




'''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,
    get_resize_output_image_size,
    normalize,
    rescale,
    resize,
    to_channel_dimension_format,
)
from ...image_utils import (
    IMAGENET_STANDARD_MEAN,
    IMAGENET_STANDARD_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    is_valid_image,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, is_vision_available, logging


if is_vision_available():
   import PIL


A     =logging.get_logger(__name__)





def 						snake_case_							(_a	:							List[Any]     ):
    if isinstance(_a    ,   (list, tuple)     ) and isinstance(videos[0]    ,   (list, tuple)     ) and is_valid_image(videos[0][0]     ):
        return videos

    elif isinstance(_a    ,   (list, tuple)     ) and is_valid_image(videos[0]     ):
        return [videos]

    elif is_valid_image(_a     ):
        return [[videos]]

    raise ValueError(F"Could not make batched video from {videos}"     )







class 			_a  (	__a ):
       __a				:     Optional[Any]   				=					["""pixel_values"""]
       def __init__(	self						:	List[str]   ,				lowercase						:	bool = True   ,				lowercase						:	Dict[str, int] = None   ,				lowercase						:	PILImageResampling = PILImageResampling.BILINEAR   ,				lowercase						:	bool = True   ,				lowercase						:	Dict[str, int] = None   ,				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						:	str   ,				):



           '''simple docstring'''

           super().__init__(**lowercase				)
           UpperCAmelCase			       =		size if size is not None else {'''shortest_edge''': 224}
           UpperCAmelCase			       =		get_size_dict(lowercase   ,				default_to_square=lowercase				)
           UpperCAmelCase			       =		crop_size if crop_size is not None else {'''height''': 224, '''width''': 224}
           UpperCAmelCase			       =		get_size_dict(lowercase   ,				param_name='''crop_size'''				)

           UpperCAmelCase			       =		do_resize
           UpperCAmelCase			       =		size
           UpperCAmelCase			       =		do_center_crop
           UpperCAmelCase			       =		crop_size
           UpperCAmelCase			       =		resample
           UpperCAmelCase			       =		do_rescale
           UpperCAmelCase			       =		rescale_factor
           UpperCAmelCase			       =		do_normalize
           UpperCAmelCase			       =		image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
           UpperCAmelCase			       =		image_std if image_std is not None else IMAGENET_STANDARD_STD
       def     A      (	self						:	Any   ,				lowercase						:	np.ndarray   ,				lowercase						:	Dict[str, int]   ,				lowercase						:	PILImageResampling = PILImageResampling.BILINEAR   ,				lowercase						:	Optional[Union[str, ChannelDimension]] = None   ,				**lowercase						:	Any   ,				):



           '''simple docstring'''

           UpperCAmelCase			       =		get_size_dict(lowercase   ,				default_to_square=lowercase				)
           if "shortest_edge" in size:
               UpperCAmelCase			       =		get_resize_output_image_size(lowercase   ,				size['''shortest_edge''']   ,				default_to_square=lowercase				)
           elif "height" in size and "width" in size:
               UpperCAmelCase			       =		(size['''height'''], size['''width'''])
           else:
               raise ValueError(f"Size must have 'height' and 'width' or 'shortest_edge' as keys. Got {size.keys()}"				)
           return resize(lowercase   ,				size=lowercase   ,				resample=lowercase   ,				data_format=lowercase   ,				**lowercase				)
       def     A      (	self						:	Optional[int]   ,				lowercase						:	np.ndarray   ,				lowercase						:	Dict[str, int]   ,				lowercase						:	Optional[Union[str, ChannelDimension]] = None   ,				**lowercase						:	Union[str, Any]   ,				):



           '''simple docstring'''

