"""simple docstring"""


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


__A       			=      logging.get_logger(__name__)

__A       			=      {
    """uw-madison/yoso-4096""": """https://huggingface.co/uw-madison/yoso-4096/resolve/main/config.json""",
    # See all YOSO models at https://huggingface.co/models?filter=yoso
}






class 					a					(				A_							):
  A_ :				List[str] 			=							'''yoso'''



  def __init__(		self   :    int						,  lowerCamelCase_   :    Optional[int]=5_02_65						,  lowerCamelCase_   :    Union[str, Any]=7_68						,  lowerCamelCase_   :    Union[str, Any]=12						,  lowerCamelCase_   :    List[Any]=12						,  lowerCamelCase_   :    List[str]=30_72						,  lowerCamelCase_   :    List[str]="gelu"						,  lowerCamelCase_   :    Tuple=0.1						,  lowerCamelCase_   :    Dict=0.1						,  lowerCamelCase_   :    Union[str, Any]=40_96						,  lowerCamelCase_   :    str=1						,  lowerCamelCase_   :    str=0.02						,  lowerCamelCase_   :    Optional[int]=1E-12						,  lowerCamelCase_   :    Optional[int]="absolute"						,  lowerCamelCase_   :    Tuple=True						,  lowerCamelCase_   :    Optional[Any]=9						,  lowerCamelCase_   :    Optional[int]=64						,  lowerCamelCase_   :    Optional[int]=None						,  lowerCamelCase_   :    int=True						,  lowerCamelCase_   :    Tuple=True						,  lowerCamelCase_   :    Tuple=1						,  lowerCamelCase_   :    Union[str, Any]=0						,  lowerCamelCase_   :    Any=2						,  **lowerCamelCase_   :    List[Any]						,  )      -> Union[str, Any]:
      super().__init__(pad_token_id=lowerCamelCase_						,  bos_token_id=lowerCamelCase_						,  eos_token_id=lowerCamelCase_						,  **lowerCamelCase_     )

      __a			      =						vocab_size
      __a			      =						max_position_embeddings
      __a			      =						hidden_size
      __a			      =						num_hidden_layers
      __a			      =						num_attention_heads
      __a			      =						intermediate_size
      __a			      =						hidden_act
      __a			      =						hidden_dropout_prob
      __a			      =						attention_probs_dropout_prob
      __a			      =						initializer_range
      __a			      =						type_vocab_size
      __a			      =						layer_norm_eps
      __a			      =						position_embedding_type
      __a			      =						use_expectation
      __a			      =						hash_code_len
      __a			      =						num_hash
      __a			      =						conv_window
      __a			      =						use_fast_hash
      __a			      =						lsh_backward

"""simple docstring"""


# DISCLAIMER: This code is strongly influenced by https://github.com/pesser/pytorch_diffusion
# and https://github.com/hojonathanho/diffusion

from dataclasses import dataclass
from typing import Optional, Tuple, Union

import flax
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					:
  A_ :				CommonSchedulerState
  A_ :				jnp.ndarray

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



  @classmethod
  def        lowerCAmelCase_				(		cls   :    Optional[int]						,  lowerCamelCase_   :    CommonSchedulerState						,  lowerCamelCase_   :    jnp.ndarray						,  lowerCamelCase_   :    jnp.ndarray						,  lowerCamelCase_   :    jnp.ndarray						,  )      -> Optional[Any]:
      return cls(
          common=lowerCamelCase_						,  final_alpha_cumprod=lowerCamelCase_						,  init_noise_sigma=lowerCamelCase_						,  timesteps=lowerCamelCase_						,  )






@dataclass
class 					a					(				A_							):
  A_ :				DDIMSchedulerState






class 					a					(				A_   ,		A_							):
  A_ :				Optional[int] 			=							[e.name for e in FlaxKarrasDiffusionSchedulers]

  A_ :				jnp.dtype



  @property
  def        lowerCAmelCase_				(		self   :    Optional[int]     )      -> Optional[Any]:
      return True



  @register_to_config
  def __init__(		self   :    Optional[int]						,  lowerCamelCase_   :    int = 10_00						,  lowerCamelCase_   :    float = 0.00_01						,  lowerCamelCase_   :    float = 0.02						,  lowerCamelCase_   :    str = "linear"						,  lowerCamelCase_   :    Optional[jnp.ndarray] = None						,  lowerCamelCase_   :    bool = True						,  lowerCamelCase_   :    int = 0						,  lowerCamelCase_   :    str = "epsilon"						,  lowerCamelCase_   :    jnp.dtype = jnp.floataa						,  )      -> Any:
      __a			      =						dtype



  def        lowerCAmelCase_				(		self   :    Dict						,  lowerCamelCase_   :    Optional[CommonSchedulerState] = None     )      -> DDIMSchedulerState:
      if common is None:
          __a			      =						CommonSchedulerState.create(self     )

      # At every step in ddim, we are looking into the previous alphas_cumprod
      # For the final step, there is no previous alphas_cumprod because we are already at 0
      # `set_alpha_to_one` decides whether we set this parameter simply to one or
      # whether we use the final alpha of the "non-previous" one.
      __a			      =						(
          jnp.array(1.0						,  dtype=self.dtype     ) if self.config.set_alpha_to_one else common.alphas_cumprod[0]
      )

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

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

      return DDIMSchedulerState.create(
          common=lowerCamelCase_						,  final_alpha_cumprod=lowerCamelCase_						,  init_noise_sigma=lowerCamelCase_						,  timesteps=lowerCamelCase_						,  )



