'''simple docstring'''






from __future__ import annotations

from typing import Any





class        A__				:





   """simple docstring"""





   def __init__(					self	:						List[str]		,	lowerCAmelCase__	:						Any		,	lowerCAmelCase__	:						List[str]		,	lowerCAmelCase__	:						Optional[Any] = 0     )			->       str:







      """simple docstring"""




      _UpperCAmelCase				:      int				=  row, column
      _UpperCAmelCase				:      Optional[int]				=  [[default_value for c in range(lowerCAmelCase_     )] for r in range(lowerCAmelCase_     )]




   def __str__(					self	:						int     )			->       Optional[Any]:







      """simple docstring"""




      _UpperCAmelCase				:      Union[str, Any]				=  F"""Matrix consist of {self.row} rows and {self.column} columns\n"""

      # Make string identifier
      _UpperCAmelCase				:      str				=  0
      for row_vector in self.array:
         for obj in row_vector:
            _UpperCAmelCase				:      int				=  max(lowerCAmelCase_		,	len(str(lowerCAmelCase_     )     )     )
      _UpperCAmelCase				:      Tuple				=  F"""%{max_element_length}s"""

      # Make string and return
      def single_line(lowerCAmelCase__	:						Union[str, Any]     ) -> str:
         nonlocal string_format_identifier
         _UpperCAmelCase				:      Tuple				=  """["""
         line += ", ".join(string_format_identifier % (obj,) for obj in row_vector     )
         line += "]"
         return line

      s += "\n".join(single_line(lowerCAmelCase_     ) for row_vector in self.array     )
      return s




   def __repr__(					self	:						Tuple     )			->       Union[str, Any]:







      """simple docstring"""




      return str(self     )




   def      _lowerCAmelCase						(					self	:						Tuple		,	lowerCAmelCase__	:						List[str]     )			->       Optional[Any]:







      """simple docstring"""




      if not (isinstance(lowerCAmelCase_		,	(list, tuple)     ) and len(lowerCAmelCase_     ) == 2):
         return False
      elif not (0 <= loc[0] < self.row and 0 <= loc[1] < self.column):
         return False
      else:
         return True




   def __getitem__(					self	:						str		,	lowerCAmelCase__	:						Tuple     )			->       Dict:







      """simple docstring"""




      assert self.validate_indicies(lowerCAmelCase_     )
      return self.array[loc[0]][loc[1]]




   def __setitem__(					self	:						List[str]		,	lowerCAmelCase__	:						str		,	lowerCAmelCase__	:						Tuple     )			->       Tuple:







      """simple docstring"""




      assert self.validate_indicies(lowerCAmelCase_     )
      _UpperCAmelCase				:      Optional[int]				=  value




   def __add__(					self	:						Tuple		,	lowerCAmelCase__	:						str     )			->       Optional[Any]:







      """simple docstring"""




      assert isinstance(lowerCAmelCase_		,	lowerCAmelCase_     )
      assert self.row == another.row and self.column == another.column

      # Add
      _UpperCAmelCase				:      Dict				=  Matrix(self.row		,	self.column     )
      for r in range(self.row     ):
         for c in range(self.column     ):
            _UpperCAmelCase				:      Optional[Any]				=  self[r, c] + another[r, c]
      return result




   def __neg__(					self	:						str     )			->       Tuple:







      """simple docstring"""




      _UpperCAmelCase				:      Any				=  Matrix(self.row		,	self.column     )
      for r in range(self.row     ):
         for c in range(self.column     ):
            _UpperCAmelCase				:      int				=  -self[r, c]
      return result




   def __sub__(					self	:						List[Any]		,	lowerCAmelCase__	:						int     )			->       Union[str, Any]:







      """simple docstring"""




      return self + (-another)




   def __mul__(					self	:						Optional[Any]		,	lowerCAmelCase__	:						List[Any]     )			->       int:







