

from typing import Dict, List, Optional, Union

import numpy as np

from .feature_extraction_utils import BatchFeature, FeatureExtractionMixin
from .utils import PaddingStrategy, TensorType, is_tf_tensor, is_torch_tensor, logging, to_numpy


a		=logging.get_logger(__name__)






class        A_		(       SCREAMING_SNAKE_CASE				):
							def __init__(		self		:			int   ,SCREAMING_SNAKE_CASE__		:			int   ,SCREAMING_SNAKE_CASE__		:			int   ,SCREAMING_SNAKE_CASE__		:			float   ,**SCREAMING_SNAKE_CASE__		:			Dict):
								__lowerCamelCase				:				List[str]					       = feature_size
								__lowerCamelCase				:				Optional[int]					       = sampling_rate
								__lowerCamelCase				:				Dict					       = padding_value

								__lowerCamelCase				:				Any					       = kwargs.pop('padding_side'   ,'right')
								__lowerCamelCase				:				int					       = kwargs.pop('return_attention_mask'   ,SCREAMING_SNAKE_CASE__)

								super().__init__(**SCREAMING_SNAKE_CASE__)
							def     lowerCAmelCase     (		self		:			int   ,SCREAMING_SNAKE_CASE__		:			Union[
							        BatchFeature,
							        List[BatchFeature],
							        Dict[str, BatchFeature],
							        Dict[str, List[BatchFeature]],
							        List[Dict[str, BatchFeature]],
							    ]   ,SCREAMING_SNAKE_CASE__		:			Union[bool, str, PaddingStrategy] = True   ,SCREAMING_SNAKE_CASE__		:			Optional[int] = None   ,SCREAMING_SNAKE_CASE__		:			bool = False   ,SCREAMING_SNAKE_CASE__		:			Optional[int] = None   ,SCREAMING_SNAKE_CASE__		:			Optional[bool] = None   ,SCREAMING_SNAKE_CASE__		:			Optional[Union[str, TensorType]] = None   ,):
								# If we have a list of dicts, let's convert it in a dict of lists
								# We do this to allow using this method as a collate_fn function in PyTorch Dataloader
								if isinstance(SCREAMING_SNAKE_CASE__   ,(list, tuple)) and isinstance(processed_features[0]   ,(dict, BatchFeature)):
									__lowerCamelCase				:				Tuple					       = {
									    key: [example[key] for example in processed_features] for key in processed_features[0].keys()
									}

								# The model's main input name, usually `input_values`, has be passed for padding
								if self.model_input_names[0] not in processed_features:
									raise ValueError(
									    'You should supply an instance of `transformers.BatchFeature` or list of `transformers.BatchFeature`'
									    F" to this method that includes {self.model_input_names[0]}, but you provided"
									    F" {list(processed_features.keys())}")

								__lowerCamelCase				:				str					       = processed_features[self.model_input_names[0]]
								__lowerCamelCase				:				Any					       = (
								    return_attention_mask if return_attention_mask is not None else self.return_attention_mask
								)

								if len(SCREAMING_SNAKE_CASE__) == 0:
									if return_attention_mask:
										__lowerCamelCase				:				List[str]					       = []
									return processed_features

								# If we have PyTorch/TF tensors or lists as inputs, we cast them as Numpy arrays
								# and rebuild them afterwards if no return_tensors is specified
								# Note that we lose the specific device the tensor may be on for PyTorch

								__lowerCamelCase				:				Optional[Any]					       = required_input[0]
								if isinstance(SCREAMING_SNAKE_CASE__   ,(list, tuple)):
									# first_element might be an empty list/tuple in some edge cases so we grab the first non empty element.
									__lowerCamelCase				:				List[str]					       = 0
									while len(required_input[index]) == 0:
										index += 1
									if index < len(SCREAMING_SNAKE_CASE__):
										__lowerCamelCase				:				Tuple					       = required_input[index][0]

								if return_tensors is None:
									if is_tf_tensor(SCREAMING_SNAKE_CASE__):
										__lowerCamelCase				:				Any					       = 'tf'
									elif is_torch_tensor(SCREAMING_SNAKE_CASE__):
										__lowerCamelCase				:				Union[str, Any]					       = 'pt'
									elif isinstance(SCREAMING_SNAKE_CASE__   ,(int, float, list, tuple, np.ndarray)):
										__lowerCamelCase				:				Union[str, Any]					       = 'np'
									else:
										raise ValueError(
										    F"type of {first_element} unknown: {type(SCREAMING_SNAKE_CASE__)}. "
										    'Should be one of a python, numpy, pytorch or tensorflow object.')

