
'''simple docstring'''







import os
import tempfile
import unittest

import numpy as np

from diffusers.utils import is_flax_available
from diffusers.utils.testing_utils import require_flax, slow


if is_flax_available():
							import jax
							import jax.numpy as jnp
							from flax.jax_utils import replicate
							from flax.training.common_utils import shard

							from diffusers import FlaxDDIMScheduler, FlaxDiffusionPipeline, FlaxStableDiffusionPipeline






@require_flax
class  lowerCAmelCase__   (  unittest.TestCase     ):






					def 	_snake_case    (   self		):


											"""simple docstring"""





											with tempfile.TemporaryDirectory() as tmpdirname:
																	# pipeline has Flax weights
																	lowercase_	:     str													=       FlaxDiffusionPipeline.from_pretrained(
																	    '''hf-internal-testing/tiny-stable-diffusion-pipe'''				,							safety_checker=__SCREAMING_SNAKE_CASE				,							cache_dir=__SCREAMING_SNAKE_CASE		)

																	lowercase_	:     int													=       [t[-1] for t in os.walk(os.path.join(__SCREAMING_SNAKE_CASE				,							os.listdir(__SCREAMING_SNAKE_CASE		)[0]				,							'''snapshots'''		)		)]
																	lowercase_	:     List[str]													=       [item for sublist in all_root_files for item in sublist]

																	# None of the downloaded files should be a PyTorch file even if we have some here:
																	# https://huggingface.co/hf-internal-testing/tiny-stable-diffusion-pipe/blob/main/unet/diffusion_pytorch_model.bin
																	assert not any(f.endswith('''.bin'''		) for f in files		)







@slow
@require_flax
class  lowerCAmelCase__   (  unittest.TestCase     ):






					def 	_snake_case    (   self		):


											"""simple docstring"""





											lowercase_						, lowercase_	:     Optional[int]													=       FlaxStableDiffusionPipeline.from_pretrained(
											    '''hf-internal-testing/tiny-stable-diffusion-pipe'''				,							safety_checker=__SCREAMING_SNAKE_CASE		)

											lowercase_	:     Any													=       (
											    '''A cinematic film still of Morgan Freeman starring as Jimi Hendrix, portrait, 40mm lens, shallow depth of'''
											    ''' field, close up, split lighting, cinematic'''
											)

											lowercase_	:     Tuple													=       jax.random.PRNGKey(0		)
											lowercase_	:     Any													=       4

											lowercase_	:     Union[str, Any]													=       jax.device_count()
											lowercase_	:     Dict													=       num_samples * [prompt]
											lowercase_	:     Tuple													=       pipeline.prepare_inputs(__SCREAMING_SNAKE_CASE		)

											# shard inputs and rng
											lowercase_	:     Optional[Any]													=       replicate(__SCREAMING_SNAKE_CASE		)
											lowercase_	:     List[Any]													=       jax.random.split(__SCREAMING_SNAKE_CASE				,							__SCREAMING_SNAKE_CASE		)
											lowercase_	:     Union[str, Any]													=       shard(__SCREAMING_SNAKE_CASE		)

											lowercase_	:     Any													=       pipeline(__SCREAMING_SNAKE_CASE				,							__SCREAMING_SNAKE_CASE				,							__SCREAMING_SNAKE_CASE				,							__SCREAMING_SNAKE_CASE				,							jit=__SCREAMING_SNAKE_CASE		).images

											assert images.shape == (num_samples, 1, 64, 64, 3)
											if jax.device_count() == 8:
																	assert np.abs(np.abs(images[0, 0, :2, :2, -2:]				,							dtype=np.floataa		).sum() - 4.1_514_745		) < 1E-3
																	assert np.abs(np.abs(__SCREAMING_SNAKE_CASE				,							dtype=np.floataa		).sum() - 49_947.875		) < 5E-1

											lowercase_	:     List[Any]													=       pipeline.numpy_to_pil(np.asarray(images.reshape((num_samples,) + images.shape[-3:]		)		)		)
											assert len(__SCREAMING_SNAKE_CASE		) == num_samples






					def 	_snake_case    (   self		):


