





'''simple docstring'''

import math
from dataclasses import dataclass
from typing import Optional, Tuple, Union

import numpy as np
import torch

from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput, randn_tensor
from .scheduling_utils import SchedulerMixin







@dataclass
# Copied from diffusers.schedulers.scheduling_ddpm.DDPMSchedulerOutput with DDPM->UnCLIP
class __a (   _snake_case			):
   __UpperCamelCase   : torch.FloatTensor
   __UpperCamelCase   : Optional[torch.FloatTensor]	      =	None





def 						__magic_name__       (							__UpperCAmelCase							,	__UpperCAmelCase=0.9_9_9							,	__UpperCAmelCase="cosine"							,	)						->					str:

   '''simple docstring'''







   if alpha_transform_type == "cosine":

      def alpha_bar_fn(__UpperCAmelCase      ):
         return math.cos((t + 0.0_0_8) / 1.0_0_8 * math.pi / 2      ) ** 2

   elif alpha_transform_type == "exp":

      def alpha_bar_fn(__UpperCAmelCase      ):
         return math.exp(t * -1_2.0      )

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

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







class __a (   _snake_case,   _snake_case			):





   @register_to_config
   def __init__(					self				:			Optional[int]				,lowerCamelCase				:			int = 1000				,lowerCamelCase				:			str = "fixed_small_log"				,lowerCamelCase				:			bool = True				,lowerCamelCase				:			Optional[float] = 1.0				,lowerCamelCase				:			str = "epsilon"				,lowerCamelCase				:			str = "squaredcos_cap_v2"				,):

      '''simple docstring'''

      if beta_schedule != "squaredcos_cap_v2":
         raise ValueError("""UnCLIPScheduler only supports `beta_schedule`: 'squaredcos_cap_v2'"""    )

      __SCREAMING_SNAKE_CASE      							=       betas_for_alpha_bar(lowerCamelCase    )

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

      # standard deviation of the initial noise distribution
      __SCREAMING_SNAKE_CASE      							=       1.0

      # setable values
      __SCREAMING_SNAKE_CASE      							=       None
      __SCREAMING_SNAKE_CASE      							=       torch.from_numpy(np.arange(0				,lowerCamelCase    )[::-1].copy()    )

      __SCREAMING_SNAKE_CASE      							=       variance_type





   def      UpperCAmelCase__     (					self				:			List[str]				,lowerCamelCase				:			torch.FloatTensor				,lowerCamelCase				:			Optional[int] = None    ):

      '''simple docstring'''

      return sample





   def      UpperCAmelCase__     (					self				:			List[Any]				,lowerCamelCase				:			int				,lowerCamelCase				:			Union[str, torch.device] = None    ):

      '''simple docstring'''

      __SCREAMING_SNAKE_CASE      							=       num_inference_steps
      __SCREAMING_SNAKE_CASE      							=       (self.config.num_train_timesteps - 1) / (self.num_inference_steps - 1)
      __SCREAMING_SNAKE_CASE      							=       (np.arange(0				,lowerCamelCase    ) * step_ratio).round()[::-1].copy().astype(np.intaa    )
      __SCREAMING_SNAKE_CASE      							=       torch.from_numpy(lowerCamelCase    ).to(lowerCamelCase    )





   def      UpperCAmelCase__     (					self				:			Optional[int]				,lowerCamelCase				:			Any				,lowerCamelCase				:			List[str]=None				,lowerCamelCase				:			Optional[int]=None				,lowerCamelCase				:			Any=None    ):

      '''simple docstring'''

      if prev_timestep is None:
         __SCREAMING_SNAKE_CASE      							=       t - 1

      __SCREAMING_SNAKE_CASE      							=       self.alphas_cumprod[t]
      __SCREAMING_SNAKE_CASE      							=       self.alphas_cumprod[prev_timestep] if prev_timestep >= 0 else self.one
      __SCREAMING_SNAKE_CASE      							=       1 - alpha_prod_t
      __SCREAMING_SNAKE_CASE      							=       1 - alpha_prod_t_prev

      if prev_timestep == t - 1:
         __SCREAMING_SNAKE_CASE      							=       self.betas[t]
      else:
         __SCREAMING_SNAKE_CASE      							=       1 - alpha_prod_t / alpha_prod_t_prev

