

import gc
import unittest

import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer

from diffusers import (
    AutoencoderKL,
    DDIMScheduler,
    StableDiffusionAttendAndExcitePipeline,
    UNetaDConditionModel,
)
from diffusers.utils import load_numpy, skip_mps, slow
from diffusers.utils.testing_utils import require_torch_gpu

from ..pipeline_params import TEXT_TO_IMAGE_BATCH_PARAMS, TEXT_TO_IMAGE_IMAGE_PARAMS, TEXT_TO_IMAGE_PARAMS
from ..test_pipelines_common import PipelineKarrasSchedulerTesterMixin, PipelineLatentTesterMixin, PipelineTesterMixin


lowercase_       		=  False


@skip_mps
class  A						(							_UpperCAmelCase    ,							_UpperCAmelCase    ,							_UpperCAmelCase    ,							unittest.TestCase ):







    """simple docstring"""





    lowerCamelCase			=							StableDiffusionAttendAndExcitePipeline
    lowerCamelCase			=							False
    lowerCamelCase			=							TEXT_TO_IMAGE_PARAMS
    lowerCamelCase			=							TEXT_TO_IMAGE_BATCH_PARAMS.union({'token_indices'} )
    lowerCamelCase			=							TEXT_TO_IMAGE_IMAGE_PARAMS
    lowerCamelCase			=							TEXT_TO_IMAGE_IMAGE_PARAMS


    @classmethod
    def 						snake_case__     (      cls :       Any		)->    Optional[Any]:





        '''simple docstring'''







        super().setUpClass()
        torch.use_deterministic_algorithms(lowercase_		)


    @classmethod
    def 						snake_case__     (      cls :       Optional[Any]		)->    Dict:





        '''simple docstring'''







        super().tearDownClass()
        torch.use_deterministic_algorithms(lowercase_		)


    def 						snake_case__     (      self :       List[str]		)->    int:





        '''simple docstring'''







        torch.manual_seed(0		)
        A__          =   UNetaDConditionModel(
            block_out_channels=(3_2, 6_4),layers_per_block=1,sample_size=3_2,in_channels=4,out_channels=4,down_block_types=('DownBlock2D', 'CrossAttnDownBlock2D'),up_block_types=('CrossAttnUpBlock2D', 'UpBlock2D'),cross_attention_dim=3_2,attention_head_dim=(2, 4),use_linear_projection=lowercase_,)
        A__          =   DDIMScheduler(
            beta_start=0.00_085,beta_end=0.012,beta_schedule='scaled_linear',clip_sample=lowercase_,set_alpha_to_one=lowercase_,)
        torch.manual_seed(0		)
        A__          =   AutoencoderKL(
            block_out_channels=[3_2, 6_4],in_channels=3,out_channels=3,down_block_types=['DownEncoderBlock2D', 'DownEncoderBlock2D'],up_block_types=['UpDecoderBlock2D', 'UpDecoderBlock2D'],latent_channels=4,sample_size=1_2_8,)
        torch.manual_seed(0		)
        A__          =   CLIPTextConfig(
            bos_token_id=0,eos_token_id=2,hidden_size=3_2,intermediate_size=3_7,layer_norm_eps=1E-05,num_attention_heads=4,num_hidden_layers=5,pad_token_id=1,vocab_size=1_0_0_0,hidden_act='gelu',projection_dim=5_1_2,)
        A__          =   CLIPTextModel(lowercase_		)
        A__          =   CLIPTokenizer.from_pretrained('hf-internal-testing/tiny-random-clip'		)

        A__          =   {
            'unet': unet,
            'scheduler': scheduler,
            'vae': vae,
            'text_encoder': text_encoder,
            'tokenizer': tokenizer,
            'safety_checker': None,
            'feature_extractor': None,
        }

        return components


    def 						snake_case__     (      self :       Tuple,lowercase_ :       str,lowercase_ :       List[Any]=0		)->    int:





        '''simple docstring'''







        if str(lowercase_		).startswith('mps'		):
            A__          =   torch.manual_seed(lowercase_		)
        else:
            A__          =   torch.Generator(device=lowercase_		).manual_seed(lowercase_		)
        A__          =   A__          =   {
            'prompt': 'a cat and a frog',
            'token_indices': [2, 5],
            'generator': generator,
            'num_inference_steps': 1,
            'guidance_scale': 6.0,
            'output_type': 'numpy',
            'max_iter_to_alter': 2,
            'thresholds': {0: 0.7},
        }
        return inputs


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





        '''simple docstring'''







        A__          =   'cpu'

        A__          =   self.get_dummy_components()
        A__          =   self.pipeline_class(**lowercase_		)
        pipe.to(lowercase_		)
        pipe.set_progress_bar_config(disable=lowercase_		)

        A__          =   self.get_dummy_inputs(lowercase_		)
        A__          =   pipe(**lowercase_		).images
        A__          =   image[0, -3:, -3:, -1]

        self.assertEqual(image.shape,(1, 6_4, 6_4, 3)		)
        A__          =   np.array(
            [0.63_905_364, 0.62_897_307, 0.48_599_017, 0.5_133_624, 0.5_550_048, 0.45_769_516, 0.50_326_973, 0.5_023_139, 0.45_384_496]		)
        A__          =   np.abs(image_slice.flatten() - expected_slice		).max()
        self.assertLessEqual(lowercase_,1E-3		)


    def 						snake_case__     (      self :       str		)->    Optional[Any]:





        '''simple docstring'''







        super().test_cpu_offload_forward_pass(expected_max_diff=5E-4		)


    def 						snake_case__     (      self :       str		)->    int:





        '''simple docstring'''







        self._test_inference_batch_consistent(batch_sizes=[1, 2]		)


    def 						snake_case__     (      self :       str		)->    Optional[int]:





        '''simple docstring'''







        self._test_inference_batch_single_identical(batch_size=2,expected_max_diff=7E-4		)


    def 						snake_case__     (      self :       Optional[Any]		)->    int:





        '''simple docstring'''







        super().test_dict_tuple_outputs_equivalent(expected_max_difference=3E-3		)


    def 						snake_case__     (      self :       Union[str, Any]		)->    str:





        '''simple docstring'''







        super().test_pt_np_pil_outputs_equivalent(expected_max_diff=5E-4		)


    def 						snake_case__     (      self :       Dict		)->    Any:





        '''simple docstring'''







        super().test_save_load_local(expected_max_difference=5E-4		)


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





        '''simple docstring'''







        super().test_save_load_optional_components(expected_max_difference=4E-4		)




@require_torch_gpu
@slow
class  A						(							unittest.TestCase ):







    """simple docstring"""





    @classmethod
    def 						snake_case__     (      cls :       Any		)->    Optional[int]:





        '''simple docstring'''







        super().setUpClass()
        torch.use_deterministic_algorithms(lowercase_		)


    @classmethod
    def 						snake_case__     (      cls :       int		)->    List[Any]:





        '''simple docstring'''







        super().tearDownClass()
        torch.use_deterministic_algorithms(lowercase_		)


    def 						snake_case__     (      self :       List[Any]		)->    Any:





        '''simple docstring'''







        super().tearDown()
        gc.collect()
        torch.cuda.empty_cache()


    def 						snake_case__     (      self :       Union[str, Any]		)->    List[Any]:





        '''simple docstring'''







        A__          =   torch.manual_seed(5_1		)

        A__          =   StableDiffusionAttendAndExcitePipeline.from_pretrained(
            'CompVis/stable-diffusion-v1-4',safety_checker=lowercase_,torch_dtype=torch.floataa		)
        pipe.to('cuda'		)

        A__          =   'a painting of an elephant with glasses'
        A__          =   [5, 7]

