




import unittest

import numpy as np
import torch
from torch import nn
from transformers import (
    CLIPImageProcessor,
    CLIPTextConfig,
    CLIPTextModelWithProjection,
    CLIPTokenizer,
    CLIPVisionConfig,
    CLIPVisionModelWithProjection,
)

from diffusers import KandinskyPriorPipeline, PriorTransformer, UnCLIPScheduler
from diffusers.utils import torch_device
from diffusers.utils.testing_utils import enable_full_determinism, skip_mps

from ..test_pipelines_common import PipelineTesterMixin


enable_full_determinism()





class 			__snake_case  (      _lowerCamelCase   ,unittest.TestCase     ):
					__lowerCamelCase	 =       KandinskyPriorPipeline
					__lowerCamelCase	 =       ["""prompt"""]
					__lowerCamelCase	 =       ["""prompt""", """negative_prompt"""]
					__lowerCamelCase	 =       [
					    """num_images_per_prompt""",
					    """generator""",
					    """num_inference_steps""",
					    """latents""",
					    """negative_prompt""",
					    """guidance_scale""",
					    """output_type""",
					    """return_dict""",
					]
					__lowerCamelCase	 =       False




					@property
					def       __a      (       self		)					->  Union[str, Any]:



									'''simple docstring'''







									return 32




					@property
					def       __a      (       self		)					->  Optional[int]:



									'''simple docstring'''







									return 32




					@property
					def       __a      (       self		)					->  str:



									'''simple docstring'''







									return self.time_input_dim




					@property
					def       __a      (       self		)					->  Any:



									'''simple docstring'''







									return self.time_input_dim * 4




					@property
					def       __a      (       self		)					->  Optional[Any]:



									'''simple docstring'''







									return 100




					@property
					def       __a      (       self		)					->  List[str]:



									'''simple docstring'''







									snake_case__ :     List[str] 							=				CLIPTokenizer.from_pretrained('hf-internal-testing/tiny-random-clip'		)
									return tokenizer




					@property
					def       __a      (       self		)					->  List[str]:



									'''simple docstring'''







									torch.manual_seed(0		)
									snake_case__ :     Union[str, Any] 							=				CLIPTextConfig(
									    bos_token_id=0   ,	eos_token_id=2   ,	hidden_size=self.text_embedder_hidden_size   ,	projection_dim=self.text_embedder_hidden_size   ,	intermediate_size=37   ,	layer_norm_eps=1E-05   ,	num_attention_heads=4   ,	num_hidden_layers=5   ,	pad_token_id=1   ,	vocab_size=1000   ,	)
									return CLIPTextModelWithProjection(__UpperCamelCase		)




					@property
					def       __a      (       self		)					->  List[str]:



									'''simple docstring'''







									torch.manual_seed(0		)

									snake_case__ :     List[Any] 							=				{
									    'num_attention_heads': 2,
									    'attention_head_dim': 12,
									    'embedding_dim': self.text_embedder_hidden_size,
									    'num_layers': 1,
									}

									snake_case__ :     int 							=				PriorTransformer(**__UpperCamelCase		)
									# clip_std and clip_mean is initialized to be 0 so PriorTransformer.post_process_latents will always return 0 - set clip_std to be 1 so it won't return 0
									snake_case__ :     Optional[int] 							=				nn.Parameter(torch.ones(model.clip_std.shape		)		)
									return model




					@property
					def       __a      (       self		)					->  int:



									'''simple docstring'''







									torch.manual_seed(0		)
									snake_case__ :     List[Any] 							=				CLIPVisionConfig(
									    hidden_size=self.text_embedder_hidden_size   ,	image_size=224   ,	projection_dim=self.text_embedder_hidden_size   ,	intermediate_size=37   ,	num_attention_heads=4   ,	num_channels=3   ,	num_hidden_layers=5   ,	patch_size=14   ,	)

									snake_case__ :     Dict 							=				CLIPVisionModelWithProjection(__UpperCamelCase		)
									return model




					@property
					def       __a      (       self		)					->  Optional[int]:



									'''simple docstring'''







									snake_case__ :     List[str] 							=				CLIPImageProcessor(
									    crop_size=224   ,	do_center_crop=__UpperCamelCase   ,	do_normalize=__UpperCamelCase   ,	do_resize=__UpperCamelCase   ,	image_mean=[0.4_8_1_4_5_4_6_6, 0.4_5_7_8_2_7_5, 0.4_0_8_2_1_0_7_3]   ,	image_std=[0.2_6_8_6_2_9_5_4, 0.2_6_1_3_0_2_5_8, 0.2_7_5_7_7_7_1_1]   ,	resample=3   ,	size=224   ,	)

									return image_processor




					def       __a      (       self		)					->  Dict:



									'''simple docstring'''







									snake_case__ :     Any 							=				self.dummy_prior
									snake_case__ :     Optional[int] 							=				self.dummy_image_encoder
									snake_case__ :     Tuple 							=				self.dummy_text_encoder
									snake_case__ :     Optional[Any] 							=				self.dummy_tokenizer
									snake_case__ :     Dict 							=				self.dummy_image_processor

									snake_case__ :     Tuple 							=				UnCLIPScheduler(
									    variance_type='fixed_small_log'   ,	prediction_type='sample'   ,	num_train_timesteps=1000   ,	clip_sample=__UpperCamelCase   ,	clip_sample_range=10.0   ,	)

									snake_case__ :     int 							=				{
									    'prior': prior,
									    'image_encoder': image_encoder,
									    'text_encoder': text_encoder,
									    'tokenizer': tokenizer,
									    'scheduler': scheduler,
									    'image_processor': image_processor,
									}

									return components




					def       __a      (       self   ,	__UpperCamelCase   ,	__UpperCamelCase=0		)					->  Any:



