


from sklearn.metrics import fa_score

import datasets


_A		:				Any									=       '''
The F1 score is the harmonic mean of the precision and recall. It can be computed with the equation:
F1 = 2 * (precision * recall) / (precision + recall)
'''


_A		:				Dict									=       '''
Args:
    predictions (`list` of `int`): Predicted labels.
    references (`list` of `int`): Ground truth labels.
    labels (`list` of `int`): The set of labels to include when `average` is not set to `\'binary\'`, and the order of the labels if `average` is `None`. Labels present in the data can be excluded, for example to calculate a multiclass average ignoring a majority negative class. Labels not present in the data will result in 0 components in a macro average. For multilabel targets, labels are column indices. By default, all labels in `predictions` and `references` are used in sorted order. Defaults to None.
    pos_label (`int`): The class to be considered the positive class, in the case where `average` is set to `binary`. Defaults to 1.
    average (`string`): This parameter is required for multiclass/multilabel targets. If set to `None`, the scores for each class are returned. Otherwise, this determines the type of averaging performed on the data. Defaults to `\'binary\'`.

        - \'binary\': Only report results for the class specified by `pos_label`. This is applicable only if the classes found in `predictions` and `references` are binary.
        - \'micro\': Calculate metrics globally by counting the total true positives, false negatives and false positives.
        - \'macro\': Calculate metrics for each label, and find their unweighted mean. This does not take label imbalance into account.
        - \'weighted\': Calculate metrics for each label, and find their average weighted by support (the number of true instances for each label). This alters `\'macro\'` to account for label imbalance. This option can result in an F-score that is not between precision and recall.
        - \'samples\': Calculate metrics for each instance, and find their average (only meaningful for multilabel classification).
    sample_weight (`list` of `float`): Sample weights Defaults to None.

Returns:
    f1 (`float` or `array` of `float`): F1 score or list of f1 scores, depending on the value passed to `average`. Minimum possible value is 0. Maximum possible value is 1. Higher f1 scores are better.

Examples:

    Example 1-A simple binary example
        >>> f1_metric = datasets.load_metric("f1")
        >>> results = f1_metric.compute(references=[0, 1, 0, 1, 0], predictions=[0, 0, 1, 1, 0])
        >>> print(results)
        {\'f1\': 0.5}

    Example 2-The same simple binary example as in Example 1, but with `pos_label` set to `0`.
        >>> f1_metric = datasets.load_metric("f1")
        >>> results = f1_metric.compute(references=[0, 1, 0, 1, 0], predictions=[0, 0, 1, 1, 0], pos_label=0)
        >>> print(round(results[\'f1\'], 2))
        0.67

    Example 3-The same simple binary example as in Example 1, but with `sample_weight` included.
        >>> f1_metric = datasets.load_metric("f1")
        >>> results = f1_metric.compute(references=[0, 1, 0, 1, 0], predictions=[0, 0, 1, 1, 0], sample_weight=[0.9, 0.5, 3.9, 1.2, 0.3])
        >>> print(round(results[\'f1\'], 2))
        0.35

    Example 4-A multiclass example, with different values for the `average` input.
        >>> predictions = [0, 2, 1, 0, 0, 1]
        >>> references = [0, 1, 2, 0, 1, 2]
        >>> results = f1_metric.compute(predictions=predictions, references=references, average="macro")
        >>> print(round(results[\'f1\'], 2))
        0.27
        >>> results = f1_metric.compute(predictions=predictions, references=references, average="micro")
        >>> print(round(results[\'f1\'], 2))
        0.33
        >>> results = f1_metric.compute(predictions=predictions, references=references, average="weighted")
        >>> print(round(results[\'f1\'], 2))
        0.27
        >>> results = f1_metric.compute(predictions=predictions, references=references, average=None)
        >>> print(results)
        {\'f1\': array([0.8, 0. , 0. ])}
'''


_A		:				Union[str, Any]									=       '''
@article{scikit-learn,
    title={Scikit-learn: Machine Learning in {P}ython},
    author={Pedregosa, F. and Varoquaux, G. and Gramfort, A. and Michel, V.
           and Thirion, B. and Grisel, O. and Blondel, M. and Prettenhofer, P.
           and Weiss, R. and Dubourg, V. and Vanderplas, J. and Passos, A. and
           Cournapeau, D. and Brucher, M. and Perrot, M. and Duchesnay, E.},
    journal={Journal of Machine Learning Research},
    volume={12},
    pages={2825--2830},
    year={2011}
}
'''



