





"""simple docstring"""






import json
import os
import unittest

from transformers import OpenAIGPTTokenizer, OpenAIGPTTokenizerFast
from transformers.models.openai.tokenization_openai import VOCAB_FILES_NAMES
from transformers.testing_utils import require_ftfy, require_spacy, require_tokenizers

from ...test_tokenization_common import TokenizerTesterMixin




@require_tokenizers
class 				__lowercase			(			_UpperCamelCase   ,      unittest.TestCase							):







					'''simple docstring'''




					__lowerCAmelCase  =  OpenAIGPTTokenizer
					__lowerCAmelCase  =  OpenAIGPTTokenizerFast
					__lowerCAmelCase  =  True
					__lowerCAmelCase  =  False







					def  _lowerCamelCase		(						self     ):
									super().setUp()

									# Adapted from Sennrich et al. 2015 and https://github.com/rsennrich/subword-nmt
									__a :						List[str]			       =  [
									    '''l''',
									    '''o''',
									    '''w''',
									    '''e''',
									    '''r''',
									    '''s''',
									    '''t''',
									    '''i''',
									    '''d''',
									    '''n''',
									    '''w</w>''',
									    '''r</w>''',
									    '''t</w>''',
									    '''lo''',
									    '''low''',
									    '''er</w>''',
									    '''low</w>''',
									    '''lowest</w>''',
									    '''newer</w>''',
									    '''wider</w>''',
									    '''<unk>''',
									]
									__a :						str			       =  dict(zip(_UpperCAmelCase	,			range(len(_UpperCAmelCase     )     )     )     )
									__a :						List[Any]			       =  ['''#version: 0.2''', '''l o''', '''lo w''', '''e r</w>''', '''''']

									__a :						Union[str, Any]			       =  os.path.join(self.tmpdirname	,			VOCAB_FILES_NAMES['''vocab_file''']     )
									__a :						Dict			       =  os.path.join(self.tmpdirname	,			VOCAB_FILES_NAMES['''merges_file''']     )
									with open(self.vocab_file	,			'''w'''     ) as fp:
													fp.write(json.dumps(_UpperCAmelCase     )     )
									with open(self.merges_file	,			'''w'''     ) as fp:
													fp.write('''\n'''.join(_UpperCAmelCase     )     )







					def  _lowerCamelCase		(						self	,			_UpperCAmelCase     ):
									return "lower newer", "lower newer"







					def  _lowerCamelCase		(						self     ):
									__a :						Tuple			       =  OpenAIGPTTokenizer(self.vocab_file	,			self.merges_file     )

									__a :						Tuple			       =  '''lower'''
									__a :						Union[str, Any]			       =  ['''low''', '''er</w>''']
									__a :						str			       =  tokenizer.tokenize(_UpperCAmelCase     )
									self.assertListEqual(_UpperCAmelCase	,			_UpperCAmelCase     )

									__a :						List[Any]			       =  tokens + ['''<unk>''']
									__a :						Tuple			       =  [14, 15, 20]
									self.assertListEqual(tokenizer.convert_tokens_to_ids(_UpperCAmelCase     )	,			_UpperCAmelCase     )







					def  _lowerCamelCase		(						self	,			_UpperCAmelCase=15     ):
									for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
													with self.subTest(f"""{tokenizer.__class__.__name__} ({pretrained_name})"""     ):
																	__a :						Any			       =  self.rust_tokenizer_class.from_pretrained(_UpperCAmelCase	,			**_UpperCAmelCase     )

																	# Simple input
																	__a :						str			       =  '''This is a simple input'''
																	__a :						Dict			       =  ['''This is a simple input 1''', '''This is a simple input 2''']
																	__a :						List[Any]			       =  ('''This is a simple input''', '''This is a pair''')
																	__a :						Optional[Any]			       =  [
																	    ('''This is a simple input 1''', '''This is a simple input 2'''),
																	    ('''This is a simple pair 1''', '''This is a simple pair 2'''),
																	]

																	# Simple input tests
																	self.assertRaises(_UpperCAmelCase	,			tokenizer_r.encode	,			_UpperCAmelCase	,			max_length=_UpperCAmelCase	,			padding='''max_length'''     )

																	# Simple input
																	self.assertRaises(_UpperCAmelCase	,			tokenizer_r.encode_plus	,			_UpperCAmelCase	,			max_length=_UpperCAmelCase	,			padding='''max_length'''     )

