


import os
from shutil import copyfile
from typing import Optional, Tuple

from ...file_utils import is_sentencepiece_available
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging


if is_sentencepiece_available():
						from .tokenization_deberta_va import DebertaVaTokenizer
else:
						__lowerCamelCase       :       Optional[int]				   =			None

__lowerCamelCase       :       Any				   =			logging.get_logger(__name__)

__lowerCamelCase       :       List[str]				   =			{'''vocab_file''': '''spm.model''', '''tokenizer_file''': '''tokenizer.json'''}

__lowerCamelCase       :       str				   =			{
    '''vocab_file''': {
        '''microsoft/deberta-v2-xlarge''': '''https://huggingface.co/microsoft/deberta-v2-xlarge/resolve/main/spm.model''',
        '''microsoft/deberta-v2-xxlarge''': '''https://huggingface.co/microsoft/deberta-v2-xxlarge/resolve/main/spm.model''',
        '''microsoft/deberta-v2-xlarge-mnli''': (
            '''https://huggingface.co/microsoft/deberta-v2-xlarge-mnli/resolve/main/spm.model'''
        ),
        '''microsoft/deberta-v2-xxlarge-mnli''': (
            '''https://huggingface.co/microsoft/deberta-v2-xxlarge-mnli/resolve/main/spm.model'''
        ),
    }
}

__lowerCamelCase       :       List[str]				   =			{
    '''microsoft/deberta-v2-xlarge''': 512,
    '''microsoft/deberta-v2-xxlarge''': 512,
    '''microsoft/deberta-v2-xlarge-mnli''': 512,
    '''microsoft/deberta-v2-xxlarge-mnli''': 512,
}

__lowerCamelCase       :       Optional[int]				   =			{
    '''microsoft/deberta-v2-xlarge''': {'''do_lower_case''': False},
    '''microsoft/deberta-v2-xxlarge''': {'''do_lower_case''': False},
    '''microsoft/deberta-v2-xlarge-mnli''': {'''do_lower_case''': False},
    '''microsoft/deberta-v2-xxlarge-mnli''': {'''do_lower_case''': False},
}



class 			a      (      A__   ):
		__lowercase													=		VOCAB_FILES_NAMES
		__lowercase													=		PRETRAINED_VOCAB_FILES_MAP
		__lowercase													=		PRETRAINED_INIT_CONFIGURATION
		__lowercase													=		PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
		__lowercase													=		DebertaVaTokenizer






		def __init__(  self	,     __UpperCamelCase=None	,     __UpperCamelCase=None	,     __UpperCamelCase=False	,     __UpperCamelCase=False	,     __UpperCamelCase="[CLS]"	,     __UpperCamelCase="[SEP]"	,     __UpperCamelCase="[UNK]"	,     __UpperCamelCase="[SEP]"	,     __UpperCamelCase="[PAD]"	,     __UpperCamelCase="[CLS]"	,     __UpperCamelCase="[MASK]"	,     **__UpperCamelCase	,     )->			Any:
						'''simple docstring'''


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

						A__     :							Union[str, Any]   				=do_lower_case
						A__     :							Optional[int]   				=split_by_punct
						A__     :							Optional[Any]   				=vocab_file
						A__     :							List[Any]   				=False if not self.vocab_file else True






		def       lowerCAmelCase_  (  self	,     __UpperCamelCase	,     __UpperCamelCase=None							)->			Dict:
						'''simple docstring'''


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






		def       lowerCAmelCase_  (  self	,     __UpperCamelCase	,     __UpperCamelCase=None	,     __UpperCamelCase=False							)->			List[str]:
						'''simple docstring'''


						if already_has_special_tokens:
										return super().get_special_tokens_mask(
										    token_ids_a=__UpperCamelCase	,     token_ids_a=__UpperCamelCase	,     already_has_special_tokens=__UpperCamelCase							)

						if token_ids_a is not None:
										return [1] + ([0] * len(__UpperCamelCase							)) + [1] + ([0] * len(__UpperCamelCase							)) + [1]
						return [1] + ([0] * len(__UpperCamelCase							)) + [1]






		def       lowerCAmelCase_  (  self	,     __UpperCamelCase	,     __UpperCamelCase=None							)->			Tuple:
						'''simple docstring'''


						A__     :							Tuple   				=[self.sep_token_id]
						A__     :							Optional[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							) * [0] + len(token_ids_a + sep							) * [1]






		def       lowerCAmelCase_  (  self	,     __UpperCamelCase	,     __UpperCamelCase = None							)->			Optional[int]:
						'''simple docstring'''


						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
						A__     :							Union[str, Any]   				=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,
)


__lowerCamelCase       :       str				   =			{
    "configuration_align": [
        "ALIGN_PRETRAINED_CONFIG_ARCHIVE_MAP",
        "AlignConfig",
        "AlignTextConfig",
        "AlignVisionConfig",
    ],
    "processing_align": ["AlignProcessor"],
}

try:
						if not is_torch_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						__lowerCamelCase       :       Dict				   =			[
						    "ALIGN_PRETRAINED_MODEL_ARCHIVE_LIST",
						    "AlignModel",
						    "AlignPreTrainedModel",
						    "AlignTextModel",
						    "AlignVisionModel",
						]

if TYPE_CHECKING:
						from .configuration_align import (
						    ALIGN_PRETRAINED_CONFIG_ARCHIVE_MAP,
						    AlignConfig,
						    AlignTextConfig,
						    AlignVisionConfig,
						)
						from .processing_align import AlignProcessor

						try:
												if not is_torch_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .modeling_align import (
												    ALIGN_PRETRAINED_MODEL_ARCHIVE_LIST,
												    AlignModel,
												    AlignPreTrainedModel,
												    AlignTextModel,
												    AlignVisionModel,
												)

else:
						import sys

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



