
'''simple docstring'''







import argparse
import os
import re


lowercase					:							str   =      "src/diffusers"

# Pattern that looks at the indentation in a line.
lowercase					:							str   =      re.compile(r"^(\s*)\S")
# Pattern that matches `"key":" and puts `key` in group 0.
lowercase					:							str   =      re.compile(r"^\s*\"([^\"]+)\":")
# Pattern that matches `_import_structure["key"]` and puts `key` in group 0.
lowercase					:							Dict   =      re.compile(r"^\s*_import_structure\[\"([^\"]+)\"\]")
# Pattern that matches `"key",` and puts `key` in group 0.
lowercase					:							List[Any]   =      re.compile(r"^\s*\"([^\"]+)\",\s*$")
# Pattern that matches any `[stuff]` and puts `stuff` in group 0.
lowercase					:							Dict   =      re.compile(r"\[([^\]]+)\]")







def        SCREAMING_SNAKE_CASE__			(     __A			) ->			Optional[Any]:
	_snake_case     		=	_re_indent.search(__A			)
	return "" if search is None else search.groups()[0]







def        SCREAMING_SNAKE_CASE__			(     __A    ,       __A=""    ,       __A=None    ,       __A=None			) ->			Dict:
	_snake_case     		=	0
	_snake_case     		=	code.split('\n'			)
	if start_prompt is not None:
		while not lines[index].startswith(__A			):
			index += 1
		_snake_case     		=	['\n'.join(lines[:index]			)]
	else:
		_snake_case     		=	[]

	# We split into blocks until we get to the `end_prompt` (or the end of the block).
	_snake_case     		=	[lines[index]]
	index += 1
	while index < len(__A			) and (end_prompt is None or not lines[index].startswith(__A			)):
		if len(lines[index]			) > 0 and get_indent(lines[index]			) == indent_level:
			if len(__A			) > 0 and get_indent(current_block[-1]			).startswith(indent_level + ' '			):
				current_block.append(lines[index]			)
				blocks.append('\n'.join(__A			)			)
				if index < len(__A			) - 1:
					_snake_case     		=	[lines[index + 1]]
					index += 1
				else:
					_snake_case     		=	[]
			else:
				blocks.append('\n'.join(__A			)			)
				_snake_case     		=	[lines[index]]
		else:
			current_block.append(lines[index]			)
		index += 1

	# Adds current block if it's nonempty.
	if len(__A			) > 0:
		blocks.append('\n'.join(__A			)			)

	# Add final block after end_prompt if provided.
	if end_prompt is not None and index < len(__A			):
		blocks.append('\n'.join(lines[index:]			)			)

	return blocks







def        SCREAMING_SNAKE_CASE__			(     __A			) ->			Optional[int]:

	def _inner(__A			):
		return key(__A			).lower().replace('_'    ,       ''			)

	return _inner







def        SCREAMING_SNAKE_CASE__			(     __A    ,       __A=None			) ->			Dict:

	# If no key is provided, we use a noop.
	def noop(__A			):
		return x

	if key is None:
		_snake_case     		=	noop
	# Constants are all uppercase, they go first.
	_snake_case     		=	[obj for obj in objects if key(__A			).isupper()]
	# Classes are not all uppercase but start with a capital, they go second.
	_snake_case     		=	[obj for obj in objects if key(__A			)[0].isupper() and not key(__A			).isupper()]
	# Functions begin with a lowercase, they go last.
	_snake_case     		=	[obj for obj in objects if not key(__A			)[0].isupper()]

	_snake_case     		=	ignore_underscore(__A			)
	return sorted(__A    ,       key=__A			) + sorted(__A    ,       key=__A			) + sorted(__A    ,       key=__A			)







def        SCREAMING_SNAKE_CASE__			(     __A			) ->			List[str]:

	# This inner function sort imports between [ ].
	def _replace(__A			):
		_snake_case     		=	match.groups()[0]
		if "," not in imports:
			return F'[{imports}]'
		_snake_case     		=	[part.strip().replace('"'    ,       ''			) for part in imports.split(','			)]
		# We will have a final empty element if the line finished with a comma.
		if len(keys[-1]			) == 0:
			_snake_case     		=	keys[:-1]
		return "[" + ", ".join([F'"{k}"' for k in sort_objects(__A			)]			) + "]"

