





from dataclasses import dataclass, field
from typing import TYPE_CHECKING, Any, ClassVar, Dict, List, Optional, Union

import pyarrow as pa


if TYPE_CHECKING:
  from .features import FeatureType







@dataclass
class   __lowerCAmelCase			:
   _UpperCamelCase :						List[str]
   _UpperCamelCase :						Optional[str]     		=				None
   # Automatically constructed
   _UpperCamelCase :						ClassVar[str]     		=				"dict"
   _UpperCamelCase :						ClassVar[Any]     		=				None
   _UpperCamelCase :						str     		=				field(default="""Translation"""		,init=__A		,repr=__A  )





   def __call__( self      )				->  List[Any]:
      """simple docstring"""





      return pa.struct({lang: pa.string() for lang in sorted(self.languages      )}      )





   def 	_snake_case ( self      )				->  Dict:
      """simple docstring"""





      from .features import Value

      return {k: Value("string"      ) for k in sorted(self.languages      )}







@dataclass
class   __lowerCAmelCase			:
   _UpperCamelCase :						Optional[List]     		=				None
   _UpperCamelCase :						Optional[int]     		=				None
   _UpperCamelCase :						Optional[str]     		=				None
   # Automatically constructed
   _UpperCamelCase :						ClassVar[str]     		=				"dict"
   _UpperCamelCase :						ClassVar[Any]     		=				None
   _UpperCamelCase :						str     		=				field(default="""TranslationVariableLanguages"""		,init=__A		,repr=__A  )





   def 	_snake_case ( self      )				->  Tuple:
      """simple docstring"""





      a__	:						Any      		=  sorted(set(self.languages      )      ) if self.languages else None
      a__	:						Dict      		=  len(self.languages      ) if self.languages else None





   def __call__( self      )				->  Any:
      """simple docstring"""





      return pa.struct({"language": pa.list_(pa.string()      ), "translation": pa.list_(pa.string()      )}      )





   def 	_snake_case ( self					,			snake_case      )				->  str:
      """simple docstring"""





      a__	:						Union[str, Any]      		=  set(self.languages      )
      if self.languages and set(UpperCamelCase__      ) - lang_set:
         raise ValueError(
             F"""Some languages in example ({", ".join(sorted(set(UpperCamelCase__      ) - lang_set      )      )}) are not in valid set ({", ".join(UpperCamelCase__      )})."""      )

      # Convert dictionary into tuples, splitting out cases where there are
      # multiple translations for a single language.
      a__	:						List[str]      		=  []
      for lang, text in translation_dict.items():
         if isinstance(UpperCamelCase__					,			UpperCamelCase__      ):
            translation_tuples.append((lang, text)      )
         else:
            translation_tuples.extend([(lang, el) for el in text]      )

        # Ensure translations are in ascending order by language code.
      a__	:						Dict      		=  zip(*sorted(UpperCamelCase__      )      )

      return {"language": languages, "translation": translations}





   def 	_snake_case ( self      )				->  Dict:
      """simple docstring"""





      from .features import Sequence, Value

      return {
          "language": Sequence(Value("string"      )      ),
          "translation": Sequence(Value("string"      )      ),
      }








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
      )

