




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


lowerCamelCase							:						List[str]					       =  "scheduler_config.json"







class     A(		_UpperCamelCase  ):



 '''simple docstring'''


 UpperCamelCase		  =		1
 UpperCamelCase		  =		2
 UpperCamelCase		  =		3
 UpperCamelCase		  =		4
 UpperCamelCase		  =		5







@dataclass
class     A(		_UpperCamelCase  ):



 '''simple docstring'''


 UpperCamelCase		  =		42







class     A:



 '''simple docstring'''


 UpperCamelCase		  =		SCHEDULER_CONFIG_NAME
 UpperCamelCase		  =		["dtype"]
 UpperCamelCase		  =		[]
 UpperCamelCase		  =		True

 @classmethod
 def 							a__					(							cls   :    str		,				A_   :    Dict[str, Any] = None		,				A_   :    Optional[str] = None		,				A_   :    Dict=False		,				**A_   :    str		,				)  ->      Any:

       """simple docstring"""






       lowerCamelCase_							=       cls.load_config(
           pretrained_model_name_or_path=a_		,				subfolder=a_		,				return_unused_kwargs=a_		,				**a_		,				)
       lowerCamelCase_							=       cls.from_config(a_		,				return_unused_kwargs=a_		,				**a_    )

       if hasattr(a_		,				'create_state'    ) and getattr(a_		,				'has_state'		,				a_    ):
             lowerCamelCase_							=       scheduler.create_state()

       if return_unused_kwargs:
             return scheduler, state, unused_kwargs

       return scheduler, state

 def 							a__					(							self   :    Any		,				A_   :    Union[str, os.PathLike]		,				A_   :    bool = False		,				**A_   :    int    )  ->      List[str]:

       """simple docstring"""






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

 @property
 def 							a__					(							self   :    List[Any]    )  ->      Any:

       """simple docstring"""






       return self._get_compatibles()





 @classmethod
 def 							a__					(							cls   :    List[Any]    )  ->      Optional[Any]:

       """simple docstring"""






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

def      _SCREAMING_SNAKE_CASE     (				lowercase      :			jnp.ndarray      ,				lowercase      :			Tuple[int]	):


      '''simple docstring'''



      assert len(lowerCAmelCase__	) >= x.ndim
      return jnp.broadcast_to(x.reshape(x.shape + (1,) * (len(lowerCAmelCase__	) - x.ndim)	)      ,				lowerCAmelCase__	)

def      _SCREAMING_SNAKE_CASE     (				lowercase      :			int      ,				lowercase      :			str=0.999      ,				lowercase      :			int=jnp.floataa	):


      '''simple docstring'''



      def alpha_bar(lowercase      :			str	):
            return math.cos((time_step + 0.008) / 1.008 * math.pi / 2	) ** 2

      lowerCamelCase_							=       []
      for i in range(lowerCAmelCase__	):
            lowerCamelCase_							=       i / num_diffusion_timesteps
            lowerCamelCase_							=       (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		  =		42
 UpperCamelCase		  =		42
 UpperCamelCase		  =		42

 @classmethod
 def 							a__					(							cls   :    List[Any]		,				A_   :    Union[str, Any]    )  ->      Tuple:

       """simple docstring"""






       lowerCamelCase_							=       scheduler.config

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

       lowerCamelCase_							=       1.0 - betas

       lowerCamelCase_							=       jnp.cumprod(a_		,				axis=0    )

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

def      _SCREAMING_SNAKE_CASE     (				lowercase      :			CommonSchedulerState      ,				lowercase      :			jnp.ndarray      ,				lowercase      :			jnp.ndarray      ,				lowercase      :			jnp.ndarray	):


      '''simple docstring'''



      lowerCamelCase_							=       state.alphas_cumprod

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

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

      return sqrt_alpha_prod, sqrt_one_minus_alpha_prod

def      _SCREAMING_SNAKE_CASE     (				lowercase      :			CommonSchedulerState      ,				lowercase      :			jnp.ndarray      ,				lowercase      :			jnp.ndarray      ,				lowercase      :			jnp.ndarray	):


