"""simple docstring"""






import json
from typing import List, Optional, Tuple

from tokenizers import pre_tokenizers, processors

from ...tokenization_utils_base import AddedToken, BatchEncoding
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_mvp import MvpTokenizer


_A   					=    logging.get_logger(__name__)


_A   					=    {'vocab_file': 'vocab.json', 'merges_file': 'merges.txt', 'tokenizer_file': 'tokenizer.json'}

# See all MVP models at https://huggingface.co/models?filter=mvp
_A   					=    {
    'vocab_file': {
        'RUCAIBox/mvp': 'https://huggingface.co/RUCAIBox/mvp/resolve/main/vocab.json',
    },
    'added_tokens.json': {
        'RUCAIBox/mvp': 'https://huggingface.co/RUCAIBox/mvp/resolve/main/added_tokens.json',
    },
    'merges_file': {
        'RUCAIBox/mvp': 'https://huggingface.co/RUCAIBox/mvp/resolve/main/merges.txt',
    },
    'tokenizer_file': {
        'RUCAIBox/mvp': 'https://huggingface.co/RUCAIBox/mvp/resolve/main/tokenizer.json',
    },
}

_A   					=    {
    'RUCAIBox/mvp': 1_0_2_4,
}

class lowerCamelCase		(_SCREAMING_SNAKE_CASE      ):






					'''simple docstring'''


					a   	=		VOCAB_FILES_NAMES
					a   	=		PRETRAINED_VOCAB_FILES_MAP
					a   	=		PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
					a   	=		["input_ids", "attention_mask"]
					a   	=		MvpTokenizer



					def __init__(						self			:				Any   , _snake_case			:				Dict=None   , _snake_case			:				str=None   , _snake_case			:				Union[str, Any]=None   , _snake_case			:				List[str]="replace"   , _snake_case			:				List[Any]="<s>"   , _snake_case			:				Optional[int]="</s>"   , _snake_case			:				Optional[int]="</s>"   , _snake_case			:				Union[str, Any]="<s>"   , _snake_case			:				List[str]="<unk>"   , _snake_case			:				List[str]="<pad>"   , _snake_case			:				Union[str, Any]="<mask>"   , _snake_case			:				List[str]=False   , _snake_case			:				Union[str, Any]=True   , **_snake_case			:				Tuple   , )							->       str:
									super().__init__(
									    _snake_case   , _snake_case   , tokenizer_file=_snake_case   , errors=_snake_case   , bos_token=_snake_case   , eos_token=_snake_case   , sep_token=_snake_case   , cls_token=_snake_case   , unk_token=_snake_case   , pad_token=_snake_case   , mask_token=_snake_case   , add_prefix_space=_snake_case   , trim_offsets=_snake_case   , **_snake_case   , )

									SCREAMING_SNAKE_CASE__		  =				json.loads(self.backend_tokenizer.pre_tokenizer.__getstate__()		)
									if pre_tok_state.get("add_prefix_space"   , _snake_case		) != add_prefix_space:
													SCREAMING_SNAKE_CASE__		  =				getattr(_snake_case   , pre_tok_state.pop("type"		)		)
													SCREAMING_SNAKE_CASE__		  =				add_prefix_space
													SCREAMING_SNAKE_CASE__		  =				pre_tok_class(**_snake_case		)

									SCREAMING_SNAKE_CASE__		  =				add_prefix_space

									# the pre_tokenizer is already updated in the GPT2TokenizerFast `__init__`
									SCREAMING_SNAKE_CASE__		  =				"post_processor"
									SCREAMING_SNAKE_CASE__		  =				getattr(self.backend_tokenizer   , _snake_case   , _snake_case		)
									if tokenizer_component_instance:
													SCREAMING_SNAKE_CASE__		  =				json.loads(tokenizer_component_instance.__getstate__()		)