        A__          =   pipe(
            prompt=lowercase_,token_indices=lowercase_,guidance_scale=7.5,generator=lowercase_,num_inference_steps=5,max_iter_to_alter=5,output_type='numpy',).images[0]

        A__          =   load_numpy(
            'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/attend-and-excite/elephant_glasses.npy'		)
        assert np.abs((expected_image - image).max()		) < 5E-1



'''simple docstring'''







import re
import tempfile
from pathlib import Path

import pytest
import yaml

from datasets.utils.readme import ReadMe


# @pytest.fixture
# def example_yaml_structure():

_lowercase :  Union[str, Any]    							=       yaml.safe_load(
    "\\nname: \"\"\nallow_empty: false\nallow_empty_text: true\nsubsections:\n  - name: \"Dataset Card for X\" # First-level markdown heading\n    allow_empty: false\n    allow_empty_text: true\n    subsections:\n      - name: \"Table of Contents\"\n        allow_empty: false\n        allow_empty_text: false\n        subsections: null\n      - name: \"Dataset Description\"\n        allow_empty: false\n        allow_empty_text: false\n        subsections:\n          - name: \"Dataset Summary\"\n            allow_empty: false\n            allow_empty_text: false\n            subsections: null\n          - name: \"Supported Tasks and Leaderboards\"\n            allow_empty: true\n            allow_empty_text: true\n            subsections: null\n          - name: Languages\n            allow_empty: false\n            allow_empty_text: true\n            subsections: null\n"
)


_lowercase :  int    							=       {
    "name": "root",
    "text": "",
    "is_empty_text": True,
    "subsections": [
        {
            "name": "Dataset Card for My Dataset",
            "text": "",
            "is_empty_text": True,
            "subsections": [
                {"name": "Table of Contents", "text": "Some text here.", "is_empty_text": False, "subsections": []},
                {
                    "name": "Dataset Description",
                    "text": "Some text here.",
                    "is_empty_text": False,
                    "subsections": [
                        {
                            "name": "Dataset Summary",
                            "text": "Some text here.",
                            "is_empty_text": False,
                            "subsections": [],
                        },
                        {
                            "name": "Supported Tasks and Leaderboards",
                            "text": "",
                            "is_empty_text": True,
                            "subsections": [],
                        },
                        {"name": "Languages", "text": "Language Text", "is_empty_text": False, "subsections": []},
                    ],
                },
            ],
        }
    ],
}


_lowercase :  Optional[Any]    							=       "\\n---\nlanguage:\n- zh\n- en\n---\n\n# Dataset Card for My Dataset\n## Table of Contents\nSome text here.\n## Dataset Description\nSome text here.\n### Dataset Summary\nSome text here.\n### Supported Tasks and Leaderboards\n### Languages\nLanguage Text\n"


_lowercase :  Union[str, Any]    							=       "\\n---\nlanguage:\n- zh\n- en\n---\n\n# Dataset Card for My Dataset\n## Table of Contents\nSome text here.\n## Dataset Description\nSome text here.\n### Dataset Summary\nSome text here.\n#### Extra Ignored Subsection\n### Supported Tasks and Leaderboards\n### Languages\nLanguage Text\n"

_lowercase :  Any    							=       {
    "name": "root",
    "text": "",
    "is_empty_text": True,
    "subsections": [
        {
            "name": "Dataset Card for My Dataset",
            "text": "",
            "is_empty_text": True,
            "subsections": [
                {"name": "Table of Contents", "text": "Some text here.", "is_empty_text": False, "subsections": []},
                {
                    "name": "Dataset Description",
                    "text": "Some text here.",
                    "is_empty_text": False,
                    "subsections": [
                        {
                            "name": "Dataset Summary",
                            "text": "Some text here.",
                            "is_empty_text": False,
                            "subsections": [
                                {
                                    "name": "Extra Ignored Subsection",
                                    "text": "",
                                    "is_empty_text": True,
                                    "subsections": [],
                                }
                            ],
                        },
                        {
                            "name": "Supported Tasks and Leaderboards",
                            "text": "",
                            "is_empty_text": True,
                            "subsections": [],
                        },
                        {"name": "Languages", "text": "Language Text", "is_empty_text": False, "subsections": []},
                    ],
                },
            ],
        }
    ],
}

