






"""simple docstring"""


from typing import List, Optional, Tuple

from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_herbert import HerbertTokenizer


lowercase_				     =     logging.get_logger(__name__)

lowercase_				     =     {'''vocab_file''': '''vocab.json''', '''merges_file''': '''merges.txt''', '''tokenizer_file''': '''tokenizer.json'''}

lowercase_				     =     {
    '''vocab_file''': {
        '''allegro/herbert-base-cased''': '''https://huggingface.co/allegro/herbert-base-cased/resolve/main/vocab.json'''
    },
    '''merges_file''': {
        '''allegro/herbert-base-cased''': '''https://huggingface.co/allegro/herbert-base-cased/resolve/main/merges.txt'''
    },
}

lowercase_				     =     {'''allegro/herbert-base-cased''': 514}
lowercase_				     =     {}


class 					snake_case		(							__UpperCAmelCase						):


   '''simple docstring'''

   A_						:	Optional[Any]						 = VOCAB_FILES_NAMES
   A_						:	int						 = PRETRAINED_VOCAB_FILES_MAP
   A_						:	Dict						 = PRETRAINED_INIT_CONFIGURATION
   A_						:	int						 = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
   A_						:	List[str]						 = HerbertTokenizer


   def __init__(	self :     List[str],       _lowerCamelCase :     Optional[int]=None,       _lowerCamelCase :     int=None,       _lowerCamelCase :     Union[str, Any]=None,       _lowerCamelCase :     List[str]="<s>",       _lowerCamelCase :     Optional[Any]="<unk>",       _lowerCamelCase :     Tuple="<pad>",       _lowerCamelCase :     Tuple="<mask>",       _lowerCamelCase :     Dict="</s>",       **_lowerCamelCase :     Any,       ):

     '''simple docstring'''
     super().__init__(
         _lowerCamelCase,       _lowerCamelCase,       tokenizer_file=_lowerCamelCase,       cls_token=_lowerCamelCase,       unk_token=_lowerCamelCase,       pad_token=_lowerCamelCase,       mask_token=_lowerCamelCase,       sep_token=_lowerCamelCase,       **_lowerCamelCase,       )


   def 				_SCREAMING_SNAKE_CASE				(	self :     List[str],       _lowerCamelCase :     Optional[Any],       _lowerCamelCase :     List[str] = None  ):

     '''simple docstring'''
     __A									= [self.cls_token_id]
     __A									= [self.sep_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 				_SCREAMING_SNAKE_CASE				(	self :     List[str],       _lowerCamelCase :     int,       _lowerCamelCase :     Tuple = None,       _lowerCamelCase :     Optional[int] = False  ):

     '''simple docstring'''
     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] + ([0] * len(_lowerCamelCase  )) + [1]


   def 				_SCREAMING_SNAKE_CASE				(	self :     Any,       _lowerCamelCase :     Optional[Any],       _lowerCamelCase :     Union[str, Any] = None  ):

     '''simple docstring'''
     __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  ) * [0] + len(token_ids_a + sep  ) * [1]






   def 				_SCREAMING_SNAKE_CASE				(	self :     str,       _lowerCamelCase :     List[Any],       _lowerCamelCase :     Any = None  ):

     '''simple docstring'''
     __A									= self._tokenizer.model.save(_lowerCamelCase,       name=_lowerCamelCase  )
     return tuple(_lowerCamelCase  )









'''simple docstring'''




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

import fsspec

from .. import Dataset, Features, NamedSplit, config
from ..formatting import query_table
from ..packaged_modules.json.json import Json
from ..utils import logging
from ..utils.typing import NestedDataStructureLike, PathLike
from .abc import AbstractDatasetReader







class 	_lowerCAmelCase							(			__UpperCAmelCase ):


	def __init__(self       ,      lowercase       ,      lowercase = None       ,      lowercase = None       ,      lowercase = None       ,      lowercase = False       ,      lowercase = False       ,      lowercase = None       ,      lowercase = None       ,      **lowercase       ,      ):
				super().__init__(
				    lowercase       ,      split=lowercase       ,      features=lowercase       ,      cache_dir=lowercase       ,      keep_in_memory=lowercase       ,      streaming=lowercase       ,      num_proc=lowercase       ,      **lowercase       ,      )
				A_	:						Any	=     field
				A_	:						Optional[int]	=     path_or_paths if isinstance(lowercase       ,      lowercase							) else {self.split: path_or_paths}
				A_	:						str	=     Json(
				    cache_dir=lowercase       ,      data_files=lowercase       ,      features=lowercase       ,      field=lowercase       ,      **lowercase       ,      )


