



from __future__ import annotations

from string import ascii_letters







def 				A			(		__UpperCAmelCase							,					__UpperCAmelCase							,					__UpperCAmelCase = None	)       ->       str:



						'''simple docstring'''






						UpperCAmelCase_     							=     alphabet or ascii_letters

						# The final result string
						UpperCAmelCase_     							=     ''''''

						for character in input_string:
												if character not in alpha:
																		# Append without encryption if character is not in the alphabet
																		result += character
												else:
																		# Get the index of the new key and make sure it isn't too large
																		UpperCAmelCase_     							=     (alpha.index(__UpperCAmelCase	) + key) % len(__UpperCAmelCase	)

																		# Append the encoded character to the alphabet
																		result += alpha[new_key]

						return result







def 				A			(		__UpperCAmelCase							,					__UpperCAmelCase							,					__UpperCAmelCase = None	)       ->       str:



						'''simple docstring'''






						key *= -1

						return encrypt(__UpperCAmelCase							,					__UpperCAmelCase							,					__UpperCAmelCase	)







def 				A			(		__UpperCAmelCase							,					__UpperCAmelCase = None	)       ->       dict[int, str]:



						'''simple docstring'''






						UpperCAmelCase_     							=     alphabet or ascii_letters

						# To store data on all the combinations
						UpperCAmelCase_     							=     {}

						# Cycle through each combination
						for key in range(1							,					len(__UpperCAmelCase	) + 1	):
												# Decrypt the message and store the result in the data
												UpperCAmelCase_     							=     decrypt(__UpperCAmelCase							,					__UpperCAmelCase							,					__UpperCAmelCase	)

						return brute_force_data


if __name__ == "__main__":
							while True:
														print(f"\n{'-' * 10}\n Menu\n{'-' * 10}")
														print(*["1.Encrypt", "2.Decrypt", "3.BruteForce", "4.Quit"], sep="\n")

														# get user input
														UpperCamelCase_	       =   input("\nWhat would you like to do?: ").strip() or "4"

														# run functions based on what the user chose
														if choice not in ("1", "2", "3", "4"):
																					print("Invalid choice, please enter a valid choice")
														elif choice == "1":
																					UpperCamelCase_	       =   input("Please enter the string to be encrypted: ")
																					UpperCamelCase_	       =   int(input("Please enter off-set: ").strip())

																					print(encrypt(input_string, key))
														elif choice == "2":
																					UpperCamelCase_	       =   input("Please enter the string to be decrypted: ")
																					UpperCamelCase_	       =   int(input("Please enter off-set: ").strip())

																					print(decrypt(input_string, key))
														elif choice == "3":
																					UpperCamelCase_	       =   input("Please enter the string to be decrypted: ")
																					UpperCamelCase_	       =   brute_force(input_string)

																					for key, value in brute_force_data.items():
																												print(f"Key: {key} | Message: {value}")

														elif choice == "4":
																					print("Goodbye.")
																					break










import datasets

from .evaluate import (
    apply_no_ans_threshold,
    find_all_best_thresh,
    get_raw_scores,
    make_eval_dict,
    make_qid_to_has_ans,
    merge_eval,
)


UpperCamelCase_	       =   "\\n@inproceedings{Rajpurkar2016SQuAD10,\n  title={SQuAD: 100, 000+ Questions for Machine Comprehension of Text},\n  author={Pranav Rajpurkar and Jian Zhang and Konstantin Lopyrev and Percy Liang},\n  booktitle={EMNLP},\n  year={2016}\n}\n"

UpperCamelCase_	       =   "\nThis metric wrap the official scoring script for version 2 of the Stanford Question\nAnswering Dataset (SQuAD).\n\nStanford Question Answering Dataset (SQuAD) is a reading comprehension dataset, consisting of questions posed by\ncrowdworkers on a set of Wikipedia articles, where the answer to every question is a segment of text, or span,\nfrom the corresponding reading passage, or the question might be unanswerable.\n\nSQuAD2.0 combines the 100,000 questions in SQuAD1.1 with over 50,000 unanswerable questions\nwritten adversarially by crowdworkers to look similar to answerable ones.\nTo do well on SQuAD2.0, systems must not only answer questions when possible, but also\ndetermine when no answer is supported by the paragraph and abstain from answering.\n"

