





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


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

__lowerCamelCase       :     Optional[int]   =     {
    """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""",
    }
}

__lowerCamelCase       :     Optional[int]   =     {
    """AI-Sweden/gpt-sw3-126m""": 2048,
    """AI-Sweden/gpt-sw3-350m""": 2048,
    """AI-Sweden/gpt-sw3-1.6b""": 2048,
    """AI-Sweden/gpt-sw3-6.7b""": 2048,
    """AI-Sweden/gpt-sw3-20b""": 2048,
}



class 					A__   (       __snake_case			):
      _UpperCAmelCase	:str						    =   VOCAB_FILES_NAMES
      _UpperCAmelCase	:Tuple						    =   PRETRAINED_VOCAB_FILES_MAP
      _UpperCAmelCase	:List[str]						    =   PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
      _UpperCAmelCase	:List[str]						    =   ['input_ids', 'attention_mask']





      def __init__( self       ,							A_       ,							A_=False       ,							A_=False       ,							A_=False       ,							A_=None       ,							A_=None       ,							A_=None       ,							A_=None       ,							A_ = None       ,							**A_       ,							):
       '''simple docstring'''




       UpperCamelCase	:					Optional[Any]       	=   {} if sp_model_kwargs is None else sp_model_kwargs

       UpperCamelCase	:					str       	=   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"   )
        UpperCamelCase	:					int       	=   "None"

       # Default definitions for our 2 tokenizer versions, with None-checks to enable proper testing
       UpperCamelCase	:					Union[str, Any]       	=   "<|endoftext|>" if eos_token is None else eos_token
       UpperCamelCase	:					List[Any]       	=   "<unk>" if unk_token is None else unk_token
       if "gpt-sw3-7b" in name_or_path:
        UpperCamelCase	:					Dict       	=   unk_token if pad_token is None else pad_token
        UpperCamelCase	:					List[str]       	=   eos_token if bos_token is None else bos_token
       else:
        UpperCamelCase	:					List[Any]       	=   "<pad>" if pad_token is None else pad_token
        UpperCamelCase	:					Dict       	=   "<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_       ,							)

       UpperCamelCase	:					List[str]       	=   do_lower_case
       UpperCamelCase	:					List[str]       	=   remove_space
       UpperCamelCase	:					Any       	=   keep_accents
       UpperCamelCase	:					str       	=   vocab_file

       UpperCamelCase	:					Union[str, Any]       	=   spm.SentencePieceProcessor(**self.sp_model_kwargs   )
       self.sp_model.Load(A_   )

       # Used for whitespace normalization in input texts
       # fmt : off
       UpperCamelCase	:					int       	=   {" ", " ", " ", " ", " ", "　", " ", " ", " ", " ", "￼", ""}
       # fmt : on

       # Regular expression to remove non-printing characters (e.g. some unicode control chars) in preprocessing
       UpperCamelCase	:					Any       	=   re.compile(
           F"""[{"".join(map(A_       ,							list(range(0       ,							9   )   ) + list(range(11       ,							32   )   ) + list(range(127       ,							160   )   ) + [160, 173, 8203]   )   )}]"""   )





      def __getstate__( self   ):
       '''simple docstring'''




       UpperCamelCase	:					Optional[int]       	=   self.__dict__.copy()
       UpperCamelCase	:					int       	=   None
       return state





      def __setstate__( self       ,							A_   ):
       '''simple docstring'''




       UpperCamelCase	:					List[str]       	=   d

       # for backward compatibility
       if not hasattr(self       ,							"sp_model_kwargs"   ):
        UpperCamelCase	:					List[Any]       	=   {}

       UpperCamelCase	:					Any       	=   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        __UpperCamelCase( self   ):
       '''simple docstring'''




       return len(self.sp_model   )





      def        __UpperCamelCase( self       ,							A_   ):
       '''simple docstring'''




       UpperCamelCase	:					List[Any]       	=   self.non_printing_characters_re.sub(""       ,							A_   )