_lowercase :  str    							=       "\\n---\n---\n# Dataset Card for My Dataset\n## Table of Contents\nSome text here.\n## Dataset Description\nSome text here.\n### Dataset Summary\nSome text here.\n### Supported Tasks and Leaderboards\n### Languages\nLanguage Text\n"

_lowercase :  List[str]    							=       (
    "The following issues were found for the README at `{path}`:\n-\tEmpty YAML markers are present in the README."
)

_lowercase :  Tuple    							=       "\\n# Dataset Card for My Dataset\n## Table of Contents\nSome text here.\n## Dataset Description\nSome text here.\n### Dataset Summary\nSome text here.\n### Supported Tasks and Leaderboards\n### Languages\nLanguage Text\n"

_lowercase :  Optional[Any]    							=       (
    "The following issues were found for the README at `{path}`:\n-\tNo YAML markers are present in the README."
)

_lowercase :  Tuple    							=       "\\n---\n# Dataset Card for My Dataset\n## Table of Contents\nSome text here.\n## Dataset Description\nSome text here.\n### Dataset Summary\nSome text here.\n### Supported Tasks and Leaderboards\n### Languages\nLanguage Text\n"

_lowercase :  Optional[int]    							=       "The following issues were found for the README at `{path}`:\n-\tOnly the start of YAML tags present in the README."

_lowercase :  List[Any]    							=       "\\n---\nlanguage:\n- zh\n- en\n---\n\n# Dataset Card for My Dataset\n## Table of Contents\nSome text here.\n## Dataset Description\nSome text here.\n### Dataset Summary\n### Supported Tasks and Leaderboards\n### Languages\nLanguage Text\n"
_lowercase :  Optional[Any]    							=       "The following issues were found for the README at `{path}`:\n-\tExpected some content in section `Dataset Summary` but it is empty.\n-\tExpected some text in section `Dataset Summary` but it is empty (text in subsections are ignored)."


_lowercase :  Optional[int]    							=       "\\n---\nlanguage:\n- zh\n- en\n---\n\n# Dataset Card for My Dataset\n"
_lowercase :  Union[str, Any]    							=       "The following issues were found for the README at `{path}`:\n-\tExpected some content in section `Dataset Card for My Dataset` but it is empty.\n-\tSection `Dataset Card for My Dataset` expected the following subsections: `Table of Contents`, `Dataset Description`. Found 'None'."

_lowercase :  Union[str, Any]    							=       "\\n---\nlanguage:\n- zh\n- en\n---\n\n# Dataset Card for My Dataset\n## Table of Contents\nSome text here.\n## Dataset Description\nSome text here.\n### Dataset Summary\nSome text here.\n### Languages\nLanguage Text\n"

_lowercase :  int    							=       "The following issues were found for the README at `{path}`:\n-\tSection `Dataset Description` is missing subsection: `Supported Tasks and Leaderboards`."


_lowercase :  List[Any]    							=       "\\n---\nlanguage:\n- zh\n- en\n---\n\n# Dataset Card for My Dataset\n## Table of Contents\nSome text here.\n## Dataset Description\nSome text here.\n### Dataset Summary\nSome text here.\n### Supported Tasks and Leaderboards\n### Languages\n"

_lowercase :  int    							=       "The following issues were found for the README at `{path}`:\n-\tExpected some content in section `Languages` but it is empty."

