



from typing import List, Optional, Tuple, Union

import torch

from ...models import UNetaDModel
from ...schedulers import ScoreSdeVeScheduler
from ...utils import randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput




class 							lowerCAmelCase__  (	UpperCamelCase						):
					__A     :    UNetaDModel
					__A     :    ScoreSdeVeScheduler







					def __init__(       self      :     int			,		_A      :     UNetaDModel			,		_A      :     ScoreSdeVeScheduler):
											super().__init__()
											self.register_modules(unet=_A			,		scheduler=_A)







					@torch.no_grad()
					def __call__(       self      :     Union[str, Any]			,		_A      :     int = 1			,		_A      :     int = 2000			,		_A      :     Optional[Union[torch.Generator, List[torch.Generator]]] = None			,		_A      :     Optional[str] = "pil"			,		_A      :     bool = True			,		**_A      :     Optional[int]			,		):
											A__	:							Any						    = self.unet.config.sample_size
											A__	:							Any						    = (batch_size, 3, img_size, img_size)

											A__	:							str						    = self.unet

											A__	:							int						    = randn_tensor(_A			,		generator=_A) * self.scheduler.init_noise_sigma
											A__	:							Optional[Any]						    = sample.to(self.device)

											self.scheduler.set_timesteps(_A)
											self.scheduler.set_sigmas(_A)

											for i, t in enumerate(self.progress_bar(self.scheduler.timesteps)):
																	A__	:							int						    = self.scheduler.sigmas[i] * torch.ones(shape[0]			,		device=self.device)

																	# correction step
																	for _ in range(self.scheduler.config.correct_steps):
																							A__	:							List[Any]						    = self.unet(_A			,		_A).sample
																							A__	:							List[str]						    = self.scheduler.step_correct(_A			,		_A			,		generator=_A).prev_sample

																	# prediction step
																	A__	:							Dict						    = model(_A			,		_A).sample
																	A__	:							List[str]						    = self.scheduler.step_pred(_A			,		_A			,		_A			,		generator=_A)

																	A__    ,       A__	:							List[Any]						    = output.prev_sample, output.prev_sample_mean

											A__	:							Dict						    = sample_mean.clamp(0			,		1)
											A__	:							Dict						    = sample.cpu().permute(0			,		2			,		3			,		1).numpy()
											if output_type == "pil":
																	A__	:							str						    = self.numpy_to_pil(_A)

											if not return_dict:
																	return (sample,)

											return ImagePipelineOutput(images=_A)




import logging
import random

import ray

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


snake_case    :						Any  =						logging.getLogger(__name__)




class 							lowerCAmelCase__  :







					def __init__(       self      :     Union[str, Any]):
											A__	:							Dict						    = False







					def  _lowercase			(       self      :     Optional[int]			,		_A      :     int			,		_A      :     Optional[int]			,		_A      :     List[str]			,		_A      :     Optional[Any]):
											if not self.initialized:
																	A__	:							List[Any]						    = RagRetriever(
																	    _A			,		question_encoder_tokenizer=_A			,		generator_tokenizer=_A			,		index=_A			,		init_retrieval=_A			,		)
																	A__	:							Dict						    = True







					def  _lowercase			(       self      :     Union[str, Any]):
											self.retriever.index.init_index()







					def  _lowercase			(       self      :     List[str]			,		_A      :     Optional[int]			,		_A      :     List[str]):
											A__    ,       A__	:							str						    = self.retriever._main_retrieve(_A			,		_A)
											return doc_ids, retrieved_doc_embeds




class 							lowerCAmelCase__  (	UpperCamelCase						):







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







					@classmethod
					def  _lowercase			(       cls      :     int			,		_A      :     Optional[int]			,		_A      :     List[str]=None			,		**_A      :     Optional[Any]):
											return super(_A			,		cls).get_tokenizers(_A			,		_A			,		**_A)







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