

"""simple docstring"""




import argparse
import json
from collections import OrderedDict
from pathlib import Path

import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image

from transformers import PoolFormerConfig, PoolFormerForImageClassification, PoolFormerImageProcessor
from transformers.utils import logging


logging.set_verbosity_info()
__UpperCAmelCase =				logging.get_logger(__name__)
def   lowerCAmelCase						(					__UpperCamelCase							,    __UpperCamelCase							,    __UpperCamelCase							,    __UpperCamelCase      ):
 '''simple docstring'''





 UpperCAmelCase__					:  List[Any]					   =					original_name.split("""."""      )[0]
 UpperCAmelCase__					:  List[str]					   =					key.split("""."""      )
 UpperCAmelCase__					:  Optional[int]					   =					int(key_list[key_list.index(__UpperCamelCase      ) - 2]      )
 UpperCAmelCase__					:  Union[str, Any]					   =					int(key_list[key_list.index(__UpperCamelCase      ) - 1]      )
 UpperCAmelCase__					:  Union[str, Any]					   =					orig_block_num - offset

 UpperCAmelCase__					:  Union[str, Any]					   =					key.replace(F"{orig_block_num}.{layer_num}.{original_name}"							,    F"block.{new_block_num}.{layer_num}.{new_name}"      )
 return key
def   lowerCAmelCase						(					__UpperCamelCase      ):
 '''simple docstring'''





 UpperCAmelCase__					:  Union[str, Any]					   =					OrderedDict()
 UpperCAmelCase__   ,      UpperCAmelCase__					:  Optional[int]					   =					0, 0
 for key, value in state_dict.items():
  if key.startswith("""network"""      ):
   UpperCAmelCase__					:  Optional[Any]					   =					key.replace("""network"""							,    """poolformer.encoder"""      )
  if "proj" in key:
   # Works for the first embedding as well as the internal embedding layers
   if key.endswith("""bias"""      ) and "patch_embed" not in key:
    patch_emb_offset += 1
   UpperCAmelCase__					:  List[Any]					   =					key[: key.find("""proj"""      )]
   UpperCAmelCase__					:  Tuple					   =					key.replace(__UpperCamelCase							,    F"patch_embeddings.{total_embed_found}."      )
   UpperCAmelCase__					:  int					   =					key.replace("""proj"""							,    """projection"""      )
   if key.endswith("""bias"""      ):
    total_embed_found += 1
  if "patch_embeddings" in key:
   UpperCAmelCase__					:  Union[str, Any]					   =					"""poolformer.encoder.""" + key
  if "mlp.fc1" in key:
   UpperCAmelCase__					:  Optional[int]					   =					replace_key_with_offset(__UpperCamelCase							,    __UpperCamelCase							,    """mlp.fc1"""							,    """output.conv1"""      )
  if "mlp.fc2" in key:
   UpperCAmelCase__					:  Dict					   =					replace_key_with_offset(__UpperCamelCase							,    __UpperCamelCase							,    """mlp.fc2"""							,    """output.conv2"""      )
  if "norm1" in key:
   UpperCAmelCase__					:  Union[str, Any]					   =					replace_key_with_offset(__UpperCamelCase							,    __UpperCamelCase							,    """norm1"""							,    """before_norm"""      )
  if "norm2" in key:
   UpperCAmelCase__					:  Any					   =					replace_key_with_offset(__UpperCamelCase							,    __UpperCamelCase							,    """norm2"""							,    """after_norm"""      )
  if "layer_scale_1" in key:
   UpperCAmelCase__					:  int					   =					replace_key_with_offset(__UpperCamelCase							,    __UpperCamelCase							,    """layer_scale_1"""							,    """layer_scale_1"""      )
  if "layer_scale_2" in key:
   UpperCAmelCase__					:  Tuple					   =					replace_key_with_offset(__UpperCamelCase							,    __UpperCamelCase							,    """layer_scale_2"""							,    """layer_scale_2"""      )
  if "head" in key:
   UpperCAmelCase__					:  Optional[int]					   =					key.replace("""head"""							,    """classifier"""      )
  UpperCAmelCase__					:  Optional[int]					   =					value
 return new_state_dict
def   lowerCAmelCase						(					):
 '''simple docstring'''





 UpperCAmelCase__					:  Union[str, Any]					   =					"""http://images.cocodataset.org/val2017/000000039769.jpg"""
 UpperCAmelCase__					:  Tuple					   =					Image.open(requests.get(__UpperCamelCase							,    stream=__UpperCamelCase      ).raw      )

 return image
@torch.no_grad()
def   lowerCAmelCase						(					__UpperCamelCase							,    __UpperCamelCase							,    __UpperCamelCase      ):
 '''simple docstring'''





 UpperCAmelCase__					:  str					   =					PoolFormerConfig()

 # set attributes based on model_name
 UpperCAmelCase__					:  int					   =					"""huggingface/label-files"""
 UpperCAmelCase__					:  Optional[int]					   =					model_name[-3:]
 UpperCAmelCase__					:  List[str]					   =					1000
 UpperCAmelCase__					:  List[Any]					   =					"""imagenet-1k-id2label.json"""
 UpperCAmelCase__					:  List[str]					   =					(1, 1000)

