



"""simple docstring"""





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 				lowercase__       :







       '''simple docstring'''


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


             '''simple docstring'''




             UpperCAmelCase_      			=							False



       def 					lowercase__    (							self      :  Tuple		,    _UpperCAmelCase      :  Optional[int]		,    _UpperCAmelCase      :  Tuple		,    _UpperCAmelCase      :  Union[str, Any]		,    _UpperCAmelCase      :  Any   )			->						Union[str, Any]:


             '''simple docstring'''




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



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


             '''simple docstring'''




             self.retriever.index.init_index()







       def 					lowercase__    (							self      :  Dict		,    _UpperCAmelCase      :  Optional[Any]		,    _UpperCAmelCase      :  Optional[int]   )			->						List[Any]:


             '''simple docstring'''




             UpperCAmelCase_			,   UpperCAmelCase_      			=							self.retriever._main_retrieve(_UpperCAmelCase		,    _UpperCAmelCase   )
             return doc_ids, retrieved_doc_embeds



class 				lowercase__       (   SCREAMING_SNAKE_CASE				):







       '''simple docstring'''


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


             '''simple docstring'''




             if index is not None and index.is_initialized() and len(_UpperCAmelCase   ) > 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__(
                 _UpperCAmelCase		,    question_encoder_tokenizer=_UpperCAmelCase		,    generator_tokenizer=_UpperCAmelCase		,    index=_UpperCAmelCase		,    init_retrieval=_UpperCAmelCase		,    )
             UpperCAmelCase_      			=							retrieval_workers
             if len(self.retrieval_workers   ) > 0:
                   ray.get(
                       [
                           worker.create_rag_retriever.remote(_UpperCAmelCase		,    _UpperCAmelCase		,    _UpperCAmelCase		,    _UpperCAmelCase   )
                           for worker in self.retrieval_workers
                       ]   )



       def 					lowercase__    (							self      :  Union[str, Any]   )			->						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 					lowercase__    (							self      :  Optional[int]		,    _UpperCAmelCase      :  str		,    _UpperCAmelCase      :  Dict   )			->						List[str]:


             '''simple docstring'''




             if len(self.retrieval_workers   ) > 0:
                   # Select a random retrieval actor.
                   UpperCAmelCase_      			=							self.retrieval_workers[random.randint(0		,    len(self.retrieval_workers   ) - 1   )]
                   UpperCAmelCase_			,   UpperCAmelCase_      			=							ray.get(random_worker.retrieve.remote(_UpperCAmelCase		,    _UpperCAmelCase   )   )
             else:
                   UpperCAmelCase_			,   UpperCAmelCase_      			=							self._main_retrieve(_UpperCAmelCase		,    _UpperCAmelCase   )
             return retrieved_doc_embeds, doc_ids, self.index.get_doc_dicts(_UpperCAmelCase   )



       @classmethod
       def 					lowercase__    (							cls      :  str		,    _UpperCAmelCase      :  Tuple		,    _UpperCAmelCase      :  Any=None		,    **_UpperCAmelCase      :  List[Any]   )			->						Union[str, Any]:


             '''simple docstring'''




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







       @classmethod
       def 					lowercase__    (							cls      :  Tuple		,    _UpperCAmelCase      :  int		,    _UpperCAmelCase      :  List[Any]		,    _UpperCAmelCase      :  Optional[int]=None		,    **_UpperCAmelCase      :  Any   )			->						Union[str, Any]:


             '''simple docstring'''




             UpperCAmelCase_      			=							kwargs.pop("config"		,    _UpperCAmelCase   ) or RagConfig.from_pretrained(_UpperCAmelCase		,    **_UpperCAmelCase   )
             UpperCAmelCase_      			=							RagTokenizer.from_pretrained(_UpperCAmelCase		,    config=_UpperCAmelCase   )
             UpperCAmelCase_      			=							rag_tokenizer.question_encoder
             UpperCAmelCase_      			=							rag_tokenizer.generator
             if indexed_dataset is not None:
                   UpperCAmelCase_      			=							"custom"
                   UpperCAmelCase_      			=							CustomHFIndex(config.retrieval_vector_size		,    _UpperCAmelCase   )
             else:
                   UpperCAmelCase_      			=							cls._build_index(_UpperCAmelCase   )
             return cls(
                 _UpperCAmelCase		,    question_encoder_tokenizer=_UpperCAmelCase		,    generator_tokenizer=_UpperCAmelCase		,    retrieval_workers=_UpperCAmelCase		,    index=_UpperCAmelCase		,    )









"""simple docstring"""





import unittest

import numpy as np

from diffusers import OnnxStableDiffusionInpaintPipelineLegacy
from diffusers.utils.testing_utils import (
    is_onnx_available,
    load_image,
    load_numpy,
    nightly,
    require_onnxruntime,
    require_torch_gpu,
)


if is_onnx_available():
  import onnxruntime as ort
@nightly
@require_onnxruntime
@require_torch_gpu
class 				lowercase__       (   unittest.TestCase				):







       '''simple docstring'''


       @property
       def 					lowercase__    (							self      :  Tuple   )			->						Any:


             '''simple docstring'''




             return (
                 "CUDAExecutionProvider",
                 {
                     "gpu_mem_limit": "15000000000",  # 15GB
                     "arena_extend_strategy": "kSameAsRequested",
                 },
             )



       @property
       def 					lowercase__    (							self      :  List[str]   )			->						int:


             '''simple docstring'''




             UpperCAmelCase_      			=							ort.SessionOptions()
             UpperCAmelCase_      			=							False
             return options







       def 					lowercase__    (							self      :  Dict   )			->						Optional[Any]:


             '''simple docstring'''




             UpperCAmelCase_      			=							load_image(
                 "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
                 "/in_paint/overture-creations-5sI6fQgYIuo.png"   )
             UpperCAmelCase_      			=							load_image(
                 "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
                 "/in_paint/overture-creations-5sI6fQgYIuo_mask.png"   )
             UpperCAmelCase_      			=							load_numpy(
                 "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
                 "/in_paint/red_cat_sitting_on_a_park_bench_onnx.npy"   )

             # using the PNDM scheduler by default
             UpperCAmelCase_      			=							OnnxStableDiffusionInpaintPipelineLegacy.from_pretrained(
                 "CompVis/stable-diffusion-v1-4"		,    revision="onnx"		,    safety_checker=_UpperCAmelCase		,    feature_extractor=_UpperCAmelCase		,    provider=self.gpu_provider		,    sess_options=self.gpu_options		,    )
             pipe.set_progress_bar_config(disable=_UpperCAmelCase   )

             UpperCAmelCase_      			=							"A red cat sitting on a park bench"

             UpperCAmelCase_      			=							np.random.RandomState(0   )
             UpperCAmelCase_      			=							pipe(
                 prompt=_UpperCAmelCase		,    image=_UpperCAmelCase		,    mask_image=_UpperCAmelCase		,    strength=0.75		,    guidance_scale=7.5		,    num_inference_steps=15		,    generator=_UpperCAmelCase		,    output_type="np"		,    )

             UpperCAmelCase_      			=							output.images[0]

             assert image.shape == (512, 512, 3)
             assert np.abs(expected_image - image   ).max() < 1e-2




