


"""simple docstring"""


import multiprocessing
import os
from typing import BinaryIO, Optional, Union

from .. import Dataset, Features, NamedSplit, config
from ..formatting import query_table
from ..packaged_modules.csv.csv import Csv
from ..utils import logging
from ..utils.typing import NestedDataStructureLike, PathLike
from .abc import AbstractDatasetReader
class snake_case					(     _UpperCamelCase):







		def __init__(    self					:				Optional[int]			,      a__					:				NestedDataStructureLike[PathLike]			,      a__					:				Optional[NamedSplit] = None			,      a__					:				Optional[Features] = None			,      a__					:				str = None			,      a__					:				bool = False			,      a__					:				bool = False			,      a__					:				Optional[int] = None			,      **a__					:				List[str]			,      )				->  int:





						'''simple docstring'''







						super().__init__(
						    a__			,      split=a__			,      features=a__			,      cache_dir=a__			,      keep_in_memory=a__			,      streaming=a__			,      num_proc=a__			,      **a__			,      )
						_A         =	path_or_paths if isinstance(a__			,      a__						) else {self.split: path_or_paths}
						_A         =	Csv(
						    cache_dir=a__			,      data_files=a__			,      features=a__			,      **a__			,      )







		def        a_       (    self					:				List[str]						)				->  Dict:





						'''simple docstring'''







						if self.streaming:
										_A         =	self.builder.as_streaming_dataset(split=self.split						)
						# Build regular (map-style) dataset
						else:
										_A         =	None
										_A         =	None
										_A         =	None
										_A         =	None

										self.builder.download_and_prepare(
										    download_config=a__			,      download_mode=a__			,      verification_mode=a__			,      base_path=a__			,      num_proc=self.num_proc			,      )
										_A         =	self.builder.as_dataset(
										    split=self.split			,      verification_mode=a__			,      in_memory=self.keep_in_memory						)
						return dataset





class snake_case					:







		def __init__(    self					:				int			,      a__					:				Dataset			,      a__					:				Union[PathLike, BinaryIO]			,      a__					:				Optional[int] = None			,      a__					:				Optional[int] = None			,      **a__					:				int			,      )				->  Tuple:





						'''simple docstring'''







						if num_proc is not None and num_proc <= 0:
										raise ValueError(F"""num_proc {num_proc} must be an integer > 0."""						)

						_A         =	dataset
						_A         =	path_or_buf
						_A         =	batch_size if batch_size else config.DEFAULT_MAX_BATCH_SIZE
						_A         =	num_proc
						_A         =	"utf-8"
						_A         =	to_csv_kwargs







		def        a_       (    self					:				Optional[int]						)				->  int:





						'''simple docstring'''







						_A         =	self.to_csv_kwargs.pop("path_or_buf"			,      a__						)
						_A         =	self.to_csv_kwargs.pop("header"			,      a__						)
						_A         =	self.to_csv_kwargs.pop("index"			,      a__						)

						if isinstance(self.path_or_buf			,      (str, bytes, os.PathLike)						):
										with open(self.path_or_buf			,      "wb+"						) as buffer:
														_A         =	self._write(file_obj=a__			,      header=a__			,      index=a__			,      **self.to_csv_kwargs						)
						else:
										_A         =	self._write(file_obj=self.path_or_buf			,      header=a__			,      index=a__			,      **self.to_csv_kwargs						)
						return written







		def        a_       (    self					:				Union[str, Any]			,      a__					:				List[Any]						)				->  List[str]:





						'''simple docstring'''







						_A     ,						_A     ,						_A     ,						_A         =	args

						_A         =	query_table(
						    table=self.dataset.data			,      key=slice(a__			,      offset + self.batch_size						)			,      indices=self.dataset._indices			,      )
						_A         =	batch.to_pandas().to_csv(
						    path_or_buf=a__			,      header=header if (offset == 0) else False			,      index=a__			,      **a__						)
						return csv_str.encode(self.encoding						)







		def        a_       (    self					:				List[str]			,      a__					:				BinaryIO			,      a__					:				Any			,      a__					:				List[str]			,      **a__					:				Any						)				->  int:





						'''simple docstring'''







						_A         =	0

						if self.num_proc is None or self.num_proc == 1:
										for offset in logging.tqdm(
										    range(0			,      len(self.dataset						)			,      self.batch_size						)			,      unit="ba"			,      disable=not logging.is_progress_bar_enabled()			,      desc="Creating CSV from Arrow format"			,      ):
														_A         =	self._batch_csv((offset, header, index, to_csv_kwargs)						)
														written += file_obj.write(a__						)

						else:
										_A     ,						_A         =	len(self.dataset						), self.batch_size
										with multiprocessing.Pool(self.num_proc						) as pool:
														for csv_str in logging.tqdm(
														    pool.imap(
														        self._batch_csv			,      [(offset, header, index, to_csv_kwargs) for offset in range(0			,      a__			,      a__						)]			,      )			,      total=(num_rows // batch_size) + 1 if num_rows % batch_size else num_rows // batch_size			,      unit="ba"			,      disable=not logging.is_progress_bar_enabled()			,      desc="Creating CSV from Arrow format"			,      ):
																		written += file_obj.write(a__						)

						return written



"""simple docstring"""


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

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


if is_sentencepiece_available():
		from .tokenization_reformer import ReformerTokenizer
else:
		a_											=			None


a_											=			logging.get_logger(__name__)


a_											=			"▁"

a_											=			{"vocab_file": "spiece.model", "tokenizer_file": "tokenizer.json"}

a_											=			{
    "vocab_file": {
        "google/reformer-crime-and-punishment": (
            "https://huggingface.co/google/reformer-crime-and-punishment/resolve/main/spiece.model"
        )
    },
    "tokenizer_file": {
        "google/reformer-crime-and-punishment": (
            "https://huggingface.co/google/reformer-crime-and-punishment/resolve/main/tokenizer.json"
        )
    },
}

a_											=			{
    "google/reformer-crime-and-punishment": 52_42_88,
}
class snake_case					(     _UpperCamelCase):
		__UpperCamelCase							=	VOCAB_FILES_NAMES
		__UpperCamelCase							=	PRETRAINED_VOCAB_FILES_MAP
		__UpperCamelCase							=	PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
		__UpperCamelCase							=	['input_ids', 'attention_mask']
		__UpperCamelCase							=	ReformerTokenizer







		def __init__(    self					:				Dict			,      a__					:				List[Any]=None			,      a__					:				int=None			,      a__					:				int="</s>"			,      a__					:				List[Any]="<unk>"			,      a__					:				Optional[Any]=[]			,      **a__					:				Tuple			,      )				->  Tuple:





						'''simple docstring'''







						super().__init__(
						    a__			,      tokenizer_file=a__			,      eos_token=a__			,      unk_token=a__			,      additional_special_tokens=a__			,      **a__			,      )

						_A         =	vocab_file
						_A         =	False if not self.vocab_file else True







		def        a_       (    self					:				Any			,      a__					:				str			,      a__					:				Optional[str] = None						)				->  Tuple[str]:





						'''simple docstring'''







						if not self.can_save_slow_tokenizer:
										raise ValueError(
										    "Your fast tokenizer does not have the necessary information to save the vocabulary for a slow "
										    "tokenizer."						)

						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__						):
										copyfile(self.vocab_file			,      a__						)

						return (out_vocab_file,)