



'''simple docstring'''






import numpy as np

UpperCamelCase__:     Any										=    [
    ["a", "b", "c", "d", "e"],
    ["f", "g", "h", "i", "k"],
    ["l", "m", "n", "o", "p"],
    ["q", "r", "s", "t", "u"],
    ["v", "w", "x", "y", "z"],
]

class        SCREAMING_SNAKE_CASE:







	"""simple docstring"""






	def __init__(		self    :  Any	)    ->	None:
								UpperCAmelCase				: Optional[int]											=	np.array(__snake_case	)







	def   A			(		self    :  str	,    __snake_case    :  str	)    ->	np.ndarray:
								UpperCAmelCase							,	UpperCAmelCase				: Dict											=	np.where(letter == self.SQUARE	)
								UpperCAmelCase				: Dict											=	np.concatenate([indexa + 1, indexa + 1]	)
								return indexes







	def   A			(		self    :  Union[str, Any]	,    __snake_case    :  int	,    __snake_case    :  int	)    ->	str:
								UpperCAmelCase				: int											=	self.SQUARE[indexa - 1, indexa - 1]
								return letter







	def   A			(		self    :  List[Any]	,    __snake_case    :  str	)    ->	str:
								UpperCAmelCase				: Any											=	message.lower()
								UpperCAmelCase				: List[Any]											=	message.replace(''' '''	,    ''''''	)
								UpperCAmelCase				: List[Any]											=	message.replace('''j'''	,    '''i'''	)

								UpperCAmelCase				: Tuple											=	np.empty((2, len(__snake_case	))	)
								for letter_index in range(len(__snake_case	)	):
															UpperCAmelCase				: str											=	self.letter_to_numbers(message[letter_index]	)

															UpperCAmelCase				: Any											=	numbers[0]
															UpperCAmelCase				: Optional[Any]											=	numbers[1]

								UpperCAmelCase				: List[str]											=	first_step.reshape(2 * len(__snake_case	)	)
								UpperCAmelCase				: Union[str, Any]											=	''''''
								for numbers_index in range(len(__snake_case	)	):
															UpperCAmelCase				: Dict											=	int(second_step[numbers_index * 2]	)
															UpperCAmelCase				: Union[str, Any]											=	int(second_step[(numbers_index * 2) + 1]	)
															UpperCAmelCase				: Dict											=	self.numbers_to_letter(__snake_case	,    __snake_case	)
															UpperCAmelCase				: int											=	encoded_message + letter

								return encoded_message







	def   A			(		self    :  Optional[Any]	,    __snake_case    :  str	)    ->	str:
								UpperCAmelCase				: str											=	message.lower()
								message.replace(''' '''	,    ''''''	)
								UpperCAmelCase				: Optional[int]											=	np.empty(2 * len(__snake_case	)	)
								for letter_index in range(len(__snake_case	)	):
															UpperCAmelCase				: int											=	self.letter_to_numbers(message[letter_index]	)
															UpperCAmelCase				: Optional[Any]											=	numbers[0]
															UpperCAmelCase				: List[Any]											=	numbers[1]

								UpperCAmelCase				: Tuple											=	first_step.reshape((2, len(__snake_case	))	)
								UpperCAmelCase				: str											=	''''''
								for numbers_index in range(len(__snake_case	)	):
															UpperCAmelCase				: Tuple											=	int(second_step[0, numbers_index]	)
															UpperCAmelCase				: List[str]											=	int(second_step[1, numbers_index]	)
															UpperCAmelCase				: Union[str, Any]											=	self.numbers_to_letter(__snake_case	,    __snake_case	)
															UpperCAmelCase				: Union[str, Any]											=	decoded_message + letter

								return decoded_message










'''simple docstring'''






from typing import List, Optional, Union

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


UpperCamelCase__:     Optional[Any]										=    logging.get_logger(__name__)

UpperCamelCase__:     Tuple										=    {
    "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        SCREAMING_SNAKE_CASE(							A__    ):







