





import unittest

import numpy as np

from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available

from ...test_image_processing_common import ImageProcessingSavingTestMixin


if is_torch_available():
					import torch

if is_vision_available():
					from PIL import Image

					from transformers import ChineseCLIPImageProcessor


class     A     (unittest.TestCase       ):
			'''simple docstring'''




			def __init__(     self  :       Optional[int]				,      __lowerCAmelCase  :       Dict				,      __lowerCAmelCase  :       Optional[int]=7				,      __lowerCAmelCase  :       Union[str, Any]=3				,      __lowerCAmelCase  :       Union[str, Any]=18				,      __lowerCAmelCase  :       List[Any]=30				,      __lowerCAmelCase  :       Dict=4_00				,      __lowerCAmelCase  :       Optional[int]=True				,      __lowerCAmelCase  :       Dict=None				,      __lowerCAmelCase  :       int=True				,      __lowerCAmelCase  :       Dict=None				,      __lowerCAmelCase  :       Any=True				,      __lowerCAmelCase  :       Dict=[0.4_8_1_4_5_4_6_6, 0.4_5_7_8_2_7_5, 0.4_0_8_2_1_0_7_3]				,      __lowerCAmelCase  :       Optional[Any]=[0.2_6_8_6_2_9_5_4, 0.2_6_1_3_0_2_5_8, 0.2_7_5_7_7_7_1_1]				,      __lowerCAmelCase  :       List[Any]=True				,      )      ->      Any:







					"""simple docstring"""






					A__     =  size if size is not None else {"""height""": 2_24, """width""": 2_24}
					A__     =  crop_size if crop_size is not None else {"""height""": 18, """width""": 18}
					A__     =  parent
					A__     =  batch_size
					A__     =  num_channels
					A__     =  image_size
					A__     =  min_resolution
					A__     =  max_resolution
					A__     =  do_resize
					A__     =  size
					A__     =  do_center_crop
					A__     =  crop_size
					A__     =  do_normalize
					A__     =  image_mean
					A__     =  image_std
					A__     =  do_convert_rgb






			def   a_			(     self  :       List[str]  )      ->      List[Any]:







					"""simple docstring"""






					return {
					    "do_resize": self.do_resize,
					    "size": self.size,
					    "do_center_crop": self.do_center_crop,
					    "crop_size": self.crop_size,
					    "do_normalize": self.do_normalize,
					    "image_mean": self.image_mean,
					    "image_std": self.image_std,
					    "do_convert_rgb": self.do_convert_rgb,
					}







			def   a_			(     self  :       Any				,      __lowerCAmelCase  :       Union[str, Any]=False				,      __lowerCAmelCase  :       int=False				,      __lowerCAmelCase  :       str=False  )      ->      Optional[Any]:







					"""simple docstring"""






					assert not (numpify and torchify), "You cannot specify both numpy and PyTorch tensors at the same time"

					if equal_resolution:
							A__     =  []
							for i in range(self.batch_size  ):
									image_inputs.append(
									    np.random.randint(
									        2_55				,      size=(self.num_channels, self.max_resolution, self.max_resolution)				,      dtype=np.uinta  )  )
					else:
							A__     =  []
							for i in range(self.batch_size  ):
									A__ ,     A__     =  np.random.choice(np.arange(self.min_resolution				,      self.max_resolution  )				,      2  )
									image_inputs.append(np.random.randint(2_55				,      size=(self.num_channels, width, height)				,      dtype=np.uinta  )  )

					if not numpify and not torchify:
							# PIL expects the channel dimension as last dimension
							A__     =  [Image.fromarray(np.moveaxis(__lowerCAmelCase				,      0				,      -1  )  ) for x in image_inputs]

					if torchify:
							A__     =  [torch.from_numpy(__lowerCAmelCase  ) for x in image_inputs]

					return image_inputs


@require_torch
@require_vision
class     A     (SCREAMING_SNAKE_CASE  ,	unittest.TestCase       ):
			'''simple docstring'''




			__lowerCamelCase      :			Dict							     =       ChineseCLIPImageProcessor if is_vision_available() else None






			def   a_			(     self  :       List[str]  )      ->      Union[str, Any]:







					"""simple docstring"""






					A__     =  ChineseCLIPImageProcessingTester(self				,      do_center_crop=__lowerCAmelCase  )






			@property
			def   a_			(     self  :       List[str]  )      ->      Tuple:







