






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     (			a ):





				"""simple docstring"""







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


				def __init__( self							,  lowerCamelCase__							,  lowerCamelCase__    ):




									"""simple docstring"""

									super().__init__(lowerCamelCase__							,  lowerCamelCase__    )


				def __call__( self							,  lowerCamelCase__							,  lowerCamelCase__ = None							,  lowerCamelCase__ = True							,  lowerCamelCase__ = False							,  lowerCamelCase__ = None							,  lowerCamelCase__ = None							,  lowerCamelCase__ = 0							,  lowerCamelCase__ = None							,  lowerCamelCase__ = None							,  lowerCamelCase__ = None							,  lowerCamelCase__ = False							,  lowerCamelCase__ = False							,  lowerCamelCase__ = False							,  lowerCamelCase__ = False							,  lowerCamelCase__ = True							,  lowerCamelCase__ = None							,  **lowerCamelCase__							,  ):




									"""simple docstring"""

									__UpperCamelCase      :					Union[str, Any]       		=self.tokenizer(
									    text=lowerCamelCase__							,  add_special_tokens=lowerCamelCase__							,  padding=lowerCamelCase__							,  truncation=lowerCamelCase__							,  max_length=lowerCamelCase__							,  stride=lowerCamelCase__							,  pad_to_multiple_of=lowerCamelCase__							,  return_token_type_ids=lowerCamelCase__							,  return_attention_mask=lowerCamelCase__							,  return_overflowing_tokens=lowerCamelCase__							,  return_special_tokens_mask=lowerCamelCase__							,  return_offsets_mapping=lowerCamelCase__							,  return_length=lowerCamelCase__							,  verbose=lowerCamelCase__							,  return_tensors=lowerCamelCase__							,  **lowerCamelCase__							,  )
									# add pixel_values + pixel_mask
									__UpperCamelCase      :					Tuple       		=self.image_processor(
									    lowerCamelCase__							,  return_tensors=lowerCamelCase__							,  do_normalize=lowerCamelCase__							,  do_center_crop=lowerCamelCase__							,  **lowerCamelCase__    )
									encoding.update(lowerCamelCase__    )

									return encoding


				def       __lowercase							( self							,  *lowerCamelCase__							,  **lowerCamelCase__    ):




									"""simple docstring"""

									return self.tokenizer.batch_decode(*lowerCamelCase__							,  **lowerCamelCase__    )


				def       __lowercase							( self							,  *lowerCamelCase__							,  **lowerCamelCase__    ):




									"""simple docstring"""

									return self.tokenizer.decode(*lowerCamelCase__							,  **lowerCamelCase__    )







				@property
				def       __lowercase							( self    ):




									"""simple docstring"""

									__UpperCamelCase      :					Union[str, Any]       		=self.tokenizer.model_input_names
									__UpperCamelCase      :					Optional[int]       		=self.image_processor.model_input_names
									return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names    )    )






def 				A				(					_lowerCamelCase  ):





							'''simple docstring'''




							if bit_count < 0:
														raise ValueError("The given input must be positive"  )

							# get the generated string sequence
							_lowerCAmelCase     :							List[str]    =    gray_code_sequence_string(_lowerCamelCase  )
							#
							# convert them to integers
							for i in range(len(_lowerCamelCase  )  ):
														_lowerCAmelCase     :							List[str]    =    int(sequence[i]				, 2  )

							return sequence






def 				A				(					_lowerCamelCase  ):





							'''simple docstring'''




							if bit_count == 0:
														return ["0"]

							if bit_count == 1:
														return ["0", "1"]

							_lowerCAmelCase     :							List[Any]    =    1 << bit_count  # defines the length of the sequence
							# 1<< n is equivalent to 2^n

							# recursive answer will generate answer for n-1 bits
							_lowerCAmelCase     :							Optional[int]    =    gray_code_sequence_string(bit_count - 1  )

							_lowerCAmelCase     :							str    =    []

							# append 0 to first half of the smaller sequence generated
							for i in range(seq_len // 2  ):
														_lowerCAmelCase     :							Dict    =    "0" + smaller_sequence[i]
														sequence.append(_lowerCamelCase  )

							# append 1 to second half ... start from the end of the list
							for i in reversed(range(seq_len // 2  )  ):
														_lowerCAmelCase     :							Optional[Any]    =    "1" + smaller_sequence[i]
														sequence.append(_lowerCamelCase  )

							return sequence


if __name__ == "__main__":
						import doctest

						doctest.testmod()
