





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      :       str									=  None


lowercase      :       Optional[Any]									=  logging.get_logger(__name__)


lowercase      :       List[Any]									=  {"""vocab_file""": """sentencepiece.bpe.model""", """tokenizer_file""": """tokenizer.json"""}

lowercase      :       List[Any]									=  {
    """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      :       List[Any]									=  {
    """facebook/nllb-large-en-ro""": 1_0_2_4,
    """facebook/nllb-200-distilled-600M""": 1_0_2_4,
}

# fmt: off
lowercase      :       Tuple									=  ["""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"""



   __A   :				List[Any]         =  VOCAB_FILES_NAMES
   __A   :				int         =  PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
   __A   :				Union[str, Any]         =  PRETRAINED_VOCAB_FILES_MAP
   __A   :				List[str]         =  ['''input_ids''', '''attention_mask''']
   __A   :				int         =  NllbTokenizer

   __A   :				List[int]         =  []
   __A   :				List[int]         =  []

   def __init__(					self					,  lowercase=None					,  lowercase=None					,  lowercase="<s>"					,  lowercase="</s>"					,  lowercase="</s>"					,  lowercase="<s>"					,  lowercase="<unk>"					,  lowercase="<pad>"					,  lowercase="<mask>"					,  lowercase=None					,  lowercase=None					,  lowercase=None					,  lowercase=False					,  **lowercase					,  )			->						Union[str, Any]:







          '''simple docstring'''

          a__						:	Optional[Any] =				AddedToken(lowercase					,  lstrip=lowercase					,  rstrip=lowercase) if isinstance(lowercase					,  lowercase) else mask_token
          a__						:	str =				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__						:	Optional[int] =				vocab_file
          a__						:	str =				False if not self.vocab_file else True

          a__						:	List[Any] =				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__						:	Any =				{
              lang_code: self.convert_tokens_to_ids(lowercase) for lang_code in FAIRSEQ_LANGUAGE_CODES
          }

          a__						:	str =				src_lang if src_lang is not None else 'eng_Latn'
          a__						:	int =				self.convert_tokens_to_ids(self._src_lang)
          a__						:	Any =				tgt_lang
          self.set_src_lang_special_tokens(self._src_lang)

   @property
   def 							__lowercase		(					self)			->						str:







          '''simple docstring'''

          return self._src_lang

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







          '''simple docstring'''

          a__						:	Tuple =				new_src_lang
          self.set_src_lang_special_tokens(self._src_lang)

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







          '''simple docstring'''

          a__						:	Optional[int] =				[self.sep_token_id]
          a__						:	Optional[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 							__lowercase		(					self					,  lowercase					,  lowercase					,  lowercase					,  lowercase					,  **lowercase)			->						str:







          '''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__						:	List[str] =				src_lang
          a__						:	Any =				self(lowercase					,  add_special_tokens=lowercase					,  return_tensors=lowercase					,  **lowercase)
          a__						:	Optional[int] =				self.convert_tokens_to_ids(lowercase)
          a__						:	List[str] =				tgt_lang_id
          return inputs

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







          '''simple docstring'''

          a__						:	Any =				src_lang
          a__						:	str =				tgt_lang
          return super().prepare_seqaseq_batch(lowercase					,  lowercase					,  **lowercase)

   def 							__lowercase		(					self)			->						Dict:







          '''simple docstring'''

          return self.set_src_lang_special_tokens(self.src_lang)

   def 							__lowercase		(					self)			->						int:







          '''simple docstring'''

          return self.set_tgt_lang_special_tokens(self.tgt_lang)

   def 							__lowercase		(					self					,  lowercase)			->						None:







          '''simple docstring'''

          a__						:	List[str] =				self.convert_tokens_to_ids(lowercase)

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

          a__						:	List[Any] =				self.convert_ids_to_tokens(self.prefix_tokens)
          a__						:	Optional[Any] =				self.convert_ids_to_tokens(self.suffix_tokens)

          a__						:	Optional[int] =				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 							__lowercase		(					self					,  lowercase)			->						None:







          '''simple docstring'''

          a__						:	Optional[Any] =				self.convert_tokens_to_ids(lowercase)
          if self.legacy_behaviour:
                 a__						:	int =				[]
                 a__						:	Union[str, Any] =				[self.eos_token_id, self.cur_lang_code]
          else:
                 a__						:	Dict =				[self.cur_lang_code]
                 a__						:	List[Any] =				[self.eos_token_id]

          a__						:	Optional[int] =				self.convert_ids_to_tokens(self.prefix_tokens)
          a__						:	int =				self.convert_ids_to_tokens(self.suffix_tokens)

          a__						:	int =				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 							__lowercase		(					self					,  lowercase					,  lowercase = 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__						:	Any =				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 transformers import OPTConfig, OPTModel
from transformers.utils import logging


logging.set_verbosity_info()
lowercase      :       Any									=  logging.get_logger(__name__)



def A_	(		A__				)  ->		List[str]:
       a__						:	Union[str, Any] =				torch.load(A__		,      map_location='cpu'				)
       if "model" in sd.keys():
              a__						:	Tuple =				torch.load(A__		,      map_location='cpu'				)['model']

       # pop unnecessary weights
       a__						:	Optional[Any] =				[
           'decoder.version',
           'decoder.output_projection.weight',
       ]
       for key in keys_to_delete:
              if key in sd:
                     sd.pop(A__				)

       a__						:	Union[str, Any] =				{
           '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:
                     a__						:	Any =				sd.pop(A__				)

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

                     a__						:	Union[str, Any] =				key.replace('.qkv_proj.'		,      '.q_proj.'				)
                     a__						:	int =				key.replace('.qkv_proj.'		,      '.k_proj.'				)
                     a__						:	Optional[int] =				key.replace('.qkv_proj.'		,      '.v_proj.'				)

                     a__						:	List[Any] =				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
                     a__ ,							a__ ,							a__						:	Any =				torch.split(A__		,      depth // 3		,      dim=0				)

                     a__						:	int =				q
                     a__						:	List[Any] =				k
                     a__						:	int =				v
                     del sd[key]

       return sd



@torch.no_grad()
def A_	(		A__		,      A__		,      A__=None				)  ->		Union[str, Any]:
       a__						:	Union[str, Any] =				load_checkpoint(A__				)

       if config is not None:
              a__						:	List[Any] =				OPTConfig.from_pretrained(A__				)
       else:
              a__						:	int =				OPTConfig()

       a__						:	Optional[int] =				OPTModel(A__				).half().eval()
       model.load_state_dict(A__				)

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


if __name__ == "__main__":
    lowercase      :       str									=  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.""")
    lowercase      :       Any									=  parser.parse_args()
    convert_opt_checkpoint(args.fairseq_path, args.pytorch_dump_folder_path, config=args.hf_config)


