



from typing import List, Optional, Union

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


lowercase__          =logging.get_logger(__name__)

lowercase__          ={
    'huggingface/informer-tourism-monthly': (
        'https://huggingface.co/huggingface/informer-tourism-monthly/resolve/main/config.json'
    ),
    # See all Informer models at https://huggingface.co/models?filter=informer
}



class 		UpperCamelCase__  (	__lowercase   ):
   _SCREAMING_SNAKE_CASE				:							Any     	=				"informer"
   _SCREAMING_SNAKE_CASE				:							str     	=				{
       "hidden_size": "d_model",
       "num_attention_heads": "encoder_attention_heads",
       "num_hidden_layers": "encoder_layers",
   }
   def __init__(self			: Dict ,    snake_case_			: Optional[int] = None ,    snake_case_			: Optional[int] = None ,    snake_case_			: str = "student_t" ,    snake_case_			: str = "nll" ,    snake_case_			: int = 1 ,    snake_case_			: List[int] = None ,    snake_case_			: Optional[Union[str, bool]] = "mean" ,    snake_case_			: int = 0 ,    snake_case_			: int = 0 ,    snake_case_			: int = 0 ,    snake_case_			: int = 0 ,    snake_case_			: Optional[List[int]] = None ,    snake_case_			: Optional[List[int]] = None ,    snake_case_			: int = 6_4 ,    snake_case_			: int = 3_2 ,    snake_case_			: int = 3_2 ,    snake_case_			: int = 2 ,    snake_case_			: int = 2 ,    snake_case_			: int = 2 ,    snake_case_			: int = 2 ,    snake_case_			: bool = True ,    snake_case_			: str = "gelu" ,    snake_case_			: float = 0.05 ,    snake_case_			: float = 0.1 ,    snake_case_			: float = 0.1 ,    snake_case_			: float = 0.1 ,    snake_case_			: float = 0.1 ,    snake_case_			: int = 1_0_0 ,    snake_case_			: float = 0.02 ,    snake_case_			: List[str]=True ,    snake_case_			: str = "prob" ,    snake_case_			: int = 5 ,    snake_case_			: bool = True ,    **snake_case_			: str ,    ):
    # time series specific configuration
    __a			:  List[Any]		    =  prediction_length
    __a			:  Union[str, Any]		    =  context_length or prediction_length
    __a			:  List[str]		    =  distribution_output
    __a			:  Optional[Any]		    =  loss
    __a			:  Union[str, Any]		    =  input_size
    __a			:  Any		    =  num_time_features
    __a			:  List[str]		    =  lags_sequence if lags_sequence is not None else [1, 2, 3, 4, 5, 6, 7]
    __a			:  Tuple		    =  scaling
    __a			:  Optional[int]		    =  num_dynamic_real_features
    __a			:  List[str]		    =  num_static_real_features
    __a			:  Tuple		    =  num_static_categorical_features

    # set cardinality
    if cardinality and num_static_categorical_features > 0:
     if len(snake_case_       ) != num_static_categorical_features:
      raise ValueError(
          '''The cardinality should be a list of the same length as `num_static_categorical_features`'''       )
     __a			:  Any		    =  cardinality
    else:
     __a			:  Tuple		    =  [0]

    # set embedding_dimension
    if embedding_dimension and num_static_categorical_features > 0:
     if len(snake_case_       ) != num_static_categorical_features:
      raise ValueError(
          '''The embedding dimension should be a list of the same length as `num_static_categorical_features`'''       )
     __a			:  Optional[Any]		    =  embedding_dimension
    else:
     __a			:  str		    =  [min(5_0 ,    (cat + 1) // 2       ) for cat in self.cardinality]

    __a			:  Tuple		    =  num_parallel_samples

    # Transformer architecture configuration
    __a			:  Optional[Any]		    =  input_size * len(self.lags_sequence       ) + self._number_of_features
    __a			:  Dict		    =  d_model
    __a			:  str		    =  encoder_attention_heads
    __a			:  Dict		    =  decoder_attention_heads
    __a			:  List[Any]		    =  encoder_ffn_dim
    __a			:  Any		    =  decoder_ffn_dim
    __a			:  List[Any]		    =  encoder_layers
    __a			:  Dict		    =  decoder_layers

    __a			:  Optional[Any]		    =  dropout
    __a			:  Tuple		    =  attention_dropout
    __a			:  int		    =  activation_dropout
    __a			:  Optional[Any]		    =  encoder_layerdrop
    __a			:  Dict		    =  decoder_layerdrop

    __a			:  Any		    =  activation_function
    __a			:  Any		    =  init_std

    __a			:  Union[str, Any]		    =  use_cache

    # Informer
    __a			:  List[str]		    =  attention_type
    __a			:  Tuple		    =  sampling_factor
    __a			:  Tuple		    =  distil

    super().__init__(is_encoder_decoder=snake_case_ ,    **snake_case_       )







   @property
   def 				lowerCAmelCase      (self			: List[str]       ):
    return (
        sum(self.embedding_dimension       )
        + self.num_dynamic_real_features
        + self.num_time_features
        + self.num_static_real_features
        + self.input_size * 2  # the log1p(abs(loc)) and log(scale) features
    )


