





from typing import List

from packaging import version
from sklearn.metrics import fa_score

import datasets
from datasets.config import PY_VERSION


if PY_VERSION < version.parse('''3.8'''):
		import importlib_metadata
else:
		import importlib.metadata as importlib_metadata


# TODO(Patrick/Anton)
SCREAMING_SNAKE_CASE_      :			Any		  =      '''\
'''

SCREAMING_SNAKE_CASE_      :			Dict		  =      '''\
    XTREME-S is a benchmark to evaluate universal cross-lingual speech representations in many languages.
    XTREME-S covers four task families: speech recognition, classification, speech-to-text translation and retrieval.
'''

SCREAMING_SNAKE_CASE_      :			List[Any]		  =      '''
Compute XTREME-S evaluation metric associated to each XTREME-S dataset.
Args:
    predictions: list of predictions to score.
        Each translation should be tokenized into a list of tokens.
    references: list of lists of references for each translation.
        Each reference should be tokenized into a list of tokens.
    bleu_kwargs: optional dict of keywords to be passed when computing \'bleu\'.
        Keywords include Dict can be one of \'smooth_method\', \'smooth_value\', \'force\', \'lowercase\',
        \'tokenize\', \'use_effective_order\'.
    wer_kwargs: optional dict of keywords to be passed when computing \'wer\' and \'cer\'.
        Keywords include \'concatenate_texts\'.
Returns: depending on the XTREME-S task, one or several of:
    "accuracy": Accuracy - for \'fleurs-lang_id\', \'minds14\'
    "f1": F1 score - for \'minds14\'
    "wer": Word error rate - for \'mls\', \'fleurs-asr\', \'voxpopuli\', \'babel\'
    "cer": Character error rate - for \'mls\', \'fleurs-asr\', \'voxpopuli\', \'babel\'
    "bleu": BLEU score according to the `sacrebleu` metric - for \'covost2\'
Examples:

    >>> xtreme_s_metric = datasets.load_metric(\'xtreme_s\', \'mls\')  # \'mls\', \'voxpopuli\', \'fleurs-asr\' or \'babel\'
    >>> references = ["it is sunny here", "paper and pen are essentials"]
    >>> predictions = ["it\'s sunny", "paper pen are essential"]
    >>> results = xtreme_s_metric.compute(predictions=predictions, references=references)
    >>> print({k: round(v, 2) for k, v in results.items()})
    {\'wer\': 0.56, \'cer\': 0.27}

    >>> xtreme_s_metric = datasets.load_metric(\'xtreme_s\', \'covost2\')
    >>> references = ["bonjour paris", "il est necessaire de faire du sport de temps en temp"]
    >>> predictions = ["bonjour paris", "il est important de faire du sport souvent"]
    >>> results = xtreme_s_metric.compute(predictions=predictions, references=references)
    >>> print({k: round(v, 2) for k, v in results.items()})
    {\'bleu\': 31.65}

    >>> xtreme_s_metric = datasets.load_metric(\'xtreme_s\', \'fleurs-lang_id\')
    >>> references = [0, 1, 0, 0, 1]
    >>> predictions = [0, 1, 1, 0, 0]
    >>> results = xtreme_s_metric.compute(predictions=predictions, references=references)
    >>> print({k: round(v, 2) for k, v in results.items()})
    {\'accuracy\': 0.6}

    >>> xtreme_s_metric = datasets.load_metric(\'xtreme_s\', \'minds14\')
    >>> references = [0, 1, 0, 0, 1]
    >>> predictions = [0, 1, 1, 0, 0]
    >>> results = xtreme_s_metric.compute(predictions=predictions, references=references)
    >>> print({k: round(v, 2) for k, v in results.items()})
    {\'f1\': 0.58, \'accuracy\': 0.6}
'''

SCREAMING_SNAKE_CASE_      :			str		  =      ['''fleurs-asr''', '''mls''', '''voxpopuli''', '''babel''', '''covost2''', '''fleurs-lang_id''', '''minds14''']
SCREAMING_SNAKE_CASE_      :			Dict		  =      ''''''

try:
		from jiwer import transforms as tr

		SCREAMING_SNAKE_CASE_      :			Optional[Any]		  =      True
except ImportError:
		SCREAMING_SNAKE_CASE_      :			str		  =      False

if _jiwer_available and version.parse(importlib_metadata.version('''jiwer''')) < version.parse('''2.3.0'''):

		class 		snake_case_      (       tr.AbstractTransform	):







			'''simple docstring'''




			def __init__(	self       :    Optional[int]				,			__lowerCamelCase       :    str = " "      )					->   List[Any]:

					'''simple docstring'''




					__lowercase					=    sentence_delimiter




			def       UpperCAmelCase (	self       :    Union[str, Any]				,			__lowerCamelCase       :    str      )					->   Optional[Any]:

					'''simple docstring'''




					return list(__lowerCamelCase      )






			def       UpperCAmelCase (	self       :    str				,			__lowerCamelCase       :    List[str]      )					->   int:

					'''simple docstring'''