																	# Simple input
																	self.assertRaises(
																	    _UpperCAmelCase	,			tokenizer_r.batch_encode_plus	,			_UpperCAmelCase	,			max_length=_UpperCAmelCase	,			padding='''max_length'''	,			)

																	# Pair input
																	self.assertRaises(_UpperCAmelCase	,			tokenizer_r.encode	,			_UpperCAmelCase	,			max_length=_UpperCAmelCase	,			padding='''max_length'''     )

																	# Pair input
																	self.assertRaises(_UpperCAmelCase	,			tokenizer_r.encode_plus	,			_UpperCAmelCase	,			max_length=_UpperCAmelCase	,			padding='''max_length'''     )

																	# Pair input
																	self.assertRaises(
																	    _UpperCAmelCase	,			tokenizer_r.batch_encode_plus	,			_UpperCAmelCase	,			max_length=_UpperCAmelCase	,			padding='''max_length'''	,			)







					def  _lowerCamelCase		(						self     ):
									pass






@require_ftfy
@require_spacy
@require_tokenizers
class 				__lowercase			(			_UpperCamelCase							):







					'''simple docstring'''




					pass






"""simple docstring"""






import gc
import random
import unittest

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

from diffusers import (
    AutoencoderKL,
    DDIMScheduler,
    EulerAncestralDiscreteScheduler,
    LMSDiscreteScheduler,
    PNDMScheduler,
    StableDiffusionInstructPixaPixPipeline,
    UNetaDConditionModel,
)
from diffusers.image_processor import VaeImageProcessor
from diffusers.utils import floats_tensor, load_image, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu

from ..pipeline_params import (
    IMAGE_TO_IMAGE_IMAGE_PARAMS,
    TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS,
    TEXT_GUIDED_IMAGE_VARIATION_PARAMS,
)
from ..test_pipelines_common import PipelineKarrasSchedulerTesterMixin, PipelineLatentTesterMixin, PipelineTesterMixin


enable_full_determinism()




class 				__lowercase			(			_UpperCamelCase   ,      _UpperCamelCase   ,      _UpperCamelCase   ,      unittest.TestCase							):







					'''simple docstring'''




					__lowerCAmelCase  =  StableDiffusionInstructPixaPixPipeline
					__lowerCAmelCase  =  TEXT_GUIDED_IMAGE_VARIATION_PARAMS - {'''height''', '''width''', '''cross_attention_kwargs'''}
					__lowerCAmelCase  =  TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS
					__lowerCAmelCase  =  IMAGE_TO_IMAGE_IMAGE_PARAMS
					__lowerCAmelCase  =  IMAGE_TO_IMAGE_IMAGE_PARAMS







					def  _lowerCamelCase		(						self     ):
									torch.manual_seed(0     )
									__a :						Optional[Any]			       =  UNetaDConditionModel(
									    block_out_channels=(32, 64)	,			layers_per_block=2	,			sample_size=32	,			in_channels=8	,			out_channels=4	,			down_block_types=('''DownBlock2D''', '''CrossAttnDownBlock2D''')	,			up_block_types=('''CrossAttnUpBlock2D''', '''UpBlock2D''')	,			cross_attention_dim=32	,			)
									__a :						List[str]			       =  PNDMScheduler(skip_prk_steps=_UpperCAmelCase     )
									torch.manual_seed(0     )
									__a :						int			       =  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	,			)
									torch.manual_seed(0     )
									__a :						str			       =  CLIPTextConfig(
									    bos_token_id=0	,			eos_token_id=2	,			hidden_size=32	,			intermediate_size=37	,			layer_norm_eps=1e-0_5	,			num_attention_heads=4	,			num_hidden_layers=5	,			pad_token_id=1	,			vocab_size=1000	,			)
									__a :						Optional[int]			       =  CLIPTextModel(_UpperCAmelCase     )
									__a :						str			       =  CLIPTokenizer.from_pretrained('''hf-internal-testing/tiny-random-clip'''     )

