





"""simple docstring"""





def 					__A  (_SCREAMING_SNAKE_CASE	,  _SCREAMING_SNAKE_CASE     )   ->List[str]:





  """simple docstring"""


  print('\nThe shortest path matrix using Floyd Warshall algorithm\n'     )
  for i in range(_SCREAMING_SNAKE_CASE     ):
    for j in range(_SCREAMING_SNAKE_CASE     ):
      if dist[i][j] != float('inf'     ):
        print(int(dist[i][j]     )	,  end='\t'     )
      else:
        print('INF'	,  end='\t'     )
    print()
def 					__A  (_SCREAMING_SNAKE_CASE	,  _SCREAMING_SNAKE_CASE     )   ->List[Any]:





  """simple docstring"""


  lowerCAmelCase__     :List[str]											=				[[float('inf'     ) for _ in range(_SCREAMING_SNAKE_CASE     )] for _ in range(_SCREAMING_SNAKE_CASE     )]

  for i in range(_SCREAMING_SNAKE_CASE     ):
    for j in range(_SCREAMING_SNAKE_CASE     ):
      lowerCAmelCase__     :Union[str, Any]											=				graph[i][j]

      # check vertex k against all other vertices (i, j)
  for k in range(_SCREAMING_SNAKE_CASE     ):
    # looping through rows of graph array
    for i in range(_SCREAMING_SNAKE_CASE     ):
      # looping through columns of graph array
      for j in range(_SCREAMING_SNAKE_CASE     ):
        if (
            dist[i][k] != float('inf'     )
            and dist[k][j] != float('inf'     )
            and dist[i][k] + dist[k][j] < dist[i][j]
        ):
          lowerCAmelCase__     :Union[str, Any]											=				dist[i][k] + dist[k][j]

  _print_dist(_SCREAMING_SNAKE_CASE	,  _SCREAMING_SNAKE_CASE     )
  return dist, v


if __name__ == "__main__":
  __A											=						int(input("""Enter number of vertices: """))
  __A											=						int(input("""Enter number of edges: """))

  __A											=						[[float("""inf""") for i in range(v)] for j in range(v)]

  for i in range(v):
    __A											=						0.0

    # src and dst are indices that must be within the array size graph[e][v]
    # failure to follow this will result in an error
  for i in range(e):
    print("""\nEdge """, i + 1)
    __A											=						int(input("""Enter source:"""))
    __A											=						int(input("""Enter destination:"""))
    __A											=						float(input("""Enter weight:"""))
    __A											=						weight

  floyd_warshall(graph, v)

  # Example Input
  # Enter number of vertices: 3
  # Enter number of edges: 2

  # # generated graph from vertex and edge inputs
  # [[inf, inf, inf], [inf, inf, inf], [inf, inf, inf]]
  # [[0.0, inf, inf], [inf, 0.0, inf], [inf, inf, 0.0]]

  # specify source, destination and weight for edge #1
  # Edge  1
  # Enter source:1
  # Enter destination:2
  # Enter weight:2

  # specify source, destination and weight for edge #2
  # Edge  2
  # Enter source:2
  # Enter destination:1
  # Enter weight:1

  # # Expected Output from the vertice, edge and src, dst, weight inputs!!
  # 0		INF	INF
  # INF	0	2
  # INF	1	0







"""simple docstring"""





import gc
import unittest

import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTextModelWithProjection, CLIPTokenizer

from diffusers import (
    AutoencoderKL,
    DDIMScheduler,
    DDPMScheduler,
    PriorTransformer,
    StableUnCLIPPipeline,
    UNetaDConditionModel,
)
from diffusers.pipelines.stable_diffusion.stable_unclip_image_normalizer import StableUnCLIPImageNormalizer
from diffusers.utils.testing_utils import enable_full_determinism, load_numpy, require_torch_gpu, slow, torch_device

from ..pipeline_params import TEXT_TO_IMAGE_BATCH_PARAMS, TEXT_TO_IMAGE_IMAGE_PARAMS, TEXT_TO_IMAGE_PARAMS
from ..test_pipelines_common import (
    PipelineKarrasSchedulerTesterMixin,
    PipelineLatentTesterMixin,
    PipelineTesterMixin,
    assert_mean_pixel_difference,
)


enable_full_determinism()






class    _lowerCAmelCase  (	a    ,  a    ,  a    ,  unittest.TestCase			):
       """simple docstring"""




