
import argparse
from pathlib import Path

import requests
import torch
from PIL import Image

from transformers import (
    RobertaTokenizer,
    TrOCRConfig,
    TrOCRForCausalLM,
    TrOCRProcessor,
    VisionEncoderDecoderModel,
    ViTConfig,
    ViTImageProcessor,
    ViTModel,
)
from transformers.utils import logging


logging.set_verbosity_info()
lowercase__          =logging.get_logger(__name__)




def        __UpperCamelCase					(    lowerCAmelCase__							:    Optional[Any]  ,       lowerCAmelCase__							:    str  ):
	__a			:  Any		    =  []
	for i in range(encoder_config.num_hidden_layers  ):
		# encoder layers: output projection, 2 feedforward neural networks and 2 layernorms
		rename_keys.append(
		    (f"encoder.deit.blocks.{i}.norm1.weight", f"encoder.encoder.layer.{i}.layernorm_before.weight")  )
		rename_keys.append((f"encoder.deit.blocks.{i}.norm1.bias", f"encoder.encoder.layer.{i}.layernorm_before.bias")  )
		rename_keys.append(
		    (f"encoder.deit.blocks.{i}.attn.proj.weight", f"encoder.encoder.layer.{i}.attention.output.dense.weight")  )
		rename_keys.append(
		    (f"encoder.deit.blocks.{i}.attn.proj.bias", f"encoder.encoder.layer.{i}.attention.output.dense.bias")  )
		rename_keys.append(
		    (f"encoder.deit.blocks.{i}.norm2.weight", f"encoder.encoder.layer.{i}.layernorm_after.weight")  )
		rename_keys.append((f"encoder.deit.blocks.{i}.norm2.bias", f"encoder.encoder.layer.{i}.layernorm_after.bias")  )
		rename_keys.append(
		    (f"encoder.deit.blocks.{i}.mlp.fc1.weight", f"encoder.encoder.layer.{i}.intermediate.dense.weight")  )
		rename_keys.append(
		    (f"encoder.deit.blocks.{i}.mlp.fc1.bias", f"encoder.encoder.layer.{i}.intermediate.dense.bias")  )
		rename_keys.append(
		    (f"encoder.deit.blocks.{i}.mlp.fc2.weight", f"encoder.encoder.layer.{i}.output.dense.weight")  )
		rename_keys.append((f"encoder.deit.blocks.{i}.mlp.fc2.bias", f"encoder.encoder.layer.{i}.output.dense.bias")  )

	# cls token, position embeddings and patch embeddings of encoder
	rename_keys.extend(
	    [
	        ('''encoder.deit.cls_token''', '''encoder.embeddings.cls_token'''),
	        ('''encoder.deit.pos_embed''', '''encoder.embeddings.position_embeddings'''),
	        ('''encoder.deit.patch_embed.proj.weight''', '''encoder.embeddings.patch_embeddings.projection.weight'''),
	        ('''encoder.deit.patch_embed.proj.bias''', '''encoder.embeddings.patch_embeddings.projection.bias'''),
	        ('''encoder.deit.norm.weight''', '''encoder.layernorm.weight'''),
	        ('''encoder.deit.norm.bias''', '''encoder.layernorm.bias'''),
	    ]  )

	return rename_keys




def        __UpperCamelCase					(    lowerCAmelCase__							:    Dict  ,       lowerCAmelCase__							:    Dict  ):
	for i in range(encoder_config.num_hidden_layers  ):
		# queries, keys and values (only weights, no biases)
		__a			:  Tuple		    =  state_dict.pop(f"encoder.deit.blocks.{i}.attn.qkv.weight"  )

