import os
from collections import namedtuple

import pytest

from datasets import ClassLabel, Features, Sequence, Value
from datasets.commands.test import TestCommand
from datasets.info import DatasetInfo, DatasetInfosDict


__lowercase							 =					namedtuple(
    '''_TestCommandArgs''',
    [
        '''dataset''',
        '''name''',
        '''cache_dir''',
        '''data_dir''',
        '''all_configs''',
        '''save_infos''',
        '''ignore_verifications''',
        '''force_redownload''',
        '''clear_cache''',
    ],
    defaults=[None, None, None, False, False, False, False, False],
)



def lowerCamelCase  (   SCREAMING_SNAKE_CASE  ,			SCREAMING_SNAKE_CASE	):
    '''simple docstring'''







    return (abs(source - target	) / target) < 0.01



@pytest.mark.integration
def lowerCamelCase  (   SCREAMING_SNAKE_CASE	):
    '''simple docstring'''







    __UpperCamelCase     :Union[str, Any]			  =		_TestCommandArgs(dataset=SCREAMING_SNAKE_CASE  ,			all_configs=SCREAMING_SNAKE_CASE  ,			save_infos=SCREAMING_SNAKE_CASE	)
    __UpperCamelCase     :List[str]			  =		TestCommand(*SCREAMING_SNAKE_CASE	)
    test_command.run()
    __UpperCamelCase     :int			  =		os.path.join(SCREAMING_SNAKE_CASE  ,			'''README.md'''	)
    assert os.path.exists(SCREAMING_SNAKE_CASE	)
    __UpperCamelCase     :List[str]			  =		DatasetInfosDict.from_directory(SCREAMING_SNAKE_CASE	)
    __UpperCamelCase     :Union[str, Any]			  =		DatasetInfosDict(
        {
            '''default''': DatasetInfo(
                features=Features(
                    {
                        '''tokens''': Sequence(Value('''string'''	)	),
                        '''ner_tags''': Sequence(
                            ClassLabel(names=['''O''', '''B-PER''', '''I-PER''', '''B-ORG''', '''I-ORG''', '''B-LOC''', '''I-LOC''']	)	),
                        '''langs''': Sequence(Value('''string'''	)	),
                        '''spans''': Sequence(Value('''string'''	)	),
                    }	)  ,			splits=[
                    {
                        '''name''': '''train''',
                        '''num_bytes''': 2_351_563,
                        '''num_examples''': 10_000,
                    },
                    {
                        '''name''': '''validation''',
                        '''num_bytes''': 238_418,
                        '''num_examples''': 1_000,
                    },
                ]  ,			download_size=3_940_680  ,			dataset_size=2_589_981  ,			)
        }	)
    assert dataset_infos.keys() == expected_dataset_infos.keys()
    for key in DatasetInfo._INCLUDED_INFO_IN_YAML:
        __UpperCamelCase  ,       __UpperCamelCase     :Optional[int]			  =		getattr(dataset_infos['''default''']  ,			SCREAMING_SNAKE_CASE	), getattr(expected_dataset_infos['''default''']  ,			SCREAMING_SNAKE_CASE	)
        if key == "num_bytes":
            assert is_apercent_close(SCREAMING_SNAKE_CASE  ,			SCREAMING_SNAKE_CASE	)
        elif key == "splits":
            assert list(SCREAMING_SNAKE_CASE	) == list(SCREAMING_SNAKE_CASE	)
            for split in result:
                assert result[split].name == expected[split].name
                assert result[split].num_examples == expected[split].num_examples
                assert is_apercent_close(result[split].num_bytes  ,			expected[split].num_bytes	)
        else:
            result == 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:
 snake_case__						:   str			 = None

snake_case__						:   Optional[Any]			 = logging.get_logger(__name__)
snake_case__						:   Optional[int]			 = {'''vocab_file''': '''spiece.model''', '''tokenizer_file''': '''tokenizer.json'''}

snake_case__						:   Dict			 = {
    '''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''',
    },
}

snake_case__						:   Any			 = {
    '''google/fnet-base''': 512,
    '''google/fnet-large''': 512,
}

snake_case__						:   Dict			 = '''▁'''



class 						snake_case_(				a__	):
     __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     :    Union[str, Any]    , UpperCamelCase_     :    Union[str, Any]=None    , UpperCamelCase_     :    Union[str, Any]=None    , UpperCamelCase_     :    Any=False    , UpperCamelCase_     :    Any=True    , UpperCamelCase_     :    Dict=True    , UpperCamelCase_     :    Tuple="<unk>"    , UpperCamelCase_     :    List[str]="[SEP]"    , UpperCamelCase_     :    List[Any]="<pad>"    , UpperCamelCase_     :    Union[str, Any]="[CLS]"    , UpperCamelCase_     :    int="[MASK]"    , **UpperCamelCase_     :    Optional[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.
           lowerCAmelCase    :		int				=      (
               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_    , )

           lowerCAmelCase    :		Optional[int]				=      do_lower_case
           lowerCAmelCase    :		str				=      remove_space
           lowerCAmelCase    :		Any				=      keep_accents
           lowerCAmelCase    :		int				=      vocab_file
           lowerCAmelCase    :		List[str]				=      False if not self.vocab_file else True






     def    lowerCamelCase__   (		self     :    List[Any]    , UpperCamelCase_     :    List[int]    , UpperCamelCase_     :    Optional[List[int]] = None ):
           lowerCAmelCase    :		Optional[int]				=      [self.sep_token_id]
           lowerCAmelCase    :		Optional[Any]				=      [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     :    List[str]    , UpperCamelCase_     :    List[int]    , UpperCamelCase_     :    Optional[List[int]] = None ):
           lowerCAmelCase    :		List[str]				=      [self.sep_token_id]
           lowerCAmelCase    :		Optional[Any]				=      [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     :    List[Any]    , UpperCamelCase_     :    str    , UpperCamelCase_     :    Optional[str] = None ):
           if not os.path.isdir(UpperCamelCase_ ):
                 logger.error(F'''Vocabulary path ({save_directory}) should be a directory''' )
                 return
           lowerCAmelCase    :		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,)