       __magic_name__	:int						    =   StableUnCLIPPipeline
       __magic_name__	:int						    =   TEXT_TO_IMAGE_PARAMS
       __magic_name__	:List[str]						    =   TEXT_TO_IMAGE_BATCH_PARAMS
       __magic_name__	:Optional[int]						    =   TEXT_TO_IMAGE_IMAGE_PARAMS
       __magic_name__	:Union[str, Any]						    =   TEXT_TO_IMAGE_IMAGE_PARAMS

       # TODO(will) Expected attn_bias.stride(1) == 0 to be true, but got false
       __magic_name__	:List[str]						    =   False






       def 	snake_case		(       self ):



         '''simple docstring'''

         lowerCAmelCase__     :Dict											=				3_2
         lowerCAmelCase__     :List[Any]											=				embedder_hidden_size

         # prior components

         torch.manual_seed(0 )
         lowerCAmelCase__     :Any											=				CLIPTokenizer.from_pretrained('hf-internal-testing/tiny-random-clip' )

         torch.manual_seed(0 )
         lowerCAmelCase__     :Any											=				CLIPTextModelWithProjection(
             CLIPTextConfig(
                 bos_token_id=0       ,					eos_token_id=2       ,					hidden_size=__UpperCAmelCase       ,					projection_dim=__UpperCAmelCase       ,					intermediate_size=3_7       ,					layer_norm_eps=1E-05       ,					num_attention_heads=4       ,					num_hidden_layers=5       ,					pad_token_id=1       ,					vocab_size=1_0_0_0       ,					) )

         torch.manual_seed(0 )
         lowerCAmelCase__     :int											=				PriorTransformer(
             num_attention_heads=2       ,					attention_head_dim=1_2       ,					embedding_dim=__UpperCAmelCase       ,					num_layers=1       ,					)

         torch.manual_seed(0 )
         lowerCAmelCase__     :Optional[int]											=				DDPMScheduler(
             variance_type='fixed_small_log'       ,					prediction_type='sample'       ,					num_train_timesteps=1_0_0_0       ,					clip_sample=__UpperCAmelCase       ,					clip_sample_range=5.0       ,					beta_schedule='squaredcos_cap_v2'       ,					)

         # regular denoising components

         torch.manual_seed(0 )
         lowerCAmelCase__     :Dict											=				StableUnCLIPImageNormalizer(embedding_dim=__UpperCAmelCase )
         lowerCAmelCase__     :List[Any]											=				DDPMScheduler(beta_schedule='squaredcos_cap_v2' )

         torch.manual_seed(0 )
         lowerCAmelCase__     :List[Any]											=				CLIPTokenizer.from_pretrained('hf-internal-testing/tiny-random-clip' )

         torch.manual_seed(0 )
         lowerCAmelCase__     :str											=				CLIPTextModel(
             CLIPTextConfig(
                 bos_token_id=0       ,					eos_token_id=2       ,					hidden_size=__UpperCAmelCase       ,					projection_dim=3_2       ,					intermediate_size=3_7       ,					layer_norm_eps=1E-05       ,					num_attention_heads=4       ,					num_hidden_layers=5       ,					pad_token_id=1       ,					vocab_size=1_0_0_0       ,					) )

         torch.manual_seed(0 )
         lowerCAmelCase__     :Optional[int]											=				UNetaDConditionModel(
             sample_size=3_2       ,					in_channels=4       ,					out_channels=4       ,					down_block_types=('CrossAttnDownBlock2D', 'DownBlock2D')       ,					up_block_types=('UpBlock2D', 'CrossAttnUpBlock2D')       ,					block_out_channels=(3_2, 6_4)       ,					attention_head_dim=(2, 4)       ,					class_embed_type='projection'       ,					projection_class_embeddings_input_dim=embedder_projection_dim * 2       ,					cross_attention_dim=__UpperCAmelCase       ,					layers_per_block=1       ,					upcast_attention=__UpperCAmelCase       ,					use_linear_projection=__UpperCAmelCase       ,					)

         torch.manual_seed(0 )
         lowerCAmelCase__     :Tuple											=				DDIMScheduler(
             beta_schedule='scaled_linear'       ,					beta_start=0.0_00_85       ,					beta_end=0.0_12       ,					prediction_type='v_prediction'       ,					set_alpha_to_one=__UpperCAmelCase       ,					steps_offset=1       ,					)

