import json
import pathlib
import unittest

import numpy as np

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

from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs


if is_torch_available():
   import torch

if is_vision_available():
   from PIL import Image

   from transformers import ConditionalDetrImageProcessor







class __snake_case       (		unittest.TestCase	):




   def __init__(					self    :       int ,			_lowercase    :       Optional[int] ,			_lowercase    :       Any=7 ,			_lowercase    :       List[str]=3 ,			_lowercase    :       Union[str, Any]=30 ,			_lowercase    :       str=4_00 ,			_lowercase    :       Any=True ,			_lowercase    :       Dict=None ,			_lowercase    :       Union[str, Any]=True ,			_lowercase    :       Any=[0.5, 0.5, 0.5] ,			_lowercase    :       Any=[0.5, 0.5, 0.5] ,			_lowercase    :       List[Any]=True ,			_lowercase    :       Any=1 / 2_55 ,			_lowercase    :       List[str]=True ,			):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__    							=   size if size is not None else {"""shortest_edge""": 18, """longest_edge""": 13_33}
     SCREAMING_SNAKE_CASE__    							=   parent
     SCREAMING_SNAKE_CASE__    							=   batch_size
     SCREAMING_SNAKE_CASE__    							=   num_channels
     SCREAMING_SNAKE_CASE__    							=   min_resolution
     SCREAMING_SNAKE_CASE__    							=   max_resolution
     SCREAMING_SNAKE_CASE__    							=   do_resize
     SCREAMING_SNAKE_CASE__    							=   size
     SCREAMING_SNAKE_CASE__    							=   do_normalize
     SCREAMING_SNAKE_CASE__    							=   image_mean
     SCREAMING_SNAKE_CASE__    							=   image_std
     SCREAMING_SNAKE_CASE__    							=   do_rescale
     SCREAMING_SNAKE_CASE__    							=   rescale_factor
     SCREAMING_SNAKE_CASE__    							=   do_pad




   def 			__a		(					self    :       Tuple   ):



     """simple docstring"""






     return {
         "do_resize": self.do_resize,
         "size": self.size,
         "do_normalize": self.do_normalize,
         "image_mean": self.image_mean,
         "image_std": self.image_std,
         "do_rescale": self.do_rescale,
         "rescale_factor": self.rescale_factor,
         "do_pad": self.do_pad,
     }




   def 			__a		(					self    :       Optional[Any] ,			_lowercase    :       Union[str, Any] ,			_lowercase    :       List[Any]=False   ):



     """simple docstring"""






     if not batched:
       SCREAMING_SNAKE_CASE__    							=   image_inputs[0]
       if isinstance(_lowercase ,			Image.Image   ):
         SCREAMING_SNAKE_CASE__							,  SCREAMING_SNAKE_CASE__    							=   image.size
       else:
         SCREAMING_SNAKE_CASE__							,  SCREAMING_SNAKE_CASE__    							=   image.shape[1], image.shape[2]
       if w < h:
         SCREAMING_SNAKE_CASE__    							=   int(self.size["""shortest_edge"""] * h / w   )
         SCREAMING_SNAKE_CASE__    							=   self.size["""shortest_edge"""]
       elif w > h:
         SCREAMING_SNAKE_CASE__    							=   self.size["""shortest_edge"""]
         SCREAMING_SNAKE_CASE__    							=   int(self.size["""shortest_edge"""] * w / h   )
       else:
         SCREAMING_SNAKE_CASE__    							=   self.size["""shortest_edge"""]
         SCREAMING_SNAKE_CASE__    							=   self.size["""shortest_edge"""]

     else:
       SCREAMING_SNAKE_CASE__    							=   []
       for image in image_inputs:
         SCREAMING_SNAKE_CASE__							,  SCREAMING_SNAKE_CASE__    							=   self.get_expected_values([image]   )
         expected_values.append((expected_height, expected_width)   )
       SCREAMING_SNAKE_CASE__    							=   max(_lowercase ,			key=lambda _lowercase   : item[0]   )[0]
       SCREAMING_SNAKE_CASE__    							=   max(_lowercase ,			key=lambda _lowercase   : item[1]   )[1]

     return expected_height, expected_width







@require_torch
@require_vision
class __snake_case       (		lowerCamelCase_     ,	unittest.TestCase	):
   lowerCAmelCase_														=							ConditionalDetrImageProcessor if is_vision_available() else None




   def 			__a		(					self    :       Dict   ):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__    							=   ConditionalDetrImageProcessingTester(self   )




