


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     __UpperCAmelCase							(       __A							):


     """simple docstring"""
     _lowerCamelCase   =      VOCAB_FILES_NAMES
     _lowerCamelCase   =      PRETRAINED_VOCAB_FILES_MAP
     _lowerCamelCase   =      PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
     _lowerCamelCase   =      ["""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  ,       ):
        __a					=  {} if sp_model_kwargs is None else sp_model_kwargs

        __a					=  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"""    )
           __a					=  """None"""

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

        __a					=  do_lower_case
        __a					=  remove_space
        __a					=  keep_accents
        __a					=  vocab_file

        __a					=  spm.SentencePieceProcessor(**self.sp_model_kwargs    )
        self.sp_model.Load(__A    )

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

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


     def __getstate__(	self    ):
        __a					=  self.__dict__.copy()
        __a					=  None
        return state


     def __setstate__(	self  ,       __A    ):
        __a					=  d

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

        __a					=  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 	snake_case_  (	self    ):
        return len(self.sp_model    )


     def 	snake_case_  (	self  ,       __A    ):
        __a					=  self.non_printing_characters_re.sub(""""""  ,       __A    )

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

        # NFC Unicode normalization
        __a					=  unicodedata.normalize("""NFC"""  ,       __A    )
        return text


     def 	snake_case_  (	self  ,       __A  ,       **__A    ):
        __a					=  self.preprocess_text(__A    )
        return self.sp_model.encode(__A  ,       out_type=__A    )


     def 	snake_case_  (	self  ,       __A    ):
        return self.sp_model.PieceToId(__A    )


     def 	snake_case_  (	self  ,       __A    ):
        return self.sp_model.IdToPiece(__A    )


     @staticmethod
     def 	snake_case_  (	__A    ):
        return out_string


     def 	snake_case_  (	self  ,       __A    ):
        __a					=  []
        __a					=  """"""
        __a					=  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
              __a					=  True
              __a					=  []
           else:
              current_sub_tokens.append(__A    )
              __a					=  False
        out_string += self.sp_model.decode(__A    )

        return out_string


     def 	snake_case_  (	self    ):
        __a					=  {self.convert_ids_to_tokens(__A    ): i for i in range(self.vocab_size    )}
        vocab.update(self.added_tokens_encoder    )
        return vocab


     def 	snake_case_  (	self  ,       __A  ,       __A = None    ):
        if not os.path.isdir(__A    ):
           logger.error(f'''Vocabulary path ({save_directory}) should be a directory'''    )
           return
        __a					=  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:
              __a					=  self.sp_model.serialized_model_proto()
              fi.write(__A    )

        return (out_vocab_file,)


     def 	snake_case_  (	self  ,       __A  ,       __A = False    ):

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

        if return_tensors is True or return_tensors == "pt":
           __a					=  torch.tensor(__A    )

        return token_ids


     def 	snake_case_  (	self  ,       __A    ):
        return self.sp_model.decode(__A    )


     def 	snake_case_  (	self  ,       __A    ):
        __a					=  [f'''User: {text}''' if is_user else f'''Bot: {text}''' for is_user, text in conversation.iter_texts()]
        __a					=  (
            f'''{self.eos_token}{self.bos_token}''' + f'''{self.bos_token}'''.join(__A    ) + f'''{self.bos_token}Bot:'''
        )
        return self.encode(text=__A    )







from typing import TYPE_CHECKING

# rely on isort to merge the imports
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


SCREAMING_SNAKE_CASE							    =							{
    'configuration_autoformer': [
        'AUTOFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP',
        'AutoformerConfig',
    ],
}

try:
  if not is_torch_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  SCREAMING_SNAKE_CASE							    =							[
      'AUTOFORMER_PRETRAINED_MODEL_ARCHIVE_LIST',
      'AutoformerForPrediction',
      'AutoformerModel',
      'AutoformerPreTrainedModel',
  ]


if TYPE_CHECKING:
  from .configuration_autoformer import (
      AUTOFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP,
      AutoformerConfig,
  )

  try:
    if not is_torch_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_autoformer import (
        AUTOFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
        AutoformerForPrediction,
        AutoformerModel,
        AutoformerPreTrainedModel,
    )

else:
  import sys

  SCREAMING_SNAKE_CASE							    =							_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)



