'''simple docstring'''


import pytest

from datasets import Dataset, DatasetDict, Features, NamedSplit, Value
from datasets.io.text import TextDatasetReader

from ..utils import assert_arrow_memory_doesnt_increase, assert_arrow_memory_increases

def   A_      (       SCREAMING_SNAKE_CASE_ ,	SCREAMING_SNAKE_CASE_						)			->Dict:
  assert isinstance(SCREAMING_SNAKE_CASE_ ,	SCREAMING_SNAKE_CASE_						)
  assert dataset.num_rows == 4
  assert dataset.num_columns == 1
  assert dataset.column_names == ["text"]
  for feature, expected_dtype in expected_features.items():
    assert dataset.features[feature].dtype == expected_dtype

@pytest.mark.parametrize("""keep_in_memory""" ,	[False, True]						)
def   A_      (       SCREAMING_SNAKE_CASE_ ,	SCREAMING_SNAKE_CASE_ ,	SCREAMING_SNAKE_CASE_						)			->List[str]:
  lowercase_     =       tmp_path / """cache"""
  lowercase_     =       {"""text""": """string"""}
  with assert_arrow_memory_increases() if keep_in_memory else assert_arrow_memory_doesnt_increase():
    lowercase_     =       TextDatasetReader(SCREAMING_SNAKE_CASE_ ,	cache_dir=SCREAMING_SNAKE_CASE_ ,	keep_in_memory=SCREAMING_SNAKE_CASE_						).read()
  _check_text_dataset(SCREAMING_SNAKE_CASE_ ,	SCREAMING_SNAKE_CASE_						)

@pytest.mark.parametrize(
    """features""" ,	[
        None,
        {"""text""": """string"""},
        {"""text""": """int32"""},
        {"""text""": """float32"""},
    ] ,	)
def   A_      (       SCREAMING_SNAKE_CASE_ ,	SCREAMING_SNAKE_CASE_ ,	SCREAMING_SNAKE_CASE_						)			->Optional[int]:
  lowercase_     =       tmp_path / """cache"""
  lowercase_     =       {"""text""": """string"""}
  lowercase_     =       features.copy() if features else default_expected_features
  lowercase_     =       (
      Features({feature: Value(SCREAMING_SNAKE_CASE_						) for feature, dtype in features.items()}						) if features is not None else None
  )
  lowercase_     =       TextDatasetReader(SCREAMING_SNAKE_CASE_ ,	features=SCREAMING_SNAKE_CASE_ ,	cache_dir=SCREAMING_SNAKE_CASE_						).read()
  _check_text_dataset(SCREAMING_SNAKE_CASE_ ,	SCREAMING_SNAKE_CASE_						)

@pytest.mark.parametrize("""split""" ,	[None, NamedSplit("""train"""						), """train""", """test"""]						)
def   A_      (       SCREAMING_SNAKE_CASE_ ,	SCREAMING_SNAKE_CASE_ ,	SCREAMING_SNAKE_CASE_						)			->Tuple:
  lowercase_     =       tmp_path / """cache"""
  lowercase_     =       {"""text""": """string"""}
  lowercase_     =       TextDatasetReader(SCREAMING_SNAKE_CASE_ ,	cache_dir=SCREAMING_SNAKE_CASE_ ,	split=SCREAMING_SNAKE_CASE_						).read()
  _check_text_dataset(SCREAMING_SNAKE_CASE_ ,	SCREAMING_SNAKE_CASE_						)
  assert dataset.split == split if split else "train"

@pytest.mark.parametrize("""path_type""" ,	[str, list]						)
def   A_      (       SCREAMING_SNAKE_CASE_ ,	SCREAMING_SNAKE_CASE_ ,	SCREAMING_SNAKE_CASE_						)			->str:
  if issubclass(SCREAMING_SNAKE_CASE_ ,	SCREAMING_SNAKE_CASE_						):
    lowercase_     =       text_path
  elif issubclass(SCREAMING_SNAKE_CASE_ ,	SCREAMING_SNAKE_CASE_						):
    lowercase_     =       [text_path]
  lowercase_     =       tmp_path / """cache"""
  lowercase_     =       {"""text""": """string"""}
  lowercase_     =       TextDatasetReader(SCREAMING_SNAKE_CASE_ ,	cache_dir=SCREAMING_SNAKE_CASE_						).read()
  _check_text_dataset(SCREAMING_SNAKE_CASE_ ,	SCREAMING_SNAKE_CASE_						)

