
'''simple docstring'''







from typing import List, Optional, Union

import torch

from ...models import UNetaDConditionModel, VQModel
from ...pipelines import DiffusionPipeline
from ...pipelines.pipeline_utils import ImagePipelineOutput
from ...schedulers import DDPMScheduler
from ...utils import (
    is_accelerate_available,
    is_accelerate_version,
    logging,
    randn_tensor,
    replace_example_docstring,
)


_lowercase :  Optional[int]    							=       logging.get_logger(__name__)  # pylint: disable=invalid-name

_lowercase :  str    							=       "\n    Examples:\n        ```py\n        >>> import torch\n        >>> import numpy as np\n\n        >>> from diffusers import KandinskyV22PriorPipeline, KandinskyV22ControlnetPipeline\n        >>> from transformers import pipeline\n        >>> from diffusers.utils import load_image\n\n\n        >>> def make_hint(image, depth_estimator):\n        ...     image = depth_estimator(image)[\"depth\"]\n        ...     image = np.array(image)\n        ...     image = image[:, :, None]\n        ...     image = np.concatenate([image, image, image], axis=2)\n        ...     detected_map = torch.from_numpy(image).float() / 255.0\n        ...     hint = detected_map.permute(2, 0, 1)\n        ...     return hint\n\n\n        >>> depth_estimator = pipeline(\"depth-estimation\")\n\n        >>> pipe_prior = KandinskyV22PriorPipeline.from_pretrained(\n        ...     \"kandinsky-community/kandinsky-2-2-prior\", torch_dtype=torch.float16\n        ... )\n        >>> pipe_prior = pipe_prior.to(\"cuda\")\n\n        >>> pipe = KandinskyV22ControlnetPipeline.from_pretrained(\n        ...     \"kandinsky-community/kandinsky-2-2-controlnet-depth\", torch_dtype=torch.float16\n        ... )\n        >>> pipe = pipe.to(\"cuda\")\n\n\n        >>> img = load_image(\n        ...     \"https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main\"\n        ...     \"/kandinsky/cat.png\"\n        ... ).resize((768, 768))\n\n        >>> hint = make_hint(img, depth_estimator).unsqueeze(0).half().to(\"cuda\")\n\n        >>> prompt = \"A robot, 4k photo\"\n        >>> negative_prior_prompt = \"lowres, text, error, cropped, worst quality, low quality, jpeg artifacts, ugly, duplicate, morbid, mutilated, out of frame, extra fingers, mutated hands, poorly drawn hands, poorly drawn face, mutation, deformed, blurry, dehydrated, bad anatomy, bad proportions, extra limbs, cloned face, disfigured, gross proportions, malformed limbs, missing arms, missing legs, extra arms, extra legs, fused fingers, too many fingers, long neck, username, watermark, signature\"\n\n        >>> generator = torch.Generator(device=\"cuda\").manual_seed(43)\n\n        >>> image_emb, zero_image_emb = pipe_prior(\n        ...     prompt=prompt, negative_prompt=negative_prior_prompt, generator=generator\n        ... ).to_tuple()\n\n        >>> images = pipe(\n        ...     image_embeds=image_emb,\n        ...     negative_image_embeds=zero_image_emb,\n        ...     hint=hint,\n        ...     num_inference_steps=50,\n        ...     generator=generator,\n        ...     height=768,\n        ...     width=768,\n        ... ).images\n\n        >>> images[0].save(\"robot_cat.png\")\n        ```\n"
def 					snake_case_		(				__SCREAMING_SNAKE_CASE					: Any      ,      __SCREAMING_SNAKE_CASE					: str      ,      __SCREAMING_SNAKE_CASE					: Union[str, Any]=8							):



						"""simple docstring"""





						lowercase_	:     List[Any]													=       height // scale_factor**2
						if height % scale_factor**2 != 0:
												new_height += 1
						lowercase_	:     Tuple													=       width // scale_factor**2
						if width % scale_factor**2 != 0:
												new_width += 1
						return new_height * scale_factor, new_width * scale_factor






class  lowerCAmelCase__   (  lowerCamelCase_     ):






					def __init__(   self				,							__SCREAMING_SNAKE_CASE				,							__SCREAMING_SNAKE_CASE				,							__SCREAMING_SNAKE_CASE				,							):


											"""simple docstring"""





											super().__init__()

											self.register_modules(
											    unet=__SCREAMING_SNAKE_CASE				,							scheduler=__SCREAMING_SNAKE_CASE				,							movq=__SCREAMING_SNAKE_CASE				,							)
											lowercase_	:     List[str]													=       2 ** (len(self.movq.config.block_out_channels		) - 1)






					def 	_snake_case    (   self				,							__SCREAMING_SNAKE_CASE				,							__SCREAMING_SNAKE_CASE				,							__SCREAMING_SNAKE_CASE				,							__SCREAMING_SNAKE_CASE				,							__SCREAMING_SNAKE_CASE				,							__SCREAMING_SNAKE_CASE		):


