






"""simple docstring"""



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 					_UpperCAmelCase       ( _A							):
					SCREAMING_SNAKE_CASE_		:				int							    =   ["image_processor", "tokenizer"]
					SCREAMING_SNAKE_CASE_		:				Union[str, Any]							    =   "BridgeTowerImageProcessor"
					SCREAMING_SNAKE_CASE_		:				List[Any]							    =   ("RobertaTokenizer", "RobertaTokenizerFast")




					def __init__(				self     :						int    ,						A     :						Any    ,						A     :						List[str]   )  ->  int:
							super().__init__(A    ,						A   )




					def __call__(				self     :						str    ,						A     :						Optional[Any]    ,						A     :						Union[TextInput, PreTokenizedInput, List[TextInput], List[PreTokenizedInput]] = None    ,						A     :						bool = True    ,						A     :						Union[bool, str, PaddingStrategy] = False    ,						A     :						Union[bool, str, TruncationStrategy] = None    ,						A     :						Optional[int] = None    ,						A     :						int = 0    ,						A     :						Optional[int] = None    ,						A     :						Optional[bool] = None    ,						A     :						Optional[bool] = None    ,						A     :						bool = False    ,						A     :						bool = False    ,						A     :						bool = False    ,						A     :						bool = False    ,						A     :						bool = True    ,						A     :						Optional[Union[str, TensorType]] = None    ,						**A     :						Optional[int]    ,						)  ->  BatchEncoding:
							lowercase_ : List[Any]							       =   self.tokenizer(
							    text=A    ,						add_special_tokens=A    ,						padding=A    ,						truncation=A    ,						max_length=A    ,						stride=A    ,						pad_to_multiple_of=A    ,						return_token_type_ids=A    ,						return_attention_mask=A    ,						return_overflowing_tokens=A    ,						return_special_tokens_mask=A    ,						return_offsets_mapping=A    ,						return_length=A    ,						verbose=A    ,						return_tensors=A    ,						**A    ,						)
							# add pixel_values + pixel_mask
							lowercase_ : int							       =   self.image_processor(
							    A    ,						return_tensors=A    ,						do_normalize=A    ,						do_center_crop=A    ,						**A   )
							encoding.update(A   )

							return encoding




					def   A  (				self     :						Tuple    ,						*A     :						str    ,						**A     :						Optional[Any]   )  ->  List[str]:
							return self.tokenizer.batch_decode(*A    ,						**A   )




					def   A  (				self     :						List[Any]    ,						*A     :						Any    ,						**A     :						Union[str, Any]   )  ->  List[str]:
							return self.tokenizer.decode(*A    ,						**A   )






					@property
					def   A  (				self     :						Optional[int]   )  ->  Tuple:
							lowercase_ : Union[str, Any]							       =   self.tokenizer.model_input_names
							lowercase_ : List[str]							       =   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


_A	:			Dict         ={
    '''configuration_swinv2''': ['''SWINV2_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''Swinv2Config'''],
}


try:
			if not is_torch_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			_A	:			List[Any]         =[
			    '''SWINV2_PRETRAINED_MODEL_ARCHIVE_LIST''',
			    '''Swinv2ForImageClassification''',
			    '''Swinv2ForMaskedImageModeling''',
			    '''Swinv2Model''',
			    '''Swinv2PreTrainedModel''',
			]


if TYPE_CHECKING:
			from .configuration_swinva import SWINV2_PRETRAINED_CONFIG_ARCHIVE_MAP, SwinvaConfig

			try:
						if not is_torch_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .modeling_swinva import (
						    SWINV2_PRETRAINED_MODEL_ARCHIVE_LIST,
						    SwinvaForImageClassification,
						    SwinvaForMaskedImageModeling,
						    SwinvaModel,
						    SwinvaPreTrainedModel,
						)


else:
			import sys

			_A	:			Optional[Any]         =_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)

