



import io
import itertools
import json
from dataclasses import dataclass
from typing import Optional

import pyarrow as pa
import pyarrow.json as paj

import datasets
from datasets.table import table_cast
from datasets.utils.file_utils import readline


UpperCAmelCase_		 =					datasets.utils.logging.get_logger(__name__)


@dataclass
class 	lowerCamelCase__	(       datasets.BuilderConfig):



 """simple docstring"""

 a__					: Optional[datasets.Features] =  None
 a__					: str =  "utf-8"
 a__					: Optional[str] =  None
 a__					: Optional[str] =  None
 a__					: bool =  True  # deprecated
 a__					: Optional[int] =  None  # deprecated
 a__					: int =  10 << 20  # 10MB
 a__					: Optional[bool] =  None


class 	lowerCamelCase__	(       datasets.ArrowBasedBuilder):



 """simple docstring"""

 a__					: List[Any] =  JsonConfig

 def     snake_case_      (    self					:					Any      )   ->	List[str]:
    if self.config.block_size is not None:
       logger.warning('''The JSON loader parameter `block_size` is deprecated. Please use `chunksize` instead'''      )
       _A      =       self.config.block_size
    if self.config.use_threads is not True:
       logger.warning(
           '''The JSON loader parameter `use_threads` is deprecated and doesn\'t have any effect anymore.'''      )
    if self.config.newlines_in_values is not None:
       raise ValueError('''The JSON loader parameter `newlines_in_values` is no longer supported'''      )
    return datasets.DatasetInfo(features=self.config.features      )

 def     snake_case_      (    self					:					int			,						__lowerCAmelCase					:					Dict      )   ->	Any:
    if not self.config.data_files:
       raise ValueError(f'''At least one data file must be specified, but got data_files={self.config.data_files}'''      )
    _A      =       dl_manager.download_and_extract(self.config.data_files      )
    if isinstance(__lowerCAmelCase			,						(str, list, tuple)      ):
       _A      =       data_files
       if isinstance(__lowerCAmelCase			,						__lowerCAmelCase      ):
          _A      =       [files]
       _A      =       [dl_manager.iter_files(__lowerCAmelCase      ) for file in files]
       return [datasets.SplitGenerator(name=datasets.Split.TRAIN			,						gen_kwargs={'''files''': files}      )]
    _A      =       []
    for split_name, files in data_files.items():
       if isinstance(__lowerCAmelCase			,						__lowerCAmelCase      ):
          _A      =       [files]
       _A      =       [dl_manager.iter_files(__lowerCAmelCase      ) for file in files]
       splits.append(datasets.SplitGenerator(name=__lowerCAmelCase			,						gen_kwargs={'''files''': files}      )      )
    return splits

 def     snake_case_      (    self					:					Dict			,						__lowerCAmelCase					:					pa.Table      )   ->	pa.Table:
    if self.config.features is not None:
       # adding missing columns
       for column_name in set(self.config.features      ) - set(pa_table.column_names      ):
          _A      =       self.config.features.arrow_schema.field(__lowerCAmelCase      ).type
          _A      =       pa_table.append_column(__lowerCAmelCase			,						pa.array([None] * len(__lowerCAmelCase      )			,						type=__lowerCAmelCase      )      )
       # more expensive cast to support nested structures with keys in a different order
       # allows str <-> int/float or str to Audio for example
       _A      =       table_cast(__lowerCAmelCase			,						self.config.features.arrow_schema      )
    return pa_table

 def     snake_case_      (    self					:					Any			,						__lowerCAmelCase					:					str      )   ->	int:
    for file_idx, file in enumerate(itertools.chain.from_iterable(__lowerCAmelCase      )      ):
       # If the file is one json object and if we need to look at the list of items in one specific field
       if self.config.field is not None:
          with open(__lowerCAmelCase			,						encoding=self.config.encoding			,						errors=self.config.encoding_errors      ) as f:
             _A      =       json.load(__lowerCAmelCase      )

          # We keep only the field we are interested in
          _A      =       dataset[self.config.field]

          # We accept two format: a list of dicts or a dict of lists
          if isinstance(__lowerCAmelCase			,						(list, tuple)      ):
             _A      =       set().union(*[row.keys() for row in dataset]      )
             _A      =       {col: [row.get(__lowerCAmelCase      ) for row in dataset] for col in keys}
          else:
             _A      =       dataset
          _A      =       pa.Table.from_pydict(__lowerCAmelCase      )
          yield file_idx, self._cast_table(__lowerCAmelCase      )

