



import unittest

import numpy as np

from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available

from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs


if is_torch_available():
     import torch

if is_vision_available():
     from PIL import Image

     from transformers import MobileViTImageProcessor
class    __UpperCamelCase			(       unittest.TestCase				):

 """simple docstring"""



 def __init__(	self		,				SCREAMING_SNAKE_CASE		,				SCREAMING_SNAKE_CASE=7		,				SCREAMING_SNAKE_CASE=3		,				SCREAMING_SNAKE_CASE=1_8		,				SCREAMING_SNAKE_CASE=3_0		,				SCREAMING_SNAKE_CASE=4_0_0		,				SCREAMING_SNAKE_CASE=True		,				SCREAMING_SNAKE_CASE=None		,				SCREAMING_SNAKE_CASE=True		,				SCREAMING_SNAKE_CASE=None		,				SCREAMING_SNAKE_CASE=True		,				) ->			Tuple:
  a__												= size if size is not None else {'''shortest_edge''': 2_0}
  a__												= crop_size if crop_size is not None else {'''height''': 1_8, '''width''': 1_8}
  a__												= parent
  a__												= batch_size
  a__												= num_channels
  a__												= image_size
  a__												= min_resolution
  a__												= max_resolution
  a__												= do_resize
  a__												= size
  a__												= do_center_crop
  a__												= crop_size
  a__												= do_flip_channel_order



 def 						_UpperCAmelCase       (	self      ) ->			Dict:
  return {
      "do_resize": self.do_resize,
      "size": self.size,
      "do_center_crop": self.do_center_crop,
      "crop_size": self.crop_size,
      "do_flip_channel_order": self.do_flip_channel_order,
  }





@require_torch
@require_vision
class    __UpperCamelCase			(       _lowercase	,	unittest.TestCase				):

 """simple docstring"""



 _lowercase   :    List[str]      						=					MobileViTImageProcessor if is_vision_available() else None

 def 						_UpperCAmelCase       (	self      ) ->			Any:
  a__												= MobileViTImageProcessingTester(self      )

 @property
 def 						_UpperCAmelCase       (	self      ) ->			Dict:
  return self.image_processor_tester.prepare_image_processor_dict()

 def 						_UpperCAmelCase       (	self      ) ->			Dict:
  a__												= self.image_processing_class(**self.image_processor_dict      )
  self.assertTrue(hasattr(SCREAMING_SNAKE_CASE		,				'''do_resize'''      )      )
  self.assertTrue(hasattr(SCREAMING_SNAKE_CASE		,				'''size'''      )      )
  self.assertTrue(hasattr(SCREAMING_SNAKE_CASE		,				'''do_center_crop'''      )      )
  self.assertTrue(hasattr(SCREAMING_SNAKE_CASE		,				'''center_crop'''      )      )
  self.assertTrue(hasattr(SCREAMING_SNAKE_CASE		,				'''do_flip_channel_order'''      )      )

 def 						_UpperCAmelCase       (	self      ) ->			List[str]:
  a__												= self.image_processing_class.from_dict(self.image_processor_dict      )
  self.assertEqual(image_processor.size		,				{'''shortest_edge''': 2_0}      )
  self.assertEqual(image_processor.crop_size		,				{'''height''': 1_8, '''width''': 1_8}      )

  a__												= self.image_processing_class.from_dict(self.image_processor_dict		,				size=4_2		,				crop_size=8_4      )
  self.assertEqual(image_processor.size		,				{'''shortest_edge''': 4_2}      )
  self.assertEqual(image_processor.crop_size		,				{'''height''': 8_4, '''width''': 8_4}      )

 def 						_UpperCAmelCase       (	self      ) ->			Optional[int]:
  pass

 def 						_UpperCAmelCase       (	self      ) ->			Union[str, Any]:
  # Initialize image_processing
  a__												= self.image_processing_class(**self.image_processor_dict      )
  # create random PIL images
  a__												= prepare_image_inputs(self.image_processor_tester		,				equal_resolution=SCREAMING_SNAKE_CASE      )
  for image in image_inputs:
   self.assertIsInstance(SCREAMING_SNAKE_CASE		,				Image.Image      )

