

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)
lowerCamelCase									= '''\
'''

lowerCamelCase									= '''\
    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.
'''

lowerCamelCase									= '''
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}
'''

lowerCamelCase									= ['''fleurs-asr''', '''mls''', '''voxpopuli''', '''babel''', '''covost2''', '''fleurs-lang_id''', '''minds14''']
lowerCamelCase									= ''''''

try:
							from jiwer import transforms as tr

							lowerCamelCase									= True
except ImportError:
							lowerCamelCase									= False

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







							class 						__magic_name__      (							tr.AbstractTransform							):

										'''simple docstring'''




										def __init__(    self,   lowercase_ = " "	)							->   Tuple:






												"""simple docstring"""






												a__  =sentence_delimiter



										def 			_UpperCAmelCase			(    self,   lowercase_	)							->   Any:






												"""simple docstring"""






												return list(lowercase_	)



										def 			_UpperCAmelCase			(    self,   lowercase_	)							->   Tuple:






												"""simple docstring"""






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





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




def       UpperCAmelCase__    (      _A				:			str     ,				_A				:			Optional[Any]							):





		'''simple docstring'''







		return float((preds == labels).mean()							)




def       UpperCAmelCase__    (      _A				:			List[str]     ,				_A				:			Tuple							):





		'''simple docstring'''







		return {
		    "f1": float(fa_score(y_true=_A     ,				y_pred=_A     ,				average='''macro'''							)							),
		    "accuracy": simple_accuracy(_A     ,				_A							),
		}




def       UpperCAmelCase__    (      _A				:			str     ,				_A				:			Any     ,				_A				:			Any="exp"     ,				_A				:			str=None     ,				_A				:			List[str]=False     ,				_A				:			Dict=False     ,				_A				:			Dict=None     ,				_A				:			Dict=False     ,				):





		'''simple docstring'''







		a__  =[[label] for label in labels]
		a__  =list(_A							)
		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"`.'''							)

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




def       UpperCAmelCase__    (      _A				:			int     ,				_A				:			Optional[int]     ,				_A				:			Any     ,				_A				:			Optional[Any]							):





		'''simple docstring'''







		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:
				a__  =compute_measures(_A     ,				_A							)['''wer''']

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

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

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







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

			'''simple docstring'''




			def 			_UpperCAmelCase			(    self	)							->   str:






					"""simple docstring"""






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

					a__  ='''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(lowercase_	), '''references''': datasets.Value(lowercase_	)}	),   codebase_urls=[],   reference_urls=[],   format='''numpy''',   )



			def 			_UpperCAmelCase			(    self,   lowercase_,   lowercase_,   lowercase_=None,   lowercase_=None	)							->   List[str]:






					"""simple docstring"""






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

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



from dataclasses import dataclass

lowerCamelCase									= '''0.1'''
lowerCamelCase									= '''Lucia Harcekova'''







@dataclass
class 						__magic_name__      :

   '''simple docstring'''




   lowerCamelCase__			:     int
   lowerCamelCase__			:     int
   lowerCamelCase__			:     str



   def __repr__(    self	)							->   str:






     """simple docstring"""






     return F"""({self.offset}, {self.length}, {self.indicator})"""







class 						__magic_name__      :

   '''simple docstring'''




   def __init__(    self,   lowercase_ = 13,   lowercase_ = 6	)							->   None:






     """simple docstring"""






     a__  =window_size
     a__  =lookahead_buffer_size
     a__  =self.window_size - self.lookahead_buffer_size



   def 			_UpperCAmelCase			(    self,   lowercase_	)							->   list[Token]:






     """simple docstring"""






     a__  =[]
     a__  =''''''

     # while there are still characters in text to compress
     while text:
       # find the next encoding phrase
       # - triplet with offset, length, indicator (the next encoding character)
       a__  =self._find_encoding_token(lowercase_,   lowercase_	)

       # update the search buffer:
       # - add new characters from text into it
       # - check if size exceed the max search buffer size, if so, drop the
       #   oldest elements
       search_buffer += text[: token.length + 1]
       if len(lowercase_	) > self.search_buffer_size:
         a__  =search_buffer[-self.search_buffer_size :]

       # update the text
       a__  =text[token.length + 1 :]

       # append the token to output
       output.append(lowercase_	)

     return output



   def 			_UpperCAmelCase			(    self,   lowercase_	)							->   str:






     """simple docstring"""






     a__  =''''''

     for token in tokens:
       for _ in range(token.length	):
         output += output[-token.offset]
       output += token.indicator

     return output



   def 			_UpperCAmelCase			(    self,   lowercase_,   lowercase_	)							->   Token:






     """simple docstring"""






     if not text:
       raise ValueError('''We need some text to work with.'''	)

     # Initialise result parameters to default values
     a__,				a__  =0, 0

     if not search_buffer:
       return Token(lowercase_,   lowercase_,   text[length]	)

     for i, character in enumerate(lowercase_	):
       a__  =len(lowercase_	) - i
       if character == text[0]:
         a__  =self._match_length_from_index(lowercase_,   lowercase_,   0,   lowercase_	)
         # if the found length is bigger than the current or if it's equal,
         # which means it's offset is smaller: update offset and length
         if found_length >= length:
           a__,				a__  =found_offset, found_length

     return Token(lowercase_,   lowercase_,   text[length]	)



   def 			_UpperCAmelCase			(    self,   lowercase_,   lowercase_,   lowercase_,   lowercase_	)							->   int:






     """simple docstring"""






     if not text or text[text_index] != window[window_index]:
       return 0
     return 1 + self._match_length_from_index(
         lowercase_,   window + text[text_index],   text_index + 1,   window_index + 1	)


if __name__ == "__main__":
       from doctest import testmod

       testmod()
       # Initialize compressor class
       lowerCamelCase									= LZaaCompressor(window_size=13, lookahead_buffer_size=6)

       # Example
       lowerCamelCase									= '''cabracadabrarrarrad'''
       lowerCamelCase									= lzaa_compressor.compress(TEXT)
       print(lzaa_compressor.compress('''ababcbababaa'''))
       lowerCamelCase									= lzaa_compressor.decompress(compressed_text)
       assert decompressed_text == TEXT, "The LZ77 algorithm returned the invalid result."
