


"""simple docstring"""







import string

# frequency taken from https://en.wikipedia.org/wiki/Letter_frequency
a__				:							Union[str, Any]						=     {
    '''E''': 12.70,
    '''T''': 9.06,
    '''A''': 8.17,
    '''O''': 7.51,
    '''I''': 6.97,
    '''N''': 6.75,
    '''S''': 6.33,
    '''H''': 6.09,
    '''R''': 5.99,
    '''D''': 4.25,
    '''L''': 4.03,
    '''C''': 2.78,
    '''U''': 2.76,
    '''M''': 2.41,
    '''W''': 2.36,
    '''F''': 2.23,
    '''G''': 2.02,
    '''Y''': 1.97,
    '''P''': 1.93,
    '''B''': 1.29,
    '''V''': 0.98,
    '''K''': 0.77,
    '''J''': 0.15,
    '''X''': 0.15,
    '''Q''': 0.10,
    '''Z''': 0.07,
}
a__				:							Optional[Any]						=     '''ETAOINSHRDLCUMWFGYPBVKJXQZ'''
a__				:							Dict						=     '''ABCDEFGHIJKLMNOPQRSTUVWXYZ'''




def    UpperCAmelCase__       (lowerCAmelCase_   ):




					'''simple docstring'''


					__SCREAMING_SNAKE_CASE						   =				{letter: 0 for letter in string.ascii_uppercase}
					for letter in message.upper():
										if letter in LETTERS:
															letter_count[letter] += 1

					return letter_count




def    UpperCAmelCase__       (lowerCAmelCase_   ):




					'''simple docstring'''


					return x[0]




def    UpperCAmelCase__       (lowerCAmelCase_   ):




					'''simple docstring'''


					__SCREAMING_SNAKE_CASE						   =				get_letter_count(lowerCAmelCase_   )
					__SCREAMING_SNAKE_CASE						   =				{
					    freq: [] for letter, freq in letter_to_freq.items()
					}
					for letter in LETTERS:
										freq_to_letter[letter_to_freq[letter]].append(lowerCAmelCase_   )

					__SCREAMING_SNAKE_CASE						   =				{}

					for freq in freq_to_letter:
										freq_to_letter[freq].sort(key=ETAOIN.find  ,						reverse=lowerCAmelCase_   )
										__SCREAMING_SNAKE_CASE						   =				"".join(freq_to_letter[freq]   )

					__SCREAMING_SNAKE_CASE						   =				list(freq_to_letter_str.items()   )
					freq_pairs.sort(key=lowerCAmelCase_  ,						reverse=lowerCAmelCase_   )

					__SCREAMING_SNAKE_CASE						   =				[freq_pair[1] for freq_pair in freq_pairs]

					return "".join(lowerCAmelCase_   )




def    UpperCAmelCase__       (lowerCAmelCase_   ):




					'''simple docstring'''


					__SCREAMING_SNAKE_CASE						   =				get_frequency_order(lowerCAmelCase_   )
					__SCREAMING_SNAKE_CASE						   =				0
					for common_letter in ETAOIN[:6]:
										if common_letter in freq_order[:6]:
															match_score += 1

					for uncommon_letter in ETAOIN[-6:]:
										if uncommon_letter in freq_order[-6:]:
															match_score += 1

					return match_score


if __name__ == "__main__":
		import doctest

		doctest.testmod()




"""simple docstring"""







import warnings

from ...utils import is_sklearn_available, requires_backends


if is_sklearn_available():
		from scipy.stats import pearsonr, spearmanr
		from sklearn.metrics import fa_score, matthews_corrcoef


a__				:							Any						=     (
    '''This metric will be removed from the library soon, metrics should be handled with the 🤗 Evaluate '''
    '''library. You can have a look at this example script for pointers: '''
    '''https://github.com/huggingface/transformers/blob/main/examples/pytorch/text-classification/run_glue.py'''
)




def    UpperCAmelCase__       (lowerCAmelCase_  ,						lowerCAmelCase_   ):




					'''simple docstring'''


					warnings.warn(lowerCAmelCase_  ,						lowerCAmelCase_   )
					requires_backends(lowerCAmelCase_  ,						"sklearn"   )
					return (preds == labels).mean()




def    UpperCAmelCase__       (lowerCAmelCase_  ,						lowerCAmelCase_   ):




