
import re
import tempfile
import unittest
from pathlib import Path

from datasets.utils.metadata import DatasetMetadata





def        lowerCAmelCase       (				snake_case__   :      str )->			str:
    A_								=					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__ )] )


__magic_name__  :  Optional[int]     			=   '\\n---\nlanguage:\n- zh\n- en\ntask_ids:\n- sentiment-classification\n---\n# Begin of markdown\n\nSome cool dataset card\n'

__magic_name__  :  Optional[int]     			=   '\\n---\n---\n# Begin of markdown\n\nSome cool dataset card\n'


__magic_name__  :  Optional[int]     			=   '\\n# Begin of markdown\n\nSome cool dataset card\n'



class 							lowerCamelCase       (		unittest.TestCase					):



  """simple docstring"""





  def 					lowercase_						(					self					):




      with tempfile.TemporaryDirectory() as tmp_dir:
          A_								=					Path(_UpperCAmelCase					) / 'README.md'
          with open(_UpperCAmelCase						,    "w+"					) as readme_file:
              readme_file.write(_UpperCAmelCase					)
          A_								=					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_								=					DatasetMetadata.from_readme(_UpperCAmelCase					)
          self.assertDictEqual(_UpperCAmelCase						,    {}					)

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

  def 					lowercase_						(					self					):
      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(_UpperCAmelCase					)

      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(_UpperCAmelCase					):
          DatasetMetadata.from_yaml_string(_UpperCAmelCase					)

      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(_UpperCAmelCase					)










from __future__ import annotations

from collections.abc import Callable
def 			__SCREAMING_SNAKE_CASE							(					lowercase__	:Callable[[int | float], int | float]   ,    lowercase__	:int | float   ,    lowercase__	:int | float   ,    lowercase__	:int = 1_0_0   ,    )							->	float:
						__magic_name__					:				str				=   x_start
						__magic_name__					:				str				=   fnc(lowercase__					)
						__magic_name__					:				Union[str, Any]				=   0.0

						for _ in range(lowercase__					):
												# Approximates small segments of curve as linear and solve
												# for trapezoidal area
												__magic_name__					:				Optional[int]				=   (x_end - x_start) / steps + xa
												__magic_name__					:				List[Any]				=   fnc(lowercase__					)
												area += abs(fxa + fxa					) * (xa - xa) / 2

												# Increment step
												__magic_name__					:				Optional[Any]				=   xa
												__magic_name__					:				Union[str, Any]				=   fxa
						return area


if __name__ == "__main__":
			def 			__SCREAMING_SNAKE_CASE							(					lowercase__	:Dict					)							->	Optional[int]:
									return x**3

			print('f(x) = x^3')
			print('The area between the curve, x = -10, x = 10 and the x axis is:')
			snake_case__  				=			10
			while i <= 10_0000:
						snake_case__  				=			trapezoidal_area(f, -5, 5, i)
						print(F"""with {i} steps: {area}""")
						i *= 10


