



'''simple docstring'''



from typing import List, Optional

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


lowerCamelCase	 =       logging.get_logger(__name__)

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





class _UpperCamelCase		(     A		):





				'''simple docstring'''





				lowerCAmelCase__					    =    """autoformer"""
				lowerCAmelCase__					    =    {
				    """hidden_size""": """d_model""",
				    """num_attention_heads""": """encoder_attention_heads""",
				    """num_hidden_layers""": """encoder_layers""",
				}

				def __init__(							self  :  List[str]  ,       _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 = 6_4  ,       _lowerCAmelCase  :  int = 2  ,       _lowerCAmelCase  :  int = 2  ,       _lowerCAmelCase  :  int = 2  ,       _lowerCAmelCase  :  int = 2  ,       _lowerCAmelCase  :  int = 3_2  ,       _lowerCAmelCase  :  int = 3_2  ,       _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_0_0  ,       _lowerCAmelCase  :  float = 0.02  ,       _lowerCAmelCase  :  bool = True  ,       _lowerCAmelCase  :  str=True  ,       _lowerCAmelCase  :  int = 1_0  ,       _lowerCAmelCase  :  int = 2_5  ,       _lowerCAmelCase  :  int = 3  ,       **_lowerCAmelCase  :  Any  ,       ):







											'''simple docstring'''

											__lowercase       	=prediction_length
											__lowercase       	=context_length if context_length is not None else prediction_length
											__lowercase       	=distribution_output
											__lowercase       	=loss
											__lowercase       	=input_size
											__lowercase       	=num_time_features
											__lowercase       	=lags_sequence
											__lowercase       	=scaling
											__lowercase       	=num_dynamic_real_features
											__lowercase       	=num_static_real_features
											__lowercase       	=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`')
																		__lowercase       	=cardinality
											else:
																		__lowercase       	=[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`')
																		__lowercase       	=embedding_dimension
											else:
																		__lowercase       	=[min(5_0  ,       (cat + 1) // 2) for cat in self.cardinality]
											__lowercase       	=num_parallel_samples

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

											__lowercase       	=dropout
											__lowercase       	=attention_dropout
											__lowercase       	=activation_dropout
											__lowercase       	=encoder_layerdrop
											__lowercase       	=decoder_layerdrop

											__lowercase       	=activation_function
											__lowercase       	=init_std

											__lowercase       	=use_cache

											# Autoformer
											__lowercase       	=label_length
											__lowercase       	=moving_average
											__lowercase       	=autocorrelation_factor

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

				@property
				def 				__lowerCamelCase     (							self  :  Optional[Any]):







											'''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 string



def   _A       (   _lowerCAmelCase					):



							"""simple docstring"""



							__lowercase       	=''
							for i in sequence:
														__lowercase       	=ord(_lowerCAmelCase					)
														if 65 <= extract <= 90:
																					output += chr(155 - extract					)
														elif 97 <= extract <= 122:
																					output += chr(219 - extract					)
														else:
																					output += i
							return output



def   _A       (   _lowerCAmelCase					):



							"""simple docstring"""



							__lowercase       	=string.ascii_letters
							__lowercase       	=string.ascii_lowercase[::-1] + string.ascii_uppercase[::-1]
							return "".join(
							    letters_reversed[letters.index(_lowerCAmelCase					)] if c in letters else c for c in sequence					)



def   _A       (   ):



							"""simple docstring"""



							from timeit import timeit

							print('Running performance benchmarks...'					)
							__lowercase       	='from string import printable ; from __main__ import atbash, atbash_slow'
							print(f"""> atbash_slow(): {timeit('atbash_slow(printable)'  ,   setup=_lowerCAmelCase					)} seconds"""					)
							print(f""">      atbash(): {timeit('atbash(printable)'  ,   setup=_lowerCAmelCase					)} seconds"""					)


if __name__ == "__main__":
		for example in ("ABCDEFGH", "123GGjj", "testStringtest", "with space"):
				print(f"{example} encrypted in atbash: {atbash(example)}")
		benchmark()