	_snake_case     		=	import_statement.split('\n'			)
	if len(__A			) > 3:
		# Here we have to sort internal imports that are on several lines (one per name):
		# key: [
		#     "object1",
		#     "object2",
		#     ...
		# ]

		# We may have to ignore one or two lines on each side.
		_snake_case     		=	2 if lines[1].strip() == '[' else 1
		_snake_case     		=	[(i, _re_strip_line.search(__A			).groups()[0]) for i, line in enumerate(lines[idx:-idx]			)]
		_snake_case     		=	sort_objects(__A    ,       key=lambda __A			: x[1]			)
		_snake_case     		=	[lines[x[0] + idx] for x in sorted_indices]
		return "\n".join(lines[:idx] + sorted_lines + lines[-idx:]			)
	elif len(__A			) == 3:
		# Here we have to sort internal imports that are on one separate line:
		# key: [
		#     "object1", "object2", ...
		# ]
		if _re_bracket_content.search(lines[1]			) is not None:
			_snake_case     		=	_re_bracket_content.sub(_replace    ,       lines[1]			)
		else:
			_snake_case     		=	[part.strip().replace('"'    ,       ''			) for part in lines[1].split(','			)]
			# We will have a final empty element if the line finished with a comma.
			if len(keys[-1]			) == 0:
				_snake_case     		=	keys[:-1]
			_snake_case     		=	get_indent(lines[1]			) + ', '.join([F'"{k}"' for k in sort_objects(__A			)]			)
		return "\n".join(__A			)
	else:
		# Finally we have to deal with imports fitting on one line
		_snake_case     		=	_re_bracket_content.sub(_replace    ,       __A			)
		return import_statement







def        SCREAMING_SNAKE_CASE__			(     __A    ,       __A=True			) ->			Any:
	with open(__A    ,       'r'			) as f:
		_snake_case     		=	f.read()

	if "_import_structure" not in code:
		return

	# Blocks of indent level 0
	_snake_case     		=	split_code_in_indented_blocks(
	    __A    ,       start_prompt='_import_structure = {'    ,       end_prompt='if TYPE_CHECKING:'			)

	# We ignore block 0 (everything until start_prompt) and the last block (everything after end_prompt).
	for block_idx in range(1    ,       len(__A			) - 1			):
		# Check if the block contains some `_import_structure`s thingy to sort.
		_snake_case     		=	main_blocks[block_idx]
		_snake_case     		=	block.split('\n'			)

		# Get to the start of the imports.
		_snake_case     		=	0
		while line_idx < len(__A			) and "_import_structure" not in block_lines[line_idx]:
			# Skip dummy import blocks
			if "import dummy" in block_lines[line_idx]:
				_snake_case     		=	len(__A			)
			else:
				line_idx += 1
		if line_idx >= len(__A			):
			continue

		# Ignore beginning and last line: they don't contain anything.
		_snake_case     		=	'\n'.join(block_lines[line_idx:-1]			)
		_snake_case     		=	get_indent(block_lines[1]			)
		# Slit the internal block into blocks of indent level 1.
		_snake_case     		=	split_code_in_indented_blocks(__A    ,       indent_level=__A			)
		# We have two categories of import key: list or _import_structure[key].append/extend
		_snake_case     		=	_re_direct_key if '_import_structure' in block_lines[0] else _re_indirect_key
		# Grab the keys, but there is a trap: some lines are empty or just comments.
		_snake_case     		=	[(pattern.search(__A			).groups()[0] if pattern.search(__A			) is not None else None) for b in internal_blocks]
		# We only sort the lines with a key.
		_snake_case     		=	[(i, key) for i, key in enumerate(__A			) if key is not None]
		_snake_case     		=	[x[0] for x in sorted(__A    ,       key=lambda __A			: x[1]			)]

		# We reorder the blocks by leaving empty lines/comments as they were and reorder the rest.
		_snake_case     		=	0
		_snake_case     		=	[]
		for i in range(len(__A			)			):
			if keys[i] is None:
				reordered_blocks.append(internal_blocks[i]			)
			else:
				_snake_case     		=	sort_objects_in_import(internal_blocks[sorted_indices[count]]			)
				reordered_blocks.append(__A			)
				count += 1