		__a			:  Tuple		    =  in_proj_weight[
		    : encoder_config.hidden_size, :
		]
		__a			:  int		    =  in_proj_weight[
		    encoder_config.hidden_size : encoder_config.hidden_size * 2, :
		]
		__a			:  Optional[Any]		    =  in_proj_weight[
		    -encoder_config.hidden_size :, :
		]




def        __UpperCamelCase					(    lowerCAmelCase__							:    Optional[int]  ,       lowerCAmelCase__							:    Optional[int]  ,       lowerCAmelCase__							:    Optional[Any]  ):
	__a			:  Tuple		    =  dct.pop(__lowerCAmelCase  )
	__a			:  List[str]		    =  val




def        __UpperCamelCase					(    lowerCAmelCase__							:    Optional[Any]  ):
	if "handwritten" in checkpoint_url:
		__a			:  str		    =  '''https://fki.tic.heia-fr.ch/static/img/a01-122-02-00.jpg'''  # industry
		# url = "https://fki.tic.heia-fr.ch/static/img/a01-122-02-12.jpg" # have
		# url = "https://fki.tic.heia-fr.ch/static/img/a01-122-02-10.jpg" # let
		# url = "https://fki.tic.heia-fr.ch/static/img/a01-122-02.jpg"  #
		# url = "https://fki.tic.heia-fr.ch/static/img/a01-122.jpg"
	elif "printed" in checkpoint_url or "stage1" in checkpoint_url:
		__a			:  Dict		    =  '''https://www.researchgate.net/profile/Dinh-Sang/publication/338099565/figure/fig8/AS:840413229350922@1577381536857/An-receipt-example-in-the-SROIE-2019-dataset_Q640.jpg'''
	__a			:  Union[str, Any]		    =  Image.open(requests.get(__lowerCAmelCase  ,       stream=__lowerCAmelCase  ).raw  ).convert('''RGB'''  )
	return im




@torch.no_grad()
def        __UpperCamelCase					(    lowerCAmelCase__							:    Optional[int]  ,       lowerCAmelCase__							:    int  ):
	__a			:  List[Any]		    =  ViTConfig(image_size=3_8_4  ,       qkv_bias=__lowerCAmelCase  )
	__a			:  int		    =  TrOCRConfig()

	# size of the architecture
	if "base" in checkpoint_url:
		__a			:  Optional[int]		    =  7_6_8
	elif "large" in checkpoint_url:
		# use ViT-large encoder
		__a			:  List[Any]		    =  1_0_2_4
		__a			:  Optional[Any]		    =  4_0_9_6
		__a			:  Dict		    =  2_4
		__a			:  List[str]		    =  1_6
		__a			:  List[Any]		    =  1_0_2_4
	else:
		raise ValueError('''Should either find \'base\' or \'large\' in checkpoint URL'''  )

	# the large-printed + stage1 checkpoints uses sinusoidal position embeddings, no layernorm afterwards
	if "large-printed" in checkpoint_url or "stage1" in checkpoint_url:
		__a			:  Optional[Any]		    =  False
		__a			:  int		    =  '''relu'''
		__a			:  Dict		    =  1_0_2_4
		__a			:  str		    =  True
		__a			:  Optional[Any]		    =  False
		__a			:  Dict		    =  False

	# load HuggingFace model
	__a			:  int		    =  ViTModel(__lowerCAmelCase  ,       add_pooling_layer=__lowerCAmelCase  )
	__a			:  List[Any]		    =  TrOCRForCausalLM(__lowerCAmelCase  )
	__a			:  Union[str, Any]		    =  VisionEncoderDecoderModel(encoder=__lowerCAmelCase  ,       decoder=__lowerCAmelCase  )
	model.eval()

	# load state_dict of original model, rename some keys
	__a			:  Optional[Any]		    =  torch.hub.load_state_dict_from_url(__lowerCAmelCase  ,       map_location='''cpu'''  ,       check_hash=__lowerCAmelCase  )['''model''']

	__a			:  Tuple		    =  create_rename_keys(__lowerCAmelCase  ,       __lowerCAmelCase  )
	for src, dest in rename_keys:
		rename_key(__lowerCAmelCase  ,       __lowerCAmelCase  ,       __lowerCAmelCase  )
	read_in_q_k_v(__lowerCAmelCase  ,       __lowerCAmelCase  )

