import json
import os
import pickle
import shutil
import tempfile
from unittest import TestCase
from unittest.mock import patch

import numpy as np
from datasets import Dataset

from transformers import is_faiss_available
from transformers.models.bart.configuration_bart import BartConfig
from transformers.models.bart.tokenization_bart import BartTokenizer
from transformers.models.bert.tokenization_bert import VOCAB_FILES_NAMES as DPR_VOCAB_FILES_NAMES
from transformers.models.dpr.configuration_dpr import DPRConfig
from transformers.models.dpr.tokenization_dpr import DPRContextEncoderTokenizer, DPRQuestionEncoderTokenizer
from transformers.models.rag.configuration_rag import RagConfig
from transformers.models.rag.retrieval_rag import CustomHFIndex, RagRetriever
from transformers.models.roberta.tokenization_roberta import VOCAB_FILES_NAMES as BART_VOCAB_FILES_NAMES
from transformers.testing_utils import require_faiss, require_sentencepiece, require_tokenizers, require_torch


if is_faiss_available():
  import faiss




@require_faiss
class 			__magic_name__	(		__a     ):





   """simple docstring"""




   def 	lowerCAmelCase    (						self       :	List[Any]				):



    """simple docstring"""



    _UpperCamelCase:  Union[str, Any]    						=			tempfile.mkdtemp()
    _UpperCamelCase:  Dict    						=			8

    # DPR tok
    _UpperCamelCase:  Optional[int]    						=			[
        '''[UNK]''',
        '''[CLS]''',
        '''[SEP]''',
        '''[PAD]''',
        '''[MASK]''',
        '''want''',
        '''##want''',
        '''##ed''',
        '''wa''',
        '''un''',
        '''runn''',
        '''##ing''',
        ''',''',
        '''low''',
        '''lowest''',
    ]
    _UpperCamelCase:  int    						=			os.path.join(self.tmpdirname		,	'''dpr_tokenizer'''				)
    os.makedirs(_lowercase		,	exist_ok=_lowercase				)
    _UpperCamelCase:  Optional[Any]    						=			os.path.join(_lowercase		,	DPR_VOCAB_FILES_NAMES['''vocab_file''']				)
    with open(self.vocab_file		,	'''w'''		,	encoding='''utf-8'''				) as vocab_writer:
     vocab_writer.write(''''''.join([x + '''\n''' for x in vocab_tokens]				)				)

    # BART tok
    _UpperCamelCase:  Tuple    						=			[
        '''l''',
        '''o''',
        '''w''',
        '''e''',
        '''r''',
        '''s''',
        '''t''',
        '''i''',
        '''d''',
        '''n''',
        '''\u0120''',
        '''\u0120l''',
        '''\u0120n''',
        '''\u0120lo''',
        '''\u0120low''',
        '''er''',
        '''\u0120lowest''',
        '''\u0120newer''',
        '''\u0120wider''',
        '''<unk>''',
    ]
    _UpperCamelCase:  int    						=			dict(zip(_lowercase		,	range(len(_lowercase				)				)				)				)
    _UpperCamelCase:  Tuple    						=			['''#version: 0.2''', '''\u0120 l''', '''\u0120l o''', '''\u0120lo w''', '''e r''', '''''']
    _UpperCamelCase:  List[str]    						=			{'''unk_token''': '''<unk>'''}

    _UpperCamelCase:  Dict    						=			os.path.join(self.tmpdirname		,	'''bart_tokenizer'''				)
    os.makedirs(_lowercase		,	exist_ok=_lowercase				)
    _UpperCamelCase:  Dict    						=			os.path.join(_lowercase		,	BART_VOCAB_FILES_NAMES['''vocab_file''']				)
    _UpperCamelCase:  Optional[Any]    						=			os.path.join(_lowercase		,	BART_VOCAB_FILES_NAMES['''merges_file''']				)
    with open(self.vocab_file		,	'''w'''		,	encoding='''utf-8'''				) as fp:
     fp.write(json.dumps(_lowercase				) + '''\n'''				)
    with open(self.merges_file		,	'''w'''		,	encoding='''utf-8'''				) as fp:
     fp.write('''\n'''.join(_lowercase				)				)



   def 	lowerCAmelCase    (						self       :	Union[str, Any]				):



    """simple docstring"""



    return DPRQuestionEncoderTokenizer.from_pretrained(os.path.join(self.tmpdirname		,	'''dpr_tokenizer'''				)				)



   def 	lowerCAmelCase    (						self       :	List[Any]				):