					"""simple docstring"""






					return self.image_processor_tester.prepare_image_processor_dict()






			def   a_			(     self  :       str  )      ->      Tuple:







					"""simple docstring"""






					A__     =  self.image_processing_class(**self.image_processor_dict  )
					self.assertTrue(hasattr(__lowerCAmelCase				,      """do_resize"""  )  )
					self.assertTrue(hasattr(__lowerCAmelCase				,      """size"""  )  )
					self.assertTrue(hasattr(__lowerCAmelCase				,      """do_center_crop"""  )  )
					self.assertTrue(hasattr(__lowerCAmelCase				,      """center_crop"""  )  )
					self.assertTrue(hasattr(__lowerCAmelCase				,      """do_normalize"""  )  )
					self.assertTrue(hasattr(__lowerCAmelCase				,      """image_mean"""  )  )
					self.assertTrue(hasattr(__lowerCAmelCase				,      """image_std"""  )  )
					self.assertTrue(hasattr(__lowerCAmelCase				,      """do_convert_rgb"""  )  )






			def   a_			(     self  :       Union[str, Any]  )      ->      Union[str, Any]:







					"""simple docstring"""






					A__     =  self.image_processing_class.from_dict(self.image_processor_dict  )
					self.assertEqual(image_processor.size				,      {"""height""": 2_24, """width""": 2_24}  )
					self.assertEqual(image_processor.crop_size				,      {"""height""": 18, """width""": 18}  )

					A__     =  self.image_processing_class.from_dict(self.image_processor_dict				,      size=42				,      crop_size=84  )
					self.assertEqual(image_processor.size				,      {"""shortest_edge""": 42}  )
					self.assertEqual(image_processor.crop_size				,      {"""height""": 84, """width""": 84}  )






			def   a_			(     self  :       int  )      ->      Dict:







					"""simple docstring"""






					pass






			def   a_			(     self  :       Optional[Any]  )      ->      int:







					"""simple docstring"""






					A__     =  self.image_processing_class(**self.image_processor_dict  )
					# create random PIL images
					A__     =  self.image_processor_tester.prepare_inputs(equal_resolution=__lowerCAmelCase  )
					for image in image_inputs:
							self.assertIsInstance(__lowerCAmelCase				,      Image.Image  )

					# Test not batched input
					A__     =  image_processing(image_inputs[0]				,      return_tensors="""pt"""  ).pixel_values
					self.assertEqual(
					    encoded_images.shape				,      (
					        1,
					        self.image_processor_tester.num_channels,
					        self.image_processor_tester.crop_size["""height"""],
					        self.image_processor_tester.crop_size["""width"""],
					    )				,      )

					# Test batched
					A__     =  image_processing(__lowerCAmelCase				,      return_tensors="""pt"""  ).pixel_values
					self.assertEqual(
					    encoded_images.shape				,      (
					        self.image_processor_tester.batch_size,
					        self.image_processor_tester.num_channels,
					        self.image_processor_tester.crop_size["""height"""],
					        self.image_processor_tester.crop_size["""width"""],
					    )				,      )






			def   a_			(     self  :       Any  )      ->      Union[str, Any]:







					"""simple docstring"""






					A__     =  self.image_processing_class(**self.image_processor_dict  )
					# create random numpy tensors
					A__     =  self.image_processor_tester.prepare_inputs(equal_resolution=__lowerCAmelCase				,      numpify=__lowerCAmelCase  )
					for image in image_inputs:
							self.assertIsInstance(__lowerCAmelCase				,      np.ndarray  )

					# Test not batched input
					A__     =  image_processing(image_inputs[0]				,      return_tensors="""pt"""  ).pixel_values
					self.assertEqual(
					    encoded_images.shape				,      (
					        1,
					        self.image_processor_tester.num_channels,
					        self.image_processor_tester.crop_size["""height"""],
					        self.image_processor_tester.crop_size["""width"""],
					    )				,      )

					# Test batched
					A__     =  image_processing(__lowerCAmelCase				,      return_tensors="""pt"""  ).pixel_values
					self.assertEqual(
					    encoded_images.shape				,      (
					        self.image_processor_tester.batch_size,
					        self.image_processor_tester.num_channels,
					        self.image_processor_tester.crop_size["""height"""],
					        self.image_processor_tester.crop_size["""width"""],
					    )				,      )







			def   a_			(     self  :       int  )      ->      Dict:







					"""simple docstring"""






