






import unittest

import numpy as np

from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available

from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs


if is_torch_available():
	import torch

if is_vision_available():
	from PIL import Image

	from transformers import OwlViTImageProcessor




class 			UpperCamelCase_				(      unittest.TestCase    ):



	'''simple docstring'''




	def __init__(	self	,      a	,      a=7	,      a=3	,      a=18	,      a=30	,      a=4_00	,      a=True	,      a=None	,      a=True	,      a=None	,      a=True	,      a=[0.48_145_466, 0.4_578_275, 0.40_821_073]	,      a=[0.26_862_954, 0.26_130_258, 0.27_577_711]	,      a=True	,      )	->   Optional[int]:
								snake_case_           =							parent
								snake_case_           =							batch_size
								snake_case_           =							num_channels
								snake_case_           =							image_size
								snake_case_           =							min_resolution
								snake_case_           =							max_resolution
								snake_case_           =							do_resize
								snake_case_           =							size if size is not None else {'height': 18, 'width': 18}
								snake_case_           =							do_center_crop
								snake_case_           =							crop_size if crop_size is not None else {'height': 18, 'width': 18}
								snake_case_           =							do_normalize
								snake_case_           =							image_mean
								snake_case_           =							image_std
								snake_case_           =							do_convert_rgb






	def 							_UpperCamelCase      (	self )	->   Optional[Any]:
								return {
								    "do_resize": self.do_resize,
								    "size": self.size,
								    "do_center_crop": self.do_center_crop,
								    "crop_size": self.crop_size,
								    "do_normalize": self.do_normalize,
								    "image_mean": self.image_mean,
								    "image_std": self.image_std,
								    "do_convert_rgb": self.do_convert_rgb,
								}






@require_torch
@require_vision
class 			UpperCamelCase_				(      snake_case_	, unittest.TestCase    ):



	'''simple docstring'''




	lowerCAmelCase													=			OwlViTImageProcessor if is_vision_available() else None




	def 							_UpperCamelCase      (	self )	->   Any:
								snake_case_           =							OwlViTImageProcessingTester(self )




	@property
	def 							_UpperCamelCase      (	self )	->   Optional[int]:
								return self.image_processor_tester.prepare_image_processor_dict()




	def 							_UpperCamelCase      (	self )	->   Optional[int]:
								snake_case_           =							self.image_processing_class(**self.image_processor_dict )
								self.assertTrue(hasattr(a	,      'do_resize' ) )
								self.assertTrue(hasattr(a	,      'size' ) )
								self.assertTrue(hasattr(a	,      'do_center_crop' ) )
								self.assertTrue(hasattr(a	,      'center_crop' ) )
								self.assertTrue(hasattr(a	,      'do_normalize' ) )
								self.assertTrue(hasattr(a	,      'image_mean' ) )
								self.assertTrue(hasattr(a	,      'image_std' ) )
								self.assertTrue(hasattr(a	,      'do_convert_rgb' ) )




	def 							_UpperCamelCase      (	self )	->   Dict:
								snake_case_           =							self.image_processing_class.from_dict(self.image_processor_dict )
								self.assertEqual(image_processor.size	,      {'height': 18, 'width': 18} )
								self.assertEqual(image_processor.crop_size	,      {'height': 18, 'width': 18} )

								snake_case_           =							self.image_processing_class.from_dict(self.image_processor_dict	,      size=42	,      crop_size=84 )
								self.assertEqual(image_processor.size	,      {'height': 42, 'width': 42} )
								self.assertEqual(image_processor.crop_size	,      {'height': 84, 'width': 84} )




	def 							_UpperCamelCase      (	self )	->   Tuple:
								# Initialize image_processing
								snake_case_           =							self.image_processing_class(**self.image_processor_dict )
								# create random PIL images
								snake_case_           =							prepare_image_inputs(self.image_processor_tester	,      equal_resolution=a )

								for image in image_inputs:
															self.assertIsInstance(a	,      Image.Image )

								# Test not batched input
								snake_case_           =							image_processing(image_inputs[0]	,      return_tensors='pt' ).pixel_values
								self.assertEqual(
								    encoded_images.shape	,      (
								        1,
								        self.image_processor_tester.num_channels,
								        self.image_processor_tester.crop_size['height'],
								        self.image_processor_tester.crop_size['width'],
								    )	,      )

								# Test batched
								snake_case_           =							image_processing(a	,      return_tensors='pt' ).pixel_values
								self.assertEqual(
								    encoded_images.shape	,      (
								        self.image_processor_tester.batch_size,
								        self.image_processor_tester.num_channels,
								        self.image_processor_tester.crop_size['height'],
								        self.image_processor_tester.crop_size['width'],
								    )	,      )




	def 							_UpperCamelCase      (	self )	->   List[str]:
								# Initialize image_processing
								snake_case_           =							self.image_processing_class(**self.image_processor_dict )
								# create random numpy tensors
								snake_case_           =							prepare_image_inputs(self.image_processor_tester	,      equal_resolution=a	,      numpify=a )
								for image in image_inputs:
															self.assertIsInstance(a	,      np.ndarray )

