


'''simple docstring'''






import warnings

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




class 	a__       (							a__						):





       '''simple docstring'''






       lowercase__    :     Dict					=   ["image_processor", "tokenizer"]
       lowercase__    :     Any					=   "ChineseCLIPImageProcessor"
       lowercase__    :     Union[str, Any]					=   ("BertTokenizer", "BertTokenizerFast")







       def __init__(	self      ,					lowerCamelCase_=None      ,					lowerCamelCase_=None      ,					**lowerCamelCase_     ) ->							Any:
              lowerCAmelCase__      =			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_      ,					)
                     lowerCAmelCase__      =			kwargs.pop('''feature_extractor'''     )

              lowerCAmelCase__      =			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_     )
              lowerCAmelCase__      =			self.image_processor







       def __call__(	self      ,					lowerCamelCase_=None      ,					lowerCamelCase_=None      ,					lowerCamelCase_=None      ,					**lowerCamelCase_     ) ->							Any:

              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:
                     lowerCAmelCase__      =			self.tokenizer(lowerCamelCase_      ,					return_tensors=lowerCamelCase_      ,					**lowerCamelCase_     )

              if images is not None:
                     lowerCAmelCase__      =			self.image_processor(lowerCamelCase_      ,					return_tensors=lowerCamelCase_      ,					**lowerCamelCase_     )

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







       def 						__SCREAMING_SNAKE_CASE							(	self      ,					*lowerCamelCase_      ,					**lowerCamelCase_     ) ->							str:
              return self.tokenizer.batch_decode(*lowerCamelCase_      ,					**lowerCamelCase_     )







       def 						__SCREAMING_SNAKE_CASE							(	self      ,					*lowerCamelCase_      ,					**lowerCamelCase_     ) ->							Any:
              return self.tokenizer.decode(*lowerCamelCase_      ,					**lowerCamelCase_     )







       @property
       def 						__SCREAMING_SNAKE_CASE							(	self     ) ->							Optional[Any]:
              lowerCAmelCase__      =			self.tokenizer.model_input_names
              lowerCAmelCase__      =			self.image_processor.model_input_names
              return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names     )     )







       @property
       def 						__SCREAMING_SNAKE_CASE							(	self     ) ->							List[str]:
              warnings.warn(
                  '''`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead.'''      ,					lowerCamelCase_      ,					)
              return self.image_processor_class



'''simple docstring'''






import doctest
from collections import deque

import numpy as np




class 	a__       :





							'''simple docstring'''






							def __init__(	self     ) ->							None:
														lowerCAmelCase__      =			[2, 1, 2, -1]
														lowerCAmelCase__      =			[1, 2, 3, 4]







							def 						__SCREAMING_SNAKE_CASE							(	self     ) ->							list[float]:
														lowerCAmelCase__      =			len(self.first_signal     )
														lowerCAmelCase__      =			len(self.second_signal     )

														lowerCAmelCase__      =			max(lowerCamelCase_      ,					lowerCamelCase_     )

														# create a zero matrix of max_length x max_length
														lowerCAmelCase__      =			[[0] * max_length for i in range(lowerCamelCase_     )]

														# fills the smaller signal with zeros to make both signals of same length
														if length_first_signal < length_second_signal:
																					self.first_signal += [0] * (max_length - length_first_signal)
														elif length_first_signal > length_second_signal:
																					self.second_signal += [0] * (max_length - length_second_signal)
														for i in range(lowerCamelCase_     ):
																					lowerCAmelCase__      =			deque(self.second_signal     )
																					rotated_signal.rotate(lowerCamelCase_     )
																					for j, item in enumerate(lowerCamelCase_     ):
																												matrix[i][j] += item

        # multiply the matrix with the first signal
														lowerCAmelCase__      =			np.matmul(np.transpose(lowerCamelCase_     )      ,					np.transpose(self.first_signal     )     )

														# rounding-off to two decimal places
														return [round(lowerCamelCase_      ,					2     ) for i in final_signal]


if __name__ == "__main__":
		doctest.testmod()