
"""simple docstring"""


import random
import unittest

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

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

from ..test_pipelines_common import PipelineTesterMixin


enable_full_determinism()







class 			lowerCAmelCase_			( _lowercase						,   unittest.TestCase      ):



						'''simple docstring'''


						_lowerCamelCase:      List[str] =      KandinskyVaaPriorEmbaEmbPipeline
						_lowerCamelCase:      List[str] =      ['''prompt''', '''image''']
						_lowerCamelCase:      Dict =      ['''prompt''', '''image''']
						_lowerCamelCase:      Optional[int] =      [
						    '''num_images_per_prompt''',
						    '''strength''',
						    '''generator''',
						    '''num_inference_steps''',
						    '''latents''',
						    '''negative_prompt''',
						    '''guidance_scale''',
						    '''output_type''',
						    '''return_dict''',
						]
						_lowerCamelCase:      Union[str, Any] =      False


						@property
						def   _SCREAMING_SNAKE_CASE (	self       : str )       ->				List[str]:
							return 32


						@property
						def   _SCREAMING_SNAKE_CASE (	self       : Union[str, Any] )       ->				Optional[Any]:
							return 32


						@property
						def   _SCREAMING_SNAKE_CASE (	self       : Optional[int] )       ->				Union[str, Any]:
							return self.time_input_dim


						@property
						def   _SCREAMING_SNAKE_CASE (	self       : Optional[Any] )       ->				List[str]:
							return self.time_input_dim * 4


						@property
						def   _SCREAMING_SNAKE_CASE (	self       : Optional[int] )       ->				Tuple:
							return 100


						@property
						def   _SCREAMING_SNAKE_CASE (	self       : Optional[int] )       ->				Optional[Any]:
							A  				=				CLIPTokenizer.from_pretrained('hf-internal-testing/tiny-random-clip' )
							return tokenizer


						@property
						def   _SCREAMING_SNAKE_CASE (	self       : Optional[int] )       ->				List[Any]:
							torch.manual_seed(0 )
							A  				=				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(A_ )


						@property
						def   _SCREAMING_SNAKE_CASE (	self       : Optional[Any] )       ->				Tuple:
							torch.manual_seed(0 )

							A  				=				{
							    'num_attention_heads': 2,
							    'attention_head_dim': 12,
							    'embedding_dim': self.text_embedder_hidden_size,
							    'num_layers': 1,
							}

							A  				=				PriorTransformer(**A_ )
							# 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
							A  				=				nn.Parameter(torch.ones(model.clip_std.shape ) )
							return model


						@property
						def   _SCREAMING_SNAKE_CASE (	self       : Any )       ->				List[str]:
							torch.manual_seed(0 )
							A  				=				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					,)

							A  				=				CLIPVisionModelWithProjection(A_ )
							return model


						@property
						def   _SCREAMING_SNAKE_CASE (	self       : int )       ->				str:
							A  				=				CLIPImageProcessor(
							    crop_size=224					,do_center_crop=A_					,do_normalize=A_					,do_resize=A_					,image_mean=[0.48_14_54_66, 0.4_57_82_75, 0.40_82_10_73]					,image_std=[0.26_86_29_54, 0.26_13_02_58, 0.27_57_77_11]					,resample=3					,size=224					,)

							return image_processor


						def   _SCREAMING_SNAKE_CASE (	self       : Optional[int] )       ->				str:
							A  				=				self.dummy_prior
							A  				=				self.dummy_image_encoder
							A  				=				self.dummy_text_encoder
							A  				=				self.dummy_tokenizer
							A  				=				self.dummy_image_processor

							A  				=				UnCLIPScheduler(
							    variance_type='fixed_small_log'					,prediction_type='sample'					,num_train_timesteps=1000					,clip_sample=A_					,clip_sample_range=10.0					,)

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

							return components


						def   _SCREAMING_SNAKE_CASE (	self       : int					,A_       : str					,A_       : List[str]=0 )       ->				int:
							if str(A_ ).startswith('mps' ):
								A  				=				torch.manual_seed(A_ )
							else:
								A  				=				torch.Generator(device=A_ ).manual_seed(A_ )

							A  				=				floats_tensor((1, 3, 64, 64)					,rng=random.Random(A_ ) ).to(A_ )
							A  				=				image.cpu().permute(0					,2					,3					,1 )[0]
							A  				=				Image.fromarray(np.uinta(A_ ) ).convert('RGB' ).resize((256, 256) )

							A  				=				{
							    'prompt': 'horse',
							    'image': init_image,
							    'strength': 0.5,
							    'generator': generator,
							    'guidance_scale': 4.0,
							    'num_inference_steps': 2,
							    'output_type': 'np',
							}
							return inputs


						def   _SCREAMING_SNAKE_CASE (	self       : List[Any] )       ->				Dict:
							A  				=				'cpu'

							A  				=				self.get_dummy_components()

							A  				=				self.pipeline_class(**A_ )
							A  				=				pipe.to(A_ )

							pipe.set_progress_bar_config(disable=A_ )

							A  				=				pipe(**self.get_dummy_inputs(A_ ) )
							A  				=				output.image_embeds

							A  				=				pipe(
							    **self.get_dummy_inputs(A_ )					,return_dict=A_					,)[0]

							A  				=				image[0, -10:]
							A  				=				image_from_tuple[0, -10:]

							assert image.shape == (1, 32)

							A  				=				np.array(
							    [
							        0.1_07_12_84,
							        1.3_33_02_71,
							        0.61_26_02_23,
							        -0.6_69_10_65,
							        -0.3_84_68_52,
							        -1.0_30_36_61,
							        0.22_71_61_11,
							        0.03_34_89_01,
							        0.30_04_06_75,
							        -0.24_80_50_29,
							    ] )

							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   _SCREAMING_SNAKE_CASE (	self       : List[Any] )       ->				int:
							A  				=				torch_device == 'cpu'
							A  				=				True
							A  				=				False

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




						@skip_mps
						def   _SCREAMING_SNAKE_CASE (	self       : List[str] )       ->				Tuple:
							A  				=				torch_device == 'cpu'
							A  				=				False

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

"""simple docstring"""


from __future__ import annotations






def  _snake_case			(						snake_case__				:       list[list[int]]		, snake_case__				:       list[list[int]]       ):
	assert len(snake_case__       ) == len(snake_case__       )

	A  				=				len(snake_case__       )
	A  				=				list(range(snake_case__       )       )
	A  				=				[-1] * n  # who the donor has donated to
	A  				=				[-1] * n  # who the recipient has received from
	A  				=				[0] * n

	while unmatched_donors:
		A  				=				unmatched_donors[0]
		A  				=				donor_pref[donor]
		A  				=				donor_preference[num_donations[donor]]
		num_donations[donor] += 1
		A  				=				recipient_pref[recipient]
		A  				=				rec_record[recipient]

		if prev_donor != -1:
			if rec_preference.index(snake_case__       ) > rec_preference.index(snake_case__       ):
				A  				=				donor
				A  				=				recipient
				unmatched_donors.append(snake_case__       )
				unmatched_donors.remove(snake_case__       )
		else:
			A  				=				donor
			A  				=				recipient
			unmatched_donors.remove(snake_case__       )
	return donor_record