         torch.manual_seed(0 )
         lowerCAmelCase__     :Optional[int]											=				AutoencoderKL()

         lowerCAmelCase__     :Optional[int]											=				{
             # prior components
             'prior_tokenizer': prior_tokenizer,
             'prior_text_encoder': prior_text_encoder,
             'prior': prior,
             'prior_scheduler': prior_scheduler,
             # image noising components
             'image_normalizer': image_normalizer,
             'image_noising_scheduler': image_noising_scheduler,
             # regular denoising components
             'tokenizer': tokenizer,
             'text_encoder': text_encoder,
             'unet': unet,
             'scheduler': scheduler,
             'vae': vae,
         }

         return components






       def 	snake_case		(       self       ,					__UpperCAmelCase       ,					__UpperCAmelCase=0 ):



         '''simple docstring'''

         if str(__UpperCAmelCase ).startswith('mps' ):
           lowerCAmelCase__     :str											=				torch.manual_seed(__UpperCAmelCase )
         else:
           lowerCAmelCase__     :Optional[int]											=				torch.Generator(device=__UpperCAmelCase ).manual_seed(__UpperCAmelCase )
         lowerCAmelCase__     :Tuple											=				{
             'prompt': 'A painting of a squirrel eating a burger',
             'generator': generator,
             'num_inference_steps': 2,
             'prior_num_inference_steps': 2,
             'output_type': 'numpy',
         }
         return inputs






       def 	snake_case		(       self ):



         '''simple docstring'''

         lowerCAmelCase__     :Optional[int]											=				torch_device == 'cpu'

         self._test_attention_slicing_forward_pass(test_max_difference=__UpperCAmelCase )






       def 	snake_case		(       self ):



         '''simple docstring'''

         lowerCAmelCase__     :str											=				torch_device in ['cpu', 'mps']

         self._test_inference_batch_single_identical(test_max_difference=__UpperCAmelCase )






@slow
@require_torch_gpu
class    _lowerCAmelCase  (	unittest.TestCase			):
       """simple docstring"""




       def 	snake_case		(       self ):



         '''simple docstring'''

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






       def 	snake_case		(       self ):



         '''simple docstring'''

         lowerCAmelCase__     :Tuple											=				load_numpy(
             'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/stable_unclip/stable_unclip_2_1_l_anime_turtle_fp16.npy' )

         lowerCAmelCase__     :List[Any]											=				StableUnCLIPPipeline.from_pretrained('fusing/stable-unclip-2-1-l'       ,					torch_dtype=torch.floataa )
         pipe.to(__UpperCAmelCase )
         pipe.set_progress_bar_config(disable=__UpperCAmelCase )
         # stable unclip will oom when integration tests are run on a V100,
         # so turn on memory savings
         pipe.enable_attention_slicing()
         pipe.enable_sequential_cpu_offload()

         lowerCAmelCase__     :List[str]											=				torch.Generator(device='cpu' ).manual_seed(0 )
         lowerCAmelCase__     :List[str]											=				pipe('anime turle'       ,					generator=__UpperCAmelCase       ,					output_type='np' )

         lowerCAmelCase__     :Union[str, Any]											=				output.images[0]

         assert image.shape == (7_6_8, 7_6_8, 3)

         assert_mean_pixel_difference(__UpperCAmelCase       ,					__UpperCAmelCase )






       def 	snake_case		(       self ):



         '''simple docstring'''

         torch.cuda.empty_cache()
         torch.cuda.reset_max_memory_allocated()
         torch.cuda.reset_peak_memory_stats()

         lowerCAmelCase__     :int											=				StableUnCLIPPipeline.from_pretrained('fusing/stable-unclip-2-1-l'       ,					torch_dtype=torch.floataa )
         lowerCAmelCase__     :List[str]											=				pipe.to(__UpperCAmelCase )
         pipe.set_progress_bar_config(disable=__UpperCAmelCase )
         pipe.enable_attention_slicing()
         pipe.enable_sequential_cpu_offload()

         lowerCAmelCase__     :Tuple											=				pipe(
             'anime turtle'       ,					prior_num_inference_steps=2       ,					num_inference_steps=2       ,					output_type='np'       ,					)

         lowerCAmelCase__     :str											=				torch.cuda.max_memory_allocated()
         # make sure that less than 7 GB is allocated
         assert mem_bytes < 7 * 1_0**9
