



'''simple docstring'''





import math

import flax.linen as nn
import jax.numpy as jnp
def 							UpperCAmelCase__		(				_SCREAMING_SNAKE_CASE   :	Any     ,				_SCREAMING_SNAKE_CASE   :	List[Any]     ,				_SCREAMING_SNAKE_CASE   :	Union[str, Any] = 1     ,				_SCREAMING_SNAKE_CASE   :	List[str] = 1     ,				_SCREAMING_SNAKE_CASE   :	Optional[Any] = 1.0e4     ,				_SCREAMING_SNAKE_CASE   :	Optional[Any] = False     ,				_SCREAMING_SNAKE_CASE   :	Dict = 1.0     ,				)->Optional[Any]:
      assert timesteps.ndim == 1, "Timesteps should be a 1d-array"
      assert embedding_dim % 2 == 0, f'''Embedding dimension {embedding_dim} should be even'''
      _lowerCAmelCase 			=  float(embedding_dim // 2				)
      _lowerCAmelCase 			=  math.log(max_timescale / min_timescale				) / (num_timescales - freq_shift)
      _lowerCAmelCase 			=  min_timescale * jnp.exp(jnp.arange(lowerCAmelCase_     ,				dtype=jnp.floataa				) * -log_timescale_increment				)
      _lowerCAmelCase 			=  jnp.expand_dims(lowerCAmelCase_     ,				1				) * jnp.expand_dims(lowerCAmelCase_     ,				0				)

      # scale embeddings
      _lowerCAmelCase 			=  scale * emb

      if flip_sin_to_cos:
            _lowerCAmelCase 			=  jnp.concatenate([jnp.cos(lowerCAmelCase_				), jnp.sin(lowerCAmelCase_				)]     ,				axis=1				)
      else:
            _lowerCAmelCase 			=  jnp.concatenate([jnp.sin(lowerCAmelCase_				), jnp.cos(lowerCAmelCase_				)]     ,				axis=1				)
      _lowerCAmelCase 			=  jnp.reshape(lowerCAmelCase_     ,				[jnp.shape(lowerCAmelCase_				)[0], embedding_dim]				)
      return signal





class 		UpperCAmelCase						(						nn.Module				):
   SCREAMING_SNAKE_CASE__							=     3_2
   SCREAMING_SNAKE_CASE__							=     jnp.floataa





   @nn.compact
   def __call__(	self       ,     _lowerCAmelCase			):
         _lowerCAmelCase 			=  nn.Dense(self.time_embed_dim       ,     dtype=self.dtype       ,     name='''linear_1'''			)(lowercase__			)
         _lowerCAmelCase 			=  nn.silu(lowercase__			)
         _lowerCAmelCase 			=  nn.Dense(self.time_embed_dim       ,     dtype=self.dtype       ,     name='''linear_2'''			)(lowercase__			)
         return temb






class 		UpperCAmelCase						(						nn.Module				):
   SCREAMING_SNAKE_CASE__							=     3_2
   SCREAMING_SNAKE_CASE__							=     False
   SCREAMING_SNAKE_CASE__							=     1





   @nn.compact
   def __call__(	self       ,     _lowerCAmelCase			):
         return get_sinusoidal_embeddings(
             lowercase__       ,     embedding_dim=self.dim       ,     flip_sin_to_cos=self.flip_sin_to_cos       ,     freq_shift=self.freq_shift			)




# DISCLAIMER: This code is strongly influenced by https://github.com/pesser/pytorch_diffusion
# and https://github.com/hojonathanho/diffusion
import math
from dataclasses import dataclass
from typing import List, Optional, Tuple, Union

import numpy as np
import torch

from diffusers.configuration_utils import ConfigMixin, register_to_config
from diffusers.schedulers.scheduling_utils import SchedulerMixin
from diffusers.utils import BaseOutput, deprecate





