






from typing import List, Optional, Union

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


__UpperCAmelCase							:   Tuple							=  logging.get_logger(__name__)

__UpperCAmelCase							:   List[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 					_snake_case						(			_A ):
	_A          =   'time_series_transformer'
	_A          =   {
	    '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					,)						->    List[Any]:
					# time series specific configuration
					snake_case__      :Optional[Any]         =   prediction_length
					snake_case__      :str         =   context_length or prediction_length
					snake_case__      :Dict         =   distribution_output
					snake_case__      :Tuple         =   loss
					snake_case__      :Optional[int]         =   input_size
					snake_case__      :Union[str, Any]         =   num_time_features
					snake_case__      :List[Any]         =   lags_sequence
					snake_case__      :Union[str, Any]         =   scaling
					snake_case__      :Union[str, Any]         =   num_dynamic_real_features
					snake_case__      :List[str]         =   num_static_real_features
					snake_case__      :List[Any]         =   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`"       )
									snake_case__      :Any         =   cardinality
					else:
									snake_case__      :Optional[Any]         =   [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`"       )
									snake_case__      :Optional[Any]         =   embedding_dimension
					else:
									snake_case__      :str         =   [min(50					,(cat + 1) // 2       ) for cat in self.cardinality]
					snake_case__      :Union[str, Any]         =   num_parallel_samples

					# Transformer architecture configuration
					snake_case__      :int         =   input_size * len(UpperCamelCase       ) + self._number_of_features
					snake_case__      :List[str]         =   d_model
					snake_case__      :Any         =   encoder_attention_heads
					snake_case__      :Dict         =   decoder_attention_heads
					snake_case__      :Any         =   encoder_ffn_dim
					snake_case__      :Tuple         =   decoder_ffn_dim
					snake_case__      :str         =   encoder_layers
					snake_case__      :int         =   decoder_layers

					snake_case__      :Any         =   dropout
					snake_case__      :Dict         =   attention_dropout
					snake_case__      :Dict         =   activation_dropout
					snake_case__      :int         =   encoder_layerdrop
					snake_case__      :Union[str, Any]         =   decoder_layerdrop

					snake_case__      :Optional[Any]         =   activation_function
					snake_case__      :Tuple         =   init_std

					snake_case__      :Optional[int]         =   use_cache

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






	@property
	def    lowerCAmelCase_							(		self       )						->    int:
					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_ (    __snake_case     :							str      )     -> list:





				'''simple docstring'''


				return [
				    txt[:a] + txt[a].upper() + txt[a + 1 :]
				    for a in range(len(__snake_case      )      )
				    if txt[a].isalpha()
				]


if __name__ == "__main__":
	__import__("doctest").testmod()