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


UpperCamelCase__    							= logging.get_logger(__name__)
UpperCamelCase__    							= {'vocab_file': 'spiece.model'}

UpperCamelCase__    							= {
    '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',
    }
}

UpperCamelCase__    							= {
    '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 					A							(    a__					):
   __UpperCAmelCase			:			Union[str, Any]              =      VOCAB_FILES_NAMES
   __UpperCAmelCase			:			Any              =      PRETRAINED_VOCAB_FILES_MAP
   __UpperCAmelCase			:			int              =      PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
   __UpperCAmelCase			:			Optional[int]              =      ["input_ids", "attention_mask"]


   def __init__(self					:						Optional[Any]	,  __UpperCAmelCase					:						Dict	,  __UpperCAmelCase					:						Any=False	,  __UpperCAmelCase					:						Optional[Any]=False	,  __UpperCAmelCase					:						str=False	,  __UpperCAmelCase					:						Tuple=None	,  __UpperCAmelCase					:						int=None	,  __UpperCAmelCase					:						Any=None	,  __UpperCAmelCase					:						List[Any]=None	,  __UpperCAmelCase					:						List[Any] = None	,  **__UpperCAmelCase					:						Optional[Any]	,  )						->    None:
         """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=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_	,  )

         UpperCAmelCase__								=  do_lower_case
         UpperCAmelCase__								=  remove_space
         UpperCAmelCase__								=  keep_accents
         UpperCAmelCase__								=  vocab_file

         UpperCAmelCase__								=  spm.SentencePieceProcessor(**self.sp_model_kwargs		)
         self.sp_model.Load(SCREAMING_SNAKE_CASE_		)

         # 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(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					:						Any		)						->    Tuple:
         """simple docstring"""



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


   def __setstate__(self					:						Optional[int]	,  __UpperCAmelCase					:						Optional[Any]		)						->    Any:
         """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       lowercase_				(self					:						Optional[int]		)						->    int:
         """simple docstring"""



         return len(self.sp_model		)


   def       lowercase_				(self					:						List[str]	,  __UpperCAmelCase					:						Any		)						->    str:
         """simple docstring"""



         UpperCAmelCase__								=  self.non_printing_characters_re.sub(""	,  SCREAMING_SNAKE_CASE_		)

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

         # NFC Unicode normalization
         UpperCAmelCase__								=  unicodedata.normalize("NFC"	,  SCREAMING_SNAKE_CASE_		)
         return text


   def       lowercase_				(self					:						str	,  __UpperCAmelCase					:						List[Any]	,  **__UpperCAmelCase					:						List[str]		)						->    List[str]:
         """simple docstring"""



         UpperCAmelCase__								=  self.preprocess_text(SCREAMING_SNAKE_CASE_		)
         return self.sp_model.encode(SCREAMING_SNAKE_CASE_	,  out_type=SCREAMING_SNAKE_CASE_		)


   def       lowercase_				(self					:						List[Any]	,  __UpperCAmelCase					:						Union[str, Any]		)						->    int:
         """simple docstring"""



         return self.sp_model.PieceToId(SCREAMING_SNAKE_CASE_		)


   def       lowercase_				(self					:						List[Any]	,  __UpperCAmelCase					:						str		)						->    str:
         """simple docstring"""



         return self.sp_model.IdToPiece(SCREAMING_SNAKE_CASE_		)


   @staticmethod
   def       lowercase_				(__UpperCAmelCase					:						Optional[Any]		)						->    str:
         """simple docstring"""



         return out_string


   def       lowercase_				(self					:						Union[str, Any]	,  __UpperCAmelCase					:						List[str]		)						->    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(SCREAMING_SNAKE_CASE_		) + token
                     UpperCAmelCase__								=  True
                     UpperCAmelCase__								=  []
               else:
                     current_sub_tokens.append(SCREAMING_SNAKE_CASE_		)
                     UpperCAmelCase__								=  False
         out_string += self.sp_model.decode(SCREAMING_SNAKE_CASE_		)

         return out_string


   def       lowercase_				(self					:						List[Any]		)						->    Dict[str, int]:
         """simple docstring"""



