


'''simple docstring'''






import json
import os
from typing import Dict, List, Optional, Tuple

from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging


__UpperCAmelCase       =    logging.get_logger(__name__)


__UpperCAmelCase       =    {
    '''vocab_file''': '''vocab.json''',
    '''tokenizer_config_file''': '''tokenizer_config.json''',
    '''merges_file''': '''merges.txt''',
}

__UpperCAmelCase       =    {
    '''vocab_file''': {
        '''facebook/s2t-wav2vec2-large-en-de''': (
            '''https://huggingface.co/facebook/s2t-wav2vec2-large-en-de/resolve/main/vocab.json'''
        ),
    },
    '''tokenizer_config_file''': {
        '''facebook/s2t-wav2vec2-large-en-de''': (
            '''https://huggingface.co/facebook/s2t-wav2vec2-large-en-de/resolve/main/tokenizer_config.json'''
        ),
    },
    '''merges_file''': {
        '''facebook/s2t-wav2vec2-large-en-de''': (
            '''https://huggingface.co/facebook/s2t-wav2vec2-large-en-de/resolve/main/merges.txt'''
        ),
    },
}

__UpperCAmelCase       =    '''</w>'''
__UpperCAmelCase       =    '''@@ '''


def  _snake_case       (					A	)						->   Tuple:
							lowerCAmelCase__	       =     set()
							lowerCAmelCase__	       =     word[0]
							for char in word[1:]:
														pairs.add((prev_char, char)	)
														lowerCAmelCase__	       =     char
							return pairs


# Speech2Text2 has no max input length
__UpperCAmelCase       =    {'''facebook/s2t-wav2vec2-large-en-de''': 1_024}






class 		a__    (							__SCREAMING_SNAKE_CASE  ):





							'''simple docstring'''






							lowercase__      :       int     						=				VOCAB_FILES_NAMES
							lowercase__      :       Union[str, Any]     						=				PRETRAINED_VOCAB_FILES_MAP
							lowercase__      :       Optional[Any]     						=				PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
							lowercase__      :       int     						=				["input_ids", "attention_mask"]







							def __init__(		self ,   lowerCamelCase_ ,   lowerCamelCase_="<s>" ,   lowerCamelCase_="<pad>" ,   lowerCamelCase_="</s>" ,   lowerCamelCase_="<unk>" ,   lowerCamelCase_=False ,   lowerCamelCase_=None ,   **lowerCamelCase_ ,   )	->							List[str]:
														super().__init__(
														    unk_token=__UpperCAmelCase ,   bos_token=__UpperCAmelCase ,   eos_token=__UpperCAmelCase ,   pad_token=__UpperCAmelCase ,   do_lower_case=__UpperCAmelCase ,   **__UpperCAmelCase ,   )

														lowerCAmelCase__	       =     do_lower_case

														with open(__UpperCAmelCase ,   encoding='''utf-8'''       ) as vocab_handle:
																					lowerCAmelCase__	       =     json.load(__UpperCAmelCase       )
														lowerCAmelCase__	       =     {v: k for k, v in self.encoder.items()}

														if merges_file is None:
																					logger.info(F"""No merges files provided. {self.__class__.__name__} can only be used for decoding."""       )

																					lowerCAmelCase__	       =     None
																					lowerCAmelCase__	       =     None
														else:
																					with open(__UpperCAmelCase ,   encoding='''utf-8'''       ) as merges_handle:
																												lowerCAmelCase__	       =     merges_handle.read().split('''\n'''       )[:-1]

																					lowerCAmelCase__	       =     [tuple(merge.split()[:2]       ) for merge in merges]
																					lowerCAmelCase__	       =     dict(zip(__UpperCAmelCase ,   range(len(__UpperCAmelCase       )       )       )       )
																					lowerCAmelCase__	       =     {}







