




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_    =None


UpperCAmelCase_    =logging.get_logger(__name__)

UpperCAmelCase_    ={"""vocab_file""": """sentencepiece.bpe.model""", """tokenizer_file""": """tokenizer.json"""}

UpperCAmelCase_    ={
    """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_    ={
    """xlm-roberta-base""": 512,
    """xlm-roberta-large""": 512,
    """xlm-roberta-large-finetuned-conll02-dutch""": 512,
    """xlm-roberta-large-finetuned-conll02-spanish""": 512,
    """xlm-roberta-large-finetuned-conll03-english""": 512,
    """xlm-roberta-large-finetuned-conll03-german""": 512,
}


class 				__UpperCamelCase      (			__UpperCAmelCase	):





				'''simple docstring'''



				__a				:			Any					    =VOCAB_FILES_NAMES
				__a				:			int					    =PRETRAINED_VOCAB_FILES_MAP
				__a				:			int					    =PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
				__a				:			Union[str, Any]					    =["""input_ids""", """attention_mask"""]
				__a				:			Tuple					    =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,)









from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_torch_available,
)


UpperCAmelCase_    ={
    """configuration_bark""": [
        """BARK_PRETRAINED_CONFIG_ARCHIVE_MAP""",
        """BarkCoarseConfig""",
        """BarkConfig""",
        """BarkFineConfig""",
        """BarkSemanticConfig""",
    ],
    """processing_bark""": ["""BarkProcessor"""],
}

try:
		if not is_torch_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		UpperCAmelCase_    =[
		    """BARK_PRETRAINED_MODEL_ARCHIVE_LIST""",
		    """BarkFineModel""",
		    """BarkSemanticModel""",
		    """BarkCoarseModel""",
		    """BarkModel""",
		    """BarkPreTrainedModel""",
		    """BarkCausalModel""",
		]

if TYPE_CHECKING:
		from .configuration_bark import (
		    BARK_PRETRAINED_CONFIG_ARCHIVE_MAP,
		    BarkCoarseConfig,
		    BarkConfig,
		    BarkFineConfig,
		    BarkSemanticConfig,
		)
		from .processing_bark import BarkProcessor

		try:
				if not is_torch_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_bark import (
				    BARK_PRETRAINED_MODEL_ARCHIVE_LIST,
				    BarkCausalModel,
				    BarkCoarseModel,
				    BarkFineModel,
				    BarkModel,
				    BarkPreTrainedModel,
				    BarkSemanticModel,
				)

else:
		import sys

		UpperCAmelCase_    =_LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)