        # And we put our main block back together with its first and last line.
		_snake_case     		=	'\n'.join(block_lines[:line_idx] + reordered_blocks + [block_lines[-1]]			)

	if code != "\n".join(__A			):
		if check_only:
			return True
		else:
			print(F'Overwriting {file}.'			)
			with open(__A    ,       'w'			) as f:
				f.write('\n'.join(__A			)			)







def        SCREAMING_SNAKE_CASE__			(     __A=True			) ->			Any:
	_snake_case     		=	[]
	for root, _, files in os.walk(__A			):
		if "__init__.py" in files:
			_snake_case     		=	sort_imports(os.path.join(__A    ,       '__init__.py'			)    ,       check_only=__A			)
			if result:
				_snake_case     		=	[os.path.join(__A    ,       '__init__.py'			)]
	if len(__A			) > 0:
		raise ValueError(F'Would overwrite {len(__A			)} files, run `make style`.'			)


if __name__ == "__main__":
					lowercase					:							Optional[Any]   =      argparse.ArgumentParser()
					parser.add_argument("--check_only", action="store_true", help="Whether to only check or fix style.")
					lowercase					:							Optional[int]   =      parser.parse_args()

					sort_imports_in_all_inits(check_only=args.check_only)



'''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 		__UpperCAmelCase   (      _lowerCamelCase		,     _lowerCamelCase		,     _lowerCamelCase		,     unittest.TestCase		):
							__lowercase   	= StableDiffusionInstructPixaPixPipeline
							__lowercase   	= TEXT_GUIDED_IMAGE_VARIATION_PARAMS - {"""height""", """width""", """cross_attention_kwargs"""}
							__lowercase   	= TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS
							__lowercase   	= IMAGE_TO_IMAGE_IMAGE_PARAMS
							__lowercase   	= IMAGE_TO_IMAGE_IMAGE_PARAMS


							def 				lowerCamelCase       ( self						):





								"""simple docstring"""






								torch.manual_seed(0						)
								_snake_case     		=	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					,     )
								_snake_case     		=	PNDMScheduler(skip_prk_steps=lowerCAmelCase_						)
								torch.manual_seed(0						)
								_snake_case     		=	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						)
								_snake_case     		=	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=10_00					,     )
								_snake_case     		=	CLIPTextModel(lowerCAmelCase_						)
								_snake_case     		=	CLIPTokenizer.from_pretrained('hf-internal-testing/tiny-random-clip'						)

								_snake_case     		=	{
								    'unet': unet,
								    'scheduler': scheduler,
								    'vae': vae,
								    'text_encoder': text_encoder,
								    'tokenizer': tokenizer,
								    'safety_checker': None,
								    'feature_extractor': None,
								}
								return components


							def 				lowerCamelCase       ( self					,     lowerCAmelCase_					,     lowerCAmelCase_=0						):





								"""simple docstring"""






								_snake_case     		=	floats_tensor((1, 3, 32, 32)					,     rng=random.Random(lowerCAmelCase_						)						).to(lowerCAmelCase_						)
								_snake_case     		=	image.cpu().permute(0					,     2					,     3					,     1						)[0]
								_snake_case     		=	Image.fromarray(np.uinta(lowerCAmelCase_						)						).convert('RGB'						)
								if str(lowerCAmelCase_						).startswith('mps'						):
									_snake_case     		=	torch.manual_seed(lowerCAmelCase_						)
								else:
									_snake_case     		=	torch.Generator(device=lowerCAmelCase_						).manual_seed(lowerCAmelCase_						)
								_snake_case     		=	{
								    '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						):





								"""simple docstring"""






								_snake_case     		=	'cpu'  # ensure determinism for the device-dependent torch.Generator
								_snake_case     		=	self.get_dummy_components()
								_snake_case     		=	StableDiffusionInstructPixaPixPipeline(**lowerCAmelCase_						)
								_snake_case     		=	sd_pipe.to(lowerCAmelCase_						)
								sd_pipe.set_progress_bar_config(disable=lowerCAmelCase_						)

