'''simple docstring'''

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


UpperCamelCase__     :	Dict				=			'scheduler_config.json'


class 				_lowercase	(      lowerCAmelCase   ):
      '''simple docstring'''


      UpperCAmelCase_      :      List[str]      							=   1
      UpperCAmelCase_      :      Union[str, Any]      							=   2
      UpperCAmelCase_      :      Optional[Any]      							=   3
      UpperCAmelCase_      :      Optional[Any]      							=   4
      UpperCAmelCase_      :      List[str]      							=   5




@dataclass
class 				_lowercase	(      lowerCAmelCase   ):
      '''simple docstring'''


      UpperCAmelCase_      :      jnp.ndarray




class 				_lowercase	:
      '''simple docstring'''


      UpperCAmelCase_      :      Union[str, Any]      							=   SCHEDULER_CONFIG_NAME
      UpperCAmelCase_      :      List[Any]      							=   ['''dtype''']
      UpperCAmelCase_      :      Union[str, Any]      							=   []
      UpperCAmelCase_      :      List[Any]      							=   True




      @classmethod
      def 		lowerCAmelCase__					(				cls					,lowerCamelCase_ = None					,lowerCamelCase_ = None					,lowerCamelCase_=False					,**lowerCamelCase_					,)			->			Optional[Any]:



         '''simple docstring'''



         UpperCAmelCase__				,      UpperCAmelCase__							:				Tuple   				=  cls.load_config(
             pretrained_model_name_or_path=lowerCamelCase_					,subfolder=lowerCamelCase_					,return_unused_kwargs=lowerCamelCase_					,**lowerCamelCase_					,)
         UpperCAmelCase__				,      UpperCAmelCase__							:				List[Any]   				=  cls.from_config(lowerCamelCase_					,return_unused_kwargs=lowerCamelCase_					,**lowerCamelCase_							)

         if hasattr(lowerCamelCase_					,'''create_state'''							) and getattr(lowerCamelCase_					,'''has_state'''					,lowerCamelCase_							):
            UpperCAmelCase__							:				Optional[int]   				=  scheduler.create_state()

         if return_unused_kwargs:
            return scheduler, state, unused_kwargs

         return scheduler, state




      def 		lowerCAmelCase__					(				self					,lowerCamelCase_					,lowerCamelCase_ = False					,**lowerCamelCase_							)			->			List[str]:



         '''simple docstring'''



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




      @property
      def 		lowerCAmelCase__					(				self							)			->			Tuple:



         '''simple docstring'''



         return self._get_compatibles()




      @classmethod
      def 		lowerCAmelCase__					(				cls							)			->			str:



         '''simple docstring'''



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







def __UpperCamelCase(   _A					:				jnp.ndarray  ,			_A					:				Tuple[int]    ):
   '''simple docstring'''
   assert len(_A    ) >= x.ndim
   return jnp.broadcast_to(x.reshape(x.shape + (1,) * (len(_A    ) - x.ndim)    )  ,			_A    )







def __UpperCamelCase(   _A					:				int  ,			_A					:				int=0.9_9_9  ,			_A					:				int=jnp.floataa    ):
   '''simple docstring'''

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

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


@flax.struct.dataclass
class 				_lowercase	:
      '''simple docstring'''


      UpperCAmelCase_      :      jnp.ndarray
      UpperCAmelCase_      :      jnp.ndarray
      UpperCAmelCase_      :      jnp.ndarray




      @classmethod
      def 		lowerCAmelCase__					(				cls					,lowerCamelCase_							)			->			Any:



         '''simple docstring'''



         UpperCAmelCase__							:				List[str]   				=  scheduler.config

         if config.trained_betas is not None:
            UpperCAmelCase__							:				List[str]   				=  jnp.asarray(config.trained_betas					,dtype=scheduler.dtype							)
         elif config.beta_schedule == "linear":
            UpperCAmelCase__							:				Optional[Any]   				=  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.
            UpperCAmelCase__							:				Optional[int]   				=  (
                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
            UpperCAmelCase__							:				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__}'''							)

         UpperCAmelCase__							:				Optional[Any]   				=  1.0 - betas

         UpperCAmelCase__							:				Tuple   				=  jnp.cumprod(lowerCamelCase_					,axis=0							)

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







def __UpperCamelCase(   _A					:				CommonSchedulerState  ,			_A					:				jnp.ndarray  ,			_A					:				jnp.ndarray  ,			_A					:				jnp.ndarray    ):
   '''simple docstring'''
   UpperCAmelCase__							:				Optional[Any]   				=  state.alphas_cumprod

   UpperCAmelCase__							:				List[str]   				=  alphas_cumprod[timesteps] ** 0.5
   UpperCAmelCase__							:				Dict   				=  sqrt_alpha_prod.flatten()
   UpperCAmelCase__							:				Any   				=  broadcast_to_shape_from_left(_A  ,			original_samples.shape    )

