



'''simple docstring'''

class __snake_case:


						'''simple docstring'''



						def __init__( self				)     ->     None:
							lowerCAmelCase   =     {}  # Mapping from char to TrieNode
							lowerCAmelCase   =     False


						def     __snake_case   ( self     ,	A_				)     ->     None:
							for word in words:
								self.insert(A_				)


						def     __snake_case   ( self     ,	A_				)     ->     None:
							lowerCAmelCase   =     self
							for char in word:
								if char not in curr.nodes:
									lowerCAmelCase   =     TrieNode()
								lowerCAmelCase   =     curr.nodes[char]
							lowerCAmelCase   =     True


						def     __snake_case   ( self     ,	A_				)     ->     bool:
							lowerCAmelCase   =     self
							for char in word:
								if char not in curr.nodes:
									return False
								lowerCAmelCase   =     curr.nodes[char]
							return curr.is_leaf




						def     __snake_case   ( self     ,	A_				)     ->     None:

							def _delete(A_     ,	A_     ,	A_				) -> bool:
								if index == len(A_				):
									# If word does not exist
									if not curr.is_leaf:
										return False
									lowerCAmelCase   =     False
									return len(curr.nodes				) == 0
								lowerCAmelCase   =     word[index]
								lowerCAmelCase   =     curr.nodes.get(A_				)
								# If char not in current trie node
								if not char_node:
									return False
								# Flag to check if node can be deleted
								lowerCAmelCase   =     _delete(A_     ,	A_     ,	index + 1				)
								if delete_curr:
									del curr.nodes[char]
									return len(curr.nodes				) == 0
								return delete_curr

							_delete(self     ,	A_     ,	0				)







def 		_snake_case		( _SCREAMING_SNAKE_CASE					:						TrieNode							,		_SCREAMING_SNAKE_CASE					:						str							)      ->						None:







	"""simple docstring"""
	if node.is_leaf:
		print(_SCREAMING_SNAKE_CASE							,		end=""" """							)

	for key, value in node.nodes.items():
		print_words(_SCREAMING_SNAKE_CASE							,		word + key							)






def 		_snake_case		( )      ->						bool:







	"""simple docstring"""
	lowerCAmelCase   =     """banana bananas bandana band apple all beast""".split()
	lowerCAmelCase   =     TrieNode()
	root.insert_many(_SCREAMING_SNAKE_CASE							)
	# print_words(root, "")
	assert all(root.find(_SCREAMING_SNAKE_CASE							) for word in words							)
	assert root.find("""banana"""							)
	assert not root.find("""bandanas"""							)
	assert not root.find("""apps"""							)
	assert root.find("""apple"""							)
	assert root.find("""all"""							)
	root.delete("""all"""							)
	assert not root.find("""all"""							)
	root.delete("""banana"""							)
	assert not root.find("""banana"""							)
	assert root.find("""bananas"""							)
	return True






def 		_snake_case		( _SCREAMING_SNAKE_CASE					:						str							,		_SCREAMING_SNAKE_CASE					:						bool							)      ->						None:







	"""simple docstring"""
	print(str(_SCREAMING_SNAKE_CASE							)							,		"""works!""" if passes else """doesn't work :("""							)






def 		_snake_case		( )      ->						None:







	"""simple docstring"""
	assert test_trie()






def 		_snake_case		( )      ->						None:







	"""simple docstring"""
	print_results("""Testing trie functionality"""							,		test_trie()							)


if __name__ == "__main__":
				main()




'''simple docstring'''

from typing import List, Optional, Union

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


UpperCAmelCase     		=   logging.get_logger(__name__)

UpperCAmelCase     		=   {
    'huggingface/time-series-transformer-tourism-monthly': (
        'https://huggingface.co/huggingface/time-series-transformer-tourism-monthly/resolve/main/config.json'
    ),
    # See all TimeSeriesTransformer models at https://huggingface.co/models?filter=time_series_transformer
}

class __snake_case(				_lowerCAmelCase ):


						'''simple docstring'''



						UpperCAmelCase							:   List[Any]									=		"time_series_transformer"
						UpperCAmelCase							:   int									=		{
						    "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_ = "mean"     ,	A_ = 0     ,	A_ = 0     ,	A_ = 0     ,	A_ = 0     ,	A_ = None     ,	A_ = None     ,	A_ = 32     ,	A_ = 32     ,	A_ = 2     ,	A_ = 2     ,	A_ = 2     ,	A_ = 2     ,	A_ = True     ,	A_ = "gelu"     ,	A_ = 64     ,	A_ = 0.1     ,	A_ = 0.1     ,	A_ = 0.1     ,	A_ = 0.1     ,	A_ = 0.1     ,	A_ = 100     ,	A_ = 0.0_2     ,	A_=True     ,	**A_     ,	)     ->     Optional[Any]:
							# time series specific configuration
							lowerCAmelCase   =     prediction_length
							lowerCAmelCase   =     context_length or 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 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`"""				)
								lowerCAmelCase   =     cardinality
							else:
								lowerCAmelCase   =     [0]
							if embedding_dimension 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`"""				)
								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(A_				) + 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

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




						@property
						def     __snake_case   ( self				)     ->     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
							)