"""simple docstring"""




import os
from glob import glob

import imageio
import torch
import torchvision
import wandb
from img_processing import custom_to_pil, loop_post_process, preprocess, preprocess_vqgan
from loaders import load_vqgan
from PIL import Image
from torch import nn

from transformers import CLIPModel, CLIPTokenizerFast
from utils import get_device, get_timestamp, show_pil
class 						__A							:






     def __init__(		self    ,					a__ = "cpu"    ,					a__ = "openai/clip-vit-large-patch14"  ):
       _lowerCAmelCase						:	Optional[int]        =					device
       _lowerCAmelCase						:	Tuple        =					CLIPTokenizerFast.from_pretrained(a__  )
       _lowerCAmelCase						:	str        =					[0.4_8_1_4_5_4_6_6, 0.4_5_7_8_2_7_5, 0.4_0_8_2_1_0_7_3]
       _lowerCAmelCase						:	List[Any]        =					[0.2_6_8_6_2_9_5_4, 0.2_6_1_3_0_2_5_8, 0.2_7_5_7_7_7_1_1]
       _lowerCAmelCase						:	Any        =					torchvision.transforms.Normalize(self.image_mean    ,					self.image_std  )
       _lowerCAmelCase						:	Union[str, Any]        =					torchvision.transforms.Resize(224  )
       _lowerCAmelCase						:	Dict        =					torchvision.transforms.CenterCrop(224  )






     def    __A  (		self    ,					a__  ):
       _lowerCAmelCase						:	str        =					self.resize(a__  )
       _lowerCAmelCase						:	Optional[Any]        =					self.center_crop(a__  )
       _lowerCAmelCase						:	List[str]        =					self.normalize(a__  )
       return images






     def __call__(		self    ,					a__=None    ,					a__=None    ,					**a__  ):
       _lowerCAmelCase						:	Optional[Any]        =					self.tokenizer(text=a__    ,					**a__  )
       _lowerCAmelCase						:	str        =					self.preprocess_img(a__  )
       _lowerCAmelCase						:	Tuple        =					{key: value.to(self.device  ) for (key, value) in encoding.items()}
       return encoding







class 						__A							(			nn.Module		):






     def __init__(		self    ,					a__=10    ,					a__=0.0_1    ,					a__=None    ,					a__=None    ,					a__=None    ,					a__=None    ,					a__=None    ,					a__=None    ,					a__=False    ,					a__=True    ,					a__="image"    ,					a__=True    ,					a__=False    ,					a__=False    ,					a__=False    ,					):
       super().__init__()
       _lowerCAmelCase						:	List[str]        =					None
       _lowerCAmelCase						:	Optional[Any]        =					device if device else get_device()
       if vqgan:
         _lowerCAmelCase						:	Optional[int]        =					vqgan
       else:
         _lowerCAmelCase						:	Optional[Any]        =					load_vqgan(self.device    ,					conf_path=a__    ,					ckpt_path=a__  )
       self.vqgan.eval()
       if clip:
         _lowerCAmelCase						:	Tuple        =					clip
       else:
         _lowerCAmelCase						:	List[Any]        =					CLIPModel.from_pretrained("""openai/clip-vit-base-patch32"""  )
       self.clip.to(self.device  )
       _lowerCAmelCase						:	Optional[int]        =					ProcessorGradientFlow(device=self.device  )

       _lowerCAmelCase						:	Dict        =					iterations
       _lowerCAmelCase						:	int        =					lr
       _lowerCAmelCase						:	Tuple        =					log
       _lowerCAmelCase						:	List[str]        =					make_grid
       _lowerCAmelCase						:	List[Any]        =					return_val
       _lowerCAmelCase						:	Any        =					quantize
       _lowerCAmelCase						:	Optional[Any]        =					self.vqgan.decoder.z_shape