									__a :						Union[str, Any]			       =  {
									    '''unet''': unet,
									    '''scheduler''': scheduler,
									    '''vae''': vae,
									    '''text_encoder''': text_encoder,
									    '''tokenizer''': tokenizer,
									    '''safety_checker''': None,
									    '''feature_extractor''': None,
									}
									return components







					def  _lowerCamelCase		(						self	,			_UpperCAmelCase	,			_UpperCAmelCase=0     ):
									__a :						Any			       =  floats_tensor((1, 3, 32, 32)	,			rng=random.Random(_UpperCAmelCase     )     ).to(_UpperCAmelCase     )
									__a :						Optional[Any]			       =  image.cpu().permute(0	,			2	,			3	,			1     )[0]
									__a :						Tuple			       =  Image.fromarray(np.uinta(_UpperCAmelCase     )     ).convert('''RGB'''     )
									if str(_UpperCAmelCase     ).startswith('''mps'''     ):
													__a :						Union[str, Any]			       =  torch.manual_seed(_UpperCAmelCase     )
									else:
													__a :						Union[str, Any]			       =  torch.Generator(device=_UpperCAmelCase     ).manual_seed(_UpperCAmelCase     )
									__a :						Union[str, Any]			       =  {
									    '''prompt''': '''A painting of a squirrel eating a burger''',
									    '''image''': image,
									    '''generator''': generator,
									    '''num_inference_steps''': 2,
									    '''guidance_scale''': 6.0,
									    '''image_guidance_scale''': 1,
									    '''output_type''': '''numpy''',
									}
									return inputs







					def  _lowerCamelCase		(						self     ):
									__a :						List[Any]			       =  '''cpu'''  # ensure determinism for the device-dependent torch.Generator
									__a :						Dict			       =  self.get_dummy_components()
									__a :						Any			       =  StableDiffusionInstructPixaPixPipeline(**_UpperCAmelCase     )
									__a :						int			       =  sd_pipe.to(_UpperCAmelCase     )
									sd_pipe.set_progress_bar_config(disable=_UpperCAmelCase     )

									__a :						Union[str, Any]			       =  self.get_dummy_inputs(_UpperCAmelCase     )
									__a :						str			       =  sd_pipe(**_UpperCAmelCase     ).images
									__a :						List[Any]			       =  image[0, -3:, -3:, -1]
									assert image.shape == (1, 32, 32, 3)
									__a :						Optional[int]			       =  np.array([0.7_5_2_6, 0.3_7_5_0, 0.4_5_4_7, 0.6_1_1_7, 0.5_8_6_6, 0.5_0_1_6, 0.4_3_2_7, 0.5_6_4_2, 0.4_8_1_5]     )

									assert np.abs(image_slice.flatten() - expected_slice     ).max() < 1e-3







					def  _lowerCamelCase		(						self     ):
									__a :						Dict			       =  '''cpu'''  # ensure determinism for the device-dependent torch.Generator
									__a :						Optional[Any]			       =  self.get_dummy_components()
									__a :						Dict			       =  StableDiffusionInstructPixaPixPipeline(**_UpperCAmelCase     )
									__a :						List[Any]			       =  sd_pipe.to(_UpperCAmelCase     )
									sd_pipe.set_progress_bar_config(disable=_UpperCAmelCase     )

									__a :						Dict			       =  self.get_dummy_inputs(_UpperCAmelCase     )
									__a :						Union[str, Any]			       =  '''french fries'''
									__a :						str			       =  sd_pipe(**_UpperCAmelCase	,			negative_prompt=_UpperCAmelCase     )
									__a :						Dict			       =  output.images
									__a :						Optional[int]			       =  image[0, -3:, -3:, -1]

									assert image.shape == (1, 32, 32, 3)
									__a :						Tuple			       =  np.array([0.7_5_1_1, 0.3_6_4_2, 0.4_5_5_3, 0.6_2_3_6, 0.5_7_9_7, 0.5_0_1_3, 0.4_3_4_3, 0.5_6_1_1, 0.4_8_3_1]     )

									assert np.abs(image_slice.flatten() - expected_slice     ).max() < 1e-3







					def  _lowerCamelCase		(						self     ):
									__a :						Union[str, Any]			       =  '''cpu'''  # ensure determinism for the device-dependent torch.Generator
									__a :						Dict			       =  self.get_dummy_components()
									__a :						str			       =  StableDiffusionInstructPixaPixPipeline(**_UpperCAmelCase     )
									__a :						Optional[int]			       =  sd_pipe.to(_UpperCAmelCase     )
									sd_pipe.set_progress_bar_config(disable=_UpperCAmelCase     )

									__a :						Union[str, Any]			       =  self.get_dummy_inputs(_UpperCAmelCase     )
									__a :						List[str]			       =  [inputs['''prompt''']] * 2

