
# DISCLAIMER: This file is strongly influenced by https://github.com/ermongroup/ddim

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

import flax
import jax
import jax.numpy as jnp

from ..configuration_utils import ConfigMixin, register_to_config
from .scheduling_utils_flax import (
    CommonSchedulerState,
    FlaxKarrasDiffusionSchedulers,
    FlaxSchedulerMixin,
    FlaxSchedulerOutput,
    add_noise_common,
    get_velocity_common,
)
@flax.struct.dataclass
class       _lowerCAmelCase  :

 """simple docstring"""
 SCREAMING_SNAKE_CASE_				:					CommonSchedulerState

 # setable values
 SCREAMING_SNAKE_CASE_				:					jnp.ndarray
 SCREAMING_SNAKE_CASE_				:					jnp.ndarray
 SCREAMING_SNAKE_CASE_				:					Optional[int]       						=None

 @classmethod
 def      __lowerCAmelCase  (						cls			:			Optional[int]					, SCREAMING_SNAKE_CASE__			:			CommonSchedulerState					, SCREAMING_SNAKE_CASE__			:			jnp.ndarray					, SCREAMING_SNAKE_CASE__			:			jnp.ndarray   ):






      """simple docstring"""



      return cls(common=SCREAMING_SNAKE_CASE__					, init_noise_sigma=SCREAMING_SNAKE_CASE__					, timesteps=SCREAMING_SNAKE_CASE__   )




@dataclass
class       _lowerCAmelCase  (		__magic_name__							):

 """simple docstring"""
 SCREAMING_SNAKE_CASE_				:					DDPMSchedulerState




class       _lowerCAmelCase  (		__magic_name__			,	__magic_name__							):

 """simple docstring"""
 SCREAMING_SNAKE_CASE_				:					Tuple       						=[e.name for e in FlaxKarrasDiffusionSchedulers]

 SCREAMING_SNAKE_CASE_				:					jnp.dtype

 @property
 def      __lowerCAmelCase  (						self			:			str   ):






      """simple docstring"""



      return True

 @register_to_config
 def __init__(						self			:			Dict					, SCREAMING_SNAKE_CASE__			:			int = 10_00					, SCREAMING_SNAKE_CASE__			:			float = 0.0001					, SCREAMING_SNAKE_CASE__			:			float = 0.02					, SCREAMING_SNAKE_CASE__			:			str = "linear"					, SCREAMING_SNAKE_CASE__			:			Optional[jnp.ndarray] = None					, SCREAMING_SNAKE_CASE__			:			str = "fixed_small"					, SCREAMING_SNAKE_CASE__			:			bool = True					, SCREAMING_SNAKE_CASE__			:			str = "epsilon"					, SCREAMING_SNAKE_CASE__			:			jnp.dtype = jnp.floataa					, ):






      """simple docstring"""



      UpperCamelCase						    =					dtype

 def      __lowerCAmelCase  (						self			:			List[str]					, SCREAMING_SNAKE_CASE__			:			Optional[CommonSchedulerState] = None   ):






      """simple docstring"""



      if common is None:
           UpperCamelCase						    =					CommonSchedulerState.create(self   )

      # standard deviation of the initial noise distribution
      UpperCamelCase						    =					jnp.array(1.0					, dtype=self.dtype   )

      UpperCamelCase						    =					jnp.arange(0					, self.config.num_train_timesteps   ).round()[::-1]

      return DDPMSchedulerState.create(
          common=SCREAMING_SNAKE_CASE__					, init_noise_sigma=SCREAMING_SNAKE_CASE__					, timesteps=SCREAMING_SNAKE_CASE__					, )

 def      __lowerCAmelCase  (						self			:			Union[str, Any]					, SCREAMING_SNAKE_CASE__			:			DDPMSchedulerState					, SCREAMING_SNAKE_CASE__			:			jnp.ndarray					, SCREAMING_SNAKE_CASE__			:			Optional[int] = None   ):






