


'''simple docstring'''





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

import torch

from ..utils import BaseOutput


__lowerCamelCase												=						'''scheduler_config.json'''




class 				UpperCAmelCase			(			_snake_case						):
 UpperCAmelCase				=					1
 UpperCAmelCase				=					2
 UpperCAmelCase				=					3
 UpperCAmelCase				=					4
 UpperCAmelCase				=					5
 UpperCAmelCase				=					6
 UpperCAmelCase				=					7
 UpperCAmelCase				=					8
 UpperCAmelCase				=					9
 UpperCAmelCase				=					10
 UpperCAmelCase				=					11
 UpperCAmelCase				=					12
 UpperCAmelCase				=					13
 UpperCAmelCase				=					14




@dataclass
class 				UpperCAmelCase			(			_snake_case						):
 UpperCAmelCase				=					42




class 				UpperCAmelCase			:
 UpperCAmelCase				=					SCHEDULER_CONFIG_NAME
 UpperCAmelCase				=					[]
 UpperCAmelCase				=					True


 @classmethod
 def        __SCREAMING_SNAKE_CASE					(      cls							:	Optional[int]			,       __lowerCamelCase							:	Dict[str, Any] = None			,       __lowerCamelCase							:	Optional[str] = None			,       __lowerCamelCase							:	Any=False			,       **__lowerCamelCase							:	List[Any]			,       ):
    UpperCAmelCase__   ,						UpperCAmelCase__   ,						UpperCAmelCase__	:int      =      cls.load_config(
        pretrained_model_name_or_path=__lowerCamelCase			,       subfolder=__lowerCamelCase			,       return_unused_kwargs=__lowerCamelCase			,       return_commit_hash=__lowerCamelCase			,       **__lowerCamelCase			,       )
    return cls.from_config(__lowerCamelCase			,       return_unused_kwargs=__lowerCamelCase			,       **__lowerCamelCase )


 def        __SCREAMING_SNAKE_CASE					(      self							:	Optional[int]			,       __lowerCamelCase							:	Union[str, os.PathLike]			,       __lowerCamelCase							:	bool = False			,       **__lowerCamelCase							:	List[str] ):
    self.save_config(save_directory=__lowerCamelCase			,       push_to_hub=__lowerCamelCase			,       **__lowerCamelCase )


 @property
 def        __SCREAMING_SNAKE_CASE					(      self							:	Union[str, Any] ):
    return self._get_compatibles()





 @classmethod
 def        __SCREAMING_SNAKE_CASE					(      cls							:	Optional[Any] ):
    UpperCAmelCase__	:Union[str, Any]      =      list(set([cls.__name__] + cls._compatibles ) )
    UpperCAmelCase__	:List[Any]      =      importlib.import_module(__name__.split('''.''' )[0] )
    UpperCAmelCase__	:Dict      =      [
        getattr(__lowerCamelCase			,       __lowerCamelCase ) for c in compatible_classes_str if hasattr(__lowerCamelCase			,       __lowerCamelCase )
    ]
    return compatible_classes







'''simple docstring'''





from typing import List, Optional

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


__lowerCamelCase												=						logging.get_logger(__name__)

__lowerCamelCase												=						{
    '''huggingface/autoformer-tourism-monthly''': '''https://huggingface.co/huggingface/autoformer-tourism-monthly/resolve/main/config.json''',
}




class 				UpperCAmelCase			(			_snake_case						):
 UpperCAmelCase				=					"autoformer"
 UpperCAmelCase				=					{
     "hidden_size": "d_model",
     "num_attention_heads": "encoder_attention_heads",
     "num_hidden_layers": "encoder_layers",
 }