       # If the file has one json object per line
       else:
          with open(__lowerCAmelCase			,						'''rb'''      ) as f:
             _A      =       0
             # Use block_size equal to the chunk size divided by 32 to leverage multithreading
             # Set a default minimum value of 16kB if the chunk size is really small
             _A      =       max(self.config.chunksize // 32			,						16 << 10      )
             _A      =       (
                 self.config.encoding_errors if self.config.encoding_errors is not None else '''strict'''
             )
             while True:
                _A      =       f.read(self.config.chunksize      )
                if not batch:
                   break
                # Finish current line
                try:
                   batch += f.readline()
                except (AttributeError, io.UnsupportedOperation):
                   batch += readline(__lowerCAmelCase      )
                # PyArrow only accepts utf-8 encoded bytes
                if self.config.encoding != "utf-8":
                   _A      =       batch.decode(self.config.encoding			,						errors=__lowerCAmelCase      ).encode('''utf-8'''      )
                try:
                   while True:
                      try:
                         _A      =       paj.read_json(
                             io.BytesIO(__lowerCAmelCase      )			,						read_options=paj.ReadOptions(block_size=__lowerCAmelCase      )      )
                         break
                      except (pa.ArrowInvalid, pa.ArrowNotImplementedError) as e:
                         if (
                             isinstance(__lowerCAmelCase			,						pa.ArrowInvalid      )
                             and "straddling" not in str(__lowerCAmelCase      )
                             or block_size > len(__lowerCAmelCase      )
                         ):
                            raise
                         else:
                            # Increase the block size in case it was too small.
                            # The block size will be reset for the next file.
                            logger.debug(
                                f'''Batch of {len(__lowerCAmelCase      )} bytes couldn\'t be parsed with block_size={block_size}. Retrying with block_size={block_size * 2}.'''      )
                            block_size *= 2
                except pa.ArrowInvalid as e:
                   try:
                      with open(
                          __lowerCAmelCase			,						encoding=self.config.encoding			,						errors=self.config.encoding_errors      ) as f:
                         _A      =       json.load(__lowerCAmelCase      )
                   except json.JSONDecodeError:
                      logger.error(f'''Failed to read file \'{file}\' with error {type(__lowerCAmelCase      )}: {e}'''      )
                      raise e
                   # If possible, parse the file as a list of json objects and exit the loop
                   if isinstance(__lowerCAmelCase			,						__lowerCAmelCase      ):  # list is the only sequence type supported in JSON
                      try:
                         _A      =       set().union(*[row.keys() for row in dataset]      )
                         _A      =       {col: [row.get(__lowerCAmelCase      ) for row in dataset] for col in keys}
                         _A      =       pa.Table.from_pydict(__lowerCAmelCase      )
                      except (pa.ArrowInvalid, AttributeError) as e:
                         logger.error(f'''Failed to read file \'{file}\' with error {type(__lowerCAmelCase      )}: {e}'''      )
                         raise ValueError(f'''Not able to read records in the JSON file at {file}.'''      ) from None
                      yield file_idx, self._cast_table(__lowerCAmelCase      )
                      break
                   else:
                      logger.error(f'''Failed to read file \'{file}\' with error {type(__lowerCAmelCase      )}: {e}'''      )
                      raise ValueError(
                          f'''Not able to read records in the JSON file at {file}. '''
                          f'''You should probably indicate the field of the JSON file containing your records. '''
                          f'''This JSON file contain the following fields: {str(list(dataset.keys()      )      )}. '''
                          f'''Select the correct one and provide it as `field=\'XXX\'` to the dataset loading method. '''      ) from None
                        # Uncomment for debugging (will print the Arrow table size and elements)
                        # logger.warning(f"pa_table: {pa_table} num rows: {pa_table.num_rows}")
                        # logger.warning('\n'.join(str(pa_table.slice(i, 1).to_pydict()) for i in range(pa_table.num_rows)))
                yield (file_idx, batch_idx), self._cast_table(__lowerCAmelCase      )
                batch_idx += 1








import os
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple

import sentencepiece as spm

from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import logging


UpperCAmelCase_		 =					logging.get_logger(__name__)