								_snake_case     		=	self.get_dummy_inputs(lowerCAmelCase_						)
								_snake_case     		=	sd_pipe(**lowerCAmelCase_						).images
								_snake_case     		=	image[0, -3:, -3:, -1]
								assert image.shape == (1, 32, 32, 3)
								_snake_case     		=	np.array([0.7526, 0.3750, 0.4547, 0.6117, 0.5866, 0.5016, 0.4327, 0.5642, 0.4815]						)

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


							def 				lowerCamelCase       ( self						):





								"""simple docstring"""






								_snake_case     		=	'cpu'  # ensure determinism for the device-dependent torch.Generator
								_snake_case     		=	self.get_dummy_components()
								_snake_case     		=	StableDiffusionInstructPixaPixPipeline(**lowerCAmelCase_						)
								_snake_case     		=	sd_pipe.to(lowerCAmelCase_						)
								sd_pipe.set_progress_bar_config(disable=lowerCAmelCase_						)

								_snake_case     		=	self.get_dummy_inputs(lowerCAmelCase_						)
								_snake_case     		=	'french fries'
								_snake_case     		=	sd_pipe(**lowerCAmelCase_					,     negative_prompt=lowerCAmelCase_						)
								_snake_case     		=	output.images
								_snake_case     		=	image[0, -3:, -3:, -1]

								assert image.shape == (1, 32, 32, 3)
								_snake_case     		=	np.array([0.7511, 0.3642, 0.4553, 0.6236, 0.5797, 0.5013, 0.4343, 0.5611, 0.4831]						)

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


							def 				lowerCamelCase       ( self						):





								"""simple docstring"""






								_snake_case     		=	'cpu'  # ensure determinism for the device-dependent torch.Generator
								_snake_case     		=	self.get_dummy_components()
								_snake_case     		=	StableDiffusionInstructPixaPixPipeline(**lowerCAmelCase_						)
								_snake_case     		=	sd_pipe.to(lowerCAmelCase_						)
								sd_pipe.set_progress_bar_config(disable=lowerCAmelCase_						)

								_snake_case     		=	self.get_dummy_inputs(lowerCAmelCase_						)
								_snake_case     		=	[inputs['prompt']] * 2

								_snake_case     		=	np.array(inputs['image']						).astype(np.floataa						) / 255.0
								_snake_case     		=	torch.from_numpy(lowerCAmelCase_						).unsqueeze(0						).to(lowerCAmelCase_						)
								_snake_case     		=	image / 2 + 0.5
								_snake_case     		=	image.permute(0					,     3					,     1					,     2						)
								_snake_case     		=	image.repeat(2					,     1					,     1					,     1						)

								_snake_case     		=	sd_pipe(**lowerCAmelCase_						).images
								_snake_case     		=	image[-1, -3:, -3:, -1]

								assert image.shape == (2, 32, 32, 3)
								_snake_case     		=	np.array([0.5812, 0.5748, 0.5222, 0.5908, 0.5695, 0.7174, 0.6804, 0.5523, 0.5579]						)

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


							def 				lowerCamelCase       ( self						):





								"""simple docstring"""






								_snake_case     		=	'cpu'  # ensure determinism for the device-dependent torch.Generator
								_snake_case     		=	self.get_dummy_components()
								_snake_case     		=	EulerAncestralDiscreteScheduler(
								    beta_start=0.00085					,     beta_end=0.012					,     beta_schedule='scaled_linear'						)
								_snake_case     		=	StableDiffusionInstructPixaPixPipeline(**lowerCAmelCase_						)
								_snake_case     		=	sd_pipe.to(lowerCAmelCase_						)
								sd_pipe.set_progress_bar_config(disable=lowerCAmelCase_						)

								_snake_case     		=	self.get_dummy_inputs(lowerCAmelCase_						)
								_snake_case     		=	sd_pipe(**lowerCAmelCase_						).images
								_snake_case     		=	image[0, -3:, -3:, -1]

								_snake_case     		=	[round(lowerCAmelCase_					,     4						) for x in image_slice.flatten().tolist()]
								print(','.join([str(lowerCAmelCase_						) for x in slice]						)						)

								assert image.shape == (1, 32, 32, 3)
								_snake_case     		=	np.array([0.7417, 0.3842, 0.4732, 0.5776, 0.5891, 0.5139, 0.4052, 0.5673, 0.4986]						)

