





from typing import List, Optional, Union

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


snake_case__					:							Dict													=     logging.get_logger(__name__)

snake_case__					:							Dict													=     {
    '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 A_					(			lowerCamelCase__   ):
   lowerCAmelCase__        = 'informer'
   lowerCAmelCase__        = {
       'hidden_size': 'd_model',
       'num_attention_heads': 'encoder_attention_heads',
       'num_hidden_layers': 'encoder_layers',
   }




   def __init__(self	:Tuple    ,   _UpperCamelCase	:Optional[int] = None    ,   _UpperCamelCase	:Optional[int] = None    ,   _UpperCamelCase	:str = "student_t"    ,   _UpperCamelCase	:str = "nll"    ,   _UpperCamelCase	:int = 1    ,   _UpperCamelCase	:List[int] = None    ,   _UpperCamelCase	:Optional[Union[str, bool]] = "mean"    ,   _UpperCamelCase	:int = 0    ,   _UpperCamelCase	:int = 0    ,   _UpperCamelCase	:int = 0    ,   _UpperCamelCase	:int = 0    ,   _UpperCamelCase	:Optional[List[int]] = None    ,   _UpperCamelCase	:Optional[List[int]] = None    ,   _UpperCamelCase	:int = 64    ,   _UpperCamelCase	:int = 32    ,   _UpperCamelCase	:int = 32    ,   _UpperCamelCase	:int = 2    ,   _UpperCamelCase	:int = 2    ,   _UpperCamelCase	:int = 2    ,   _UpperCamelCase	:int = 2    ,   _UpperCamelCase	:bool = True    ,   _UpperCamelCase	:str = "gelu"    ,   _UpperCamelCase	:float = 0.0_5    ,   _UpperCamelCase	:float = 0.1    ,   _UpperCamelCase	:float = 0.1    ,   _UpperCamelCase	:float = 0.1    ,   _UpperCamelCase	:float = 0.1    ,   _UpperCamelCase	:int = 100    ,   _UpperCamelCase	:float = 0.0_2    ,   _UpperCamelCase	:Any=True    ,   _UpperCamelCase	:str = "prob"    ,   _UpperCamelCase	:int = 5    ,   _UpperCamelCase	:bool = True    ,   **_UpperCamelCase	:List[str]    ,   )->					List[str]:
         # 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 if lags_sequence is not None else [1, 2, 3, 4, 5, 6, 7]
         __A          =				scaling
         __A          =				num_dynamic_real_features
         __A          =				num_static_real_features
         __A          =				num_static_categorical_features

         # set cardinality
         if cardinality and num_static_categorical_features > 0:
               if len(lowercase__ ) != 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]

         # set embedding_dimension
         if embedding_dimension and num_static_categorical_features > 0:
               if len(lowercase__ ) != 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(self.lags_sequence ) + 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

         # Informer
         __A          =				attention_type
         __A          =				sampling_factor
         __A          =				distil

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






   @property
   def _lowerCAmelCase      (self	:List[Any] )->					Tuple:
         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'''






import os

lowerCAmelCase__											=	{'''I''': 1, '''V''': 5, '''X''': 10, '''L''': 50, '''C''': 100, '''D''': 500, '''M''': 1000}


def 		_A     (    A__      ):






 """simple docstring"""



 __lowercase							  = 0

 __lowercase							  = 0
 while index < len(A__      ) - 1:
  __lowercase							  = SYMBOLS[numerals[index]]
  __lowercase							  = SYMBOLS[numerals[index + 1]]
  if current_value < next_value:
   total_value -= current_value
  else:
   total_value += current_value
  index += 1
 total_value += SYMBOLS[numerals[index]]

 return total_value


def 		_A     (    A__      ):






 """simple docstring"""



 __lowercase							  = ''''''

 __lowercase							  = num // 1000
 numerals += m_count * "M"
 num %= 1000

 __lowercase							  = num // 100
 if c_count == 9:
  numerals += "CM"
  c_count -= 9
 elif c_count == 4:
  numerals += "CD"
  c_count -= 4
 if c_count >= 5:
  numerals += "D"
  c_count -= 5
 numerals += c_count * "C"
 num %= 100

 __lowercase							  = num // 10
 if x_count == 9:
  numerals += "XC"
  x_count -= 9
 elif x_count == 4:
  numerals += "XL"
  x_count -= 4
 if x_count >= 5:
  numerals += "L"
  x_count -= 5
 numerals += x_count * "X"
 num %= 10

 if num == 9:
  numerals += "IX"
  num -= 9
 elif num == 4:
  numerals += "IV"
  num -= 4
 if num >= 5:
  numerals += "V"
  num -= 5
 numerals += num * "I"

 return numerals


def 		_A     (    A__ = "/p089_roman.txt"      ):






 """simple docstring"""



 __lowercase							  = 0

 with open(os.path.dirname(A__      ) + roman_numerals_filename      ) as filea:
  __lowercase							  = filea.readlines()

 for line in lines:
  __lowercase							  = line.strip()
  __lowercase							  = parse_roman_numerals(A__      )
  __lowercase							  = generate_roman_numerals(A__      )
  savings += len(A__      ) - len(A__      )

 return savings


if __name__ == "__main__":
 print(f'{solution() = }')

