






"""simple docstring"""






import numpy as np

from transformers import BatchFeature
from transformers.testing_utils import require_tf, require_torch

from .test_feature_extraction_common import FeatureExtractionSavingTestMixin




class 							lowerCAmelCase__       (    UpperCAmelCase__				):






      '''simple docstring'''

      __UpperCamelCase							=						None
      __UpperCamelCase							=						None
      @property
      def _SCREAMING_SNAKE_CASE				(		self  :						Union[str, Any]):




          '''simple docstring'''


          return self.feat_extract_tester.prepare_feat_extract_dict()
      def _SCREAMING_SNAKE_CASE				(		self  :						List[str]):




          '''simple docstring'''


          SCREAMING_SNAKE_CASE_   :						Optional[Any]							=	self.feature_extraction_class(**self.feat_extract_dict)
          self.assertTrue(hasattr(lowercase_		,			'''feature_size'''))
          self.assertTrue(hasattr(lowercase_		,			'''sampling_rate'''))
          self.assertTrue(hasattr(lowercase_		,			'''padding_value'''))
      def _SCREAMING_SNAKE_CASE				(		self  :						Union[str, Any]):




          '''simple docstring'''


          SCREAMING_SNAKE_CASE_   :						Optional[Any]							=	self.feat_extract_tester.prepare_inputs_for_common()
          SCREAMING_SNAKE_CASE_   :						Dict							=	self.feature_extraction_class(**self.feat_extract_dict)
          SCREAMING_SNAKE_CASE_   :						Tuple							=	feat_extract.model_input_names[0]

          SCREAMING_SNAKE_CASE_   :						Dict							=	BatchFeature({input_name: speech_inputs})

          self.assertTrue(all(len(lowercase_) == len(lowercase_) for x, y in zip(lowercase_		,			processed_features[input_name])))

          SCREAMING_SNAKE_CASE_   :						Union[str, Any]							=	self.feat_extract_tester.prepare_inputs_for_common(equal_length=lowercase_)
          SCREAMING_SNAKE_CASE_   :						List[str]							=	BatchFeature({input_name: speech_inputs}		,			tensor_type='''np''')

          SCREAMING_SNAKE_CASE_   :						Tuple							=	processed_features[input_name]

          if len(batch_features_input.shape) < 3:
              SCREAMING_SNAKE_CASE_   :						Optional[Any]							=	batch_features_input[:, :, None]

          self.assertTrue(
              batch_features_input.shape
              == (self.feat_extract_tester.batch_size, len(speech_inputs[0]), self.feat_extract_tester.feature_size))
      @require_torch
      def _SCREAMING_SNAKE_CASE				(		self  :						List[str]):




          '''simple docstring'''


          SCREAMING_SNAKE_CASE_   :						int							=	self.feat_extract_tester.prepare_inputs_for_common(equal_length=lowercase_)
          SCREAMING_SNAKE_CASE_   :						Union[str, Any]							=	self.feature_extraction_class(**self.feat_extract_dict)
          SCREAMING_SNAKE_CASE_   :						Union[str, Any]							=	feat_extract.model_input_names[0]

          SCREAMING_SNAKE_CASE_   :						Tuple							=	BatchFeature({input_name: speech_inputs}		,			tensor_type='''pt''')

          SCREAMING_SNAKE_CASE_   :						Dict							=	processed_features[input_name]

          if len(batch_features_input.shape) < 3:
              SCREAMING_SNAKE_CASE_   :						Tuple							=	batch_features_input[:, :, None]

          self.assertTrue(
              batch_features_input.shape
              == (self.feat_extract_tester.batch_size, len(speech_inputs[0]), self.feat_extract_tester.feature_size))
      @require_tf
      def _SCREAMING_SNAKE_CASE				(		self  :						Dict):




          '''simple docstring'''


          SCREAMING_SNAKE_CASE_   :						str							=	self.feat_extract_tester.prepare_inputs_for_common(equal_length=lowercase_)
          SCREAMING_SNAKE_CASE_   :						List[Any]							=	self.feature_extraction_class(**self.feat_extract_dict)
          SCREAMING_SNAKE_CASE_   :						Tuple							=	feat_extract.model_input_names[0]

          SCREAMING_SNAKE_CASE_   :						Optional[int]							=	BatchFeature({input_name: speech_inputs}		,			tensor_type='''tf''')