    """simple docstring"""



    return DPRContextEncoderTokenizer.from_pretrained(os.path.join(self.tmpdirname		,	'''dpr_tokenizer'''				)				)



   def 	lowerCAmelCase    (						self       :	Any				):



    """simple docstring"""



    return BartTokenizer.from_pretrained(os.path.join(self.tmpdirname		,	'''bart_tokenizer'''				)				)



   def 	lowerCAmelCase    (						self       :	Optional[int]				):



    """simple docstring"""



    shutil.rmtree(self.tmpdirname				)



   def 	lowerCAmelCase    (						self       :	List[str]				):



    """simple docstring"""



    _UpperCamelCase:  List[str]    						=			Dataset.from_dict(
        {
            '''id''': ['''0''', '''1'''],
            '''text''': ['''foo''', '''bar'''],
            '''title''': ['''Foo''', '''Bar'''],
            '''embeddings''': [np.ones(self.retrieval_vector_size				), 2 * np.ones(self.retrieval_vector_size				)],
        }				)
    dataset.add_faiss_index('''embeddings'''		,	string_factory='''Flat'''		,	metric_type=faiss.METRIC_INNER_PRODUCT				)
    return dataset



   def 	lowerCAmelCase    (						self       :	Any				):



    """simple docstring"""



    _UpperCamelCase:  Union[str, Any]    						=			self.get_dummy_dataset()
    _UpperCamelCase:  List[Any]    						=			RagConfig(
        retrieval_vector_size=self.retrieval_vector_size		,	question_encoder=DPRConfig().to_dict()		,	generator=BartConfig().to_dict()		,	)
    with patch('''transformers.models.rag.retrieval_rag.load_dataset'''				) as mock_load_dataset:
     _UpperCamelCase:  List[Any]    						=			dataset
     _UpperCamelCase:  str    						=			RagRetriever(
         _lowercase		,	question_encoder_tokenizer=self.get_dpr_tokenizer()		,	generator_tokenizer=self.get_bart_tokenizer()		,	)
    return retriever



   def 	lowerCAmelCase    (						self       :	str		,	_lowercase       :	bool				):



    """simple docstring"""



    _UpperCamelCase:  Optional[Any]    						=			self.get_dummy_dataset()
    _UpperCamelCase:  Optional[int]    						=			RagConfig(
        retrieval_vector_size=self.retrieval_vector_size		,	question_encoder=DPRConfig().to_dict()		,	generator=BartConfig().to_dict()		,	index_name='''custom'''		,	)
    if from_disk:
     _UpperCamelCase:  int    						=			os.path.join(self.tmpdirname		,	'''dataset'''				)
     _UpperCamelCase:  Union[str, Any]    						=			os.path.join(self.tmpdirname		,	'''index.faiss'''				)
     dataset.get_index('''embeddings'''				).save(os.path.join(self.tmpdirname		,	'''index.faiss'''				)				)
     dataset.drop_index('''embeddings'''				)
     dataset.save_to_disk(os.path.join(self.tmpdirname		,	'''dataset'''				)				)
     del dataset
     _UpperCamelCase:  Dict    						=			RagRetriever(
         _lowercase		,	question_encoder_tokenizer=self.get_dpr_tokenizer()		,	generator_tokenizer=self.get_bart_tokenizer()		,	)
    else:
     _UpperCamelCase:  List[str]    						=			RagRetriever(
         _lowercase		,	question_encoder_tokenizer=self.get_dpr_tokenizer()		,	generator_tokenizer=self.get_bart_tokenizer()		,	index=CustomHFIndex(config.retrieval_vector_size		,	_lowercase				)		,	)
    return retriever



   def 	lowerCAmelCase    (						self       :	List[str]				):



    """simple docstring"""



    _UpperCamelCase:  str    						=			Dataset.from_dict(
        {
            '''id''': ['''0''', '''1'''],
            '''text''': ['''foo''', '''bar'''],
            '''title''': ['''Foo''', '''Bar'''],
            '''embeddings''': [np.ones(self.retrieval_vector_size + 1				), 2 * np.ones(self.retrieval_vector_size + 1				)],
        }				)
    dataset.add_faiss_index('''embeddings'''		,	string_factory='''Flat'''		,	metric_type=faiss.METRIC_INNER_PRODUCT				)

    _UpperCamelCase:  Dict    						=			os.path.join(self.tmpdirname		,	'''hf_bert_base.hnswSQ8_correct_phi_128.c_index'''				)
    dataset.save_faiss_index('''embeddings'''		,	index_file_name + '''.index.dpr'''				)
    pickle.dump(dataset['''id''']		,	open(index_file_name + '''.index_meta.dpr'''		,	'''wb'''				)				)

