

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_       :				Dict		   =      None

UpperCAmelCase_       :				Dict		   =      logging.get_logger(__name__)

UpperCAmelCase_       :				Tuple		   =      {'''vocab_file''': '''spm.model''', '''tokenizer_file''': '''tokenizer.json'''}

UpperCAmelCase_       :				List[Any]		   =      {
    '''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_       :				List[Any]		   =      {
    '''microsoft/deberta-v2-xlarge''': 512,
    '''microsoft/deberta-v2-xxlarge''': 512,
    '''microsoft/deberta-v2-xlarge-mnli''': 512,
    '''microsoft/deberta-v2-xxlarge-mnli''': 512,
}

UpperCAmelCase_       :				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     lowerCamelCase_			(  _lowercase  ):
	_lowercase		:      Optional[int]    				=							VOCAB_FILES_NAMES
	_lowercase		:      List[str]    				=							PRETRAINED_VOCAB_FILES_MAP
	_lowercase		:      Optional[Any]    				=							PRETRAINED_INIT_CONFIGURATION
	_lowercase		:      Optional[Any]    				=							PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
	_lowercase		:      Tuple    				=							DebertaVaTokenizer




	def __init__(    self	:    Any				,			__A	:    Tuple=None				,			__A	:    Optional[int]=None				,			__A	:    List[str]=False				,			__A	:    Union[str, Any]=False				,			__A	:    List[Any]="[CLS]"				,			__A	:    Union[str, Any]="[SEP]"				,			__A	:    Any="[UNK]"				,			__A	:    List[Any]="[SEP]"				,			__A	:    str="[PAD]"				,			__A	:    List[str]="[CLS]"				,			__A	:    List[Any]="[MASK]"				,			**__A	:    int				,			):
		super().__init__(
		    __A				,			tokenizer_file=__A				,			do_lower_case=__A				,			bos_token=__A				,			eos_token=__A				,			unk_token=__A				,			sep_token=__A				,			pad_token=__A				,			cls_token=__A				,			mask_token=__A				,			split_by_punct=__A				,			**__A				,			)

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




	def 					lowerCAmelCase_					(    self	:    List[str]				,			__A	:    Optional[int]				,			__A	:    int=None       ):

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




	def 					lowerCAmelCase_					(    self	:    Dict				,			__A	:    Optional[int]				,			__A	:    Tuple=None				,			__A	:    List[str]=False       ):

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

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




	def 					lowerCAmelCase_					(    self	:    int				,			__A	:    List[str]				,			__A	:    Any=None       ):
		__A   : int								= [self.sep_token_id]
		__A   : 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	:    Union[str, Any]				,			__A	:    str				,			__A	:    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(__A       ):
			logger.error(F"""Vocabulary path ({save_directory}) should be a directory"""       )
			return
		__A   : Union[str, Any]								= os.path.join(
		    __A				,			(filename_prefix + """-""" if filename_prefix else """""") + VOCAB_FILES_NAMES["""vocab_file"""]       )

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

		return (out_vocab_file,)








from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


UpperCAmelCase_       :				Optional[Any]		   =      {
    '''configuration_prophetnet''': ['''PROPHETNET_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''ProphetNetConfig'''],
    '''tokenization_prophetnet''': ['''ProphetNetTokenizer'''],
}

try:
			if not is_torch_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			UpperCAmelCase_       :				List[str]		   =      [
			    '''PROPHETNET_PRETRAINED_MODEL_ARCHIVE_LIST''',
			    '''ProphetNetDecoder''',
			    '''ProphetNetEncoder''',
			    '''ProphetNetForCausalLM''',
			    '''ProphetNetForConditionalGeneration''',
			    '''ProphetNetModel''',
			    '''ProphetNetPreTrainedModel''',
			]


if TYPE_CHECKING:
			from .configuration_prophetnet import PROPHETNET_PRETRAINED_CONFIG_ARCHIVE_MAP, ProphetNetConfig
			from .tokenization_prophetnet import ProphetNetTokenizer

			try:
						if not is_torch_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .modeling_prophetnet import (
						    PROPHETNET_PRETRAINED_MODEL_ARCHIVE_LIST,
						    ProphetNetDecoder,
						    ProphetNetEncoder,
						    ProphetNetForCausalLM,
						    ProphetNetForConditionalGeneration,
						    ProphetNetModel,
						    ProphetNetPreTrainedModel,
						)

else:
			import sys

			UpperCAmelCase_       :				Any		   =      _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)





