




"""simple docstring"""







import unittest

from transformers import SPIECE_UNDERLINE, ReformerTokenizer, ReformerTokenizerFast
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, require_torch, slow
from transformers.utils import cached_property

from ...test_tokenization_common import TokenizerTesterMixin


_a  :  Any=				get_tests_dir("fixtures/test_sentencepiece.model")

@require_sentencepiece
@require_tokenizers
class 	UpperCamelCase    (					lowercase   ,			unittest.TestCase		):
				UpperCAmelCase       :   int					=	ReformerTokenizer
				UpperCAmelCase       :   Optional[int]					=	ReformerTokenizerFast
				UpperCAmelCase       :   Optional[Any]					=	True
				UpperCAmelCase       :   int					=	False
				UpperCAmelCase       :   Union[str, Any]					=	True



				def       _lowercase  (self       :   Optional[int])					->			List[Any]:
					super().setUp()

					__snake_case    :							Optional[int]   	=				ReformerTokenizer(_A			,						keep_accents=_A)
					tokenizer.save_pretrained(self.tmpdirname)



				def       _lowercase  (self       :   Union[str, Any])					->			str:
					__snake_case    :							Optional[int]   	=				'<s>'
					__snake_case    :							int   	=				1

					self.assertEqual(self.get_tokenizer()._convert_token_to_id(_A)			,						_A)
					self.assertEqual(self.get_tokenizer()._convert_id_to_token(_A)			,						_A)



				def       _lowercase  (self       :   str)					->			Union[str, Any]:
					__snake_case    :							List[str]   	=				list(self.get_tokenizer().get_vocab().keys())

					self.assertEqual(vocab_keys[0]			,						'<unk>')
					self.assertEqual(vocab_keys[1]			,						'<s>')
					self.assertEqual(vocab_keys[-1]			,						'j')
					self.assertEqual(len(_A)			,						10_00)



				def       _lowercase  (self       :   Tuple)					->			Tuple:
					self.assertEqual(self.get_tokenizer().vocab_size			,						10_00)



				def       _lowercase  (self       :   Optional[Any])					->			Optional[Any]:
					if not self.test_rust_tokenizer:
						return

					__snake_case    :							List[str]   	=				self.get_tokenizer()
					__snake_case    :							Tuple   	=				self.get_rust_tokenizer()

					__snake_case    :							str   	=				'I was born in 92000, and this is falsé.'

					__snake_case    :							Any   	=				tokenizer.tokenize(_A)
					__snake_case    :							Any   	=				rust_tokenizer.tokenize(_A)
					self.assertListEqual(_A			,						_A)

					__snake_case    :							Union[str, Any]   	=				tokenizer.encode(_A			,						add_special_tokens=_A)
					__snake_case    :							Any   	=				rust_tokenizer.encode(_A			,						add_special_tokens=_A)
					self.assertListEqual(_A			,						_A)

					__snake_case    :							Optional[int]   	=				self.get_rust_tokenizer()
					__snake_case    :							str   	=				tokenizer.encode(_A)
					__snake_case    :							Tuple   	=				rust_tokenizer.encode(_A)
					self.assertListEqual(_A			,						_A)



				def       _lowercase  (self       :   int			,						_A       :   List[str]=15)					->			str:
					for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
						with self.subTest(f"{tokenizer.__class__.__name__} ({pretrained_name})"):
							__snake_case    :							Dict   	=				self.rust_tokenizer_class.from_pretrained(_A			,						**_A)

							# Simple input
							__snake_case    :							int   	=				'This is a simple input'
							__snake_case    :							int   	=				['This is a simple input 1', 'This is a simple input 2']
							__snake_case    :							Dict   	=				('This is a simple input', 'This is a pair')
							__snake_case    :							int   	=				[
							    ('This is a simple input 1', 'This is a simple input 2'),
							    ('This is a simple pair 1', 'This is a simple pair 2'),
							]

							# Simple input tests
							self.assertRaises(_A			,						tokenizer_r.encode			,						_A			,						max_length=_A			,						padding='max_length')

							# Simple input
							self.assertRaises(_A			,						tokenizer_r.encode_plus			,						_A			,						max_length=_A			,						padding='max_length')

