





import gc
import random
import tempfile
import unittest

import numpy as np
import torch
from PIL import Image
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer

from diffusers import (
    AutoencoderKL,
    DDIMInverseScheduler,
    DDIMScheduler,
    DPMSolverMultistepInverseScheduler,
    DPMSolverMultistepScheduler,
    StableDiffusionDiffEditPipeline,
    UNetaDConditionModel,
)
from diffusers.utils import load_image, slow
from diffusers.utils.testing_utils import enable_full_determinism, floats_tensor, require_torch_gpu, torch_device

from ..pipeline_params import TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS, TEXT_GUIDED_IMAGE_INPAINTING_PARAMS
from ..test_pipelines_common import PipelineLatentTesterMixin, PipelineTesterMixin


enable_full_determinism()

class 		UpperCamelCase__			(				lowerCAmelCase_							,				lowerCAmelCase_							,				unittest.TestCase       ):



     '''simple docstring'''

     __snake_case   :     Tuple		     =      StableDiffusionDiffEditPipeline
     __snake_case   :     Union[str, Any]		     =      TEXT_GUIDED_IMAGE_INPAINTING_PARAMS - {"height", "width", "image"} | {"image_latents"}
     __snake_case   :     Optional[Any]		     =      TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS - {"image"} | {"image_latents"}
     __snake_case   :     Tuple		     =      frozenset(
         []       )  # TO-DO: update image_params once pipeline is refactored with VaeImageProcessor.preprocess
     __snake_case   :     Tuple		     =      frozenset([]       )






     def     SCREAMING_SNAKE_CASE__  (							self						:   Union[str, Any]	)					->		List[Any]:




           '''simple docstring'''


           torch.manual_seed(0	)
           SCREAMING_SNAKE_CASE		=						UNetaDConditionModel(
               block_out_channels=(32, 64)						,layers_per_block=2						,sample_size=32						,in_channels=4						,out_channels=4						,down_block_types=("""DownBlock2D""", """CrossAttnDownBlock2D""")						,up_block_types=("""CrossAttnUpBlock2D""", """UpBlock2D""")						,cross_attention_dim=32						,attention_head_dim=(2, 4)						,use_linear_projection=lowerCamelCase__						,)
           SCREAMING_SNAKE_CASE		=						DDIMScheduler(
               beta_start=0.00085						,beta_end=0.012						,beta_schedule="""scaled_linear"""						,clip_sample=lowerCamelCase__						,set_alpha_to_one=lowerCamelCase__						,)
           SCREAMING_SNAKE_CASE		=						DDIMInverseScheduler(
               beta_start=0.00085						,beta_end=0.012						,beta_schedule="""scaled_linear"""						,clip_sample=lowerCamelCase__						,set_alpha_to_zero=lowerCamelCase__						,)
           torch.manual_seed(0	)
           SCREAMING_SNAKE_CASE		=						AutoencoderKL(
               block_out_channels=[32, 64]						,in_channels=3						,out_channels=3						,down_block_types=["""DownEncoderBlock2D""", """DownEncoderBlock2D"""]						,up_block_types=["""UpDecoderBlock2D""", """UpDecoderBlock2D"""]						,latent_channels=4						,sample_size=128						,)
           torch.manual_seed(0	)
           SCREAMING_SNAKE_CASE		=						CLIPTextConfig(
               bos_token_id=0						,eos_token_id=2						,hidden_size=32						,intermediate_size=37						,layer_norm_eps=1e-0_5						,num_attention_heads=4						,num_hidden_layers=5						,pad_token_id=1						,vocab_size=1000						,hidden_act="""gelu"""						,projection_dim=512						,)
           SCREAMING_SNAKE_CASE		=						CLIPTextModel(lowerCamelCase__	)
           SCREAMING_SNAKE_CASE		=						CLIPTokenizer.from_pretrained("""hf-internal-testing/tiny-random-clip"""	)

