




'''simple docstring'''

import os

import requests
from bsa import BeautifulSoup
from fake_useragent import UserAgent

a__         =     {'''UserAgent''': UserAgent().random}
a__         =     '''https://www.mywaifulist.moe/random'''




def    snake_case__     (  a       ,      a	)			->       None:





			'''simple docstring'''






			snake_case__          =						requests.get(a       ,      headers=a	)
			with open(a       ,      """wb"""	) as file:
						file.write(image.content	)




def    snake_case__     (  )			->       tuple[str, str, str]:





			'''simple docstring'''






			snake_case__          =						BeautifulSoup(requests.get(a       ,      headers=a	).text       ,      """html.parser"""	)
			snake_case__          =						soup.find("""meta"""       ,      attrs={"""property""": """og:title"""}	).attrs["""content"""]
			snake_case__          =						soup.find("""meta"""       ,      attrs={"""property""": """og:image"""}	).attrs["""content"""]
			snake_case__          =						soup.find("""p"""       ,      id="""description"""	).get_text()
			snake_case__    ,			snake_case__          =						os.path.splitext(os.path.basename(a	)	)
			snake_case__          =						title.strip().replace(""" """       ,      """_"""	)
			snake_case__          =						F"""{image_title}{image_extension}"""
			save_image(a       ,      a	)
			return (title, description, image_title)


if __name__ == "__main__":
		a__    , a__    , a__         =     random_anime_character()
		print(F"{title}\n\n{desc}\n\nImage saved : {image_title}")

"""simple docstring"""







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 DeiTImageProcessor




class 					A__			(      unittest.TestCase):

		def __init__( self			,						_SCREAMING_SNAKE_CASE			,						_SCREAMING_SNAKE_CASE=7			,						_SCREAMING_SNAKE_CASE=3			,						_SCREAMING_SNAKE_CASE=18			,						_SCREAMING_SNAKE_CASE=30			,						_SCREAMING_SNAKE_CASE=4_00			,						_SCREAMING_SNAKE_CASE=True			,						_SCREAMING_SNAKE_CASE=None			,						_SCREAMING_SNAKE_CASE=True			,						_SCREAMING_SNAKE_CASE=None			,						_SCREAMING_SNAKE_CASE=True			,						_SCREAMING_SNAKE_CASE=[0.5, 0.5, 0.5]			,						_SCREAMING_SNAKE_CASE=[0.5, 0.5, 0.5]			,						):
			__lowerCAmelCase     :		List[str]		     = size if size is not None else {'height': 20, 'width': 20}
			__lowerCAmelCase     :		List[str]		     = crop_size if crop_size is not None else {'height': 18, 'width': 18}

			__lowerCAmelCase     :		Any		     = parent
			__lowerCAmelCase     :		Optional[Any]		     = batch_size
			__lowerCAmelCase     :		Any		     = num_channels
			__lowerCAmelCase     :		Dict		     = image_size
			__lowerCAmelCase     :		Tuple		     = min_resolution
			__lowerCAmelCase     :		Union[str, Any]		     = max_resolution
			__lowerCAmelCase     :		Optional[int]		     = do_resize
			__lowerCAmelCase     :		Any		     = size
			__lowerCAmelCase     :		Union[str, Any]		     = do_center_crop
			__lowerCAmelCase     :		List[Any]		     = crop_size
			__lowerCAmelCase     :		Optional[int]		     = do_normalize
			__lowerCAmelCase     :		int		     = image_mean
			__lowerCAmelCase     :		Tuple		     = image_std







		def 				__lowerCamelCase				( self					):
			return {
			    "do_resize": self.do_resize,
			    "size": self.size,
			    "do_center_crop": self.do_center_crop,
			    "crop_size": self.crop_size,
			    "do_normalize": self.do_normalize,
			    "image_mean": self.image_mean,
			    "image_std": self.image_std,
			}




@require_torch
@require_vision
class 					A__			(      _lowerCamelCase			,	unittest.TestCase):
		A_		:			List[str]							  =      DeiTImageProcessor if is_vision_available() else None
		A_		:			int							  =      True

		def 				__lowerCamelCase				( self					):
			__lowerCAmelCase     :		Optional[int]		     = DeiTImageProcessingTester(self					)