           UpperCAmelCase			       =		get_size_dict(lowercase				)
           if "height" not in size or "width" not in size:
               raise ValueError(f"Size must have 'height' and 'width' as keys. Got {size.keys()}"				)
           return center_crop(lowercase   ,				size=(size['''height'''], size['''width'''])   ,				data_format=lowercase   ,				**lowercase				)
       def     A      (	self						:	Dict   ,				lowercase						:	np.ndarray   ,				lowercase						:	Union[int, float]   ,				lowercase						:	Optional[Union[str, ChannelDimension]] = None   ,				**lowercase						:	Tuple   ,				):



           '''simple docstring'''

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



           '''simple docstring'''

           return normalize(lowercase   ,				mean=lowercase   ,				std=lowercase   ,				data_format=lowercase   ,				**lowercase				)
       def     A      (	self						:	List[str]   ,				lowercase						:	ImageInput   ,				lowercase						:	bool = None   ,				lowercase						:	Dict[str, int] = None   ,				lowercase						:	PILImageResampling = None   ,				lowercase						:	bool = None   ,				lowercase						:	Dict[str, int] = None   ,				lowercase						:	bool = None   ,				lowercase						:	float = None   ,				lowercase						:	bool = None   ,				lowercase						:	Optional[Union[float, List[float]]] = None   ,				lowercase						:	Optional[Union[float, List[float]]] = None   ,				lowercase						:	Optional[ChannelDimension] = ChannelDimension.FIRST   ,				):



           '''simple docstring'''

           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_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.'''				)

           # All transformations expect numpy arrays.
           UpperCAmelCase			       =		to_numpy_array(lowercase				)

           if do_resize:
               UpperCAmelCase			       =		self.resize(image=lowercase   ,				size=lowercase   ,				resample=lowercase				)

           if do_center_crop:
               UpperCAmelCase			       =		self.center_crop(lowercase   ,				size=lowercase				)

           if do_rescale:
               UpperCAmelCase			       =		self.rescale(image=lowercase   ,				scale=lowercase				)

           if do_normalize:
               UpperCAmelCase			       =		self.normalize(image=lowercase   ,				mean=lowercase   ,				std=lowercase				)

           UpperCAmelCase			       =		to_channel_dimension_format(lowercase   ,				lowercase				)
           return image







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



           '''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_center_crop if do_center_crop is not None else self.do_center_crop
           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			       =		size if size is not None else self.size
           UpperCAmelCase			       =		get_size_dict(lowercase   ,				default_to_square=lowercase				)
           UpperCAmelCase			       =		crop_size if crop_size is not None else self.crop_size
           UpperCAmelCase			       =		get_size_dict(lowercase   ,				param_name='''crop_size'''				)

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

           UpperCAmelCase			       =		make_batched(lowercase				)

           UpperCAmelCase			       =		[
               [
                   self._preprocess_image(
                       image=lowercase   ,				do_resize=lowercase   ,				size=lowercase   ,				resample=lowercase   ,				do_center_crop=lowercase   ,				crop_size=lowercase   ,				do_rescale=lowercase   ,				rescale_factor=lowercase   ,				do_normalize=lowercase   ,				image_mean=lowercase   ,				image_std=lowercase   ,				data_format=lowercase   ,				)
                   for img in video
               ]
               for video in videos
           ]

           UpperCAmelCase			       =		{'''pixel_values''': videos}
           return BatchFeature(data=lowercase   ,				tensor_type=lowercase				)










'''simple docstring'''







from datetime import datetime

import requests
from bsa import BeautifulSoup

if __name__ == "__main__":
   A     =input('Enter image url: ').strip()
   print(f"""Downloading image from {url} ...""")
   A     =BeautifulSoup(requests.get(url).content, 'html.parser')
   # The image URL is in the content field of the first meta tag with property og:image
   A     =soup.find('meta', {'property': 'og:image'})['content']
   A     =requests.get(image_url).content
   A     =f"""{datetime.now():%Y-%m-%d_%H:%M:%S}.jpg"""
   with open(file_name, 'wb') as fp:
      fp.write(image_data)
   print(f"""Done. Image saved to disk as {file_name}.""")




