

'''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


__snake_case			:     str										=      logging.get_logger(__name__)


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

# See all MVP models at https://huggingface.co/models?filter=mvp
__snake_case			:     Tuple										=      {
    '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',
    },
}

__snake_case			:     int										=      {
    'RUCAIBox/mvp': 1024,
}



class lowerCamelCase			(  lowercase_						):



			'''simple docstring'''






			__snake_case        =	VOCAB_FILES_NAMES
			__snake_case        =	PRETRAINED_VOCAB_FILES_MAP
			__snake_case        =	PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
			__snake_case        =	['input_ids', 'attention_mask']
			__snake_case        =	MvpTokenizer


			def __init__(				self	:						List[str]		,      lowerCAmelCase_	:						Any=None		,      lowerCAmelCase_	:						Union[str, Any]=None		,      lowerCAmelCase_	:						Optional[Any]=None		,      lowerCAmelCase_	:						Optional[int]="replace"		,      lowerCAmelCase_	:						Dict="<s>"		,      lowerCAmelCase_	:						Any="</s>"		,      lowerCAmelCase_	:						Optional[Any]="</s>"		,      lowerCAmelCase_	:						int="<s>"		,      lowerCAmelCase_	:						int="<unk>"		,      lowerCAmelCase_	:						Tuple="<pad>"		,      lowerCAmelCase_	:						Union[str, Any]="<mask>"		,      lowerCAmelCase_	:						List[Any]=False		,      lowerCAmelCase_	:						Any=True		,      **lowerCAmelCase_	:						Dict		,      )      ->		Dict:





									'''simple docstring'''
									super().__init__(
									    lowerCAmelCase_		,      lowerCAmelCase_		,      tokenizer_file=lowerCAmelCase_		,      errors=lowerCAmelCase_		,      bos_token=lowerCAmelCase_		,      eos_token=lowerCAmelCase_		,      sep_token=lowerCAmelCase_		,      cls_token=lowerCAmelCase_		,      unk_token=lowerCAmelCase_		,      pad_token=lowerCAmelCase_		,      mask_token=lowerCAmelCase_		,      add_prefix_space=lowerCAmelCase_		,      trim_offsets=lowerCAmelCase_		,      **lowerCAmelCase_		,      )

									A__					:       Dict     =json.loads(self.backend_tokenizer.pre_tokenizer.__getstate__()			)
									if pre_tok_state.get("""add_prefix_space"""		,      lowerCAmelCase_			) != add_prefix_space:
															A__					:       Optional[int]     =getattr(lowerCAmelCase_		,      pre_tok_state.pop("""type"""			)			)
															A__					:       str     =add_prefix_space
															A__					:       List[str]     =pre_tok_class(**lowerCAmelCase_			)

									A__					:       Union[str, Any]     =add_prefix_space

									# the pre_tokenizer is already updated in the GPT2TokenizerFast `__init__`
									A__					:       Tuple     ="""post_processor"""
									A__					:       Optional[int]     =getattr(self.backend_tokenizer		,      lowerCAmelCase_		,      lowerCAmelCase_			)
									if tokenizer_component_instance:
															A__					:       Dict     =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__					:       Dict     =tuple(state["""sep"""]			)
															if "cls" in state:
																					A__					:       Dict     =tuple(state["""cls"""]			)

															A__					:       Any     =False

															if state.get("""add_prefix_space"""		,      lowerCAmelCase_			) != add_prefix_space:
																					A__					:       Optional[Any]     =add_prefix_space
																					A__					:       Union[str, Any]     =True

															if state.get("""trim_offsets"""		,      lowerCAmelCase_			) != trim_offsets:
																					A__					:       Optional[Any]     =trim_offsets
																					A__					:       str     =True

															if changes_to_apply:
																					A__					:       Optional[int]     =getattr(lowerCAmelCase_		,      state.pop("""type"""			)			)
																					A__					:       List[str]     =component_class(**lowerCAmelCase_			)
																					setattr(self.backend_tokenizer		,      lowerCAmelCase_		,      lowerCAmelCase_			)


