






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_ ):





       """simple docstring"""







       def __init__( self  ,      _lowerCamelCase  ,      _lowerCamelCase = None  ,      _lowerCamelCase = None  ,      _lowerCamelCase = None  ,      _lowerCamelCase = False  ,      _lowerCamelCase = False  ,      _lowerCamelCase = None  ,      _lowerCamelCase = None  ,      **_lowerCamelCase  ,      ):
         super().__init__(
             _lowerCamelCase  ,      split=_lowerCamelCase  ,      features=_lowerCamelCase  ,      cache_dir=_lowerCamelCase  ,      keep_in_memory=_lowerCamelCase  ,      streaming=_lowerCamelCase  ,      num_proc=_lowerCamelCase  ,      **_lowerCamelCase  ,      )
         UpperCamelCase_: List[Any]   				=    field
         UpperCamelCase_: List[Any]   				=    path_or_paths if isinstance(_lowerCamelCase  ,      _lowerCamelCase							) else {self.split: path_or_paths}
         UpperCamelCase_: Any   				=    Json(
             cache_dir=_lowerCamelCase  ,      data_files=_lowerCamelCase  ,      features=_lowerCamelCase  ,      field=_lowerCamelCase  ,      **_lowerCamelCase  ,      )





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

           self.builder.download_and_prepare(
               download_config=_lowerCamelCase  ,      download_mode=_lowerCamelCase  ,      verification_mode=_lowerCamelCase  ,      base_path=_lowerCamelCase  ,      num_proc=self.num_proc  ,      )
           UpperCamelCase_: Optional[Any]   				=    self.builder.as_dataset(
               split=self.split  ,      verification_mode=_lowerCamelCase  ,      in_memory=self.keep_in_memory							)
         return dataset







class     _lowerCAmelCase:





       """simple docstring"""







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

         UpperCamelCase_: int   				=    dataset
         UpperCamelCase_: int   				=    path_or_buf
         UpperCamelCase_: int   				=    batch_size if batch_size else config.DEFAULT_MAX_BATCH_SIZE
         UpperCamelCase_: str   				=    num_proc
         UpperCamelCase_: List[str]   				=    'utf-8'
         UpperCamelCase_: List[Any]   				=    to_json_kwargs





       def       _a   ( self							):
         UpperCamelCase_: Dict   				=    self.to_json_kwargs.pop('path_or_buf'  ,      _lowerCamelCase							)
         UpperCamelCase_: List[Any]   				=    self.to_json_kwargs.pop('orient'  ,      'records'							)
         UpperCamelCase_: List[Any]   				=    self.to_json_kwargs.pop('lines'  ,      True if orient == 'records' else False							)
         UpperCamelCase_: List[str]   				=    self.to_json_kwargs.pop('index'  ,      False if orient in ['split', 'table'] else True							)
         UpperCamelCase_: List[Any]   				=    self.to_json_kwargs.pop('compression'  ,      _lowerCamelCase							)

         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=_lowerCamelCase							) as buffer:
             UpperCamelCase_: str   				=    self._write(file_obj=_lowerCamelCase  ,      orient=_lowerCamelCase  ,      lines=_lowerCamelCase  ,      index=_lowerCamelCase  ,      **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.'							)
           UpperCamelCase_: List[Any]   				=    self._write(
               file_obj=self.path_or_buf  ,      orient=_lowerCamelCase  ,      lines=_lowerCamelCase  ,      index=_lowerCamelCase  ,      **self.to_json_kwargs							)
         return written





       def       _a   ( self  ,      _lowerCamelCase							):
         UpperCamelCase_				,UpperCamelCase_				,UpperCamelCase_				,UpperCamelCase_				,UpperCamelCase_: str   				=    args

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





       def       _a   ( self  ,      _lowerCamelCase  ,      _lowerCamelCase  ,      _lowerCamelCase  ,      _lowerCamelCase  ,      **_lowerCamelCase  ,      ):
         UpperCamelCase_: 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'  ,      ):
             UpperCamelCase_: Any   				=    self._batch_json((offset, orient, lines, index, to_json_kwargs)							)
             written += file_obj.write(_lowerCamelCase							)
         else:
           UpperCamelCase_				,UpperCamelCase_: Optional[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  ,      _lowerCamelCase  ,      _lowerCamelCase							)]  ,      )  ,      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(_lowerCamelCase							)

         return written







import unittest

from transformers import RoFormerTokenizer, RoFormerTokenizerFast
from transformers.testing_utils import require_rjieba, require_tokenizers

from ...test_tokenization_common import TokenizerTesterMixin




@require_rjieba
@require_tokenizers
class     _lowerCAmelCase(	UpperCAmelCase_							,     unittest.TestCase ):





       """simple docstring"""







       a    :		Optional[int]									=RoFormerTokenizer
       a    :		int									=RoFormerTokenizerFast
       a    :		int									=True
       a    :		Optional[int]									=True





       def       _a   ( self							):
         super().setUp()





       def       _a   ( self  ,      **_lowerCamelCase							):
         return self.tokenizer_class.from_pretrained('junnyu/roformer_chinese_base'  ,      **_lowerCamelCase							)





       def       _a   ( self  ,      **_lowerCamelCase							):
         return self.rust_tokenizer_class.from_pretrained('junnyu/roformer_chinese_base'  ,      **_lowerCamelCase							)





       def       _a   ( self							):
         UpperCamelCase_: Optional[int]   				=    '永和服装饰品有限公司,今天天气非常好'
         UpperCamelCase_: Any   				=    '永和 服装 饰品 有限公司 , 今 天 天 气 非常 好'
         return input_text, output_text





       def       _a   ( self							):
         UpperCamelCase_: int   				=    self.get_tokenizer()
         UpperCamelCase_				,UpperCamelCase_: int   				=    self.get_chinese_input_output_texts()
         UpperCamelCase_: Tuple   				=    tokenizer.tokenize(_lowerCamelCase							)

         self.assertListEqual(_lowerCamelCase  ,      output_text.split()							)

         UpperCamelCase_: Dict   				=    tokens + [tokenizer.unk_token]
         UpperCamelCase_: Dict   				=    [2_2_9_4_3, 2_1_3_3_2, 3_4_4_3_1, 4_5_9_0_4, 1_1_7, 3_0_6, 1_2_3_1, 1_2_3_1, 2_6_5_3, 3_3_9_9_4, 1_2_6_6, 1_0_0]
         self.assertListEqual(tokenizer.convert_tokens_to_ids(_lowerCamelCase							)  ,      _lowerCamelCase							)





       def       _a   ( self							):
         UpperCamelCase_: Optional[Any]   				=    self.get_rust_tokenizer()
         UpperCamelCase_				,UpperCamelCase_: Tuple   				=    self.get_chinese_input_output_texts()
         UpperCamelCase_: Optional[Any]   				=    tokenizer.tokenize(_lowerCamelCase							)
         self.assertListEqual(_lowerCamelCase  ,      output_text.split()							)
         UpperCamelCase_: str   				=    tokens + [tokenizer.unk_token]
         UpperCamelCase_: Optional[Any]   				=    [2_2_9_4_3, 2_1_3_3_2, 3_4_4_3_1, 4_5_9_0_4, 1_1_7, 3_0_6, 1_2_3_1, 1_2_3_1, 2_6_5_3, 3_3_9_9_4, 1_2_6_6, 1_0_0]
         self.assertListEqual(tokenizer.convert_tokens_to_ids(_lowerCamelCase							)  ,      _lowerCamelCase							)





       def       _a   ( self							):
         pass





       def       _a   ( self							):
         pass





       def       _a   ( self							):
         pass