"""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:
	a							=None

a							=logging.get_logger(__name__)

a							={"""vocab_file""": """spm.model""", """tokenizer_file""": """tokenizer.json"""}

a							={
    """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"""
        ),
    }
}

a							={
    """microsoft/deberta-v2-xlarge""": 512,
    """microsoft/deberta-v2-xxlarge""": 512,
    """microsoft/deberta-v2-xlarge-mnli""": 512,
    """microsoft/deberta-v2-xxlarge-mnli""": 512,
}

a							={
    """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    __UpperCAmelCase						(	__lowerCAmelCase	):
				A__      :   Tuple  							=       VOCAB_FILES_NAMES
				A__      :   Any  							=       PRETRAINED_VOCAB_FILES_MAP
				A__      :   Any  							=       PRETRAINED_INIT_CONFIGURATION
				A__      :   Any  							=       PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
				A__      :   str  							=       DebertaVaTokenizer

				def __init__(      self	, _lowerCamelCase=None	, _lowerCamelCase=None	, _lowerCamelCase=False	, _lowerCamelCase=False	, _lowerCamelCase="[CLS]"	, _lowerCamelCase="[SEP]"	, _lowerCamelCase="[UNK]"	, _lowerCamelCase="[SEP]"	, _lowerCamelCase="[PAD]"	, _lowerCamelCase="[CLS]"	, _lowerCamelCase="[MASK]"	, **_lowerCamelCase	, ):
										super().__init__(
										    snake_case__	, tokenizer_file=snake_case__	, do_lower_case=snake_case__	, bos_token=snake_case__	, eos_token=snake_case__	, unk_token=snake_case__	, sep_token=snake_case__	, pad_token=snake_case__	, cls_token=snake_case__	, mask_token=snake_case__	, split_by_punct=snake_case__	, **snake_case__	, )

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

				def 		_a	(      self	, _lowerCamelCase	, _lowerCamelCase=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 + token_ids_a + sep

				def 		_a	(      self	, _lowerCamelCase	, _lowerCamelCase=None	, _lowerCamelCase=False      ):

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

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

				def 		_a	(      self	, _lowerCamelCase	, _lowerCamelCase=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      ) * [0] + len(token_ids_a + sep      ) * [1]






				def 		_a	(      self	, _lowerCamelCase	, _lowerCamelCase = 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(snake_case__      ):
																logger.error(F'''Vocabulary path ({save_directory}) should be a directory'''      )
																return
										lowerCamelCase__					=os.path.join(
										    snake_case__	, (filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"]      )

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

										return (out_vocab_file,)










"""simple docstring"""



from transformers import PretrainedConfig




class        lowerCAmelCase__       (  __magic_name__					):
   SCREAMING_SNAKE_CASE_					      ='''custom'''
   def __init__(						self	:     Any				,       snake_case__	:     Any=1				,       **snake_case__	:     Optional[Any]				):


        '''simple docstring'''
        UpperCAmelCase__		:  List[str]				=				attribute
        super().__init__(**snake_case__				)




class        lowerCAmelCase__       (  __magic_name__					):
   SCREAMING_SNAKE_CASE_					      ='''custom'''
   def __init__(						self	:     List[str]				,       snake_case__	:     Tuple=1				,       **snake_case__	:     str				):


        '''simple docstring'''
        UpperCAmelCase__		:  List[str]				=				attribute


