



"""simple docstring"""


import pyarrow.parquet as pq
import pytest

from datasets import Audio, Dataset, DatasetDict, Features, NamedSplit, Sequence, Value, config
from datasets.features.image import Image
from datasets.io.parquet import ParquetDatasetReader, ParquetDatasetWriter, get_writer_batch_size

from ..utils import assert_arrow_memory_doesnt_increase, assert_arrow_memory_increases







def  lowercase	(      _SCREAMING_SNAKE_CASE   :						int					,     _SCREAMING_SNAKE_CASE   :						Optional[int]				):






				'''simple docstring'''



				assert isinstance(lowerCAmelCase__					,     lowerCAmelCase__				)
				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  lowercase	(      _SCREAMING_SNAKE_CASE   :						Optional[int]					,     _SCREAMING_SNAKE_CASE   :						List[str]					,     _SCREAMING_SNAKE_CASE   :						Any				):






				'''simple docstring'''



				_UpperCAmelCase					=    tmp_path / '''cache'''
				_UpperCAmelCase					=    {'''col_1''': '''string''', '''col_2''': '''int64''', '''col_3''': '''float64'''}
				with assert_arrow_memory_increases() if keep_in_memory else assert_arrow_memory_doesnt_increase():
								_UpperCAmelCase					=    ParquetDatasetReader(lowerCAmelCase__					,     cache_dir=lowerCAmelCase__					,     keep_in_memory=lowerCAmelCase__				).read()
				_check_parquet_dataset(lowerCAmelCase__					,     lowerCAmelCase__				)







@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  lowercase	(      _SCREAMING_SNAKE_CASE   :						List[str]					,     _SCREAMING_SNAKE_CASE   :						Optional[int]					,     _SCREAMING_SNAKE_CASE   :						Optional[int]				):






				'''simple docstring'''



				_UpperCAmelCase					=    tmp_path / '''cache'''
				_UpperCAmelCase					=    {'''col_1''': '''string''', '''col_2''': '''int64''', '''col_3''': '''float64'''}
				_UpperCAmelCase					=    features.copy() if features else default_expected_features
				_UpperCAmelCase					=    (
				    Features({feature: Value(lowerCAmelCase__				) for feature, dtype in features.items()}				) if features is not None else None
				)
				_UpperCAmelCase					=    ParquetDatasetReader(lowerCAmelCase__					,     features=lowerCAmelCase__					,     cache_dir=lowerCAmelCase__				).read()
				_check_parquet_dataset(lowerCAmelCase__					,     lowerCAmelCase__				)







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






				'''simple docstring'''



				_UpperCAmelCase					=    tmp_path / '''cache'''
				_UpperCAmelCase					=    {'''col_1''': '''string''', '''col_2''': '''int64''', '''col_3''': '''float64'''}
				_UpperCAmelCase					=    ParquetDatasetReader(lowerCAmelCase__					,     cache_dir=lowerCAmelCase__					,     split=lowerCAmelCase__				).read()
				_check_parquet_dataset(lowerCAmelCase__					,     lowerCAmelCase__				)
				assert dataset.split == split if split else "train"







@pytest.mark.parametrize('''path_type'''					,     [str, list]				)
def  lowercase	(      _SCREAMING_SNAKE_CASE   :						List[str]					,     _SCREAMING_SNAKE_CASE   :						int					,     _SCREAMING_SNAKE_CASE   :						List[str]				):






				'''simple docstring'''



				if issubclass(lowerCAmelCase__					,     lowerCAmelCase__				):
								_UpperCAmelCase					=    parquet_path
				elif issubclass(lowerCAmelCase__					,     lowerCAmelCase__				):
								_UpperCAmelCase					=    [parquet_path]
				_UpperCAmelCase					=    tmp_path / '''cache'''
				_UpperCAmelCase					=    {'''col_1''': '''string''', '''col_2''': '''int64''', '''col_3''': '''float64'''}
				_UpperCAmelCase					=    ParquetDatasetReader(lowerCAmelCase__					,     cache_dir=lowerCAmelCase__				).read()
				_check_parquet_dataset(lowerCAmelCase__					,     lowerCAmelCase__				)







def  lowercase	(      _SCREAMING_SNAKE_CASE   :						Any					,     _SCREAMING_SNAKE_CASE   :						Any					,     _SCREAMING_SNAKE_CASE   :						List[Any]=("train",)				):






				'''simple docstring'''



				assert isinstance(lowerCAmelCase__					,     lowerCAmelCase__				)
				for split in splits:
								_UpperCAmelCase					=    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  lowercase	(      _SCREAMING_SNAKE_CASE   :						int					,     _SCREAMING_SNAKE_CASE   :						Tuple					,     _SCREAMING_SNAKE_CASE   :						Any				):