UpperCamelCase_	       =   "\nComputes SQuAD v2 scores (F1 and EM).\nArgs:\n    predictions: List of triple for question-answers to score with the following elements:\n        - the question-answer 'id' field as given in the references (see below)\n        - the text of the answer\n        - the probability that the question has no answer\n    references: List of question-answers dictionaries with the following key-values:\n            - 'id': id of the question-answer pair (see above),\n            - 'answers': a list of Dict {'text': text of the answer as a string}\n    no_answer_threshold: float\n        Probability threshold to decide that a question has no answer.\nReturns:\n    'exact': Exact match (the normalized answer exactly match the gold answer)\n    'f1': The F-score of predicted tokens versus the gold answer\n    'total': Number of score considered\n    'HasAns_exact': Exact match (the normalized answer exactly match the gold answer)\n    'HasAns_f1': The F-score of predicted tokens versus the gold answer\n    'HasAns_total': Number of score considered\n    'NoAns_exact': Exact match (the normalized answer exactly match the gold answer)\n    'NoAns_f1': The F-score of predicted tokens versus the gold answer\n    'NoAns_total': Number of score considered\n    'best_exact': Best exact match (with varying threshold)\n    'best_exact_thresh': No-answer probability threshold associated to the best exact match\n    'best_f1': Best F1 (with varying threshold)\n    'best_f1_thresh': No-answer probability threshold associated to the best F1\nExamples:\n\n    >>> predictions = [{'prediction_text': '1976', 'id': '56e10a3be3433e1400422b22', 'no_answer_probability': 0.}]\n    >>> references = [{'answers': {'answer_start': [97], 'text': ['1976']}, 'id': '56e10a3be3433e1400422b22'}]\n    >>> squad_v2_metric = datasets.load_metric(\"squad_v2\")\n    >>> results = squad_v2_metric.compute(predictions=predictions, references=references)\n    >>> print(results)\n    {'exact': 100.0, 'f1': 100.0, 'total': 1, 'HasAns_exact': 100.0, 'HasAns_f1': 100.0, 'HasAns_total': 1, 'best_exact': 100.0, 'best_exact_thresh': 0.0, 'best_f1': 100.0, 'best_f1_thresh': 0.0}\n"







@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION					, _KWARGS_DESCRIPTION				)
class 							a_  (		datasets.Metric				):

						def 	__a       (    self	:List[Any])		->					List[str]:
												return datasets.MetricInfo(
												    description=_DESCRIPTION   ,						citation=_CITATION   ,						inputs_description=_KWARGS_DESCRIPTION   ,						features=datasets.Features(
												        {
												            '''predictions''': {
												                '''id''': datasets.Value('''string'''),
												                '''prediction_text''': datasets.Value('''string'''),
												                '''no_answer_probability''': datasets.Value('''float32'''),
												            },
												            '''references''': {
												                '''id''': datasets.Value('''string'''),
												                '''answers''': datasets.features.Sequence(
												                    {'''text''': datasets.Value('''string'''), '''answer_start''': datasets.Value('''int32''')}),
												            },
												        })   ,						codebase_urls=['''https://rajpurkar.github.io/SQuAD-explorer/''']   ,						reference_urls=['''https://rajpurkar.github.io/SQuAD-explorer/''']   ,						)




						def 	__a       (    self	:int   ,						_lowercase	:Tuple   ,						_lowercase	:List[str]   ,						_lowercase	:Optional[Any]=1.0)		->					str:
												UpperCAmelCase_     							=     {p['''id''']: p['''no_answer_probability'''] for p in predictions}
												UpperCAmelCase_     							=     [{'''paragraphs''': [{'''qas''': references}]}]
												UpperCAmelCase_     							=     {p['''id''']: p['''prediction_text'''] for p in predictions}

												UpperCAmelCase_     							=     make_qid_to_has_ans(_lowercase)  # maps qid to True/False
												UpperCAmelCase_     							=     [k for k, v in qid_to_has_ans.items() if v]
												UpperCAmelCase_     							=     [k for k, v in qid_to_has_ans.items() if not v]

												UpperCAmelCase_							,      UpperCAmelCase_     							=     get_raw_scores(_lowercase   ,						_lowercase)
												UpperCAmelCase_     							=     apply_no_ans_threshold(_lowercase   ,						_lowercase   ,						_lowercase   ,						_lowercase)
												UpperCAmelCase_     							=     apply_no_ans_threshold(_lowercase   ,						_lowercase   ,						_lowercase   ,						_lowercase)
												UpperCAmelCase_     							=     make_eval_dict(_lowercase   ,						_lowercase)

												if has_ans_qids:
																		UpperCAmelCase_     							=     make_eval_dict(_lowercase   ,						_lowercase   ,						qid_list=_lowercase)
																		merge_eval(_lowercase   ,						_lowercase   ,						'''HasAns''')
												if no_ans_qids:
																		UpperCAmelCase_     							=     make_eval_dict(_lowercase   ,						_lowercase   ,						qid_list=_lowercase)
																		merge_eval(_lowercase   ,						_lowercase   ,						'''NoAns''')
												find_all_best_thresh(_lowercase   ,						_lowercase   ,						_lowercase   ,						_lowercase   ,						_lowercase   ,						_lowercase)
												return dict(_lowercase)





