

# 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      UpperCamelCase :
    __UpperCamelCase         =42

    # setable values
    __UpperCamelCase         =42
    __UpperCamelCase         =42
    __UpperCamelCase         =None

    @classmethod
    def UpperCamelCase			(			cls :      Any							,    snake_case__ :      CommonSchedulerState							,    snake_case__ :      jnp.ndarray							,    snake_case__ :      jnp.ndarray    ):

     """simple docstring"""






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




@dataclass
class      UpperCamelCase (     SCREAMING_SNAKE_CASE   ):
    __UpperCamelCase         =42




class      UpperCamelCase (     SCREAMING_SNAKE_CASE	,			SCREAMING_SNAKE_CASE   ):
    __UpperCamelCase         =[e.name for e in FlaxKarrasDiffusionSchedulers]

    __UpperCamelCase         =42

    @property
    def UpperCamelCase			(			self :      Tuple    ):

     """simple docstring"""






     return True

    @register_to_config
    def __init__(			self :      List[Any]							,    snake_case__ :      int = 1_0_0_0							,    snake_case__ :      float = 0.0_001							,    snake_case__ :      float = 0.02							,    snake_case__ :      str = "linear"							,    snake_case__ :      Optional[jnp.ndarray] = None							,    snake_case__ :      str = "fixed_small"							,    snake_case__ :      bool = True							,    snake_case__ :      str = "epsilon"							,    snake_case__ :      jnp.dtype = jnp.floataa							,    ):

     """simple docstring"""






     SCREAMING_SNAKE_CASE								=			dtype

    def UpperCamelCase			(			self :      List[Any]							,    snake_case__ :      Optional[CommonSchedulerState] = None    ):

     """simple docstring"""






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

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

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

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

    def UpperCamelCase			(			self :      int							,    snake_case__ :      DDPMSchedulerState							,    snake_case__ :      jnp.ndarray							,    snake_case__ :      Optional[int] = None    ):

     """simple docstring"""






     return sample

    def UpperCamelCase			(			self :      Dict							,    snake_case__ :      DDPMSchedulerState							,    snake_case__ :      int							,    snake_case__ :      Tuple = ()    ):

     """simple docstring"""






     SCREAMING_SNAKE_CASE								=			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
     SCREAMING_SNAKE_CASE								=			(jnp.arange(0							,    snake_case__    ) * step_ratio).round()[::-1]

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

    def UpperCamelCase			(			self :      str							,    snake_case__ :      DDPMSchedulerState							,    snake_case__ :      List[Any]							,    snake_case__ :      Dict=None							,    snake_case__ :      int=None    ):

     """simple docstring"""






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

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

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

     return variance

    def UpperCamelCase			(			self :      List[Any]							,    snake_case__ :      DDPMSchedulerState							,    snake_case__ :      jnp.ndarray							,    snake_case__ :      int							,    snake_case__ :      jnp.ndarray							,    snake_case__ :      Optional[jax.random.KeyArray] = None							,    snake_case__ :      bool = True							,    ):

     """simple docstring"""






     SCREAMING_SNAKE_CASE								=			timestep

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

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

     # 1. compute alphas, betas
     SCREAMING_SNAKE_CASE								=			state.common.alphas_cumprod[t]
     SCREAMING_SNAKE_CASE								=			jnp.where(t > 0							,    state.common.alphas_cumprod[t - 1]							,    jnp.array(1.0							,    dtype=self.dtype    )    )
     SCREAMING_SNAKE_CASE								=			1 - alpha_prod_t
     SCREAMING_SNAKE_CASE								=			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":
      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
     elif self.config.prediction_type == "v_prediction":
      SCREAMING_SNAKE_CASE								=			(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:
      SCREAMING_SNAKE_CASE								=			jnp.clip(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
     SCREAMING_SNAKE_CASE								=			(alpha_prod_t_prev ** 0.5 * state.common.betas[t]) / beta_prod_t
     SCREAMING_SNAKE_CASE								=			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
     SCREAMING_SNAKE_CASE								=			pred_original_sample_coeff * pred_original_sample + current_sample_coeff * sample

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

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

     SCREAMING_SNAKE_CASE								=			pred_prev_sample + variance

     if not return_dict:
      return (pred_prev_sample, state)

     return FlaxDDPMSchedulerOutput(prev_sample=snake_case__							,    state=snake_case__    )

    def UpperCamelCase			(			self :      List[str]							,    snake_case__ :      DDPMSchedulerState							,    snake_case__ :      jnp.ndarray							,    snake_case__ :      jnp.ndarray							,    snake_case__ :      jnp.ndarray							,    ):

     """simple docstring"""






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

    def UpperCamelCase			(			self :      List[Any]							,    snake_case__ :      DDPMSchedulerState							,    snake_case__ :      jnp.ndarray							,    snake_case__ :      jnp.ndarray							,    snake_case__ :      jnp.ndarray							,    ):

     """simple docstring"""






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




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

     """simple docstring"""






     return self.config.num_train_timesteps




from ....configuration_utils import PretrainedConfig
from ....utils import logging


a_    :						Any    		=      logging.get_logger(__name__)

a_    :						Dict    		=      {
    "Visual-Attention-Network/van-base": (
        "https://huggingface.co/Visual-Attention-Network/van-base/blob/main/config.json"
    ),
}




class      UpperCamelCase (     SCREAMING_SNAKE_CASE   ):
    __UpperCamelCase         ="van"

    def __init__(			self :      Optional[Any]							,    snake_case__ :      Tuple=2_2_4							,    snake_case__ :      Dict=3							,    snake_case__ :      Union[str, Any]=[7, 3, 3, 3]							,    snake_case__ :      str=[4, 2, 2, 2]							,    snake_case__ :      Optional[Any]=[6_4, 1_2_8, 3_2_0, 5_1_2]							,    snake_case__ :      Optional[Any]=[3, 3, 1_2, 3]							,    snake_case__ :      Tuple=[8, 8, 4, 4]							,    snake_case__ :      Any="gelu"							,    snake_case__ :      Dict=0.02							,    snake_case__ :      List[str]=1E-6							,    snake_case__ :      int=1E-2							,    snake_case__ :      Any=0.0							,    snake_case__ :      Tuple=0.0							,    **snake_case__ :      Any							,    ):

     """simple docstring"""






     super().__init__(**snake_case__    )
     SCREAMING_SNAKE_CASE								=			image_size
     SCREAMING_SNAKE_CASE								=			num_channels
     SCREAMING_SNAKE_CASE								=			patch_sizes
     SCREAMING_SNAKE_CASE								=			strides
     SCREAMING_SNAKE_CASE								=			hidden_sizes
     SCREAMING_SNAKE_CASE								=			depths
     SCREAMING_SNAKE_CASE								=			mlp_ratios
     SCREAMING_SNAKE_CASE								=			hidden_act
     SCREAMING_SNAKE_CASE								=			initializer_range
     SCREAMING_SNAKE_CASE								=			layer_norm_eps
     SCREAMING_SNAKE_CASE								=			layer_scale_init_value
     SCREAMING_SNAKE_CASE								=			drop_path_rate
     SCREAMING_SNAKE_CASE								=			dropout_rate

