"""simple docstring"""




import argparse

import requests
import torch

# pip3 install salesforce-lavis
# I'm actually installing a slightly modified version: pip3 install git+https://github.com/nielsrogge/LAVIS.git@fix_lavis
from lavis.models import load_model_and_preprocess
from PIL import Image

from transformers import (
    AutoTokenizer,
    BlipaConfig,
    BlipaForConditionalGeneration,
    BlipaProcessor,
    BlipaVisionConfig,
    BlipImageProcessor,
    OPTConfig,
    TaConfig,
)
from transformers.utils.constants import OPENAI_CLIP_MEAN, OPENAI_CLIP_STD





def 							SCREAMING_SNAKE_CASE					(      )	->				Any:
  _lowerCAmelCase						:	str        =					"""https://storage.googleapis.com/sfr-vision-language-research/LAVIS/assets/merlion.png"""
  _lowerCAmelCase						:	List[str]        =					Image.open(requests.get(_lowerCamelCase      ,stream=_lowerCamelCase     ).raw     ).convert("""RGB"""     )

  return image





def 							SCREAMING_SNAKE_CASE					(      _lowerCamelCase						:	Optional[int]     )	->				str:
  _lowerCAmelCase						:	Any        =					[]
  # fmt: off

  # vision encoder
  rename_keys.append(("""visual_encoder.cls_token""", """vision_model.embeddings.class_embedding""")     )
  rename_keys.append(("""visual_encoder.pos_embed""", """vision_model.embeddings.position_embedding""")     )
  rename_keys.append(("""visual_encoder.patch_embed.proj.weight""", """vision_model.embeddings.patch_embedding.weight""")     )
  rename_keys.append(("""visual_encoder.patch_embed.proj.bias""", """vision_model.embeddings.patch_embedding.bias""")     )
  rename_keys.append(("""ln_vision.weight""", """vision_model.post_layernorm.weight""")     )
  rename_keys.append(("""ln_vision.bias""", """vision_model.post_layernorm.bias""")     )

  for i in range(config.vision_config.num_hidden_layers     ):
    rename_keys.append((f"visual_encoder.blocks.{i}.norm1.weight", f"vision_model.encoder.layers.{i}.layer_norm1.weight")     )
    rename_keys.append((f"visual_encoder.blocks.{i}.norm1.bias", f"vision_model.encoder.layers.{i}.layer_norm1.bias")     )
    rename_keys.append((f"visual_encoder.blocks.{i}.norm2.weight", f"vision_model.encoder.layers.{i}.layer_norm2.weight")     )
    rename_keys.append((f"visual_encoder.blocks.{i}.norm2.bias", f"vision_model.encoder.layers.{i}.layer_norm2.bias")     )
    rename_keys.append((f"visual_encoder.blocks.{i}.attn.qkv.weight", f"vision_model.encoder.layers.{i}.self_attn.qkv.weight")     )
    rename_keys.append((f"visual_encoder.blocks.{i}.attn.proj.weight", f"vision_model.encoder.layers.{i}.self_attn.projection.weight",)     )
    rename_keys.append((f"visual_encoder.blocks.{i}.attn.proj.bias", f"vision_model.encoder.layers.{i}.self_attn.projection.bias")     )
    rename_keys.append((f"visual_encoder.blocks.{i}.mlp.fc1.weight", f"vision_model.encoder.layers.{i}.mlp.fc1.weight")     )
    rename_keys.append((f"visual_encoder.blocks.{i}.mlp.fc1.bias", f"vision_model.encoder.layers.{i}.mlp.fc1.bias")     )
    rename_keys.append((f"visual_encoder.blocks.{i}.mlp.fc2.weight", f"vision_model.encoder.layers.{i}.mlp.fc2.weight")     )
    rename_keys.append((f"visual_encoder.blocks.{i}.mlp.fc2.bias", f"vision_model.encoder.layers.{i}.mlp.fc2.bias")     )

  # QFormer
  rename_keys.append(("""Qformer.bert.embeddings.LayerNorm.weight""", """qformer.layernorm.weight""")     )
  rename_keys.append(("""Qformer.bert.embeddings.LayerNorm.bias""", """qformer.layernorm.bias""")     )