									'''simple docstring'''







									if str(__UpperCamelCase		).startswith('mps'		):
													snake_case__ :     str 							=				torch.manual_seed(__UpperCamelCase		)
									else:
													snake_case__ :     Optional[int] 							=				torch.Generator(device=__UpperCamelCase		).manual_seed(__UpperCamelCase		)
									snake_case__ :     Any 							=				{
									    'prompt': 'horse',
									    'generator': generator,
									    'guidance_scale': 4.0,
									    'num_inference_steps': 2,
									    'output_type': 'np',
									}
									return inputs




					def       __a      (       self		)					->  List[str]:



									'''simple docstring'''







									snake_case__ :     Optional[Any] 							=				'cpu'

									snake_case__ :     Tuple 							=				self.get_dummy_components()

									snake_case__ :     List[Any] 							=				self.pipeline_class(**__UpperCamelCase		)
									snake_case__ :     List[Any] 							=				pipe.to(__UpperCamelCase		)

									pipe.set_progress_bar_config(disable=__UpperCamelCase		)

									snake_case__ :     str 							=				pipe(**self.get_dummy_inputs(__UpperCamelCase		)		)
									snake_case__ :     str 							=				output.image_embeds

									snake_case__ :     Optional[int] 							=				pipe(
									    **self.get_dummy_inputs(__UpperCamelCase		)   ,	return_dict=__UpperCamelCase   ,	)[0]

									snake_case__ :     Tuple 							=				image[0, -10:]
									snake_case__ :     List[Any] 							=				image_from_tuple[0, -10:]

									assert image.shape == (1, 32)

									snake_case__ :     Tuple 							=				np.array(
									    [-0.0_5_3_2, 1.7_1_2_0, 0.3_6_5_6, -1.0_8_5_2, -0.8_9_4_6, -1.1_7_5_6, 0.4_3_4_8, 0.2_4_8_2, 0.5_1_4_6, -0.1_1_5_6]		)

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




					@skip_mps
					def       __a      (       self		)					->  Optional[Any]:



									'''simple docstring'''







									snake_case__ :     Union[str, Any] 							=				torch_device == 'cpu'
									snake_case__ :     Any 							=				True
									snake_case__ :     Optional[Any] 							=				False

									self._test_inference_batch_single_identical(
									    test_max_difference=__UpperCamelCase   ,	relax_max_difference=__UpperCamelCase   ,	test_mean_pixel_difference=__UpperCamelCase   ,	)




					@skip_mps
					def       __a      (       self		)					->  Tuple:



									'''simple docstring'''







									snake_case__ :     int 							=				torch_device == 'cpu'
									snake_case__ :     Optional[Any] 							=				False

									self._test_attention_slicing_forward_pass(
									    test_max_difference=__UpperCamelCase   ,	test_mean_pixel_difference=__UpperCamelCase   ,	)



from __future__ import annotations

import random

# Adjacency list representation of this graph:
# https://en.wikipedia.org/wiki/File:Single_run_of_Karger%E2%80%99s_Mincut_algorithm.svg
lowerCAmelCase__					:       Dict		=       {
    '''1''': ['''2''', '''3''', '''4''', '''5'''],
    '''2''': ['''1''', '''3''', '''4''', '''5'''],
    '''3''': ['''1''', '''2''', '''4''', '''5''', '''10'''],
    '''4''': ['''1''', '''2''', '''3''', '''5''', '''6'''],
    '''5''': ['''1''', '''2''', '''3''', '''4''', '''7'''],
    '''6''': ['''7''', '''8''', '''9''', '''10''', '''4'''],
    '''7''': ['''6''', '''8''', '''9''', '''10''', '''5'''],
    '''8''': ['''6''', '''7''', '''9''', '''10'''],
    '''9''': ['''6''', '''7''', '''8''', '''10'''],
    '''10''': ['''6''', '''7''', '''8''', '''9''', '''3'''],
}
def 			UpperCamelCase__						(    A__   )       -> set[tuple[str, str]]:
				snake_case__ :     Tuple 							=				{node: {node} for node in graph}

				snake_case__ :     Tuple 							=				{node: graph[node][:] for node in graph}

				while len(A__   ) > 2:
								# Choose a random edge.
								snake_case__ :     Union[str, Any] 							=				random.choice(list(graph_copy.keys()   )   )
								snake_case__ :     Any 							=				random.choice(graph_copy[u]   )

								# Contract edge (u, v) to new node uv
								snake_case__ :     Tuple 							=				u + v
								snake_case__ :     Dict 							=				list(set(graph_copy[u] + graph_copy[v]   )   )
								uv_neighbors.remove(A__   )
								uv_neighbors.remove(A__   )
								snake_case__ :     Optional[Any] 							=				uv_neighbors
								for neighbor in uv_neighbors:
												graph_copy[neighbor].append(A__   )

								snake_case__ :     Tuple 							=				set(contracted_nodes[u].union(contracted_nodes[v]   )   )

								# Remove nodes u and v.
								del graph_copy[u]
								del graph_copy[v]
								for neighbor in uv_neighbors:
												if u in graph_copy[neighbor]:
																graph_copy[neighbor].remove(A__   )
												if v in graph_copy[neighbor]:
																graph_copy[neighbor].remove(A__   )

    # Find cutset.
				snake_case__ :     Tuple 							=				[contracted_nodes[node] for node in graph_copy]
				return {
				    (node, neighbor)
				    for node in groups[0]
				    for neighbor in graph[node]
				    if neighbor in groups[1]
				}


if __name__ == "__main__":
				print(partition_graph(TEST_GRAPH))


