
'''simple docstring'''







from abc import ABC, abstractmethod
from typing import List, Optional


class 		__UpperCAmelCase   (      snake_case__		):

							'''simple docstring'''





							def __init__( self						):





								"""simple docstring"""






								self.test()


							def 				lowerCamelCase       ( self						):





								"""simple docstring"""






								_snake_case     		=	0
								_snake_case     		=	False
								while not completed:
									if counter == 1:
										self.reset()
									_snake_case     		=	self.advance()
									if not self.does_advance(UpperCAmelCase_						):
										raise Exception(
										    'Custom Constraint is not defined correctly. self.does_advance(self.advance()) must be true.'						)

									_snake_case		,				_snake_case		,				_snake_case     		=	self.update(UpperCAmelCase_						)
									counter += 1

									if counter > 1_00_00:
										raise Exception('update() does not fulfill the constraint.'						)

								if self.remaining() != 0:
									raise Exception('Custom Constraint is not defined correctly.'						)


							@abstractmethod
							def 				lowerCamelCase       ( self						):





								"""simple docstring"""






								raise NotImplementedError(
								    F'{self.__class__} is an abstract class. Only classes inheriting this class can be called.'						)


							@abstractmethod
							def 				lowerCamelCase       ( self					,     lowerCAmelCase_						):





								"""simple docstring"""






								raise NotImplementedError(
								    F'{self.__class__} is an abstract class. Only classes inheriting this class can be called.'						)


							@abstractmethod
							def 				lowerCamelCase       ( self					,     lowerCAmelCase_						):





								"""simple docstring"""






								raise NotImplementedError(
								    F'{self.__class__} is an abstract class. Only classes inheriting this class can be called.'						)


							@abstractmethod
							def 				lowerCamelCase       ( self						):





								"""simple docstring"""






								raise NotImplementedError(
								    F'{self.__class__} is an abstract class. Only classes inheriting this class can be called.'						)


							@abstractmethod
							def 				lowerCamelCase       ( self						):





								"""simple docstring"""






								raise NotImplementedError(
								    F'{self.__class__} is an abstract class. Only classes inheriting this class can be called.'						)




							@abstractmethod
							def 				lowerCamelCase       ( self					,     lowerCAmelCase_=False						):





								"""simple docstring"""






								raise NotImplementedError(
								    F'{self.__class__} is an abstract class. Only classes inheriting this class can be called.'						)


class 		__UpperCAmelCase   (      snake_case__		):

							'''simple docstring'''





							def __init__( self					,     lowerCAmelCase_						):





								"""simple docstring"""






								super(UpperCAmelCase_					,     self						).__init__()

								if not isinstance(UpperCAmelCase_					,     UpperCAmelCase_						) or len(UpperCAmelCase_						) == 0:
									raise ValueError(F'`token_ids` has to be a non-empty list, but is {token_ids}.'						)
								if any((not isinstance(UpperCAmelCase_					,     UpperCAmelCase_						) or token_id < 0) for token_id in token_ids						):
									raise ValueError(F'Each list in `token_ids` has to be a list of positive integers, but is {token_ids}.'						)

								_snake_case     		=	token_ids

								_snake_case     		=	len(self.token_ids						)
								_snake_case     		=	-1  # the index of the currently fulfilled step
								_snake_case     		=	False


							def 				lowerCamelCase       ( self						):





								"""simple docstring"""






								if self.completed:
									return None
								return self.token_ids[self.fulfilled_idx + 1]


							def 				lowerCamelCase       ( self					,     lowerCAmelCase_						):





								"""simple docstring"""






								if not isinstance(UpperCAmelCase_					,     UpperCAmelCase_						):
									raise ValueError(F'`token_id` has to be an `int`, but is {token_id} of type {type(UpperCAmelCase_						)}'						)

								if self.completed:
									return False

								return token_id == self.token_ids[self.fulfilled_idx + 1]


							def 				lowerCamelCase       ( self					,     lowerCAmelCase_						):





								"""simple docstring"""






								if not isinstance(UpperCAmelCase_					,     UpperCAmelCase_						):
									raise ValueError(F'`token_id` has to be an `int`, but is {token_id} of type {type(UpperCAmelCase_						)}'						)