					'''simple docstring'''


					warnings.warn(lowerCAmelCase_  ,						lowerCAmelCase_   )
					requires_backends(lowerCAmelCase_  ,						"sklearn"   )
					__SCREAMING_SNAKE_CASE						   =				simple_accuracy(lowerCAmelCase_  ,						lowerCAmelCase_   )
					__SCREAMING_SNAKE_CASE						   =				fa_score(y_true=lowerCAmelCase_  ,						y_pred=lowerCAmelCase_   )
					return {
					    "acc": acc,
					    "f1": fa,
					    "acc_and_f1": (acc + fa) / 2,
					}




def    UpperCAmelCase__       (lowerCAmelCase_  ,						lowerCAmelCase_   ):




					'''simple docstring'''


					warnings.warn(lowerCAmelCase_  ,						lowerCAmelCase_   )
					requires_backends(lowerCAmelCase_  ,						"sklearn"   )
					__SCREAMING_SNAKE_CASE						   =				pearsonr(lowerCAmelCase_  ,						lowerCAmelCase_   )[0]
					__SCREAMING_SNAKE_CASE						   =				spearmanr(lowerCAmelCase_  ,						lowerCAmelCase_   )[0]
					return {
					    "pearson": pearson_corr,
					    "spearmanr": spearman_corr,
					    "corr": (pearson_corr + spearman_corr) / 2,
					}




def    UpperCAmelCase__       (lowerCAmelCase_  ,						lowerCAmelCase_  ,						lowerCAmelCase_   ):




					'''simple docstring'''


					warnings.warn(lowerCAmelCase_  ,						lowerCAmelCase_   )
					requires_backends(lowerCAmelCase_  ,						"sklearn"   )
					assert len(lowerCAmelCase_   ) == len(lowerCAmelCase_   ), f"""Predictions and labels have mismatched lengths {len(lowerCAmelCase_   )} and {len(lowerCAmelCase_   )}"""
					if task_name == "cola":
										return {"mcc": matthews_corrcoef(lowerCAmelCase_  ,						lowerCAmelCase_   )}
					elif task_name == "sst-2":
										return {"acc": simple_accuracy(lowerCAmelCase_  ,						lowerCAmelCase_   )}
					elif task_name == "mrpc":
										return acc_and_fa(lowerCAmelCase_  ,						lowerCAmelCase_   )
					elif task_name == "sts-b":
										return pearson_and_spearman(lowerCAmelCase_  ,						lowerCAmelCase_   )
					elif task_name == "qqp":
										return acc_and_fa(lowerCAmelCase_  ,						lowerCAmelCase_   )
					elif task_name == "mnli":
										return {"mnli/acc": simple_accuracy(lowerCAmelCase_  ,						lowerCAmelCase_   )}
					elif task_name == "mnli-mm":
										return {"mnli-mm/acc": simple_accuracy(lowerCAmelCase_  ,						lowerCAmelCase_   )}
					elif task_name == "qnli":
										return {"acc": simple_accuracy(lowerCAmelCase_  ,						lowerCAmelCase_   )}
					elif task_name == "rte":
										return {"acc": simple_accuracy(lowerCAmelCase_  ,						lowerCAmelCase_   )}
					elif task_name == "wnli":
										return {"acc": simple_accuracy(lowerCAmelCase_  ,						lowerCAmelCase_   )}
					elif task_name == "hans":
										return {"acc": simple_accuracy(lowerCAmelCase_  ,						lowerCAmelCase_   )}
					else:
										raise KeyError(lowerCAmelCase_   )




def    UpperCAmelCase__       (lowerCAmelCase_  ,						lowerCAmelCase_  ,						lowerCAmelCase_   ):




					'''simple docstring'''


					warnings.warn(lowerCAmelCase_  ,						lowerCAmelCase_   )
					requires_backends(lowerCAmelCase_  ,						"sklearn"   )
					if len(lowerCAmelCase_   ) != len(lowerCAmelCase_   ):
										raise ValueError(f"""Predictions and labels have mismatched lengths {len(lowerCAmelCase_   )} and {len(lowerCAmelCase_   )}"""   )
					if task_name == "xnli":
										return {"acc": simple_accuracy(lowerCAmelCase_  ,						lowerCAmelCase_   )}
					else:
										raise KeyError(lowerCAmelCase_   )
