

"""simple docstring"""



import unittest

import numpy as np
import torch

from diffusers import PNDMPipeline, PNDMScheduler, UNetaDModel
from diffusers.utils.testing_utils import enable_full_determinism, require_torch, slow, torch_device


enable_full_determinism()



class 					a						(       unittest.TestCase				):



    """simple docstring"""



    @property
    def 				UpperCamelCase	(     self: Any       ):






         """simple docstring"""


         torch.manual_seed(0       )
         A__										=  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





    def 				UpperCamelCase	(     self: str       ):






         """simple docstring"""


         A__										=  self.dummy_uncond_unet
         A__										=  PNDMScheduler()

         A__										=  PNDMPipeline(unet=UpperCamelCase    ,						scheduler=UpperCamelCase       )
         pndm.to(UpperCamelCase       )
         pndm.set_progress_bar_config(disable=UpperCamelCase       )

         A__										=  torch.manual_seed(0       )
         A__										=  pndm(generator=UpperCamelCase    ,						num_inference_steps=20    ,						output_type="""numpy"""       ).images

         A__										=  torch.manual_seed(0       )
         A__										=  pndm(generator=UpperCamelCase    ,						num_inference_steps=20    ,						output_type="""numpy"""    ,						return_dict=UpperCamelCase       )[0]

         A__										=  image[0, -3:, -3:, -1]
         A__										=  image_from_tuple[0, -3:, -3:, -1]

         assert image.shape == (1, 32, 32, 3)
         A__										=  np.array([1.0, 1.0, 0.0, 1.0, 0.0, 1.0, 0.0, 0.0, 0.0]       )

         assert np.abs(image_slice.flatten() - expected_slice       ).max() < 1e-2
         assert np.abs(image_from_tuple_slice.flatten() - expected_slice       ).max() < 1e-2



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



    """simple docstring"""



    def 				UpperCamelCase	(     self: Dict       ):






         """simple docstring"""


         A__										=  """google/ddpm-cifar10-32"""

         A__										=  UNetaDModel.from_pretrained(UpperCamelCase       )
         A__										=  PNDMScheduler()

         A__										=  PNDMPipeline(unet=UpperCamelCase    ,						scheduler=UpperCamelCase       )
         pndm.to(UpperCamelCase       )
         pndm.set_progress_bar_config(disable=UpperCamelCase       )
         A__										=  torch.manual_seed(0       )
         A__										=  pndm(generator=UpperCamelCase    ,						output_type="""numpy"""       ).images

         A__										=  image[0, -3:, -3:, -1]

         assert image.shape == (1, 32, 32, 3)
         A__										=  np.array([0.1_564, 0.14_645, 0.1_406, 0.14_715, 0.12_425, 0.14_045, 0.13_115, 0.12_175, 0.125]       )

         assert np.abs(image_slice.flatten() - expected_slice       ).max() < 1e-2









"""simple docstring"""



def  _snake_case     (    UpperCAmelCase_  :					List[str]			):
     A__										=  [0] * len(UpperCAmelCase_			)
     A__										=  []
     A__										=  [1] * len(UpperCAmelCase_			)

     for values in graph.values():
          for i in values:
               indegree[i] += 1

     for i in range(len(UpperCAmelCase_			)			):
          if indegree[i] == 0:
               queue.append(UpperCAmelCase_			)

     while queue:
          A__										=  queue.pop(0			)
          for x in graph[vertex]:
               indegree[x] -= 1

               if long_dist[vertex] + 1 > long_dist[x]:
                    A__										=  long_dist[vertex] + 1

               if indegree[x] == 0:
                    queue.append(UpperCAmelCase_			)

     print(max(UpperCAmelCase_			)			)


# Adjacency list of Graph
SCREAMING_SNAKE_CASE_					:	Any							  =						{0: [2, 3, 4], 1: [2, 7], 2: [5], 3: [5, 7], 4: [7], 5: [6], 6: [7], 7: []}

longest_distance(graph)






