code: 
  
  
  
  
  
  import cva
  import numpy as np
  
  from digital_image_processing.filters.convolve import img_convolve
  from digital_image_processing.filters.sobel_filter import sobel_filter
  
  _lowerCamelCase				:	Optional[int]				    =       180
  
  
  
  
  def    __a (     UpperCAmelCase       ,			UpperCAmelCase    )      ->int:
  
  
  
  	"""simple docstring"""
  
  
  
  
  
  
  
  	A							  =      k_size // 2
  	A  ,    A							  =      np.mgrid[0 - center : k_size - center, 0 - center : k_size - center]
  	A							  =      (
  	    1
  	    / (2 * np.pi * sigma)
  	    * np.exp(-(np.square(UpperCAmelCase    ) + np.square(UpperCAmelCase    )) / (2 * np.square(UpperCAmelCase    ))    )
  	)
  	return g
  
  
  
  
  def    __a (     UpperCAmelCase       ,			UpperCAmelCase       ,			UpperCAmelCase    )      ->Optional[int]:
  
  
  
  	"""simple docstring"""
  
  
  
  
  
  
  
  	A							  =      np.zeros(UpperCAmelCase    )
  
  	for row in range(1       ,			image_shape[0] - 1    ):
  		for col in range(1       ,			image_shape[1] - 1    ):
  			A							  =      gradient_direction[row, col]
  
  			if (
  			    0 <= direction < PI / 8
  			    or 15 * PI / 8 <= direction <= 2 * PI
  			    or 7 * PI / 8 <= direction <= 9 * PI / 8
  			):
  				A							  =      sobel_grad[row, col - 1]
  				A							  =      sobel_grad[row, col + 1]
  				if sobel_grad[row, col] >= w and sobel_grad[row, col] >= e:
  					A							  =      sobel_grad[row, col]
  
  			elif (
  			    PI / 8 <= direction < 3 * PI / 8
  			    or 9 * PI / 8 <= direction < 11 * PI / 8
  			):
  				A							  =      sobel_grad[row + 1, col - 1]
  				A							  =      sobel_grad[row - 1, col + 1]
  				if sobel_grad[row, col] >= sw and sobel_grad[row, col] >= ne:
  					A							  =      sobel_grad[row, col]
  
  			elif (
  			    3 * PI / 8 <= direction < 5 * PI / 8
  			    or 11 * PI / 8 <= direction < 13 * PI / 8
  			):
  				A							  =      sobel_grad[row - 1, col]
  				A							  =      sobel_grad[row + 1, col]
  				if sobel_grad[row, col] >= n and sobel_grad[row, col] >= s:
  					A							  =      sobel_grad[row, col]
  
  			elif (
  			    5 * PI / 8 <= direction < 7 * PI / 8
  			    or 13 * PI / 8 <= direction < 15 * PI / 8
  			):
  				A							  =      sobel_grad[row - 1, col - 1]
  				A							  =      sobel_grad[row + 1, col + 1]
  				if sobel_grad[row, col] >= nw and sobel_grad[row, col] >= se:
  					A							  =      sobel_grad[row, col]
  
  	return destination
  
  
  
  
  def    __a (     UpperCAmelCase       ,			UpperCAmelCase       ,			UpperCAmelCase       ,			UpperCAmelCase       ,			UpperCAmelCase       ,			UpperCAmelCase    )      ->Tuple:
  
  
  
  	"""simple docstring"""
  
  
  
  
  
  
  
  	for row in range(1       ,			image_shape[0] - 1    ):
  		for col in range(1       ,			image_shape[1] - 1    ):
  			if destination[row, col] >= threshold_high:
  				A							  =      strong
  			elif destination[row, col] <= threshold_low:
  				A							  =      0
  			else:
  				A							  =      weak
  
  
  
  
  def    __a (     UpperCAmelCase       ,			UpperCAmelCase       ,			UpperCAmelCase       ,			UpperCAmelCase    )      ->str:
  
  
  
  	"""simple docstring"""
  
  
  
  
  
  
  
  	for row in range(1       ,			image_shape[0]    ):
  		for col in range(1       ,			image_shape[1]    ):
  			if destination[row, col] == weak:
  				if 255 in (
  				    destination[row, col + 1],
  				    destination[row, col - 1],
  				    destination[row - 1, col],
  				    destination[row + 1, col],
  				    destination[row - 1, col - 1],
  				    destination[row + 1, col - 1],
  				    destination[row - 1, col + 1],
  				    destination[row + 1, col + 1],
  				):
  					A							  =      strong
  				else:
  					A							  =      0
  