   @property
   def 			__a		(					self    :       Optional[int]   ):



     """simple docstring"""






     return self.image_processor_tester.prepare_image_processor_dict()




   def 			__a		(					self    :       Tuple   ):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__    							=   self.image_processing_class(**self.image_processor_dict   )
     self.assertTrue(hasattr(_lowercase ,			"""image_mean"""   )   )
     self.assertTrue(hasattr(_lowercase ,			"""image_std"""   )   )
     self.assertTrue(hasattr(_lowercase ,			"""do_normalize"""   )   )
     self.assertTrue(hasattr(_lowercase ,			"""do_resize"""   )   )
     self.assertTrue(hasattr(_lowercase ,			"""size"""   )   )




   def 			__a		(					self    :       int   ):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__    							=   self.image_processing_class.from_dict(self.image_processor_dict   )
     self.assertEqual(image_processor.size ,			{"""shortest_edge""": 18, """longest_edge""": 13_33}   )
     self.assertEqual(image_processor.do_pad ,			_lowercase   )

     SCREAMING_SNAKE_CASE__    							=   self.image_processing_class.from_dict(
         self.image_processor_dict ,			size=42 ,			max_size=84 ,			pad_and_return_pixel_mask=_lowercase   )
     self.assertEqual(image_processor.size ,			{"""shortest_edge""": 42, """longest_edge""": 84}   )
     self.assertEqual(image_processor.do_pad ,			_lowercase   )




   def 			__a		(					self    :       str   ):



     """simple docstring"""






     pass




   def 			__a		(					self    :       Tuple   ):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__    							=   self.image_processing_class(**self.image_processor_dict   )
     # create random PIL images
     SCREAMING_SNAKE_CASE__    							=   prepare_image_inputs(self.image_processor_tester ,			equal_resolution=_lowercase   )
     for image in image_inputs:
       self.assertIsInstance(_lowercase ,			Image.Image   )

     # Test not batched input
     SCREAMING_SNAKE_CASE__    							=   image_processing(image_inputs[0] ,			return_tensors="""pt"""   ).pixel_values

     SCREAMING_SNAKE_CASE__							,  SCREAMING_SNAKE_CASE__    							=   self.image_processor_tester.get_expected_values(_lowercase   )

     self.assertEqual(
         encoded_images.shape ,			(1, self.image_processor_tester.num_channels, expected_height, expected_width) ,			)

     # Test batched
     SCREAMING_SNAKE_CASE__							,  SCREAMING_SNAKE_CASE__    							=   self.image_processor_tester.get_expected_values(_lowercase ,			batched=_lowercase   )

     SCREAMING_SNAKE_CASE__    							=   image_processing(_lowercase ,			return_tensors="""pt"""   ).pixel_values
     self.assertEqual(
         encoded_images.shape ,			(
             self.image_processor_tester.batch_size,
             self.image_processor_tester.num_channels,
             expected_height,
             expected_width,
         ) ,			)




   def 			__a		(					self    :       Optional[int]   ):



     """simple docstring"""






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

     # Test not batched input
     SCREAMING_SNAKE_CASE__    							=   image_processing(image_inputs[0] ,			return_tensors="""pt"""   ).pixel_values

     SCREAMING_SNAKE_CASE__							,  SCREAMING_SNAKE_CASE__    							=   self.image_processor_tester.get_expected_values(_lowercase   )

     self.assertEqual(
         encoded_images.shape ,			(1, self.image_processor_tester.num_channels, expected_height, expected_width) ,			)

     # Test batched
     SCREAMING_SNAKE_CASE__    							=   image_processing(_lowercase ,			return_tensors="""pt"""   ).pixel_values

     SCREAMING_SNAKE_CASE__							,  SCREAMING_SNAKE_CASE__    							=   self.image_processor_tester.get_expected_values(_lowercase ,			batched=_lowercase   )

     self.assertEqual(
         encoded_images.shape ,			(
             self.image_processor_tester.batch_size,
             self.image_processor_tester.num_channels,
             expected_height,
             expected_width,
         ) ,			)




   def 			__a		(					self    :       Tuple   ):



     """simple docstring"""






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

     # Test not batched input
     SCREAMING_SNAKE_CASE__    							=   image_processing(image_inputs[0] ,			return_tensors="""pt"""   ).pixel_values

