
'''simple docstring'''


import re
import tempfile
import unittest
from pathlib import Path

from datasets.utils.metadata import DatasetMetadata
def 					__magic_name__(       UpperCAmelCase__ :       str	):
						UpperCamelCase_     :	List[Any]					     =     min(re.search("""^ +"""							,				lowerCAmelCase__	).end() if t.startswith(""" """	) else 0 for t in string.splitlines()	)
						return "\n".join([line[indent_level:] for line in string.splitlines() if indent_level < len(lowerCAmelCase__	)]	)


__SCREAMING_SNAKE_CASE						=      "\\n---\nlanguage:\n- zh\n- en\ntask_ids:\n- sentiment-classification\n---\n# Begin of markdown\n\nSome cool dataset card\n"

__SCREAMING_SNAKE_CASE						=      "\\n---\n---\n# Begin of markdown\n\nSome cool dataset card\n"


__SCREAMING_SNAKE_CASE						=      "\\n# Begin of markdown\n\nSome cool dataset card\n"







class     __snake_case			(    unittest.TestCase	):




		def 	_snake_case	(  self					:List[Any])					->			List[str]:






								'''simple docstring'''






								with tempfile.TemporaryDirectory() as tmp_dir:
														UpperCamelCase_     :	int					     =     Path(_SCREAMING_SNAKE_CASE) / """README.md"""
														with open(_SCREAMING_SNAKE_CASE			,    """w+""") as readme_file:
																				readme_file.write(_SCREAMING_SNAKE_CASE)
														UpperCamelCase_     :	Union[str, Any]					     =     DatasetMetadata.from_readme(_SCREAMING_SNAKE_CASE)
														self.assertDictEqual(_SCREAMING_SNAKE_CASE			,    {"""language""": ["""zh""", """en"""], """task_ids""": ["""sentiment-classification"""]})

														with open(_SCREAMING_SNAKE_CASE			,    """w+""") as readme_file:
																				readme_file.write(_SCREAMING_SNAKE_CASE)
														UpperCamelCase_     :	Any					     =     DatasetMetadata.from_readme(_SCREAMING_SNAKE_CASE)
														self.assertDictEqual(_SCREAMING_SNAKE_CASE			,    {})

														with open(_SCREAMING_SNAKE_CASE			,    """w+""") as readme_file:
																				readme_file.write(_SCREAMING_SNAKE_CASE)
														UpperCamelCase_     :	int					     =     DatasetMetadata.from_readme(_SCREAMING_SNAKE_CASE)
														self.assertEqual(_SCREAMING_SNAKE_CASE			,    {})





		def 	_snake_case	(  self					:str)					->			Tuple:






								'''simple docstring'''






								UpperCamelCase_     :	Dict					     =     _dedent(
								    """\\n            annotations_creators:\n            - found\n            language_creators:\n            - found\n            language:\n            - en\n            license:\n            - unknown\n            multilinguality:\n            - monolingual\n            pretty_name: Test Dataset\n            size_categories:\n            - 10K<n<100K\n            source_datasets:\n            - extended|other-yahoo-webscope-l6\n            task_categories:\n            - question-answering\n            task_ids:\n            - open-domain-qa\n            """)
								assert DatasetMetadata.from_yaml_string(_SCREAMING_SNAKE_CASE)

								UpperCamelCase_     :	Any					     =     _dedent(
								    """\\n            annotations_creators:\n            - found\n            language:\n            - en\n            license:\n            - unknown\n            multilinguality:\n            - monolingual\n            pretty_name: Test Dataset\n            size_categories:\n            - 10K<n<100K\n            source_datasets:\n            - extended|other-yahoo-webscope-l6\n            task_categories:\n            - question-answering\n            task_ids:\n            - open-domain-qa\n            task_ids:\n            - open-domain-qa\n            """)
								with self.assertRaises(_SCREAMING_SNAKE_CASE):
														DatasetMetadata.from_yaml_string(_SCREAMING_SNAKE_CASE)

								UpperCamelCase_     :	int					     =     _dedent(
								    """\\n            annotations_creators:\n            - found\n            language_creators:\n            - found\n            language:\n            - en\n            license:\n            - unknown\n            multilinguality:\n            - monolingual\n            pretty_name: Test Dataset\n            size_categories:\n            - 10K<n<100K\n            source_datasets:\n            - extended|other-yahoo-webscope-l6\n            task_categories:\n            - text-classification\n            task_ids:\n            - multi-class-classification\n            paperswithcode_id:\n            - squad\n            configs:\n            - en\n            train-eval-index:\n            - config: en\n              task: text-classification\n              task_id: multi_class_classification\n              splits:\n                train_split: train\n                eval_split: test\n              col_mapping:\n                text: text\n                label: target\n              metrics:\n                - type: accuracy\n                  name: Accuracy\n            extra_gated_prompt: |\n              By clicking on “Access repository” below, you also agree to ImageNet Terms of Access:\n              [RESEARCHER_FULLNAME] (the \"Researcher\") has requested permission to use the ImageNet database (the \"Database\") at Princeton University and Stanford University. In exchange for such permission, Researcher hereby agrees to the following terms and conditions:\n              1. Researcher shall use the Database only for non-commercial research and educational purposes.\n            extra_gated_fields:\n              Company: text\n              Country: text\n              I agree to use this model for non-commerical use ONLY: checkbox\n            """)
								assert DatasetMetadata.from_yaml_string(_SCREAMING_SNAKE_CASE)








'''simple docstring'''






from dataclasses import dataclass
from enum import Enum
from typing import List, Optional, Union

import numpy as np
import PIL
from PIL import Image

from ...utils import BaseOutput, is_torch_available, is_transformers_available




@dataclass
class 			a  (  _SCREAMING_SNAKE_CASE							):
 _lowerCAmelCase			       =						{
     """sld_warmup_steps""": 1_5,
     """sld_guidance_scale""": 2_0,
     """sld_threshold""": 0.0,
     """sld_momentum_scale""": 0.0,
     """sld_mom_beta""": 0.0,
 }
 _lowerCAmelCase			       =						{
     """sld_warmup_steps""": 1_0,
     """sld_guidance_scale""": 1_0_0_0,
     """sld_threshold""": 0.01,
     """sld_momentum_scale""": 0.3,
     """sld_mom_beta""": 0.4,
 }
 _lowerCAmelCase			       =						{
     """sld_warmup_steps""": 7,
     """sld_guidance_scale""": 2_0_0_0,
     """sld_threshold""": 0.025,
     """sld_momentum_scale""": 0.5,
     """sld_mom_beta""": 0.7,
 }
 _lowerCAmelCase			       =						{
     """sld_warmup_steps""": 0,
     """sld_guidance_scale""": 5_0_0_0,
     """sld_threshold""": 1.0,
     """sld_momentum_scale""": 0.5,
     """sld_mom_beta""": 0.7,
 }





@dataclass
class 			a  (  _SCREAMING_SNAKE_CASE							):
 _lowerCAmelCase			       =						42
 _lowerCAmelCase			       =						42
 _lowerCAmelCase			       =						42
 _lowerCAmelCase			       =						42


if is_transformers_available() and is_torch_available():
 from .pipeline_stable_diffusion_safe import StableDiffusionPipelineSafe
 from .safety_checker import SafeStableDiffusionSafetyChecker

