


'''simple docstring'''



def       _UpperCamelCase (						UpperCamelCase__   ):

			# bit count represents no. of bits in the gray code
			if bit_count < 0:
						raise ValueError("""The given input must be positive"""   )

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

			return sequence







def       _UpperCamelCase (						UpperCamelCase__   ):

			# The approach is a recursive one
			# Base case achieved when either n = 0 or n=1
			if bit_count == 0:
						return ["0"]

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

			UpperCAmelCase__					:   Tuple       				=       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
			UpperCAmelCase__					:   Dict       				=       gray_code_sequence_string(bit_count - 1   )

			UpperCAmelCase__					:   str       				=       []

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

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

			return sequence


if __name__ == "__main__":
		import doctest

		doctest.testmod()



'''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 						_snake_case   (       a__		):
					lowerCAmelCase :Optional[int]						=   ['''image_processor''', '''tokenizer''']
					lowerCAmelCase :Optional[int]						=   '''BridgeTowerImageProcessor'''
					lowerCAmelCase :List[str]						=   ('''RobertaTokenizer''', '''RobertaTokenizerFast''')




					def __init__( self							,							_lowerCamelCase							,							_lowerCamelCase):
								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							,							):
								UpperCAmelCase__					:   List[str]       				=       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__					:   Optional[Any]       				=       self.image_processor(
								    _lowerCamelCase							,							return_tensors=_lowerCamelCase							,							do_normalize=_lowerCamelCase							,							do_center_crop=_lowerCamelCase							,							**_lowerCamelCase)
								encoding.update(_lowerCamelCase)

								return encoding




					def 			snake_case__  ( self							,							*_lowerCamelCase							,							**_lowerCamelCase):
								return self.tokenizer.batch_decode(*_lowerCamelCase							,							**_lowerCamelCase)




					def 			snake_case__  ( self							,							*_lowerCamelCase							,							**_lowerCamelCase):
								return self.tokenizer.decode(*_lowerCamelCase							,							**_lowerCamelCase)




					@property
					def 			snake_case__  ( self):
								UpperCAmelCase__					:   Optional[int]       				=       self.tokenizer.model_input_names
								UpperCAmelCase__					:   str       				=       self.image_processor.model_input_names
								return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names))