






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 :      Union[str, Any]										=				logging.get_logger(__name__)
__lowerCamelCase :      List[str]										=				{"""vocab_file""": """spiece.model"""}

__lowerCamelCase :      Dict										=				{
    """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 :      List[Any]										=				{
    """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 							SCREAMING_SNAKE_CASE__						(    a__						):





     """simple docstring"""






     a_     =		VOCAB_FILES_NAMES
     a_     =		PRETRAINED_VOCAB_FILES_MAP
     a_     =		PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
     a_     =		["input_ids", "attention_mask"]



     def __init__( self					:  List[Any]							,		__A					:  Optional[int]							,		__A					:  Union[str, Any]=False							,		__A					:  Optional[Any]=False							,		__A					:  List[str]=False							,		__A					:  int=None							,		__A					:  str=None							,		__A					:  Optional[Any]=None							,		__A					:  Optional[int]=None							,		__A					:  Union[str, Any] = None							,		**__A					:  int							,		):
      snake_case__			:				Optional[Any]       =  {} if sp_model_kwargs is None else sp_model_kwargs

      snake_case__			:				Dict       =  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"	)
       snake_case__			:				Tuple       =  'None'

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

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

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

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

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

      # Regular expression to remove non-printing characters (e.g. some unicode control chars) in preprocessing
      snake_case__			:				List[Any]       =  re.compile(
          f'''[{''.join(map(SCREAMING_SNAKE_CASE_							,		list(range(0							,		9	)	) + list(range(1_1							,		3_2	)	) + list(range(1_2_7							,		1_6_0	)	) + [1_6_0, 1_7_3, 8_2_0_3]	)	)}]'''	)



     def __getstate__( self					:  Tuple	):
      snake_case__			:				List[Any]       =  self.__dict__.copy()
      snake_case__			:				Optional[int]       =  None
      return state



     def __setstate__( self					:  List[Any]							,		__A					:  List[Any]	):
      snake_case__			:				Any       =  d

      # for backward compatibility
      if not hasattr(self							,		"sp_model_kwargs"	):
       snake_case__			:				Optional[int]       =  {}

      snake_case__			:				int       =  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 	_lowercase					( self					:  List[str]	):
      return len(self.sp_model	)



     def 	_lowercase					( self					:  Any							,		__A					:  int	):
      snake_case__			:				Dict       =  self.non_printing_characters_re.sub(""							,		SCREAMING_SNAKE_CASE_	)

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

      # NFC Unicode normalization
      snake_case__			:				Dict       =  unicodedata.normalize("NFC"							,		SCREAMING_SNAKE_CASE_	)
      return text



     def 	_lowercase					( self					:  List[Any]							,		__A					:  str							,		**__A					:  Tuple	):
      snake_case__			:				Any       =  self.preprocess_text(SCREAMING_SNAKE_CASE_	)
      return self.sp_model.encode(SCREAMING_SNAKE_CASE_							,		out_type=SCREAMING_SNAKE_CASE_	)



     def 	_lowercase					( self					:  Optional[Any]							,		__A					:  int	):
      return self.sp_model.PieceToId(SCREAMING_SNAKE_CASE_	)



     def 	_lowercase					( self					:  List[str]							,		__A					:  str	):
      return self.sp_model.IdToPiece(SCREAMING_SNAKE_CASE_	)



     @staticmethod
     def 	_lowercase					( __A					:  List[Any]	):
      return out_string



     def 	_lowercase					( self					:  Optional[int]							,		__A					:  Dict	):
      snake_case__			:				Optional[Any]       =  []
      snake_case__			:				List[Any]       =  ''
      snake_case__			:				str       =  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(SCREAMING_SNAKE_CASE_	) + token
        snake_case__			:				Dict       =  True
        snake_case__			:				Optional[Any]       =  []
       else:
        current_sub_tokens.append(SCREAMING_SNAKE_CASE_	)
        snake_case__			:				str       =  False
      out_string += self.sp_model.decode(SCREAMING_SNAKE_CASE_	)

