

"""simple docstring"""




# Copyright 2021 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.


import warnings
from typing import List
from unittest.mock import Mock

import torch
from torch.utils.data import DataLoader, IterableDataset, TensorDataset

from accelerate.accelerator import Accelerator
from accelerate.utils.dataclasses import DistributedType







class      __lowercase			(    __lowerCamelCase					):

  def __init__(  self		:			int					,A		:			List[str]    ):






   '''simple docstring'''
   UpperCAmelCase__					:  List[str]					   =					data




  def __iter__(  self		:			int    ):






   '''simple docstring'''
   for element in self.data:
    yield element







def   lowerCAmelCase						(					__UpperCamelCase=True      ):
 '''simple docstring'''





 UpperCAmelCase__					:  Any					   =					Accelerator(even_batches=__UpperCamelCase      )
 assert accelerator.num_processes == 2, "this script expects that two GPUs are available"
 return accelerator
def   lowerCAmelCase						(					__UpperCamelCase							,    __UpperCamelCase							,    __UpperCamelCase							,    __UpperCamelCase = False      ):
 '''simple docstring'''





 if iterable:
  UpperCAmelCase__					:  Dict					   =					DummyIterableDataset(torch.as_tensor(range(__UpperCamelCase      )      )      )
 else:
  UpperCAmelCase__					:  List[Any]					   =					TensorDataset(torch.as_tensor(range(__UpperCamelCase      )      )      )

 UpperCAmelCase__					:  Any					   =					DataLoader(__UpperCamelCase							,    batch_size=__UpperCamelCase      )
 UpperCAmelCase__					:  Optional[Any]					   =					accelerator.prepare(__UpperCamelCase      )

 return dl
def   lowerCAmelCase						(					__UpperCamelCase							,    __UpperCamelCase							,    __UpperCamelCase							,    __UpperCamelCase							,    __UpperCamelCase							,    ):
 '''simple docstring'''





 UpperCAmelCase__					:  Optional[int]					   =					create_dataloader(accelerator=__UpperCamelCase							,    dataset_size=__UpperCamelCase							,    batch_size=__UpperCamelCase      )

 UpperCAmelCase__					:  Union[str, Any]					   =					[len(batch[0]      ) for batch in dl]

 if accelerator.process_index == 0:
  assert batch_sizes == process_0_expected_batch_sizes
 elif accelerator.process_index == 1:
  assert batch_sizes == process_1_expected_batch_sizes
def   lowerCAmelCase						(					):
 '''simple docstring'''





 UpperCAmelCase__					:  Optional[Any]					   =					create_accelerator()

 # without padding, we would expect a different number of batches
 verify_dataloader_batch_sizes(
     __UpperCamelCase							,    dataset_size=3							,    batch_size=1							,    process_0_expected_batch_sizes=[1, 1]							,    process_1_expected_batch_sizes=[1, 1]							,    )

 # without padding, we would expect the same number of batches, but different sizes
 verify_dataloader_batch_sizes(
     __UpperCamelCase							,    dataset_size=7							,    batch_size=2							,    process_0_expected_batch_sizes=[2, 2]							,    process_1_expected_batch_sizes=[2, 2]							,    )
def   lowerCAmelCase						(					):
 '''simple docstring'''





 UpperCAmelCase__					:  str					   =					create_accelerator(even_batches=__UpperCamelCase      )

 verify_dataloader_batch_sizes(
     __UpperCamelCase							,    dataset_size=3							,    batch_size=1							,    process_0_expected_batch_sizes=[1, 1]							,    process_1_expected_batch_sizes=[1]							,    )

 verify_dataloader_batch_sizes(
     __UpperCamelCase							,    dataset_size=7							,    batch_size=2							,    process_0_expected_batch_sizes=[2, 2]							,    process_1_expected_batch_sizes=[2, 1]							,    )
def   lowerCAmelCase						(					):
 '''simple docstring'''





 UpperCAmelCase__					:  Any					   =					create_accelerator(even_batches=__UpperCamelCase      )

 UpperCAmelCase__					:  Dict					   =					torch.nn.Linear(1							,    1      )
 UpperCAmelCase__					:  str					   =					accelerator.prepare(__UpperCamelCase      )

 UpperCAmelCase__					:  List[Any]					   =					create_dataloader(__UpperCamelCase							,    dataset_size=3							,    batch_size=1      )