     SCREAMING_SNAKE_CASE__							,  SCREAMING_SNAKE_CASE__    							=   self.image_processor_tester.get_expected_values(_lowercase   )

     self.assertEqual(
         encoded_images.shape ,			(1, self.image_processor_tester.num_channels, expected_height, expected_width) ,			)

     # Test batched
     SCREAMING_SNAKE_CASE__    							=   image_processing(_lowercase ,			return_tensors="""pt"""   ).pixel_values

     SCREAMING_SNAKE_CASE__							,  SCREAMING_SNAKE_CASE__    							=   self.image_processor_tester.get_expected_values(_lowercase ,			batched=_lowercase   )

     self.assertEqual(
         encoded_images.shape ,			(
             self.image_processor_tester.batch_size,
             self.image_processor_tester.num_channels,
             expected_height,
             expected_width,
         ) ,			)




   @slow
   def 			__a		(					self    :       List[Any]   ):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__    							=   Image.open("""./tests/fixtures/tests_samples/COCO/000000039769.png"""   )
     with open("""./tests/fixtures/tests_samples/COCO/coco_annotations.txt""" ,			"""r"""   ) as f:
       SCREAMING_SNAKE_CASE__    							=   json.loads(f.read()   )

     SCREAMING_SNAKE_CASE__    							=   {"""image_id""": 3_97_69, """annotations""": target}

     # encode them
     SCREAMING_SNAKE_CASE__    							=   ConditionalDetrImageProcessor.from_pretrained("""microsoft/conditional-detr-resnet-50"""   )
     SCREAMING_SNAKE_CASE__    							=   image_processing(images=_lowercase ,			annotations=_lowercase ,			return_tensors="""pt"""   )

     # verify pixel values
     SCREAMING_SNAKE_CASE__    							=   torch.Size([1, 3, 8_00, 10_66]   )
     self.assertEqual(encoding["""pixel_values"""].shape ,			_lowercase   )

     SCREAMING_SNAKE_CASE__    							=   torch.tensor([0.27_96, 0.31_38, 0.34_81]   )
     self.assertTrue(torch.allclose(encoding["""pixel_values"""][0, 0, 0, :3] ,			_lowercase ,			atol=1E-4   )   )

     # verify area
     SCREAMING_SNAKE_CASE__    							=   torch.tensor([58_87.96_00, 1_12_50.20_61, 48_93_53.84_38, 83_71_22.75_00, 14_79_67.51_56, 16_57_32.34_38]   )
     self.assertTrue(torch.allclose(encoding["""labels"""][0]["""area"""] ,			_lowercase   )   )
     # verify boxes
     SCREAMING_SNAKE_CASE__    							=   torch.Size([6, 4]   )
     self.assertEqual(encoding["""labels"""][0]["""boxes"""].shape ,			_lowercase   )
     SCREAMING_SNAKE_CASE__    							=   torch.tensor([0.55_03, 0.27_65, 0.06_04, 0.22_15]   )
     self.assertTrue(torch.allclose(encoding["""labels"""][0]["""boxes"""][0] ,			_lowercase ,			atol=1E-3   )   )
     # verify image_id
     SCREAMING_SNAKE_CASE__    							=   torch.tensor([3_97_69]   )
     self.assertTrue(torch.allclose(encoding["""labels"""][0]["""image_id"""] ,			_lowercase   )   )
     # verify is_crowd
     SCREAMING_SNAKE_CASE__    							=   torch.tensor([0, 0, 0, 0, 0, 0]   )
     self.assertTrue(torch.allclose(encoding["""labels"""][0]["""iscrowd"""] ,			_lowercase   )   )
     # verify class_labels
     SCREAMING_SNAKE_CASE__    							=   torch.tensor([75, 75, 63, 65, 17, 17]   )
     self.assertTrue(torch.allclose(encoding["""labels"""][0]["""class_labels"""] ,			_lowercase   )   )
     # verify orig_size
     SCREAMING_SNAKE_CASE__    							=   torch.tensor([4_80, 6_40]   )
     self.assertTrue(torch.allclose(encoding["""labels"""][0]["""orig_size"""] ,			_lowercase   )   )
     # verify size
     SCREAMING_SNAKE_CASE__    							=   torch.tensor([8_00, 10_66]   )
     self.assertTrue(torch.allclose(encoding["""labels"""][0]["""size"""] ,			_lowercase   )   )