       # Normalize whitespaces
       UpperCamelCase	:					Tuple       	=   "".join([char if char not in self.whitespaces else " " for char in text]   )

       # NFC Unicode normalization
       UpperCamelCase	:					List[str]       	=   unicodedata.normalize("NFC"       ,							A_   )
       return text





      def        __UpperCamelCase( self       ,							A_       ,							**A_   ):
       '''simple docstring'''




       UpperCamelCase	:					List[Any]       	=   self.preprocess_text(A_   )
       return self.sp_model.encode(A_       ,							out_type=A_   )





      def        __UpperCamelCase( self       ,							A_   ):
       '''simple docstring'''




       return self.sp_model.PieceToId(A_   )





      def        __UpperCamelCase( self       ,							A_   ):
       '''simple docstring'''




       return self.sp_model.IdToPiece(A_   )





      @staticmethod
      def        __UpperCamelCase( A_   ):
       '''simple docstring'''




       return out_string





      def        __UpperCamelCase( self       ,							A_   ):
       '''simple docstring'''




       UpperCamelCase	:					Any       	=   []
       UpperCamelCase	:					Optional[int]       	=   ""
       UpperCamelCase	:					Optional[Any]       	=   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
         UpperCamelCase	:					Optional[int]       	=   True
         UpperCamelCase	:					List[str]       	=   []
        else:
         current_sub_tokens.append(A_   )
         UpperCamelCase	:					Dict       	=   False
       out_string += self.sp_model.decode(A_   )

       return out_string





      def        __UpperCamelCase( self   ):
       '''simple docstring'''




       UpperCamelCase	:					List[str]       	=   {self.convert_ids_to_tokens(A_   ): i for i in range(self.vocab_size   )}
       vocab.update(self.added_tokens_encoder   )
       return vocab





      def        __UpperCamelCase( self       ,							A_       ,							A_ = None   ):
       '''simple docstring'''




       if not os.path.isdir(A_   ):
        logger.error(F"""Vocabulary path ({save_directory}) should be a directory"""   )
        return
       UpperCamelCase	:					Tuple       	=   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:
         UpperCamelCase	:					List[str]       	=   self.sp_model.serialized_model_proto()
         fi.write(A_   )

       return (out_vocab_file,)





      def        __UpperCamelCase( self       ,							A_       ,							A_ = False   ):
       '''simple docstring'''




       if isinstance(A_       ,							A_   ):
        UpperCamelCase	:					int       	=   self.preprocess_text(A_   )
        UpperCamelCase	:					List[str]       	=   self.sp_model.encode(A_   )
       else:
        UpperCamelCase	:					Optional[int]       	=   [self.preprocess_text(A_   ) for t in text]
        UpperCamelCase	:					Union[str, Any]       	=   self.sp_model.encode(A_   )

       if return_tensors is True or return_tensors == "pt":
        UpperCamelCase	:					int       	=   torch.tensor(A_   )

       return token_ids





      def        __UpperCamelCase( self       ,							A_   ):
       '''simple docstring'''




       return self.sp_model.decode(A_   )





      def        __UpperCamelCase( self       ,							A_   ):
       '''simple docstring'''




       UpperCamelCase	:					Union[str, Any]       	=   [F"""User: {text}""" if is_user else F"""Bot: {text}""" for is_user, text in conversation.iter_texts()]
       UpperCamelCase	:					str       	=   (
           F"""{self.eos_token}{self.bos_token}""" + F"""{self.bos_token}""".join(A_   ) + F"""{self.bos_token}Bot:"""
       )
       return self.encode(text=A_   )







"""simple docstring"""
import argparse
import os

import torch

from transformers import FlavaImageCodebook, FlavaImageCodebookConfig



def      __lowerCamelCase   (							a_     :		str ,   a_     :		Dict ,   a_     :		Any ,   a_     :		str							)			->      str:
      __SCREAMING_SNAKE_CASE   :int						=  s.rsplit(a_ ,   a_							)
      return new.join(a_							)



def      __lowerCamelCase   (							a_     :		List[str]							)			->      Dict:
      # encoder.embeddings are double copied in original FLAVA
      return sum(param.float().sum() if '''encoder.embeddings''' not in key else 0 for key, param in state_dict.items()							)



def      __lowerCamelCase   (							a_     :		Optional[int]							)			->      Any:
      __SCREAMING_SNAKE_CASE   :Optional[int]						=  {}

