




"""simple docstring"""







from __future__ import annotations

from typing import Any






class       SCREAMING_SNAKE_CASE__					:


						def __init__(							self				:			Tuple						,						lowerCAmelCase				:			int						,						lowerCAmelCase				:			int						,						lowerCAmelCase				:			float = 0					):
										lowerCAmelCase ,						lowerCAmelCase  =				row, column
										lowerCAmelCase  =				[[default_value for c in range(lowerCAmelCase					)] for r in range(lowerCAmelCase					)]


						def __str__(							self				:			Dict					):
										lowerCAmelCase  =				f'''Matrix consist of {self.row} rows and {self.column} columns\n'''

										# Make string identifier
										lowerCAmelCase  =				0
										for row_vector in self.array:
														for obj in row_vector:
																		lowerCAmelCase  =				max(lowerCAmelCase						,						len(str(lowerCAmelCase					)					)					)
										lowerCAmelCase  =				f'''%{max_element_length}s'''

										# Make string and return
										def single_line(lowerCAmelCase				:			list[float]					) -> str:
														nonlocal string_format_identifier
														lowerCAmelCase  =				"""["""
														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				:			List[Any]					):
										return str(self					)


						def   __lowercase (							self				:			Dict						,						lowerCAmelCase				:			tuple[int, int]					):
										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				:			int						,						lowerCAmelCase				:			tuple[int, int]					):
										assert self.validate_indicies(lowerCAmelCase					)
										return self.array[loc[0]][loc[1]]


						def __setitem__(							self				:			Tuple						,						lowerCAmelCase				:			tuple[int, int]						,						lowerCAmelCase				:			float					):
										assert self.validate_indicies(lowerCAmelCase					)
										lowerCAmelCase  =				value


						def __add__(							self				:			Any						,						lowerCAmelCase				:			Matrix					):
										assert isinstance(lowerCAmelCase						,						lowerCAmelCase					)
										assert self.row == another.row and self.column == another.column

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


						def __neg__(							self				:			List[str]					):
										lowerCAmelCase  =				Matrix(self.row						,						self.column					)
										for r in range(self.row					):
														for c in range(self.column					):
																		lowerCAmelCase  =				-self[r, c]
										return result


						def __sub__(							self				:			str						,						lowerCAmelCase				:			Matrix					):
										return self + (-another)


						def __mul__(							self				:			Optional[Any]						,						lowerCAmelCase				:			int | float | Matrix					):

										if isinstance(lowerCAmelCase						,						(int, float)					):  # Scalar multiplication
														lowerCAmelCase  =				Matrix(self.row						,						self.column					)
														for r in range(self.row					):
																		for c in range(self.column					):
																						lowerCAmelCase  =				self[r, c] * another
														return result
										elif isinstance(lowerCAmelCase						,						lowerCAmelCase					):  # Matrix multiplication
														assert self.column == another.row
														lowerCAmelCase  =				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:
														lowerCAmelCase  =				f'''Unsupported type given for another ({type(lowerCAmelCase					)})'''
														raise TypeError(lowerCAmelCase					)


						def   __lowercase (							self				:			Tuple					):
										lowerCAmelCase  =				Matrix(self.column						,						self.row					)
										for r in range(self.row					):
														for c in range(self.column					):
																		lowerCAmelCase  =				self[r, c]
										return result




						def   __lowercase (							self				:			Any						,						lowerCAmelCase				:			Matrix						,						lowerCAmelCase				:			Matrix					):
										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
										lowerCAmelCase  =				v.transpose()
										lowerCAmelCase  =				(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 							lowercase					()     ->  None:


						'''simple docstring'''






						lowerCAmelCase  =				Matrix(3      ,       3      ,       0 )
						for i in range(3 ):
										lowerCAmelCase  =				1
						print(f'''a^(-1) is {ainv}''' )
						# u, v
						lowerCAmelCase  =				Matrix(3      ,       1      ,       0 )
						lowerCAmelCase ,						lowerCAmelCase ,						lowerCAmelCase  =				1, 2, -3
						lowerCAmelCase  =				Matrix(3      ,       1      ,       0 )
						lowerCAmelCase ,						lowerCAmelCase ,						lowerCAmelCase  =				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 							lowercase					()     ->  None:


						'''simple docstring'''






						import doctest

						doctest.testmod()






		testa()