													# The lists 'sep' and 'cls' must be cased in tuples for the object `post_processor_class`
													if "sep" in state:
																	SCREAMING_SNAKE_CASE__		  =				tuple(state["sep"]		)
													if "cls" in state:
																	SCREAMING_SNAKE_CASE__		  =				tuple(state["cls"]		)

													SCREAMING_SNAKE_CASE__		  =				False

													if state.get("add_prefix_space"   , _snake_case		) != add_prefix_space:
																	SCREAMING_SNAKE_CASE__		  =				add_prefix_space
																	SCREAMING_SNAKE_CASE__		  =				True

													if state.get("trim_offsets"   , _snake_case		) != trim_offsets:
																	SCREAMING_SNAKE_CASE__		  =				trim_offsets
																	SCREAMING_SNAKE_CASE__		  =				True

													if changes_to_apply:
																	SCREAMING_SNAKE_CASE__		  =				getattr(_snake_case   , state.pop("type"		)		)
																	SCREAMING_SNAKE_CASE__		  =				component_class(**_snake_case		)
																	setattr(self.backend_tokenizer   , _snake_case   , _snake_case		)



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



					def 			lowerCAmelCase_					(						self			:				List[str]   , *_snake_case			:				List[str]   , **_snake_case			:				str		)							->       BatchEncoding:
									SCREAMING_SNAKE_CASE__		  =				kwargs.get("is_split_into_words"   , _snake_case		)

									if is_split_into_words and not self.add_prefix_space:
													raise ValueError(
													    F"""You need to instantiate {self.__class__.__name__} with add_prefix_space=True """
													    "to use it with pretokenized inputs."		)

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



					def 			lowerCAmelCase_					(						self			:				int   , *_snake_case			:				Optional[int]   , **_snake_case			:				str		)							->       BatchEncoding:
									SCREAMING_SNAKE_CASE__		  =				kwargs.get("is_split_into_words"   , _snake_case		)

									if is_split_into_words and not self.add_prefix_space:
													raise ValueError(
													    F"""You need to instantiate {self.__class__.__name__} with add_prefix_space=True """
													    "to use it with pretokenized inputs."		)

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



					def 			lowerCAmelCase_					(						self			:				List[Any]   , _snake_case			:				str   , _snake_case			:				Optional[str] = None		)							->       Tuple[str]:
									SCREAMING_SNAKE_CASE__		  =				self._tokenizer.model.save(_snake_case   , name=_snake_case		)
									return tuple(_snake_case		)



					def 			lowerCAmelCase_					(						self			:				List[str]   , _snake_case			:				Dict   , _snake_case			:				List[Any]=None		)							->       Any:
									SCREAMING_SNAKE_CASE__		  =				[self.bos_token_id] + token_ids_a + [self.eos_token_id]
									if token_ids_a is None:
													return output

									return output + [self.eos_token_id] + token_ids_a + [self.eos_token_id]



					def 			lowerCAmelCase_					(						self			:				Union[str, Any]   , _snake_case			:				List[int]   , _snake_case			:				Optional[List[int]] = None		)							->       List[int]:
									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 + sep + token_ids_a + sep		) * [0]





"""simple docstring"""






from typing import TYPE_CHECKING

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


_A   					=    {'configuration_van': ['VAN_PRETRAINED_CONFIG_ARCHIVE_MAP', 'VanConfig']}


try:
						if not is_torch_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						_A   					=    [
						    'VAN_PRETRAINED_MODEL_ARCHIVE_LIST',
						    'VanForImageClassification',
						    'VanModel',
						    'VanPreTrainedModel',
						]

if TYPE_CHECKING:
						from .configuration_van import VAN_PRETRAINED_CONFIG_ARCHIVE_MAP, VanConfig

						try:
												if not is_torch_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .modeling_van import (
												    VAN_PRETRAINED_MODEL_ARCHIVE_LIST,
												    VanForImageClassification,
												    VanModel,
												    VanPreTrainedModel,
												)

else:
						import sys

						_A   					=    _LazyModule(__name__, globals()['__file__'], _import_structure)




