





from typing import List, Optional

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


A    :					Any =      logging.get_logger(__name__)

A    :					Optional[int] =      {
    '''huggingface/autoformer-tourism-monthly''': '''https://huggingface.co/huggingface/autoformer-tourism-monthly/resolve/main/config.json''',
}


class   A			(SCREAMING_SNAKE_CASE    ):
			'''simple docstring'''




			__lowerCamelCase	:       List[str]							=    '''autoformer'''
			__lowerCamelCase	:       List[str]							=    {
			    '''hidden_size''': '''d_model''',
			    '''num_attention_heads''': '''encoder_attention_heads''',
			    '''num_hidden_layers''': '''encoder_layers''',
			}






			def __init__(			self   :    List[Any]							,	__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 = 1_00							,	__lowerCAmelCase   :    float = 0.0_2							,	__lowerCAmelCase   :    bool = True							,	__lowerCAmelCase   :    str=True							,	__lowerCAmelCase   :    int = 10							,	__lowerCAmelCase   :    int = 25							,	__lowerCAmelCase   :    int = 3							,	**__lowerCAmelCase   :    Optional[int]							,	)	->	Union[str, Any]:







					"""simple docstring"""






					A__        =   prediction_length
					A__        =   context_length if context_length is not None else prediction_length
					A__        =   distribution_output
					A__        =   loss
					A__        =   input_size
					A__        =   num_time_features
					A__        =   lags_sequence
					A__        =   scaling
					A__        =   num_dynamic_real_features
					A__        =   num_static_real_features
					A__        =   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`"""   )
							A__        =   cardinality
					else:
							A__        =   [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`"""   )
							A__        =   embedding_dimension
					else:
							A__        =   [min(50							,	(cat + 1) // 2   ) for cat in self.cardinality]
					A__        =   num_parallel_samples

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

					A__        =   dropout
					A__        =   attention_dropout
					A__        =   activation_dropout
					A__        =   encoder_layerdrop
					A__        =   decoder_layerdrop

					A__        =   activation_function
					A__        =   init_std

					A__        =   use_cache

					# Autoformer
					A__        =   label_length
					A__        =   moving_average
					A__        =   autocorrelation_factor

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







			@property
			def    a_       (			self   :    Optional[int]   )	->	int:







					"""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
					)










class   A			(SCREAMING_SNAKE_CASE    ):
			'''simple docstring'''




			pass


class   A			(SCREAMING_SNAKE_CASE    ):
			'''simple docstring'''




			pass


class   A			:
			'''simple docstring'''




			def __init__(			self   :    List[Any]   )	->	str:







					"""simple docstring"""






					A__        =   [
					    [],
					    [],
					    [],
					]






			def    a_       (			self   :    Dict							,	__lowerCAmelCase   :    int							,	__lowerCAmelCase   :    int   )	->	None:







					"""simple docstring"""






					try:
							if len(self.queues[priority]   ) >= 1_00:
									raise OverflowError("""Maximum queue size is 100"""   )
							self.queues[priority].append(__lowerCAmelCase   )
					except IndexError:
							raise ValueError("""Valid priorities are 0, 1, and 2"""   )






			def    a_       (			self   :    Optional[Any]   )	->	int:







					"""simple docstring"""






					for queue in self.queues:
							if queue:
									return queue.pop(0   )
					raise UnderFlowError("""All queues are empty"""   )







			def __str__(			self   :    Tuple   )	->	str:







					"""simple docstring"""






					return "\n".join(f'Priority {i}: {q}' for i, q in enumerate(self.queues   )   )


class   A			:
			'''simple docstring'''




			def __init__(			self   :    int   )	->	str:







					"""simple docstring"""






					A__        =   []






			def    a_       (			self   :    int							,	__lowerCAmelCase   :    int   )	->	None:







					"""simple docstring"""






					if len(self.queue   ) == 1_00:
							raise OverFlowError("""Maximum queue size is 100"""   )
					self.queue.append(__lowerCAmelCase   )






			def    a_       (			self   :    List[str]   )	->	int:







					"""simple docstring"""






					if not self.queue:
							raise UnderFlowError("""The queue is empty"""   )
					else:
							A__        =   min(self.queue   )
							self.queue.remove(__lowerCAmelCase   )
							return data







			def __str__(			self   :    List[Any]   )	->	str:







					"""simple docstring"""






					return str(self.queue   )







def    __lowerCamelCase					(							)		->				Optional[Any]:






		"""simple docstring"""

		A__        =   FixedPriorityQueue()
		fpq.enqueue(0    ,     1_0    )
		fpq.enqueue(1    ,     7_0    )
		fpq.enqueue(0    ,     1_0_0    )
		fpq.enqueue(2    ,     1    )
		fpq.enqueue(2    ,     5    )
		fpq.enqueue(1    ,     7    )
		fpq.enqueue(2    ,     4    )
		fpq.enqueue(1    ,     6_4    )
		fpq.enqueue(0    ,     1_2_8    )
		print(__a    )
		print(fpq.dequeue()    )
		print(fpq.dequeue()    )
		print(fpq.dequeue()    )
		print(fpq.dequeue()    )
		print(fpq.dequeue()    )
		print(__a    )
		print(fpq.dequeue()    )
		print(fpq.dequeue()    )
		print(fpq.dequeue()    )
		print(fpq.dequeue()    )
		print(fpq.dequeue()    )







def    __lowerCamelCase					(							)		->				int:






		"""simple docstring"""

		A__        =   ElementPriorityQueue()
		epq.enqueue(1_0    )
		epq.enqueue(7_0    )
		epq.enqueue(1_0_0    )
		epq.enqueue(1    )
		epq.enqueue(5    )
		epq.enqueue(7    )
		epq.enqueue(4    )
		epq.enqueue(6_4    )
		epq.enqueue(1_2_8    )
		print(__a    )
		print(epq.dequeue()    )
		print(epq.dequeue()    )
		print(epq.dequeue()    )
		print(epq.dequeue()    )
		print(epq.dequeue()    )
		print(__a    )
		print(epq.dequeue()    )
		print(epq.dequeue()    )
		print(epq.dequeue()    )
		print(epq.dequeue()    )
		print(epq.dequeue()    )


if __name__ == "__main__":
					fixed_priority_queue()
					element_priority_queue()



