






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 GLPNImageProcessor


class 							__a							(  unittest.TestCase						):





		'''simple docstring'''



		def __init__(						self ,    UpperCamelCase__ ,    UpperCamelCase__=7 ,    UpperCamelCase__=3 ,    UpperCamelCase__=18 ,    UpperCamelCase__=30 ,    UpperCamelCase__=400 ,    UpperCamelCase__=True ,    UpperCamelCase__=32 ,    UpperCamelCase__=True ,    ):
						SCREAMING_SNAKE_CASE_   :	List[Any]													=   parent
						SCREAMING_SNAKE_CASE_   :	Optional[Any]													=   batch_size
						SCREAMING_SNAKE_CASE_   :	Optional[int]													=   num_channels
						SCREAMING_SNAKE_CASE_   :	str													=   image_size
						SCREAMING_SNAKE_CASE_   :	Dict													=   min_resolution
						SCREAMING_SNAKE_CASE_   :	Optional[int]													=   max_resolution
						SCREAMING_SNAKE_CASE_   :	Dict													=   do_resize
						SCREAMING_SNAKE_CASE_   :	Optional[Any]													=   size_divisor
						SCREAMING_SNAKE_CASE_   :	Tuple													=   do_rescale






		def   __snake_case			(						self       ):
						return {
						    "do_resize": self.do_resize,
						    "size_divisor": self.size_divisor,
						    "do_rescale": self.do_rescale,
						}






@require_torch
@require_vision
class 							__a							(  snake_case__ ,	unittest.TestCase						):





		'''simple docstring'''



		UpperCAmelCase__      :				Union[str, Any]				    =						GLPNImageProcessor if is_vision_available() else None


		def   __snake_case			(						self       ):
						SCREAMING_SNAKE_CASE_   :	int													=   GLPNImageProcessingTester(self       )


		@property
		def   __snake_case			(						self       ):
						return self.image_processor_tester.prepare_image_processor_dict()


		def   __snake_case			(						self       ):
						SCREAMING_SNAKE_CASE_   :	Tuple													=   self.image_processing_class(**self.image_processor_dict       )
						self.assertTrue(hasattr(_SCREAMING_SNAKE_CASE ,    'do_resize'       )       )
						self.assertTrue(hasattr(_SCREAMING_SNAKE_CASE ,    'size_divisor'       )       )
						self.assertTrue(hasattr(_SCREAMING_SNAKE_CASE ,    'resample'       )       )
						self.assertTrue(hasattr(_SCREAMING_SNAKE_CASE ,    'do_rescale'       )       )


		def   __snake_case			(						self       ):
						pass


		def   __snake_case			(						self       ):
						SCREAMING_SNAKE_CASE_   :	Any													=   self.image_processing_class(**self.image_processor_dict       )
						# create random PIL images
						SCREAMING_SNAKE_CASE_   :	Tuple													=   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 (GLPNImageProcessor doesn't support batching)
						SCREAMING_SNAKE_CASE_   :	str													=   image_processing(image_inputs[0] ,    return_tensors='pt'       ).pixel_values
						self.assertTrue(encoded_images.shape[-1] % self.image_processor_tester.size_divisor == 0       )
						self.assertTrue(encoded_images.shape[-2] % self.image_processor_tester.size_divisor == 0       )


		def   __snake_case			(						self       ):
						SCREAMING_SNAKE_CASE_   :	Optional[Any]													=   self.image_processing_class(**self.image_processor_dict       )
						# create random numpy tensors
						SCREAMING_SNAKE_CASE_   :	Union[str, Any]													=   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 (GLPNImageProcessor doesn't support batching)
						SCREAMING_SNAKE_CASE_   :	Optional[Any]													=   image_processing(image_inputs[0] ,    return_tensors='pt'       ).pixel_values
						self.assertTrue(encoded_images.shape[-1] % self.image_processor_tester.size_divisor == 0       )
						self.assertTrue(encoded_images.shape[-2] % self.image_processor_tester.size_divisor == 0       )






		def   __snake_case			(						self       ):
						SCREAMING_SNAKE_CASE_   :	Optional[int]													=   self.image_processing_class(**self.image_processor_dict       )
						# create random PyTorch tensors
						SCREAMING_SNAKE_CASE_   :	List[Any]													=   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 (GLPNImageProcessor doesn't support batching)
						SCREAMING_SNAKE_CASE_   :	int													=   image_processing(image_inputs[0] ,    return_tensors='pt'       ).pixel_values
						self.assertTrue(encoded_images.shape[-1] % self.image_processor_tester.size_divisor == 0       )
						self.assertTrue(encoded_images.shape[-2] % self.image_processor_tester.size_divisor == 0       )







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}.""")