 def __init__(      self							:	List[str]			,       __lowerCamelCase							:	Optional[int] = None			,       __lowerCamelCase							:	Optional[int] = None			,       __lowerCamelCase							:	str = "student_t"			,       __lowerCamelCase							:	str = "nll"			,       __lowerCamelCase							:	int = 1			,       __lowerCamelCase							:	List[int] = [1, 2, 3, 4, 5, 6, 7]			,       __lowerCamelCase							:	bool = True			,       __lowerCamelCase							:	int = 0			,       __lowerCamelCase							:	int = 0			,       __lowerCamelCase							:	int = 0			,       __lowerCamelCase							:	int = 0			,       __lowerCamelCase							:	Optional[List[int]] = None			,       __lowerCamelCase							:	Optional[List[int]] = None			,       __lowerCamelCase							:	int = 6_4			,       __lowerCamelCase							:	int = 2			,       __lowerCamelCase							:	int = 2			,       __lowerCamelCase							:	int = 2			,       __lowerCamelCase							:	int = 2			,       __lowerCamelCase							:	int = 3_2			,       __lowerCamelCase							:	int = 3_2			,       __lowerCamelCase							:	str = "gelu"			,       __lowerCamelCase							:	float = 0.1			,       __lowerCamelCase							:	float = 0.1			,       __lowerCamelCase							:	float = 0.1			,       __lowerCamelCase							:	float = 0.1			,       __lowerCamelCase							:	float = 0.1			,       __lowerCamelCase							:	int = 1_0_0			,       __lowerCamelCase							:	float = 0.02			,       __lowerCamelCase							:	bool = True			,       __lowerCamelCase							:	Optional[int]=True			,       __lowerCamelCase							:	int = 1_0			,       __lowerCamelCase							:	int = 2_5			,       __lowerCamelCase							:	int = 3			,       **__lowerCamelCase							:	int			,       ):
    # time series specific configuration
    UpperCAmelCase__	:Any      =      prediction_length
    UpperCAmelCase__	:Optional[int]      =      context_length if context_length is not None else prediction_length
    UpperCAmelCase__	:int      =      distribution_output
    UpperCAmelCase__	:str      =      loss
    UpperCAmelCase__	:Tuple      =      input_size
    UpperCAmelCase__	:Optional[int]      =      num_time_features
    UpperCAmelCase__	:List[Any]      =      lags_sequence
    UpperCAmelCase__	:Any      =      scaling
    UpperCAmelCase__	:Union[str, Any]      =      num_dynamic_real_features
    UpperCAmelCase__	:Dict      =      num_static_real_features
    UpperCAmelCase__	:Optional[int]      =      num_static_categorical_features
    if cardinality is not None and num_static_categorical_features > 0:
       if len(__lowerCamelCase ) != num_static_categorical_features:
          raise ValueError(
              '''The cardinality should be a list of the same length as `num_static_categorical_features`''' )
       UpperCAmelCase__	:Any      =      cardinality
    else:
       UpperCAmelCase__	:Dict      =      [0]
    if embedding_dimension is not None and num_static_categorical_features > 0:
       if len(__lowerCamelCase ) != 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__	:int      =      [min(5_0			,       (cat + 1) // 2 ) for cat in self.cardinality]
    UpperCAmelCase__	:Optional[Any]      =      num_parallel_samples

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

    UpperCAmelCase__	:Optional[Any]      =      dropout
    UpperCAmelCase__	:str      =      attention_dropout
    UpperCAmelCase__	:int      =      activation_dropout
    UpperCAmelCase__	:Optional[Any]      =      encoder_layerdrop
    UpperCAmelCase__	:Dict      =      decoder_layerdrop

    UpperCAmelCase__	:Union[str, Any]      =      activation_function
    UpperCAmelCase__	:List[Any]      =      init_std

    UpperCAmelCase__	:List[str]      =      use_cache

    # Autoformer
    UpperCAmelCase__	:List[str]      =      label_length
    UpperCAmelCase__	:Dict      =      moving_average
    UpperCAmelCase__	:str      =      autocorrelation_factor

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





 @property
 def        __SCREAMING_SNAKE_CASE					(      self							:	int ):
    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
    )