								for key, value in processed_features.items():
									if isinstance(value[0]   ,(int, float)):
										__lowerCamelCase				:				Union[str, Any]					       = to_numpy(SCREAMING_SNAKE_CASE__)
									else:
										__lowerCamelCase				:				Dict					       = [to_numpy(SCREAMING_SNAKE_CASE__) for v in value]

        # Convert padding_strategy in PaddingStrategy
								__lowerCamelCase				:				Tuple					       = self._get_padding_strategies(padding=SCREAMING_SNAKE_CASE__   ,max_length=SCREAMING_SNAKE_CASE__)

								__lowerCamelCase				:				Union[str, Any]					       = processed_features[self.model_input_names[0]]

								__lowerCamelCase				:				Union[str, Any]					       = len(SCREAMING_SNAKE_CASE__)
								if not all(len(SCREAMING_SNAKE_CASE__) == batch_size for v in processed_features.values()):
									raise ValueError('Some items in the output dictionary have a different batch size than others.')

								__lowerCamelCase				:				Any					       = []
								for i in range(SCREAMING_SNAKE_CASE__):
									__lowerCamelCase				:				int					       = {k: v[i] for k, v in processed_features.items()}
									# truncation
									__lowerCamelCase				:				Union[str, Any]					       = self._truncate(
									    SCREAMING_SNAKE_CASE__   ,max_length=SCREAMING_SNAKE_CASE__   ,pad_to_multiple_of=SCREAMING_SNAKE_CASE__   ,truncation=SCREAMING_SNAKE_CASE__   ,)
									truncated_inputs.append(SCREAMING_SNAKE_CASE__)

								if padding_strategy == PaddingStrategy.LONGEST:
									# make sure that `max_length` cannot be longer than the longest truncated length
									__lowerCamelCase				:				Any					       = max(len(input_slice[self.model_input_names[0]]) for input_slice in truncated_inputs)
									__lowerCamelCase				:				Optional[Any]					       = PaddingStrategy.MAX_LENGTH

								__lowerCamelCase				:				Optional[Any]					       = {}
								for i in range(SCREAMING_SNAKE_CASE__):
									# padding
									__lowerCamelCase				:				List[str]					       = self._pad(
									    truncated_inputs[i]   ,max_length=SCREAMING_SNAKE_CASE__   ,padding_strategy=SCREAMING_SNAKE_CASE__   ,pad_to_multiple_of=SCREAMING_SNAKE_CASE__   ,return_attention_mask=SCREAMING_SNAKE_CASE__   ,)

									for key, value in outputs.items():
										if key not in batch_outputs:
											__lowerCamelCase				:				Union[str, Any]					       = []
										if value.dtype is np.dtype(np.floataa):
											__lowerCamelCase				:				Any					       = value.astype(np.floataa)
										batch_outputs[key].append(SCREAMING_SNAKE_CASE__)

								return BatchFeature(SCREAMING_SNAKE_CASE__   ,tensor_type=SCREAMING_SNAKE_CASE__)
							def     lowerCAmelCase     (		self		:			Dict   ,SCREAMING_SNAKE_CASE__		:			Union[Dict[str, np.ndarray], BatchFeature]   ,SCREAMING_SNAKE_CASE__		:			Optional[int] = None   ,SCREAMING_SNAKE_CASE__		:			PaddingStrategy = PaddingStrategy.DO_NOT_PAD   ,SCREAMING_SNAKE_CASE__		:			Optional[int] = None   ,SCREAMING_SNAKE_CASE__		:			Optional[bool] = None   ,):
								__lowerCamelCase				:				Optional[int]					       = processed_features[self.model_input_names[0]]

								if padding_strategy == PaddingStrategy.LONGEST:
									__lowerCamelCase				:				int					       = len(SCREAMING_SNAKE_CASE__)

								if max_length is not None and pad_to_multiple_of is not None and (max_length % pad_to_multiple_of != 0):
									__lowerCamelCase				:				List[Any]					       = ((max_length // pad_to_multiple_of) + 1) * pad_to_multiple_of