									__a :						Optional[Any]			       =  np.array(inputs['''image''']     ).astype(np.floataa     ) / 2_5_5.0
									__a :						Optional[Any]			       =  torch.from_numpy(_UpperCAmelCase     ).unsqueeze(0     ).to(_UpperCAmelCase     )
									__a :						Tuple			       =  image / 2 + 0.5
									__a :						str			       =  image.permute(0	,			3	,			1	,			2     )
									__a :						List[str]			       =  image.repeat(2	,			1	,			1	,			1     )

									__a :						int			       =  sd_pipe(**_UpperCAmelCase     ).images
									__a :						Optional[Any]			       =  image[-1, -3:, -3:, -1]

									assert image.shape == (2, 32, 32, 3)
									__a :						List[str]			       =  np.array([0.5_8_1_2, 0.5_7_4_8, 0.5_2_2_2, 0.5_9_0_8, 0.5_6_9_5, 0.7_1_7_4, 0.6_8_0_4, 0.5_5_2_3, 0.5_5_7_9]     )

									assert np.abs(image_slice.flatten() - expected_slice     ).max() < 1e-3







					def  _lowerCamelCase		(						self     ):
									__a :						Dict			       =  '''cpu'''  # ensure determinism for the device-dependent torch.Generator
									__a :						List[str]			       =  self.get_dummy_components()
									__a :						Union[str, Any]			       =  EulerAncestralDiscreteScheduler(
									    beta_start=0.0_0_0_8_5	,			beta_end=0.0_1_2	,			beta_schedule='''scaled_linear'''     )
									__a :						str			       =  StableDiffusionInstructPixaPixPipeline(**_UpperCAmelCase     )
									__a :						List[str]			       =  sd_pipe.to(_UpperCAmelCase     )
									sd_pipe.set_progress_bar_config(disable=_UpperCAmelCase     )

									__a :						Dict			       =  self.get_dummy_inputs(_UpperCAmelCase     )
									__a :						Any			       =  sd_pipe(**_UpperCAmelCase     ).images
									__a :						Dict			       =  image[0, -3:, -3:, -1]

									__a :						Optional[int]			       =  [round(_UpperCAmelCase	,			4     ) for x in image_slice.flatten().tolist()]
									print(''','''.join([str(_UpperCAmelCase     ) for x in slice]     )     )

									assert image.shape == (1, 32, 32, 3)
									__a :						int			       =  np.array([0.7_4_1_7, 0.3_8_4_2, 0.4_7_3_2, 0.5_7_7_6, 0.5_8_9_1, 0.5_1_3_9, 0.4_0_5_2, 0.5_6_7_3, 0.4_9_8_6]     )

									assert np.abs(image_slice.flatten() - expected_slice     ).max() < 1e-3







					def  _lowerCamelCase		(						self     ):
									super().test_inference_batch_single_identical(expected_max_diff=3e-3     )







					def  _lowerCamelCase		(						self     ):
									__a :						Any			       =  self.get_dummy_components()
									__a :						Optional[Any]			       =  StableDiffusionInstructPixaPixPipeline(**_UpperCAmelCase     )
									__a :						Any			       =  VaeImageProcessor(do_resize=_UpperCAmelCase	,			do_normalize=_UpperCAmelCase     )
									__a :						Dict			       =  pipe.to(_UpperCAmelCase     )
									pipe.set_progress_bar_config(disable=_UpperCAmelCase     )

									__a :						str			       =  pipe(**self.get_dummy_inputs_by_type(_UpperCAmelCase	,			input_image_type='''pt'''     )     )[0]

									__a :						List[Any]			       =  components['''vae''']
									__a :						List[Any]			       =  self.get_dummy_inputs_by_type(_UpperCAmelCase	,			input_image_type='''pt'''     )

									for image_param in self.image_latents_params:
													if image_param in inputs.keys():
																	__a :						Optional[Any]			       =  vae.encode(inputs[image_param]     ).latent_dist.mode()

									__a :						str			       =  pipe(**_UpperCAmelCase     )[0]

									__a :						Union[str, Any]			       =  np.abs(out - out_latents_inputs     ).max()
									self.assertLess(_UpperCAmelCase	,			1e-4	,			'''passing latents as image input generate different result from passing image'''     )