 UpperCAmelCase__					:  Optional[int]					   =					[]
 with accelerator.join_uneven_inputs([ddp_model]      ):
  for batch_idx, batch in enumerate(__UpperCamelCase      ):
   UpperCAmelCase__					:  List[Any]					   =					ddp_model(batch[0].float()      )
   UpperCAmelCase__					:  Optional[Any]					   =					output.sum()
   loss.backward()
   batch_idxs.append(__UpperCamelCase      )

 accelerator.wait_for_everyone()

 if accelerator.process_index == 0:
  assert batch_idxs == [0, 1]
 elif accelerator.process_index == 1:
  assert batch_idxs == [0]
def   lowerCAmelCase						(					__UpperCamelCase      ):
 '''simple docstring'''





 with warnings.catch_warnings(record=__UpperCamelCase      ) as w:
  with accelerator.join_uneven_inputs([Mock()]      ):
   pass

  assert issubclass(w[-1].category							,    __UpperCamelCase      )
  assert "only supported for multi-GPU" in str(w[-1].message      )
def   lowerCAmelCase						(					):
 '''simple docstring'''





 UpperCAmelCase__					:  Union[str, Any]					   =					True
 UpperCAmelCase__					:  List[str]					   =					False
 UpperCAmelCase__					:  str					   =					create_accelerator(even_batches=__UpperCamelCase      )
 UpperCAmelCase__					:  Any					   =					torch.nn.Linear(1							,    1      )
 UpperCAmelCase__					:  Optional[int]					   =					accelerator.prepare(__UpperCamelCase      )
 UpperCAmelCase__					:  List[Any]					   =					create_dataloader(__UpperCamelCase							,    dataset_size=3							,    batch_size=1      )
 UpperCAmelCase__					:  str					   =					create_dataloader(__UpperCamelCase							,    dataset_size=3							,    batch_size=1      )

 with accelerator.join_uneven_inputs([ddp_model]							,    even_batches=__UpperCamelCase      ):
  UpperCAmelCase__					:  int					   =					train_dl.batch_sampler.even_batches
  UpperCAmelCase__					:  List[str]					   =					valid_dl.batch_sampler.even_batches

 assert train_dl_overridden_value == overridden_even_batches
 assert valid_dl_overridden_value == overridden_even_batches
 assert train_dl.batch_sampler.even_batches == default_even_batches
 assert valid_dl.batch_sampler.even_batches == default_even_batches
def   lowerCAmelCase						(					):
 '''simple docstring'''





 UpperCAmelCase__					:  Tuple					   =					True
 UpperCAmelCase__					:  str					   =					False
 UpperCAmelCase__					:  Optional[Any]					   =					create_accelerator(even_batches=__UpperCamelCase      )
 UpperCAmelCase__					:  int					   =					torch.nn.Linear(1							,    1      )
 UpperCAmelCase__					:  str					   =					accelerator.prepare(__UpperCamelCase      )
 create_dataloader(__UpperCamelCase							,    dataset_size=3							,    batch_size=1							,    iterable=__UpperCamelCase      )
 UpperCAmelCase__					:  Any					   =					create_dataloader(__UpperCamelCase							,    dataset_size=3							,    batch_size=1      )

 with warnings.catch_warnings():
  warnings.filterwarnings("""ignore"""      )
  try:
   with accelerator.join_uneven_inputs([ddp_model]							,    even_batches=__UpperCamelCase      ):
    UpperCAmelCase__					:  List[Any]					   =					batch_dl.batch_sampler.even_batches
  except AttributeError:
   # ensure attribute error is not raised when processing iterable dl
   raise AssertionError

 assert batch_dl_overridden_value == overridden_even_batches
 assert batch_dl.batch_sampler.even_batches == default_even_batches
def   lowerCAmelCase						(					):
 '''simple docstring'''





 UpperCAmelCase__					:  List[Any]					   =					create_accelerator()
 UpperCAmelCase__					:  int					   =					torch.nn.Linear(1							,    1      )
 UpperCAmelCase__					:  Tuple					   =					accelerator.prepare(__UpperCamelCase      )
 create_dataloader(__UpperCamelCase							,    dataset_size=3							,    batch_size=1							,    iterable=__UpperCamelCase      )

 with warnings.catch_warnings(record=__UpperCamelCase      ) as w:
  with accelerator.join_uneven_inputs([ddp_model]							,    even_batches=__UpperCamelCase      ):
   pass

  assert issubclass(w[-1].category							,    __UpperCamelCase      )
  assert "only supported for map-style datasets" in str(w[-1].message      )
def   lowerCAmelCase						(					):
 '''simple docstring'''





