






import re
import tempfile
import unittest
from pathlib import Path

from datasets.utils.metadata import DatasetMetadata

def        _lowerCamelCase		(  __A      :							int	) ->   List[str]:
    _UpperCAmelCase			: int     					=			min(re.search('''^ +'''						,						lowercase__	).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(lowercase__	)]	)


SCREAMING_SNAKE_CASE								=  '\\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								=  '\\n---\n---\n# Begin of markdown\n\nSome cool dataset card\n'


SCREAMING_SNAKE_CASE								=  '\\n# Begin of markdown\n\nSome cool dataset card\n'


class 							__magic_name__      (  unittest.TestCase     ):






 '''simple docstring'''




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

     """simple docstring"""



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

         with open(lowerCamelCase__							,					'''w+''') as readme_file:
             readme_file.write(lowerCamelCase__)
         _UpperCAmelCase			: List[str]     					=			DatasetMetadata.from_readme(lowerCamelCase__)
         self.assertDictEqual(lowerCamelCase__							,					{})

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


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

     """simple docstring"""



     _UpperCAmelCase			: Tuple     					=			_dedent(
         '''\
            annotations_creators:
            - found
            language_creators:
            - found
            language:
            - en
            license:
            - unknown
            multilinguality:
            - monolingual
            pretty_name: Test Dataset
            size_categories:
            - 10K<n<100K
            source_datasets:
            - extended|other-yahoo-webscope-l6
            task_categories:
            - question-answering
            task_ids:
            - open-domain-qa
            ''')
     assert DatasetMetadata.from_yaml_string(lowerCamelCase__)

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

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












from collections import OrderedDict
from typing import Mapping

from packaging import version

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


SCREAMING_SNAKE_CASE								=  logging.get_logger(__name__)

SCREAMING_SNAKE_CASE								=  {
    'MBZUAI/swiftformer-xs': 'https://huggingface.co/MBZUAI/swiftformer-xs/resolve/main/config.json',
}
class 							A_      (  __lowercase     ):






 '''simple docstring'''




 _SCREAMING_SNAKE_CASE							:     Dict	=   "swiftformer"


 def __init__(		self							,					_A=3							,					_A=[3, 3, 6, 4]							,					_A=[48, 56, 112, 220]							,					_A=4							,					_A=[True, True, True, True]							,					_A="gelu"							,					_A=3							,					_A=2							,					_A=1							,					_A=0.0							,					_A=True							,					_A=1e-5							,					_A=1e-5							,					**_A							,					)	->						List[Any]:

     """simple docstring"""



     super().__init__(**_A)
     _UpperCAmelCase			: Tuple     					=			num_channels
     _UpperCAmelCase			: Optional[int]     					=			depths
     _UpperCAmelCase			: Union[str, Any]     					=			embed_dims
     _UpperCAmelCase			: Dict     					=			mlp_ratio
     _UpperCAmelCase			: Tuple     					=			downsamples
     _UpperCAmelCase			: str     					=			hidden_act
     _UpperCAmelCase			: int     					=			down_patch_size
     _UpperCAmelCase			: Dict     					=			down_stride
     _UpperCAmelCase			: Dict     					=			down_pad
     _UpperCAmelCase			: Tuple     					=			drop_path_rate
     _UpperCAmelCase			: Tuple     					=			use_layer_scale
     _UpperCAmelCase			: int     					=			layer_scale_init_value
     _UpperCAmelCase			: int     					=			batch_norm_eps



class 							A_      (  __lowercase     ):






 '''simple docstring'''




 _SCREAMING_SNAKE_CASE							:     List[Any]	=   version.parse("1.11"     )


 @property
 def 	snake_case__			(		self)	->						Mapping[str, Mapping[int, str]]:

     """simple docstring"""



     return OrderedDict(
         [
             ('''pixel_values''', {0: '''batch''', 1: '''num_channels''', 2: '''height''', 3: '''width'''}),
         ])


 @property
 def 	snake_case__			(		self)	->						float:

     """simple docstring"""



     return 1e-4