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







					'''simple docstring'''




					def  _lowerCamelCase		(						self     ):
									super().tearDown()
									gc.collect()
									torch.cuda.empty_cache()







					def  _lowerCamelCase		(						self	,			_UpperCAmelCase=0     ):
									__a :						List[str]			       =  torch.manual_seed(_UpperCAmelCase     )
									__a :						str			       =  load_image(
									    '''https://huggingface.co/datasets/diffusers/test-arrays/resolve/main/stable_diffusion_pix2pix/example.jpg'''     )
									__a :						List[str]			       =  {
									    '''prompt''': '''turn him into a cyborg''',
									    '''image''': image,
									    '''generator''': generator,
									    '''num_inference_steps''': 3,
									    '''guidance_scale''': 7.5,
									    '''image_guidance_scale''': 1.0,
									    '''output_type''': '''numpy''',
									}
									return inputs







					def  _lowerCamelCase		(						self     ):
									__a :						List[str]			       =  StableDiffusionInstructPixaPixPipeline.from_pretrained(
									    '''timbrooks/instruct-pix2pix'''	,			safety_checker=_UpperCAmelCase     )
									pipe.to(_UpperCAmelCase     )
									pipe.set_progress_bar_config(disable=_UpperCAmelCase     )
									pipe.enable_attention_slicing()

									__a :						str			       =  self.get_inputs()
									__a :						Optional[Any]			       =  pipe(**_UpperCAmelCase     ).images
									__a :						List[str]			       =  image[0, -3:, -3:, -1].flatten()

									assert image.shape == (1, 512, 512, 3)
									__a :						Tuple			       =  np.array([0.5_9_0_2, 0.6_0_1_5, 0.6_0_2_7, 0.5_9_8_3, 0.6_0_9_2, 0.6_0_6_1, 0.5_7_6_5, 0.5_7_8_5, 0.5_5_5_5]     )

									assert np.abs(expected_slice - image_slice     ).max() < 1e-3







					def  _lowerCamelCase		(						self     ):
									__a :						Tuple			       =  StableDiffusionInstructPixaPixPipeline.from_pretrained(
									    '''timbrooks/instruct-pix2pix'''	,			safety_checker=_UpperCAmelCase     )
									__a :						Optional[int]			       =  LMSDiscreteScheduler.from_config(pipe.scheduler.config     )
									pipe.to(_UpperCAmelCase     )
									pipe.set_progress_bar_config(disable=_UpperCAmelCase     )
									pipe.enable_attention_slicing()

									__a :						List[str]			       =  self.get_inputs()
									__a :						Optional[int]			       =  pipe(**_UpperCAmelCase     ).images
									__a :						Optional[Any]			       =  image[0, -3:, -3:, -1].flatten()

									assert image.shape == (1, 512, 512, 3)
									__a :						Tuple			       =  np.array([0.6_5_7_8, 0.6_8_1_7, 0.6_9_7_2, 0.6_7_6_1, 0.6_8_5_6, 0.6_9_1_6, 0.6_4_2_8, 0.6_5_1_6, 0.6_3_0_1]     )

									assert np.abs(expected_slice - image_slice     ).max() < 1e-3







					def  _lowerCamelCase		(						self     ):
									__a :						List[Any]			       =  StableDiffusionInstructPixaPixPipeline.from_pretrained(
									    '''timbrooks/instruct-pix2pix'''	,			safety_checker=_UpperCAmelCase     )
									__a :						Optional[int]			       =  DDIMScheduler.from_config(pipe.scheduler.config     )
									pipe.to(_UpperCAmelCase     )
									pipe.set_progress_bar_config(disable=_UpperCAmelCase     )
									pipe.enable_attention_slicing()

									__a :						List[str]			       =  self.get_inputs()
									__a :						str			       =  pipe(**_UpperCAmelCase     ).images
									__a :						Optional[int]			       =  image[0, -3:, -3:, -1].flatten()

									assert image.shape == (1, 512, 512, 3)
									__a :						Any			       =  np.array([0.3_8_2_8, 0.3_8_3_4, 0.3_8_1_8, 0.3_7_9_2, 0.3_8_6_5, 0.3_7_5_2, 0.3_7_9_2, 0.3_8_4_7, 0.3_7_5_3]     )

