





'''simple docstring'''




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


_SCREAMING_SNAKE_CASE							     =			datasets.utils.logging.get_logger(__name__)
@dataclass
class 			_lowerCAmelCase		(			datasets.BuilderConfig	):






     """simple docstring"""






     snake_case_						      =   None
     snake_case_						      =   "utf-8"
     snake_case_						      =   None
     snake_case_						      =   None
     snake_case_						      =   True  # deprecated
     snake_case_						      =   None  # deprecated
     snake_case_						      =   10 << 20  # 10MB
     snake_case_						      =   None




class 			_lowerCAmelCase		(			datasets.ArrowBasedBuilder	):






     """simple docstring"""






     snake_case_						      =   JsonConfig



     def      lowerCAmelCase			(   self							:			str						)->	str:
        if self.config.block_size is not None:
           logger.warning("""The JSON loader parameter `block_size` is deprecated. Please use `chunksize` instead"""						)
           snake_case 						=   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      lowerCAmelCase			(   self							:			List[Any]   ,       __snake_case							:			List[str]						)->	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}'''						)
        snake_case 						=   dl_manager.download_and_extract(self.config.data_files						)
        if isinstance(__snake_case   ,       (str, list, tuple)						):
           snake_case 						=   data_files
           if isinstance(__snake_case   ,       __snake_case						):
              snake_case 						=   [files]
           snake_case 						=   [dl_manager.iter_files(__snake_case						) for file in files]
           return [datasets.SplitGenerator(name=datasets.Split.TRAIN   ,       gen_kwargs={"""files""": files}						)]
        snake_case 						=   []
        for split_name, files in data_files.items():
           if isinstance(__snake_case   ,       __snake_case						):
              snake_case 						=   [files]
           snake_case 						=   [dl_manager.iter_files(__snake_case						) for file in files]
           splits.append(datasets.SplitGenerator(name=__snake_case   ,       gen_kwargs={"""files""": files}						)						)
        return splits



     def      lowerCAmelCase			(   self							:			str   ,       __snake_case							:			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						):
              snake_case 						=   self.config.features.arrow_schema.field(__snake_case						).type
              snake_case 						=   pa_table.append_column(__snake_case   ,       pa.array([None] * len(__snake_case						)   ,       type=__snake_case						)						)
           # more expensive cast to support nested structures with keys in a different order
           # allows str <-> int/float or str to Audio for example
           snake_case 						=   table_cast(__snake_case   ,       self.config.features.arrow_schema						)
        return pa_table



     def      lowerCAmelCase			(   self							:			Optional[Any]   ,       __snake_case							:			List[str]						)->	Dict:
        for file_idx, file in enumerate(itertools.chain.from_iterable(__snake_case						)						):
           # 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(__snake_case   ,       encoding=self.config.encoding   ,       errors=self.config.encoding_errors						) as f:
                 snake_case 						=   json.load(__snake_case						)

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

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

           # If the file has one json object per line
           else:
              with open(__snake_case   ,       """rb"""						) as f:
                 snake_case 						=   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
                 snake_case 						=   max(self.config.chunksize // 32   ,       16 << 10						)
                 snake_case 						=   (
                     self.config.encoding_errors if self.config.encoding_errors is not None else """strict"""
                 )
                 while True:
                    snake_case 						=   f.read(self.config.chunksize						)
                    if not batch:
                       break
                    # Finish current line
                    try:
                       batch += f.readline()
                    except (AttributeError, io.UnsupportedOperation):
                       batch += readline(__snake_case						)
                    # PyArrow only accepts utf-8 encoded bytes
                    if self.config.encoding != "utf-8":
                       snake_case 						=   batch.decode(self.config.encoding   ,       errors=__snake_case						).encode("""utf-8"""						)
                    try:
                       while True:
                          try:
                             snake_case 						=   paj.read_json(
                                 io.BytesIO(__snake_case						)   ,       read_options=paj.ReadOptions(block_size=__snake_case						)						)
                             break
                          except (pa.ArrowInvalid, pa.ArrowNotImplementedError) as e:
                             if (
                                 isinstance(__snake_case   ,       pa.ArrowInvalid						)
                                 and "straddling" not in str(__snake_case						)
                                 or block_size > len(__snake_case						)
                             ):
                                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(__snake_case						)} 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(
                              __snake_case   ,       encoding=self.config.encoding   ,       errors=self.config.encoding_errors						) as f:
                             snake_case 						=   json.load(__snake_case						)
                       except json.JSONDecodeError:
                          logger.error(f'''Failed to read file \'{file}\' with error {type(__snake_case						)}: {e}'''						)
                          raise e
                       # If possible, parse the file as a list of json objects and exit the loop
                       if isinstance(__snake_case   ,       __snake_case						):  # list is the only sequence type supported in JSON
                          try:
                             snake_case 						=   set().union(*[row.keys() for row in dataset]						)
                             snake_case 						=   {col: [row.get(__snake_case						) for row in dataset] for col in keys}
                             snake_case 						=   pa.Table.from_pydict(__snake_case						)
                          except (pa.ArrowInvalid, AttributeError) as e:
                             logger.error(f'''Failed to read file \'{file}\' with error {type(__snake_case						)}: {e}'''						)
                             raise ValueError(f'''Not able to read records in the JSON file at {file}.'''						) from None
                          yield file_idx, self._cast_table(__snake_case						)
                          break
                       else:
                          logger.error(f'''Failed to read file \'{file}\' with error {type(__snake_case						)}: {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(__snake_case						)
                    batch_idx += 1