  # fmt: on
  return rename_keys





def 							SCREAMING_SNAKE_CASE					(      _lowerCamelCase						:	str      ,_lowerCamelCase						:	List[str]      ,_lowerCamelCase						:	List[Any]     )	->				Any:
  _lowerCAmelCase						:	List[str]        =					dct.pop(_lowerCamelCase     )
  _lowerCAmelCase						:	Optional[int]        =					val





def 							SCREAMING_SNAKE_CASE					(      _lowerCamelCase						:	Optional[Any]      ,_lowerCamelCase						:	Dict     )	->				List[Any]:
  for i in range(config.vision_config.num_hidden_layers     ):
    # read in original q and v biases
    _lowerCAmelCase						:	int        =					state_dict.pop(f"visual_encoder.blocks.{i}.attn.q_bias"     )
    _lowerCAmelCase						:	Optional[Any]        =					state_dict.pop(f"visual_encoder.blocks.{i}.attn.v_bias"     )

    # next, set bias in the state dict
    _lowerCAmelCase						:	Optional[Any]        =					torch.cat((q_bias, torch.zeros_like(_lowerCamelCase      ,requires_grad=_lowerCamelCase     ), v_bias)     )
    _lowerCAmelCase						:	Optional[int]        =					qkv_bias





def 							SCREAMING_SNAKE_CASE					(      _lowerCamelCase						:	Optional[int]      ,_lowerCamelCase						:	int     )	->				Dict:
  _lowerCAmelCase						:	Tuple        =					364 if """coco""" in model_name else 224
  _lowerCAmelCase						:	Optional[int]        =					BlipaVisionConfig(image_size=_lowerCamelCase     ).to_dict()

  # make sure the models have proper bos_token_id and eos_token_id set (important for generation)
  # seems like flan-T5 models don't have bos_token_id properly set?
  if "opt-2.7b" in model_name:
    _lowerCAmelCase						:	Union[str, Any]        =					OPTConfig.from_pretrained("""facebook/opt-2.7b"""      ,eos_token_id=_lowerCamelCase     ).to_dict()
  elif "opt-6.7b" in model_name:
    _lowerCAmelCase						:	Tuple        =					OPTConfig.from_pretrained("""facebook/opt-6.7b"""      ,eos_token_id=_lowerCamelCase     ).to_dict()
  elif "t5-xl" in model_name:
    _lowerCAmelCase						:	Any        =					TaConfig.from_pretrained("""google/flan-t5-xl"""      ,dense_act_fn="""gelu"""      ,bos_token_id=1     ).to_dict()
  elif "t5-xxl" in model_name:
    _lowerCAmelCase						:	List[str]        =					TaConfig.from_pretrained("""google/flan-t5-xxl"""      ,dense_act_fn="""gelu"""      ,bos_token_id=1     ).to_dict()

  _lowerCAmelCase						:	int        =					BlipaConfig(vision_config=_lowerCamelCase      ,text_config=_lowerCamelCase     )

  return config, image_size





@torch.no_grad()
def 							SCREAMING_SNAKE_CASE					(      _lowerCamelCase						:	Union[str, Any]      ,_lowerCamelCase						:	Dict=None      ,_lowerCamelCase						:	int=False     )	->				Tuple:
  _lowerCAmelCase						:	Tuple        =					(
      AutoTokenizer.from_pretrained("""facebook/opt-2.7b"""     )
      if """opt""" in model_name
      else AutoTokenizer.from_pretrained("""google/flan-t5-xl"""     )
  )
  _lowerCAmelCase						:	Dict        =					tokenizer("""\n"""      ,add_special_tokens=_lowerCamelCase     ).input_ids[0]
  _lowerCAmelCase						:	List[str]        =					get_blipa_config(_lowerCamelCase      ,eos_token_id=_lowerCamelCase     )

  _lowerCAmelCase						:	int        =					BlipaForConditionalGeneration(_lowerCamelCase     ).eval()

  _lowerCAmelCase						:	Tuple        =					{
      """blip2-opt-2.7b""": ("""blip2_opt""", """pretrain_opt2.7b"""),
      """blip2-opt-6.7b""": ("""blip2_opt""", """pretrain_opt6.7b"""),
      """blip2-opt-2.7b-coco""": ("""blip2_opt""", """caption_coco_opt2.7b"""),
      """blip2-opt-6.7b-coco""": ("""blip2_opt""", """caption_coco_opt6.7b"""),
      """blip2-flan-t5-xl""": ("""blip2_t5""", """pretrain_flant5xl"""),
      """blip2-flan-t5-xl-coco""": ("""blip2_t5""", """caption_coco_flant5xl"""),
      """blip2-flan-t5-xxl""": ("""blip2_t5""", """pretrain_flant5xxl"""),
  }