          SCREAMING_SNAKE_CASE_   :						List[Any]							=	processed_features[input_name]

          if len(batch_features_input.shape) < 3:
              SCREAMING_SNAKE_CASE_   :						Any							=	batch_features_input[:, :, None]

          self.assertTrue(
              batch_features_input.shape
              == (self.feat_extract_tester.batch_size, len(speech_inputs[0]), self.feat_extract_tester.feature_size))
      def _SCREAMING_SNAKE_CASE				(		self  :						int		,			lowercase_  :						List[Any]=False):




          '''simple docstring'''


          def _inputs_have_equal_length(lowercase_  :						Union[str, Any]):
              SCREAMING_SNAKE_CASE_   :						Union[str, Any]							=	len(input[0])
              for input_slice in input[1:]:
                  if len(lowercase_) != length:
                      return False
              return True

          def _inputs_are_equal(lowercase_  :						Tuple		,			lowercase_  :						str):
              if len(lowercase_) != len(lowercase_):
                  return False

              for input_slice_a, input_slice_a in zip(lowercase_		,			lowercase_):
                  if not np.allclose(np.asarray(lowercase_)		,			np.asarray(lowercase_)		,			atol=1e-3):
                      return False
              return True

          SCREAMING_SNAKE_CASE_   :						Optional[Any]							=	self.feature_extraction_class(**self.feat_extract_dict)
          SCREAMING_SNAKE_CASE_   :						Dict							=	self.feat_extract_tester.prepare_inputs_for_common(numpify=lowercase_)
          SCREAMING_SNAKE_CASE_   :						Tuple							=	feat_extract.model_input_names[0]

          SCREAMING_SNAKE_CASE_   :						int							=	BatchFeature({input_name: speech_inputs})

          SCREAMING_SNAKE_CASE_   :						Any							=	self.feat_extract_tester.seq_length_diff
          SCREAMING_SNAKE_CASE_   :						str							=	self.feat_extract_tester.max_seq_length + pad_diff
          SCREAMING_SNAKE_CASE_   :						Dict							=	self.feat_extract_tester.min_seq_length
          SCREAMING_SNAKE_CASE_   :						Any							=	self.feat_extract_tester.batch_size
          SCREAMING_SNAKE_CASE_   :						str							=	self.feat_extract_tester.feature_size

          # test padding for List[int] + numpy
          SCREAMING_SNAKE_CASE_   :						Dict							=	feat_extract.pad(lowercase_		,			padding=lowercase_)
          SCREAMING_SNAKE_CASE_   :						Tuple							=	input_a[input_name]

          SCREAMING_SNAKE_CASE_   :						Optional[int]							=	feat_extract.pad(lowercase_		,			padding='''longest''')
          SCREAMING_SNAKE_CASE_   :						Optional[int]							=	input_a[input_name]

          SCREAMING_SNAKE_CASE_   :						Optional[Any]							=	feat_extract.pad(lowercase_		,			padding='''max_length'''		,			max_length=len(speech_inputs[-1]))
          SCREAMING_SNAKE_CASE_   :						Optional[Any]							=	input_a[input_name]

          SCREAMING_SNAKE_CASE_   :						Dict							=	feat_extract.pad(lowercase_		,			padding='''longest'''		,			return_tensors='''np''')
          SCREAMING_SNAKE_CASE_   :						Optional[int]							=	input_a[input_name]

          # max_length parameter has to be provided when setting `padding="max_length"`
          with self.assertRaises(lowercase_):
              feat_extract.pad(lowercase_		,			padding='''max_length''')[input_name]

          SCREAMING_SNAKE_CASE_   :						int							=	feat_extract.pad(
              lowercase_		,			padding='''max_length'''		,			max_length=lowercase_		,			return_tensors='''np''')
          SCREAMING_SNAKE_CASE_   :						int							=	input_a[input_name]

          self.assertFalse(_inputs_have_equal_length(lowercase_))
          self.assertTrue(_inputs_have_equal_length(lowercase_))
          self.assertTrue(_inputs_have_equal_length(lowercase_))
          self.assertTrue(_inputs_are_equal(lowercase_		,			lowercase_))
          self.assertTrue(len(input_a[0]) == pad_min_length)
          self.assertTrue(len(input_a[1]) == pad_min_length + pad_diff)
          self.assertTrue(input_a.shape[:2] == (batch_size, len(input_a[0])))
          self.assertTrue(input_a.shape[:2] == (batch_size, pad_max_length))

          if feature_size > 1:
              self.assertTrue(input_a.shape[2] == input_a.shape[2] == feature_size)

