from typing import TYPE_CHECKING

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


UpperCAmelCase_           =				{'''configuration_opt''': ['''OPT_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''OPTConfig''']}

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

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

try:
  if not is_flax_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  UpperCAmelCase_           =				[
      '''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

  UpperCAmelCase_           =				_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)
from __future__ import annotations




def    lowerCAmelCase_  (		lowercase:				str	,       lowercase:				list[str] | None = None	,       lowercase:				dict[str, float] | None = None	,       lowercase:				bool = False	,       )     ->					tuple[int, float, str]:







 '''simple docstring'''
 _UpperCamelCase:  Any    						=			cipher_alphabet or [chr(lowercase		) 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:  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
  _UpperCamelCase:  str    						=			frequencies_dict

 if not case_sensitive:
  _UpperCamelCase:  Union[str, Any]    						=			ciphertext.lower()

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

 # cycle through all of the shifts
 for shift in range(len(lowercase		)		):
  _UpperCamelCase:  Tuple    						=			''''''

  # 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(
        lowercase		)
    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:  Optional[int]    						=			0.0

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

     # Get the excepcted amount of times the letter should appear based
     # on letter frequencies
     _UpperCamelCase:  int    						=			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:  List[Any]    						=			decrypted_with_shift.count(lowercase		)

     # 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:  List[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:  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(lowercase:				int		) -> tuple[float, str]:
  return chi_squared_statistic_values[key]

 _UpperCamelCase:  int    						=			min(
     lowercase	,       key=lowercase	,       )

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