  
  
  
  def    __a (     UpperCAmelCase       ,			UpperCAmelCase=15       ,			UpperCAmelCase=30       ,			UpperCAmelCase=128       ,			UpperCAmelCase=255    )      ->int:
  
  
  
  	"""simple docstring"""
  
  
  
  
  
  
  
  	A							  =      img_convolve(UpperCAmelCase       ,			gen_gaussian_kernel(9       ,			sigma=1.4    )    )
  	# get the gradient and degree by sobel_filter
  	A  ,    A							  =      sobel_filter(UpperCAmelCase    )
  	A							  =      PI + np.radadeg(UpperCAmelCase    )
  
  	A							  =      suppress_non_maximum(image.shape       ,			UpperCAmelCase       ,			UpperCAmelCase    )
  
  	detect_high_low_threshold(
  	    image.shape       ,			UpperCAmelCase       ,			UpperCAmelCase       ,			UpperCAmelCase       ,			UpperCAmelCase       ,			UpperCAmelCase    )
  
  	track_edge(image.shape       ,			UpperCAmelCase       ,			UpperCAmelCase       ,			UpperCAmelCase    )
  
  	return destination
  
  
  if __name__ == "__main__":
  				# read original image in gray mode
  				_lowerCamelCase				:	List[str]				    =       cva.imread(R'../image_data/lena.jpg', 0)
  				# canny edge detection
  				_lowerCamelCase				:	Tuple				    =       canny(lena)
  				cva.imshow('canny', canny_destination)
  				cva.waitKey(0)
  
  
style_context: 
  
  
  
  
  
  '''simple docstring'''
  
  
  import logging
  import random
  
  import ray
  
  from transformers import RagConfig, RagRetriever, RagTokenizer
  from transformers.models.rag.retrieval_rag import CustomHFIndex
  
  
  _lowerCamelCase				:	Tuple				    =       logging.getLogger(__name__)
  
  
  
  class 							__UpperCAmelCase  :
  
  
  
  
  
  
  
  		'''simple docstring'''
  
  
  
  
  
  
  
  		def __init__(self						:      Union[str, Any]      ):
  			A							  =      False
  
  
  
  
  
  
  
  		def        A     (self						:      Any      ,      _lowerCAmelCase						:      str      ,      _lowerCAmelCase						:      List[str]      ,      _lowerCAmelCase						:      str      ,      _lowerCAmelCase						:      Any      ):
  			if not self.initialized:
  				A							  =      RagRetriever(
  				    _lowerCAmelCase      ,      question_encoder_tokenizer=_lowerCAmelCase      ,      generator_tokenizer=_lowerCAmelCase      ,      index=_lowerCAmelCase      ,      init_retrieval=_lowerCAmelCase      ,      )
  				A							  =      True
  
  
  
  
  
  
  
  		def        A     (self						:      Optional[int]      ):
  			self.retriever.index.init_index()
  
  
  
  
  
  
  
  		def        A     (self						:      Optional[int]      ):
  			# delete the old self.retriever object before assigning the new index
  			del self.retriever
  			A							  =      False
  
  
  
  
  
  
  
  		def        A     (self						:      List[str]      ,      _lowerCAmelCase						:      int      ,      _lowerCAmelCase						:      List[str]      ):
  			A  ,    A							  =      self.retriever._main_retrieve(_lowerCAmelCase      ,      _lowerCAmelCase      )
  			A							  =      self.retriever.index.get_doc_dicts(_lowerCAmelCase      )
  			return doc_ids, retrieved_doc_embeds, doc_dicts
  
  
  
  
  
  
  class 							__UpperCAmelCase  (       A__						):
  
  
  
  
  
  
  
  		'''simple docstring'''
  
  
  
  
  
  
  
  		def __init__(self						:      Tuple      ,      _lowerCAmelCase						:      int      ,      _lowerCAmelCase						:      int      ,      _lowerCAmelCase						:      List[str]      ,      _lowerCAmelCase						:      Tuple      ,      _lowerCAmelCase						:      str=None      ):
  			if index is not None and index.is_initialized() and len(_lowerCAmelCase      ) > 0:
  				raise ValueError(
  				    """When using Ray for distributed fine-tuning, """
  				    """you'll need to provide the paths instead, """
  				    """as the dataset and the index are loaded """
  				    """separately. More info in examples/rag/use_own_knowledge_dataset.py """      )
  
  			super().__init__(
  			    _lowerCAmelCase      ,      question_encoder_tokenizer=_lowerCAmelCase      ,      generator_tokenizer=_lowerCAmelCase      ,      index=_lowerCAmelCase      ,      init_retrieval=_lowerCAmelCase      ,      )
  
