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

import sentencepiece as spm

from ...tokenization_utils import AddedToken, BatchEncoding, PreTrainedTokenizer
from ...utils import logging


lowerCamelCase      =logging.get_logger(__name__)

lowerCamelCase      ="▁"

lowerCamelCase      ={"vocab_file": "sentencepiece.bpe.model"}

lowerCamelCase      ={
    "vocab_file": {
        "facebook/nllb-200-distilled-600M": (
            "https://huggingface.co/facebook/nllb-200-distilled-600M/blob/main/sentencepiece.bpe.model"
        ),
    }
}

lowerCamelCase      ={
    "facebook/nllb-200-distilled-600M": 1_0_2_4,
}

# fmt: off
lowerCamelCase      =["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_ ):





     """simple docstring"""







     SCREAMING_SNAKE_CASE_		   =  VOCAB_FILES_NAMES
     SCREAMING_SNAKE_CASE_		   =  PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
     SCREAMING_SNAKE_CASE_		   =  PRETRAINED_VOCAB_FILES_MAP
     SCREAMING_SNAKE_CASE_		   =  ['''input_ids''', '''attention_mask''']

     SCREAMING_SNAKE_CASE_		   =  []
     SCREAMING_SNAKE_CASE_		   =  []






     def __init__(							self						,						__SCREAMING_SNAKE_CASE						,						__SCREAMING_SNAKE_CASE="<s>"						,						__SCREAMING_SNAKE_CASE="</s>"						,						__SCREAMING_SNAKE_CASE="</s>"						,						__SCREAMING_SNAKE_CASE="<s>"						,						__SCREAMING_SNAKE_CASE="<unk>"						,						__SCREAMING_SNAKE_CASE="<pad>"						,						__SCREAMING_SNAKE_CASE="<mask>"						,						__SCREAMING_SNAKE_CASE=None						,						__SCREAMING_SNAKE_CASE=None						,						__SCREAMING_SNAKE_CASE=None						,						__SCREAMING_SNAKE_CASE = None						,						__SCREAMING_SNAKE_CASE=None						,						__SCREAMING_SNAKE_CASE=False						,						**__SCREAMING_SNAKE_CASE						,						)  ->   Optional[int]:




           """simple docstring"""







           UpperCamelCase__    :   Optional[Any]				=				AddedToken(__SCREAMING_SNAKE_CASE						,						lstrip=__SCREAMING_SNAKE_CASE						,						rstrip=__SCREAMING_SNAKE_CASE  ) if isinstance(__SCREAMING_SNAKE_CASE						,						__SCREAMING_SNAKE_CASE  ) else mask_token

           UpperCamelCase__    :   Union[str, Any]				=				{} if sp_model_kwargs is None else sp_model_kwargs
           UpperCamelCase__    :   Union[str, Any]				=				legacy_behaviour

           super().__init__(
               bos_token=__SCREAMING_SNAKE_CASE						,						eos_token=__SCREAMING_SNAKE_CASE						,						unk_token=__SCREAMING_SNAKE_CASE						,						sep_token=__SCREAMING_SNAKE_CASE						,						cls_token=__SCREAMING_SNAKE_CASE						,						pad_token=__SCREAMING_SNAKE_CASE						,						mask_token=__SCREAMING_SNAKE_CASE						,						tokenizer_file=__SCREAMING_SNAKE_CASE						,						src_lang=__SCREAMING_SNAKE_CASE						,						tgt_lang=__SCREAMING_SNAKE_CASE						,						additional_special_tokens=__SCREAMING_SNAKE_CASE						,						sp_model_kwargs=self.sp_model_kwargs						,						legacy_behaviour=__SCREAMING_SNAKE_CASE						,						**__SCREAMING_SNAKE_CASE						,						)

           UpperCamelCase__    :   Tuple				=				spm.SentencePieceProcessor(**self.sp_model_kwargs  )
           self.sp_model.Load(str(__SCREAMING_SNAKE_CASE  )  )
           UpperCamelCase__    :   Any				=				vocab_file
           # Original fairseq vocab and spm vocab must be "aligned":
           # Vocab    |    0    |    1    |   2    |    3    |  4   |  5   |  6   |   7  |   8  |  9
           # -------- | ------- | ------- | ------ | ------- | ---- | ---- | ---- | ---- | ---- | ----
           # fairseq  | '<s>'   | '<pad>' | '</s>' | '<unk>' | 'an' | '▁n' | '▁m' | '▁t' | '▁k' | '▁a'
           # spm      | '<unk>' | '<s>'   | '</s>' | 'an'    | '▁n' | '▁m' | '▁t' | '▁k' | '▁a' | '▁s'