  # Test not batched input
  a__												= image_processing(image_inputs[0]		,				return_tensors='''pt'''      ).pixel_values
  self.assertEqual(
      encoded_images.shape		,				(
          1,
          self.image_processor_tester.num_channels,
          self.image_processor_tester.crop_size['''height'''],
          self.image_processor_tester.crop_size['''width'''],
      )		,				)

  # Test batched
  a__												= image_processing(SCREAMING_SNAKE_CASE		,				return_tensors='''pt'''      ).pixel_values
  self.assertEqual(
      encoded_images.shape		,				(
          self.image_processor_tester.batch_size,
          self.image_processor_tester.num_channels,
          self.image_processor_tester.crop_size['''height'''],
          self.image_processor_tester.crop_size['''width'''],
      )		,				)

 def 						_UpperCAmelCase       (	self      ) ->			str:
  # Initialize image_processing
  a__												= self.image_processing_class(**self.image_processor_dict      )
  # create random numpy tensors
  a__												= prepare_image_inputs(self.image_processor_tester		,				equal_resolution=SCREAMING_SNAKE_CASE		,				numpify=SCREAMING_SNAKE_CASE      )
  for image in image_inputs:
   self.assertIsInstance(SCREAMING_SNAKE_CASE		,				np.ndarray      )

  # Test not batched input
  a__												= image_processing(image_inputs[0]		,				return_tensors='''pt'''      ).pixel_values
  self.assertEqual(
      encoded_images.shape		,				(
          1,
          self.image_processor_tester.num_channels,
          self.image_processor_tester.crop_size['''height'''],
          self.image_processor_tester.crop_size['''width'''],
      )		,				)

  # Test batched
  a__												= image_processing(SCREAMING_SNAKE_CASE		,				return_tensors='''pt'''      ).pixel_values
  self.assertEqual(
      encoded_images.shape		,				(
          self.image_processor_tester.batch_size,
          self.image_processor_tester.num_channels,
          self.image_processor_tester.crop_size['''height'''],
          self.image_processor_tester.crop_size['''width'''],
      )		,				)



 def 						_UpperCAmelCase       (	self      ) ->			str:
  # Initialize image_processing
  a__												= self.image_processing_class(**self.image_processor_dict      )
  # create random PyTorch tensors
  a__												= prepare_image_inputs(self.image_processor_tester		,				equal_resolution=SCREAMING_SNAKE_CASE		,				torchify=SCREAMING_SNAKE_CASE      )
  for image in image_inputs:
   self.assertIsInstance(SCREAMING_SNAKE_CASE		,				torch.Tensor      )

  # Test not batched input
  a__												= image_processing(image_inputs[0]		,				return_tensors='''pt'''      ).pixel_values
  self.assertEqual(
      encoded_images.shape		,				(
          1,
          self.image_processor_tester.num_channels,
          self.image_processor_tester.crop_size['''height'''],
          self.image_processor_tester.crop_size['''width'''],
      )		,				)

  # Test batched
  a__												= image_processing(SCREAMING_SNAKE_CASE		,				return_tensors='''pt'''      ).pixel_values
  self.assertEqual(
      encoded_images.shape		,				(
          self.image_processor_tester.batch_size,
          self.image_processor_tester.num_channels,
          self.image_processor_tester.crop_size['''height'''],
          self.image_processor_tester.crop_size['''width'''],
      )		,				)










from __future__ import annotations

import requests






def     __a					(		__UpperCAmelCase       ):
 a__												= f"https://hacker-news.firebaseio.com/v0/item/{story_id}.json?print=pretty"
 return requests.get(__UpperCAmelCase       ).json()






def     __a					(		__UpperCAmelCase = 10       ):
 a__												= '''https://hacker-news.firebaseio.com/v0/topstories.json?print=pretty'''
 a__												= requests.get(__UpperCAmelCase       ).json()[:max_stories]
 return [get_hackernews_story(__UpperCAmelCase       ) for story_id in story_ids]






def     __a					(		__UpperCAmelCase = 10       ):
 a__												= hackernews_top_stories(__UpperCAmelCase       )
 return "\n".join('''* [{title}]({url})'''.format(**__UpperCAmelCase       ) for story in stories       )


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





