



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    __lowerCAmelCase					(	lowerCamelCase__			):




				def __init__(				self		,							_snake_case		,							_snake_case = None		,							_snake_case = None		,							_snake_case = None		,							_snake_case = False		,							_snake_case = False		,							_snake_case = None		,							**_snake_case		,							):



									"""simple docstring"""





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




				def 				snake_case  (				self						):



									"""simple docstring"""





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

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





class    __lowerCAmelCase					:




				def __init__(				self		,							_snake_case		,							_snake_case		,							_snake_case = None		,							_snake_case = None		,							**_snake_case		,							):



									"""simple docstring"""





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

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




				def 				snake_case  (				self						):



									"""simple docstring"""





									_lowerCAmelCase         =       self.to_csv_kwargs.pop("""path_or_buf"""		,							_snake_case						)
									_lowerCAmelCase         =       self.to_csv_kwargs.pop("""header"""		,							_snake_case						)
									_lowerCAmelCase         =       self.to_csv_kwargs.pop("""index"""		,							_snake_case						)

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




				def 				snake_case  (				self		,							_snake_case						):



									"""simple docstring"""





									_lowerCAmelCase      ,      _lowerCAmelCase      ,      _lowerCAmelCase      ,      _lowerCAmelCase         =       args

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




				def 				snake_case  (				self		,							_snake_case		,							_snake_case		,							_snake_case		,							**_snake_case						):



									"""simple docstring"""





									_lowerCAmelCase         =       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"""		,							):
																			_lowerCAmelCase         =       self._batch_csv((offset, header, index, to_csv_kwargs)						)
																			written += file_obj.write(_snake_case						)

									else:
														_lowerCAmelCase      ,      _lowerCAmelCase         =       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		,							_snake_case		,							_snake_case						)]		,							)		,							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(_snake_case						)

									return written








"""simple docstring"""
from transformers.models.cpm.tokenization_cpm import CpmTokenizer
from transformers.testing_utils import custom_tokenizers

from ..xlnet.test_modeling_xlnet import XLNetModelTest
@custom_tokenizers
class 							lowerCamelCase    (     _lowerCAmelCase							):






					'''simple docstring'''





					def   lowerCAmelCase_							(			self:				Optional[int]		,	snake_case:				Optional[Any]		,	snake_case:				Optional[Any]		,	snake_case:				Tuple		,	snake_case:				int		,	snake_case:				Optional[Any]		)  ->      int:
											return True






					def   lowerCAmelCase_							(			self:				Tuple		)  ->      Optional[int]:
											snake_case_					:Dict		=     CpmTokenizer.from_pretrained("""TsinghuaAI/CPM-Generate"""		)
											snake_case_					:Any		=     """Hugging Face大法好，谁用谁知道。"""
											snake_case_					:Union[str, Any]		=     """Hugging Face大法好,谁用谁知道。<unk>"""
											snake_case_					:Dict		=     """▁Hu gg ing ▁ ▂ ▁F ace ▁大法 ▁好 ▁ , ▁谁 ▁用 ▁谁 ▁知 道 ▁ 。""".split()

											snake_case_					:str		=     tokenizer.tokenize(snake_case		)
											self.assertListEqual(snake_case		,	snake_case		)

											snake_case_					:List[Any]		=     tokens + [tokenizer.unk_token]

											snake_case_					:Optional[Any]		=     [13_789, 13_283, 1_421, 8, 10, 1_164, 13_608, 16_528, 63, 8, 9, 440, 108, 440, 121, 90, 8, 12, 0]
											self.assertListEqual(tokenizer.convert_tokens_to_ids(snake_case		)		,	snake_case		)

											snake_case_					:int		=     tokenizer.decode(snake_case		)
											self.assertEqual(snake_case		,	snake_case		)
