

import torch
from torch import autograd


class 					A		(				autograd.Function					):







    """simple docstring"""





    @staticmethod
    def 		snake_case__	( lowercase_					:			Union[str, Any],lowercase_					:			torch.tensor,lowercase_					:			float,lowercase_					:			bool						)->       Optional[int]:





        '''simple docstring'''







        A__        =  inputs.numel()
        A__        =  int(0.005 * nb_elems						) + 1
        if sigmoid:
            A__        =  (torch.sigmoid(lowercase_						) > threshold).type(inputs.type()						)
        else:
            A__        =  (inputs > threshold).type(inputs.type()						)
        if mask.sum() < nb_min:
            # We limit the pruning so that at least 0.5% (half a percent) of the weights are remaining
            A__        =  inputs.flatten().kthvalue(max(nb_elems - nb_min,1						)						).values
            A__        =  (inputs > k_threshold).type(inputs.type()						)
        return mask


    @staticmethod
    def 		snake_case__	( lowercase_					:			Tuple,lowercase_					:			str						)->       str:





        '''simple docstring'''







        return gradOutput, None, None




class 					A		(				autograd.Function					):







    """simple docstring"""





    @staticmethod
    def 		snake_case__	( lowercase_					:			int,lowercase_					:			torch.tensor,lowercase_					:			float						)->       Any:





        '''simple docstring'''







        A__        =  inputs.clone()
        A__ ,		A__        =  inputs.flatten().sort(descending=lowercase_						)
        A__        =  int(threshold * inputs.numel()						)

        # flat_out and mask access the same memory.
        A__        =  mask.flatten()
        A__        =  0
        A__        =  1
        return mask


    @staticmethod
    def 		snake_case__	( lowercase_					:			Tuple,lowercase_					:			Tuple						)->       Any:





        '''simple docstring'''







        return gradOutput, None




class 					A		(				_UpperCAmelCase					):







    """simple docstring"""





    @staticmethod
    def 		snake_case__	( lowercase_					:			torch.tensor,lowercase_					:			float						)->       List[str]:





        '''simple docstring'''







        A__        =  inputs.clone()
        A__ ,		A__        =  inputs.abs().flatten().sort(descending=lowercase_						)
        A__        =  int(threshold * inputs.numel()						)

        # flat_out and mask access the same memory.
        A__        =  mask.flatten()
        A__        =  0
        A__        =  1
        return mask




import re
import tempfile
import unittest
from pathlib import Path

from datasets.utils.metadata import DatasetMetadata
def 		_snake_case(       SCREAMING_SNAKE_CASE__     :      str )				-> str:





    '''simple docstring'''






    A__        =  min(re.search('^ +'       ,     SCREAMING_SNAKE_CASE__ ).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(SCREAMING_SNAKE_CASE__ )] )


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

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


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


class 					A		(				unittest.TestCase					):







    """simple docstring"""





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





        '''simple docstring'''







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

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

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


    def 		snake_case__	( self					:			Tuple						)->       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(lowercase_						)

        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(lowercase_						):
            DatasetMetadata.from_yaml_string(lowercase_						)

        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(lowercase_						)

