



"""simple docstring"""
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()
A:   Tuple								=		logging.get_logger(__name__)

def       _snake_case						(						UpperCamelCase						:		List[Any]			,		UpperCamelCase						:		Optional[int]						):
 UpperCAmelCase	:					int	=	[]
 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       _snake_case						(						UpperCamelCase						:		Dict			,		UpperCamelCase						:		Optional[Any]						):
 for i in range(encoder_config.num_hidden_layers						):
  # queries, keys and values (only weights, no biases)
  UpperCAmelCase	:					Optional[int]	=	state_dict.pop(F"encoder.deit.blocks.{i}.attn.qkv.weight"						)

  UpperCAmelCase	:					str	=	in_proj_weight[
      : encoder_config.hidden_size, :
  ]
  UpperCAmelCase	:					Dict	=	in_proj_weight[
      encoder_config.hidden_size : encoder_config.hidden_size * 2, :
  ]
  UpperCAmelCase	:					Optional[int]	=	in_proj_weight[
      -encoder_config.hidden_size :, :
  ]

def       _snake_case						(						UpperCamelCase						:		str			,		UpperCamelCase						:		Dict			,		UpperCamelCase						:		int						):
 UpperCAmelCase	:					Optional[int]	=	dct.pop(UpperCamelCase						)
 UpperCAmelCase	:					str	=	val

def       _snake_case						(						UpperCamelCase						:		Any						):
 if "handwritten" in checkpoint_url:
  UpperCAmelCase	:					int	=	"""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:
  UpperCAmelCase	:					Tuple	=	"""https://www.researchgate.net/profile/Dinh-Sang/publication/338099565/figure/fig8/AS:840413229350922@1577381536857/An-receipt-example-in-the-SROIE-2019-dataset_Q640.jpg"""
 UpperCAmelCase	:					int	=	Image.open(requests.get(UpperCamelCase			,		stream=UpperCamelCase						).raw						).convert("""RGB"""						)
 return im

@torch.no_grad()
def       _snake_case						(						UpperCamelCase						:		Dict			,		UpperCamelCase						:		int						):
 UpperCAmelCase	:					Dict	=	ViTConfig(image_size=384			,		qkv_bias=UpperCamelCase						)
 UpperCAmelCase	:					List[Any]	=	TrOCRConfig()

 # size of the architecture
 if "base" in checkpoint_url:
  UpperCAmelCase	:					List[Any]	=	768
 elif "large" in checkpoint_url:
  # use ViT-large encoder
  UpperCAmelCase	:					Tuple	=	1024
  UpperCAmelCase	:					Any	=	4096
  UpperCAmelCase	:					List[str]	=	24
  UpperCAmelCase	:					str	=	16
  UpperCAmelCase	:					Optional[Any]	=	1024
 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:
  UpperCAmelCase	:					Optional[Any]	=	False
  UpperCAmelCase	:					int	=	"""relu"""
  UpperCAmelCase	:					Dict	=	1024
  UpperCAmelCase	:					List[str]	=	True
  UpperCAmelCase	:					List[str]	=	False
  UpperCAmelCase	:					Any	=	False

 # load HuggingFace model
 UpperCAmelCase	:					Any	=	ViTModel(UpperCamelCase			,		add_pooling_layer=UpperCamelCase						)
 UpperCAmelCase	:					Any	=	TrOCRForCausalLM(UpperCamelCase						)
 UpperCAmelCase	:					Dict	=	VisionEncoderDecoderModel(encoder=UpperCamelCase			,		decoder=UpperCamelCase						)
 model.eval()

 # load state_dict of original model, rename some keys
 UpperCAmelCase	:					str	=	torch.hub.load_state_dict_from_url(UpperCamelCase			,		map_location="""cpu"""			,		check_hash=UpperCamelCase						)["""model"""]

