





import json
from typing import List, Optional, Tuple

from tokenizers import normalizers
from tokenizers.pre_tokenizers import BertPreTokenizer, PreTokenizer

from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_roformer import RoFormerTokenizer
from .tokenization_utils import JiebaPreTokenizer


SCREAMING_SNAKE_CASE_   :      Dict			=   logging.get_logger(__name__)

SCREAMING_SNAKE_CASE_   :      Optional[int]			=   {'''vocab_file''': '''vocab.txt''', '''tokenizer_file''': '''tokenizer.json'''}

SCREAMING_SNAKE_CASE_   :      Any			=   {
    '''vocab_file''': {
        '''junnyu/roformer_chinese_small''': '''https://huggingface.co/junnyu/roformer_chinese_small/resolve/main/vocab.txt''',
        '''junnyu/roformer_chinese_base''': '''https://huggingface.co/junnyu/roformer_chinese_base/resolve/main/vocab.txt''',
        '''junnyu/roformer_chinese_char_small''': (
            '''https://huggingface.co/junnyu/roformer_chinese_char_small/resolve/main/vocab.txt'''
        ),
        '''junnyu/roformer_chinese_char_base''': (
            '''https://huggingface.co/junnyu/roformer_chinese_char_base/resolve/main/vocab.txt'''
        ),
        '''junnyu/roformer_small_discriminator''': (
            '''https://huggingface.co/junnyu/roformer_small_discriminator/resolve/main/vocab.txt'''
        ),
        '''junnyu/roformer_small_generator''': (
            '''https://huggingface.co/junnyu/roformer_small_generator/resolve/main/vocab.txt'''
        ),
    }
}

SCREAMING_SNAKE_CASE_   :      Any			=   {
    '''junnyu/roformer_chinese_small''': 1536,
    '''junnyu/roformer_chinese_base''': 1536,
    '''junnyu/roformer_chinese_char_small''': 512,
    '''junnyu/roformer_chinese_char_base''': 512,
    '''junnyu/roformer_small_discriminator''': 128,
    '''junnyu/roformer_small_generator''': 128,
}


SCREAMING_SNAKE_CASE_   :      List[str]			=   {
    '''junnyu/roformer_chinese_small''': {'''do_lower_case''': True},
    '''junnyu/roformer_chinese_base''': {'''do_lower_case''': True},
    '''junnyu/roformer_chinese_char_small''': {'''do_lower_case''': True},
    '''junnyu/roformer_chinese_char_base''': {'''do_lower_case''': True},
    '''junnyu/roformer_small_discriminator''': {'''do_lower_case''': True},
    '''junnyu/roformer_small_generator''': {'''do_lower_case''': True},
}

class 		snake_case_   (							UpperCAmelCase_						):







	'''simple docstring'''
	__UpperCamelCase   =		VOCAB_FILES_NAMES
	__UpperCamelCase   =		PRETRAINED_VOCAB_FILES_MAP
	__UpperCamelCase   =		PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
	__UpperCamelCase   =		PRETRAINED_INIT_CONFIGURATION
	__UpperCamelCase   =		RoFormerTokenizer




	def __init__(						self							:     str					,      __lowerCamelCase							:     int=None					,      __lowerCamelCase							:     List[str]=None					,      __lowerCamelCase							:     int=True					,      __lowerCamelCase							:     Optional[Any]="[UNK]"					,      __lowerCamelCase							:     List[str]="[SEP]"					,      __lowerCamelCase							:     Any="[PAD]"					,      __lowerCamelCase							:     Optional[int]="[CLS]"					,      __lowerCamelCase							:     Any="[MASK]"					,      __lowerCamelCase							:     Tuple=True					,      __lowerCamelCase							:     Optional[Any]=None					,      **__lowerCamelCase							:     Optional[Any]					,      )       -> List[str]:

			'''simple docstring'''




			super().__init__(
			    __lowerCamelCase					,      tokenizer_file=__lowerCamelCase					,      do_lower_case=__lowerCamelCase					,      unk_token=__lowerCamelCase					,      sep_token=__lowerCamelCase					,      pad_token=__lowerCamelCase					,      cls_token=__lowerCamelCase					,      mask_token=__lowerCamelCase					,      tokenize_chinese_chars=__lowerCamelCase					,      strip_accents=__lowerCamelCase					,      **__lowerCamelCase					,      )

