






import json
import os
import tempfile

from transformers.testing_utils import check_json_file_has_correct_format, require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available


if is_torch_available():
       import numpy as np
       import torch

if is_vision_available():
       from PIL import Image




def       _a   (							lowerCamelCase__ ,			lowerCamelCase__=False ,			lowerCamelCase__=False ,			lowerCamelCase__=False				)  ->      Optional[Any]:
 assert not (numpify and torchify), "You cannot specify both numpy and PyTorch tensors at the same time"

 lowerCamelCase_							:    Tuple							=     []
 for i in range(image_processor_tester.batch_size				):
  if equal_resolution:
   lowerCamelCase_							:    Any							=     image_processor_tester.max_resolution
  else:
   # To avoid getting image width/height 0
   lowerCamelCase_							:    Union[str, Any]							=     image_processor_tester.min_resolution
   if getattr(lowerCamelCase__ ,			'size_divisor' ,			lowerCamelCase__				):
    # If `size_divisor` is defined, the image needs to have width/size >= `size_divisor`
    lowerCamelCase_							:    Union[str, Any]							=     max(image_processor_tester.size_divisor ,			lowerCamelCase__				)
   lowerCamelCase_		,      lowerCamelCase_							:    Any							=     np.random.choice(np.arange(lowerCamelCase__ ,			image_processor_tester.max_resolution				) ,			2				)
  image_inputs.append(
      np.random.randint(2_55 ,			size=(image_processor_tester.num_channels, width, height) ,			dtype=np.uinta				)				)

 if not numpify and not torchify:
  # PIL expects the channel dimension as last dimension
  lowerCamelCase_							:    Optional[Any]							=     [Image.fromarray(np.moveaxis(lowerCamelCase__ ,			0 ,			-1				)				) for image in image_inputs]

 if torchify:
  lowerCamelCase_							:    Optional[Any]							=     [torch.from_numpy(lowerCamelCase__				) for image in image_inputs]

 return image_inputs




def       _a   (							lowerCamelCase__ ,			lowerCamelCase__=10 ,			lowerCamelCase__=10 ,			lowerCamelCase__=False ,			lowerCamelCase__=False				)  ->      Optional[int]:
 lowerCamelCase_							:    Union[str, Any]							=     []
 for i in range(image_processor_tester.num_frames				):
  video.append(np.random.randint(2_55 ,			size=(image_processor_tester.num_channels, width, height) ,			dtype=np.uinta				)				)

 if not numpify and not torchify:
  # PIL expects the channel dimension as last dimension
  lowerCamelCase_							:    List[Any]							=     [Image.fromarray(np.moveaxis(lowerCamelCase__ ,			0 ,			-1				)				) for frame in video]

 if torchify:
  lowerCamelCase_							:    int							=     [torch.from_numpy(lowerCamelCase__				) for frame in video]

 return video




def       _a   (							lowerCamelCase__ ,			lowerCamelCase__=False ,			lowerCamelCase__=False ,			lowerCamelCase__=False				)  ->      Union[str, Any]:
 assert not (numpify and torchify), "You cannot specify both numpy and PyTorch tensors at the same time"

 lowerCamelCase_							:    Dict							=     []
 for i in range(image_processor_tester.batch_size				):
  if equal_resolution:
   lowerCamelCase_							:    Any							=     image_processor_tester.max_resolution
  else:
   lowerCamelCase_		,      lowerCamelCase_							:    Dict							=     np.random.choice(
       np.arange(image_processor_tester.min_resolution ,			image_processor_tester.max_resolution				) ,			2				)
   lowerCamelCase_							:    int							=     prepare_video(
       image_processor_tester=lowerCamelCase__ ,			width=lowerCamelCase__ ,			height=lowerCamelCase__ ,			numpify=lowerCamelCase__ ,			torchify=lowerCamelCase__ ,			)
  video_inputs.append(lowerCamelCase__				)

 return video_inputs





class 	lowerCamelCase__				:
      lowerCamelCase_							:   int											=	None






      def      UpperCAmelCase_     (self					:    List[str]		)       ->   Optional[Any]:


       """simple docstring"""






       lowerCamelCase_							:    Union[str, Any]							=     self.image_processing_class(**self.image_processor_dict		)
       lowerCamelCase_							:    Optional[int]							=     json.loads(image_processor.to_json_string()		)
       for key, value in self.image_processor_dict.items():
        self.assertEqual(obj[key]		,		_snake_case		)






      def      UpperCAmelCase_     (self					:    Any		)       ->   str:


       """simple docstring"""






       lowerCamelCase_							:    int							=     self.image_processing_class(**self.image_processor_dict		)

       with tempfile.TemporaryDirectory() as tmpdirname:
        lowerCamelCase_							:    Optional[int]							=     os.path.join(_snake_case		,		'image_processor.json'		)
        image_processor_first.to_json_file(_snake_case		)
        lowerCamelCase_							:    str							=     self.image_processing_class.from_json_file(_snake_case		)

       self.assertEqual(image_processor_second.to_dict()		,		image_processor_first.to_dict()		)






      def      UpperCAmelCase_     (self					:    List[str]		)       ->   int:


       """simple docstring"""






       lowerCamelCase_							:    Any							=     self.image_processing_class(**self.image_processor_dict		)

       with tempfile.TemporaryDirectory() as tmpdirname:
        lowerCamelCase_							:    Union[str, Any]							=     image_processor_first.save_pretrained(_snake_case		)[0]
        check_json_file_has_correct_format(_snake_case		)
        lowerCamelCase_							:    Tuple							=     self.image_processing_class.from_pretrained(_snake_case		)

       self.assertEqual(image_processor_second.to_dict()		,		image_processor_first.to_dict()		)






      def      UpperCAmelCase_     (self					:    List[str]		)       ->   int:


       """simple docstring"""






       lowerCamelCase_							:    List[str]							=     self.image_processing_class()
       self.assertIsNotNone(_snake_case		)






      @require_torch
      @require_vision
      def      UpperCAmelCase_     (self					:    Optional[Any]		)       ->   Optional[Any]:


       """simple docstring"""






       if self.test_cast_dtype is not None:
        # Initialize image_processor
        lowerCamelCase_							:    Tuple							=     self.image_processing_class(**self.image_processor_dict		)

        # create random PyTorch tensors
        lowerCamelCase_							:    Tuple							=     prepare_image_inputs(self.image_processor_tester		,		equal_resolution=_snake_case		,		torchify=_snake_case		)

        lowerCamelCase_							:    List[str]							=     image_processor(_snake_case		,		return_tensors='pt'		)
        # for layoutLM compatiblity
        self.assertEqual(encoding.pixel_values.device		,		torch.device('cpu'		)		)
        self.assertEqual(encoding.pixel_values.dtype		,		torch.floataa		)

        lowerCamelCase_							:    List[str]							=     image_processor(_snake_case		,		return_tensors='pt'		).to(torch.floataa		)
        self.assertEqual(encoding.pixel_values.device		,		torch.device('cpu'		)		)
        self.assertEqual(encoding.pixel_values.dtype		,		torch.floataa		)

        lowerCamelCase_							:    int							=     image_processor(_snake_case		,		return_tensors='pt'		).to('cpu'		,		torch.bfloataa		)
        self.assertEqual(encoding.pixel_values.device		,		torch.device('cpu'		)		)
        self.assertEqual(encoding.pixel_values.dtype		,		torch.bfloataa		)

        with self.assertRaises(_snake_case		):
         lowerCamelCase_							:    Optional[Any]							=     image_processor(_snake_case		,		return_tensors='pt'		).to(torch.bfloataa		,		'cpu'		)

        # Try with text + image feature
        lowerCamelCase_							:    Dict							=     image_processor(_snake_case		,		return_tensors='pt'		)
        encoding.update({'input_ids': torch.LongTensor([[1, 2, 3], [4, 5, 6]]		)}		)
        lowerCamelCase_							:    Optional[Any]							=     encoding.to(torch.floataa		)

        self.assertEqual(encoding.pixel_values.device		,		torch.device('cpu'		)		)
        self.assertEqual(encoding.pixel_values.dtype		,		torch.floataa		)
        self.assertEqual(encoding.input_ids.dtype		,		torch.long		)







import os

import requests
from bsa import BeautifulSoup
from fake_useragent import UserAgent

lowercase     :			Optional[int]     						=	{"""UserAgent""": UserAgent().random}
lowercase     :			Union[str, Any]     						=	"""https://www.mywaifulist.moe/random"""


def      SCREAMING_SNAKE_CASE      (   lowerCAmelCase     ,							lowerCAmelCase       ):
       _UpperCamelCase														=    requests.get(lowerCAmelCase     ,							headers=lowerCAmelCase       )
       with open(lowerCAmelCase     ,							'''wb'''       ) as file:
              file.write(image.content       )


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


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