@dataclass
# Copied from diffusers.schedulers.scheduling_ddpm.DDPMSchedulerOutput with DDPM->DDIM
class 		UpperCAmelCase						(						snake_case_				):
   SCREAMING_SNAKE_CASE__							=     42
   SCREAMING_SNAKE_CASE__							=     None






def 							UpperCAmelCase__		(				_SCREAMING_SNAKE_CASE   :	List[str]     ,				_SCREAMING_SNAKE_CASE   :	int=0.999     ,				_SCREAMING_SNAKE_CASE   :	List[str]="cosine"     ,				)->Optional[int]:
      if alpha_transform_type == "cosine":

            def alpha_bar_fn(_SCREAMING_SNAKE_CASE   :	List[str]				):
                  return math.cos((t + 0.008) / 1.008 * math.pi / 2				) ** 2

      elif alpha_transform_type == "exp":

            def alpha_bar_fn(_SCREAMING_SNAKE_CASE   :	List[str]				):
                  return math.exp(t * -12.0				)

      else:
            raise ValueError(f'''Unsupported alpha_tranform_type: {alpha_transform_type}'''				)

      _lowerCAmelCase 			=  []
      for i in range(_SCREAMING_SNAKE_CASE				):
            _lowerCAmelCase 			=  i / num_diffusion_timesteps
            _lowerCAmelCase 			=  (i + 1) / num_diffusion_timesteps
            betas.append(min(1 - alpha_bar_fn(_SCREAMING_SNAKE_CASE				) / alpha_bar_fn(_SCREAMING_SNAKE_CASE				)     ,				_SCREAMING_SNAKE_CASE				)				)
      return torch.tensor(_SCREAMING_SNAKE_CASE     ,				dtype=torch.floataa				)





class 		UpperCAmelCase						(						snake_case_       ,snake_case_				):
   SCREAMING_SNAKE_CASE__							=     1





   @register_to_config
   def __init__(	self       ,     _lowerCAmelCase = 1_000       ,     _lowerCAmelCase = 0.0_001       ,     _lowerCAmelCase = 0.02       ,     _lowerCAmelCase = "linear"       ,     _lowerCAmelCase = None       ,     _lowerCAmelCase = True       ,     _lowerCAmelCase = True       ,     _lowerCAmelCase = 0       ,     _lowerCAmelCase = "epsilon"       ,     _lowerCAmelCase = 1.0       ,     **_lowerCAmelCase       ,     ):
         if kwargs.get('''set_alpha_to_one'''       ,     _lowerCAmelCase			) is not None:
               _lowerCAmelCase 			=  (
                   '''The `set_alpha_to_one` argument is deprecated. Please use `set_alpha_to_zero` instead.'''
               )
               deprecate('''set_alpha_to_one'''       ,     '''1.0.0'''       ,     _lowerCAmelCase       ,     standard_warn=_lowerCAmelCase			)
               _lowerCAmelCase 			=  kwargs['''set_alpha_to_one''']
         if trained_betas is not None:
               _lowerCAmelCase 			=  torch.tensor(_lowerCAmelCase       ,     dtype=torch.floataa			)
         elif beta_schedule == "linear":
               _lowerCAmelCase 			=  torch.linspace(_lowerCAmelCase       ,     _lowerCAmelCase       ,     _lowerCAmelCase       ,     dtype=torch.floataa			)
         elif beta_schedule == "scaled_linear":
               # this schedule is very specific to the latent diffusion model.
               _lowerCAmelCase 			=  (
                   torch.linspace(beta_start**0.5       ,     beta_end**0.5       ,     _lowerCAmelCase       ,     dtype=torch.floataa			) ** 2
               )
         elif beta_schedule == "squaredcos_cap_v2":
               # Glide cosine schedule
               _lowerCAmelCase 			=  betas_for_alpha_bar(_lowerCAmelCase			)
         else:
               raise NotImplementedError(F'''{beta_schedule} does is not implemented for {self.__class__}'''			)

         _lowerCAmelCase 			=  1.0 - self.betas
         _lowerCAmelCase 			=  torch.cumprod(self.alphas       ,     dim=0			)

         # At every step in inverted ddim, we are looking into the next alphas_cumprod
         # For the final step, there is no next alphas_cumprod, and the index is out of bounds
         # `set_alpha_to_zero` decides whether we set this parameter simply to zero
         # in this case, self.step() just output the predicted noise
         # or whether we use the final alpha of the "non-previous" one.
         _lowerCAmelCase 			=  torch.tensor(0.0			) if set_alpha_to_zero else self.alphas_cumprod[-1]

