





from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


UpperCAmelCase__  :	Any     ={'''configuration_umt5''': ['''UMT5Config''', '''UMT5OnnxConfig''']}


try:
							if not is_torch_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							UpperCAmelCase__  :	Optional[Any]     =[
							    '''UMT5EncoderModel''',
							    '''UMT5ForConditionalGeneration''',
							    '''UMT5ForQuestionAnswering''',
							    '''UMT5Model''',
							    '''UMT5PreTrainedModel''',
							]

if TYPE_CHECKING:
							from .configuration_umta import UMTaConfig, UMTaOnnxConfig

							try:
														if not is_torch_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_umta import (
														    UMTaEncoderModel,
														    UMTaForConditionalGeneration,
														    UMTaForQuestionAnswering,
														    UMTaModel,
														    UMTaPreTrainedModel,
														)
else:
							import sys

							UpperCAmelCase__  :	List[str]     =_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)












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__  :	Union[str, Any]     =None


UpperCAmelCase__  :	str     =logging.get_logger(__name__)

UpperCAmelCase__  :	Optional[int]     ={'''vocab_file''': '''sentencepiece.bpe.model''', '''tokenizer_file''': '''tokenizer.json'''}

UpperCAmelCase__  :	str     ={
    '''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__  :	int     ={
    '''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 					__A   (  a			):
				__A            = VOCAB_FILES_NAMES
				__A            = PRETRAINED_VOCAB_FILES_MAP
				__A            = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
				__A            = ["""input_ids""", """attention_mask"""]
				__A            = XLMRobertaTokenizer


				def __init__(						self      ,       UpperCAmelCase_=None      ,       UpperCAmelCase_=None      ,       UpperCAmelCase_="<s>"      ,       UpperCAmelCase_="</s>"      ,       UpperCAmelCase_="</s>"      ,       UpperCAmelCase_="<s>"      ,       UpperCAmelCase_="<unk>"      ,       UpperCAmelCase_="<pad>"      ,       UpperCAmelCase_="<mask>"      ,       **UpperCAmelCase_      ,       ):
								# Mask token behave like a normal word, i.e. include the space before it
								lowerCamelCase		       =AddedToken(UpperCAmelCase_      ,       lstrip=UpperCAmelCase_      ,       rstrip=UpperCAmelCase_							) if isinstance(UpperCAmelCase_      ,       UpperCAmelCase_							) else mask_token

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

								lowerCamelCase		       =vocab_file
								lowerCamelCase		       =False if not self.vocab_file else True


				def _snake_case				(						self      ,       UpperCAmelCase_      ,       UpperCAmelCase_ = None							):

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


				def _snake_case				(						self      ,       UpperCAmelCase_      ,       UpperCAmelCase_ = None							):
								lowerCamelCase		       =[self.sep_token_id]
								lowerCamelCase		       =[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 _snake_case				(						self      ,       UpperCAmelCase_      ,       UpperCAmelCase_ = 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(UpperCAmelCase_							):
												logger.error(f"""Vocabulary path ({save_directory}) should be a directory."""							)
												return
								lowerCamelCase		       =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,)