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





     def  SCREAMING_SNAKE_CASE__       (    self					:				Union[str, Any]							):
        return datasets.MetricInfo(
            description=_DESCRIPTION    ,	citation=_CITATION    ,	inputs_description=_KWARGS_DESCRIPTION    ,	features=datasets.Features(
                {
                    """predictions""": datasets.Sequence(datasets.Value("""int32"""							)							),
                    """references""": datasets.Sequence(datasets.Value("""int32"""							)							),
                }
                if self.config_name == """multilabel"""
                else {
                    """predictions""": datasets.Value("""int32"""							),
                    """references""": datasets.Value("""int32"""							),
                }							)    ,	reference_urls=["""https://scikit-learn.org/stable/modules/generated/sklearn.metrics.f1_score.html"""]    ,	)







     def  SCREAMING_SNAKE_CASE__       (    self					:				int    ,	SCREAMING_SNAKE_CASE_					:				Optional[Any]    ,	SCREAMING_SNAKE_CASE_					:				str    ,	SCREAMING_SNAKE_CASE_					:				str=None    ,	SCREAMING_SNAKE_CASE_					:				str=1    ,	SCREAMING_SNAKE_CASE_					:				List[str]="binary"    ,	SCREAMING_SNAKE_CASE_					:				List[str]=None							):
        __lowerCamelCase:				str     =	fa_score(
            SCREAMING_SNAKE_CASE_    ,	SCREAMING_SNAKE_CASE_    ,	labels=SCREAMING_SNAKE_CASE_    ,	pos_label=SCREAMING_SNAKE_CASE_    ,	average=SCREAMING_SNAKE_CASE_    ,	sample_weight=SCREAMING_SNAKE_CASE_							)
        return {"f1": float(SCREAMING_SNAKE_CASE_							) if score.size == 1 else score}






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


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


_A		:				Any									=       {'''vocab_file''': '''vocab.json''', '''merges_file''': '''merges.txt'''}

# See all LED models at https://huggingface.co/models?filter=LED
_A		:				int									=       {
    '''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''',
    },
}

_A		:				Dict									=       {
    '''allenai/led-base-16384''': 16_384,
}



@lru_cache()
# Copied from transformers.models.bart.tokenization_bart.bytes_to_unicode
def     __lowerCAmelCase						(							)       ->							List[Any]:
   __lowerCamelCase:				Optional[Any]     =	(
       list(range(ord("""!""" )						,      ord("""~""" ) + 1 ) ) + list(range(ord("""¡""" )						,      ord("""¬""" ) + 1 ) ) + list(range(ord("""®""" )						,      ord("""ÿ""" ) + 1 ) )
   )
   __lowerCamelCase:				List[str]     =	bs[:]
   __lowerCamelCase:				Tuple     =	0
   for b in range(2**8 ):
      if b not in bs:
         bs.append(snake_case )
         cs.append(2**8 + n )
         n += 1
   __lowerCamelCase:				List[Any]     =	[chr(snake_case ) for n in cs]
   return dict(zip(snake_case						,      snake_case ) )



def     __lowerCAmelCase						(							snake_case	:    List[str] )       ->							str:
   __lowerCamelCase:				int     =	set()
   __lowerCamelCase:				str     =	word[0]
   for char in word[1:]:
      pairs.add((prev_char, char) )
      __lowerCamelCase:				Any     =	char
   return pairs







class        a					(			_UpperCAmelCase   ):
     UpperCAmelCase__ :  Optional[Any]						      =	VOCAB_FILES_NAMES
     UpperCAmelCase__ :  Optional[Any]						      =	PRETRAINED_VOCAB_FILES_MAP
     UpperCAmelCase__ :  List[str]						      =	PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
     UpperCAmelCase__ :  List[str]						      =	["input_ids", "attention_mask"]