_lowercase :  List[str]    							=       "\\n---\nlanguage:\n- zh\n- en\n---\n\n## Table of Contents\nSome text here.\n## Dataset Description\nSome text here.\n### Dataset Summary\nSome text here.\n### Supported Tasks and Leaderboards\n### Languages\nLanguage Text\n"
_lowercase :  str    							=       "The following issues were found for the README at `{path}`:\n-\tThe README has no first-level headings. One heading is expected. Skipping further validation for this README."

_lowercase :  Dict    							=       "\\n---\nlanguage:\n- zh\n- en\n---\n\n# Dataset Card for My Dataset\n## Table of Contents\nSome text here.\n## Dataset Description\nSome text here.\n### Dataset Summary\nSome text here.\n### Supported Tasks and Leaderboards\n### Languages\nLanguage Text\n# Dataset Card My Dataset\n"

_lowercase :  List[str]    							=       "The following issues were found for the README at `{path}`:\n-\tThe README has several first-level headings: `Dataset Card for My Dataset`, `Dataset Card My Dataset`. Only one heading is expected. Skipping further validation for this README."

_lowercase :  str    							=       "\\n---\nlanguage:\n- zh\n- en\n---\n\n# Dataset Card My Dataset\n## Table of Contents\nSome text here.\n## Dataset Description\nSome text here.\n### Dataset Summary\nSome text here.\n### Supported Tasks and Leaderboards\n### Languages\nLanguage Text\n"

_lowercase :  Union[str, Any]    							=       "The following issues were found for the README at `{path}`:\n-\tNo first-level heading starting with `Dataset Card for` found in README. Skipping further validation for this README."

_lowercase :  List[Any]    							=       ""

_lowercase :  Optional[Any]    							=       "The following issues were found for the README at `{path}`:\n-\tThe README has no first-level headings. One heading is expected. Skipping further validation for this README.\n-\tNo YAML markers are present in the README."

_lowercase :  List[Any]    							=       "\\n---\nlanguage:\n- zh\n- en\n---\n\n# Dataset Card for My Dataset\n# Dataset Card for My Dataset\n## Table of Contents\nSome text here.\n## Dataset Description\nSome text here.\n### Dataset Summary\nSome text here.\n### Supported Tasks and Leaderboards\n### Languages\nLanguage Text\n"

_lowercase :  Optional[Any]    							=       "The following issues were found while parsing the README at `{path}`:\n-\tMultiple sections with the same heading `Dataset Card for My Dataset` have been found. Please keep only one of these sections."
@pytest.mark.parametrize(
    '''readme_md, expected_dict'''      ,      [
        (README_CORRECT, CORRECT_DICT),
        (README_CORRECT_FOUR_LEVEL, CORRECT_DICT_FOUR_LEVEL),
    ]      ,      )
def 					snake_case_		(				__SCREAMING_SNAKE_CASE					: int      ,      __SCREAMING_SNAKE_CASE					: Optional[Any]							):



						"""simple docstring"""





						assert ReadMe.from_string(__SCREAMING_SNAKE_CASE      ,      __SCREAMING_SNAKE_CASE							).to_dict() == expected_dict
@pytest.mark.parametrize(
    '''readme_md, expected_error'''      ,      [
        (README_NO_YAML, EXPECTED_ERROR_README_NO_YAML),
        (README_EMPTY_YAML, EXPECTED_ERROR_README_EMPTY_YAML),
        (README_INCORRECT_YAML, EXPECTED_ERROR_README_INCORRECT_YAML),
        (README_EMPTY, EXPECTED_ERROR_README_EMPTY),
        (README_NONE_SUBSECTION, EXPECTED_ERROR_README_NONE_SUBSECTION),
        (README_MISSING_FIRST_LEVEL, EXPECTED_ERROR_README_MISSING_FIRST_LEVEL),
        (README_MISSING_SUBSECTION, EXPECTED_ERROR_README_MISSING_SUBSECTION),
        (README_MISSING_TEXT, EXPECTED_ERROR_README_MISSING_TEXT),
        (README_WRONG_FIRST_LEVEL, EXPECTED_ERROR_README_WRONG_FIRST_LEVEL),
        (README_MULTIPLE_WRONG_FIRST_LEVEL, EXPECTED_ERROR_README_MULTIPLE_WRONG_FIRST_LEVEL),
        (README_MISSING_CONTENT, EXPECTED_ERROR_README_MISSING_CONTENT),
    ]      ,      )
def 					snake_case_		(				__SCREAMING_SNAKE_CASE					: Optional[Any]      ,      __SCREAMING_SNAKE_CASE					: Dict							):