								_snake_case     		=	False
								_snake_case     		=	False
								_snake_case     		=	False

								if self.does_advance(UpperCAmelCase_						):
									self.fulfilled_idx += 1
									_snake_case     		=	True
									if self.fulfilled_idx == (self.seqlen - 1):
										_snake_case     		=	True
									_snake_case     		=	completed
								else:
									# failed to make progress.
									_snake_case     		=	True
									self.reset()
								return stepped, completed, reset


							def 				lowerCamelCase       ( self						):





								"""simple docstring"""






								_snake_case     		=	False
								_snake_case     		=	0


							def 				lowerCamelCase       ( self						):





								"""simple docstring"""






								return self.seqlen - (self.fulfilled_idx + 1)




							def 				lowerCamelCase       ( self					,     lowerCAmelCase_=False						):





								"""simple docstring"""






								_snake_case     		=	PhrasalConstraint(self.token_ids						)

								if stateful:
									_snake_case     		=	self.seqlen
									_snake_case     		=	self.fulfilled_idx
									_snake_case     		=	self.completed

								return new_constraint


class 		__UpperCAmelCase   :

							'''simple docstring'''





							def __init__( self					,     lowerCAmelCase_					,     lowerCAmelCase_=True						):





								"""simple docstring"""






								_snake_case     		=	max([len(UpperCAmelCase_						) for one in nested_token_ids]						)

								_snake_case     		=	{}
								for token_ids in nested_token_ids:
									_snake_case     		=	root
									for tidx, token_id in enumerate(UpperCAmelCase_						):
										if token_id not in level:
											_snake_case     		=	{}

										_snake_case     		=	level[token_id]

								if no_subsets and self.has_subsets(UpperCAmelCase_					,     UpperCAmelCase_						):
									raise ValueError(
									    'Each list in `nested_token_ids` can\'t be a complete subset of another list, but is'
									    F' {nested_token_ids}.'						)

								_snake_case     		=	root


							def 				lowerCamelCase       ( self					,     lowerCAmelCase_						):





								"""simple docstring"""






								_snake_case     		=	self.trie

								for current_token in current_seq:
									_snake_case     		=	start[current_token]

								_snake_case     		=	list(start.keys()						)

								return next_tokens


							def 				lowerCamelCase       ( self					,     lowerCAmelCase_						):





								"""simple docstring"""






								_snake_case     		=	self.next_tokens(UpperCAmelCase_						)

								return len(UpperCAmelCase_						) == 0


							def 				lowerCamelCase       ( self					,     lowerCAmelCase_						):





								"""simple docstring"""






								_snake_case     		=	list(root.values()						)
								if len(UpperCAmelCase_						) == 0:
									return 1
								else:
									return sum([self.count_leaves(UpperCAmelCase_						) for nn in next_nodes]						)




							def 				lowerCamelCase       ( self					,     lowerCAmelCase_					,     lowerCAmelCase_						):





								"""simple docstring"""






								_snake_case     		=	self.count_leaves(UpperCAmelCase_						)
								return len(UpperCAmelCase_						) != leaf_count


class 		__UpperCAmelCase   (      snake_case__		):

							'''simple docstring'''





							def __init__( self					,     lowerCAmelCase_						):





								"""simple docstring"""






								super(UpperCAmelCase_					,     self						).__init__()

								if not isinstance(UpperCAmelCase_					,     UpperCAmelCase_						) or len(UpperCAmelCase_						) == 0:
									raise ValueError(F'`nested_token_ids` has to be a non-empty list, but is {nested_token_ids}.'						)
								if any(not isinstance(UpperCAmelCase_					,     UpperCAmelCase_						) for token_ids in nested_token_ids						):
									raise ValueError(F'`nested_token_ids` has to be a list of lists, but is {nested_token_ids}.'						)
								if any(
								    any((not isinstance(UpperCAmelCase_					,     UpperCAmelCase_						) or token_id < 0) for token_id in token_ids						)
								    for token_ids in nested_token_ids						):
									raise ValueError(
									    F'Each list in `nested_token_ids` has to be a list of positive integers, but is {nested_token_ids}.'						)

