'''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_roberta import RobertaTokenizer


UpperCamelCase_      	=	logging.get_logger(__name__)

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

UpperCamelCase_      	=	{
    '''vocab_file''': {
        '''roberta-base''': '''https://huggingface.co/roberta-base/resolve/main/vocab.json''',
        '''roberta-large''': '''https://huggingface.co/roberta-large/resolve/main/vocab.json''',
        '''roberta-large-mnli''': '''https://huggingface.co/roberta-large-mnli/resolve/main/vocab.json''',
        '''distilroberta-base''': '''https://huggingface.co/distilroberta-base/resolve/main/vocab.json''',
        '''roberta-base-openai-detector''': '''https://huggingface.co/roberta-base-openai-detector/resolve/main/vocab.json''',
        '''roberta-large-openai-detector''': (
            '''https://huggingface.co/roberta-large-openai-detector/resolve/main/vocab.json'''
        ),
    },
    '''merges_file''': {
        '''roberta-base''': '''https://huggingface.co/roberta-base/resolve/main/merges.txt''',
        '''roberta-large''': '''https://huggingface.co/roberta-large/resolve/main/merges.txt''',
        '''roberta-large-mnli''': '''https://huggingface.co/roberta-large-mnli/resolve/main/merges.txt''',
        '''distilroberta-base''': '''https://huggingface.co/distilroberta-base/resolve/main/merges.txt''',
        '''roberta-base-openai-detector''': '''https://huggingface.co/roberta-base-openai-detector/resolve/main/merges.txt''',
        '''roberta-large-openai-detector''': (
            '''https://huggingface.co/roberta-large-openai-detector/resolve/main/merges.txt'''
        ),
    },
    '''tokenizer_file''': {
        '''roberta-base''': '''https://huggingface.co/roberta-base/resolve/main/tokenizer.json''',
        '''roberta-large''': '''https://huggingface.co/roberta-large/resolve/main/tokenizer.json''',
        '''roberta-large-mnli''': '''https://huggingface.co/roberta-large-mnli/resolve/main/tokenizer.json''',
        '''distilroberta-base''': '''https://huggingface.co/distilroberta-base/resolve/main/tokenizer.json''',
        '''roberta-base-openai-detector''': (
            '''https://huggingface.co/roberta-base-openai-detector/resolve/main/tokenizer.json'''
        ),
        '''roberta-large-openai-detector''': (
            '''https://huggingface.co/roberta-large-openai-detector/resolve/main/tokenizer.json'''
        ),
    },
}

UpperCamelCase_      	=	{
    '''roberta-base''': 5_12,
    '''roberta-large''': 5_12,
    '''roberta-large-mnli''': 5_12,
    '''distilroberta-base''': 5_12,
    '''roberta-base-openai-detector''': 5_12,
    '''roberta-large-openai-detector''': 5_12,
}




class _SCREAMING_SNAKE_CASE(							_SCREAMING_SNAKE_CASE					):
						A_			:							int     		= VOCAB_FILES_NAMES
						A_			:							Tuple     		= PRETRAINED_VOCAB_FILES_MAP
						A_			:							Any     		= PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
						A_			:							Dict     		= ['input_ids', 'attention_mask']
						A_			:							Dict     		= RobertaTokenizer




						def __init__(    self :       Union[str, Any]							,							UpperCamelCase_ :       int=None							,							UpperCamelCase_ :       int=None							,							UpperCamelCase_ :       Dict=None							,							UpperCamelCase_ :       Optional[int]="replace"							,							UpperCamelCase_ :       Union[str, Any]="<s>"							,							UpperCamelCase_ :       Dict="</s>"							,							UpperCamelCase_ :       str="</s>"							,							UpperCamelCase_ :       Optional[Any]="<s>"							,							UpperCamelCase_ :       List[Any]="<unk>"							,							UpperCamelCase_ :       Any="<pad>"							,							UpperCamelCase_ :       Any="<mask>"							,							UpperCamelCase_ :       Optional[int]=False							,							UpperCamelCase_ :       List[str]=True							,							**UpperCamelCase_ :       List[str]							,							)     ->				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__     :Union[str, Any]						    = json.loads(self.backend_tokenizer.pre_tokenizer.__getstate__()      )
													if pre_tok_state.get('add_prefix_space'							,							UpperCamelCase_      ) != add_prefix_space:
																				SCREAMING_SNAKE_CASE__     :Any						    = getattr(UpperCamelCase_							,							pre_tok_state.pop('type'      )      )
																				SCREAMING_SNAKE_CASE__     :int						    = add_prefix_space
																				SCREAMING_SNAKE_CASE__     :str						    = pre_tok_class(**UpperCamelCase_      )

													SCREAMING_SNAKE_CASE__     :Optional[int]						    = add_prefix_space

													SCREAMING_SNAKE_CASE__     :Optional[int]						    = 'post_processor'
													SCREAMING_SNAKE_CASE__     :str						    = getattr(self.backend_tokenizer							,							UpperCamelCase_							,							UpperCamelCase_      )
													if tokenizer_component_instance:
																				SCREAMING_SNAKE_CASE__     :List[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:
																											SCREAMING_SNAKE_CASE__     :List[str]						    = tuple(state['sep']      )
																				if "cls" in state:
																											SCREAMING_SNAKE_CASE__     :Optional[Any]						    = tuple(state['cls']      )