      """simple docstring"""




      if isinstance(lowerCAmelCase_		,	(int, float)     ):  # Scalar multiplication
         _UpperCAmelCase				:      str				=  Matrix(self.row		,	self.column     )
         for r in range(self.row     ):
            for c in range(self.column     ):
               _UpperCAmelCase				:      Tuple				=  self[r, c] * another
         return result
      elif isinstance(lowerCAmelCase_		,	lowerCAmelCase_     ):  # Matrix multiplication
         assert self.column == another.row
         _UpperCAmelCase				:      int				=  Matrix(self.row		,	another.column     )
         for r in range(self.row     ):
            for c in range(another.column     ):
               for i in range(self.column     ):
                  result[r, c] += self[r, i] * another[i, c]
         return result
      else:
         _UpperCAmelCase				:      str				=  F"""Unsupported type given for another ({type(lowerCAmelCase_     )})"""
         raise TypeError(lowerCAmelCase_     )




   def      _lowerCAmelCase						(					self	:						Tuple     )			->       List[str]:







      """simple docstring"""




      _UpperCAmelCase				:      Optional[Any]				=  Matrix(self.column		,	self.row     )
      for r in range(self.row     ):
         for c in range(self.column     ):
            _UpperCAmelCase				:      int				=  self[r, c]
      return result




   def      _lowerCAmelCase						(					self	:						Any		,	lowerCAmelCase__	:						Optional[Any]		,	lowerCAmelCase__	:						Optional[int]     )			->       Optional[int]:







      """simple docstring"""




      assert isinstance(lowerCAmelCase_		,	lowerCAmelCase_     ) and isinstance(lowerCAmelCase_		,	lowerCAmelCase_     )
      assert self.row == self.column == u.row == v.row  # u, v should be column vector
      assert u.column == v.column == 1  # u, v should be column vector

      # Calculate
      _UpperCAmelCase				:      Optional[Any]				=  v.transpose()
      _UpperCAmelCase				:      Tuple				=  (v_t * self * u)[0, 0] + 1
      if numerator_factor == 0:
         return None  # It's not invertable
      return self - ((self * u) * (v_t * self) * (1.0 / numerator_factor))


# Testing
if __name__ == "__main__":






     def   __UpperCAmelCase     (    ):
        # a^(-1)
        _UpperCAmelCase				:      str				=  Matrix(3,   3,   0				)
        for i in range(3				):
           _UpperCAmelCase				:      Union[str, Any]				=  1
        print(f"""a^(-1) is {ainv}"""				)
        # u, v
        _UpperCAmelCase				:      Dict				=  Matrix(3,   1,   0				)
        _UpperCAmelCase				:      int				=  1, 2, -3
        _UpperCAmelCase				:      str				=  Matrix(3,   1,   0				)
        _UpperCAmelCase				:      Tuple				=  4, -2, 5
        print(f"""u is {u}"""				)
        print(f"""v is {v}"""				)
        print(f"""uv^T is {u * v.transpose()}"""				)
        # Sherman Morrison
        print(f"""(a + uv^T)^(-1) is {ainv.sherman_morrison(snake_case__,   snake_case__				)}"""				)






     def   __UpperCAmelCase     (    ):
        import doctest

        doctest.testmod()






     testa()







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

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


_lowerCAmelCase       =				logging.get_logger(__name__)

_lowerCAmelCase       =				{
    "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   SCREAMING_SNAKE_CASE	(					_SCREAMING_SNAKE_CASE    ):





	"""simple docstring"""