											"""simple docstring"""





											if latents is None:
																	lowercase_	:     List[Any]													=       randn_tensor(__SCREAMING_SNAKE_CASE				,							generator=__SCREAMING_SNAKE_CASE				,							device=__SCREAMING_SNAKE_CASE				,							dtype=__SCREAMING_SNAKE_CASE		)
											else:
																	if latents.shape != shape:
																							raise ValueError(F'''Unexpected latents shape, got {latents.shape}, expected {shape}'''		)
																	lowercase_	:     int													=       latents.to(__SCREAMING_SNAKE_CASE		)

											lowercase_	:     Optional[int]													=       latents * scheduler.init_noise_sigma
											return latents






					def 	_snake_case    (   self				,							__SCREAMING_SNAKE_CASE=0		):


											"""simple docstring"""





											if is_accelerate_available():
																	from accelerate import cpu_offload
											else:
																	raise ImportError('''Please install accelerate via `pip install accelerate`'''		)

											lowercase_	:     Dict													=       torch.device(F'''cuda:{gpu_id}'''		)

											lowercase_	:     Dict													=       [
											    self.unet,
											    self.movq,
											]
											for cpu_offloaded_model in models:
																	if cpu_offloaded_model is not None:
																							cpu_offload(__SCREAMING_SNAKE_CASE				,							__SCREAMING_SNAKE_CASE		)






					def 	_snake_case    (   self				,							__SCREAMING_SNAKE_CASE=0		):


											"""simple docstring"""





											if is_accelerate_available() and is_accelerate_version('''>='''				,							'''0.17.0.dev0'''		):
																	from accelerate import cpu_offload_with_hook
											else:
																	raise ImportError('''`enable_model_cpu_offload` requires `accelerate v0.17.0` or higher.'''		)

											lowercase_	:     Dict													=       torch.device(F'''cuda:{gpu_id}'''		)

											if self.device.type != "cpu":
																	self.to('''cpu'''				,							silence_dtype_warnings=__SCREAMING_SNAKE_CASE		)
																	torch.cuda.empty_cache()  # otherwise we don't see the memory savings (but they probably exist)

											lowercase_	:     List[str]													=       None
											for cpu_offloaded_model in [self.unet, self.movq]:
																	lowercase_						, lowercase_	:     List[str]													=       cpu_offload_with_hook(__SCREAMING_SNAKE_CASE				,							__SCREAMING_SNAKE_CASE				,							prev_module_hook=__SCREAMING_SNAKE_CASE		)

											# We'll offload the last model manually.
											lowercase_	:     List[str]													=       hook






					@property
					# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline._execution_device
					def 	_snake_case    (   self		):


											"""simple docstring"""





											if not hasattr(self.unet				,							'''_hf_hook'''		):
																	return self.device
											for module in self.unet.modules():
																	if (
																	    hasattr(__SCREAMING_SNAKE_CASE				,							'''_hf_hook'''		)
																	    and hasattr(module._hf_hook				,							'''execution_device'''		)
																	    and module._hf_hook.execution_device is not None
																	):
																							return torch.device(module._hf_hook.execution_device		)
											return self.device






					@torch.no_grad()
					@replace_example_docstring(__SCREAMING_SNAKE_CASE		)
					def __call__(   self				,							__SCREAMING_SNAKE_CASE				,							__SCREAMING_SNAKE_CASE				,							__SCREAMING_SNAKE_CASE				,							__SCREAMING_SNAKE_CASE = 5_12				,							__SCREAMING_SNAKE_CASE = 5_12				,							__SCREAMING_SNAKE_CASE = 1_00				,							__SCREAMING_SNAKE_CASE = 4.0				,							__SCREAMING_SNAKE_CASE = 1				,							__SCREAMING_SNAKE_CASE = None				,							__SCREAMING_SNAKE_CASE = None				,							__SCREAMING_SNAKE_CASE = "pil"				,							__SCREAMING_SNAKE_CASE = True				,							):


											"""simple docstring"""





											lowercase_	:     Optional[Any]													=       self._execution_device

											lowercase_	:     List[str]													=       guidance_scale > 1.0

											if isinstance(__SCREAMING_SNAKE_CASE				,							__SCREAMING_SNAKE_CASE		):
																	lowercase_	:     Union[str, Any]													=       torch.cat(__SCREAMING_SNAKE_CASE				,							dim=0		)
											if isinstance(__SCREAMING_SNAKE_CASE				,							__SCREAMING_SNAKE_CASE		):
																	lowercase_	:     Any													=       torch.cat(__SCREAMING_SNAKE_CASE				,							dim=0		)
											if isinstance(__SCREAMING_SNAKE_CASE				,							__SCREAMING_SNAKE_CASE		):
																	lowercase_	:     Tuple													=       torch.cat(__SCREAMING_SNAKE_CASE				,							dim=0		)

