





from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


SCREAMING_SNAKE_CASE__       :			List[Any]			      =						{'configuration_vit_msn': ['VIT_MSN_PRETRAINED_CONFIG_ARCHIVE_MAP', 'ViTMSNConfig']}

try:
  if not is_torch_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  SCREAMING_SNAKE_CASE__       :			int			      =						[
      'VIT_MSN_PRETRAINED_MODEL_ARCHIVE_LIST',
      'ViTMSNModel',
      'ViTMSNForImageClassification',
      'ViTMSNPreTrainedModel',
  ]

if TYPE_CHECKING:
  from .configuration_vit_msn import VIT_MSN_PRETRAINED_CONFIG_ARCHIVE_MAP, ViTMSNConfig

  try:
    if not is_torch_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_vit_msn import (
        VIT_MSN_PRETRAINED_MODEL_ARCHIVE_LIST,
        ViTMSNForImageClassification,
        ViTMSNModel,
        ViTMSNPreTrainedModel,
    )

else:
  import sys

  SCREAMING_SNAKE_CASE__       :			Union[str, Any]			      =						_LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)








from typing import List, Optional, Union

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


SCREAMING_SNAKE_CASE__       :			Any			      =						logging.get_logger(__name__)

SCREAMING_SNAKE_CASE__       :			str			      =						{
    """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   __lowerCAmelCase			(					_UpperCamelCase  ):
   _UpperCamelCase :						Optional[Any]     		=				"""informer"""
   _UpperCamelCase :						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 = None					,			snake_case = "mean"					,			snake_case = 0					,			snake_case = 0					,			snake_case = 0					,			snake_case = 0					,			snake_case = None					,			snake_case = None					,			snake_case = 64					,			snake_case = 32					,			snake_case = 32					,			snake_case = 2					,			snake_case = 2					,			snake_case = 2					,			snake_case = 2					,			snake_case = True					,			snake_case = "gelu"					,			snake_case = 0.05					,			snake_case = 0.1					,			snake_case = 0.1					,			snake_case = 0.1					,			snake_case = 0.1					,			snake_case = 100					,			snake_case = 0.02					,			snake_case=True					,			snake_case = "prob"					,			snake_case = 5					,			snake_case = True					,			**snake_case					,			)				->  Union[str, Any]:
      """simple docstring"""





      a__	:						Optional[Any]      		=  prediction_length
      a__	:						Optional[int]      		=  context_length or prediction_length
      a__	:						Optional[int]      		=  distribution_output
      a__	:						str      		=  loss
      a__	:						Optional[Any]      		=  input_size
      a__	:						int      		=  num_time_features
      a__	:						Optional[int]      		=  lags_sequence if lags_sequence is not None else [1, 2, 3, 4, 5, 6, 7]
      a__	:						Optional[int]      		=  scaling
      a__	:						List[str]      		=  num_dynamic_real_features
      a__	:						Optional[int]      		=  num_static_real_features
      a__	:						Optional[int]      		=  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__	:						List[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__	:						Tuple      		=  embedding_dimension
      else:
         a__	:						int      		=  [min(50					,			(cat + 1) // 2      ) for cat in self.cardinality]

      a__	:						Optional[Any]      		=  num_parallel_samples

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

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

      a__	:						Any      		=  activation_function
      a__	:						Tuple      		=  init_std

      a__	:						Optional[int]      		=  use_cache

      # Informer
      a__	:						Union[str, Any]      		=  attention_type
      a__	:						List[str]      		=  sampling_factor
      a__	:						Optional[int]      		=  distil

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





   @property
   def 	_snake_case ( self      )				->  int:
      """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
      )