							@property
							def   __SCREAMING_SNAKE_CASE							(		self       )	->							int:
														return len(self.decoder       )







							def   __SCREAMING_SNAKE_CASE							(		self       )	->							Dict:
														return dict(self.encoder ,   **self.added_tokens_encoder       )







							def   __SCREAMING_SNAKE_CASE							(		self ,   lowerCamelCase_       )	->							List[Any]:
														lowerCAmelCase__	       =     tuple(token[:-1]       ) + (token[-1] + BPE_TOKEN_MERGES,)
														if token in self.cache:
																					return self.cache[token]
														lowerCAmelCase__	       =     get_pairs(__UpperCAmelCase       )

														if not pairs:
																					return token

														while True:
																					lowerCAmelCase__	       =     min(__UpperCAmelCase ,   key=lambda lowerCamelCase_       : self.bpe_ranks.get(__UpperCAmelCase ,   float('''inf'''       )       )       )
																					if bigram not in self.bpe_ranks:
																												break
																					lowerCAmelCase__	       =     bigram
																					lowerCAmelCase__	       =     []
																					lowerCAmelCase__	       =     0
																					while i < len(__UpperCAmelCase       ):
																												try:
																																			lowerCAmelCase__	       =     word.index(__UpperCAmelCase ,   __UpperCAmelCase       )
																												except ValueError:
																																			new_word.extend(word[i:]       )
																																			break
																												else:
																																			new_word.extend(word[i:j]       )
																																			lowerCAmelCase__	       =     j

																												if word[i] == first and i < len(__UpperCAmelCase       ) - 1 and word[i + 1] == second:
																																			new_word.append(first + second       )
																																			i += 2
																												else:
																																			new_word.append(word[i]       )
																																			i += 1
																					lowerCAmelCase__	       =     tuple(__UpperCAmelCase       )
																					lowerCAmelCase__	       =     new_word
																					if len(__UpperCAmelCase       ) == 1:
																												break
																					else:
																												lowerCAmelCase__	       =     get_pairs(__UpperCAmelCase       )
														lowerCAmelCase__	       =     """ """.join(__UpperCAmelCase       )
														if word == "\n  " + BPE_TOKEN_MERGES:
																					lowerCAmelCase__	       =     """\n""" + BPE_TOKEN_MERGES

														if word.endswith(__UpperCAmelCase       ):
																					lowerCAmelCase__	       =     word.replace(__UpperCAmelCase ,   ''''''       )

														lowerCAmelCase__	       =     word.replace(''' ''' ,   __UpperCAmelCase       )
														lowerCAmelCase__	       =     word
														return word







							def   __SCREAMING_SNAKE_CASE							(		self ,   lowerCamelCase_       )	->							List[Any]:

														if self.bpe_ranks is None:
																					raise ValueError(
																					    '''This tokenizer was instantiated without a `merges.txt` file, so'''
																					    ''' that it can only be used for decoding, not for encoding.'''
																					    '''Make sure to provide `merges.txt` file at instantiation to enable '''
																					    '''encoding.'''       )

														if self.do_lower_case:
																					lowerCAmelCase__	       =     text.lower()

														lowerCAmelCase__	       =     text.split()

														lowerCAmelCase__	       =     []
														for token in text:
																					if token:
																												split_tokens.extend(list(self.bpe(__UpperCAmelCase       ).split(''' '''       )       )       )

														return split_tokens







							def   __SCREAMING_SNAKE_CASE							(		self ,   lowerCamelCase_       )	->							int:
														return self.encoder.get(__UpperCAmelCase ,   self.encoder.get(self.unk_token       )       )







							def   __SCREAMING_SNAKE_CASE							(		self ,   lowerCamelCase_       )	->							str:
														lowerCAmelCase__	       =     self.decoder.get(__UpperCAmelCase ,   self.unk_token       )
														return result







							def   __SCREAMING_SNAKE_CASE							(		self ,   lowerCamelCase_       )	->							str:
														lowerCAmelCase__	       =     """ """.join(__UpperCAmelCase       )

