






import math
from typing import Dict, Iterable, List, Optional, Tuple, Union

import numpy as np

from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import normalize, rescale, resize, to_channel_dimension_format
from ...image_utils import (
    IMAGENET_STANDARD_MEAN,
    IMAGENET_STANDARD_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    get_image_size,
    is_torch_available,
    is_torch_tensor,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, is_vision_available, logging


if is_torch_available():
				import torch

if is_vision_available():
				import PIL


A        =			logging.get_logger(__name__)



def 					_lowerCamelCase(			lowerCAmelCase__				:		np.ndarray    ,    lowerCAmelCase__				:		Union[int, Iterable[int]]    ,    lowerCAmelCase__				:		bool    ,    lowerCAmelCase__				:		int      ):





				'''simple docstring'''







				def constraint_to_multiple_of(lowerCAmelCase__				:		str    ,    lowerCAmelCase__				:		Optional[Any]    ,    lowerCAmelCase__				:		Optional[Any]=0    ,    lowerCAmelCase__				:		Any=None      ):
								SCREAMING_SNAKE_CASE_   :	Optional[Any]													=   round(val / multiple      ) * multiple

								if max_val is not None and x > max_val:
												SCREAMING_SNAKE_CASE_   :	Union[str, Any]													=   math.floor(val / multiple      ) * multiple

								if x < min_val:
												SCREAMING_SNAKE_CASE_   :	Dict													=   math.ceil(val / multiple      ) * multiple

								return x

				SCREAMING_SNAKE_CASE_   :	str													=   (output_size, output_size) if isinstance(lowerCAmelCase__    ,    lowerCAmelCase__      ) else output_size

				SCREAMING_SNAKE_CASE_							,       SCREAMING_SNAKE_CASE_   :	Any													=   get_image_size(lowerCAmelCase__      )
				SCREAMING_SNAKE_CASE_							,       SCREAMING_SNAKE_CASE_   :	List[str]													=   output_size

				# determine new height and width
				SCREAMING_SNAKE_CASE_   :	Optional[int]													=   output_height / input_height
				SCREAMING_SNAKE_CASE_   :	Union[str, Any]													=   output_width / input_width

				if keep_aspect_ratio:
								# scale as little as possible
								if abs(1 - scale_width      ) < abs(1 - scale_height      ):
												# fit width
												SCREAMING_SNAKE_CASE_   :	str													=   scale_width
								else:
												# fit height
												SCREAMING_SNAKE_CASE_   :	Optional[Any]													=   scale_height

				SCREAMING_SNAKE_CASE_   :	Any													=   constraint_to_multiple_of(scale_height * input_height    ,    multiple=lowerCAmelCase__      )
				SCREAMING_SNAKE_CASE_   :	int													=   constraint_to_multiple_of(scale_width * input_width    ,    multiple=lowerCAmelCase__      )

				return (new_height, new_width)


class 							__a							(  __A						):





		'''simple docstring'''



		UpperCAmelCase__      :				List[Any]				    =						["""pixel_values"""]


		def __init__(						self ,    UpperCamelCase__ = True ,    UpperCamelCase__ = None ,    UpperCamelCase__ = PILImageResampling.BILINEAR ,    UpperCamelCase__ = False ,    UpperCamelCase__ = 1 ,    UpperCamelCase__ = True ,    UpperCamelCase__ = 1 / 255 ,    UpperCamelCase__ = True ,    UpperCamelCase__ = None ,    UpperCamelCase__ = None ,    **UpperCamelCase__ ,    ):
						super().__init__(**UpperCamelCase__       )
						SCREAMING_SNAKE_CASE_   :	Any													=   size if size is not None else {'height': 384, 'width': 384}
						SCREAMING_SNAKE_CASE_   :	str													=   get_size_dict(UpperCamelCase__       )
						SCREAMING_SNAKE_CASE_   :	str													=   do_resize
						SCREAMING_SNAKE_CASE_   :	str													=   size
						SCREAMING_SNAKE_CASE_   :	int													=   keep_aspect_ratio
						SCREAMING_SNAKE_CASE_   :	Tuple													=   ensure_multiple_of
						SCREAMING_SNAKE_CASE_   :	Optional[int]													=   resample
						SCREAMING_SNAKE_CASE_   :	int													=   do_rescale
						SCREAMING_SNAKE_CASE_   :	int													=   rescale_factor
						SCREAMING_SNAKE_CASE_   :	str													=   do_normalize
						SCREAMING_SNAKE_CASE_   :	Union[str, Any]													=   image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
						SCREAMING_SNAKE_CASE_   :	int													=   image_std if image_std is not None else IMAGENET_STANDARD_STD


		def   __snake_case			(						self ,    UpperCamelCase__ ,    UpperCamelCase__ ,    UpperCamelCase__ = False ,    UpperCamelCase__ = 1 ,    UpperCamelCase__ = PILImageResampling.BICUBIC ,    UpperCamelCase__ = None ,    **UpperCamelCase__ ,    ):
						SCREAMING_SNAKE_CASE_   :	List[Any]													=   get_size_dict(UpperCamelCase__       )
						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()}'''       )
						SCREAMING_SNAKE_CASE_   :	Optional[int]													=   get_resize_output_image_size(
						    UpperCamelCase__ ,    output_size=(size['height'], size['width']) ,    keep_aspect_ratio=UpperCamelCase__ ,    multiple=UpperCamelCase__ ,    )
						return resize(UpperCamelCase__ ,    size=UpperCamelCase__ ,    resample=UpperCamelCase__ ,    data_format=UpperCamelCase__ ,    **UpperCamelCase__       )


