


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 	UpperCAmelCase_					(					UpperCamelCase    ):

   '''simple docstring'''




   __A :      Union[str, Any]     		=		"time_series_transformer"
   __A :      List[Any]     		=		{
       "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						,				):






     """simple docstring"""





     lowerCamelCase :					Union[str, Any]    =				prediction_length
     lowerCamelCase :					str    =				context_length or prediction_length
     lowerCamelCase :					Any    =				distribution_output
     lowerCamelCase :					int    =				loss
     lowerCamelCase :					Optional[int]    =				input_size
     lowerCamelCase :					str    =				num_time_features
     lowerCamelCase :					List[Any]    =				lags_sequence
     lowerCamelCase :					Tuple    =				scaling
     lowerCamelCase :					int    =				num_dynamic_real_features
     lowerCamelCase :					Any    =				num_static_real_features
     lowerCamelCase :					Any    =				num_static_categorical_features
     if cardinality and num_static_categorical_features > 0:
       if len(__A ) != num_static_categorical_features:
         raise ValueError(
             "The cardinality should be a list of the same length as `num_static_categorical_features`" )
       lowerCamelCase :					str    =				cardinality
     else:
       lowerCamelCase :					Optional[int]    =				[0]
     if embedding_dimension and num_static_categorical_features > 0:
       if len(__A ) != num_static_categorical_features:
         raise ValueError(
             "The embedding dimension should be a list of the same length as `num_static_categorical_features`" )
       lowerCamelCase :					Optional[int]    =				embedding_dimension
     else:
       lowerCamelCase :					Optional[int]    =				[min(50						,				(cat + 1) // 2 ) for cat in self.cardinality]
     lowerCamelCase :					Union[str, Any]    =				num_parallel_samples

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

     lowerCamelCase :					Optional[Any]    =				dropout
     lowerCamelCase :					Optional[Any]    =				attention_dropout
     lowerCamelCase :					Optional[Any]    =				activation_dropout
     lowerCamelCase :					List[str]    =				encoder_layerdrop
     lowerCamelCase :					int    =				decoder_layerdrop

     lowerCamelCase :					Dict    =				activation_function
     lowerCamelCase :					Dict    =				init_std

     lowerCamelCase :					int    =				use_cache

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






   @property
   def _snake_case    (		self ):






     """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
     )







def 		lowercase_(  SCREAMING_SNAKE_CASE_	):


  '''simple docstring'''

  if divisor % 5 == 0 or divisor % 2 == 0:
    return 0
  lowerCamelCase :					List[Any]    =				1
  lowerCamelCase :					Union[str, Any]    =				1
  while repunit:
    lowerCamelCase :					Union[str, Any]    =				(10 * repunit + 1) % divisor
    repunit_index += 1
  return repunit_index






def 		lowercase_(  SCREAMING_SNAKE_CASE_ = 1000000	):


  '''simple docstring'''

  lowerCamelCase :					List[str]    =				limit - 1
  if divisor % 2 == 0:
    divisor += 1
  while least_divisible_repunit(SCREAMING_SNAKE_CASE_	) <= limit:
    divisor += 2
  return divisor


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



