
"""simple docstring"""



import numpy as np
import torch
from imwatermark import WatermarkEncoder


# Copied from https://github.com/Stability-AI/generative-models/blob/613af104c6b85184091d42d374fef420eddb356d/scripts/demo/streamlit_helpers.py#L66
_snake_case          =						0b1_0_1_1_0_0_1_1_1_1_1_0_1_1_0_0_1_0_0_1_0_0_0_0_0_1_1_1_1_0_1_1_1_0_1_1_0_0_0_1_1_0_0_1_1_1_1_0
# bin(x)[2:] gives bits of x as str, use int to convert them to 0/1
_snake_case          =						[int(bit) for bit in bin(WATERMARK_MESSAGE)[2:]]
class        _SCREAMING_SNAKE_CASE  :






				'''simple docstring'''



				def __init__(					self	:			Dict  )		->  List[str]:







											"""simple docstring"""




											_lowerCAmelCase  	=			WATERMARK_BITS
											_lowerCAmelCase  	=			WatermarkEncoder()

											self.encoder.set_watermark('bits'				,     self.watermark  )







				def 				__lowerCamelCase	(					self	:			Optional[Any]				,     UpperCAmelCase_	:			torch.FloatTensor  )		->  List[Any]:







											"""simple docstring"""




											if images.shape[-1] < 256:
																		return images

											_lowerCAmelCase  	=			(255 * (images / 2 + 0.5)).cpu().permute(0				,     2				,     3				,     1  ).float().numpy()

											_lowerCAmelCase  	=			[self.encoder.encode(UpperCAmelCase_				,     'dwtDct'  ) for image in images]

											_lowerCAmelCase  	=			torch.from_numpy(np.array(UpperCAmelCase_  )  ).permute(0				,     3				,     1				,     2  )

											_lowerCAmelCase  	=			torch.clamp(2 * (images / 255 - 0.5)				,     min=-1.0				,     max=1.0  )
											return images








"""simple docstring"""



import pytest


_snake_case          =						'''__dummy_dataset1__'''

_snake_case          =						'''
import json
import os

import datasets


REPO_URL = "https://huggingface.co/datasets/albertvillanova/tests-raw-jsonl/resolve/main/"
URLS = {"train": REPO_URL + "wikiann-bn-train.jsonl", "validation": REPO_URL + "wikiann-bn-validation.jsonl"}


class __DummyDataset1__(datasets.GeneratorBasedBuilder):

    def _info(self):
        features = datasets.Features(
            {
                "tokens": datasets.Sequence(datasets.Value("string")),
                "ner_tags": datasets.Sequence(
                    datasets.features.ClassLabel(
                        names=[
                            "O",
                            "B-PER",
                            "I-PER",
                            "B-ORG",
                            "I-ORG",
                            "B-LOC",
                            "I-LOC",
                        ]
                    )
                ),
                "langs": datasets.Sequence(datasets.Value("string")),
                "spans": datasets.Sequence(datasets.Value("string")),
            }
        )
        return datasets.DatasetInfo(features=features)

    def _split_generators(self, dl_manager):
        dl_path = dl_manager.download(URLS)
        return [
            datasets.SplitGenerator(datasets.Split.TRAIN, gen_kwargs={"filepath": dl_path["train"]}),
            datasets.SplitGenerator(datasets.Split.VALIDATION, gen_kwargs={"filepath": dl_path["validation"]}),
        ]

    def _generate_examples(self, filepath):
        with open(filepath, "r", encoding="utf-8") as f:
            for i, line in enumerate(f):
                yield i, json.loads(line)
'''






@pytest.fixture
def 	__snake_case  (    ):






							"""simple docstring"""



							return DATASET_LOADING_SCRIPT_NAME






@pytest.fixture
def 	__snake_case  (    ):






							"""simple docstring"""



							return DATASET_LOADING_SCRIPT_CODE






@pytest.fixture
def 	__snake_case  (    SCREAMING_SNAKE_CASE:					List[str]     ,  SCREAMING_SNAKE_CASE:					Optional[int]     ,  SCREAMING_SNAKE_CASE:					List[str]						):






							"""simple docstring"""



							_lowerCAmelCase  	=			dataset_loading_script_name
							_lowerCAmelCase  	=			tmp_path / 'datasets' / script_name
							script_dir.mkdir(parents=SCREAMING_SNAKE_CASE						)
							_lowerCAmelCase  	=			script_dir / f"""{script_name}.py"""
							with open(SCREAMING_SNAKE_CASE     ,  'w'						) as f:
														f.write(SCREAMING_SNAKE_CASE						)
							return str(SCREAMING_SNAKE_CASE						)






