


import os

import pytest

from datasets import (
    get_dataset_config_info,
    get_dataset_config_names,
    get_dataset_infos,
    get_dataset_split_names,
    inspect_dataset,
    inspect_metric,
)


lowerCAmelCase__			:      int								=pytest.mark.integration






@pytest.mark.parametrize('path',      ['paws', 'csv']				)
def 						a__			(				A__,      A__				):
			inspect_dataset(A__,      A__				)
			SCREAMING_SNAKE_CASE_		:     Optional[Any]					=					path + '.py'
			assert script_name in os.listdir(A__				)
			assert "__pycache__" not in os.listdir(A__				)






@pytest.mark.filterwarnings('ignore:inspect_metric is deprecated:FutureWarning'				)
@pytest.mark.filterwarnings('ignore:metric_module_factory is deprecated:FutureWarning'				)
@pytest.mark.parametrize('path',      ['accuracy']				)
def 						a__			(				A__,      A__				):
			inspect_metric(A__,      A__				)
			SCREAMING_SNAKE_CASE_		:     Optional[int]					=					path + '.py'
			assert script_name in os.listdir(A__				)
			assert "__pycache__" not in os.listdir(A__				)






@pytest.mark.parametrize(
    'path, config_name, expected_splits',      [
        ('squad', 'plain_text', ['train', 'validation']),
        ('dalle-mini/wit', 'dalle-mini--wit', ['train']),
        ('paws', 'labeled_final', ['train', 'test', 'validation']),
    ],      )
def 						a__			(				A__,      A__,      A__				):
			SCREAMING_SNAKE_CASE_		:     int					=					get_dataset_config_info(A__,      config_name=A__				)
			assert info.config_name == config_name
			assert list(info.splits.keys()				) == expected_splits






@pytest.mark.parametrize(
    'path, config_name, expected_exception',      [
        ('paws', None, ValueError),
    ],      )
def 						a__			(				A__,      A__,      A__				):
			with pytest.raises(A__				):
						get_dataset_config_info(A__,      config_name=A__				)






@pytest.mark.parametrize(
    'path, expected',      [
        ('squad', 'plain_text'),
        ('acronym_identification', 'default'),
        ('lhoestq/squad', 'plain_text'),
        ('lhoestq/test', 'default'),
        ('lhoestq/demo1', 'lhoestq--demo1'),
        ('dalle-mini/wit', 'dalle-mini--wit'),
    ],      )
def 						a__			(				A__,      A__				):
			SCREAMING_SNAKE_CASE_		:     Optional[int]					=					get_dataset_config_names(A__				)
			assert expected in config_names






@pytest.mark.parametrize(
    'path, expected_configs, expected_splits_in_first_config',      [
        ('squad', ['plain_text'], ['train', 'validation']),
        ('dalle-mini/wit', ['dalle-mini--wit'], ['train']),
        ('paws', ['labeled_final', 'labeled_swap', 'unlabeled_final'], ['train', 'test', 'validation']),
    ],      )
def 						a__			(				A__,      A__,      A__				):
			SCREAMING_SNAKE_CASE_		:     Tuple					=					get_dataset_infos(A__				)
			assert list(infos.keys()				) == expected_configs
			SCREAMING_SNAKE_CASE_		:     List[str]					=					expected_configs[0]
			assert expected_config in infos
			SCREAMING_SNAKE_CASE_		:     Any					=					infos[expected_config]
			assert info.config_name == expected_config
			assert list(info.splits.keys()				) == expected_splits_in_first_config






@pytest.mark.parametrize(
    'path, expected_config, expected_splits',      [
        ('squad', 'plain_text', ['train', 'validation']),
        ('dalle-mini/wit', 'dalle-mini--wit', ['train']),
        ('paws', 'labeled_final', ['train', 'test', 'validation']),
    ],      )
def 						a__			(				A__,      A__,      A__				):
			SCREAMING_SNAKE_CASE_		:     List[str]					=					get_dataset_infos(A__				)
			assert expected_config in infos
			SCREAMING_SNAKE_CASE_		:     int					=					infos[expected_config]
			assert info.config_name == expected_config
			assert list(info.splits.keys()				) == expected_splits






@pytest.mark.parametrize(
    'path, config_name, expected_exception',      [
        ('paws', None, ValueError),
    ],      )
def 						a__			(				A__,      A__,      A__				):
			with pytest.raises(A__				):
						get_dataset_split_names(A__,      config_name=A__				)









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
lowerCAmelCase__			:      Optional[int]								=0B1011_0011_1110_1100_1001_0000_0111_1011_1011_0001_1001_1110
# bin(x)[2:] gives bits of x as str, use int to convert them to 0/1
lowerCAmelCase__			:      str								=[int(bit) for bit in bin(WATERMARK_MESSAGE)[2:]]







class 	__lowercase    :





							"""simple docstring"""







							def __init__(      self       ):





										"""simple docstring"""




										SCREAMING_SNAKE_CASE_		:     List[Any]					=					WATERMARK_BITS
										SCREAMING_SNAKE_CASE_		:     int					=					WatermarkEncoder()

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






							def 					UpperCamelCase__     (      self			,						lowerCAmelCase__       ):





										"""simple docstring"""




										if images.shape[-1] < 2_5_6:
													return images

										SCREAMING_SNAKE_CASE_		:     Tuple					=					(2_5_5 * (images / 2 + 0.5)).cpu().permute(0			,						2			,						3			,						1       ).float().numpy()

										SCREAMING_SNAKE_CASE_		:     Optional[int]					=					[self.encoder.encode(lowerCAmelCase__			,						'dwtDct'       ) for image in images]

										SCREAMING_SNAKE_CASE_		:     List[str]					=					torch.from_numpy(np.array(lowerCAmelCase__       )       ).permute(0			,						3			,						1			,						2       )

										SCREAMING_SNAKE_CASE_		:     Union[str, Any]					=					torch.clamp(2 * (images / 2_5_5 - 0.5)			,						min=-1.0			,						max=1.0       )
										return images





