

import logging
import random

import ray

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


_lowerCamelCase								=logging.getLogger(__name__)



class 		a_	:



      """simple docstring"""







      def __init__(				self					:							Union[str, Any]    ):
           SCREAMING_SNAKE_CASE										=False
      def 	_lowerCAmelCase					(				self					:							Dict      ,snake_case					:							List[str]      ,snake_case					:							List[str]      ,snake_case					:							List[Any]      ,snake_case					:							Dict    ):
           if not self.initialized:
                SCREAMING_SNAKE_CASE										=RagRetriever(
                    snake_case      ,question_encoder_tokenizer=snake_case      ,generator_tokenizer=snake_case      ,index=snake_case      ,init_retrieval=snake_case      ,)
                SCREAMING_SNAKE_CASE										=True
      def 	_lowerCAmelCase					(				self					:							str    ):
           self.retriever.index.init_index()
      def 	_lowerCAmelCase					(				self					:							Optional[int]    ):
           # delete the old self.retriever object before assigning the new index
           del self.retriever
           SCREAMING_SNAKE_CASE										=False


      def 	_lowerCAmelCase					(				self					:							List[str]      ,snake_case					:							List[Any]      ,snake_case					:							Optional[Any]    ):
           SCREAMING_SNAKE_CASE						,  SCREAMING_SNAKE_CASE										=self.retriever._main_retrieve(snake_case      ,snake_case    )
           SCREAMING_SNAKE_CASE										=self.retriever.index.get_doc_dicts(snake_case    )
           return doc_ids, retrieved_doc_embeds, doc_dicts







class 		a_	(							lowerCamelCase_					):



      """simple docstring"""







      def __init__(				self					:							Tuple      ,snake_case					:							List[Any]      ,snake_case					:							List[Any]      ,snake_case					:							Dict      ,snake_case					:							Optional[int]      ,snake_case					:							List[Any]=None    ):
           if index is not None and index.is_initialized() and len(snake_case    ) > 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__(
               snake_case      ,question_encoder_tokenizer=snake_case      ,generator_tokenizer=snake_case      ,index=snake_case      ,init_retrieval=snake_case      ,)

           SCREAMING_SNAKE_CASE										=retrieval_workers
           SCREAMING_SNAKE_CASE										=question_encoder_tokenizer
           SCREAMING_SNAKE_CASE										=generator_tokenizer
           if len(self.retrieval_workers    ) > 0:
                ray.get(
                    [
                        worker.create_rag_retriever.remote(snake_case      ,snake_case      ,snake_case      ,snake_case    )
                        for worker in self.retrieval_workers
                    ]    )
      def 	_lowerCAmelCase					(				self					:							List[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 	_lowerCAmelCase					(				self					:							Tuple      ,snake_case					:							Any      ,snake_case					:							List[Any]    ):
           if len(self.retrieval_workers    ) > 0:
                # Select a random retrieval actor.
                SCREAMING_SNAKE_CASE										=self.retrieval_workers[random.randint(0      ,len(self.retrieval_workers    ) - 1    )]
                SCREAMING_SNAKE_CASE						,  SCREAMING_SNAKE_CASE						,  SCREAMING_SNAKE_CASE										=ray.get(
                    random_worker.retrieve.remote(snake_case      ,snake_case    )    )
           else:
                SCREAMING_SNAKE_CASE						,  SCREAMING_SNAKE_CASE										=self._main_retrieve(snake_case      ,snake_case    )
                SCREAMING_SNAKE_CASE										=self.index.get_doc_dicts(snake_case    )
           return retrieved_doc_embeds, doc_ids, doc_dicts
      @classmethod
      def 	_lowerCAmelCase					(				cls					:							Any      ,snake_case					:							List[str]      ,snake_case					:							str=None      ,**snake_case					:							Optional[Any]    ):
           return super(snake_case      ,cls    ).get_tokenizers(snake_case      ,snake_case      ,**snake_case    )
      @classmethod
      def 	_lowerCAmelCase					(				cls					:							List[str]      ,snake_case					:							Optional[int]      ,snake_case					:							Optional[Any]      ,snake_case					:							List[str]=None      ,**snake_case					:							Tuple    ):
           SCREAMING_SNAKE_CASE										=kwargs.pop('config'      ,snake_case    ) or RagConfig.from_pretrained(snake_case      ,**snake_case    )
           SCREAMING_SNAKE_CASE										=RagTokenizer.from_pretrained(snake_case      ,config=snake_case    )
           SCREAMING_SNAKE_CASE										=rag_tokenizer.question_encoder
           SCREAMING_SNAKE_CASE										=rag_tokenizer.generator