														# make sure @@ tokens are concatenated
														lowerCAmelCase__	       =     """""".join(string.split(__UpperCAmelCase       )       )

														return string







							def   __SCREAMING_SNAKE_CASE							(		self ,   lowerCamelCase_ ,   lowerCamelCase_ = None       )	->							Tuple[str]:
														if not os.path.isdir(__UpperCAmelCase       ):
																					logger.error(F"""Vocabulary path ({save_directory}) should be a directory"""       )
																					return
														lowerCAmelCase__	       =     os.path.join(
														    __UpperCAmelCase ,   (filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''vocab_file''']       )
														lowerCAmelCase__	       =     os.path.join(
														    __UpperCAmelCase ,   (filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''merges_file''']       )

														with open(__UpperCAmelCase ,   '''w''' ,   encoding='''utf-8'''       ) as f:
																					f.write(json.dumps(self.encoder ,   indent=2 ,   sort_keys=__UpperCAmelCase ,   ensure_ascii=__UpperCAmelCase       ) + '''\n'''       )

														lowerCAmelCase__	       =     0
														if self.bpe_ranks is None:
																					return (vocab_file,)

														with open(__UpperCAmelCase ,   '''w''' ,   encoding='''utf-8'''       ) as writer:
																					for bpe_tokens, token_index in sorted(self.bpe_ranks.items() ,   key=lambda lowerCamelCase_       : kv[1]       ):
																												if index != token_index:
																																			logger.warning(
																																			    F"""Saving vocabulary to {merges_file}: BPE merge indices are not consecutive."""
																																			    ''' Please check that the tokenizer is not corrupted!'''       )
																																			lowerCAmelCase__	       =     token_index
																												writer.write(''' '''.join(__UpperCAmelCase       ) + '''\n'''       )
																												index += 1

														return (vocab_file, merges_file)



'''simple docstring'''






from sklearn.metrics import fa_score, matthews_corrcoef

import datasets

from .record_evaluation import evaluate as evaluate_record


__UpperCAmelCase       =							'''\
@article{wang2019superglue,
  title={SuperGLUE: A Stickier Benchmark for General-Purpose Language Understanding Systems},
  author={Wang, Alex and Pruksachatkun, Yada and Nangia, Nikita and Singh, Amanpreet and Michael, Julian and Hill, Felix and Levy, Omer and Bowman, Samuel R},
  journal={arXiv preprint arXiv:1905.00537},
  year={2019}
}
'''

__UpperCAmelCase       =							'''\
SuperGLUE (https://super.gluebenchmark.com/) is a new benchmark styled after
GLUE with a new set of more difficult language understanding tasks, improved
resources, and a new public leaderboard.
'''

__UpperCAmelCase       =							'''
Compute SuperGLUE evaluation metric associated to each SuperGLUE dataset.
Args:
    predictions: list of predictions to score. Depending on the SuperGlUE subset:
        - for \'record\': list of question-answer dictionaries with the following keys:
            - \'idx\': index of the question as specified by the dataset
            - \'prediction_text\': the predicted answer text
        - for \'multirc\': list of question-answer dictionaries with the following keys:
            - \'idx\': index of the question-answer pair as specified by the dataset
            - \'prediction\': the predicted answer label
        - otherwise: list of predicted labels
    references: list of reference labels. Depending on the SuperGLUE subset:
        - for \'record\': list of question-answers dictionaries with the following keys:
            - \'idx\': index of the question as specified by the dataset
            - \'answers\': list of possible answers
        - otherwise: list of reference labels
Returns: depending on the SuperGLUE subset:
    - for \'record\':
        - \'exact_match\': Exact match between answer and gold answer
        - \'f1\': F1 score
    - for \'multirc\':
        - \'exact_match\': Exact match between answer and gold answer
        - \'f1_m\': Per-question macro-F1 score
        - \'f1_a\': Average F1 score over all answers
    - for \'axb\':
        \'matthews_correlation\': Matthew Correlation
    - for \'cb\':
        - \'accuracy\': Accuracy
        - \'f1\': F1 score
    - for all others:
        - \'accuracy\': Accuracy
Examples:

    >>> super_glue_metric = datasets.load_metric(\'super_glue\', \'copa\')  # any of ["copa", "rte", "wic", "wsc", "wsc.fixed", "boolq", "axg"]
    >>> predictions = [0, 1]
    >>> references = [0, 1]
    >>> results = super_glue_metric.compute(predictions=predictions, references=references)
    >>> print(results)
    {\'accuracy\': 1.0}

    >>> super_glue_metric = datasets.load_metric(\'super_glue\', \'cb\')
    >>> predictions = [0, 1]
    >>> references = [0, 1]
    >>> results = super_glue_metric.compute(predictions=predictions, references=references)
    >>> print(results)
    {\'accuracy\': 1.0, \'f1\': 1.0}

    >>> super_glue_metric = datasets.load_metric(\'super_glue\', \'record\')
    >>> predictions = [{\'idx\': {\'passage\': 0, \'query\': 0}, \'prediction_text\': \'answer\'}]
    >>> references = [{\'idx\': {\'passage\': 0, \'query\': 0}, \'answers\': [\'answer\', \'another_answer\']}]
    >>> results = super_glue_metric.compute(predictions=predictions, references=references)
    >>> print(results)
    {\'exact_match\': 1.0, \'f1\': 1.0}

    >>> super_glue_metric = datasets.load_metric(\'super_glue\', \'multirc\')
    >>> predictions = [{\'idx\': {\'answer\': 0, \'paragraph\': 0, \'question\': 0}, \'prediction\': 0}, {\'idx\': {\'answer\': 1, \'paragraph\': 2, \'question\': 3}, \'prediction\': 1}]
    >>> references = [0, 1]
    >>> results = super_glue_metric.compute(predictions=predictions, references=references)
    >>> print(results)
    {\'exact_match\': 1.0, \'f1_m\': 1.0, \'f1_a\': 1.0}

    >>> super_glue_metric = datasets.load_metric(\'super_glue\', \'axb\')
    >>> references = [0, 1]
    >>> predictions = [0, 1]
    >>> results = super_glue_metric.compute(predictions=predictions, references=references)
    >>> print(results)
    {\'matthews_correlation\': 1.0}
'''


def  _snake_case       (					A			,   A	)						->   List[Any]:
							return float((preds == labels).mean()	)


def  _snake_case       (					A			,   A			,   A="binary"	)						->   int:
							lowerCAmelCase__	       =     simple_accuracy(A			,   A	)
							lowerCAmelCase__	       =     float(fa_score(y_true=A			,   y_pred=A			,   average=A	)	)
							return {
							    "accuracy": acc,
							    "f1": fa,
							}


def  _snake_case       (					A			,   A	)						->   List[Any]:
							lowerCAmelCase__	       =     {}
							for id_pred, label in zip(A			,   A	):
														lowerCAmelCase__	       =     F"""{id_pred["idx"]["paragraph"]}-{id_pred["idx"]["question"]}"""
														lowerCAmelCase__	       =     id_pred['''prediction''']
														if question_id in question_map:
																					question_map[question_id].append((pred, label)	)
														else:
																					lowerCAmelCase__	       =     [(pred, label)]
							lowerCAmelCase__       , lowerCAmelCase__	       =     [], []
							for question, preds_labels in question_map.items():
														lowerCAmelCase__       , lowerCAmelCase__	       =     zip(*A	)
														lowerCAmelCase__	       =     fa_score(y_true=A			,   y_pred=A			,   average='''macro'''	)
														fas.append(A	)
														lowerCAmelCase__	       =     int(sum(pred == label for pred, label in preds_labels	) == len(A	)	)
														ems.append(A	)
							lowerCAmelCase__	       =     float(sum(A	) / len(A	)	)
							lowerCAmelCase__	       =     sum(A	) / len(A	)
							lowerCAmelCase__	       =     float(fa_score(y_true=A			,   y_pred=[id_pred['''prediction'''] for id_pred in ids_preds]	)	)
							return {"exact_match": em, "f1_m": fa_m, "f1_a": fa_a}






