




import json
from typing import TYPE_CHECKING, List, Optional, Tuple

from tokenizers import pre_tokenizers

from ...tokenization_utils_base import AddedToken, BatchEncoding
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_deberta import DebertaTokenizer


if TYPE_CHECKING:
				from transformers.pipelines.conversational import Conversation


SCREAMING_SNAKE_CASE__    :    List[Any]								=					logging.get_logger(__name__)

SCREAMING_SNAKE_CASE__    :    Dict								=					{"vocab_file": "vocab.json", "merges_file": "merges.txt", "tokenizer_file": "tokenizer.json"}

SCREAMING_SNAKE_CASE__    :    List[str]								=					{
    "vocab_file": {
        "microsoft/deberta-base": "https://huggingface.co/microsoft/deberta-base/resolve/main/vocab.json",
        "microsoft/deberta-large": "https://huggingface.co/microsoft/deberta-large/resolve/main/vocab.json",
        "microsoft/deberta-xlarge": "https://huggingface.co/microsoft/deberta-xlarge/resolve/main/vocab.json",
        "microsoft/deberta-base-mnli": "https://huggingface.co/microsoft/deberta-base-mnli/resolve/main/vocab.json",
        "microsoft/deberta-large-mnli": "https://huggingface.co/microsoft/deberta-large-mnli/resolve/main/vocab.json",
        "microsoft/deberta-xlarge-mnli": (
            "https://huggingface.co/microsoft/deberta-xlarge-mnli/resolve/main/vocab.json"
        ),
    },
    "merges_file": {
        "microsoft/deberta-base": "https://huggingface.co/microsoft/deberta-base/resolve/main/merges.txt",
        "microsoft/deberta-large": "https://huggingface.co/microsoft/deberta-large/resolve/main/merges.txt",
        "microsoft/deberta-xlarge": "https://huggingface.co/microsoft/deberta-xlarge/resolve/main/merges.txt",
        "microsoft/deberta-base-mnli": "https://huggingface.co/microsoft/deberta-base-mnli/resolve/main/merges.txt",
        "microsoft/deberta-large-mnli": "https://huggingface.co/microsoft/deberta-large-mnli/resolve/main/merges.txt",
        "microsoft/deberta-xlarge-mnli": (
            "https://huggingface.co/microsoft/deberta-xlarge-mnli/resolve/main/merges.txt"
        ),
    },
}

SCREAMING_SNAKE_CASE__    :    int								=					{
    "microsoft/deberta-base": 5_12,
    "microsoft/deberta-large": 5_12,
    "microsoft/deberta-xlarge": 5_12,
    "microsoft/deberta-base-mnli": 5_12,
    "microsoft/deberta-large-mnli": 5_12,
    "microsoft/deberta-xlarge-mnli": 5_12,
}

SCREAMING_SNAKE_CASE__    :    Optional[Any]								=					{
    "microsoft/deberta-base": {"do_lower_case": False},
    "microsoft/deberta-large": {"do_lower_case": False},
}



class 			a_					(    SCREAMING_SNAKE_CASE__    ):
	A						       =   VOCAB_FILES_NAMES
	A						       =   PRETRAINED_VOCAB_FILES_MAP
	A						       =   PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
	A						       =   ['''input_ids''', '''attention_mask''', '''token_type_ids''']
	A						       =   DebertaTokenizer





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




				"""simple docstring"""



				super().__init__(
				    SCREAMING_SNAKE_CASE			,    SCREAMING_SNAKE_CASE			,    tokenizer_file=SCREAMING_SNAKE_CASE			,    errors=SCREAMING_SNAKE_CASE			,    bos_token=SCREAMING_SNAKE_CASE			,    eos_token=SCREAMING_SNAKE_CASE			,    unk_token=SCREAMING_SNAKE_CASE			,    sep_token=SCREAMING_SNAKE_CASE			,    cls_token=SCREAMING_SNAKE_CASE			,    pad_token=SCREAMING_SNAKE_CASE			,    mask_token=SCREAMING_SNAKE_CASE			,    add_prefix_space=SCREAMING_SNAKE_CASE			,    **SCREAMING_SNAKE_CASE			,    )
				SCREAMING_SNAKE_CASE_			=	kwargs.pop('add_bos_token'			,    SCREAMING_SNAKE_CASE			)

				SCREAMING_SNAKE_CASE_			=	json.loads(self.backend_tokenizer.pre_tokenizer.__getstate__()			)
				if pre_tok_state.get('add_prefix_space'			,    SCREAMING_SNAKE_CASE			) != add_prefix_space:
							SCREAMING_SNAKE_CASE_			=	getattr(SCREAMING_SNAKE_CASE			,    pre_tok_state.pop('type'			)			)
							SCREAMING_SNAKE_CASE_			=	add_prefix_space
							SCREAMING_SNAKE_CASE_			=	pre_tok_class(**SCREAMING_SNAKE_CASE			)