  _lowerCAmelCase						:	Union[str, Any]        =					model_name_to_original[model_name]

  # load original model
  print("""Loading original model..."""     )
  _lowerCAmelCase						:	Optional[Any]        =					"""cuda""" if torch.cuda.is_available() else """cpu"""
  _lowerCAmelCase						:	int        =					load_model_and_preprocess(
      name=_lowerCamelCase      ,model_type=_lowerCamelCase      ,is_eval=_lowerCamelCase      ,device=_lowerCamelCase     )
  original_model.eval()
  print("""Done!"""     )

  # update state dict keys
  _lowerCAmelCase						:	str        =					original_model.state_dict()
  _lowerCAmelCase						:	Optional[int]        =					create_rename_keys(_lowerCamelCase     )
  for src, dest in rename_keys:
    rename_key(_lowerCamelCase      ,_lowerCamelCase      ,_lowerCamelCase     )

  # some keys can be renamed efficiently
  for key, val in state_dict.copy().items():
    _lowerCAmelCase						:	Any        =					state_dict.pop(_lowerCamelCase     )
    if key.startswith("""Qformer.bert"""     ):
      _lowerCAmelCase						:	Optional[Any]        =					key.replace("""Qformer.bert"""      ,"""qformer"""     )
    if "attention.self" in key:
      _lowerCAmelCase						:	Union[str, Any]        =					key.replace("""self"""      ,"""attention"""     )
    if "opt_proj" in key:
      _lowerCAmelCase						:	str        =					key.replace("""opt_proj"""      ,"""language_projection"""     )
    if "t5_proj" in key:
      _lowerCAmelCase						:	Union[str, Any]        =					key.replace("""t5_proj"""      ,"""language_projection"""     )
    if key.startswith("""opt"""     ):
      _lowerCAmelCase						:	Optional[int]        =					key.replace("""opt"""      ,"""language"""     )
    if key.startswith("""t5"""     ):
      _lowerCAmelCase						:	List[str]        =					key.replace("""t5"""      ,"""language"""     )
    _lowerCAmelCase						:	List[Any]        =					val

  # read in qv biases
  read_in_q_v_bias(_lowerCamelCase      ,_lowerCamelCase     )

  _lowerCAmelCase						:	Union[str, Any]        =					hf_model.load_state_dict(_lowerCamelCase      ,strict=_lowerCamelCase     )
  assert len(_lowerCamelCase     ) == 0
  assert unexpected_keys == ["qformer.embeddings.position_ids"]

  _lowerCAmelCase						:	Optional[Any]        =					load_demo_image()
  _lowerCAmelCase						:	List[str]        =					vis_processors["""eval"""](_lowerCamelCase     ).unsqueeze(0     ).to(_lowerCamelCase     )
  _lowerCAmelCase						:	Dict        =					tokenizer(["""\n"""]      ,return_tensors="""pt"""     ).input_ids.to(_lowerCamelCase     )

  # create processor
  _lowerCAmelCase						:	Union[str, Any]        =					BlipImageProcessor(
      size={"""height""": image_size, """width""": image_size}      ,image_mean=_lowerCamelCase      ,image_std=_lowerCamelCase     )
  _lowerCAmelCase						:	List[str]        =					BlipaProcessor(image_processor=_lowerCamelCase      ,tokenizer=_lowerCamelCase     )
  _lowerCAmelCase						:	Tuple        =					processor(images=_lowerCamelCase      ,return_tensors="""pt"""     ).pixel_values.to(_lowerCamelCase     )

  # make sure processor creates exact same pixel values
  assert torch.allclose(_lowerCamelCase      ,_lowerCamelCase     )

  original_model.to(_lowerCamelCase     )
  hf_model.to(_lowerCamelCase     )
  with torch.no_grad():
    if "opt" in model_name:
      _lowerCAmelCase						:	List[str]        =					original_model({"""image""": original_pixel_values, """text_input""": [""""""]}     ).logits
      _lowerCAmelCase						:	List[Any]        =					hf_model(_lowerCamelCase      ,_lowerCamelCase     ).logits
    else:
      _lowerCAmelCase						:	List[Any]        =					original_model(
          {"""image""": original_pixel_values, """text_input""": ["""\n"""], """text_output""": ["""\n"""]}     ).logits
      _lowerCAmelCase						:	Dict        =					input_ids.masked_fill(input_ids == tokenizer.pad_token_id      ,-100     )
      _lowerCAmelCase						:	Any        =					hf_model(_lowerCamelCase      ,_lowerCamelCase      ,labels=_lowerCamelCase     ).logits

  assert original_logits.shape == logits.shape
  print("""First values of original logits:"""      ,original_logits[0, :3, :3]     )
  print("""First values of HF logits:"""      ,logits[0, :3, :3]     )

