

import pytest

from datasets import Dataset, DatasetDict, Features, NamedSplit, Value
from datasets.io.text import TextDatasetReader

from ..utils import assert_arrow_memory_doesnt_increase, assert_arrow_memory_increases
def 							__SCREAMING_SNAKE_CASE     (   a__    :   Optional[int]			,a__    :   Any       )					->				Union[str, Any]:
	assert isinstance(a__			,a__       )
	assert dataset.num_rows == 4
	assert dataset.num_columns == 1
	assert dataset.column_names == ["text"]
	for feature, expected_dtype in expected_features.items():
		assert dataset.features[feature].dtype == expected_dtype
@pytest.mark.parametrize("""keep_in_memory"""			,[False, True]       )
def 							__SCREAMING_SNAKE_CASE     (   a__    :   Dict			,a__    :   Optional[Any]			,a__    :   Tuple       )					->				List[Any]:
	__A :			int		    =			tmp_path / """cache"""
	__A :			List[str]		    =			{"""text""": """string"""}
	with assert_arrow_memory_increases() if keep_in_memory else assert_arrow_memory_doesnt_increase():
		__A :			Any		    =			TextDatasetReader(a__			,cache_dir=a__			,keep_in_memory=a__       ).read()
	_check_text_dataset(a__			,a__       )
@pytest.mark.parametrize(
    """features"""			,[
        None,
        {"""text""": """string"""},
        {"""text""": """int32"""},
        {"""text""": """float32"""},
    ]			,)
def 							__SCREAMING_SNAKE_CASE     (   a__    :   Union[str, Any]			,a__    :   List[str]			,a__    :   List[str]       )					->				List[str]:
	__A :			Optional[int]		    =			tmp_path / """cache"""
	__A :			List[Any]		    =			{"""text""": """string"""}
	__A :			List[str]		    =			features.copy() if features else default_expected_features
	__A :			Union[str, Any]		    =			(
	    Features({feature: Value(a__       ) for feature, dtype in features.items()}       ) if features is not None else None
	)
	__A :			str		    =			TextDatasetReader(a__			,features=a__			,cache_dir=a__       ).read()
	_check_text_dataset(a__			,a__       )
@pytest.mark.parametrize("""split"""			,[None, NamedSplit("""train"""       ), """train""", """test"""]       )
def 							__SCREAMING_SNAKE_CASE     (   a__    :   Any			,a__    :   List[str]			,a__    :   str       )					->				Dict:
	__A :			Optional[int]		    =			tmp_path / """cache"""
	__A :			List[Any]		    =			{"""text""": """string"""}
	__A :			List[Any]		    =			TextDatasetReader(a__			,cache_dir=a__			,split=a__       ).read()
	_check_text_dataset(a__			,a__       )
	assert dataset.split == split if split else "train"
@pytest.mark.parametrize("""path_type"""			,[str, list]       )
def 							__SCREAMING_SNAKE_CASE     (   a__    :   Dict			,a__    :   List[Any]			,a__    :   Optional[int]       )					->				Optional[int]:
	if issubclass(a__			,a__       ):
		__A :			Optional[Any]		    =			text_path
	elif issubclass(a__			,a__       ):
		__A :			str		    =			[text_path]
	__A :			List[str]		    =			tmp_path / """cache"""
	__A :			Tuple		    =			{"""text""": """string"""}
	__A :			List[Any]		    =			TextDatasetReader(a__			,cache_dir=a__       ).read()
	_check_text_dataset(a__			,a__       )
def 							__SCREAMING_SNAKE_CASE     (   a__    :   Union[str, Any]			,a__    :   List[Any]			,a__    :   Optional[Any]=("train",)       )					->				Tuple:
	assert isinstance(a__			,a__       )
	for split in splits:
		__A :			Union[str, Any]		    =			dataset_dict[split]
		assert dataset.num_rows == 4
		assert dataset.num_columns == 1
		assert dataset.column_names == ["text"]
		for feature, expected_dtype in expected_features.items():
			assert dataset.features[feature].dtype == expected_dtype
@pytest.mark.parametrize("""keep_in_memory"""			,[False, True]       )
def 							__SCREAMING_SNAKE_CASE     (   a__    :   str			,a__    :   str			,a__    :   str       )					->				Any:
	__A :			Dict		    =			tmp_path / """cache"""
	__A :			Optional[int]		    =			{"""text""": """string"""}
	with assert_arrow_memory_increases() if keep_in_memory else assert_arrow_memory_doesnt_increase():
		__A :			Dict		    =			TextDatasetReader({"""train""": text_path}			,cache_dir=a__			,keep_in_memory=a__       ).read()
	_check_text_datasetdict(a__			,a__       )
@pytest.mark.parametrize(
    """features"""			,[
        None,
        {"""text""": """string"""},
        {"""text""": """int32"""},
        {"""text""": """float32"""},
    ]			,)
def 							__SCREAMING_SNAKE_CASE     (   a__    :   str			,a__    :   int			,a__    :   Dict       )					->				Tuple:
	__A :			Any		    =			tmp_path / """cache"""
	# CSV file loses col_1 string dtype information: default now is "int64" instead of "string"
	__A :			Optional[Any]		    =			{"""text""": """string"""}
	__A :			int		    =			features.copy() if features else default_expected_features
	__A :			Optional[Any]		    =			(
	    Features({feature: Value(a__       ) for feature, dtype in features.items()}       ) if features is not None else None
	)
	__A :			Tuple		    =			TextDatasetReader({"""train""": text_path}			,features=a__			,cache_dir=a__       ).read()
	_check_text_datasetdict(a__			,a__       )