	# remove parameters we don't need
	del state_dict["encoder.deit.head.weight"]
	del state_dict["encoder.deit.head.bias"]
	del state_dict["decoder.version"]

	# add prefix to decoder keys
	for key, val in state_dict.copy().items():
		__a			:  Optional[int]		    =  state_dict.pop(__lowerCAmelCase  )
		if key.startswith('''decoder'''  ) and "output_projection" not in key:
			__a			:  Union[str, Any]		    =  val
		else:
			__a			:  Any		    =  val

    # load state dict
	model.load_state_dict(__lowerCAmelCase  )

	# Check outputs on an image
	__a			:  List[str]		    =  ViTImageProcessor(size=encoder_config.image_size  )
	__a			:  List[str]		    =  RobertaTokenizer.from_pretrained('''roberta-large'''  )
	__a			:  Any		    =  TrOCRProcessor(__lowerCAmelCase  ,       __lowerCAmelCase  )

	__a			:  str		    =  processor(images=prepare_img(__lowerCAmelCase  )  ,       return_tensors='''pt'''  ).pixel_values

	# verify logits
	__a			:  List[Any]		    =  torch.tensor([[model.config.decoder.decoder_start_token_id]]  )
	__a			:  str		    =  model(pixel_values=__lowerCAmelCase  ,       decoder_input_ids=__lowerCAmelCase  )
	__a			:  int		    =  outputs.logits

	__a			:  List[Any]		    =  torch.Size([1, 1, 5_0_2_6_5]  )
	if "trocr-base-handwritten" in checkpoint_url:
		__a			:  Any		    =  torch.tensor(
		    [-1.45_02, -4.66_83, -0.53_47, -2.92_91, 9.14_35, -3.05_71, 8.97_64, 1.75_60, 8.73_58, -1.53_11]  )
	elif "trocr-large-handwritten" in checkpoint_url:
		__a			:  List[Any]		    =  torch.tensor(
		    [-2.64_37, -1.31_29, -2.25_96, -5.34_55, 6.35_39, 1.76_04, 5.49_91, 1.47_02, 5.61_13, 2.01_70]  )
	elif "trocr-base-printed" in checkpoint_url:
		__a			:  Any		    =  torch.tensor(
		    [-5.68_16, -5.83_88, 1.13_98, -6.90_34, 6.85_05, -2.43_93, 1.22_84, -1.02_32, -1.96_61, -3.92_10]  )
	elif "trocr-large-printed" in checkpoint_url:
		__a			:  Tuple		    =  torch.tensor(
		    [-6.01_62, -7.09_59, 4.41_55, -5.10_63, 7.04_68, -3.16_31, 2.64_66, -0.30_81, -0.81_06, -1.75_35]  )

	if "stage1" not in checkpoint_url:
		assert logits.shape == expected_shape, "Shape of logits not as expected"
		assert torch.allclose(logits[0, 0, :1_0]  ,       __lowerCAmelCase  ,       atol=1e-3  ), "First elements of logits not as expected"

	Path(__lowerCAmelCase  ).mkdir(exist_ok=__lowerCAmelCase  )
	print(f"Saving model to {pytorch_dump_folder_path}"  )
	model.save_pretrained(__lowerCAmelCase  )
	print(f"Saving processor to {pytorch_dump_folder_path}"  )
	processor.save_pretrained(__lowerCAmelCase  )


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

						parser.add_argument(
						    '--checkpoint_url',
						    default='https://layoutlm.blob.core.windows.net/trocr/model_zoo/fairseq/trocr-base-handwritten.pt',
						    type=str,
						    help='URL 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.'
						)
						lowercase__          =parser.parse_args()
						convert_tr_ocr_checkpoint(args.checkpoint_url, args.pytorch_dump_folder_path)


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


lowercase__          =logging.get_logger(__name__)