							# Simple input
							self.assertRaises(
							    _A			,						tokenizer_r.batch_encode_plus			,						_A			,						max_length=_A			,						padding='max_length'			,						)

							# Pair input
							self.assertRaises(_A			,						tokenizer_r.encode			,						_A			,						max_length=_A			,						padding='max_length')

							# Pair input
							self.assertRaises(_A			,						tokenizer_r.encode_plus			,						_A			,						max_length=_A			,						padding='max_length')

							# Pair input
							self.assertRaises(
							    _A			,						tokenizer_r.batch_encode_plus			,						_A			,						max_length=_A			,						padding='max_length'			,						)



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



				def       _lowercase  (self       :   List[str])					->			int:
					__snake_case    :							Dict   	=				ReformerTokenizer(_A			,						keep_accents=_A)

					__snake_case    :							Union[str, Any]   	=				tokenizer.tokenize('This is a test')
					self.assertListEqual(_A			,						['▁This', '▁is', '▁a', '▁t', 'est'])

					self.assertListEqual(
					    tokenizer.convert_tokens_to_ids(_A)			,						[2_85, 46, 10, 1_70, 3_82]			,						)

					__snake_case    :							Optional[int]   	=				tokenizer.tokenize('I was born in 92000, and this is falsé.')
					self.assertListEqual(
					    _A			,						[
					        SPIECE_UNDERLINE + 'I',
					        SPIECE_UNDERLINE + 'was',
					        SPIECE_UNDERLINE + 'b',
					        'or',
					        'n',
					        SPIECE_UNDERLINE + 'in',
					        SPIECE_UNDERLINE + '',
					        '9',
					        '2',
					        '0',
					        '0',
					        '0',
					        ',',
					        SPIECE_UNDERLINE + 'and',
					        SPIECE_UNDERLINE + 'this',
					        SPIECE_UNDERLINE + 'is',
					        SPIECE_UNDERLINE + 'f',
					        'al',
					        's',
					        'é',
					        '.',
					    ]			,						)
					__snake_case    :							Optional[int]   	=				tokenizer.convert_tokens_to_ids(_A)
					self.assertListEqual(
					    _A			,						[8, 21, 84, 55, 24, 19, 7, 0, 6_02, 3_47, 3_47, 3_47, 3, 12, 66, 46, 72, 80, 6, 0, 4]			,						)

					__snake_case    :							Dict   	=				tokenizer.convert_ids_to_tokens(_A)
					self.assertListEqual(
					    _A			,						[
					        SPIECE_UNDERLINE + 'I',
					        SPIECE_UNDERLINE + 'was',
					        SPIECE_UNDERLINE + 'b',
					        'or',
					        'n',
					        SPIECE_UNDERLINE + 'in',
					        SPIECE_UNDERLINE + '',
					        '<unk>',
					        '2',
					        '0',
					        '0',
					        '0',
					        ',',
					        SPIECE_UNDERLINE + 'and',
					        SPIECE_UNDERLINE + 'this',
					        SPIECE_UNDERLINE + 'is',
					        SPIECE_UNDERLINE + 'f',
					        'al',
					        's',
					        '<unk>',
					        '.',
					    ]			,						)



				@cached_property
				def       _lowercase  (self       :   List[Any])					->			Any:
					return ReformerTokenizer.from_pretrained('google/reformer-crime-and-punishment')



				@slow
				def       _lowercase  (self       :   int)					->			Any:
					__snake_case    :							Tuple   	=				'Hello World!'
					__snake_case    :							List[str]   	=				[1_26, 32, 2_62, 1_52, 38, 72, 2_87]

					self.assertListEqual(_A			,						self.big_tokenizer.encode(_A))