def   A_      (       SCREAMING_SNAKE_CASE_ ,	SCREAMING_SNAKE_CASE_ ,	SCREAMING_SNAKE_CASE_=("train",)						)			->List[str]:
  assert isinstance(SCREAMING_SNAKE_CASE_ ,	SCREAMING_SNAKE_CASE_						)
  for split in splits:
    lowercase_     =       dataset_dict[split]
    assert dataset.num_rows == 4
    assert dataset.num_columns == 1
    assert dataset.column_names == ["text"]
    for feature, expected_dtype in expected_features.items():
      assert dataset.features[feature].dtype == expected_dtype

@pytest.mark.parametrize("""keep_in_memory""" ,	[False, True]						)
def   A_      (       SCREAMING_SNAKE_CASE_ ,	SCREAMING_SNAKE_CASE_ ,	SCREAMING_SNAKE_CASE_						)			->Dict:
  lowercase_     =       tmp_path / """cache"""
  lowercase_     =       {"""text""": """string"""}
  with assert_arrow_memory_increases() if keep_in_memory else assert_arrow_memory_doesnt_increase():
    lowercase_     =       TextDatasetReader({"""train""": text_path} ,	cache_dir=SCREAMING_SNAKE_CASE_ ,	keep_in_memory=SCREAMING_SNAKE_CASE_						).read()
  _check_text_datasetdict(SCREAMING_SNAKE_CASE_ ,	SCREAMING_SNAKE_CASE_						)

@pytest.mark.parametrize(
    """features""" ,	[
        None,
        {"""text""": """string"""},
        {"""text""": """int32"""},
        {"""text""": """float32"""},
    ] ,	)
def   A_      (       SCREAMING_SNAKE_CASE_ ,	SCREAMING_SNAKE_CASE_ ,	SCREAMING_SNAKE_CASE_						)			->str:
  lowercase_     =       tmp_path / """cache"""
  # CSV file loses col_1 string dtype information: default now is "int64" instead of "string"
  lowercase_     =       {"""text""": """string"""}
  lowercase_     =       features.copy() if features else default_expected_features
  lowercase_     =       (
      Features({feature: Value(SCREAMING_SNAKE_CASE_						) for feature, dtype in features.items()}						) if features is not None else None
  )
  lowercase_     =       TextDatasetReader({"""train""": text_path} ,	features=SCREAMING_SNAKE_CASE_ ,	cache_dir=SCREAMING_SNAKE_CASE_						).read()
  _check_text_datasetdict(SCREAMING_SNAKE_CASE_ ,	SCREAMING_SNAKE_CASE_						)




@pytest.mark.parametrize("""split""" ,	[None, NamedSplit("""train"""						), """train""", """test"""]						)
def   A_      (       SCREAMING_SNAKE_CASE_ ,	SCREAMING_SNAKE_CASE_ ,	SCREAMING_SNAKE_CASE_						)			->List[str]:
  if split:
    lowercase_     =       {split: text_path}
  else:
    lowercase_     =       """train"""
    lowercase_     =       {"""train""": text_path, """test""": text_path}
  lowercase_     =       tmp_path / """cache"""
  lowercase_     =       {"""text""": """string"""}
  lowercase_     =       TextDatasetReader(SCREAMING_SNAKE_CASE_ ,	cache_dir=SCREAMING_SNAKE_CASE_						).read()
  _check_text_datasetdict(SCREAMING_SNAKE_CASE_ ,	SCREAMING_SNAKE_CASE_ ,	splits=list(path.keys()						)						)
  assert all(dataset[split].split == split for split in path.keys()						)


'''simple docstring'''


