





import logging
import random

import ray

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


lowercase     :			str     						=	logging.getLogger(__name__)

class 			__A:

     def __init__(   self   ):





            """simple docstring"""

            _UpperCamelCase														=    False

     def 						_UpperCamelCase  (   self,							A,							A,							A,							A   ):





            """simple docstring"""

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

     def 						_UpperCamelCase  (   self   ):





            """simple docstring"""

            self.retriever.index.init_index()

     def 						_UpperCamelCase  (   self   ):





            """simple docstring"""

            del self.retriever
            _UpperCamelCase														=    False



     def 						_UpperCamelCase  (   self,							A,							A   ):





            """simple docstring"""

            _UpperCamelCase							,		_UpperCamelCase														=    self.retriever._main_retrieve(A,							A   )
            _UpperCamelCase														=    self.retriever.index.get_doc_dicts(A   )
            return doc_ids, retrieved_doc_embeds, doc_dicts






class 			__A(    __UpperCAmelCase			):

     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,							)

            _UpperCamelCase														=    retrieval_workers
            _UpperCamelCase														=    question_encoder_tokenizer
            _UpperCamelCase														=    generator_tokenizer
            if len(self.retrieval_workers   ) > 0:
                   ray.get(
                       [
                           worker.create_rag_retriever.remote(A,							A,							A,							A   )
                           for worker in self.retrieval_workers
                       ]   )

     def 						_UpperCamelCase  (   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 						_UpperCamelCase  (   self,							A,							A   ):





            """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							,		_UpperCamelCase														=    ray.get(
                       random_worker.retrieve.remote(A,							A   )   )
            else:
                   _UpperCamelCase							,		_UpperCamelCase														=    self._main_retrieve(A,							A   )
                   _UpperCamelCase														=    self.index.get_doc_dicts(A   )
            return retrieved_doc_embeds, doc_ids, doc_dicts

     @classmethod
     def 						_UpperCamelCase  (   cls,							A,							A=None,							**A   ):





            """simple docstring"""

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

     @classmethod
     def 						_UpperCamelCase  (   cls,							A,							A,							A=None,							**A   ):





            """simple docstring"""

            _UpperCamelCase														=    kwargs.pop('''config''',							A   ) or RagConfig.from_pretrained(A,							**A   )
            _UpperCamelCase														=    RagTokenizer.from_pretrained(A,							config=A   )
            _UpperCamelCase														=    rag_tokenizer.question_encoder
            _UpperCamelCase														=    rag_tokenizer.generator

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

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



     def 						_UpperCamelCase  (   self   ):





            """simple docstring"""

            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
            _UpperCamelCase														=    self._build_index(self.config   )

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








'''simple docstring'''







from typing import List, Optional, Tuple, Union

import torch

from ...utils import randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput


class       lowerCAmelCase			(					__UpperCAmelCase      ):




		def __init__(					self       ,    UpperCamelCase       ,    UpperCamelCase   ):
								super().__init__()
								self.register_modules(unet=UpperCamelCase       ,    scheduler=UpperCamelCase   )




		@torch.no_grad()
		def __call__(					self       ,    UpperCamelCase = 1       ,    UpperCamelCase = None       ,    UpperCamelCase = 1_000       ,    UpperCamelCase = "pil"       ,    UpperCamelCase = True       ,    ):
								# Sample gaussian noise to begin loop
								if isinstance(self.unet.config.sample_size       ,    UpperCamelCase   ):
														_SCREAMING_SNAKE_CASE					=			(
														    batch_size,
														    self.unet.config.in_channels,
														    self.unet.config.sample_size,
														    self.unet.config.sample_size,
														)
								else:
														_SCREAMING_SNAKE_CASE					=			(batch_size, self.unet.config.in_channels, *self.unet.config.sample_size)

								if self.device.type == "mps":
														# randn does not work reproducibly on mps
														_SCREAMING_SNAKE_CASE					=			randn_tensor(UpperCamelCase       ,    generator=UpperCamelCase   )
														_SCREAMING_SNAKE_CASE					=			image.to(self.device   )
								else:
														_SCREAMING_SNAKE_CASE					=			randn_tensor(UpperCamelCase       ,    generator=UpperCamelCase       ,    device=self.device   )

								# set step values
								self.scheduler.set_timesteps(UpperCamelCase   )

								for t in self.progress_bar(self.scheduler.timesteps   ):
														# 1. predict noise model_output
														_SCREAMING_SNAKE_CASE					=			self.unet(UpperCamelCase       ,    UpperCamelCase   ).sample

														# 2. compute previous image: x_t -> x_t-1
														_SCREAMING_SNAKE_CASE					=			self.scheduler.step(UpperCamelCase       ,    UpperCamelCase       ,    UpperCamelCase       ,    generator=UpperCamelCase   ).prev_sample

								_SCREAMING_SNAKE_CASE					=			(image / 2 + 0.5).clamp(0       ,    1   )
								_SCREAMING_SNAKE_CASE					=			image.cpu().permute(0       ,    2       ,    3       ,    1   ).numpy()
								if output_type == "pil":
														_SCREAMING_SNAKE_CASE					=			self.numpy_to_pil(UpperCamelCase   )

								if not return_dict:
														return (image,)

								return ImagePipelineOutput(images=UpperCamelCase   )