



'''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  			=						None


__a  			=						logging.get_logger(__name__)


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

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

# fmt: off
__a  			=						["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        UpperCAmelCase_			(			_a      ):
    """simple docstring"""







    lowercase   =						VOCAB_FILES_NAMES
    lowercase   =						PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
    lowercase   =						PRETRAINED_VOCAB_FILES_MAP
    lowercase   =						["input_ids", "attention_mask"]
    lowercase   =						NllbTokenizer

    lowercase   =						[]
    lowercase   =						[]




    def __init__(       self   :	Union[str, Any]					,	snake_case_   :	List[str]=None					,	snake_case_   :	Union[str, Any]=None					,	snake_case_   :	Optional[Any]="<s>"					,	snake_case_   :	List[str]="</s>"					,	snake_case_   :	List[str]="</s>"					,	snake_case_   :	List[Any]="<s>"					,	snake_case_   :	Optional[Any]="<unk>"					,	snake_case_   :	Optional[int]="<pad>"					,	snake_case_   :	str="<mask>"					,	snake_case_   :	Tuple=None					,	snake_case_   :	int=None					,	snake_case_   :	str=None					,	snake_case_   :	Union[str, Any]=False					,	**snake_case_   :	Tuple					,	):
          snake_case__    :       Optional[Any]       		= AddedToken(__UpperCAmelCase					,	lstrip=__UpperCAmelCase					,	rstrip=__UpperCAmelCase		) if isinstance(__UpperCAmelCase					,	__UpperCAmelCase		) else mask_token
          snake_case__    :       str       		= 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					,	)

          snake_case__    :       int       		= vocab_file
          snake_case__    :       Optional[Any]       		= False if not self.vocab_file else True

          snake_case__    :       Optional[int]       		= 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}		)
          snake_case__    :       List[str]       		= {
              lang_code: self.convert_tokens_to_ids(__UpperCAmelCase		) for lang_code in FAIRSEQ_LANGUAGE_CODES
          }

          snake_case__    :       List[str]       		= src_lang if src_lang is not None else "eng_Latn"
          snake_case__    :       Tuple       		= self.convert_tokens_to_ids(self._src_lang		)
          snake_case__    :       Any       		= tgt_lang
          self.set_src_lang_special_tokens(self._src_lang		)




    @property
    def lowerCamelCase			(       self   :	Optional[Any]		):
          return self._src_lang




    @src_lang.setter
    def lowerCamelCase			(       self   :	Any					,	snake_case_   :	Any		):
          snake_case__    :       List[Any]       		= new_src_lang
          self.set_src_lang_special_tokens(self._src_lang		)




    def lowerCamelCase			(       self   :	Any					,	snake_case_   :	Any					,	snake_case_   :	Any = 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 lowerCamelCase			(       self   :	Dict					,	snake_case_   :	Any					,	snake_case_   :	Optional[Any] = None		):
          snake_case__    :       Union[str, Any]       		= [self.sep_token_id]
          snake_case__    :       Optional[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 lowerCamelCase			(       self   :	str					,	snake_case_   :	Tuple					,	snake_case_   :	int					,	snake_case_   :	Dict					,	snake_case_   :	List[str]					,	**snake_case_   :	int		):



          if src_lang is None or tgt_lang is None:
                raise ValueError("""Translation requires a `src_lang` and a `tgt_lang` for this model"""		)
          snake_case__    :       Any       		= src_lang
          snake_case__    :       Optional[Any]       		= self(__UpperCAmelCase					,	add_special_tokens=__UpperCAmelCase					,	return_tensors=__UpperCAmelCase					,	**__UpperCAmelCase		)
          snake_case__    :       Union[str, Any]       		= self.convert_tokens_to_ids(__UpperCAmelCase		)
          snake_case__    :       Union[str, Any]       		= tgt_lang_id
          return inputs




    def lowerCamelCase			(       self   :	Tuple					,	snake_case_   :	Tuple					,	snake_case_   :	Tuple = "eng_Latn"					,	snake_case_   :	Dict = None					,	snake_case_   :	Dict = "fra_Latn"					,	**snake_case_   :	int					,	):
          snake_case__    :       Tuple       		= src_lang
          snake_case__    :       Optional[Any]       		= tgt_lang
          return super().prepare_seqaseq_batch(__UpperCAmelCase					,	__UpperCAmelCase					,	**__UpperCAmelCase		)




    def lowerCamelCase			(       self   :	str		):
          return self.set_src_lang_special_tokens(self.src_lang		)




    def lowerCamelCase			(       self   :	str		):
          return self.set_tgt_lang_special_tokens(self.tgt_lang		)




    def lowerCamelCase			(       self   :	Optional[int]					,	snake_case_   :	Optional[Any]		):
          snake_case__    :       List[Any]       		= self.convert_tokens_to_ids(__UpperCAmelCase		)

          if self.legacy_behaviour:
                snake_case__    :       str       		= []
                snake_case__    :       Optional[int]       		= [self.eos_token_id, self.cur_lang_code]
          else:
                snake_case__    :       Tuple       		= [self.cur_lang_code]
                snake_case__    :       Any       		= [self.eos_token_id]

