






import logging
import random

import ray

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


snake_case_   :	Optional[Any]							      =logging.getLogger(__name__)





class        a__      :






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






		def 					_lowerCamelCase					(      self							, lowercase__							, lowercase__							, lowercase__							, lowercase__						)				->				Any:
				if not self.initialized:
						__A    =							RagRetriever(
						    lowercase__							, question_encoder_tokenizer=lowercase__							, generator_tokenizer=lowercase__							, index=lowercase__							, init_retrieval=lowercase__							, )
						__A    =							True






		def 					_lowerCamelCase					(      self						)				->				Tuple:
				self.retriever.index.init_index()






		def 					_lowerCamelCase					(      self						)				->				Tuple:
				# delete the old self.retriever object before assigning the new index
				del self.retriever
				__A    =							False






		def 					_lowerCamelCase					(      self							, lowercase__							, lowercase__						)				->				Any:
				__A ,   __A    =							self.retriever._main_retrieve(lowercase__							, lowercase__						)
				__A    =							self.retriever.index.get_doc_dicts(lowercase__						)
				return doc_ids, retrieved_doc_embeds, doc_dicts





class        a__      (	lowerCAmelCase__		):






		def __init__(      self							, lowercase__							, lowercase__							, lowercase__							, lowercase__							, lowercase__=None						)				->				Optional[int]:
				if index is not None and index.is_initialized() and len(lowercase__						) > 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__(
				    lowercase__							, question_encoder_tokenizer=lowercase__							, generator_tokenizer=lowercase__							, index=lowercase__							, init_retrieval=lowercase__							, )

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






		def 					_lowerCamelCase					(      self						)				->				Optional[int]:
				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							, lowercase__							, lowercase__						)				->				Optional[Any]:
				if len(self.retrieval_workers						) > 0:
						# Select a random retrieval actor.
						__A    =							self.retrieval_workers[random.randint(0							, len(self.retrieval_workers						) - 1						)]
						__A ,   __A ,   __A    =							ray.get(
						    random_worker.retrieve.remote(lowercase__							, lowercase__						)						)
				else:
						__A ,   __A    =							self._main_retrieve(lowercase__							, lowercase__						)
						__A    =							self.index.get_doc_dicts(lowercase__						)
				return retrieved_doc_embeds, doc_ids, doc_dicts






		@classmethod
		def 					_lowerCamelCase					(      cls							, lowercase__							, lowercase__=None							, **lowercase__						)				->				str:
				return super(lowercase__							, cls						).get_tokenizers(lowercase__							, lowercase__							, **lowercase__						)






		@classmethod
		def 					_lowerCamelCase					(      cls							, lowercase__							, lowercase__							, lowercase__=None							, **lowercase__						)				->				Any:
				__A    =							kwargs.pop("config"							, lowercase__						) or RagConfig.from_pretrained(lowercase__							, **lowercase__						)
				__A    =							RagTokenizer.from_pretrained(lowercase__							, config=lowercase__						)
				__A    =							rag_tokenizer.question_encoder
				__A    =							rag_tokenizer.generator

				if indexed_dataset is not None:
						__A    =							"custom"
						__A    =							CustomHFIndex(config.retrieval_vector_size							, lowercase__						)
				else:
						__A    =							cls._build_index(lowercase__						)

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






		def 					_lowerCamelCase					(      self						)				->				Dict:
				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
				__A    =							self._build_index(self.config						)

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









import sys
from typing import Tuple

import numpy as np
import torch
from PIL import Image
from torch import nn

from transformers.image_utils import PILImageResampling
from utils import img_tensorize





class        a__      :






		def __init__(      self							, lowercase__							, lowercase__=sys.maxsize						)				->				str:
				__A    =							"bilinear"
				__A    =							max_size
				__A    =							short_edge_length






		def __call__(      self							, lowercase__						)				->				Dict:
				__A    =							[]
				for img in imgs:
						__A ,   __A    =							img.shape[:2]
						# later: provide list and randomly choose index for resize
						__A    =							np.random.randint(self.short_edge_length[0]							, self.short_edge_length[1] + 1						)
						if size == 0:
								return img
						__A    =							size * 1.0 / min(lowercase__							, lowercase__						)
						if h < w:
								__A ,   __A    =							size, scale * w
						else:
								__A ,   __A    =							scale * h, size
						if max(lowercase__							, lowercase__						) > self.max_size:
								__A    =							self.max_size * 1.0 / max(lowercase__							, lowercase__						)
								__A    =							newh * scale
								__A    =							neww * scale
						__A    =							int(neww + 0.5						)
						__A    =							int(newh + 0.5						)

