		SCREAMING_SNAKE_CASE_					      =							sqrt_one_minus_alpha_prod.flatten()
		SCREAMING_SNAKE_CASE_					      =							broadcast_to_shape_from_left(lowerCAmelCase    ,						original_samples.shape )

		return sqrt_alpha_prod, sqrt_one_minus_alpha_prod


def 					_a							(					lowerCAmelCase    ,						lowerCAmelCase    ,						lowerCAmelCase    ,						lowerCAmelCase )->	Tuple:
		SCREAMING_SNAKE_CASE_      ,				SCREAMING_SNAKE_CASE_					      =							get_sqrt_alpha_prod(lowerCAmelCase    ,						lowerCAmelCase    ,						lowerCAmelCase    ,						lowerCAmelCase )
		SCREAMING_SNAKE_CASE_					      =							sqrt_alpha_prod * original_samples + sqrt_one_minus_alpha_prod * noise
		return noisy_samples




def 					_a							(					lowerCAmelCase    ,						lowerCAmelCase    ,						lowerCAmelCase    ,						lowerCAmelCase )->	Dict:
		SCREAMING_SNAKE_CASE_      ,				SCREAMING_SNAKE_CASE_					      =							get_sqrt_alpha_prod(lowerCAmelCase    ,						lowerCAmelCase    ,						lowerCAmelCase    ,						lowerCAmelCase )
		SCREAMING_SNAKE_CASE_					      =							sqrt_alpha_prod * noise - sqrt_one_minus_alpha_prod * sample
		return velocity





import torch

from diffusers import KDPMaDiscreteScheduler
from diffusers.utils import torch_device

from .test_schedulers import SchedulerCommonTest



class    lowercase_		(SCREAMING_SNAKE_CASE__ ):
						lowerCAmelCase__			      =(KDPMaDiscreteScheduler,)
						lowerCAmelCase__			      =10



						def 	__a					(			self	:   Dict					,							**snake_case__	:   str  ):

								"""simple docstring"""

								SCREAMING_SNAKE_CASE_					      =							{
								    'num_train_timesteps': 11_00,
								    'beta_start': 0.00_01,
								    'beta_end': 0.02,
								    'beta_schedule': 'linear',
								}

								config.update(**snake_case__  )
								return config



						def 	__a					(			self	:   str  ):

								"""simple docstring"""

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



						def 	__a					(			self	:   List[Any]  ):

								"""simple docstring"""

								for beta_start, beta_end in zip([0.0_00_01, 0.00_01, 0.0_01]					,							[0.00_02, 0.0_02, 0.02]  ):
										self.check_over_configs(beta_start=snake_case__					,							beta_end=snake_case__  )



						def 	__a					(			self	:   Optional[int]  ):

								"""simple docstring"""

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



						def 	__a					(			self	:   Optional[Any]  ):

								"""simple docstring"""

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



						def 	__a					(			self	:   Optional[Any]  ):

								"""simple docstring"""

								SCREAMING_SNAKE_CASE_					      =							self.scheduler_classes[0]
								SCREAMING_SNAKE_CASE_					      =							self.get_scheduler_config(prediction_type='v_prediction'  )
								SCREAMING_SNAKE_CASE_					      =							scheduler_class(**snake_case__  )

								scheduler.set_timesteps(self.num_inference_steps  )

								SCREAMING_SNAKE_CASE_					      =							self.dummy_model()
								SCREAMING_SNAKE_CASE_					      =							self.dummy_sample_deter * scheduler.init_noise_sigma
								SCREAMING_SNAKE_CASE_					      =							sample.to(snake_case__  )

								for i, t in enumerate(scheduler.timesteps  ):
										SCREAMING_SNAKE_CASE_					      =							scheduler.scale_model_input(snake_case__					,							snake_case__  )

										SCREAMING_SNAKE_CASE_					      =							model(snake_case__					,							snake_case__  )

