

from typing import List, Optional, Union

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



class        A__(_UpperCamelCase							):
						"""simple docstring"""






						_A    :				int       					=			["""image_processor""", """tokenizer"""]
						_A    :				List[str]       					=			"""BridgeTowerImageProcessor"""
						_A    :				Optional[int]       					=			("""RobertaTokenizer""", """RobertaTokenizerFast""")





						def __init__(    self  ,      _lowercase  ,      _lowercase    )     -> Dict:
									super().__init__(_lowercase  ,      _lowercase    )





						def __call__(    self  ,      _lowercase  ,      _lowercase = None  ,      _lowercase = True  ,      _lowercase = False  ,      _lowercase = None  ,      _lowercase = None  ,      _lowercase = 0  ,      _lowercase = None  ,      _lowercase = None  ,      _lowercase = None  ,      _lowercase = False  ,      _lowercase = False  ,      _lowercase = False  ,      _lowercase = False  ,      _lowercase = True  ,      _lowercase = None  ,      **_lowercase  ,      )     -> BatchEncoding:
									a_						:						List[Any] 							= self.tokenizer(
									    text=_lowercase  ,      add_special_tokens=_lowercase  ,      padding=_lowercase  ,      truncation=_lowercase  ,      max_length=_lowercase  ,      stride=_lowercase  ,      pad_to_multiple_of=_lowercase  ,      return_token_type_ids=_lowercase  ,      return_attention_mask=_lowercase  ,      return_overflowing_tokens=_lowercase  ,      return_special_tokens_mask=_lowercase  ,      return_offsets_mapping=_lowercase  ,      return_length=_lowercase  ,      verbose=_lowercase  ,      return_tensors=_lowercase  ,      **_lowercase  ,      )
									# add pixel_values + pixel_mask
									a_						:						str 							= self.image_processor(
									    _lowercase  ,      return_tensors=_lowercase  ,      do_normalize=_lowercase  ,      do_center_crop=_lowercase  ,      **_lowercase    )
									encoding.update(_lowercase    )

									return encoding





						def 			UpperCamelCase__					(    self  ,      *_lowercase  ,      **_lowercase    )     -> List[str]:
									return self.tokenizer.batch_decode(*_lowercase  ,      **_lowercase    )





						def 			UpperCamelCase__					(    self  ,      *_lowercase  ,      **_lowercase    )     -> List[str]:
									return self.tokenizer.decode(*_lowercase  ,      **_lowercase    )





						@property
						def 			UpperCamelCase__					(    self    )     -> Any:
									a_						:						Optional[int] 							= self.tokenizer.model_input_names
									a_						:						List[Any] 							= self.image_processor.model_input_names
									return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names    )    )









from packaging import version

from .import_utils import is_accelerate_available


if is_accelerate_available():
  import accelerate






def    _A	(   lowerCamelCase ):
   if not is_accelerate_available():
      return method
   a__						:    List[Any]   		=	version.parse(accelerate.__version__ ).base_version
   if version.parse(lowerCamelCase ) < version.parse("0.17.0" ):
      return method

   def wrapper(self				,			*lowerCamelCase				,			**lowerCamelCase ):
      if hasattr(self				,			"_hf_hook" ) and hasattr(self._hf_hook				,			"pre_forward" ):
         self._hf_hook.pre_forward(self )
      return method(self				,			*lowerCamelCase				,			**lowerCamelCase )

   return wrapper

