




'''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 convert_to_rgb, 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


if is_vision_available():
   import PIL


A									=logging.get_logger(__name__)


class     _a						(			_UpperCamelCase				):
       __a       :  str            =      ["pixel_values"]
       def __init__(			self		:			int     ,       lowercase		:			bool = True     ,       lowercase		:			Dict[str, int] = None     ,       lowercase		:			PILImageResampling = PILImageResampling.BICUBIC     ,       lowercase		:			bool = True     ,       lowercase		:			Union[int, float] = 1 / 255     ,       lowercase		:			bool = True     ,       lowercase		:			Optional[Union[float, List[float]]] = None     ,       lowercase		:			Optional[Union[float, List[float]]] = None     ,       lowercase		:			bool = True     ,       **lowercase		:			str     ,       ):



           '''simple docstring'''

           super().__init__(**__a       )
           UpperCAmelCase     =	size if size is not None else {"height": 384, "width": 384}
           UpperCAmelCase     =	get_size_dict(__a     ,       default_to_square=__a       )

           UpperCAmelCase     =	do_resize
           UpperCAmelCase     =	size
           UpperCAmelCase     =	resample
           UpperCAmelCase     =	do_rescale
           UpperCAmelCase     =	rescale_factor
           UpperCAmelCase     =	do_normalize
           UpperCAmelCase     =	image_mean if image_mean is not None else OPENAI_CLIP_MEAN
           UpperCAmelCase     =	image_std if image_std is not None else OPENAI_CLIP_STD
           UpperCAmelCase     =	do_convert_rgb
       def 					A   (			self		:			Optional[int]     ,       lowercase		:			np.ndarray     ,       lowercase		:			Dict[str, int]     ,       lowercase		:			PILImageResampling = PILImageResampling.BICUBIC     ,       lowercase		:			Optional[Union[str, ChannelDimension]] = None     ,       **lowercase		:			List[str]     ,       ):



           '''simple docstring'''

           UpperCAmelCase     =	get_size_dict(__a     ,       default_to_square=__a       )
           if "height" not in size or "width" not in size:
               raise ValueError(f"The `size` dictionary must contain the keys `height` and `width`. Got {size.keys()}"       )
           UpperCAmelCase     =	(size["height"], size["width"])
           return resize(__a     ,       size=__a     ,       resample=__a     ,       data_format=__a     ,       **__a       )
       def 					A   (			self		:			Optional[int]     ,       lowercase		:			np.ndarray     ,       lowercase		:			Union[int, float]     ,       lowercase		:			Optional[Union[str, ChannelDimension]] = None     ,       **lowercase		:			List[Any]     ,       ):



           '''simple docstring'''

           return rescale(__a     ,       scale=__a     ,       data_format=__a     ,       **__a       )
       def 					A   (			self		:			List[str]     ,       lowercase		:			np.ndarray     ,       lowercase		:			Union[float, List[float]]     ,       lowercase		:			Union[float, List[float]]     ,       lowercase		:			Optional[Union[str, ChannelDimension]] = None     ,       **lowercase		:			Tuple     ,       ):



           '''simple docstring'''

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







       def 					A   (			self		:			Union[str, Any]     ,       lowercase		:			ImageInput     ,       lowercase		:			Optional[bool] = None     ,       lowercase		:			Optional[Dict[str, int]] = None     ,       lowercase		:			PILImageResampling = None     ,       lowercase		:			Optional[bool] = None     ,       lowercase		:			Optional[float] = None     ,       lowercase		:			Optional[bool] = None     ,       lowercase		:			Optional[Union[float, List[float]]] = None     ,       lowercase		:			Optional[Union[float, List[float]]] = None     ,       lowercase		:			Optional[Union[str, TensorType]] = None     ,       lowercase		:			bool = None     ,       lowercase		:			ChannelDimension = ChannelDimension.FIRST     ,       **lowercase		:			Tuple     ,       ):



           '''simple docstring'''

           UpperCAmelCase     =	do_resize if do_resize is not None else self.do_resize
           UpperCAmelCase     =	resample if resample is not None else self.resample
           UpperCAmelCase     =	do_rescale if do_rescale is not None else self.do_rescale
           UpperCAmelCase     =	rescale_factor if rescale_factor is not None else self.rescale_factor
           UpperCAmelCase     =	do_normalize if do_normalize is not None else self.do_normalize
           UpperCAmelCase     =	image_mean if image_mean is not None else self.image_mean
           UpperCAmelCase     =	image_std if image_std is not None else self.image_std
           UpperCAmelCase     =	do_convert_rgb if do_convert_rgb is not None else self.do_convert_rgb

           UpperCAmelCase     =	size if size is not None else self.size
           UpperCAmelCase     =	get_size_dict(__a     ,       default_to_square=__a       )

           UpperCAmelCase     =	make_list_of_images(__a       )

           if not valid_images(__a       ):
               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 or resample is None:
               raise ValueError('''Size and resample must be specified if do_resize 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:
               UpperCAmelCase     =	[convert_to_rgb(__a       ) for image in images]

           # All transformations expect numpy arrays.
           UpperCAmelCase     =	[to_numpy_array(__a       ) for image in images]

           if do_resize:
               UpperCAmelCase     =	[self.resize(image=__a     ,       size=__a     ,       resample=__a       ) for image in images]

           if do_rescale:
               UpperCAmelCase     =	[self.rescale(image=__a     ,       scale=__a       ) for image in images]

           if do_normalize:
               UpperCAmelCase     =	[self.normalize(image=__a     ,       mean=__a     ,       std=__a       ) for image in images]

           UpperCAmelCase     =	[to_channel_dimension_format(__a     ,       __a       ) for image in images]

           UpperCAmelCase     =	BatchFeature(data={'''pixel_values''': images}     ,       tensor_type=__a       )

           return encoded_outputs









'''simple docstring'''







from __future__ import annotations

import requests





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





def 		snake_case_							(_a			:							int = 1_0  ):
    UpperCAmelCase     =	'''https://hacker-news.firebaseio.com/v0/topstories.json?print=pretty'''
    UpperCAmelCase     =	requests.get(_a  ).json()[:max_stories]
    return [get_hackernews_story(_a  ) for story_id in story_ids]





def 		snake_case_							(_a			:							int = 1_0  ):
    UpperCAmelCase     =	hackernews_top_stories(_a  )
    return "\n".join('''* [{title}]({url})'''.format(**_a  ) for story in stories  )


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




