



'''simple docstring'''





from sklearn.metrics import fa_score, matthews_corrcoef

import datasets

from .record_evaluation import evaluate as evaluate_record


snake_case__					:     List[Any]    		=   """\
@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}
}
"""

snake_case__					:     Optional[int]    		=   """\
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.
"""

snake_case__					:     Optional[int]    		=   """
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_				(    _SCREAMING_SNAKE_CASE  ,	_SCREAMING_SNAKE_CASE   ):
	return float((preds == labels).mean()   )



def    snake_case_				(    _SCREAMING_SNAKE_CASE  ,	_SCREAMING_SNAKE_CASE  ,	_SCREAMING_SNAKE_CASE="binary"   ):
	__lowercase		       =			simple_accuracy(_SCREAMING_SNAKE_CASE  ,	_SCREAMING_SNAKE_CASE   )
	__lowercase		       =			float(fa_score(y_true=_SCREAMING_SNAKE_CASE  ,	y_pred=_SCREAMING_SNAKE_CASE  ,	average=_SCREAMING_SNAKE_CASE   )   )
	return {
	    "accuracy": acc,
	    "f1": fa,
	}



def    snake_case_				(    _SCREAMING_SNAKE_CASE  ,	_SCREAMING_SNAKE_CASE   ):
	__lowercase		       =			{}
	for id_pred, label in zip(_SCREAMING_SNAKE_CASE  ,	_SCREAMING_SNAKE_CASE   ):
		__lowercase		       =			F"""{id_pred['idx']['paragraph']}-{id_pred['idx']['question']}"""
		__lowercase		       =			id_pred["prediction"]
		if question_id in question_map:
			question_map[question_id].append((pred, label)   )
		else:
			__lowercase		       =			[(pred, label)]
	__lowercase       ,    __lowercase		       =			[], []
	for question, preds_labels in question_map.items():
		__lowercase       ,    __lowercase		       =			zip(*_SCREAMING_SNAKE_CASE   )
		__lowercase		       =			fa_score(y_true=_SCREAMING_SNAKE_CASE  ,	y_pred=_SCREAMING_SNAKE_CASE  ,	average="macro"   )
		fas.append(_SCREAMING_SNAKE_CASE   )
		__lowercase		       =			int(sum(pred == label for pred, label in preds_labels   ) == len(_SCREAMING_SNAKE_CASE   )   )
		ems.append(_SCREAMING_SNAKE_CASE   )
	__lowercase		       =			float(sum(_SCREAMING_SNAKE_CASE   ) / len(_SCREAMING_SNAKE_CASE   )   )
	__lowercase		       =			sum(_SCREAMING_SNAKE_CASE   ) / len(_SCREAMING_SNAKE_CASE   )
	__lowercase		       =			float(fa_score(y_true=_SCREAMING_SNAKE_CASE  ,	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     _snake_case	(	self	:							Tuple	):






							'''simple docstring'''



							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     _snake_case	(	self	:							Tuple	):






							'''simple docstring'''



							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     _snake_case	(	self	:							Optional[Any]							,      lowerCamelCase	:							Optional[Any]							,      lowerCamelCase	:							Dict	):






							'''simple docstring'''



							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":
								__lowercase		       =			[
								    {
								        "qas": [
								            {"id": ref["idx"]["query"], "answers": [{"text": ans} for ans in ref["answers"]]}
								            for ref in references
								        ]
								    }
								]
								__lowercase		       =			{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\",]"	)








import json
import os
from functools import lru_cache
from typing import Dict, List, Optional, Tuple, Union

import regex as re

from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...tokenization_utils_base import BatchEncoding, EncodedInput
from ...utils import PaddingStrategy, logging


snake_case__					:     List[str]    		=   logging.get_logger(__name__)


snake_case__					:     Optional[Any]    		=   {"""vocab_file""": """vocab.json""", """merges_file""": """merges.txt"""}

# See all LED models at https://huggingface.co/models?filter=LED
snake_case__					:     Optional[Any]    		=   {
    """vocab_file""": {
        """allenai/led-base-16384""": """https://huggingface.co/allenai/led-base-16384/resolve/main/vocab.json""",
    },
    """merges_file""": {
        """allenai/led-base-16384""": """https://huggingface.co/allenai/led-base-16384/resolve/main/merges.txt""",
    },
    """tokenizer_file""": {
        """allenai/led-base-16384""": """https://huggingface.co/allenai/led-base-16384/resolve/main/tokenizer.json""",
    },
}

snake_case__					:     List[str]    		=   {
    """allenai/led-base-16384""": 1_63_84,
}