																				SCREAMING_SNAKE_CASE__     :Any						    = False

																				if state.get('add_prefix_space'							,							UpperCamelCase_      ) != add_prefix_space:
																											SCREAMING_SNAKE_CASE__     :int						    = add_prefix_space
																											SCREAMING_SNAKE_CASE__     :List[str]						    = True

																				if state.get('trim_offsets'							,							UpperCamelCase_      ) != trim_offsets:
																											SCREAMING_SNAKE_CASE__     :Union[str, Any]						    = trim_offsets
																											SCREAMING_SNAKE_CASE__     :Tuple						    = True

																				if changes_to_apply:
																											SCREAMING_SNAKE_CASE__     :List[Any]						    = getattr(UpperCamelCase_							,							state.pop('type'      )      )
																											SCREAMING_SNAKE_CASE__     :List[str]						    = component_class(**UpperCamelCase_      )
																											setattr(self.backend_tokenizer							,							UpperCamelCase_							,							UpperCamelCase_      )




						@property
						def      __lowerCamelCase       (    self :       Union[str, 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 :       str							,							UpperCamelCase_ :       str      )     ->				str:
													SCREAMING_SNAKE_CASE__     :Optional[int]						    = AddedToken(UpperCamelCase_							,							lstrip=UpperCamelCase_							,							rstrip=UpperCamelCase_      ) if isinstance(UpperCamelCase_							,							UpperCamelCase_      ) else value
													SCREAMING_SNAKE_CASE__     :int						    = value




						def      __lowerCamelCase       (    self :       Optional[Any]							,							*UpperCamelCase_ :       Optional[Any]							,							**UpperCamelCase_ :       Tuple      )     ->				BatchEncoding:
													SCREAMING_SNAKE_CASE__     :Optional[int]						    = kwargs.get('is_split_into_words'							,							UpperCamelCase_      )
													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(*UpperCamelCase_							,							**UpperCamelCase_      )




						def      __lowerCamelCase       (    self :       str							,							*UpperCamelCase_ :       List[str]							,							**UpperCamelCase_ :       Union[str, Any]      )     ->				BatchEncoding:
													SCREAMING_SNAKE_CASE__     :List[Any]						    = kwargs.get('is_split_into_words'							,							UpperCamelCase_      )

													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(*UpperCamelCase_							,							**UpperCamelCase_      )




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




						def      __lowerCamelCase       (    self :       Union[str, Any]							,							UpperCamelCase_ :       List[str]							,							UpperCamelCase_ :       Optional[Any]=None      )     ->				List[Any]:
													SCREAMING_SNAKE_CASE__     :int						    = [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 :       List[Any]							,							UpperCamelCase_ :       List[int]							,							UpperCamelCase_ :       Optional[List[int]] = None      )     ->				List[int]:
													SCREAMING_SNAKE_CASE__     :Any						    = [self.sep_token_id]
													SCREAMING_SNAKE_CASE__     :str						    = [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_tf_available,
    is_torch_available,
    is_vision_available,
)


UpperCamelCase_      	=	{
    '''configuration_blip''': [
        '''BLIP_PRETRAINED_CONFIG_ARCHIVE_MAP''',
        '''BlipConfig''',
        '''BlipTextConfig''',
        '''BlipVisionConfig''',
    ],
    '''processing_blip''': ['''BlipProcessor'''],
}

try:
		if not is_vision_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		UpperCamelCase_      	=	['''BlipImageProcessor''']


try:
		if not is_torch_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		UpperCamelCase_      	=	[
		    '''BLIP_PRETRAINED_MODEL_ARCHIVE_LIST''',
		    '''BlipModel''',
		    '''BlipPreTrainedModel''',
		    '''BlipForConditionalGeneration''',
		    '''BlipForQuestionAnswering''',
		    '''BlipVisionModel''',
		    '''BlipTextModel''',
		    '''BlipForImageTextRetrieval''',
		]

try:
		if not is_tf_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		UpperCamelCase_      	=	[
		    '''TF_BLIP_PRETRAINED_MODEL_ARCHIVE_LIST''',
		    '''TFBlipModel''',
		    '''TFBlipPreTrainedModel''',
		    '''TFBlipForConditionalGeneration''',
		    '''TFBlipForQuestionAnswering''',
		    '''TFBlipVisionModel''',
		    '''TFBlipTextModel''',
		    '''TFBlipForImageTextRetrieval''',
		]

if TYPE_CHECKING:
		from .configuration_blip import BLIP_PRETRAINED_CONFIG_ARCHIVE_MAP, BlipConfig, BlipTextConfig, BlipVisionConfig
		from .processing_blip import BlipProcessor

		try:
				if not is_vision_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .image_processing_blip import BlipImageProcessor

		try:
				if not is_torch_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_blip import (
				    BLIP_PRETRAINED_MODEL_ARCHIVE_LIST,
				    BlipForConditionalGeneration,
				    BlipForImageTextRetrieval,
				    BlipForQuestionAnswering,
				    BlipModel,
				    BlipPreTrainedModel,
				    BlipTextModel,
				    BlipVisionModel,
				)

		try:
				if not is_tf_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_tf_blip import (
				    TF_BLIP_PRETRAINED_MODEL_ARCHIVE_LIST,
				    TFBlipForConditionalGeneration,
				    TFBlipForImageTextRetrieval,
				    TFBlipForQuestionAnswering,
				    TFBlipModel,
				    TFBlipPreTrainedModel,
				    TFBlipTextModel,
				    TFBlipVisionModel,
				)

else:
		import sys

		UpperCamelCase_      	=	_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)