			__lowercase											=     json.loads(self.backend_tokenizer.normalizer.__getstate__()   )
			if (
			    pre_tok_state.get('lowercase'					,      __lowerCamelCase   ) != do_lower_case
			    or pre_tok_state.get('strip_accents'					,      __lowerCamelCase   ) != strip_accents
			):
					__lowercase											=     getattr(__lowerCamelCase					,      pre_tok_state.pop('type'   )   )
					__lowercase											=     do_lower_case
					__lowercase											=     strip_accents
					__lowercase											=     pre_tok_class(**__lowerCamelCase   )

			__lowercase											=     do_lower_case




	def __getstate__(						self							:     Optional[Any]   )       -> List[Any]:

			'''simple docstring'''




			__lowercase											=     self.__dict__.copy()
			__lowercase											=     BertPreTokenizer()
			return state




	def __setstate__(						self							:     List[str]					,      __lowerCamelCase							:     List[Any]   )       -> Optional[Any]:

			'''simple docstring'''




			__lowercase											=     d
			__lowercase											=     self.__dict__['_tokenizer'].get_vocab()
			__lowercase											=     PreTokenizer.custom(JiebaPreTokenizer(__lowerCamelCase   )   )




	def    UpperCAmelCase			(						self							:     Any					,      __lowerCamelCase							:     Optional[int]					,      __lowerCamelCase							:     int=None   )       -> Union[str, Any]:

			'''simple docstring'''




			__lowercase											=     [self.cls_token_id] + token_ids_a + [self.sep_token_id]

			if token_ids_a:
					output += token_ids_a + [self.sep_token_id]

			return output




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

			'''simple docstring'''




			__lowercase											=     [self.sep_token_id]
			__lowercase											=     [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    UpperCAmelCase			(						self							:     List[Any]					,      __lowerCamelCase							:     str					,      __lowerCamelCase							:     Optional[str] = None   )       -> Tuple[str]:

			'''simple docstring'''




			__lowercase											=     self._tokenizer.model.save(__lowerCamelCase					,      name=__lowerCamelCase   )
			return tuple(__lowerCamelCase   )






	def    UpperCAmelCase			(						self							:     Optional[Any]					,      __lowerCamelCase							:     Any					,      __lowerCamelCase							:     Tuple=None					,      __lowerCamelCase							:     str=None					,      __lowerCamelCase							:     Any=False					,      **__lowerCamelCase							:     List[str]					,      )       -> List[str]:

			'''simple docstring'''




			__lowercase											=     BertPreTokenizer()
			return super().save_pretrained(__lowerCamelCase					,      __lowerCamelCase					,      __lowerCamelCase					,      __lowerCamelCase					,      **__lowerCamelCase   )











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


SCREAMING_SNAKE_CASE_   :      Optional[Any]			=   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  SCREAMING_SNAKE_CASE				(      snake_case						,   snake_case							)		->    Optional[int]:
		return (abs(source - target							) / target) < 0.01



@pytest.mark.integration
def  SCREAMING_SNAKE_CASE				(      snake_case							)		->    Dict:
		__lowercase											=     _TestCommandArgs(dataset=snake_case						,   all_configs=snake_case						,   save_infos=snake_case							)
		__lowercase											=     TestCommand(*snake_case							)
		test_command.run()
		__lowercase											=     os.path.join(snake_case						,   'README.md'							)
		assert os.path.exists(snake_case							)
		__lowercase											=     DatasetInfosDict.from_directory(snake_case							)
		__lowercase											=     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:
				__lowercase						,      __lowercase											=     getattr(dataset_infos['default']						,   snake_case							), getattr(expected_dataset_infos['default']						,   snake_case							)
				if key == "num_bytes":
						assert is_apercent_close(snake_case						,   snake_case							)
				elif key == "splits":
						assert list(snake_case							) == list(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




