"""simple docstring"""







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


_a	:	str=					logging.get_logger(__name__)

_a	:	Optional[int]=					{"vocab_file": "vocab.json", "merges_file": "merges.txt", "tokenizer_file": "tokenizer.json"}

_a	:	Dict=					{
    "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"
        ),
    },
}

_a	:	Optional[int]=					{
    "microsoft/deberta-base": 512,
    "microsoft/deberta-large": 512,
    "microsoft/deberta-xlarge": 512,
    "microsoft/deberta-base-mnli": 512,
    "microsoft/deberta-large-mnli": 512,
    "microsoft/deberta-xlarge-mnli": 512,
}

_a	:	str=					{
    "microsoft/deberta-base": {"do_lower_case": False},
    "microsoft/deberta-large": {"do_lower_case": False},
}

class 						UpperCamelCase     (       lowercase		):
				UpperCAmelCase							: int       =						VOCAB_FILES_NAMES
				UpperCAmelCase							: str       =						PRETRAINED_VOCAB_FILES_MAP
				UpperCAmelCase							: str       =						PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
				UpperCAmelCase							: List[str]       =						["""input_ids""", """attention_mask""", """token_type_ids"""]
				UpperCAmelCase							: Optional[Any]       =						DebertaTokenizer



				def __init__(self							: str      ,    _A							: str=None      ,    _A							: int=None      ,    _A							: Any=None      ,    _A							: Any="replace"      ,    _A							: Union[str, Any]="[CLS]"      ,    _A							: Union[str, Any]="[SEP]"      ,    _A							: Optional[int]="[SEP]"      ,    _A							: Any="[CLS]"      ,    _A							: Tuple="[UNK]"      ,    _A							: List[Any]="[PAD]"      ,    _A							: Union[str, Any]="[MASK]"      ,    _A							: Optional[Any]=False      ,    **_A							: Optional[int]      ,    )       ->      int:
					super().__init__(
					    _A      ,    _A      ,    tokenizer_file=_A      ,    errors=_A      ,    bos_token=_A      ,    eos_token=_A      ,    unk_token=_A      ,    sep_token=_A      ,    cls_token=_A      ,    pad_token=_A      ,    mask_token=_A      ,    add_prefix_space=_A      ,    **_A      ,    )
					__snake_case     :				Optional[int] 						=					kwargs.pop('add_bos_token'      ,    _A)

					__snake_case     :				Dict 						=					json.loads(self.backend_tokenizer.pre_tokenizer.__getstate__())
					if pre_tok_state.get('add_prefix_space'      ,    _A) != add_prefix_space:
						__snake_case     :				Tuple 						=					getattr(_A      ,    pre_tok_state.pop('type'))
						__snake_case     :				Dict 						=					add_prefix_space
						__snake_case     :				Dict 						=					pre_tok_class(**_A)

					__snake_case     :				List[Any] 						=					add_prefix_space



				@property
				def    _lowercase	(self							: str)       ->      str:
					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    _lowercase	(self							: int      ,    _A							: List[Any])       ->      Optional[Any]:
					__snake_case     :				int 						=					AddedToken(_A      ,    lstrip=_A      ,    rstrip=_A) if isinstance(_A      ,    _A) else value
					__snake_case     :				Any 						=					value



				def    _lowercase	(self							: Tuple      ,    _A							: List[int]      ,    _A							: Optional[List[int]] = None)       ->      List[int]:
					if token_ids_a is None:
						return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
					__snake_case     :				List[Any] 						=					[self.cls_token_id]
					__snake_case     :				Optional[Any] 						=					[self.sep_token_id]
					return cls + token_ids_a + sep + token_ids_a + sep



				def    _lowercase	(self							: Optional[Any]      ,    _A							: List[int]      ,    _A							: Optional[List[int]] = None)       ->      List[int]:
					__snake_case     :				List[Any] 						=					[self.sep_token_id]
					__snake_case     :				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    _lowercase	(self							: int      ,    *_A							: Tuple      ,    **_A							: List[Any])       ->      BatchEncoding:
					__snake_case     :				Union[str, Any] 						=					kwargs.get('is_split_into_words'      ,    _A)
					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(*_A      ,    **_A)



				def    _lowercase	(self							: Tuple      ,    *_A							: str      ,    **_A							: int)       ->      BatchEncoding:
					__snake_case     :				Optional[Any] 						=					kwargs.get('is_split_into_words'      ,    _A)

					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(*_A      ,    **_A)



				def    _lowercase	(self							: Optional[Any]      ,    _A							: str      ,    _A							: Optional[str] = None)       ->      Tuple[str]:
					__snake_case     :				Tuple 						=					self._tokenizer.model.save(_A      ,    name=_A)
					return tuple(_A)






				def    _lowercase	(self							: Tuple      ,    _A							: "Conversation")       ->      List[int]:
					__snake_case     :				List[str] 						=					[]
					for is_user, text in conversation.iter_texts():
						input_ids.extend(self.encode(_A      ,    add_special_tokens=_A) + [self.eos_token_id])

					if len(_A) > self.model_max_length:
						__snake_case     :				Union[str, Any] 						=					input_ids[-self.model_max_length :]
					return input_ids






"""simple docstring"""







from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_flax_available,
    is_tf_available,
    is_torch_available,
)


_a	:	Optional[Any]=					{"configuration_gptj": ["GPTJ_PRETRAINED_CONFIG_ARCHIVE_MAP", "GPTJConfig", "GPTJOnnxConfig"]}

try:
	if not is_torch_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	_a	:	Dict=					[
	    "GPTJ_PRETRAINED_MODEL_ARCHIVE_LIST",
	    "GPTJForCausalLM",
	    "GPTJForQuestionAnswering",
	    "GPTJForSequenceClassification",
	    "GPTJModel",
	    "GPTJPreTrainedModel",
	]

try:
	if not is_tf_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	_a	:	Optional[Any]=					[
	    "TFGPTJForCausalLM",
	    "TFGPTJForQuestionAnswering",
	    "TFGPTJForSequenceClassification",
	    "TFGPTJModel",
	    "TFGPTJPreTrainedModel",
	]

try:
	if not is_flax_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	_a	:	List[str]=					[
	    "FlaxGPTJForCausalLM",
	    "FlaxGPTJModel",
	    "FlaxGPTJPreTrainedModel",
	]


if TYPE_CHECKING:
	from .configuration_gptj import GPTJ_PRETRAINED_CONFIG_ARCHIVE_MAP, GPTJConfig, GPTJOnnxConfig

	try:
		if not is_torch_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_gptj import (
		    GPTJ_PRETRAINED_MODEL_ARCHIVE_LIST,
		    GPTJForCausalLM,
		    GPTJForQuestionAnswering,
		    GPTJForSequenceClassification,
		    GPTJModel,
		    GPTJPreTrainedModel,
		)

	try:
		if not is_tf_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_tf_gptj import (
		    TFGPTJForCausalLM,
		    TFGPTJForQuestionAnswering,
		    TFGPTJForSequenceClassification,
		    TFGPTJModel,
		    TFGPTJPreTrainedModel,
		)

	try:
		if not is_flax_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_flax_gptj import FlaxGPTJForCausalLM, FlaxGPTJModel, FlaxGPTJPreTrainedModel

else:
	import sys

	_a	:	Any=					_LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)