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

          snake_case__    :       List[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 lowerCamelCase			(       self   :	str					,	snake_case_   :	Tuple		):
          snake_case__    :       Optional[int]       		= self.convert_tokens_to_ids(__UpperCAmelCase		)
          if self.legacy_behaviour:
                snake_case__    :       Dict       		= []
                snake_case__    :       List[str]       		= [self.eos_token_id, self.cur_lang_code]
          else:
                snake_case__    :       int       		= [self.cur_lang_code]
                snake_case__    :       Tuple       		= [self.eos_token_id]

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

          snake_case__    :       Tuple       		= 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 lowerCamelCase			(       self   :	int					,	snake_case_   :	Any					,	snake_case_   :	List[str] = 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(__UpperCAmelCase		):
                logger.error(f"Vocabulary path ({save_directory}) should be a directory."		)
                return
          snake_case__    :       Optional[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,)





import argparse
import json
import os

import torch
from torch import nn

from transformers import NllbMoeConfig, NllbMoeModel
from transformers.modeling_utils import dtype_byte_size
from transformers.utils import WEIGHTS_INDEX_NAME, WEIGHTS_NAME






def     SCREAMING_SNAKE_CASE(      __UpperCamelCase )	->						List[Any]:
				a__	:							Any      						= [
				    "encoder.version",
				    "decoder.version",
				    "model.encoder.version",
				    "model.decoder.version",
				    "decoder.output_projection.weight",
				    "_float_tensor",
				    "encoder.embed_positions._float_tensor",
				    "decoder.embed_positions._float_tensor",
				]
				for k in ignore_keys:
								state_dict.pop(__UpperCamelCase	,				__UpperCamelCase )






def     SCREAMING_SNAKE_CASE(      __UpperCamelCase )	->						Optional[Any]:
				a__						,   a__	:							Optional[int]      						= emb.weight.shape
				a__	:							Union[str, Any]      						= nn.Linear(__UpperCamelCase	,				__UpperCamelCase	,				bias=__UpperCamelCase )
				a__	:							str      						= emb.weight.data
				return lin_layer






def     SCREAMING_SNAKE_CASE(      __UpperCamelCase	,				__UpperCamelCase=None )	->						Optional[int]:
				a__	:							Union[str, Any]      						= {}
				for old_key in state_dict.keys():
								a__	:							List[Any]      						= old_key
								if "moe_layer.experts." in key:
												if expert_idx is not None:
																a__	:							List[str]      						= key.replace("moe_layer.experts.0"	,				F'ffn.experts.expert_{expert_idx}' )
												else:
																a__	:							Optional[Any]      						= key.replace("moe_layer.experts."	,				"ffn.experts.expert_" )
								if "gate" in key:
												a__	:							Optional[Any]      						= key.replace(".moe_layer.gate.wg"	,				".ffn.router.classifier" )
								if "fc2" and "experts" not in key:
												a__	:							int      						= key.replace(".fc2."	,				".ffn.fc2." )
								if "fc1" and "experts" not in key:
												a__	:							Any      						= key.replace(".fc1."	,				".ffn.fc1." )
								if ".encoder_attn." in key:
												a__	:							str      						= key.replace(".encoder_attn."	,				".cross_attention." )
								if "encoder_attn_layer_norm" in key:
												a__	:							Optional[int]      						= key.replace("encoder_attn_layer_norm"	,				"cross_attention_layer_norm" )
								if "final_layer_norm" in key:
												a__	:							int      						= key.replace("final_layer_norm"	,				"ff_layer_norm" )
								a__	:							Optional[int]      						= state_dict[old_key]
				return new_dict






def     SCREAMING_SNAKE_CASE(      __UpperCamelCase	,				__UpperCamelCase	,				__UpperCamelCase	,				__UpperCamelCase	,				__UpperCamelCase = WEIGHTS_NAME )	->						Dict:
				a__	:							Any      						= []
				a__	:							List[str]      						= 0
				os.makedirs(__UpperCamelCase	,				exist_ok=__UpperCamelCase )

