


'''simple docstring'''




import argparse
from pathlib import Path

import torch

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


logging.set_verbosity_info()
_a   :   str												=   logging.get_logger(__name__)





def   _a		(lowercase__			:   Tuple						)			->			Optional[int]:






     """simple docstring"""
     __snake_case           =						torch.load(lowercase__							,   map_location='cpu'						)
     if "model" in sd.keys():
          __snake_case           =						torch.load(lowercase__							,   map_location='cpu'						)['model']

     # pop unnecessary weights
     __snake_case           =						[
         'decoder.version',
         'decoder.output_projection.weight',
     ]
     for key in keys_to_delete:
          if key in sd:
               sd.pop(lowercase__						)

     __snake_case           =						{
         '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:
               __snake_case           =						sd.pop(lowercase__						)

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

               __snake_case           =						key.replace('.qkv_proj.'							,   '.q_proj.'						)
               __snake_case           =						key.replace('.qkv_proj.'							,   '.k_proj.'						)
               __snake_case           =						key.replace('.qkv_proj.'							,   '.v_proj.'						)

               __snake_case           =						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
               __snake_case      ,       __snake_case      ,       __snake_case           =						torch.split(lowercase__							,   depth // 3							,   dim=0						)

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

     return sd





@torch.no_grad()
def   _a		(lowercase__			:   str							,   lowercase__			:   Tuple							,   lowercase__			:   List[Any]=None						)			->			Optional[int]:






     """simple docstring"""
     __snake_case           =						load_checkpoint(lowercase__						)

     if config is not None:
          __snake_case           =						OPTConfig.from_pretrained(lowercase__						)
     else:
          __snake_case           =						OPTConfig()

     __snake_case           =						OPTModel(lowercase__						).half().eval()
     model.load_state_dict(lowercase__						)

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


if __name__ == "__main__":
       _a   :   List[Any]												=   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   :   List[Any]												=   parser.parse_args()
       convert_opt_checkpoint(args.fairseq_path, args.pytorch_dump_folder_path, config=args.hf_config)









'''simple docstring'''





import json
from typing import Iterator, List, Union

from tokenizers import AddedToken, Regex, Tokenizer, decoders, normalizers, pre_tokenizers, trainers
from tokenizers.implementations.base_tokenizer import BaseTokenizer
from tokenizers.models import Unigram
from tokenizers.processors import TemplateProcessing


class 					snake_case			(UpperCamelCase ):



       def __init__(					self  ,UpperCAmelCase_ = "▁"  ,UpperCAmelCase_ = True  ,UpperCAmelCase_ = "<unk>"  ,UpperCAmelCase_ = "</s>"  ,UpperCAmelCase_ = "<pad>"  ,)	->				Optional[int]:
           lowercase__									=    {
               "pad": {"id": 0, "token": pad_token},
               "eos": {"id": 1, "token": eos_token},
               "unk": {"id": 2, "token": unk_token},
           }

           lowercase__									=    [None] * len(self.special_tokens   )
           for token_dict in self.special_tokens.values():
               lowercase__									=    token_dict["token"]

           lowercase__									=    Tokenizer(Unigram()   )

           lowercase__									=    normalizers.Sequence(
               [
                   normalizers.Nmt(),
                   normalizers.NFKC(),
                   normalizers.Replace(Regex(" {2,}"   )  ," "   ),
                   normalizers.Lowercase(),
               ]   )
           lowercase__									=    pre_tokenizers.Sequence(
               [
                   pre_tokenizers.Metaspace(replacement=UpperCAmelCase_  ,add_prefix_space=UpperCAmelCase_   ),
                   pre_tokenizers.Digits(individual_digits=UpperCAmelCase_   ),
                   pre_tokenizers.Punctuation(),
               ]   )
           lowercase__									=    decoders.Metaspace(replacement=UpperCAmelCase_  ,add_prefix_space=UpperCAmelCase_   )

           lowercase__									=    TemplateProcessing(
               single=F'''$A {self.special_tokens['eos']['token']}'''  ,special_tokens=[(self.special_tokens["eos"]["token"], self.special_tokens["eos"]["id"])]  ,)

           lowercase__									=    {
               "model": "SentencePieceUnigram",
               "replacement": replacement,
               "add_prefix_space": add_prefix_space,
           }

           super().__init__(UpperCAmelCase_  ,UpperCAmelCase_   )



       def 				_a   (					self  ,UpperCAmelCase_  ,UpperCAmelCase_ = 8_000  ,UpperCAmelCase_ = True  ,)	->				List[str]:
           lowercase__									=    trainers.UnigramTrainer(
               vocab_size=UpperCAmelCase_  ,special_tokens=self.special_tokens_list  ,show_progress=UpperCAmelCase_  ,)

           if isinstance(UpperCAmelCase_  ,UpperCAmelCase_   ):
               lowercase__									=    [files]
           self._tokenizer.train(UpperCAmelCase_  ,trainer=UpperCAmelCase_   )

           self.add_unk_id()



       def 				_a   (					self  ,UpperCAmelCase_  ,UpperCAmelCase_ = 8_000  ,UpperCAmelCase_ = True  ,)	->				Union[str, Any]:
           lowercase__									=    trainers.UnigramTrainer(
               vocab_size=UpperCAmelCase_  ,special_tokens=self.special_tokens_list  ,show_progress=UpperCAmelCase_  ,)

           self._tokenizer.train_from_iterator(UpperCAmelCase_  ,trainer=UpperCAmelCase_   )

           self.add_unk_id()







       def 				_a   (					self   )	->				str:
           lowercase__									=    json.loads(self._tokenizer.to_str()   )

           lowercase__									=    self.special_tokens["unk"]["id"]

           lowercase__									=    Tokenizer.from_str(json.dumps(UpperCAmelCase_   )   )