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


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

__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""",
    }
}

__snake_case						  =		{
    """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    (		__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      :       str   ,				lowercase_      :       List[str]   ,				lowercase_      :       List[str]=False   ,				lowercase_      :       List[str]=False   ,				lowercase_      :       Optional[int]=False   ,				lowercase_      :       Dict=None   ,				lowercase_      :       Union[str, Any]=None   ,				lowercase_      :       List[Any]=None   ,				lowercase_      :       Union[str, Any]=None   ,				lowercase_      :       Optional[Dict[str, Any]] = None   ,				**lowercase_      :       Tuple   ,				):







    '''simple docstring'''


    lowercase_     =       {} if sp_model_kwargs is None else sp_model_kwargs

    lowercase_     =       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"""      )
      lowercase_     =       """None"""

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

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

    lowercase_     =       do_lower_case
    lowercase_     =       remove_space
    lowercase_     =       keep_accents
    lowercase_     =       vocab_file

    lowercase_     =       spm.SentencePieceProcessor(**self.sp_model_kwargs      )
    self.sp_model.Load(lowercase_      )

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

    # Regular expression to remove non-printing characters (e.g. some unicode control chars) in preprocessing
    lowercase_     =       re.compile(
        F"""[{"".join(map(lowercase_   ,				list(range(0   ,				9      )      ) + list(range(11   ,				32      )      ) + list(range(127   ,				160      )      ) + [160, 173, 8_203]      )      )}]"""      )




  def __getstate__(		self      :       Tuple      ):







    '''simple docstring'''


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




  def __setstate__(		self      :       Tuple   ,				lowercase_      :       Union[str, Any]      ):







    '''simple docstring'''


    lowercase_     =       d

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

    lowercase_     =       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      :       int      ):







    '''simple docstring'''


    return len(self.sp_model      )




  def 		lowerCamelCase__  (		self      :       List[Any]   ,				lowercase_      :       str      ):







    '''simple docstring'''


    lowercase_     =       self.non_printing_characters_re.sub(""""""   ,				lowercase_      )

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

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




  def 		lowerCamelCase__  (		self      :       Any   ,				lowercase_      :       str   ,				**lowercase_      :       Any      ):







    '''simple docstring'''


    lowercase_     =       self.preprocess_text(lowercase_      )
    return self.sp_model.encode(lowercase_   ,				out_type=lowercase_      )




  def 		lowerCamelCase__  (		self      :       Dict   ,				lowercase_      :       str      ):







    '''simple docstring'''


    return self.sp_model.PieceToId(lowercase_      )




  def 		lowerCamelCase__  (		self      :       Any   ,				lowercase_      :       int      ):







    '''simple docstring'''


    return self.sp_model.IdToPiece(lowercase_      )




  @staticmethod
  def 		lowerCamelCase__  (		lowercase_      :       str      ):







    '''simple docstring'''


    return out_string




  def 		lowerCamelCase__  (		self      :       Dict   ,				lowercase_      :       List[str]      ):







    '''simple docstring'''


    lowercase_     =       []
    lowercase_     =       """"""
    lowercase_     =       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(lowercase_      ) + token
        lowercase_     =       True
        lowercase_     =       []
      else:
        current_sub_tokens.append(lowercase_      )
        lowercase_     =       False
    out_string += self.sp_model.decode(lowercase_      )

    return out_string




  def 		lowerCamelCase__  (		self      :       Tuple      ):







    '''simple docstring'''


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




  def 		lowerCamelCase__  (		self      :       Tuple   ,				lowercase_      :       str   ,				lowercase_      :       Optional[str] = None      ):







    '''simple docstring'''


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

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

    return (out_vocab_file,)




  def 		lowerCamelCase__  (		self      :       Any   ,				lowercase_      :       Union[str, List[str]]   ,				lowercase_      :       Union[str, bool] = False      ):







    '''simple docstring'''


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

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

    return token_ids




  def 		lowerCamelCase__  (		self      :       Union[str, Any]   ,				lowercase_      :       Union[int, List[int]]      ):







    '''simple docstring'''


    return self.sp_model.decode(lowercase_      )





  def 		lowerCamelCase__  (		self      :       Any   ,				lowercase_      :       "Conversation"      ):







    '''simple docstring'''


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

