


'''simple docstring'''






def 	_A				(lowerCAmelCase__  :List[str]		)   ->  int:  # noqa: E741




     '''simple docstring'''



     _a			      =							len(lowerCAmelCase__		)
     _a			      =							0
     _a			      =							[0] * n
     _a			      =							[False] * n
     _a			      =							[False] * n

     def dfs(lowerCAmelCase__  :Tuple				,						lowerCAmelCase__  :List[str]				,						lowerCAmelCase__  :Tuple				,						lowerCAmelCase__  :Optional[int]		):
          if parent == root:
               out_edge_count += 1
          _a			      =							True
          _a			      =							at

          for to in l[at]:
               if to == parent:
                    pass
               elif not visited[to]:
                    _a			      =							dfs(lowerCAmelCase__				,						lowerCAmelCase__				,						lowerCAmelCase__				,						lowerCAmelCase__		)
                    _a			      =							min(low[at]				,						low[to]		)

                    # AP found via bridge
                    if at < low[to]:
                         _a			      =							True
                    # AP found via cycle
                    if at == low[to]:
                         _a			      =							True
               else:
                    _a			      =							min(low[at]				,						lowerCAmelCase__		)
          return out_edge_count

     for i in range(lowerCAmelCase__		):
          if not visited[i]:
               _a			      =							0
               _a			      =							dfs(lowerCAmelCase__				,						lowerCAmelCase__				,						-1				,						lowerCAmelCase__		)
               _a			      =							out_edge_count > 1

     for x in range(len(lowerCAmelCase__		)		):
          if is_art[x] is True:
               print(lowerCAmelCase__		)


# Adjacency list of graph
a_					:    int          =    {
    0: [1, 2],
    1: [0, 2],
    2: [0, 1, 3, 5],
    3: [2, 4],
    4: [3],
    5: [2, 6, 8],
    6: [5, 7],
    7: [6, 8],
    8: [5, 7],
}






compute_ap(data)





'''simple docstring'''






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  a     (     _SCREAMING_SNAKE_CASE  ,				unittest.TestCase       ):
 _lowerCAmelCase 					=	DiTPipeline
 _lowerCAmelCase 					=	CLASS_CONDITIONED_IMAGE_GENERATION_PARAMS
 _lowerCAmelCase 					=	PipelineTesterMixin.required_optional_params - {
     """latents""",
     """num_images_per_prompt""",
     """callback""",
     """callback_steps""",
 }
 _lowerCAmelCase 					=	CLASS_CONDITIONED_IMAGE_GENERATION_BATCH_PARAMS
 _lowerCAmelCase 					=	False
 def 							__UpperCAmelCase		( self						)					->							Dict:
      torch.manual_seed(0						)
      _a			      =							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=__magic_name__      ,				activation_fn='gelu-approximate'      ,				num_embeds_ada_norm=10_00      ,				norm_type='ada_norm_zero'      ,				norm_elementwise_affine=__magic_name__      ,				)
      _a			      =							AutoencoderKL()
      _a			      =							DDIMScheduler()
      _a			      =							{'transformer': transformer.eval(), 'vae': vae.eval(), 'scheduler': scheduler}
      return components
 def 							__UpperCAmelCase		( self      ,				__magic_name__      ,				__magic_name__=0						)					->							Union[str, Any]:
      if str(__magic_name__						).startswith('mps'						):
           _a			      =							torch.manual_seed(__magic_name__						)
      else:
           _a			      =							torch.Generator(device=__magic_name__						).manual_seed(__magic_name__						)
      _a			      =							{
          'class_labels': [1],
          'generator': generator,
          'num_inference_steps': 2,
          'output_type': 'numpy',
      }
      return inputs
 def 							__UpperCAmelCase		( self						)					->							Tuple:
      _a			      =							'cpu'

      _a			      =							self.get_dummy_components()
      _a			      =							self.pipeline_class(**__magic_name__						)
      pipe.to(__magic_name__						)
      pipe.set_progress_bar_config(disable=__magic_name__						)

      _a			      =							self.get_dummy_inputs(__magic_name__						)
      _a			      =							pipe(**__magic_name__						).images
      _a			      =							image[0, -3:, -3:, -1]

      self.assertEqual(image.shape      ,				(1, 16, 16, 3)						)
      _a			      =							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]						)
      _a			      =							np.abs(image_slice.flatten() - expected_slice						).max()
      self.assertLessEqual(__magic_name__      ,				1e-3						)
 def 							__UpperCAmelCase		( self						)					->							Any:
      self._test_inference_batch_single_identical(relax_max_difference=__magic_name__      ,				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 							__UpperCAmelCase		( self						)					->							Any:
      self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=1e-3						)





@require_torch_gpu
@slow
class  a     (     unittest.TestCase       ):
 def 							__UpperCAmelCase		( self						)					->							List[str]:
      super().tearDown()
      gc.collect()
      torch.cuda.empty_cache()
 def 							__UpperCAmelCase		( self						)					->							Union[str, Any]:
      _a			      =							torch.manual_seed(0						)

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

      _a			      =							['vase', 'umbrella', 'white shark', 'white wolf']
      _a			      =							pipe.get_label_ids(__magic_name__						)

      _a			      =							pipe(__magic_name__      ,				generator=__magic_name__      ,				num_inference_steps=40      ,				output_type='np'						).images

      for word, image in zip(__magic_name__      ,				__magic_name__						):
           _a			      =							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 							__UpperCAmelCase		( self						)					->							Any:
      _a			      =							DiTPipeline.from_pretrained('facebook/DiT-XL-2-512'						)
      _a			      =							DPMSolverMultistepScheduler.from_config(pipe.scheduler.config						)
      pipe.to('cuda'						)

      _a			      =							['vase', 'umbrella']
      _a			      =							pipe.get_label_ids(__magic_name__						)

      _a			      =							torch.manual_seed(0						)
      _a			      =							pipe(__magic_name__      ,				generator=__magic_name__      ,				num_inference_steps=25      ,				output_type='np'						).images

      for word, image in zip(__magic_name__      ,				__magic_name__						):
           _a			      =							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