			@property
			def lowercase__						(				self	:						int			)      ->		str:





									'''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 lowercase__						(				self	:						Optional[Any]		,      lowerCAmelCase_	:						Optional[int]			)      ->		Tuple:





									'''simple docstring'''
									A__					:       Any     =AddedToken(lowerCAmelCase_		,      lstrip=lowerCAmelCase_		,      rstrip=lowerCAmelCase_			) if isinstance(lowerCAmelCase_		,      lowerCAmelCase_			) else value
									A__					:       List[str]     =value


			def lowercase__						(				self	:						Union[str, Any]		,      *lowerCAmelCase_	:						List[str]		,      **lowerCAmelCase_	:						Optional[Any]			)      ->		BatchEncoding:





									'''simple docstring'''
									A__					:       Union[str, Any]     =kwargs.get("""is_split_into_words"""		,      lowerCAmelCase_			)

									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(*lowerCAmelCase_		,      **lowerCAmelCase_			)


			def lowercase__						(				self	:						Any		,      *lowerCAmelCase_	:						Optional[Any]		,      **lowerCAmelCase_	:						str			)      ->		BatchEncoding:





									'''simple docstring'''
									A__					:       Dict     =kwargs.get("""is_split_into_words"""		,      lowerCAmelCase_			)

									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(*lowerCAmelCase_		,      **lowerCAmelCase_			)


			def lowercase__						(				self	:						List[Any]		,      lowerCAmelCase_	:						str		,      lowerCAmelCase_	:						Optional[str] = None			)      ->		Tuple[str]:





									'''simple docstring'''
									A__					:       str     =self._tokenizer.model.save(lowerCAmelCase_		,      name=lowerCAmelCase_			)
									return tuple(lowerCAmelCase_			)


			def lowercase__						(				self	:						Union[str, Any]		,      lowerCAmelCase_	:						Optional[int]		,      lowerCAmelCase_	:						List[str]=None			)      ->		List[str]:





									'''simple docstring'''
									A__					:       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 lowercase__						(				self	:						int		,      lowerCAmelCase_	:						List[int]		,      lowerCAmelCase_	:						Optional[List[int]] = None			)      ->		List[int]:





									'''simple docstring'''
									A__					:       List[Any]     =[self.sep_token_id]
									A__					:       Union[str, Any]     =[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'''



import warnings
from typing import List, Optional, Union

from ...image_utils import ImageInput
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding, PaddingStrategy, PreTokenizedInput, TextInput, TruncationStrategy
from ...utils import TensorType



class lowerCamelCase			(  lowercase_						):



			'''simple docstring'''






			__snake_case        =	['image_processor', 'tokenizer']
			__snake_case        =	'FlavaImageProcessor'
			__snake_case        =	('BertTokenizer', 'BertTokenizerFast')


			def __init__(				self	:						Optional[int]		,      lowerCAmelCase_	:						List[str]=None		,      lowerCAmelCase_	:						Optional[Any]=None		,      **lowerCAmelCase_	:						Optional[Any]			)      ->		List[str]:





									'''simple docstring'''
									A__					:       Any     =None
									if "feature_extractor" in kwargs:
															warnings.warn(
															    """The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`"""
															    """ instead."""		,      lowerCAmelCase_		,      )
															A__					:       Optional[Any]     =kwargs.pop("""feature_extractor"""			)

									A__					:       List[Any]     =image_processor if image_processor is not None else feature_extractor
									if image_processor is None:
															raise ValueError("""You need to specify an `image_processor`."""			)
									if tokenizer is None:
															raise ValueError("""You need to specify a `tokenizer`."""			)

									super().__init__(lowerCAmelCase_		,      lowerCAmelCase_			)
									A__					:       int     =self.image_processor