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

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

lowercase__          ={
    'allenai/led-base-16384': 16384,
}




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




def        __UpperCamelCase					(    lowerCAmelCase__							:    List[Any]  ):
 __a			:  Any		    =  set()
 __a			:  List[str]		    =  word[0]
 for char in word[1:]:
  pairs.add((prev_char, char)  )
  __a			:  Tuple		    =  char
 return pairs



class 		UpperCamelCase__  (	__lowercase   ):
   _SCREAMING_SNAKE_CASE				:							List[str]     	=				VOCAB_FILES_NAMES
   _SCREAMING_SNAKE_CASE				:							Optional[Any]     	=				PRETRAINED_VOCAB_FILES_MAP
   _SCREAMING_SNAKE_CASE				:							Tuple     	=				PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
   _SCREAMING_SNAKE_CASE				:							Union[str, Any]     	=				["input_ids", "attention_mask"]
   def __init__(self			: Any ,    snake_case_			: int ,    snake_case_			: Dict ,    snake_case_			: Union[str, Any]="replace" ,    snake_case_			: Any="<s>" ,    snake_case_			: Any="</s>" ,    snake_case_			: Tuple="</s>" ,    snake_case_			: Dict="<s>" ,    snake_case_			: Any="<unk>" ,    snake_case_			: List[Any]="<pad>" ,    snake_case_			: Dict="<mask>" ,    snake_case_			: Dict=False ,    **snake_case_			: Tuple ,    ):
    __a			:  List[str]		    =  AddedToken(snake_case_ ,    lstrip=snake_case_ ,    rstrip=snake_case_       ) if isinstance(snake_case_ ,    snake_case_       ) else bos_token
    __a			:  Tuple		    =  AddedToken(snake_case_ ,    lstrip=snake_case_ ,    rstrip=snake_case_       ) if isinstance(snake_case_ ,    snake_case_       ) else eos_token
    __a			:  Union[str, Any]		    =  AddedToken(snake_case_ ,    lstrip=snake_case_ ,    rstrip=snake_case_       ) if isinstance(snake_case_ ,    snake_case_       ) else sep_token
    __a			:  Dict		    =  AddedToken(snake_case_ ,    lstrip=snake_case_ ,    rstrip=snake_case_       ) if isinstance(snake_case_ ,    snake_case_       ) else cls_token
    __a			:  List[str]		    =  AddedToken(snake_case_ ,    lstrip=snake_case_ ,    rstrip=snake_case_       ) if isinstance(snake_case_ ,    snake_case_       ) else unk_token
    __a			:  str		    =  AddedToken(snake_case_ ,    lstrip=snake_case_ ,    rstrip=snake_case_       ) if isinstance(snake_case_ ,    snake_case_       ) else pad_token

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

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

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

    # Should have added re.IGNORECASE so BPE merges can happen for capitalized versions of contractions
    __a			:  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 				lowerCAmelCase      (self			: int       ):
    return len(self.encoder       )
   def 				lowerCAmelCase      (self			: str       ):
    return dict(self.encoder ,    **self.added_tokens_encoder       )
   def 				lowerCAmelCase      (self			: Dict ,    snake_case_			: Optional[Any]       ):
    if token in self.cache:
     return self.cache[token]
    __a			:  str		    =  tuple(snake_case_       )
    __a			:  Optional[Any]		    =  get_pairs(snake_case_       )