      # For t > 0, compute predicted variance βt (see formula (6) and (7) from https://arxiv.org/pdf/2006.11239.pdf)
      # and sample from it to get previous sample
      # x_{t-1} ~ N(pred_prev_sample, variance) == add variance to pred_sample
      __SCREAMING_SNAKE_CASE      							=       beta_prod_t_prev / beta_prod_t * beta

      if variance_type is None:
         __SCREAMING_SNAKE_CASE      							=       self.config.variance_type

      # hacks - were probably added for training stability
      if variance_type == "fixed_small_log":
         __SCREAMING_SNAKE_CASE      							=       torch.log(torch.clamp(lowerCamelCase				,min=1E-2_0    )    )
         __SCREAMING_SNAKE_CASE      							=       torch.exp(0.5 * variance    )
      elif variance_type == "learned_range":
         # NOTE difference with DDPM scheduler
         __SCREAMING_SNAKE_CASE      							=       variance.log()
         __SCREAMING_SNAKE_CASE      							=       beta.log()

         __SCREAMING_SNAKE_CASE      							=       (predicted_variance + 1) / 2
         __SCREAMING_SNAKE_CASE      							=       frac * max_log + (1 - frac) * min_log

      return variance





   def      UpperCAmelCase__     (					self				:			List[Any]				,lowerCamelCase				:			torch.FloatTensor				,lowerCamelCase				:			int				,lowerCamelCase				:			torch.FloatTensor				,lowerCamelCase				:			Optional[int] = None				,lowerCamelCase				:			List[str]=None				,lowerCamelCase				:			bool = True				,):

      '''simple docstring'''

      __SCREAMING_SNAKE_CASE      							=       timestep

      if model_output.shape[1] == sample.shape[1] * 2 and self.variance_type == "learned_range":
         __SCREAMING_SNAKE_CASE							,     __SCREAMING_SNAKE_CASE      							=       torch.split(lowerCamelCase				,sample.shape[1]				,dim=1    )
      else:
         __SCREAMING_SNAKE_CASE      							=       None

      # 1. compute alphas, betas
      if prev_timestep is None:
         __SCREAMING_SNAKE_CASE      							=       t - 1

      __SCREAMING_SNAKE_CASE      							=       self.alphas_cumprod[t]
      __SCREAMING_SNAKE_CASE      							=       self.alphas_cumprod[prev_timestep] if prev_timestep >= 0 else self.one
      __SCREAMING_SNAKE_CASE      							=       1 - alpha_prod_t
      __SCREAMING_SNAKE_CASE      							=       1 - alpha_prod_t_prev

      if prev_timestep == t - 1:
         __SCREAMING_SNAKE_CASE      							=       self.betas[t]
         __SCREAMING_SNAKE_CASE      							=       self.alphas[t]
      else:
         __SCREAMING_SNAKE_CASE      							=       1 - alpha_prod_t / alpha_prod_t_prev
         __SCREAMING_SNAKE_CASE      							=       1 - beta

      # 2. compute predicted original sample from predicted noise also called
      # "predicted x_0" of formula (15) from https://arxiv.org/pdf/2006.11239.pdf
      if self.config.prediction_type == "epsilon":
         __SCREAMING_SNAKE_CASE      							=       (sample - beta_prod_t ** 0.5 * model_output) / alpha_prod_t ** 0.5
      elif self.config.prediction_type == "sample":
         __SCREAMING_SNAKE_CASE      							=       model_output
      else:
         raise ValueError(
             f"""prediction_type given as {self.config.prediction_type} must be one of `epsilon` or `sample`"""
             """ for the UnCLIPScheduler."""    )

      # 3. Clip "predicted x_0"
      if self.config.clip_sample:
         __SCREAMING_SNAKE_CASE      							=       torch.clamp(
             lowerCamelCase				,-self.config.clip_sample_range				,self.config.clip_sample_range    )

      # 4. Compute coefficients for pred_original_sample x_0 and current sample x_t
      # See formula (7) from https://arxiv.org/pdf/2006.11239.pdf
      __SCREAMING_SNAKE_CASE      							=       (alpha_prod_t_prev ** 0.5 * beta) / beta_prod_t
      __SCREAMING_SNAKE_CASE      							=       alpha ** 0.5 * beta_prod_t_prev / beta_prod_t

