"""simple docstring"""






import unittest

import numpy as np
import torch

from diffusers import KarrasVePipeline, KarrasVeScheduler, UNetaDModel
from diffusers.utils.testing_utils import enable_full_determinism, require_torch, slow, torch_device


enable_full_determinism()
class 				snake_case       ( unittest.TestCase				):




					"""simple docstring"""

					@property
					def 		__lowerCAmelCase						(				self   :	List[str]			):
									torch.manual_seed(0			)
									UpperCAmelCase__             =							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')    ,)
									return model







					def 		__lowerCAmelCase						(				self   :	Dict			):
									UpperCAmelCase__             =							self.dummy_uncond_unet
									UpperCAmelCase__             =							KarrasVeScheduler()

									UpperCAmelCase__             =							KarrasVePipeline(unet=lowerCamelCase__    ,scheduler=lowerCamelCase__			)
									pipe.to(lowerCamelCase__			)
									pipe.set_progress_bar_config(disable=lowerCamelCase__			)

									UpperCAmelCase__             =							torch.manual_seed(0			)
									UpperCAmelCase__             =							pipe(num_inference_steps=2    ,generator=lowerCamelCase__    ,output_type='numpy'			).images

									UpperCAmelCase__             =							torch.manual_seed(0			)
									UpperCAmelCase__             =							pipe(num_inference_steps=2    ,generator=lowerCamelCase__    ,output_type='numpy'    ,return_dict=lowerCamelCase__			)[0]

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

									assert image.shape == (1, 32, 32, 3)
									UpperCAmelCase__             =							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 				snake_case       ( unittest.TestCase				):




					"""simple docstring"""

					def 		__lowerCAmelCase						(				self   :	List[Any]			):
									UpperCAmelCase__             =							'google/ncsnpp-celebahq-256'
									UpperCAmelCase__             =							UNetaDModel.from_pretrained(lowerCamelCase__			)
									UpperCAmelCase__             =							KarrasVeScheduler()

									UpperCAmelCase__             =							KarrasVePipeline(unet=lowerCamelCase__    ,scheduler=lowerCamelCase__			)
									pipe.to(lowerCamelCase__			)
									pipe.set_progress_bar_config(disable=lowerCamelCase__			)

									UpperCAmelCase__             =							torch.manual_seed(0			)
									UpperCAmelCase__             =							pipe(num_inference_steps=20    ,generator=lowerCamelCase__    ,output_type='numpy'			).images

									UpperCAmelCase__             =							image[0, -3:, -3:, -1]
									assert image.shape == (1, 256, 256, 3)
									UpperCAmelCase__             =							np.array([0.5_7_8, 0.5_8_1_1, 0.5_9_2_4, 0.5_8_0_9, 0.5_8_7, 0.5_8_8_6, 0.5_8_6_1, 0.5_8_0_2, 0.5_8_6]			)

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




"""simple docstring"""






import logging
import random

import ray

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


__lowerCAmelCase    :       int					     =logging.getLogger(__name__)





class  _A						:

      def __init__(						self						):







            """simple docstring"""



            lowercase														=	False

      def 				A__       (						self		,       __lowerCAmelCase		,       __lowerCAmelCase		,       __lowerCAmelCase		,       __lowerCAmelCase						):







            """simple docstring"""



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

      def 				A__       (						self						):







            """simple docstring"""



            self.retriever.index.init_index()







      def 				A__       (						self		,       __lowerCAmelCase		,       __lowerCAmelCase						):







            """simple docstring"""



            lowercase							,							lowercase														=	self.retriever._main_retrieve(__lowerCAmelCase		,       __lowerCAmelCase						)
            return doc_ids, retrieved_doc_embeds






class  _A						(       lowerCAmelCase	):

      def __init__(						self		,       __lowerCAmelCase		,       __lowerCAmelCase		,       __lowerCAmelCase		,       __lowerCAmelCase		,       __lowerCAmelCase=None						):







            """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		,       )
            lowercase														=	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 				A__       (						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 				A__       (						self		,       __lowerCAmelCase		,       __lowerCAmelCase						):







            """simple docstring"""



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

      @classmethod
      def 				A__       (						cls		,       __lowerCAmelCase		,       __lowerCAmelCase=None		,       **__lowerCAmelCase						):







            """simple docstring"""



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







      @classmethod
      def 				A__       (						cls		,       __lowerCAmelCase		,       __lowerCAmelCase		,       __lowerCAmelCase=None		,       **__lowerCAmelCase						):







            """simple docstring"""



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