	"""simple docstring"""






	lowerCamelCase__    				= """time_series_transformer"""
	lowerCamelCase__    				= {
	    """hidden_size""": """d_model""",
	    """num_attention_heads""": """encoder_attention_heads""",
	    """num_hidden_layers""": """encoder_layers""",
	}







	def __init__(		self    :  str	,    __snake_case    :  Optional[int] = None	,    __snake_case    :  Optional[int] = None	,    __snake_case    :  str = "student_t"	,    __snake_case    :  str = "nll"	,    __snake_case    :  int = 1	,    __snake_case    :  List[int] = [1, 2, 3, 4, 5, 6, 7]	,    __snake_case    :  Optional[Union[str, bool]] = "mean"	,    __snake_case    :  int = 0	,    __snake_case    :  int = 0	,    __snake_case    :  int = 0	,    __snake_case    :  int = 0	,    __snake_case    :  Optional[List[int]] = None	,    __snake_case    :  Optional[List[int]] = None	,    __snake_case    :  int = 32	,    __snake_case    :  int = 32	,    __snake_case    :  int = 2	,    __snake_case    :  int = 2	,    __snake_case    :  int = 2	,    __snake_case    :  int = 2	,    __snake_case    :  bool = True	,    __snake_case    :  str = "gelu"	,    __snake_case    :  int = 64	,    __snake_case    :  float = 0.1	,    __snake_case    :  float = 0.1	,    __snake_case    :  float = 0.1	,    __snake_case    :  float = 0.1	,    __snake_case    :  float = 0.1	,    __snake_case    :  int = 100	,    __snake_case    :  float = 0.02	,    __snake_case    :  Optional[Any]=True	,    **__snake_case    :  List[Any]	,    )    ->	List[str]:
								# time series specific configuration
								UpperCAmelCase				: List[Any]											=	prediction_length
								UpperCAmelCase				: List[Any]											=	context_length or prediction_length
								UpperCAmelCase				: Tuple											=	distribution_output
								UpperCAmelCase				: Optional[Any]											=	loss
								UpperCAmelCase				: Tuple											=	input_size
								UpperCAmelCase				: Optional[int]											=	num_time_features
								UpperCAmelCase				: Dict											=	lags_sequence
								UpperCAmelCase				: Any											=	scaling
								UpperCAmelCase				: Tuple											=	num_dynamic_real_features
								UpperCAmelCase				: Any											=	num_static_real_features
								UpperCAmelCase				: Optional[int]											=	num_static_categorical_features
								if cardinality and num_static_categorical_features > 0:
															if len(__snake_case	) != num_static_categorical_features:
																						raise ValueError(
																						    '''The cardinality should be a list of the same length as `num_static_categorical_features`'''	)
															UpperCAmelCase				: Any											=	cardinality
								else:
															UpperCAmelCase				: Optional[Any]											=	[0]
								if embedding_dimension and num_static_categorical_features > 0:
															if len(__snake_case	) != num_static_categorical_features:
																						raise ValueError(
																						    '''The embedding dimension should be a list of the same length as `num_static_categorical_features`'''	)
															UpperCAmelCase				: Optional[int]											=	embedding_dimension
								else:
															UpperCAmelCase				: Optional[Any]											=	[min(50	,    (cat + 1) // 2	) for cat in self.cardinality]
								UpperCAmelCase				: List[Any]											=	num_parallel_samples

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

								UpperCAmelCase				: Optional[int]											=	dropout
								UpperCAmelCase				: Union[str, Any]											=	attention_dropout
								UpperCAmelCase				: List[Any]											=	activation_dropout
								UpperCAmelCase				: Optional[int]											=	encoder_layerdrop
								UpperCAmelCase				: Dict											=	decoder_layerdrop

								UpperCAmelCase				: Tuple											=	activation_function
								UpperCAmelCase				: Dict											=	init_std

								UpperCAmelCase				: Optional[int]											=	use_cache

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







	@property
	def   A			(		self    :  Union[str, Any]	)    ->	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
								)





