






"""simple docstring"""







import gc
import unittest

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

from diffusers import (
    AutoencoderKL,
    DDIMScheduler,
    StableDiffusionAttendAndExcitePipeline,
    UNetaDConditionModel,
)
from diffusers.utils import load_numpy, skip_mps, slow
from diffusers.utils.testing_utils import require_torch_gpu

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


__a       :	int             = False







@skip_mps
class        _SCREAMING_SNAKE_CASE						(  __snake_case       , __snake_case       , __snake_case       , unittest.TestCase							):





							"""simple docstring"""





							_SCREAMING_SNAKE_CASE    =StableDiffusionAttendAndExcitePipeline
							_SCREAMING_SNAKE_CASE    =False
							_SCREAMING_SNAKE_CASE    =TEXT_TO_IMAGE_PARAMS
							_SCREAMING_SNAKE_CASE    =TEXT_TO_IMAGE_BATCH_PARAMS.union({'token_indices'}							)
							_SCREAMING_SNAKE_CASE    =TEXT_TO_IMAGE_IMAGE_PARAMS
							_SCREAMING_SNAKE_CASE    =TEXT_TO_IMAGE_IMAGE_PARAMS




							@classmethod
							def  lowercase		(   cls:     List[str]    ):

												'''simple docstring'''

												super().setUpClass()
												torch.use_deterministic_algorithms(__A    )




							@classmethod
							def  lowercase		(   cls:     str    ):

												'''simple docstring'''

												super().tearDownClass()
												torch.use_deterministic_algorithms(__A    )




							def  lowercase		(   self:     Any    ):

												'''simple docstring'''

												torch.manual_seed(0    )
												a__          =  UNetaDConditionModel(
												    block_out_channels=(32, 64)				,		layers_per_block=1				,		sample_size=32				,		in_channels=4				,		out_channels=4				,		down_block_types=('''DownBlock2D''', '''CrossAttnDownBlock2D''')				,		up_block_types=('''CrossAttnUpBlock2D''', '''UpBlock2D''')				,		cross_attention_dim=32				,		attention_head_dim=(2, 4)				,		use_linear_projection=__A				,		)
												a__          =  DDIMScheduler(
												    beta_start=0.0_0_0_8_5				,		beta_end=0.0_1_2				,		beta_schedule='''scaled_linear'''				,		clip_sample=__A				,		set_alpha_to_one=__A				,		)
												torch.manual_seed(0    )
												a__          =  AutoencoderKL(
												    block_out_channels=[32, 64]				,		in_channels=3				,		out_channels=3				,		down_block_types=['''DownEncoderBlock2D''', '''DownEncoderBlock2D''']				,		up_block_types=['''UpDecoderBlock2D''', '''UpDecoderBlock2D''']				,		latent_channels=4				,		sample_size=128				,		)
												torch.manual_seed(0    )
												a__          =  CLIPTextConfig(
												    bos_token_id=0				,		eos_token_id=2				,		hidden_size=32				,		intermediate_size=37				,		layer_norm_eps=1e-05				,		num_attention_heads=4				,		num_hidden_layers=5				,		pad_token_id=1				,		vocab_size=1000				,		hidden_act='''gelu'''				,		projection_dim=512				,		)
												a__          =  CLIPTextModel(__A    )
												a__          =  CLIPTokenizer.from_pretrained('''hf-internal-testing/tiny-random-clip'''    )

												a__          =  {
												    '''unet''': unet,
												    '''scheduler''': scheduler,
												    '''vae''': vae,
												    '''text_encoder''': text_encoder,
												    '''tokenizer''': tokenizer,
												    '''safety_checker''': None,
												    '''feature_extractor''': None,
												}

												return components




							def  lowercase		(   self:     int				,		__A:     List[Any]				,		__A:     int=0    ):

												'''simple docstring'''

												if str(__A    ).startswith('''mps'''    ):
																	a__          =  torch.manual_seed(__A    )
												else:
																	a__          =  torch.Generator(device=__A    ).manual_seed(__A    )
												a__          =  a__          =  {
												    '''prompt''': '''a cat and a frog''',
												    '''token_indices''': [2, 5],
												    '''generator''': generator,
												    '''num_inference_steps''': 1,
												    '''guidance_scale''': 6.0,
												    '''output_type''': '''numpy''',
												    '''max_iter_to_alter''': 2,
												    '''thresholds''': {0: 0.7},
												}
												return inputs




							def  lowercase		(   self:     List[Any]    ):

