
'''simple docstring'''

# Lint as: python3
import dataclasses
import re
from dataclasses import dataclass
from functools import total_ordering
from typing import Optional, Union


UpperCAmelCase__ :			Optional[Any]											=		re.compile(R"^(?P<major>\d+)" R"\.(?P<minor>\d+)" R"\.(?P<patch>\d+)$")






@total_ordering
@dataclass
class  A  :
	snake_case__   :str
	snake_case__   :Optional[str]   =  None
	snake_case__   :Optional[Union[str, int]]   =  None
	snake_case__   :Optional[Union[str, int]]   =  None
	snake_case__   :Optional[Union[str, int]]   =  None





	def 				__SCREAMING_SNAKE_CASE	(       self      :							Union[str, Any]				):


				"""simple docstring"""

				lowerCAmelCase__						,lowerCAmelCase__						,lowerCAmelCase__										=				_str_to_version_tuple(self.version_str				)





	def __repr__(       self      :							Tuple				):


				"""simple docstring"""

				return f"""{self.tuple[0]}.{self.tuple[1]}.{self.tuple[2]}"""





	@property
	def 				__SCREAMING_SNAKE_CASE	(       self      :							str				):


				"""simple docstring"""

				return self.major, self.minor, self.patch





	def 				__SCREAMING_SNAKE_CASE	(       self      :							List[Any]   ,				__magic_name__      :							str				):


				"""simple docstring"""

				if isinstance(__magic_name__   ,				__magic_name__				):
							return Version(__magic_name__				)
				elif isinstance(__magic_name__   ,				__magic_name__				):
							return other
				raise TypeError(f"""{other} (type {type(__magic_name__				)}) cannot be compared to version."""				)





	def __eq__(       self      :							List[Any]   ,				__magic_name__      :							str				):


				"""simple docstring"""

				try:
							lowerCAmelCase__										=				self._validate_operand(__magic_name__				)
				except (TypeError, ValueError):
							return False
				else:
							return self.tuple == other.tuple





	def __lt__(       self      :							str   ,				__magic_name__      :							Dict				):


				"""simple docstring"""

				lowerCAmelCase__										=				self._validate_operand(__magic_name__				)
				return self.tuple < other.tuple





	def __hash__(       self      :							Tuple				):


				"""simple docstring"""

				return hash(_version_tuple_to_str(self.tuple				)				)





	@classmethod
	def 				__SCREAMING_SNAKE_CASE	(       cls      :							Optional[int]   ,				__magic_name__      :							Any				):


				"""simple docstring"""

				lowerCAmelCase__										=				{f.name for f in dataclasses.fields(cls				)}
				return cls(**{k: v for k, v in dic.items() if k in field_names}				)







	def 				__SCREAMING_SNAKE_CASE	(       self      :							Tuple				):


				"""simple docstring"""

				return self.version_str







def 	A  (   UpperCamelCase_       :  Any   )       ->							Dict:







			'''simple docstring'''



			lowerCAmelCase__										=				_VERSION_REG.match(UpperCamelCase_   )
			if not res:
						raise ValueError(F"""Invalid version '{version_str}'. Format should be x.y.z with {{x,y,z}} being digits."""   )
			return tuple(int(UpperCamelCase_   ) for v in [res.group("major"   ), res.group("minor"   ), res.group("patch"   )]   )







def 	A  (   UpperCamelCase_       :  List[Any]   )       ->							int:







			'''simple docstring'''



			return ".".join(str(UpperCamelCase_   ) for v in version_tuple   )













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__ (							__snake_case							):
		__snake_case      :       Optional[Any]     =   ["image_processor", "tokenizer"]
		__snake_case      :       Tuple     =   "BridgeTowerImageProcessor"
		__snake_case      :       List[str]     =   ("RobertaTokenizer", "RobertaTokenizerFast")







		def __init__(					self       ,A__       ,A__		):
						super().__init__(A__       ,A__		)







		def __call__(					self       ,A__       ,A__ = None       ,A__ = True       ,A__ = False       ,A__ = None       ,A__ = None       ,A__ = 0       ,A__ = None       ,A__ = None       ,A__ = None       ,A__ = False       ,A__ = False       ,A__ = False       ,A__ = False       ,A__ = True       ,A__ = None       ,**A__       ,):
						_A	:				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
						_A	:				Optional[Any]   =      self.image_processor(
						    A__       ,return_tensors=A__       ,do_normalize=A__       ,do_center_crop=A__       ,**A__		)
						encoding.update(A__		)

						return encoding







		def  A__      (					self       ,*A__       ,**A__		):
						return self.tokenizer.batch_decode(*A__       ,**A__		)







		def  A__      (					self       ,*A__       ,**A__		):
						return self.tokenizer.decode(*A__       ,**A__		)







		@property
		def  A__      (					self		):
						_A	:				Union[str, Any]   =      self.tokenizer.model_input_names
						_A	:				str   =      self.image_processor.model_input_names
						return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names		)		)