								__lowerCamelCase				:				Dict					       = padding_strategy != PaddingStrategy.DO_NOT_PAD and len(SCREAMING_SNAKE_CASE__) < max_length

								if return_attention_mask and "attention_mask" not in processed_features:
									__lowerCamelCase				:				int					       = np.ones(len(SCREAMING_SNAKE_CASE__)   ,dtype=np.intaa)

								if needs_to_be_padded:
									__lowerCamelCase				:				List[Any]					       = max_length - len(SCREAMING_SNAKE_CASE__)
									if self.padding_side == "right":
										if return_attention_mask:
											__lowerCamelCase				:				Any					       = np.pad(
											    processed_features['attention_mask']   ,(0, difference))
										__lowerCamelCase				:				str					       = ((0, difference), (0, 0)) if self.feature_size > 1 else (0, difference)
										__lowerCamelCase				:				Tuple					       = np.pad(
										    SCREAMING_SNAKE_CASE__   ,SCREAMING_SNAKE_CASE__   ,'constant'   ,constant_values=self.padding_value)
									elif self.padding_side == "left":
										if return_attention_mask:
											__lowerCamelCase				:				List[Any]					       = np.pad(
											    processed_features['attention_mask']   ,(difference, 0))
										__lowerCamelCase				:				Any					       = ((difference, 0), (0, 0)) if self.feature_size > 1 else (difference, 0)
										__lowerCamelCase				:				Union[str, Any]					       = np.pad(
										    SCREAMING_SNAKE_CASE__   ,SCREAMING_SNAKE_CASE__   ,'constant'   ,constant_values=self.padding_value)
									else:
										raise ValueError('Invalid padding strategy:' + str(self.padding_side))

								return processed_features
							def     lowerCAmelCase     (		self		:			Dict   ,SCREAMING_SNAKE_CASE__		:			Union[Dict[str, np.ndarray], BatchFeature]   ,SCREAMING_SNAKE_CASE__		:			Optional[int] = None   ,SCREAMING_SNAKE_CASE__		:			Optional[int] = None   ,SCREAMING_SNAKE_CASE__		:			Optional[bool] = None   ,):
								if not truncation:
									return processed_features
								elif truncation and max_length is None:
									raise ValueError('When setting ``truncation=True``, make sure that ``max_length`` is defined.')

								__lowerCamelCase				:				Union[str, Any]					       = processed_features[self.model_input_names[0]]

								# find `max_length` that fits `pad_to_multiple_of`
								if max_length is not None and pad_to_multiple_of is not None and (max_length % pad_to_multiple_of != 0):
									__lowerCamelCase				:				Tuple					       = ((max_length // pad_to_multiple_of) + 1) * pad_to_multiple_of

								__lowerCamelCase				:				Optional[int]					       = len(SCREAMING_SNAKE_CASE__) > max_length

								if needs_to_be_truncated:
									__lowerCamelCase				:				Dict					       = processed_features[self.model_input_names[0]][:max_length]
									if "attention_mask" in processed_features:
										__lowerCamelCase				:				Optional[Any]					       = processed_features['attention_mask'][:max_length]

								return processed_features


							def     lowerCAmelCase     (		self		:			int   ,SCREAMING_SNAKE_CASE__		:			Any=False   ,SCREAMING_SNAKE_CASE__		:			List[Any]=None):

								# Get padding strategy
								if padding is not False:
									if padding is True:
										__lowerCamelCase				:				Any					       = PaddingStrategy.LONGEST  # Default to pad to the longest sequence in the batch
									elif not isinstance(SCREAMING_SNAKE_CASE__   ,SCREAMING_SNAKE_CASE__):
										__lowerCamelCase				:				Optional[int]					       = PaddingStrategy(SCREAMING_SNAKE_CASE__)
									elif isinstance(SCREAMING_SNAKE_CASE__   ,SCREAMING_SNAKE_CASE__):
										__lowerCamelCase				:				Optional[Any]					       = padding
								else:
									__lowerCamelCase				:				Union[str, Any]					       = PaddingStrategy.DO_NOT_PAD

								# Set max length if needed
								if max_length is None:
									if padding_strategy == PaddingStrategy.MAX_LENGTH:
										raise ValueError(
										    F"When setting ``padding={PaddingStrategy.MAX_LENGTH}``, make sure that max_length is defined")

        # Test if we have a padding value
								if padding_strategy != PaddingStrategy.DO_NOT_PAD and (self.padding_value is None):
									raise ValueError(
									    'Asking to pad but the feature_extractor does not have a padding value. Please select a value to use'
									    ' as `padding_value`. For example: `feature_extractor.padding_value = 0.0`.')

								return padding_strategy






'''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							)



