



from typing import List, Optional, Union

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


a_								=       logging.get_logger(__name__)

a_								=       {
    """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  (						lowerCAmelCase__ ):
 lowerCAmelCase__							       =	"""time_series_transformer"""
 lowerCAmelCase__							       =	{
     """hidden_size""": """d_model""",
     """num_attention_heads""": """encoder_attention_heads""",
     """num_hidden_layers""": """encoder_layers""",
 }




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



    '''simple docstring'''
    # time series specific configuration
    __lowerCamelCase    				=     prediction_length
    __lowerCamelCase    				=     context_length or prediction_length
    __lowerCamelCase    				=     distribution_output
    __lowerCamelCase    				=     loss
    __lowerCamelCase    				=     input_size
    __lowerCamelCase    				=     num_time_features
    __lowerCamelCase    				=     lags_sequence
    __lowerCamelCase    				=     scaling
    __lowerCamelCase    				=     num_dynamic_real_features
    __lowerCamelCase    				=     num_static_real_features
    __lowerCamelCase    				=     num_static_categorical_features
    if cardinality and num_static_categorical_features > 0:
       if len(__UpperCAmelCase					) != num_static_categorical_features:
          raise ValueError(
              '''The cardinality should be a list of the same length as `num_static_categorical_features`'''					)
       __lowerCamelCase    				=     cardinality
    else:
       __lowerCamelCase    				=     [0]
    if embedding_dimension and num_static_categorical_features > 0:
       if len(__UpperCAmelCase					) != num_static_categorical_features:
          raise ValueError(
              '''The embedding dimension should be a list of the same length as `num_static_categorical_features`'''					)
       __lowerCamelCase    				=     embedding_dimension
    else:
       __lowerCamelCase    				=     [min(50						,					(cat + 1) // 2					) for cat in self.cardinality]
    __lowerCamelCase    				=     num_parallel_samples

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

    __lowerCamelCase    				=     dropout
    __lowerCamelCase    				=     attention_dropout
    __lowerCamelCase    				=     activation_dropout
    __lowerCamelCase    				=     encoder_layerdrop
    __lowerCamelCase    				=     decoder_layerdrop

    __lowerCamelCase    				=     activation_function
    __lowerCamelCase    				=     init_std

    __lowerCamelCase    				=     use_cache

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




 @property
 def 							lowerCamelCase   (    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
    )











from __future__ import annotations





def 		a__      (		_UpperCamelCase      :			list[float]				):
   if len(_UpperCamelCase				) < 2:
      raise ValueError('''Monogons and Digons are not polygons in the Euclidean space'''				)
   if any(i <= 0 for i in nums				):
      raise ValueError('''All values must be greater than 0'''				)
   __lowerCamelCase    				=     nums.copy()
   copy_nums.sort()
   return copy_nums[-1] < sum(copy_nums[:-1]				)


if __name__ == "__main__":
  import doctest

  doctest.testmod()