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



  def        lowerCAmelCase_				(		self   :    List[str]						,  lowerCamelCase_   :    DDIMSchedulerState						,  lowerCamelCase_   :    int						,  lowerCamelCase_   :    Tuple = ()     )      -> DDIMSchedulerState:
      __a			      =						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			      =						(jnp.arange(0						,  lowerCamelCase_     ) * step_ratio).round()[::-1] + self.config.steps_offset

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



  def        lowerCAmelCase_				(		self   :    int						,  lowerCamelCase_   :    DDIMSchedulerState						,  lowerCamelCase_   :    int						,  lowerCamelCase_   :    Any     )      -> str:
      __a			      =						state.common.alphas_cumprod[timestep]
      __a			      =						jnp.where(
          prev_timestep >= 0						,  state.common.alphas_cumprod[prev_timestep]						,  state.final_alpha_cumprod     )
      __a			      =						1 - alpha_prod_t
      __a			      =						1 - alpha_prod_t_prev

      __a			      =						(beta_prod_t_prev / beta_prod_t) * (1 - alpha_prod_t / alpha_prod_t_prev)

      return variance



  def        lowerCAmelCase_				(		self   :    List[str]						,  lowerCamelCase_   :    DDIMSchedulerState						,  lowerCamelCase_   :    jnp.ndarray						,  lowerCamelCase_   :    int						,  lowerCamelCase_   :    jnp.ndarray						,  lowerCamelCase_   :    float = 0.0						,  lowerCamelCase_   :    bool = True						,  )      -> Union[FlaxDDIMSchedulerOutput, Tuple]:
      if state.num_inference_steps is None:
          raise ValueError(
              """Number of inference steps is 'None', you need to run 'set_timesteps' after creating the scheduler"""     )

      # See formulas (12) and (16) of DDIM paper https://arxiv.org/pdf/2010.02502.pdf
      # Ideally, read DDIM paper in-detail understanding

      # Notation (<variable name> -> <name in paper>
      # - pred_noise_t -> e_theta(x_t, t)
      # - pred_original_sample -> f_theta(x_t, t) or x_0
      # - std_dev_t -> sigma_t
      # - eta -> η
      # - pred_sample_direction -> "direction pointing to x_t"
      # - pred_prev_sample -> "x_t-1"

      # 1. get previous step value (=t-1)
      __a			      =						timestep - self.config.num_train_timesteps // state.num_inference_steps

      __a			      =						state.common.alphas_cumprod
      __a			      =						state.final_alpha_cumprod

      # 2. compute alphas, betas
      __a			      =						alphas_cumprod[timestep]
      __a			      =						jnp.where(prev_timestep >= 0						,  alphas_cumprod[prev_timestep]						,  lowerCamelCase_     )

      __a			      =						1 - alpha_prod_t

      # 3. compute predicted original sample from predicted noise also called
      # "predicted x_0" of formula (12) from https://arxiv.org/pdf/2010.02502.pdf
      if self.config.prediction_type == "epsilon":
          __a			      =						(sample - beta_prod_t ** 0.5 * model_output) / alpha_prod_t ** 0.5
          __a			      =						model_output
      elif self.config.prediction_type == "sample":
          __a			      =						model_output
          __a			      =						(sample - alpha_prod_t ** 0.5 * pred_original_sample) / beta_prod_t ** 0.5
      elif self.config.prediction_type == "v_prediction":
          __a			      =						(alpha_prod_t**0.5) * sample - (beta_prod_t**0.5) * model_output
          __a			      =						(alpha_prod_t**0.5) * model_output + (beta_prod_t**0.5) * sample
      else:
          raise ValueError(
              F"""prediction_type given as {self.config.prediction_type} must be one of `epsilon`, `sample`, or"""
              """ `v_prediction`"""     )

      # 4. compute variance: "sigma_t(η)" -> see formula (16)
      # σ_t = sqrt((1 − α_t−1)/(1 − α_t)) * sqrt(1 − α_t/α_t−1)
      __a			      =						self._get_variance(lowerCamelCase_						,  lowerCamelCase_						,  lowerCamelCase_     )
      __a			      =						eta * variance ** 0.5

      # 5. compute "direction pointing to x_t" of formula (12) from https://arxiv.org/pdf/2010.02502.pdf
      __a			      =						(1 - alpha_prod_t_prev - std_dev_t**2) ** 0.5 * pred_epsilon

      # 6. compute x_t without "random noise" of formula (12) from https://arxiv.org/pdf/2010.02502.pdf
      __a			      =						alpha_prod_t_prev ** 0.5 * pred_original_sample + pred_sample_direction

      if not return_dict:
          return (prev_sample, state)

      return FlaxDDIMSchedulerOutput(prev_sample=lowerCamelCase_						,  state=lowerCamelCase_     )



  def        lowerCAmelCase_				(		self   :    str						,  lowerCamelCase_   :    DDIMSchedulerState						,  lowerCamelCase_   :    jnp.ndarray						,  lowerCamelCase_   :    jnp.ndarray						,  lowerCamelCase_   :    jnp.ndarray						,  )      -> jnp.ndarray:
      return add_noise_common(state.common						,  lowerCamelCase_						,  lowerCamelCase_						,  lowerCamelCase_     )



  def        lowerCAmelCase_				(		self   :    Any						,  lowerCamelCase_   :    DDIMSchedulerState						,  lowerCamelCase_   :    jnp.ndarray						,  lowerCamelCase_   :    jnp.ndarray						,  lowerCamelCase_   :    jnp.ndarray						,  )      -> jnp.ndarray:
      return get_velocity_common(state.common						,  lowerCamelCase_						,  lowerCamelCase_						,  lowerCamelCase_     )



  def __len__(		self   :    Tuple     )      -> Union[str, Any]:
      return self.config.num_train_timesteps
