

"""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      __lowercase			:
  snake_case_				       =       42

  # setable values
  snake_case_				       =       42
  snake_case_				       =       42
  snake_case_				       =       None

  @classmethod
  def    __lowercase       (  cls		:			Optional[Any]					,A		:			CommonSchedulerState					,A		:			jnp.ndarray					,A		:			jnp.ndarray    ):






   '''simple docstring'''
   return cls(common=A					,init_noise_sigma=A					,timesteps=A    )







@dataclass
class      __lowercase			(    __lowerCamelCase					):
  snake_case_				       =       42







class      __lowercase			(    __lowerCamelCase	,			__lowerCamelCase					):
  snake_case_				       =       [e.name for e in FlaxKarrasDiffusionSchedulers]

  snake_case_				       =       42

  @property
  def    __lowercase       (  self		:			Union[str, Any]    ):






   '''simple docstring'''
   return True

  @register_to_config
  def __init__(  self		:			Any					,A		:			int = 1_000					,A		:			float = 0.0_0_0_1					,A		:			float = 0.0_2					,A		:			str = "linear"					,A		:			Optional[jnp.ndarray] = None					,A		:			str = "fixed_small"					,A		:			bool = True					,A		:			str = "epsilon"					,A		:			jnp.dtype = jnp.floataa					,):






   '''simple docstring'''
   UpperCAmelCase__					:  Any					   =					dtype

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






   '''simple docstring'''
   if common is None:
    UpperCAmelCase__					:  Dict					   =					CommonSchedulerState.create(self    )

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

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

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

  def    __lowercase       (  self		:			Dict					,A		:			DDPMSchedulerState					,A		:			jnp.ndarray					,A		:			Optional[int] = None    ):






   '''simple docstring'''
   return sample

  def    __lowercase       (  self		:			Optional[Any]					,A		:			DDPMSchedulerState					,A		:			int					,A		:			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					,A    ) * step_ratio).round()[::-1]

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

  def    __lowercase       (  self		:			str					,A		:			DDPMSchedulerState					,A		:			Tuple					,A		:			Tuple=None					,A		:			Optional[Any]=None    ):






   '''simple docstring'''
   UpperCAmelCase__					:  Optional[int]					   =					state.common.alphas_cumprod[t]
   UpperCAmelCase__					:  int					   =					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__					:  List[str]					   =					(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__					:  Dict					   =					jnp.clip(A					,a_min=1e-20    )
   # for rl-diffuser https://arxiv.org/abs/2205.09991
   elif variance_type == "fixed_small_log":
    UpperCAmelCase__					:  Any					   =					jnp.log(jnp.clip(A					,a_min=1e-20    )    )
   elif variance_type == "fixed_large":
    UpperCAmelCase__					:  List[str]					   =					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__					:  Union[str, Any]					   =					variance
    UpperCAmelCase__					:  Dict					   =					state.common.betas[t]
    UpperCAmelCase__					:  Tuple					   =					(predicted_variance + 1) / 2
    UpperCAmelCase__					:  str					   =					frac * max_log + (1 - frac) * min_log

   return variance

  def    __lowercase       (  self		:			Tuple					,A		:			DDPMSchedulerState					,A		:			jnp.ndarray					,A		:			int					,A		:			jnp.ndarray					,A		:			Optional[jax.random.KeyArray] = None					,A		:			bool = True					,):






   '''simple docstring'''
   UpperCAmelCase__					:  Any					   =					timestep

   if key is None:
    UpperCAmelCase__					:  Optional[int]					   =					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(A					,sample.shape[1]					,axis=1    )
   else:
    UpperCAmelCase__					:  Tuple					   =					None

   # 1. compute alphas, betas
   UpperCAmelCase__					:  Tuple					   =					state.common.alphas_cumprod[t]
   UpperCAmelCase__					:  Optional[Any]					   =					jnp.where(t > 0					,state.common.alphas_cumprod[t - 1]					,jnp.array(1.0					,dtype=self.dtype    )    )
   UpperCAmelCase__					:  Tuple					   =					1 - alpha_prod_t
   UpperCAmelCase__					:  Optional[int]					   =					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__					:  Tuple					   =					(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__					:  Tuple					   =					(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[Any]					   =					jnp.clip(A					,-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__					:  Any					   =					(alpha_prod_t_prev ** 0.5 * state.common.betas[t]) / beta_prod_t
   UpperCAmelCase__					:  Union[str, Any]					   =					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__					:  Optional[int]					   =					pred_original_sample_coeff * pred_original_sample + current_sample_coeff * sample