      """simple docstring"""



      return sample

 def      __lowerCAmelCase  (						self			:			int					, SCREAMING_SNAKE_CASE__			:			DDPMSchedulerState					, SCREAMING_SNAKE_CASE__			:			int					, SCREAMING_SNAKE_CASE__			:			Tuple = ()   ):






      """simple docstring"""



      UpperCamelCase						    =					self.config.num_train_timesteps // num_inference_steps
      # creates integer timesteps by multiplying by ratio
      # rounding to avoid issues when num_inference_step is power of 3
      UpperCamelCase						    =					(jnp.arange(0					, SCREAMING_SNAKE_CASE__   ) * step_ratio).round()[::-1]

      return state.replace(
          num_inference_steps=SCREAMING_SNAKE_CASE__					, timesteps=SCREAMING_SNAKE_CASE__					, )

 def      __lowerCAmelCase  (						self			:			List[str]					, SCREAMING_SNAKE_CASE__			:			DDPMSchedulerState					, SCREAMING_SNAKE_CASE__			:			Tuple					, SCREAMING_SNAKE_CASE__			:			Optional[Any]=None					, SCREAMING_SNAKE_CASE__			:			str=None   ):






      """simple docstring"""



      UpperCamelCase						    =					state.common.alphas_cumprod[t]
      UpperCamelCase						    =					jnp.where(t > 0					, state.common.alphas_cumprod[t - 1]					, jnp.array(1.0					, dtype=self.dtype   )   )

      # 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
      UpperCamelCase						    =					(1 - alpha_prod_t_prev) / (1 - alpha_prod_t) * state.common.betas[t]

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

      # hacks - were probably added for training stability
      if variance_type == "fixed_small":
           UpperCamelCase						    =					jnp.clip(SCREAMING_SNAKE_CASE__					, a_min=1e-20   )
      # for rl-diffuser https://arxiv.org/abs/2205.09991
      elif variance_type == "fixed_small_log":
           UpperCamelCase						    =					jnp.log(jnp.clip(SCREAMING_SNAKE_CASE__					, a_min=1e-20   )   )
      elif variance_type == "fixed_large":
           UpperCamelCase						    =					state.common.betas[t]
      elif variance_type == "fixed_large_log":
           # Glide max_log
           UpperCamelCase						    =					jnp.log(state.common.betas[t]   )
      elif variance_type == "learned":
           return predicted_variance
      elif variance_type == "learned_range":
           UpperCamelCase						    =					variance
           UpperCamelCase						    =					state.common.betas[t]
           UpperCamelCase						    =					(predicted_variance + 1) / 2
           UpperCamelCase						    =					frac * max_log + (1 - frac) * min_log

      return variance

 def      __lowerCAmelCase  (						self			:			List[str]					, SCREAMING_SNAKE_CASE__			:			DDPMSchedulerState					, SCREAMING_SNAKE_CASE__			:			jnp.ndarray					, SCREAMING_SNAKE_CASE__			:			int					, SCREAMING_SNAKE_CASE__			:			jnp.ndarray					, SCREAMING_SNAKE_CASE__			:			Optional[jax.random.KeyArray] = None					, SCREAMING_SNAKE_CASE__			:			bool = True					, ):






      """simple docstring"""



      UpperCamelCase						    =					timestep

      if key is None:
           UpperCamelCase						    =					jax.random.PRNGKey(0   )

      if model_output.shape[1] == sample.shape[1] * 2 and self.config.variance_type in ["learned", "learned_range"]:
           UpperCamelCase    ,		UpperCamelCase						    =					jnp.split(SCREAMING_SNAKE_CASE__					, sample.shape[1]					, axis=1   )
      else:
           UpperCamelCase						    =					None

      # 1. compute alphas, betas
      UpperCamelCase						    =					state.common.alphas_cumprod[t]
      UpperCamelCase						    =					jnp.where(t > 0					, state.common.alphas_cumprod[t - 1]					, jnp.array(1.0					, dtype=self.dtype   )   )
      UpperCamelCase						    =					1 - alpha_prod_t
      UpperCamelCase						    =					1 - alpha_prod_t_prev