      return out_string



     def 	_lowercase					( self					:  Optional[int]	):
      snake_case__			:				Tuple       =  {self.convert_ids_to_tokens(SCREAMING_SNAKE_CASE_	): i for i in range(self.vocab_size	)}
      vocab.update(self.added_tokens_encoder	)
      return vocab



     def 	_lowercase					( self					:  Optional[int]							,		__A					:  Dict							,		__A					:  Tuple = None	):
      if not os.path.isdir(SCREAMING_SNAKE_CASE_	):
       logger.error(f'''Vocabulary path ({save_directory}) should be a directory'''	)
       return
      snake_case__			:				List[str]       =  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_	) and os.path.isfile(self.vocab_file	):
       copyfile(self.vocab_file							,		SCREAMING_SNAKE_CASE_	)
      elif not os.path.isfile(self.vocab_file	):
       with open(SCREAMING_SNAKE_CASE_							,		"wb"	) as fi:
        snake_case__			:				Any       =  self.sp_model.serialized_model_proto()
        fi.write(SCREAMING_SNAKE_CASE_	)

      return (out_vocab_file,)



     def 	_lowercase					( self					:  List[str]							,		__A					:  str							,		__A					:  Dict = False	):

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

      if return_tensors is True or return_tensors == "pt":
       snake_case__			:				List[Any]       =  torch.tensor(SCREAMING_SNAKE_CASE_	)

      return token_ids



     def 	_lowercase					( self					:  Any							,		__A					:  Any	):
      return self.sp_model.decode(SCREAMING_SNAKE_CASE_	)






     def 	_lowercase					( self					:  Dict							,		__A					:  List[Any]	):
      snake_case__			:				List[Any]       =  [f'''User: {text}''' if is_user else f'''Bot: {text}''' for is_user, text in conversation.iter_texts()]
      snake_case__			:				Optional[Any]       =  (
          f'''{self.eos_token}{self.bos_token}''' + f'''{self.bos_token}'''.join(SCREAMING_SNAKE_CASE_	) + f'''{self.bos_token}Bot:'''
      )
      return self.encode(text=SCREAMING_SNAKE_CASE_	)











import argparse

import torch

from transformers import MobileBertConfig, MobileBertForPreTraining, load_tf_weights_in_mobilebert
from transformers.utils import logging


logging.set_verbosity_info()







def 					SCREAMING_SNAKE_CASE    (			snake_case_ :							Tuple					,  snake_case_ :							str					,  snake_case_ :							Union[str, Any]       ):
	# Initialise PyTorch model
	snake_case__			:				List[str]       =  MobileBertConfig.from_json_file(snake_case_       )
	print(F'''Building PyTorch model from configuration: {config}'''       )
	snake_case__			:				Dict       =  MobileBertForPreTraining(snake_case_       )
	# Load weights from tf checkpoint
	snake_case__			:				Any       =  load_tf_weights_in_mobilebert(snake_case_					,  snake_case_					,  snake_case_       )
	# Save pytorch-model
	print(F'''Save PyTorch model to {pytorch_dump_path}'''       )
	torch.save(model.state_dict()					,  snake_case_       )


if __name__ == "__main__":
	__lowerCamelCase :      List[Any]										=				argparse.ArgumentParser()
	# Required parameters
	parser.add_argument(
	    """--tf_checkpoint_path""", default=None, type=str, required=True, help="""Path to the TensorFlow checkpoint path."""
	)
	parser.add_argument(
	    """--mobilebert_config_file""",
	    default=None,
	    type=str,
	    required=True,
	    help=(
	        """The config json file corresponding to the pre-trained MobileBERT model. \n"""
	        """This specifies the model architecture."""
	    ),
	)
	parser.add_argument(
	    """--pytorch_dump_path""", default=None, type=str, required=True, help="""Path to the output PyTorch model."""
	)
	__lowerCamelCase :      int										=				parser.parse_args()
	convert_tf_checkpoint_to_pytorch(args.tf_checkpoint_path, args.mobilebert_config_file, args.pytorch_dump_path)



