






import re
import tempfile
import unittest
from pathlib import Path

from datasets.utils.metadata import DatasetMetadata

def   SCREAMING_SNAKE_CASE_				(				__magic_name__   :    str   )						->     str:






	"""simple docstring"""



	UpperCamelCase	:int		=      min(re.search("""^ +"""      ,     __magic_name__   ).end() if t.startswith(""" """   ) else 0 for t in string.splitlines()   )
	return "\n".join([line[indent_level:] for line in string.splitlines() if indent_level < len(__magic_name__   )]   )


UpperCAmelCase_					:		Dict           = '''\
---
language:
- zh
- en
task_ids:
- sentiment-classification
---
# Begin of markdown

Some cool dataset card
'''

UpperCAmelCase_					:		Tuple           = '''\
---
---
# Begin of markdown

Some cool dataset card
'''


UpperCAmelCase_					:		Dict           = '''\
# Begin of markdown

Some cool dataset card
'''

class        _SCREAMING_SNAKE_CASE			(     unittest.TestCase ):





	def 		_A					(						self							:   Union[str, Any]      ):
		with tempfile.TemporaryDirectory() as tmp_dir:
			UpperCamelCase	:Optional[int]		=      Path(__lowerCamelCase      ) / """README.md"""
			with open(__lowerCamelCase					,    """w+"""      ) as readme_file:
				readme_file.write(__lowerCamelCase      )
			UpperCamelCase	:Optional[int]		=      DatasetMetadata.from_readme(__lowerCamelCase      )
			self.assertDictEqual(__lowerCamelCase					,    {"""language""": ["""zh""", """en"""], """task_ids""": ["""sentiment-classification"""]}      )

			with open(__lowerCamelCase					,    """w+"""      ) as readme_file:
				readme_file.write(__lowerCamelCase      )
			UpperCamelCase	:Dict		=      DatasetMetadata.from_readme(__lowerCamelCase      )
			self.assertDictEqual(__lowerCamelCase					,    {}      )

			with open(__lowerCamelCase					,    """w+"""      ) as readme_file:
				readme_file.write(__lowerCamelCase      )
			UpperCamelCase	:Optional[int]		=      DatasetMetadata.from_readme(__lowerCamelCase      )
			self.assertEqual(__lowerCamelCase					,    {}      )





	def 		_A					(						self							:   Dict      ):
		UpperCamelCase	:str		=      _dedent(
		    """\
            annotations_creators:
            - found
            language_creators:
            - found
            language:
            - en
            license:
            - unknown
            multilinguality:
            - monolingual
            pretty_name: Test Dataset
            size_categories:
            - 10K<n<100K
            source_datasets:
            - extended|other-yahoo-webscope-l6
            task_categories:
            - question-answering
            task_ids:
            - open-domain-qa
            """      )
		assert DatasetMetadata.from_yaml_string(__lowerCamelCase      )

		UpperCamelCase	:Union[str, Any]		=      _dedent(
		    """\
            annotations_creators:
            - found
            language:
            - en
            license:
            - unknown
            multilinguality:
            - monolingual
            pretty_name: Test Dataset
            size_categories:
            - 10K<n<100K
            source_datasets:
            - extended|other-yahoo-webscope-l6
            task_categories:
            - question-answering
            task_ids:
            - open-domain-qa
            task_ids:
            - open-domain-qa
            """      )
		with self.assertRaises(__lowerCamelCase      ):
			DatasetMetadata.from_yaml_string(__lowerCamelCase      )

		UpperCamelCase	:List[Any]		=      _dedent(
		    """\
            annotations_creators:
            - found
            language_creators:
            - found
            language:
            - en
            license:
            - unknown
            multilinguality:
            - monolingual
            pretty_name: Test Dataset
            size_categories:
            - 10K<n<100K
            source_datasets:
            - extended|other-yahoo-webscope-l6
            task_categories:
            - text-classification
            task_ids:
            - multi-class-classification
            paperswithcode_id:
            - squad
            configs:
            - en
            train-eval-index:
            - config: en
              task: text-classification
              task_id: multi_class_classification
              splits:
                train_split: train
                eval_split: test
              col_mapping:
                text: text
                label: target
              metrics:
                - type: accuracy
                  name: Accuracy
            extra_gated_prompt: |
              By clicking on “Access repository” below, you also agree to ImageNet Terms of Access:
              [RESEARCHER_FULLNAME] (the \"Researcher\") has requested permission to use the ImageNet database (the \"Database\") at Princeton University and Stanford University. In exchange for such permission, Researcher hereby agrees to the following terms and conditions:
              1. Researcher shall use the Database only for non-commercial research and educational purposes.
            extra_gated_fields:
              Company: text
              Country: text
              I agree to use this model for non-commerical use ONLY: checkbox
            """      )
		assert DatasetMetadata.from_yaml_string(__lowerCamelCase      )








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_xlm_roberta import XLMRobertaTokenizer
else:
	UpperCAmelCase_					:		str           = None


UpperCAmelCase_					:		int           = logging.get_logger(__name__)

UpperCAmelCase_					:		List[str]           = {'''vocab_file''': '''sentencepiece.bpe.model''', '''tokenizer_file''': '''tokenizer.json'''}

