


"""simple docstring"""


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 	_a   (   SCREAMING_SNAKE_CASE_			):
    a_     :					List[Any]   							=     ['image_processor', 'tokenizer']
    a_     :					Union[str, Any]   							=     'BlipImageProcessor'
    a_     :					List[Any]   							=     ('BertTokenizer', 'BertTokenizerFast')
    def __init__(			self				:       Tuple				,       SCREAMING_SNAKE_CASE__				:       List[str]				,       SCREAMING_SNAKE_CASE__				:       Tuple					):
          lowerCamelCase__							     =		False
          super().__init__(_a				,       _a					)
          lowerCamelCase__							     =		self.image_processor
    def __call__(			self				:       str				,       SCREAMING_SNAKE_CASE__				:       ImageInput = None				,       SCREAMING_SNAKE_CASE__				:       Union[TextInput, PreTokenizedInput, List[TextInput], List[PreTokenizedInput]] = None				,       SCREAMING_SNAKE_CASE__				:       bool = True				,       SCREAMING_SNAKE_CASE__				:       Union[bool, str, PaddingStrategy] = False				,       SCREAMING_SNAKE_CASE__				:       Union[bool, str, TruncationStrategy] = None				,       SCREAMING_SNAKE_CASE__				:       Optional[int] = None				,       SCREAMING_SNAKE_CASE__				:       int = 0				,       SCREAMING_SNAKE_CASE__				:       Optional[int] = None				,       SCREAMING_SNAKE_CASE__				:       Optional[bool] = None				,       SCREAMING_SNAKE_CASE__				:       bool = False				,       SCREAMING_SNAKE_CASE__				:       bool = False				,       SCREAMING_SNAKE_CASE__				:       bool = False				,       SCREAMING_SNAKE_CASE__				:       bool = False				,       SCREAMING_SNAKE_CASE__				:       bool = False				,       SCREAMING_SNAKE_CASE__				:       bool = True				,       SCREAMING_SNAKE_CASE__				:       Optional[Union[str, TensorType]] = None				,       **SCREAMING_SNAKE_CASE__				:       List[Any]				,       ):
          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:
                lowerCamelCase__							     =		self.tokenizer
                lowerCamelCase__							     =		self.tokenizer(
                    text=_a				,       add_special_tokens=_a				,       padding=_a				,       truncation=_a				,       max_length=_a				,       stride=_a				,       pad_to_multiple_of=_a				,       return_attention_mask=_a				,       return_overflowing_tokens=_a				,       return_special_tokens_mask=_a				,       return_offsets_mapping=_a				,       return_token_type_ids=_a				,       return_length=_a				,       verbose=_a				,       return_tensors=_a				,       **_a				,       )
                return text_encoding

          # add pixel_values
          lowerCamelCase__							     =		self.image_processor(_a				,       return_tensors=_a					)

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

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

          return encoding_image_processor
    def   _UpperCamelCase							(			self				:       Tuple				,       *SCREAMING_SNAKE_CASE__				:       Dict				,       **SCREAMING_SNAKE_CASE__				:       int					):
          return self.tokenizer.batch_decode(*_a				,       **_a					)
    def   _UpperCamelCase							(			self				:       Optional[int]				,       *SCREAMING_SNAKE_CASE__				:       str				,       **SCREAMING_SNAKE_CASE__				:       Dict					):
          return self.tokenizer.decode(*_a				,       **_a					)


    @property
    def   _UpperCamelCase							(			self				:       Tuple					):
          lowerCamelCase__							     =		self.tokenizer.model_input_names
          lowerCamelCase__							     =		self.image_processor.model_input_names
          return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names					)					)








'''simple docstring'''





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)






