


"""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


__UpperCamelCase		= logging.get_logger(__name__)


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

# See all MVP models at https://huggingface.co/models?filter=mvp
__UpperCamelCase		= {
    "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",
    },
}

__UpperCamelCase		= {
    "RUCAIBox/mvp": 1024,
}
class    lowerCAmelCase      (   __a       ):




		'''simple docstring'''







		SCREAMING_SNAKE_CASE_		:	str														=	VOCAB_FILES_NAMES
		SCREAMING_SNAKE_CASE_		:	Tuple														=	PRETRAINED_VOCAB_FILES_MAP
		SCREAMING_SNAKE_CASE_		:	Optional[Any]														=	PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
		SCREAMING_SNAKE_CASE_		:	List[str]														=	['''input_ids''', '''attention_mask''']
		SCREAMING_SNAKE_CASE_		:	Optional[Any]														=	MvpTokenizer







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

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

								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		,			__UpperCamelCase		,			__UpperCamelCase							)
								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'		,			__UpperCamelCase							) != add_prefix_space:
																				SCREAMING_SNAKE_CASE						=   add_prefix_space
																				SCREAMING_SNAKE_CASE						=   True

														if state.get('trim_offsets'		,			__UpperCamelCase							) != trim_offsets:
																				SCREAMING_SNAKE_CASE						=   trim_offsets
																				SCREAMING_SNAKE_CASE						=   True

														if changes_to_apply:
																				SCREAMING_SNAKE_CASE						=   getattr(__UpperCamelCase		,			state.pop('type'							)							)
																				SCREAMING_SNAKE_CASE						=   component_class(**__UpperCamelCase							)
																				setattr(self.backend_tokenizer		,			__UpperCamelCase		,			__UpperCamelCase							)







		@property
		def 				__A		(      self							)	->				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 				__A		(      self		,			lowerCAmelCase__							)	->				Tuple:
								SCREAMING_SNAKE_CASE						=   AddedToken(__UpperCamelCase		,			lstrip=__UpperCamelCase		,			rstrip=__UpperCamelCase							) if isinstance(__UpperCamelCase		,			__UpperCamelCase							) else value
								SCREAMING_SNAKE_CASE						=   value







		def 				__A		(      self		,			*lowerCAmelCase__		,			**lowerCAmelCase__							)	->				BatchEncoding:
								SCREAMING_SNAKE_CASE						=   kwargs.get('is_split_into_words'		,			__UpperCamelCase							)

								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(*__UpperCamelCase		,			**__UpperCamelCase							)







		def 				__A		(      self		,			*lowerCAmelCase__		,			**lowerCAmelCase__							)	->				BatchEncoding:
								SCREAMING_SNAKE_CASE						=   kwargs.get('is_split_into_words'		,			__UpperCamelCase							)

								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(*__UpperCamelCase		,			**__UpperCamelCase							)







		def 				__A		(      self		,			lowerCAmelCase__		,			lowerCAmelCase__ = None							)	->				Tuple[str]:
								SCREAMING_SNAKE_CASE						=   self._tokenizer.model.save(__UpperCamelCase		,			name=__UpperCamelCase							)
								return tuple(__UpperCamelCase							)







		def 				__A		(      self		,			lowerCAmelCase__		,			lowerCAmelCase__=None							)	->				str:
								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 				__A		(      self		,			lowerCAmelCase__		,			lowerCAmelCase__ = 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]



from typing import TYPE_CHECKING

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


SCREAMING_SNAKE_CASE		:     Union[str, Any]         =	{
    "configuration_bridgetower": [
        "BRIDGETOWER_PRETRAINED_CONFIG_ARCHIVE_MAP",
        "BridgeTowerConfig",
        "BridgeTowerTextConfig",
        "BridgeTowerVisionConfig",
    ],
    "processing_bridgetower": ["BridgeTowerProcessor"],
}

try:
	if not is_vision_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	SCREAMING_SNAKE_CASE		:     Optional[Any]         =	["BridgeTowerImageProcessor"]

try:
	if not is_torch_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	SCREAMING_SNAKE_CASE		:     Tuple         =	[
	    "BRIDGETOWER_PRETRAINED_MODEL_ARCHIVE_LIST",
	    "BridgeTowerForContrastiveLearning",
	    "BridgeTowerForImageAndTextRetrieval",
	    "BridgeTowerForMaskedLM",
	    "BridgeTowerModel",
	    "BridgeTowerPreTrainedModel",
	]


if TYPE_CHECKING:
	from .configuration_bridgetower import (
	    BRIDGETOWER_PRETRAINED_CONFIG_ARCHIVE_MAP,
	    BridgeTowerConfig,
	    BridgeTowerTextConfig,
	    BridgeTowerVisionConfig,
	)
	from .processing_bridgetower import BridgeTowerProcessor

	try:
		if not is_vision_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .image_processing_bridgetower import BridgeTowerImageProcessor

	try:
		if not is_torch_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_bridgetower import (
		    BRIDGETOWER_PRETRAINED_MODEL_ARCHIVE_LIST,
		    BridgeTowerForContrastiveLearning,
		    BridgeTowerForImageAndTextRetrieval,
		    BridgeTowerForMaskedLM,
		    BridgeTowerModel,
		    BridgeTowerPreTrainedModel,
		)


else:
	import sys

	SCREAMING_SNAKE_CASE		:     List[Any]         =	_LazyModule(__name__, globals()["__file__"], _import_structure)