    if not pairs:
     return token

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

      if word[i] == first and i < len(snake_case_       ) - 1 and word[i + 1] == second:
       new_word.append(first + second       )
       i += 2
      else:
       new_word.append(word[i]       )
       i += 1
     __a			:  Tuple		    =  tuple(snake_case_       )
     __a			:  List[Any]		    =  new_word
     if len(snake_case_       ) == 1:
      break
     else:
      __a			:  Tuple		    =  get_pairs(snake_case_       )
    __a			:  Optional[int]		    =  ''' '''.join(snake_case_       )
    __a			:  Union[str, Any]		    =  word
    return word
   def 				lowerCAmelCase      (self			: Dict ,    snake_case_			: str       ):
    __a			:  Optional[Any]		    =  []
    for token in re.findall(self.pat ,    snake_case_       ):
     __a			:  str		    =  ''''''.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(snake_case_       ).split(''' '''       )       )
    return bpe_tokens
   def 				lowerCAmelCase      (self			: Optional[Any] ,    snake_case_			: int       ):
    return self.encoder.get(snake_case_ ,    self.encoder.get(self.unk_token       )       )
   def 				lowerCAmelCase      (self			: Union[str, Any] ,    snake_case_			: List[str]       ):
    return self.decoder.get(snake_case_       )
   def 				lowerCAmelCase      (self			: Optional[int] ,    snake_case_			: str       ):
    __a			:  Any		    =  ''''''.join(snake_case_       )
    __a			:  Union[str, Any]		    =  bytearray([self.byte_decoder[c] for c in text]       ).decode('''utf-8''' ,    errors=self.errors       )
    return text
   def 				lowerCAmelCase      (self			: List[Any] ,    snake_case_			: str ,    snake_case_			: Optional[str] = None       ):
    if not os.path.isdir(snake_case_       ):
     logger.error(f"Vocabulary path ({save_directory}) should be a directory"       )
     return
    __a			:  Any		    =  os.path.join(
        snake_case_ ,    (filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''vocab_file''']       )
    __a			:  str		    =  os.path.join(
        snake_case_ ,    (filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''merges_file''']       )

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

    __a			:  Optional[int]		    =  0
    with open(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 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!'''       )
       __a			:  Union[str, Any]		    =  token_index
      writer.write(''' '''.join(snake_case_       ) + '''\n'''       )
      index += 1

    return vocab_file, merge_file
   def 				lowerCAmelCase      (self			: int ,    snake_case_			: List[int] ,    snake_case_			: Optional[List[int]] = None       ):
    if token_ids_a is None:
     return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
    __a			:  Union[str, Any]		    =  [self.cls_token_id]
    __a			:  Union[str, Any]		    =  [self.sep_token_id]
    return cls + token_ids_a + sep + sep + token_ids_a + sep
   def 				lowerCAmelCase      (self			: str ,    snake_case_			: List[int] ,    snake_case_			: Optional[List[int]] = None ,    snake_case_			: bool = False       ):
    if already_has_special_tokens:
     return super().get_special_tokens_mask(
         token_ids_a=snake_case_ ,    token_ids_a=snake_case_ ,    already_has_special_tokens=snake_case_       )

    if token_ids_a is None:
     return [1] + ([0] * len(snake_case_       )) + [1]
    return [1] + ([0] * len(snake_case_       )) + [1, 1] + ([0] * len(snake_case_       )) + [1]
   def 				lowerCAmelCase      (self			: str ,    snake_case_			: List[int] ,    snake_case_			: Optional[List[int]] = None       ):
    __a			:  Union[str, Any]		    =  [self.sep_token_id]
    __a			:  Optional[int]		    =  [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 				lowerCAmelCase      (self			: str ,    snake_case_			: int ,    snake_case_			: Union[str, Any]=False ,    **snake_case_			: str       ):
    __a			:  List[str]		    =  kwargs.pop('''add_prefix_space''' ,    self.add_prefix_space       )
    if (is_split_into_words or add_prefix_space) and (len(snake_case_       ) > 0 and not text[0].isspace()):
     __a			:  str		    =  ''' ''' + text
    return (text, kwargs)







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

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

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

     if needs_to_be_padded:
      __a			:  int		    =  len(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`
       __a			:  List[str]		    =  (
           encoded_inputs['''global_attention_mask'''] + [-1] * difference
       )
      elif self.padding_side == "left":
       __a			:  List[str]		    =  [-1] * difference + encoded_inputs[
           '''global_attention_mask'''
       ]
      else:
       raise ValueError('''Invalid padding strategy:''' + str(self.padding_side       )       )

    return encoded_inputs
