

import itertools
import string
from collections.abc import Generator, Iterable

def 							__SCREAMING_SNAKE_CASE							(			lowerCAmelCase:					Iterable[str]					, lowerCAmelCase:					int	)				->    Generator[tuple[str, ...], None, None]:
				_UpperCAmelCase    :						List[str]							=      iter(lowerCAmelCase	)
				while True:
								_UpperCAmelCase    :						List[str]							=      tuple(itertools.islice(lowerCAmelCase					, lowerCAmelCase	)	)
								if not chunk:
												return
								yield chunk

def 							__SCREAMING_SNAKE_CASE							(			lowerCAmelCase:					str	)				->    str:
				_UpperCAmelCase    :						List[str]							=      "".join([c.upper() for c in dirty if c in string.ascii_letters]	)
				_UpperCAmelCase    :						Any							=      ""

				if len(lowerCAmelCase	) < 2:
								return dirty

				for i in range(len(lowerCAmelCase	) - 1	):
								clean += dirty[i]

								if dirty[i] == dirty[i + 1]:
												clean += "X"

				clean += dirty[-1]

				if len(lowerCAmelCase	) & 1:
								clean += "X"

				return clean

def 							__SCREAMING_SNAKE_CASE							(			lowerCAmelCase:					str	)				->    list[str]:
				# I and J are used interchangeably to allow
				# us to use a 5x5 table (25 letters)
				_UpperCAmelCase    :						str							=      "ABCDEFGHIKLMNOPQRSTUVWXYZ"
				# we're using a list instead of a '2d' array because it makes the math
				# for setting up the table and doing the actual encoding/decoding simpler
				_UpperCAmelCase    :						str							=      []

				# copy key chars into the table if they are in `alphabet` ignoring duplicates
				for char in key.upper():
								if char not in table and char in alphabet:
												table.append(lowerCAmelCase	)

    # fill the rest of the table in with the remaining alphabet chars
				for char in alphabet:
								if char not in table:
												table.append(lowerCAmelCase	)

				return table

def 							__SCREAMING_SNAKE_CASE							(			lowerCAmelCase:					str					, lowerCAmelCase:					str	)				->    str:
				_UpperCAmelCase    :						str							=      generate_table(lowerCAmelCase	)
				_UpperCAmelCase    :						List[str]							=      prepare_input(lowerCAmelCase	)
				_UpperCAmelCase    :						List[Any]							=      ""

				# https://en.wikipedia.org/wiki/Playfair_cipher#Description
				for chara, chara in chunker(lowerCAmelCase					, 2	):
								_UpperCAmelCase						,							_UpperCAmelCase    :						Any							=      divmod(table.index(lowerCAmelCase	)					, 5	)
								_UpperCAmelCase						,							_UpperCAmelCase    :						Any							=      divmod(table.index(lowerCAmelCase	)					, 5	)

								if rowa == rowa:
												ciphertext += table[rowa * 5 + (cola + 1) % 5]
												ciphertext += table[rowa * 5 + (cola + 1) % 5]
								elif cola == cola:
												ciphertext += table[((rowa + 1) % 5) * 5 + cola]
												ciphertext += table[((rowa + 1) % 5) * 5 + cola]
								else:  # rectangle
												ciphertext += table[rowa * 5 + cola]
												ciphertext += table[rowa * 5 + cola]

				return ciphertext





def 							__SCREAMING_SNAKE_CASE							(			lowerCAmelCase:					str					, lowerCAmelCase:					str	)				->    str:
				_UpperCAmelCase    :						Optional[int]							=      generate_table(lowerCAmelCase	)
				_UpperCAmelCase    :						List[str]							=      ""

