
from typing import List, Optional, Union

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


_A   =			logging.get_logger(__name__)

_A   =			{
    "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 					lowerCamelCase		(  A_  ):
     UpperCAmelCase__    :							Tuple									=		"informer"
     UpperCAmelCase__    :							Union[str, Any]									=		{
         "hidden_size": "d_model",
         "num_attention_heads": "encoder_attention_heads",
         "num_hidden_layers": "encoder_layers",
     }





     def __init__(self				:   List[str]    ,   _A				:   Optional[int] = None    ,   _A				:   Optional[int] = None    ,   _A				:   str = "student_t"    ,   _A				:   str = "nll"    ,   _A				:   int = 1    ,   _A				:   List[int] = None    ,   _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 = 6_4    ,   _A				:   int = 3_2    ,   _A				:   int = 3_2    ,   _A				:   int = 2    ,   _A				:   int = 2    ,   _A				:   int = 2    ,   _A				:   int = 2    ,   _A				:   bool = True    ,   _A				:   str = "gelu"    ,   _A				:   float = 0.05    ,   _A				:   float = 0.1    ,   _A				:   float = 0.1    ,   _A				:   float = 0.1    ,   _A				:   float = 0.1    ,   _A				:   int = 1_0_0    ,   _A				:   float = 0.02    ,   _A				:   Optional[int]=True    ,   _A				:   str = "prob"    ,   _A				:   int = 5    ,   _A				:   bool = True    ,   **_A				:   int    ,   )						->						int:
         # time series specific configuration
         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(_A       ) != 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(_A       ) != 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(5_0    ,   (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=_A    ,   **_A       )





     @property
     def 		UpperCAmelCase(self				:   Dict       )						->						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
         )



from __future__ import annotations

from decimal import Decimal

from numpy import array



def 						lowercase_      (       A__		)							->							list[list[float]]:
    """simple docstring"""

    snake_case								= Decimal

    # Check if the provided matrix has 2 rows and 2 columns
    # since this implementation only works for 2x2 matrices
    if len(A__		) == 2 and len(matrix[0]		) == 2 and len(matrix[1]		) == 2:
        # Calculate the determinant of the matrix
        snake_case								= float(
            d(matrix[0][0]		) * d(matrix[1][1]		) - d(matrix[1][0]		) * d(matrix[0][1]		)		)
        if determinant == 0:
            raise ValueError("This matrix has no inverse."		)

        # Creates a copy of the matrix with swapped positions of the elements
        snake_case								= [[0.0, 0.0], [0.0, 0.0]]
        snake_case					,							snake_case								= matrix[1][1], matrix[0][0]
        snake_case					,							snake_case								= -matrix[1][0], -matrix[0][1]

        # Calculate the inverse of the matrix
        return [
            [(float(d(A__		)		) / determinant) or 0.0 for n in row] for row in swapped_matrix
        ]
    elif (
        len(A__		) == 3
        and len(matrix[0]		) == 3
        and len(matrix[1]		) == 3
        and len(matrix[2]		) == 3
    ):
        # Calculate the determinant of the matrix using Sarrus rule
        snake_case								= float(
            (
                (d(matrix[0][0]		) * d(matrix[1][1]		) * d(matrix[2][2]		))
                + (d(matrix[0][1]		) * d(matrix[1][2]		) * d(matrix[2][0]		))
                + (d(matrix[0][2]		) * d(matrix[1][0]		) * d(matrix[2][1]		))
            )
            - (
                (d(matrix[0][2]		) * d(matrix[1][1]		) * d(matrix[2][0]		))
                + (d(matrix[0][1]		) * d(matrix[1][0]		) * d(matrix[2][2]		))
                + (d(matrix[0][0]		) * d(matrix[1][2]		) * d(matrix[2][1]		))
            )		)
        if determinant == 0:
            raise ValueError("This matrix has no inverse."		)

        # Creating cofactor matrix
        snake_case								= [
            [d(0.0		), d(0.0		), d(0.0		)],
            [d(0.0		), d(0.0		), d(0.0		)],
            [d(0.0		), d(0.0		), d(0.0		)],
        ]
        snake_case								= (d(matrix[1][1]		) * d(matrix[2][2]		)) - (
            d(matrix[1][2]		) * d(matrix[2][1]		)
        )
        snake_case								= -(
            (d(matrix[1][0]		) * d(matrix[2][2]		)) - (d(matrix[1][2]		) * d(matrix[2][0]		))
        )
        snake_case								= (d(matrix[1][0]		) * d(matrix[2][1]		)) - (
            d(matrix[1][1]		) * d(matrix[2][0]		)
        )
        snake_case								= -(
            (d(matrix[0][1]		) * d(matrix[2][2]		)) - (d(matrix[0][2]		) * d(matrix[2][1]		))
        )
        snake_case								= (d(matrix[0][0]		) * d(matrix[2][2]		)) - (
            d(matrix[0][2]		) * d(matrix[2][0]		)
        )
        snake_case								= -(
            (d(matrix[0][0]		) * d(matrix[2][1]		)) - (d(matrix[0][1]		) * d(matrix[2][0]		))
        )
        snake_case								= (d(matrix[0][1]		) * d(matrix[1][2]		)) - (
            d(matrix[0][2]		) * d(matrix[1][1]		)
        )
        snake_case								= -(
            (d(matrix[0][0]		) * d(matrix[1][2]		)) - (d(matrix[0][2]		) * d(matrix[1][0]		))
        )
        snake_case								= (d(matrix[0][0]		) * d(matrix[1][1]		)) - (
            d(matrix[0][1]		) * d(matrix[1][0]		)
        )

        # Transpose the cofactor matrix (Adjoint matrix)
        snake_case								= array(A__		)
        for i in range(3		):
            for j in range(3		):
                snake_case								= cofactor_matrix[j][i]

        # Inverse of the matrix using the formula (1/determinant) * adjoint matrix
        snake_case								= array(A__		)
        for i in range(3		):
            for j in range(3		):
                inverse_matrix[i][j] /= d(A__		)

        # Calculate the inverse of the matrix
        return [[float(d(A__		)		) or 0.0 for n in row] for row in inverse_matrix]
    raise ValueError("Please provide a matrix of size 2x2 or 3x3."		)

