


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						( __lowerCamelCase			,  __lowerCamelCase=100			,  __lowerCamelCase=" "							)		->Union[str, Any]:


 '''simple docstring'''
 snake_case :			Tuple							=   text.split(__lowerCamelCase							)
 return [character.join(text[i : i + n]							).strip() for i in range(0			,  len(__lowerCamelCase							)			,  __lowerCamelCase							)]






def  _UpperCAmelCase						( __lowerCamelCase							)		->Dict:


 '''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(__lowerCamelCase							):
    titles.append(title if title is not None else ''''''							)
    texts.append(__lowerCamelCase							)
 return {"title": titles, "text": texts}






def  _UpperCAmelCase						( __lowerCamelCase			,  __lowerCamelCase			,  __lowerCamelCase			,  __lowerCamelCase			,  __lowerCamelCase			,  __lowerCamelCase							)		->Union[str, Any]:


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

 snake_case :			Union[str, Any]							=   '''data_''' + str(__lowerCamelCase							)
 snake_case :			Any							=   os.path.join(__lowerCamelCase			,  __lowerCamelCase							)

 snake_case :			Union[str, Any]							=   DPRContextEncoderTokenizerFast.from_pretrained('''facebook/dpr-ctx_encoder-multiset-base'''							)
 snake_case :			List[Any]							=   ctx_encoder.to(device=__lowerCamelCase							)

 def embed(
     __lowerCamelCase			,  __lowerCamelCase			,  __lowerCamelCase			,  __lowerCamelCase							) -> dict:
  snake_case :			Union[str, Any]							=   ctx_tokenizer(
      documents['''title''']			,  documents['''text''']			,  truncation=__lowerCamelCase			,  padding='''longest'''			,  return_tensors='''pt'''							)['''input_ids''']
  snake_case :			Optional[int]							=   ctx_encoder(input_ids.to(device=__lowerCamelCase							)			,  return_dict=__lowerCamelCase							).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(__lowerCamelCase			,  ctx_encoder=__lowerCamelCase			,  ctx_tokenizer=__lowerCamelCase			,  device=__lowerCamelCase							)			,  batched=__lowerCamelCase			,  batch_size=16			,  features=__lowerCamelCase			,  )
 dataset.save_to_disk(__lowerCamelCase							)






def  _UpperCAmelCase						( __lowerCamelCase			,  __lowerCamelCase							)		->Any:


 '''simple docstring'''
 snake_case :			Dict							=   []

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

 snake_case :			int							=   concatenate_datasets(__lowerCamelCase							)
 faiss.omp_set_num_threads(96							)

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










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


a					:				Optional[int]       =      False

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

       def 					UpperCAmelCase					(      self       ):






        '''simple docstring'''






        super().tearDown()
        gc.collect()
        torch.cuda.empty_cache()

       def 					UpperCAmelCase					(      self       ):






        '''simple docstring'''






        snake_case :			Optional[int]							=   VersatileDiffusionDualGuidedPipeline.from_pretrained('''shi-labs/versatile-diffusion'''       )
        # remove text_unet
        pipe.remove_unused_weights()
        pipe.to(a__       )
        pipe.set_progress_bar_config(disable=a__       )

        snake_case :			Union[str, Any]							=   load_image(
            '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/versatile_diffusion/benz.jpg'''       )

        snake_case :			str							=   torch.manual_seed(0       )
        snake_case :			Tuple							=   pipe(
            prompt='''first prompt'''						,    image=a__						,    text_to_image_strength=0.75						,    generator=a__						,    guidance_scale=7.5						,    num_inference_steps=2						,    output_type='''numpy'''						,    ).images

        with tempfile.TemporaryDirectory() as tmpdirname:
         pipe.save_pretrained(a__       )
         snake_case :			str							=   VersatileDiffusionDualGuidedPipeline.from_pretrained(a__       )

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

        snake_case :			Optional[Any]							=   generator.manual_seed(0       )
        snake_case :			List[str]							=   pipe(
            prompt='''first prompt'''						,    image=a__						,    text_to_image_strength=0.75						,    generator=a__						,    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 					UpperCAmelCase					(      self       ):






        '''simple docstring'''






        snake_case :			Tuple							=   VersatileDiffusionDualGuidedPipeline.from_pretrained('''shi-labs/versatile-diffusion'''       )
        pipe.remove_unused_weights()
        pipe.to(a__       )
        pipe.set_progress_bar_config(disable=a__       )

        snake_case :			int							=   '''cyberpunk 2077'''
        snake_case :			Dict							=   load_image(
            '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/versatile_diffusion/benz.jpg'''       )
        snake_case :			Optional[int]							=   torch.manual_seed(0       )
        snake_case :			Union[str, Any]							=   pipe(
            prompt=a__						,    image=a__						,    text_to_image_strength=0.75						,    generator=a__						,    guidance_scale=7.5						,    num_inference_steps=50						,    output_type='''numpy'''						,    ).images

        snake_case :			str							=   image[0, 2_53:2_56, 2_53:2_56, -1]

        assert image.shape == (1, 5_12, 5_12, 3)
        snake_case :			int							=   np.array([0.0787, 0.0849, 0.0826, 0.0812, 0.0807, 0.0795, 0.0818, 0.0798, 0.0779]       )

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