     def __init__(    self					:				List[str]    ,	SCREAMING_SNAKE_CASE_					:				Dict    ,	SCREAMING_SNAKE_CASE_					:				Any    ,	SCREAMING_SNAKE_CASE_					:				Dict="replace"    ,	SCREAMING_SNAKE_CASE_					:				Dict="<s>"    ,	SCREAMING_SNAKE_CASE_					:				Optional[int]="</s>"    ,	SCREAMING_SNAKE_CASE_					:				int="</s>"    ,	SCREAMING_SNAKE_CASE_					:				Any="<s>"    ,	SCREAMING_SNAKE_CASE_					:				Union[str, Any]="<unk>"    ,	SCREAMING_SNAKE_CASE_					:				Tuple="<pad>"    ,	SCREAMING_SNAKE_CASE_					:				Dict="<mask>"    ,	SCREAMING_SNAKE_CASE_					:				int=False    ,	**SCREAMING_SNAKE_CASE_					:				List[str]    ,	):
        __lowerCamelCase:				Tuple     =	AddedToken(SCREAMING_SNAKE_CASE_    ,	lstrip=SCREAMING_SNAKE_CASE_    ,	rstrip=SCREAMING_SNAKE_CASE_							) if isinstance(SCREAMING_SNAKE_CASE_    ,	SCREAMING_SNAKE_CASE_							) else bos_token
        __lowerCamelCase:				Union[str, Any]     =	AddedToken(SCREAMING_SNAKE_CASE_    ,	lstrip=SCREAMING_SNAKE_CASE_    ,	rstrip=SCREAMING_SNAKE_CASE_							) if isinstance(SCREAMING_SNAKE_CASE_    ,	SCREAMING_SNAKE_CASE_							) else eos_token
        __lowerCamelCase:				List[Any]     =	AddedToken(SCREAMING_SNAKE_CASE_    ,	lstrip=SCREAMING_SNAKE_CASE_    ,	rstrip=SCREAMING_SNAKE_CASE_							) if isinstance(SCREAMING_SNAKE_CASE_    ,	SCREAMING_SNAKE_CASE_							) else sep_token
        __lowerCamelCase:				Dict     =	AddedToken(SCREAMING_SNAKE_CASE_    ,	lstrip=SCREAMING_SNAKE_CASE_    ,	rstrip=SCREAMING_SNAKE_CASE_							) if isinstance(SCREAMING_SNAKE_CASE_    ,	SCREAMING_SNAKE_CASE_							) else cls_token
        __lowerCamelCase:				Any     =	AddedToken(SCREAMING_SNAKE_CASE_    ,	lstrip=SCREAMING_SNAKE_CASE_    ,	rstrip=SCREAMING_SNAKE_CASE_							) if isinstance(SCREAMING_SNAKE_CASE_    ,	SCREAMING_SNAKE_CASE_							) else unk_token
        __lowerCamelCase:				Tuple     =	AddedToken(SCREAMING_SNAKE_CASE_    ,	lstrip=SCREAMING_SNAKE_CASE_    ,	rstrip=SCREAMING_SNAKE_CASE_							) if isinstance(SCREAMING_SNAKE_CASE_    ,	SCREAMING_SNAKE_CASE_							) else pad_token

        # Mask token behave like a normal word, i.e. include the space before it
        __lowerCamelCase:				List[str]     =	AddedToken(SCREAMING_SNAKE_CASE_    ,	lstrip=SCREAMING_SNAKE_CASE_    ,	rstrip=SCREAMING_SNAKE_CASE_							) if isinstance(SCREAMING_SNAKE_CASE_    ,	SCREAMING_SNAKE_CASE_							) else mask_token

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

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

        # Should have added re.IGNORECASE so BPE merges can happen for capitalized versions of contractions
        __lowerCamelCase:				str     =	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  SCREAMING_SNAKE_CASE__       (    self					:				Any							):
        return len(self.encoder							)





     def  SCREAMING_SNAKE_CASE__       (    self					:				List[str]							):
        return dict(self.encoder    ,	**self.added_tokens_encoder							)





     def  SCREAMING_SNAKE_CASE__       (    self					:				List[str]    ,	SCREAMING_SNAKE_CASE_					:				int							):
        if token in self.cache:
           return self.cache[token]
        __lowerCamelCase:				List[str]     =	tuple(SCREAMING_SNAKE_CASE_							)
        __lowerCamelCase:				List[Any]     =	get_pairs(SCREAMING_SNAKE_CASE_							)