          # test padding for `pad_to_multiple_of` for List[int] + numpy
          SCREAMING_SNAKE_CASE_   :						Optional[int]							=	feat_extract.pad(lowercase_		,			pad_to_multiple_of=10)
          SCREAMING_SNAKE_CASE_   :						Any							=	input_a[input_name]

          SCREAMING_SNAKE_CASE_   :						List[Any]							=	feat_extract.pad(lowercase_		,			padding='''longest'''		,			pad_to_multiple_of=10)
          SCREAMING_SNAKE_CASE_   :						Any							=	input_a[input_name]

          SCREAMING_SNAKE_CASE_   :						Any							=	feat_extract.pad(
              lowercase_		,			padding='''max_length'''		,			pad_to_multiple_of=10		,			max_length=lowercase_)
          SCREAMING_SNAKE_CASE_   :						Union[str, Any]							=	input_a[input_name]

          SCREAMING_SNAKE_CASE_   :						int							=	feat_extract.pad(
              lowercase_		,			padding='''max_length'''		,			pad_to_multiple_of=10		,			max_length=lowercase_		,			return_tensors='''np'''		,			)
          SCREAMING_SNAKE_CASE_   :						Union[str, Any]							=	input_a[input_name]

          self.assertTrue(all(len(lowercase_) % 10 == 0 for x in input_a))
          self.assertTrue(_inputs_are_equal(lowercase_		,			lowercase_))

          SCREAMING_SNAKE_CASE_   :						Union[str, Any]							=	pad_max_length if pad_max_length % 10 == 0 else (pad_max_length // 10 + 1) * 10
          self.assertTrue(all(len(lowercase_) == expected_mult_pad_length for x in input_a))
          self.assertEqual(input_a.shape[:2]		,			(batch_size, expected_mult_pad_length))

          if feature_size > 1:
              self.assertTrue(input_a.shape[2] == feature_size)

          # Check padding value is correct
          SCREAMING_SNAKE_CASE_   :						int							=	(np.ones(self.feat_extract_tester.feature_size) * feat_extract.padding_value).sum()
          self.assertTrue(
              abs(np.asarray(input_a[0])[pad_min_length:].sum() - padding_vector_sum * (pad_max_length - pad_min_length))
              < 1e-3)
          self.assertTrue(
              abs(
                  np.asarray(input_a[1])[pad_min_length + pad_diff :].sum()
                  - padding_vector_sum * (pad_max_length - pad_min_length - pad_diff))
              < 1e-3)
          self.assertTrue(
              abs(
                  np.asarray(input_a[2])[pad_min_length + 2 * pad_diff :].sum()
                  - padding_vector_sum * (pad_max_length - pad_min_length - 2 * pad_diff))
              < 1e-3)
          self.assertTrue(
              abs(input_a[0, pad_min_length:].sum() - padding_vector_sum * (pad_max_length - pad_min_length)) < 1e-3)
          self.assertTrue(
              abs(input_a[0, pad_min_length:].sum() - padding_vector_sum * (expected_mult_pad_length - pad_min_length))
              < 1e-3)
      def _SCREAMING_SNAKE_CASE				(		self  :						List[Any]		,			lowercase_  :						str=False):




          '''simple docstring'''


          def _inputs_have_equal_length(lowercase_  :						Optional[int]):
              SCREAMING_SNAKE_CASE_   :						str							=	len(input[0])
              for input_slice in input[1:]:
                  if len(lowercase_) != length:
                      return False
              return True

          def _inputs_are_equal(lowercase_  :						str		,			lowercase_  :						str):
              if len(lowercase_) != len(lowercase_):
                  return False

              for input_slice_a, input_slice_a in zip(lowercase_		,			lowercase_):
                  if not np.allclose(np.asarray(lowercase_)		,			np.asarray(lowercase_)		,			atol=1e-3):
                      return False
              return True