      # 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":
           UpperCamelCase						    =					(sample - beta_prod_t ** 0.5 * model_output) / alpha_prod_t ** 0.5
      elif self.config.prediction_type == "sample":
           UpperCamelCase						    =					model_output
      elif self.config.prediction_type == "v_prediction":
           UpperCamelCase						    =					(alpha_prod_t**0.5) * sample - (beta_prod_t**0.5) * model_output
      else:
           raise ValueError(
               F'prediction_type given as {self.config.prediction_type} must be one of `epsilon`, `sample` '
               ' for the FlaxDDPMScheduler.'   )

      # 3. Clip "predicted x_0"
      if self.config.clip_sample:
           UpperCamelCase						    =					jnp.clip(SCREAMING_SNAKE_CASE__					, -1					, 1   )

      # 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
      UpperCamelCase						    =					(alpha_prod_t_prev ** 0.5 * state.common.betas[t]) / beta_prod_t
      UpperCamelCase						    =					state.common.alphas[t] ** 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
      UpperCamelCase						    =					pred_original_sample_coeff * pred_original_sample + current_sample_coeff * sample

      # 6. Add noise
      def random_variance():
           UpperCamelCase						    =					jax.random.split(SCREAMING_SNAKE_CASE__					, num=1   )
           UpperCamelCase						    =					jax.random.normal(SCREAMING_SNAKE_CASE__					, shape=model_output.shape					, dtype=self.dtype   )
           return (self._get_variance(SCREAMING_SNAKE_CASE__					, SCREAMING_SNAKE_CASE__					, predicted_variance=SCREAMING_SNAKE_CASE__   ) ** 0.5) * noise

      UpperCamelCase						    =					jnp.where(t > 0					, random_variance()					, jnp.zeros(model_output.shape					, dtype=self.dtype   )   )

      UpperCamelCase						    =					pred_prev_sample + variance

      if not return_dict:
           return (pred_prev_sample, state)

      return FlaxDDPMSchedulerOutput(prev_sample=SCREAMING_SNAKE_CASE__					, state=SCREAMING_SNAKE_CASE__   )

 def      __lowerCAmelCase  (						self			:			Optional[Any]					, SCREAMING_SNAKE_CASE__			:			DDPMSchedulerState					, SCREAMING_SNAKE_CASE__			:			jnp.ndarray					, SCREAMING_SNAKE_CASE__			:			jnp.ndarray					, SCREAMING_SNAKE_CASE__			:			jnp.ndarray					, ):






      """simple docstring"""



      return add_noise_common(state.common					, SCREAMING_SNAKE_CASE__					, SCREAMING_SNAKE_CASE__					, SCREAMING_SNAKE_CASE__   )

 def      __lowerCAmelCase  (						self			:			Optional[Any]					, SCREAMING_SNAKE_CASE__			:			DDPMSchedulerState					, SCREAMING_SNAKE_CASE__			:			jnp.ndarray					, SCREAMING_SNAKE_CASE__			:			jnp.ndarray					, SCREAMING_SNAKE_CASE__			:			jnp.ndarray					, ):






      """simple docstring"""



      return get_velocity_common(state.common					, SCREAMING_SNAKE_CASE__					, SCREAMING_SNAKE_CASE__					, SCREAMING_SNAKE_CASE__   )






 def __len__(						self			:			List[str]   ):






      """simple docstring"""



      return self.config.num_train_timesteps




from ...utils import (
    OptionalDependencyNotAvailable,
    is_flax_available,
    is_torch_available,
    is_transformers_available,
)


try:
 if not (is_transformers_available() and is_torch_available()):
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 from ...utils.dummy_torch_and_transformers_objects import *  # noqa F403
else:
 from .multicontrolnet import MultiControlNetModel
 from .pipeline_controlnet import StableDiffusionControlNetPipeline
 from .pipeline_controlnet_imgaimg import StableDiffusionControlNetImgaImgPipeline
 from .pipeline_controlnet_inpaint import StableDiffusionControlNetInpaintPipeline


if is_transformers_available() and is_flax_available():
 from .pipeline_flax_controlnet import FlaxStableDiffusionControlNetPipeline