   # 6. Add noise
   def random_variance():
    UpperCAmelCase__					:  Union[str, Any]					   =					jax.random.split(A					,num=1    )
    UpperCAmelCase__					:  int					   =					jax.random.normal(A					,shape=model_output.shape					,dtype=self.dtype    )
    return (self._get_variance(A					,A					,predicted_variance=A    ) ** 0.5) * noise

   UpperCAmelCase__					:  List[str]					   =					jnp.where(t > 0					,random_variance()					,jnp.zeros(model_output.shape					,dtype=self.dtype    )    )

   UpperCAmelCase__					:  Dict					   =					pred_prev_sample + variance

   if not return_dict:
    return (pred_prev_sample, state)

   return FlaxDDPMSchedulerOutput(prev_sample=A					,state=A    )

  def    __lowercase       (  self		:			Optional[int]					,A		:			DDPMSchedulerState					,A		:			jnp.ndarray					,A		:			jnp.ndarray					,A		:			jnp.ndarray					,):






   '''simple docstring'''
   return add_noise_common(state.common					,A					,A					,A    )

  def    __lowercase       (  self		:			str					,A		:			DDPMSchedulerState					,A		:			jnp.ndarray					,A		:			jnp.ndarray					,A		:			jnp.ndarray					,):






   '''simple docstring'''
   return get_velocity_common(state.common					,A					,A					,A    )




  def __len__(  self		:			Dict    ):






   '''simple docstring'''
   return self.config.num_train_timesteps








"""simple docstring"""




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


__UpperCAmelCase =				logging.get_logger(__name__)

__UpperCAmelCase =				{
    'microsoft/markuplm-base': 'https://huggingface.co/microsoft/markuplm-base/resolve/main/config.json',
    'microsoft/markuplm-large': 'https://huggingface.co/microsoft/markuplm-large/resolve/main/config.json',
}







class      __lowercase			(    __lowerCamelCase					):
  snake_case_				       =       """markuplm"""

  def __init__(  self		:			str					,A		:			List[Any]=30_522					,A		:			Tuple=768					,A		:			str=12					,A		:			int=12					,A		:			int=3_072					,A		:			Optional[int]="gelu"					,A		:			Optional[int]=0.1					,A		:			Optional[int]=0.1					,A		:			Any=512					,A		:			Any=2					,A		:			str=0.0_2					,A		:			int=1e-12					,A		:			int=0					,A		:			str=0					,A		:			List[Any]=2					,A		:			List[str]=256					,A		:			Union[str, Any]=1_024					,A		:			List[Any]=216					,A		:			Any=1_001					,A		:			Optional[int]=32					,A		:			Any=50					,A		:			int="absolute"					,A		:			Dict=True					,A		:			int=None					,**A		:			Optional[int]					,):






   '''simple docstring'''
   super().__init__(
       pad_token_id=A					,bos_token_id=A					,eos_token_id=A					,**A					,)
   UpperCAmelCase__					:  Dict					   =					vocab_size
   UpperCAmelCase__					:  Union[str, Any]					   =					hidden_size
   UpperCAmelCase__					:  Optional[int]					   =					num_hidden_layers
   UpperCAmelCase__					:  Tuple					   =					num_attention_heads
   UpperCAmelCase__					:  Optional[Any]					   =					hidden_act
   UpperCAmelCase__					:  Any					   =					intermediate_size
   UpperCAmelCase__					:  Dict					   =					hidden_dropout_prob
   UpperCAmelCase__					:  Optional[Any]					   =					attention_probs_dropout_prob
   UpperCAmelCase__					:  Optional[int]					   =					max_position_embeddings
   UpperCAmelCase__					:  Dict					   =					type_vocab_size
   UpperCAmelCase__					:  Optional[Any]					   =					initializer_range
   UpperCAmelCase__					:  str					   =					layer_norm_eps
   UpperCAmelCase__					:  Any					   =					position_embedding_type
   UpperCAmelCase__					:  int					   =					use_cache
   UpperCAmelCase__					:  List[str]					   =					classifier_dropout
   # additional properties
   UpperCAmelCase__					:  Optional[int]					   =					max_depth
   UpperCAmelCase__					:  List[str]					   =					max_xpath_tag_unit_embeddings
   UpperCAmelCase__					:  Any					   =					max_xpath_subs_unit_embeddings
   UpperCAmelCase__					:  str					   =					tag_pad_id
   UpperCAmelCase__					:  Dict					   =					subs_pad_id
   UpperCAmelCase__					:  List[str]					   =					xpath_unit_hidden_size