@lru_cache()
# Copied from transformers.models.bart.tokenization_bart.bytes_to_unicode
def    snake_case_				(    ):
	__lowercase		       =			(
	    list(range(ord("!"   )  ,	ord("~"   ) + 1   )   ) + list(range(ord("¡"   )  ,	ord("¬"   ) + 1   )   ) + list(range(ord("®"   )  ,	ord("ÿ"   ) + 1   )   )
	)
	__lowercase		       =			bs[:]
	__lowercase		       =			0
	for b in range(2**8   ):
		if b not in bs:
			bs.append(_SCREAMING_SNAKE_CASE   )
			cs.append(2**8 + n   )
			n += 1
	__lowercase		       =			[chr(_SCREAMING_SNAKE_CASE   ) for n in cs]
	return dict(zip(_SCREAMING_SNAKE_CASE  ,	_SCREAMING_SNAKE_CASE   )   )



def    snake_case_				(    _SCREAMING_SNAKE_CASE   ):
	__lowercase		       =			set()
	__lowercase		       =			word[0]
	for char in word[1:]:
		pairs.add((prev_char, char)   )
		__lowercase		       =			char
	return pairs

class   _A   ( _lowercase	):


						'''simple docstring'''





						_snake_case			:     List[str]      							=      VOCAB_FILES_NAMES
						_snake_case			:     Union[str, Any]      							=      PRETRAINED_VOCAB_FILES_MAP
						_snake_case			:     Dict      							=      PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
						_snake_case			:     Union[str, Any]      							=      ["""input_ids""", """attention_mask"""]



						def __init__(	self	:							List[str]							,      lowerCamelCase	:							Tuple							,      lowerCamelCase	:							Tuple							,      lowerCamelCase	:							Optional[int]="replace"							,      lowerCamelCase	:							Dict="<s>"							,      lowerCamelCase	:							Dict="</s>"							,      lowerCamelCase	:							Optional[Any]="</s>"							,      lowerCamelCase	:							Any="<s>"							,      lowerCamelCase	:							List[str]="<unk>"							,      lowerCamelCase	:							Union[str, Any]="<pad>"							,      lowerCamelCase	:							Any="<mask>"							,      lowerCamelCase	:							str=False							,      **lowerCamelCase	:							Optional[Any]							,      ):






							'''simple docstring'''



							__lowercase		       =			AddedToken(lowerCamelCase							,      lstrip=lowerCamelCase							,      rstrip=lowerCamelCase	) if isinstance(lowerCamelCase							,      lowerCamelCase	) else bos_token
							__lowercase		       =			AddedToken(lowerCamelCase							,      lstrip=lowerCamelCase							,      rstrip=lowerCamelCase	) if isinstance(lowerCamelCase							,      lowerCamelCase	) else eos_token
							__lowercase		       =			AddedToken(lowerCamelCase							,      lstrip=lowerCamelCase							,      rstrip=lowerCamelCase	) if isinstance(lowerCamelCase							,      lowerCamelCase	) else sep_token
							__lowercase		       =			AddedToken(lowerCamelCase							,      lstrip=lowerCamelCase							,      rstrip=lowerCamelCase	) if isinstance(lowerCamelCase							,      lowerCamelCase	) else cls_token
							__lowercase		       =			AddedToken(lowerCamelCase							,      lstrip=lowerCamelCase							,      rstrip=lowerCamelCase	) if isinstance(lowerCamelCase							,      lowerCamelCase	) else unk_token
							__lowercase		       =			AddedToken(lowerCamelCase							,      lstrip=lowerCamelCase							,      rstrip=lowerCamelCase	) if isinstance(lowerCamelCase							,      lowerCamelCase	) else pad_token

