






"""simple docstring"""



from typing import List, Optional

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


__A		:       Any  				=		logging.get_logger(__name__)

__A		:       List[str]  				=		{
    '''huggingface/autoformer-tourism-monthly''': '''https://huggingface.co/huggingface/autoformer-tourism-monthly/resolve/main/config.json''',
}





class 					_UpperCAmelCase       ( _A							):
					SCREAMING_SNAKE_CASE_		:				Tuple							    =   "autoformer"
					SCREAMING_SNAKE_CASE_		:				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] = [1, 2, 3, 4, 5, 6, 7]    ,						A     :						bool = True    ,						A     :						int = 0    ,						A     :						int = 0    ,						A     :						int = 0    ,						A     :						int = 0    ,						A     :						Optional[List[int]] = None    ,						A     :						Optional[List[int]] = None    ,						A     :						int = 64    ,						A     :						int = 2    ,						A     :						int = 2    ,						A     :						int = 2    ,						A     :						int = 2    ,						A     :						int = 32    ,						A     :						int = 32    ,						A     :						str = "gelu"    ,						A     :						float = 0.1    ,						A     :						float = 0.1    ,						A     :						float = 0.1    ,						A     :						float = 0.1    ,						A     :						float = 0.1    ,						A     :						int = 1_00    ,						A     :						float = 0.02    ,						A     :						bool = True    ,						A     :						int=True    ,						A     :						int = 10    ,						A     :						int = 25    ,						A     :						int = 3    ,						**A     :						str    ,						)  ->  str:
							# time series specific configuration
							lowercase_ : List[Any]							       =   prediction_length
							lowercase_ : str							       =   context_length if context_length is not None else prediction_length
							lowercase_ : Tuple							       =   distribution_output
							lowercase_ : str							       =   loss
							lowercase_ : Any							       =   input_size
							lowercase_ : List[str]							       =   num_time_features
							lowercase_ : int							       =   lags_sequence
							lowercase_ : Union[str, Any]							       =   scaling
							lowercase_ : Optional[int]							       =   num_dynamic_real_features
							lowercase_ : List[Any]							       =   num_static_real_features
							lowercase_ : Optional[Any]							       =   num_static_categorical_features
							if cardinality is not None 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`'''   )
									lowercase_ : int							       =   cardinality
							else:
									lowercase_ : Tuple							       =   [0]
							if embedding_dimension is not None 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`'''   )
									lowercase_ : str							       =   embedding_dimension
							else:
									lowercase_ : str							       =   [min(50    ,						(cat + 1) // 2   ) for cat in self.cardinality]
							lowercase_ : Optional[Any]							       =   num_parallel_samples

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

							lowercase_ : int							       =   dropout
							lowercase_ : Optional[Any]							       =   attention_dropout
							lowercase_ : str							       =   activation_dropout
							lowercase_ : List[str]							       =   encoder_layerdrop
							lowercase_ : str							       =   decoder_layerdrop

							lowercase_ : Any							       =   activation_function
							lowercase_ : Any							       =   init_std

							lowercase_ : Union[str, Any]							       =   use_cache

							# Autoformer
							lowercase_ : Union[str, Any]							       =   label_length
							lowercase_ : str							       =   moving_average
							lowercase_ : Union[str, Any]							       =   autocorrelation_factor

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






					@property
					def   A  (				self     :						int   )  ->  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
							)











"""simple docstring"""



def 						lowercase    (				__snake_case				: Optional[int]	):
		lowercase_ : int							       =   0
		lowercase_ : Optional[Any]							       =   len(__snake_case	)

		for i in range(n - 1	):
				for j in range(i + 1		,					__snake_case	):
						if arr[i] > arr[j]:
								num_inversions += 1

		return num_inversions



def 						lowercase    (				__snake_case				: str	):
		if len(__snake_case	) <= 1:
				return arr, 0
		lowercase_ : Optional[Any]							       =   len(__snake_case	) // 2
		lowercase_ : List[Any]							       =   arr[0:mid]
		lowercase_ : Union[str, Any]							       =   arr[mid:]

		lowercase_       ,					lowercase_ : Tuple							       =   count_inversions_recursive(__snake_case	)
		lowercase_       ,					lowercase_ : List[Any]							       =   count_inversions_recursive(__snake_case	)
		lowercase_       ,					lowercase_ : List[Any]							       =   _count_cross_inversions(__snake_case		,					__snake_case	)

		lowercase_ : List[Any]							       =   inversion_p + inversions_q + cross_inversions
		return c, num_inversions



def 						lowercase    (				__snake_case				: str		,					__snake_case				: Optional[int]	):
		lowercase_ : Optional[Any]							       =   []
		lowercase_ : Any							       =   0
		while i < len(__snake_case	) and j < len(__snake_case	):
				if p[i] > q[j]:
						# if P[1] > Q[j], then P[k] > Q[k] for all  i < k <= len(P)
						# These are all inversions. The claim emerges from the
						# property that P is sorted.
						num_inversion += len(__snake_case	) - i
						r.append(q[j]	)
						j += 1
				else:
						r.append(p[i]	)
						i += 1

		if i < len(__snake_case	):
				r.extend(p[i:]	)
		else:
				r.extend(q[j:]	)

		return r, num_inversion



def 						lowercase    (				):
		lowercase_ : Union[str, Any]							       =   [1_0, 2, 1, 5, 5, 2, 1_1]

		# this arr has 8 inversions:
		# (10, 2), (10, 1), (10, 5), (10, 5), (10, 2), (2, 1), (5, 2), (5, 2)

		lowercase_ : int							       =   count_inversions_bf(__snake_case	)
		lowercase_       ,					lowercase_ : Dict							       =   count_inversions_recursive(__snake_case	)

		assert num_inversions_bf == num_inversions_recursive == 8

		print('''number of inversions = '''		,					__snake_case	)

		# testing an array with zero inversion (a sorted arr_1)

		arr_a.sort()
		lowercase_ : Dict							       =   count_inversions_bf(__snake_case	)
		lowercase_       ,					lowercase_ : Dict							       =   count_inversions_recursive(__snake_case	)

		assert num_inversions_bf == num_inversions_recursive == 0
		print('''number of inversions = '''		,					__snake_case	)

		# an empty list should also have zero inversions
		lowercase_ : List[Any]							       =   []
		lowercase_ : Any							       =   count_inversions_bf(__snake_case	)
		lowercase_       ,					lowercase_ : List[str]							       =   count_inversions_recursive(__snake_case	)

		assert num_inversions_bf == num_inversions_recursive == 0
		print('''number of inversions = '''		,					__snake_case	)


if __name__ == "__main__":
						main()