           SCREAMING_SNAKE_CASE		=						{
               """unet""": unet,
               """scheduler""": scheduler,
               """inverse_scheduler""": inverse_scheduler,
               """vae""": vae,
               """text_encoder""": text_encoder,
               """tokenizer""": tokenizer,
               """safety_checker""": None,
               """feature_extractor""": None,
           }

           return components






     def     SCREAMING_SNAKE_CASE__  (							self						:   int						,lowerCamelCase__						:   Optional[Any]						,lowerCamelCase__						:   Any=0	)					->		Optional[int]:




           '''simple docstring'''


           SCREAMING_SNAKE_CASE		=						floats_tensor((1, 16, 16)						,rng=random.Random(lowerCamelCase__	)	).to(lowerCamelCase__	)
           SCREAMING_SNAKE_CASE		=						floats_tensor((1, 2, 4, 16, 16)						,rng=random.Random(lowerCamelCase__	)	).to(lowerCamelCase__	)
           if str(lowerCamelCase__	).startswith("""mps"""	):
                 SCREAMING_SNAKE_CASE		=						torch.manual_seed(lowerCamelCase__	)
           else:
                 SCREAMING_SNAKE_CASE		=						torch.Generator(device=lowerCamelCase__	).manual_seed(lowerCamelCase__	)
           SCREAMING_SNAKE_CASE		=						{
               """prompt""": """a dog and a newt""",
               """mask_image""": mask,
               """image_latents""": latents,
               """generator""": generator,
               """num_inference_steps""": 2,
               """inpaint_strength""": 1.0,
               """guidance_scale""": 6.0,
               """output_type""": """numpy""",
           }

           return inputs






     def     SCREAMING_SNAKE_CASE__  (							self						:   List[str]						,lowerCamelCase__						:   Any						,lowerCamelCase__						:   Dict=0	)					->		Optional[int]:




           '''simple docstring'''


           SCREAMING_SNAKE_CASE		=						floats_tensor((1, 3, 32, 32)						,rng=random.Random(lowerCamelCase__	)	).to(lowerCamelCase__	)
           SCREAMING_SNAKE_CASE		=						image.cpu().permute(0						,2						,3						,1	)[0]
           SCREAMING_SNAKE_CASE		=						Image.fromarray(np.uinta(lowerCamelCase__	)	).convert("""RGB"""	)
           if str(lowerCamelCase__	).startswith("""mps"""	):
                 SCREAMING_SNAKE_CASE		=						torch.manual_seed(lowerCamelCase__	)
           else:
                 SCREAMING_SNAKE_CASE		=						torch.Generator(device=lowerCamelCase__	).manual_seed(lowerCamelCase__	)
           SCREAMING_SNAKE_CASE		=						{
               """image""": image,
               """source_prompt""": """a cat and a frog""",
               """target_prompt""": """a dog and a newt""",
               """generator""": generator,
               """num_inference_steps""": 2,
               """num_maps_per_mask""": 2,
               """mask_encode_strength""": 1.0,
               """guidance_scale""": 6.0,
               """output_type""": """numpy""",
           }

           return inputs






     def     SCREAMING_SNAKE_CASE__  (							self						:   int						,lowerCamelCase__						:   Optional[Any]						,lowerCamelCase__						:   List[Any]=0	)					->		Dict:




           '''simple docstring'''


           SCREAMING_SNAKE_CASE		=						floats_tensor((1, 3, 32, 32)						,rng=random.Random(lowerCamelCase__	)	).to(lowerCamelCase__	)
           SCREAMING_SNAKE_CASE		=						image.cpu().permute(0						,2						,3						,1	)[0]
           SCREAMING_SNAKE_CASE		=						Image.fromarray(np.uinta(lowerCamelCase__	)	).convert("""RGB"""	)
           if str(lowerCamelCase__	).startswith("""mps"""	):
                 SCREAMING_SNAKE_CASE		=						torch.manual_seed(lowerCamelCase__	)
           else:
                 SCREAMING_SNAKE_CASE		=						torch.Generator(device=lowerCamelCase__	).manual_seed(lowerCamelCase__	)
           SCREAMING_SNAKE_CASE		=						{
               """image""": image,
               """prompt""": """a cat and a frog""",
               """generator""": generator,
               """num_inference_steps""": 2,
               """inpaint_strength""": 1.0,
               """guidance_scale""": 6.0,
               """decode_latents""": True,
               """output_type""": """numpy""",
           }
           return inputs






     def     SCREAMING_SNAKE_CASE__  (							self						:   Optional[Any]	)					->		Any:




