





import gc
import math
import unittest

import torch

from diffusers import UNetaDModel
from diffusers.utils import floats_tensor, logging, slow, torch_all_close, torch_device
from diffusers.utils.testing_utils import enable_full_determinism

from .test_modeling_common import ModelTesterMixin, UNetTesterMixin


snake_case								=							logging.get_logger(__name__)

enable_full_determinism()





class 					A_					(   UpperCAmelCase       ,						UpperCAmelCase       ,						unittest.TestCase						):







       """simple docstring"""





       SCREAMING_SNAKE_CASE_   :			Any  			=	UNetaDModel
       SCREAMING_SNAKE_CASE_   :			Dict  			=	'''sample'''







       @property
       def  __UpperCAmelCase			(     self						:						str	)    ->       List[Any]:
         _lowercase						    =			4
         _lowercase						    =			3
         _lowercase						    =			(32, 32)

         _lowercase						    =			floats_tensor((batch_size, num_channels) + sizes	).to(__A	)
         _lowercase						    =			torch.tensor([10]	).to(__A	)

         return {"sample": noise, "timestep": time_step}







       @property
       def  __UpperCAmelCase			(     self						:						Dict	)    ->       Optional[int]:
         return (3, 32, 32)







       @property
       def  __UpperCAmelCase			(     self						:						List[Any]	)    ->       Optional[Any]:
         return (3, 32, 32)







       def  __UpperCAmelCase			(     self						:						int	)    ->       Optional[int]:
         _lowercase						    =			{
             'block_out_channels': (32, 64),
             'down_block_types': ('DownBlock2D', 'AttnDownBlock2D'),
             'up_block_types': ('AttnUpBlock2D', 'UpBlock2D'),
             'attention_head_dim': 3,
             'out_channels': 3,
             'in_channels': 3,
             'layers_per_block': 2,
             'sample_size': 32,
         }
         _lowercase						    =			self.dummy_input
         return init_dict, inputs_dict





class 					A_					(   UpperCAmelCase       ,						UpperCAmelCase       ,						unittest.TestCase						):







       """simple docstring"""





       SCREAMING_SNAKE_CASE_   :			int  			=	UNetaDModel
       SCREAMING_SNAKE_CASE_   :			Dict  			=	'''sample'''







       @property
       def  __UpperCAmelCase			(     self						:						Union[str, Any]	)    ->       Tuple:
         _lowercase						    =			4
         _lowercase						    =			4
         _lowercase						    =			(32, 32)

         _lowercase						    =			floats_tensor((batch_size, num_channels) + sizes	).to(__A	)
         _lowercase						    =			torch.tensor([10]	).to(__A	)

         return {"sample": noise, "timestep": time_step}







       @property
       def  __UpperCAmelCase			(     self						:						Any	)    ->       Dict:
         return (4, 32, 32)







       @property
       def  __UpperCAmelCase			(     self						:						Any	)    ->       List[Any]:
         return (4, 32, 32)







       def  __UpperCAmelCase			(     self						:						int	)    ->       str:
         _lowercase						    =			{
             'sample_size': 32,
             'in_channels': 4,
             'out_channels': 4,
             'layers_per_block': 2,
             'block_out_channels': (32, 64),
             'attention_head_dim': 32,
             'down_block_types': ('DownBlock2D', 'DownBlock2D'),
             'up_block_types': ('UpBlock2D', 'UpBlock2D'),
         }
         _lowercase						    =			self.dummy_input
         return init_dict, inputs_dict







       def  __UpperCAmelCase			(     self						:						List[Any]	)    ->       Optional[int]:
         _lowercase    ,		_lowercase						    =			UNetaDModel.from_pretrained('fusing/unet-ldm-dummy-update'    ,output_loading_info=__A	)

         self.assertIsNotNone(__A	)
         self.assertEqual(len(loading_info['missing_keys']	)    ,0	)

         model.to(__A	)
         _lowercase						    =			model(**self.dummy_input	).sample

         assert image is not None, "Make sure output is not None"







       @unittest.skipIf(torch_device != 'cuda'    ,'This test is supposed to run on GPU'	)
       def  __UpperCAmelCase			(     self						:						Dict	)    ->       Union[str, Any]:
         _lowercase    ,		_lowercase						    =			UNetaDModel.from_pretrained('fusing/unet-ldm-dummy-update'    ,output_loading_info=__A	)
         model.to(__A	)
         _lowercase						    =			model(**self.dummy_input	).sample

         assert image is not None, "Make sure output is not None"







       @unittest.skipIf(torch_device != 'cuda'    ,'This test is supposed to run on GPU'	)
       def  __UpperCAmelCase			(     self						:						Any	)    ->       str:
         # by defautl model loading will use accelerate as `low_cpu_mem_usage=True`
         _lowercase    ,		_lowercase						    =			UNetaDModel.from_pretrained('fusing/unet-ldm-dummy-update'    ,output_loading_info=__A	)
         model_accelerate.to(__A	)
         model_accelerate.eval()

