
'''simple docstring'''




from collections import deque







def 		lowerCAmelCase_				(   snake_case__						):




    '''simple docstring'''



    A       :   Dict					=						len(snake_case__						)
    A       :   str					=						deque()
    A       :   int					=						[False for _ in range(snake_case__						)]
    A       :   List[Any]					=						[-1 for _ in range(snake_case__						)]
    A       :   Optional[int]					=						index_of[:]

    def strong_connect(snake_case__		,				snake_case__		,				snake_case__						):
        A       :   int					=						index  # the number when this node is seen
        A       :   Tuple					=						index  # lowest rank node reachable from here
        index += 1
        stack.append(snake_case__						)
        A       :   List[str]					=						True

        for w in g[v]:
            if index_of[w] == -1:
                A       :   List[str]					=						strong_connect(snake_case__		,				snake_case__		,				snake_case__						)
                A       :   str					=						(
                    lowlink_of[w] if lowlink_of[w] < lowlink_of[v] else lowlink_of[v]
                )
            elif on_stack[w]:
                A       :   List[Any]					=						(
                    lowlink_of[w] if lowlink_of[w] < lowlink_of[v] else lowlink_of[v]
                )

        if lowlink_of[v] == index_of[v]:
            A       :   Optional[int]					=						[]
            A       :   List[str]					=						stack.pop()
            A       :   List[str]					=						False
            component.append(snake_case__						)
            while w != v:
                A       :   Optional[Any]					=						stack.pop()
                A       :   List[str]					=						False
                component.append(snake_case__						)
            components.append(snake_case__						)
        return index

    A       :   Any					=						[]
    for v in range(snake_case__						):
        if index_of[v] == -1:
            strong_connect(snake_case__		,				0		,				snake_case__						)

    return components







def 		lowerCAmelCase_				(   snake_case__		,				snake_case__						):




    '''simple docstring'''



    A       :   Optional[Any]					=						[[] for _ in range(snake_case__						)]
    for u, v in edges:
        g[u].append(snake_case__						)
    return g


if __name__ == "__main__":
      # Test
      lowercase			:				str    		=			7
      lowercase			:				Any    		=			[0, 0, 1, 2, 3, 3, 4, 4, 6]
      lowercase			:				Tuple    		=			[1, 3, 2, 0, 1, 4, 5, 6, 5]
      lowercase			:				Optional[int]    		=			[(u, v) for u, v in zip(source, target)]
      lowercase			:				Any    		=			create_graph(n_vertices, edges)

      assert [[5], [6], [4], [3, 2, 1, 0]] == tarjan(g)






'''simple docstring'''







import unittest

import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel

from diffusers import DDIMScheduler, LDMPipeline, UNetaDModel, VQModel
from diffusers.utils.testing_utils import enable_full_determinism, require_torch, slow, torch_device


enable_full_determinism()







class UpperCAmelCase__	( unittest.TestCase	):





  """simple docstring"""

  @property
  def   __lowercase		(		self      :     Union[str, Any]       ):


     '''simple docstring'''






     torch.manual_seed(0       )
     _a				:	Any			=   UNetaDModel(
         block_out_channels=(32, 64)							,layers_per_block=2							,sample_size=32							,in_channels=3							,out_channels=3							,down_block_types=('DownBlock2D', 'AttnDownBlock2D')							,up_block_types=('AttnUpBlock2D', 'UpBlock2D')							,)
     return model


  @property
  def   __lowercase		(		self      :     Optional[int]       ):


     '''simple docstring'''






     torch.manual_seed(0       )
     _a				:	Union[str, Any]			=   VQModel(
         block_out_channels=[32, 64]							,in_channels=3							,out_channels=3							,down_block_types=['DownEncoderBlock2D', 'DownEncoderBlock2D']							,up_block_types=['UpDecoderBlock2D', 'UpDecoderBlock2D']							,latent_channels=3							,)
     return model


  @property
  def   __lowercase		(		self      :     Optional[int]       ):


     '''simple docstring'''






     torch.manual_seed(0       )
     _a				:	Any			=   CLIPTextConfig(
         bos_token_id=0							,eos_token_id=2							,hidden_size=32							,intermediate_size=37							,layer_norm_eps=1E-05							,num_attention_heads=4							,num_hidden_layers=5							,pad_token_id=1							,vocab_size=1000							,)
     return CLIPTextModel(_a       )


  def   __lowercase		(		self      :     Tuple       ):


     '''simple docstring'''






     _a				:	Dict			=   self.dummy_uncond_unet
     _a				:	List[Any]			=   DDIMScheduler()
     _a				:	List[Any]			=   self.dummy_vq_model

     _a				:	str			=   LDMPipeline(unet=_a							,vqvae=_a							,scheduler=_a       )
     ldm.to(_a       )
     ldm.set_progress_bar_config(disable=_a       )

     _a				:	List[str]			=   torch.manual_seed(0       )
     _a				:	List[str]			=   ldm(generator=_a							,num_inference_steps=2							,output_type='numpy'       ).images

     _a				:	List[str]			=   torch.manual_seed(0       )
     _a				:	Union[str, Any]			=   ldm(generator=_a							,num_inference_steps=2							,output_type='numpy'							,return_dict=_a       )[0]

     _a				:	Tuple			=   image[0, -3:, -3:, -1]
     _a				:	Optional[Any]			=   image_from_tuple[0, -3:, -3:, -1]

     assert image.shape == (1, 64, 64, 3)
     _a				:	int			=   np.array([0.8512, 0.818, 0.6411, 0.6808, 0.4465, 0.5618, 0.46, 0.6231, 0.5172]       )
     _a				:	Any			=   1E-2 if torch_device != 'mps' else 3E-2

     assert np.abs(image_slice.flatten() - expected_slice       ).max() < tolerance
     assert np.abs(image_from_tuple_slice.flatten() - expected_slice       ).max() < tolerance







@slow
@require_torch
class UpperCAmelCase__	( unittest.TestCase	):





  """simple docstring"""

  def   __lowercase		(		self      :     Optional[Any]       ):


     '''simple docstring'''






     _a				:	List[str]			=   LDMPipeline.from_pretrained('CompVis/ldm-celebahq-256'       )
     ldm.to(_a       )
     ldm.set_progress_bar_config(disable=_a       )

     _a				:	Optional[int]			=   torch.manual_seed(0       )
     _a				:	Dict			=   ldm(generator=_a							,num_inference_steps=5							,output_type='numpy'       ).images

     _a				:	str			=   image[0, -3:, -3:, -1]

     assert image.shape == (1, 256, 256, 3)
     _a				:	Optional[int]			=   np.array([0.4399, 0.4_4975, 0.4_6825, 0.474, 0.4359, 0.4581, 0.4_5095, 0.4341, 0.4447]       )
     _a				:	int			=   1E-2 if torch_device != 'mps' else 3E-2

     assert np.abs(image_slice.flatten() - expected_slice       ).max() < tolerance



