



"""simple docstring"""
from __future__ import annotations





def  lowerCamelCase_						(UpperCamelCase__		:		str     ,       UpperCamelCase__		:		list[str] | None = None     ,       UpperCamelCase__		:		dict[str, float] | None = None     ,       UpperCamelCase__		:		bool = False     ,       ):
    _UpperCAmelCase							:  str											=    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)
        _UpperCAmelCase							:  Optional[int]											=    {
            '''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							:  Tuple											=    ciphertext.lower()

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

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

        # decrypt the message with the shift
        for letter in ciphertext:
            try:
                # Try to index the letter in the alphabet
                _UpperCAmelCase							:  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

        _UpperCAmelCase							:  Union[str, Any]											=    0.0

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

                    # 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							:  int											=    ((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							:  int											=    decrypted_with_shift.count(UpperCamelCase__							)

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

                    # Complete the chi squared statistic formula
                    _UpperCAmelCase							:  str											=    ((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							:  int											=    (
            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]

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

    # Get all the data from the most likely cipher (key, decoded message)
    (
        (
        _UpperCAmelCase
    )							,				(
        _UpperCAmelCase
    )							,				
    )							:  Optional[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,
)


_lowerCAmelCase    :List[Any]     =					{'configuration_opt': ['OPT_PRETRAINED_CONFIG_ARCHIVE_MAP', 'OPTConfig']}

try:
  if not is_torch_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  _lowerCAmelCase    :Any     =					[
      'OPT_PRETRAINED_MODEL_ARCHIVE_LIST',
      'OPTForCausalLM',
      'OPTModel',
      'OPTPreTrainedModel',
      'OPTForSequenceClassification',
      'OPTForQuestionAnswering',
  ]

try:
  if not is_tf_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  _lowerCAmelCase    :Optional[int]     =					['TFOPTForCausalLM', 'TFOPTModel', 'TFOPTPreTrainedModel']

try:
  if not is_flax_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  _lowerCAmelCase    :Any     =					[
      'FlaxOPTForCausalLM',
      'FlaxOPTModel',
      'FlaxOPTPreTrainedModel',
  ]


if TYPE_CHECKING:
  from .configuration_opt import OPT_PRETRAINED_CONFIG_ARCHIVE_MAP, OPTConfig

  try:
    if not is_torch_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_opt import (
        OPT_PRETRAINED_MODEL_ARCHIVE_LIST,
        OPTForCausalLM,
        OPTForQuestionAnswering,
        OPTForSequenceClassification,
        OPTModel,
        OPTPreTrainedModel,
    )

  try:
    if not is_tf_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_tf_opt import TFOPTForCausalLM, TFOPTModel, TFOPTPreTrainedModel

  try:
    if not is_flax_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_flax_opt import FlaxOPTForCausalLM, FlaxOPTModel, FlaxOPTPreTrainedModel

else:
  import sys

  _lowerCAmelCase    :int     =					_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)


