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,
)


_SCREAMING_SNAKE_CASE 		=      pytest.mark.integration





@pytest.mark.parametrize("""path"""							,					["""paws""", """csv"""]       )
def 			lowercase(	UpperCamelCase_							,					UpperCamelCase_       )		->	Tuple:







 '''simple docstring'''
 inspect_dataset(A__							,					A__       )
 UpperCamelCase			  =      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 			lowercase(	UpperCamelCase_							,					UpperCamelCase_       )		->	Optional[int]:







 '''simple docstring'''
 inspect_metric(A__							,					A__       )
 UpperCamelCase			  =      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 			lowercase(	UpperCamelCase_							,					UpperCamelCase_							,					UpperCamelCase_       )		->	Union[str, Any]:







 '''simple docstring'''
 UpperCamelCase			  =      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 			lowercase(	UpperCamelCase_							,					UpperCamelCase_							,					UpperCamelCase_       )		->	Dict:







 '''simple docstring'''
 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 			lowercase(	UpperCamelCase_							,					UpperCamelCase_       )		->	List[Any]:







 '''simple docstring'''
 UpperCamelCase			  =      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 			lowercase(	UpperCamelCase_							,					UpperCamelCase_							,					UpperCamelCase_       )		->	int:







 '''simple docstring'''
 UpperCamelCase			  =      get_dataset_infos(A__       )
 assert list(infos.keys()       ) == expected_configs
 UpperCamelCase			  =      expected_configs[0]
 assert expected_config in infos
 UpperCamelCase			  =      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 			lowercase(	UpperCamelCase_							,					UpperCamelCase_							,					UpperCamelCase_       )		->	Optional[Any]:







 '''simple docstring'''
 UpperCamelCase			  =      get_dataset_infos(A__       )
 assert expected_config in infos
 UpperCamelCase			  =      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 			lowercase(	UpperCamelCase_							,					UpperCamelCase_							,					UpperCamelCase_       )		->	List[Any]:







 '''simple docstring'''
 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