         # standard deviation of the initial noise distribution
         _lowerCAmelCase 			=  1.0

         # setable values
         _lowerCAmelCase 			=  None
         _lowerCAmelCase 			=  torch.from_numpy(np.arange(0       ,     _lowerCAmelCase			).copy().astype(np.intaa			)			)





   def 	__lowerCAmelCase							(	self       ,     _lowerCAmelCase       ,     _lowerCAmelCase = None			):
         return sample





   def 	__lowerCAmelCase							(	self       ,     _lowerCAmelCase       ,     _lowerCAmelCase = None			):

         if num_inference_steps > self.config.num_train_timesteps:
               raise ValueError(
                   F'''`num_inference_steps`: {num_inference_steps} cannot be larger than `self.config.train_timesteps`:'''
                   F''' {self.config.num_train_timesteps} as the unet model trained with this scheduler can only handle'''
                   F''' maximal {self.config.num_train_timesteps} timesteps.'''			)

         _lowerCAmelCase 			=  num_inference_steps
         _lowerCAmelCase 			=  self.config.num_train_timesteps // self.num_inference_steps
         # creates integer timesteps by multiplying by ratio
         # casting to int to avoid issues when num_inference_step is power of 3
         _lowerCAmelCase 			=  (np.arange(0       ,     _lowerCAmelCase			) * step_ratio).round().copy().astype(np.intaa			)
         _lowerCAmelCase 			=  torch.from_numpy(_lowerCAmelCase			).to(_lowerCAmelCase			)
         self.timesteps += self.config.steps_offset





   def 	__lowerCAmelCase							(	self       ,     _lowerCAmelCase       ,     _lowerCAmelCase       ,     _lowerCAmelCase       ,     _lowerCAmelCase = 0.0       ,     _lowerCAmelCase = False       ,     _lowerCAmelCase = None       ,     _lowerCAmelCase = True       ,     ):
         # 1. get previous step value (=t+1)
         _lowerCAmelCase 			=  timestep + self.config.num_train_timesteps // self.num_inference_steps

         # 2. compute alphas, betas
         # change original implementation to exactly match noise levels for analogous forward process
         _lowerCAmelCase 			=  self.alphas_cumprod[timestep]
         _lowerCAmelCase 			=  (
             self.alphas_cumprod[prev_timestep]
             if prev_timestep < self.config.num_train_timesteps
             else self.final_alpha_cumprod
         )

         _lowerCAmelCase 			=  1 - alpha_prod_t

         # 3. compute predicted original sample from predicted noise also called
         # "predicted x_0" of formula (12) from https://arxiv.org/pdf/2010.02502.pdf
         if self.config.prediction_type == "epsilon":
               _lowerCAmelCase 			=  (sample - beta_prod_t ** 0.5 * model_output) / alpha_prod_t ** 0.5
               _lowerCAmelCase 			=  model_output
         elif self.config.prediction_type == "sample":
               _lowerCAmelCase 			=  model_output
               _lowerCAmelCase 			=  (sample - alpha_prod_t ** 0.5 * pred_original_sample) / beta_prod_t ** 0.5
         elif self.config.prediction_type == "v_prediction":
               _lowerCAmelCase 			=  (alpha_prod_t**0.5) * sample - (beta_prod_t**0.5) * model_output
               _lowerCAmelCase 			=  (alpha_prod_t**0.5) * model_output + (beta_prod_t**0.5) * sample
         else:
               raise ValueError(
                   F'''prediction_type given as {self.config.prediction_type} must be one of `epsilon`, `sample`, or'''
                   ''' `v_prediction`'''			)

         # 4. Clip or threshold "predicted x_0"
         if self.config.clip_sample:
               _lowerCAmelCase 			=  pred_original_sample.clamp(
                   -self.config.clip_sample_range       ,     self.config.clip_sample_range			)

         # 5. compute "direction pointing to x_t" of formula (12) from https://arxiv.org/pdf/2010.02502.pdf
         _lowerCAmelCase 			=  (1 - alpha_prod_t_prev) ** 0.5 * pred_epsilon

         # 6. compute x_t without "random noise" of formula (12) from https://arxiv.org/pdf/2010.02502.pdf
         _lowerCAmelCase 			=  alpha_prod_t_prev ** 0.5 * pred_original_sample + pred_sample_direction

         if not return_dict:
               return (prev_sample, pred_original_sample)
         return DDIMSchedulerOutput(prev_sample=_lowerCAmelCase       ,     pred_original_sample=_lowerCAmelCase			)





   def __len__(	self			):
         return self.config.num_train_timesteps