		def   __snake_case			(						self ,    UpperCamelCase__ ,    UpperCamelCase__ ,    UpperCamelCase__ = None ,    **UpperCamelCase__ ,    ):
						return rescale(UpperCamelCase__ ,    scale=UpperCamelCase__ ,    data_format=UpperCamelCase__ ,    **UpperCamelCase__       )


		def   __snake_case			(						self ,    UpperCamelCase__ ,    UpperCamelCase__ ,    UpperCamelCase__ ,    UpperCamelCase__ = None ,    **UpperCamelCase__ ,    ):
						return normalize(UpperCamelCase__ ,    mean=UpperCamelCase__ ,    std=UpperCamelCase__ ,    data_format=UpperCamelCase__ ,    **UpperCamelCase__       )


		def   __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__ = ChannelDimension.FIRST ,    **UpperCamelCase__ ,    ):
						SCREAMING_SNAKE_CASE_   :	Union[str, Any]													=   do_resize if do_resize is not None else self.do_resize
						SCREAMING_SNAKE_CASE_   :	Optional[Any]													=   size if size is not None else self.size
						SCREAMING_SNAKE_CASE_   :	str													=   get_size_dict(UpperCamelCase__       )
						SCREAMING_SNAKE_CASE_   :	Dict													=   keep_aspect_ratio if keep_aspect_ratio is not None else self.keep_aspect_ratio
						SCREAMING_SNAKE_CASE_   :	str													=   ensure_multiple_of if ensure_multiple_of is not None else self.ensure_multiple_of
						SCREAMING_SNAKE_CASE_   :	Union[str, Any]													=   resample if resample is not None else self.resample
						SCREAMING_SNAKE_CASE_   :	Dict													=   do_rescale if do_rescale is not None else self.do_rescale
						SCREAMING_SNAKE_CASE_   :	Dict													=   rescale_factor if rescale_factor is not None else self.rescale_factor
						SCREAMING_SNAKE_CASE_   :	Union[str, Any]													=   do_normalize if do_normalize is not None else self.do_normalize
						SCREAMING_SNAKE_CASE_   :	Optional[int]													=   image_mean if image_mean is not None else self.image_mean
						SCREAMING_SNAKE_CASE_   :	Dict													=   image_std if image_std is not None else self.image_std

						SCREAMING_SNAKE_CASE_   :	List[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 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.'       )

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

						if do_resize:
										SCREAMING_SNAKE_CASE_   :	Tuple													=   [self.resize(image=UpperCamelCase__ ,    size=UpperCamelCase__ ,    resample=UpperCamelCase__       ) for image in images]

						if do_rescale:
										SCREAMING_SNAKE_CASE_   :	Any													=   [self.rescale(image=UpperCamelCase__ ,    scale=UpperCamelCase__       ) for image in images]

						if do_normalize:
										SCREAMING_SNAKE_CASE_   :	Any													=   [self.normalize(image=UpperCamelCase__ ,    mean=UpperCamelCase__ ,    std=UpperCamelCase__       ) for image in images]

						SCREAMING_SNAKE_CASE_   :	str													=   [to_channel_dimension_format(UpperCamelCase__ ,    UpperCamelCase__       ) for image in images]

						SCREAMING_SNAKE_CASE_   :	Dict													=   {'pixel_values': images}
						return BatchFeature(data=UpperCamelCase__ ,    tensor_type=UpperCamelCase__       )






		def   __snake_case			(						self ,    UpperCamelCase__ ,    UpperCamelCase__ = None       ):
						SCREAMING_SNAKE_CASE_   :	Optional[int]													=   outputs.logits

						# Resize logits and compute semantic segmentation maps
						if target_sizes is not None:
										if len(UpperCamelCase__       ) != len(UpperCamelCase__       ):
														raise ValueError(
														    'Make sure that you pass in as many target sizes as the batch dimension of the logits'       )

										if is_torch_tensor(UpperCamelCase__       ):
														SCREAMING_SNAKE_CASE_   :	int													=   target_sizes.numpy()

										SCREAMING_SNAKE_CASE_   :	Tuple													=   []

										for idx in range(len(UpperCamelCase__       )       ):
														SCREAMING_SNAKE_CASE_   :	Optional[Any]													=   torch.nn.functional.interpolate(
														    logits[idx].unsqueeze(dim=0       ) ,    size=target_sizes[idx] ,    mode='bilinear' ,    align_corners=UpperCamelCase__       )
														SCREAMING_SNAKE_CASE_   :	Union[str, Any]													=   resized_logits[0].argmax(dim=0       )
														semantic_segmentation.append(UpperCamelCase__       )
						else:
										SCREAMING_SNAKE_CASE_   :	List[Any]													=   logits.argmax(dim=1       )
										SCREAMING_SNAKE_CASE_   :	Any													=   [semantic_segmentation[i] for i in range(semantic_segmentation.shape[0]       )]

						return semantic_segmentation







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}.""")