



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:
    __UpperCAmelCase			  =			None


__UpperCAmelCase			  =			logging.get_logger(__name__)


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

__UpperCAmelCase			  =			{
    '''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'''
        ),
    },
}

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

# fmt: off
__UpperCAmelCase			  =			['''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_						(    a__				):
       UpperCAmelCase__							:   List[str]  	=    VOCAB_FILES_NAMES
       UpperCAmelCase__							:   List[Any]  	=    PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
       UpperCAmelCase__							:   Optional[Any]  	=    PRETRAINED_VOCAB_FILES_MAP
       UpperCAmelCase__							:   str  	=    ["input_ids", "attention_mask"]
       UpperCAmelCase__							:   int  	=    NllbTokenizer

       UpperCAmelCase__							:   List[int]  	=    []
       UpperCAmelCase__							:   List[int]  	=    []
       def __init__(     self,     SCREAMING_SNAKE_CASE_=None,     SCREAMING_SNAKE_CASE_=None,     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_=False,     **SCREAMING_SNAKE_CASE_,     )	->						Union[str, Any]:
          # Mask token behave like a normal word, i.e. include the space before it
          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       :    Dict		      =      legacy_behaviour
          super().__init__(
              vocab_file=SCREAMING_SNAKE_CASE_,     tokenizer_file=SCREAMING_SNAKE_CASE_,     bos_token=SCREAMING_SNAKE_CASE_,     eos_token=SCREAMING_SNAKE_CASE_,     sep_token=SCREAMING_SNAKE_CASE_,     cls_token=SCREAMING_SNAKE_CASE_,     unk_token=SCREAMING_SNAKE_CASE_,     pad_token=SCREAMING_SNAKE_CASE_,     mask_token=SCREAMING_SNAKE_CASE_,     src_lang=SCREAMING_SNAKE_CASE_,     tgt_lang=SCREAMING_SNAKE_CASE_,     additional_special_tokens=SCREAMING_SNAKE_CASE_,     legacy_behaviour=SCREAMING_SNAKE_CASE_,     **SCREAMING_SNAKE_CASE_,     )

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

          UpperCamelCase       :    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}			)
          UpperCamelCase       :    Optional[int]		      =      {
              lang_code: self.convert_tokens_to_ids(SCREAMING_SNAKE_CASE_			) for lang_code in FAIRSEQ_LANGUAGE_CODES
          }

          UpperCamelCase       :    Optional[int]		      =      src_lang if src_lang is not None else 'eng_Latn'
          UpperCamelCase       :    Optional[int]		      =      self.convert_tokens_to_ids(self._src_lang			)
          UpperCamelCase       :    Any		      =      tgt_lang
          self.set_src_lang_special_tokens(self._src_lang			)
       @property
       def    snake_case_			(     self			)	->						str:
          return self._src_lang
       @src_lang.setter
       def    snake_case_			(     self,     SCREAMING_SNAKE_CASE_			)	->						None:
          UpperCamelCase       :    Dict		      =      new_src_lang
          self.set_src_lang_special_tokens(self._src_lang			)
       def    snake_case_			(     self,     SCREAMING_SNAKE_CASE_,     SCREAMING_SNAKE_CASE_ = None			)	->						List[int]:
          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,     SCREAMING_SNAKE_CASE_,     SCREAMING_SNAKE_CASE_ = None			)	->						List[int]:
          UpperCamelCase       :    Dict		      =      [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    snake_case_			(     self,     SCREAMING_SNAKE_CASE_,     SCREAMING_SNAKE_CASE_,     SCREAMING_SNAKE_CASE_,     SCREAMING_SNAKE_CASE_,     **SCREAMING_SNAKE_CASE_			)	->						Tuple:
          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       :    str		      =      src_lang
          UpperCamelCase       :    List[str]		      =      self(SCREAMING_SNAKE_CASE_,     add_special_tokens=SCREAMING_SNAKE_CASE_,     return_tensors=SCREAMING_SNAKE_CASE_,     **SCREAMING_SNAKE_CASE_			)
          UpperCamelCase       :    List[Any]		      =      self.convert_tokens_to_ids(SCREAMING_SNAKE_CASE_			)
          UpperCamelCase       :    Optional[Any]		      =      tgt_lang_id
          return inputs
       def    snake_case_			(     self,     SCREAMING_SNAKE_CASE_,     SCREAMING_SNAKE_CASE_ = "eng_Latn",     SCREAMING_SNAKE_CASE_ = None,     SCREAMING_SNAKE_CASE_ = "fra_Latn",     **SCREAMING_SNAKE_CASE_,     )	->						BatchEncoding:
          UpperCamelCase       :    Union[str, Any]		      =      src_lang
          UpperCamelCase       :    str		      =      tgt_lang
          return super().prepare_seqaseq_batch(SCREAMING_SNAKE_CASE_,     SCREAMING_SNAKE_CASE_,     **SCREAMING_SNAKE_CASE_			)
       def    snake_case_			(     self			)	->						str:
          return self.set_src_lang_special_tokens(self.src_lang			)
       def    snake_case_			(     self			)	->						str:
          return self.set_tgt_lang_special_tokens(self.tgt_lang			)
       def    snake_case_			(     self,     SCREAMING_SNAKE_CASE_			)	->						None:
          UpperCamelCase       :    List[Any]		      =      self.convert_tokens_to_ids(SCREAMING_SNAKE_CASE_			)

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

          UpperCamelCase       :    int		      =      self.convert_ids_to_tokens(self.prefix_tokens			)
          UpperCamelCase       :    List[str]		      =      self.convert_ids_to_tokens(self.suffix_tokens			)

