




'''simple docstring'''



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:
     UpperCAmelCase_				:     List[Any]     			=						None

UpperCAmelCase_				:     Dict     			=						logging.get_logger(__name__)

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

UpperCAmelCase_				:     Tuple     			=						{
    '''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'''
        ),
    }
}

UpperCAmelCase_				:     str     			=						{
    '''microsoft/deberta-v2-xlarge''': 5_1_2,
    '''microsoft/deberta-v2-xxlarge''': 5_1_2,
    '''microsoft/deberta-v2-xlarge-mnli''': 5_1_2,
    '''microsoft/deberta-v2-xxlarge-mnli''': 5_1_2,
}

UpperCAmelCase_				:     Any     			=						{
    '''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 			lowerCAmelCase      (       _snake_case):
       __lowercase     :					Dict       				=	VOCAB_FILES_NAMES
       __lowercase     :					Union[str, Any]       				=	PRETRAINED_VOCAB_FILES_MAP
       __lowercase     :					List[Any]       				=	PRETRAINED_INIT_CONFIGURATION
       __lowercase     :					Any       				=	PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
       __lowercase     :					Optional[Any]       				=	DebertaVaTokenizer


       def __init__(      self			,     __SCREAMING_SNAKE_CASE=None			,     __SCREAMING_SNAKE_CASE=None			,     __SCREAMING_SNAKE_CASE=False			,     __SCREAMING_SNAKE_CASE=False			,     __SCREAMING_SNAKE_CASE="[CLS]"			,     __SCREAMING_SNAKE_CASE="[SEP]"			,     __SCREAMING_SNAKE_CASE="[UNK]"			,     __SCREAMING_SNAKE_CASE="[SEP]"			,     __SCREAMING_SNAKE_CASE="[PAD]"			,     __SCREAMING_SNAKE_CASE="[CLS]"			,     __SCREAMING_SNAKE_CASE="[MASK]"			,     **__SCREAMING_SNAKE_CASE			,     )					->     None:



            '''simple docstring'''




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

            __snake_case			    =     do_lower_case
            __snake_case			    =     split_by_punct
            __snake_case			    =     vocab_file
            __snake_case			    =     False if not self.vocab_file else True


       def 					lowerCAmelCase					(      self			,     __SCREAMING_SNAKE_CASE			,     __SCREAMING_SNAKE_CASE=None     )					->     str:



            '''simple docstring'''




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


       def 					lowerCAmelCase					(      self			,     __SCREAMING_SNAKE_CASE			,     __SCREAMING_SNAKE_CASE=None			,     __SCREAMING_SNAKE_CASE=False     )					->     str:



            '''simple docstring'''




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

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


       def 					lowerCAmelCase					(      self			,     __SCREAMING_SNAKE_CASE			,     __SCREAMING_SNAKE_CASE=None     )					->     str:



            '''simple docstring'''




            __snake_case			    =     [self.sep_token_id]
            __snake_case			    =     [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			,     __SCREAMING_SNAKE_CASE			,     __SCREAMING_SNAKE_CASE = None     )					->     Tuple[str]:



            '''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(_lowercase     ):
                 logger.error(F'''Vocabulary path ({save_directory}) should be a directory'''     )
                 return
            __snake_case			    =     os.path.join(
                _lowercase			,     (filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''vocab_file''']     )

            if os.path.abspath(self.vocab_file     ) != os.path.abspath(_lowercase     ):
                 copyfile(self.vocab_file			,     _lowercase     )

            return (out_vocab_file,)











from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available


UpperCamelCase_	       =   {"configuration_vit_hybrid": ["VIT_HYBRID_PRETRAINED_CONFIG_ARCHIVE_MAP", "ViTHybridConfig"]}

try:
							if not is_torch_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							UpperCamelCase_	       =   [
							    "VIT_HYBRID_PRETRAINED_MODEL_ARCHIVE_LIST",
							    "ViTHybridForImageClassification",
							    "ViTHybridModel",
							    "ViTHybridPreTrainedModel",
							]

try:
							if not is_vision_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							UpperCamelCase_	       =   ["ViTHybridImageProcessor"]


if TYPE_CHECKING:
							from .configuration_vit_hybrid import VIT_HYBRID_PRETRAINED_CONFIG_ARCHIVE_MAP, ViTHybridConfig

							try:
														if not is_torch_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_vit_hybrid import (
														    VIT_HYBRID_PRETRAINED_MODEL_ARCHIVE_LIST,
														    ViTHybridForImageClassification,
														    ViTHybridModel,
														    ViTHybridPreTrainedModel,
														)

							try:
														if not is_vision_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .image_processing_vit_hybrid import ViTHybridImageProcessor


else:
							import sys

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





