





"""simple docstring"""






import gc
import unittest

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

from diffusers import TransformeraDModel, VQDiffusionPipeline, VQDiffusionScheduler, VQModel
from diffusers.pipelines.vq_diffusion.pipeline_vq_diffusion import LearnedClassifierFreeSamplingEmbeddings
from diffusers.utils import load_numpy, slow, torch_device
from diffusers.utils.testing_utils import require_torch_gpu


A 			=  False




class 				__lowercase			(			unittest.TestCase							):







					'''simple docstring'''




					def  _lowerCamelCase		(						self     ):
									# clean up the VRAM after each test
									super().tearDown()
									gc.collect()
									torch.cuda.empty_cache()







					@property
					def  _lowerCamelCase		(						self     ):
									return 12







					@property
					def  _lowerCamelCase		(						self     ):
									return 12







					@property
					def  _lowerCamelCase		(						self     ):
									return 32







					@property
					def  _lowerCamelCase		(						self     ):
									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	,			num_vq_embeddings=self.num_embed	,			vq_embed_dim=3	,			)
									return model







					@property
					def  _lowerCamelCase		(						self     ):
									__a :						str			       =  CLIPTokenizer.from_pretrained('''hf-internal-testing/tiny-random-clip'''     )
									return tokenizer







					@property
					def  _lowerCamelCase		(						self     ):
									torch.manual_seed(0     )
									__a :						Tuple			       =  CLIPTextConfig(
									    bos_token_id=0	,			eos_token_id=2	,			hidden_size=self.text_embedder_hidden_size	,			intermediate_size=37	,			layer_norm_eps=1e-0_5	,			num_attention_heads=4	,			num_hidden_layers=5	,			pad_token_id=1	,			vocab_size=1000	,			)
									return CLIPTextModel(_UpperCAmelCase     )







					@property
					def  _lowerCamelCase		(						self     ):
									torch.manual_seed(0     )

									__a :						str			       =  12
									__a :						Optional[Any]			       =  12

									__a :						Dict			       =  {
									    '''attention_bias''': True,
									    '''cross_attention_dim''': 32,
									    '''attention_head_dim''': height * width,
									    '''num_attention_heads''': 1,
									    '''num_vector_embeds''': self.num_embed,
									    '''num_embeds_ada_norm''': self.num_embeds_ada_norm,
									    '''norm_num_groups''': 32,
									    '''sample_size''': width,
									    '''activation_fn''': '''geglu-approximate''',
									}

									__a :						Optional[Any]			       =  TransformeraDModel(**_UpperCAmelCase     )
									return model







					def  _lowerCamelCase		(						self     ):
									__a :						List[str]			       =  '''cpu'''

									__a :						int			       =  self.dummy_vqvae
									__a :						int			       =  self.dummy_text_encoder
									__a :						List[Any]			       =  self.dummy_tokenizer
									__a :						Tuple			       =  self.dummy_transformer
									__a :						Union[str, Any]			       =  VQDiffusionScheduler(self.num_embed     )
									__a :						Optional[Any]			       =  LearnedClassifierFreeSamplingEmbeddings(learnable=_UpperCAmelCase     )

									__a :						Optional[Any]			       =  VQDiffusionPipeline(
									    vqvae=_UpperCAmelCase	,			text_encoder=_UpperCAmelCase	,			tokenizer=_UpperCAmelCase	,			transformer=_UpperCAmelCase	,			scheduler=_UpperCAmelCase	,			learned_classifier_free_sampling_embeddings=_UpperCAmelCase	,			)
									__a :						str			       =  pipe.to(_UpperCAmelCase     )
									pipe.set_progress_bar_config(disable=_UpperCAmelCase     )

									__a :						Tuple			       =  '''teddy bear playing in the pool'''

									__a :						Dict			       =  torch.Generator(device=_UpperCAmelCase     ).manual_seed(0     )
									__a :						str			       =  pipe([prompt]	,			generator=_UpperCAmelCase	,			num_inference_steps=2	,			output_type='''np'''     )
									__a :						List[Any]			       =  output.images

									__a :						str			       =  torch.Generator(device=_UpperCAmelCase     ).manual_seed(0     )
									__a :						int			       =  pipe(
									    [prompt]	,			generator=_UpperCAmelCase	,			output_type='''np'''	,			return_dict=_UpperCAmelCase	,			num_inference_steps=2     )[0]

									__a :						Optional[Any]			       =  image[0, -3:, -3:, -1]
									__a :						Optional[int]			       =  image_from_tuple[0, -3:, -3:, -1]

									assert image.shape == (1, 24, 24, 3)

									__a :						Optional[Any]			       =  np.array([0.6_5_5_1, 0.6_1_6_8, 0.5_0_0_8, 0.5_6_7_6, 0.5_6_5_9, 0.4_2_9_5, 0.6_0_7_3, 0.5_5_9_9, 0.4_9_9_2]     )

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







					def  _lowerCamelCase		(						self     ):
									__a :						Optional[Any]			       =  '''cpu'''