          SCREAMING_SNAKE_CASE_   :						str							=	self.feature_extraction_class(**self.feat_extract_dict)
          SCREAMING_SNAKE_CASE_   :						List[Any]							=	self.feat_extract_tester.prepare_inputs_for_common(numpify=lowercase_)
          SCREAMING_SNAKE_CASE_   :						Optional[Any]							=	feat_extract.model_input_names[0]

          SCREAMING_SNAKE_CASE_   :						Optional[int]							=	BatchFeature({input_name: speech_inputs})

          # truncate to smallest
          SCREAMING_SNAKE_CASE_   :						Any							=	feat_extract.pad(
              lowercase_		,			padding='''max_length'''		,			max_length=len(speech_inputs[0])		,			truncation=lowercase_)
          SCREAMING_SNAKE_CASE_   :						int							=	input_a[input_name]

          SCREAMING_SNAKE_CASE_   :						Tuple							=	feat_extract.pad(lowercase_		,			padding='''max_length'''		,			max_length=len(speech_inputs[0]))
          SCREAMING_SNAKE_CASE_   :						Optional[int]							=	input_a[input_name]

          self.assertTrue(_inputs_have_equal_length(lowercase_))
          self.assertFalse(_inputs_have_equal_length(lowercase_))

          # truncate to smallest with np
          SCREAMING_SNAKE_CASE_   :						Tuple							=	feat_extract.pad(
              lowercase_		,			padding='''max_length'''		,			max_length=len(speech_inputs[0])		,			return_tensors='''np'''		,			truncation=lowercase_		,			)
          SCREAMING_SNAKE_CASE_   :						List[str]							=	input_a[input_name]

          SCREAMING_SNAKE_CASE_   :						Tuple							=	feat_extract.pad(
              lowercase_		,			padding='''max_length'''		,			max_length=len(speech_inputs[0])		,			return_tensors='''np''')
          SCREAMING_SNAKE_CASE_   :						Dict							=	input_a[input_name]

          self.assertTrue(_inputs_have_equal_length(lowercase_))
          self.assertTrue(input_a.shape[1] == len(speech_inputs[0]))

          # since truncation forces padding to be smaller than longest input
          # function can't return `np.ndarray`, but has to return list
          self.assertFalse(_inputs_have_equal_length(lowercase_))

          # truncate to middle
          SCREAMING_SNAKE_CASE_   :						List[Any]							=	feat_extract.pad(
              lowercase_		,			padding='''max_length'''		,			max_length=len(speech_inputs[1])		,			truncation=lowercase_		,			return_tensors='''np'''		,			)
          SCREAMING_SNAKE_CASE_   :						Any							=	input_a[input_name]

          SCREAMING_SNAKE_CASE_   :						List[Any]							=	feat_extract.pad(
              lowercase_		,			padding='''max_length'''		,			max_length=len(speech_inputs[1])		,			truncation=lowercase_)
          SCREAMING_SNAKE_CASE_   :						Tuple							=	input_a[input_name]

          SCREAMING_SNAKE_CASE_   :						Tuple							=	feat_extract.pad(
              lowercase_		,			padding='''max_length'''		,			max_length=len(speech_inputs[1])		,			return_tensors='''np''')
          SCREAMING_SNAKE_CASE_   :						List[Any]							=	input_a[input_name]

          self.assertTrue(input_a.shape[1] == len(speech_inputs[1]))
          self.assertTrue(_inputs_have_equal_length(lowercase_))
          self.assertTrue(_inputs_have_equal_length(lowercase_))
          self.assertTrue(_inputs_are_equal(lowercase_		,			lowercase_))

          # since truncation forces padding to be smaller than longest input
          # function can't return `np.ndarray`, but has to return list
          self.assertFalse(_inputs_have_equal_length(lowercase_))
          self.assertTrue(len(input_a[-1]) == len(speech_inputs[-1]))

          # padding has to be max_length when setting `truncation=True`
          with self.assertRaises(lowercase_):
              feat_extract.pad(lowercase_		,			truncation=lowercase_)[input_name]

          # padding has to be max_length when setting `truncation=True`
          with self.assertRaises(lowercase_):
              feat_extract.pad(lowercase_		,			padding='''longest'''		,			truncation=lowercase_)[input_name]

          # padding has to be max_length when setting `truncation=True`
          with self.assertRaises(lowercase_):
              feat_extract.pad(lowercase_		,			padding='''longest'''		,			truncation=lowercase_)[input_name]

