


"""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 						lowercase				(	_snake_case		: List[Any]	,  _snake_case		: List[Any]=100	,  _snake_case		: Dict=" "	)	->Dict:
	"""simple docstring"""





	__snake_case      :		List[str]         =							text.split(_snake_case	)
	return [character.join(text[i : i + n]	).strip() for i in range(0	,  len(_snake_case	)	,  _snake_case	)]






def 						lowercase				(	_snake_case		: List[str]	)	->Optional[Any]:
	"""simple docstring"""





	__snake_case      ,				__snake_case      :		Union[str, Any]         =							[], []
	for title, text in zip(documents['''title''']	,  documents['''text''']	):
		if text is not None:
			for passage in split_text(_snake_case	):
				titles.append(title if title is not None else ''''''	)
				texts.append(_snake_case	)
	return {"title": titles, "text": texts}






def 						lowercase				(	_snake_case		: List[Any]	,  _snake_case		: Any	,  _snake_case		: Dict	,  _snake_case		: Optional[Any]	,  _snake_case		: Dict	,  _snake_case		: Dict	)	->Dict:
	"""simple docstring"""





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

	__snake_case      :		List[Any]         =							'''data_''' + str(_snake_case	)
	__snake_case      :		Tuple         =							os.path.join(_snake_case	,  _snake_case	)

	__snake_case      :		Any         =							DPRContextEncoderTokenizerFast.from_pretrained('''facebook/dpr-ctx_encoder-multiset-base'''	)
	__snake_case      :		List[Any]         =							ctx_encoder.to(device=_snake_case	)

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

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

	__snake_case      :		Union[str, Any]         =							data_shrad.map(
	    partial(_snake_case	,  ctx_encoder=_snake_case	,  ctx_tokenizer=_snake_case	,  device=_snake_case	)	,  batched=_snake_case	,  batch_size=16	,  features=_snake_case	,  )
	dataset.save_to_disk(_snake_case	)






def 						lowercase				(	_snake_case		: List[str]	,  _snake_case		: Any	)	->Union[str, Any]:
	"""simple docstring"""





	__snake_case      :		Dict         =							[]

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

	__snake_case      :		str         =							concatenate_datasets(_snake_case	)
	faiss.omp_set_num_threads(96	)

	__snake_case      :		str         =							faiss.IndexHNSWFlat(768	,  128	,  faiss.METRIC_INNER_PRODUCT	)
	concat.add_faiss_index('''embeddings'''	,  custom_index=_snake_case	)
	concat.get_index('''embeddings'''	).save(
	    _snake_case	)  # since we load the index in to memory,we can directly update the index in the disk









"""simple docstring"""






import gc
import tempfile
import unittest

import numpy as np
import torch

from diffusers import VersatileDiffusionDualGuidedPipeline
from diffusers.utils.testing_utils import load_image, nightly, require_torch_gpu, torch_device


_lowerCamelCase  =	False


@nightly
@require_torch_gpu
class       lowerCamelCase_		(      unittest.TestCase ):





				"""simple docstring"""







				def 	lowerCAmelCase__		(    self  ):
								# clean up the VRAM after each test
								super().tearDown()
								gc.collect()
								torch.cuda.empty_cache()



				def 	lowerCAmelCase__		(    self  ):
								SCREAMING_SNAKE_CASE__  				=					VersatileDiffusionDualGuidedPipeline.from_pretrained("shi-labs/versatile-diffusion"  )
								# remove text_unet
								pipe.remove_unused_weights()
								pipe.to(UpperCAmelCase__  )
								pipe.set_progress_bar_config(disable=UpperCAmelCase__  )

								SCREAMING_SNAKE_CASE__  				=					load_image(
								    "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/versatile_diffusion/benz.jpg"  )

								SCREAMING_SNAKE_CASE__  				=					torch.manual_seed(0  )
								SCREAMING_SNAKE_CASE__  				=					pipe(
								    prompt="first prompt"							,		image=UpperCAmelCase__							,		text_to_image_strength=0.75							,		generator=UpperCAmelCase__							,		guidance_scale=7.5							,		num_inference_steps=2							,		output_type="numpy"							,		).images

								with tempfile.TemporaryDirectory() as tmpdirname:
												pipe.save_pretrained(UpperCAmelCase__  )
												SCREAMING_SNAKE_CASE__  				=					VersatileDiffusionDualGuidedPipeline.from_pretrained(UpperCAmelCase__  )

								pipe.to(UpperCAmelCase__  )
								pipe.set_progress_bar_config(disable=UpperCAmelCase__  )

								SCREAMING_SNAKE_CASE__  				=					generator.manual_seed(0  )
								SCREAMING_SNAKE_CASE__  				=					pipe(
								    prompt="first prompt"							,		image=UpperCAmelCase__							,		text_to_image_strength=0.75							,		generator=UpperCAmelCase__							,		guidance_scale=7.5							,		num_inference_steps=2							,		output_type="numpy"							,		).images

								assert np.abs(image - new_image  ).sum() < 1e-5, "Models don't have the same forward pass"






				def 	lowerCAmelCase__		(    self  ):
								SCREAMING_SNAKE_CASE__  				=					VersatileDiffusionDualGuidedPipeline.from_pretrained("shi-labs/versatile-diffusion"  )
								pipe.remove_unused_weights()
								pipe.to(UpperCAmelCase__  )
								pipe.set_progress_bar_config(disable=UpperCAmelCase__  )

								SCREAMING_SNAKE_CASE__  				=					"cyberpunk 2077"
								SCREAMING_SNAKE_CASE__  				=					load_image(
								    "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/versatile_diffusion/benz.jpg"  )
								SCREAMING_SNAKE_CASE__  				=					torch.manual_seed(0  )
								SCREAMING_SNAKE_CASE__  				=					pipe(
								    prompt=UpperCAmelCase__							,		image=UpperCAmelCase__							,		text_to_image_strength=0.75							,		generator=UpperCAmelCase__							,		guidance_scale=7.5							,		num_inference_steps=50							,		output_type="numpy"							,		).images

								SCREAMING_SNAKE_CASE__  				=					image[0, 253:256, 253:256, -1]

								assert image.shape == (1, 512, 512, 3)
								SCREAMING_SNAKE_CASE__  				=					np.array([0.0_787, 0.0_849, 0.0_826, 0.0_812, 0.0_807, 0.0_795, 0.0_818, 0.0_798, 0.0_779]  )

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