






from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ...utils.backbone_utils import BackboneConfigMixin, get_aligned_output_features_output_indices


lowerCamelCase     :							int    =logging.get_logger(__name__)

lowerCamelCase     :							List[Any]    ={
    'microsoft/focalnet-tiny': 'https://huggingface.co/microsoft/focalnet-tiny/resolve/main/config.json',
}





class       __a  (   lowerCamelCase__	,       lowerCamelCase__	):
	_lowerCAmelCase	:	Union[str, Any]				=			'''focalnet'''







	def __init__(		self :       Tuple							,       SCREAMING_SNAKE_CASE :       str=2_24							,       SCREAMING_SNAKE_CASE :       Dict=4							,       SCREAMING_SNAKE_CASE :       str=3							,       SCREAMING_SNAKE_CASE :       Union[str, Any]=96							,       SCREAMING_SNAKE_CASE :       List[str]=False							,       SCREAMING_SNAKE_CASE :       int=[1_92, 3_84, 7_68, 7_68]							,       SCREAMING_SNAKE_CASE :       List[str]=[2, 2, 6, 2]							,       SCREAMING_SNAKE_CASE :       int=[2, 2, 2, 2]							,       SCREAMING_SNAKE_CASE :       Tuple=[3, 3, 3, 3]							,       SCREAMING_SNAKE_CASE :       List[str]="gelu"							,       SCREAMING_SNAKE_CASE :       List[Any]=4.0							,       SCREAMING_SNAKE_CASE :       Optional[Any]=0.0							,       SCREAMING_SNAKE_CASE :       Optional[int]=0.1							,       SCREAMING_SNAKE_CASE :       List[Any]=False							,       SCREAMING_SNAKE_CASE :       Any=1e-4							,       SCREAMING_SNAKE_CASE :       Optional[Any]=False							,       SCREAMING_SNAKE_CASE :       int=False							,       SCREAMING_SNAKE_CASE :       Dict=False							,       SCREAMING_SNAKE_CASE :       str=0.0_2							,       SCREAMING_SNAKE_CASE :       int=1e-5							,       SCREAMING_SNAKE_CASE :       Optional[Any]=32							,       SCREAMING_SNAKE_CASE :       Dict=None							,       SCREAMING_SNAKE_CASE :       Dict=None							,       **SCREAMING_SNAKE_CASE :       List[str]							,       ):


						'''simple docstring'''






						super().__init__(**__snake_case						)

						UpperCamelCase__      :       Optional[int]	    =  image_size
						UpperCamelCase__      :       Union[str, Any]	    =  patch_size
						UpperCamelCase__      :       int	    =  num_channels
						UpperCamelCase__      :       List[str]	    =  embed_dim
						UpperCamelCase__      :       Union[str, Any]	    =  use_conv_embed
						UpperCamelCase__      :       Any	    =  hidden_sizes
						UpperCamelCase__      :       List[Any]	    =  depths
						UpperCamelCase__      :       Any	    =  focal_levels
						UpperCamelCase__      :       Any	    =  focal_windows
						UpperCamelCase__      :       Optional[int]	    =  hidden_act
						UpperCamelCase__      :       List[Any]	    =  mlp_ratio
						UpperCamelCase__      :       Optional[Any]	    =  hidden_dropout_prob
						UpperCamelCase__      :       Dict	    =  drop_path_rate
						UpperCamelCase__      :       Optional[int]	    =  use_layerscale
						UpperCamelCase__      :       Tuple	    =  layerscale_value
						UpperCamelCase__      :       Optional[Any]	    =  use_post_layernorm
						UpperCamelCase__      :       List[str]	    =  use_post_layernorm_in_modulation
						UpperCamelCase__      :       Optional[int]	    =  normalize_modulator
						UpperCamelCase__      :       Union[str, Any]	    =  initializer_range
						UpperCamelCase__      :       Optional[Any]	    =  layer_norm_eps
						UpperCamelCase__      :       Optional[Any]	    =  encoder_stride
						UpperCamelCase__      :       str	    =  ['stem'] + [F'stage{idx}' for idx in range(1							,       len(self.depths						) + 1						)]
						UpperCamelCase__      :       Optional[int]	    =  get_aligned_output_features_output_indices(
						    out_features=__snake_case							,       out_indices=__snake_case							,       stage_names=self.stage_names						)







