




from typing import List, Optional, Union

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


_snake_case            = logging.get_logger(__name__)

_snake_case            = {
    "huggingface/time-series-transformer-tourism-monthly": (
        "https://huggingface.co/huggingface/time-series-transformer-tourism-monthly/resolve/main/config.json"
    ),
    # See all TimeSeriesTransformer models at https://huggingface.co/models?filter=time_series_transformer
}







class 		lowercase							(     UpperCamelCase__	):
     _a											=		"time_series_transformer"
     _a											=		{
         "hidden_size": "d_model",
         "num_attention_heads": "encoder_attention_heads",
         "num_hidden_layers": "encoder_layers",
     }

     def __init__(						self	,  _a = None	,  _a = None	,  _a = "student_t"	,  _a = "nll"	,  _a = 1	,  _a = [1, 2, 3, 4, 5, 6, 7]	,  _a = "mean"	,  _a = 0	,  _a = 0	,  _a = 0	,  _a = 0	,  _a = None	,  _a = None	,  _a = 32	,  _a = 32	,  _a = 2	,  _a = 2	,  _a = 2	,  _a = 2	,  _a = True	,  _a = "gelu"	,  _a = 64	,  _a = 0.1	,  _a = 0.1	,  _a = 0.1	,  _a = 0.1	,  _a = 0.1	,  _a = 100	,  _a = 0.02	,  _a=True	,  **_a	,  )		->							Optional[int]:
           # time series specific configuration
           _A							:  int    =		prediction_length
           _A							:  Optional[Any]    =		context_length or prediction_length
           _A							:  Optional[int]    =		distribution_output
           _A							:  List[str]    =		loss
           _A							:  Dict    =		input_size
           _A							:  Optional[int]    =		num_time_features
           _A							:  str    =		lags_sequence
           _A							:  Any    =		scaling
           _A							:  int    =		num_dynamic_real_features
           _A							:  Optional[Any]    =		num_static_real_features
           _A							:  List[str]    =		num_static_categorical_features
           if cardinality and num_static_categorical_features > 0:
                 if len(UpperCAmelCase__       ) != num_static_categorical_features:
                       raise ValueError(
                           """The cardinality should be a list of the same length as `num_static_categorical_features`"""       )
                 _A							:  str    =		cardinality
           else:
                 _A							:  Optional[int]    =		[0]
           if embedding_dimension and num_static_categorical_features > 0:
                 if len(UpperCAmelCase__       ) != num_static_categorical_features:
                       raise ValueError(
                           """The embedding dimension should be a list of the same length as `num_static_categorical_features`"""       )
                 _A							:  Dict    =		embedding_dimension
           else:
                 _A							:  str    =		[min(50	,  (cat + 1) // 2       ) for cat in self.cardinality]
           _A							:  int    =		num_parallel_samples

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

           _A							:  Dict    =		dropout
           _A							:  Any    =		attention_dropout
           _A							:  Any    =		activation_dropout
           _A							:  List[Any]    =		encoder_layerdrop
           _A							:  Optional[Any]    =		decoder_layerdrop

           _A							:  Dict    =		activation_function
           _A							:  Tuple    =		init_std

           _A							:  Union[str, Any]    =		use_cache

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



     @property
     def       a__			(						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
           )







from __future__ import annotations







def        lowerCAmelCase_			(  snake_case_,snake_case_ = None  ):
      _A							:  Tuple    =		word_bank or []
      # create a table
      _A							:  int    =		len(snake_case_  ) + 1

      _A							:  list[list[list[str]]]    =		[]
      for _ in range(snake_case_  ):
            table.append([]  )
      # seed value
      _A							:  Dict    =		[[]]  # because empty string has empty combination

      # iterate through the indices
      for i in range(snake_case_  ):
            # condition
            if table[i] != []:
                  for word in word_bank:
                        # slice condition
                        if target[i : i + len(snake_case_  )] == word:
                              _A							:  list[list[str]]    =		[
                                  [word, *way] for way in table[i]
                              ]
                              # adds the word to every combination the current position holds
                              # now,push that combination to the table[i+len(word)]
                              table[i + len(snake_case_  )] += new_combinations

    # combinations are in reverse order so reverse for better output
      for combination in table[len(snake_case_  )]:
            combination.reverse()

      return table[len(snake_case_  )]


if __name__ == "__main__":
  print(all_construct("jwajalapa", ["jwa", "j", "w", "a", "la", "lapa"]))
  print(all_construct("rajamati", ["s", "raj", "amat", "raja", "ma", "i", "t"]))
  print(
      all_construct(
          "hexagonosaurus",
          ["h", "ex", "hex", "ag", "ago", "ru", "auru", "rus", "go", "no", "o", "s"],
      )
  )

