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


UpperCamelCase  							=						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 			lowercase_				(			_lowerCamelCase							:       Union[str, Any]		,	_lowerCamelCase							:       Any):
    return (abs(source - target) / target) < 0.01

@pytest.mark.integration
def 			lowercase_				(			_lowerCamelCase							:       Any):
    lowercase__ :   Optional[int]    					=				_TestCommandArgs(dataset=_lowerCamelCase		,	all_configs=_lowerCamelCase		,	save_infos=_lowerCamelCase)
    lowercase__ :   int    					=				TestCommand(*_lowerCamelCase)
    test_command.run()
    lowercase__ :   Dict    					=				os.path.join(_lowerCamelCase		,	"README.md")
    assert os.path.exists(_lowerCamelCase)
    lowercase__ :   str    					=				DatasetInfosDict.from_directory(_lowerCamelCase)
    lowercase__ :   Dict    					=				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": 235_1563,
                        "num_examples": 1_0000,
                    },
                    {
                        "name": "validation",
                        "num_bytes": 23_8418,
                        "num_examples": 1000,
                    },
                ]		,	download_size=394_0680		,	dataset_size=258_9981		,	)
        })
    assert dataset_infos.keys() == expected_dataset_infos.keys()
    for key in DatasetInfo._INCLUDED_INFO_IN_YAML:
        lowercase__					,   lowercase__ :   Union[str, Any]    					=				getattr(dataset_infos["default"]		,	_lowerCamelCase), getattr(expected_dataset_infos["default"]		,	_lowerCamelCase)
        if key == "num_bytes":
            assert is_apercent_close(_lowerCamelCase		,	_lowerCamelCase)
        elif key == "splits":
            assert list(_lowerCamelCase) == list(_lowerCamelCase)
            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:
						lowerCamelCase__					=   None

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

lowerCamelCase__					=   {
    '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__					=   {
    'google/fnet-base': 512,
    'google/fnet-large': 512,
}

lowerCamelCase__					=   '▁'






class 		lowerCAmelCase__	(	UpperCAmelCase__		):
			lowerCAmelCase       :		Dict				      =   VOCAB_FILES_NAMES
			lowerCAmelCase       :		Optional[Any]				      =   PRETRAINED_VOCAB_FILES_MAP
			lowerCAmelCase       :		List[str]				      =   PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
			lowerCAmelCase       :		Optional[int]				      =   ["input_ids", "token_type_ids"]
			lowerCAmelCase       :		Optional[Any]				      =   FNetTokenizer




			def __init__(						self     :   Dict		,     lowerCamelCase__     :   int=None		,     lowerCamelCase__     :   List[Any]=None		,     lowerCamelCase__     :   Any=False		,     lowerCamelCase__     :   List[str]=True		,     lowerCamelCase__     :   Dict=True		,     lowerCamelCase__     :   str="<unk>"		,     lowerCamelCase__     :   List[str]="[SEP]"		,     lowerCamelCase__     :   Union[str, Any]="<pad>"		,     lowerCamelCase__     :   Optional[Any]="[CLS]"		,     lowerCamelCase__     :   Any="[MASK]"		,     **lowerCamelCase__     :   Any		,     )      ->List[Any]:



					'''simple docstring'''







					_UpperCAmelCase						:				Dict	    =					(
					    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__		,     )

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




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



					'''simple docstring'''







					_UpperCAmelCase						:				Optional[Any]	    =					[self.sep_token_id]
					_UpperCAmelCase						:				Tuple	    =					[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     :   int		,     lowerCamelCase__     :   List[int]		,     lowerCamelCase__     :   Optional[List[int]] = None       )      ->List[int]:



					'''simple docstring'''







					_UpperCAmelCase						:				Optional[int]	    =					[self.sep_token_id]
					_UpperCAmelCase						:				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     :   Dict		,     lowerCamelCase__     :   str		,     lowerCamelCase__     :   Optional[str] = None       )      ->Tuple[str]:



					'''simple docstring'''







					if not os.path.isdir(lowerCamelCase__       ):
							logger.error(F"""Vocabulary path ({save_directory}) should be a directory"""       )
							return
					_UpperCAmelCase						:				Union[str, Any]	    =					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,)