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


							def 				lowerCamelCase       ( self						):





								"""simple docstring"""






								super().test_inference_batch_single_identical(expected_max_diff=3E-3						)




							def 				lowerCamelCase       ( self						):





								"""simple docstring"""






								_snake_case     		=	self.get_dummy_components()
								_snake_case     		=	StableDiffusionInstructPixaPixPipeline(**lowerCAmelCase_						)
								_snake_case     		=	VaeImageProcessor(do_resize=lowerCAmelCase_					,     do_normalize=lowerCAmelCase_						)
								_snake_case     		=	pipe.to(lowerCAmelCase_						)
								pipe.set_progress_bar_config(disable=lowerCAmelCase_						)

								_snake_case     		=	pipe(**self.get_dummy_inputs_by_type(lowerCAmelCase_					,     input_image_type='pt'						)						)[0]

								_snake_case     		=	components['vae']
								_snake_case     		=	self.get_dummy_inputs_by_type(lowerCAmelCase_					,     input_image_type='pt'						)

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

								_snake_case     		=	pipe(**lowerCAmelCase_						)[0]

								_snake_case     		=	np.abs(out - out_latents_inputs						).max()
								self.assertLess(lowerCAmelCase_					,     1E-4					,     'passing latents as image input generate different result from passing image'						)


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


							def 				lowerCamelCase       ( self						):





								"""simple docstring"""






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


							def 				lowerCamelCase       ( self					,     lowerCAmelCase_=0						):





								"""simple docstring"""






								_snake_case     		=	torch.manual_seed(lowerCAmelCase_						)
								_snake_case     		=	load_image(
								    'https://huggingface.co/datasets/diffusers/test-arrays/resolve/main/stable_diffusion_pix2pix/example.jpg'						)
								_snake_case     		=	{
								    '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						):





								"""simple docstring"""






								_snake_case     		=	StableDiffusionInstructPixaPixPipeline.from_pretrained(
								    'timbrooks/instruct-pix2pix'					,     safety_checker=lowerCAmelCase_						)
								pipe.to(lowerCAmelCase_						)
								pipe.set_progress_bar_config(disable=lowerCAmelCase_						)
								pipe.enable_attention_slicing()

								_snake_case     		=	self.get_inputs()
								_snake_case     		=	pipe(**lowerCAmelCase_						).images
								_snake_case     		=	image[0, -3:, -3:, -1].flatten()

								assert image.shape == (1, 5_12, 5_12, 3)
								_snake_case     		=	np.array([0.5902, 0.6015, 0.6027, 0.5983, 0.6092, 0.6061, 0.5765, 0.5785, 0.5555]						)

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


							def 				lowerCamelCase       ( self						):





								"""simple docstring"""






								_snake_case     		=	StableDiffusionInstructPixaPixPipeline.from_pretrained(
								    'timbrooks/instruct-pix2pix'					,     safety_checker=lowerCAmelCase_						)
								_snake_case     		=	LMSDiscreteScheduler.from_config(pipe.scheduler.config						)
								pipe.to(lowerCAmelCase_						)
								pipe.set_progress_bar_config(disable=lowerCAmelCase_						)
								pipe.enable_attention_slicing()

								_snake_case     		=	self.get_inputs()
								_snake_case     		=	pipe(**lowerCAmelCase_						).images
								_snake_case     		=	image[0, -3:, -3:, -1].flatten()

								assert image.shape == (1, 5_12, 5_12, 3)
								_snake_case     		=	np.array([0.6578, 0.6817, 0.6972, 0.6761, 0.6856, 0.6916, 0.6428, 0.6516, 0.6301]						)

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


							def 				lowerCamelCase       ( self						):





								"""simple docstring"""






								_snake_case     		=	StableDiffusionInstructPixaPixPipeline.from_pretrained(
								    'timbrooks/instruct-pix2pix'					,     safety_checker=lowerCAmelCase_						)
								_snake_case     		=	DDIMScheduler.from_config(pipe.scheduler.config						)
								pipe.to(lowerCAmelCase_						)
								pipe.set_progress_bar_config(disable=lowerCAmelCase_						)
								pipe.enable_attention_slicing()