         UpperCAmelCase__								=  {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[Any]	,  __UpperCAmelCase					:						Tuple	,  __UpperCAmelCase					:						int = None		)						->    Tuple[str]:
         """simple docstring"""



         if not os.path.isdir(SCREAMING_SNAKE_CASE_		):
               logger.error(f"""Vocabulary path ({save_directory}) should be a directory"""		)
               return
         UpperCAmelCase__								=  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:
                     UpperCAmelCase__								=  self.sp_model.serialized_model_proto()
                     fi.write(SCREAMING_SNAKE_CASE_		)

         return (out_vocab_file,)


   def       lowercase_				(self					:						str	,  __UpperCAmelCase					:						str	,  __UpperCAmelCase					:						List[Any] = False		)						->    Union[List[int], List[List[int]], "torch.Tensor"]:
         """simple docstring"""



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

         if return_tensors is True or return_tensors == "pt":
               UpperCAmelCase__								=  torch.tensor(SCREAMING_SNAKE_CASE_		)

         return token_ids


   def       lowercase_				(self					:						str	,  __UpperCAmelCase					:						Dict		)						->    str:
         """simple docstring"""



         return self.sp_model.decode(SCREAMING_SNAKE_CASE_		)





   def       lowercase_				(self					:						List[Any]	,  __UpperCAmelCase					:						Optional[Any]		)						->    List[int]:
         """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(SCREAMING_SNAKE_CASE_		) + f"""{self.bos_token}Bot:"""
         )
         return self.encode(text=SCREAMING_SNAKE_CASE_		)




# Lint as: python3
import itertools
import os
import re


UpperCamelCase__    							= re.compile(R'([A-Z]+)([A-Z][a-z])')
UpperCamelCase__    							= re.compile(R'([a-z\d])([A-Z])')

UpperCamelCase__    							= re.compile(R'(?<!_)_(?!_)')
UpperCamelCase__    							= re.compile(R'(_{2,})')

UpperCamelCase__    							= R'^\w+(\.\w+)*$'

UpperCamelCase__    							= R'<>:/\|?*'




def        lowerCAmelCase_			(					__A							)					->    Dict:







      '''simple docstring'''


      UpperCAmelCase__								=  _uppercase_uppercase_re.sub(r"\1_\2",  __A							)
      UpperCAmelCase__								=  _lowercase_uppercase_re.sub(r"\1_\2",  __A							)
      return name.lower()




def        lowerCAmelCase_			(					__A							)					->    Optional[Any]:







      '''simple docstring'''


      UpperCAmelCase__								=  _single_underscore_re.split(__A							)
      UpperCAmelCase__								=  [_multiple_underscores_re.split(__A							) for n in name]
      return "".join(n.capitalize() for n in itertools.chain.from_iterable(__A							) if n != ""							)




def        lowerCAmelCase_			(					__A							)					->    List[str]:







      '''simple docstring'''


      if os.path.basename(__A							) != name:
            raise ValueError(f"""Should be a dataset name, not a path: {name}"""							)
      return camelcase_to_snakecase(__A							)




def        lowerCAmelCase_			(					__A,  __A							)					->    Optional[int]:







      '''simple docstring'''


      if os.path.basename(__A							) != name:
            raise ValueError(f"""Should be a dataset name, not a path: {name}"""							)
      if not re.match(_split_re,  __A							):
            raise ValueError(f"""Split name should match '{_split_re}'' but got '{split}'."""							)
      return f"""{filename_prefix_for_name(__A							)}-{split}"""




def        lowerCAmelCase_			(					__A,  __A,  __A,  __A=None							)					->    str:







      '''simple docstring'''


      UpperCAmelCase__								=  filename_prefix_for_split(__A,  __A							)
      if filetype_suffix:
            prefix += f""".{filetype_suffix}"""
      UpperCAmelCase__								=  os.path.join(__A,  __A							)
      return f"""{filepath}*"""







def        lowerCAmelCase_			(					__A,  __A,  __A,  __A=None,  __A=None							)					->    Any:







      '''simple docstring'''


      UpperCAmelCase__								=  filename_prefix_for_split(__A,  __A							)
      UpperCAmelCase__								=  os.path.join(__A,  __A							)

      if shard_lengths:
            UpperCAmelCase__								=  len(__A							)
            UpperCAmelCase__								=  [f"""{prefix}-{shard_id:05d}-of-{num_shards:05d}""" for shard_id in range(__A							)]
            if filetype_suffix:
                  UpperCAmelCase__								=  [filename + f""".{filetype_suffix}""" for filename in filenames]
            return filenames
      else:
            UpperCAmelCase__								=  prefix
            if filetype_suffix:
                  filename += f""".{filetype_suffix}"""
            return [filename]