  # assert values
  if model_name == "blip2-flan-t5-xl":
    _lowerCAmelCase						:	Optional[Any]        =					torch.tensor(
        [[-41.58_50, -4.44_40, -8.99_22], [-47.43_22, -5.91_43, -1.73_40]]      ,device=_lowerCamelCase     )
    assert torch.allclose(logits[0, :3, :3]      ,_lowerCamelCase      ,atol=1e-4     )
  elif model_name == "blip2-flan-t5-xl-coco":
    _lowerCAmelCase						:	List[str]        =					torch.tensor(
        [[-57.01_09, -9.89_67, -12.62_80], [-68.65_78, -12.71_91, -10.50_65]]      ,device=_lowerCamelCase     )
  else:
    # cast to same type
    _lowerCAmelCase						:	Optional[int]        =					logits.dtype
    assert torch.allclose(original_logits.to(_lowerCamelCase     )      ,_lowerCamelCase      ,atol=1e-2     )
  print("""Looks ok!"""     )

  print("""Generating a caption..."""     )
  _lowerCAmelCase						:	Optional[int]        =					""""""
  _lowerCAmelCase						:	Union[str, Any]        =					tokenizer(_lowerCamelCase      ,return_tensors="""pt"""     ).input_ids.to(_lowerCamelCase     )

  _lowerCAmelCase						:	Dict        =					original_model.generate({"""image""": original_pixel_values}     )
  _lowerCAmelCase						:	Dict        =					hf_model.generate(
      _lowerCamelCase      ,_lowerCamelCase      ,do_sample=_lowerCamelCase      ,num_beams=5      ,max_length=30      ,min_length=1      ,top_p=0.9      ,repetition_penalty=1.0      ,length_penalty=1.0      ,temperature=1      ,)
  print("""Original generation:"""      ,_lowerCamelCase     )
  _lowerCAmelCase						:	Union[str, Any]        =					input_ids.shape[1]
  _lowerCAmelCase						:	Dict        =					processor.batch_decode(outputs[:, prompt_length:]      ,skip_special_tokens=_lowerCamelCase     )
  _lowerCAmelCase						:	Dict        =					[text.strip() for text in output_text]
  print("""HF generation:"""      ,_lowerCamelCase     )

  if pytorch_dump_folder_path is not None:
    processor.save_pretrained(_lowerCamelCase     )
    hf_model.save_pretrained(_lowerCamelCase     )

  if push_to_hub:
    processor.push_to_hub(f"nielsr/{model_name}"     )
    hf_model.push_to_hub(f"nielsr/{model_name}"     )


if __name__ == "__main__":
       _a				:		Dict	   =       argparse.ArgumentParser()
       _a				:		List[Any]	   =       [
           'blip2-opt-2.7b',
           'blip2-opt-6.7b',
           'blip2-opt-2.7b-coco',
           'blip2-opt-6.7b-coco',
           'blip2-flan-t5-xl',
           'blip2-flan-t5-xl-coco',
           'blip2-flan-t5-xxl',
       ]
       parser.add_argument(
           '--model_name',
           default='blip2-opt-2.7b',
           choices=choices,
           type=str,
           help='Path to hf config.json of model to convert',
       )
       parser.add_argument('--pytorch_dump_folder_path', default=None, type=str, help='Path to the output PyTorch model.')
       parser.add_argument(
           '--push_to_hub',
           action='store_true',
           help='Whether to push the model and processor to the hub after converting',
       )

       _a				:		Tuple	   =       parser.parse_args()

       convert_blipa_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.push_to_hub)





"""simple docstring"""




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:
     _a				:		Tuple	   =       None


_a				:		str	   =       logging.get_logger(__name__)


_a				:		str	   =       {'vocab_file': 'sentencepiece.bpe.model', 'tokenizer_file': 'tokenizer.json'}

_a				:		Tuple	   =       {
    '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'
        ),
    },
}

_a				:		int	   =       {
    'facebook/nllb-large-en-ro': 1_024,
    'facebook/nllb-200-distilled-600M': 1_024,
}