									__a :						Dict			       =  self.dummy_vqvae
									__a :						List[str]			       =  self.dummy_text_encoder
									__a :						List[str]			       =  self.dummy_tokenizer
									__a :						List[str]			       =  self.dummy_transformer
									__a :						List[str]			       =  VQDiffusionScheduler(self.num_embed     )
									__a :						List[str]			       =  LearnedClassifierFreeSamplingEmbeddings(
									    learnable=_UpperCAmelCase	,			hidden_size=self.text_embedder_hidden_size	,			length=tokenizer.model_max_length     )

									__a :						List[Any]			       =  VQDiffusionPipeline(
									    vqvae=_UpperCAmelCase	,			text_encoder=_UpperCAmelCase	,			tokenizer=_UpperCAmelCase	,			transformer=_UpperCAmelCase	,			scheduler=_UpperCAmelCase	,			learned_classifier_free_sampling_embeddings=_UpperCAmelCase	,			)
									__a :						List[Any]			       =  pipe.to(_UpperCAmelCase     )
									pipe.set_progress_bar_config(disable=_UpperCAmelCase     )

									__a :						List[str]			       =  '''teddy bear playing in the pool'''

									__a :						List[Any]			       =  torch.Generator(device=_UpperCAmelCase     ).manual_seed(0     )
									__a :						Dict			       =  pipe([prompt]	,			generator=_UpperCAmelCase	,			num_inference_steps=2	,			output_type='''np'''     )
									__a :						str			       =  output.images

									__a :						List[Any]			       =  torch.Generator(device=_UpperCAmelCase     ).manual_seed(0     )
									__a :						Tuple			       =  pipe(
									    [prompt]	,			generator=_UpperCAmelCase	,			output_type='''np'''	,			return_dict=_UpperCAmelCase	,			num_inference_steps=2     )[0]

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

									assert image.shape == (1, 24, 24, 3)

									__a :						Optional[int]			       =  np.array([0.6_6_9_3, 0.6_0_7_5, 0.4_9_5_9, 0.5_7_0_1, 0.5_5_8_3, 0.4_3_3_3, 0.6_1_7_1, 0.5_6_8_4, 0.4_9_8_8]     )

									assert np.abs(image_slice.flatten() - expected_slice     ).max() < 2.0
									assert np.abs(image_from_tuple_slice.flatten() - expected_slice     ).max() < 1e-2






@slow
@require_torch_gpu
class 				__lowercase			(			unittest.TestCase							):







					'''simple docstring'''




					def  _lowerCamelCase		(						self     ):
									# clean up the VRAM after each test
									super().tearDown()
									gc.collect()
									torch.cuda.empty_cache()







					def  _lowerCamelCase		(						self     ):
									__a :						Union[str, Any]			       =  load_numpy(
									    '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
									    '''/vq_diffusion/teddy_bear_pool_classifier_free_sampling.npy'''     )

									__a :						Dict			       =  VQDiffusionPipeline.from_pretrained('''microsoft/vq-diffusion-ithq'''     )
									__a :						Optional[Any]			       =  pipeline.to(_UpperCAmelCase     )
									pipeline.set_progress_bar_config(disable=_UpperCAmelCase     )

									# requires GPU generator for gumbel softmax
									# don't use GPU generator in tests though
									__a :						Dict			       =  torch.Generator(device=_UpperCAmelCase     ).manual_seed(0     )
									__a :						str			       =  pipeline(
									    '''teddy bear playing in the pool'''	,			num_images_per_prompt=1	,			generator=_UpperCAmelCase	,			output_type='''np'''	,			)

									__a :						Any			       =  output.images[0]

									assert image.shape == (256, 256, 3)
									assert np.abs(expected_image - image     ).max() < 2.0






"""simple docstring"""






from __future__ import annotations

import os
from collections.abc import Mapping

A 			=  tuple[int, int]




class 				__lowercase			:







					'''simple docstring'''




					def __init__(						self	,			_UpperCAmelCase	,			_UpperCAmelCase     ):
									__a :						set[int]			       =  vertices
									__a :						dict[EdgeT, int]			       =  {
									    (min(_UpperCAmelCase     ), max(_UpperCAmelCase     )): weight for edge, weight in edges.items()
									}







					def  _lowerCamelCase		(						self	,			_UpperCAmelCase	,			_UpperCAmelCase     ):
									self.vertices.add(edge[0]     )
									self.vertices.add(edge[1]     )
									__a :						Any			       =  weight







					def  _lowerCamelCase		(						self     ):
									__a :						Graph			       =  Graph({min(self.vertices     )}	,			{}     )
									__a :						EdgeT
									__a :						int
									__a :						EdgeT
									__a :						int

									while len(subgraph.vertices     ) < len(self.vertices     ):
													__a :						List[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:
																									__a :						str			       =  edge
																									__a :						int			       =  weight

													subgraph.add_edge(_UpperCAmelCase	,			_UpperCAmelCase     )

									return subgraph






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

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

				__a :						Union[str, Any]			       =  [line.split(''',''') for line in data]

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

				__a :						Graph			       =  Graph(set(range(len(a_)))   ,       a_)

				__a :						Graph			       =  graph.prims_algorithm()

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

				return initial_total - optimal_total


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