




"""simple docstring"""

import os
from functools import partial
from glob import glob

import faiss
from datasets import Features, Sequence, Value, concatenate_datasets, load_dataset, load_from_disk

from transformers import DPRContextEncoder, DPRContextEncoderTokenizerFast



def   UpperCamelCase__     (		lowercase__  :     int       ,     lowercase__  :     Any=100       ,     lowercase__  :     List[str]=" "   ):
		snake_case		:						Union[str, Any]								=				text.split(lowercase__   )
		return [character.join(text[i : i + n]   ).strip() for i in range(0       ,     len(lowercase__   )       ,     lowercase__   )]



def   UpperCamelCase__     (		lowercase__  :     int   ):
		snake_case			,				snake_case		:						Tuple								=				[], []
		for title, text in zip(documents["title"]       ,     documents["text"]   ):
				if text is not None:
						for passage in split_text(lowercase__   ):
								titles.append(title if title is not None else ""   )
								texts.append(lowercase__   )
		return {"title": titles, "text": texts}



def   UpperCamelCase__     (		lowercase__  :     Optional[int]       ,     lowercase__  :     List[str]       ,     lowercase__  :     Dict       ,     lowercase__  :     Dict       ,     lowercase__  :     str       ,     lowercase__  :     int   ):
		snake_case		:						Optional[Any]								=				load_dataset(
		    "csv"       ,     data_files=[csv_path]       ,     split="train"       ,     delimiter="\t"       ,     column_names=["title", "text"]   )
		snake_case		:						Optional[Any]								=				kb_dataset.map(
		    lowercase__       ,     batched=lowercase__       ,     num_proc=1   )  # if you want you can load already splitted csv.
		snake_case		:						Dict								=				[kb_dataset.shard(lowercase__       ,     lowercase__       ,     contiguous=lowercase__   ) for i in range(lowercase__   )]
		snake_case		:						List[Any]								=				kb_list[process_num]

		snake_case		:						Dict								=				"data_" + str(lowercase__   )
		snake_case		:						str								=				os.path.join(lowercase__       ,     lowercase__   )

		snake_case		:						Optional[Any]								=				DPRContextEncoderTokenizerFast.from_pretrained("facebook/dpr-ctx_encoder-multiset-base"   )
		snake_case		:						Union[str, Any]								=				ctx_encoder.to(device=lowercase__   )

		def embed(
		    lowercase__  :     dict       ,     lowercase__  :     DPRContextEncoder       ,     lowercase__  :     DPRContextEncoderTokenizerFast       ,     lowercase__  :     str   ) -> dict:
				snake_case		:						List[Any]								=				ctx_tokenizer(
				    documents["title"]       ,     documents["text"]       ,     truncation=lowercase__       ,     padding="longest"       ,     return_tensors="pt"   )["input_ids"]
				snake_case		:						Union[str, Any]								=				ctx_encoder(input_ids.to(device=lowercase__   )       ,     return_dict=lowercase__   ).pooler_output
				return {"embeddings": embeddings.detach().cpu().numpy()}

		snake_case		:						Tuple								=				Features(
		    {"text": Value("string"   ), "title": Value("string"   ), "embeddings": Sequence(Value("float32"   )   )}   )  # optional, save as float32 instead of float64 to save space

		snake_case		:						Dict								=				data_shrad.map(
		    partial(lowercase__       ,     ctx_encoder=lowercase__       ,     ctx_tokenizer=lowercase__       ,     device=lowercase__   )       ,     batched=lowercase__       ,     batch_size=16       ,     features=lowercase__       ,     )
		dataset.save_to_disk(lowercase__   )



def   UpperCamelCase__     (		lowercase__  :     Tuple       ,     lowercase__  :     int   ):
		snake_case		:						Any								=				[]

		for shard_address in glob(str(lowercase__   ) + "/*/"   ):
				data_shard_list.append(load_from_disk(lowercase__   )   )

		snake_case		:						Union[str, Any]								=				concatenate_datasets(lowercase__   )
		faiss.omp_set_num_threads(96   )

		snake_case		:						List[Any]								=				faiss.IndexHNSWFlat(768       ,     128       ,     faiss.METRIC_INNER_PRODUCT   )
		concat.add_faiss_index("embeddings"       ,     custom_index=lowercase__   )
		concat.get_index("embeddings"   ).save(
		    lowercase__   )  # since we load the index in to memory,we can directly update the index in the disk







"""simple docstring"""

import unittest

import numpy as np
import PIL
import torch

from diffusers.image_processor import VaeImageProcessor
class 			lowerCamelCase__    (  unittest.TestCase			):


			@property
			def 							lowerCamelCase_				(     self							):



					"""simple docstring"""







					snake_case		:						int								=				1
					snake_case		:						Dict								=				3
					snake_case		:						int								=				8
					snake_case		:						int								=				8

					snake_case		:						List[str]								=				torch.rand((batch_size, num_channels, height, width)							)

					return sample


			def 							lowerCamelCase_				(     self				, SCREAMING_SNAKE_CASE							):



					"""simple docstring"""







					if isinstance(image[0]				, PIL.Image.Image							):
							return np.stack([np.array(SCREAMING_SNAKE_CASE							) for i in image]				, axis=0							)
					elif isinstance(SCREAMING_SNAKE_CASE				, torch.Tensor							):
							return image.cpu().numpy().transpose(0				, 2				, 3				, 1							)
					return image


			def 							lowerCamelCase_				(     self							):