     def    __A  (		self    ,					a__=None    ,					a__=None    ,					a__=5    ,					a__=True  ):
       _lowerCAmelCase						:	List[Any]        =					[]
       if output_path is None:
         _lowerCAmelCase						:	Any        =					"""./animation.gif"""
       if input_path is None:
         _lowerCAmelCase						:	int        =					self.save_path
       _lowerCAmelCase						:	Any        =					sorted(glob(input_path + """/*"""  )  )
       if not len(a__  ):
         raise ValueError(
             """No images found in save path, aborting (did you pass save_intermediate=True to the generate"""
             """ function?)"""  )
       if len(a__  ) == 1:
         print("""Only one image found in save path, (did you pass save_intermediate=True to the generate function?)"""  )
       _lowerCAmelCase						:	str        =					total_duration / len(a__  )
       _lowerCAmelCase						:	int        =					[frame_duration] * len(a__  )
       if extend_frames:
         _lowerCAmelCase						:	List[str]        =					1.5
         _lowerCAmelCase						:	List[Any]        =					3
       for file_name in paths:
         if file_name.endswith(""".png"""  ):
           images.append(imageio.imread(a__  )  )
       imageio.mimsave(a__    ,					a__    ,					duration=a__  )
       print(F"gif saved to {output_path}"  )






     def    __A  (		self    ,					a__=None    ,					a__=None  ):
       if not (path or img):
         raise ValueError("""Input either path or tensor"""  )
       if img is not None:
         raise NotImplementedError
       _lowerCAmelCase						:	Optional[Any]        =					preprocess(Image.open(a__  )    ,					target_image_size=256  ).to(self.device  )
       _lowerCAmelCase						:	List[str]        =					preprocess_vqgan(a__  )
       _lowerCAmelCase						:	str        =					self.vqgan.encode(a__  )
       return z






     def    __A  (		self    ,					a__  ):
       _lowerCAmelCase						:	Tuple        =					self.latent.detach().requires_grad_()
       _lowerCAmelCase						:	Optional[Any]        =					base_latent + transform_vector
       if self.quantize:
         _lowerCAmelCase						:	Union[str, Any]        =					self.vqgan.quantize(a__  )
       else:
         _lowerCAmelCase						:	List[str]        =					trans_latent
       return self.vqgan.decode(a__  )






     def    __A  (		self    ,					a__    ,					a__    ,					a__=None  ):
       _lowerCAmelCase						:	Dict        =					self.clip_preprocessor(text=a__    ,					images=a__    ,					return_tensors="""pt"""    ,					padding=a__  )
       _lowerCAmelCase						:	List[Any]        =					self.clip(**a__  )
       _lowerCAmelCase						:	Optional[Any]        =					clip_outputs.logits_per_image
       if weights is not None:
         _lowerCAmelCase						:	Union[str, Any]        =					similarity_logits * weights
       return similarity_logits.sum()






     def    __A  (		self    ,					a__    ,					a__    ,					a__  ):
       _lowerCAmelCase						:	Tuple        =					self._get_clip_similarity(pos_prompts["""prompts"""]    ,					a__    ,					weights=(1 / pos_prompts["""weights"""])  )
       if neg_prompts:
         _lowerCAmelCase						:	Any        =					self._get_clip_similarity(neg_prompts["""prompts"""]    ,					a__    ,					weights=neg_prompts["""weights"""]  )
       else:
         _lowerCAmelCase						:	List[str]        =					torch.tensor([1]    ,					device=self.device  )
       _lowerCAmelCase						:	Any        =					-torch.log(a__  ) + torch.log(a__  )
       return loss






     def    __A  (		self    ,					a__    ,					a__    ,					a__  ):
       _lowerCAmelCase						:	Union[str, Any]        =					torch.randn_like(self.latent    ,					requires_grad=a__    ,					device=self.device  )
       _lowerCAmelCase						:	Optional[Any]        =					torch.optim.Adam([vector]    ,					lr=self.lr  )

       for i in range(self.iterations  ):
         optim.zero_grad()
         _lowerCAmelCase						:	Dict        =					self._add_vector(a__  )
         _lowerCAmelCase						:	Any        =					loop_post_process(a__  )
         _lowerCAmelCase						:	Dict        =					self._get_CLIP_loss(a__    ,					a__    ,					a__  )
         print("""CLIP loss"""    ,					a__  )
         if self.log:
           wandb.log({"""CLIP Loss""": clip_loss}  )
         clip_loss.backward(retain_graph=a__  )
         optim.step()
         if self.return_val == "image":
           yield custom_to_pil(transformed_img[0]  )
         else:
           yield vector