        if not pairs:
           return token

        while True:
           __lowerCamelCase:				List[str]     =	min(SCREAMING_SNAKE_CASE_    ,	key=lambda SCREAMING_SNAKE_CASE_							: self.bpe_ranks.get(SCREAMING_SNAKE_CASE_    ,	float("""inf"""							)							)							)
           if bigram not in self.bpe_ranks:
              break
           __lowerCamelCase   ,     __lowerCamelCase:				List[str]     =	bigram
           __lowerCamelCase:				Optional[int]     =	[]
           __lowerCamelCase:				Optional[Any]     =	0
           while i < len(SCREAMING_SNAKE_CASE_							):
              try:
                 __lowerCamelCase:				int     =	word.index(SCREAMING_SNAKE_CASE_    ,	SCREAMING_SNAKE_CASE_							)
              except ValueError:
                 new_word.extend(word[i:]							)
                 break
              else:
                 new_word.extend(word[i:j]							)
                 __lowerCamelCase:				Tuple     =	j

              if word[i] == first and i < len(SCREAMING_SNAKE_CASE_							) - 1 and word[i + 1] == second:
                 new_word.append(first + second							)
                 i += 2
              else:
                 new_word.append(word[i]							)
                 i += 1
           __lowerCamelCase:				Optional[int]     =	tuple(SCREAMING_SNAKE_CASE_							)
           __lowerCamelCase:				Any     =	new_word
           if len(SCREAMING_SNAKE_CASE_							) == 1:
              break
           else:
              __lowerCamelCase:				Tuple     =	get_pairs(SCREAMING_SNAKE_CASE_							)
        __lowerCamelCase:				Dict     =	""" """.join(SCREAMING_SNAKE_CASE_							)
        __lowerCamelCase:				Any     =	word
        return word





     def  SCREAMING_SNAKE_CASE__       (    self					:				int    ,	SCREAMING_SNAKE_CASE_					:				Any							):
        __lowerCamelCase:				Any     =	[]
        for token in re.findall(self.pat    ,	SCREAMING_SNAKE_CASE_							):
           __lowerCamelCase:				int     =	"""""".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(SCREAMING_SNAKE_CASE_							).split(""" """							)							)
        return bpe_tokens





     def  SCREAMING_SNAKE_CASE__       (    self					:				str    ,	SCREAMING_SNAKE_CASE_					:				Tuple							):
        return self.encoder.get(SCREAMING_SNAKE_CASE_    ,	self.encoder.get(self.unk_token							)							)





     def  SCREAMING_SNAKE_CASE__       (    self					:				Any    ,	SCREAMING_SNAKE_CASE_					:				str							):
        return self.decoder.get(SCREAMING_SNAKE_CASE_							)





     def  SCREAMING_SNAKE_CASE__       (    self					:				str    ,	SCREAMING_SNAKE_CASE_					:				Union[str, Any]							):
        __lowerCamelCase:				Any     =	"""""".join(SCREAMING_SNAKE_CASE_							)
        __lowerCamelCase:				str     =	bytearray([self.byte_decoder[c] for c in text]							).decode("""utf-8"""    ,	errors=self.errors							)
        return text





     def  SCREAMING_SNAKE_CASE__       (    self					:				Any    ,	SCREAMING_SNAKE_CASE_					:				str    ,	SCREAMING_SNAKE_CASE_					:				Optional[str] = None							):
        if not os.path.isdir(SCREAMING_SNAKE_CASE_							):
           logger.error(F'Vocabulary path ({save_directory}) should be a directory'							)
           return
        __lowerCamelCase:				Dict     =	os.path.join(
            SCREAMING_SNAKE_CASE_    ,	(filename_prefix + """-""" if filename_prefix else """""") + VOCAB_FILES_NAMES["""vocab_file"""]							)
        __lowerCamelCase:				str     =	os.path.join(
            SCREAMING_SNAKE_CASE_    ,	(filename_prefix + """-""" if filename_prefix else """""") + VOCAB_FILES_NAMES["""merges_file"""]							)

