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

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


SCREAMING_SNAKE_CASE__				=							logging.get_logger(__name__)

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

SCREAMING_SNAKE_CASE__				=							{
    """vocab_file""": {
        """TsinghuaAI/CPM-Generate""": """https://huggingface.co/TsinghuaAI/CPM-Generate/resolve/main/spiece.model""",
    },
    """tokenizer_file""": {
        """TsinghuaAI/CPM-Generate""": """https://huggingface.co/TsinghuaAI/CPM-Generate/resolve/main/tokenizer.json""",
    },
}
class       A__  (		lowerCAmelCase__							):





							def __init__(					self    :  Optional[int]	,					_UpperCAmelCase    :  Tuple=None	,					_UpperCAmelCase    :  int=None	,					_UpperCAmelCase    :  Union[str, Any]=False	,					_UpperCAmelCase    :  str=True	,					_UpperCAmelCase    :  List[str]=False	,					_UpperCAmelCase    :  Dict="<s>"	,					_UpperCAmelCase    :  Optional[int]="</s>"	,					_UpperCAmelCase    :  Optional[Any]="<unk>"	,					_UpperCAmelCase    :  str="<sep>"	,					_UpperCAmelCase    :  int="<pad>"	,					_UpperCAmelCase    :  Any="<cls>"	,					_UpperCAmelCase    :  Dict="<mask>"	,					_UpperCAmelCase    :  int=["<eop>", "<eod>"]	,					**_UpperCAmelCase    :  Union[str, Any]	,					)		->						Union[str, Any]:



											"""simple docstring"""


											__lowercase								=   AddedToken(_UpperCAmelCase	,					lstrip=_UpperCAmelCase	,					rstrip=_UpperCAmelCase							) if isinstance(_UpperCAmelCase	,					_UpperCAmelCase							) else mask_token

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

											__lowercase								=   3
											__lowercase								=   do_lower_case
											__lowercase								=   remove_space
											__lowercase								=   keep_accents
											__lowercase								=   vocab_file
											__lowercase								=   False if not self.vocab_file else True

											try:
															import jieba
											except ModuleNotFoundError as error:
															raise error.__class__(
															    'You need to install jieba to use CpmTokenizer or CpmTokenizerFast. '
															    'See https://pypi.org/project/jieba/ for installation.'							)
											__lowercase								=   jieba
											__lowercase								=   str.maketrans(' \n'	,					'\u2582\u2583'							)





							def      a__					(					self    :  str	,					_UpperCAmelCase    :  List[int]	,					_UpperCAmelCase    :  Optional[List[int]] = None							)		->						List[int]:



											"""simple docstring"""


											__lowercase								=   [self.sep_token_id]
											__lowercase								=   [self.cls_token_id]
											if token_ids_a is None:
															return token_ids_a + sep + cls
											return token_ids_a + sep + token_ids_a + sep + cls





							def      a__					(					self    :  Union[str, Any]	,					_UpperCAmelCase    :  List[int]	,					_UpperCAmelCase    :  Optional[List[int]] = None							)		->						List[int]:



											"""simple docstring"""


											__lowercase								=   [self.sep_token_id]
											__lowercase								=   [2]

											if token_ids_a is None:
															return len(token_ids_a + sep							) * [0] + cls_segment_id
											return len(token_ids_a + sep							) * [0] + len(token_ids_a + sep							) * [1] + cls_segment_id





							def      a__					(					self    :  Dict	,					_UpperCAmelCase    :  str	,					_UpperCAmelCase    :  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(_UpperCAmelCase							):
															logger.error(f"""Vocabulary path ({save_directory}) should be a directory"""							)
															return
											__lowercase								=   os.path.join(
											    _UpperCAmelCase	,					(filename_prefix + '-' if filename_prefix else '') + VOCAB_FILES_NAMES['vocab_file']							)

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

											return (out_vocab_file,)





							def      a__					(					self    :  str	,					_UpperCAmelCase    :  Any	,					*_UpperCAmelCase    :  Optional[Any]	,					**_UpperCAmelCase    :  Optional[int]							)		->						int:



											"""simple docstring"""


											__lowercase								=   [
											    ' '.join([x.translate(self.translator							) for x in self.jieba.cut(_UpperCAmelCase	,					cut_all=_UpperCAmelCase							)]							)
											    for text in batch_text_or_text_pairs
											]
											return super()._batch_encode_plus(_UpperCAmelCase	,					*_UpperCAmelCase	,					**_UpperCAmelCase							)





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



											"""simple docstring"""


											__lowercase								=   super()._decode(*_UpperCAmelCase	,					**_UpperCAmelCase							)
											__lowercase								=   text.replace(' '	,					''							).replace('\u2582'	,					' '							).replace('\u2583'	,					'\n'							)
											return text







import itertools
from dataclasses import dataclass
from io import StringIO
from typing import Optional

import pyarrow as pa

import datasets
from datasets.features.features import require_storage_cast
from datasets.table import table_cast


