



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__	(       _A):



 """simple docstring"""

 a__					: Tuple =  ["image_processor", "tokenizer"]
 a__					: Optional[Any] =  "BlipImageProcessor"
 a__					: Any =  ("BertTokenizer", "BertTokenizerFast")

 def __init__(    self					:					List[Any]			,						__lowerCAmelCase					:					Union[str, Any]			,						__lowerCAmelCase					:					Optional[Any]      )   ->	Optional[int]:
    _A      =       False
    super().__init__(__lowerCAmelCase			,						__lowerCAmelCase      )
    _A      =       self.image_processor

 def __call__(    self					:					List[Any]			,						__lowerCAmelCase					:					ImageInput = None			,						__lowerCAmelCase					:					Union[TextInput, PreTokenizedInput, List[TextInput], List[PreTokenizedInput]] = None			,						__lowerCAmelCase					:					bool = True			,						__lowerCAmelCase					:					Union[bool, str, PaddingStrategy] = False			,						__lowerCAmelCase					:					Union[bool, str, TruncationStrategy] = None			,						__lowerCAmelCase					:					Optional[int] = None			,						__lowerCAmelCase					:					int = 0			,						__lowerCAmelCase					:					Optional[int] = None			,						__lowerCAmelCase					:					Optional[bool] = None			,						__lowerCAmelCase					:					bool = False			,						__lowerCAmelCase					:					bool = False			,						__lowerCAmelCase					:					bool = False			,						__lowerCAmelCase					:					bool = False			,						__lowerCAmelCase					:					bool = False			,						__lowerCAmelCase					:					bool = True			,						__lowerCAmelCase					:					Optional[Union[str, TensorType]] = None			,						**__lowerCAmelCase					:					Union[str, Any]			,						)   ->	BatchEncoding:
    if images is None and text is None:
       raise ValueError('''You have to specify either images or text.'''      )

    # Get only text
    if images is None:
       _A      =       self.tokenizer
       _A      =       self.tokenizer(
           text=__lowerCAmelCase			,						add_special_tokens=__lowerCAmelCase			,						padding=__lowerCAmelCase			,						truncation=__lowerCAmelCase			,						max_length=__lowerCAmelCase			,						stride=__lowerCAmelCase			,						pad_to_multiple_of=__lowerCAmelCase			,						return_attention_mask=__lowerCAmelCase			,						return_overflowing_tokens=__lowerCAmelCase			,						return_special_tokens_mask=__lowerCAmelCase			,						return_offsets_mapping=__lowerCAmelCase			,						return_token_type_ids=__lowerCAmelCase			,						return_length=__lowerCAmelCase			,						verbose=__lowerCAmelCase			,						return_tensors=__lowerCAmelCase			,						**__lowerCAmelCase			,						)
       return text_encoding

    # add pixel_values
    _A      =       self.image_processor(__lowerCAmelCase			,						return_tensors=__lowerCAmelCase      )

    if text is not None:
       _A      =       self.tokenizer(
           text=__lowerCAmelCase			,						add_special_tokens=__lowerCAmelCase			,						padding=__lowerCAmelCase			,						truncation=__lowerCAmelCase			,						max_length=__lowerCAmelCase			,						stride=__lowerCAmelCase			,						pad_to_multiple_of=__lowerCAmelCase			,						return_attention_mask=__lowerCAmelCase			,						return_overflowing_tokens=__lowerCAmelCase			,						return_special_tokens_mask=__lowerCAmelCase			,						return_offsets_mapping=__lowerCAmelCase			,						return_token_type_ids=__lowerCAmelCase			,						return_length=__lowerCAmelCase			,						verbose=__lowerCAmelCase			,						return_tensors=__lowerCAmelCase			,						**__lowerCAmelCase			,						)
    else:
       _A      =       None

    if text_encoding is not None:
       encoding_image_processor.update(__lowerCAmelCase      )

    return encoding_image_processor

 def     snake_case_      (    self					:					str			,						*__lowerCAmelCase					:					Any			,						**__lowerCAmelCase					:					Any      )   ->	int:
    return self.tokenizer.batch_decode(*__lowerCAmelCase			,						**__lowerCAmelCase      )

 def     snake_case_      (    self					:					int			,						*__lowerCAmelCase					:					int			,						**__lowerCAmelCase					:					Optional[int]      )   ->	Tuple:
    return self.tokenizer.decode(*__lowerCAmelCase			,						**__lowerCAmelCase      )

 @property
 def     snake_case_      (    self					:					Optional[int]      )   ->	Dict:
    _A      =       self.tokenizer.model_input_names
    _A      =       self.image_processor.model_input_names
    return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names      )      )








from .configuration_bert_masked import MaskedBertConfig
from .modeling_bert_masked import (
    MaskedBertForMultipleChoice,
    MaskedBertForQuestionAnswering,
    MaskedBertForSequenceClassification,
    MaskedBertForTokenClassification,
    MaskedBertModel,
)
from .modules import *