 UpperCAmelCase	:					str	=	create_rename_keys(UpperCamelCase			,		UpperCamelCase						)
 for src, dest in rename_keys:
  rename_key(UpperCamelCase			,		UpperCamelCase			,		UpperCamelCase						)
 read_in_q_k_v(UpperCamelCase			,		UpperCamelCase						)

 # 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():
  UpperCAmelCase	:					Any	=	state_dict.pop(UpperCamelCase						)
  if key.startswith("""decoder"""						) and "output_projection" not in key:
   UpperCAmelCase	:					Union[str, Any]	=	val
  else:
   UpperCAmelCase	:					List[str]	=	val

    # load state dict
 model.load_state_dict(UpperCamelCase						)

 # Check outputs on an image
 UpperCAmelCase	:					Optional[Any]	=	ViTImageProcessor(size=encoder_config.image_size						)
 UpperCAmelCase	:					List[str]	=	RobertaTokenizer.from_pretrained("""roberta-large"""						)
 UpperCAmelCase	:					Union[str, Any]	=	TrOCRProcessor(UpperCamelCase			,		UpperCamelCase						)

 UpperCAmelCase	:					Tuple	=	processor(images=prepare_img(UpperCamelCase						)			,		return_tensors="""pt"""						).pixel_values

 # verify logits
 UpperCAmelCase	:					Dict	=	torch.tensor([[model.config.decoder.decoder_start_token_id]]						)
 UpperCAmelCase	:					Dict	=	model(pixel_values=UpperCamelCase			,		decoder_input_ids=UpperCamelCase						)
 UpperCAmelCase	:					List[Any]	=	outputs.logits

 UpperCAmelCase	:					Optional[int]	=	torch.Size([1, 1, 50265]						)
 if "trocr-base-handwritten" in checkpoint_url:
  UpperCAmelCase	:					Optional[Any]	=	torch.tensor(
      [-1.4502, -4.6683, -0.5347, -2.9291, 9.1435, -3.0571, 8.9764, 1.7560, 8.7358, -1.5311]						)
 elif "trocr-large-handwritten" in checkpoint_url:
  UpperCAmelCase	:					Optional[int]	=	torch.tensor(
      [-2.6437, -1.3129, -2.2596, -5.3455, 6.3539, 1.7604, 5.4991, 1.4702, 5.6113, 2.0170]						)
 elif "trocr-base-printed" in checkpoint_url:
  UpperCAmelCase	:					Tuple	=	torch.tensor(
      [-5.6816, -5.8388, 1.1398, -6.9034, 6.8505, -2.4393, 1.2284, -1.0232, -1.9661, -3.9210]						)
 elif "trocr-large-printed" in checkpoint_url:
  UpperCAmelCase	:					Tuple	=	torch.tensor(
      [-6.0162, -7.0959, 4.4155, -5.1063, 7.0468, -3.1631, 2.6466, -0.3081, -0.8106, -1.7535]						)

 if "stage1" not in checkpoint_url:
  assert logits.shape == expected_shape, "Shape of logits not as expected"
  assert torch.allclose(logits[0, 0, :10]			,		UpperCamelCase			,		atol=1e-3						), "First elements of logits not as expected"

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


if __name__ == "__main__":
      A:   List[str]								=		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."
      )
      A:   List[str]								=		parser.parse_args()
      convert_tr_ocr_checkpoint(args.checkpoint_url, args.pytorch_dump_folder_path)







import os
from shutil import copyfile
from typing import List, Optional, Tuple

from tokenizers import processors

from ...tokenization_utils import AddedToken, BatchEncoding
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging


if is_sentencepiece_available():
    from .tokenization_nllb import NllbTokenizer
else:
    _UpperCAmelCase				:       int	   =				None


_UpperCAmelCase				:       Dict	   =				logging.get_logger(__name__)


_UpperCAmelCase				:       Optional[int]	   =				{"""vocab_file""": """sentencepiece.bpe.model""", """tokenizer_file""": """tokenizer.json"""}

