





# 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    :

  """simple docstring"""


  a_        = 42

  # setable values
  a_        = 42
  a_        = 42
  a_        = None






  @classmethod
  def 				lowercase		(				cls :		List[str]					,  lowerCAmelCase_ :		CommonSchedulerState					,  lowerCAmelCase_ :		jnp.ndarray					,  lowerCAmelCase_ :		jnp.ndarray     )							->						Dict:
     return cls(common=lowerCAmelCase_					,  init_noise_sigma=lowerCAmelCase_					,  timesteps=lowerCAmelCase_     )





@dataclass
class 				_UpperCAmelCase    (    _UpperCamelCase    ):

  """simple docstring"""


  a_        = 42





class 				_UpperCAmelCase    (    _UpperCamelCase		,    _UpperCamelCase    ):

  """simple docstring"""


  a_        = [e.name for e in FlaxKarrasDiffusionSchedulers]

  a_        = 42






  @property
  def 				lowercase		(				self :		str     )							->						Optional[int]:
     return True






  @register_to_config
  def __init__(				self :		int					,  lowerCAmelCase_ :		int = 1_0_0_0					,  lowerCAmelCase_ :		float = 0.00_01					,  lowerCAmelCase_ :		float = 0.02					,  lowerCAmelCase_ :		str = "linear"					,  lowerCAmelCase_ :		Optional[jnp.ndarray] = None					,  lowerCAmelCase_ :		str = "fixed_small"					,  lowerCAmelCase_ :		bool = True					,  lowerCAmelCase_ :		str = "epsilon"					,  lowerCAmelCase_ :		jnp.dtype = jnp.floataa					,  )							->						Optional[Any]:
     __lowerCAmelCase								=	dtype






  def 				lowercase		(				self :		Dict					,  lowerCAmelCase_ :		Optional[CommonSchedulerState] = None     )							->						DDPMSchedulerState:
     if common is None:
        __lowerCAmelCase								=	CommonSchedulerState.create(self     )

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

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

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






  def 				lowercase		(				self :		str					,  lowerCAmelCase_ :		DDPMSchedulerState					,  lowerCAmelCase_ :		jnp.ndarray					,  lowerCAmelCase_ :		Optional[int] = None     )							->						jnp.ndarray:
     return sample






  def 				lowercase		(				self :		Tuple					,  lowerCAmelCase_ :		DDPMSchedulerState					,  lowerCAmelCase_ :		int					,  lowerCAmelCase_ :		Tuple = ()     )							->						DDPMSchedulerState:
     __lowerCAmelCase								=	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
     __lowerCAmelCase								=	(jnp.arange(0					,  lowerCAmelCase_     ) * step_ratio).round()[::-1]

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






  def 				lowercase		(				self :		Tuple					,  lowerCAmelCase_ :		DDPMSchedulerState					,  lowerCAmelCase_ :		Optional[Any]					,  lowerCAmelCase_ :		int=None					,  lowerCAmelCase_ :		List[Any]=None     )							->						Tuple:
     __lowerCAmelCase								=	state.common.alphas_cumprod[t]
     __lowerCAmelCase								=	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
     __lowerCAmelCase								=	(1 - alpha_prod_t_prev) / (1 - alpha_prod_t) * state.common.betas[t]

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

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

     return variance






  def 				lowercase		(				self :		Optional[int]					,  lowerCAmelCase_ :		DDPMSchedulerState					,  lowerCAmelCase_ :		jnp.ndarray					,  lowerCAmelCase_ :		int					,  lowerCAmelCase_ :		jnp.ndarray					,  lowerCAmelCase_ :		Optional[jax.random.KeyArray] = None					,  lowerCAmelCase_ :		bool = True					,  )							->						Union[FlaxDDPMSchedulerOutput, Tuple]:
     __lowerCAmelCase								=	timestep

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

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

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

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

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

     __lowerCAmelCase								=	pred_prev_sample + variance

     if not return_dict:
        return (pred_prev_sample, state)

     return FlaxDDPMSchedulerOutput(prev_sample=lowerCAmelCase_					,  state=lowerCAmelCase_     )






  def 				lowercase		(				self :		Optional[Any]					,  lowerCAmelCase_ :		DDPMSchedulerState					,  lowerCAmelCase_ :		jnp.ndarray					,  lowerCAmelCase_ :		jnp.ndarray					,  lowerCAmelCase_ :		jnp.ndarray					,  )							->						jnp.ndarray:
     return add_noise_common(state.common					,  lowerCAmelCase_					,  lowerCAmelCase_					,  lowerCAmelCase_     )






  def 				lowercase		(				self :		Optional[Any]					,  lowerCAmelCase_ :		DDPMSchedulerState					,  lowerCAmelCase_ :		jnp.ndarray					,  lowerCAmelCase_ :		jnp.ndarray					,  lowerCAmelCase_ :		jnp.ndarray					,  )							->						jnp.ndarray:
     return get_velocity_common(state.common					,  lowerCAmelCase_					,  lowerCAmelCase_					,  lowerCAmelCase_     )






  def __len__(				self :		str     )							->						Optional[int]:
     return self.config.num_train_timesteps








from collections import OrderedDict
from typing import Mapping

from packaging import version

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


_snake_case  :    List[Any]						    =  logging.get_logger(__name__)