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

		def 				__lowerCamelCase				( self					):
			__lowerCAmelCase     :		Any		     = self.image_processing_class(**self.image_processor_dict					)
			self.assertTrue(hasattr(_SCREAMING_SNAKE_CASE			,						'do_resize'					)					)
			self.assertTrue(hasattr(_SCREAMING_SNAKE_CASE			,						'size'					)					)
			self.assertTrue(hasattr(_SCREAMING_SNAKE_CASE			,						'do_center_crop'					)					)
			self.assertTrue(hasattr(_SCREAMING_SNAKE_CASE			,						'center_crop'					)					)
			self.assertTrue(hasattr(_SCREAMING_SNAKE_CASE			,						'do_normalize'					)					)
			self.assertTrue(hasattr(_SCREAMING_SNAKE_CASE			,						'image_mean'					)					)
			self.assertTrue(hasattr(_SCREAMING_SNAKE_CASE			,						'image_std'					)					)

		def 				__lowerCamelCase				( self					):
			__lowerCAmelCase     :		Tuple		     = self.image_processing_class.from_dict(self.image_processor_dict					)
			self.assertEqual(image_processor.size			,						{'height': 20, 'width': 20}					)
			self.assertEqual(image_processor.crop_size			,						{'height': 18, 'width': 18}					)

			__lowerCAmelCase     :		str		     = self.image_processing_class.from_dict(self.image_processor_dict			,						size=42			,						crop_size=84					)
			self.assertEqual(image_processor.size			,						{'height': 42, 'width': 42}					)
			self.assertEqual(image_processor.crop_size			,						{'height': 84, 'width': 84}					)

		def 				__lowerCamelCase				( self					):
			pass

		def 				__lowerCamelCase				( self					):
			# Initialize image_processing
			__lowerCAmelCase     :		Dict		     = self.image_processing_class(**self.image_processor_dict					)
			# create random PIL images
			__lowerCAmelCase     :		List[str]		     = 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
			__lowerCAmelCase     :		Dict		     = image_processing(image_inputs[0]			,						return_tensors='pt'					).pixel_values
			self.assertEqual(
			    encoded_images.shape			,						(
			        1,
			        self.image_processor_tester.num_channels,
			        self.image_processor_tester.crop_size['height'],
			        self.image_processor_tester.crop_size['width'],
			    )			,						)

			# Test batched
			__lowerCAmelCase     :		int		     = image_processing(_SCREAMING_SNAKE_CASE			,						return_tensors='pt'					).pixel_values
			self.assertEqual(
			    encoded_images.shape			,						(
			        self.image_processor_tester.batch_size,
			        self.image_processor_tester.num_channels,
			        self.image_processor_tester.crop_size['height'],
			        self.image_processor_tester.crop_size['width'],
			    )			,						)

		def 				__lowerCamelCase				( self					):
			# Initialize image_processing
			__lowerCAmelCase     :		Optional[int]		     = self.image_processing_class(**self.image_processor_dict					)
			# create random numpy tensors
			__lowerCAmelCase     :		List[str]		     = 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
			__lowerCAmelCase     :		str		     = image_processing(image_inputs[0]			,						return_tensors='pt'					).pixel_values
			self.assertEqual(
			    encoded_images.shape			,						(
			        1,
			        self.image_processor_tester.num_channels,
			        self.image_processor_tester.crop_size['height'],
			        self.image_processor_tester.crop_size['width'],
			    )			,						)

			# Test batched
			__lowerCAmelCase     :		Dict		     = image_processing(_SCREAMING_SNAKE_CASE			,						return_tensors='pt'					).pixel_values
			self.assertEqual(
			    encoded_images.shape			,						(
			        self.image_processor_tester.batch_size,
			        self.image_processor_tester.num_channels,
			        self.image_processor_tester.crop_size['height'],
			        self.image_processor_tester.crop_size['width'],
			    )			,						)







		def 				__lowerCamelCase				( self					):
			# Initialize image_processing
			__lowerCAmelCase     :		Union[str, Any]		     = self.image_processing_class(**self.image_processor_dict					)
			# create random PyTorch tensors
			__lowerCAmelCase     :		List[str]		     = 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
			__lowerCAmelCase     :		Union[str, Any]		     = image_processing(image_inputs[0]			,						return_tensors='pt'					).pixel_values
			self.assertEqual(
			    encoded_images.shape			,						(
			        1,
			        self.image_processor_tester.num_channels,
			        self.image_processor_tester.crop_size['height'],
			        self.image_processor_tester.crop_size['width'],
			    )			,						)

			# Test batched
			__lowerCAmelCase     :		Optional[int]		     = image_processing(_SCREAMING_SNAKE_CASE			,						return_tensors='pt'					).pixel_values
			self.assertEqual(
			    encoded_images.shape			,						(
			        self.image_processor_tester.batch_size,
			        self.image_processor_tester.num_channels,
			        self.image_processor_tester.crop_size['height'],
			        self.image_processor_tester.crop_size['width'],
			    )			,						)