	def     _a	(self							):
				# Build iterable dataset
				if self.streaming:
							A_	:						Optional[int]	=     self.builder.as_streaming_dataset(split=self.split							)
				# Build regular (map-style) dataset
				else:
							A_	:						Optional[Any]	=     None
							A_	:						Optional[Any]	=     None
							A_	:						Tuple	=     None
							A_	:						Optional[int]	=     None

							self.builder.download_and_prepare(
							    download_config=lowercase       ,      download_mode=lowercase       ,      verification_mode=lowercase       ,      base_path=lowercase       ,      num_proc=self.num_proc       ,      )
							A_	:						Union[str, Any]	=     self.builder.as_dataset(
							    split=self.split       ,      verification_mode=lowercase       ,      in_memory=self.keep_in_memory							)
				return dataset







class 	_lowerCAmelCase							:


	def __init__(self       ,      lowercase       ,      lowercase       ,      lowercase = None       ,      lowercase = None       ,      **lowercase       ,      ):
				if num_proc is not None and num_proc <= 0:
							raise ValueError(F'num_proc {num_proc} must be an integer > 0.'							)

				A_	:						Union[str, Any]	=     dataset
				A_	:						Optional[int]	=     path_or_buf
				A_	:						str	=     batch_size if batch_size else config.DEFAULT_MAX_BATCH_SIZE
				A_	:						List[str]	=     num_proc
				A_	:						Union[str, Any]	=     """utf-8"""
				A_	:						Dict	=     to_json_kwargs


	def     _a	(self							):
				A_	:						Optional[Any]	=     self.to_json_kwargs.pop("""path_or_buf"""       ,      lowercase							)
				A_	:						Tuple	=     self.to_json_kwargs.pop("""orient"""       ,      """records"""							)
				A_	:						Union[str, Any]	=     self.to_json_kwargs.pop("""lines"""       ,      True if orient == """records""" else False							)
				A_	:						str	=     self.to_json_kwargs.pop("""index"""       ,      False if orient in ["""split""", """table"""] else True							)
				A_	:						Dict	=     self.to_json_kwargs.pop("""compression"""       ,      lowercase							)

				if compression not in [None, "infer", "gzip", "bz2", "xz"]:
							raise NotImplementedError(F'`datasets` currently does not support {compression} compression'							)

				if isinstance(self.path_or_buf       ,      (str, bytes, os.PathLike)							):
							with fsspec.open(self.path_or_buf       ,      """wb"""       ,      compression=lowercase							) as buffer:
										A_	:						List[str]	=     self._write(file_obj=lowercase       ,      orient=lowercase       ,      lines=lowercase       ,      index=lowercase       ,      **self.to_json_kwargs							)
				else:
							if compression:
										raise NotImplementedError(
										    F'The compression parameter is not supported when writing to a buffer, but compression={compression}'
										    """ was passed. Please provide a local path instead."""							)
							A_	:						List[Any]	=     self._write(
							    file_obj=self.path_or_buf       ,      orient=lowercase       ,      lines=lowercase       ,      index=lowercase       ,      **self.to_json_kwargs							)
				return written


	def     _a	(self       ,      lowercase							):
				A_,    A_,    A_,    A_,    A_	:						Dict	=     args

				A_	:						Any	=     query_table(
				    table=self.dataset.data       ,      key=slice(lowercase       ,      offset + self.batch_size							)       ,      indices=self.dataset._indices       ,      )
				A_	:						Union[str, Any]	=     batch.to_pandas().to_json(
				    path_or_buf=lowercase       ,      orient=lowercase       ,      lines=lowercase       ,      index=lowercase       ,      **lowercase							)
				if not json_str.endswith("""\n"""							):
							json_str += "\n"
				return json_str.encode(self.encoding							)


	def     _a	(self       ,      lowercase       ,      lowercase       ,      lowercase       ,      lowercase       ,      **lowercase       ,      ):
				A_	:						Optional[Any]	=     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 json from Arrow format"""       ,      ):
										A_	:						Any	=     self._batch_json((offset, orient, lines, index, to_json_kwargs)							)
										written += file_obj.write(lowercase							)
				else:
							A_,    A_	:						List[Any]	=     len(self.dataset							), self.batch_size
							with multiprocessing.Pool(self.num_proc							) as pool:
										for json_str in logging.tqdm(
										    pool.imap(
										        self._batch_json       ,      [(offset, orient, lines, index, to_json_kwargs) for offset in range(0       ,      lowercase       ,      lowercase							)]       ,      )       ,      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 json from Arrow format"""       ,      ):
													written += file_obj.write(lowercase							)

				return written