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

        __lowerCamelCase:				List[str]     =	0
        with open(SCREAMING_SNAKE_CASE_    ,	"""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 SCREAMING_SNAKE_CASE_							: 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!"""							)
                 __lowerCamelCase:				Tuple     =	token_index
              writer.write(""" """.join(SCREAMING_SNAKE_CASE_							) + """\n"""							)
              index += 1

        return vocab_file, merge_file





     def  SCREAMING_SNAKE_CASE__       (    self					:				Dict    ,	SCREAMING_SNAKE_CASE_					:				List[int]    ,	SCREAMING_SNAKE_CASE_					:				Optional[List[int]] = None							):
        if token_ids_a is None:
           return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
        __lowerCamelCase:				List[str]     =	[self.cls_token_id]
        __lowerCamelCase:				int     =	[self.sep_token_id]
        return cls + token_ids_a + sep + sep + token_ids_a + sep





     def  SCREAMING_SNAKE_CASE__       (    self					:				int    ,	SCREAMING_SNAKE_CASE_					:				List[int]    ,	SCREAMING_SNAKE_CASE_					:				Optional[List[int]] = None    ,	SCREAMING_SNAKE_CASE_					:				bool = False							):
        if already_has_special_tokens:
           return super().get_special_tokens_mask(
               token_ids_a=SCREAMING_SNAKE_CASE_    ,	token_ids_a=SCREAMING_SNAKE_CASE_    ,	already_has_special_tokens=SCREAMING_SNAKE_CASE_							)

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





     def  SCREAMING_SNAKE_CASE__       (    self					:				Any    ,	SCREAMING_SNAKE_CASE_					:				List[int]    ,	SCREAMING_SNAKE_CASE_					:				Optional[List[int]] = None							):
        __lowerCamelCase:				int     =	[self.sep_token_id]
        __lowerCamelCase:				Tuple     =	[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  SCREAMING_SNAKE_CASE__       (    self					:				Union[str, Any]    ,	SCREAMING_SNAKE_CASE_					:				Tuple    ,	SCREAMING_SNAKE_CASE_					:				Optional[Any]=False    ,	**SCREAMING_SNAKE_CASE_					:				Union[str, Any]							):
        __lowerCamelCase:				Any     =	kwargs.pop("""add_prefix_space"""    ,	self.add_prefix_space							)
        if (is_split_into_words or add_prefix_space) and (len(SCREAMING_SNAKE_CASE_							) > 0 and not text[0].isspace()):
           __lowerCamelCase:				Dict     =	""" """ + text
        return (text, kwargs)







     def  SCREAMING_SNAKE_CASE__       (    self					:				Dict    ,	SCREAMING_SNAKE_CASE_					:				Union[Dict[str, EncodedInput], BatchEncoding]    ,	SCREAMING_SNAKE_CASE_					:				Optional[int] = None    ,	SCREAMING_SNAKE_CASE_					:				PaddingStrategy = PaddingStrategy.DO_NOT_PAD    ,	SCREAMING_SNAKE_CASE_					:				Optional[int] = None    ,	SCREAMING_SNAKE_CASE_					:				Optional[bool] = None    ,	):
        __lowerCamelCase:				Optional[Any]     =	super()._pad(
            encoded_inputs=SCREAMING_SNAKE_CASE_    ,	max_length=SCREAMING_SNAKE_CASE_    ,	padding_strategy=SCREAMING_SNAKE_CASE_    ,	pad_to_multiple_of=SCREAMING_SNAKE_CASE_    ,	return_attention_mask=SCREAMING_SNAKE_CASE_    ,	)

        # Load from model defaults
        if return_attention_mask is None:
           __lowerCamelCase:				Optional[Any]     =	"""attention_mask""" in self.model_input_names

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

           if needs_to_be_padded:
              __lowerCamelCase:				Union[str, Any]     =	len(SCREAMING_SNAKE_CASE_							) - 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`
                 __lowerCamelCase:				str     =	(
                     encoded_inputs["""global_attention_mask"""] + [-1] * difference
                 )
              elif self.padding_side == "left":
                 __lowerCamelCase:				Optional[int]     =	[-1] * difference + encoded_inputs[
                     """global_attention_mask"""
                 ]
              else:
                 raise ValueError("""Invalid padding strategy:""" + str(self.padding_side							)							)

        return encoded_inputs