SCREAMING_SNAKE_CASE__				=							datasets.utils.logging.get_logger(__name__)
@dataclass
class       A__  (		datasets.BuilderConfig							):
							lowerCAmelCase__						:		Optional[datasets.Features]            =				None
							lowerCAmelCase__						:		str            =				"utf-8"
							lowerCAmelCase__						:		Optional[str]            =				None
							lowerCAmelCase__						:		int            =				10 << 20  # 10MB
							lowerCAmelCase__						:		bool            =				False
							lowerCAmelCase__						:		str            =				"line"






class       A__  (		datasets.ArrowBasedBuilder							):
							lowerCAmelCase__						:		Optional[int]            =				TextConfig





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



											"""simple docstring"""


											return datasets.DatasetInfo(features=self.config.features							)





							def      a__					(					self    :  Tuple	,					_UpperCAmelCase    :  str							)		->						List[str]:



											"""simple docstring"""


											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}"""							)
											__lowercase								=   dl_manager.download_and_extract(self.config.data_files							)
											if isinstance(_UpperCAmelCase	,					(str, list, tuple)							):
															__lowercase								=   data_files
															if isinstance(_UpperCAmelCase	,					_UpperCAmelCase							):
																			__lowercase								=   [files]
															__lowercase								=   [dl_manager.iter_files(_UpperCAmelCase							) for file in files]
															return [datasets.SplitGenerator(name=datasets.Split.TRAIN	,					gen_kwargs={'files': files}							)]
											__lowercase								=   []
											for split_name, files in data_files.items():
															if isinstance(_UpperCAmelCase	,					_UpperCAmelCase							):
																			__lowercase								=   [files]
															__lowercase								=   [dl_manager.iter_files(_UpperCAmelCase							) for file in files]
															splits.append(datasets.SplitGenerator(name=_UpperCAmelCase	,					gen_kwargs={'files': files}							)							)
											return splits





							def      a__					(					self    :  int	,					_UpperCAmelCase    :  pa.Table							)		->						pa.Table:



											"""simple docstring"""


											if self.config.features is not None:
															__lowercase								=   self.config.features.arrow_schema
															if all(not require_storage_cast(_UpperCAmelCase							) for feature in self.config.features.values()							):
																			# cheaper cast
																			__lowercase								=   pa_table.cast(_UpperCAmelCase							)
															else:
																			# more expensive cast; allows str <-> int/float or str to Audio for example
																			__lowercase								=   table_cast(_UpperCAmelCase	,					_UpperCAmelCase							)
															return pa_table
											else:
															return pa_table.cast(pa.schema({'text': pa.string()}							)							)





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



											"""simple docstring"""


											__lowercase								=   list(self.config.features							) if self.config.features is not None else ['text']
											for file_idx, file in enumerate(itertools.chain.from_iterable(_UpperCAmelCase							)							):
															# open in text mode, by default translates universal newlines ("\n", "\r\n" and "\r") into "\n"
															with open(_UpperCAmelCase	,					encoding=self.config.encoding	,					errors=self.config.errors							) as f:
																			if self.config.sample_by == "line":
																							__lowercase								=   0
																							while True:
																											__lowercase								=   f.read(self.config.chunksize							)
																											if not batch:
																															break
																											batch += f.readline()  # finish current line
																											# StringIO.readlines, by default splits only on "\n" (and keeps line breaks)
																											__lowercase								=   StringIO(_UpperCAmelCase							).readlines()
																											if not self.config.keep_linebreaks:
																															__lowercase								=   [line.rstrip('\n'							) for line in batch]
																											__lowercase								=   pa.Table.from_arrays([pa.array(_UpperCAmelCase							)]	,					names=_UpperCAmelCase							)
																											# 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(_UpperCAmelCase							)
																											batch_idx += 1
																			elif self.config.sample_by == "paragraph":
																							__lowercase								=   0
																							__lowercase								=   ''
																							while True:
																											__lowercase								=   f.read(self.config.chunksize							)
																											if not new_batch:
																															break
																											batch += new_batch
																											batch += f.readline()  # finish current line
																											__lowercase								=   batch.split('\n\n'							)
																											__lowercase								=   pa.Table.from_arrays(
																											    [pa.array([example for example in batch[:-1] if example]							)]	,					names=_UpperCAmelCase							)
																											# 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(_UpperCAmelCase							)
																											batch_idx += 1
																											__lowercase								=   batch[-1]
																							if batch:
																											__lowercase								=   pa.Table.from_arrays([pa.array([batch]							)]	,					names=_UpperCAmelCase							)
																											yield (file_idx, batch_idx), self._cast_table(_UpperCAmelCase							)
																			elif self.config.sample_by == "document":
																							__lowercase								=   f.read()
																							__lowercase								=   pa.Table.from_arrays([pa.array([text]							)]	,					names=_UpperCAmelCase							)
																							yield file_idx, self._cast_table(_UpperCAmelCase							)