      __SCREAMING_SNAKE_CASE   :Union[str, Any]						=  ['''group_1''', '''group_2''', '''group_3''', '''group_4''']
      for key, value in state_dict.items():
            for group_key in group_keys:
                  if group_key in key:
                        __SCREAMING_SNAKE_CASE   :Optional[Any]						=  key.replace(f'''{group_key}.''' ,   f'''{group_key}.group.'''							)

            if "res_path" in key:
                  __SCREAMING_SNAKE_CASE   :str						=  key.replace('''res_path.''' ,   '''res_path.path.'''							)

            if key.endswith('''.w'''							):
                  __SCREAMING_SNAKE_CASE   :List[Any]						=  rreplace(a_ ,   '''.w''' ,   '''.weight''' ,   1							)
            if key.endswith('''.b'''							):
                  __SCREAMING_SNAKE_CASE   :List[Any]						=  rreplace(a_ ,   '''.b''' ,   '''.bias''' ,   1							)

            __SCREAMING_SNAKE_CASE   :Optional[Any]						=  value.float()

      return upgrade



@torch.no_grad()
def      __lowerCamelCase   (							a_     :		List[Any] ,   a_     :		Optional[int] ,   a_     :		Optional[int]=None ,   a_     :		Dict=True							)			->      Union[str, Any]:
      from dall_e import Encoder

      __SCREAMING_SNAKE_CASE   :int						=  Encoder()
      if os.path.exists(a_							):
            __SCREAMING_SNAKE_CASE   :Dict						=  torch.load(a_							)
      else:
            __SCREAMING_SNAKE_CASE   :List[str]						=  torch.hub.load_state_dict_from_url(a_							)

      if isinstance(a_ ,   a_							):
            __SCREAMING_SNAKE_CASE   :List[str]						=  ckpt.state_dict()
      encoder.load_state_dict(a_							)

      if config_path is not None:
            __SCREAMING_SNAKE_CASE   :Any						=  FlavaImageCodebookConfig.from_pretrained(a_							)
      else:
            __SCREAMING_SNAKE_CASE   :Optional[int]						=  FlavaImageCodebookConfig()

      __SCREAMING_SNAKE_CASE   :Tuple						=  FlavaImageCodebook(a_							).eval()
      __SCREAMING_SNAKE_CASE   :List[str]						=  encoder.state_dict()

      __SCREAMING_SNAKE_CASE   :Union[str, Any]						=  upgrade_state_dict(a_							)
      hf_model.load_state_dict(a_							)
      __SCREAMING_SNAKE_CASE   :Union[str, Any]						=  hf_model.state_dict()
      __SCREAMING_SNAKE_CASE   :Union[str, Any]						=  count_parameters(a_							)
      __SCREAMING_SNAKE_CASE   :Any						=  count_parameters(a_							)

      assert torch.allclose(a_ ,   a_ ,   atol=1e-3							)

      if save_checkpoint:
            hf_model.save_pretrained(a_							)
      else:
            return hf_state_dict


if __name__ == "__main__":
  lowerCamelCase_								=		argparse.ArgumentParser()
  parser.add_argument("--pytorch_dump_folder_path", default=None, type=str, help="Path to the output PyTorch model.")
  parser.add_argument("--checkpoint_path", default=None, type=str, help="Path to flava checkpoint")
  parser.add_argument("--config_path", default=None, type=str, help="Path to hf config.json of model to convert")
  lowerCamelCase_								=		parser.parse_args()

  convert_dalle_checkpoint(args.checkpoint_path, args.pytorch_dump_folder_path, args.config_path)