   UpperCAmelCase__							:				Tuple   				=  (1 - alphas_cumprod[timesteps]) ** 0.5
   UpperCAmelCase__							:				Any   				=  sqrt_one_minus_alpha_prod.flatten()
   UpperCAmelCase__							:				Tuple   				=  broadcast_to_shape_from_left(_A  ,			original_samples.shape    )

   return sqrt_alpha_prod, sqrt_one_minus_alpha_prod







def __UpperCamelCase(   _A					:				CommonSchedulerState  ,			_A					:				jnp.ndarray  ,			_A					:				jnp.ndarray  ,			_A					:				jnp.ndarray    ):
   '''simple docstring'''
   UpperCAmelCase__				,      UpperCAmelCase__							:				Dict   				=  get_sqrt_alpha_prod(_A  ,			_A  ,			_A  ,			_A    )
   UpperCAmelCase__							:				Dict   				=  sqrt_alpha_prod * original_samples + sqrt_one_minus_alpha_prod * noise
   return noisy_samples







def __UpperCamelCase(   _A					:				CommonSchedulerState  ,			_A					:				jnp.ndarray  ,			_A					:				jnp.ndarray  ,			_A					:				jnp.ndarray    ):
   '''simple docstring'''
   UpperCAmelCase__				,      UpperCAmelCase__							:				List[Any]   				=  get_sqrt_alpha_prod(_A  ,			_A  ,			_A  ,			_A    )
   UpperCAmelCase__							:				List[str]   				=  sqrt_alpha_prod * noise - sqrt_one_minus_alpha_prod * sample
   return velocity





'''simple docstring'''

import unittest

import numpy as np







def __UpperCamelCase(   _A					:				np.ndarray  ,			_A					:				np.ndarray  ,			_A					:				np.ndarray  ,			_A					:				np.ndarray | None = None  ,			):
   '''simple docstring'''
   UpperCAmelCase__							:				Any   				=  np.shape(_A    )
   UpperCAmelCase__							:				List[Any]   				=  np.shape(_A    )
   UpperCAmelCase__							:				Tuple   				=  np.shape(_A    )

   if shape_a[0] != shape_b[0]:
      UpperCAmelCase__							:				Any   				=  (
          '''Expected the same number of rows for A and B. '''
          F'''Instead found A of size {shape_a} and B of size {shape_b}'''
      )
      raise ValueError(_A    )

   if shape_b[1] != shape_c[1]:
      UpperCAmelCase__							:				List[str]   				=  (
          '''Expected the same number of columns for B and C. '''
          F'''Instead found B of size {shape_b} and C of size {shape_c}'''
      )
      raise ValueError(_A    )

   UpperCAmelCase__							:				Optional[Any]   				=  pseudo_inv
   if a_inv is None:
      try:
         UpperCAmelCase__							:				str   				=  np.linalg.inv(_A    )
      except np.linalg.LinAlgError:
         raise ValueError(
             '''Input matrix A is not invertible. Cannot compute Schur complement.'''    )

   return mat_c - mat_b.T @ a_inv @ mat_b


class 				_lowercase	(      unittest.TestCase   ):
      '''simple docstring'''


      def 		lowerCAmelCase__					(				self							)			->			None:



         '''simple docstring'''



         UpperCAmelCase__							:				int   				=  np.array([[1, 2, 1], [2, 1, 2], [3, 2, 4]]							)
         UpperCAmelCase__							:				List[Any]   				=  np.array([[0, 3], [3, 0], [2, 3]]							)
         UpperCAmelCase__							:				Optional[Any]   				=  np.array([[2, 1], [6, 3]]							)

         UpperCAmelCase__							:				Tuple   				=  schur_complement(lowerCamelCase_					,lowerCamelCase_					,lowerCamelCase_							)

         UpperCAmelCase__							:				Tuple   				=  np.block([[a, b], [b.T, c]]							)

         UpperCAmelCase__							:				int   				=  np.linalg.det(lowerCamelCase_							)
         UpperCAmelCase__							:				Optional[Any]   				=  np.linalg.det(lowerCamelCase_							)
         UpperCAmelCase__							:				Union[str, Any]   				=  np.linalg.det(lowerCamelCase_							)

         self.assertAlmostEqual(lowerCamelCase_					,det_a * det_s							)




      def 		lowerCAmelCase__					(				self							)			->			None:



         '''simple docstring'''



         UpperCAmelCase__							:				Optional[Any]   				=  np.array([[1, 2, 1], [2, 1, 2], [3, 2, 4]]							)
         UpperCAmelCase__							:				Tuple   				=  np.array([[0, 3], [3, 0], [2, 3]]							)
         UpperCAmelCase__							:				Tuple   				=  np.array([[2, 1], [6, 3]]							)

         with self.assertRaises(lowerCamelCase_							):
            schur_complement(lowerCamelCase_					,lowerCamelCase_					,lowerCamelCase_							)




      def 		lowerCAmelCase__					(				self							)			->			None:



         '''simple docstring'''



         UpperCAmelCase__							:				Dict   				=  np.array([[1, 2, 1], [2, 1, 2], [3, 2, 4]]							)
         UpperCAmelCase__							:				str   				=  np.array([[0, 3], [3, 0], [2, 3]]							)
         UpperCAmelCase__							:				Any   				=  np.array([[2, 1, 3], [6, 3, 5]]							)

         with self.assertRaises(lowerCamelCase_							):
            schur_complement(lowerCamelCase_					,lowerCamelCase_					,lowerCamelCase_							)


if __name__ == "__main__":
  import doctest

  doctest.testmod()
  unittest.main()




