





'''simple docstring'''

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

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


lowerCAmelCase__	:					Dict	     =       logging.get_logger(__name__)

lowerCAmelCase__	:					str	     =       {'vocab_file': 'spiece.model', 'tokenizer_file': 'tokenizer.json'}

lowerCAmelCase__	:					str	     =       {
    '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 			SCREAMING_SNAKE_CASE__	(					snake_case__						):







					"""simple docstring"""






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







							"""simple docstring"""
							__UpperCAmelCase     :				Tuple					   =			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_						,     )

							__UpperCAmelCase     :				Dict					   =			3
							__UpperCAmelCase     :				List[Any]					   =			do_lower_case
							__UpperCAmelCase     :				Dict					   =			remove_space
							__UpperCAmelCase     :				Dict					   =			keep_accents
							__UpperCAmelCase     :				int					   =			vocab_file
							__UpperCAmelCase     :				int					   =			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."						)
							__UpperCAmelCase     :				Optional[Any]					   =			jieba
							__UpperCAmelCase     :				Union[str, Any]					   =			str.maketrans(" \n"						,     "\u2582\u2583"						)
					def 						lowerCamelCase_					(					self    :	List[Any]						,     UpperCAmelCase_    :	List[int]						,     UpperCAmelCase_    :	Optional[List[int]] = None						):







							"""simple docstring"""
							__UpperCAmelCase     :				int					   =			[self.sep_token_id]
							__UpperCAmelCase     :				str					   =			[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 						lowerCamelCase_					(					self    :	Any						,     UpperCAmelCase_    :	List[int]						,     UpperCAmelCase_    :	Optional[List[int]] = None						):







							"""simple docstring"""
							__UpperCAmelCase     :				Optional[int]					   =			[self.sep_token_id]
							__UpperCAmelCase     :				Optional[Any]					   =			[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 						lowerCamelCase_					(					self    :	Optional[Any]						,     UpperCAmelCase_    :	str						,     UpperCAmelCase_    :	Optional[str] = None						):







							"""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
							__UpperCAmelCase     :				List[str]					   =			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 						lowerCamelCase_					(					self    :	Any						,     UpperCAmelCase_    :	str						,     *UpperCAmelCase_    :	str						,     **UpperCAmelCase_    :	Dict						):







							"""simple docstring"""
							__UpperCAmelCase     :				Dict					   =			[
							    ' '.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 						lowerCamelCase_					(					self    :	Any						,     *UpperCAmelCase_    :	Optional[Any]						,     **UpperCAmelCase_    :	Union[str, Any]						):







							"""simple docstring"""
							__UpperCAmelCase     :				Optional[int]					   =			super()._decode(*UpperCAmelCase_						,     **UpperCAmelCase_						)
							__UpperCAmelCase     :				Dict					   =			text.replace(" "						,     ""						).replace("\u2582"						,     " "						).replace("\u2583"						,     "\n"						)
							return text












"""simple docstring"""


import csv
import os

import pytest

from datasets import Dataset, DatasetDict, Features, NamedSplit, Value
from datasets.io.csv import CsvDatasetReader, CsvDatasetWriter

from ..utils import assert_arrow_memory_doesnt_increase, assert_arrow_memory_increases




def 			snake_case  (A_      :Dict	,			A_      :Tuple      ):







  '''simple docstring'''

  assert isinstance(A_	,			A_      )
  assert dataset.num_rows == 4
  assert dataset.num_columns == 3
  assert dataset.column_names == ["col_1", "col_2", "col_3"]
  for feature, expected_dtype in expected_features.items():
    assert dataset.features[feature].dtype == expected_dtype




@pytest.mark.parametrize('keep_in_memory'	,			[False, True]      )
def 			snake_case  (A_      :str	,			A_      :List[Any]	,			A_      :Optional[Any]      ):







  '''simple docstring'''

  a     :			List[Any]				=   tmp_path / 'cache'
  a     :			str				=   {'col_1': 'int64', 'col_2': 'int64', 'col_3': 'float64'}
  with assert_arrow_memory_increases() if keep_in_memory else assert_arrow_memory_doesnt_increase():
    a     :			Union[str, Any]				=   CsvDatasetReader(A_	,			cache_dir=A_	,			keep_in_memory=A_      ).read()
  _check_csv_dataset(A_	,			A_      )




