


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

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


__SCREAMING_SNAKE_CASE							:      Union[str, Any]						=      logging.get_logger(__name__)

__SCREAMING_SNAKE_CASE							:      List[Any]						=      {
    'huggingface/informer-tourism-monthly': (
        'https://huggingface.co/huggingface/informer-tourism-monthly/resolve/main/config.json'
    ),
    # See all Informer models at https://huggingface.co/models?filter=informer
}





class        __A       (snake_case__):





							'''simple docstring'''

							__lowercase:						List[Any]							 =      """informer"""
							__lowercase:						Any							 =      {
							    """hidden_size""": """d_model""",
							    """num_attention_heads""": """encoder_attention_heads""",
							    """num_hidden_layers""": """encoder_layers""",
							}



							def __init__(			self							: Dict						,    UpperCAmelCase_							: Optional[int] = None						,    UpperCAmelCase_							: Optional[int] = None						,    UpperCAmelCase_							: str = "student_t"						,    UpperCAmelCase_							: str = "nll"						,    UpperCAmelCase_							: int = 1						,    UpperCAmelCase_							: List[int] = None						,    UpperCAmelCase_							: Optional[Union[str, bool]] = "mean"						,    UpperCAmelCase_							: int = 0						,    UpperCAmelCase_							: int = 0						,    UpperCAmelCase_							: int = 0						,    UpperCAmelCase_							: int = 0						,    UpperCAmelCase_							: Optional[List[int]] = None						,    UpperCAmelCase_							: Optional[List[int]] = None						,    UpperCAmelCase_							: int = 64						,    UpperCAmelCase_							: int = 32						,    UpperCAmelCase_							: int = 32						,    UpperCAmelCase_							: int = 2						,    UpperCAmelCase_							: int = 2						,    UpperCAmelCase_							: int = 2						,    UpperCAmelCase_							: int = 2						,    UpperCAmelCase_							: bool = True						,    UpperCAmelCase_							: str = "gelu"						,    UpperCAmelCase_							: float = 0.05						,    UpperCAmelCase_							: float = 0.1						,    UpperCAmelCase_							: float = 0.1						,    UpperCAmelCase_							: float = 0.1						,    UpperCAmelCase_							: float = 0.1						,    UpperCAmelCase_							: int = 100						,    UpperCAmelCase_							: float = 0.02						,    UpperCAmelCase_							: Tuple=True						,    UpperCAmelCase_							: str = "prob"						,    UpperCAmelCase_							: int = 5						,    UpperCAmelCase_							: bool = True						,    **UpperCAmelCase_							: str						,    )   ->int:



														"""simple docstring"""



														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 if lags_sequence is not None else [1, 2, 3, 4, 5, 6, 7]
														snake_case_          =						scaling
														snake_case_          =						num_dynamic_real_features
														snake_case_          =						num_static_real_features
														snake_case_          =						num_static_categorical_features

														# set cardinality
														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_          =						cardinality
														else:
																					snake_case_          =						[0]

														# set embedding_dimension
														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_          =						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(self.lags_sequence    ) + 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

														# Informer
														snake_case_          =						attention_type
														snake_case_          =						sampling_factor
														snake_case_          =						distil

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



							@property
							def 						lowerCAmelCase    (			self							: Dict    )   ->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"""
def   _a			( _SCREAMING_SNAKE_CASE			,	_SCREAMING_SNAKE_CASE			)      ->			str:
							return "\n".join(
							    f"""{number} * {i} = {number * i}""" for i in range(1			,	number_of_terms + 1			)			)


if __name__ == "__main__":
				print(multiplication_table(number=5, number_of_terms=10))