									assert np.abs(expected_slice - image_slice     ).max() < 1e-3







					def  _lowerCamelCase		(						self     ):
									__a :						Dict			       =  0

									def callback_fn(_UpperCAmelCase	,			_UpperCAmelCase	,			_UpperCAmelCase     ) -> None:
													__a :						Optional[Any]			       =  True
													nonlocal number_of_steps
													number_of_steps += 1
													if step == 1:
																	__a :						Optional[int]			       =  latents.detach().cpu().numpy()
																	assert latents.shape == (1, 4, 64, 64)
																	__a :						int			       =  latents[0, -3:, -3:, -1]
																	__a :						int			       =  np.array([-0.2_4_6_3, -0.4_6_4_4, -0.9_7_5_6, 1.5_1_7_6, 1.4_4_1_4, 0.7_8_6_6, 0.9_8_9_7, 0.8_5_2_1, 0.7_9_8_3]     )

																	assert np.abs(latents_slice.flatten() - expected_slice     ).max() < 5e-2
													elif step == 2:
																	__a :						Union[str, Any]			       =  latents.detach().cpu().numpy()
																	assert latents.shape == (1, 4, 64, 64)
																	__a :						List[str]			       =  latents[0, -3:, -3:, -1]
																	__a :						Tuple			       =  np.array([-0.2_6_4_4, -0.4_6_2_6, -0.9_6_5_3, 1.5_1_7_6, 1.4_5_5_1, 0.7_6_8_6, 0.9_8_0_5, 0.8_4_5_2, 0.8_1_1_5]     )

																	assert np.abs(latents_slice.flatten() - expected_slice     ).max() < 5e-2

									__a :						Union[str, Any]			       =  False

									__a :						Optional[int]			       =  StableDiffusionInstructPixaPixPipeline.from_pretrained(
									    '''timbrooks/instruct-pix2pix'''	,			safety_checker=_UpperCAmelCase	,			torch_dtype=torch.floataa     )
									__a :						Optional[int]			       =  pipe.to(_UpperCAmelCase     )
									pipe.set_progress_bar_config(disable=_UpperCAmelCase     )
									pipe.enable_attention_slicing()

									__a :						str			       =  self.get_inputs()
									pipe(**_UpperCAmelCase	,			callback=_UpperCAmelCase	,			callback_steps=1     )
									assert callback_fn.has_been_called
									assert number_of_steps == 3







					def  _lowerCamelCase		(						self     ):
									torch.cuda.empty_cache()
									torch.cuda.reset_max_memory_allocated()
									torch.cuda.reset_peak_memory_stats()

									__a :						Any			       =  StableDiffusionInstructPixaPixPipeline.from_pretrained(
									    '''timbrooks/instruct-pix2pix'''	,			safety_checker=_UpperCAmelCase	,			torch_dtype=torch.floataa     )
									__a :						Optional[int]			       =  pipe.to(_UpperCAmelCase     )
									pipe.set_progress_bar_config(disable=_UpperCAmelCase     )
									pipe.enable_attention_slicing(1     )
									pipe.enable_sequential_cpu_offload()

									__a :						List[Any]			       =  self.get_inputs()
									__a :						Tuple			       =  pipe(**_UpperCAmelCase     )

									__a :						List[str]			       =  torch.cuda.max_memory_allocated()
									# make sure that less than 2.2 GB is allocated
									assert mem_bytes < 2.2 * 10**9







					def  _lowerCamelCase		(						self     ):
									__a :						List[Any]			       =  self.get_inputs()
									# resize to resolution that is divisible by 8 but not 16 or 32
									__a :						str			       =  inputs['''image'''].resize((504, 504)     )

									__a :						Tuple			       =  '''timbrooks/instruct-pix2pix'''
									__a :						Any			       =  StableDiffusionInstructPixaPixPipeline.from_pretrained(
									    _UpperCAmelCase	,			safety_checker=_UpperCAmelCase	,			)
									pipe.to(_UpperCAmelCase     )
									pipe.set_progress_bar_config(disable=_UpperCAmelCase     )
									pipe.enable_attention_slicing()

									__a :						List[Any]			       =  pipe(**_UpperCAmelCase     )
									__a :						int			       =  output.images[0]

									__a :						Optional[int]			       =  image[255:258, 383:386, -1]

									assert image.shape == (504, 504, 3)
									__a :						Union[str, Any]			       =  np.array([0.2_7_2_6, 0.2_5_2_9, 0.2_6_6_4, 0.2_6_5_5, 0.2_6_4_1, 0.2_6_4_2, 0.2_5_9_1, 0.2_6_4_9, 0.2_5_9_0]     )

									assert np.abs(image_slice.flatten() - expected_slice     ).max() < 5e-3