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





							'''simple docstring'''






							def   __SCREAMING_SNAKE_CASE							(		self       )	->							Dict:
														if self.config_name not in [
														    "boolq",
														    "cb",
														    "copa",
														    "multirc",
														    "record",
														    "rte",
														    "wic",
														    "wsc",
														    "wsc.fixed",
														    "axb",
														    "axg",
														]:
																					raise KeyError(
																					    '''You should supply a configuration name selected in '''
																					    '''["boolq", "cb", "copa", "multirc", "record", "rte", "wic", "wsc", "wsc.fixed", "axb", "axg",]'''       )
														return datasets.MetricInfo(
														    description=_DESCRIPTION ,   citation=_CITATION ,   inputs_description=_KWARGS_DESCRIPTION ,   features=datasets.Features(self._get_feature_types()       ) ,   codebase_urls=[] ,   reference_urls=[] ,   format='''numpy''' if not self.config_name == '''record''' and not self.config_name == '''multirc''' else None ,   )







							def   __SCREAMING_SNAKE_CASE							(		self       )	->							Optional[int]:
														if self.config_name == "record":
																					return {
																					    "predictions": {
																					        "idx": {
																					            "passage": datasets.Value('''int64'''       ),
																					            "query": datasets.Value('''int64'''       ),
																					        },
																					        "prediction_text": datasets.Value('''string'''       ),
																					    },
																					    "references": {
																					        "idx": {
																					            "passage": datasets.Value('''int64'''       ),
																					            "query": datasets.Value('''int64'''       ),
																					        },
																					        "answers": datasets.Sequence(datasets.Value('''string'''       )       ),
																					    },
																					}
														elif self.config_name == "multirc":
																					return {
																					    "predictions": {
																					        "idx": {
																					            "answer": datasets.Value('''int64'''       ),
																					            "paragraph": datasets.Value('''int64'''       ),
																					            "question": datasets.Value('''int64'''       ),
																					        },
																					        "prediction": datasets.Value('''int64'''       ),
																					    },
																					    "references": datasets.Value('''int64'''       ),
																					}
														else:
																					return {
																					    "predictions": datasets.Value('''int64'''       ),
																					    "references": datasets.Value('''int64'''       ),
																					}







							def   __SCREAMING_SNAKE_CASE							(		self ,   lowerCamelCase_ ,   lowerCamelCase_       )	->							Dict:
														if self.config_name == "axb":
																					return {"matthews_correlation": matthews_corrcoef(lowerCamelCase_ ,   lowerCamelCase_       )}
														elif self.config_name == "cb":
																					return acc_and_fa(lowerCamelCase_ ,   lowerCamelCase_ ,   fa_avg='''macro'''       )
														elif self.config_name == "record":
																					lowerCAmelCase__	       =     [
																					    {
																					        '''qas''': [
																					            {'''id''': ref['''idx''']['''query'''], '''answers''': [{'''text''': ans} for ans in ref['''answers''']]}
																					            for ref in references
																					        ]
																					    }
																					]
																					lowerCAmelCase__	       =     {pred['''idx''']['''query''']: pred['''prediction_text'''] for pred in predictions}
																					return evaluate_record(lowerCamelCase_ ,   lowerCamelCase_       )[0]
														elif self.config_name == "multirc":
																					return evaluate_multirc(lowerCamelCase_ ,   lowerCamelCase_       )
														elif self.config_name in ["copa", "rte", "wic", "wsc", "wsc.fixed", "boolq", "axg"]:
																					return {"accuracy": simple_accuracy(lowerCamelCase_ ,   lowerCamelCase_       )}
														else:
																					raise KeyError(
																					    '''You should supply a configuration name selected in '''
																					    '''["boolq", "cb", "copa", "multirc", "record", "rte", "wic", "wsc", "wsc.fixed", "axb", "axg",]'''       )