 # set config attributes
 UpperCAmelCase__					:  Optional[Any]					   =					json.load(open(hf_hub_download(__UpperCamelCase							,    __UpperCamelCase							,    repo_type="""dataset"""      )							,    """r"""      )      )
 UpperCAmelCase__					:  Optional[int]					   =					{int(__UpperCamelCase      ): v for k, v in idalabel.items()}
 UpperCAmelCase__					:  Optional[Any]					   =					idalabel
 UpperCAmelCase__					:  Dict					   =					{v: k for k, v in idalabel.items()}
 if size == "s12":
  UpperCAmelCase__					:  str					   =					[2, 2, 6, 2]
  UpperCAmelCase__					:  Any					   =					[64, 128, 320, 512]
  UpperCAmelCase__					:  Optional[int]					   =					4.0
  UpperCAmelCase__					:  Dict					   =					0.9
 elif size == "s24":
  UpperCAmelCase__					:  Tuple					   =					[4, 4, 12, 4]
  UpperCAmelCase__					:  str					   =					[64, 128, 320, 512]
  UpperCAmelCase__					:  Tuple					   =					4.0
  UpperCAmelCase__					:  Dict					   =					0.9
 elif size == "s36":
  UpperCAmelCase__					:  List[str]					   =					[6, 6, 18, 6]
  UpperCAmelCase__					:  List[str]					   =					[64, 128, 320, 512]
  UpperCAmelCase__					:  Optional[Any]					   =					4.0
  UpperCAmelCase__					:  Tuple					   =					1e-6
  UpperCAmelCase__					:  str					   =					0.9
 elif size == "m36":
  UpperCAmelCase__					:  Any					   =					[6, 6, 18, 6]
  UpperCAmelCase__					:  Optional[int]					   =					[96, 192, 384, 768]
  UpperCAmelCase__					:  str					   =					4.0
  UpperCAmelCase__					:  List[Any]					   =					1e-6
  UpperCAmelCase__					:  Optional[Any]					   =					0.95
 elif size == "m48":
  UpperCAmelCase__					:  Union[str, Any]					   =					[8, 8, 24, 8]
  UpperCAmelCase__					:  Optional[int]					   =					[96, 192, 384, 768]
  UpperCAmelCase__					:  str					   =					4.0
  UpperCAmelCase__					:  Optional[int]					   =					1e-6
  UpperCAmelCase__					:  Optional[int]					   =					0.95
 else:
  raise ValueError(F"Size {size} not supported"      )

 # load image processor
 UpperCAmelCase__					:  Optional[int]					   =					PoolFormerImageProcessor(crop_pct=__UpperCamelCase      )

 # Prepare image
 UpperCAmelCase__					:  str					   =					prepare_img()
 UpperCAmelCase__					:  Optional[Any]					   =					image_processor(images=__UpperCamelCase							,    return_tensors="""pt"""      ).pixel_values

 logger.info(F"Converting model {model_name}..."      )

 # load original state dict
 UpperCAmelCase__					:  Union[str, Any]					   =					torch.load(__UpperCamelCase							,    map_location=torch.device("""cpu"""      )      )

 # rename keys
 UpperCAmelCase__					:  List[Any]					   =					rename_keys(__UpperCamelCase      )

 # create HuggingFace model and load state dict
 UpperCAmelCase__					:  Tuple					   =					PoolFormerForImageClassification(__UpperCamelCase      )
 model.load_state_dict(__UpperCamelCase      )
 model.eval()

 # Define image processor
 UpperCAmelCase__					:  str					   =					PoolFormerImageProcessor(crop_pct=__UpperCamelCase      )
 UpperCAmelCase__					:  Optional[int]					   =					image_processor(images=prepare_img()							,    return_tensors="""pt"""      ).pixel_values

 # forward pass
 UpperCAmelCase__					:  str					   =					model(__UpperCamelCase      )
 UpperCAmelCase__					:  Union[str, Any]					   =					outputs.logits

 # define expected logit slices for different models
 if size == "s12":
  UpperCAmelCase__					:  Union[str, Any]					   =					torch.tensor([-0.3045, -0.6758, -0.4869]      )
 elif size == "s24":
  UpperCAmelCase__					:  Any					   =					torch.tensor([0.4402, -0.1374, -0.8045]      )
 elif size == "s36":
  UpperCAmelCase__					:  Any					   =					torch.tensor([-0.6080, -0.5133, -0.5898]      )
 elif size == "m36":
  UpperCAmelCase__					:  Union[str, Any]					   =					torch.tensor([0.3952, 0.2263, -1.2668]      )
 elif size == "m48":
  UpperCAmelCase__					:  Union[str, Any]					   =					torch.tensor([0.1167, -0.0656, -0.3423]      )
 else:
  raise ValueError(F"Size {size} not supported"      )

 # verify logits
 assert logits.shape == expected_shape
 assert torch.allclose(logits[0, :3]							,    __UpperCamelCase							,    atol=1e-2      )

 # finally, save model and image processor
 logger.info(F"Saving PyTorch model and image processor to {pytorch_dump_folder_path}..."      )
 Path(__UpperCamelCase      ).mkdir(exist_ok=__UpperCamelCase      )
 model.save_pretrained(__UpperCamelCase      )
 print(F"Saving image processor to {pytorch_dump_folder_path}"      )
 image_processor.save_pretrained(__UpperCamelCase      )


if __name__ == "__main__":
      __UpperCAmelCase =				argparse.ArgumentParser()

      parser.add_argument(
          '--model_name',
          default='poolformer_s12',
          type=str,
          help='Name of the model you\'d like to convert.',
      )
      parser.add_argument(
          '--checkpoint_path', default=None, type=str, help='Path to the original PyTorch checkpoint (.pth file).'
      )
      parser.add_argument(
          '--pytorch_dump_folder_path', default=None, type=str, help='Path to the folder to output PyTorch model.'
      )
      __UpperCAmelCase =				parser.parse_args()
      convert_poolformer_checkpoint(args.model_name, args.checkpoint_path, args.pytorch_dump_folder_path)








"""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





