






"""simple docstring"""






from typing import List, Optional, Union

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


SCREAMING_SNAKE_CASE    :    str        =							logging.get_logger(__name__)

SCREAMING_SNAKE_CASE    :    Optional[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    snake_case_							(     _lowerCamelCase							):



	"""simple docstring"""

	SCREAMING_SNAKE_CASE_:				Optional[Any]       =       """time_series_transformer"""
	SCREAMING_SNAKE_CASE_:				Optional[Any]       =       {
	    """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	,       ):


					"""simple docstring"""

					A__							=							prediction_length
					A__							=							context_length or 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 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 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(lowerCamelCase_    ) + 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

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






	@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
					)





"""simple docstring"""


import random





class lowerCamelCase__   :



    '''simple docstring'''




    @staticmethod
    def UpperCamelCase__		( lowerCamelCase_    )    ->      tuple[list[int], list[int]]:
          A				  =		[ord(lowerCamelCase_    ) for i in text]
          A				  =		[]
          A				  =		[]
          for i in plain:
                A				  =		random.randint(1 ,3_0_0    )
                A				  =		(i + k) * k
                cipher.append(lowerCamelCase_    )
                key.append(lowerCamelCase_    )
          return cipher, key




    @staticmethod
    def UpperCamelCase__		( lowerCamelCase_ ,lowerCamelCase_    )    ->      str:
          A				  =		[]
          for i in range(len(lowerCamelCase_    )    ):
                A				  =		int((cipher[i] - (key[i]) ** 2) / key[i]    )
                plain.append(chr(lowerCamelCase_    )    )
          return "".join(lowerCamelCase_    )


if __name__ == "__main__":
  UpperCAmelCase							,			UpperCAmelCase													=Onepad().encrypt("Hello")
  print(c, k)
  print(Onepad().decrypt(c, k))