					A__     =  self.image_processing_class(**self.image_processor_dict  )
					# create random PyTorch tensors
					A__     =  self.image_processor_tester.prepare_inputs(equal_resolution=__lowerCAmelCase				,      torchify=__lowerCAmelCase  )
					for image in image_inputs:
							self.assertIsInstance(__lowerCAmelCase				,      torch.Tensor  )

					# Test not batched input
					A__     =  image_processing(image_inputs[0]				,      return_tensors="""pt"""  ).pixel_values
					self.assertEqual(
					    encoded_images.shape				,      (
					        1,
					        self.image_processor_tester.num_channels,
					        self.image_processor_tester.crop_size["""height"""],
					        self.image_processor_tester.crop_size["""width"""],
					    )				,      )

					# Test batched
					A__     =  image_processing(__lowerCAmelCase				,      return_tensors="""pt"""  ).pixel_values
					self.assertEqual(
					    encoded_images.shape				,      (
					        self.image_processor_tester.batch_size,
					        self.image_processor_tester.num_channels,
					        self.image_processor_tester.crop_size["""height"""],
					        self.image_processor_tester.crop_size["""width"""],
					    )				,      )


@require_torch
@require_vision
class     A     (SCREAMING_SNAKE_CASE  ,	unittest.TestCase       ):
			'''simple docstring'''




			__lowerCamelCase      :			int							     =       ChineseCLIPImageProcessor if is_vision_available() else None






			def   a_			(     self  :       List[Any]  )      ->      Dict:







					"""simple docstring"""






					A__     =  ChineseCLIPImageProcessingTester(self				,      num_channels=4				,      do_center_crop=__lowerCAmelCase  )
					A__     =  3






			@property
			def   a_			(     self  :       str  )      ->      Dict:







					"""simple docstring"""






					return self.image_processor_tester.prepare_image_processor_dict()






			def   a_			(     self  :       Union[str, Any]  )      ->      Optional[Any]:







					"""simple docstring"""






					A__     =  self.image_processing_class(**self.image_processor_dict  )
					self.assertTrue(hasattr(__lowerCAmelCase				,      """do_resize"""  )  )
					self.assertTrue(hasattr(__lowerCAmelCase				,      """size"""  )  )
					self.assertTrue(hasattr(__lowerCAmelCase				,      """do_center_crop"""  )  )
					self.assertTrue(hasattr(__lowerCAmelCase				,      """center_crop"""  )  )
					self.assertTrue(hasattr(__lowerCAmelCase				,      """do_normalize"""  )  )
					self.assertTrue(hasattr(__lowerCAmelCase				,      """image_mean"""  )  )
					self.assertTrue(hasattr(__lowerCAmelCase				,      """image_std"""  )  )
					self.assertTrue(hasattr(__lowerCAmelCase				,      """do_convert_rgb"""  )  )






			def   a_			(     self  :       Dict  )      ->      Dict:







					"""simple docstring"""






					pass







			def   a_			(     self  :       List[str]  )      ->      Union[str, Any]:







					"""simple docstring"""






					A__     =  self.image_processing_class(**self.image_processor_dict  )
					# create random PIL images
					A__     =  self.image_processor_tester.prepare_inputs(equal_resolution=__lowerCAmelCase  )
					for image in image_inputs:
							self.assertIsInstance(__lowerCAmelCase				,      Image.Image  )

					# Test not batched input
					A__     =  image_processing(image_inputs[0]				,      return_tensors="""pt"""  ).pixel_values
					self.assertEqual(
					    encoded_images.shape				,      (
					        1,
					        self.expected_encoded_image_num_channels,
					        self.image_processor_tester.crop_size["""height"""],
					        self.image_processor_tester.crop_size["""width"""],
					    )				,      )

					# Test batched
					A__     =  image_processing(__lowerCAmelCase				,      return_tensors="""pt"""  ).pixel_values
					self.assertEqual(
					    encoded_images.shape				,      (
					        self.image_processor_tester.batch_size,
					        self.expected_encoded_image_num_channels,
					        self.image_processor_tester.crop_size["""height"""],
					        self.image_processor_tester.crop_size["""width"""],
					    )				,      )










