




"""simple docstring"""







import argparse

import evaluate
import torch
from datasets import load_dataset
from torch.optim import AdamW
from torch.utils.data import DataLoader
from transformers import AutoModelForSequenceClassification, AutoTokenizer, get_linear_schedule_with_warmup, set_seed

from accelerate import Accelerator, DistributedType


########################################################################
# This is a fully working simple example to use Accelerate
#
# This example trains a Bert base model on GLUE MRPC
# in any of the following settings (with the same script):
#   - single CPU or single GPU
#   - multi GPUS (using PyTorch distributed mode)
#   - (multi) TPUs
#   - fp16 (mixed-precision) or fp32 (normal precision)
#
# To run it in each of these various modes, follow the instructions
# in the readme for examples:
# https://github.com/huggingface/accelerate/tree/main/examples
#
########################################################################


_a  :  int=				16
_a  :  Optional[Any]=				32
def 						__UpperCAmelCase       (					UpperCAmelCase_					:       Accelerator    ,							UpperCAmelCase_					:       int = 16						)							->    Tuple:

	'''simple docstring'''





	__snake_case    :							Tuple   	=				AutoTokenizer.from_pretrained('bert-base-cased'						)
	__snake_case    :							Optional[Any]   	=				load_dataset('glue'    ,							'mrpc'						)

	def tokenize_function(UpperCAmelCase_					:       Any						):
		# max_length=None => use the model max length (it's actually the default)
		__snake_case    :							Any   	=				tokenizer(examples['sentence1']    ,							examples['sentence2']    ,							truncation=UpperCAmelCase_    ,							max_length=UpperCAmelCase_						)
		return outputs

	# Apply the method we just defined to all the examples in all the splits of the dataset
	# starting with the main process first:
	with accelerator.main_process_first():
		__snake_case    :							Any   	=				datasets.map(
		    UpperCAmelCase_    ,							batched=UpperCAmelCase_    ,							remove_columns=['idx', 'sentence1', 'sentence2']    ,							)

	# We also rename the 'label' column to 'labels' which is the expected name for labels by the models of the
	# transformers library
	__snake_case    :							int   	=				tokenized_datasets.rename_column('label'    ,							'labels'						)

	def collate_fn(UpperCAmelCase_					:       Any						):
		# On TPU it's best to pad everything to the same length or training will be very slow.
		__snake_case    :							List[Any]   	=				1_28 if accelerator.distributed_type == DistributedType.TPU else None
		# When using mixed precision we want round multiples of 8/16
		if accelerator.mixed_precision == "fp8":
			__snake_case    :							Optional[int]   	=				16
		elif accelerator.mixed_precision != "no":
			__snake_case    :							List[Any]   	=				8
		else:
			__snake_case    :							Any   	=				None

		return tokenizer.pad(
		    UpperCAmelCase_    ,							padding='longest'    ,							max_length=UpperCAmelCase_    ,							pad_to_multiple_of=UpperCAmelCase_    ,							return_tensors='pt'    ,							)

	# Instantiate dataloaders.
	__snake_case    :							Dict   	=				DataLoader(
	    tokenized_datasets['train']    ,							shuffle=UpperCAmelCase_    ,							collate_fn=UpperCAmelCase_    ,							batch_size=UpperCAmelCase_    ,							drop_last=UpperCAmelCase_						)
	__snake_case    :							str   	=				DataLoader(
	    tokenized_datasets['validation']    ,							shuffle=UpperCAmelCase_    ,							collate_fn=UpperCAmelCase_    ,							batch_size=UpperCAmelCase_    ,							drop_last=(accelerator.mixed_precision == 'fp8')    ,							)

	return train_dataloader, eval_dataloader
def 						__UpperCAmelCase       (					UpperCAmelCase_					:       Tuple    ,							UpperCAmelCase_					:       Tuple						)							->    List[Any]:

	'''simple docstring'''





	__snake_case    :							str   	=				Accelerator(cpu=args.cpu    ,							mixed_precision=args.mixed_precision						)
	# Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
	__snake_case    :							Optional[Any]   	=				config['lr']
	__snake_case    :							Any   	=				int(config['num_epochs']						)
	__snake_case    :							int   	=				int(config['seed']						)
	__snake_case    :							Tuple   	=				int(config['batch_size']						)