      # 5. Compute predicted previous sample µ_t
      # See formula (7) from https://arxiv.org/pdf/2006.11239.pdf
      __SCREAMING_SNAKE_CASE      							=       pred_original_sample_coeff * pred_original_sample + current_sample_coeff * sample

      # 6. Add noise
      __SCREAMING_SNAKE_CASE      							=       0
      if t > 0:
         __SCREAMING_SNAKE_CASE      							=       randn_tensor(
             model_output.shape				,dtype=model_output.dtype				,generator=lowerCamelCase				,device=model_output.device    )

         __SCREAMING_SNAKE_CASE      							=       self._get_variance(
             lowerCamelCase				,predicted_variance=lowerCamelCase				,prev_timestep=lowerCamelCase				,)

         if self.variance_type == "fixed_small_log":
            __SCREAMING_SNAKE_CASE      							=       variance
         elif self.variance_type == "learned_range":
            __SCREAMING_SNAKE_CASE      							=       (0.5 * variance).exp()
         else:
            raise ValueError(
                f"""variance_type given as {self.variance_type} must be one of `fixed_small_log` or `learned_range`"""
                """ for the UnCLIPScheduler."""    )

         __SCREAMING_SNAKE_CASE      							=       variance * variance_noise

      __SCREAMING_SNAKE_CASE      							=       pred_prev_sample + variance

      if not return_dict:
         return (pred_prev_sample,)

      return UnCLIPSchedulerOutput(prev_sample=lowerCamelCase				,pred_original_sample=lowerCamelCase    )





   def      UpperCAmelCase__     (					self				:			str				,lowerCamelCase				:			torch.FloatTensor				,lowerCamelCase				:			torch.FloatTensor				,lowerCamelCase				:			torch.IntTensor				,):

      '''simple docstring'''

      __SCREAMING_SNAKE_CASE      							=       self.alphas_cumprod.to(device=original_samples.device				,dtype=original_samples.dtype    )
      __SCREAMING_SNAKE_CASE      							=       timesteps.to(original_samples.device    )

      __SCREAMING_SNAKE_CASE      							=       alphas_cumprod[timesteps] ** 0.5
      __SCREAMING_SNAKE_CASE      							=       sqrt_alpha_prod.flatten()
      while len(sqrt_alpha_prod.shape    ) < len(original_samples.shape    ):
         __SCREAMING_SNAKE_CASE      							=       sqrt_alpha_prod.unsqueeze(-1    )

      __SCREAMING_SNAKE_CASE      							=       (1 - alphas_cumprod[timesteps]) ** 0.5
      __SCREAMING_SNAKE_CASE      							=       sqrt_one_minus_alpha_prod.flatten()
      while len(sqrt_one_minus_alpha_prod.shape    ) < len(original_samples.shape    ):
         __SCREAMING_SNAKE_CASE      							=       sqrt_one_minus_alpha_prod.unsqueeze(-1    )

      __SCREAMING_SNAKE_CASE      							=       sqrt_alpha_prod * original_samples + sqrt_one_minus_alpha_prod * noise
      return noisy_samples












'''simple docstring'''

import flax.linen as nn
import jax
import jax.numpy as jnp







class __a (   nn.Module			):
   __UpperCamelCase   : int
   __UpperCamelCase   : jnp.dtype	      =	jnp.floataa





   def      UpperCAmelCase__     (					self				:			List[Any]    ):

      '''simple docstring'''

      __SCREAMING_SNAKE_CASE      							=       nn.Conv(
          self.out_channels				,kernel_size=(3, 3)				,strides=(1, 1)				,padding=((1, 1), (1, 1))				,dtype=self.dtype				,)





   def __call__(					self				:			List[Any]				,lowerCamelCase				:			Tuple    ):

      '''simple docstring'''

      __SCREAMING_SNAKE_CASE							,     __SCREAMING_SNAKE_CASE							,     __SCREAMING_SNAKE_CASE							,     __SCREAMING_SNAKE_CASE      							=       hidden_states.shape
      __SCREAMING_SNAKE_CASE      							=       jax.image.resize(
          lowerCamelCase				,shape=(batch, height * 2, width * 2, channels)				,method="""nearest"""				,)
      __SCREAMING_SNAKE_CASE      							=       self.conv(lowerCamelCase    )
      return hidden_states







class __a (   nn.Module			):
   __UpperCamelCase   : int
   __UpperCamelCase   : jnp.dtype	      =	jnp.floataa





   def      UpperCAmelCase__     (					self				:			Optional[int]    ):

