
"""simple docstring"""



from typing import List, Optional, Union

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


UpperCAmelCase__          = logging.get_logger(__name__)

UpperCAmelCase__          = {
    '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       lowerCAmelCase__				(						A_	):
     __a						=					"""time_series_transformer"""
     __a						=					{
         """hidden_size""": """d_model""",
         """num_attention_heads""": """encoder_attention_heads""",
         """num_hidden_layers""": """encoder_layers""",
     }







     def __init__(							self			:			Optional[Any]    ,      _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 = 0.0_2    ,      _lowerCamelCase			:			Tuple=True    ,      **_lowerCamelCase			:			Union[str, Any]    ,      ):
       # time series specific configuration
       _snake_case					     =       prediction_length
       _snake_case					     =       context_length or prediction_length
       _snake_case					     =       distribution_output
       _snake_case					     =       loss
       _snake_case					     =       input_size
       _snake_case					     =       num_time_features
       _snake_case					     =       lags_sequence
       _snake_case					     =       scaling
       _snake_case					     =       num_dynamic_real_features
       _snake_case					     =       num_static_real_features
       _snake_case					     =       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`'''			)
         _snake_case					     =       cardinality
       else:
         _snake_case					     =       [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`'''			)
         _snake_case					     =       embedding_dimension
       else:
         _snake_case					     =       [min(50    ,      (cat + 1) // 2			) for cat in self.cardinality]
       _snake_case					     =       num_parallel_samples

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

       _snake_case					     =       dropout
       _snake_case					     =       attention_dropout
       _snake_case					     =       activation_dropout
       _snake_case					     =       encoder_layerdrop
       _snake_case					     =       decoder_layerdrop

       _snake_case					     =       activation_function
       _snake_case					     =       init_std

       _snake_case					     =       use_cache

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







     @property
     def      lowercase	(							self			:			str			):
       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"""



def 					_UpperCAmelCase      ( __lowerCamelCase   :       Tuple		)     ->					Any:
  stooge(__lowerCamelCase ,					0 ,					len(__lowerCamelCase		) - 1		)
  return arr
def 					_UpperCAmelCase      ( __lowerCamelCase   :       str ,					__lowerCamelCase   :       List[str] ,					__lowerCamelCase   :       str		)     ->					int:
  if i >= h:
    return

  # If first element is smaller than the last then swap them
  if arr[i] > arr[h]:
    _snake_case     ,					_snake_case					     =       arr[h], arr[i]

  # If there are more than 2 elements in the array
  if h - i + 1 > 2:
    _snake_case					     =       (int)((h - i + 1) / 3		)

    # Recursively sort first 2/3 elements
    stooge(__lowerCamelCase ,					__lowerCamelCase ,					(h - t)		)

    # Recursively sort last 2/3 elements
    stooge(__lowerCamelCase ,					i + t ,					(__lowerCamelCase)		)

    # Recursively sort first 2/3 elements
    stooge(__lowerCamelCase ,					__lowerCamelCase ,					(h - t)		)


if __name__ == "__main__":
 UpperCAmelCase__          = input('Enter numbers separated by a comma:\n').strip()
 UpperCAmelCase__          = [int(item) for item in user_input.split(',')]
 print(stooge_sort(unsorted))