    _UpperCamelCase:  List[Any]    						=			os.path.join(self.tmpdirname		,	'''psgs_w100.tsv.pkl'''				)
    _UpperCamelCase:  List[str]    						=			{sample['''id''']: [sample['''text'''], sample['''title''']] for sample in dataset}
    pickle.dump(_lowercase		,	open(_lowercase		,	'''wb'''				)				)

    _UpperCamelCase:  List[Any]    						=			RagConfig(
        retrieval_vector_size=self.retrieval_vector_size		,	question_encoder=DPRConfig().to_dict()		,	generator=BartConfig().to_dict()		,	index_name='''legacy'''		,	index_path=self.tmpdirname		,	)
    _UpperCamelCase:  Any    						=			RagRetriever(
        _lowercase		,	question_encoder_tokenizer=self.get_dpr_tokenizer()		,	generator_tokenizer=self.get_bart_tokenizer()				)
    return retriever



   def 	lowerCAmelCase    (						self       :	List[str]				):



    """simple docstring"""



    _UpperCamelCase:  List[str]    						=			1
    _UpperCamelCase:  List[str]    						=			self.get_dummy_canonical_hf_index_retriever()
    _UpperCamelCase:  Optional[int]    						=			np.array(
        [np.ones(self.retrieval_vector_size				), -np.ones(self.retrieval_vector_size				)]		,	dtype=np.floataa				)
    _UpperCamelCase						,				_UpperCamelCase						,				_UpperCamelCase:  Optional[int]    						=			retriever.retrieve(_lowercase		,	n_docs=_lowercase				)
    self.assertEqual(retrieved_doc_embeds.shape		,	(2, n_docs, self.retrieval_vector_size)				)
    self.assertEqual(len(_lowercase				)		,	2				)
    self.assertEqual(sorted(doc_dicts[0]				)		,	['''embeddings''', '''id''', '''text''', '''title''']				)
    self.assertEqual(len(doc_dicts[0]['''id''']				)		,	_lowercase				)
    self.assertEqual(doc_dicts[0]['''id'''][0]		,	'''1'''				)  # max inner product is reached with second doc
    self.assertEqual(doc_dicts[1]['''id'''][0]		,	'''0'''				)  # max inner product is reached with first doc
    self.assertListEqual(doc_ids.tolist()		,	[[1], [0]]				)



   def 	lowerCAmelCase    (						self       :	Any				):



    """simple docstring"""



    _UpperCamelCase:  int    						=			self.get_dummy_canonical_hf_index_retriever()
    with tempfile.TemporaryDirectory() as tmp_dirname:
     with patch('''transformers.models.rag.retrieval_rag.load_dataset'''				) as mock_load_dataset:
      _UpperCamelCase:  Optional[int]    						=			self.get_dummy_dataset()
      retriever.save_pretrained(_lowercase				)
      _UpperCamelCase:  Any    						=			RagRetriever.from_pretrained(_lowercase				)
      self.assertIsInstance(_lowercase		,	_lowercase				)
      _UpperCamelCase:  int    						=			np.array(
          [np.ones(self.retrieval_vector_size				), -np.ones(self.retrieval_vector_size				)]		,	dtype=np.floataa				)
      _UpperCamelCase:  Tuple    						=			retriever.retrieve(_lowercase		,	n_docs=1				)
      self.assertTrue(out is not None				)



   def 	lowerCAmelCase    (						self       :	Optional[int]				):



    """simple docstring"""



    _UpperCamelCase:  str    						=			1
    _UpperCamelCase:  str    						=			self.get_dummy_custom_hf_index_retriever(from_disk=_lowercase				)
    _UpperCamelCase:  List[Any]    						=			np.array(
        [np.ones(self.retrieval_vector_size				), -np.ones(self.retrieval_vector_size				)]		,	dtype=np.floataa				)
    _UpperCamelCase						,				_UpperCamelCase						,				_UpperCamelCase:  Any    						=			retriever.retrieve(_lowercase		,	n_docs=_lowercase				)
    self.assertEqual(retrieved_doc_embeds.shape		,	(2, n_docs, self.retrieval_vector_size)				)
    self.assertEqual(len(_lowercase				)		,	2				)
    self.assertEqual(sorted(doc_dicts[0]				)		,	['''embeddings''', '''id''', '''text''', '''title''']				)
    self.assertEqual(len(doc_dicts[0]['''id''']				)		,	_lowercase				)
    self.assertEqual(doc_dicts[0]['''id'''][0]		,	'''1'''				)  # max inner product is reached with second doc
    self.assertEqual(doc_dicts[1]['''id'''][0]		,	'''0'''				)  # max inner product is reached with first doc
    self.assertListEqual(doc_ids.tolist()		,	[[1], [0]]				)



   def 	lowerCAmelCase    (						self       :	str				):