         _lowercase						    =			torch.randn(
             1    ,model_accelerate.config.in_channels    ,model_accelerate.config.sample_size    ,model_accelerate.config.sample_size    ,generator=torch.manual_seed(0	)    ,)
         _lowercase						    =			noise.to(__A	)
         _lowercase						    =			torch.tensor([10] * noise.shape[0]	).to(__A	)

         _lowercase						    =			model_accelerate(__A    ,__A	)['sample']

         # two models don't need to stay in the device at the same time
         del model_accelerate
         torch.cuda.empty_cache()
         gc.collect()

         _lowercase    ,		_lowercase						    =			UNetaDModel.from_pretrained(
             'fusing/unet-ldm-dummy-update'    ,output_loading_info=__A    ,low_cpu_mem_usage=__A	)
         model_normal_load.to(__A	)
         model_normal_load.eval()
         _lowercase						    =			model_normal_load(__A    ,__A	)['sample']

         assert torch_all_close(__A    ,__A    ,rtol=1e-3	)







       def  __UpperCAmelCase			(     self						:						Union[str, Any]	)    ->       Optional[Any]:
         _lowercase						    =			UNetaDModel.from_pretrained('fusing/unet-ldm-dummy-update'	)
         model.eval()
         model.to(__A	)

         _lowercase						    =			torch.randn(
             1    ,model.config.in_channels    ,model.config.sample_size    ,model.config.sample_size    ,generator=torch.manual_seed(0	)    ,)
         _lowercase						    =			noise.to(__A	)
         _lowercase						    =			torch.tensor([10] * noise.shape[0]	).to(__A	)

         with torch.no_grad():
           _lowercase						    =			model(__A    ,__A	).sample

         _lowercase						    =			output[0, -1, -3:, -3:].flatten().cpu()
         # fmt: off
         _lowercase						    =			torch.tensor([-13.3258, -20.1100, -15.9873, -17.6617, -23.0596, -17.9419, -13.3675, -16.1889, -12.3800]	)
         # fmt: on

         self.assertTrue(torch_all_close(__A    ,__A    ,rtol=1e-3	)	)





class 					A_					(   UpperCAmelCase       ,						UpperCAmelCase       ,						unittest.TestCase						):







       """simple docstring"""





       SCREAMING_SNAKE_CASE_   :			List[str]  			=	UNetaDModel
       SCREAMING_SNAKE_CASE_   :			List[str]  			=	'''sample'''







       @property
       def  __UpperCAmelCase			(     self						:						Optional[Any]    ,__A						:						str=(32, 32)	)    ->       Tuple:
         _lowercase						    =			4
         _lowercase						    =			3

         _lowercase						    =			floats_tensor((batch_size, num_channels) + sizes	).to(__A	)
         _lowercase						    =			torch.tensor(batch_size * [10]	).to(dtype=torch.intaa    ,device=__A	)

         return {"sample": noise, "timestep": time_step}







       @property
       def  __UpperCAmelCase			(     self						:						Any	)    ->       Optional[Any]:
         return (3, 32, 32)







       @property
       def  __UpperCAmelCase			(     self						:						int	)    ->       Dict:
         return (3, 32, 32)







       def  __UpperCAmelCase			(     self						:						int	)    ->       Tuple:
         _lowercase						    =			{
             'block_out_channels': [32, 64, 64, 64],
             'in_channels': 3,
             'layers_per_block': 1,
             'out_channels': 3,
             'time_embedding_type': 'fourier',
             'norm_eps': 1e-6,
             'mid_block_scale_factor': math.sqrt(2.0	),
             'norm_num_groups': None,
             'down_block_types': [
                 'SkipDownBlock2D',
                 'AttnSkipDownBlock2D',
                 'SkipDownBlock2D',
                 'SkipDownBlock2D',
             ],
             'up_block_types': [
                 'SkipUpBlock2D',
                 'SkipUpBlock2D',
                 'AttnSkipUpBlock2D',
                 'SkipUpBlock2D',
             ],
         }
         _lowercase						    =			self.dummy_input
         return init_dict, inputs_dict







       @slow
       def  __UpperCAmelCase			(     self						:						str	)    ->       List[Any]:
         _lowercase    ,		_lowercase						    =			UNetaDModel.from_pretrained('google/ncsnpp-celebahq-256'    ,output_loading_info=__A	)
         self.assertIsNotNone(__A	)
         self.assertEqual(len(loading_info['missing_keys']	)    ,0	)

         model.to(__A	)
         _lowercase						    =			self.dummy_input
         _lowercase						    =			floats_tensor((4, 3) + (256, 256)	).to(__A	)
         _lowercase						    =			noise
         _lowercase						    =			model(**__A	)

         assert image is not None, "Make sure output is not None"







       @slow
       def  __UpperCAmelCase			(     self						:						int	)    ->       int:
         _lowercase						    =			UNetaDModel.from_pretrained('google/ncsnpp-celebahq-256'	)
         model.to(__A	)

         _lowercase						    =			4
         _lowercase						    =			3
         _lowercase						    =			(256, 256)

         _lowercase						    =			torch.ones((batch_size, num_channels) + sizes	).to(__A	)
         _lowercase						    =			torch.tensor(batch_size * [1e-4]	).to(__A	)

         with torch.no_grad():
           _lowercase						    =			model(__A    ,__A	).sample