@pytest.mark.parametrize(
    'features'	,			[
        None,
        {'col_1': 'string', 'col_2': 'int64', 'col_3': 'float64'},
        {'col_1': 'string', 'col_2': 'string', 'col_3': 'string'},
        {'col_1': 'int32', 'col_2': 'int32', 'col_3': 'int32'},
        {'col_1': 'float32', 'col_2': 'float32', 'col_3': 'float32'},
    ]	,			)
def 			snake_case  (A_      :List[str]	,			A_      :Union[str, Any]	,			A_      :Union[str, Any]      ):







  '''simple docstring'''

  a     :			str				=   tmp_path / 'cache'
  # CSV file loses col_1 string dtype information: default now is "int64" instead of "string"
  a     :			Optional[int]				=   {'col_1': 'int64', 'col_2': 'int64', 'col_3': 'float64'}
  a     :			Dict				=   features.copy() if features else default_expected_features
  a     :			Dict				=   (
      Features({feature: Value(A_      ) for feature, dtype in features.items()}      ) if features is not None else None
  )
  a     :			Tuple				=   CsvDatasetReader(A_	,			features=A_	,			cache_dir=A_      ).read()
  _check_csv_dataset(A_	,			A_      )




@pytest.mark.parametrize('split'	,			[None, NamedSplit('train'      ), 'train', 'test']      )
def 			snake_case  (A_      :List[str]	,			A_      :str	,			A_      :int      ):







  '''simple docstring'''

  a     :			str				=   tmp_path / 'cache'
  a     :			Tuple				=   {'col_1': 'int64', 'col_2': 'int64', 'col_3': 'float64'}
  a     :			List[str]				=   CsvDatasetReader(A_	,			cache_dir=A_	,			split=A_      ).read()
  _check_csv_dataset(A_	,			A_      )
  assert dataset.split == split if split else "train"




@pytest.mark.parametrize('path_type'	,			[str, list]      )
def 			snake_case  (A_      :int	,			A_      :List[Any]	,			A_      :str      ):







  '''simple docstring'''

  if issubclass(A_	,			A_      ):
    a     :			Dict				=   csv_path
  elif issubclass(A_	,			A_      ):
    a     :			Optional[Any]				=   [csv_path]
  a     :			Tuple				=   tmp_path / 'cache'
  a     :			str				=   {'col_1': 'int64', 'col_2': 'int64', 'col_3': 'float64'}
  a     :			Any				=   CsvDatasetReader(A_	,			cache_dir=A_      ).read()
  _check_csv_dataset(A_	,			A_      )




def 			snake_case  (A_      :List[str]	,			A_      :Dict	,			A_      :Optional[Any]=("train",)      ):







  '''simple docstring'''

  assert isinstance(A_	,			A_      )
  for split in splits:
    a     :			List[Any]				=   dataset_dict[split]
    assert dataset.num_rows == 4
    assert dataset.num_columns == 3
    assert dataset.column_names == ["col_1", "col_2", "col_3"]
    for feature, expected_dtype in expected_features.items():
      assert dataset.features[feature].dtype == expected_dtype




@pytest.mark.parametrize('keep_in_memory'	,			[False, True]      )
def 			snake_case  (A_      :str	,			A_      :Any	,			A_      :List[str]      ):







  '''simple docstring'''

  a     :			Any				=   tmp_path / 'cache'
  a     :			int				=   {'col_1': 'int64', 'col_2': 'int64', 'col_3': 'float64'}
  with assert_arrow_memory_increases() if keep_in_memory else assert_arrow_memory_doesnt_increase():
    a     :			str				=   CsvDatasetReader({'train': csv_path}	,			cache_dir=A_	,			keep_in_memory=A_      ).read()
  _check_csv_datasetdict(A_	,			A_      )