    """simple docstring"""



    _UpperCamelCase:  str    						=			self.get_dummy_custom_hf_index_retriever(from_disk=_lowercase				)
    with tempfile.TemporaryDirectory() as tmp_dirname:
     retriever.save_pretrained(_lowercase				)
     _UpperCamelCase:  Optional[int]    						=			RagRetriever.from_pretrained(_lowercase				)
     self.assertIsInstance(_lowercase		,	_lowercase				)
     _UpperCamelCase:  Union[str, Any]    						=			np.array(
         [np.ones(self.retrieval_vector_size				), -np.ones(self.retrieval_vector_size				)]		,	dtype=np.floataa				)
     _UpperCamelCase:  Any    						=			retriever.retrieve(_lowercase		,	n_docs=1				)
     self.assertTrue(out is not None				)



   def 	lowerCAmelCase    (						self       :	Union[str, Any]				):



    """simple docstring"""



    _UpperCamelCase:  List[Any]    						=			1
    _UpperCamelCase:  Optional[int]    						=			self.get_dummy_custom_hf_index_retriever(from_disk=_lowercase				)
    _UpperCamelCase:  Optional[int]    						=			np.array(
        [np.ones(self.retrieval_vector_size				), -np.ones(self.retrieval_vector_size				)]		,	dtype=np.floataa				)
    _UpperCamelCase						,				_UpperCamelCase						,				_UpperCamelCase:  str    						=			retriever.retrieve(_lowercase		,	n_docs=_lowercase				)
    self.assertEqual(retrieved_doc_embeds.shape		,	(2, n_docs, self.retrieval_vector_size)				)
    self.assertEqual(len(_lowercase				)		,	2				)
    self.assertEqual(sorted(doc_dicts[0]				)		,	['''embeddings''', '''id''', '''text''', '''title''']				)
    self.assertEqual(len(doc_dicts[0]['''id''']				)		,	_lowercase				)
    self.assertEqual(doc_dicts[0]['''id'''][0]		,	'''1'''				)  # max inner product is reached with second doc
    self.assertEqual(doc_dicts[1]['''id'''][0]		,	'''0'''				)  # max inner product is reached with first doc
    self.assertListEqual(doc_ids.tolist()		,	[[1], [0]]				)



   def 	lowerCAmelCase    (						self       :	List[str]				):



    """simple docstring"""



    _UpperCamelCase:  Dict    						=			self.get_dummy_custom_hf_index_retriever(from_disk=_lowercase				)
    with tempfile.TemporaryDirectory() as tmp_dirname:
     retriever.save_pretrained(_lowercase				)
     _UpperCamelCase:  Union[str, Any]    						=			RagRetriever.from_pretrained(_lowercase				)
     self.assertIsInstance(_lowercase		,	_lowercase				)
     _UpperCamelCase:  str    						=			np.array(
         [np.ones(self.retrieval_vector_size				), -np.ones(self.retrieval_vector_size				)]		,	dtype=np.floataa				)
     _UpperCamelCase:  List[str]    						=			retriever.retrieve(_lowercase		,	n_docs=1				)
     self.assertTrue(out is not None				)



   def 	lowerCAmelCase    (						self       :	Any				):



    """simple docstring"""



    _UpperCamelCase:  List[str]    						=			1
    _UpperCamelCase:  Any    						=			self.get_dummy_legacy_index_retriever()
    _UpperCamelCase:  Union[str, Any]    						=			np.array(
        [np.ones(self.retrieval_vector_size				), -np.ones(self.retrieval_vector_size				)]		,	dtype=np.floataa				)
    _UpperCamelCase						,				_UpperCamelCase						,				_UpperCamelCase:  Union[str, Any]    						=			retriever.retrieve(_lowercase		,	n_docs=_lowercase				)
    self.assertEqual(retrieved_doc_embeds.shape		,	(2, n_docs, self.retrieval_vector_size)				)
    self.assertEqual(len(_lowercase				)		,	2				)
    self.assertEqual(sorted(doc_dicts[0]				)		,	['''text''', '''title''']				)
    self.assertEqual(len(doc_dicts[0]['''text''']				)		,	_lowercase				)
    self.assertEqual(doc_dicts[0]['''text'''][0]		,	'''bar'''				)  # max inner product is reached with second doc
    self.assertEqual(doc_dicts[1]['''text'''][0]		,	'''foo'''				)  # max inner product is reached with first doc
    self.assertListEqual(doc_ids.tolist()		,	[[1], [0]]				)



   def 	lowerCAmelCase    (						self       :	int				):



