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 UpperCAmelCase						( __A  ):


				'''simple docstring'''






				lowerCamelCase_    = VOCAB_FILES_NAMES
				lowerCamelCase_    = PRETRAINED_VOCAB_FILES_MAP
				lowerCamelCase_    = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
				lowerCamelCase_    = ['''input_ids''', '''attention_mask''']
				lowerCamelCase_    = MvpTokenizer
				def __init__(						self		,     lowercase=None		,     lowercase=None		,     lowercase=None		,     lowercase="replace"		,     lowercase="<s>"		,     lowercase="</s>"		,     lowercase="</s>"		,     lowercase="<s>"		,     lowercase="<unk>"		,     lowercase="<pad>"		,     lowercase="<mask>"		,     lowercase=False		,     lowercase=True		,     **lowercase		,     ):



						"""simple docstring"""





						super().__init__(
						    lowercase		,     lowercase		,     tokenizer_file=lowercase		,     errors=lowercase		,     bos_token=lowercase		,     eos_token=lowercase		,     sep_token=lowercase		,     cls_token=lowercase		,     unk_token=lowercase		,     pad_token=lowercase		,     mask_token=lowercase		,     add_prefix_space=lowercase		,     trim_offsets=lowercase		,     **lowercase		,     )

						A_		:    Union[str, Any]         =				json.loads(self.backend_tokenizer.pre_tokenizer.__getstate__() )
						if pre_tok_state.get('add_prefix_space'		,     lowercase ) != add_prefix_space:
								A_		:    Tuple         =				getattr(lowercase		,     pre_tok_state.pop('type' ) )
								A_		:    str         =				add_prefix_space
								A_		:    List[Any]         =				pre_tok_class(**lowercase )

						A_		:    Optional[int]         =				add_prefix_space

						# the pre_tokenizer is already updated in the GPT2TokenizerFast `__init__`
						A_		:    Dict         =				'post_processor'
						A_		:    Any         =				getattr(self.backend_tokenizer		,     lowercase		,     lowercase )
						if tokenizer_component_instance:
								A_		:    str         =				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:
										A_		:    List[Any]         =				tuple(state['sep'] )
								if "cls" in state:
										A_		:    Union[str, Any]         =				tuple(state['cls'] )

								A_		:    Union[str, Any]         =				False

								if state.get('add_prefix_space'		,     lowercase ) != add_prefix_space:
										A_		:    List[Any]         =				add_prefix_space
										A_		:    List[Any]         =				True

								if state.get('trim_offsets'		,     lowercase ) != trim_offsets:
										A_		:    int         =				trim_offsets
										A_		:    str         =				True

								if changes_to_apply:
										A_		:    Dict         =				getattr(lowercase		,     state.pop('type' ) )
										A_		:    Tuple         =				component_class(**lowercase )
										setattr(self.backend_tokenizer		,     lowercase		,     lowercase )
				@property
				def   lowerCAmelCase_       (						self ):



						"""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   lowerCAmelCase_       (						self		,     lowercase ):



						"""simple docstring"""





						A_		:    Dict         =				AddedToken(lowercase		,     lstrip=lowercase		,     rstrip=lowercase ) if isinstance(lowercase		,     lowercase ) else value
						A_		:    Tuple         =				value
				def   lowerCAmelCase_       (						self		,     *lowercase		,     **lowercase ):



						"""simple docstring"""





						A_		:    List[str]         =				kwargs.get('is_split_into_words'		,     lowercase )

						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(*lowercase		,     **lowercase )
				def   lowerCAmelCase_       (						self		,     *lowercase		,     **lowercase ):



						"""simple docstring"""





						A_		:    Optional[Any]         =				kwargs.get('is_split_into_words'		,     lowercase )

						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(*lowercase		,     **lowercase )
				def   lowerCAmelCase_       (						self		,     lowercase		,     lowercase = None ):



						"""simple docstring"""





						A_		:    Tuple         =				self._tokenizer.model.save(lowercase		,     name=lowercase )
						return tuple(lowercase )
				def   lowerCAmelCase_       (						self		,     lowercase		,     lowercase=None ):



						"""simple docstring"""





						A_		:    List[str]         =				[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		,     lowercase		,     lowercase = None ):



						"""simple docstring"""





						A_		:    List[str]         =				[self.sep_token_id]
						A_		:    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 + sep + token_ids_a + sep ) * [0]







from typing import TYPE_CHECKING

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


_UpperCAmelCase            =		{
    """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:
	_UpperCAmelCase            =		["""BridgeTowerImageProcessor"""]

try:
	if not is_torch_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	_UpperCAmelCase            =		[
	    """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

	_UpperCAmelCase            =		_LazyModule(__name__, globals()["""__file__"""], _import_structure)






