
from typing import List, Optional, Union

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


_UpperCAmelCase      :				Dict          = logging.get_logger(__name__)

_UpperCAmelCase      :				Any          = {
    "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        lowercase      (	_SCREAMING_SNAKE_CASE     ):
		__lowercase : List[str]			  =       "time_series_transformer"
		__lowercase : 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_ = 0.02       ,   A_=True       ,   **A_       ,   )						->      List[str]:
				"""simple docstring"""
				UpperCamelCase						=  prediction_length
				UpperCamelCase						=  context_length or prediction_length
				UpperCamelCase						=  distribution_output
				UpperCamelCase						=  loss
				UpperCamelCase						=  input_size
				UpperCamelCase						=  num_time_features
				UpperCamelCase						=  lags_sequence
				UpperCamelCase						=  scaling
				UpperCamelCase						=  num_dynamic_real_features
				UpperCamelCase						=  num_static_real_features
				UpperCamelCase						=  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`' )
						UpperCamelCase						=  cardinality
				else:
						UpperCamelCase						=  [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`' )
						UpperCamelCase						=  embedding_dimension
				else:
						UpperCamelCase						=  [min(50       ,   (cat + 1) // 2 ) for cat in self.cardinality]
				UpperCamelCase						=  num_parallel_samples

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

				UpperCamelCase						=  dropout
				UpperCamelCase						=  attention_dropout
				UpperCamelCase						=  activation_dropout
				UpperCamelCase						=  encoder_layerdrop
				UpperCamelCase						=  decoder_layerdrop

				UpperCamelCase						=  activation_function
				UpperCamelCase						=  init_std

				UpperCamelCase						=  use_cache

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



		@property
		def __UpperCamelCase				(				self )						->      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
				)


def    A (      lowercase					, lowercase				)     ->						str:
		'''simple docstring'''

		if a < 0 or b < 0:
				raise ValueError('the value of both inputs must be positive'				)

		UpperCamelCase						=  str(bin(lowercase				)				)[2:]  # remove the leading "0b"
		UpperCamelCase						=  str(bin(lowercase				)				)[2:]  # remove the leading "0b"

		UpperCamelCase						=  max(len(lowercase				)					, len(lowercase				)				)

		return "0b" + "".join(
		    str(int(char_a != char_b				)				)
		    for char_a, char_b in zip(a_binary.zfill(lowercase				)					, b_binary.zfill(lowercase				)				)				)


if __name__ == "__main__":
					import doctest

					doctest.testmod()