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       :   Dict					= yaml.safe_load(
    '''\
name: ""
allow_empty: false
allow_empty_text: true
subsections:
  - name: "Dataset Card for X" # First-level markdown heading
    allow_empty: false
    allow_empty_text: true
    subsections:
      - name: "Table of Contents"
        allow_empty: false
        allow_empty_text: false
        subsections: null
      - name: "Dataset Description"
        allow_empty: false
        allow_empty_text: false
        subsections:
          - name: "Dataset Summary"
            allow_empty: false
            allow_empty_text: false
            subsections: null
          - name: "Supported Tasks and Leaderboards"
            allow_empty: true
            allow_empty_text: true
            subsections: null
          - name: Languages
            allow_empty: false
            allow_empty_text: true
            subsections: null
'''
)


A       :   str					= {
    '''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       :   Union[str, Any]					= '''\
---
language:
- zh
- en
---

# Dataset Card for My Dataset
## Table of Contents
Some text here.
## Dataset Description
Some text here.
### Dataset Summary
Some text here.
### Supported Tasks and Leaderboards
### Languages
Language Text
'''


A       :   int					= '''\
---
language:
- zh
- en
---

# Dataset Card for My Dataset
## Table of Contents
Some text here.
## Dataset Description
Some text here.
### Dataset Summary
Some text here.
#### Extra Ignored Subsection
### Supported Tasks and Leaderboards
### Languages
Language Text
'''

A       :   Optional[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''': '''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					= '''\
---
---
# Dataset Card for My Dataset
## Table of Contents
Some text here.
## Dataset Description
Some text here.
### Dataset Summary
Some text here.
### Supported Tasks and Leaderboards
### Languages
Language Text
'''

A       :   Optional[Any]					= (
    '''The following issues were found for the README at `{path}`:\n-\tEmpty YAML markers are present in the README.'''
)

A       :   List[Any]					= '''\
# Dataset Card for My Dataset
## Table of Contents
Some text here.
## Dataset Description
Some text here.
### Dataset Summary
Some text here.
### Supported Tasks and Leaderboards
### Languages
Language Text
'''

A       :   Union[str, Any]					= (
    '''The following issues were found for the README at `{path}`:\n-\tNo YAML markers are present in the README.'''
)

A       :   int					= '''\
---
# Dataset Card for My Dataset
## Table of Contents
Some text here.
## Dataset Description
Some text here.
### Dataset Summary
Some text here.
### Supported Tasks and Leaderboards
### Languages
Language Text
'''

A       :   Dict					= '''The following issues were found for the README at `{path}`:\n-\tOnly the start of YAML tags present in the README.'''

A       :   List[Any]					= '''\
---
language:
- zh
- en
---

# Dataset Card for My Dataset
## Table of Contents
Some text here.
## Dataset Description
Some text here.
### Dataset Summary
### Supported Tasks and Leaderboards
### Languages
Language Text
'''
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       :   Optional[int]					= '''\
---
language:
- zh
- en
---

# Dataset Card for My Dataset
'''
A       :   Optional[int]					= '''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       :   Optional[int]					= '''\
---
language:
- zh
- en
---

# Dataset Card for My Dataset
## Table of Contents
Some text here.
## Dataset Description
Some text here.
### Dataset Summary
Some text here.
### Languages
Language Text
'''

A       :   List[str]					= '''The following issues were found for the README at `{path}`:\n-\tSection `Dataset Description` is missing subsection: `Supported Tasks and Leaderboards`.'''


A       :   Optional[int]					= '''\
---
language:
- zh
- en
---

# Dataset Card for My Dataset
## Table of Contents
Some text here.
## Dataset Description
Some text here.
### Dataset Summary
Some text here.
### Supported Tasks and Leaderboards
### Languages
'''

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       :   List[Any]					= '''\
---
language:
- zh
- en
---

## Table of Contents
Some text here.
## Dataset Description
Some text here.
### Dataset Summary
Some text here.
### Supported Tasks and Leaderboards
### Languages
Language Text
'''
A       :   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.'''

A       :   int					= '''\
---
language:
- zh
- en
---

# Dataset Card for My Dataset
## Table of Contents
Some text here.
## Dataset Description
Some text here.
### Dataset Summary
Some text here.
### Supported Tasks and Leaderboards
### Languages
Language Text
# Dataset Card My Dataset
'''

A       :   Dict					= '''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       :   str					= '''\
---
language:
- zh
- en
---

# Dataset Card My Dataset
## Table of Contents
Some text here.
## Dataset Description
Some text here.
### Dataset Summary
Some text here.
### Supported Tasks and Leaderboards
### Languages
Language Text
'''

A       :   int					= '''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       :   str					= ''''''

A       :   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.'''