					__lowercase					=    []
					for sent_idx, sentence in enumerate(__lowerCamelCase      ):
							chars.extend(self.process_string(__lowerCamelCase      )      )
							if self.sentence_delimiter is not None and self.sentence_delimiter != "" and sent_idx < len(__lowerCamelCase      ) - 1:
									chars.append(self.sentence_delimiter      )
					return chars





		SCREAMING_SNAKE_CASE_      :			str		  =      tr.Compose(
		    [tr.RemoveMultipleSpaces(), tr.Strip(), SentencesToListOfCharacters(SENTENCE_DELIMITER)]
		)
elif _jiwer_available:
		SCREAMING_SNAKE_CASE_      :			str		  =      tr.Compose(
		    [
		        tr.RemoveMultipleSpaces(),
		        tr.Strip(),
		        tr.ReduceToSingleSentence(SENTENCE_DELIMITER),
		        tr.ReduceToListOfListOfChars(),
		    ]
		)
else:
		SCREAMING_SNAKE_CASE_      :			Tuple		  =      None



def    SCREAMING_SNAKE_CASE							(			snake_case	,      snake_case       )		->						Optional[Any]:
		return float((preds == labels).mean()       )



def    SCREAMING_SNAKE_CASE							(			snake_case	,      snake_case       )		->						Dict:
		return {
		    "f1": float(fa_score(y_true=snake_case	,      y_pred=snake_case	,      average='macro'       )       ),
		    "accuracy": simple_accuracy(snake_case	,      snake_case       ),
		}



def    SCREAMING_SNAKE_CASE							(			snake_case	,      snake_case	,      snake_case="exp"	,      snake_case=None	,      snake_case=False	,      snake_case=False	,      snake_case=None	,      snake_case=False	,      )		->						Optional[int]:
		# xtreme-s can only have one label
		__lowercase					=    [[label] for label in labels]
		__lowercase					=    list(snake_case       )
		try:
				import sacrebleu as scb
		except ImportError:
				raise ValueError(
				    'sacrebleu has to be installed in order to apply the bleu metric for covost2.'
				    'You can install it via `pip install sacrebleu`.'       )

		if version.parse(scb.__version__       ) < version.parse('1.4.12'       ):
				raise ImportWarning(
				    'To use `sacrebleu`, the module `sacrebleu>=1.4.12` is required, and the current version of `sacrebleu` doesn\'t match this condition.\n'
				    'You can install it with `pip install "sacrebleu>=1.4.12"`.'       )

		__lowercase					=    len(labels[0]       )
		if any(len(snake_case       ) != references_per_prediction for refs in labels       ):
				raise ValueError('Sacrebleu requires the same number of references for each prediction'       )
		__lowercase					=    [[refs[i] for refs in labels] for i in range(snake_case       )]
		__lowercase					=    scb.corpus_bleu(
		    snake_case	,      snake_case	,      smooth_method=snake_case	,      smooth_value=snake_case	,      force=snake_case	,      lowercase=snake_case	,      use_effective_order=snake_case	,      **({'tokenize': tokenize} if tokenize else {})	,      )
		return {"bleu": output.score}



def    SCREAMING_SNAKE_CASE							(			snake_case	,      snake_case	,      snake_case	,      snake_case       )		->						List[Any]:
		try:
				from jiwer import compute_measures
		except ImportError:
				raise ValueError(
				    F"jiwer has to be installed in order to apply the wer metric for {config_name}."
				    'You can install it via `pip install jiwer`.'       )

		if concatenate_texts:
				__lowercase					=    compute_measures(snake_case	,      snake_case       )['wer']

				__lowercase					=    compute_measures(snake_case	,      snake_case	,      truth_transform=snake_case	,      hypothesis_transform=snake_case       )['wer']
				return {"wer": wer, "cer": cer}
		else:

				def compute_score(snake_case	,      snake_case	,      snake_case="wer"       ):
						__lowercase					=    0
						__lowercase					=    0
						for prediction, reference in zip(snake_case	,      snake_case       ):
								if score_type == "wer":
										__lowercase					=    compute_measures(snake_case	,      snake_case       )
								elif score_type == "cer":
										__lowercase					=    compute_measures(
										    snake_case	,      snake_case	,      truth_transform=snake_case	,      hypothesis_transform=snake_case       )
								incorrect += measures["substitutions"] + measures["deletions"] + measures["insertions"]
								total += measures["substitutions"] + measures["deletions"] + measures["hits"]
						return incorrect / total

				return {"wer": compute_score(snake_case	,      snake_case	,      'wer'       ), "cer": compute_score(snake_case	,      snake_case	,      'cer'       )}


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







	'''simple docstring'''




	def       UpperCAmelCase (	self       :    Any      )					->   int:

			'''simple docstring'''




			if self.config_name not in _CONFIG_NAMES:
					raise KeyError(F"You should supply a configuration name selected in {_CONFIG_NAMES}"      )