				SCREAMING_SNAKE_CASE_			=	add_prefix_space





	@property
	def 	A_(       self			)     ->				str:




				"""simple docstring"""



				if self._mask_token is None:
							if self.verbose:
										logger.error('Using mask_token, but it is not set yet.'			)
							return None
				return str(self._mask_token			)





	@mask_token.setter
	def 	A_(       self			,    SCREAMING_SNAKE_CASE			)     ->				Tuple:




				"""simple docstring"""



				SCREAMING_SNAKE_CASE_			=	AddedToken(SCREAMING_SNAKE_CASE			,    lstrip=SCREAMING_SNAKE_CASE			,    rstrip=SCREAMING_SNAKE_CASE			) if isinstance(SCREAMING_SNAKE_CASE			,    SCREAMING_SNAKE_CASE			) else value
				SCREAMING_SNAKE_CASE_			=	value





	def 	A_(       self			,    SCREAMING_SNAKE_CASE			,    SCREAMING_SNAKE_CASE = None			)     ->				List[int]:




				"""simple docstring"""



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





	def 	A_(       self			,    SCREAMING_SNAKE_CASE			,    SCREAMING_SNAKE_CASE = None			)     ->				List[int]:




				"""simple docstring"""



				SCREAMING_SNAKE_CASE_			=	[self.sep_token_id]
				SCREAMING_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 	A_(       self			,    *SCREAMING_SNAKE_CASE			,    **SCREAMING_SNAKE_CASE			)     ->				BatchEncoding:




				"""simple docstring"""



				SCREAMING_SNAKE_CASE_			=	kwargs.get('is_split_into_words'			,    SCREAMING_SNAKE_CASE			)
				assert self.add_prefix_space or not is_split_into_words, (
				    f'You need to instantiate {self.__class__.__name__} with add_prefix_space=True '
				    "to use it with pretokenized inputs."
				)

				return super()._batch_encode_plus(*SCREAMING_SNAKE_CASE			,    **SCREAMING_SNAKE_CASE			)





	def 	A_(       self			,    *SCREAMING_SNAKE_CASE			,    **SCREAMING_SNAKE_CASE			)     ->				BatchEncoding:




				"""simple docstring"""



				SCREAMING_SNAKE_CASE_			=	kwargs.get('is_split_into_words'			,    SCREAMING_SNAKE_CASE			)

				assert self.add_prefix_space or not is_split_into_words, (
				    f'You need to instantiate {self.__class__.__name__} with add_prefix_space=True '
				    "to use it with pretokenized inputs."
				)

				return super()._encode_plus(*SCREAMING_SNAKE_CASE			,    **SCREAMING_SNAKE_CASE			)





	def 	A_(       self			,    SCREAMING_SNAKE_CASE			,    SCREAMING_SNAKE_CASE = None			)     ->				Tuple[str]:




				"""simple docstring"""



				SCREAMING_SNAKE_CASE_			=	self._tokenizer.model.save(SCREAMING_SNAKE_CASE			,    name=SCREAMING_SNAKE_CASE			)
				return tuple(SCREAMING_SNAKE_CASE			)





	def 	A_(       self			,    SCREAMING_SNAKE_CASE			)     ->				List[int]:




				"""simple docstring"""



				SCREAMING_SNAKE_CASE_			=	[]
				for is_user, text in conversation.iter_texts():
							input_ids.extend(self.encode(SCREAMING_SNAKE_CASE			,    add_special_tokens=SCREAMING_SNAKE_CASE			) + [self.eos_token_id]			)

				if len(SCREAMING_SNAKE_CASE			) > self.model_max_length:
							SCREAMING_SNAKE_CASE_			=	input_ids[-self.model_max_length :]
				return input_ids






SCREAMING_SNAKE_CASE__    :    List[str]								=					"\n# Transformers installation\n! pip install transformers datasets\n# To install from source instead of the last release, comment the command above and uncomment the following one.\n# ! pip install git+https://github.com/huggingface/transformers.git\n"

SCREAMING_SNAKE_CASE__    :    List[str]								=					[{"type": "code", "content": INSTALL_CONTENT}]
SCREAMING_SNAKE_CASE__    :    List[Any]								=					{
    "{processor_class}": "FakeProcessorClass",
    "{model_class}": "FakeModelClass",
    "{object_class}": "FakeObjectClass",
}
