


import logging
import random

import ray

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


lowerCAmelCase__ :			Union[str, Any]         =logging.getLogger(__name__)







class   __lowercase						:





							"""simple docstring"""







							def __init__(			self					):





										"""simple docstring"""




										SCREAMING_SNAKE_CASE_		:    List[Any] 		=				False






							def 				UpperCamelCase__    (			self ,	lowerCAmelCase__ ,	lowerCAmelCase__ ,	lowerCAmelCase__ ,	lowerCAmelCase__					):





										"""simple docstring"""




										if not self.initialized:
													SCREAMING_SNAKE_CASE_		:    int 		=				RagRetriever(
													    lowerCAmelCase__ ,	question_encoder_tokenizer=lowerCAmelCase__ ,	generator_tokenizer=lowerCAmelCase__ ,	index=lowerCAmelCase__ ,	init_retrieval=lowerCAmelCase__ ,	)
													SCREAMING_SNAKE_CASE_		:    Any 		=				True






							def 				UpperCamelCase__    (			self					):





										"""simple docstring"""




										self.retriever.index.init_index()






							def 				UpperCamelCase__    (			self					):





										"""simple docstring"""




										del self.retriever
										SCREAMING_SNAKE_CASE_		:    List[Any] 		=				False






							def 				UpperCamelCase__    (			self ,	lowerCAmelCase__ ,	lowerCAmelCase__					):





										"""simple docstring"""




										SCREAMING_SNAKE_CASE_		,			SCREAMING_SNAKE_CASE_		:    Optional[int] 		=				self.retriever._main_retrieve(lowerCAmelCase__ ,	lowerCAmelCase__					)
										SCREAMING_SNAKE_CASE_		:    Optional[int] 		=				self.retriever.index.get_doc_dicts(lowerCAmelCase__					)
										return doc_ids, retrieved_doc_embeds, doc_dicts







class   __lowercase						(__SCREAMING_SNAKE_CASE     ):





							"""simple docstring"""







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





										"""simple docstring"""




										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__ ,	)

										SCREAMING_SNAKE_CASE_		:    Union[str, Any] 		=				retrieval_workers
										SCREAMING_SNAKE_CASE_		:    List[str] 		=				question_encoder_tokenizer
										SCREAMING_SNAKE_CASE_		:    Optional[Any] 		=				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 				UpperCamelCase__    (			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 				UpperCamelCase__    (			self ,	lowerCAmelCase__ ,	lowerCAmelCase__					):





										"""simple docstring"""




										if len(self.retrieval_workers					) > 0:
													# Select a random retrieval actor.
													SCREAMING_SNAKE_CASE_		:    str 		=				self.retrieval_workers[random.randint(0 ,	len(self.retrieval_workers					) - 1					)]
													SCREAMING_SNAKE_CASE_		,			SCREAMING_SNAKE_CASE_		,			SCREAMING_SNAKE_CASE_		:    Tuple 		=				ray.get(
													    random_worker.retrieve.remote(lowerCAmelCase__ ,	lowerCAmelCase__					)					)
										else:
													SCREAMING_SNAKE_CASE_		,			SCREAMING_SNAKE_CASE_		:    Tuple 		=				self._main_retrieve(lowerCAmelCase__ ,	lowerCAmelCase__					)
													SCREAMING_SNAKE_CASE_		:    Optional[int] 		=				self.index.get_doc_dicts(lowerCAmelCase__					)
										return retrieved_doc_embeds, doc_ids, doc_dicts






							@classmethod
							def 				UpperCamelCase__    (			cls ,	lowerCAmelCase__ ,	lowerCAmelCase__=None ,	**lowerCAmelCase__					):





										"""simple docstring"""




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






							@classmethod
							def 				UpperCamelCase__    (			cls ,	lowerCAmelCase__ ,	lowerCAmelCase__ ,	lowerCAmelCase__=None ,	**lowerCAmelCase__					):





										"""simple docstring"""