	__snake_case    :							Any   	=				evaluate.load('glue'    ,							'mrpc'						)

	# If the batch size is too big we use gradient accumulation
	__snake_case    :							List[str]   	=				1
	if batch_size > MAX_GPU_BATCH_SIZE and accelerator.distributed_type != DistributedType.TPU:
		__snake_case    :							Dict   	=				batch_size // MAX_GPU_BATCH_SIZE
		__snake_case    :							Optional[int]   	=				MAX_GPU_BATCH_SIZE

	set_seed(UpperCAmelCase_						)
	__snake_case    :							List[str]   	=				get_dataloaders(UpperCAmelCase_    ,							UpperCAmelCase_						)
	# Instantiate the model (we build the model here so that the seed also control new weights initialization)
	__snake_case    :							Union[str, Any]   	=				AutoModelForSequenceClassification.from_pretrained('bert-base-cased'    ,							return_dict=UpperCAmelCase_						)

	# We could avoid this line since the accelerator is set with `device_placement=True` (default value).
	# Note that if you are placing tensors on devices manually, this line absolutely needs to be before the optimizer
	# creation otherwise training will not work on TPU (`accelerate` will kindly throw an error to make us aware of that).
	__snake_case    :							Any   	=				model.to(accelerator.device						)
	# Instantiate optimizer
	__snake_case    :							Optional[Any]   	=				AdamW(params=model.parameters()    ,							lr=UpperCAmelCase_						)

	# Instantiate scheduler
	__snake_case    :							Dict   	=				get_linear_schedule_with_warmup(
	    optimizer=UpperCAmelCase_    ,							num_warmup_steps=1_00    ,							num_training_steps=(len(UpperCAmelCase_						) * num_epochs) // gradient_accumulation_steps    ,							)

	# Prepare everything
	# There is no specific order to remember, we just need to unpack the objects in the same order we gave them to the
	# prepare method.

	__snake_case    :							Any   	=				accelerator.prepare(
	    UpperCAmelCase_    ,							UpperCAmelCase_    ,							UpperCAmelCase_    ,							UpperCAmelCase_    ,							UpperCAmelCase_						)

	# Now we train the model
	for epoch in range(UpperCAmelCase_						):
		model.train()
		for step, batch in enumerate(UpperCAmelCase_						):
			# We could avoid this line since we set the accelerator with `device_placement=True`.
			batch.to(accelerator.device						)
			__snake_case    :							int   	=				model(**UpperCAmelCase_						)
			__snake_case    :							List[Any]   	=				outputs.loss
			__snake_case    :							Optional[Any]   	=				loss / gradient_accumulation_steps
			accelerator.backward(UpperCAmelCase_						)
			if step % gradient_accumulation_steps == 0:
				optimizer.step()
				lr_scheduler.step()
				optimizer.zero_grad()

		model.eval()
		for step, batch in enumerate(UpperCAmelCase_						):
			# We could avoid this line since we set the accelerator with `device_placement=True`.
			batch.to(accelerator.device						)
			with torch.no_grad():
				__snake_case    :							List[Any]   	=				model(**UpperCAmelCase_						)
			__snake_case    :							int   	=				outputs.logits.argmax(dim=-1						)
			__snake_case    :							str   	=				accelerator.gather_for_metrics((predictions, batch['labels'])						)
			metric.add_batch(
			    predictions=UpperCAmelCase_    ,							references=UpperCAmelCase_    ,							)

		__snake_case    :							Any   	=				metric.compute()
		# Use accelerator.print to print only on the main process.
		accelerator.print(F"epoch {epoch}:"    ,							UpperCAmelCase_						)
def 						__UpperCAmelCase       (					)							->    Any:

	'''simple docstring'''





	__snake_case    :							Union[str, Any]   	=				argparse.ArgumentParser(description='Simple example of training script.'						)
	parser.add_argument(
	    '--mixed_precision'    ,							type=UpperCAmelCase_    ,							default=UpperCAmelCase_    ,							choices=['no', 'fp16', 'bf16', 'fp8']    ,							help='Whether to use mixed precision. Choose'
	    'between fp16 and bf16 (bfloat16). Bf16 requires PyTorch >= 1.10.'
	    'and an Nvidia Ampere GPU.'    ,							)
	parser.add_argument('--cpu'    ,							action='store_true'    ,							help='If passed, will train on the CPU.'						)
	__snake_case    :							Optional[int]   	=				parser.parse_args()
	__snake_case    :							int   	=				{'lr': 2E-5, 'num_epochs': 3, 'seed': 42, 'batch_size': 16}
	training_function(UpperCAmelCase_    ,							UpperCAmelCase_						)


if __name__ == "__main__":
	main()






"""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():

_a  :  List[str]=				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"
)


_a  :  Dict=				{
    "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": []},
                    ],
                },
            ],
        }
    ],
}


_a  :  str=				"\\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"


_a  :  Tuple=				"\\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"

_a  :  Dict=				{
    "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": []},
                    ],
                },
            ],
        }
    ],
}

_a  :  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"

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

_a  :  Optional[int]=				"\\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"

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

_a  :  str=				"\\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"

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

_a  :  int=				"\\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"
_a  :  str=				"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)."


