'''simple docstring'''


import importlib
import math
import os
from dataclasses import dataclass
from enum import Enum
from typing import Any, Dict, Optional, Tuple, Union

import flax
import jax.numpy as jnp

from ..utils import BaseOutput


snake_case__					:      List[Any]				      =  '''scheduler_config.json'''






class 					__SCREAMING_SNAKE_CASE						(	lowerCamelCase_	):







  '''simple docstring'''






  lowerCamelCase_		:str						   =		1
  lowerCamelCase_		:Union[str, Any]						   =		2
  lowerCamelCase_		:List[str]						   =		3
  lowerCamelCase_		:Optional[int]						   =		4
  lowerCamelCase_		:str						   =		5







@dataclass
class 					__SCREAMING_SNAKE_CASE						(	lowerCamelCase_	):







  '''simple docstring'''






  lowerCamelCase_		:jnp.ndarray







class 					__SCREAMING_SNAKE_CASE						:







  '''simple docstring'''






  lowerCamelCase_		:Any						   =		SCHEDULER_CONFIG_NAME
  lowerCamelCase_		:List[str]						   =		['''dtype''']
  lowerCamelCase_		:List[str]						   =		[]
  lowerCamelCase_		:Optional[Any]						   =		True


  @classmethod
  def _UpperCamelCase					(			cls				,		snake_case_ = None				,		snake_case_ = None				,		snake_case_=False				,		**snake_case_				,		):




       '''simple docstring'''

       UpperCAmelCase_							:       List[Any]       =				cls.load_config(
           pretrained_model_name_or_path=snake_case_				,		subfolder=snake_case_				,		return_unused_kwargs=snake_case_				,		**snake_case_				,		)
       UpperCAmelCase_							:       Union[str, Any]       =				cls.from_config(snake_case_				,		return_unused_kwargs=snake_case_				,		**snake_case_						)

       if hasattr(snake_case_				,		'create_state'						) and getattr(snake_case_				,		'has_state'				,		snake_case_						):
            UpperCAmelCase_							:       List[Any]       =				scheduler.create_state()

       if return_unused_kwargs:
            return scheduler, state, unused_kwargs

       return scheduler, state


  def _UpperCamelCase					(			self				,		snake_case_				,		snake_case_ = False				,		**snake_case_						):




       '''simple docstring'''

       self.save_config(save_directory=snake_case_				,		push_to_hub=snake_case_				,		**snake_case_						)


  @property
  def _UpperCamelCase					(			self						):




       '''simple docstring'''

       return self._get_compatibles()






  @classmethod
  def _UpperCamelCase					(			cls						):




       '''simple docstring'''

       UpperCAmelCase_							:       List[str]       =				list(set([cls.__name__] + cls._compatibles						)						)
       UpperCAmelCase_							:       Optional[int]       =				importlib.import_module(__name__.split('.'						)[0]						)
       UpperCAmelCase_							:       Any       =				[
           getattr(snake_case_				,		snake_case_						) for c in compatible_classes_str if hasattr(snake_case_				,		snake_case_						)
       ]
       return compatible_classes







def  _lowerCamelCase   (				lowerCamelCase_ :							jnp.ndarray     ,					lowerCamelCase_ :							Tuple[int]       ):



     """simple docstring"""





     assert len(lowerCamelCase_       ) >= x.ndim
     return jnp.broadcast_to(x.reshape(x.shape + (1,) * (len(lowerCamelCase_       ) - x.ndim)       )     ,					lowerCamelCase_       )


def  _lowerCamelCase   (				lowerCamelCase_ :							int     ,					lowerCamelCase_ :							Optional[Any]=0.999     ,					lowerCamelCase_ :							List[str]=jnp.floataa       ):



     """simple docstring"""





     def alpha_bar(lowerCamelCase_ :							Optional[Any]       ):
          return math.cos((time_step + 0.008) / 1.008 * math.pi / 2       ) ** 2

     UpperCAmelCase_							:       int       =				[]
     for i in range(lowerCamelCase_       ):
          UpperCAmelCase_							:       Optional[int]       =				i / num_diffusion_timesteps
          UpperCAmelCase_							:       Any       =				(i + 1) / num_diffusion_timesteps
          betas.append(min(1 - alpha_bar(lowerCamelCase_       ) / alpha_bar(lowerCamelCase_       )     ,					lowerCamelCase_       )       )
     return jnp.array(lowerCamelCase_     ,					dtype=lowerCamelCase_       )






@flax.struct.dataclass
class 					__SCREAMING_SNAKE_CASE						:







