


'''simple docstring'''
from __future__ import annotations


def       _lowercase							(					UpperCamelCase__							:       str,					UpperCamelCase__							:       list[str] | None = None,					UpperCamelCase__							:       dict[str, float] | None = None,					UpperCamelCase__							:       bool = False,					):
 __A	:		Union[str, Any]	  =      cipher_alphabet or [chr(UpperCamelCase__      ) 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)
  __A	:		Union[str, Any]	  =      {
      '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	:		Optional[Any]	  =      frequencies_dict

 if not case_sensitive:
  __A	:		Optional[Any]	  =      ciphertext.lower()

 # Chi squared statistic values
 __A	:		dict[int, tuple[float, str]]	  =      {}

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

  # decrypt the message with the shift
  for letter in ciphertext:
   try:
    # Try to index the letter in the alphabet
    __A	:		Union[str, Any]	  =      (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	:		Dict	  =      0.0

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

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

     # Complete the chi squared statistic formula
     __A	:		Union[str, Any]	  =      ((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	:		Optional[int]	  =      decrypted_with_shift.count(UpperCamelCase__      )

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

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

 __A	:		int	  =      min(
     UpperCamelCase__,					key=UpperCamelCase__,					)

 # Get all the data from the most likely cipher (key, decoded message)
 (
     (
     __A
 )  ,(
     __A
 )  ,
 )	:		Union[str, Any]	  =      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 TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_flax_available,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


UpperCAmelCase_			:  Dict										=   {
    'configuration_distilbert': [
        'DISTILBERT_PRETRAINED_CONFIG_ARCHIVE_MAP',
        'DistilBertConfig',
        'DistilBertOnnxConfig',
    ],
    'tokenization_distilbert': ['DistilBertTokenizer'],
}

try:
 if not is_tokenizers_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 UpperCAmelCase_			:  List[Any]										=   ['DistilBertTokenizerFast']

try:
 if not is_torch_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 UpperCAmelCase_			:  int										=   [
     'DISTILBERT_PRETRAINED_MODEL_ARCHIVE_LIST',
     'DistilBertForMaskedLM',
     'DistilBertForMultipleChoice',
     'DistilBertForQuestionAnswering',
     'DistilBertForSequenceClassification',
     'DistilBertForTokenClassification',
     'DistilBertModel',
     'DistilBertPreTrainedModel',
 ]

try:
 if not is_tf_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 UpperCAmelCase_			:  Optional[int]										=   [
     'TF_DISTILBERT_PRETRAINED_MODEL_ARCHIVE_LIST',
     'TFDistilBertForMaskedLM',
     'TFDistilBertForMultipleChoice',
     'TFDistilBertForQuestionAnswering',
     'TFDistilBertForSequenceClassification',
     'TFDistilBertForTokenClassification',
     'TFDistilBertMainLayer',
     'TFDistilBertModel',
     'TFDistilBertPreTrainedModel',
 ]

try:
 if not is_flax_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 UpperCAmelCase_			:  List[str]										=   [
     'FlaxDistilBertForMaskedLM',
     'FlaxDistilBertForMultipleChoice',
     'FlaxDistilBertForQuestionAnswering',
     'FlaxDistilBertForSequenceClassification',
     'FlaxDistilBertForTokenClassification',
     'FlaxDistilBertModel',
     'FlaxDistilBertPreTrainedModel',
 ]


if TYPE_CHECKING:
 from .configuration_distilbert import (
     DISTILBERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
     DistilBertConfig,
     DistilBertOnnxConfig,
 )
 from .tokenization_distilbert import DistilBertTokenizer

 try:
  if not is_tokenizers_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .tokenization_distilbert_fast import DistilBertTokenizerFast

 try:
  if not is_torch_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_distilbert import (
      DISTILBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
      DistilBertForMaskedLM,
      DistilBertForMultipleChoice,
      DistilBertForQuestionAnswering,
      DistilBertForSequenceClassification,
      DistilBertForTokenClassification,
      DistilBertModel,
      DistilBertPreTrainedModel,
  )

 try:
  if not is_tf_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_tf_distilbert import (
      TF_DISTILBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
      TFDistilBertForMaskedLM,
      TFDistilBertForMultipleChoice,
      TFDistilBertForQuestionAnswering,
      TFDistilBertForSequenceClassification,
      TFDistilBertForTokenClassification,
      TFDistilBertMainLayer,
      TFDistilBertModel,
      TFDistilBertPreTrainedModel,
  )

 try:
  if not is_flax_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_flax_distilbert import (
      FlaxDistilBertForMaskedLM,
      FlaxDistilBertForMultipleChoice,
      FlaxDistilBertForQuestionAnswering,
      FlaxDistilBertForSequenceClassification,
      FlaxDistilBertForTokenClassification,
      FlaxDistilBertModel,
      FlaxDistilBertPreTrainedModel,
  )

else:
 import sys

 UpperCAmelCase_			:  Optional[Any]										=   _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)