   @slow
   def 			__a		(					self    :       List[str]   ):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__    							=   Image.open("""./tests/fixtures/tests_samples/COCO/000000039769.png"""   )
     with open("""./tests/fixtures/tests_samples/COCO/coco_panoptic_annotations.txt""" ,			"""r"""   ) as f:
       SCREAMING_SNAKE_CASE__    							=   json.loads(f.read()   )

     SCREAMING_SNAKE_CASE__    							=   {"""file_name""": """000000039769.png""", """image_id""": 3_97_69, """segments_info""": target}

     SCREAMING_SNAKE_CASE__    							=   pathlib.Path("""./tests/fixtures/tests_samples/COCO/coco_panoptic"""   )

     # encode them
     SCREAMING_SNAKE_CASE__    							=   ConditionalDetrImageProcessor(format="""coco_panoptic"""   )
     SCREAMING_SNAKE_CASE__    							=   image_processing(images=_lowercase ,			annotations=_lowercase ,			masks_path=_lowercase ,			return_tensors="""pt"""   )

     # verify pixel values
     SCREAMING_SNAKE_CASE__    							=   torch.Size([1, 3, 8_00, 10_66]   )
     self.assertEqual(encoding["""pixel_values"""].shape ,			_lowercase   )

     SCREAMING_SNAKE_CASE__    							=   torch.tensor([0.27_96, 0.31_38, 0.34_81]   )
     self.assertTrue(torch.allclose(encoding["""pixel_values"""][0, 0, 0, :3] ,			_lowercase ,			atol=1E-4   )   )

     # verify area
     SCREAMING_SNAKE_CASE__    							=   torch.tensor([14_79_79.68_75, 16_55_27.04_69, 48_46_38.59_38, 1_12_92.93_75, 58_79.65_62, 76_34.11_47]   )
     self.assertTrue(torch.allclose(encoding["""labels"""][0]["""area"""] ,			_lowercase   )   )
     # verify boxes
     SCREAMING_SNAKE_CASE__    							=   torch.Size([6, 4]   )
     self.assertEqual(encoding["""labels"""][0]["""boxes"""].shape ,			_lowercase   )
     SCREAMING_SNAKE_CASE__    							=   torch.tensor([0.26_25, 0.54_37, 0.46_88, 0.86_25]   )
     self.assertTrue(torch.allclose(encoding["""labels"""][0]["""boxes"""][0] ,			_lowercase ,			atol=1E-3   )   )
     # verify image_id
     SCREAMING_SNAKE_CASE__    							=   torch.tensor([3_97_69]   )
     self.assertTrue(torch.allclose(encoding["""labels"""][0]["""image_id"""] ,			_lowercase   )   )
     # verify is_crowd
     SCREAMING_SNAKE_CASE__    							=   torch.tensor([0, 0, 0, 0, 0, 0]   )
     self.assertTrue(torch.allclose(encoding["""labels"""][0]["""iscrowd"""] ,			_lowercase   )   )
     # verify class_labels
     SCREAMING_SNAKE_CASE__    							=   torch.tensor([17, 17, 63, 75, 75, 93]   )
     self.assertTrue(torch.allclose(encoding["""labels"""][0]["""class_labels"""] ,			_lowercase   )   )
     # verify masks
     SCREAMING_SNAKE_CASE__    							=   82_28_73
     self.assertEqual(encoding["""labels"""][0]["""masks"""].sum().item() ,			_lowercase   )
     # verify orig_size
     SCREAMING_SNAKE_CASE__    							=   torch.tensor([4_80, 6_40]   )
     self.assertTrue(torch.allclose(encoding["""labels"""][0]["""orig_size"""] ,			_lowercase   )   )
     # verify size
     SCREAMING_SNAKE_CASE__    							=   torch.tensor([8_00, 10_66]   )
     self.assertTrue(torch.allclose(encoding["""labels"""][0]["""size"""] ,			_lowercase   )   )

import re
import tempfile
from pathlib import Path

import pytest
import yaml

from datasets.utils.readme import ReadMe


# @pytest.fixture
# def example_yaml_structure():

__lowerCamelCase    :				str							     =	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
'''
)


__lowerCamelCase    :				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''': []},
                    ],
                },
            ],
        }
    ],
}


__lowerCamelCase    :				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
'''


__lowerCamelCase    :				List[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.
#### Extra Ignored Subsection
### Supported Tasks and Leaderboards
### Languages
Language Text
'''

__lowerCamelCase    :				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''': []},
                    ],
                },
            ],
        }
    ],
}

__lowerCamelCase    :				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
'''

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

__lowerCamelCase    :				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
'''

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

__lowerCamelCase    :				List[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
'''

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

__lowerCamelCase    :				str							     =	'''\
---
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
'''
__lowerCamelCase    :				Any							     =	'''The following issues were found for the README at `{path}`:\n-\tExpected some content in section `Dataset Summary` but it is empty.\n-\tExpected some text in section `Dataset Summary` but it is empty (text in subsections are ignored).'''