          UpperCamelCase       :    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    snake_case_			(     self,     SCREAMING_SNAKE_CASE_			)	->						None:
          UpperCamelCase       :    List[str]		      =      self.convert_tokens_to_ids(SCREAMING_SNAKE_CASE_			)
          if self.legacy_behaviour:
             UpperCamelCase       :    Optional[Any]		      =      []
             UpperCamelCase       :    Any		      =      [self.eos_token_id, self.cur_lang_code]
          else:
             UpperCamelCase       :    int		      =      [self.cur_lang_code]
             UpperCamelCase       :    Tuple		      =      [self.eos_token_id]

          UpperCamelCase       :    Union[str, Any]		      =      self.convert_ids_to_tokens(self.prefix_tokens			)
          UpperCamelCase       :    Union[str, Any]		      =      self.convert_ids_to_tokens(self.suffix_tokens			)

          UpperCamelCase       :    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    snake_case_			(     self,     SCREAMING_SNAKE_CASE_,     SCREAMING_SNAKE_CASE_ = None			)	->						Tuple[str]:
          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(SCREAMING_SNAKE_CASE_			):
             logger.error(F"""Vocabulary path ({save_directory}) should be a directory."""			)
             return
          UpperCamelCase       :    Tuple		      =      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_			):
             copyfile(self.vocab_file,     SCREAMING_SNAKE_CASE_			)

          return (out_vocab_file,)





import argparse
from pathlib import Path

import torch

from transformers import OPTConfig, OPTModel
from transformers.utils import logging


logging.set_verbosity_info()
_A  =	logging.get_logger(__name__)






def   lowerCamelCase__							(							__lowerCAmelCase    :    Any							):




			"""simple docstring"""


			lowerCAmelCase_         =      torch.load(__lowerCAmelCase       ,				map_location="cpu"							)
			if "model" in sd.keys():
						lowerCAmelCase_         =      torch.load(__lowerCAmelCase       ,				map_location="cpu"							)["model"]

			# pop unnecessary weights
			lowerCAmelCase_         =      [
			    "decoder.version",
			    "decoder.output_projection.weight",
			]
			for key in keys_to_delete:
						if key in sd:
									sd.pop(__lowerCAmelCase							)

			lowerCAmelCase_         =      {
			    "decoder.project_in_dim.weight": "decoder.project_in.weight",
			    "decoder.project_out_dim.weight": "decoder.project_out.weight",
			    "decoder.layer_norm.weight": "decoder.final_layer_norm.weight",
			    "decoder.layer_norm.bias": "decoder.final_layer_norm.bias",
			}
			for old_key, new_key in keys_to_rename.items():
						if old_key in sd:
									lowerCAmelCase_         =      sd.pop(__lowerCAmelCase							)

			lowerCAmelCase_         =      list(sd.keys()							)
			for key in keys:
						if ".qkv_proj." in key:
									lowerCAmelCase_         =      sd[key]
									# We split QKV in separate Q,K,V

									lowerCAmelCase_         =      key.replace(".qkv_proj."       ,				".q_proj."							)
									lowerCAmelCase_         =      key.replace(".qkv_proj."       ,				".k_proj."							)
									lowerCAmelCase_         =      key.replace(".qkv_proj."       ,				".v_proj."							)

									lowerCAmelCase_         =      value.shape[0]
									assert depth % 3 == 0
									# `SequeuceParallelTransformerBlock` has QKV weight is separated in K,V,Q despite the naming:
									# https://cs.github.com/facebookresearch/metaseq/blob/51871bd73cd04c038f239ea2a26db1d7f6b37927/metaseq/modules/sequence_parallel_transformer_layer.py#L97
									lowerCAmelCase_      ,    lowerCAmelCase_      ,    lowerCAmelCase_         =      torch.split(__lowerCAmelCase       ,				depth // 3       ,				dim=0							)

									lowerCAmelCase_         =      q
									lowerCAmelCase_         =      k
									lowerCAmelCase_         =      v
									del sd[key]

			return sd






@torch.no_grad()
def   lowerCamelCase__							(							__lowerCAmelCase    :    Optional[int]       ,				__lowerCAmelCase    :    List[str]       ,				__lowerCAmelCase    :    Optional[Any]=None							):




			"""simple docstring"""


			lowerCAmelCase_         =      load_checkpoint(__lowerCAmelCase							)

			if config is not None:
						lowerCAmelCase_         =      OPTConfig.from_pretrained(__lowerCAmelCase							)
			else:
						lowerCAmelCase_         =      OPTConfig()

			lowerCAmelCase_         =      OPTModel(__lowerCAmelCase							).half().eval()
			model.load_state_dict(__lowerCAmelCase							)

			# Check results
			Path(__lowerCAmelCase							).mkdir(exist_ok=__lowerCAmelCase							)
			model.save_pretrained(__lowerCAmelCase							)


if __name__ == "__main__":
			_A  =	argparse.ArgumentParser()
			# Required parameters
			parser.add_argument(
			    "--fairseq_path",
			    type=str,
			    help=(
			        "path to fairseq checkpoint in correct format. You can find all checkpoints in the correct format here:"
			        " https://huggingface.co/models?other=opt_metasq"
			    ),
			)
			parser.add_argument("--pytorch_dump_folder_path", default=None, type=str, help="Path to the output PyTorch model.")
			parser.add_argument("--hf_config", default=None, type=str, help="Define HF config.")
			_A  =	parser.parse_args()
			convert_opt_checkpoint(args.fairseq_path, args.pytorch_dump_folder_path, config=args.hf_config)



