




"""simple docstring"""

from __future__ import annotations






def 		_snake_case	(   _snake_case				:   str							,    _snake_case				:   list[str] | None = None							,    _snake_case				:   dict[str, float] | None = None							,    _snake_case				:   bool = False							,    )       ->   List[str]:






      '''simple docstring'''


      _A     =  cipher_alphabet or [chr(UpperCamelCase__				) for i in range(97							,    1_23				)]

      # If the argument is None or the user provided an empty dictionary
      if not frequencies_dict:
            # Frequencies of letters in the english language (how much they show up)
            _A     =  {
                'a': 0.08497,
                'b': 0.01492,
                'c': 0.02202,
                'd': 0.04253,
                'e': 0.11162,
                'f': 0.02228,
                'g': 0.02015,
                'h': 0.06094,
                'i': 0.07546,
                'j': 0.00153,
                'k': 0.01292,
                'l': 0.04025,
                'm': 0.02406,
                'n': 0.06749,
                'o': 0.07507,
                'p': 0.01929,
                'q': 0.00095,
                'r': 0.07587,
                's': 0.06327,
                't': 0.09356,
                'u': 0.02758,
                'v': 0.00978,
                'w': 0.02560,
                'x': 0.00150,
                'y': 0.01994,
                'z': 0.00077,
            }
      else:
            # Custom frequencies dictionary
            _A     =  frequencies_dict

      if not case_sensitive:
            _A     =  ciphertext.lower()

      # Chi squared statistic values
      _A     =  {}

      # cycle through all of the shifts
      for shift in range(len(UpperCamelCase__				)				):
            _A     =  ''

            # decrypt the message with the shift
            for letter in ciphertext:
                  try:
                        # Try to index the letter in the alphabet
                        _A     =  (alphabet_letters.index(letter.lower()				) - shift) % len(
                            UpperCamelCase__				)
                        decrypted_with_shift += (
                            alphabet_letters[new_key].upper()
                            if case_sensitive and letter.isupper()
                            else alphabet_letters[new_key]
                        )
                  except ValueError:
                        # Append the character if it isn't in the alphabet
                        decrypted_with_shift += letter

            _A     =  0.0

            # Loop through each letter in the decoded message with the shift
            for letter in decrypted_with_shift:
                  if case_sensitive:
                        _A     =  letter.lower()
                        if letter in frequencies:
                              # Get the amount of times the letter occurs in the message
                              _A     =  decrypted_with_shift.lower().count(UpperCamelCase__				)

                              # Get the excepcted amount of times the letter should appear based
                              # on letter frequencies
                              _A     =  frequencies[letter] * occurrences

                              # Complete the chi squared statistic formula
                              _A     =  ((occurrences - expected) ** 2) / expected

                              # Add the margin of error to the total chi squared statistic
                              chi_squared_statistic += chi_letter_value
                  else:
                        if letter.lower() in frequencies:
                              # Get the amount of times the letter occurs in the message
                              _A     =  decrypted_with_shift.count(UpperCamelCase__				)

                              # Get the excepcted amount of times the letter should appear based
                              # on letter frequencies
                              _A     =  frequencies[letter] * occurrences

                              # Complete the chi squared statistic formula
                              _A     =  ((occurrences - expected) ** 2) / expected

                              # Add the margin of error to the total chi squared statistic
                              chi_squared_statistic += chi_letter_value

        # Add the data to the chi_squared_statistic_values dictionary
            _A     =  (
                chi_squared_statistic,
                decrypted_with_shift,
            )

      # Get the most likely cipher by finding the cipher with the smallest chi squared
      # statistic
      def chi_squared_statistic_values_sorting_key(_snake_case				:   int				) -> tuple[float, str]:
            return chi_squared_statistic_values[key]

      _A     =  min(
          UpperCamelCase__							,    key=UpperCamelCase__							,    )

      # Get all the data from the most likely cipher (key, decoded message)
      (
          (
          _A      
      )  ,       (
          _A      
      )  ,             
      )     =  chi_squared_statistic_values[most_likely_cipher]

      # Return the data on the most likely shift
      return (
          most_likely_cipher,
          most_likely_cipher_chi_squared_value,
          decoded_most_likely_cipher,
      )


'''simple docstring'''


from typing import List, Optional, Union

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


__lowerCAmelCase						:							Union[str, Any]    							=						logging.get_logger(__name__)

__lowerCAmelCase						:							Optional[int]    							=						{
    "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     A ( UpperCAmelCase ):
							a_										=   '''time_series_transformer'''
							a_										=   {
							    '''hidden_size''': '''d_model''',
							    '''num_attention_heads''': '''encoder_attention_heads''',
							    '''num_hidden_layers''': '''encoder_layers''',
							}





							def __init__(   self					:				str  ,		__a					:				Optional[int] = None  ,		__a					:				Optional[int] = None  ,		__a					:				str = "student_t"  ,		__a					:				str = "nll"  ,		__a					:				int = 1  ,		__a					:				List[int] = [1, 2, 3, 4, 5, 6, 7]  ,		__a					:				Optional[Union[str, bool]] = "mean"  ,		__a					:				int = 0  ,		__a					:				int = 0  ,		__a					:				int = 0  ,		__a					:				int = 0  ,		__a					:				Optional[List[int]] = None  ,		__a					:				Optional[List[int]] = None  ,		__a					:				int = 3_2  ,		__a					:				int = 3_2  ,		__a					:				int = 2  ,		__a					:				int = 2  ,		__a					:				int = 2  ,		__a					:				int = 2  ,		__a					:				bool = True  ,		__a					:				str = "gelu"  ,		__a					:				int = 6_4  ,		__a					:				float = 0.1  ,		__a					:				float = 0.1  ,		__a					:				float = 0.1  ,		__a					:				float = 0.1  ,		__a					:				float = 0.1  ,		__a					:				int = 1_0_0  ,		__a					:				float = 0.0_2  ,		__a					:				Optional[int]=True  ,		**__a					:				str  ,		)				->						int:
								# time series specific configuration
								__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(5_0  ,		(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    snake_case__    (   self					:				List[Any]						)				->						int:
								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
								)