UpperCAmelCase_		 =					"""▁"""

UpperCAmelCase_		 =					{"""vocab_file""": """sentencepiece.bpe.model""", """monolingual_vocab_file""": """dict.txt"""}

UpperCAmelCase_		 =					{
    """vocab_file""": {
        """vinai/bartpho-syllable""": """https://huggingface.co/vinai/bartpho-syllable/resolve/main/sentencepiece.bpe.model""",
    },
    """monolingual_vocab_file""": {
        """vinai/bartpho-syllable""": """https://huggingface.co/vinai/bartpho-syllable/resolve/main/dict.txt""",
    },
}

UpperCAmelCase_		 =					{"""vinai/bartpho-syllable""": 1_0_2_4}


class 	lowerCamelCase__	(       _A):



 """simple docstring"""

 a__					: int =  VOCAB_FILES_NAMES
 a__					: Tuple =  PRETRAINED_VOCAB_FILES_MAP
 a__					: Optional[int] =  PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
 a__					: Tuple =  ["input_ids", "attention_mask"]

 def __init__(    self					:					Union[str, Any]			,						__lowerCAmelCase					:					Dict			,						__lowerCAmelCase					:					List[str]			,						__lowerCAmelCase					:					Union[str, Any]="<s>"			,						__lowerCAmelCase					:					Dict="</s>"			,						__lowerCAmelCase					:					List[Any]="</s>"			,						__lowerCAmelCase					:					Optional[Any]="<s>"			,						__lowerCAmelCase					:					Tuple="<unk>"			,						__lowerCAmelCase					:					int="<pad>"			,						__lowerCAmelCase					:					Optional[Any]="<mask>"			,						__lowerCAmelCase					:					Optional[Dict[str, Any]] = None			,						**__lowerCAmelCase					:					Tuple			,						)   ->	None:
    # Mask token behave like a normal word, i.e. include the space before it
    _A      =       AddedToken(__lowerCAmelCase			,						lstrip=__lowerCAmelCase			,						rstrip=__lowerCAmelCase      ) if isinstance(__lowerCAmelCase			,						__lowerCAmelCase      ) else mask_token

    _A      =       {} if sp_model_kwargs is None else sp_model_kwargs

    super().__init__(
        bos_token=__lowerCAmelCase			,						eos_token=__lowerCAmelCase			,						unk_token=__lowerCAmelCase			,						sep_token=__lowerCAmelCase			,						cls_token=__lowerCAmelCase			,						pad_token=__lowerCAmelCase			,						mask_token=__lowerCAmelCase			,						sp_model_kwargs=self.sp_model_kwargs			,						**__lowerCAmelCase			,						)

    _A      =       vocab_file
    _A      =       monolingual_vocab_file
    _A      =       spm.SentencePieceProcessor(**self.sp_model_kwargs      )
    self.sp_model.Load(str(__lowerCAmelCase      )      )

    # Load the reduced vocab

    # Keep order of special tokens for backward compatibility
    _A      =       {}
    _A      =       0
    for token in [bos_token, pad_token, eos_token, unk_token, sep_token, cls_token]:
       if str(__lowerCAmelCase      ) not in self.fairseq_tokens_to_ids:
          _A      =       cnt
          cnt += 1
    with open(__lowerCAmelCase			,						'''r'''			,						encoding='''utf-8'''      ) as f:
       for line in f.readlines():
          _A      =       line.strip().split()[0]
          _A      =       len(self.fairseq_tokens_to_ids      )
    if str(__lowerCAmelCase      ) not in self.fairseq_tokens_to_ids:
       _A      =       len(self.fairseq_tokens_to_ids      )

    _A      =       {v: k for k, v in self.fairseq_tokens_to_ids.items()}

 def __getstate__(    self					:					Any      )   ->	List[Any]:
    _A      =       self.__dict__.copy()
    _A      =       None
    _A      =       self.sp_model.serialized_model_proto()
    return state

 def __setstate__(    self					:					Union[str, Any]			,						__lowerCAmelCase					:					Dict      )   ->	List[Any]:
    _A      =       d