           '''simple docstring'''


           if not hasattr(self.pipeline_class						,"""_optional_components"""	):
                 return

           SCREAMING_SNAKE_CASE		=						self.get_dummy_components()
           SCREAMING_SNAKE_CASE		=						self.pipeline_class(**lowerCamelCase__	)
           pipe.to(lowerCamelCase__	)
           pipe.set_progress_bar_config(disable=lowerCamelCase__	)

           # set all optional components to None and update pipeline config accordingly
           for optional_component in pipe._optional_components:
                 setattr(lowerCamelCase__						,lowerCamelCase__						,lowerCamelCase__	)
           pipe.register_modules(**{optional_component: None for optional_component in pipe._optional_components}	)

           SCREAMING_SNAKE_CASE		=						self.get_dummy_inputs(lowerCamelCase__	)
           SCREAMING_SNAKE_CASE		=						pipe(**lowerCamelCase__	)[0]

           with tempfile.TemporaryDirectory() as tmpdir:
                 pipe.save_pretrained(lowerCamelCase__	)
                 SCREAMING_SNAKE_CASE		=						self.pipeline_class.from_pretrained(lowerCamelCase__	)
                 pipe_loaded.to(lowerCamelCase__	)
                 pipe_loaded.set_progress_bar_config(disable=lowerCamelCase__	)

           for optional_component in pipe._optional_components:
                 self.assertTrue(
                     getattr(lowerCamelCase__						,lowerCamelCase__	) is None						,F"""`{optional_component}` did not stay set to None after loading."""						,)

           SCREAMING_SNAKE_CASE		=						self.get_dummy_inputs(lowerCamelCase__	)
           SCREAMING_SNAKE_CASE		=						pipe_loaded(**lowerCamelCase__	)[0]

           SCREAMING_SNAKE_CASE		=						np.abs(output - output_loaded	).max()
           self.assertLess(lowerCamelCase__						,1e-4	)






     def     SCREAMING_SNAKE_CASE__  (							self						:   Optional[Any]	)					->		Optional[int]:




           '''simple docstring'''


           SCREAMING_SNAKE_CASE		=						"""cpu"""

           SCREAMING_SNAKE_CASE		=						self.get_dummy_components()
           SCREAMING_SNAKE_CASE		=						self.pipeline_class(**lowerCamelCase__	)
           pipe.to(lowerCamelCase__	)
           pipe.set_progress_bar_config(disable=lowerCamelCase__	)

           SCREAMING_SNAKE_CASE		=						self.get_dummy_mask_inputs(lowerCamelCase__	)
           SCREAMING_SNAKE_CASE		=						pipe.generate_mask(**lowerCamelCase__	)
           SCREAMING_SNAKE_CASE		=						mask[0, -3:, -3:]

           self.assertEqual(mask.shape						,(1, 16, 16)	)
           SCREAMING_SNAKE_CASE		=						np.array([0] * 9	)
           SCREAMING_SNAKE_CASE		=						np.abs(mask_slice.flatten() - expected_slice	).max()
           self.assertLessEqual(lowerCamelCase__						,1e-3	)
           self.assertEqual(mask[0, -3, -4]						,0	)






     def     SCREAMING_SNAKE_CASE__  (							self						:   Any	)					->		str:




           '''simple docstring'''


           SCREAMING_SNAKE_CASE		=						"""cpu"""

           SCREAMING_SNAKE_CASE		=						self.get_dummy_components()
           SCREAMING_SNAKE_CASE		=						self.pipeline_class(**lowerCamelCase__	)
           pipe.to(lowerCamelCase__	)
           pipe.set_progress_bar_config(disable=lowerCamelCase__	)