  '''simple docstring'''






  lowerCamelCase_		:jnp.ndarray
  lowerCamelCase_		:jnp.ndarray
  lowerCamelCase_		:jnp.ndarray


  @classmethod
  def _UpperCamelCase					(			cls				,		snake_case_						):




       '''simple docstring'''

       UpperCAmelCase_							:       List[Any]       =				scheduler.config

       if config.trained_betas is not None:
            UpperCAmelCase_							:       List[Any]       =				jnp.asarray(config.trained_betas				,		dtype=scheduler.dtype						)
       elif config.beta_schedule == "linear":
            UpperCAmelCase_							:       Tuple       =				jnp.linspace(config.beta_start				,		config.beta_end				,		config.num_train_timesteps				,		dtype=scheduler.dtype						)
       elif config.beta_schedule == "scaled_linear":
            # this schedule is very specific to the latent diffusion model.
            UpperCAmelCase_							:       Optional[Any]       =				(
                jnp.linspace(
                    config.beta_start**0.5				,		config.beta_end**0.5				,		config.num_train_timesteps				,		dtype=scheduler.dtype						)
                ** 2
            )
       elif config.beta_schedule == "squaredcos_cap_v2":
            # Glide cosine schedule
            UpperCAmelCase_							:       Any       =				betas_for_alpha_bar(config.num_train_timesteps				,		dtype=scheduler.dtype						)
       else:
            raise NotImplementedError(
                F'''beta_schedule {config.beta_schedule} is not implemented for scheduler {scheduler.__class__.__name__}'''						)

       UpperCAmelCase_							:       Union[str, Any]       =				1.0 - betas

       UpperCAmelCase_							:       Optional[int]       =				jnp.cumprod(snake_case_				,		axis=0						)

       return cls(
           alphas=snake_case_				,		betas=snake_case_				,		alphas_cumprod=snake_case_				,		)







def  _lowerCamelCase   (				lowerCamelCase_ :							CommonSchedulerState     ,					lowerCamelCase_ :							jnp.ndarray     ,					lowerCamelCase_ :							jnp.ndarray     ,					lowerCamelCase_ :							jnp.ndarray       ):



     """simple docstring"""





     UpperCAmelCase_							:       List[str]       =				state.alphas_cumprod

     UpperCAmelCase_							:       int       =				alphas_cumprod[timesteps] ** 0.5
     UpperCAmelCase_							:       int       =				sqrt_alpha_prod.flatten()
     UpperCAmelCase_							:       Any       =				broadcast_to_shape_from_left(lowerCamelCase_     ,					original_samples.shape       )

     UpperCAmelCase_							:       Dict       =				(1 - alphas_cumprod[timesteps]) ** 0.5
     UpperCAmelCase_							:       int       =				sqrt_one_minus_alpha_prod.flatten()
     UpperCAmelCase_							:       Dict       =				broadcast_to_shape_from_left(lowerCamelCase_     ,					original_samples.shape       )

     return sqrt_alpha_prod, sqrt_one_minus_alpha_prod


def  _lowerCamelCase   (				lowerCamelCase_ :							CommonSchedulerState     ,					lowerCamelCase_ :							jnp.ndarray     ,					lowerCamelCase_ :							jnp.ndarray     ,					lowerCamelCase_ :							jnp.ndarray       ):



     """simple docstring"""





     UpperCAmelCase_							:       Optional[Any]       =				get_sqrt_alpha_prod(lowerCamelCase_     ,					lowerCamelCase_     ,					lowerCamelCase_     ,					lowerCamelCase_       )
     UpperCAmelCase_							:       Any       =				sqrt_alpha_prod * original_samples + sqrt_one_minus_alpha_prod * noise
     return noisy_samples





def  _lowerCamelCase   (				lowerCamelCase_ :							CommonSchedulerState     ,					lowerCamelCase_ :							jnp.ndarray     ,					lowerCamelCase_ :							jnp.ndarray     ,					lowerCamelCase_ :							jnp.ndarray       ):



     """simple docstring"""





     UpperCAmelCase_							:       Optional[int]       =				get_sqrt_alpha_prod(lowerCamelCase_     ,					lowerCamelCase_     ,					lowerCamelCase_     ,					lowerCamelCase_       )
     UpperCAmelCase_							:       int       =				sqrt_alpha_prod * noise - sqrt_one_minus_alpha_prod * sample
     return velocity







