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


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

_SCREAMING_SNAKE_CASE      						=	{
    "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",
    }
}

_SCREAMING_SNAKE_CASE      						=	{
    "AI-Sweden/gpt-sw3-126m": 2_0_4_8,
    "AI-Sweden/gpt-sw3-350m": 2_0_4_8,
    "AI-Sweden/gpt-sw3-1.6b": 2_0_4_8,
    "AI-Sweden/gpt-sw3-6.7b": 2_0_4_8,
    "AI-Sweden/gpt-sw3-20b": 2_0_4_8,
}
class SCREAMING_SNAKE_CASE_						(	lowerCAmelCase_						):
   __lowerCAmelCase      =							VOCAB_FILES_NAMES
   __lowerCAmelCase      =							PRETRAINED_VOCAB_FILES_MAP
   __lowerCAmelCase      =							PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
   __lowerCAmelCase      =							["""input_ids""", """attention_mask"""]







   def __init__(			self		:		Dict					,					lowerCamelCase_		:		Tuple					,					lowerCamelCase_		:		List[str]=False					,					lowerCamelCase_		:		str=False					,					lowerCamelCase_		:		List[Any]=False					,					lowerCamelCase_		:		List[str]=None					,					lowerCamelCase_		:		Optional[Any]=None					,					lowerCamelCase_		:		List[str]=None					,					lowerCamelCase_		:		Optional[int]=None					,					lowerCamelCase_		:		Optional[Dict[str, Any]] = None					,					**lowerCamelCase_		:		Dict					,					):





    """simple docstring"""


    UpperCamelCase          =	{} if sp_model_kwargs is None else sp_model_kwargs

    UpperCamelCase          =	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          =	'''None'''

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

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

    UpperCamelCase          =	do_lower_case
    UpperCamelCase          =	remove_space
    UpperCamelCase          =	keep_accents
    UpperCamelCase          =	vocab_file

    UpperCamelCase          =	spm.SentencePieceProcessor(**self.sp_model_kwargs							)
    self.sp_model.Load(lowerCamelCase_							)

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

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







   def __getstate__(			self		:		Any							):





    """simple docstring"""


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







   def __setstate__(			self		:		Optional[Any]					,					lowerCamelCase_		:		Dict							):





    """simple docstring"""


    UpperCamelCase          =	d

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

    UpperCamelCase          =	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 		lowerCamelCase_     (			self		:		List[str]							):





    """simple docstring"""


    return len(self.sp_model							)







   def 		lowerCamelCase_     (			self		:		List[str]					,					lowerCamelCase_		:		str							):





    """simple docstring"""


    UpperCamelCase          =	self.non_printing_characters_re.sub(""""""					,					lowerCamelCase_							)

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

    # NFC Unicode normalization
    UpperCamelCase          =	unicodedata.normalize("""NFC"""					,					lowerCamelCase_							)
    return text







   def 		lowerCamelCase_     (			self		:		int					,					lowerCamelCase_		:		str					,					**lowerCamelCase_		:		str							):





    """simple docstring"""


    UpperCamelCase          =	self.preprocess_text(lowerCamelCase_							)
    return self.sp_model.encode(lowerCamelCase_					,					out_type=lowerCamelCase_							)







   def 		lowerCamelCase_     (			self		:		Any					,					lowerCamelCase_		:		str							):





    """simple docstring"""


    return self.sp_model.PieceToId(lowerCamelCase_							)







   def 		lowerCamelCase_     (			self		:		Optional[int]					,					lowerCamelCase_		:		int							):





    """simple docstring"""


    return self.sp_model.IdToPiece(lowerCamelCase_							)







   @staticmethod
   def 		lowerCamelCase_     (			lowerCamelCase_		:		str							):





    """simple docstring"""


    return out_string







   def 		lowerCamelCase_     (			self		:		List[Any]					,					lowerCamelCase_		:		List[str]							):





