

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__	(   _a							,     _a=100							,     _a=" "):
	SCREAMING_SNAKE_CASE				: List[str]			       =    text.split(_a)
	return [character.join(text[i : i + n]).strip() for i in range(0							,     len(_a)							,     _a)]

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

	SCREAMING_SNAKE_CASE				: List[str]			       =    "data_" + str(_a)
	SCREAMING_SNAKE_CASE				: str			       =    os.path.join(_a							,     _a)

	SCREAMING_SNAKE_CASE				: Optional[Any]			       =    DPRContextEncoderTokenizerFast.from_pretrained("facebook/dpr-ctx_encoder-multiset-base")
	SCREAMING_SNAKE_CASE				: List[str]			       =    ctx_encoder.to(device=_a)

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

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

	SCREAMING_SNAKE_CASE				: Tuple			       =    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 	lowerCamelCase__	(   _a							,     _a):
	SCREAMING_SNAKE_CASE				: Union[str, Any]			       =    []

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

	SCREAMING_SNAKE_CASE				: int			       =    concatenate_datasets(_a)
	faiss.omp_set_num_threads(96)

	SCREAMING_SNAKE_CASE				: str			       =    faiss.IndexHNSWFlat(768							,     128							,     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


import math
import os

import cva
import numpy as np

a_         =      255.0

def 	lowerCamelCase__	(   _a							,     _a):
	SCREAMING_SNAKE_CASE				: Optional[int]			       =    np.mean((original - contrast) ** 2)
	if mse == 0:
		return 100

	return 20 * math.logaa(PIXEL_MAX / math.sqrt(_a))

def 	lowerCamelCase__	(   ):
	SCREAMING_SNAKE_CASE				: List[Any]			       =    os.path.dirname(os.path.realpath(_a))
	# Loading images (original image and compressed image)
	SCREAMING_SNAKE_CASE				: Any			       =    cva.imread(os.path.join(_a							,     "image_data/original_image.png"))
	SCREAMING_SNAKE_CASE				: List[str]			       =    cva.imread(os.path.join(_a							,     "image_data/compressed_image.png")							,     1)

	SCREAMING_SNAKE_CASE				: List[Any]			       =    cva.imread(os.path.join(_a							,     "image_data/PSNR-example-base.png"))
	SCREAMING_SNAKE_CASE				: List[str]			       =    cva.imread(
	    os.path.join(_a							,     "image_data/PSNR-example-comp-10.jpg")							,     1)

	# Value expected: 29.73dB
	print("-- First Test --")
	print(f"PSNR value is {peak_signal_to_noise_ratio(_a							,     _a)} dB")

	# # Value expected: 31.53dB (Wikipedia Example)
	print("\n-- Second Test --")
	print(f"PSNR value is {peak_signal_to_noise_ratio(_a							,     _a)} dB")


if __name__ == "__main__":
			main()