


'''simple docstring'''
# 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'''
 __lowercase					:		CommonSchedulerState

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


 @classmethod
 def 			snake_case__ (   cls   ,   __lowercase   ,   __lowercase   ,   __lowercase    ):

  """simple docstring"""







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


@dataclass
class     _lowerCamelCase       (     snake_case_					):




 '''simple docstring'''
 __lowercase					:		DDPMSchedulerState


class     _lowerCamelCase       (     snake_case_     ,		snake_case_					):




 '''simple docstring'''
 __lowercase					:		Optional[int]     				=    [e.name for e in FlaxKarrasDiffusionSchedulers]

 __lowercase					:		jnp.dtype


 @property
 def 			snake_case__ (   self    ):

  """simple docstring"""







  return True


 @register_to_config
 def __init__(   self   ,   __lowercase = 1_000   ,   __lowercase = 0.0_0_0_1   ,   __lowercase = 0.0_2   ,   __lowercase = "linear"   ,   __lowercase = None   ,   __lowercase = "fixed_small"   ,   __lowercase = True   ,   __lowercase = "epsilon"   ,   __lowercase = jnp.floataa   ,   ):

  """simple docstring"""







  __A	:		List[Any]	  =      dtype


 def 			snake_case__ (   self   ,   __lowercase = None    ):

  """simple docstring"""







  if common is None:
   __A	:		List[Any]	  =      CommonSchedulerState.create(self    )

  # standard deviation of the initial noise distribution
  __A	:		List[Any]	  =      jnp.array(1.0   ,   dtype=self.dtype    )

  __A	:		Union[str, Any]	  =      jnp.arange(0   ,   self.config.num_train_timesteps    ).round()[::-1]

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


 def 			snake_case__ (   self   ,   __lowercase   ,   __lowercase   ,   __lowercase = None    ):

  """simple docstring"""







  return sample


 def 			snake_case__ (   self   ,   __lowercase   ,   __lowercase   ,   __lowercase = ()    ):

  """simple docstring"""







  __A	:		int	  =      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
  __A	:		Tuple	  =      (jnp.arange(0   ,   __lowercase    ) * step_ratio).round()[::-1]

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


 def 			snake_case__ (   self   ,   __lowercase   ,   __lowercase   ,   __lowercase=None   ,   __lowercase=None    ):

  """simple docstring"""







  __A	:		Optional[Any]	  =      state.common.alphas_cumprod[t]
  __A	:		List[Any]	  =      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
  __A	:		List[Any]	  =      (1 - alpha_prod_t_prev) / (1 - alpha_prod_t) * state.common.betas[t]

  if variance_type is None:
   __A	:		Union[str, Any]	  =      self.config.variance_type

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

  return variance


 def 			snake_case__ (   self   ,   __lowercase   ,   __lowercase   ,   __lowercase   ,   __lowercase   ,   __lowercase = None   ,   __lowercase = True   ,   ):

  """simple docstring"""







  __A	:		Union[str, Any]	  =      timestep

  if key is None:
   __A	:		Union[str, Any]	  =      jax.random.PRNGKey(0    )

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

  # 1. compute alphas, betas
  __A	:		Union[str, Any]	  =      state.common.alphas_cumprod[t]
  __A	:		Optional[Any]	  =      jnp.where(t > 0   ,   state.common.alphas_cumprod[t - 1]   ,   jnp.array(1.0   ,   dtype=self.dtype    )    )
  __A	:		int	  =      1 - alpha_prod_t
  __A	:		Any	  =      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":
   __A	:		Optional[int]	  =      (sample - beta_prod_t ** 0.5 * model_output) / alpha_prod_t ** 0.5
  elif self.config.prediction_type == "sample":
   __A	:		List[str]	  =      model_output
  elif self.config.prediction_type == "v_prediction":
   __A	:		List[str]	  =      (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:
   __A	:		Optional[int]	  =      jnp.clip(__lowercase   ,   -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
  __A	:		Tuple	  =      (alpha_prod_t_prev ** 0.5 * state.common.betas[t]) / beta_prod_t
  __A	:		Dict	  =      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
  __A	:		Optional[int]	  =      pred_original_sample_coeff * pred_original_sample + current_sample_coeff * sample

  # 6. Add noise
  def random_variance():
   __A	:		Tuple	  =      jax.random.split(__lowercase   ,   num=1    )
   __A	:		List[Any]	  =      jax.random.normal(__lowercase   ,   shape=model_output.shape   ,   dtype=self.dtype    )
   return (self._get_variance(__lowercase   ,   __lowercase   ,   predicted_variance=__lowercase    ) ** 0.5) * noise