				'''simple docstring'''



				_UpperCAmelCase					=    tmp_path / '''cache'''
				_UpperCAmelCase					=    {'''col_1''': '''string''', '''col_2''': '''int64''', '''col_3''': '''float64'''}
				with assert_arrow_memory_increases() if keep_in_memory else assert_arrow_memory_doesnt_increase():
								_UpperCAmelCase					=    ParquetDatasetReader(
								    {'''train''': parquet_path}					,     cache_dir=lowerCAmelCase__					,     keep_in_memory=lowerCAmelCase__				).read()
				_check_parquet_datasetdict(lowerCAmelCase__					,     lowerCAmelCase__				)







@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  lowercase	(      _SCREAMING_SNAKE_CASE   :						str					,     _SCREAMING_SNAKE_CASE   :						Any					,     _SCREAMING_SNAKE_CASE   :						List[Any]				):






				'''simple docstring'''



				_UpperCAmelCase					=    tmp_path / '''cache'''
				_UpperCAmelCase					=    {'''col_1''': '''string''', '''col_2''': '''int64''', '''col_3''': '''float64'''}
				_UpperCAmelCase					=    features.copy() if features else default_expected_features
				_UpperCAmelCase					=    (
				    Features({feature: Value(lowerCAmelCase__				) for feature, dtype in features.items()}				) if features is not None else None
				)
				_UpperCAmelCase					=    ParquetDatasetReader({'''train''': parquet_path}					,     features=lowerCAmelCase__					,     cache_dir=lowerCAmelCase__				).read()
				_check_parquet_datasetdict(lowerCAmelCase__					,     lowerCAmelCase__				)







@pytest.mark.parametrize('''split'''					,     [None, NamedSplit('''train'''				), '''train''', '''test''']				)
def  lowercase	(      _SCREAMING_SNAKE_CASE   :						str					,     _SCREAMING_SNAKE_CASE   :						Optional[int]					,     _SCREAMING_SNAKE_CASE   :						Optional[Any]				):






				'''simple docstring'''



				if split:
								_UpperCAmelCase					=    {split: parquet_path}
				else:
								_UpperCAmelCase					=    '''train'''
								_UpperCAmelCase					=    {'''train''': parquet_path, '''test''': parquet_path}
				_UpperCAmelCase					=    tmp_path / '''cache'''
				_UpperCAmelCase					=    {'''col_1''': '''string''', '''col_2''': '''int64''', '''col_3''': '''float64'''}
				_UpperCAmelCase					=    ParquetDatasetReader(lowerCAmelCase__					,     cache_dir=lowerCAmelCase__				).read()
				_check_parquet_datasetdict(lowerCAmelCase__					,     lowerCAmelCase__					,     splits=list(path.keys()				)				)
				assert all(dataset[split].split == split for split in path.keys()				)







def  lowercase	(      _SCREAMING_SNAKE_CASE   :						List[Any]					,     _SCREAMING_SNAKE_CASE   :						List[Any]				):






				'''simple docstring'''



				_UpperCAmelCase					=    ParquetDatasetWriter(lowerCAmelCase__					,     tmp_path / '''foo.parquet'''				)
				assert writer.write() > 0
				_UpperCAmelCase					=    pq.ParquetFile(tmp_path / '''foo.parquet'''				)
				_UpperCAmelCase					=    pf.read()
				assert dataset.data.table == output_table







def  lowercase	(      _SCREAMING_SNAKE_CASE   :						Any					,     _SCREAMING_SNAKE_CASE   :						Dict				):






				'''simple docstring'''



				_UpperCAmelCase					=    str(shared_datadir / '''test_image_rgb.jpg'''				)
				_UpperCAmelCase					=    {'''image''': [image_path]}
				_UpperCAmelCase					=    Features({'''image''': Image()}				)
				_UpperCAmelCase					=    Dataset.from_dict(lowerCAmelCase__					,     features=lowerCAmelCase__				)
				_UpperCAmelCase					=    ParquetDatasetWriter(lowerCAmelCase__					,     tmp_path / '''foo.parquet'''				)
				assert writer.write() > 0

				_UpperCAmelCase					=    Dataset.from_parquet(str(tmp_path / '''foo.parquet'''				)				)
				assert dataset.features == reloaded_dataset.features

				_UpperCAmelCase					=    ParquetDatasetReader(str(tmp_path / '''foo.parquet'''				)					,     streaming=lowerCAmelCase__				).read()
				assert dataset.features == reloaded_iterable_dataset.features