           if indexed_dataset is not None:
                SCREAMING_SNAKE_CASE										='custom'
                SCREAMING_SNAKE_CASE										=CustomHFIndex(config.retrieval_vector_size      ,snake_case    )
           else:
                SCREAMING_SNAKE_CASE										=cls._build_index(snake_case    )

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


      def 	_lowerCAmelCase					(				self					:							Optional[Any]    ):
           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										=self._build_index(self.config    )

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




import gc
import unittest

import numpy as np
import torch

from diffusers import RePaintPipeline, RePaintScheduler, UNetaDModel
from diffusers.utils.testing_utils import (
    enable_full_determinism,
    load_image,
    load_numpy,
    nightly,
    require_torch_gpu,
    skip_mps,
    torch_device,
)

from ..pipeline_params import IMAGE_INPAINTING_BATCH_PARAMS, IMAGE_INPAINTING_PARAMS
from ..test_pipelines_common import PipelineTesterMixin


enable_full_determinism()



class 		a_	(							lowerCamelCase_			,      unittest.TestCase					):



      """simple docstring"""







      __UpperCAmelCase      		=				RePaintPipeline
      __UpperCAmelCase      		=				IMAGE_INPAINTING_PARAMS - {'width', 'height', 'guidance_scale'}
      __UpperCAmelCase      		=				PipelineTesterMixin.required_optional_params - {
          'latents',
          'num_images_per_prompt',
          'callback',
          'callback_steps',
      }
      __UpperCAmelCase      		=				IMAGE_INPAINTING_BATCH_PARAMS
      __UpperCAmelCase      		=				False
      def 	_lowerCAmelCase					(				self					:							Tuple    ):
           torch.manual_seed(0    )
           torch.manual_seed(0    )
           SCREAMING_SNAKE_CASE										=UNetaDModel(
               block_out_channels=(32, 64)      ,layers_per_block=2      ,sample_size=32      ,in_channels=3      ,out_channels=3      ,down_block_types=('DownBlock2D', 'AttnDownBlock2D')      ,up_block_types=('AttnUpBlock2D', 'UpBlock2D')      ,)
           SCREAMING_SNAKE_CASE										=RePaintScheduler()
           SCREAMING_SNAKE_CASE										={'unet': unet, 'scheduler': scheduler}
           return components
      def 	_lowerCAmelCase					(				self					:							Tuple      ,snake_case					:							Optional[int]      ,snake_case					:							Dict=0    ):
           if str(snake_case    ).startswith('mps'    ):
                SCREAMING_SNAKE_CASE										=torch.manual_seed(snake_case    )
           else:
                SCREAMING_SNAKE_CASE										=torch.Generator(device=snake_case    ).manual_seed(snake_case    )
           SCREAMING_SNAKE_CASE										=np.random.RandomState(snake_case    ).standard_normal((1, 3, 32, 32)    )
           SCREAMING_SNAKE_CASE										=torch.from_numpy(snake_case    ).to(device=snake_case      ,dtype=torch.floataa    )
           SCREAMING_SNAKE_CASE										=(image > 0).to(device=snake_case      ,dtype=torch.floataa    )
           SCREAMING_SNAKE_CASE										={
               'image': image,
               'mask_image': mask,
               'generator': generator,
               'num_inference_steps': 5,
               'eta': 0.0,
               'jump_length': 2,
               'jump_n_sample': 2,
               'output_type': 'numpy',
           }
           return inputs
      def 	_lowerCAmelCase					(				self					:							Optional[Any]    ):
           SCREAMING_SNAKE_CASE										='cpu'  # ensure determinism for the device-dependent torch.Generator
           SCREAMING_SNAKE_CASE										=self.get_dummy_components()
           SCREAMING_SNAKE_CASE										=RePaintPipeline(**snake_case    )
           SCREAMING_SNAKE_CASE										=sd_pipe.to(snake_case    )
           sd_pipe.set_progress_bar_config(disable=snake_case    )

