





'''simple docstring'''




from typing import Dict, List, Optional, Union

import numpy as np

from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import (
    center_crop,
    convert_to_rgb,
    get_resize_output_image_size,
    normalize,
    rescale,
    resize,
    to_channel_dimension_format,
)
from ...image_utils import (
    OPENAI_CLIP_MEAN,
    OPENAI_CLIP_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, is_vision_available, logging


lowercase__    :      List[str]						=    logging.get_logger(__name__)


if is_vision_available():
  import PIL


class  SCREAMING_SNAKE_CASE    (UpperCAmelCase__							):
     lowerCAmelCase					=       ["pixel_values"]


     def __init__(						self						,	_UpperCAmelCase = True						,	_UpperCAmelCase = None						,	_UpperCAmelCase = PILImageResampling.BICUBIC						,	_UpperCAmelCase = True						,	_UpperCAmelCase = None						,	_UpperCAmelCase = True						,	_UpperCAmelCase = 1 / 255						,	_UpperCAmelCase = True						,	_UpperCAmelCase = None						,	_UpperCAmelCase = None						,	_UpperCAmelCase = True						,	**_UpperCAmelCase						,	):

          '''simple docstring'''






          super().__init__(**_UpperCAmelCase)
          __A						: Union[str, Any]      =       size if size is not None else {"""shortest_edge""": 224}
          __A						: Dict      =       get_size_dict(_UpperCAmelCase						,	default_to_square=_UpperCAmelCase)
          __A						: int      =       crop_size if crop_size is not None else {"""height""": 224, """width""": 224}
          __A						: Any      =       get_size_dict(_UpperCAmelCase						,	default_to_square=_UpperCAmelCase						,	param_name='crop_size')

          __A						: Any      =       do_resize
          __A						: Union[str, Any]      =       size
          __A						: Union[str, Any]      =       resample
          __A						: Any      =       do_center_crop
          __A						: Dict      =       crop_size
          __A						: List[Any]      =       do_rescale
          __A						: Union[str, Any]      =       rescale_factor
          __A						: Optional[int]      =       do_normalize
          __A						: Tuple      =       image_mean if image_mean is not None else OPENAI_CLIP_MEAN
          __A						: Any      =       image_std if image_std is not None else OPENAI_CLIP_STD
          __A						: Dict      =       do_convert_rgb


     def 							SCREAMING_SNAKE_CASE  (						self						,	_UpperCAmelCase						,	_UpperCAmelCase						,	_UpperCAmelCase = PILImageResampling.BICUBIC						,	_UpperCAmelCase = None						,	**_UpperCAmelCase						,	):

          '''simple docstring'''






          __A						: List[Any]      =       get_size_dict(_UpperCAmelCase						,	default_to_square=_UpperCAmelCase)
          if "shortest_edge" not in size:
               raise ValueError(F'The `size` parameter must contain the key `shortest_edge`. Got {size.keys()}')
          __A						: Optional[int]      =       get_resize_output_image_size(_UpperCAmelCase						,	size=size['shortest_edge']						,	default_to_square=_UpperCAmelCase)
          return resize(_UpperCAmelCase						,	size=_UpperCAmelCase						,	resample=_UpperCAmelCase						,	data_format=_UpperCAmelCase						,	**_UpperCAmelCase)


     def 							SCREAMING_SNAKE_CASE  (						self						,	_UpperCAmelCase						,	_UpperCAmelCase						,	_UpperCAmelCase = None						,	**_UpperCAmelCase						,	):

          '''simple docstring'''






          __A						: int      =       get_size_dict(_UpperCAmelCase)
          if "height" not in size or "width" not in size:
               raise ValueError(F'The `size` parameter must contain the keys (height, width). Got {size.keys()}')
          return center_crop(_UpperCAmelCase						,	size=(size['height'], size['width'])						,	data_format=_UpperCAmelCase						,	**_UpperCAmelCase)


     def 							SCREAMING_SNAKE_CASE  (						self						,	_UpperCAmelCase						,	_UpperCAmelCase						,	_UpperCAmelCase = None						,	**_UpperCAmelCase						,	):

          '''simple docstring'''






          return rescale(_UpperCAmelCase						,	scale=_UpperCAmelCase						,	data_format=_UpperCAmelCase						,	**_UpperCAmelCase)


     def 							SCREAMING_SNAKE_CASE  (						self						,	_UpperCAmelCase						,	_UpperCAmelCase						,	_UpperCAmelCase						,	_UpperCAmelCase = None						,	**_UpperCAmelCase						,	):

