




import numpy as np
from cva import COLOR_BGR2GRAY, CV_8UC3, cvtColor, filteraD, imread, imshow, waitKey







def      _a				(						SCREAMING_SNAKE_CASE_							:    int				,				SCREAMING_SNAKE_CASE_							:    int				,				SCREAMING_SNAKE_CASE_							:    int				,				SCREAMING_SNAKE_CASE_							:    int				,				SCREAMING_SNAKE_CASE_							:    int				,				SCREAMING_SNAKE_CASE_							:    int					):

					# prepare kernel
					# the kernel size have to be odd
					if (ksize % 2) == 0:
										__lowerCAmelCase           =	ksize + 1
					__lowerCAmelCase           =	np.zeros((ksize, ksize)				,				dtype=np.floataa					)

					# each value
					for y in range(SCREAMING_SNAKE_CASE_					):
										for x in range(SCREAMING_SNAKE_CASE_					):
															# distance from center
															__lowerCAmelCase           =	x - ksize // 2
															__lowerCAmelCase           =	y - ksize // 2

															# degree to radiant
															__lowerCAmelCase           =	theta / 1_80 * np.pi
															__lowerCAmelCase           =	np.cos(_theta					)
															__lowerCAmelCase           =	np.sin(_theta					)

															# get kernel x
															__lowerCAmelCase           =	cos_theta * px + sin_theta * py

															# get kernel y
															__lowerCAmelCase           =	-sin_theta * px + cos_theta * py

															# fill kernel
															__lowerCAmelCase           =	np.exp(
															    -(_x**2 + gamma**2 * _y**2) / (2 * sigma**2)					) * np.cos(2 * np.pi * _x / lambd + psi					)

					return gabor


if __name__ == "__main__":
					import doctest

					doctest.testmod()
					# read original image
					UpperCamelCase__         =      imread("""../image_data/lena.jpg""")
					# turn image in gray scale value
					UpperCamelCase__         =      cvtColor(img, COLOR_BGR2GRAY)

					# Apply multiple Kernel to detect edges
					UpperCamelCase__         =      np.zeros(gray.shape[:2])
					for theta in [0, 30, 60, 90, 120, 150]:
										UpperCamelCase__         =      gabor_filter_kernel(10, 8, theta, 10, 0, 0)
										out += filteraD(gray, CV_8UC3, kernel_aa)
					UpperCamelCase__         =      out / out.max() * 255
					UpperCamelCase__         =      out.astype(np.uinta)

					imshow("""Original""", gray)
					imshow("""Gabor filter with 20x20 mask and 6 directions""", out)

					waitKey(0)










import logging
import random

import ray

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


UpperCamelCase__         =      logging.getLogger(__name__)
class       a__   :






						def __init__(       self      ):




											"""simple docstring"""



											__lowerCAmelCase           =	False






						def     __SCREAMING_SNAKE_CASE(       self		,      _A		,      _A		,      _A		,      _A      ):




											"""simple docstring"""



											if not self.initialized:
																__lowerCAmelCase           =	RagRetriever(
																    _A		,      question_encoder_tokenizer=_A		,      generator_tokenizer=_A		,      index=_A		,      init_retrieval=_A		,      )
																__lowerCAmelCase           =	True






						def     __SCREAMING_SNAKE_CASE(       self      ):




											"""simple docstring"""



											self.retriever.index.init_index()






						def     __SCREAMING_SNAKE_CASE(       self		,      _A		,      _A      ):




											"""simple docstring"""



											__lowerCAmelCase       ,							__lowerCAmelCase           =	self.retriever._main_retrieve(_A		,      _A      )
											return doc_ids, retrieved_doc_embeds






class       a__   ( snake_case__				):






						def __init__(       self		,      _A		,      _A		,      _A		,      _A		,      _A=None      ):




											"""simple docstring"""



											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		,      )
											__lowerCAmelCase           =	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     __SCREAMING_SNAKE_CASE(       self      ):




											"""simple docstring"""



											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     __SCREAMING_SNAKE_CASE(       self		,      _A		,      _A      ):




											"""simple docstring"""



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






						@classmethod
						def     __SCREAMING_SNAKE_CASE(       cls		,      _A		,      _A=None		,      **_A      ):




											"""simple docstring"""



											return super(_A		,      cls      ).get_tokenizers(_A		,      _A		,      **_A      )






						@classmethod
						def     __SCREAMING_SNAKE_CASE(       cls		,      _A		,      _A		,      _A=None		,      **_A      ):




											"""simple docstring"""



											__lowerCAmelCase           =	kwargs.pop("config"		,      _A      ) or RagConfig.from_pretrained(_A		,      **_A      )
											__lowerCAmelCase           =	RagTokenizer.from_pretrained(_A		,      config=_A      )
											__lowerCAmelCase           =	rag_tokenizer.question_encoder
											__lowerCAmelCase           =	rag_tokenizer.generator
											if indexed_dataset is not None:
																__lowerCAmelCase           =	"custom"
																__lowerCAmelCase           =	CustomHFIndex(config.retrieval_vector_size		,      _A      )
											else:
																__lowerCAmelCase           =	cls._build_index(_A      )
											return cls(
											    _A		,      question_encoder_tokenizer=_A		,      generator_tokenizer=_A		,      retrieval_workers=_A		,      index=_A		,      )




