

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:
     lowercase_       		=  None


lowercase_       		=  logging.get_logger(__name__)


lowercase_       		=  {"vocab_file": "sentencepiece.bpe.model", "tokenizer_file": "tokenizer.json"}

lowercase_       		=  {
    "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"
        ),
    },
}

lowercase_       		=  {
    "facebook/nllb-large-en-ro": 1024,
    "facebook/nllb-200-distilled-600M": 1024,
}

# fmt: off
lowercase_       		=  ["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						(							_UpperCAmelCase ):







    """simple docstring"""





    lowerCamelCase			=							VOCAB_FILES_NAMES
    lowerCamelCase			=							PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
    lowerCamelCase			=							PRETRAINED_VOCAB_FILES_MAP
    lowerCamelCase			=							['input_ids', 'attention_mask']
    lowerCamelCase			=							NllbTokenizer

    lowerCamelCase			=							[]
    lowerCamelCase			=							[]


    def __init__(      self :       Optional[Any],lowercase_ :       List[str]=None,lowercase_ :       Union[str, Any]=None,lowercase_ :       List[str]="<s>",lowercase_ :       Tuple="</s>",lowercase_ :       List[Any]="</s>",lowercase_ :       Dict="<s>",lowercase_ :       Union[str, Any]="<unk>",lowercase_ :       Union[str, Any]="<pad>",lowercase_ :       int="<mask>",lowercase_ :       Tuple=None,lowercase_ :       Union[str, Any]=None,lowercase_ :       Optional[Any]=None,lowercase_ :       int=False,**lowercase_ :       List[str],)->    int:





        '''simple docstring'''







        A__          =   AddedToken(lowercase_,lstrip=lowercase_,rstrip=lowercase_		) if isinstance(lowercase_,lowercase_		) else mask_token
        A__          =   legacy_behaviour
        super().__init__(
            vocab_file=lowercase_,tokenizer_file=lowercase_,bos_token=lowercase_,eos_token=lowercase_,sep_token=lowercase_,cls_token=lowercase_,unk_token=lowercase_,pad_token=lowercase_,mask_token=lowercase_,src_lang=lowercase_,tgt_lang=lowercase_,additional_special_tokens=lowercase_,legacy_behaviour=lowercase_,**lowercase_,)

        A__          =   vocab_file
        A__          =   False if not self.vocab_file else True

        A__          =   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}		)
        A__          =   {
            lang_code: self.convert_tokens_to_ids(lowercase_		) for lang_code in FAIRSEQ_LANGUAGE_CODES
        }

        A__          =   src_lang if src_lang is not None else 'eng_Latn'
        A__          =   self.convert_tokens_to_ids(self._src_lang		)
        A__          =   tgt_lang
        self.set_src_lang_special_tokens(self._src_lang		)


    @property
    def 						snake_case__     (      self :       Union[str, Any]		)->    str:





        '''simple docstring'''







        return self._src_lang


    @src_lang.setter
    def 						snake_case__     (      self :       List[Any],lowercase_ :       str		)->    None:





        '''simple docstring'''







        A__          =   new_src_lang
        self.set_src_lang_special_tokens(self._src_lang		)


    def 						snake_case__     (      self :       Any,lowercase_ :       List[int],lowercase_ :       Optional[List[int]] = None		)->    List[int]:





        '''simple docstring'''







        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 						snake_case__     (      self :       str,lowercase_ :       List[int],lowercase_ :       Optional[List[int]] = None		)->    List[int]:





        '''simple docstring'''







        A__          =   [self.sep_token_id]
        A__          =   [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 						snake_case__     (      self :       Optional[int],lowercase_ :       Dict,lowercase_ :       str,lowercase_ :       Optional[str],lowercase_ :       Optional[str],**lowercase_ :       Tuple		)->    Optional[Any]:





        '''simple docstring'''







        if src_lang is None or tgt_lang is None:
            raise ValueError('Translation requires a `src_lang` and a `tgt_lang` for this model'		)
        A__          =   src_lang
        A__          =   self(lowercase_,add_special_tokens=lowercase_,return_tensors=lowercase_,**lowercase_		)
        A__          =   self.convert_tokens_to_ids(lowercase_		)
        A__          =   tgt_lang_id
        return inputs


    def 						snake_case__     (      self :       Dict,lowercase_ :       List[str],lowercase_ :       str = "eng_Latn",lowercase_ :       Optional[List[str]] = None,lowercase_ :       str = "fra_Latn",**lowercase_ :       Tuple,)->    BatchEncoding:





        '''simple docstring'''







        A__          =   src_lang
        A__          =   tgt_lang
        return super().prepare_seqaseq_batch(lowercase_,lowercase_,**lowercase_		)


    def 						snake_case__     (      self :       Dict		)->    Any:





        '''simple docstring'''







        return self.set_src_lang_special_tokens(self.src_lang		)


    def 						snake_case__     (      self :       Optional[int]		)->    int:





        '''simple docstring'''







        return self.set_tgt_lang_special_tokens(self.tgt_lang		)


    def 						snake_case__     (      self :       str,lowercase_ :       Tuple		)->    None:





        '''simple docstring'''







        A__          =   self.convert_tokens_to_ids(lowercase_		)

        if self.legacy_behaviour:
            A__          =   []
            A__          =   [self.eos_token_id, self.cur_lang_code]
        else:
            A__          =   [self.cur_lang_code]
            A__          =   [self.eos_token_id]

        A__          =   self.convert_ids_to_tokens(self.prefix_tokens		)
        A__          =   self.convert_ids_to_tokens(self.suffix_tokens		)

        A__          =   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 						snake_case__     (      self :       Optional[Any],lowercase_ :       str		)->    None:





        '''simple docstring'''