@pytest.mark.parametrize("""split"""			,[None, NamedSplit("""train"""       ), """train""", """test"""]       )
def 							__SCREAMING_SNAKE_CASE     (   a__    :   str			,a__    :   List[Any]			,a__    :   Tuple       )					->				Any:
	if split:
		__A :			int		    =			{split: text_path}
	else:
		__A :			Union[str, Any]		    =			"""train"""
		__A :			int		    =			{"""train""": text_path, """test""": text_path}
	__A :			Any		    =			tmp_path / """cache"""
	__A :			str		    =			{"""text""": """string"""}
	__A :			List[str]		    =			TextDatasetReader(a__			,cache_dir=a__       ).read()
	_check_text_datasetdict(a__			,a__			,splits=list(path.keys()       )       )
	assert all(dataset[split].split == split for split in path.keys()       )











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:
   lowerCamelCase__					:  Optional[Any]      							=				None

lowerCamelCase__					:  List[str]      							=				logging.get_logger(__name__)
lowerCamelCase__					:  List[str]      							=				{'vocab_file': 'spiece.model', 'tokenizer_file': 'tokenizer.json'}

lowerCamelCase__					:  List[str]      							=				{
    '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',
    },
}

lowerCamelCase__					:  Optional[Any]      							=				{
    'google/fnet-base': 512,
    'google/fnet-large': 512,
}

lowerCamelCase__					:  List[Any]      							=				'▁'





class 			lowerCamelCase_				( _SCREAMING_SNAKE_CASE   ):


 '''simple docstring'''







 lowercase_							       =       VOCAB_FILES_NAMES
 lowercase_							       =       PRETRAINED_VOCAB_FILES_MAP
 lowercase_							       =       PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
 lowercase_							       =       ["input_ids", "token_type_ids"]
 lowercase_							       =       FNetTokenizer



 def __init__(				self    :						List[Any]							,							_lowerCAmelCase    :						Dict=None							,							_lowerCAmelCase    :						Dict=None							,							_lowerCAmelCase    :						List[str]=False							,							_lowerCAmelCase    :						Optional[Any]=True							,							_lowerCAmelCase    :						Tuple=True							,							_lowerCAmelCase    :						List[Any]="<unk>"							,							_lowerCAmelCase    :						Optional[Any]="[SEP]"							,							_lowerCAmelCase    :						Optional[Any]="<pad>"							,							_lowerCAmelCase    :						Optional[int]="[CLS]"							,							_lowerCAmelCase    :						Optional[Any]="[MASK]"							,							**_lowerCAmelCase    :						Any							,							):
       # 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.
       SCREAMING_SNAKE_CASE_      		=     (
           AddedToken(_lowerCAmelCase							,							lstrip=_lowerCAmelCase							,							rstrip=_lowerCAmelCase							,							normalized=_lowerCAmelCase		)
           if isinstance(_lowerCAmelCase							,							_lowerCAmelCase		)
           else mask_token
       )

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

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



 def  lowerCAmelCase_    (				self    :						Any							,							_lowerCAmelCase    :						List[int]							,							_lowerCAmelCase    :						Optional[List[int]] = None		):
       SCREAMING_SNAKE_CASE_      		=     [self.sep_token_id]
       SCREAMING_SNAKE_CASE_      		=     [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  lowerCAmelCase_    (				self    :						Optional[int]							,							_lowerCAmelCase    :						List[int]							,							_lowerCAmelCase    :						Optional[List[int]] = None		):
       SCREAMING_SNAKE_CASE_      		=     [self.sep_token_id]
       SCREAMING_SNAKE_CASE_      		=     [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  lowerCAmelCase_    (				self    :						Optional[Any]							,							_lowerCAmelCase    :						str							,							_lowerCAmelCase    :						Optional[str] = None		):
       if not os.path.isdir(_lowerCAmelCase		):
             logger.error(F"Vocabulary path ({save_directory}) should be a directory"		)
             return
       SCREAMING_SNAKE_CASE_      		=     os.path.join(
           _lowerCAmelCase							,							(filename_prefix + '-' if filename_prefix else '') + VOCAB_FILES_NAMES['vocab_file']		)

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

       return (out_vocab_file,)