	_A							:					Optional[Any] 			=					"""informer"""
	_A							:					Dict 			=					{
	    """hidden_size""": """d_model""",
	    """num_attention_heads""": """encoder_attention_heads""",
	    """num_hidden_layers""": """encoder_layers""",
	}





	def __init__(self  ,  lowerCAmelCase_ = None  ,  lowerCAmelCase_ = None  ,  lowerCAmelCase_ = "student_t"  ,  lowerCAmelCase_ = "nll"  ,  lowerCAmelCase_ = 1  ,  lowerCAmelCase_ = None  ,  lowerCAmelCase_ = "mean"  ,  lowerCAmelCase_ = 0  ,  lowerCAmelCase_ = 0  ,  lowerCAmelCase_ = 0  ,  lowerCAmelCase_ = 0  ,  lowerCAmelCase_ = None  ,  lowerCAmelCase_ = None  ,  lowerCAmelCase_ = 64  ,  lowerCAmelCase_ = 32  ,  lowerCAmelCase_ = 32  ,  lowerCAmelCase_ = 2  ,  lowerCAmelCase_ = 2  ,  lowerCAmelCase_ = 2  ,  lowerCAmelCase_ = 2  ,  lowerCAmelCase_ = True  ,  lowerCAmelCase_ = "gelu"  ,  lowerCAmelCase_ = 0.05  ,  lowerCAmelCase_ = 0.1  ,  lowerCAmelCase_ = 0.1  ,  lowerCAmelCase_ = 0.1  ,  lowerCAmelCase_ = 0.1  ,  lowerCAmelCase_ = 100  ,  lowerCAmelCase_ = 0.02  ,  lowerCAmelCase_=True  ,  lowerCAmelCase_ = "prob"  ,  lowerCAmelCase_ = 5  ,  lowerCAmelCase_ = True  ,  **lowerCAmelCase_  ,  ):
		# time series specific configuration
		A_		:    Optional[Any]						     = prediction_length
		A_		:    Dict						     = context_length or prediction_length
		A_		:    Dict						     = distribution_output
		A_		:    Tuple						     = loss
		A_		:    Dict						     = input_size
		A_		:    Union[str, Any]						     = num_time_features
		A_		:    List[str]						     = lags_sequence if lags_sequence is not None else [1, 2, 3, 4, 5, 6, 7]
		A_		:    Optional[int]						     = scaling
		A_		:    Optional[Any]						     = num_dynamic_real_features
		A_		:    Tuple						     = num_static_real_features
		A_		:    Tuple						     = num_static_categorical_features

		# set cardinality
		if cardinality and num_static_categorical_features > 0:
			if len(lowerCAmelCase_				) != num_static_categorical_features:
				raise ValueError(
				    """The cardinality should be a list of the same length as `num_static_categorical_features`"""				)
			A_		:    List[str]						     = cardinality
		else:
			A_		:    List[Any]						     = [0]

		# set embedding_dimension
		if embedding_dimension and num_static_categorical_features > 0:
			if len(lowerCAmelCase_				) != num_static_categorical_features:
				raise ValueError(
				    """The embedding dimension should be a list of the same length as `num_static_categorical_features`"""				)
			A_		:    int						     = embedding_dimension
		else:
			A_		:    Optional[int]						     = [min(50  ,  (cat + 1) // 2				) for cat in self.cardinality]

		A_		:    Optional[int]						     = num_parallel_samples

		# Transformer architecture configuration
		A_		:    Union[str, Any]						     = input_size * len(self.lags_sequence				) + self._number_of_features
		A_		:    Dict						     = d_model
		A_		:    Dict						     = encoder_attention_heads
		A_		:    Dict						     = decoder_attention_heads
		A_		:    Any						     = encoder_ffn_dim
		A_		:    Tuple						     = decoder_ffn_dim
		A_		:    Tuple						     = encoder_layers
		A_		:    Optional[int]						     = decoder_layers

		A_		:    List[str]						     = dropout
		A_		:    List[str]						     = attention_dropout
		A_		:    Any						     = activation_dropout
		A_		:    Any						     = encoder_layerdrop
		A_		:    List[Any]						     = decoder_layerdrop

		A_		:    str						     = activation_function
		A_		:    Optional[Any]						     = init_std

		A_		:    Optional[int]						     = use_cache

		# Informer
		A_		:    Dict						     = attention_type
		A_		:    List[Any]						     = sampling_factor
		A_		:    List[Any]						     = distil

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





	@property
	def  lowerCamelCase(self				):
		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
		)

