


from __future__ import annotations






def  __lowerCAmelCase					(SCREAMING_SNAKE_CASE      ,      SCREAMING_SNAKE_CASE = None      ,      SCREAMING_SNAKE_CASE = None      ,      SCREAMING_SNAKE_CASE = False      ,      )->						tuple[int, float, str]:




      """simple docstring"""

      snake_case_ =   cipher_alphabet or [chr(SCREAMING_SNAKE_CASE      ) for i in range(97      ,      123      )]

      # 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)
            snake_case_ =   {
                '''a''': 0.0_8_4_9_7,
                '''b''': 0.0_1_4_9_2,
                '''c''': 0.0_2_2_0_2,
                '''d''': 0.0_4_2_5_3,
                '''e''': 0.1_1_1_6_2,
                '''f''': 0.0_2_2_2_8,
                '''g''': 0.0_2_0_1_5,
                '''h''': 0.0_6_0_9_4,
                '''i''': 0.0_7_5_4_6,
                '''j''': 0.0_0_1_5_3,
                '''k''': 0.0_1_2_9_2,
                '''l''': 0.0_4_0_2_5,
                '''m''': 0.0_2_4_0_6,
                '''n''': 0.0_6_7_4_9,
                '''o''': 0.0_7_5_0_7,
                '''p''': 0.0_1_9_2_9,
                '''q''': 0.0_0_0_9_5,
                '''r''': 0.0_7_5_8_7,
                '''s''': 0.0_6_3_2_7,
                '''t''': 0.0_9_3_5_6,
                '''u''': 0.0_2_7_5_8,
                '''v''': 0.0_0_9_7_8,
                '''w''': 0.0_2_5_6_0,
                '''x''': 0.0_0_1_5_0,
                '''y''': 0.0_1_9_9_4,
                '''z''': 0.0_0_0_7_7,
            }
      else:
            # Custom frequencies dictionary
            snake_case_ =   frequencies_dict

      if not case_sensitive:
            snake_case_ =   ciphertext.lower()

      # Chi squared statistic values
      snake_case_ =   {}

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

            # decrypt the message with the shift
            for letter in ciphertext:
                  try:
                        # Try to index the letter in the alphabet
                        snake_case_ =   (alphabet_letters.index(letter.lower()      ) - shift) % len(
                            SCREAMING_SNAKE_CASE      )
                        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

            snake_case_ =   0.0

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

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

                              # Complete the chi squared statistic formula
                              snake_case_ =   ((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
                              snake_case_ =   decrypted_with_shift.count(SCREAMING_SNAKE_CASE      )

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

                              # Complete the chi squared statistic formula
                              snake_case_ =   ((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
            snake_case_ =   (
                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(SCREAMING_SNAKE_CASE      ) -> tuple[float, str]:
            return chi_squared_statistic_values[key]

      snake_case_ =   min(
          SCREAMING_SNAKE_CASE      ,      key=SCREAMING_SNAKE_CASE      ,      )

      # Get all the data from the most likely cipher (key, decoded message)
      (
          (
          snake_case_
      ) ,					(
          snake_case_
      ) ,					
      ) =   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,
      )



from typing import List, Optional, Union

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


UpperCAmelCase    =					logging.get_logger(__name__)

UpperCAmelCase    =					{
    """huggingface/informer-tourism-monthly""": (
        """https://huggingface.co/huggingface/informer-tourism-monthly/resolve/main/config.json"""
    ),
    # See all Informer models at https://huggingface.co/models?filter=informer
}







class lowerCAmelCase_		(				lowerCamelCase__ ):

 '''simple docstring'''

 __snake_case				=						"informer"
 __snake_case				=						{
     "hidden_size": "d_model",
     "num_attention_heads": "encoder_attention_heads",
     "num_hidden_layers": "encoder_layers",
 }



 def __init__(			self	,   _UpperCAmelCase = None	,   _UpperCAmelCase = None	,   _UpperCAmelCase = "student_t"	,   _UpperCAmelCase = "nll"	,   _UpperCAmelCase = 1	,   _UpperCAmelCase = None	,   _UpperCAmelCase = "mean"	,   _UpperCAmelCase = 0	,   _UpperCAmelCase = 0	,   _UpperCAmelCase = 0	,   _UpperCAmelCase = 0	,   _UpperCAmelCase = None	,   _UpperCAmelCase = None	,   _UpperCAmelCase = 64	,   _UpperCAmelCase = 32	,   _UpperCAmelCase = 32	,   _UpperCAmelCase = 2	,   _UpperCAmelCase = 2	,   _UpperCAmelCase = 2	,   _UpperCAmelCase = 2	,   _UpperCAmelCase = True	,   _UpperCAmelCase = "gelu"	,   _UpperCAmelCase = 0.05	,   _UpperCAmelCase = 0.1	,   _UpperCAmelCase = 0.1	,   _UpperCAmelCase = 0.1	,   _UpperCAmelCase = 0.1	,   _UpperCAmelCase = 1_00	,   _UpperCAmelCase = 0.02	,   _UpperCAmelCase=True	,   _UpperCAmelCase = "prob"	,   _UpperCAmelCase = 5	,   _UpperCAmelCase = True	,   **_UpperCAmelCase	,   ):
       # time series specific configuration
       snake_case_ =   prediction_length
       snake_case_ =   context_length or prediction_length
       snake_case_ =   distribution_output
       snake_case_ =   loss
       snake_case_ =   input_size
       snake_case_ =   num_time_features
       snake_case_ =   lags_sequence if lags_sequence is not None else [1, 2, 3, 4, 5, 6, 7]
       snake_case_ =   scaling
       snake_case_ =   num_dynamic_real_features
       snake_case_ =   num_static_real_features
       snake_case_ =   num_static_categorical_features

       # set cardinality
       if cardinality and num_static_categorical_features > 0:
             if len(_UpperCAmelCase      ) != num_static_categorical_features:
                   raise ValueError(
                       '''The cardinality should be a list of the same length as `num_static_categorical_features`'''      )
             snake_case_ =   cardinality
       else:
             snake_case_ =   [0]

       # set embedding_dimension
       if embedding_dimension and num_static_categorical_features > 0:
             if len(_UpperCAmelCase      ) != num_static_categorical_features:
                   raise ValueError(
                       '''The embedding dimension should be a list of the same length as `num_static_categorical_features`'''      )
             snake_case_ =   embedding_dimension
       else:
             snake_case_ =   [min(50	,   (cat + 1) // 2      ) for cat in self.cardinality]

       snake_case_ =   num_parallel_samples

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

       snake_case_ =   dropout
       snake_case_ =   attention_dropout
       snake_case_ =   activation_dropout
       snake_case_ =   encoder_layerdrop
       snake_case_ =   decoder_layerdrop

       snake_case_ =   activation_function
       snake_case_ =   init_std

       snake_case_ =   use_cache

       # Informer
       snake_case_ =   attention_type
       snake_case_ =   sampling_factor
       snake_case_ =   distil

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





 @property
 def 		UpperCamelCase__  (			self      ):
       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
       )