




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     :   List[str]						       =   VOCAB_FILES_NAMES
	A     :   List[Any]						       =   PRETRAINED_VOCAB_FILES_MAP
	A     :   Optional[int]						       =   PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
	A     :   Any						       =   ['''input_ids''', '''attention_mask''', '''token_type_ids''']
	A     :   Union[str, Any]						       =   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






from typing import TYPE_CHECKING

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


SCREAMING_SNAKE_CASE__    :    List[Any]								=					{"configuration_detr": ["DETR_PRETRAINED_CONFIG_ARCHIVE_MAP", "DetrConfig", "DetrOnnxConfig"]}

try:
				if not is_vision_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				SCREAMING_SNAKE_CASE__    :    int								=					["DetrFeatureExtractor"]
				SCREAMING_SNAKE_CASE__    :    Any								=					["DetrImageProcessor"]

try:
				if not is_torch_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				SCREAMING_SNAKE_CASE__    :    List[Any]								=					[
				    "DETR_PRETRAINED_MODEL_ARCHIVE_LIST",
				    "DetrForObjectDetection",
				    "DetrForSegmentation",
				    "DetrModel",
				    "DetrPreTrainedModel",
				]


if TYPE_CHECKING:
				from .configuration_detr import DETR_PRETRAINED_CONFIG_ARCHIVE_MAP, DetrConfig, DetrOnnxConfig

				try:
								if not is_vision_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .feature_extraction_detr import DetrFeatureExtractor
								from .image_processing_detr import DetrImageProcessor

				try:
								if not is_torch_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .modeling_detr import (
								    DETR_PRETRAINED_MODEL_ARCHIVE_LIST,
								    DetrForObjectDetection,
								    DetrForSegmentation,
								    DetrModel,
								    DetrPreTrainedModel,
								)

else:
				import sys

				SCREAMING_SNAKE_CASE__    :    Tuple								=					_LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)