     def    __A  (		self    ,					a__    ,					a__    ,					a__  ):
       wandb.init(reinit=a__    ,					project="""face-editor"""  )
       wandb.config.update({"""Positive Prompts""": positive_prompts}  )
       wandb.config.update({"""Negative Prompts""": negative_prompts}  )
       wandb.config.update({"""lr""": self.lr, """iterations""": self.iterations}  )
       if image_path:
         _lowerCAmelCase						:	List[str]        =					Image.open(a__  )
         _lowerCAmelCase						:	int        =					image.resize((256, 256)  )
         wandb.log("""Original Image"""    ,					wandb.Image(a__  )  )






     def    __A  (		self    ,					a__  ):
       if not prompts:
         return []
       _lowerCAmelCase						:	Dict        =					[]
       _lowerCAmelCase						:	str        =					[]
       if isinstance(a__    ,					a__  ):
         _lowerCAmelCase						:	Dict        =					[prompt.strip() for prompt in prompts.split("""|"""  )]
       for prompt in prompts:
         if isinstance(a__    ,					(tuple, list)  ):
           _lowerCAmelCase						:	List[str]        =					prompt[0]
           _lowerCAmelCase						:	List[Any]        =					float(prompt[1]  )
         elif ":" in prompt:
           _lowerCAmelCase						:	Optional[int]        =					prompt.split(""":"""  )
           _lowerCAmelCase						:	Tuple        =					float(a__  )
         else:
           _lowerCAmelCase						:	List[str]        =					prompt
           _lowerCAmelCase						:	Dict        =					1.0
         processed_prompts.append(a__  )
         weights.append(a__  )
       return {
           "prompts": processed_prompts,
           "weights": torch.tensor(a__    ,					device=self.device  ),
       }






     def    __A  (		self    ,					a__    ,					a__=None    ,					a__=None    ,					a__=True    ,					a__=False    ,					a__=True    ,					a__=True    ,					a__=None    ,					):
       if image_path:
         _lowerCAmelCase						:	List[Any]        =					self._get_latent(a__  )
       else:
         _lowerCAmelCase						:	Optional[int]        =					torch.randn(self.latent_dim    ,					device=self.device  )
       if self.log:
         self._init_logging(a__    ,					a__    ,					a__  )

       assert pos_prompts, "You must provide at least one positive prompt."
       _lowerCAmelCase						:	Any        =					self.process_prompts(a__  )
       _lowerCAmelCase						:	Optional[Any]        =					self.process_prompts(a__  )
       if save_final and save_path is None:
         _lowerCAmelCase						:	Optional[Any]        =					os.path.join("""./outputs/"""    ,					"""_""".join(pos_prompts["""prompts"""]  )  )
       if not os.path.exists(a__  ):
         os.makedirs(a__  )
       else:
         _lowerCAmelCase						:	str        =					save_path + """_""" + get_timestamp()
         os.makedirs(a__  )
       _lowerCAmelCase						:	Optional[int]        =					save_path

       _lowerCAmelCase						:	Optional[int]        =					self.vqgan.decode(self.latent  )[0]
       if show_intermediate:
         print("""Original Image"""  )
         show_pil(custom_to_pil(a__  )  )

       _lowerCAmelCase						:	List[str]        =					loop_post_process(a__  )
       for iter, transformed_img in enumerate(self._optimize_CLIP(a__    ,					a__    ,					a__  )  ):
         if show_intermediate:
           show_pil(a__  )
         if save_intermediate:
           transformed_img.save(os.path.join(self.save_path    ,					F"iter_{iter:03d}.png"  )  )
         if self.log:
           wandb.log({"""Image""": wandb.Image(a__  )}  )
       if show_final:
         show_pil(a__  )
       if save_final:
         transformed_img.save(os.path.join(self.save_path    ,					F"iter_{iter:03d}_final.png"  )  )





"""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,)