								_snake_case     		=	self.get_inputs()
								_snake_case     		=	pipe(**lowerCAmelCase_						).images
								_snake_case     		=	image[0, -3:, -3:, -1].flatten()

								assert image.shape == (1, 5_12, 5_12, 3)
								_snake_case     		=	np.array([0.3828, 0.3834, 0.3818, 0.3792, 0.3865, 0.3752, 0.3792, 0.3847, 0.3753]						)

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


							def 				lowerCamelCase       ( self						):





								"""simple docstring"""






								_snake_case     		=	0

								def callback_fn(lowerCAmelCase_					,     lowerCAmelCase_					,     lowerCAmelCase_						) -> None:
									_snake_case     		=	True
									nonlocal number_of_steps
									number_of_steps += 1
									if step == 1:
										_snake_case     		=	latents.detach().cpu().numpy()
										assert latents.shape == (1, 4, 64, 64)
										_snake_case     		=	latents[0, -3:, -3:, -1]
										_snake_case     		=	np.array([-0.2463, -0.4644, -0.9756, 1.5176, 1.4414, 0.7866, 0.9897, 0.8521, 0.7983]						)

										assert np.abs(latents_slice.flatten() - expected_slice						).max() < 5E-2
									elif step == 2:
										_snake_case     		=	latents.detach().cpu().numpy()
										assert latents.shape == (1, 4, 64, 64)
										_snake_case     		=	latents[0, -3:, -3:, -1]
										_snake_case     		=	np.array([-0.2644, -0.4626, -0.9653, 1.5176, 1.4551, 0.7686, 0.9805, 0.8452, 0.8115]						)

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

								_snake_case     		=	False

								_snake_case     		=	StableDiffusionInstructPixaPixPipeline.from_pretrained(
								    'timbrooks/instruct-pix2pix'					,     safety_checker=lowerCAmelCase_					,     torch_dtype=torch.floataa						)
								_snake_case     		=	pipe.to(lowerCAmelCase_						)
								pipe.set_progress_bar_config(disable=lowerCAmelCase_						)
								pipe.enable_attention_slicing()

								_snake_case     		=	self.get_inputs()
								pipe(**lowerCAmelCase_					,     callback=lowerCAmelCase_					,     callback_steps=1						)
								assert callback_fn.has_been_called
								assert number_of_steps == 3


							def 				lowerCamelCase       ( self						):





								"""simple docstring"""






								torch.cuda.empty_cache()
								torch.cuda.reset_max_memory_allocated()
								torch.cuda.reset_peak_memory_stats()

								_snake_case     		=	StableDiffusionInstructPixaPixPipeline.from_pretrained(
								    'timbrooks/instruct-pix2pix'					,     safety_checker=lowerCAmelCase_					,     torch_dtype=torch.floataa						)
								_snake_case     		=	pipe.to(lowerCAmelCase_						)
								pipe.set_progress_bar_config(disable=lowerCAmelCase_						)
								pipe.enable_attention_slicing(1						)
								pipe.enable_sequential_cpu_offload()

								_snake_case     		=	self.get_inputs()
								_snake_case     		=	pipe(**lowerCAmelCase_						)

								_snake_case     		=	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						):





								"""simple docstring"""






								_snake_case     		=	self.get_inputs()
								# resize to resolution that is divisible by 8 but not 16 or 32
								_snake_case     		=	inputs['image'].resize((5_04, 5_04)						)

								_snake_case     		=	'timbrooks/instruct-pix2pix'
								_snake_case     		=	StableDiffusionInstructPixaPixPipeline.from_pretrained(
								    lowerCAmelCase_					,     safety_checker=lowerCAmelCase_					,     )
								pipe.to(lowerCAmelCase_						)
								pipe.set_progress_bar_config(disable=lowerCAmelCase_						)
								pipe.enable_attention_slicing()

								_snake_case     		=	pipe(**lowerCAmelCase_						)
								_snake_case     		=	output.images[0]

								_snake_case     		=	image[2_55:2_58, 3_83:3_86, -1]

								assert image.shape == (5_04, 5_04, 3)
								_snake_case     		=	np.array([0.2726, 0.2529, 0.2664, 0.2655, 0.2641, 0.2642, 0.2591, 0.2649, 0.2590]						)

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

