


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     _lowercase				(							lowercase__							,						unittest.TestCase):


       """simple docstring"""




       A__      		=    DiTPipeline
       A__      		=    CLASS_CONDITIONED_IMAGE_GENERATION_PARAMS
       A__      		=    PipelineTesterMixin.required_optional_params - {
           "latents",
           "num_images_per_prompt",
           "callback",
           "callback_steps",
       }
       A__      		=    CLASS_CONDITIONED_IMAGE_GENERATION_BATCH_PARAMS
       A__      		=    False





       def 		lowerCAmelCase  (     self							:    Optional[Any] ):


             '''simple docstring'''



             torch.manual_seed(0 )
             lowerCamelCase__	:       Dict					=						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      ,   )
             lowerCamelCase__	:       int					=						AutoencoderKL()
             lowerCamelCase__	:       int					=						DDIMScheduler()
             lowerCamelCase__	:       str					=						{"transformer": transformer.eval(), "vae": vae.eval(), "scheduler": scheduler}
             return components





       def 		lowerCAmelCase  (     self							:    List[str]      ,   __lowerCamelCase							:    Tuple      ,   __lowerCamelCase							:    Optional[Any]=0 ):


             '''simple docstring'''



             if str(__lowerCamelCase ).startswith("mps" ):
                   lowerCamelCase__	:       Dict					=						torch.manual_seed(__lowerCamelCase )
             else:
                   lowerCamelCase__	:       Union[str, Any]					=						torch.Generator(device=__lowerCamelCase ).manual_seed(__lowerCamelCase )
             lowerCamelCase__	:       Any					=						{
                 "class_labels": [1],
                 "generator": generator,
                 "num_inference_steps": 2,
                 "output_type": "numpy",
             }
             return inputs





       def 		lowerCAmelCase  (     self							:    str ):


             '''simple docstring'''



             lowerCamelCase__	:       Tuple					=						"cpu"

             lowerCamelCase__	:       int					=						self.get_dummy_components()
             lowerCamelCase__	:       Any					=						self.pipeline_class(**__lowerCamelCase )
             pipe.to(__lowerCamelCase )
             pipe.set_progress_bar_config(disable=__lowerCamelCase )

             lowerCamelCase__	:       Union[str, Any]					=						self.get_dummy_inputs(__lowerCamelCase )
             lowerCamelCase__	:       Dict					=						pipe(**__lowerCamelCase ).images
             lowerCamelCase__	:       Tuple					=						image[0, -3:, -3:, -1]

             self.assertEqual(image.shape      ,   (1, 16, 16, 3) )
             lowerCamelCase__	:       List[Any]					=						np.array([0.2_9_4_6, 0.6_6_0_1, 0.4_3_2_9, 0.3_2_9_6, 0.4_1_4_4, 0.5_3_1_9, 0.7_2_7_3, 0.5_0_1_3, 0.4_4_5_7] )
             lowerCamelCase__	:       List[Any]					=						np.abs(image_slice.flatten() - expected_slice ).max()
             self.assertLessEqual(__lowerCamelCase      ,   1E-3 )





       def 		lowerCAmelCase  (     self							:    int ):


             '''simple docstring'''



             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 		lowerCAmelCase  (     self							:    List[str] ):


             '''simple docstring'''



             self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=1E-3 )







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


       """simple docstring"""




       def 		lowerCAmelCase  (     self							:    int ):


             '''simple docstring'''



             super().tearDown()
             gc.collect()
             torch.cuda.empty_cache()





       def 		lowerCAmelCase  (     self							:    str ):


             '''simple docstring'''



             lowerCamelCase__	:       Dict					=						torch.manual_seed(0 )

             lowerCamelCase__	:       Optional[int]					=						DiTPipeline.from_pretrained("facebook/DiT-XL-2-256" )
             pipe.to("cuda" )

             lowerCamelCase__	:       Tuple					=						["vase", "umbrella", "white shark", "white wolf"]
             lowerCamelCase__	:       List[str]					=						pipe.get_label_ids(__lowerCamelCase )

             lowerCamelCase__	:       Optional[int]					=						pipe(__lowerCamelCase      ,   generator=__lowerCamelCase      ,   num_inference_steps=40      ,   output_type="np" ).images

             for word, image in zip(__lowerCamelCase      ,   __lowerCamelCase ):
                   lowerCamelCase__	:       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 		lowerCAmelCase  (     self							:    Dict ):


             '''simple docstring'''



             lowerCamelCase__	:       str					=						DiTPipeline.from_pretrained("facebook/DiT-XL-2-512" )
             lowerCamelCase__	:       Any					=						DPMSolverMultistepScheduler.from_config(pipe.scheduler.config )
             pipe.to("cuda" )

             lowerCamelCase__	:       Any					=						["vase", "umbrella"]
             lowerCamelCase__	:       int					=						pipe.get_label_ids(__lowerCamelCase )

             lowerCamelCase__	:       int					=						torch.manual_seed(0 )
             lowerCamelCase__	:       Optional[int]					=						pipe(__lowerCamelCase      ,   generator=__lowerCamelCase      ,   num_inference_steps=25      ,   output_type="np" ).images

             for word, image in zip(__lowerCamelCase      ,   __lowerCamelCase ):
                   lowerCamelCase__	:       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






import sys
import webbrowser

import requests
from bsa import BeautifulSoup
from fake_useragent import UserAgent

if __name__ == "__main__":
    print("Googling.....")
    A   :					str	=      "https://www.google.com/search?q=" + " ".join(sys.argv[1:])
    A   :					Optional[int]	=      requests.get(url, headers={"UserAgent": UserAgent().random})
    # res.raise_for_status()
    with open("project1a.html", "wb") as out_file:  # only for knowing the class
        for data in res.iter_content(10000):
            out_file.write(data)
    A   :					int	=      BeautifulSoup(res.text, "html.parser")
    A   :					Any	=      list(soup.select(".eZt8xd"))[:5]

    print(len(links))
    for link in links:
        if link.text == "Maps":
            webbrowser.open(link.get("href"))
        else:
            webbrowser.open(f'https://google.com{link.get("href")}')


