


import torch
import torch.nn.functional as F

from diffusers import VQDiffusionScheduler

from .test_schedulers import SchedulerCommonTest





class    lowerCAmelCase__				(			__lowerCamelCase       ):






						"""simple docstring"""
						__UpperCAmelCase   :		Tuple  					=	(VQDiffusionScheduler,)




						def 					_UpperCamelCase			(   self				,							**a_     ):
							lowerCamelCase_			:							Optional[int]      =       {
							    "num_vec_classes": 4097,
							    "num_train_timesteps": 100,
							}

							config.update(**a_     )
							return config




						def 					_UpperCamelCase			(   self				,							a_     ):
							lowerCamelCase_			:							Optional[int]      =       4
							lowerCamelCase_			:							Dict      =       8
							lowerCamelCase_			:							List[Any]      =       8

							lowerCamelCase_			:							Any      =       torch.randint(0				,							a_				,							(batch_size, height * width)     )

							return sample




						@property
						def 					_UpperCamelCase			(   self     ):
							assert False




						def 					_UpperCamelCase			(   self				,							a_     ):
							def model(a_				,							a_				,							*a_     ):
								lowerCamelCase_   ,lowerCamelCase_			:							str      =       sample.shape
								lowerCamelCase_			:							Tuple      =       torch.rand((batch_size, num_vec_classes - 1, num_latent_pixels)     )
								lowerCamelCase_			:							Optional[Any]      =       F.log_softmax(logits.double()				,							dim=1     ).float()
								return return_value

							return model




						def 					_UpperCamelCase			(   self     ):
							for timesteps in [2, 5, 100, 1000]:
								self.check_over_configs(num_train_timesteps=a_     )




						def 					_UpperCamelCase			(   self     ):
							for num_vec_classes in [5, 100, 1000, 4000]:
								self.check_over_configs(num_vec_classes=a_     )




						def 					_UpperCamelCase			(   self     ):
							for t in [0, 50, 99]:
								self.check_over_forward(time_step=a_     )




						def 					_UpperCamelCase			(   self     ):
							pass





import logging
import random

import ray

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


__magic_name__       					=      logging.getLogger(__name__)





class    lowerCAmelCase__				:






						"""simple docstring"""




						def __init__(   self     ):
							lowerCamelCase_			:							List[Any]      =       False




						def 					_UpperCamelCase			(   self				,							a_				,							a_				,							a_				,							a_     ):
							if not self.initialized:
								lowerCamelCase_			:							Optional[int]      =       RagRetriever(
								    a_				,							question_encoder_tokenizer=a_				,							generator_tokenizer=a_				,							index=a_				,							init_retrieval=a_				,							)
								lowerCamelCase_			:							Any      =       True




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




						def 					_UpperCamelCase			(   self     ):
							# delete the old self.retriever object before assigning the new index
							del self.retriever
							lowerCamelCase_			:							Optional[int]      =       False




						def 					_UpperCamelCase			(   self				,							a_				,							a_     ):
							lowerCamelCase_   ,lowerCamelCase_			:							List[Any]      =       self.retriever._main_retrieve(a_				,							a_     )
							lowerCamelCase_			:							str      =       self.retriever.index.get_doc_dicts(a_     )
							return doc_ids, retrieved_doc_embeds, doc_dicts





class    lowerCAmelCase__				(			__lowerCamelCase       ):






						"""simple docstring"""




						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_				,							)

							lowerCamelCase_			:							List[str]      =       retrieval_workers
							lowerCamelCase_			:							str      =       question_encoder_tokenizer
							lowerCamelCase_			:							Optional[int]      =       generator_tokenizer
							if len(self.retrieval_workers     ) > 0:
								ray.get(
								    [
								        worker.create_rag_retriever.remote(a_				,							a_				,							a_				,							a_     )
								        for worker in self.retrieval_workers
								    ]     )




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




						@classmethod
						def 					_UpperCamelCase			(   cls				,							a_				,							a_=None				,							**a_     ):
							return super(a_				,							cls     ).get_tokenizers(a_				,							a_				,							**a_     )




						@classmethod
						def 					_UpperCamelCase			(   cls				,							a_				,							a_				,							a_=None				,							**a_     ):
							lowerCamelCase_			:							str      =       kwargs.pop("config"				,							a_     ) or RagConfig.from_pretrained(a_				,							**a_     )
							lowerCamelCase_			:							Tuple      =       RagTokenizer.from_pretrained(a_				,							config=a_     )
							lowerCamelCase_			:							str      =       rag_tokenizer.question_encoder
							lowerCamelCase_			:							int      =       rag_tokenizer.generator

							if indexed_dataset is not None:
								lowerCamelCase_			:							int      =       "custom"
								lowerCamelCase_			:							List[str]      =       CustomHFIndex(config.retrieval_vector_size				,							a_     )
							else:
								lowerCamelCase_			:							str      =       cls._build_index(a_     )

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




						def 					_UpperCamelCase			(   self     ):
							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
							lowerCamelCase_			:							Optional[int]      =       self._build_index(self.config     )

							ray.get(
							    [
							        worker.create_rag_retriever.remote(
							            self.config				,							self.question_encoder_tokenizer				,							self.generator_tokenizer				,							a_     )
							        for worker in self.retrieval_workers
							    ]     )

