


import random
import unittest

import torch

from diffusers import IFImgaImgPipeline
from diffusers.utils import floats_tensor
from diffusers.utils.import_utils import is_xformers_available
from diffusers.utils.testing_utils import skip_mps, torch_device

from ..pipeline_params import (
    TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS,
    TEXT_GUIDED_IMAGE_VARIATION_PARAMS,
)
from ..test_pipelines_common import PipelineTesterMixin
from . import IFPipelineTesterMixin


@skip_mps
class       __lowerCAmelCase       (	a	,					a	,					unittest.TestCase   ):






  """simple docstring"""





  _SCREAMING_SNAKE_CASE						      =    IFImgaImgPipeline
  _SCREAMING_SNAKE_CASE						      =    TEXT_GUIDED_IMAGE_VARIATION_PARAMS - {'width', 'height'}
  _SCREAMING_SNAKE_CASE						      =    TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS
  _SCREAMING_SNAKE_CASE						      =    PipelineTesterMixin.required_optional_params - {'latents'}



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




     """simple docstring"""
     return self._get_dummy_components()



  def    lowerCAmelCase__					(       self    :    Any      ,			_lowerCAmelCase    :    Dict      ,			_lowerCAmelCase    :    List[str]=0					)				->	Optional[Any]:




     """simple docstring"""
     if str(_lowerCAmelCase					).startswith("mps"					):
        snake_case_         =   torch.manual_seed(_lowerCAmelCase					)
     else:
        snake_case_         =   torch.Generator(device=_lowerCAmelCase					).manual_seed(_lowerCAmelCase					)

     snake_case_         =   floats_tensor((1, 3, 3_2, 3_2)      ,			rng=random.Random(_lowerCAmelCase					)					).to(_lowerCAmelCase					)

     snake_case_         =   {
         "prompt": "A painting of a squirrel eating a burger",
         "image": image,
         "generator": generator,
         "num_inference_steps": 2,
         "output_type": "numpy",
     }

     return inputs



  def    lowerCAmelCase__					(       self    :    Tuple					)				->	Optional[Any]:




     """simple docstring"""
     self._test_save_load_optional_components()



  @unittest.skipIf(
      torch_device != "cuda" or not is_xformers_available()      ,			reason="XFormers attention is only available with CUDA and `xformers` installed"      ,			)
  def    lowerCAmelCase__					(       self    :    Dict					)				->	Optional[int]:




     """simple docstring"""
     self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=1e-3					)



  @unittest.skipIf(torch_device != "cuda"      ,			reason="float16 requires CUDA"					)
  def    lowerCAmelCase__					(       self    :    Optional[Any]					)				->	Union[str, Any]:




     """simple docstring"""
     # Due to non-determinism in save load of the hf-internal-testing/tiny-random-t5 text encoder
     super().test_save_load_floataa(expected_max_diff=1e-1					)



  @unittest.skipIf(torch_device != "cuda"      ,			reason="float16 requires CUDA"					)
  def    lowerCAmelCase__					(       self    :    Optional[int]					)				->	Dict:




     """simple docstring"""
     super().test_floataa_inference(expected_max_diff=1e-1					)



  def    lowerCAmelCase__					(       self    :    Tuple					)				->	Dict:




     """simple docstring"""
     self._test_attention_slicing_forward_pass(expected_max_diff=1e-2					)



  def    lowerCAmelCase__					(       self    :    Tuple					)				->	Tuple:




     """simple docstring"""
     self._test_save_load_local()



  def    lowerCAmelCase__					(       self    :    Optional[Any]					)				->	Tuple:




     """simple docstring"""
     self._test_inference_batch_single_identical(
         expected_max_diff=1e-2      ,			)








import os

import requests
from bsa import BeautifulSoup
from fake_useragent import UserAgent

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



def      _lowerCAmelCase  (							lowerCAmelCase_							:str						,  lowerCAmelCase_							:str							)->None:
   '''simple docstring'''






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



def      _lowerCAmelCase  (							)->tuple[str, str, str]:
   '''simple docstring'''






   snake_case_         =   BeautifulSoup(requests.get(lowerCAmelCase_						,  headers=lowerCAmelCase_							).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(lowerCAmelCase_							)							)
   snake_case_         =   title.strip().replace(" "						,  "_"							)
   snake_case_         =   F'''{image_title}{image_extension}'''
   save_image(lowerCAmelCase_						,  lowerCAmelCase_							)
   return (title, description, image_title)


if __name__ == "__main__":
   SCREAMING_SNAKE_CASE						,		SCREAMING_SNAKE_CASE						,		SCREAMING_SNAKE_CASE		:Optional[Any]											=       random_anime_character()
   print(F'''{title}\n\n{desc}\n\nImage saved : {image_title}''')