# fmt: off
_a				:		Union[str, Any]	   =       ['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 						__A							(			SCREAMING_SNAKE_CASE_		):
     _UpperCamelCase							:      Union[str, Any]		 =		VOCAB_FILES_NAMES
     _UpperCamelCase							:      Union[str, Any]		 =		PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
     _UpperCamelCase							:      Dict		 =		PRETRAINED_VOCAB_FILES_MAP
     _UpperCamelCase							:      str		 =		["input_ids", "attention_mask"]
     _UpperCamelCase							:      Tuple		 =		NllbTokenizer

     _UpperCamelCase							:      List[int]		 =		[]
     _UpperCamelCase							:      List[int]		 =		[]






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

       _lowerCAmelCase						:	List[Any]        =					vocab_file
       _lowerCAmelCase						:	List[str]        =					False if not self.vocab_file else True

       _lowerCAmelCase						:	List[str]        =					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						:	Tuple        =					{
           lang_code: self.convert_tokens_to_ids(a__  ) for lang_code in FAIRSEQ_LANGUAGE_CODES
       }

       _lowerCAmelCase						:	List[str]        =					src_lang if src_lang is not None else """eng_Latn"""
       _lowerCAmelCase						:	int        =					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  ):
       return self._src_lang






     @src_lang.setter
     def    __A  (		self    ,					a__  ):
       _lowerCAmelCase						:	List[Any]        =					new_src_lang
       self.set_src_lang_special_tokens(self._src_lang  )






     def    __A  (		self    ,					a__    ,					a__ = None  ):
       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    ,					a__    ,					a__ = None  ):
       _lowerCAmelCase						:	Tuple        =					[self.sep_token_id]
       _lowerCAmelCase						:	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    __A  (		self    ,					a__    ,					a__    ,					a__    ,					a__    ,					**a__  ):
       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						:	str        =					src_lang
       _lowerCAmelCase						:	List[Any]        =					self(a__    ,					add_special_tokens=a__    ,					return_tensors=a__    ,					**a__  )
       _lowerCAmelCase						:	Dict        =					self.convert_tokens_to_ids(a__  )
       _lowerCAmelCase						:	Dict        =					tgt_lang_id
       return inputs






     def    __A  (		self    ,					a__    ,					a__ = "eng_Latn"    ,					a__ = None    ,					a__ = "fra_Latn"    ,					**a__    ,					):
       _lowerCAmelCase						:	List[Any]        =					src_lang
       _lowerCAmelCase						:	Optional[int]        =					tgt_lang
       return super().prepare_seqaseq_batch(a__    ,					a__    ,					**a__  )






     def    __A  (		self  ):
       return self.set_src_lang_special_tokens(self.src_lang  )






     def    __A  (		self  ):
       return self.set_tgt_lang_special_tokens(self.tgt_lang  )






     def    __A  (		self    ,					a__  ):
       _lowerCAmelCase						:	Optional[Any]        =					self.convert_tokens_to_ids(a__  )

       if self.legacy_behaviour:
         _lowerCAmelCase						:	Optional[int]        =					[]
         _lowerCAmelCase						:	Union[str, Any]        =					[self.eos_token_id, self.cur_lang_code]
       else:
         _lowerCAmelCase						:	str        =					[self.cur_lang_code]
         _lowerCAmelCase						:	List[Any]        =					[self.eos_token_id]

       _lowerCAmelCase						:	List[Any]        =					self.convert_ids_to_tokens(self.prefix_tokens  )
       _lowerCAmelCase						:	Any        =					self.convert_ids_to_tokens(self.suffix_tokens  )

       _lowerCAmelCase						:	Optional[Any]        =					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    ,					a__  ):
       _lowerCAmelCase						:	Any        =					self.convert_tokens_to_ids(a__  )
       if self.legacy_behaviour:
         _lowerCAmelCase						:	Optional[int]        =					[]
         _lowerCAmelCase						:	str        =					[self.eos_token_id, self.cur_lang_code]
       else:
         _lowerCAmelCase						:	int        =					[self.cur_lang_code]
         _lowerCAmelCase						:	Optional[int]        =					[self.eos_token_id]

       _lowerCAmelCase						:	Union[str, Any]        =					self.convert_ids_to_tokens(self.prefix_tokens  )
       _lowerCAmelCase						:	int        =					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    ,					a__    ,					a__ = None  ):
       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(a__  ):
         logger.error(F"Vocabulary path ({save_directory}) should be a directory."  )
         return
       _lowerCAmelCase						:	Optional[Any]        =					os.path.join(
           a__    ,					(filename_prefix + """-""" if filename_prefix else """""") + VOCAB_FILES_NAMES["""vocab_file"""]  )

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

       return (out_vocab_file,)