								_snake_case     		=	DisjunctiveTrie(UpperCAmelCase_						)
								_snake_case     		=	nested_token_ids

								_snake_case     		=	self.trie.max_height
								_snake_case     		=	[]
								_snake_case     		=	False


							def 				lowerCamelCase       ( self						):





								"""simple docstring"""






								_snake_case     		=	self.trie.next_tokens(self.current_seq						)

								if len(UpperCAmelCase_						) == 0:
									return None
								else:
									return token_list


							def 				lowerCamelCase       ( self					,     lowerCAmelCase_						):





								"""simple docstring"""






								if not isinstance(UpperCAmelCase_					,     UpperCAmelCase_						):
									raise ValueError(F'`token_id` is supposed to be type `int`, but is {token_id} of type {type(UpperCAmelCase_						)}'						)

								_snake_case     		=	self.trie.next_tokens(self.current_seq						)

								return token_id in next_tokens


							def 				lowerCamelCase       ( self					,     lowerCAmelCase_						):





								"""simple docstring"""






								if not isinstance(UpperCAmelCase_					,     UpperCAmelCase_						):
									raise ValueError(F'`token_id` is supposed to be type `int`, but is {token_id} of type {type(UpperCAmelCase_						)}'						)

								_snake_case     		=	False
								_snake_case     		=	False
								_snake_case     		=	False

								if self.does_advance(UpperCAmelCase_						):
									self.current_seq.append(UpperCAmelCase_						)
									_snake_case     		=	True
								else:
									_snake_case     		=	True
									self.reset()

								_snake_case     		=	self.trie.reached_leaf(self.current_seq						)
								_snake_case     		=	completed

								return stepped, completed, reset


							def 				lowerCamelCase       ( self						):





								"""simple docstring"""






								_snake_case     		=	False
								_snake_case     		=	[]


							def 				lowerCamelCase       ( self						):





								"""simple docstring"""






								if self.completed:
									# since this can be completed without reaching max height
									return 0
								else:
									return self.seqlen - len(self.current_seq						)




							def 				lowerCamelCase       ( self					,     lowerCAmelCase_=False						):





								"""simple docstring"""






								_snake_case     		=	DisjunctiveConstraint(self.token_ids						)

								if stateful:
									_snake_case     		=	self.seqlen
									_snake_case     		=	self.current_seq
									_snake_case     		=	self.completed

								return new_constraint


class 		__UpperCAmelCase   :

							'''simple docstring'''





							def __init__( self					,     lowerCAmelCase_						):





								"""simple docstring"""






								_snake_case     		=	constraints

								# max # of steps required to fulfill a given constraint
								_snake_case     		=	max([c.seqlen for c in constraints]						)
								_snake_case     		=	len(UpperCAmelCase_						)
								_snake_case     		=	False

								self.init_state()


							def 				lowerCamelCase       ( self						):





								"""simple docstring"""






								_snake_case     		=	[]
								_snake_case     		=	None
								_snake_case     		=	[constraint.copy(stateful=UpperCAmelCase_						) for constraint in self.constraints]


							def 				lowerCamelCase       ( self						):





								"""simple docstring"""






								_snake_case     		=	0
								if self.inprogress_constraint:
									# extra points for having a constraint mid-fulfilled
									add += self.max_seqlen - self.inprogress_constraint.remaining()

								return (len(self.complete_constraints						) * self.max_seqlen) + add


							def 				lowerCamelCase       ( self						):





								"""simple docstring"""






								_snake_case     		=	[]
								if self.inprogress_constraint is None:
									for constraint in self.pending_constraints:  # "pending" == "unfulfilled yet"
										_snake_case     		=	constraint.advance()
										if isinstance(UpperCAmelCase_					,     UpperCAmelCase_						):
											token_list.append(UpperCAmelCase_						)
										elif isinstance(UpperCAmelCase_					,     UpperCAmelCase_						):
											token_list.extend(UpperCAmelCase_						)
								else:
									_snake_case     		=	self.inprogress_constraint.advance()
									if isinstance(UpperCAmelCase_					,     UpperCAmelCase_						):
										token_list.append(UpperCAmelCase_						)
									elif isinstance(UpperCAmelCase_					,     UpperCAmelCase_						):
										token_list.extend(UpperCAmelCase_						)

								if len(UpperCAmelCase_						) == 0:
									return None
								else:
									return token_list


							def 				lowerCamelCase       ( self					,     lowerCAmelCase_						):





