





"""simple docstring"""






def     __A      (			a_						:list[list])		->		list[list]:
				__a :						Union[str, Any]			       =  current_set.copy()
				for row_index, row in enumerate(a_):
								__a :						Dict			       =  row[0]
								for column_index, column in enumerate(a_):
												if magnitude == 0:
																__a :						Any			       =  column
																continue
												__a :						List[str]			       =  column / magnitude
    # Subtract to cancel term
				__a :						Optional[Any]			       =  current_set[0]
				__a :						List[str]			       =  [first_row]
				__a :						Any			       =  current_set[1::]
				for row in current_set:
								__a :						str			       =  []
								# If first term is 0, it is already in form we want, so we preserve it
								if row[0] == 0:
												final_set.append(a_)
												continue
								for column_index in range(len(a_)):
												temp_row.append(first_row[column_index] - row[column_index])
								final_set.append(a_)
				# Create next recursion iteration set
				if len(final_set[0]) != 3:
								__a :						str			       =  final_set[0]
								__a :						Optional[int]			       =  []
								__a :						List[Any]			       =  []
								for row in final_set[1::]:
												current_first_column.append(row[0])
												next_iteration.append(row[1::])
								__a :						Optional[int]			       =  simplify(a_)
								for i in range(len(a_)):
												resultant[i].insert(0   ,       current_first_column[i])
								resultant.insert(0   ,       a_)
								__a :						List[str]			       =  resultant
				return final_set

def     __A      (			a_						:list[list])		->		list:
				if len(a_) == 0:
								raise IndexError('''solve_simultaneous() requires n lists of length n+1''')
				__a :						Any			       =  len(a_) + 1
				if any(len(a_) != _length for item in equations):
								raise IndexError('''solve_simultaneous() requires n lists of length n+1''')
				for row in equations:
								if any(not isinstance(a_   ,       (int, float)) for column in row):
												raise ValueError('''solve_simultaneous() requires lists of integers''')
				if len(a_) == 1:
								return [equations[0][-1] / equations[0][0]]
				__a :						str			       =  equations.copy()
				if any(0 in row for row in data_set):
								__a :						Optional[int]			       =  data_set.copy()
								__a :						Optional[Any]			       =  []
								for row_index, row in enumerate(a_):
												if 0 not in row:
																__a :						int			       =  data_set.pop(a_)
																break
								if not full_row:
												raise ValueError('''solve_simultaneous() requires at least 1 full equation''')
								data_set.insert(0   ,       a_)
				__a :						Tuple			       =  data_set.copy()
				__a :						int			       =  simplify(a_)
				__a :						Tuple			       =  simplified[::-1]
				__a :						list			       =  []
				for row in simplified:
								__a :						Optional[Any]			       =  row[-1]
								if not solutions:
												if row[-2] == 0:
																solutions.append(0)
																continue
												solutions.append(current_solution / row[-2])
												continue
								__a :						List[Any]			       =  row.copy()[: len(a_) - 1 :]
								while temp_row[0] == 0:
												temp_row.pop(0)
								if len(a_) == 0:
												solutions.append(0)
												continue
								__a :						Any			       =  temp_row[1::]
								__a :						List[Any]			       =  temp_row[::-1]
								for column_index, column in enumerate(a_):
												current_solution -= column * solutions[column_index]
								solutions.append(a_)
				__a :						str			       =  []
				for item in solutions:
								final.append(float(round(a_   ,       5)))
				return final[::-1]


if __name__ == "__main__":
		import doctest

		doctest.testmod()
		A 			=  [
		    [2, 1, 1, 1, 1, 4],
		    [1, 2, 1, 1, 1, 5],
		    [1, 1, 2, 1, 1, 6],
		    [1, 1, 1, 2, 1, 7],
		    [1, 1, 1, 1, 2, 8],
		]
		print(solve_simultaneous(eq))
		print(solve_simultaneous([[4, 2]]))






"""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 				__lowercase			(			_UpperCamelCase							):







					'''simple docstring'''




					__lowerCAmelCase  =  '''autoformer'''
					__lowerCAmelCase  =  {
					    '''hidden_size''': '''d_model''',
					    '''num_attention_heads''': '''encoder_attention_heads''',
					    '''num_hidden_layers''': '''encoder_layers''',
					}







					def __init__(						self	,			_UpperCAmelCase = None	,			_UpperCAmelCase = None	,			_UpperCAmelCase = "student_t"	,			_UpperCAmelCase = "nll"	,			_UpperCAmelCase = 1	,			_UpperCAmelCase = [1, 2, 3, 4, 5, 6, 7]	,			_UpperCAmelCase = True	,			_UpperCAmelCase = 0	,			_UpperCAmelCase = 0	,			_UpperCAmelCase = 0	,			_UpperCAmelCase = 0	,			_UpperCAmelCase = None	,			_UpperCAmelCase = None	,			_UpperCAmelCase = 64	,			_UpperCAmelCase = 2	,			_UpperCAmelCase = 2	,			_UpperCAmelCase = 2	,			_UpperCAmelCase = 2	,			_UpperCAmelCase = 32	,			_UpperCAmelCase = 32	,			_UpperCAmelCase = "gelu"	,			_UpperCAmelCase = 0.1	,			_UpperCAmelCase = 0.1	,			_UpperCAmelCase = 0.1	,			_UpperCAmelCase = 0.1	,			_UpperCAmelCase = 0.1	,			_UpperCAmelCase = 100	,			_UpperCAmelCase = 0.0_2	,			_UpperCAmelCase = True	,			_UpperCAmelCase=True	,			_UpperCAmelCase = 10	,			_UpperCAmelCase = 25	,			_UpperCAmelCase = 3	,			**_UpperCAmelCase	,			):
									# time series specific configuration
									__a :						Optional[int]			       =  prediction_length
									__a :						Optional[int]			       =  context_length if context_length is not None else prediction_length
									__a :						Union[str, Any]			       =  distribution_output
									__a :						str			       =  loss
									__a :						Optional[Any]			       =  input_size
									__a :						str			       =  num_time_features
									__a :						Optional[Any]			       =  lags_sequence
									__a :						Any			       =  scaling
									__a :						Optional[Any]			       =  num_dynamic_real_features
									__a :						str			       =  num_static_real_features
									__a :						Any			       =  num_static_categorical_features
									if cardinality is not None 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`'''     )
													__a :						List[str]			       =  cardinality
									else:
													__a :						Any			       =  [0]
									if embedding_dimension is not None 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`'''     )
													__a :						Any			       =  embedding_dimension
									else:
													__a :						Tuple			       =  [min(50	,			(cat + 1) // 2     ) for cat in self.cardinality]
									__a :						Union[str, Any]			       =  num_parallel_samples

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

									__a :						Union[str, Any]			       =  dropout
									__a :						Union[str, Any]			       =  attention_dropout
									__a :						Optional[Any]			       =  activation_dropout
									__a :						List[str]			       =  encoder_layerdrop
									__a :						Any			       =  decoder_layerdrop

									__a :						Union[str, Any]			       =  activation_function
									__a :						Union[str, Any]			       =  init_std

									__a :						List[str]			       =  use_cache

									# Autoformer
									__a :						int			       =  label_length
									__a :						List[Any]			       =  moving_average
									__a :						str			       =  autocorrelation_factor

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







					@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
									)