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

  __A	:		int	  =      pred_prev_sample + variance

  if not return_dict:
   return (pred_prev_sample, state)

  return FlaxDDPMSchedulerOutput(prev_sample=__lowercase   ,   state=__lowercase    )


 def 			snake_case__ (   self   ,   __lowercase   ,   __lowercase   ,   __lowercase   ,   __lowercase   ,   ):

  """simple docstring"""







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


 def 			snake_case__ (   self   ,   __lowercase   ,   __lowercase   ,   __lowercase   ,   __lowercase   ,   ):

  """simple docstring"""







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







 def __len__(   self    ):

  """simple docstring"""







  return self.config.num_train_timesteps










'''simple docstring'''
from dataclasses import dataclass
from typing import Optional

import torch
from torch import nn

from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput
from .attention import BasicTransformerBlock
from .modeling_utils import ModelMixin

@dataclass
class     _lowerCamelCase       (     snake_case_					):




 '''simple docstring'''
 __lowercase					:		torch.FloatTensor


class     _lowerCamelCase       (     snake_case_     ,		snake_case_					):




 '''simple docstring'''


 @register_to_config
 def __init__(   self   ,   __lowercase = 16   ,   __lowercase = 88   ,   __lowercase = None   ,   __lowercase = None   ,   __lowercase = 1   ,   __lowercase = 0.0   ,   __lowercase = 32   ,   __lowercase = None   ,   __lowercase = False   ,   __lowercase = None   ,   __lowercase = "geglu"   ,   __lowercase = True   ,   __lowercase = True   ,   ):

  """simple docstring"""







  super().__init__()
  __A	:		Optional[int]	  =      num_attention_heads
  __A	:		Dict	  =      attention_head_dim
  __A	:		Optional[int]	  =      num_attention_heads * attention_head_dim

  __A	:		Optional[Any]	  =      in_channels

  __A	:		Optional[int]	  =      torch.nn.GroupNorm(num_groups=__lowercase   ,   num_channels=__lowercase   ,   eps=1E-6   ,   affine=__lowercase    )
  __A	:		Optional[Any]	  =      nn.Linear(__lowercase   ,   __lowercase    )

  # 3. Define transformers blocks
  __A	:		List[Any]	  =      nn.ModuleList(
      [
          BasicTransformerBlock(
              __lowercase   ,   __lowercase   ,   __lowercase   ,   dropout=__lowercase   ,   cross_attention_dim=__lowercase   ,   activation_fn=__lowercase   ,   attention_bias=__lowercase   ,   double_self_attention=__lowercase   ,   norm_elementwise_affine=__lowercase   ,   )
          for d in range(__lowercase    )
      ]    )

  __A	:		Union[str, Any]	  =      nn.Linear(__lowercase   ,   __lowercase    )







 def 			snake_case__ (   self   ,   __lowercase   ,   __lowercase=None   ,   __lowercase=None   ,   __lowercase=None   ,   __lowercase=1   ,   __lowercase=None   ,   __lowercase = True   ,   ):

  """simple docstring"""







  __A  ,__A  ,__A  ,__A	:		int	  =      hidden_states.shape
  __A	:		Union[str, Any]	  =      batch_frames // num_frames

  __A	:		Union[str, Any]	  =      hidden_states

  __A	:		List[str]	  =      hidden_states[None, :].reshape(__lowercase   ,   __lowercase   ,   __lowercase   ,   __lowercase   ,   __lowercase    )
  __A	:		List[Any]	  =      hidden_states.permute(0   ,   2   ,   1   ,   3   ,   4    )

  __A	:		Dict	  =      self.norm(__lowercase    )
  __A	:		List[Any]	  =      hidden_states.permute(0   ,   3   ,   4   ,   2   ,   1    ).reshape(batch_size * height * width   ,   __lowercase   ,   __lowercase    )

  __A	:		Optional[Any]	  =      self.proj_in(__lowercase    )

  # 2. Blocks
  for block in self.transformer_blocks:
   __A	:		List[Any]	  =      block(
       __lowercase   ,   encoder_hidden_states=__lowercase   ,   timestep=__lowercase   ,   cross_attention_kwargs=__lowercase   ,   class_labels=__lowercase   ,   )

  # 3. Output
  __A	:		Optional[int]	  =      self.proj_out(__lowercase    )
  __A	:		int	  =      (
      hidden_states[None, None, :]
      .reshape(__lowercase   ,   __lowercase   ,   __lowercase   ,   __lowercase   ,   __lowercase    )
      .permute(0   ,   3   ,   4   ,   1   ,   2    )
      .contiguous()
  )
  __A	:		List[str]	  =      hidden_states.reshape(__lowercase   ,   __lowercase   ,   __lowercase   ,   __lowercase    )

  __A	:		str	  =      hidden_states + residual

  if not return_dict:
   return (output,)

  return TransformerTemporalModelOutput(sample=__lowercase    )