				@slow
				def       _lowercase  (self       :   Optional[Any])					->			Optional[Any]:
					__snake_case    :							Optional[int]   	=				(
					    'This is a very long text with a lot of weird characters, such as: . , ~ ? ( ) " [ ] ! : - . Also we will'
					    ' add words that should not exsist and be tokenized to <unk>, such as saoneuhaoesuth'
					)
					__snake_case    :							List[Any]   	=				[
					    1_08,
					    2_65,
					    24,
					    1_11,
					    4,
					    2_58,
					    1_56,
					    35,
					    28,
					    2_75,
					    3,
					    2_59,
					    2_97,
					    2_60,
					    84,
					    4,
					    35,
					    1_10,
					    44,
					    8,
					    2_59,
					    91,
					    2_68,
					    21,
					    11,
					    2_09,
					    2_74,
					    1_09,
					    2_66,
					    2_77,
					    1_17,
					    86,
					    93,
					    3_15,
					    2_58,
					    2_78,
					    2_58,
					    2_77,
					    2_58,
					    0,
					    2_58,
					    2_88,
					    2_58,
					    3_19,
					    2_58,
					    0,
					    2_58,
					    0,
					    2_58,
					    0,
					    2_58,
					    0,
					    2_58,
					    2_87,
					    2_58,
					    3_15,
					    2_58,
					    2_89,
					    2_58,
					    2_78,
					    99,
					    2_69,
					    2_66,
					    2_62,
					    8,
					    2_59,
					    2_41,
					    4,
					    2_17,
					    2_30,
					    2_68,
					    2_66,
					    55,
					    1_68,
					    1_06,
					    75,
					    1_93,
					    2_66,
					    2_23,
					    27,
					    49,
					    26,
					    2_82,
					    25,
					    2_64,
					    2_99,
					    19,
					    26,
					    0,
					    2_58,
					    2_77,
					    1_17,
					    86,
					    93,
					    1_76,
					    1_83,
					    2_70,
					    11,
					    2_62,
					    42,
					    61,
					    2_65,
					]

					self.assertListEqual(_A			,						self.big_tokenizer.encode(_A))



				@require_torch
				@slow
				def       _lowercase  (self       :   Dict)					->			Optional[int]:
					import torch

					from transformers import ReformerConfig, ReformerModel

					# Build sequence
					__snake_case    :							Optional[int]   	=				list(self.big_tokenizer.get_vocab().keys())[:10]
					__snake_case    :							Optional[int]   	=				' '.join(_A)
					__snake_case    :							List[str]   	=				self.big_tokenizer.encode_plus(_A			,						return_tensors='pt')
					__snake_case    :							str   	=				self.big_tokenizer.batch_encode_plus([sequence, sequence]			,						return_tensors='pt')

					__snake_case    :							Optional[int]   	=				ReformerConfig()
					# The input gets padded during training so adjust the axial position encodings from the pretrained model value of (512, 1024)
					__snake_case    :							List[Any]   	=				encoded_sequence['input_ids'].shape
					__snake_case    :							str   	=				ReformerModel(_A)

					# Reformer has config.vocab_size == tokenizer.vocab_size == len(tokenizer) - 1 = 320; len(tokenizer) is 321 (including a pad token with id 320)
					assert model.get_input_embeddings().weight.shape[0] >= self.big_tokenizer.vocab_size

					with torch.no_grad():
						model(**_A)
						model(**_A)






				@slow
				def       _lowercase  (self       :   Dict)					->			Dict:
					# fmt: off
					__snake_case    :							List[Any]   	=				{'input_ids': [[1_08, 2_65, 24, 1_11, 4, 2_58, 1_56, 7, 51, 2_79, 58, 7, 76, 25, 69, 2_78], [1_40, 2_43, 2_64, 1_34, 17, 2_67, 77, 2_63, 22, 2_62, 2_97, 2_58, 3_04, 1_77, 2_79, 2_66, 14, 89, 13, 35, 2_61, 2_99, 2_72, 1_37, 2_75, 2_78]], 'attention_mask': [[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]]}  # noqa: E501
					# fmt: on

					# This tokenizer does not know some characters like ")".
					# That is the reason why we use very simple texts here.
					# Also see https://github.com/huggingface/transformers/pull/11737#issuecomment-850769064
					__snake_case    :							int   	=				[
					    'This is a very simple sentence.',
					    'The quick brown fox jumps over the lazy dog.',
					]

					self.tokenizer_integration_test_util(
					    expected_encoding=_A			,						model_name='google/reformer-crime-and-punishment'			,						revision='0e6c3decb8211d49bf881013425dc8b0448b3f5a'			,						padding=_A			,						sequences=_A			,						)






"""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_						)





