






'''simple docstring'''







from typing import List

import PIL
import torch
from PIL import Image

from ...configuration_utils import ConfigMixin
from ...models.modeling_utils import ModelMixin
from ...utils import PIL_INTERPOLATION

class _lowercase      (							_UpperCAmelCase							, _UpperCAmelCase						):




							def __init__(				self:							Any	):
														super().__init__()

														self.register_buffer("""watermark_image"""     ,				torch.zeros((62, 62, 4)	)	)
														lowerCamelCase__   :      Optional[Any]			    =			None




							def 						lowerCamelCase_							(				self:							List[Any]     ,				UpperCamelCase__:							Any     ,				UpperCamelCase__:							Any=None	):
														# copied from https://github.com/deep-floyd/IF/blob/b77482e36ca2031cb94dbca1001fc1e6400bf4ab/deepfloyd_if/modules/base.py#L287

														lowerCamelCase__   :      Union[str, Any]			    =			images[0].height
														lowerCamelCase__   :      int			    =			images[0].width

														lowerCamelCase__   :      Optional[int]			    =			sample_size or h

														lowerCamelCase__   :      Optional[int]			    =			min(h / sample_size     ,				w / sample_size	)
														lowerCamelCase__   :      Union[str, Any]			    =			(int(h / coef	), int(w / coef	)) if coef < 1 else (h, w)

														lowerCamelCase__   :      Optional[int]			    =			1_024**2, img_w * img_h
														lowerCamelCase__   :      Dict			    =			(Sa / Sa) ** 0.5
														lowerCamelCase__   :      int			    =			int(K * 62	), img_w - int(14 * K	), img_h - int(14 * K	)

														if self.watermark_image_as_pil is None:
																					lowerCamelCase__   :      Tuple			    =			self.watermark_image.to(torch.uinta	).cpu().numpy()
																					lowerCamelCase__   :      Tuple			    =			Image.fromarray(__UpperCAmelCase     ,				mode="""RGBA"""	)
																					lowerCamelCase__   :      List[Any]			    =			watermark_image

														lowerCamelCase__   :      Optional[Any]			    =			self.watermark_image_as_pil.resize(
														    (wm_size, wm_size)     ,				PIL_INTERPOLATION["""bicubic"""]     ,				reducing_gap=__UpperCAmelCase	)

														for pil_img in images:
																					pil_img.paste(__UpperCAmelCase     ,				box=(wm_x - wm_size, wm_y - wm_size, wm_x, wm_y)     ,				mask=wm_img.split()[-1]	)

														return images





"""simple docstring"""






import re
import tempfile
import unittest
from pathlib import Path

from datasets.utils.metadata import DatasetMetadata




def        A_					(					UpperCAmelCase__				)     ->     str:
   a   :	Optional[int]						=    min(re.search('^ +'       ,   UpperCAmelCase__				).end() if t.startswith(' '				) else 0 for t in string.splitlines()				)
   return "\n".join([line[indent_level:] for line in string.splitlines() if indent_level < len(UpperCAmelCase__				)]				)


SCREAMING_SNAKE_CASE__				:  Union[str, Any]						= "\\n---\nlanguage:\n- zh\n- en\ntask_ids:\n- sentiment-classification\n---\n# Begin of markdown\n\nSome cool dataset card\n"

SCREAMING_SNAKE_CASE__				:  List[str]						= "\\n---\n---\n# Begin of markdown\n\nSome cool dataset card\n"


SCREAMING_SNAKE_CASE__				:  Dict						= "\\n# Begin of markdown\n\nSome cool dataset card\n"


class 	A_       (     unittest.TestCase   ):





  """simple docstring"""

  def 							lowercase_     (						self						)				->					Any:
     with tempfile.TemporaryDirectory() as tmp_dir:
        a   :	Optional[Any]						=    Path(__UpperCAmelCase						) / 'README.md'
        with open(__UpperCAmelCase  ,					'w+'						) as readme_file:
           readme_file.write(__UpperCAmelCase						)
        a   :	str						=    DatasetMetadata.from_readme(__UpperCAmelCase						)
        self.assertDictEqual(__UpperCAmelCase  ,					{'language': ['zh', 'en'], 'task_ids': ['sentiment-classification']}						)

        with open(__UpperCAmelCase  ,					'w+'						) as readme_file:
           readme_file.write(__UpperCAmelCase						)
        a   :	Optional[Any]						=    DatasetMetadata.from_readme(__UpperCAmelCase						)
        self.assertDictEqual(__UpperCAmelCase  ,					{}						)

        with open(__UpperCAmelCase  ,					'w+'						) as readme_file:
           readme_file.write(__UpperCAmelCase						)
        a   :	str						=    DatasetMetadata.from_readme(__UpperCAmelCase						)
        self.assertEqual(__UpperCAmelCase  ,					{}						)






  def 							lowercase_     (						self						)				->					Tuple:
     a   :	List[str]						=    _dedent(
         '\\n            annotations_creators:\n            - found\n            language_creators:\n            - found\n            language:\n            - en\n            license:\n            - unknown\n            multilinguality:\n            - monolingual\n            pretty_name: Test Dataset\n            size_categories:\n            - 10K<n<100K\n            source_datasets:\n            - extended|other-yahoo-webscope-l6\n            task_categories:\n            - question-answering\n            task_ids:\n            - open-domain-qa\n            '						)
     assert DatasetMetadata.from_yaml_string(__UpperCAmelCase						)

     a   :	Optional[Any]						=    _dedent(
         '\\n            annotations_creators:\n            - found\n            language:\n            - en\n            license:\n            - unknown\n            multilinguality:\n            - monolingual\n            pretty_name: Test Dataset\n            size_categories:\n            - 10K<n<100K\n            source_datasets:\n            - extended|other-yahoo-webscope-l6\n            task_categories:\n            - question-answering\n            task_ids:\n            - open-domain-qa\n            task_ids:\n            - open-domain-qa\n            '						)
     with self.assertRaises(__UpperCAmelCase						):
        DatasetMetadata.from_yaml_string(__UpperCAmelCase						)

     a   :	List[Any]						=    _dedent(
         '\\n            annotations_creators:\n            - found\n            language_creators:\n            - found\n            language:\n            - en\n            license:\n            - unknown\n            multilinguality:\n            - monolingual\n            pretty_name: Test Dataset\n            size_categories:\n            - 10K<n<100K\n            source_datasets:\n            - extended|other-yahoo-webscope-l6\n            task_categories:\n            - text-classification\n            task_ids:\n            - multi-class-classification\n            paperswithcode_id:\n            - squad\n            configs:\n            - en\n            train-eval-index:\n            - config: en\n              task: text-classification\n              task_id: multi_class_classification\n              splits:\n                train_split: train\n                eval_split: test\n              col_mapping:\n                text: text\n                label: target\n              metrics:\n                - type: accuracy\n                  name: Accuracy\n            extra_gated_prompt: |\n              By clicking on “Access repository” below, you also agree to ImageNet Terms of Access:\n              [RESEARCHER_FULLNAME] (the "Researcher") has requested permission to use the ImageNet database (the "Database") at Princeton University and Stanford University. In exchange for such permission, Researcher hereby agrees to the following terms and conditions:\n              1. Researcher shall use the Database only for non-commercial research and educational purposes.\n            extra_gated_fields:\n              Company: text\n              Country: text\n              I agree to use this model for non-commerical use ONLY: checkbox\n            '						)
     assert DatasetMetadata.from_yaml_string(__UpperCAmelCase						)





