


import gc
import unittest

import numpy as np
import torch

from diffusers import AutoencoderKL, DDIMScheduler, DiTPipeline, DPMSolverMultistepScheduler, TransformeraDModel
from diffusers.utils import is_xformers_available, load_numpy, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu

from ..pipeline_params import (
    CLASS_CONDITIONED_IMAGE_GENERATION_BATCH_PARAMS,
    CLASS_CONDITIONED_IMAGE_GENERATION_PARAMS,
)
from ..test_pipelines_common import PipelineTesterMixin


enable_full_determinism()







class 		_snake_case  (      _snake_case						,    unittest.TestCase							):
 SCREAMING_SNAKE_CASE__      =       DiTPipeline
 SCREAMING_SNAKE_CASE__      =       CLASS_CONDITIONED_IMAGE_GENERATION_PARAMS
 SCREAMING_SNAKE_CASE__      =       PipelineTesterMixin.required_optional_params - {
     'latents',
     'num_images_per_prompt',
     'callback',
     'callback_steps',
 }
 SCREAMING_SNAKE_CASE__      =       CLASS_CONDITIONED_IMAGE_GENERATION_BATCH_PARAMS
 SCREAMING_SNAKE_CASE__      =       False




 def        SCREAMING_SNAKE_CASE__				(		self    ):
       torch.manual_seed(0    )
       a						:Union[str, Any] 							=       TransformeraDModel(
           sample_size=16					,  num_layers=2					,  patch_size=4					,  attention_head_dim=8					,  num_attention_heads=2					,  in_channels=4					,  out_channels=8					,  attention_bias=_lowerCamelCase					,  activation_fn='''gelu-approximate'''					,  num_embeds_ada_norm=1000					,  norm_type='''ada_norm_zero'''					,  norm_elementwise_affine=_lowerCamelCase					,  )
       a						:Optional[int] 							=       AutoencoderKL()
       a						:str 							=       DDIMScheduler()
       a						:List[str] 							=       {'''transformer''': transformer.eval(), '''vae''': vae.eval(), '''scheduler''': scheduler}
       return components




 def        SCREAMING_SNAKE_CASE__				(		self					,  _lowerCamelCase					,  _lowerCamelCase=0    ):
       if str(_lowerCamelCase    ).startswith('''mps'''    ):
             a						:str 							=       torch.manual_seed(_lowerCamelCase    )
       else:
             a						:Union[str, Any] 							=       torch.Generator(device=_lowerCamelCase    ).manual_seed(_lowerCamelCase    )
       a						:Optional[Any] 							=       {
           '''class_labels''': [1],
           '''generator''': generator,
           '''num_inference_steps''': 2,
           '''output_type''': '''numpy''',
       }
       return inputs




 def        SCREAMING_SNAKE_CASE__				(		self    ):
       a						:Optional[Any] 							=       '''cpu'''

       a						:int 							=       self.get_dummy_components()
       a						:Union[str, Any] 							=       self.pipeline_class(**_lowerCamelCase    )
       pipe.to(_lowerCamelCase    )
       pipe.set_progress_bar_config(disable=_lowerCamelCase    )

       a						:Tuple 							=       self.get_dummy_inputs(_lowerCamelCase    )
       a						:Optional[Any] 							=       pipe(**_lowerCamelCase    ).images
       a						:int 							=       image[0, -3:, -3:, -1]

       self.assertEqual(image.shape					,  (1, 16, 16, 3)    )
       a						:Optional[Any] 							=       np.array([0.2946, 0.6601, 0.4329, 0.3296, 0.4144, 0.5319, 0.7273, 0.5013, 0.4457]    )
       a						:Optional[Any] 							=       np.abs(image_slice.flatten() - expected_slice    ).max()
       self.assertLessEqual(_lowerCamelCase					,  1e-3    )




 def        SCREAMING_SNAKE_CASE__				(		self    ):
       self._test_inference_batch_single_identical(relax_max_difference=_lowerCamelCase					,  expected_max_diff=1e-3    )




 @unittest.skipIf(
     torch_device != '''cuda''' or not is_xformers_available()					,  reason='''XFormers attention is only available with CUDA and `xformers` installed'''					,  )
 def        SCREAMING_SNAKE_CASE__				(		self    ):
       self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=1e-3    )







@require_torch_gpu
@slow
class 		_snake_case  (      unittest.TestCase							):




 def        SCREAMING_SNAKE_CASE__				(		self    ):
       super().tearDown()
       gc.collect()
       torch.cuda.empty_cache()




 def        SCREAMING_SNAKE_CASE__				(		self    ):
       a						:Any 							=       torch.manual_seed(0    )

       a						:str 							=       DiTPipeline.from_pretrained('''facebook/DiT-XL-2-256'''    )
       pipe.to('''cuda'''    )

       a						:str 							=       ['''vase''', '''umbrella''', '''white shark''', '''white wolf''']
       a						:Any 							=       pipe.get_label_ids(_lowerCamelCase    )

       a						:Tuple 							=       pipe(_lowerCamelCase					,  generator=_lowerCamelCase					,  num_inference_steps=40					,  output_type='''np'''    ).images

       for word, image in zip(_lowerCamelCase					,  _lowerCamelCase    ):
             a						:Optional[Any] 							=       load_numpy(
                 F'''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/dit/{word}.npy'''    )
             assert np.abs((expected_image - image).max()    ) < 1e-2




 def        SCREAMING_SNAKE_CASE__				(		self    ):
       a						:Dict 							=       DiTPipeline.from_pretrained('''facebook/DiT-XL-2-512'''    )
       a						:Optional[int] 							=       DPMSolverMultistepScheduler.from_config(pipe.scheduler.config    )
       pipe.to('''cuda'''    )

       a						:Optional[int] 							=       ['''vase''', '''umbrella''']
       a						:int 							=       pipe.get_label_ids(_lowerCamelCase    )

       a						:Union[str, Any] 							=       torch.manual_seed(0    )
       a						:List[str] 							=       pipe(_lowerCamelCase					,  generator=_lowerCamelCase					,  num_inference_steps=25					,  output_type='''np'''    ).images

       for word, image in zip(_lowerCamelCase					,  _lowerCamelCase    ):
             a						:List[str] 							=       load_numpy(
                 '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
                 F'''/dit/{word}_512.npy'''    )

             assert np.abs((expected_image - image).max()    ) < 1e-1











'''simple docstring'''







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}.""")