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

# Dataset Card for My Dataset
'''
__lowerCamelCase    :				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\'.'''

__lowerCamelCase    :				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
'''

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


__lowerCamelCase    :				Optional[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
'''

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

__lowerCamelCase    :				List[str]							     =	'''\
---
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
'''
__lowerCamelCase    :				List[str]							     =	'''The following issues were found for the README at `{path}`:\n-\tThe README has no first-level headings. One heading is expected. Skipping further validation for this README.'''

__lowerCamelCase    :				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
'''

__lowerCamelCase    :				int							     =	'''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.'''

__lowerCamelCase    :				List[Any]							     =	'''\
---
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
'''

__lowerCamelCase    :				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.'''

__lowerCamelCase    :				Optional[Any]							     =	''''''

__lowerCamelCase    :				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.'''

__lowerCamelCase    :				str							     =	'''\
---
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
'''

__lowerCamelCase    :				Dict							     =	'''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 			__SCREAMING_SNAKE_CASE						(      __UpperCamelCase					: Union[str, Any]				,  __UpperCamelCase					: Union[str, Any]			)							->      Dict:







  """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 			__SCREAMING_SNAKE_CASE						(      __UpperCamelCase					: Optional[Any]				,  __UpperCamelCase					: Dict			)							->      Union[str, Any]:







  """simple docstring"""





  with pytest.raises(__UpperCamelCase				,  match=re.escape(expected_error.format(path="""root"""			)			)			):
    SCREAMING_SNAKE_CASE__    							=   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 			__SCREAMING_SNAKE_CASE						(      __UpperCamelCase					: Optional[int]				,  __UpperCamelCase					: Optional[Any]			)							->      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 			__SCREAMING_SNAKE_CASE						(      __UpperCamelCase					: Optional[int]			)							->      List[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 			__SCREAMING_SNAKE_CASE						(      __UpperCamelCase					: Any				,  __UpperCamelCase					: Union[str, Any]			)							->      Dict:







  """simple docstring"""





  with tempfile.TemporaryDirectory() as tmp_dir:
    SCREAMING_SNAKE_CASE__    							=   Path(__UpperCamelCase			) / """README.md"""
    with open(__UpperCamelCase				,  """w+"""			) as readme_file:
      readme_file.write(__UpperCamelCase			)
    SCREAMING_SNAKE_CASE__    							=   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 			__SCREAMING_SNAKE_CASE						(      __UpperCamelCase					: List[str]				,  __UpperCamelCase					: Any			)							->      Optional[Any]:







  """simple docstring"""





  with tempfile.TemporaryDirectory() as tmp_dir:
    SCREAMING_SNAKE_CASE__    							=   Path(__UpperCamelCase			) / """README.md"""
    with open(__UpperCamelCase				,  """w+"""			) as readme_file:
      readme_file.write(__UpperCamelCase			)
    SCREAMING_SNAKE_CASE__    							=   expected_error.format(path=__UpperCamelCase			)
    with pytest.raises(__UpperCamelCase				,  match=re.escape(__UpperCamelCase			)			):
      SCREAMING_SNAKE_CASE__    							=   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 			__SCREAMING_SNAKE_CASE						(      __UpperCamelCase					: List[Any]				,  __UpperCamelCase					: Optional[int]			)							->      Optional[Any]:







  """simple docstring"""





  with tempfile.TemporaryDirectory() as tmp_dir:
    SCREAMING_SNAKE_CASE__    							=   Path(__UpperCamelCase			) / """README.md"""
    with open(__UpperCamelCase				,  """w+"""			) as readme_file:
      readme_file.write(__UpperCamelCase			)
    SCREAMING_SNAKE_CASE__    							=   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 			__SCREAMING_SNAKE_CASE						(      __UpperCamelCase					: Optional[Any]			)							->      List[Any]:







  """simple docstring"""





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