

"""simple docstring"""




def 							__SCREAMING_SNAKE_CASE							(			lowerCAmelCase:					Any	)				->    int:
				if not head:
								return True
				# split the list to two parts
				_UpperCAmelCase    :						Dict							=      head.next, head
				while fast and fast.next:
								_UpperCAmelCase    :						Union[str, Any]							=      fast.next.next
								_UpperCAmelCase    :						int							=      slow.next
				_UpperCAmelCase    :						List[str]							=      slow.next
				_UpperCAmelCase    :						Optional[int]							=      None  # Don't forget here! But forget still works!
				# reverse the second part
				_UpperCAmelCase    :						List[Any]							=      None
				while second:
								_UpperCAmelCase    :						Optional[Any]							=      second.next
								_UpperCAmelCase    :						Union[str, Any]							=      node
								_UpperCAmelCase    :						Optional[int]							=      second
								_UpperCAmelCase    :						Union[str, Any]							=      nxt
				# compare two parts
				# second part has the same or one less node
				while node:
								if node.val != head.val:
												return False
								_UpperCAmelCase    :						Tuple							=      node.next
								_UpperCAmelCase    :						int							=      head.next
				return True

def 							__SCREAMING_SNAKE_CASE							(			lowerCAmelCase:					Union[str, Any]	)				->    Tuple:
				if not head or not head.next:
								return True

				# 1. Get the midpoint (slow)
				_UpperCAmelCase    :						int							=      head
				while fast and fast.next:
								_UpperCAmelCase    :						int							=      fast.next.next, slow.next

				# 2. Push the second half into the stack
				_UpperCAmelCase    :						int							=      [slow.val]
				while slow.next:
								_UpperCAmelCase    :						Union[str, Any]							=      slow.next
								stack.append(slow.val	)

				# 3. Comparison
				while stack:
								if stack.pop() != cur.val:
												return False
								_UpperCAmelCase    :						Optional[int]							=      cur.next

				return True





def 							__SCREAMING_SNAKE_CASE							(			lowerCAmelCase:					Union[str, Any]	)				->    Any:
				if not head or not head.next:
								return True
				_UpperCAmelCase    :						int							=      {}
				_UpperCAmelCase    :						int							=      0
				while head:
								if head.val in d:
												d[head.val].append(lowerCAmelCase	)
								else:
												_UpperCAmelCase    :						Any							=      [pos]
								_UpperCAmelCase    :						Tuple							=      head.next
								pos += 1
				_UpperCAmelCase    :						int							=      pos - 1
				_UpperCAmelCase    :						Dict							=      0
				for v in d.values():
								if len(lowerCAmelCase	) % 2 != 0:
												middle += 1
								else:
												_UpperCAmelCase    :						str							=      0
												for i in range(0					, len(lowerCAmelCase	)	):
																if v[i] + v[len(lowerCAmelCase	) - 1 - step] != checksum:
																				return False
																step += 1
								if middle > 1:
												return False
				return True





from typing import List, Optional

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


SCREAMING_SNAKE_CASE_            = logging.get_logger(__name__)

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

class 			a					(		UpperCAmelCase			):
						_lowercase        =     "autoformer"
						_lowercase        =     {
						    "hidden_size": "d_model",
						    "num_attention_heads": "encoder_attention_heads",
						    "num_hidden_layers": "encoder_layers",
						}


						def __init__(    self  ,   A_ = None  ,   A_ = None  ,   A_ = "student_t"  ,   A_ = "nll"  ,   A_ = 1  ,   A_ = [1, 2, 3, 4, 5, 6, 7]  ,   A_ = True  ,   A_ = 0  ,   A_ = 0  ,   A_ = 0  ,   A_ = 0  ,   A_ = None  ,   A_ = None  ,   A_ = 64  ,   A_ = 2  ,   A_ = 2  ,   A_ = 2  ,   A_ = 2  ,   A_ = 32  ,   A_ = 32  ,   A_ = "gelu"  ,   A_ = 0.1  ,   A_ = 0.1  ,   A_ = 0.1  ,   A_ = 0.1  ,   A_ = 0.1  ,   A_ = 100  ,   A_ = 0.02  ,   A_ = True  ,   A_=True  ,   A_ = 10  ,   A_ = 25  ,   A_ = 3  ,   **A_  ,   ):







										'''simple docstring'''


										_UpperCAmelCase    :						List[Any]							=      prediction_length
										_UpperCAmelCase    :						Dict							=      context_length if context_length is not None else prediction_length
										_UpperCAmelCase    :						Tuple							=      distribution_output
										_UpperCAmelCase    :						List[Any]							=      loss
										_UpperCAmelCase    :						Optional[Any]							=      input_size
										_UpperCAmelCase    :						int							=      num_time_features
										_UpperCAmelCase    :						str							=      lags_sequence
										_UpperCAmelCase    :						Union[str, Any]							=      scaling
										_UpperCAmelCase    :						Union[str, Any]							=      num_dynamic_real_features
										_UpperCAmelCase    :						int							=      num_static_real_features
										_UpperCAmelCase    :						int							=      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`"		)
														_UpperCAmelCase    :						Union[str, Any]							=      cardinality
										else:
														_UpperCAmelCase    :						Any							=      [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`"		)
														_UpperCAmelCase    :						int							=      embedding_dimension
										else:
														_UpperCAmelCase    :						int							=      [min(50  ,   (cat + 1) // 2		) for cat in self.cardinality]
										_UpperCAmelCase    :						Union[str, Any]							=      num_parallel_samples

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

										_UpperCAmelCase    :						Optional[Any]							=      dropout
										_UpperCAmelCase    :						Union[str, Any]							=      attention_dropout
										_UpperCAmelCase    :						Dict							=      activation_dropout
										_UpperCAmelCase    :						Dict							=      encoder_layerdrop
										_UpperCAmelCase    :						int							=      decoder_layerdrop

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

										_UpperCAmelCase    :						Tuple							=      use_cache

										# Autoformer
										_UpperCAmelCase    :						str							=      label_length
										_UpperCAmelCase    :						Any							=      moving_average
										_UpperCAmelCase    :						Optional[Any]							=      autocorrelation_factor

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





						@property
						def     _UpperCAmelCase			(    self		):







										'''simple docstring'''


										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
										)