						"""simple docstring"""





						with pytest.raises(__SCREAMING_SNAKE_CASE      ,      match=re.escape(expected_error.format(path='''root'''							)							)							):
												lowercase_	:     Optional[int]													=       ReadMe.from_string(__SCREAMING_SNAKE_CASE      ,      __SCREAMING_SNAKE_CASE							)
												readme.validate()
@pytest.mark.parametrize(
    '''readme_md, expected_error'''      ,      [
        (README_MULTIPLE_SAME_HEADING_1, EXPECTED_ERROR_README_MULTIPLE_SAME_HEADING_1),
    ]      ,      )
def 					snake_case_		(				__SCREAMING_SNAKE_CASE					: Union[str, Any]      ,      __SCREAMING_SNAKE_CASE					: Dict							):



						"""simple docstring"""





						with pytest.raises(__SCREAMING_SNAKE_CASE      ,      match=re.escape(expected_error.format(path='''root'''							)							)							):
												ReadMe.from_string(__SCREAMING_SNAKE_CASE      ,      __SCREAMING_SNAKE_CASE							)
@pytest.mark.parametrize(
    '''readme_md,'''      ,      [
        (README_MULTIPLE_SAME_HEADING_1),
    ]      ,      )
def 					snake_case_		(				__SCREAMING_SNAKE_CASE					: Any							):



						"""simple docstring"""





						ReadMe.from_string(__SCREAMING_SNAKE_CASE      ,      __SCREAMING_SNAKE_CASE      ,      suppress_parsing_errors=__SCREAMING_SNAKE_CASE							)
@pytest.mark.parametrize(
    '''readme_md, expected_dict'''      ,      [
        (README_CORRECT, CORRECT_DICT),
        (README_CORRECT_FOUR_LEVEL, CORRECT_DICT_FOUR_LEVEL),
    ]      ,      )
def 					snake_case_		(				__SCREAMING_SNAKE_CASE					: List[Any]      ,      __SCREAMING_SNAKE_CASE					: str							):



						"""simple docstring"""





						with tempfile.TemporaryDirectory() as tmp_dir:
												lowercase_	:     Optional[int]													=       Path(__SCREAMING_SNAKE_CASE							) / '''README.md'''
												with open(__SCREAMING_SNAKE_CASE      ,      '''w+'''							) as readme_file:
																		readme_file.write(__SCREAMING_SNAKE_CASE							)
												lowercase_	:     Any													=       ReadMe.from_readme(__SCREAMING_SNAKE_CASE      ,      __SCREAMING_SNAKE_CASE							).to_dict()
												assert out["name"] == path
												assert out["text"] == ""
												assert out["is_empty_text"]
												assert out["subsections"] == expected_dict["subsections"]
@pytest.mark.parametrize(
    '''readme_md, expected_error'''      ,      [
        (README_NO_YAML, EXPECTED_ERROR_README_NO_YAML),
        (README_EMPTY_YAML, EXPECTED_ERROR_README_EMPTY_YAML),
        (README_INCORRECT_YAML, EXPECTED_ERROR_README_INCORRECT_YAML),
        (README_EMPTY, EXPECTED_ERROR_README_EMPTY),
        (README_NONE_SUBSECTION, EXPECTED_ERROR_README_NONE_SUBSECTION),
        (README_MISSING_FIRST_LEVEL, EXPECTED_ERROR_README_MISSING_FIRST_LEVEL),
        (README_MISSING_SUBSECTION, EXPECTED_ERROR_README_MISSING_SUBSECTION),
        (README_MISSING_TEXT, EXPECTED_ERROR_README_MISSING_TEXT),
        (README_WRONG_FIRST_LEVEL, EXPECTED_ERROR_README_WRONG_FIRST_LEVEL),
        (README_MULTIPLE_WRONG_FIRST_LEVEL, EXPECTED_ERROR_README_MULTIPLE_WRONG_FIRST_LEVEL),
        (README_MISSING_CONTENT, EXPECTED_ERROR_README_MISSING_CONTENT),
    ]      ,      )
def 					snake_case_		(				__SCREAMING_SNAKE_CASE					: List[str]      ,      __SCREAMING_SNAKE_CASE					: Union[str, Any]							):