			def __call__(				self	:						Union[str, Any]		,      lowerCAmelCase_	:						Optional[ImageInput] = None		,      lowerCAmelCase_	:						Optional[Union[TextInput, PreTokenizedInput, List[TextInput], List[PreTokenizedInput]]] = None		,      lowerCAmelCase_	:						bool = True		,      lowerCAmelCase_	:						Union[bool, str, PaddingStrategy] = False		,      lowerCAmelCase_	:						Union[bool, str, TruncationStrategy] = False		,      lowerCAmelCase_	:						Optional[int] = None		,      lowerCAmelCase_	:						int = 0		,      lowerCAmelCase_	:						Optional[int] = None		,      lowerCAmelCase_	:						Optional[bool] = None		,      lowerCAmelCase_	:						Optional[bool] = None		,      lowerCAmelCase_	:						Optional[bool] = None		,      lowerCAmelCase_	:						Optional[bool] = None		,      lowerCAmelCase_	:						bool = False		,      lowerCAmelCase_	:						bool = False		,      lowerCAmelCase_	:						bool = False		,      lowerCAmelCase_	:						bool = False		,      lowerCAmelCase_	:						bool = True		,      lowerCAmelCase_	:						Optional[Union[str, TensorType]] = None		,      **lowerCAmelCase_	:						Optional[int]		,      )      ->		str:





									'''simple docstring'''

									if text is None and images is None:
															raise ValueError("""You have to specify either text or images. Both cannot be none."""			)

									if text is not None:
															A__					:       int     =self.tokenizer(
															    text=lowerCAmelCase_		,      add_special_tokens=lowerCAmelCase_		,      padding=lowerCAmelCase_		,      truncation=lowerCAmelCase_		,      max_length=lowerCAmelCase_		,      stride=lowerCAmelCase_		,      pad_to_multiple_of=lowerCAmelCase_		,      return_token_type_ids=lowerCAmelCase_		,      return_attention_mask=lowerCAmelCase_		,      return_overflowing_tokens=lowerCAmelCase_		,      return_special_tokens_mask=lowerCAmelCase_		,      return_offsets_mapping=lowerCAmelCase_		,      return_length=lowerCAmelCase_		,      verbose=lowerCAmelCase_		,      return_tensors=lowerCAmelCase_		,      **lowerCAmelCase_		,      )
									if images is not None:
															A__					:       List[str]     =self.image_processor(
															    lowerCAmelCase_		,      return_image_mask=lowerCAmelCase_		,      return_codebook_pixels=lowerCAmelCase_		,      return_tensors=lowerCAmelCase_		,      **lowerCAmelCase_		,      )

									if text is not None and images is not None:
															encoding.update(lowerCAmelCase_			)
															return encoding
									elif text is not None:
															return encoding
									else:
															return BatchEncoding(data=dict(**lowerCAmelCase_			)		,      tensor_type=lowerCAmelCase_			)


			def lowercase__						(				self	:						Any		,      *lowerCAmelCase_	:						str		,      **lowerCAmelCase_	:						Optional[int]			)      ->		str:





									'''simple docstring'''
									return self.tokenizer.batch_decode(*lowerCAmelCase_		,      **lowerCAmelCase_			)


			def lowercase__						(				self	:						List[Any]		,      *lowerCAmelCase_	:						List[Any]		,      **lowerCAmelCase_	:						Any			)      ->		Tuple:





									'''simple docstring'''
									return self.tokenizer.decode(*lowerCAmelCase_		,      **lowerCAmelCase_			)


			@property
			def lowercase__						(				self	:						Tuple			)      ->		Tuple:





									'''simple docstring'''
									A__					:       Any     =self.tokenizer.model_input_names
									A__					:       Optional[Any]     =self.image_processor.model_input_names
									return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names			)			)


			@property
			def lowercase__						(				self	:						Tuple			)      ->		int:





									'''simple docstring'''
									warnings.warn(
									    """`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead."""		,      lowerCAmelCase_		,      )
									return self.image_processor_class



			@property
			def lowercase__						(				self	:						Tuple			)      ->		Dict:





									'''simple docstring'''
									warnings.warn(
									    """`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead."""		,      lowerCAmelCase_		,      )
									return self.image_processor

