'''simple docstring'''

from typing import List, Optional, Union

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


lowerCAmelCase_ : Dict						  =      logging.get_logger(__name__)

lowerCAmelCase_ : Any						  =      {
    "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						(						__lowerCamelCase ):
   lowerCamelCase_    					='time_series_transformer'
   lowerCamelCase_    					={
       '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				:	Optional[Union[str, bool]] = "mean"   ,		__lowerCAmelCase				:	int = 0   ,		__lowerCAmelCase				:	int = 0   ,		__lowerCAmelCase				:	int = 0   ,		__lowerCAmelCase				:	int = 0   ,		__lowerCAmelCase				:	Optional[List[int]] = None   ,		__lowerCAmelCase				:	Optional[List[int]] = None   ,		__lowerCAmelCase				:	int = 32   ,		__lowerCAmelCase				:	int = 32   ,		__lowerCAmelCase				:	int = 2   ,		__lowerCAmelCase				:	int = 2   ,		__lowerCAmelCase				:	int = 2   ,		__lowerCAmelCase				:	int = 2   ,		__lowerCAmelCase				:	bool = True   ,		__lowerCAmelCase				:	str = "gelu"   ,		__lowerCAmelCase				:	int = 64   ,		__lowerCAmelCase				:	float = 0.1   ,		__lowerCAmelCase				:	float = 0.1   ,		__lowerCAmelCase				:	float = 0.1   ,		__lowerCAmelCase				:	float = 0.1   ,		__lowerCAmelCase				:	float = 0.1   ,		__lowerCAmelCase				:	int = 100   ,		__lowerCAmelCase				:	float = 0.02   ,		__lowerCAmelCase				:	str=True   ,		**__lowerCAmelCase				:	int   ,		)      ->					List[str]:
         # time series specific configuration
         lowercase_     =      prediction_length
         lowercase_     =      context_length or prediction_length
         lowercase_     =      distribution_output
         lowercase_     =      loss
         lowercase_     =      input_size
         lowercase_     =      num_time_features
         lowercase_     =      lags_sequence
         lowercase_     =      scaling
         lowercase_     =      num_dynamic_real_features
         lowercase_     =      num_static_real_features
         lowercase_     =      num_static_categorical_features
         if cardinality 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`")
               lowercase_     =      cardinality
         else:
               lowercase_     =      [0]
         if embedding_dimension 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`")
               lowercase_     =      embedding_dimension
         else:
               lowercase_     =      [min(50   ,		(cat + 1) // 2) for cat in self.cardinality]
         lowercase_     =      num_parallel_samples

         # Transformer architecture configuration
         lowercase_     =      input_size * len(__lowerCAmelCase) + self._number_of_features
         lowercase_     =      d_model
         lowercase_     =      encoder_attention_heads
         lowercase_     =      decoder_attention_heads
         lowercase_     =      encoder_ffn_dim
         lowercase_     =      decoder_ffn_dim
         lowercase_     =      encoder_layers
         lowercase_     =      decoder_layers

         lowercase_     =      dropout
         lowercase_     =      attention_dropout
         lowercase_     =      activation_dropout
         lowercase_     =      encoder_layerdrop
         lowercase_     =      decoder_layerdrop

         lowercase_     =      activation_function
         lowercase_     =      init_std

         lowercase_     =      use_cache

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


   @property
   def __UpperCAmelCase       (   self				:	Optional[int])      ->					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
         )







'''simple docstring'''

from __future__ import annotations


def __a (	__lowerCamelCase			:						list[list[int]]				)	->							bool:
      '''simple docstring'''


      lowercase_     =      len(__lowerCamelCase				)
      # We need to create solution object to save path.
      lowercase_     =      [[0 for _ in range(__lowerCamelCase				)] for _ in range(__lowerCamelCase				)]
      lowercase_     =      run_maze(__lowerCamelCase ,		0 ,		0 ,		__lowerCamelCase				)
      if solved:
            print("\n".join(str(__lowerCamelCase				) for row in solutions				)				)
      else:
            print("No solution exists!"				)
      return solved


def __a (	__lowerCamelCase			:						list[list[int]] ,		__lowerCamelCase			:						int ,		__lowerCamelCase			:						int ,		__lowerCamelCase			:						list[list[int]]				)	->							bool:
      '''simple docstring'''


      lowercase_     =      len(__lowerCamelCase				)
      # Final check point.
      if i == j == (size - 1):
            lowercase_     =      1
            return True

      lowercase_     =      (not i < 0) and (not j < 0)  # Check lower bounds
      lowercase_     =      (i < size) and (j < size)  # Check upper bounds

      if lower_flag and upper_flag:
            # check for already visited and block points.
            lowercase_     =      (not solutions[i][j]) and (not maze[i][j])
            if block_flag:
                  # check visited
                  lowercase_     =      1

                  # check for directions
                  if (
                      run_maze(__lowerCamelCase ,		i + 1 ,		__lowerCamelCase ,		__lowerCamelCase				)
                      or run_maze(__lowerCamelCase ,		__lowerCamelCase ,		j + 1 ,		__lowerCamelCase				)
                      or run_maze(__lowerCamelCase ,		i - 1 ,		__lowerCamelCase ,		__lowerCamelCase				)
                      or run_maze(__lowerCamelCase ,		__lowerCamelCase ,		j - 1 ,		__lowerCamelCase				)
                  ):
                        return True

                  lowercase_     =      0
                  return False
      return False


if __name__ == "__main__":
       import doctest

       doctest.testmod()