_snake_case  :    List[Any]						    =  {
    'microsoft/beit-base-patch16-224-pt22k': (
        'https://huggingface.co/microsoft/beit-base-patch16-224-pt22k/resolve/main/config.json'
    ),
    # See all BEiT models at https://huggingface.co/models?filter=beit
}



class 				_UpperCAmelCase    (    _UpperCamelCase    ):

  """simple docstring"""


  a_        = """beit"""






  def __init__(				self :		List[Any]					,  lowerCAmelCase_ :		Tuple=8_1_9_2					,  lowerCAmelCase_ :		Optional[int]=7_6_8					,  lowerCAmelCase_ :		int=1_2					,  lowerCAmelCase_ :		Optional[int]=1_2					,  lowerCAmelCase_ :		Any=3_0_7_2					,  lowerCAmelCase_ :		Optional[int]="gelu"					,  lowerCAmelCase_ :		Any=0.0					,  lowerCAmelCase_ :		Any=0.0					,  lowerCAmelCase_ :		Any=0.02					,  lowerCAmelCase_ :		int=1e-12					,  lowerCAmelCase_ :		int=2_2_4					,  lowerCAmelCase_ :		str=1_6					,  lowerCAmelCase_ :		int=3					,  lowerCAmelCase_ :		Dict=False					,  lowerCAmelCase_ :		int=False					,  lowerCAmelCase_ :		List[Any]=False					,  lowerCAmelCase_ :		int=False					,  lowerCAmelCase_ :		List[str]=0.1					,  lowerCAmelCase_ :		Union[str, Any]=0.1					,  lowerCAmelCase_ :		List[str]=True					,  lowerCAmelCase_ :		List[Any]=[3, 5, 7, 1_1]					,  lowerCAmelCase_ :		Optional[Any]=[1, 2, 3, 6]					,  lowerCAmelCase_ :		Tuple=True					,  lowerCAmelCase_ :		Dict=0.4					,  lowerCAmelCase_ :		Tuple=2_5_6					,  lowerCAmelCase_ :		Any=1					,  lowerCAmelCase_ :		Any=False					,  lowerCAmelCase_ :		Optional[int]=2_5_5					,  **lowerCAmelCase_ :		Any					,  )							->						Dict:
     super().__init__(**lowerCAmelCase_     )

     __lowerCAmelCase								=	vocab_size
     __lowerCAmelCase								=	hidden_size
     __lowerCAmelCase								=	num_hidden_layers
     __lowerCAmelCase								=	num_attention_heads
     __lowerCAmelCase								=	intermediate_size
     __lowerCAmelCase								=	hidden_act
     __lowerCAmelCase								=	hidden_dropout_prob
     __lowerCAmelCase								=	attention_probs_dropout_prob
     __lowerCAmelCase								=	initializer_range
     __lowerCAmelCase								=	layer_norm_eps

     __lowerCAmelCase								=	image_size
     __lowerCAmelCase								=	patch_size
     __lowerCAmelCase								=	num_channels
     __lowerCAmelCase								=	use_mask_token
     __lowerCAmelCase								=	use_absolute_position_embeddings
     __lowerCAmelCase								=	use_relative_position_bias
     __lowerCAmelCase								=	use_shared_relative_position_bias
     __lowerCAmelCase								=	layer_scale_init_value
     __lowerCAmelCase								=	drop_path_rate
     __lowerCAmelCase								=	use_mean_pooling
     # decode head attributes (semantic segmentation)
     __lowerCAmelCase								=	out_indices
     __lowerCAmelCase								=	pool_scales
     # auxiliary head attributes (semantic segmentation)
     __lowerCAmelCase								=	use_auxiliary_head
     __lowerCAmelCase								=	auxiliary_loss_weight
     __lowerCAmelCase								=	auxiliary_channels
     __lowerCAmelCase								=	auxiliary_num_convs
     __lowerCAmelCase								=	auxiliary_concat_input
     __lowerCAmelCase								=	semantic_loss_ignore_index





class 				_UpperCAmelCase    (    _UpperCamelCase    ):

  """simple docstring"""


  a_        = version.parse("""1.11"""    )






  @property
  def 				lowercase		(				self :		Tuple     )							->						Mapping[str, Mapping[int, str]]:
     return OrderedDict(
         [
             ('pixel_values', {0: 'batch', 1: 'num_channels', 2: 'height', 3: 'width'}),
         ]     )






  @property
  def 				lowercase		(				self :		Optional[Any]     )							->						float:
     return 1e-4