_UpperCAmelCase				:       List[Any]	   =				{
    """vocab_file""": {
        """facebook/nllb-200-distilled-600M""": (
            """https://huggingface.co/facebook/nllb-200-distilled-600M/resolve/main/sentencepiece.bpe.model"""
        ),
    },
    """tokenizer_file""": {
        """facebook/nllb-200-distilled-600M""": (
            """https://huggingface.co/facebook/nllb-200-distilled-600M/resolve/main/tokenizer.json"""
        ),
    },
}

_UpperCAmelCase				:       List[str]	   =				{
    """facebook/nllb-large-en-ro""": 10_24,
    """facebook/nllb-200-distilled-600M""": 10_24,
}

# fmt: off
_UpperCAmelCase				:       Optional[int]	   =				["""ace_Arab""", """ace_Latn""", """acm_Arab""", """acq_Arab""", """aeb_Arab""", """afr_Latn""", """ajp_Arab""", """aka_Latn""", """amh_Ethi""", """apc_Arab""", """arb_Arab""", """ars_Arab""", """ary_Arab""", """arz_Arab""", """asm_Beng""", """ast_Latn""", """awa_Deva""", """ayr_Latn""", """azb_Arab""", """azj_Latn""", """bak_Cyrl""", """bam_Latn""", """ban_Latn""", """bel_Cyrl""", """bem_Latn""", """ben_Beng""", """bho_Deva""", """bjn_Arab""", """bjn_Latn""", """bod_Tibt""", """bos_Latn""", """bug_Latn""", """bul_Cyrl""", """cat_Latn""", """ceb_Latn""", """ces_Latn""", """cjk_Latn""", """ckb_Arab""", """crh_Latn""", """cym_Latn""", """dan_Latn""", """deu_Latn""", """dik_Latn""", """dyu_Latn""", """dzo_Tibt""", """ell_Grek""", """eng_Latn""", """epo_Latn""", """est_Latn""", """eus_Latn""", """ewe_Latn""", """fao_Latn""", """pes_Arab""", """fij_Latn""", """fin_Latn""", """fon_Latn""", """fra_Latn""", """fur_Latn""", """fuv_Latn""", """gla_Latn""", """gle_Latn""", """glg_Latn""", """grn_Latn""", """guj_Gujr""", """hat_Latn""", """hau_Latn""", """heb_Hebr""", """hin_Deva""", """hne_Deva""", """hrv_Latn""", """hun_Latn""", """hye_Armn""", """ibo_Latn""", """ilo_Latn""", """ind_Latn""", """isl_Latn""", """ita_Latn""", """jav_Latn""", """jpn_Jpan""", """kab_Latn""", """kac_Latn""", """kam_Latn""", """kan_Knda""", """kas_Arab""", """kas_Deva""", """kat_Geor""", """knc_Arab""", """knc_Latn""", """kaz_Cyrl""", """kbp_Latn""", """kea_Latn""", """khm_Khmr""", """kik_Latn""", """kin_Latn""", """kir_Cyrl""", """kmb_Latn""", """kon_Latn""", """kor_Hang""", """kmr_Latn""", """lao_Laoo""", """lvs_Latn""", """lij_Latn""", """lim_Latn""", """lin_Latn""", """lit_Latn""", """lmo_Latn""", """ltg_Latn""", """ltz_Latn""", """lua_Latn""", """lug_Latn""", """luo_Latn""", """lus_Latn""", """mag_Deva""", """mai_Deva""", """mal_Mlym""", """mar_Deva""", """min_Latn""", """mkd_Cyrl""", """plt_Latn""", """mlt_Latn""", """mni_Beng""", """khk_Cyrl""", """mos_Latn""", """mri_Latn""", """zsm_Latn""", """mya_Mymr""", """nld_Latn""", """nno_Latn""", """nob_Latn""", """npi_Deva""", """nso_Latn""", """nus_Latn""", """nya_Latn""", """oci_Latn""", """gaz_Latn""", """ory_Orya""", """pag_Latn""", """pan_Guru""", """pap_Latn""", """pol_Latn""", """por_Latn""", """prs_Arab""", """pbt_Arab""", """quy_Latn""", """ron_Latn""", """run_Latn""", """rus_Cyrl""", """sag_Latn""", """san_Deva""", """sat_Beng""", """scn_Latn""", """shn_Mymr""", """sin_Sinh""", """slk_Latn""", """slv_Latn""", """smo_Latn""", """sna_Latn""", """snd_Arab""", """som_Latn""", """sot_Latn""", """spa_Latn""", """als_Latn""", """srd_Latn""", """srp_Cyrl""", """ssw_Latn""", """sun_Latn""", """swe_Latn""", """swh_Latn""", """szl_Latn""", """tam_Taml""", """tat_Cyrl""", """tel_Telu""", """tgk_Cyrl""", """tgl_Latn""", """tha_Thai""", """tir_Ethi""", """taq_Latn""", """taq_Tfng""", """tpi_Latn""", """tsn_Latn""", """tso_Latn""", """tuk_Latn""", """tum_Latn""", """tur_Latn""", """twi_Latn""", """tzm_Tfng""", """uig_Arab""", """ukr_Cyrl""", """umb_Latn""", """urd_Arab""", """uzn_Latn""", """vec_Latn""", """vie_Latn""", """war_Latn""", """wol_Latn""", """xho_Latn""", """ydd_Hebr""", """yor_Latn""", """yue_Hant""", """zho_Hans""", """zho_Hant""", """zul_Latn"""]