											"""simple docstring"""





											lowercase_						, lowercase_	:     int													=       FlaxStableDiffusionPipeline.from_pretrained(
											    '''CompVis/stable-diffusion-v1-4'''				,							revision='''flax'''				,							safety_checker=__SCREAMING_SNAKE_CASE		)

											lowercase_	:     List[str]													=       (
											    '''A cinematic film still of Morgan Freeman starring as Jimi Hendrix, portrait, 40mm lens, shallow depth of'''
											    ''' field, close up, split lighting, cinematic'''
											)

											lowercase_	:     Any													=       jax.random.PRNGKey(0		)
											lowercase_	:     List[str]													=       50

											lowercase_	:     Any													=       jax.device_count()
											lowercase_	:     Tuple													=       num_samples * [prompt]
											lowercase_	:     str													=       pipeline.prepare_inputs(__SCREAMING_SNAKE_CASE		)

											# shard inputs and rng
											lowercase_	:     Optional[Any]													=       replicate(__SCREAMING_SNAKE_CASE		)
											lowercase_	:     Dict													=       jax.random.split(__SCREAMING_SNAKE_CASE				,							__SCREAMING_SNAKE_CASE		)
											lowercase_	:     Dict													=       shard(__SCREAMING_SNAKE_CASE		)

											lowercase_	:     Optional[Any]													=       pipeline(__SCREAMING_SNAKE_CASE				,							__SCREAMING_SNAKE_CASE				,							__SCREAMING_SNAKE_CASE				,							__SCREAMING_SNAKE_CASE				,							jit=__SCREAMING_SNAKE_CASE		).images

											assert images.shape == (num_samples, 1, 5_12, 5_12, 3)
											if jax.device_count() == 8:
																	assert np.abs((np.abs(images[0, 0, :2, :2, -2:]				,							dtype=np.floataa		).sum() - 0.05_652_401)		) < 1E-3
																	assert np.abs((np.abs(__SCREAMING_SNAKE_CASE				,							dtype=np.floataa		).sum() - 2_383_808.2)		) < 5E-1






					def 	_snake_case    (   self		):


											"""simple docstring"""





											lowercase_						, lowercase_	:     Union[str, Any]													=       FlaxStableDiffusionPipeline.from_pretrained(
											    '''CompVis/stable-diffusion-v1-4'''				,							revision='''bf16'''				,							dtype=jnp.bfloataa				,							safety_checker=__SCREAMING_SNAKE_CASE		)

											lowercase_	:     List[Any]													=       (
											    '''A cinematic film still of Morgan Freeman starring as Jimi Hendrix, portrait, 40mm lens, shallow depth of'''
											    ''' field, close up, split lighting, cinematic'''
											)

											lowercase_	:     Optional[int]													=       jax.random.PRNGKey(0		)
											lowercase_	:     str													=       50

											lowercase_	:     int													=       jax.device_count()
											lowercase_	:     str													=       num_samples * [prompt]
											lowercase_	:     List[str]													=       pipeline.prepare_inputs(__SCREAMING_SNAKE_CASE		)

											# shard inputs and rng
											lowercase_	:     int													=       replicate(__SCREAMING_SNAKE_CASE		)
											lowercase_	:     Any													=       jax.random.split(__SCREAMING_SNAKE_CASE				,							__SCREAMING_SNAKE_CASE		)
											lowercase_	:     int													=       shard(__SCREAMING_SNAKE_CASE		)

											lowercase_	:     str													=       pipeline(__SCREAMING_SNAKE_CASE				,							__SCREAMING_SNAKE_CASE				,							__SCREAMING_SNAKE_CASE				,							__SCREAMING_SNAKE_CASE				,							jit=__SCREAMING_SNAKE_CASE		).images

											assert images.shape == (num_samples, 1, 5_12, 5_12, 3)
											if jax.device_count() == 8:
																	assert np.abs((np.abs(images[0, 0, :2, :2, -2:]				,							dtype=np.floataa		).sum() - 0.04_003_906)		) < 1E-3
																	assert np.abs((np.abs(__SCREAMING_SNAKE_CASE				,							dtype=np.floataa		).sum() - 2_373_516.75)		) < 5E-1






					def 	_snake_case    (   self		):