_a  :  int=				"\\n---\nlanguage:\n- zh\n- en\n---\n\n# Dataset Card for My Dataset\n"
_a  :  Optional[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'."

_a  :  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### Languages\nLanguage Text\n"

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


_a  :  int=				"\\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"

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

_a  :  Optional[int]=				"\\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"
_a  :  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."

_a  :  Tuple=				"\\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"

_a  :  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."

_a  :  List[Any]=				"\\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"

_a  :  Tuple=				"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."

_a  :  Optional[Any]=				""

_a  :  Tuple=				"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."

_a  :  Union[str, 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"

_a  :  List[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 						__UpperCAmelCase       (					UpperCAmelCase_					:       Tuple    ,							UpperCAmelCase_					:       Tuple						)							->    Tuple:

	'''simple docstring'''





	assert ReadMe.from_string(UpperCAmelCase_    ,							UpperCAmelCase_						).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 						__UpperCAmelCase       (					UpperCAmelCase_					:       List[str]    ,							UpperCAmelCase_					:       Tuple						)							->    Union[str, Any]:

	'''simple docstring'''





	with pytest.raises(UpperCAmelCase_    ,							match=re.escape(expected_error.format(path='root'						)						)						):
		__snake_case    :							Tuple   	=				ReadMe.from_string(UpperCAmelCase_    ,							UpperCAmelCase_						)
		readme.validate()
@pytest.mark.parametrize(
    'readme_md, expected_error'    ,							[
        (README_MULTIPLE_SAME_HEADING_1, EXPECTED_ERROR_README_MULTIPLE_SAME_HEADING_1),
    ]    ,							)
def 						__UpperCAmelCase       (					UpperCAmelCase_					:       List[str]    ,							UpperCAmelCase_					:       int						)							->    Optional[Any]:

	'''simple docstring'''





	with pytest.raises(UpperCAmelCase_    ,							match=re.escape(expected_error.format(path='root'						)						)						):
		ReadMe.from_string(UpperCAmelCase_    ,							UpperCAmelCase_						)
@pytest.mark.parametrize(
    'readme_md,'    ,							[
        (README_MULTIPLE_SAME_HEADING_1),
    ]    ,							)
def 						__UpperCAmelCase       (					UpperCAmelCase_					:       Optional[int]						)							->    Any:

	'''simple docstring'''





	ReadMe.from_string(UpperCAmelCase_    ,							UpperCAmelCase_    ,							suppress_parsing_errors=UpperCAmelCase_						)
@pytest.mark.parametrize(
    'readme_md, expected_dict'    ,							[
        (README_CORRECT, CORRECT_DICT),
        (README_CORRECT_FOUR_LEVEL, CORRECT_DICT_FOUR_LEVEL),
    ]    ,							)
def 						__UpperCAmelCase       (					UpperCAmelCase_					:       Optional[int]    ,							UpperCAmelCase_					:       Union[str, Any]						)							->    str:

	'''simple docstring'''





	with tempfile.TemporaryDirectory() as tmp_dir:
		__snake_case    :							Union[str, Any]   	=				Path(UpperCAmelCase_						) / 'README.md'
		with open(UpperCAmelCase_    ,							'w+'						) as readme_file:
			readme_file.write(UpperCAmelCase_						)
		__snake_case    :							Optional[Any]   	=				ReadMe.from_readme(UpperCAmelCase_    ,							UpperCAmelCase_						).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 						__UpperCAmelCase       (					UpperCAmelCase_					:       Optional[Any]    ,							UpperCAmelCase_					:       Dict						)							->    int:

	'''simple docstring'''





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

	'''simple docstring'''





	with tempfile.TemporaryDirectory() as tmp_dir:
		__snake_case    :							Tuple   	=				Path(UpperCAmelCase_						) / 'README.md'
		with open(UpperCAmelCase_    ,							'w+'						) as readme_file:
			readme_file.write(UpperCAmelCase_						)
		__snake_case    :							List[str]   	=				expected_error.format(path=UpperCAmelCase_						)
		with pytest.raises(UpperCAmelCase_    ,							match=re.escape(UpperCAmelCase_						)						):
			ReadMe.from_readme(UpperCAmelCase_    ,							UpperCAmelCase_						)

@pytest.mark.parametrize(
    'readme_md,'    ,							[
        (README_MULTIPLE_SAME_HEADING_1),
    ]    ,							)
def 						__UpperCAmelCase       (					UpperCAmelCase_					:       Dict						)							->    Optional[Any]:

	'''simple docstring'''





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





