

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

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

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

_a       =							'\\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       =							'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       =							'\\n---\nlanguage:\n- zh\n- en\n---\n\n# Dataset Card for My Dataset\n'
_a       =							'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       =							'\\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       =							'The following issues were found for the README at `{path}`:\n-\tSection `Dataset Description` is missing subsection: `Supported Tasks and Leaderboards`.'


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

_a       =							'\\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       =							'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       =							'\\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       =							'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       =							'\\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       =							'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       =							''

_a       =							'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       =							'\\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       =							'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   _A  (  UpperCamelCase_     :       str,			UpperCamelCase_     :       int)	->						List[str]:
   '''simple docstring'''


   assert ReadMe.from_string(lowerCAmelCase__,			lowerCAmelCase__).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   _A  (  UpperCamelCase_     :       Union[str, Any],			UpperCamelCase_     :       Tuple)	->						str:
   '''simple docstring'''


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




@pytest.mark.parametrize(
    "readme_md, expected_error",			[
        (README_MULTIPLE_SAME_HEADING_1, EXPECTED_ERROR_README_MULTIPLE_SAME_HEADING_1),
    ],			)
def   _A  (  UpperCamelCase_     :       List[Any],			UpperCamelCase_     :       Optional[Any])	->						Union[str, Any]:
   '''simple docstring'''


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




@pytest.mark.parametrize(
    "readme_md,",			[
        (README_MULTIPLE_SAME_HEADING_1),
    ],			)
def   _A  (  UpperCamelCase_     :       Dict)	->						List[str]:
   '''simple docstring'''


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




@pytest.mark.parametrize(
    "readme_md, expected_dict",			[
        (README_CORRECT, CORRECT_DICT),
        (README_CORRECT_FOUR_LEVEL, CORRECT_DICT_FOUR_LEVEL),
    ],			)
def   _A  (  UpperCamelCase_     :       Any,			UpperCamelCase_     :       Optional[int])	->						Any:
   '''simple docstring'''


   with tempfile.TemporaryDirectory() as tmp_dir:
      __lowercase								=						Path(lowerCAmelCase__) / '''README.md'''
      with open(lowerCAmelCase__,			"w+") as readme_file:
         readme_file.write(lowerCAmelCase__)
      __lowercase								=						ReadMe.from_readme(lowerCAmelCase__,			lowerCAmelCase__).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   _A  (  UpperCamelCase_     :       int,			UpperCamelCase_     :       Optional[int])	->						str:
   '''simple docstring'''


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




@pytest.mark.parametrize(
    "readme_md, expected_error",			[
        (README_MULTIPLE_SAME_HEADING_1, EXPECTED_ERROR_README_MULTIPLE_SAME_HEADING_1),
    ],			)
def   _A  (  UpperCamelCase_     :       Any,			UpperCamelCase_     :       List[Any])	->						Tuple:
   '''simple docstring'''


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




@pytest.mark.parametrize(
    "readme_md,",			[
        (README_MULTIPLE_SAME_HEADING_1),
    ],			)
def   _A  (  UpperCamelCase_     :       str)	->						Dict:
   '''simple docstring'''


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






from typing import Dict, List, Optional, Union

import numpy as np

from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import (
    center_crop,
    get_resize_output_image_size,
    normalize,
    rescale,
    resize,
    to_channel_dimension_format,
)
from ...image_utils import (
    IMAGENET_STANDARD_MEAN,
    IMAGENET_STANDARD_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, is_vision_available, logging


if is_vision_available():
      import PIL


lowercase__       =logging.get_logger(__name__)



class 		UpperCamelCase__  (       __lowercase    ):
   _SCREAMING_SNAKE_CASE      :							Optional[Any]	       =      ["pixel_values"]
   def __init__(self :					str					,				snake_case_ :					bool = True					,				snake_case_ :					Dict[str, int] = None					,				snake_case_ :					float = None					,				snake_case_ :					PILImageResampling = PILImageResampling.BILINEAR					,				snake_case_ :					bool = True					,				snake_case_ :					Union[int, float] = 1 / 2_5_5					,				snake_case_ :					bool = True					,				snake_case_ :					Optional[Union[float, List[float]]] = None					,				snake_case_ :					Optional[Union[float, List[float]]] = None					,				**snake_case_ :					Tuple					,				):
    super().__init__(**snake_case_   )
    __a :  Optional[Any]						=  size if size is not None else {'''shortest_edge''': 3_8_4}
    __a :  str						=  get_size_dict(snake_case_					,				default_to_square=snake_case_   )

    __a :  Tuple						=  do_resize
    __a :  List[str]						=  size
    # Default value set here for backwards compatibility where the value in config is None
    __a :  List[str]						=  crop_pct if crop_pct is not None else 2_2_4 / 2_5_6
    __a :  Tuple						=  resample
    __a :  Any						=  do_rescale
    __a :  Optional[Any]						=  rescale_factor
    __a :  int						=  do_normalize
    __a :  List[str]						=  image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
    __a :  Union[str, Any]						=  image_std if image_std is not None else IMAGENET_STANDARD_STD
   def       lowerCAmelCase			(self :					int					,				snake_case_ :					np.ndarray					,				snake_case_ :					Dict[str, int]					,				snake_case_ :					float					,				snake_case_ :					PILImageResampling = PILImageResampling.BICUBIC					,				snake_case_ :					Optional[Union[str, ChannelDimension]] = None					,				**snake_case_ :					str					,				):
    __a :  str						=  get_size_dict(snake_case_					,				default_to_square=snake_case_   )
    if "shortest_edge" not in size:
     raise ValueError(f"Size dictionary must contain 'shortest_edge' key. Got {size.keys()}"   )
    __a :  Any						=  size['''shortest_edge''']