# 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       __a  :
 _lowerCAmelCase	:	CommonSchedulerState

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







 @classmethod
 def  __lowercase (		cls :       Optional[Any]							,       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       __a  (   A__	):
 _lowerCAmelCase	:	DDPMSchedulerState







class       __a  (   A__	,       A__	):
 _lowerCAmelCase	:	Tuple				=			[e.name for e in FlaxKarrasDiffusionSchedulers]

 _lowerCAmelCase	:	jnp.dtype







 @property
 def  __lowercase (		self :       List[str]						):


      '''simple docstring'''






      return True







 @register_to_config
 def __init__(		self :       Dict							,       SCREAMING_SNAKE_CASE :       int = 10_00							,       SCREAMING_SNAKE_CASE :       float = 0.0_0_0_1							,       SCREAMING_SNAKE_CASE :       float = 0.0_2							,       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__      :       int	    =  dtype







 def  __lowercase (		self :       List[Any]							,       SCREAMING_SNAKE_CASE :       Optional[CommonSchedulerState] = None						):


      '''simple docstring'''






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

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

      UpperCamelCase__      :       Any	    =  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  __lowercase (		self :       Tuple							,       SCREAMING_SNAKE_CASE :       DDPMSchedulerState							,       SCREAMING_SNAKE_CASE :       jnp.ndarray							,       SCREAMING_SNAKE_CASE :       Optional[int] = None						):


      '''simple docstring'''






      return sample







 def  __lowercase (		self :       Tuple							,       SCREAMING_SNAKE_CASE :       DDPMSchedulerState							,       SCREAMING_SNAKE_CASE :       int							,       SCREAMING_SNAKE_CASE :       Tuple = ()						):


      '''simple docstring'''






      UpperCamelCase__      :       Optional[Any]	    =  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__      :       Optional[int]	    =  (jnp.arange(0							,       SCREAMING_SNAKE_CASE						) * step_ratio).round()[::-1]

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







 def  __lowercase (		self :       str							,       SCREAMING_SNAKE_CASE :       DDPMSchedulerState							,       SCREAMING_SNAKE_CASE :       List[str]							,       SCREAMING_SNAKE_CASE :       Dict=None							,       SCREAMING_SNAKE_CASE :       Optional[Any]=None						):


      '''simple docstring'''






      UpperCamelCase__      :       Optional[int]	    =  state.common.alphas_cumprod[t]
      UpperCamelCase__      :       Tuple	    =  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__      :       int	    =  (1 - alpha_prod_t_prev) / (1 - alpha_prod_t) * state.common.betas[t]

      if variance_type is None:
           UpperCamelCase__      :       List[Any]	    =  self.config.variance_type

      # hacks - were probably added for training stability
      if variance_type == "fixed_small":
           UpperCamelCase__      :       Union[str, Any]	    =  jnp.clip(SCREAMING_SNAKE_CASE							,       a_min=1e-2_0						)
      # for rl-diffuser https://arxiv.org/abs/2205.09991
      elif variance_type == "fixed_small_log":
           UpperCamelCase__      :       Optional[Any]	    =  jnp.log(jnp.clip(SCREAMING_SNAKE_CASE							,       a_min=1e-2_0						)						)
      elif variance_type == "fixed_large":
           UpperCamelCase__      :       Dict	    =  state.common.betas[t]
      elif variance_type == "fixed_large_log":
           # Glide max_log
           UpperCamelCase__      :       List[str]	    =  jnp.log(state.common.betas[t]						)
      elif variance_type == "learned":
           return predicted_variance
      elif variance_type == "learned_range":
           UpperCamelCase__      :       Tuple	    =  variance
           UpperCamelCase__      :       int	    =  state.common.betas[t]
           UpperCamelCase__      :       Union[str, Any]	    =  (predicted_variance + 1) / 2
           UpperCamelCase__      :       List[str]	    =  frac * max_log + (1 - frac) * min_log

      return variance







 def  __lowercase (		self :       Optional[Any]							,       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__      :       str	    =  timestep

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

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

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

      # 6. Add noise
      def random_variance():
           UpperCamelCase__      :       str	    =  jax.random.split(SCREAMING_SNAKE_CASE							,       num=1						)
           UpperCamelCase__      :       Tuple	    =  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__      :       Any	    =  jnp.where(t > 0							,       random_variance()							,       jnp.zeros(model_output.shape							,       dtype=self.dtype						)						)

      UpperCamelCase__      :       List[str]	    =  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  __lowercase (		self :       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  __lowercase (		self :       Dict							,       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 :       int						):


      '''simple docstring'''






      return self.config.num_train_timesteps