    """simple docstring"""


    UpperCamelCase          =	[]
    UpperCamelCase          =	''''''
    UpperCamelCase          =	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(lowerCamelCase_							) + token
      UpperCamelCase          =	True
      UpperCamelCase          =	[]
     else:
      current_sub_tokens.append(lowerCamelCase_							)
      UpperCamelCase          =	False
    out_string += self.sp_model.decode(lowerCamelCase_							)

    return out_string







   def 		lowerCamelCase_     (			self		:		Union[str, Any]							):





    """simple docstring"""


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







   def 		lowerCamelCase_     (			self		:		Optional[Any]					,					lowerCamelCase_		:		str					,					lowerCamelCase_		:		Optional[str] = None							):





    """simple docstring"""


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

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

    return (out_vocab_file,)







   def 		lowerCamelCase_     (			self		:		int					,					lowerCamelCase_		:		Union[str, List[str]]					,					lowerCamelCase_		:		Union[str, bool] = False							):





    """simple docstring"""


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

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

    return token_ids







   def 		lowerCamelCase_     (			self		:		int					,					lowerCamelCase_		:		Union[int, List[int]]							):





    """simple docstring"""


    return self.sp_model.decode(lowerCamelCase_							)







   def 		lowerCamelCase_     (			self		:		Tuple					,					lowerCamelCase_		:		"Conversation"							):





    """simple docstring"""


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





"""simple docstring"""






import argparse

import torch

from transformers import GPTaConfig, GPTaModel, load_tf_weights_in_gpta
from transformers.utils import CONFIG_NAME, WEIGHTS_NAME, logging


logging.set_verbosity_info()






def      snake_case__   (		__lowerCamelCase				:    List[Any]							,		__lowerCamelCase				:    List[str]							,		__lowerCamelCase				:    List[Any]	):
			"""simple docstring"""
			# Construct model
			if gpta_config_file == "":
						lowerCamelCase__		:		Dict			=GPTaConfig()
			else:
						lowerCamelCase__		:		Tuple			=GPTaConfig.from_json_file(__lowerCamelCase	)
			lowerCamelCase__		:		Optional[Any]			=GPTaModel(__lowerCamelCase	)

			# Load weights from numpy
			load_tf_weights_in_gpta(__lowerCamelCase							,		__lowerCamelCase							,		__lowerCamelCase	)

			# Save pytorch-model
			lowerCamelCase__		:		List[str]			=pytorch_dump_folder_path + '''/''' + WEIGHTS_NAME
			lowerCamelCase__		:		int			=pytorch_dump_folder_path + '''/''' + CONFIG_NAME
			print(f'''Save PyTorch model to {pytorch_weights_dump_path}'''	)
			torch.save(model.state_dict()							,		__lowerCamelCase	)
			print(f'''Save configuration file to {pytorch_config_dump_path}'''	)
			with open(__lowerCamelCase							,		'''w'''							,		encoding='''utf-8'''	) as f:
						f.write(config.to_json_string()	)


if __name__ == "__main__":
		_lowercase : Optional[Any]      						=						argparse.ArgumentParser()
		# Required parameters
		parser.add_argument(
		    "--gpt2_checkpoint_path", default=None, type=str, required=True, help="Path to the TensorFlow checkpoint path."
		)
		parser.add_argument(
		    "--pytorch_dump_folder_path", default=None, type=str, required=True, help="Path to the output PyTorch model."
		)
		parser.add_argument(
		    "--gpt2_config_file",
		    default="",
		    type=str,
		    help=(
		        "An optional config json file corresponding to the pre-trained OpenAI model. \n"
		        "This specifies the model architecture."
		    ),
		)
		_lowercase : Any      						=						parser.parse_args()
		convert_gpta_checkpoint_to_pytorch(args.gpta_checkpoint_path, args.gpta_config_file, args.pytorch_dump_folder_path)