 UpperCAmelCase__					:  Tuple					   =					create_accelerator()

 accelerator.print("""Test that even_batches variable ensures uniform batches across processes"""      )
 test_default_ensures_even_batch_sizes()

 accelerator.print("""Run tests with even_batches disabled"""      )
 test_can_disable_even_batches()

 accelerator.print("""Test joining uneven inputs"""      )
 test_can_join_uneven_inputs()

 accelerator.print("""Test overriding even_batches when joining uneven inputs"""      )
 test_join_can_override_even_batches()

 accelerator.print("""Test overriding even_batches for mixed dataloader types"""      )
 test_join_can_override_for_mixed_type_dataloaders()

 accelerator.print("""Test overriding even_batches raises a warning for iterable dataloaders"""      )
 test_join_raises_warning_for_iterable_when_overriding_even_batches()

 accelerator.print("""Test join with non DDP distributed raises warning"""      )
 UpperCAmelCase__					:  int					   =					accelerator.state.distributed_type
 UpperCAmelCase__					:  Any					   =					DistributedType.FSDP
 test_join_raises_warning_for_non_ddp_distributed(__UpperCamelCase      )
 UpperCAmelCase__					:  str					   =					original_state


if __name__ == "__main__":
      main()








"""simple docstring"""




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


__UpperCAmelCase =				logging.get_logger(__name__)


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

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

__UpperCAmelCase =				{
    'allenai/led-base-16384': 1_6384,
}
@lru_cache()
# Copied from transformers.models.bart.tokenization_bart.bytes_to_unicode
def   lowerCAmelCase						(					):
 '''simple docstring'''





 UpperCAmelCase__					:  Tuple					   =					(
     list(range(ord("""!"""      )							,    ord("""~"""      ) + 1      )      ) + list(range(ord("""¡"""      )							,    ord("""¬"""      ) + 1      )      ) + list(range(ord("""®"""      )							,    ord("""ÿ"""      ) + 1      )      )
 )
 UpperCAmelCase__					:  int					   =					bs[:]
 UpperCAmelCase__					:  Union[str, Any]					   =					0
 for b in range(2**8      ):
  if b not in bs:
   bs.append(__UpperCamelCase      )
   cs.append(2**8 + n      )
   n += 1
 UpperCAmelCase__					:  Tuple					   =					[chr(__UpperCamelCase      ) for n in cs]
 return dict(zip(__UpperCamelCase							,    __UpperCamelCase      )      )
def   lowerCAmelCase						(					__UpperCamelCase      ):
 '''simple docstring'''





 UpperCAmelCase__					:  int					   =					set()
 UpperCAmelCase__					:  Tuple					   =					word[0]
 for char in word[1:]:
  pairs.add((prev_char, char)      )
  UpperCAmelCase__					:  Optional[Any]					   =					char
 return pairs







class      __lowercase			(    __lowerCamelCase					):
  snake_case_				       =       VOCAB_FILES_NAMES
  snake_case_				       =       PRETRAINED_VOCAB_FILES_MAP
  snake_case_				       =       PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
  snake_case_				       =       ["""input_ids""", """attention_mask"""]

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






   '''simple docstring'''
   UpperCAmelCase__					:  List[str]					   =					AddedToken(A					,lstrip=A					,rstrip=A    ) if isinstance(A					,A    ) else bos_token
   UpperCAmelCase__					:  Any					   =					AddedToken(A					,lstrip=A					,rstrip=A    ) if isinstance(A					,A    ) else eos_token
   UpperCAmelCase__					:  List[str]					   =					AddedToken(A					,lstrip=A					,rstrip=A    ) if isinstance(A					,A    ) else sep_token
   UpperCAmelCase__					:  Tuple					   =					AddedToken(A					,lstrip=A					,rstrip=A    ) if isinstance(A					,A    ) else cls_token
   UpperCAmelCase__					:  Tuple					   =					AddedToken(A					,lstrip=A					,rstrip=A    ) if isinstance(A					,A    ) else unk_token
   UpperCAmelCase__					:  List[str]					   =					AddedToken(A					,lstrip=A					,rstrip=A    ) if isinstance(A					,A    ) else pad_token

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

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

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

   # Should have added re.IGNORECASE so BPE merges can happen for capitalized versions of contractions
   UpperCAmelCase__					:  Union[str, Any]					   =					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    __lowercase       (  self		:			List[Any]    ):






   '''simple docstring'''
   return len(self.encoder    )

  def    __lowercase       (  self		:			Optional[Any]    ):






   '''simple docstring'''
   return dict(self.encoder					,**self.added_tokens_encoder    )

  def    __lowercase       (  self		:			Optional[int]					,A		:			Union[str, Any]    ):






   '''simple docstring'''
   if token in self.cache:
    return self.cache[token]
   UpperCAmelCase__					:  Optional[Any]					   =					tuple(A    )
   UpperCAmelCase__					:  int					   =					get_pairs(A    )

   if not pairs:
    return token

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

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

  def    __lowercase       (  self		:			Optional[Any]					,A		:			Any    ):