           SCREAMING_SNAKE_CASE		=						self.get_dummy_inversion_inputs(lowerCamelCase__	)
           SCREAMING_SNAKE_CASE		=						pipe.invert(**lowerCamelCase__	).images
           SCREAMING_SNAKE_CASE		=						image[0, -1, -3:, -3:]

           self.assertEqual(image.shape						,(2, 32, 32, 3)	)
           SCREAMING_SNAKE_CASE		=						np.array(
               [0.5150, 0.5134, 0.5043, 0.5376, 0.4694, 0.51050, 0.5015, 0.4407, 0.4799]						,)
           SCREAMING_SNAKE_CASE		=						np.abs(image_slice.flatten() - expected_slice	).max()
           self.assertLessEqual(lowerCamelCase__						,1e-3	)






     def     SCREAMING_SNAKE_CASE__  (							self						:   int	)					->		Any:




           '''simple docstring'''


           super().test_inference_batch_single_identical(expected_max_diff=5e-3	)






     def     SCREAMING_SNAKE_CASE__  (							self						:   Optional[int]	)					->		Optional[int]:




           '''simple docstring'''


           SCREAMING_SNAKE_CASE		=						"""cpu"""

           SCREAMING_SNAKE_CASE		=						self.get_dummy_components()

           SCREAMING_SNAKE_CASE		=						{"""beta_start""": 0.00085, """beta_end""": 0.012, """beta_schedule""": """scaled_linear"""}
           SCREAMING_SNAKE_CASE		=						DPMSolverMultistepScheduler(**lowerCamelCase__	)
           SCREAMING_SNAKE_CASE		=						DPMSolverMultistepInverseScheduler(**lowerCamelCase__	)

           SCREAMING_SNAKE_CASE		=						self.pipeline_class(**lowerCamelCase__	)
           pipe.to(lowerCamelCase__	)
           pipe.set_progress_bar_config(disable=lowerCamelCase__	)

           SCREAMING_SNAKE_CASE		=						self.get_dummy_inversion_inputs(lowerCamelCase__	)
           SCREAMING_SNAKE_CASE		=						pipe.invert(**lowerCamelCase__	).images
           SCREAMING_SNAKE_CASE		=						image[0, -1, -3:, -3:]

           self.assertEqual(image.shape						,(2, 32, 32, 3)	)
           SCREAMING_SNAKE_CASE		=						np.array(
               [0.5150, 0.5134, 0.5043, 0.5376, 0.4694, 0.51050, 0.5015, 0.4407, 0.4799]						,)
           SCREAMING_SNAKE_CASE		=						np.abs(image_slice.flatten() - expected_slice	).max()
           self.assertLessEqual(lowerCamelCase__						,1e-3	)

@require_torch_gpu
@slow
class 		UpperCamelCase__			(				unittest.TestCase       ):



     '''simple docstring'''

     def     SCREAMING_SNAKE_CASE__  (							self						:   Optional[int]	)					->		str:




           '''simple docstring'''


           super().tearDown()
           gc.collect()
           torch.cuda.empty_cache()






     @classmethod
     def     SCREAMING_SNAKE_CASE__  (							cls						:   List[Any]	)					->		Optional[Any]:




           '''simple docstring'''


           SCREAMING_SNAKE_CASE		=						load_image(
               """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/diffedit/fruit.png"""	)

           SCREAMING_SNAKE_CASE		=						raw_image.convert("""RGB"""	).resize((768, 768)	)

           SCREAMING_SNAKE_CASE		=						raw_image






     def     SCREAMING_SNAKE_CASE__  (							self						:   str	)					->		Union[str, Any]:




           '''simple docstring'''


           SCREAMING_SNAKE_CASE		=						torch.manual_seed(0	)

           SCREAMING_SNAKE_CASE		=						StableDiffusionDiffEditPipeline.from_pretrained(
               """stabilityai/stable-diffusion-2-1"""						,safety_checker=lowerCamelCase__						,torch_dtype=torch.floataa	)
           SCREAMING_SNAKE_CASE		=						DDIMScheduler.from_config(pipe.scheduler.config	)
           SCREAMING_SNAKE_CASE		=						DDIMInverseScheduler.from_config(pipe.scheduler.config	)
           pipe.enable_model_cpu_offload()
           pipe.set_progress_bar_config(disable=lowerCamelCase__	)

