





import argparse
from pathlib import Path

import torch

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


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




def       __UpperCamelCase  (_SCREAMING_SNAKE_CASE		)    ->       Union[str, Any]:
  lowercase__           =  torch.load(_SCREAMING_SNAKE_CASE		,						map_location='cpu'		)
  if "model" in sd.keys():
    lowercase__           =  torch.load(_SCREAMING_SNAKE_CASE		,						map_location='cpu'		)['model']

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

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

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

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

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

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

  return sd




@torch.no_grad()
def       __UpperCamelCase  (_SCREAMING_SNAKE_CASE		,						_SCREAMING_SNAKE_CASE		,						_SCREAMING_SNAKE_CASE=None		)    ->       List[Any]:
  lowercase__           =  load_checkpoint(_SCREAMING_SNAKE_CASE		)

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

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

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


if __name__ == "__main__":
      lowercase_          =		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_          =		parser.parse_args()
      convert_opt_checkpoint(args.fairseq_path, args.pytorch_dump_folder_path, config=args.hf_config)









import os
import re
import unicodedata
from shutil import copyfile
from typing import TYPE_CHECKING, Any, Dict, List, Optional, Tuple, Union

import sentencepiece as spm

from ...tokenization_utils import PreTrainedTokenizer
from ...utils import is_torch_available, logging


if is_torch_available():
      import torch


if TYPE_CHECKING:
      from transformers.pipelines.conversational import Conversation


lowercase_          =		logging.get_logger(__name__)
lowercase_          =		{"""vocab_file""": """spiece.model"""}

lowercase_          =		{
    """vocab_file""": {
        """AI-Sweden/gpt-sw3-126m""": """https://huggingface.co/AI-Sweden/gpt-sw3-126m/resolve/main/spiece.model""",
        """AI-Sweden/gpt-sw3-350m""": """https://huggingface.co/AI-Sweden/gpt-sw3-350m/resolve/main/spiece.model""",
        """AI-Sweden/gpt-sw3-1.6b""": """https://huggingface.co/AI-Sweden/gpt-sw3-1.6b/resolve/main/spiece.model""",
        """AI-Sweden/gpt-sw3-6.7b""": """https://huggingface.co/AI-Sweden/gpt-sw3-6.7b/resolve/main/spiece.model""",
        """AI-Sweden/gpt-sw3-20b""": """https://huggingface.co/AI-Sweden/gpt-sw3-20b/resolve/main/spiece.model""",
    }
}

lowercase_          =		{
    """AI-Sweden/gpt-sw3-126m""": 2_048,
    """AI-Sweden/gpt-sw3-350m""": 2_048,
    """AI-Sweden/gpt-sw3-1.6b""": 2_048,
    """AI-Sweden/gpt-sw3-6.7b""": 2_048,
    """AI-Sweden/gpt-sw3-20b""": 2_048,
}
class     SCREAMING_SNAKE_CASE       (UpperCAmelCase				):
   _UpperCamelCase   :		List[str]			 =	VOCAB_FILES_NAMES
   _UpperCamelCase   :		List[Any]			 =	PRETRAINED_VOCAB_FILES_MAP
   _UpperCamelCase   :		Union[str, Any]			 =	PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
   _UpperCamelCase   :		Any			 =	['input_ids', 'attention_mask']


   def __init__(   self      :    Optional[Any]      ,					a      :    Tuple      ,					a      :    Optional[int]=False      ,					a      :    str=False      ,					a      :    str=False      ,					a      :    Tuple=None      ,					a      :    Any=None      ,					a      :    Union[str, Any]=None      ,					a      :    Union[str, Any]=None      ,					a      :    Optional[Dict[str, Any]] = None      ,					**a      :    Optional[int]      ,					)->       None:





     """simple docstring"""







     lowercase__           =  {} if sp_model_kwargs is None else sp_model_kwargs

     lowercase__           =  kwargs.get('name_or_path'	)
     if name_or_path is None:
       logger.warning(
           'name_or_path not provided, will work for all GPTSw3 models except gpt-sw3-7b,'
           ' you are testing the model, this can safely be ignored'	)
       lowercase__           =  'None'

     # Default definitions for our 2 tokenizer versions, with None-checks to enable proper testing
     lowercase__           =  '<|endoftext|>' if eos_token is None else eos_token
     lowercase__           =  '<unk>' if unk_token is None else unk_token
     if "gpt-sw3-7b" in name_or_path:
       lowercase__           =  unk_token if pad_token is None else pad_token
       lowercase__           =  eos_token if bos_token is None else bos_token
     else:
       lowercase__           =  '<pad>' if pad_token is None else pad_token
       lowercase__           =  '<s>' if bos_token is None else bos_token

     super().__init__(
         do_lower_case=a      ,					remove_space=a      ,					keep_accents=a      ,					bos_token=a      ,					eos_token=a      ,					unk_token=a      ,					pad_token=a      ,					sp_model_kwargs=self.sp_model_kwargs      ,					**a      ,					)

     lowercase__           =  do_lower_case
     lowercase__           =  remove_space
     lowercase__           =  keep_accents
     lowercase__           =  vocab_file

     lowercase__           =  spm.SentencePieceProcessor(**self.sp_model_kwargs	)
     self.sp_model.Load(a	)

     # Used for whitespace normalization in input texts
     # fmt : off
     lowercase__           =  {' ', ' ', ' ', ' ', ' ', '　', ' ', ' ', ' ', ' ', '￼', ''}
     # fmt : on