      '''simple docstring'''

      __SCREAMING_SNAKE_CASE      							=       nn.Conv(
          self.out_channels				,kernel_size=(3, 3)				,strides=(2, 2)				,padding=((1, 1), (1, 1))				,dtype=self.dtype				,)





   def __call__(					self				:			List[str]				,lowerCamelCase				:			Tuple    ):

      '''simple docstring'''

      __SCREAMING_SNAKE_CASE      							=       self.conv(lowerCamelCase    )
      return hidden_states







class __a (   nn.Module			):
   __UpperCamelCase   : int
   __UpperCamelCase   : int	      =	None
   __UpperCamelCase   : float	      =	0.0
   __UpperCamelCase   : bool	      =	None
   __UpperCamelCase   : jnp.dtype	      =	jnp.floataa





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

      '''simple docstring'''

      __SCREAMING_SNAKE_CASE      							=       self.in_channels if self.out_channels is None else self.out_channels

      __SCREAMING_SNAKE_CASE      							=       nn.GroupNorm(num_groups=32				,epsilon=1E-5    )
      __SCREAMING_SNAKE_CASE      							=       nn.Conv(
          lowerCamelCase				,kernel_size=(3, 3)				,strides=(1, 1)				,padding=((1, 1), (1, 1))				,dtype=self.dtype				,)

      __SCREAMING_SNAKE_CASE      							=       nn.Dense(lowerCamelCase				,dtype=self.dtype    )

      __SCREAMING_SNAKE_CASE      							=       nn.GroupNorm(num_groups=32				,epsilon=1E-5    )
      __SCREAMING_SNAKE_CASE      							=       nn.Dropout(self.dropout_prob    )
      __SCREAMING_SNAKE_CASE      							=       nn.Conv(
          lowerCamelCase				,kernel_size=(3, 3)				,strides=(1, 1)				,padding=((1, 1), (1, 1))				,dtype=self.dtype				,)

      __SCREAMING_SNAKE_CASE      							=       self.in_channels != out_channels if self.use_nin_shortcut is None else self.use_nin_shortcut

      __SCREAMING_SNAKE_CASE      							=       None
      if use_nin_shortcut:
         __SCREAMING_SNAKE_CASE      							=       nn.Conv(
             lowerCamelCase				,kernel_size=(1, 1)				,strides=(1, 1)				,padding="""VALID"""				,dtype=self.dtype				,)





   def __call__(					self				:			List[str]				,lowerCamelCase				:			Optional[int]				,lowerCamelCase				:			Tuple				,lowerCamelCase				:			Union[str, Any]=True    ):

      '''simple docstring'''

      __SCREAMING_SNAKE_CASE      							=       hidden_states
      __SCREAMING_SNAKE_CASE      							=       self.norma(lowerCamelCase    )
      __SCREAMING_SNAKE_CASE      							=       nn.swish(lowerCamelCase    )
      __SCREAMING_SNAKE_CASE      							=       self.conva(lowerCamelCase    )

      __SCREAMING_SNAKE_CASE      							=       self.time_emb_proj(nn.swish(lowerCamelCase    )    )
      __SCREAMING_SNAKE_CASE      							=       jnp.expand_dims(jnp.expand_dims(lowerCamelCase				,1    )				,1    )
      __SCREAMING_SNAKE_CASE      							=       hidden_states + temb

      __SCREAMING_SNAKE_CASE      							=       self.norma(lowerCamelCase    )
      __SCREAMING_SNAKE_CASE      							=       nn.swish(lowerCamelCase    )
      __SCREAMING_SNAKE_CASE      							=       self.dropout(lowerCamelCase				,lowerCamelCase    )
      __SCREAMING_SNAKE_CASE      							=       self.conva(lowerCamelCase    )

      if self.conv_shortcut is not None:
         __SCREAMING_SNAKE_CASE      							=       self.conv_shortcut(lowerCamelCase    )

      return hidden_states + residual