								"""simple docstring"""






								self.init_state()

								if token_ids is not None:
									for token in token_ids:
										# completes or steps **one** constraint
										_snake_case		,				_snake_case     		=	self.add(UpperCAmelCase_						)

										# the entire list of constraints are fulfilled
										if self.completed:
											break


							def 				lowerCamelCase       ( self					,     lowerCAmelCase_						):





								"""simple docstring"""






								if not isinstance(UpperCAmelCase_					,     UpperCAmelCase_						):
									raise ValueError(F'`token_id` should be an `int`, but is `{token_id}`.'						)

								_snake_case		,				_snake_case     		=	False, False

								if self.completed:
									_snake_case     		=	True
									_snake_case     		=	False
									return complete, stepped

								if self.inprogress_constraint is not None:
									# In the middle of fulfilling a constraint. If the `token_id` *does* makes an incremental progress to current
									# job, simply update the state

									_snake_case		,				_snake_case		,				_snake_case     		=	self.inprogress_constraint.update(UpperCAmelCase_						)
									if reset:
										# 1. If the next token breaks the progress, then we must restart.
										#     e.g. constraint = "I love pies" and sequence so far is "I love" but `token_id` == "books".

										#     But that doesn't mean we self.init_state(), since we only reset the state for this particular
										#     constraint, not the full list of constraints.

										self.pending_constraints.append(self.inprogress_constraint.copy(stateful=UpperCAmelCase_						)						)
										_snake_case     		=	None

									if complete:
										# 2. If the next token completes the constraint, move it to completed list, set
										#     inprogress to None. If there are no pending constraints either, then this full list of constraints
										#     is complete.

										self.complete_constraints.append(self.inprogress_constraint						)
										_snake_case     		=	None

										if len(self.pending_constraints						) == 0:
											# we're done!
											_snake_case     		=	True

								else:
									# Not in the middle of fulfilling a constraint. So does this `token_id` helps us step towards any of our list
									# of constraints?

									for cidx, pending_constraint in enumerate(self.pending_constraints						):
										if pending_constraint.does_advance(UpperCAmelCase_						):
											_snake_case		,				_snake_case		,				_snake_case     		=	pending_constraint.update(UpperCAmelCase_						)

											if not stepped:
												raise Exception(
												    '`constraint.update(token_id)` is not yielding incremental progress, '
												    'even though `constraint.does_advance(token_id)` is true.'						)

											if complete:
												self.complete_constraints.append(UpperCAmelCase_						)
												_snake_case     		=	None

											if not complete and stepped:
												_snake_case     		=	pending_constraint

											if complete or stepped:
												# If we made any progress at all, then it's at least not a "pending constraint".

												_snake_case     		=	(
												    self.pending_constraints[:cidx] + self.pending_constraints[cidx + 1 :]
												)

												if len(self.pending_constraints						) == 0 and self.inprogress_constraint is None:
													# If there's no longer any pending after this and no inprogress either, then we must be
													# complete.

													_snake_case     		=	True

												break  # prevent accidentally stepping through multiple constraints with just one token.

								return complete, stepped




							def 				lowerCamelCase       ( self					,     lowerCAmelCase_=True						):





								"""simple docstring"""






								_snake_case     		=	ConstraintListState(self.constraints						)  # we actually never though self.constraints objects
								# throughout this process. So it's at initialization state.

								if stateful:
									_snake_case     		=	[
									    constraint.copy(stateful=UpperCAmelCase_						) for constraint in self.complete_constraints
									]
									if self.inprogress_constraint is not None:
										_snake_case     		=	self.inprogress_constraint.copy(stateful=UpperCAmelCase_						)
									_snake_case     		=	[constraint.copy() for constraint in self.pending_constraints]

								return new_state



'''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