           SCREAMING_SNAKE_CASE		=						"""a bowl of fruit"""
           SCREAMING_SNAKE_CASE		=						"""a bowl of pears"""

           SCREAMING_SNAKE_CASE		=						pipe.generate_mask(
               image=self.raw_image						,source_prompt=lowerCamelCase__						,target_prompt=lowerCamelCase__						,generator=lowerCamelCase__						,)

           SCREAMING_SNAKE_CASE		=						pipe.invert(
               prompt=lowerCamelCase__						,image=self.raw_image						,inpaint_strength=0.7						,generator=lowerCamelCase__	).latents

           SCREAMING_SNAKE_CASE		=						pipe(
               prompt=lowerCamelCase__						,mask_image=lowerCamelCase__						,image_latents=lowerCamelCase__						,generator=lowerCamelCase__						,negative_prompt=lowerCamelCase__						,inpaint_strength=0.7						,output_type="""numpy"""						,).images[0]

           SCREAMING_SNAKE_CASE		=						(
               np.array(
                   load_image(
                       """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"""
                       """/diffedit/pears.png"""	).resize((768, 768)	)	)
               / 255
           )
           assert np.abs((expected_image - image).max()	) < 5e-1






     def     SCREAMING_SNAKE_CASE__  (							self						:   List[Any]	)					->		int:




           '''simple docstring'''


           SCREAMING_SNAKE_CASE		=						torch.manual_seed(0	)

           SCREAMING_SNAKE_CASE		=						StableDiffusionDiffEditPipeline.from_pretrained(
               """stabilityai/stable-diffusion-2-1"""						,safety_checker=lowerCamelCase__						,torch_dtype=torch.floataa	)
           SCREAMING_SNAKE_CASE		=						DPMSolverMultistepScheduler.from_config(pipe.scheduler.config	)
           SCREAMING_SNAKE_CASE		=						DPMSolverMultistepInverseScheduler.from_config(pipe.scheduler.config	)
           pipe.enable_model_cpu_offload()
           pipe.set_progress_bar_config(disable=lowerCamelCase__	)

           SCREAMING_SNAKE_CASE		=						"""a bowl of fruit"""
           SCREAMING_SNAKE_CASE		=						"""a bowl of pears"""

           SCREAMING_SNAKE_CASE		=						pipe.generate_mask(
               image=self.raw_image						,source_prompt=lowerCamelCase__						,target_prompt=lowerCamelCase__						,generator=lowerCamelCase__						,)

           SCREAMING_SNAKE_CASE		=						pipe.invert(
               prompt=lowerCamelCase__						,image=self.raw_image						,inpaint_strength=0.7						,generator=lowerCamelCase__						,num_inference_steps=25						,).latents

           SCREAMING_SNAKE_CASE		=						pipe(
               prompt=lowerCamelCase__						,mask_image=lowerCamelCase__						,image_latents=lowerCamelCase__						,generator=lowerCamelCase__						,negative_prompt=lowerCamelCase__						,inpaint_strength=0.7						,num_inference_steps=25						,output_type="""numpy"""						,).images[0]

           SCREAMING_SNAKE_CASE		=						(
               np.array(
                   load_image(
                       """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"""
                       """/diffedit/pears.png"""	).resize((768, 768)	)	)
               / 255
           )
           assert np.abs((expected_image - image).max()	) < 5e-1









import logging
import random

import ray

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


SCREAMING_SNAKE_CASE_						       =	logging.getLogger(__name__)

class 		UpperCamelCase__			:



     '''simple docstring'''

     def __init__(							self						:   str	)					->		List[str]:




           '''simple docstring'''