           # Mimic fairseq token-to-id alignment for the first 4 token
           UpperCamelCase__    :   List[str]				=				{'''<s>''': 0, '''<pad>''': 1, '''</s>''': 2, '''<unk>''': 3}

           # The first "real" token "," has position 4 in the original fairseq vocab and position 3 in the spm vocab
           UpperCamelCase__    :   str				=				1

           UpperCamelCase__    :   str				=				len(self.sp_model  )
           UpperCamelCase__    :   int				=				{
               code: self.sp_model_size + i + self.fairseq_offset for i, code in enumerate(__SCREAMING_SNAKE_CASE  )
           }
           UpperCamelCase__    :   Union[str, Any]				=				{v: k for k, v in self.lang_code_to_id.items()}
           UpperCamelCase__    :   Optional[int]				=				len(self.sp_model  ) + len(self.lang_code_to_id  ) + self.fairseq_offset

           self.fairseq_tokens_to_ids.update(self.lang_code_to_id  )
           UpperCamelCase__    :   Optional[Any]				=				{v: k for k, v in self.fairseq_tokens_to_ids.items()}
           UpperCamelCase__    :   Any				=				list(self.lang_code_to_id.keys()  )

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

           UpperCamelCase__    :   Union[str, Any]				=				src_lang if src_lang is not None else '''eng_Latn'''
           UpperCamelCase__    :   List[str]				=				self.lang_code_to_id[self._src_lang]
           UpperCamelCase__    :   Union[str, Any]				=				tgt_lang
           self.set_src_lang_special_tokens(self._src_lang  )






     def __getstate__(							self  )  ->   Union[str, Any]:




           """simple docstring"""







           UpperCamelCase__    :   Union[str, Any]				=				self.__dict__.copy()
           UpperCamelCase__    :   Optional[Any]				=				None
           UpperCamelCase__    :   Tuple				=				self.sp_model.serialized_model_proto()
           return state






     def __setstate__(							self						,						__SCREAMING_SNAKE_CASE  )  ->   int:




           """simple docstring"""







           UpperCamelCase__    :   Union[str, Any]				=				d

           # for backward compatibility
           if not hasattr(self						,						'''sp_model_kwargs'''  ):
                 UpperCamelCase__    :   Optional[int]				=				{}

           UpperCamelCase__    :   str				=				spm.SentencePieceProcessor(**self.sp_model_kwargs  )
           self.sp_model.LoadFromSerializedProto(self.sp_model_proto  )






     @property
     def    __SCREAMING_SNAKE_CASE			(							self  )  ->   int:




           """simple docstring"""







           return len(self.sp_model  ) + len(self.lang_code_to_id  ) + self.fairseq_offset + 1  # Plus 1 for the mask token






     @property
     def    __SCREAMING_SNAKE_CASE			(							self  )  ->   str:




           """simple docstring"""







           return self._src_lang






     @src_lang.setter
     def    __SCREAMING_SNAKE_CASE			(							self						,						__SCREAMING_SNAKE_CASE  )  ->   None:




           """simple docstring"""







           UpperCamelCase__    :   Optional[Any]				=				new_src_lang
           self.set_src_lang_special_tokens(self._src_lang  )






     def    __SCREAMING_SNAKE_CASE			(							self						,						__SCREAMING_SNAKE_CASE						,						__SCREAMING_SNAKE_CASE = None						,						__SCREAMING_SNAKE_CASE = False  )  ->   List[int]:




           """simple docstring"""







           if already_has_special_tokens:
                 return super().get_special_tokens_mask(
                     token_ids_a=__SCREAMING_SNAKE_CASE						,						token_ids_a=__SCREAMING_SNAKE_CASE						,						already_has_special_tokens=__SCREAMING_SNAKE_CASE  )

           UpperCamelCase__    :   List[str]				=				[1] * len(self.prefix_tokens  )
           UpperCamelCase__    :   Any				=				[1] * len(self.suffix_tokens  )
           if token_ids_a is None:
                 return prefix_ones + ([0] * len(__SCREAMING_SNAKE_CASE  )) + suffix_ones
           return prefix_ones + ([0] * len(__SCREAMING_SNAKE_CASE  )) + ([0] * len(__SCREAMING_SNAKE_CASE  )) + suffix_ones






     def    __SCREAMING_SNAKE_CASE			(							self						,						__SCREAMING_SNAKE_CASE						,						__SCREAMING_SNAKE_CASE = 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    __SCREAMING_SNAKE_CASE			(							self						,						__SCREAMING_SNAKE_CASE						,						__SCREAMING_SNAKE_CASE = None  )  ->   List[int]:




           """simple docstring"""







           UpperCamelCase__    :   Optional[int]				=				[self.sep_token_id]
           UpperCamelCase__    :   List[Any]				=				[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    __SCREAMING_SNAKE_CASE			(							self						,						__SCREAMING_SNAKE_CASE						,						__SCREAMING_SNAKE_CASE						,						__SCREAMING_SNAKE_CASE						,						__SCREAMING_SNAKE_CASE						,						**__SCREAMING_SNAKE_CASE  )  ->   List[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'''  )
           UpperCamelCase__    :   int				=				src_lang
           UpperCamelCase__    :   List[str]				=				self(__SCREAMING_SNAKE_CASE						,						add_special_tokens=__SCREAMING_SNAKE_CASE						,						return_tensors=__SCREAMING_SNAKE_CASE						,						**__SCREAMING_SNAKE_CASE  )
           UpperCamelCase__    :   str				=				self.convert_tokens_to_ids(__SCREAMING_SNAKE_CASE  )
           UpperCamelCase__    :   Dict				=				tgt_lang_id
           return inputs






     def    __SCREAMING_SNAKE_CASE			(							self  )  ->   str:




           """simple docstring"""







           UpperCamelCase__    :   Any				=				{self.convert_ids_to_tokens(__SCREAMING_SNAKE_CASE  ): i for i in range(self.vocab_size  )}
           vocab.update(self.added_tokens_encoder  )
           return vocab






     def    __SCREAMING_SNAKE_CASE			(							self						,						__SCREAMING_SNAKE_CASE  )  ->   List[str]:




           """simple docstring"""







           return self.sp_model.encode(__SCREAMING_SNAKE_CASE						,						out_type=__SCREAMING_SNAKE_CASE  )






     def    __SCREAMING_SNAKE_CASE			(							self						,						__SCREAMING_SNAKE_CASE  )  ->   int:




           """simple docstring"""







           if token in self.fairseq_tokens_to_ids:
                 return self.fairseq_tokens_to_ids[token]
           UpperCamelCase__    :   Any				=				self.sp_model.PieceToId(__SCREAMING_SNAKE_CASE  )

           # Need to return unknown token if the SP model returned 0
           return spm_id + self.fairseq_offset if spm_id else self.unk_token_id






     def    __SCREAMING_SNAKE_CASE			(							self						,						__SCREAMING_SNAKE_CASE  )  ->   List[str]:




           """simple docstring"""







           if index in self.fairseq_ids_to_tokens:
                 return self.fairseq_ids_to_tokens[index]
           return self.sp_model.IdToPiece(index - self.fairseq_offset  )






     def    __SCREAMING_SNAKE_CASE			(							self						,						__SCREAMING_SNAKE_CASE  )  ->   Tuple:




           """simple docstring"""







           UpperCamelCase__    :   Tuple				=				''''''.join(__SCREAMING_SNAKE_CASE  ).replace(__SCREAMING_SNAKE_CASE						,						''' '''  ).strip()
           return out_string






     def    __SCREAMING_SNAKE_CASE			(							self						,						__SCREAMING_SNAKE_CASE						,						__SCREAMING_SNAKE_CASE = None  )  ->   Tuple[str]:




           """simple docstring"""







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

           if os.path.abspath(self.vocab_file  ) != os.path.abspath(__SCREAMING_SNAKE_CASE  ) and os.path.isfile(self.vocab_file  ):
                 copyfile(self.vocab_file						,						__SCREAMING_SNAKE_CASE  )
           elif not os.path.isfile(self.vocab_file  ):
                 with open(__SCREAMING_SNAKE_CASE						,						'''wb'''  ) as fi:
                       UpperCamelCase__    :   List[Any]				=				self.sp_model.serialized_model_proto()
                       fi.write(__SCREAMING_SNAKE_CASE  )

           return (out_vocab_file,)






     def    __SCREAMING_SNAKE_CASE			(							self						,						__SCREAMING_SNAKE_CASE						,						__SCREAMING_SNAKE_CASE = "eng_Latn"						,						__SCREAMING_SNAKE_CASE = None						,						__SCREAMING_SNAKE_CASE = "fra_Latn"						,						**__SCREAMING_SNAKE_CASE						,						)  ->   BatchEncoding:




           """simple docstring"""







           UpperCamelCase__    :   int				=				src_lang
           UpperCamelCase__    :   Tuple				=				tgt_lang
           return super().prepare_seqaseq_batch(__SCREAMING_SNAKE_CASE						,						__SCREAMING_SNAKE_CASE						,						**__SCREAMING_SNAKE_CASE  )






