





'''simple docstring'''




from itertools import product

from cva import COLOR_BGR2GRAY, cvtColor, imread, imshow, waitKey
from numpy import dot, exp, mgrid, pi, ravel, square, uinta, zeros







def _lowerCAmelCase     (      _lowerCAmelCase       ,    _lowerCAmelCase    )->     Optional[int]:
 __UpperCAmelCase					=   k_size // 2
 __UpperCAmelCase					,    __UpperCAmelCase					=   mgrid[0 - center : k_size - center, 0 - center : k_size - center]
 __UpperCAmelCase					=   1 / (2 * pi * sigma) * exp(-(square(UpperCamelCase__    ) + square(UpperCamelCase__    )) / (2 * square(UpperCamelCase__    ))    )
 return g







def _lowerCAmelCase     (      _lowerCAmelCase       ,    _lowerCAmelCase       ,    _lowerCAmelCase    )->     List[Any]:
 __UpperCAmelCase					,    __UpperCAmelCase					=   image.shape[0], image.shape[1]
 # dst image height and width
 __UpperCAmelCase					=   height - k_size + 1
 __UpperCAmelCase					=   width - k_size + 1

 # im2col, turn the k_size*k_size pixels into a row and np.vstack all rows
 __UpperCAmelCase					=   zeros((dst_height * dst_width, k_size * k_size)    )
 __UpperCAmelCase					=   0
 for i, j in product(range(UpperCamelCase__    )       ,    range(UpperCamelCase__    )    ):
  __UpperCAmelCase					=   ravel(image[i : i + k_size, j : j + k_size]    )
  __UpperCAmelCase					=   window
  row += 1

 #  turn the kernel into shape(k*k, 1)
 __UpperCAmelCase					=   gen_gaussian_kernel(UpperCamelCase__       ,    UpperCamelCase__    )
 __UpperCAmelCase					=   ravel(UpperCamelCase__    )

 # reshape and get the dst image
 __UpperCAmelCase					=   dot(UpperCamelCase__       ,    UpperCamelCase__    ).reshape(UpperCamelCase__       ,    UpperCamelCase__    ).astype(UpperCamelCase__    )

 return dst


if __name__ == "__main__":
    # read original image
    _A:					Union[str, Any]							=				imread(r"""../image_data/lena.jpg""")
    # turn image in gray scale value
    _A:					List[Any]							=				cvtColor(img, COLOR_BGR2GRAY)

    # get values with two different mask size
    _A:					Union[str, Any]							=				gaussian_filter(gray, 3, sigma=1)
    _A:					Optional[int]							=				gaussian_filter(gray, 5, sigma=0.8)

    # show result images
    imshow("""gaussian filter with 3x3 mask""", gaussianaxa)
    imshow("""gaussian filter with 5x5 mask""", gaussianaxa)
    waitKey()












'''simple docstring'''




import logging
import random

import ray

from transformers import RagConfig, RagRetriever, RagTokenizer
from transformers.models.rag.retrieval_rag import CustomHFIndex


_A:					int							=				logging.getLogger(__name__)






class UpperCAmelCase							:



    def __init__(  self					):
     __UpperCAmelCase					=   False



    def 					__lowerCamelCase						(  self     ,			__A     ,			__A     ,			__A     ,			__A					):
     if not self.initialized:
      __UpperCAmelCase					=   RagRetriever(
          __A     ,			question_encoder_tokenizer=__A     ,			generator_tokenizer=__A     ,			index=__A     ,			init_retrieval=__A     ,			)
      __UpperCAmelCase					=   True



    def 					__lowerCamelCase						(  self					):
     self.retriever.index.init_index()




    def 					__lowerCamelCase						(  self     ,			__A     ,			__A					):
     __UpperCAmelCase					,    __UpperCAmelCase					=   self.retriever._main_retrieve(__A     ,			__A					)
     return doc_ids, retrieved_doc_embeds






class UpperCAmelCase							(		UpperCAmelCase_							):



    def __init__(  self     ,			__A     ,			__A     ,			__A     ,			__A     ,			__A=None					):
     if index is not None and index.is_initialized() and len(__A					) > 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__(
         __A     ,			question_encoder_tokenizer=__A     ,			generator_tokenizer=__A     ,			index=__A     ,			init_retrieval=__A     ,			)
     __UpperCAmelCase					=   retrieval_workers
     if len(self.retrieval_workers					) > 0:
      ray.get(
          [
              worker.create_rag_retriever.remote(__A     ,			__A     ,			__A     ,			__A					)
              for worker in self.retrieval_workers
          ]					)



    def 					__lowerCamelCase						(  self					):
     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 					__lowerCamelCase						(  self     ,			__A     ,			__A					):
     if len(self.retrieval_workers					) > 0:
      # Select a random retrieval actor.
      __UpperCAmelCase					=   self.retrieval_workers[random.randint(0     ,			len(self.retrieval_workers					) - 1					)]
      __UpperCAmelCase					,    __UpperCAmelCase					=   ray.get(random_worker.retrieve.remote(__A     ,			__A					)					)
     else:
      __UpperCAmelCase					,    __UpperCAmelCase					=   self._main_retrieve(__A     ,			__A					)
     return retrieved_doc_embeds, doc_ids, self.index.get_doc_dicts(__A					)



    @classmethod
    def 					__lowerCamelCase						(  cls     ,			__A     ,			__A=None     ,			**__A					):
     return super(__A     ,			cls					).get_tokenizers(__A     ,			__A     ,			**__A					)




    @classmethod
    def 					__lowerCamelCase						(  cls     ,			__A     ,			__A     ,			__A=None     ,			**__A					):
     __UpperCAmelCase					=   kwargs.pop('config'     ,			__A					) or RagConfig.from_pretrained(__A     ,			**__A					)
     __UpperCAmelCase					=   RagTokenizer.from_pretrained(__A     ,			config=__A					)
     __UpperCAmelCase					=   rag_tokenizer.question_encoder
     __UpperCAmelCase					=   rag_tokenizer.generator
     if indexed_dataset is not None:
      __UpperCAmelCase					=   'custom'
      __UpperCAmelCase					=   CustomHFIndex(config.retrieval_vector_size     ,			__A					)
     else:
      __UpperCAmelCase					=   cls._build_index(__A					)
     return cls(
         __A     ,			question_encoder_tokenizer=__A     ,			generator_tokenizer=__A     ,			retrieval_workers=__A     ,			index=__A     ,			)