           SCREAMING_SNAKE_CASE		=						False






     def     SCREAMING_SNAKE_CASE__  (							self						:   Any						,lowerCamelCase__						:   int						,lowerCamelCase__						:   Dict						,lowerCamelCase__						:   List[Any]						,lowerCamelCase__						:   Tuple	)					->		Optional[Any]:




           '''simple docstring'''


           if not self.initialized:
                 SCREAMING_SNAKE_CASE		=						RagRetriever(
                     lowerCamelCase__						,question_encoder_tokenizer=lowerCamelCase__						,generator_tokenizer=lowerCamelCase__						,index=lowerCamelCase__						,init_retrieval=lowerCamelCase__						,)
                 SCREAMING_SNAKE_CASE		=						True






     def     SCREAMING_SNAKE_CASE__  (							self						:   Dict	)					->		Any:




           '''simple docstring'''


           self.retriever.index.init_index()






     def     SCREAMING_SNAKE_CASE__  (							self						:   Any						,lowerCamelCase__						:   List[Any]						,lowerCamelCase__						:   List[str]	)					->		List[Any]:




           '''simple docstring'''


           SCREAMING_SNAKE_CASE, SCREAMING_SNAKE_CASE		=						self.retriever._main_retrieve(lowerCamelCase__						,lowerCamelCase__	)
           return doc_ids, retrieved_doc_embeds

class 		UpperCamelCase__			(				lowerCAmelCase_       ):



     '''simple docstring'''

     def __init__(							self						:   int						,lowerCamelCase__						:   Dict						,lowerCamelCase__						:   Optional[Any]						,lowerCamelCase__						:   List[str]						,lowerCamelCase__						:   Optional[Any]						,lowerCamelCase__						:   Dict=None	)					->		Any:




           '''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		=						retrieval_workers
           if len(self.retrieval_workers	) > 0:
                 ray.get(
                     [
                         worker.create_rag_retriever.remote(lowerCamelCase__						,lowerCamelCase__						,lowerCamelCase__						,lowerCamelCase__	)
                         for worker in self.retrieval_workers
                     ]	)






     def     SCREAMING_SNAKE_CASE__  (							self						:   List[str]	)					->		Union[str, Any]:




           '''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						:   Any						,lowerCamelCase__						:   Any						,lowerCamelCase__						:   int	)					->		Dict:




           '''simple docstring'''


           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		=						ray.get(random_worker.retrieve.remote(lowerCamelCase__						,lowerCamelCase__	)	)
           else:
                 SCREAMING_SNAKE_CASE, SCREAMING_SNAKE_CASE		=						self._main_retrieve(lowerCamelCase__						,lowerCamelCase__	)
           return retrieved_doc_embeds, doc_ids, self.index.get_doc_dicts(lowerCamelCase__	)






     @classmethod
     def     SCREAMING_SNAKE_CASE__  (							cls						:   Optional[Any]						,lowerCamelCase__						:   List[Any]						,lowerCamelCase__						:   Union[str, Any]=None						,**lowerCamelCase__						:   Optional[Any]	)					->		Any:




           '''simple docstring'''


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






     @classmethod
     def     SCREAMING_SNAKE_CASE__  (							cls						:   Any						,lowerCamelCase__						:   Tuple						,lowerCamelCase__						:   List[Any]						,lowerCamelCase__						:   List[Any]=None						,**lowerCamelCase__						:   Any	)					->		Tuple:




           '''simple docstring'''


           SCREAMING_SNAKE_CASE		=						kwargs.pop("""config"""						,lowerCamelCase__	) or RagConfig.from_pretrained(lowerCamelCase__						,**lowerCamelCase__	)
           SCREAMING_SNAKE_CASE		=						RagTokenizer.from_pretrained(lowerCamelCase__						,config=lowerCamelCase__	)
           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						,lowerCamelCase__	)
           else:
                 SCREAMING_SNAKE_CASE		=						cls._build_index(lowerCamelCase__	)
           return cls(
               lowerCamelCase__						,question_encoder_tokenizer=lowerCamelCase__						,generator_tokenizer=lowerCamelCase__						,retrieval_workers=lowerCamelCase__						,index=lowerCamelCase__						,)