							# Mask token behave like a normal word, i.e. include the space before it
							__lowercase		       =			AddedToken(lowerCamelCase							,      lstrip=lowerCamelCase							,      rstrip=lowerCamelCase	) if isinstance(lowerCamelCase							,      lowerCamelCase	) else mask_token

							super().__init__(
							    errors=lowerCamelCase							,      bos_token=lowerCamelCase							,      eos_token=lowerCamelCase							,      unk_token=lowerCamelCase							,      sep_token=lowerCamelCase							,      cls_token=lowerCamelCase							,      pad_token=lowerCamelCase							,      mask_token=lowerCamelCase							,      add_prefix_space=lowerCamelCase							,      **lowerCamelCase							,      )

							with open(lowerCamelCase							,      encoding="utf-8"	) as vocab_handle:
								__lowercase		       =			json.load(lowerCamelCase	)
							__lowercase		       =			{v: k for k, v in self.encoder.items()}
							__lowercase		       =			errors  # how to handle errors in decoding
							__lowercase		       =			bytes_to_unicode()
							__lowercase		       =			{v: k for k, v in self.byte_encoder.items()}
							with open(lowerCamelCase							,      encoding="utf-8"	) as merges_handle:
								__lowercase		       =			merges_handle.read().split("\n"	)[1:-1]
							__lowercase		       =			[tuple(merge.split()	) for merge in bpe_merges]
							__lowercase		       =			dict(zip(lowerCamelCase							,      range(len(lowerCamelCase	)	)	)	)
							__lowercase		       =			{}
							__lowercase		       =			add_prefix_space

							# Should have added re.IGNORECASE so BPE merges can happen for capitalized versions of contractions
							__lowercase		       =			re.compile(R"'s|'t|'re|'ve|'m|'ll|'d| ?\p{L}+| ?\p{N}+| ?[^\s\p{L}\p{N}]+|\s+(?!\S)|\s+"	)



						@property
						# Copied from transformers.models.bart.tokenization_bart.BartTokenizer.vocab_size
						def     _snake_case	(	self	:							Optional[int]	):






							'''simple docstring'''



							return len(self.encoder	)



						def     _snake_case	(	self	:							Optional[int]	):






							'''simple docstring'''



							return dict(self.encoder							,      **self.added_tokens_encoder	)



						def     _snake_case	(	self	:							List[Any]							,      lowerCamelCase	:							str	):






							'''simple docstring'''



							if token in self.cache:
								return self.cache[token]
							__lowercase		       =			tuple(lowerCamelCase	)
							__lowercase		       =			get_pairs(lowerCamelCase	)

							if not pairs:
								return token

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

									if word[i] == first and i < len(lowerCamelCase	) - 1 and word[i + 1] == second:
										new_word.append(first + second	)
										i += 2
									else:
										new_word.append(word[i]	)
										i += 1
								__lowercase		       =			tuple(lowerCamelCase	)
								__lowercase		       =			new_word
								if len(lowerCamelCase	) == 1:
									break
								else:
									__lowercase		       =			get_pairs(lowerCamelCase	)
							__lowercase		       =			" ".join(lowerCamelCase	)
							__lowercase		       =			word
							return word



						def     _snake_case	(	self	:							List[Any]							,      lowerCamelCase	:							Tuple	):






							'''simple docstring'''



							__lowercase		       =			[]
							for token in re.findall(self.pat							,      lowerCamelCase	):
								__lowercase		       =			"".join(
								    self.byte_encoder[b] for b in token.encode("utf-8"	)	)  # Maps all our bytes to unicode strings, avoiding control tokens of the BPE (spaces in our case)
								bpe_tokens.extend(bpe_token for bpe_token in self.bpe(lowerCamelCase	).split(" "	)	)
							return bpe_tokens



						def     _snake_case	(	self	:							Dict							,      lowerCamelCase	:							Optional[int]	):






							'''simple docstring'''



							return self.encoder.get(lowerCamelCase							,      self.encoder.get(self.unk_token	)	)



						def     _snake_case	(	self	:							str							,      lowerCamelCase	:							Optional[Any]	):






							'''simple docstring'''



							return self.decoder.get(lowerCamelCase	)



						def     _snake_case	(	self	:							Union[str, Any]							,      lowerCamelCase	:							int	):






							'''simple docstring'''



							__lowercase		       =			"".join(lowerCamelCase	)
							__lowercase		       =			bytearray([self.byte_decoder[c] for c in text]	).decode("utf-8"							,      errors=self.errors	)
							return text



						def     _snake_case	(	self	:							Optional[Any]							,      lowerCamelCase	:							str							,      lowerCamelCase	:							Optional[str] = None	):






							'''simple docstring'''



							if not os.path.isdir(lowerCamelCase	):
								logger.error(f"""Vocabulary path ({save_directory}) should be a directory"""	)
								return
							__lowercase		       =			os.path.join(
							    lowerCamelCase							,      (filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"]	)
							__lowercase		       =			os.path.join(
							    lowerCamelCase							,      (filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["merges_file"]	)