												'''simple docstring'''

												a__          =  '''cpu'''

												a__          =  self.get_dummy_components()
												a__          =  self.pipeline_class(**__A    )
												pipe.to(__A    )
												pipe.set_progress_bar_config(disable=__A    )

												a__          =  self.get_dummy_inputs(__A    )
												a__          =  pipe(**__A    ).images
												a__          =  image[0, -3:, -3:, -1]

												self.assertEqual(image.shape				,		(1, 64, 64, 3)    )
												a__          =  np.array(
												    [0.6_3_9_0_5_3_6_4, 0.6_2_8_9_7_3_0_7, 0.4_8_5_9_9_0_1_7, 0.5_1_3_3_6_2_4, 0.5_5_5_0_0_4_8, 0.4_5_7_6_9_5_1_6, 0.5_0_3_2_6_9_7_3, 0.5_0_2_3_1_3_9, 0.4_5_3_8_4_4_9_6]    )
												a__          =  np.abs(image_slice.flatten() - expected_slice    ).max()
												self.assertLessEqual(__A				,		1e-3    )




							def  lowercase		(   self:     Tuple    ):

												'''simple docstring'''

												super().test_cpu_offload_forward_pass(expected_max_diff=5e-4    )




							def  lowercase		(   self:     Any    ):

												'''simple docstring'''

												self._test_inference_batch_consistent(batch_sizes=[1, 2]    )




							def  lowercase		(   self:     Optional[Any]    ):

												'''simple docstring'''

												self._test_inference_batch_single_identical(batch_size=2				,		expected_max_diff=7e-4    )




							def  lowercase		(   self:     Dict    ):

												'''simple docstring'''

												super().test_dict_tuple_outputs_equivalent(expected_max_difference=3e-3    )




							def  lowercase		(   self:     Any    ):

												'''simple docstring'''

												super().test_pt_np_pil_outputs_equivalent(expected_max_diff=5e-4    )




							def  lowercase		(   self:     Union[str, Any]    ):

												'''simple docstring'''

												super().test_save_load_local(expected_max_difference=5e-4    )





							def  lowercase		(   self:     Tuple    ):

												'''simple docstring'''

												super().test_save_load_optional_components(expected_max_difference=4e-4    )







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





							"""simple docstring"""





							@classmethod
							def  lowercase		(   cls:     Any    ):

												'''simple docstring'''

												super().setUpClass()
												torch.use_deterministic_algorithms(__A    )




							@classmethod
							def  lowercase		(   cls:     int    ):

												'''simple docstring'''

												super().tearDownClass()
												torch.use_deterministic_algorithms(__A    )




							def  lowercase		(   self:     List[str]    ):

												'''simple docstring'''

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





							def  lowercase		(   self:     List[str]    ):

												'''simple docstring'''

												a__          =  torch.manual_seed(51    )

												a__          =  StableDiffusionAttendAndExcitePipeline.from_pretrained(
												    '''CompVis/stable-diffusion-v1-4'''				,		safety_checker=__A				,		torch_dtype=torch.floataa    )
												pipe.to('''cuda'''    )

												a__          =  '''a painting of an elephant with glasses'''
												a__          =  [5, 7]

												a__          =  pipe(
												    prompt=__A				,		token_indices=__A				,		guidance_scale=7.5				,		generator=__A				,		num_inference_steps=5				,		max_iter_to_alter=5				,		output_type='''numpy'''				,		).images[0]

												a__          =  load_numpy(
												    '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/attend-and-excite/elephant_glasses.npy'''    )
												assert np.abs((expected_image - image).max()    ) < 5e-1










"""simple docstring"""







class        _SCREAMING_SNAKE_CASE						:





							"""simple docstring"""





							def __init__(   self:     Any    ):

												'''simple docstring'''

												a__          =  {}




							def  lowercase		(   self:     Optional[int]    ):

												'''simple docstring'''

												print(self.vertex    )
												for i in self.vertex:
																	print(__A				,		''' -> '''				,		''' -> '''.join([str(__A    ) for j in self.vertex[i]]    )    )




							def  lowercase		(   self:     Union[str, Any]				,		__A:     int				,		__A:     int    ):

												'''simple docstring'''

												if from_vertex in self.vertex:
																	self.vertex[from_vertex].append(__A    )
												else:
																	# else make a new vertex
																	a__          =  [to_vertex]




							def  lowercase		(   self:     Dict    ):

												'''simple docstring'''

												a__          =  [False] * len(self.vertex    )

												# call the recursive helper function
												for i in range(len(self.vertex    )    ):
																	if not visited[i]:
																						self.dfs_recursive(__A				,		__A    )





							def  lowercase		(   self:     Optional[int]				,		__A:     int				,		__A:     list    ):

												'''simple docstring'''

												a__          =  True

												print(__A				,		end=''' '''    )

												# Recur for all the vertices that are adjacent to this node
												for i in self.vertex:
																	if not visited[i]:
																						self.dfs_recursive(__A				,		__A    )


if __name__ == "__main__":
	__a       :	Any             = Graph()
	g.add_edge(0, 1)
	g.add_edge(0, 2)
	g.add_edge(1, 2)
	g.add_edge(2, 0)
	g.add_edge(2, 3)
	g.add_edge(3, 3)

	g.print_graph()
	print('DFS:')
	g.dfs()

	# OUTPUT:
	# 0  ->  1 -> 2
	# 1  ->  2
	# 2  ->  0 -> 3
	# 3  ->  3
	# DFS:
	#  0 1 2 3