'''simple docstring'''




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

from ...tokenization_utils import AddedToken
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging


if is_sentencepiece_available():
 from .tokenization_rembert import RemBertTokenizer
else:
 _SCREAMING_SNAKE_CASE							     =			None

_SCREAMING_SNAKE_CASE							     =			logging.get_logger(__name__)
_SCREAMING_SNAKE_CASE							     =			{"vocab_file": "sentencepiece.model", "tokenizer_file": "tokenizer.json"}

_SCREAMING_SNAKE_CASE							     =			{
    "vocab_file": {
        "google/rembert": "https://huggingface.co/google/rembert/resolve/main/sentencepiece.model",
    },
    "tokenizer_file": {
        "google/rembert": "https://huggingface.co/google/rembert/resolve/main/tokenizer.json",
    },
}

_SCREAMING_SNAKE_CASE							     =			{
    "google/rembert": 256,
}

_SCREAMING_SNAKE_CASE							     =			"▁"
class 			_lowerCAmelCase		(			A__	):






     """simple docstring"""






     snake_case_						      =   VOCAB_FILES_NAMES
     snake_case_						      =   PRETRAINED_VOCAB_FILES_MAP
     snake_case_						      =   PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
     snake_case_						      =   RemBertTokenizer



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

        super().__init__(
            __snake_case   ,       tokenizer_file=__snake_case   ,       do_lower_case=__snake_case   ,       remove_space=__snake_case   ,       keep_accents=__snake_case   ,       bos_token=__snake_case   ,       eos_token=__snake_case   ,       unk_token=__snake_case   ,       sep_token=__snake_case   ,       pad_token=__snake_case   ,       cls_token=__snake_case   ,       mask_token=__snake_case   ,       **__snake_case   ,       )

        snake_case 						=   do_lower_case
        snake_case 						=   remove_space
        snake_case 						=   keep_accents
        snake_case 						=   vocab_file
        snake_case 						=   False if not self.vocab_file else True



     def      lowerCAmelCase			(   self							:			List[str]   ,       __snake_case							:			List[int]   ,       __snake_case							:			Optional[List[int]] = None						)->	List[int]:
        snake_case 						=   [self.sep_token_id]
        snake_case 						=   [self.cls_token_id]
        if token_ids_a is None:
           return cls + token_ids_a + sep
        return cls + token_ids_a + sep + token_ids_a + sep



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

        if already_has_special_tokens:
           if token_ids_a is not None:
              raise ValueError(
                  """You should not supply a second sequence if the provided sequence of """
                  """ids is already formatted with special tokens for the model."""						)
           return [1 if x in [self.sep_token_id, self.cls_token_id] else 0 for x in token_ids_a]

        if token_ids_a is not None:
           return [1] + ([0] * len(__snake_case						)) + [1] + ([0] * len(__snake_case						)) + [1]
        return [1] + ([0] * len(__snake_case						)) + [1]



     def      lowerCAmelCase			(   self							:			int   ,       __snake_case							:			List[int]   ,       __snake_case							:			Optional[List[int]] = None						)->	List[int]:
        snake_case 						=   [self.sep_token_id]
        snake_case 						=   [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						) * [0] + len(token_ids_a + sep						) * [1]



     def      lowerCAmelCase			(   self							:			Tuple   ,       __snake_case							:			str   ,       __snake_case							:			Optional[str] = None						)->	Tuple[str]:
        if not os.path.isdir(__snake_case						):
           logger.error("""Vocabulary path ({}) should be a directory""".format(__snake_case						)						)
           return
        snake_case 						=   os.path.join(
            __snake_case   ,       (filename_prefix + """-""" if filename_prefix else """""") + VOCAB_FILES_NAMES["""vocab_file"""]						)

        if os.path.abspath(self.vocab_file						) != os.path.abspath(__snake_case						):
           copyfile(self.vocab_file   ,       __snake_case						)

        return (out_vocab_file,)

