'''simple docstring'''


from __future__ import annotations


def  _lowerCamelCase   (				lowerCamelCase_ :							str     ,					lowerCamelCase_ :							list[str] | None = None     ,					lowerCamelCase_ :							dict[str, float] | None = None     ,					lowerCamelCase_ :							bool = False     ,					):



     """simple docstring"""





     UpperCAmelCase_							:       Union[str, Any]       =				cipher_alphabet or [chr(lowerCAmelCase__       ) 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)
          UpperCAmelCase_							:       Dict       =				{
              'a': 0.0_8497,
              'b': 0.0_1492,
              'c': 0.0_2202,
              'd': 0.0_4253,
              'e': 0.1_1162,
              'f': 0.0_2228,
              'g': 0.0_2015,
              'h': 0.0_6094,
              'i': 0.0_7546,
              'j': 0.0_0153,
              'k': 0.0_1292,
              'l': 0.0_4025,
              'm': 0.0_2406,
              'n': 0.0_6749,
              'o': 0.0_7507,
              'p': 0.0_1929,
              'q': 0.0_0095,
              'r': 0.0_7587,
              's': 0.0_6327,
              't': 0.0_9356,
              'u': 0.0_2758,
              'v': 0.0_0978,
              'w': 0.0_2560,
              'x': 0.0_0150,
              'y': 0.0_1994,
              'z': 0.0_0077,
          }
     else:
          # Custom frequencies dictionary
          UpperCAmelCase_							:       List[Any]       =				frequencies_dict

     if not case_sensitive:
          UpperCAmelCase_							:       str       =				ciphertext.lower()

     # Chi squared statistic values
     UpperCAmelCase_							:       Optional[Any]       =				{}

     # cycle through all of the shifts
     for shift in range(len(lowerCAmelCase__       )       ):
          UpperCAmelCase_							:       Optional[int]       =				''

          # decrypt the message with the shift
          for letter in ciphertext:
               try:
                    # Try to index the letter in the alphabet
                    UpperCAmelCase_							:       Optional[int]       =				(alphabet_letters.index(letter.lower()       ) - shift) % len(
                        lowerCAmelCase__       )
                    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

          UpperCAmelCase_							:       List[Any]       =				0.0

          # Loop through each letter in the decoded message with the shift
          for letter in decrypted_with_shift:
               if case_sensitive:
                    UpperCAmelCase_							:       List[Any]       =				letter.lower()
                    if letter in frequencies:
                         # Get the amount of times the letter occurs in the message
                         UpperCAmelCase_							:       Optional[Any]       =				decrypted_with_shift.lower().count(lowerCAmelCase__       )

                         # Get the excepcted amount of times the letter should appear based
                         # on letter frequencies
                         UpperCAmelCase_							:       List[Any]       =				frequencies[letter] * occurrences

                         # Complete the chi squared statistic formula
                         UpperCAmelCase_							:       List[str]       =				((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
                         UpperCAmelCase_							:       Optional[int]       =				decrypted_with_shift.count(lowerCAmelCase__       )

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

                         # Complete the chi squared statistic formula
                         UpperCAmelCase_							:       Union[str, Any]       =				((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
          UpperCAmelCase_							:       List[str]       =				(
              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(lowerCamelCase_ :							int       ) -> tuple[float, str]:
          return chi_squared_statistic_values[key]

     UpperCAmelCase_							:       str       =				min(
         lowerCAmelCase__     ,					key=lowerCAmelCase__     ,					)

     # Get all the data from the most likely cipher (key, decoded message)
     (
         (
         UpperCAmelCase_				
     )       ,      (
         UpperCAmelCase_				
     )       ,      				
     )							:       Tuple       =				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 collections import OrderedDict
from typing import Mapping

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig


snake_case__					:      Dict				      =  {
    '''albert-base-v1''': '''https://huggingface.co/albert-base-v1/resolve/main/config.json''',
    '''albert-large-v1''': '''https://huggingface.co/albert-large-v1/resolve/main/config.json''',
    '''albert-xlarge-v1''': '''https://huggingface.co/albert-xlarge-v1/resolve/main/config.json''',
    '''albert-xxlarge-v1''': '''https://huggingface.co/albert-xxlarge-v1/resolve/main/config.json''',
    '''albert-base-v2''': '''https://huggingface.co/albert-base-v2/resolve/main/config.json''',
    '''albert-large-v2''': '''https://huggingface.co/albert-large-v2/resolve/main/config.json''',
    '''albert-xlarge-v2''': '''https://huggingface.co/albert-xlarge-v2/resolve/main/config.json''',
    '''albert-xxlarge-v2''': '''https://huggingface.co/albert-xxlarge-v2/resolve/main/config.json''',
}






class 					__SCREAMING_SNAKE_CASE						(	lowerCamelCase_	):







  '''simple docstring'''






  lowerCamelCase_		:str						   =		'''albert'''


  def __init__(			self				,		snake_case_=3_0_0_0_0				,		snake_case_=1_2_8				,		snake_case_=4_0_9_6				,		snake_case_=1_2				,		snake_case_=1				,		snake_case_=6_4				,		snake_case_=1_6_3_8_4				,		snake_case_=1				,		snake_case_="gelu_new"				,		snake_case_=0				,		snake_case_=0				,		snake_case_=5_1_2				,		snake_case_=2				,		snake_case_=0.02				,		snake_case_=1E-12				,		snake_case_=0.1				,		snake_case_="absolute"				,		snake_case_=0				,		snake_case_=2				,		snake_case_=3				,		**snake_case_				,		):




       '''simple docstring'''

       super().__init__(pad_token_id=snake_case_				,		bos_token_id=snake_case_				,		eos_token_id=snake_case_				,		**snake_case_						)

       UpperCAmelCase_							:       List[Any]       =				vocab_size
       UpperCAmelCase_							:       Dict       =				embedding_size
       UpperCAmelCase_							:       str       =				hidden_size
       UpperCAmelCase_							:       Any       =				num_hidden_layers
       UpperCAmelCase_							:       Union[str, Any]       =				num_hidden_groups
       UpperCAmelCase_							:       List[str]       =				num_attention_heads
       UpperCAmelCase_							:       Any       =				inner_group_num
       UpperCAmelCase_							:       Optional[int]       =				hidden_act
       UpperCAmelCase_							:       Tuple       =				intermediate_size
       UpperCAmelCase_							:       Any       =				hidden_dropout_prob
       UpperCAmelCase_							:       List[str]       =				attention_probs_dropout_prob
       UpperCAmelCase_							:       Union[str, Any]       =				max_position_embeddings
       UpperCAmelCase_							:       Dict       =				type_vocab_size
       UpperCAmelCase_							:       Union[str, Any]       =				initializer_range
       UpperCAmelCase_							:       Optional[Any]       =				layer_norm_eps
       UpperCAmelCase_							:       Dict       =				classifier_dropout_prob
       UpperCAmelCase_							:       Tuple       =				position_embedding_type







class 					__SCREAMING_SNAKE_CASE						(	lowerCamelCase_	):







  '''simple docstring'''






  @property
  def _UpperCamelCase					(			self						):




       '''simple docstring'''

       if self.task == "multiple-choice":
            UpperCAmelCase_							:       Optional[Any]       =				{0: 'batch', 1: 'choice', 2: 'sequence'}
       else:
            UpperCAmelCase_							:       List[Any]       =				{0: 'batch', 1: 'sequence'}
       return OrderedDict(
           [
               ('input_ids', dynamic_axis),
               ('attention_mask', dynamic_axis),
               ('token_type_ids', dynamic_axis),
           ]						)