          # max_length parameter has to be provided when setting `truncation=True` and padding="max_length"
          with self.assertRaises(lowercase_):
              feat_extract.pad(lowercase_		,			padding='''max_length'''		,			truncation=lowercase_)[input_name]

          # test truncation for `pad_to_multiple_of` for List[int] + numpy
          SCREAMING_SNAKE_CASE_   :						Union[str, Any]							=	12
          SCREAMING_SNAKE_CASE_   :						Optional[int]							=	feat_extract.pad(
              lowercase_		,			padding='''max_length'''		,			max_length=len(speech_inputs[0])		,			pad_to_multiple_of=lowercase_		,			truncation=lowercase_		,			)
          SCREAMING_SNAKE_CASE_   :						Optional[Any]							=	input_a[input_name]

          SCREAMING_SNAKE_CASE_   :						int							=	feat_extract.pad(
              lowercase_		,			padding='''max_length'''		,			max_length=len(speech_inputs[0])		,			pad_to_multiple_of=lowercase_		,			)
          SCREAMING_SNAKE_CASE_   :						List[str]							=	input_a[input_name]

          # retrieve expected_length as multiple of pad_to_multiple_of
          SCREAMING_SNAKE_CASE_   :						List[Any]							=	len(speech_inputs[0])
          if expected_length % pad_to_multiple_of != 0:
              SCREAMING_SNAKE_CASE_   :						Optional[Any]							=	((len(speech_inputs[0]) // pad_to_multiple_of) + 1) * pad_to_multiple_of

          self.assertTrue(len(input_a[0]) == expected_length)
          self.assertTrue(_inputs_have_equal_length(lowercase_))
          self.assertFalse(_inputs_have_equal_length(lowercase_))
      def _SCREAMING_SNAKE_CASE				(		self  :						Dict):




          '''simple docstring'''


          self._check_padding(numpify=lowercase_)
      def _SCREAMING_SNAKE_CASE				(		self  :						Dict):




          '''simple docstring'''


          self._check_padding(numpify=lowercase_)
      def _SCREAMING_SNAKE_CASE				(		self  :						Dict):




          '''simple docstring'''


          self._check_truncation(numpify=lowercase_)
      def _SCREAMING_SNAKE_CASE				(		self  :						int):




          '''simple docstring'''


          self._check_truncation(numpify=lowercase_)
      @require_torch
      def _SCREAMING_SNAKE_CASE				(		self  :						Optional[Any]):




          '''simple docstring'''


          SCREAMING_SNAKE_CASE_   :						int							=	self.feature_extraction_class(**self.feat_extract_dict)
          SCREAMING_SNAKE_CASE_   :						Dict							=	self.feat_extract_tester.prepare_inputs_for_common()
          SCREAMING_SNAKE_CASE_   :						Dict							=	feat_extract.model_input_names[0]

          SCREAMING_SNAKE_CASE_   :						Optional[int]							=	BatchFeature({input_name: speech_inputs})

          SCREAMING_SNAKE_CASE_   :						str							=	feat_extract.pad(lowercase_		,			padding='''longest'''		,			return_tensors='''np''')[input_name]
          SCREAMING_SNAKE_CASE_   :						str							=	feat_extract.pad(lowercase_		,			padding='''longest'''		,			return_tensors='''pt''')[input_name]

          self.assertTrue(abs(input_np.astype(np.floataa).sum() - input_pt.numpy().astype(np.floataa).sum()) < 1e-2)
      @require_tf
      def _SCREAMING_SNAKE_CASE				(		self  :						List[str]):




          '''simple docstring'''


          SCREAMING_SNAKE_CASE_   :						Dict							=	self.feature_extraction_class(**self.feat_extract_dict)
          SCREAMING_SNAKE_CASE_   :						List[str]							=	self.feat_extract_tester.prepare_inputs_for_common()
          SCREAMING_SNAKE_CASE_   :						Dict							=	feat_extract.model_input_names[0]

          SCREAMING_SNAKE_CASE_   :						Dict							=	BatchFeature({input_name: speech_inputs})