           SCREAMING_SNAKE_CASE										=self.get_dummy_inputs(snake_case    )
           SCREAMING_SNAKE_CASE										=sd_pipe(**snake_case    ).images
           SCREAMING_SNAKE_CASE										=image[0, -3:, -3:, -1]

           assert image.shape == (1, 32, 32, 3)
           SCREAMING_SNAKE_CASE										=np.array([1.0_000, 0.5_426, 0.5_497, 0.2_200, 1.0_000, 1.0_000, 0.5_623, 1.0_000, 0.6_274]    )

           assert np.abs(image_slice.flatten() - expected_slice    ).max() < 1e-3
      @skip_mps
      def 	_lowerCAmelCase					(				self					:							Optional[int]    ):
           return super().test_save_load_local()
      @unittest.skip('non-deterministic pipeline'    )
      def 	_lowerCAmelCase					(				self					:							Tuple    ):
           return super().test_inference_batch_single_identical()
      @skip_mps
      def 	_lowerCAmelCase					(				self					:							Optional[Any]    ):
           return super().test_dict_tuple_outputs_equivalent()
      @skip_mps
      def 	_lowerCAmelCase					(				self					:							Optional[Any]    ):
           return super().test_save_load_optional_components()


      @skip_mps
      def 	_lowerCAmelCase					(				self					:							Any    ):
           return super().test_attention_slicing_forward_pass()







@nightly
@require_torch_gpu
class 		a_	(							unittest.TestCase					):



      """simple docstring"""







      def 	_lowerCAmelCase					(				self					:							Union[str, Any]    ):
           super().tearDown()
           gc.collect()
           torch.cuda.empty_cache()


      def 	_lowerCAmelCase					(				self					:							Tuple    ):
           SCREAMING_SNAKE_CASE										=load_image(
               'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/'
               'repaint/celeba_hq_256.png'    )
           SCREAMING_SNAKE_CASE										=load_image(
               'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/repaint/mask_256.png'    )
           SCREAMING_SNAKE_CASE										=load_numpy(
               'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/'
               'repaint/celeba_hq_256_result.npy'    )

           SCREAMING_SNAKE_CASE										='google/ddpm-ema-celebahq-256'
           SCREAMING_SNAKE_CASE										=UNetaDModel.from_pretrained(snake_case    )
           SCREAMING_SNAKE_CASE										=RePaintScheduler.from_pretrained(snake_case    )

           SCREAMING_SNAKE_CASE										=RePaintPipeline(unet=snake_case      ,scheduler=snake_case    ).to(snake_case    )
           repaint.set_progress_bar_config(disable=snake_case    )
           repaint.enable_attention_slicing()

           SCREAMING_SNAKE_CASE										=torch.manual_seed(0    )
           SCREAMING_SNAKE_CASE										=repaint(
               snake_case      ,snake_case      ,num_inference_steps=250      ,eta=0.0      ,jump_length=10      ,jump_n_sample=10      ,generator=snake_case      ,output_type='np'      ,)
           SCREAMING_SNAKE_CASE										=output.images[0]

           assert image.shape == (256, 256, 3)
           assert np.abs(expected_image - image    ).mean() < 1e-2