   '''simple docstring'''
   UpperCAmelCase__					:  Any					   =					[]
   for token in re.findall(self.pat					,A    ):
    UpperCAmelCase__					:  Any					   =					"""""".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(A    ).split(""" """    )    )
   return bpe_tokens

  def    __lowercase       (  self		:			Dict					,A		:			int    ):






   '''simple docstring'''
   return self.encoder.get(A					,self.encoder.get(self.unk_token    )    )

  def    __lowercase       (  self		:			Dict					,A		:			Optional[Any]    ):






   '''simple docstring'''
   return self.decoder.get(A    )

  def    __lowercase       (  self		:			Optional[Any]					,A		:			int    ):






   '''simple docstring'''
   UpperCAmelCase__					:  int					   =					"""""".join(A    )
   UpperCAmelCase__					:  Dict					   =					bytearray([self.byte_decoder[c] for c in text]    ).decode("""utf-8"""					,errors=self.errors    )
   return text

  def    __lowercase       (  self		:			Optional[int]					,A		:			str					,A		:			Optional[str] = None    ):






   '''simple docstring'''
   if not os.path.isdir(A    ):
    logger.error(f"Vocabulary path ({save_directory}) should be a directory"    )
    return
   UpperCAmelCase__					:  Any					   =					os.path.join(
       A					,(filename_prefix + """-""" if filename_prefix else """""") + VOCAB_FILES_NAMES["""vocab_file"""]    )
   UpperCAmelCase__					:  List[str]					   =					os.path.join(
       A					,(filename_prefix + """-""" if filename_prefix else """""") + VOCAB_FILES_NAMES["""merges_file"""]    )

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

   UpperCAmelCase__					:  Any					   =					0
   with open(A					,"""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 A    : 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!"""    )
      UpperCAmelCase__					:  Optional[int]					   =					token_index
     writer.write(""" """.join(A    ) + """\n"""    )
     index += 1

   return vocab_file, merge_file

  def    __lowercase       (  self		:			Union[str, Any]					,A		:			List[int]					,A		:			Optional[List[int]] = None    ):






   '''simple docstring'''
   if token_ids_a is None:
    return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
   UpperCAmelCase__					:  Dict					   =					[self.cls_token_id]
   UpperCAmelCase__					:  Optional[Any]					   =					[self.sep_token_id]
   return cls + token_ids_a + sep + sep + token_ids_a + sep

  def    __lowercase       (  self		:			int					,A		:			List[int]					,A		:			Optional[List[int]] = None					,A		:			bool = False    ):






   '''simple docstring'''
   if already_has_special_tokens:
    return super().get_special_tokens_mask(
        token_ids_a=A					,token_ids_a=A					,already_has_special_tokens=A    )

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

  def    __lowercase       (  self		:			Tuple					,A		:			List[int]					,A		:			Optional[List[int]] = None    ):






   '''simple docstring'''
   UpperCAmelCase__					:  str					   =					[self.sep_token_id]
   UpperCAmelCase__					:  List[str]					   =					[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    __lowercase       (  self		:			Any					,A		:			str					,A		:			List[Any]=False					,**A		:			Tuple    ):






   '''simple docstring'''
   UpperCAmelCase__					:  Any					   =					kwargs.pop("""add_prefix_space"""					,self.add_prefix_space    )
   if (is_split_into_words or add_prefix_space) and (len(A    ) > 0 and not text[0].isspace()):
    UpperCAmelCase__					:  Dict					   =					""" """ + text
   return (text, kwargs)




  def    __lowercase       (  self		:			Dict					,A		:			Union[Dict[str, EncodedInput], BatchEncoding]					,A		:			Optional[int] = None					,A		:			PaddingStrategy = PaddingStrategy.DO_NOT_PAD					,A		:			Optional[int] = None					,A		:			Optional[bool] = None					,):






   '''simple docstring'''
   UpperCAmelCase__					:  Union[str, Any]					   =					super()._pad(
       encoded_inputs=A					,max_length=A					,padding_strategy=A					,pad_to_multiple_of=A					,return_attention_mask=A					,)

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

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

    if needs_to_be_padded:
     UpperCAmelCase__					:  List[Any]					   =					len(A    ) - 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`
      UpperCAmelCase__					:  Tuple					   =					(
          encoded_inputs["""global_attention_mask"""] + [-1] * difference
      )
     elif self.padding_side == "left":
      UpperCAmelCase__					:  Dict					   =					[-1] * difference + encoded_inputs[
          """global_attention_mask"""
      ]
     else:
      raise ValueError("""Invalid padding strategy:""" + str(self.padding_side    )    )

   return encoded_inputs