class    lowerCAmelCase							(					__UpperCamelCase			):
 UpperCAmelCase__													=		VOCAB_FILES_NAMES
 UpperCAmelCase__													=		PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
 UpperCAmelCase__													=		PRETRAINED_VOCAB_FILES_MAP
 UpperCAmelCase__													=		["""input_ids""", """attention_mask"""]
 UpperCAmelCase__													=		NllbTokenizer

 UpperCAmelCase__													=		[]
 UpperCAmelCase__													=		[]
 def __init__(      self  :      Tuple			,     UpperCAmelCase  :      int=None			,     UpperCAmelCase  :      Any=None			,     UpperCAmelCase  :      str="<s>"			,     UpperCAmelCase  :      Optional[Any]="</s>"			,     UpperCAmelCase  :      str="</s>"			,     UpperCAmelCase  :      Tuple="<s>"			,     UpperCAmelCase  :      Optional[Any]="<unk>"			,     UpperCAmelCase  :      List[str]="<pad>"			,     UpperCAmelCase  :      Union[str, Any]="<mask>"			,     UpperCAmelCase  :      Tuple=None			,     UpperCAmelCase  :      int=None			,     UpperCAmelCase  :      Dict=None			,     UpperCAmelCase  :      Any=False			,     **UpperCAmelCase  :      Optional[int]			,     )			->	Tuple:
     # Mask token behave like a normal word, i.e. include the space before it
     lowerCamelCase__    :      List[Any]					 =		AddedToken(UpperCAmelCase			,     lstrip=UpperCAmelCase			,     rstrip=UpperCAmelCase	) if isinstance(UpperCAmelCase			,     UpperCAmelCase	) else mask_token
     lowerCamelCase__    :      Union[str, Any]					 =		legacy_behaviour
     super().__init__(
         vocab_file=UpperCAmelCase			,     tokenizer_file=UpperCAmelCase			,     bos_token=UpperCAmelCase			,     eos_token=UpperCAmelCase			,     sep_token=UpperCAmelCase			,     cls_token=UpperCAmelCase			,     unk_token=UpperCAmelCase			,     pad_token=UpperCAmelCase			,     mask_token=UpperCAmelCase			,     src_lang=UpperCAmelCase			,     tgt_lang=UpperCAmelCase			,     additional_special_tokens=UpperCAmelCase			,     legacy_behaviour=UpperCAmelCase			,     **UpperCAmelCase			,     )

