




import unittest

import numpy as np
import torch

from diffusers import ScoreSdeVePipeline, ScoreSdeVeScheduler, UNetaDModel
from diffusers.utils.testing_utils import enable_full_determinism, require_torch, slow, torch_device


enable_full_determinism()
class       a__   ( unittest.TestCase				):






						@property
						def     __SCREAMING_SNAKE_CASE(       self      ):




											"""simple docstring"""



											torch.manual_seed(0      )
											__lowerCAmelCase           =	UNetaDModel(
											    block_out_channels=(3_2, 6_4)		,      layers_per_block=2		,      sample_size=3_2		,      in_channels=3		,      out_channels=3		,      down_block_types=("DownBlock2D", "AttnDownBlock2D")		,      up_block_types=("AttnUpBlock2D", "UpBlock2D")		,      )
											return model






						def     __SCREAMING_SNAKE_CASE(       self      ):




											"""simple docstring"""



											__lowerCAmelCase           =	self.dummy_uncond_unet
											__lowerCAmelCase           =	ScoreSdeVeScheduler()

											__lowerCAmelCase           =	ScoreSdeVePipeline(unet=_A		,      scheduler=_A      )
											sde_ve.to(_A      )
											sde_ve.set_progress_bar_config(disable=_A      )

											__lowerCAmelCase           =	torch.manual_seed(0      )
											__lowerCAmelCase           =	sde_ve(num_inference_steps=2		,      output_type="numpy"		,      generator=_A      ).images

											__lowerCAmelCase           =	torch.manual_seed(0      )
											__lowerCAmelCase           =	sde_ve(num_inference_steps=2		,      output_type="numpy"		,      generator=_A		,      return_dict=_A      )[
											    0
											]

											__lowerCAmelCase           =	image[0, -3:, -3:, -1]
											__lowerCAmelCase           =	image_from_tuple[0, -3:, -3:, -1]

											assert image.shape == (1, 3_2, 3_2, 3)
											__lowerCAmelCase           =	np.array([0.0, 1.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0]      )

											assert np.abs(image_slice.flatten() - expected_slice      ).max() < 1E-2
											assert np.abs(image_from_tuple_slice.flatten() - expected_slice      ).max() < 1E-2






@slow
@require_torch
class       a__   ( unittest.TestCase				):






						def     __SCREAMING_SNAKE_CASE(       self      ):




											"""simple docstring"""



											__lowerCAmelCase           =	"google/ncsnpp-church-256"
											__lowerCAmelCase           =	UNetaDModel.from_pretrained(_A      )

											__lowerCAmelCase           =	ScoreSdeVeScheduler.from_pretrained(_A      )

											__lowerCAmelCase           =	ScoreSdeVePipeline(unet=_A		,      scheduler=_A      )
											sde_ve.to(_A      )
											sde_ve.set_progress_bar_config(disable=_A      )

											__lowerCAmelCase           =	torch.manual_seed(0      )
											__lowerCAmelCase           =	sde_ve(num_inference_steps=1_0		,      output_type="numpy"		,      generator=_A      ).images

											__lowerCAmelCase           =	image[0, -3:, -3:, -1]

											assert image.shape == (1, 2_5_6, 2_5_6, 3)

											__lowerCAmelCase           =	np.array([0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 0.0]      )

											assert np.abs(image_slice.flatten() - expected_slice      ).max() < 1E-2










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		,      )