				for expert in range(__UpperCamelCase ):
								a__	:							Union[str, Any]      						= switch_checkpoint_path + F'-rank-{expert}.pt'
								if os.path.isfile(__UpperCamelCase ):
												a__	:							str      						= torch.load(__UpperCamelCase )["model"]
												remove_ignore_keys_(__UpperCamelCase )
												a__	:							Tuple      						= rename_fairseq_keys(__UpperCamelCase	,				__UpperCamelCase )
												a__	:							str      						= os.path.join(
												    __UpperCamelCase	,				weights_name.replace(".bin"	,				F'-{len(__UpperCamelCase )+1:05d}-of-???.bin' ) )
												torch.save(__UpperCamelCase	,				__UpperCamelCase )
												sharded_state_dicts.append(expert_state.keys() )
												total_size += sum([value.numel() for key, value in expert_state.items()] ) * dtype_byte_size(
												    expert_state[list(__UpperCamelCase )[0]].dtype )

    # Add the last block
				a__	:							int      						= os.path.join(__UpperCamelCase	,				weights_name.replace(".bin"	,				F'-{len(__UpperCamelCase )+1:05d}-of-???.bin' ) )
				a__	:							Any      						= torch.load(switch_checkpoint_path + "-shared.pt" )["model"]
				remove_ignore_keys_(__UpperCamelCase )
				a__	:							Optional[Any]      						= rename_fairseq_keys(__UpperCamelCase	,				__UpperCamelCase )
				a__	:							List[Any]      						= shared_weights["decoder.embed_tokens.weight"]
				sharded_state_dicts.append(shared_weights.keys() )

				# If we only have the shared weights (dummy model/experts saved on the same file)
				if len(__UpperCamelCase ) == 1:
								a__	:							Union[str, Any]      						= os.path.join(__UpperCamelCase	,				__UpperCamelCase )
								torch.save(__UpperCamelCase	,				__UpperCamelCase )
								return {weights_name: sharded_state_dicts[0]}, None
				else:
								torch.save(__UpperCamelCase	,				__UpperCamelCase )
				# Otherwise, let's build the index
				a__	:							Any      						= {}
				for idx, shard in enumerate(__UpperCamelCase ):
								a__	:							Any      						= weights_name.replace(".bin"	,				F'-{idx+1:05d}-of-{len(__UpperCamelCase ):05d}.bin' )
								a__	:							Optional[Any]      						= os.path.join(__UpperCamelCase	,				weights_name.replace(".bin"	,				F'-{idx+1:05d}-of-???.bin' ) )
								os.rename(__UpperCamelCase	,				os.path.join(__UpperCamelCase	,				__UpperCamelCase ) )
								for key in shard:
												a__	:							Any      						= shard_file

    # Add the metadata
				a__	:							Optional[int]      						= {"total_size": total_size}
				a__	:							Optional[Any]      						= {"metadata": metadata, "weight_map": weight_map}

				with open(os.path.join(__UpperCamelCase	,				__UpperCamelCase )	,				"w"	,				encoding="utf-8" ) as f:
								a__	:							Any      						= json.dumps(__UpperCamelCase	,				indent=2	,				sort_keys=__UpperCamelCase ) + "\n"
								f.write(__UpperCamelCase )

				return metadata, index


if __name__ == "__main__":
						lowerCamelCase		       =      argparse.ArgumentParser()
						# Required parameters
						parser.add_argument(
						    """--nllb_moe_checkpoint_path""",
						    default="""/home/arthur_huggingface_co/fairseq/weights/checkpoints/model_moe_54b/checkpoint_2_300000""",
						    type=str,
						    required=False,
						    help="""Path to a directory containing a folder per layer. Follows the original Google format.""",
						)
						parser.add_argument("""--dtype""", default="""float32""", type=str, required=False, help="""dtype of the saved model""")
						parser.add_argument(
						    """--pytorch_dump_folder_path""",
						    default="""/home/arthur_huggingface_co/fairseq/weights/checkpoints/hf-converted-moe-54b""",
						    type=str,
						    required=False,
						    help="""Path to the output pytorch model.""",
						)
						lowerCamelCase		       =      parser.parse_args()
						lowerCamelCase       ,   lowerCamelCase		       =      shard_on_the_fly(
						    args.nllb_moe_checkpoint_path,
						    args.pytorch_dump_folder_path,
						    1_28,
						    args.dtype,
						)

						lowerCamelCase		       =      NllbMoeConfig.from_pretrained(
						    """facebook/nllb-200-3.3B""", encoder_sparse_step=4, decoder_sparse_step=4, num_experts=1_28
						)
						config.save_pretrained(args.pytorch_dump_folder_path)
						lowerCamelCase		       =      NllbMoeModel.from_pretrained(args.pytorch_dump_folder_path)
						print("""Done""")
						model.save_pretrained(args.pytorch_dump_folder_path)