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

    _A      =       spm.SentencePieceProcessor(**self.sp_model_kwargs      )
    self.sp_model.LoadFromSerializedProto(self.sp_model_proto      )

 def     snake_case_      (    self					:					Optional[Any]			,						__lowerCAmelCase					:					List[int]			,						__lowerCAmelCase					:					Optional[List[int]] = None      )   ->	List[int]:

    if token_ids_a is None:
       return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
    _A      =       [self.cls_token_id]
    _A      =       [self.sep_token_id]
    return cls + token_ids_a + sep + sep + token_ids_a + sep

 def     snake_case_      (    self					:					List[Any]			,						__lowerCAmelCase					:					List[int]			,						__lowerCAmelCase					:					Optional[List[int]] = None			,						__lowerCAmelCase					:					bool = False      )   ->	List[int]:

    if already_has_special_tokens:
       return super().get_special_tokens_mask(
           token_ids_a=__lowerCAmelCase			,						token_ids_a=__lowerCAmelCase			,						already_has_special_tokens=__lowerCAmelCase      )

    if token_ids_a is None:
       return [1] + ([0] * len(__lowerCAmelCase      )) + [1]
    return [1] + ([0] * len(__lowerCAmelCase      )) + [1, 1] + ([0] * len(__lowerCAmelCase      )) + [1]

 def     snake_case_      (    self					:					Any			,						__lowerCAmelCase					:					List[int]			,						__lowerCAmelCase					:					Optional[List[int]] = None      )   ->	List[int]:
    _A      =       [self.sep_token_id]
    _A      =       [self.cls_token_id]

    if token_ids_a is None:
       return len(cls + token_ids_a + sep      ) * [0]
    return len(cls + token_ids_a + sep + sep + token_ids_a + sep      ) * [0]

 @property
 def     snake_case_      (    self					:					Optional[int]      )   ->	Union[str, Any]:
    return len(self.fairseq_ids_to_tokens      )

 def     snake_case_      (    self					:					Dict      )   ->	Optional[Any]:
    _A      =       {self.convert_ids_to_tokens(__lowerCAmelCase      ): i for i in range(self.vocab_size      )}
    vocab.update(self.added_tokens_encoder      )
    return vocab

 def     snake_case_      (    self					:					List[str]			,						__lowerCAmelCase					:					str      )   ->	List[str]:
    return self.sp_model.encode(__lowerCAmelCase			,						out_type=__lowerCAmelCase      )

 def     snake_case_      (    self					:					str			,						__lowerCAmelCase					:					Optional[Any]      )   ->	Dict:
    if token in self.fairseq_tokens_to_ids:
       return self.fairseq_tokens_to_ids[token]
    else:
       return self.unk_token_id

 def     snake_case_      (    self					:					int			,						__lowerCAmelCase					:					Optional[int]      )   ->	List[str]:
    return self.fairseq_ids_to_tokens[index]

 def     snake_case_      (    self					:					List[str]			,						__lowerCAmelCase					:					Union[str, Any]      )   ->	Tuple:
    _A      =       ''''''.join(__lowerCAmelCase      ).replace(__lowerCAmelCase			,						''' '''      ).strip()
    return out_string

 def     snake_case_      (    self					:					str			,						__lowerCAmelCase					:					str			,						__lowerCAmelCase					:					Optional[str] = None      )   ->	Tuple[str]:
    if not os.path.isdir(__lowerCAmelCase      ):
       logger.error(f'''Vocabulary path ({save_directory}) should be a directory'''      )
       return
    _A      =       os.path.join(
        __lowerCAmelCase			,						(filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''vocab_file''']      )
    _A      =       os.path.join(
        __lowerCAmelCase			,						(filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''monolingual_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:
          _A      =       self.sp_model.serialized_model_proto()
          fi.write(__lowerCAmelCase      )

    if os.path.abspath(self.monolingual_vocab_file      ) != os.path.abspath(
        __lowerCAmelCase      ) and os.path.isfile(self.monolingual_vocab_file      ):
       copyfile(self.monolingual_vocab_file			,						__lowerCAmelCase      )
    elif not os.path.isfile(self.monolingual_vocab_file      ):
       with open(__lowerCAmelCase			,						'''w'''			,						encoding='''utf-8'''      ) as fp:
          for token in self.fairseq_tokens_to_ids:
             if token not in self.all_special_tokens:
                fp.write(f'''{str(__lowerCAmelCase      )} \n'''      )

    return out_vocab_file, out_monolingual_vocab_file