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




           """simple docstring"""







           return self.set_src_lang_special_tokens(self.src_lang  )






     def    __SCREAMING_SNAKE_CASE			(							self  )  ->   Any:




           """simple docstring"""







           return self.set_tgt_lang_special_tokens(self.tgt_lang  )






     def    __SCREAMING_SNAKE_CASE			(							self						,						__SCREAMING_SNAKE_CASE  )  ->   None:




           """simple docstring"""







           UpperCamelCase__    :   Union[str, Any]				=				self.lang_code_to_id[src_lang]
           if self.legacy_behaviour:
                 UpperCamelCase__    :   Any				=				[]
                 UpperCamelCase__    :   str				=				[self.eos_token_id, self.cur_lang_code]
           else:
                 UpperCamelCase__    :   str				=				[self.cur_lang_code]
                 UpperCamelCase__    :   List[str]				=				[self.eos_token_id]






     def    __SCREAMING_SNAKE_CASE			(							self						,						__SCREAMING_SNAKE_CASE  )  ->   None:




           """simple docstring"""







           UpperCamelCase__    :   Union[str, Any]				=				self.lang_code_to_id[lang]
           if self.legacy_behaviour:
                 UpperCamelCase__    :   Union[str, Any]				=				[]
                 UpperCamelCase__    :   List[str]				=				[self.eos_token_id, self.cur_lang_code]
           else:
                 UpperCamelCase__    :   Dict				=				[self.cur_lang_code]
                 UpperCamelCase__    :   Optional[Any]				=				[self.eos_token_id]




import argparse
import json
import os

import torch
from transformers.file_utils import has_file

from diffusers import UNetaDConditionModel, UNetaDModel


lowerCamelCase      =False
lowerCamelCase      =True
lowerCamelCase      =False


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

    parser.add_argument(
        "--repo_path",
        default=None,
        type=str,
        required=True,
        help="The config json file corresponding to the architecture.",
    )

    parser.add_argument("--dump_path", default=None, type=str, required=True, help="Path to the output model.")

    lowerCamelCase      =parser.parse_args()

    lowerCamelCase      ={
        "image_size": "sample_size",
        "num_res_blocks": "layers_per_block",
        "block_channels": "block_out_channels",
        "down_blocks": "down_block_types",
        "up_blocks": "up_block_types",
        "downscale_freq_shift": "freq_shift",
        "resnet_num_groups": "norm_num_groups",
        "resnet_act_fn": "act_fn",
        "resnet_eps": "norm_eps",
        "num_head_channels": "attention_head_dim",
    }

    lowerCamelCase      ={
        "time_steps": "time_proj",
        "mid": "mid_block",
        "downsample_blocks": "down_blocks",
        "upsample_blocks": "up_blocks",
    }

    lowerCamelCase      ="" if has_file(args.repo_path, "config.json") else "unet"

    with open(os.path.join(args.repo_path, subfolder, "config.json"), "r", encoding="utf-8") as reader:
        lowerCamelCase      =reader.read()
        lowerCamelCase      =json.loads(text)

    if do_only_config:
        for key in config_parameters_to_change.keys():
            config.pop(key, None)

    if has_file(args.repo_path, "config.json"):
        lowerCamelCase      =UNetaDModel(**config)
    else:
        lowerCamelCase      =UNetaDConditionModel if "ldm-text2im-large-256" in args.repo_path else UNetaDModel
        lowerCamelCase      =class_name(**config)

    if do_only_config:
        model.save_config(os.path.join(args.repo_path, subfolder))

    lowerCamelCase      =dict(model.config)

    if do_only_renaming:
        for key, value in config_parameters_to_change.items():
            if key in config:
                lowerCamelCase      =config[key]
                del config[key]

        lowerCamelCase      =[k.replace("UNetRes", "") for k in config["down_block_types"]]
        lowerCamelCase      =[k.replace("UNetRes", "") for k in config["up_block_types"]]

    if do_only_weights:
        lowerCamelCase      =torch.load(os.path.join(args.repo_path, subfolder, "diffusion_pytorch_model.bin"))

        lowerCamelCase      ={}
        for param_key, param_value in state_dict.items():
            if param_key.endswith(".op.bias") or param_key.endswith(".op.weight"):
                continue
            lowerCamelCase      =False
            for key, new_key in key_parameters_to_change.items():
                if not has_changed and param_key.split(".")[0] == key:
                    lowerCamelCase      =param_value
                    lowerCamelCase      =True
            if not has_changed:
                lowerCamelCase      =param_value

        model.load_state_dict(new_state_dict)
        model.save_pretrained(os.path.join(args.repo_path, subfolder))