     lowerCamelCase__    :      List[Any]					 =		vocab_file
     lowerCamelCase__    :      Dict					 =		False if not self.vocab_file else True

     lowerCamelCase__    :      Optional[Any]					 =		FAIRSEQ_LANGUAGE_CODES.copy()

     if additional_special_tokens is not None:
         # Only add those special tokens if they are not already there.
         _additional_special_tokens.extend(
             [t for t in additional_special_tokens if t not in _additional_special_tokens]	)

     self.add_special_tokens({'additional_special_tokens': _additional_special_tokens}	)
     lowerCamelCase__    :      str					 =		{
         lang_code: self.convert_tokens_to_ids(UpperCAmelCase	) for lang_code in FAIRSEQ_LANGUAGE_CODES
     }

     lowerCamelCase__    :      int					 =		src_lang if src_lang is not None else 'eng_Latn'
     lowerCamelCase__    :      List[Any]					 =		self.convert_tokens_to_ids(self._src_lang	)
     lowerCamelCase__    :      str					 =		tgt_lang
     self.set_src_lang_special_tokens(self._src_lang	)
 @property
 def      A_	(      self  :      int	)			->	str:
     return self._src_lang
 @src_lang.setter
 def      A_	(      self  :      List[Any]			,     UpperCAmelCase  :      str	)			->	None:
     lowerCamelCase__    :      Any					 =		new_src_lang
     self.set_src_lang_special_tokens(self._src_lang	)
 def      A_	(      self  :      Optional[Any]			,     UpperCAmelCase  :      List[int]			,     UpperCAmelCase  :      Optional[List[int]] = None	)			->	List[int]:
     if token_ids_a is None:
         return self.prefix_tokens + token_ids_a + self.suffix_tokens
     # We don't expect to process pairs, but leave the pair logic for API consistency
     return self.prefix_tokens + token_ids_a + token_ids_a + self.suffix_tokens
 def      A_	(      self  :      Optional[Any]			,     UpperCAmelCase  :      List[int]			,     UpperCAmelCase  :      Optional[List[int]] = None	)			->	List[int]:
     lowerCamelCase__    :      Dict					 =		[self.sep_token_id]
     lowerCamelCase__    :      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      A_	(      self  :      int			,     UpperCAmelCase  :      int			,     UpperCAmelCase  :      str			,     UpperCAmelCase  :      Optional[str]			,     UpperCAmelCase  :      Optional[str]			,     **UpperCAmelCase  :      List[str]	)			->	Dict:
     if src_lang is None or tgt_lang is None:
         raise ValueError('Translation requires a `src_lang` and a `tgt_lang` for this model'	)
     lowerCamelCase__    :      Optional[int]					 =		src_lang
     lowerCamelCase__    :      Optional[int]					 =		self(UpperCAmelCase			,     add_special_tokens=UpperCAmelCase			,     return_tensors=UpperCAmelCase			,     **UpperCAmelCase	)
     lowerCamelCase__    :      Optional[Any]					 =		self.convert_tokens_to_ids(UpperCAmelCase	)
     lowerCamelCase__    :      Union[str, Any]					 =		tgt_lang_id
     return inputs
 def      A_	(      self  :      Dict			,     UpperCAmelCase  :      List[str]			,     UpperCAmelCase  :      str = "eng_Latn"			,     UpperCAmelCase  :      Optional[List[str]] = None			,     UpperCAmelCase  :      str = "fra_Latn"			,     **UpperCAmelCase  :      Dict			,     )			->	BatchEncoding:
     lowerCamelCase__    :      Any					 =		src_lang
     lowerCamelCase__    :      int					 =		tgt_lang
     return super().prepare_seqaseq_batch(UpperCAmelCase			,     UpperCAmelCase			,     **UpperCAmelCase	)
 def      A_	(      self  :      Union[str, Any]	)			->	Optional[int]:
     return self.set_src_lang_special_tokens(self.src_lang	)
 def      A_	(      self  :      Any	)			->	Union[str, Any]:
     return self.set_tgt_lang_special_tokens(self.tgt_lang	)
 def      A_	(      self  :      str			,     UpperCAmelCase  :      Optional[Any]	)			->	None:
     lowerCamelCase__    :      int					 =		self.convert_tokens_to_ids(UpperCAmelCase	)