          SCREAMING_SNAKE_CASE_   :						Optional[int]							=	feat_extract.pad(lowercase_		,			padding='''longest'''		,			return_tensors='''np''')[input_name]
          SCREAMING_SNAKE_CASE_   :						int							=	feat_extract.pad(lowercase_		,			padding='''longest'''		,			return_tensors='''tf''')[input_name]

          self.assertTrue(abs(input_np.astype(np.floataa).sum() - input_tf.numpy().astype(np.floataa).sum()) < 1e-2)
      def _SCREAMING_SNAKE_CASE				(		self  :						List[Any]):




          '''simple docstring'''


          SCREAMING_SNAKE_CASE_   :						List[str]							=	self.feat_extract_dict
          SCREAMING_SNAKE_CASE_   :						Optional[Any]							=	True
          SCREAMING_SNAKE_CASE_   :						Dict							=	self.feature_extraction_class(**lowercase_)
          SCREAMING_SNAKE_CASE_   :						Optional[int]							=	self.feat_extract_tester.prepare_inputs_for_common()
          SCREAMING_SNAKE_CASE_   :						Optional[Any]							=	[len(lowercase_) for x in speech_inputs]
          SCREAMING_SNAKE_CASE_   :						Dict							=	feat_extract.model_input_names[0]

          SCREAMING_SNAKE_CASE_   :						Any							=	BatchFeature({input_name: speech_inputs})

          SCREAMING_SNAKE_CASE_   :						int							=	feat_extract.pad(lowercase_		,			padding='''longest'''		,			return_tensors='''np''')
          self.assertIn('''attention_mask'''		,			lowercase_)
          self.assertListEqual(list(processed.attention_mask.shape)		,			list(processed[input_name].shape[:2]))
          self.assertListEqual(processed.attention_mask.sum(-1).tolist()		,			lowercase_)



      def _SCREAMING_SNAKE_CASE				(		self  :						str):




          '''simple docstring'''


          SCREAMING_SNAKE_CASE_   :						List[str]							=	self.feat_extract_dict
          SCREAMING_SNAKE_CASE_   :						Any							=	True
          SCREAMING_SNAKE_CASE_   :						Tuple							=	self.feature_extraction_class(**lowercase_)
          SCREAMING_SNAKE_CASE_   :						List[Any]							=	self.feat_extract_tester.prepare_inputs_for_common()
          SCREAMING_SNAKE_CASE_   :						Any							=	[len(lowercase_) for x in speech_inputs]
          SCREAMING_SNAKE_CASE_   :						Optional[Any]							=	feat_extract.model_input_names[0]

          SCREAMING_SNAKE_CASE_   :						List[str]							=	BatchFeature({input_name: speech_inputs})
          SCREAMING_SNAKE_CASE_   :						Dict							=	min(lowercase_)

          SCREAMING_SNAKE_CASE_   :						int							=	feat_extract.pad(
              lowercase_		,			padding='''max_length'''		,			max_length=lowercase_		,			truncation=lowercase_		,			return_tensors='''np''')
          self.assertIn('''attention_mask'''		,			lowercase_)
          self.assertListEqual(
              list(processed_pad.attention_mask.shape)		,			[processed_pad[input_name].shape[0], max_length])
          self.assertListEqual(
              processed_pad.attention_mask[:, :max_length].sum(-1).tolist()		,			[max_length for x in speech_inputs])










"""simple docstring"""






from __future__ import annotations

import requests



def       _A    (__a							)				-> dict:
    """simple docstring"""






    SCREAMING_SNAKE_CASE_   :						Any							=	f'https://hacker-news.firebaseio.com/v0/item/{story_id}.json?print=pretty'
    return requests.get(__a							).json()



def       _A    (__a = 10							)				-> list[dict]:
    """simple docstring"""






    SCREAMING_SNAKE_CASE_   :						Any							=	'''https://hacker-news.firebaseio.com/v0/topstories.json?print=pretty'''
    SCREAMING_SNAKE_CASE_   :						Dict							=	requests.get(__a							).json()[:max_stories]
    return [get_hackernews_story(__a							) for story_id in story_ids]



def       _A    (__a = 10							)				-> str:
    """simple docstring"""






    SCREAMING_SNAKE_CASE_   :						Tuple							=	hackernews_top_stories(__a							)
    return "\n".join('''* [{title}]({url})'''.format(**__a							) for story in stories							)


if __name__ == "__main__":
  print(hackernews_top_stories_as_markdown())