UpperCAmelCase_					:		Any           = {
    '''vocab_file''': {
        '''xlm-roberta-base''': '''https://huggingface.co/xlm-roberta-base/resolve/main/sentencepiece.bpe.model''',
        '''xlm-roberta-large''': '''https://huggingface.co/xlm-roberta-large/resolve/main/sentencepiece.bpe.model''',
        '''xlm-roberta-large-finetuned-conll02-dutch''': (
            '''https://huggingface.co/xlm-roberta-large-finetuned-conll02-dutch/resolve/main/sentencepiece.bpe.model'''
        ),
        '''xlm-roberta-large-finetuned-conll02-spanish''': (
            '''https://huggingface.co/xlm-roberta-large-finetuned-conll02-spanish/resolve/main/sentencepiece.bpe.model'''
        ),
        '''xlm-roberta-large-finetuned-conll03-english''': (
            '''https://huggingface.co/xlm-roberta-large-finetuned-conll03-english/resolve/main/sentencepiece.bpe.model'''
        ),
        '''xlm-roberta-large-finetuned-conll03-german''': (
            '''https://huggingface.co/xlm-roberta-large-finetuned-conll03-german/resolve/main/sentencepiece.bpe.model'''
        ),
    },
    '''tokenizer_file''': {
        '''xlm-roberta-base''': '''https://huggingface.co/xlm-roberta-base/resolve/main/tokenizer.json''',
        '''xlm-roberta-large''': '''https://huggingface.co/xlm-roberta-large/resolve/main/tokenizer.json''',
        '''xlm-roberta-large-finetuned-conll02-dutch''': (
            '''https://huggingface.co/xlm-roberta-large-finetuned-conll02-dutch/resolve/main/tokenizer.json'''
        ),
        '''xlm-roberta-large-finetuned-conll02-spanish''': (
            '''https://huggingface.co/xlm-roberta-large-finetuned-conll02-spanish/resolve/main/tokenizer.json'''
        ),
        '''xlm-roberta-large-finetuned-conll03-english''': (
            '''https://huggingface.co/xlm-roberta-large-finetuned-conll03-english/resolve/main/tokenizer.json'''
        ),
        '''xlm-roberta-large-finetuned-conll03-german''': (
            '''https://huggingface.co/xlm-roberta-large-finetuned-conll03-german/resolve/main/tokenizer.json'''
        ),
    },
}

UpperCAmelCase_					:		Any           = {
    '''xlm-roberta-base''': 5_12,
    '''xlm-roberta-large''': 5_12,
    '''xlm-roberta-large-finetuned-conll02-dutch''': 5_12,
    '''xlm-roberta-large-finetuned-conll02-spanish''': 5_12,
    '''xlm-roberta-large-finetuned-conll03-english''': 5_12,
    '''xlm-roberta-large-finetuned-conll03-german''': 5_12,
}

class        _SCREAMING_SNAKE_CASE			(     _a ):
	snake_case__				:	Dict	      =    VOCAB_FILES_NAMES
	snake_case__				:	Optional[int]	      =    PRETRAINED_VOCAB_FILES_MAP
	snake_case__				:	str	      =    PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
	snake_case__				:	int	      =    ["""input_ids""", """attention_mask"""]
	snake_case__				:	Dict	      =    XLMRobertaTokenizer





	def __init__(						self							:   List[str]					,    __lowerCamelCase							:   List[Any]=None					,    __lowerCamelCase							:   int=None					,    __lowerCamelCase							:   Dict="<s>"					,    __lowerCamelCase							:   Optional[int]="</s>"					,    __lowerCamelCase							:   int="</s>"					,    __lowerCamelCase							:   Dict="<s>"					,    __lowerCamelCase							:   Optional[Any]="<unk>"					,    __lowerCamelCase							:   Union[str, Any]="<pad>"					,    __lowerCamelCase							:   Any="<mask>"					,    **__lowerCamelCase							:   List[str]					,    ):
		# Mask token behave like a normal word, i.e. include the space before it
		UpperCamelCase	:int		=      AddedToken(__lowerCamelCase					,    lstrip=__lowerCamelCase					,    rstrip=__lowerCamelCase      ) if isinstance(__lowerCamelCase					,    __lowerCamelCase      ) else mask_token

		super().__init__(
		    __lowerCamelCase					,    tokenizer_file=__lowerCamelCase					,    bos_token=__lowerCamelCase					,    eos_token=__lowerCamelCase					,    sep_token=__lowerCamelCase					,    cls_token=__lowerCamelCase					,    unk_token=__lowerCamelCase					,    pad_token=__lowerCamelCase					,    mask_token=__lowerCamelCase					,    **__lowerCamelCase					,    )

		UpperCamelCase	:Optional[int]		=      vocab_file
		UpperCamelCase	:List[Any]		=      False if not self.vocab_file else True





	def 		_A					(						self							:   Dict					,    __lowerCamelCase							:   List[int]					,    __lowerCamelCase							:   Optional[List[int]] = None      ):

		if token_ids_a is None:
			return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
		UpperCamelCase	:int		=      [self.cls_token_id]
		UpperCamelCase	:Any		=      [self.sep_token_id]
		return cls + token_ids_a + sep + sep + token_ids_a + sep





	def 		_A					(						self							:   List[str]					,    __lowerCamelCase							:   List[int]					,    __lowerCamelCase							:   Optional[List[int]] = None      ):
		UpperCamelCase	:str		=      [self.sep_token_id]
		UpperCamelCase	:int		=      [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 + sep + token_ids_a + sep      ) * [0]





	def 		_A					(						self							:   Optional[int]					,    __lowerCamelCase							:   str					,    __lowerCamelCase							:   Optional[str] = None      ):
		if not self.can_save_slow_tokenizer:
			raise ValueError(
			    """Your fast tokenizer does not have the necessary information to save the vocabulary for a slow """
			    """tokenizer."""      )

		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,)