    if shortest_edge < 3_8_4:
     # maintain same ratio, resizing shortest edge to shortest_edge/crop_pct
     __a :  Union[str, Any]						=  int(shortest_edge / crop_pct   )
     __a :  Tuple						=  get_resize_output_image_size(snake_case_					,				size=snake_case_					,				default_to_square=snake_case_   )
     __a :  List[str]						=  resize(image=snake_case_					,				size=snake_case_					,				resample=snake_case_					,				data_format=snake_case_					,				**snake_case_   )
     # then crop to (shortest_edge, shortest_edge)
     return center_crop(image=snake_case_					,				size=(shortest_edge, shortest_edge)					,				data_format=snake_case_					,				**snake_case_   )
    else:
     # warping (no cropping) when evaluated at 384 or larger
     return resize(
         snake_case_					,				size=(shortest_edge, shortest_edge)					,				resample=snake_case_					,				data_format=snake_case_					,				**snake_case_   )
   def       lowerCAmelCase			(self :					Optional[int]					,				snake_case_ :					np.ndarray					,				snake_case_ :					Union[int, float]					,				snake_case_ :					Optional[Union[str, ChannelDimension]] = None					,				**snake_case_ :					Union[str, Any]					,				):
    return rescale(snake_case_					,				scale=snake_case_					,				data_format=snake_case_					,				**snake_case_   )
   def       lowerCAmelCase			(self :					Union[str, Any]					,				snake_case_ :					np.ndarray					,				snake_case_ :					Union[float, List[float]]					,				snake_case_ :					Union[float, List[float]]					,				snake_case_ :					Optional[Union[str, ChannelDimension]] = None					,				**snake_case_ :					Union[str, Any]					,				):
    return normalize(snake_case_					,				mean=snake_case_					,				std=snake_case_					,				data_format=snake_case_					,				**snake_case_   )







   def       lowerCAmelCase			(self :					Dict					,				snake_case_ :					ImageInput					,				snake_case_ :					bool = None					,				snake_case_ :					Dict[str, int] = None					,				snake_case_ :					float = None					,				snake_case_ :					PILImageResampling = None					,				snake_case_ :					bool = None					,				snake_case_ :					float = None					,				snake_case_ :					bool = None					,				snake_case_ :					Optional[Union[float, List[float]]] = None					,				snake_case_ :					Optional[Union[float, List[float]]] = None					,				snake_case_ :					Optional[Union[str, TensorType]] = None					,				snake_case_ :					ChannelDimension = ChannelDimension.FIRST					,				**snake_case_ :					Dict					,				):
    __a :  List[Any]						=  do_resize if do_resize is not None else self.do_resize
    __a :  Dict						=  crop_pct if crop_pct is not None else self.crop_pct
    __a :  Tuple						=  resample if resample is not None else self.resample
    __a :  Optional[int]						=  do_rescale if do_rescale is not None else self.do_rescale
    __a :  Optional[int]						=  rescale_factor if rescale_factor is not None else self.rescale_factor
    __a :  int						=  do_normalize if do_normalize is not None else self.do_normalize
    __a :  Optional[int]						=  image_mean if image_mean is not None else self.image_mean
    __a :  Optional[int]						=  image_std if image_std is not None else self.image_std

    __a :  Dict						=  size if size is not None else self.size
    __a :  List[str]						=  get_size_dict(snake_case_					,				default_to_square=snake_case_   )

    __a :  List[str]						=  make_list_of_images(snake_case_   )

    if not valid_images(snake_case_   ):
     raise ValueError(
         '''Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, '''
         '''torch.Tensor, tf.Tensor or jax.ndarray.'''   )

    if do_resize and size is None or resample is None:
     raise ValueError('''Size and resample must be specified if do_resize is True.'''   )

    if do_resize and size["shortest_edge"] < 3_8_4 and crop_pct is None:
     raise ValueError('''crop_pct must be specified if size < 384.'''   )

    if do_rescale and rescale_factor is None:
     raise ValueError('''Rescale factor must be specified if do_rescale is True.'''   )

    if do_normalize and (image_mean is None or image_std is None):
     raise ValueError('''Image mean and std must be specified if do_normalize is True.'''   )

    # All transformations expect numpy arrays.
    __a :  List[Any]						=  [to_numpy_array(snake_case_   ) for image in images]

    if do_resize:
     __a :  List[str]						=  [self.resize(image=snake_case_					,				size=snake_case_					,				crop_pct=snake_case_					,				resample=snake_case_   ) for image in images]

    if do_rescale:
     __a :  List[Any]						=  [self.rescale(image=snake_case_					,				scale=snake_case_   ) for image in images]

    if do_normalize:
     __a :  Optional[int]						=  [self.normalize(image=snake_case_					,				mean=snake_case_					,				std=snake_case_   ) for image in images]

    __a :  str						=  [to_channel_dimension_format(snake_case_					,				snake_case_   ) for image in images]

    __a :  Tuple						=  {'''pixel_values''': images}
    return BatchFeature(data=snake_case_					,				tensor_type=snake_case_   )
