






'''simple docstring'''



from typing import List, Optional, Union

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


__UpperCamelCase   :							int         =   logging.get_logger(__name__)

__UpperCamelCase   :							Optional[int]         =   {
    """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 				__SCREAMING_SNAKE_CASE							(				_lowerCAmelCase  ):
      __a	       ="time_series_transformer"
      __a	       ={
          "hidden_size": "d_model",
          "num_attention_heads": "encoder_attention_heads",
          "num_hidden_layers": "encoder_layers",
      }




      def __init__(	self			, lowerCamelCase = None			, lowerCamelCase = None			, lowerCamelCase = "student_t"			, lowerCamelCase = "nll"			, lowerCamelCase = 1			, lowerCamelCase = [1, 2, 3, 4, 5, 6, 7]			, lowerCamelCase = "mean"			, lowerCamelCase = 0			, lowerCamelCase = 0			, lowerCamelCase = 0			, lowerCamelCase = 0			, lowerCamelCase = None			, lowerCamelCase = None			, lowerCamelCase = 32			, lowerCamelCase = 32			, lowerCamelCase = 2			, lowerCamelCase = 2			, lowerCamelCase = 2			, lowerCamelCase = 2			, lowerCamelCase = True			, lowerCamelCase = "gelu"			, lowerCamelCase = 64			, lowerCamelCase = 0.1			, lowerCamelCase = 0.1			, lowerCamelCase = 0.1			, lowerCamelCase = 0.1			, lowerCamelCase = 0.1			, lowerCamelCase = 100			, lowerCamelCase = 0.02			, lowerCamelCase=True			, **lowerCamelCase			, )				->Dict:



         '''simple docstring'''
         # time series specific configuration
         __a					  =     prediction_length
         __a					  =     context_length or prediction_length
         __a					  =     distribution_output
         __a					  =     loss
         __a					  =     input_size
         __a					  =     num_time_features
         __a					  =     lags_sequence
         __a					  =     scaling
         __a					  =     num_dynamic_real_features
         __a					  =     num_static_real_features
         __a					  =     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`'   )
            __a					  =     cardinality
         else:
            __a					  =     [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`'   )
            __a					  =     embedding_dimension
         else:
            __a					  =     [min(50			, (cat + 1) // 2   ) for cat in self.cardinality]
         __a					  =     num_parallel_samples

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

         __a					  =     dropout
         __a					  =     attention_dropout
         __a					  =     activation_dropout
         __a					  =     encoder_layerdrop
         __a					  =     decoder_layerdrop

         __a					  =     activation_function
         __a					  =     init_std

         __a					  =     use_cache

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






      @property
      def 			__UpperCamelCase				(	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
         )







'''simple docstring'''



from __future__ import annotations

from typing import TypedDict
class 				__SCREAMING_SNAKE_CASE							(				_lowerCAmelCase  ):
      __a	       =42
      __a	       =42







def  __UpperCAmelCase						(					SCREAMING_SNAKE_CASE__:						str     )	->      list[str]:

   """simple docstring"""
   if not isinstance(SCREAMING_SNAKE_CASE__,       SCREAMING_SNAKE_CASE__     ):
      raise TypeError('The parameter s type must be str.'     )

   return [s[i:] + s[:i] for i in range(len(SCREAMING_SNAKE_CASE__     )     )]



def  __UpperCAmelCase						(					SCREAMING_SNAKE_CASE__:						str     )	->      BWTTransformDict:

   """simple docstring"""
   if not isinstance(SCREAMING_SNAKE_CASE__,       SCREAMING_SNAKE_CASE__     ):
      raise TypeError('The parameter s type must be str.'     )
   if not s:
      raise ValueError('The parameter s must not be empty.'     )

   __a					  =     all_rotations(SCREAMING_SNAKE_CASE__     )
   rotations.sort()  # sort the list of rotations in alphabetically order
   # make a string composed of the last char of each rotation
   __a					  =     {
       "bwt_string": "".join([word[-1] for word in rotations]     ),
       "idx_original_string": rotations.index(SCREAMING_SNAKE_CASE__     ),
   }
   return response



def  __UpperCAmelCase						(					SCREAMING_SNAKE_CASE__:						str,       SCREAMING_SNAKE_CASE__:						int     )	->      str:

   """simple docstring"""
   if not isinstance(SCREAMING_SNAKE_CASE__,       SCREAMING_SNAKE_CASE__     ):
      raise TypeError('The parameter bwt_string type must be str.'     )
   if not bwt_string:
      raise ValueError('The parameter bwt_string must not be empty.'     )
   try:
      __a					  =     int(SCREAMING_SNAKE_CASE__     )
   except ValueError:
      raise TypeError(
          'The parameter idx_original_string type must be int or passive'
          ' of cast to int.'     )
   if idx_original_string < 0:
      raise ValueError('The parameter idx_original_string must not be lower than 0.'     )
   if idx_original_string >= len(SCREAMING_SNAKE_CASE__     ):
      raise ValueError(
          'The parameter idx_original_string must be lower than' ' len(bwt_string).'     )

   __a					  =     [''] * len(SCREAMING_SNAKE_CASE__     )
   for _ in range(len(SCREAMING_SNAKE_CASE__     )     ):
      for i in range(len(SCREAMING_SNAKE_CASE__     )     ):
         __a					  =     bwt_string[i] + ordered_rotations[i]
      ordered_rotations.sort()
   return ordered_rotations[idx_original_string]


if __name__ == "__main__":
 __UpperCamelCase   :							Any         =   """Provide a string that I will generate its BWT transform: """
 __UpperCamelCase   :							Dict         =   input(entry_msg).strip()
 __UpperCamelCase   :							Optional[int]         =   bwt_transform(s)
 print(
     f"""Burrows Wheeler transform for string '{s}' results """
     f"""in '{result["bwt_string"]}'"""
 )
 __UpperCamelCase   :							Dict         =   reverse_bwt(result["""bwt_string"""], result["""idx_original_string"""])
 print(
     f"""Reversing Burrows Wheeler transform for entry '{result["bwt_string"]}' """
     f"""we get original string '{original_string}'"""
 )