											"""simple docstring"""





											lowercase_						, lowercase_	:     List[str]													=       FlaxStableDiffusionPipeline.from_pretrained(
											    '''CompVis/stable-diffusion-v1-4'''				,							revision='''bf16'''				,							dtype=jnp.bfloataa		)

											lowercase_	:     str													=       (
											    '''A cinematic film still of Morgan Freeman starring as Jimi Hendrix, portrait, 40mm lens, shallow depth of'''
											    ''' field, close up, split lighting, cinematic'''
											)

											lowercase_	:     int													=       jax.random.PRNGKey(0		)
											lowercase_	:     List[Any]													=       50

											lowercase_	:     Tuple													=       jax.device_count()
											lowercase_	:     Any													=       num_samples * [prompt]
											lowercase_	:     int													=       pipeline.prepare_inputs(__SCREAMING_SNAKE_CASE		)

											# shard inputs and rng
											lowercase_	:     Dict													=       replicate(__SCREAMING_SNAKE_CASE		)
											lowercase_	:     Optional[int]													=       jax.random.split(__SCREAMING_SNAKE_CASE				,							__SCREAMING_SNAKE_CASE		)
											lowercase_	:     Union[str, Any]													=       shard(__SCREAMING_SNAKE_CASE		)

											lowercase_	:     int													=       pipeline(__SCREAMING_SNAKE_CASE				,							__SCREAMING_SNAKE_CASE				,							__SCREAMING_SNAKE_CASE				,							__SCREAMING_SNAKE_CASE				,							jit=__SCREAMING_SNAKE_CASE		).images

											assert images.shape == (num_samples, 1, 5_12, 5_12, 3)
											if jax.device_count() == 8:
																	assert np.abs((np.abs(images[0, 0, :2, :2, -2:]				,							dtype=np.floataa		).sum() - 0.04_003_906)		) < 1E-3
																	assert np.abs((np.abs(__SCREAMING_SNAKE_CASE				,							dtype=np.floataa		).sum() - 2_373_516.75)		) < 5E-1






					def 	_snake_case    (   self		):


											"""simple docstring"""





											lowercase_	:     Union[str, Any]													=       FlaxDDIMScheduler(
											    beta_start=0.00_085				,							beta_end=0.012				,							beta_schedule='''scaled_linear'''				,							set_alpha_to_one=__SCREAMING_SNAKE_CASE				,							steps_offset=1				,							)

											lowercase_						, lowercase_	:     str													=       FlaxStableDiffusionPipeline.from_pretrained(
											    '''CompVis/stable-diffusion-v1-4'''				,							revision='''bf16'''				,							dtype=jnp.bfloataa				,							scheduler=__SCREAMING_SNAKE_CASE				,							safety_checker=__SCREAMING_SNAKE_CASE				,							)
											lowercase_	:     str													=       scheduler.create_state()

											lowercase_	:     Optional[int]													=       scheduler_state

											lowercase_	:     Optional[Any]													=       (
											    '''A cinematic film still of Morgan Freeman starring as Jimi Hendrix, portrait, 40mm lens, shallow depth of'''
											    ''' field, close up, split lighting, cinematic'''
											)

											lowercase_	:     Tuple													=       jax.random.PRNGKey(0		)
											lowercase_	:     int													=       50

											lowercase_	:     str													=       jax.device_count()
											lowercase_	:     List[Any]													=       num_samples * [prompt]
											lowercase_	:     Tuple													=       pipeline.prepare_inputs(__SCREAMING_SNAKE_CASE		)