				# https://en.wikipedia.org/wiki/Playfair_cipher#Description
				for chara, chara in chunker(lowerCAmelCase					, 2	):
								_UpperCAmelCase						,							_UpperCAmelCase    :						Dict							=      divmod(table.index(lowerCAmelCase	)					, 5	)
								_UpperCAmelCase						,							_UpperCAmelCase    :						Tuple							=      divmod(table.index(lowerCAmelCase	)					, 5	)

								if rowa == rowa:
												plaintext += table[rowa * 5 + (cola - 1) % 5]
												plaintext += table[rowa * 5 + (cola - 1) % 5]
								elif cola == cola:
												plaintext += table[((rowa - 1) % 5) * 5 + cola]
												plaintext += table[((rowa - 1) % 5) * 5 + cola]
								else:  # rectangle
												plaintext += table[rowa * 5 + cola]
												plaintext += table[rowa * 5 + cola]

				return plaintext





from typing import List, Optional

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


SCREAMING_SNAKE_CASE_            = logging.get_logger(__name__)

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

class 			a					(		UpperCAmelCase			):
						_lowercase        =     "autoformer"
						_lowercase        =     {
						    "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_ = True  ,   A_ = 0  ,   A_ = 0  ,   A_ = 0  ,   A_ = 0  ,   A_ = None  ,   A_ = None  ,   A_ = 64  ,   A_ = 2  ,   A_ = 2  ,   A_ = 2  ,   A_ = 2  ,   A_ = 32  ,   A_ = 32  ,   A_ = "gelu"  ,   A_ = 0.1  ,   A_ = 0.1  ,   A_ = 0.1  ,   A_ = 0.1  ,   A_ = 0.1  ,   A_ = 100  ,   A_ = 0.02  ,   A_ = True  ,   A_=True  ,   A_ = 10  ,   A_ = 25  ,   A_ = 3  ,   **A_  ,   ):







										'''simple docstring'''


										_UpperCAmelCase    :						List[Any]							=      prediction_length
										_UpperCAmelCase    :						Dict							=      context_length if context_length is not None else prediction_length
										_UpperCAmelCase    :						Tuple							=      distribution_output
										_UpperCAmelCase    :						List[Any]							=      loss
										_UpperCAmelCase    :						Optional[Any]							=      input_size
										_UpperCAmelCase    :						int							=      num_time_features
										_UpperCAmelCase    :						str							=      lags_sequence
										_UpperCAmelCase    :						Union[str, Any]							=      scaling
										_UpperCAmelCase    :						Union[str, Any]							=      num_dynamic_real_features
										_UpperCAmelCase    :						int							=      num_static_real_features
										_UpperCAmelCase    :						int							=      num_static_categorical_features
										if cardinality is not None 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    :						Union[str, Any]							=      cardinality
										else:
														_UpperCAmelCase    :						Any							=      [0]
										if embedding_dimension is not None 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    :						int							=      embedding_dimension
										else:
														_UpperCAmelCase    :						int							=      [min(50  ,   (cat + 1) // 2		) for cat in self.cardinality]
										_UpperCAmelCase    :						Union[str, Any]							=      num_parallel_samples

										# Transformer architecture configuration
										_UpperCAmelCase    :						Union[str, Any]							=      input_size * len(self.lags_sequence		) + self._number_of_features
										_UpperCAmelCase    :						int							=      d_model
										_UpperCAmelCase    :						Any							=      encoder_attention_heads
										_UpperCAmelCase    :						str							=      decoder_attention_heads
										_UpperCAmelCase    :						Union[str, Any]							=      encoder_ffn_dim
										_UpperCAmelCase    :						Any							=      decoder_ffn_dim
										_UpperCAmelCase    :						Any							=      encoder_layers
										_UpperCAmelCase    :						Optional[int]							=      decoder_layers

										_UpperCAmelCase    :						Optional[Any]							=      dropout
										_UpperCAmelCase    :						Union[str, Any]							=      attention_dropout
										_UpperCAmelCase    :						Dict							=      activation_dropout
										_UpperCAmelCase    :						Dict							=      encoder_layerdrop
										_UpperCAmelCase    :						int							=      decoder_layerdrop

										_UpperCAmelCase    :						Union[str, Any]							=      activation_function
										_UpperCAmelCase    :						str							=      init_std

										_UpperCAmelCase    :						Tuple							=      use_cache

										# Autoformer
										_UpperCAmelCase    :						str							=      label_length
										_UpperCAmelCase    :						Any							=      moving_average
										_UpperCAmelCase    :						Optional[Any]							=      autocorrelation_factor

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





						@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
										)