								# Test not batched input
								snake_case_           =							image_processing(image_inputs[0]	,      return_tensors='pt' ).pixel_values
								self.assertEqual(
								    encoded_images.shape	,      (
								        1,
								        self.image_processor_tester.num_channels,
								        self.image_processor_tester.crop_size['height'],
								        self.image_processor_tester.crop_size['width'],
								    )	,      )

								# Test batched
								snake_case_           =							image_processing(a	,      return_tensors='pt' ).pixel_values
								self.assertEqual(
								    encoded_images.shape	,      (
								        self.image_processor_tester.batch_size,
								        self.image_processor_tester.num_channels,
								        self.image_processor_tester.crop_size['height'],
								        self.image_processor_tester.crop_size['width'],
								    )	,      )






	def 							_UpperCamelCase      (	self )	->   str:
								# Initialize image_processing
								snake_case_           =							self.image_processing_class(**self.image_processor_dict )
								# create random PyTorch tensors
								snake_case_           =							prepare_image_inputs(self.image_processor_tester	,      equal_resolution=a	,      torchify=a )
								for image in image_inputs:
															self.assertIsInstance(a	,      torch.Tensor )

								# Test not batched input
								snake_case_           =							image_processing(image_inputs[0]	,      return_tensors='pt' ).pixel_values
								self.assertEqual(
								    encoded_images.shape	,      (
								        1,
								        self.image_processor_tester.num_channels,
								        self.image_processor_tester.crop_size['height'],
								        self.image_processor_tester.crop_size['width'],
								    )	,      )

								# Test batched
								snake_case_           =							image_processing(a	,      return_tensors='pt' ).pixel_values
								self.assertEqual(
								    encoded_images.shape	,      (
								        self.image_processor_tester.batch_size,
								        self.image_processor_tester.num_channels,
								        self.image_processor_tester.crop_size['height'],
								        self.image_processor_tester.crop_size['width'],
								    )	,      )













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      (					a_							,					a_=1_00							,					a_=" "):
							snake_case_           =							text.split(a_)
							return [character.join(text[i : i + n]).strip() for i in range(0							,					len(a_)							,					a_)]





def 						__UpperCAmelCase      (					a_):
							snake_case_      ,		snake_case_           =							[], []
							for title, text in zip(documents['title']							,					documents['text']):
														if text is not None:
																					for passage in split_text(a_):
																												titles.append(title if title is not None else '')
																												texts.append(a_)
							return {"title": titles, "text": texts}





def 						__UpperCAmelCase      (					a_							,					a_							,					a_							,					a_							,					a_							,					a_):
							snake_case_           =							load_dataset(
							    'csv'							,					data_files=[csv_path]							,					split='train'							,					delimiter='\t'							,					column_names=['title', 'text'])
							snake_case_           =							kb_dataset.map(
							    a_							,					batched=a_							,					num_proc=1)  # if you want you can load already splitted csv.
							snake_case_           =							[kb_dataset.shard(a_							,					a_							,					contiguous=a_) for i in range(a_)]
							snake_case_           =							kb_list[process_num]

							snake_case_           =							'data_' + str(a_)
							snake_case_           =							os.path.join(a_							,					a_)

							snake_case_           =							DPRContextEncoderTokenizerFast.from_pretrained('facebook/dpr-ctx_encoder-multiset-base')
							snake_case_           =							ctx_encoder.to(device=a_)

							def embed(
							    a_							,					a_							,					a_							,					a_) -> dict:
														snake_case_           =							ctx_tokenizer(
														    documents['title']							,					documents['text']							,					truncation=a_							,					padding='longest'							,					return_tensors='pt')['input_ids']
														snake_case_           =							ctx_encoder(input_ids.to(device=a_)							,					return_dict=a_).pooler_output
														return {"embeddings": embeddings.detach().cpu().numpy()}

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

							snake_case_           =							data_shrad.map(
							    partial(a_							,					ctx_encoder=a_							,					ctx_tokenizer=a_							,					device=a_)							,					batched=a_							,					batch_size=16							,					features=a_							,					)
							dataset.save_to_disk(a_)





def 						__UpperCAmelCase      (					a_							,					a_):
							snake_case_           =							[]

							for shard_address in glob(str(a_) + '/*/'):
														data_shard_list.append(load_from_disk(a_))

							snake_case_           =							concatenate_datasets(a_)
							faiss.omp_set_num_threads(96)

							snake_case_           =							faiss.IndexHNSWFlat(7_68							,					1_28							,					faiss.METRIC_INNER_PRODUCT)
							concat.add_faiss_index('embeddings'							,					custom_index=a_)
							concat.get_index('embeddings').save(
							    a_)  # since we load the index in to memory,we can directly update the index in the disk