@pytest.mark.parametrize(
    '''feature, expected'''					,     [
        (Features({'''foo''': Value('''int32'''				)}				), None),
        (Features({'''image''': Image(), '''foo''': Value('''int32'''				)}				), config.PARQUET_ROW_GROUP_SIZE_FOR_IMAGE_DATASETS),
        (Features({'''nested''': Sequence(Audio()				)}				), config.PARQUET_ROW_GROUP_SIZE_FOR_AUDIO_DATASETS),
    ]					,     )
def  lowercase	(      _SCREAMING_SNAKE_CASE   :						int					,     _SCREAMING_SNAKE_CASE   :						str				):






				'''simple docstring'''



				assert get_writer_batch_size(lowerCAmelCase__				) == expected








"""simple docstring"""


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

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


if is_sentencepiece_available():
					from .tokenization_fnet import FNetTokenizer
else:
					__A   :							Tuple										=   None

__A   :							Optional[Any]										=   logging.get_logger(__name__)
__A   :							Union[str, Any]										=   {"vocab_file": "spiece.model", "tokenizer_file": "tokenizer.json"}

__A   :							int										=   {
    "vocab_file": {
        "google/fnet-base": "https://huggingface.co/google/fnet-base/resolve/main/spiece.model",
        "google/fnet-large": "https://huggingface.co/google/fnet-large/resolve/main/spiece.model",
    },
    "tokenizer_file": {
        "google/fnet-base": "https://huggingface.co/google/fnet-base/resolve/main/tokenizer.json",
        "google/fnet-large": "https://huggingface.co/google/fnet-large/resolve/main/tokenizer.json",
    },
}

__A   :							Optional[int]										=   {
    "google/fnet-base": 512,
    "google/fnet-large": 512,
}

__A   :							List[Any]										=   "▁"
class 			_a (					lowerCAmelCase):
			"""simple docstring"""
			UpperCamelCase__			      =	VOCAB_FILES_NAMES
			UpperCamelCase__			      =	PRETRAINED_VOCAB_FILES_MAP
			UpperCamelCase__			      =	PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
			UpperCamelCase__			      =	["""input_ids""", """token_type_ids"""]
			UpperCamelCase__			      =	FNetTokenizer



			def __init__(		self     :    Optional[Any]				,   __UpperCamelCase     :    Tuple=None				,   __UpperCamelCase     :    Dict=None				,   __UpperCamelCase     :    List[str]=False				,   __UpperCamelCase     :    Union[str, Any]=True				,   __UpperCamelCase     :    List[str]=True				,   __UpperCamelCase     :    List[Any]="<unk>"				,   __UpperCamelCase     :    Any="[SEP]"				,   __UpperCamelCase     :    Dict="<pad>"				,   __UpperCamelCase     :    Optional[int]="[CLS]"				,   __UpperCamelCase     :    List[Any]="[MASK]"				,   **__UpperCamelCase     :    Optional[Any]				,   )->Dict:
							# Mask token behave like a normal word, i.e. include the space before it and
							# is included in the raw text, there should be a match in a non-normalized sentence.
							_UpperCAmelCase					=    (
							    AddedToken(__UpperCamelCase				,   lstrip=__UpperCamelCase				,   rstrip=__UpperCamelCase				,   normalized=__UpperCamelCase   )
							    if isinstance(__UpperCamelCase				,   __UpperCamelCase   )
							    else mask_token
							)

							super().__init__(
							    __UpperCamelCase				,   tokenizer_file=__UpperCamelCase				,   do_lower_case=__UpperCamelCase				,   remove_space=__UpperCamelCase				,   keep_accents=__UpperCamelCase				,   unk_token=__UpperCamelCase				,   sep_token=__UpperCamelCase				,   pad_token=__UpperCamelCase				,   cls_token=__UpperCamelCase				,   mask_token=__UpperCamelCase				,   **__UpperCamelCase				,   )

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



			def  lowercase__						(		self     :    int				,   __UpperCamelCase     :    List[int]				,   __UpperCamelCase     :    Optional[List[int]] = None   )->List[int]:
							_UpperCAmelCase					=    [self.sep_token_id]
							_UpperCAmelCase					=    [self.cls_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  lowercase__						(		self     :    Optional[Any]				,   __UpperCamelCase     :    List[int]				,   __UpperCamelCase     :    Optional[List[int]] = None   )->List[int]:
							_UpperCAmelCase					=    [self.sep_token_id]
							_UpperCAmelCase					=    [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  lowercase__						(		self     :    List[str]				,   __UpperCamelCase     :    str				,   __UpperCamelCase     :    Optional[str] = None   )->Tuple[str]:
							if not os.path.isdir(__UpperCamelCase   ):
											logger.error(F'Vocabulary path ({save_directory}) should be a directory'   )
											return
							_UpperCAmelCase					=    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,)