      '''simple docstring'''



      lowerCamelCase_							=       get_sqrt_alpha_prod(lowerCAmelCase__      ,				lowerCAmelCase__      ,				lowerCAmelCase__      ,				lowerCAmelCase__	)
      lowerCamelCase_							=       sqrt_alpha_prod * original_samples + sqrt_one_minus_alpha_prod * noise
      return noisy_samples






def      _SCREAMING_SNAKE_CASE     (				lowercase      :			CommonSchedulerState      ,				lowercase      :			jnp.ndarray      ,				lowercase      :			jnp.ndarray      ,				lowercase      :			jnp.ndarray	):


      '''simple docstring'''



      lowerCamelCase_							=       get_sqrt_alpha_prod(lowerCAmelCase__      ,				lowerCAmelCase__      ,				lowerCAmelCase__      ,				lowerCAmelCase__	)
      lowerCamelCase_							=       sqrt_alpha_prod * noise - sqrt_one_minus_alpha_prod * sample
      return velocity










"""simple docstring"""






import unittest

import torch

from diffusers import DDIMScheduler, DDPMScheduler, UNetaDModel
from diffusers.training_utils import set_seed
from diffusers.utils.testing_utils import slow


__UpperCAmelCase						=			False



class 					__UpperCAmelCase							(						unittest.TestCase  ):
				def       UpperCAmelCase      (		self    :			Tuple	, a_    :			Optional[Any]=32    )	->	Union[str, Any]:



							'''simple docstring'''



							set_seed(0    )
							a__  :	Any		 =			UNetaDModel(sample_size=a_	, in_channels=3	, out_channels=3    )
							a__  :	Union[str, Any]		 =			torch.optim.SGD(model.parameters()	, lr=0.0001    )
							return model, optimizer






				@slow
				def       UpperCAmelCase      (		self    :			Union[str, Any]    )	->	Any:



							'''simple docstring'''



							a__  :	Optional[Any]		 =			"cpu"  # ensure full determinism without setting the CUBLAS_WORKSPACE_CONFIG env variable
							a__  :	Dict		 =			DDPMScheduler(
							    num_train_timesteps=10_00	, beta_start=0.0001	, beta_end=0.02	, beta_schedule="linear"	, clip_sample=a_	, )
							a__  :	Optional[Any]		 =			DDIMScheduler(
							    num_train_timesteps=10_00	, beta_start=0.0001	, beta_end=0.02	, beta_schedule="linear"	, clip_sample=a_	, )

							assert ddpm_scheduler.config.num_train_timesteps == ddim_scheduler.config.num_train_timesteps

							# shared batches for DDPM and DDIM
							set_seed(0    )
							a__  :	List[str]		 =			[torch.randn((4, 3, 32, 32)    ).clip(-1	, 1    ).to(a_    ) for _ in range(4    )]
							a__  :	List[str]		 =			[torch.randn((4, 3, 32, 32)    ).to(a_    ) for _ in range(4    )]
							a__  :	Optional[Any]		 =			[torch.randint(0	, 10_00	, (4,)    ).long().to(a_    ) for _ in range(4    )]

							# train with a DDPM scheduler
							a__ ,	a__  :	Union[str, Any]		 =			self.get_model_optimizer(resolution=32    )
							model.train().to(a_    )
							for i in range(4    ):
										optimizer.zero_grad()
										a__  :	Optional[int]		 =			ddpm_scheduler.add_noise(clean_images[i]	, noise[i]	, timesteps[i]    )
										a__  :	List[str]		 =			model(a_	, timesteps[i]    ).sample
										a__  :	Optional[int]		 =			torch.nn.functional.mse_loss(a_	, noise[i]    )
										loss.backward()
										optimizer.step()
							del model, optimizer

							# recreate the model and optimizer, and retry with DDIM
							a__ ,	a__  :	Optional[Any]		 =			self.get_model_optimizer(resolution=32    )
							model.train().to(a_    )
							for i in range(4    ):
										optimizer.zero_grad()
										a__  :	List[str]		 =			ddim_scheduler.add_noise(clean_images[i]	, noise[i]	, timesteps[i]    )
										a__  :	Optional[int]		 =			model(a_	, timesteps[i]    ).sample
										a__  :	Union[str, Any]		 =			torch.nn.functional.mse_loss(a_	, noise[i]    )
										loss.backward()
										optimizer.step()
							del model, optimizer

							self.assertTrue(torch.allclose(a_	, a_	, atol=1E-5    )    )
							self.assertTrue(torch.allclose(a_	, a_	, atol=1E-5    )    )