						if img.dtype == np.uinta:
								__A    =							Image.fromarray(lowercase__						)
								__A    =							pil_image.resize((neww, newh)							, PILImageResampling.BILINEAR						)
								__A    =							np.asarray(lowercase__						)
						else:
								__A    =							img.permute(2							, 0							, 1						).unsqueeze(0						)  # 3, 0, 1)  # hw(c) -> nchw
								__A    =							nn.functional.interpolate(
								    lowercase__							, (newh, neww)							, mode=self.interp_method							, align_corners=lowercase__						).squeeze(0						)
						img_augs.append(lowercase__						)

				return img_augs





class        a__      :






		def __init__(      self							, lowercase__						)				->				Union[str, Any]:
				__A    =							ResizeShortestEdge([cfg.INPUT.MIN_SIZE_TEST, cfg.INPUT.MIN_SIZE_TEST]							, cfg.INPUT.MAX_SIZE_TEST						)
				__A    =							cfg.INPUT.FORMAT
				__A    =							cfg.SIZE_DIVISIBILITY
				__A    =							cfg.PAD_VALUE
				__A    =							cfg.INPUT.MAX_SIZE_TEST
				__A    =							cfg.MODEL.DEVICE
				__A    =							torch.tensor(cfg.MODEL.PIXEL_STD						).to(self.device						).view(len(cfg.MODEL.PIXEL_STD						)							, 1							, 1						)
				__A    =							torch.tensor(cfg.MODEL.PIXEL_MEAN						).to(self.device						).view(len(cfg.MODEL.PIXEL_STD						)							, 1							, 1						)
				__A    =							lambda lowercase__						: (x - self.pixel_mean) / self.pixel_std






		def 					_lowerCamelCase					(      self							, lowercase__						)				->				Optional[int]:
				__A    =							tuple(max(lowercase__						) for s in zip(*[img.shape for img in images]						)						)
				__A    =							[im.shape[-2:] for im in images]
				__A    =							[
				    nn.functional.pad(
				        lowercase__							, [0, max_size[-1] - size[1], 0, max_size[-2] - size[0]]							, value=self.pad_value							, )
				    for size, im in zip(lowercase__							, lowercase__						)
				]

				return torch.stack(lowercase__						), torch.tensor(lowercase__						)






		def __call__(      self							, lowercase__							, lowercase__=False						)				->				int:
				with torch.no_grad():
						if not isinstance(lowercase__							, lowercase__						):
								__A    =							[images]
						if single_image:
								assert len(lowercase__						) == 1
						for i in range(len(lowercase__						)						):
								if isinstance(images[i]							, torch.Tensor						):
										images.insert(lowercase__							, images.pop(lowercase__						).to(self.device						).float()						)
								elif not isinstance(images[i]							, torch.Tensor						):
										images.insert(
										    lowercase__							, torch.as_tensor(img_tensorize(images.pop(lowercase__						)							, input_format=self.input_format						)						)
										    .to(self.device						)
										    .float()							, )
            # resize smallest edge
						__A    =							torch.tensor([im.shape[:2] for im in images]						)
						__A    =							self.aug(lowercase__						)
						# transpose images and convert to torch tensors
						# images = [torch.as_tensor(i.astype("float32")).permute(2, 0, 1).to(self.device) for i in images]
						# now normalize before pad to avoid useless arithmetic
						__A    =							[self.normalizer(lowercase__						) for x in images]
						# now pad them to do the following operations
						__A ,   __A    =							self.pad(lowercase__						)
						# Normalize

						if self.size_divisibility > 0:
								raise NotImplementedError()
						# pad
						__A    =							torch.true_divide(lowercase__							, lowercase__						)
						if single_image:
								return images[0], sizes[0], scales_yx[0]
						else:
								return images, sizes, scales_yx



def      UpperCAmelCase						( lowerCAmelCase__					,     lowerCAmelCase__	):


		'''simple docstring'''

		boxes[:, 0::2] *= scale_yx[:, 1]
		boxes[:, 1::2] *= scale_yx[:, 0]
		return boxes





def      UpperCAmelCase						( lowerCAmelCase__					,     lowerCAmelCase__	):


		'''simple docstring'''

		assert torch.isfinite(lowerCAmelCase__	).all(), "Box tensor contains infinite or NaN!"
		__A ,   __A    =							box_size
		tensor[:, 0].clamp_(min=0					,     max=lowerCAmelCase__	)
		tensor[:, 1].clamp_(min=0					,     max=lowerCAmelCase__	)
		tensor[:, 2].clamp_(min=0					,     max=lowerCAmelCase__	)
		tensor[:, 3].clamp_(min=0					,     max=lowerCAmelCase__	)

