
import warnings

from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding



class    a_ (			_a    ):
			a	:			Optional[Any]					   =							['''image_processor''', '''tokenizer''']
			a	:			Optional[Any]					   =							'''ChineseCLIPImageProcessor'''
			a	:			List[str]					   =							('''BertTokenizer''', '''BertTokenizerFast''')






			def __init__(      self				,			__UpperCamelCase=None				,			__UpperCamelCase=None				,			**__UpperCamelCase  ):
									_lowercase					       =					None
									if "feature_extractor" in kwargs:
															warnings.warn(
															    """The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`"""
															    """ instead."""				,			__UpperCamelCase				,			)
															_lowercase					       =					kwargs.pop("""feature_extractor"""  )

									_lowercase					       =					image_processor if image_processor is not None else feature_extractor
									if image_processor is None:
															raise ValueError("""You need to specify an `image_processor`."""  )
									if tokenizer is None:
															raise ValueError("""You need to specify a `tokenizer`."""  )

									super().__init__(__UpperCamelCase				,			__UpperCamelCase  )
									_lowercase					       =					self.image_processor






			def __call__(      self				,			__UpperCamelCase=None				,			__UpperCamelCase=None				,			__UpperCamelCase=None				,			**__UpperCamelCase  ):

									if text is None and images is None:
															raise ValueError("""You have to specify either text or images. Both cannot be none."""  )

									if text is not None:
															_lowercase					       =					self.tokenizer(__UpperCamelCase				,			return_tensors=__UpperCamelCase				,			**__UpperCamelCase  )

									if images is not None:
															_lowercase					       =					self.image_processor(__UpperCamelCase				,			return_tensors=__UpperCamelCase				,			**__UpperCamelCase  )

									if text is not None and images is not None:
															_lowercase					       =					image_features.pixel_values
															return encoding
									elif text is not None:
															return encoding
									else:
															return BatchEncoding(data=dict(**__UpperCamelCase  )				,			tensor_type=__UpperCamelCase  )






			def    UpperCamelCase_				(      self				,			*__UpperCamelCase				,			**__UpperCamelCase  ):
									return self.tokenizer.batch_decode(*__UpperCamelCase				,			**__UpperCamelCase  )






			def    UpperCamelCase_				(      self				,			*__UpperCamelCase				,			**__UpperCamelCase  ):
									return self.tokenizer.decode(*__UpperCamelCase				,			**__UpperCamelCase  )






			@property
			def    UpperCamelCase_				(      self  ):
									_lowercase					       =					self.tokenizer.model_input_names
									_lowercase					       =					self.image_processor.model_input_names
									return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names  )  )






			@property
			def    UpperCamelCase_				(      self  ):
									warnings.warn(
									    """`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead."""				,			__UpperCamelCase				,			)
									return self.image_processor_class

def 	UpperCamelCase__  (     SCREAMING_SNAKE_CASE_	:	int       )  ->     list:

						# 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
						_lowercase					       =					gray_code_sequence_string(SCREAMING_SNAKE_CASE_       )
						#
						# convert them to integers
						for i in range(len(SCREAMING_SNAKE_CASE_       )       ):
												_lowercase					       =					int(sequence[i]		,   2       )

						return sequence




def 	UpperCamelCase__  (     SCREAMING_SNAKE_CASE_	:	int       )  ->     list:

						# 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"]

						_lowercase					       =					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
						_lowercase					       =					gray_code_sequence_string(bit_count - 1       )

						_lowercase					       =					[]

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

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

						return sequence


if __name__ == "__main__":
		import doctest

		doctest.testmod()