     # Regular expression to remove non-printing characters (e.g. some unicode control chars) in preprocessing
     lowercase__           =  re.compile(
         f"""[{"".join(map(a      ,					list(range(0      ,					9	)	) + list(range(11      ,					32	)	) + list(range(127      ,					160	)	) + [160, 173, 8_203]	)	)}]"""	)


   def __getstate__(   self      :    Any	)->       str:





     """simple docstring"""







     lowercase__           =  self.__dict__.copy()
     lowercase__           =  None
     return state


   def __setstate__(   self      :    int      ,					a      :    Optional[Any]	)->       int:





     """simple docstring"""







     lowercase__           =  d

     # for backward compatibility
     if not hasattr(self      ,					'sp_model_kwargs'	):
       lowercase__           =  {}

     lowercase__           =  spm.SentencePieceProcessor(**self.sp_model_kwargs	)
     self.sp_model.Load(self.vocab_file	)


   @property
   # Copied from transformers.models.albert.tokenization_albert.AlbertTokenizer.vocab_size
   def 	SCREAMING_SNAKE_CASE_							(   self      :    Union[str, Any]	)->       int:





     """simple docstring"""







     return len(self.sp_model	)


   def 	SCREAMING_SNAKE_CASE_							(   self      :    Tuple      ,					a      :    str	)->       str:





     """simple docstring"""







     lowercase__           =  self.non_printing_characters_re.sub(''      ,					a	)

     # Normalize whitespaces
     lowercase__           =  ''.join([char if char not in self.whitespaces else ' ' for char in text]	)

     # NFC Unicode normalization
     lowercase__           =  unicodedata.normalize('NFC'      ,					a	)
     return text


   def 	SCREAMING_SNAKE_CASE_							(   self      :    Any      ,					a      :    str      ,					**a      :    Tuple	)->       List[str]:





     """simple docstring"""







     lowercase__           =  self.preprocess_text(a	)
     return self.sp_model.encode(a      ,					out_type=a	)


   def 	SCREAMING_SNAKE_CASE_							(   self      :    Optional[int]      ,					a      :    str	)->       int:





     """simple docstring"""







     return self.sp_model.PieceToId(a	)


   def 	SCREAMING_SNAKE_CASE_							(   self      :    Optional[Any]      ,					a      :    int	)->       str:





     """simple docstring"""







     return self.sp_model.IdToPiece(a	)


   @staticmethod
   def 	SCREAMING_SNAKE_CASE_							(   a      :    str	)->       str:





     """simple docstring"""







     return out_string


   def 	SCREAMING_SNAKE_CASE_							(   self      :    Union[str, Any]      ,					a      :    List[str]	)->       str:





     """simple docstring"""







     lowercase__           =  []
     lowercase__           =  ''
     lowercase__           =  False
     for token in tokens:
       # make sure that special tokens are not decoded using sentencepiece model
       if token in self.all_special_tokens:
         # TODO: Check if this is needed, as it ensures that decode(encode(doc)) != doc by adding extra whitespace in the decoded document
         if not prev_is_special:
           out_string += " "

         out_string += self.sp_model.decode(a	) + token
         lowercase__           =  True
         lowercase__           =  []
       else:
         current_sub_tokens.append(a	)
         lowercase__           =  False
     out_string += self.sp_model.decode(a	)

     return out_string


   def 	SCREAMING_SNAKE_CASE_							(   self      :    List[str]	)->       Dict[str, int]:





     """simple docstring"""







     lowercase__           =  {self.convert_ids_to_tokens(a	): i for i in range(self.vocab_size	)}
     vocab.update(self.added_tokens_encoder	)
     return vocab


   def 	SCREAMING_SNAKE_CASE_							(   self      :    Any      ,					a      :    str      ,					a      :    Optional[str] = None	)->       Tuple[str]:





     """simple docstring"""







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

     if os.path.abspath(self.vocab_file	) != os.path.abspath(a	) and os.path.isfile(self.vocab_file	):
       copyfile(self.vocab_file      ,					a	)
     elif not os.path.isfile(self.vocab_file	):
       with open(a      ,					'wb'	) as fi:
         lowercase__           =  self.sp_model.serialized_model_proto()
         fi.write(a	)

     return (out_vocab_file,)


   def 	SCREAMING_SNAKE_CASE_							(   self      :    Union[str, Any]      ,					a      :    Union[str, List[str]]      ,					a      :    Union[str, bool] = False	)->       Union[List[int], List[List[int]], "torch.Tensor"]:





     """simple docstring"""







     if isinstance(a      ,					a	):
       lowercase__           =  self.preprocess_text(a	)
       lowercase__           =  self.sp_model.encode(a	)
     else:
       lowercase__           =  [self.preprocess_text(a	) for t in text]
       lowercase__           =  self.sp_model.encode(a	)

     if return_tensors is True or return_tensors == "pt":
       lowercase__           =  torch.tensor(a	)

     return token_ids


   def 	SCREAMING_SNAKE_CASE_							(   self      :    Optional[Any]      ,					a      :    Union[int, List[int]]	)->       str:





     """simple docstring"""







     return self.sp_model.decode(a	)


   def 	SCREAMING_SNAKE_CASE_							(   self      :    Any      ,					a      :    "Conversation"	)->       List[int]:





     """simple docstring"""







     lowercase__           =  [f"""User: {text}""" if is_user else f"""Bot: {text}""" for is_user, text in conversation.iter_texts()]
     lowercase__           =  (
         f"""{self.eos_token}{self.bos_token}""" + f"""{self.bos_token}""".join(a	) + f"""{self.bos_token}Bot:"""
     )
     return self.encode(text=a	)