"""simple docstring"""







from typing import List, Optional

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


a      =							logging.get_logger(__name__)

a      =							{
    'huggingface/autoformer-tourism-monthly': 'https://huggingface.co/huggingface/autoformer-tourism-monthly/resolve/main/config.json',
}






class       SCREAMING_SNAKE_CASE__					(					_a					):
						_a    						=						'autoformer'
						_a    						=						{
						    'hidden_size': 'd_model',
						    'num_attention_heads': 'encoder_attention_heads',
						    'num_hidden_layers': 'encoder_layers',
						}


						def __init__(							self				:			Dict						,						lowerCAmelCase				:			Optional[int] = None						,						lowerCAmelCase				:			Optional[int] = None						,						lowerCAmelCase				:			str = "student_t"						,						lowerCAmelCase				:			str = "nll"						,						lowerCAmelCase				:			int = 1						,						lowerCAmelCase				:			List[int] = [1, 2, 3, 4, 5, 6, 7]						,						lowerCAmelCase				:			bool = True						,						lowerCAmelCase				:			int = 0						,						lowerCAmelCase				:			int = 0						,						lowerCAmelCase				:			int = 0						,						lowerCAmelCase				:			int = 0						,						lowerCAmelCase				:			Optional[List[int]] = None						,						lowerCAmelCase				:			Optional[List[int]] = None						,						lowerCAmelCase				:			int = 64						,						lowerCAmelCase				:			int = 2						,						lowerCAmelCase				:			int = 2						,						lowerCAmelCase				:			int = 2						,						lowerCAmelCase				:			int = 2						,						lowerCAmelCase				:			int = 32						,						lowerCAmelCase				:			int = 32						,						lowerCAmelCase				:			str = "gelu"						,						lowerCAmelCase				:			float = 0.1						,						lowerCAmelCase				:			float = 0.1						,						lowerCAmelCase				:			float = 0.1						,						lowerCAmelCase				:			float = 0.1						,						lowerCAmelCase				:			float = 0.1						,						lowerCAmelCase				:			int = 100						,						lowerCAmelCase				:			float = 0.02						,						lowerCAmelCase				:			bool = True						,						lowerCAmelCase				:			Tuple=True						,						lowerCAmelCase				:			int = 10						,						lowerCAmelCase				:			int = 25						,						lowerCAmelCase				:			int = 3						,						**lowerCAmelCase				:			Tuple						,						):
										# time series specific configuration
										lowerCAmelCase  =				prediction_length
										lowerCAmelCase  =				context_length if context_length is not None else prediction_length
										lowerCAmelCase  =				distribution_output
										lowerCAmelCase  =				loss
										lowerCAmelCase  =				input_size
										lowerCAmelCase  =				num_time_features
										lowerCAmelCase  =				lags_sequence
										lowerCAmelCase  =				scaling
										lowerCAmelCase  =				num_dynamic_real_features
										lowerCAmelCase  =				num_static_real_features
										lowerCAmelCase  =				num_static_categorical_features
										if cardinality is not None 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`"""					)
														lowerCAmelCase  =				cardinality
										else:
														lowerCAmelCase  =				[0]
										if embedding_dimension is not None 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`"""					)
														lowerCAmelCase  =				embedding_dimension
										else:
														lowerCAmelCase  =				[min(50						,						(cat + 1) // 2					) for cat in self.cardinality]
										lowerCAmelCase  =				num_parallel_samples

										# Transformer architecture configuration
										lowerCAmelCase  =				input_size * len(self.lags_sequence					) + self._number_of_features
										lowerCAmelCase  =				d_model
										lowerCAmelCase  =				encoder_attention_heads
										lowerCAmelCase  =				decoder_attention_heads
										lowerCAmelCase  =				encoder_ffn_dim
										lowerCAmelCase  =				decoder_ffn_dim
										lowerCAmelCase  =				encoder_layers
										lowerCAmelCase  =				decoder_layers

										lowerCAmelCase  =				dropout
										lowerCAmelCase  =				attention_dropout
										lowerCAmelCase  =				activation_dropout
										lowerCAmelCase  =				encoder_layerdrop
										lowerCAmelCase  =				decoder_layerdrop

										lowerCAmelCase  =				activation_function
										lowerCAmelCase  =				init_std

										lowerCAmelCase  =				use_cache

										# Autoformer
										lowerCAmelCase  =				label_length
										lowerCAmelCase  =				moving_average
										lowerCAmelCase  =				autocorrelation_factor

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




						@property
						def   __lowercase (							self				:			Tuple					):
										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
										)