											lowercase_	:     Dict													=       image_embeds.shape[0] * num_images_per_prompt

											if do_classifier_free_guidance:
																	lowercase_	:     int													=       image_embeds.repeat_interleave(__SCREAMING_SNAKE_CASE				,							dim=0		)
																	lowercase_	:     List[str]													=       negative_image_embeds.repeat_interleave(__SCREAMING_SNAKE_CASE				,							dim=0		)
																	lowercase_	:     int													=       hint.repeat_interleave(__SCREAMING_SNAKE_CASE				,							dim=0		)

											lowercase_	:     Any													=       torch.cat([negative_image_embeds, image_embeds]				,							dim=0		).to(dtype=self.unet.dtype				,							device=__SCREAMING_SNAKE_CASE		)
											lowercase_	:     List[str]													=       torch.cat([hint, hint]				,							dim=0		).to(dtype=self.unet.dtype				,							device=__SCREAMING_SNAKE_CASE		)
											self.scheduler.set_timesteps(__SCREAMING_SNAKE_CASE				,							device=__SCREAMING_SNAKE_CASE		)
											lowercase_	:     List[str]													=       self.scheduler.timesteps

											lowercase_	:     Optional[int]													=       self.movq.config.latent_channels

											lowercase_						, lowercase_	:     List[str]													=       downscale_height_and_width(__SCREAMING_SNAKE_CASE				,							__SCREAMING_SNAKE_CASE				,							self.movq_scale_factor		)

											# create initial latent
											lowercase_	:     Dict													=       self.prepare_latents(
											    (batch_size, num_channels_latents, height, width)				,							image_embeds.dtype				,							__SCREAMING_SNAKE_CASE				,							__SCREAMING_SNAKE_CASE				,							__SCREAMING_SNAKE_CASE				,							self.scheduler				,							)

											for i, t in enumerate(self.progress_bar(__SCREAMING_SNAKE_CASE		)		):
																	# expand the latents if we are doing classifier free guidance
																	lowercase_	:     Optional[int]													=       torch.cat([latents] * 2		) if do_classifier_free_guidance else latents

																	lowercase_	:     Optional[int]													=       {'''image_embeds''': image_embeds, '''hint''': hint}
																	lowercase_	:     Dict													=       self.unet(
																	    sample=__SCREAMING_SNAKE_CASE				,							timestep=__SCREAMING_SNAKE_CASE				,							encoder_hidden_states=__SCREAMING_SNAKE_CASE				,							added_cond_kwargs=__SCREAMING_SNAKE_CASE				,							return_dict=__SCREAMING_SNAKE_CASE				,							)[0]

																	if do_classifier_free_guidance:
																							lowercase_						, lowercase_	:     Union[str, Any]													=       noise_pred.split(latents.shape[1]				,							dim=1		)
																							lowercase_						, lowercase_	:     List[Any]													=       noise_pred.chunk(2		)
																							lowercase_						, lowercase_	:     Union[str, Any]													=       variance_pred.chunk(2		)
																							lowercase_	:     List[Any]													=       noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)
																							lowercase_	:     Any													=       torch.cat([noise_pred, variance_pred_text]				,							dim=1		)

																	if not (
																	    hasattr(self.scheduler.config				,							'''variance_type'''		)
																	    and self.scheduler.config.variance_type in ["learned", "learned_range"]
																	):
																							lowercase_						, lowercase_	:     Optional[Any]													=       noise_pred.split(latents.shape[1]				,							dim=1		)

																	# compute the previous noisy sample x_t -> x_t-1
																	lowercase_	:     List[str]													=       self.scheduler.step(
																	    __SCREAMING_SNAKE_CASE				,							__SCREAMING_SNAKE_CASE				,							__SCREAMING_SNAKE_CASE				,							generator=__SCREAMING_SNAKE_CASE				,							)[0]
											# post-processing
											lowercase_	:     Tuple													=       self.movq.decode(__SCREAMING_SNAKE_CASE				,							force_not_quantize=__SCREAMING_SNAKE_CASE		)['''sample''']

											if output_type not in ["pt", "np", "pil"]:
																	raise ValueError(F'''Only the output types `pt`, `pil` and `np` are supported not output_type={output_type}'''		)

											if output_type in ["np", "pil"]:
																	lowercase_	:     List[str]													=       image * 0.5 + 0.5
																	lowercase_	:     Union[str, Any]													=       image.clamp(0				,							1		)
																	lowercase_	:     Any													=       image.cpu().permute(0				,							2				,							3				,							1		).float().numpy()

											if output_type == "pil":
																	lowercase_	:     List[Any]													=       self.numpy_to_pil(__SCREAMING_SNAKE_CASE		)

											if not return_dict:
																	return (image,)

											return ImagePipelineOutput(images=__SCREAMING_SNAKE_CASE		)





'''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							)