					"""simple docstring"""







					snake_case		:						Optional[int]								=				VaeImageProcessor(do_resize=SCREAMING_SNAKE_CASE				, do_normalize=SCREAMING_SNAKE_CASE							)

					snake_case		:						Any								=				self.dummy_sample
					snake_case		:						Optional[Any]								=				self.to_np(SCREAMING_SNAKE_CASE							)

					for output_type in ["pt", "np", "pil"]:
							snake_case		:						Optional[Any]								=				image_processor.postprocess(
							    image_processor.preprocess(SCREAMING_SNAKE_CASE							)				, output_type=SCREAMING_SNAKE_CASE				, )
							snake_case		:						Optional[Any]								=				self.to_np(SCREAMING_SNAKE_CASE							)
							snake_case		:						Any								=				(input_np * 255).round() if output_type == "pil" else input_np
							assert (
							    np.abs(in_np - out_np							).max() < 1E-6
							), F'''decoded output does not match input for output_type {output_type}'''


			def 							lowerCamelCase_				(     self							):



					"""simple docstring"""







					snake_case		:						Dict								=				VaeImageProcessor(do_resize=SCREAMING_SNAKE_CASE				, do_normalize=SCREAMING_SNAKE_CASE							)
					snake_case		:						Tuple								=				self.dummy_sample.cpu().numpy().transpose(0				, 2				, 3				, 1							)

					for output_type in ["pt", "np", "pil"]:
							snake_case		:						Dict								=				image_processor.postprocess(image_processor.preprocess(SCREAMING_SNAKE_CASE							)				, output_type=SCREAMING_SNAKE_CASE							)

							snake_case		:						Union[str, Any]								=				self.to_np(SCREAMING_SNAKE_CASE							)
							snake_case		:						Tuple								=				(input_np * 255).round() if output_type == "pil" else input_np
							assert (
							    np.abs(in_np - out_np							).max() < 1E-6
							), F'''decoded output does not match input for output_type {output_type}'''


			def 							lowerCamelCase_				(     self							):



					"""simple docstring"""







					snake_case		:						Optional[Any]								=				VaeImageProcessor(do_resize=SCREAMING_SNAKE_CASE				, do_normalize=SCREAMING_SNAKE_CASE							)

					snake_case		:						str								=				self.dummy_sample.cpu().numpy().transpose(0				, 2				, 3				, 1							)
					snake_case		:						int								=				image_processor.numpy_to_pil(SCREAMING_SNAKE_CASE							)

					for output_type in ["pt", "np", "pil"]:
							snake_case		:						Dict								=				image_processor.postprocess(image_processor.preprocess(SCREAMING_SNAKE_CASE							)				, output_type=SCREAMING_SNAKE_CASE							)
							for i, o in zip(SCREAMING_SNAKE_CASE				, SCREAMING_SNAKE_CASE							):
									snake_case		:						Dict								=				np.array(SCREAMING_SNAKE_CASE							)
									snake_case		:						Tuple								=				self.to_np(SCREAMING_SNAKE_CASE							) if output_type == "pil" else (self.to_np(SCREAMING_SNAKE_CASE							) * 255).round()
									assert (
									    np.abs(in_np - out_np							).max() < 1E-6
									), F'''decoded output does not match input for output_type {output_type}'''


			def 							lowerCamelCase_				(     self							):



					"""simple docstring"""







					snake_case		:						Tuple								=				VaeImageProcessor(do_resize=SCREAMING_SNAKE_CASE				, do_normalize=SCREAMING_SNAKE_CASE							)

					snake_case		:						List[str]								=				self.dummy_sample
					snake_case		:						Optional[int]								=				input_pt_ad.squeeze(0							)

					snake_case		:						Any								=				image_processor.postprocess(
					    image_processor.preprocess(SCREAMING_SNAKE_CASE							)				, output_type="np"				, )
					snake_case		:						Union[str, Any]								=				image_processor.postprocess(
					    image_processor.preprocess(SCREAMING_SNAKE_CASE							)				, output_type="np"				, )

					snake_case		:						Union[str, Any]								=				self.to_np(self.dummy_sample							)
					snake_case		:						Tuple								=				input_np_ad.squeeze(0							)

					snake_case		:						str								=				image_processor.postprocess(
					    image_processor.preprocess(SCREAMING_SNAKE_CASE							)				, output_type="np"				, )
					snake_case		:						Union[str, Any]								=				image_processor.postprocess(
					    image_processor.preprocess(SCREAMING_SNAKE_CASE							)				, output_type="np"				, )

					assert np.abs(out_pt_ad - out_pt_ad							).max() < 1E-6
					assert np.abs(out_np_ad - out_np_ad							).max() < 1E-6



			def 							lowerCamelCase_				(     self							):



					"""simple docstring"""







					snake_case		:						Optional[Any]								=				VaeImageProcessor(do_resize=SCREAMING_SNAKE_CASE				, do_normalize=SCREAMING_SNAKE_CASE							)

					snake_case		:						Union[str, Any]								=				self.dummy_sample
					snake_case		:						List[str]								=				list(SCREAMING_SNAKE_CASE							)

					snake_case		:						str								=				image_processor.postprocess(
					    image_processor.preprocess(SCREAMING_SNAKE_CASE							)				, output_type="np"				, )

					snake_case		:						Any								=				image_processor.postprocess(
					    image_processor.preprocess(SCREAMING_SNAKE_CASE							)				, output_type="np"				, )

					snake_case		:						Dict								=				self.to_np(self.dummy_sample							)
					list(SCREAMING_SNAKE_CASE							)

					snake_case		:						str								=				image_processor.postprocess(
					    image_processor.preprocess(SCREAMING_SNAKE_CASE							)				, output_type="np"				, )

					snake_case		:						Union[str, Any]								=				image_processor.postprocess(
					    image_processor.preprocess(SCREAMING_SNAKE_CASE							)				, output_type="np"				, )

					assert np.abs(out_pt_ad - out_pt_list							).max() < 1E-6
					assert np.abs(out_np_ad - out_np_list							).max() < 1E-6