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

							__lowercase		       =			0
							with open(lowerCamelCase							,      "w"							,      encoding="utf-8"	) as writer:
								writer.write("#version: 0.2\n"	)
								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 {merge_file}: BPE merge indices are not consecutive."""
										    " Please check that the tokenizer is not corrupted!"	)
										__lowercase		       =			token_index
									writer.write(" ".join(lowerCamelCase	) + "\n"	)
									index += 1

							return vocab_file, merge_file



						def     _snake_case	(	self	:							Tuple							,      lowerCamelCase	:							List[int]							,      lowerCamelCase	:							Optional[List[int]] = None	):






							'''simple docstring'''



							if token_ids_a is None:
								return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
							__lowercase		       =			[self.cls_token_id]
							__lowercase		       =			[self.sep_token_id]
							return cls + token_ids_a + sep + sep + token_ids_a + sep



						def     _snake_case	(	self	:							str							,      lowerCamelCase	:							List[int]							,      lowerCamelCase	:							Optional[List[int]] = None							,      lowerCamelCase	:							bool = False	):






							'''simple docstring'''



							if already_has_special_tokens:
								return super().get_special_tokens_mask(
								    token_ids_a=lowerCamelCase							,      token_ids_a=lowerCamelCase							,      already_has_special_tokens=lowerCamelCase	)

							if token_ids_a is None:
								return [1] + ([0] * len(lowerCamelCase	)) + [1]
							return [1] + ([0] * len(lowerCamelCase	)) + [1, 1] + ([0] * len(lowerCamelCase	)) + [1]



						def     _snake_case	(	self	:							int							,      lowerCamelCase	:							List[int]							,      lowerCamelCase	:							Optional[List[int]] = None	):






							'''simple docstring'''



							__lowercase		       =			[self.sep_token_id]
							__lowercase		       =			[self.cls_token_id]

							if token_ids_a is None:
								return len(cls + token_ids_a + sep	) * [0]
							return len(cls + token_ids_a + sep + sep + token_ids_a + sep	) * [0]



						def     _snake_case	(	self	:							Dict							,      lowerCamelCase	:							Any							,      lowerCamelCase	:							Tuple=False							,      **lowerCamelCase	:							Any	):






							'''simple docstring'''



							__lowercase		       =			kwargs.pop("add_prefix_space"							,      self.add_prefix_space	)
							if (is_split_into_words or add_prefix_space) and (len(lowerCamelCase	) > 0 and not text[0].isspace()):
								__lowercase		       =			" " + text
							return (text, kwargs)



						def     _snake_case	(	self	:							List[Any]							,      lowerCamelCase	:							Union[Dict[str, EncodedInput], BatchEncoding]							,      lowerCamelCase	:							Optional[int] = None							,      lowerCamelCase	:							PaddingStrategy = PaddingStrategy.DO_NOT_PAD							,      lowerCamelCase	:							Optional[int] = None							,      lowerCamelCase	:							Optional[bool] = None							,      ):






							'''simple docstring'''



							__lowercase		       =			super()._pad(
							    encoded_inputs=lowerCamelCase							,      max_length=lowerCamelCase							,      padding_strategy=lowerCamelCase							,      pad_to_multiple_of=lowerCamelCase							,      return_attention_mask=lowerCamelCase							,      )

							# Load from model defaults
							if return_attention_mask is None:
								__lowercase		       =			"attention_mask" in self.model_input_names

							if return_attention_mask and "global_attention_mask" in encoded_inputs:
								__lowercase		       =			encoded_inputs[self.model_input_names[0]]
								# `global_attention_mask` need to have the same length as other (sequential) inputs.
								__lowercase		       =			len(encoded_inputs["global_attention_mask"]	) != len(lowerCamelCase	)

								if needs_to_be_padded:
									__lowercase		       =			len(lowerCamelCase	) - len(encoded_inputs["global_attention_mask"]	)

									if self.padding_side == "right":
										# Use `-1` since `0` in `global_attention_mask` means `local attention` instead of `not to attend`
										__lowercase		       =			(
										    encoded_inputs["global_attention_mask"] + [-1] * difference
										)
									elif self.padding_side == "left":
										__lowercase		       =			[-1] * difference + encoded_inputs[
										    "global_attention_mask"
										]
									else:
										raise ValueError("Invalid padding strategy:" + str(self.padding_side	)	)

							return encoded_inputs