     if self.legacy_behaviour:
         lowerCamelCase__    :      int					 =		[]
         lowerCamelCase__    :      str					 =		[self.eos_token_id, self.cur_lang_code]
     else:
         lowerCamelCase__    :      int					 =		[self.cur_lang_code]
         lowerCamelCase__    :      Tuple					 =		[self.eos_token_id]

     lowerCamelCase__    :      Any					 =		self.convert_ids_to_tokens(self.prefix_tokens	)
     lowerCamelCase__    :      Optional[Any]					 =		self.convert_ids_to_tokens(self.suffix_tokens	)

     lowerCamelCase__    :      str					 =		processors.TemplateProcessing(
         single=prefix_tokens_str + ['$A'] + suffix_tokens_str			,     pair=prefix_tokens_str + ['$A', '$B'] + suffix_tokens_str			,     special_tokens=list(zip(prefix_tokens_str + suffix_tokens_str			,     self.prefix_tokens + self.suffix_tokens	)	)			,     )
 def      A_	(      self  :      int			,     UpperCAmelCase  :      str	)			->	None:
     lowerCamelCase__    :      Union[str, Any]					 =		self.convert_tokens_to_ids(UpperCAmelCase	)
     if self.legacy_behaviour:
         lowerCamelCase__    :      Dict					 =		[]
         lowerCamelCase__    :      Union[str, Any]					 =		[self.eos_token_id, self.cur_lang_code]
     else:
         lowerCamelCase__    :      Any					 =		[self.cur_lang_code]
         lowerCamelCase__    :      Optional[Any]					 =		[self.eos_token_id]

     lowerCamelCase__    :      Union[str, Any]					 =		self.convert_ids_to_tokens(self.prefix_tokens	)
     lowerCamelCase__    :      List[Any]					 =		self.convert_ids_to_tokens(self.suffix_tokens	)

     lowerCamelCase__    :      Optional[int]					 =		processors.TemplateProcessing(
         single=prefix_tokens_str + ['$A'] + suffix_tokens_str			,     pair=prefix_tokens_str + ['$A', '$B'] + suffix_tokens_str			,     special_tokens=list(zip(prefix_tokens_str + suffix_tokens_str			,     self.prefix_tokens + self.suffix_tokens	)	)			,     )







 def      A_	(      self  :      Union[str, Any]			,     UpperCAmelCase  :      str			,     UpperCAmelCase  :      Optional[str] = None	)			->	Tuple[str]:
     if not self.can_save_slow_tokenizer:
         raise ValueError(
             'Your fast tokenizer does not have the necessary information to save the vocabulary for a slow '
             'tokenizer.'	)

     if not os.path.isdir(UpperCAmelCase	):
         logger.error(F"""Vocabulary path ({save_directory}) should be a directory."""	)
         return
     lowerCamelCase__    :      int					 =		os.path.join(
         UpperCAmelCase			,     (filename_prefix + '-' if filename_prefix else '') + VOCAB_FILES_NAMES['vocab_file']	)

     if os.path.abspath(self.vocab_file	) != os.path.abspath(UpperCAmelCase	):
         copyfile(self.vocab_file			,     UpperCAmelCase	)

     return (out_vocab_file,)