  			A							  =      retrieval_workers
  			A							  =      question_encoder_tokenizer
  			A							  =      generator_tokenizer
  			if len(self.retrieval_workers      ) > 0:
  				ray.get(
  				    [
  				        worker.create_rag_retriever.remote(_lowerCAmelCase      ,      _lowerCAmelCase      ,      _lowerCAmelCase      ,      _lowerCAmelCase      )
  				        for worker in self.retrieval_workers
  				    ]      )
  
  
  
  
  
  
  
  		def        A     (self						:      List[Any]      ):
  			logger.info("""initializing retrieval"""      )
  
  			if len(self.retrieval_workers      ) > 0:
  				ray.get([worker.init_retrieval.remote() for worker in self.retrieval_workers]      )
  			else:
  				# Non-distributed training. Load index into this same process.
  				self.index.init_index()
  
  
  
  
  
  
  
  		def        A     (self						:      str      ,      _lowerCAmelCase						:      Dict      ,      _lowerCAmelCase						:      Tuple      ):
  			if len(self.retrieval_workers      ) > 0:
  				# Select a random retrieval actor.
  				A							  =      self.retrieval_workers[random.randint(0      ,      len(self.retrieval_workers      ) - 1      )]
  				A  ,    A  ,    A							  =      ray.get(
  				    random_worker.retrieve.remote(_lowerCAmelCase      ,      _lowerCAmelCase      )      )
  			else:
  				A  ,    A							  =      self._main_retrieve(_lowerCAmelCase      ,      _lowerCAmelCase      )
  				A							  =      self.index.get_doc_dicts(_lowerCAmelCase      )
  			return retrieved_doc_embeds, doc_ids, doc_dicts
  
  
  
  
  
  
  
  		@classmethod
  		def        A     (cls						:      Union[str, Any]      ,      _lowerCAmelCase						:      int      ,      _lowerCAmelCase						:      Optional[Any]=None      ,      **_lowerCAmelCase						:      Union[str, Any]      ):
  			return super(_lowerCAmelCase      ,      cls      ).get_tokenizers(_lowerCAmelCase      ,      _lowerCAmelCase      ,      **_lowerCAmelCase      )
  
  
  
  
  
  
  
  		@classmethod
  		def        A     (cls						:      List[Any]      ,      _lowerCAmelCase						:      Optional[int]      ,      _lowerCAmelCase						:      List[str]      ,      _lowerCAmelCase						:      str=None      ,      **_lowerCAmelCase						:      List[str]      ):
  			A							  =      kwargs.pop("""config"""      ,      _lowerCAmelCase      ) or RagConfig.from_pretrained(_lowerCAmelCase      ,      **_lowerCAmelCase      )
  			A							  =      RagTokenizer.from_pretrained(_lowerCAmelCase      ,      config=_lowerCAmelCase      )
  			A							  =      rag_tokenizer.question_encoder
  			A							  =      rag_tokenizer.generator
  
  			if indexed_dataset is not None:
  				A							  =      """custom"""
  				A							  =      CustomHFIndex(config.retrieval_vector_size      ,      _lowerCAmelCase      )
  			else:
  				A							  =      cls._build_index(_lowerCAmelCase      )
  
  			return cls(
  			    _lowerCAmelCase      ,      question_encoder_tokenizer=_lowerCAmelCase      ,      generator_tokenizer=_lowerCAmelCase      ,      retrieval_workers=_lowerCAmelCase      ,      index=_lowerCAmelCase      ,      )
  
  
  
  
  
  
  
  		def        A     (self						:      List[Any]      ):
  			logger.info("""re-loading the new dataset with embeddings"""      )
  			# access from the training loop
  
  			ray.get([worker.clear_object.remote() for worker in self.retrieval_workers]      )
  
  			# build the index object again
  			A							  =      self._build_index(self.config      )
  
  			ray.get(
  			    [
  			        worker.create_rag_retriever.remote(
  			            self.config      ,      self.question_encoder_tokenizer      ,      self.generator_tokenizer      ,      _lowerCAmelCase      )
  			        for worker in self.retrieval_workers
  			    ]      )
  
  