										SCREAMING_SNAKE_CASE_					      =							scheduler.step(snake_case__					,							snake_case__					,							snake_case__  )
										SCREAMING_SNAKE_CASE_					      =							output.prev_sample

								SCREAMING_SNAKE_CASE_					      =							torch.sum(torch.abs(snake_case__  )  )
								SCREAMING_SNAKE_CASE_					      =							torch.mean(torch.abs(snake_case__  )  )

								if torch_device in ["cpu", "mps"]:
										assert abs(result_sum.item() - 4.69_34e-07  ) < 1e-2
										assert abs(result_mean.item() - 6.11_12e-10  ) < 1e-3
								else:
										# CUDA
										assert abs(result_sum.item() - 4.6_93_42_86_50_17_09_72e-07  ) < 1e-2
										assert abs(result_mean.item() - 0.00_02  ) < 1e-3



						def 	__a					(			self	:   Union[str, Any]  ):

								"""simple docstring"""

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

								scheduler.set_timesteps(self.num_inference_steps  )

								SCREAMING_SNAKE_CASE_					      =							self.dummy_model()
								SCREAMING_SNAKE_CASE_					      =							self.dummy_sample_deter * scheduler.init_noise_sigma
								SCREAMING_SNAKE_CASE_					      =							sample.to(snake_case__  )

								for i, t in enumerate(scheduler.timesteps  ):
										SCREAMING_SNAKE_CASE_					      =							scheduler.scale_model_input(snake_case__					,							snake_case__  )

										SCREAMING_SNAKE_CASE_					      =							model(snake_case__					,							snake_case__  )

										SCREAMING_SNAKE_CASE_					      =							scheduler.step(snake_case__					,							snake_case__					,							snake_case__  )
										SCREAMING_SNAKE_CASE_					      =							output.prev_sample

								SCREAMING_SNAKE_CASE_					      =							torch.sum(torch.abs(snake_case__  )  )
								SCREAMING_SNAKE_CASE_					      =							torch.mean(torch.abs(snake_case__  )  )

								if torch_device in ["cpu", "mps"]:
										assert abs(result_sum.item() - 20.41_25  ) < 1e-2
										assert abs(result_mean.item() - 0.02_66  ) < 1e-3
								else:
										# CUDA
										assert abs(result_sum.item() - 20.41_25  ) < 1e-2
										assert abs(result_mean.item() - 0.02_66  ) < 1e-3






						def 	__a					(			self	:   Optional[int]  ):

								"""simple docstring"""

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

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

								SCREAMING_SNAKE_CASE_					      =							self.dummy_model()
								SCREAMING_SNAKE_CASE_					      =							self.dummy_sample_deter.to(snake_case__  ) * scheduler.init_noise_sigma

								for t in scheduler.timesteps:
										SCREAMING_SNAKE_CASE_					      =							scheduler.scale_model_input(snake_case__					,							snake_case__  )

										SCREAMING_SNAKE_CASE_					      =							model(snake_case__					,							snake_case__  )

										SCREAMING_SNAKE_CASE_					      =							scheduler.step(snake_case__					,							snake_case__					,							snake_case__  )
										SCREAMING_SNAKE_CASE_					      =							output.prev_sample

								SCREAMING_SNAKE_CASE_					      =							torch.sum(torch.abs(snake_case__  )  )
								SCREAMING_SNAKE_CASE_					      =							torch.mean(torch.abs(snake_case__  )  )

								if str(snake_case__  ).startswith('cpu'  ):
										# The following sum varies between 148 and 156 on mps. Why?
										assert abs(result_sum.item() - 20.41_25  ) < 1e-2
										assert abs(result_mean.item() - 0.02_66  ) < 1e-3
								else:
										# CUDA
										assert abs(result_sum.item() - 20.41_25  ) < 1e-2
										assert abs(result_mean.item() - 0.02_66  ) < 1e-3