






import logging
import random

import ray

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


lowercase    			=       logging.getLogger(__name__)

class 						A_							:







				def __init__(							self							:						Dict	)				->					List[Any]:
										__magic_name__            =     False







				def     _snake_case					(							self							:						Optional[Any]						,    __lowerCamelCase							:						Optional[Any]						,    __lowerCamelCase							:						Dict						,    __lowerCamelCase							:						List[Any]						,    __lowerCamelCase							:						Any	)				->					int:
										if not self.initialized:
																__magic_name__            =     RagRetriever(
																    __lowerCamelCase						,    question_encoder_tokenizer=__lowerCamelCase						,    generator_tokenizer=__lowerCamelCase						,    index=__lowerCamelCase						,    init_retrieval=__lowerCamelCase						,    )
																__magic_name__            =     True







				def     _snake_case					(							self							:						Dict	)				->					List[str]:
										self.retriever.index.init_index()







				def     _snake_case					(							self							:						Any						,    __lowerCamelCase							:						Optional[Any]						,    __lowerCamelCase							:						str	)				->					Optional[Any]:
										__magic_name__      ,    __magic_name__            =     self.retriever._main_retrieve(__lowerCamelCase						,    __lowerCamelCase	)
										return doc_ids, retrieved_doc_embeds





class 						A_							(      snake_case_  ):







				def __init__(							self							:						Tuple						,    __lowerCamelCase							:						List[Any]						,    __lowerCamelCase							:						Union[str, Any]						,    __lowerCamelCase							:						List[str]						,    __lowerCamelCase							:						Tuple						,    __lowerCamelCase							:						Any=None	)				->					Union[str, Any]:
										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						,    )
										__magic_name__            =     retrieval_workers
										if len(self.retrieval_workers	) > 0:
																ray.get(
																    [
																        worker.create_rag_retriever.remote(__lowerCamelCase						,    __lowerCamelCase						,    __lowerCamelCase						,    __lowerCamelCase	)
																        for worker in self.retrieval_workers
																    ]	)







				def     _snake_case					(							self							:						List[str]	)				->					List[str]:
										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     _snake_case					(							self							:						Union[str, Any]						,    __lowerCamelCase							:						Tuple						,    __lowerCamelCase							:						Optional[Any]	)				->					List[str]:
										if len(self.retrieval_workers	) > 0:
																# Select a random retrieval actor.
																__magic_name__            =     self.retrieval_workers[random.randint(0						,    len(self.retrieval_workers	) - 1	)]
																__magic_name__      ,    __magic_name__            =     ray.get(random_worker.retrieve.remote(__lowerCamelCase						,    __lowerCamelCase	)	)
										else:
																__magic_name__      ,    __magic_name__            =     self._main_retrieve(__lowerCamelCase						,    __lowerCamelCase	)
										return retrieved_doc_embeds, doc_ids, self.index.get_doc_dicts(__lowerCamelCase	)







				@classmethod
				def     _snake_case					(							cls							:						Any						,    __lowerCamelCase							:						List[Any]						,    __lowerCamelCase							:						int=None						,    **__lowerCamelCase							:						List[str]	)				->					List[Any]:
										return super(__lowerCamelCase						,    cls	).get_tokenizers(__lowerCamelCase						,    __lowerCamelCase						,    **__lowerCamelCase	)







				@classmethod
				def     _snake_case					(							cls							:						Optional[Any]						,    __lowerCamelCase							:						List[str]						,    __lowerCamelCase							:						List[str]						,    __lowerCamelCase							:						List[str]=None						,    **__lowerCamelCase							:						Dict	)				->					Tuple:
										__magic_name__            =     kwargs.pop("config"						,    __lowerCamelCase	) or RagConfig.from_pretrained(__lowerCamelCase						,    **__lowerCamelCase	)
										__magic_name__            =     RagTokenizer.from_pretrained(__lowerCamelCase						,    config=__lowerCamelCase	)
										__magic_name__            =     rag_tokenizer.question_encoder
										__magic_name__            =     rag_tokenizer.generator
										if indexed_dataset is not None:
																__magic_name__            =     "custom"
																__magic_name__            =     CustomHFIndex(config.retrieval_vector_size						,    __lowerCamelCase	)
										else:
																__magic_name__            =     cls._build_index(__lowerCamelCase	)
										return cls(
										    __lowerCamelCase						,    question_encoder_tokenizer=__lowerCamelCase						,    generator_tokenizer=__lowerCamelCase						,    retrieval_workers=__lowerCamelCase						,    index=__lowerCamelCase						,    )











"""simple docstring"""

import colorsys

from PIL import Image  # type: ignore






def _lowerCAmelCase	(  __lowerCamelCase:float ,						__lowerCamelCase:float ,						__lowerCamelCase:int   ):

						'''simple docstring'''






						__magic_name__            =     x
						__magic_name__            =     y
						for step in range(__lowerCamelCase   ):  # noqa: B007
												__magic_name__            =     a * a - b * b + x
												__magic_name__            =     2 * a * b + y
												__magic_name__            =     a_new

												# divergence happens for all complex number with an absolute value
												# greater than 4
												if a * a + b * b > 4:
																		break
						return step / (max_step - 1)






def _lowerCAmelCase	(  __lowerCamelCase:float   ):

						'''simple docstring'''






						if distance == 1:
												return (0, 0, 0)
						else:
												return (2_5_5, 2_5_5, 2_5_5)






def _lowerCAmelCase	(  __lowerCamelCase:float   ):

						'''simple docstring'''






						if distance == 1:
												return (0, 0, 0)
						else:
												return tuple(round(i * 2_5_5   ) for i in colorsys.hsv_to_rgb(__lowerCamelCase ,						1 ,						1   )   )






def _lowerCAmelCase	(  __lowerCamelCase:int = 8_0_0 ,						__lowerCamelCase:int = 6_0_0 ,						__lowerCamelCase:float = -0.6 ,						__lowerCamelCase:float = 0 ,						__lowerCamelCase:float = 3.2 ,						__lowerCamelCase:int = 5_0 ,						__lowerCamelCase:bool = True ,						):

						'''simple docstring'''






						__magic_name__            =     Image.new("RGB" ,						(image_width, image_height)   )
						__magic_name__            =     img.load()

						# loop through the image-coordinates
						for image_x in range(__lowerCamelCase   ):
												for image_y in range(__lowerCamelCase   ):
																		# determine the figure-coordinates based on the image-coordinates
																		__magic_name__            =     figure_width / image_width * image_height
																		__magic_name__            =     figure_center_x + (image_x / image_width - 0.5) * figure_width
																		__magic_name__            =     figure_center_y + (image_y / image_height - 0.5) * figure_height

																		__magic_name__            =     get_distance(__lowerCamelCase ,						__lowerCamelCase ,						__lowerCamelCase   )

																		# color the corresponding pixel based on the selected coloring-function
																		if use_distance_color_coding:
																								__magic_name__            =     get_color_coded_rgb(__lowerCamelCase   )
																		else:
																								__magic_name__            =     get_black_and_white_rgb(__lowerCamelCase   )

						return img


if __name__ == "__main__":
			import doctest

			doctest.testmod()

			# colored version, full figure
			lowercase    			=       get_image()

			# uncomment for colored version, different section, zoomed in
			# img = get_image(figure_center_x = -0.6, figure_center_y = -0.4,
			# figure_width = 0.8)

			# uncomment for black and white version, full figure
			# img = get_image(use_distance_color_coding = False)

			# uncomment to save the image
			# img.save("mandelbrot.png")

			img.show()