A       :   int					= '''\
---
language:
- zh
- en
---

# Dataset Card for My Dataset
# Dataset Card for My Dataset
## Table of Contents
Some text here.
## Dataset Description
Some text here.
### Dataset Summary
Some text here.
### Supported Tasks and Leaderboards
### Languages
Language Text
'''

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   __lowerCamelCase   (				__a  :Union[str, Any]       ,	__a  :List[Any]       )						->							List[Any]:






		"""simple docstring"""

		assert ReadMe.from_string(__a       ,	__a       ).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   __lowerCamelCase   (				__a  :Any       ,	__a  :Union[str, Any]       )						->							Dict:






		"""simple docstring"""

		with pytest.raises(__a       ,	match=re.escape(expected_error.format(path="""root"""       )       )       ):
				A__     =  ReadMe.from_string(__a       ,	__a       )
				readme.validate()







@pytest.mark.parametrize(
    """readme_md, expected_error"""       ,	[
        (README_MULTIPLE_SAME_HEADING_1, EXPECTED_ERROR_README_MULTIPLE_SAME_HEADING_1),
    ]       ,	)
def   __lowerCamelCase   (				__a  :List[str]       ,	__a  :Tuple       )						->							Dict:






		"""simple docstring"""

		with pytest.raises(__a       ,	match=re.escape(expected_error.format(path="""root"""       )       )       ):
				ReadMe.from_string(__a       ,	__a       )







@pytest.mark.parametrize(
    """readme_md,"""       ,	[
        (README_MULTIPLE_SAME_HEADING_1),
    ]       ,	)
def   __lowerCamelCase   (				__a  :Any       )						->							List[str]:






		"""simple docstring"""

		ReadMe.from_string(__a       ,	__a       ,	suppress_parsing_errors=__a       )







@pytest.mark.parametrize(
    """readme_md, expected_dict"""       ,	[
        (README_CORRECT, CORRECT_DICT),
        (README_CORRECT_FOUR_LEVEL, CORRECT_DICT_FOUR_LEVEL),
    ]       ,	)
def   __lowerCamelCase   (				__a  :List[Any]       ,	__a  :Optional[int]       )						->							Optional[int]:






		"""simple docstring"""

		with tempfile.TemporaryDirectory() as tmp_dir:
				A__     =  Path(__a       ) / """README.md"""
				with open(__a       ,	"""w+"""       ) as readme_file:
						readme_file.write(__a       )
				A__     =  ReadMe.from_readme(__a       ,	__a       ).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   __lowerCamelCase   (				__a  :List[str]       ,	__a  :Union[str, Any]       )						->							List[str]:






		"""simple docstring"""

		with tempfile.TemporaryDirectory() as tmp_dir:
				A__     =  Path(__a       ) / """README.md"""
				with open(__a       ,	"""w+"""       ) as readme_file:
						readme_file.write(__a       )
				A__     =  expected_error.format(path=__a       )
				with pytest.raises(__a       ,	match=re.escape(__a       )       ):
						A__     =  ReadMe.from_readme(__a       ,	__a       )
						readme.validate()







@pytest.mark.parametrize(
    """readme_md, expected_error"""       ,	[
        (README_MULTIPLE_SAME_HEADING_1, EXPECTED_ERROR_README_MULTIPLE_SAME_HEADING_1),
    ]       ,	)
def   __lowerCamelCase   (				__a  :List[Any]       ,	__a  :Any       )						->							int:






		"""simple docstring"""

		with tempfile.TemporaryDirectory() as tmp_dir:
				A__     =  Path(__a       ) / """README.md"""
				with open(__a       ,	"""w+"""       ) as readme_file:
						readme_file.write(__a       )
				A__     =  expected_error.format(path=__a       )
				with pytest.raises(__a       ,	match=re.escape(__a       )       ):
						ReadMe.from_readme(__a       ,	__a       )







@pytest.mark.parametrize(
    """readme_md,"""       ,	[
        (README_MULTIPLE_SAME_HEADING_1),
    ]       ,	)
def   __lowerCamelCase   (				__a  :Union[str, Any]       )						->							Optional[Any]:






		"""simple docstring"""

		with tempfile.TemporaryDirectory() as tmp_dir:
				A__     =  Path(__a       ) / """README.md"""
				with open(__a       ,	"""w+"""       ) as readme_file:
						readme_file.write(__a       )
				ReadMe.from_readme(__a       ,	__a       ,	suppress_parsing_errors=__a       )