    """simple docstring"""



    _UpperCamelCase:  Dict    						=			self.get_dummy_legacy_index_retriever()
    with tempfile.TemporaryDirectory() as tmp_dirname:
     retriever.save_pretrained(_lowercase				)
     _UpperCamelCase:  Dict    						=			RagRetriever.from_pretrained(_lowercase				)
     self.assertIsInstance(_lowercase		,	_lowercase				)
     _UpperCamelCase:  Any    						=			np.array(
         [np.ones(self.retrieval_vector_size				), -np.ones(self.retrieval_vector_size				)]		,	dtype=np.floataa				)
     _UpperCamelCase:  Optional[int]    						=			retriever.retrieve(_lowercase		,	n_docs=1				)
     self.assertTrue(out is not None				)



   @require_torch
   @require_tokenizers
   @require_sentencepiece
   def 	lowerCAmelCase    (						self       :	str				):



    """simple docstring"""



    import torch

    _UpperCamelCase:  List[str]    						=			1
    _UpperCamelCase:  Dict    						=			self.get_dummy_canonical_hf_index_retriever()
    _UpperCamelCase:  Any    						=			[[5, 7], [10, 11]]
    _UpperCamelCase:  int    						=			np.array(
        [np.ones(self.retrieval_vector_size				), -np.ones(self.retrieval_vector_size				)]		,	dtype=np.floataa				)
    _UpperCamelCase:  Optional[int]    						=			retriever(_lowercase		,	_lowercase		,	prefix=retriever.config.generator.prefix		,	n_docs=_lowercase				)
    _UpperCamelCase						,				_UpperCamelCase						,				_UpperCamelCase:  List[Any]    						=			(
        out['''context_input_ids'''],
        out['''context_attention_mask'''],
        out['''retrieved_doc_embeds'''],
    )
    self.assertEqual(retrieved_doc_embeds.shape		,	(2, n_docs, self.retrieval_vector_size)				)
    self.assertIsInstance(_lowercase		,	_lowercase				)
    self.assertIsInstance(_lowercase		,	_lowercase				)
    self.assertIsInstance(_lowercase		,	np.ndarray				)

    _UpperCamelCase:  Union[str, Any]    						=			retriever(
        _lowercase		,	_lowercase		,	prefix=retriever.config.generator.prefix		,	n_docs=_lowercase		,	return_tensors='''pt'''		,	)
    _UpperCamelCase						,				_UpperCamelCase						,				_UpperCamelCase						,				_UpperCamelCase:  Tuple    						=			(  # noqa: F841
        out['''context_input_ids'''],
        out['''context_attention_mask'''],
        out['''retrieved_doc_embeds'''],
        out['''doc_ids'''],
    )
    self.assertEqual(retrieved_doc_embeds.shape		,	(2, n_docs, self.retrieval_vector_size)				)
    self.assertIsInstance(_lowercase		,	torch.Tensor				)
    self.assertIsInstance(_lowercase		,	torch.Tensor				)
    self.assertIsInstance(_lowercase		,	torch.Tensor				)



   @require_torch
   @require_tokenizers
   @require_sentencepiece
   def 	lowerCAmelCase    (						self       :	Optional[int]				):



    """simple docstring"""



    _UpperCamelCase:  Optional[Any]    						=			self.get_dpr_ctx_encoder_tokenizer()
    _UpperCamelCase:  List[str]    						=			1
    _UpperCamelCase:  Union[str, Any]    						=			self.get_dummy_custom_hf_index_retriever(from_disk=_lowercase				)
    retriever.set_ctx_encoder_tokenizer(_lowercase				)

    _UpperCamelCase:  str    						=			[[5, 7], [10, 11]]
    _UpperCamelCase:  str    						=			np.array(
        [np.ones(self.retrieval_vector_size				), -np.ones(self.retrieval_vector_size				)]		,	dtype=np.floataa				)
    _UpperCamelCase:  Optional[int]    						=			retriever(_lowercase		,	_lowercase		,	prefix=retriever.config.generator.prefix		,	n_docs=_lowercase				)

    self.assertEqual(
        len(_lowercase				)		,	6				)  # check whether the retriever output consist of 6 attributes including tokenized docs
    self.assertEqual(
        all(k in out for k in ('''tokenized_doc_ids''', '''tokenized_doc_attention_mask''')				)		,	_lowercase				)  # check for doc token related keys in dictionary.
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,
 )