											# shard inputs and rng
											lowercase_	:     Tuple													=       replicate(__SCREAMING_SNAKE_CASE		)
											lowercase_	:     List[Any]													=       jax.random.split(__SCREAMING_SNAKE_CASE				,							__SCREAMING_SNAKE_CASE		)
											lowercase_	:     List[str]													=       shard(__SCREAMING_SNAKE_CASE		)

											lowercase_	:     int													=       pipeline(__SCREAMING_SNAKE_CASE				,							__SCREAMING_SNAKE_CASE				,							__SCREAMING_SNAKE_CASE				,							__SCREAMING_SNAKE_CASE				,							jit=__SCREAMING_SNAKE_CASE		).images

											assert images.shape == (num_samples, 1, 5_12, 5_12, 3)
											if jax.device_count() == 8:
																	assert np.abs((np.abs(images[0, 0, :2, :2, -2:]				,							dtype=np.floataa		).sum() - 0.045_043_945)		) < 1E-3
																	assert np.abs((np.abs(__SCREAMING_SNAKE_CASE				,							dtype=np.floataa		).sum() - 2_347_693.5)		) < 5E-1






					def 	_snake_case    (   self		):


											"""simple docstring"""





											lowercase_	:     Optional[int]													=       (
											    '''A cinematic film still of Morgan Freeman starring as Jimi Hendrix, portrait, 40mm lens, shallow depth of'''
											    ''' field, close up, split lighting, cinematic'''
											)

											lowercase_	:     List[Any]													=       jax.device_count()
											lowercase_	:     List[Any]													=       num_samples * [prompt]
											lowercase_	:     Optional[Any]													=       jax.random.split(jax.random.PRNGKey(0		)				,							__SCREAMING_SNAKE_CASE		)

											lowercase_						, lowercase_	:     Dict													=       FlaxStableDiffusionPipeline.from_pretrained(
											    '''CompVis/stable-diffusion-v1-4'''				,							revision='''bf16'''				,							dtype=jnp.bfloataa				,							safety_checker=__SCREAMING_SNAKE_CASE				,							)

											lowercase_	:     List[str]													=       replicate(__SCREAMING_SNAKE_CASE		)
											lowercase_	:     str													=       pipeline.prepare_inputs(__SCREAMING_SNAKE_CASE		)
											lowercase_	:     Tuple													=       shard(__SCREAMING_SNAKE_CASE		)
											lowercase_	:     Union[str, Any]													=       pipeline(__SCREAMING_SNAKE_CASE				,							__SCREAMING_SNAKE_CASE				,							__SCREAMING_SNAKE_CASE				,							jit=__SCREAMING_SNAKE_CASE		).images
											assert images.shape == (num_samples, 1, 5_12, 5_12, 3)
											lowercase_	:     Union[str, Any]													=       images[2, 0, 2_56, 10:17, 1]

											# With memory efficient attention
											lowercase_						, lowercase_	:     int													=       FlaxStableDiffusionPipeline.from_pretrained(
											    '''CompVis/stable-diffusion-v1-4'''				,							revision='''bf16'''				,							dtype=jnp.bfloataa				,							safety_checker=__SCREAMING_SNAKE_CASE				,							use_memory_efficient_attention=__SCREAMING_SNAKE_CASE				,							)

											lowercase_	:     Optional[Any]													=       replicate(__SCREAMING_SNAKE_CASE		)
											lowercase_	:     List[str]													=       pipeline.prepare_inputs(__SCREAMING_SNAKE_CASE		)
											lowercase_	:     Dict													=       shard(__SCREAMING_SNAKE_CASE		)
											lowercase_	:     List[str]													=       pipeline(__SCREAMING_SNAKE_CASE				,							__SCREAMING_SNAKE_CASE				,							__SCREAMING_SNAKE_CASE				,							jit=__SCREAMING_SNAKE_CASE		).images
											assert images_eff.shape == (num_samples, 1, 5_12, 5_12, 3)
											lowercase_	:     Tuple													=       images[2, 0, 2_56, 10:17, 1]

											# I checked the results visually and they are very similar. However, I saw that the max diff is `1` and the `sum`
											# over the 8 images is exactly `256`, which is very suspicious. Testing a random slice for now.
											assert abs(slice_eff - slice		).max() < 1E-2





'''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							)