'''simple docstring'''


from typing import List, Optional

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


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

snake_case__					:      Optional[Any]				      =  {
    '''huggingface/autoformer-tourism-monthly''': '''https://huggingface.co/huggingface/autoformer-tourism-monthly/resolve/main/config.json''',
}






class 					__SCREAMING_SNAKE_CASE						(	lowerCamelCase_	):







  '''simple docstring'''






  lowerCamelCase_		:List[str]						   =		'''autoformer'''
  lowerCamelCase_		:Optional[Any]						   =		{
      '''hidden_size''': '''d_model''',
      '''num_attention_heads''': '''encoder_attention_heads''',
      '''num_hidden_layers''': '''encoder_layers''',
  }


  def __init__(			self				,		snake_case_ = None				,		snake_case_ = None				,		snake_case_ = "student_t"				,		snake_case_ = "nll"				,		snake_case_ = 1				,		snake_case_ = [1, 2, 3, 4, 5, 6, 7]				,		snake_case_ = True				,		snake_case_ = 0				,		snake_case_ = 0				,		snake_case_ = 0				,		snake_case_ = 0				,		snake_case_ = None				,		snake_case_ = None				,		snake_case_ = 6_4				,		snake_case_ = 2				,		snake_case_ = 2				,		snake_case_ = 2				,		snake_case_ = 2				,		snake_case_ = 3_2				,		snake_case_ = 3_2				,		snake_case_ = "gelu"				,		snake_case_ = 0.1				,		snake_case_ = 0.1				,		snake_case_ = 0.1				,		snake_case_ = 0.1				,		snake_case_ = 0.1				,		snake_case_ = 1_0_0				,		snake_case_ = 0.02				,		snake_case_ = True				,		snake_case_=True				,		snake_case_ = 1_0				,		snake_case_ = 2_5				,		snake_case_ = 3				,		**snake_case_				,		):




       '''simple docstring'''

       UpperCAmelCase_							:       List[Any]       =				prediction_length
       UpperCAmelCase_							:       List[str]       =				context_length if context_length is not None else prediction_length
       UpperCAmelCase_							:       Optional[int]       =				distribution_output
       UpperCAmelCase_							:       Optional[int]       =				loss
       UpperCAmelCase_							:       Union[str, Any]       =				input_size
       UpperCAmelCase_							:       int       =				num_time_features
       UpperCAmelCase_							:       List[str]       =				lags_sequence
       UpperCAmelCase_							:       Any       =				scaling
       UpperCAmelCase_							:       Any       =				num_dynamic_real_features
       UpperCAmelCase_							:       int       =				num_static_real_features
       UpperCAmelCase_							:       Optional[Any]       =				num_static_categorical_features
       if cardinality is not None 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`'						)
            UpperCAmelCase_							:       List[Any]       =				cardinality
       else:
            UpperCAmelCase_							:       Optional[int]       =				[0]
       if embedding_dimension is not None 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`'						)
            UpperCAmelCase_							:       List[str]       =				embedding_dimension
       else:
            UpperCAmelCase_							:       List[Any]       =				[min(5_0				,		(cat + 1) // 2						) for cat in self.cardinality]
       UpperCAmelCase_							:       List[str]       =				num_parallel_samples

       # Transformer architecture configuration
       UpperCAmelCase_							:       Union[str, Any]       =				input_size * len(self.lags_sequence						) + self._number_of_features
       UpperCAmelCase_							:       str       =				d_model
       UpperCAmelCase_							:       str       =				encoder_attention_heads
       UpperCAmelCase_							:       str       =				decoder_attention_heads
       UpperCAmelCase_							:       str       =				encoder_ffn_dim
       UpperCAmelCase_							:       str       =				decoder_ffn_dim
       UpperCAmelCase_							:       str       =				encoder_layers
       UpperCAmelCase_							:       str       =				decoder_layers

       UpperCAmelCase_							:       str       =				dropout
       UpperCAmelCase_							:       Optional[int]       =				attention_dropout
       UpperCAmelCase_							:       Tuple       =				activation_dropout
       UpperCAmelCase_							:       Any       =				encoder_layerdrop
       UpperCAmelCase_							:       Tuple       =				decoder_layerdrop

       UpperCAmelCase_							:       List[str]       =				activation_function
       UpperCAmelCase_							:       Tuple       =				init_std

       UpperCAmelCase_							:       Union[str, Any]       =				use_cache

       # Autoformer
       UpperCAmelCase_							:       Any       =				label_length
       UpperCAmelCase_							:       Union[str, Any]       =				moving_average
       UpperCAmelCase_							:       Tuple       =				autocorrelation_factor

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






  @property
  def _UpperCamelCase					(			self						):




       '''simple docstring'''

       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
       )