# 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__  :
   _SCREAMING_SNAKE_CASE				:							CommonSchedulerState

   # setable values
   _SCREAMING_SNAKE_CASE				:							jnp.ndarray
   _SCREAMING_SNAKE_CASE				:							jnp.ndarray
   _SCREAMING_SNAKE_CASE				:							Optional[int]     	=				None
   @classmethod
   def 				lowerCAmelCase      (cls			: int ,    snake_case_			: CommonSchedulerState ,    snake_case_			: jnp.ndarray ,    snake_case_			: jnp.ndarray       ):
    return cls(common=snake_case_ ,    init_noise_sigma=snake_case_ ,    timesteps=snake_case_       )





@dataclass
class 		UpperCamelCase__  (	__lowercase   ):
   _SCREAMING_SNAKE_CASE				:							DDPMSchedulerState





class 		UpperCamelCase__  (	__lowercase   ,__lowercase   ):
   _SCREAMING_SNAKE_CASE				:							str     	=				[e.name for e in FlaxKarrasDiffusionSchedulers]

   _SCREAMING_SNAKE_CASE				:							jnp.dtype
   @property
   def 				lowerCAmelCase      (self			: Optional[Any]       ):
    return True
   @register_to_config
   def __init__(self			: Any ,    snake_case_			: int = 1_0_0_0 ,    snake_case_			: float = 0.0001 ,    snake_case_			: float = 0.02 ,    snake_case_			: str = "linear" ,    snake_case_			: Optional[jnp.ndarray] = None ,    snake_case_			: str = "fixed_small" ,    snake_case_			: bool = True ,    snake_case_			: str = "epsilon" ,    snake_case_			: jnp.dtype = jnp.floataa ,    ):
    __a			:  str		    =  dtype
   def 				lowerCAmelCase      (self			: Any ,    snake_case_			: Optional[CommonSchedulerState] = None       ):
    if common is None:
     __a			:  Optional[Any]		    =  CommonSchedulerState.create(self       )

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

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

    return DDPMSchedulerState.create(
        common=snake_case_ ,    init_noise_sigma=snake_case_ ,    timesteps=snake_case_ ,    )
   def 				lowerCAmelCase      (self			: Dict ,    snake_case_			: DDPMSchedulerState ,    snake_case_			: jnp.ndarray ,    snake_case_			: Optional[int] = None       ):
    return sample
   def 				lowerCAmelCase      (self			: List[Any] ,    snake_case_			: DDPMSchedulerState ,    snake_case_			: int ,    snake_case_			: Tuple = ()       ):
    __a			:  Tuple		    =  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			:  Any		    =  (jnp.arange(0 ,    snake_case_       ) * step_ratio).round()[::-1]

    return state.replace(
        num_inference_steps=snake_case_ ,    timesteps=snake_case_ ,    )
   def 				lowerCAmelCase      (self			: List[Any] ,    snake_case_			: DDPMSchedulerState ,    snake_case_			: Optional[Any] ,    snake_case_			: List[str]=None ,    snake_case_			: Union[str, Any]=None       ):
    __a			:  Optional[Any]		    =  state.common.alphas_cumprod[t]
    __a			:  List[str]		    =  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			:  Optional[int]		    =  (1 - alpha_prod_t_prev) / (1 - alpha_prod_t) * state.common.betas[t]

    if variance_type is None:
     __a			:  List[str]		    =  self.config.variance_type

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

    return variance
   def 				lowerCAmelCase      (self			: Any ,    snake_case_			: DDPMSchedulerState ,    snake_case_			: jnp.ndarray ,    snake_case_			: int ,    snake_case_			: jnp.ndarray ,    snake_case_			: Optional[jax.random.KeyArray] = None ,    snake_case_			: bool = True ,    ):
    __a			:  int		    =  timestep

    if key is None:
     __a			:  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			:  List[str]		    =  jnp.split(snake_case_ ,    sample.shape[1] ,    axis=1       )
    else:
     __a			:  int		    =  None

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

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

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

    __a			:  Tuple		    =  pred_prev_sample + variance

    if not return_dict:
     return (pred_prev_sample, state)

    return FlaxDDPMSchedulerOutput(prev_sample=snake_case_ ,    state=snake_case_       )
   def 				lowerCAmelCase      (self			: List[str] ,    snake_case_			: DDPMSchedulerState ,    snake_case_			: jnp.ndarray ,    snake_case_			: jnp.ndarray ,    snake_case_			: jnp.ndarray ,    ):
    return add_noise_common(state.common ,    snake_case_ ,    snake_case_ ,    snake_case_       )
   def 				lowerCAmelCase      (self			: str ,    snake_case_			: DDPMSchedulerState ,    snake_case_			: jnp.ndarray ,    snake_case_			: jnp.ndarray ,    snake_case_			: jnp.ndarray ,    ):
    return get_velocity_common(state.common ,    snake_case_ ,    snake_case_ ,    snake_case_       )







   def __len__(self			: List[str]       ):
    return self.config.num_train_timesteps