										SCREAMING_SNAKE_CASE_		:    Dict 		=				kwargs.pop('config' ,	lowerCAmelCase__					) or RagConfig.from_pretrained(lowerCAmelCase__ ,	**lowerCAmelCase__					)
										SCREAMING_SNAKE_CASE_		:    Tuple 		=				RagTokenizer.from_pretrained(lowerCAmelCase__ ,	config=lowerCAmelCase__					)
										SCREAMING_SNAKE_CASE_		:    List[Any] 		=				rag_tokenizer.question_encoder
										SCREAMING_SNAKE_CASE_		:    Any 		=				rag_tokenizer.generator

										if indexed_dataset is not None:
													SCREAMING_SNAKE_CASE_		:    List[Any] 		=				'custom'
													SCREAMING_SNAKE_CASE_		:    int 		=				CustomHFIndex(config.retrieval_vector_size ,	lowerCAmelCase__					)
										else:
													SCREAMING_SNAKE_CASE_		:    Dict 		=				cls._build_index(lowerCAmelCase__					)

										return cls(
										    lowerCAmelCase__ ,	question_encoder_tokenizer=lowerCAmelCase__ ,	generator_tokenizer=lowerCAmelCase__ ,	retrieval_workers=lowerCAmelCase__ ,	index=lowerCAmelCase__ ,	)






							def 				UpperCamelCase__    (			self					):





										"""simple docstring"""




										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
										SCREAMING_SNAKE_CASE_		:    List[str] 		=				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
										    ]					)









import unittest
from dataclasses import dataclass
from typing import List, Union

import numpy as np
import PIL.Image

from diffusers.utils.outputs import BaseOutput







@dataclass
class   __lowercase						(__SCREAMING_SNAKE_CASE     ):





							"""simple docstring"""







							_UpperCAmelCase						     =	42







class   __lowercase						(unittest.TestCase     ):





							"""simple docstring"""







							def 				UpperCamelCase__    (			self					):





										"""simple docstring"""




										SCREAMING_SNAKE_CASE_		:    Union[str, Any] 		=				CustomOutput(images=np.random.rand(1 ,	3 ,	4 ,	4					)					)

										# check every way of getting the attribute
										assert isinstance(outputs.images ,	np.ndarray					)
										assert outputs.images.shape == (1, 3, 4, 4)
										assert isinstance(outputs['images'] ,	np.ndarray					)
										assert outputs["images"].shape == (1, 3, 4, 4)
										assert isinstance(outputs[0] ,	np.ndarray					)
										assert outputs[0].shape == (1, 3, 4, 4)

										# test with a non-tensor attribute
										SCREAMING_SNAKE_CASE_		:    Tuple 		=				CustomOutput(images=[PIL.Image.new('RGB' ,	(4, 4)					)]					)

										# check every way of getting the attribute
										assert isinstance(outputs.images ,	lowerCAmelCase__					)
										assert isinstance(outputs.images[0] ,	PIL.Image.Image					)
										assert isinstance(outputs['images'] ,	lowerCAmelCase__					)
										assert isinstance(outputs['images'][0] ,	PIL.Image.Image					)
										assert isinstance(outputs[0] ,	lowerCAmelCase__					)
										assert isinstance(outputs[0][0] ,	PIL.Image.Image					)






							def 				UpperCamelCase__    (			self					):





										"""simple docstring"""




										SCREAMING_SNAKE_CASE_		:    Union[str, Any] 		=				CustomOutput({'images': np.random.rand(1 ,	3 ,	4 ,	4					)}					)

										# check every way of getting the attribute
										assert isinstance(outputs.images ,	np.ndarray					)
										assert outputs.images.shape == (1, 3, 4, 4)
										assert isinstance(outputs['images'] ,	np.ndarray					)
										assert outputs["images"].shape == (1, 3, 4, 4)
										assert isinstance(outputs[0] ,	np.ndarray					)
										assert outputs[0].shape == (1, 3, 4, 4)

										# test with a non-tensor attribute
										SCREAMING_SNAKE_CASE_		:    Any 		=				CustomOutput({'images': [PIL.Image.new('RGB' ,	(4, 4)					)]}					)

										# check every way of getting the attribute
										assert isinstance(outputs.images ,	lowerCAmelCase__					)
										assert isinstance(outputs.images[0] ,	PIL.Image.Image					)
										assert isinstance(outputs['images'] ,	lowerCAmelCase__					)
										assert isinstance(outputs['images'][0] ,	PIL.Image.Image					)
										assert isinstance(outputs[0] ,	lowerCAmelCase__					)
										assert isinstance(outputs[0][0] ,	PIL.Image.Image					)





