



'''simple docstring'''


import warnings

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







class 	UpperCAmelCase	(		__a):


	'''simple docstring'''




	__magic_name__					: int  =      ["image_processor", "tokenizer"]
	__magic_name__					: Optional[int]  =      "CLIPImageProcessor"
	__magic_name__					: Dict  =      ("XLMRobertaTokenizer", "XLMRobertaTokenizerFast")




	def __init__(   self							,  lowerCAmelCase_=None							,  lowerCAmelCase_=None							,  **lowerCAmelCase_) ->      List[str]:



						"""simple docstring"""







						a_       		=None
						if "feature_extractor" in kwargs:
											warnings.warn(
											    "The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`"
											    " instead."							,  lowerCAmelCase_							,  )
											a_       		=kwargs.pop("feature_extractor")

						a_       		=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__(lowerCAmelCase_							,  lowerCAmelCase_)




	def __call__(   self							,  lowerCAmelCase_=None							,  lowerCAmelCase_=None							,  lowerCAmelCase_=None							,  **lowerCAmelCase_) ->      Optional[int]:



						"""simple docstring"""







						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:
											a_       		=self.tokenizer(lowerCAmelCase_							,  return_tensors=lowerCAmelCase_							,  **lowerCAmelCase_)

						if images is not None:
											a_       		=self.image_processor(lowerCAmelCase_							,  return_tensors=lowerCAmelCase_							,  **lowerCAmelCase_)

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




	def       lowercase_  (   self							,  *lowerCAmelCase_							,  **lowerCAmelCase_) ->      Tuple:



						"""simple docstring"""







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




	def       lowercase_  (   self							,  *lowerCAmelCase_							,  **lowerCAmelCase_) ->      Tuple:



						"""simple docstring"""







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




	@property
	def       lowercase_  (   self) ->      Optional[Any]:



						"""simple docstring"""







						a_       		=self.tokenizer.model_input_names
						a_       		=self.image_processor.model_input_names
						return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names))







'''simple docstring'''


from typing import Any

import numpy as np






def  UpperCAmelCase_       (		lowercase__       ):


     '''simple docstring'''







     return np.array_equal(lowercase__       ,	matrix.conjugate().T       )






def  UpperCAmelCase_       (		lowercase__       ,	lowercase__       ):


     '''simple docstring'''







     a_       		=v.conjugate().T
     a_       		=v_star.dot(lowercase__       )
     assert isinstance(lowercase__       ,	np.ndarray       )
     return (v_star_dot.dot(lowercase__       )) / (v_star.dot(lowercase__       ))






def  UpperCAmelCase_       (		):


     '''simple docstring'''







     a_       		=np.array([[2, 2 + 1j, 4], [2 - 1j, 3, 1j], [4, -1j, 1]]       )
     a_       		=np.array([[1], [2], [3]]       )
     assert is_hermitian(lowercase__       ), F"""{a} is not hermitian."""
     print(rayleigh_quotient(lowercase__       ,	lowercase__       )       )

     a_       		=np.array([[1, 2, 4], [2, 3, -1], [4, -1, 1]]       )
     assert is_hermitian(lowercase__       ), F"""{a} is not hermitian."""
     assert rayleigh_quotient(lowercase__       ,	lowercase__       ) == float(3       )


if __name__ == "__main__":
  import doctest

  doctest.testmod()
  tests()