						"""simple docstring"""





						with tempfile.TemporaryDirectory() as tmp_dir:
												lowercase_	:     str													=       Path(__SCREAMING_SNAKE_CASE							) / '''README.md'''
												with open(__SCREAMING_SNAKE_CASE      ,      '''w+'''							) as readme_file:
																		readme_file.write(__SCREAMING_SNAKE_CASE							)
												lowercase_	:     List[str]													=       expected_error.format(path=__SCREAMING_SNAKE_CASE							)
												with pytest.raises(__SCREAMING_SNAKE_CASE      ,      match=re.escape(__SCREAMING_SNAKE_CASE							)							):
																		lowercase_	:     int													=       ReadMe.from_readme(__SCREAMING_SNAKE_CASE      ,      __SCREAMING_SNAKE_CASE							)
																		readme.validate()
@pytest.mark.parametrize(
    '''readme_md, expected_error'''      ,      [
        (README_MULTIPLE_SAME_HEADING_1, EXPECTED_ERROR_README_MULTIPLE_SAME_HEADING_1),
    ]      ,      )
def 					snake_case_		(				__SCREAMING_SNAKE_CASE					: Tuple      ,      __SCREAMING_SNAKE_CASE					: Optional[Any]							):



						"""simple docstring"""





						with tempfile.TemporaryDirectory() as tmp_dir:
												lowercase_	:     Dict													=       Path(__SCREAMING_SNAKE_CASE							) / '''README.md'''
												with open(__SCREAMING_SNAKE_CASE      ,      '''w+'''							) as readme_file:
																		readme_file.write(__SCREAMING_SNAKE_CASE							)
												lowercase_	:     Tuple													=       expected_error.format(path=__SCREAMING_SNAKE_CASE							)
												with pytest.raises(__SCREAMING_SNAKE_CASE      ,      match=re.escape(__SCREAMING_SNAKE_CASE							)							):
																		ReadMe.from_readme(__SCREAMING_SNAKE_CASE      ,      __SCREAMING_SNAKE_CASE							)




@pytest.mark.parametrize(
    '''readme_md,'''      ,      [
        (README_MULTIPLE_SAME_HEADING_1),
    ]      ,      )
def 					snake_case_		(				__SCREAMING_SNAKE_CASE					: Tuple							):



						"""simple docstring"""





						with tempfile.TemporaryDirectory() as tmp_dir:
												lowercase_	:     Optional[int]													=       Path(__SCREAMING_SNAKE_CASE							) / '''README.md'''
												with open(__SCREAMING_SNAKE_CASE      ,      '''w+'''							) as readme_file:
																		readme_file.write(__SCREAMING_SNAKE_CASE							)
												ReadMe.from_readme(__SCREAMING_SNAKE_CASE      ,      __SCREAMING_SNAKE_CASE      ,      suppress_parsing_errors=__SCREAMING_SNAKE_CASE							)