         _lowercase						    =			output[0, -3:, -3:, -1].flatten().cpu()
         # fmt: off
         _lowercase						    =			torch.tensor([-4842.8691, -6499.6631, -3800.1953, -7978.2686, -10980.7129, -20028.8535, 8148.2822, 2342.2905, 567.7608]	)
         # fmt: on

         self.assertTrue(torch_all_close(__A    ,__A    ,rtol=1e-2	)	)







       def  __UpperCAmelCase			(     self						:						Dict	)    ->       Union[str, Any]:
         _lowercase						    =			UNetaDModel.from_pretrained('fusing/ncsnpp-ffhq-ve-dummy-update'	)
         model.to(__A	)

         _lowercase						    =			4
         _lowercase						    =			3
         _lowercase						    =			(32, 32)

         _lowercase						    =			torch.ones((batch_size, num_channels) + sizes	).to(__A	)
         _lowercase						    =			torch.tensor(batch_size * [1e-4]	).to(__A	)

         with torch.no_grad():
           _lowercase						    =			model(__A    ,__A	).sample

         _lowercase						    =			output[0, -3:, -3:, -1].flatten().cpu()
         # fmt: off
         _lowercase						    =			torch.tensor([-0.0325, -0.0900, -0.0869, -0.0332, -0.0725, -0.0270, -0.0101, 0.0227, 0.0256]	)
         # fmt: on

         self.assertTrue(torch_all_close(__A    ,__A    ,rtol=1e-2	)	)







       def  __UpperCAmelCase			(     self						:						int	)    ->       Optional[int]:
         # not required for this model
         pass



'''simple docstring'''



from __future__ import annotations

import os
from collections.abc import Mapping

A__						:   Optional[Any]				=    tuple[int, int]





class   UpperCAmelCase_    :





    """simple docstring"""






    def __init__(				self     ,	SCREAMING_SNAKE_CASE_     ,	SCREAMING_SNAKE_CASE_      )							->	None:
         __lowerCamelCase  :      set[int]														=						vertices
         __lowerCamelCase  :      dict[EdgeT, int]														=						{
             (min(SCREAMING_SNAKE_CASE_      ), max(SCREAMING_SNAKE_CASE_      )): weight for edge, weight in edges.items()
         }







    def  lowercase_							(				self     ,	SCREAMING_SNAKE_CASE_     ,	SCREAMING_SNAKE_CASE_      )							->	None:
         self.vertices.add(edge[0]      )
         self.vertices.add(edge[1]      )
         __lowerCamelCase  :      Union[str, Any]														=						weight







    def  lowercase_							(				self      )							->	Graph:
         __lowerCamelCase  :      Graph														=						Graph({min(self.vertices      )}     ,	{}      )
         __lowerCamelCase  :      EdgeT
         __lowerCamelCase  :      int
         __lowerCamelCase  :      EdgeT
         __lowerCamelCase  :      int

         while len(subgraph.vertices      ) < len(self.vertices      ):
              __lowerCamelCase  :      Any														=						max(self.edges.values()      ) + 1
              for edge, weight in self.edges.items():
                   if (edge[0] in subgraph.vertices) ^ (edge[1] in subgraph.vertices):
                        if weight < min_weight:
                             __lowerCamelCase  :      Optional[int]														=						edge
                             __lowerCamelCase  :      List[str]														=						weight

              subgraph.add_edge(SCREAMING_SNAKE_CASE_     ,	SCREAMING_SNAKE_CASE_      )

         return subgraph







def      UpperCAmelCase__   (      UpperCAmelCase_       :							str = "p107_network.txt"				)	->						int:
     __lowerCamelCase  :      str														=						os.path.abspath(os.path.dirname(UpperCAmelCase_				)				)
     __lowerCamelCase  :      str														=						os.path.join(UpperCAmelCase_ ,       UpperCAmelCase_				)
     __lowerCamelCase  :      dict[EdgeT, int]														=						{}
     __lowerCamelCase  :      list[str]
     __lowerCamelCase  :      int
     __lowerCamelCase  :      int

     with open(UpperCAmelCase_				) as f:
          __lowerCamelCase  :      Any														=						f.read().strip().split('\n'				)

     __lowerCamelCase  :      Any														=						[line.split(','				) for line in data]

     for edgea in range(1 ,       len(UpperCAmelCase_				)				):
          for edgea in range(UpperCAmelCase_				):
               if adjaceny_matrix[edgea][edgea] != "-":
                    __lowerCamelCase  :      int														=						int(adjaceny_matrix[edgea][edgea]				)

     __lowerCamelCase  :      Graph														=						Graph(set(range(len(UpperCAmelCase_				)				)				) ,       UpperCAmelCase_				)

     __lowerCamelCase  :      Graph														=						graph.prims_algorithm()

     __lowerCamelCase  :      int														=						sum(graph.edges.values()				)
     __lowerCamelCase  :      int														=						sum(subgraph.edges.values()				)

     return initial_total - optimal_total


if __name__ == "__main__":
      print(f'''{solution() = }''')






