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



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



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

				A_       	=  '''data_''' + str(__UpperCamelCase     )
				A_       	=  os.path.join(__UpperCamelCase ,						__UpperCamelCase     )

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

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

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

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



def        lowerCamelCase_  (   __UpperCamelCase ,						__UpperCamelCase     ):
				A_       	=  []

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

				A_       	=  concatenate_datasets(__UpperCamelCase     )
				faiss.omp_set_num_threads(96     )

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

from cva import destroyAllWindows, imread, imshow, waitKey



def        lowerCamelCase_  (   __UpperCamelCase ,						__UpperCamelCase     ):
				A_	,					A_       	=  img.shape[0], img.shape[1]

				def to_grayscale(__UpperCamelCase ,						__UpperCamelCase ,						__UpperCamelCase     ):
								return 0.2126 * red + 0.587 * green + 0.114 * blue

				def normalize(__UpperCamelCase     ):
								return min(__UpperCamelCase ,						2_55     )

				for i in range(__UpperCamelCase     ):
								for j in range(__UpperCamelCase     ):
												A_       	=  int(to_grayscale(*img[i][j]     )     )
												A_       	=  [
												    normalize(__UpperCamelCase     ),
												    normalize(greyscale + factor     ),
												    normalize(greyscale + 2 * factor     ),
												]

				return img


if __name__ == "__main__":
						# read original image
						SCREAMING_SNAKE_CASE   :		Optional[Any]					 =       {
						    percentage: imread("image_data/lena.jpg", 1) for percentage in (10, 20, 30, 40)
						}

						for percentage, img in images.items():
												make_sepia(img, percentage)

						for percentage, img in images.items():
												imshow(f'''Original image with sepia (factor: {percentage})''', img)

						waitKey(0)
						destroyAllWindows()