"""simple docstring"""



import inspect

from diffusers.utils import is_flax_available
from diffusers.utils.testing_utils import require_flax


if is_flax_available():
     import jax

@require_flax
class SCREAMING_SNAKE_CASE     :







 """simple docstring"""


 def        _lowerCAmelCase				(   self		:      Tuple				)				->			Optional[int]:




    '''simple docstring'''






    a__				,       a__     				=		self.prepare_init_args_and_inputs_for_common()

    a__     				=		self.model_class(**_snake_case				)
    a__     				=		model.init(inputs_dict['prng_key']					,   inputs_dict['sample']				)
    jax.lax.stop_gradient(_snake_case				)

    a__     				=		model.apply(_snake_case					,   inputs_dict['sample']				)

    if isinstance(_snake_case					,   _snake_case				):
       a__     				=		output.sample

    self.assertIsNotNone(_snake_case				)
    a__     				=		inputs_dict['sample'].shape
    self.assertEqual(output.shape					,   _snake_case					,   'Input and output shapes do not match'				)


 def        _lowerCAmelCase				(   self		:      int				)				->			List[Any]:




    '''simple docstring'''






    a__				,       a__     				=		self.prepare_init_args_and_inputs_for_common()

    a__     				=		16
    a__     				=		(16, 32)

    a__     				=		self.model_class(**_snake_case				)
    a__     				=		model.init(inputs_dict['prng_key']					,   inputs_dict['sample']				)
    jax.lax.stop_gradient(_snake_case				)

    a__     				=		model.apply(_snake_case					,   inputs_dict['sample']				)

    if isinstance(_snake_case					,   _snake_case				):
       a__     				=		output.sample

    self.assertIsNotNone(_snake_case				)
    a__     				=		inputs_dict['sample'].shape
    self.assertEqual(output.shape					,   _snake_case					,   'Input and output shapes do not match'				)



 def        _lowerCAmelCase				(   self		:      List[str]				)				->			Tuple:




    '''simple docstring'''






    a__     				=		'kwargs' in inspect.signature(self.model_class.__init__				).parameters
    a__     				=		len(self.model_class._deprecated_kwargs				) > 0

    if has_kwarg_in_model_class and not has_deprecated_kwarg:
       raise ValueError(
           F'''{self.model_class} has `**kwargs` in its __init__ method but has not defined any deprecated kwargs'''
           ' under the `_deprecated_kwargs` class attribute. Make sure to either remove `**kwargs` if there are'
           ' no deprecated arguments or add the deprecated argument with `_deprecated_kwargs ='
           ' [<deprecated_argument>]`'				)

    if not has_kwarg_in_model_class and has_deprecated_kwarg:
       raise ValueError(
           F'''{self.model_class} doesn\'t have `**kwargs` in its __init__ method but has defined deprecated kwargs'''
           ' under the `_deprecated_kwargs` class attribute. Make sure to either add the `**kwargs` argument to'
           F''' {self.model_class}.__init__ if there are deprecated arguments or remove the deprecated argument'''
           ' from `_deprecated_kwargs = [<deprecated_argument>]`'				)



"""simple docstring"""



import re
import tempfile
import unittest
from pathlib import Path

from datasets.utils.metadata import DatasetMetadata





def   _lowerCamelCase ( UpperCAmelCase__					)							->     str:


   '''simple docstring'''


   a__     				=		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__					)]					)


__magic_name__    				=					"\\n---\nlanguage:\n- zh\n- en\ntask_ids:\n- sentiment-classification\n---\n# Begin of markdown\n\nSome cool dataset card\n"

__magic_name__    				=					"\\n---\n---\n# Begin of markdown\n\nSome cool dataset card\n"


__magic_name__    				=					"\\n# Begin of markdown\n\nSome cool dataset card\n"




class SCREAMING_SNAKE_CASE     (    unittest.TestCase	):







 """simple docstring"""


 def        _lowerCAmelCase				(   self		:      Tuple				)				->			Dict:




    '''simple docstring'''






    with tempfile.TemporaryDirectory() as tmp_dir:
       a__     				=		Path(_snake_case				) / 'README.md'
       with open(_snake_case					,   'w+'				) as readme_file:
          readme_file.write(_snake_case				)
       a__     				=		DatasetMetadata.from_readme(_snake_case				)
       self.assertDictEqual(_snake_case					,   {'language': ['zh', 'en'], 'task_ids': ['sentiment-classification']}				)

       with open(_snake_case					,   'w+'				) as readme_file:
          readme_file.write(_snake_case				)
       a__     				=		DatasetMetadata.from_readme(_snake_case				)
       self.assertDictEqual(_snake_case					,   {}				)

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



 def        _lowerCAmelCase				(   self		:      List[Any]				)				->			List[Any]:




    '''simple docstring'''






    a__     				=		_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(_snake_case				)

    a__     				=		_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(_snake_case				):
       DatasetMetadata.from_yaml_string(_snake_case				)

    a__     				=		_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(_snake_case				)