@pytest.mark.parametrize(
    'features'	,			[
        None,
        {'col_1': 'string', 'col_2': 'int64', 'col_3': 'float64'},
        {'col_1': 'string', 'col_2': 'string', 'col_3': 'string'},
        {'col_1': 'int32', 'col_2': 'int32', 'col_3': 'int32'},
        {'col_1': 'float32', 'col_2': 'float32', 'col_3': 'float32'},
    ]	,			)
def 			snake_case  (A_      :Optional[int]	,			A_      :List[Any]	,			A_      :Tuple      ):







  '''simple docstring'''

  a     :			Dict				=   tmp_path / 'cache'
  # CSV file loses col_1 string dtype information: default now is "int64" instead of "string"
  a     :			Any				=   {'col_1': 'int64', 'col_2': 'int64', 'col_3': 'float64'}
  a     :			Tuple				=   features.copy() if features else default_expected_features
  a     :			Optional[int]				=   (
      Features({feature: Value(A_      ) for feature, dtype in features.items()}      ) if features is not None else None
  )
  a     :			Any				=   CsvDatasetReader({'train': csv_path}	,			features=A_	,			cache_dir=A_      ).read()
  _check_csv_datasetdict(A_	,			A_      )




@pytest.mark.parametrize('split'	,			[None, NamedSplit('train'      ), 'train', 'test']      )
def 			snake_case  (A_      :List[str]	,			A_      :Optional[Any]	,			A_      :str      ):







  '''simple docstring'''

  if split:
    a     :			List[Any]				=   {split: csv_path}
  else:
    a     :			str				=   {'train': csv_path, 'test': csv_path}
  a     :			Union[str, Any]				=   tmp_path / 'cache'
  a     :			Tuple				=   {'col_1': 'int64', 'col_2': 'int64', 'col_3': 'float64'}
  a     :			List[str]				=   CsvDatasetReader(A_	,			cache_dir=A_      ).read()
  _check_csv_datasetdict(A_	,			A_	,			splits=list(path.keys()      )      )
  assert all(dataset[split].split == split for split in path.keys()      )




def 			snake_case  (A_      :Dict      ):







  '''simple docstring'''

  with open(A_	,			encoding='utf-8'      ) as csvfile:
    yield from csv.reader(A_      )




def 			snake_case  (A_      :int	,			A_      :Optional[int]      ):







  '''simple docstring'''

  a     :			str				=   tmp_path / 'cache'
  a     :			List[str]				=   os.path.join(A_	,			'tmp.csv'      )
  a     :			Union[str, Any]				=   CsvDatasetReader({'train': csv_path}	,			cache_dir=A_      ).read()
  CsvDatasetWriter(dataset['train']	,			A_	,			num_proc=1      ).write()

  a     :			int				=   iter_csv_file(A_      )
  a     :			Optional[Any]				=   iter_csv_file(A_      )

  for rowa, rowa in zip(A_	,			A_      ):
    assert rowa == rowa




def 			snake_case  (A_      :Tuple	,			A_      :Tuple      ):







  '''simple docstring'''

  a     :			Union[str, Any]				=   tmp_path / 'cache'
  a     :			List[Any]				=   os.path.join(A_	,			'tmp.csv'      )
  a     :			Union[str, Any]				=   CsvDatasetReader({'train': csv_path}	,			cache_dir=A_      ).read()
  CsvDatasetWriter(dataset['train']	,			A_	,			num_proc=2      ).write()

  a     :			Dict				=   iter_csv_file(A_      )
  a     :			List[Any]				=   iter_csv_file(A_      )

  for rowa, rowa in zip(A_	,			A_      ):
    assert rowa == rowa






def 			snake_case  (A_      :Union[str, Any]	,			A_      :List[Any]      ):







  '''simple docstring'''

  a     :			List[Any]				=   tmp_path / 'cache'
  a     :			Dict				=   os.path.join(A_	,			'tmp.csv'      )
  a     :			Union[str, Any]				=   CsvDatasetReader({'train': csv_path}	,			cache_dir=A_      ).read()
  with pytest.raises(A_      ):
    CsvDatasetWriter(dataset['train']	,			A_	,			num_proc=0      )