			__lowercase					=    'int64' if self.config_name in ['fleurs-lang_id', 'minds14'] else 'string'

			return datasets.MetricInfo(
			    description=_DESCRIPTION				,			citation=_CITATION				,			inputs_description=_KWARGS_DESCRIPTION				,			features=datasets.Features(
			        {'predictions': datasets.Value(__lowerCamelCase      ), 'references': datasets.Value(__lowerCamelCase      )}      )				,			codebase_urls=[]				,			reference_urls=[]				,			format='numpy'				,			)






	def       UpperCAmelCase (	self       :    Optional[int]				,			__lowerCamelCase       :    Any				,			__lowerCamelCase       :    int				,			__lowerCamelCase       :    Dict=None				,			__lowerCamelCase       :    Optional[int]=None      )					->   Optional[int]:

			'''simple docstring'''




			__lowercase					=    bleu_kwargs if bleu_kwargs is not None else {}
			__lowercase					=    wer_kwargs if wer_kwargs is not None else {}

			if self.config_name == "fleurs-lang_id":
					return {"accuracy": simple_accuracy(__lowerCamelCase				,			__lowerCamelCase      )}
			elif self.config_name == "minds14":
					return fa_and_simple_accuracy(__lowerCamelCase				,			__lowerCamelCase      )
			elif self.config_name == "covost2":
					__lowercase					=    bleu_kwargs.pop('smooth_method'				,			'exp'      )
					__lowercase					=    bleu_kwargs.pop('smooth_value'				,			__lowerCamelCase      )
					__lowercase					=    bleu_kwargs.pop('force'				,			__lowerCamelCase      )
					__lowercase					=    bleu_kwargs.pop('lowercase'				,			__lowerCamelCase      )
					__lowercase					=    bleu_kwargs.pop('tokenize'				,			__lowerCamelCase      )
					__lowercase					=    bleu_kwargs.pop('use_effective_order'				,			__lowerCamelCase      )
					return bleu(
					    preds=__lowerCamelCase				,			labels=__lowerCamelCase				,			smooth_method=__lowerCamelCase				,			smooth_value=__lowerCamelCase				,			force=__lowerCamelCase				,			lowercase=__lowerCamelCase				,			tokenize=__lowerCamelCase				,			use_effective_order=__lowerCamelCase				,			)
			elif self.config_name in ["fleurs-asr", "mls", "voxpopuli", "babel"]:
					__lowercase					=    wer_kwargs.pop('concatenate_texts'				,			__lowerCamelCase      )
					return wer_and_cer(__lowerCamelCase				,			__lowerCamelCase				,			__lowerCamelCase				,			self.config_name      )
			else:
					raise KeyError(F"You should supply a configuration name selected in {_CONFIG_NAMES}"      )











SCREAMING_SNAKE_CASE_      :			str		  =      [chr(i) for i in range(32, 126)]
SCREAMING_SNAKE_CASE_      :			Any		  =      list(range(len(alphabets)))
SCREAMING_SNAKE_CASE_      :			Any		  =      list(range(len(alphabets)))
SCREAMING_SNAKE_CASE_      :			List[str]		  =      list(range(len(alphabets)))
SCREAMING_SNAKE_CASE_      :			Dict		  =      list(reversed(range(len(alphabets))))
SCREAMING_SNAKE_CASE_      :			int		  =      []
SCREAMING_SNAKE_CASE_      :			Union[str, Any]		  =      0



def    SCREAMING_SNAKE_CASE							(			)		->						int:
		global gear_one_pos
		global gear_two_pos
		global gear_three_pos
		__lowercase					=    gear_one[0]
		gear_one.append(snake_case       )
		del gear_one[0]
		gear_one_pos += 1
		if gear_one_pos % int(len(snake_case       )       ) == 0:
				__lowercase					=    gear_two[0]
				gear_two.append(snake_case       )
				del gear_two[0]
				gear_two_pos += 1
				if gear_two_pos % int(len(snake_case       )       ) == 0:
						__lowercase					=    gear_three[0]
						gear_three.append(snake_case       )
						del gear_three[0]
						gear_three_pos += 1



def    SCREAMING_SNAKE_CASE							(			snake_case       )		->						int:
		__lowercase					=    alphabets.index(snake_case       )
		__lowercase					=    gear_one[target]
		__lowercase					=    gear_two[target]
		__lowercase					=    gear_three[target]
		__lowercase					=    reflector[target]
		__lowercase					=    gear_three.index(snake_case       )
		__lowercase					=    gear_two.index(snake_case       )
		__lowercase					=    gear_one.index(snake_case       )
		code.append(alphabets[target]       )
		rotator()


if __name__ == "__main__":
		SCREAMING_SNAKE_CASE_      :			Tuple		  =      list(input('''Type your message:\n'''))
		while True:
				try:
						SCREAMING_SNAKE_CASE_      :			List[str]		  =      int(input('''Please set token:(must be only digits)\n'''))
						break
				except Exception as error:
						print(error)
		for _ in range(token):
				rotator()
		for j in decode:
				engine(j)
		print('''\n''' + ''''''.join(code))
		print(
		    F"""\nYour Token is {token} please write it down.\nIf you want to decode """
		    '''this message again you should input same digits as token!'''
		)