        A__          =   self.convert_tokens_to_ids(lowercase_		)
        if self.legacy_behaviour:
            A__          =   []
            A__          =   [self.eos_token_id, self.cur_lang_code]
        else:
            A__          =   [self.cur_lang_code]
            A__          =   [self.eos_token_id]

        A__          =   self.convert_ids_to_tokens(self.prefix_tokens		)
        A__          =   self.convert_ids_to_tokens(self.suffix_tokens		)

        A__          =   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 						snake_case__     (      self :       Dict,lowercase_ :       str,lowercase_ :       Optional[str] = None		)->    Tuple[str]:





        '''simple docstring'''







        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(lowercase_		):
            logger.error(F'Vocabulary path ({save_directory}) should be a directory.'		)
            return
        A__          =   os.path.join(
            lowercase_,(filename_prefix + '-' if filename_prefix else '') + VOCAB_FILES_NAMES['vocab_file']		)

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

        return (out_vocab_file,)




import argparse
from pathlib import Path

import torch
from packaging import version
from torch.onnx import export

from diffusers import AutoencoderKL


lowercase_       		=  version.parse(version.parse(torch.__version__).base_version) < version.parse("1.11")
def 						_snake_case(    SCREAMING_SNAKE_CASE__			:	Dict    ,			SCREAMING_SNAKE_CASE__			:	tuple    ,			SCREAMING_SNAKE_CASE__			:	Path    ,			SCREAMING_SNAKE_CASE__			:	List[Any]    ,			SCREAMING_SNAKE_CASE__			:	Any    ,			SCREAMING_SNAKE_CASE__			:	Optional[int]    ,			SCREAMING_SNAKE_CASE__			:	Tuple    ,			SCREAMING_SNAKE_CASE__			:	Union[str, Any]=False    ,			)							->      Union[str, Any]:





    '''simple docstring'''






    output_path.parent.mkdir(parents=SCREAMING_SNAKE_CASE__    ,			exist_ok=SCREAMING_SNAKE_CASE__      )
    # PyTorch deprecated the `enable_onnx_checker` and `use_external_data_format` arguments in v1.11,
    # so we check the torch version for backwards compatibility
    if is_torch_less_than_1_11:
        export(
            SCREAMING_SNAKE_CASE__    ,			SCREAMING_SNAKE_CASE__    ,			f=output_path.as_posix()    ,			input_names=SCREAMING_SNAKE_CASE__    ,			output_names=SCREAMING_SNAKE_CASE__    ,			dynamic_axes=SCREAMING_SNAKE_CASE__    ,			do_constant_folding=SCREAMING_SNAKE_CASE__    ,			use_external_data_format=SCREAMING_SNAKE_CASE__    ,			enable_onnx_checker=SCREAMING_SNAKE_CASE__    ,			opset_version=SCREAMING_SNAKE_CASE__    ,			)
    else:
        export(
            SCREAMING_SNAKE_CASE__    ,			SCREAMING_SNAKE_CASE__    ,			f=output_path.as_posix()    ,			input_names=SCREAMING_SNAKE_CASE__    ,			output_names=SCREAMING_SNAKE_CASE__    ,			dynamic_axes=SCREAMING_SNAKE_CASE__    ,			do_constant_folding=SCREAMING_SNAKE_CASE__    ,			opset_version=SCREAMING_SNAKE_CASE__    ,			)
@torch.no_grad()
def 						_snake_case(    SCREAMING_SNAKE_CASE__			:	str    ,			SCREAMING_SNAKE_CASE__			:	str    ,			SCREAMING_SNAKE_CASE__			:	int    ,			SCREAMING_SNAKE_CASE__			:	bool = False      )							->      Tuple:





    '''simple docstring'''






    A__          =   torch.floataa if fpaa else torch.floataa
    if fpaa and torch.cuda.is_available():
        A__          =   'cuda'
    elif fpaa and not torch.cuda.is_available():
        raise ValueError('`float16` model export is only supported on GPUs with CUDA'      )
    else:
        A__          =   'cpu'
    A__          =   Path(SCREAMING_SNAKE_CASE__      )

    # VAE DECODER
    A__          =   AutoencoderKL.from_pretrained(model_path + '/vae'      )
    A__          =   vae_decoder.config.latent_channels
    # forward only through the decoder part
    A__          =   vae_decoder.decode
    onnx_export(
        SCREAMING_SNAKE_CASE__    ,			model_args=(
            torch.randn(1    ,			SCREAMING_SNAKE_CASE__    ,			25    ,			25      ).to(device=SCREAMING_SNAKE_CASE__    ,			dtype=SCREAMING_SNAKE_CASE__      ),
            False,
        )    ,			output_path=output_path / 'vae_decoder' / 'model.onnx'    ,			ordered_input_names=['latent_sample', 'return_dict']    ,			output_names=['sample']    ,			dynamic_axes={
            'latent_sample': {0: 'batch', 1: 'channels', 2: 'height', 3: 'width'},
        }    ,			opset=SCREAMING_SNAKE_CASE__    ,			)
    del vae_decoder


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

     parser.add_argument(
         "--model_path",
         type=str,
         required=True,
         help="Path to the `diffusers` checkpoint to convert (either a local directory or on the Hub).",
     )

     parser.add_argument("--output_path", type=str, required=True, help="Path to the output model.")
     parser.add_argument(
         "--opset",
         default=14,
         type=int,
         help="The version of the ONNX operator set to use.",
     )
     parser.add_argument("--fp16", action="store_true", default=False, help="Export the models in `float16` mode")

     lowercase_       		=  parser.parse_args()
     print(args.output_path)
     convert_models(args.model_path, args.output_path, args.opset, args.fpaa)
     print("SD: Done: ONNX")