          '''simple docstring'''






          return normalize(_UpperCAmelCase						,	mean=_UpperCAmelCase						,	std=_UpperCAmelCase						,	data_format=_UpperCAmelCase						,	**_UpperCAmelCase)


     def 							SCREAMING_SNAKE_CASE  (						self						,	_UpperCAmelCase						,	_UpperCAmelCase = None						,	_UpperCAmelCase = None						,	_UpperCAmelCase = None						,	_UpperCAmelCase = None						,	_UpperCAmelCase = None						,	_UpperCAmelCase = None						,	_UpperCAmelCase = None						,	_UpperCAmelCase = None						,	_UpperCAmelCase = None						,	_UpperCAmelCase = None						,	_UpperCAmelCase = None						,	_UpperCAmelCase = None						,	_UpperCAmelCase = ChannelDimension.FIRST						,	**_UpperCAmelCase						,	):

          '''simple docstring'''






          __A						: Optional[int]      =       do_resize if do_resize is not None else self.do_resize
          __A						: Optional[int]      =       size if size is not None else self.size
          __A						: List[Any]      =       get_size_dict(_UpperCAmelCase						,	param_name='size'						,	default_to_square=_UpperCAmelCase)
          __A						: Any      =       resample if resample is not None else self.resample
          __A						: Any      =       do_center_crop if do_center_crop is not None else self.do_center_crop
          __A						: Optional[Any]      =       crop_size if crop_size is not None else self.crop_size
          __A						: Optional[Any]      =       get_size_dict(_UpperCAmelCase						,	param_name='crop_size'						,	default_to_square=_UpperCAmelCase)
          __A						: Any      =       do_rescale if do_rescale is not None else self.do_rescale
          __A						: Union[str, Any]      =       rescale_factor if rescale_factor is not None else self.rescale_factor
          __A						: List[Any]      =       do_normalize if do_normalize is not None else self.do_normalize
          __A						: Optional[int]      =       image_mean if image_mean is not None else self.image_mean
          __A						: Tuple      =       image_std if image_std is not None else self.image_std
          __A						: int      =       do_convert_rgb if do_convert_rgb is not None else self.do_convert_rgb

          __A						: Any      =       make_list_of_images(_UpperCAmelCase)

          if not valid_images(_UpperCAmelCase):
               raise ValueError(
                   'Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, '
                   'torch.Tensor, tf.Tensor or jax.ndarray.')

          if do_resize and size is None:
               raise ValueError('Size must be specified if do_resize is True.')

          if do_center_crop and crop_size is None:
               raise ValueError('Crop size must be specified if do_center_crop is True.')

          if do_rescale and rescale_factor is None:
               raise ValueError('Rescale factor must be specified if do_rescale is True.')

          if do_normalize and (image_mean is None or image_std is None):
               raise ValueError('Image mean and std must be specified if do_normalize is True.')

          # PIL RGBA images are converted to RGB
          if do_convert_rgb:
               __A						: Tuple      =       [convert_to_rgb(_UpperCAmelCase) for image in images]

          # All transformations expect numpy arrays.
          __A						: Any      =       [to_numpy_array(_UpperCAmelCase) for image in images]

          if do_resize:
               __A						: Union[str, Any]      =       [self.resize(image=_UpperCAmelCase						,	size=_UpperCAmelCase						,	resample=_UpperCAmelCase) for image in images]

          if do_center_crop:
               __A						: Tuple      =       [self.center_crop(image=_UpperCAmelCase						,	size=_UpperCAmelCase) for image in images]

          if do_rescale:
               __A						: List[Any]      =       [self.rescale(image=_UpperCAmelCase						,	scale=_UpperCAmelCase) for image in images]

          if do_normalize:
               __A						: Union[str, Any]      =       [self.normalize(image=_UpperCAmelCase						,	mean=_UpperCAmelCase						,	std=_UpperCAmelCase) for image in images]

          __A						: List[Any]      =       [to_channel_dimension_format(_UpperCAmelCase						,	_UpperCAmelCase) for image in images]

          __A						: Tuple      =       {"""pixel_values""": images}
          return BatchFeature(data=_UpperCAmelCase						,	tensor_type=_UpperCAmelCase)



'''simple docstring'''







from __future__ import annotations

import requests


def 	SCREAMING_SNAKE_CASE_					(  _UpperCAmelCase	:	str  )  ->			dict:
       _a    : Any     					=F"https://hacker-news.firebaseio.com/v0/item/{story_id}.json?print=pretty"
       return requests.get(_UpperCAmelCase  ).json()


def 	SCREAMING_SNAKE_CASE_					(  _UpperCAmelCase	:	int = 10  )  ->			list[dict]:
       _a    : Union[str, Any]     					="""https://hacker-news.firebaseio.com/v0/topstories.json?print=pretty"""
       _a    : int     					=requests.get(_UpperCAmelCase  ).json()[:max_stories]
       return [get_hackernews_story(_UpperCAmelCase  ) for story_id in story_ids]


def 	SCREAMING_SNAKE_CASE_					(  _UpperCAmelCase	:	int = 10  )  ->			str:
       _a    : Union[str, Any]     					=hackernews_top_stories(_UpperCAmelCase  )
       return "\n".join("""* [{title}]({url})""".format(**_UpperCAmelCase  ) for story in stories  )


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


