

'''simple docstring'''
from typing import List, Optional, Union

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


lowerCamelCase :      List[Any]					 =     logging.get_logger(__name__)

lowerCamelCase :      str					 =     {
    "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 							A__    (       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[int]							,					_a      :	Optional[int] = None							,					_a      :	Optional[int] = None							,					_a      :	str = "student_t"							,					_a      :	str = "nll"							,					_a      :	int = 1							,					_a      :	List[int] = [1, 2, 3, 4, 5, 6, 7]							,					_a      :	Optional[Union[str, bool]] = "mean"							,					_a      :	int = 0							,					_a      :	int = 0							,					_a      :	int = 0							,					_a      :	int = 0							,					_a      :	Optional[List[int]] = None							,					_a      :	Optional[List[int]] = None							,					_a      :	int = 32							,					_a      :	int = 32							,					_a      :	int = 2							,					_a      :	int = 2							,					_a      :	int = 2							,					_a      :	int = 2							,					_a      :	bool = True							,					_a      :	str = "gelu"							,					_a      :	int = 64							,					_a      :	float = 0.1							,					_a      :	float = 0.1							,					_a      :	float = 0.1							,					_a      :	float = 0.1							,					_a      :	float = 0.1							,					_a      :	int = 100							,					_a      :	float = 0.02							,					_a      :	Union[str, Any]=True							,					**_a      :	Optional[Any]							,					)	->    Optional[Any]:





       '''simple docstring'''






       _SCREAMING_SNAKE_CASE							    =prediction_length
       _SCREAMING_SNAKE_CASE							    =context_length or prediction_length
       _SCREAMING_SNAKE_CASE							    =distribution_output
       _SCREAMING_SNAKE_CASE							    =loss
       _SCREAMING_SNAKE_CASE							    =input_size
       _SCREAMING_SNAKE_CASE							    =num_time_features
       _SCREAMING_SNAKE_CASE							    =lags_sequence
       _SCREAMING_SNAKE_CASE							    =scaling
       _SCREAMING_SNAKE_CASE							    =num_dynamic_real_features
       _SCREAMING_SNAKE_CASE							    =num_static_real_features
       _SCREAMING_SNAKE_CASE							    =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`' )
            _SCREAMING_SNAKE_CASE							    =cardinality
       else:
            _SCREAMING_SNAKE_CASE							    =[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`' )
            _SCREAMING_SNAKE_CASE							    =embedding_dimension
       else:
            _SCREAMING_SNAKE_CASE							    =[min(50							,					(cat + 1) // 2 ) for cat in self.cardinality]
       _SCREAMING_SNAKE_CASE							    =num_parallel_samples

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

       _SCREAMING_SNAKE_CASE							    =dropout
       _SCREAMING_SNAKE_CASE							    =attention_dropout
       _SCREAMING_SNAKE_CASE							    =activation_dropout
       _SCREAMING_SNAKE_CASE							    =encoder_layerdrop
       _SCREAMING_SNAKE_CASE							    =decoder_layerdrop

       _SCREAMING_SNAKE_CASE							    =activation_function
       _SCREAMING_SNAKE_CASE							    =init_std

       _SCREAMING_SNAKE_CASE							    =use_cache

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





  @property
  def 						A  (		self      :	List[Any] )	->    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'''
lowerCamelCase :      Any					 =     "\n# Transformers installation\n! pip install transformers datasets\n# To install from source instead of the last release, comment the command above and uncomment the following one.\n# ! pip install git+https://github.com/huggingface/transformers.git\n"

lowerCamelCase :      int					 =     [{"type": "code", "content": INSTALL_CONTENT}]
lowerCamelCase :      str					 =     {
    "{processor_class}": "FakeProcessorClass",
    "{model_class}": "FakeModelClass",
    "{object_class}": "FakeObjectClass",
}
