





'''simple docstring'''



import gc
import unittest

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

from diffusers import (
    AutoencoderKL,
    DDIMScheduler,
    EulerAncestralDiscreteScheduler,
    PNDMScheduler,
    StableDiffusionModelEditingPipeline,
    UNetaDConditionModel,
)
from diffusers.utils import slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu, skip_mps

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


enable_full_determinism()






@skip_mps
class 							__lowercase				(					lowerCAmelCase__       ,					lowerCAmelCase__       ,					lowerCAmelCase__       ,					unittest.TestCase  ):







	'''simple docstring'''

	a			: str      =				StableDiffusionModelEditingPipeline
	a			: int      =				TEXT_TO_IMAGE_PARAMS
	a			: List[Any]      =				TEXT_TO_IMAGE_BATCH_PARAMS
	a			: Tuple      =				TEXT_TO_IMAGE_IMAGE_PARAMS
	a			: Any      =				TEXT_TO_IMAGE_IMAGE_PARAMS


	def   _UpperCAmelCase   (self       )							->							Dict:







				'''simple docstring'''





				torch.manual_seed(0       )
				__lowercase   =							UNetaDConditionModel(
				    block_out_channels=(32, 64)   ,layers_per_block=2   ,sample_size=32   ,in_channels=4   ,out_channels=4   ,down_block_types=('''DownBlock2D''', '''CrossAttnDownBlock2D''')   ,up_block_types=('''CrossAttnUpBlock2D''', '''UpBlock2D''')   ,cross_attention_dim=32   ,)
				__lowercase   =							DDIMScheduler()
				torch.manual_seed(0       )
				__lowercase   =							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       )
				__lowercase   =							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   ,)
				__lowercase   =							CLIPTextModel(_lowerCamelCase       )
				__lowercase   =							CLIPTokenizer.from_pretrained('''hf-internal-testing/tiny-random-clip'''       )

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


	def   _UpperCAmelCase   (self   ,_lowerCamelCase   ,_lowerCamelCase=0       )							->							Any:







				'''simple docstring'''





				__lowercase   =							torch.manual_seed(_lowerCamelCase       )
				__lowercase   =							{
				    '''prompt''': '''A field of roses''',
				    '''generator''': generator,
				    # Setting height and width to None to prevent OOMs on CPU.
				    '''height''': None,
				    '''width''': None,
				    '''num_inference_steps''': 2,
				    '''guidance_scale''': 6.0,
				    '''output_type''': '''numpy''',
				}
				return inputs


	def   _UpperCAmelCase   (self       )							->							Tuple:







				'''simple docstring'''





				__lowercase   =							'''cpu'''  # ensure determinism for the device-dependent torch.Generator
				__lowercase   =							self.get_dummy_components()
				__lowercase   =							StableDiffusionModelEditingPipeline(**_lowerCamelCase       )
				__lowercase   =							sd_pipe.to(_lowerCamelCase       )
				sd_pipe.set_progress_bar_config(disable=_lowerCamelCase       )

				__lowercase   =							self.get_dummy_inputs(_lowerCamelCase       )
				__lowercase   =							sd_pipe(**_lowerCamelCase       ).images
				__lowercase   =							image[0, -3:, -3:, -1]
				assert image.shape == (1, 64, 64, 3)

				__lowercase   =							np.array([0.4_7_5_5, 0.5_1_3_2, 0.4_9_7_6, 0.3_9_0_4, 0.3_5_5_4, 0.4_7_6_5, 0.5_1_3_9, 0.5_1_5_8, 0.4_8_8_9]       )

				assert np.abs(image_slice.flatten() - expected_slice       ).max() < 1E-2


	def   _UpperCAmelCase   (self       )							->							Optional[int]:







				'''simple docstring'''





				__lowercase   =							'''cpu'''  # ensure determinism for the device-dependent torch.Generator
				__lowercase   =							self.get_dummy_components()
				__lowercase   =							StableDiffusionModelEditingPipeline(**_lowerCamelCase       )
				__lowercase   =							sd_pipe.to(_lowerCamelCase       )
				sd_pipe.set_progress_bar_config(disable=_lowerCamelCase       )

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

				assert image.shape == (1, 64, 64, 3)

				__lowercase   =							np.array([0.4_9_9_2, 0.5_1_0_1, 0.5_0_0_4, 0.3_9_4_9, 0.3_6_0_4, 0.4_7_3_5, 0.5_2_1_6, 0.5_2_0_4, 0.4_9_1_3]       )

				assert np.abs(image_slice.flatten() - expected_slice       ).max() < 1E-2


	def   _UpperCAmelCase   (self       )							->							Tuple:







				'''simple docstring'''





				__lowercase   =							'''cpu'''  # ensure determinism for the device-dependent torch.Generator
				__lowercase   =							self.get_dummy_components()
				__lowercase   =							EulerAncestralDiscreteScheduler(
				    beta_start=0.0_0_0_8_5   ,beta_end=0.0_1_2   ,beta_schedule='''scaled_linear'''       )
				__lowercase   =							StableDiffusionModelEditingPipeline(**_lowerCamelCase       )
				__lowercase   =							sd_pipe.to(_lowerCamelCase       )
				sd_pipe.set_progress_bar_config(disable=_lowerCamelCase       )

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

				assert image.shape == (1, 64, 64, 3)

				__lowercase   =							np.array([0.4_7_4_7, 0.5_3_7_2, 0.4_7_7_9, 0.4_9_8_2, 0.5_5_4_3, 0.4_8_1_6, 0.5_2_3_8, 0.4_9_0_4, 0.5_0_2_7]       )

				assert np.abs(image_slice.flatten() - expected_slice       ).max() < 1E-2


	def   _UpperCAmelCase   (self       )							->							Optional[int]:







				'''simple docstring'''





				__lowercase   =							'''cpu'''  # ensure determinism for the device-dependent torch.Generator
				__lowercase   =							self.get_dummy_components()
				__lowercase   =							PNDMScheduler()
				__lowercase   =							StableDiffusionModelEditingPipeline(**_lowerCamelCase       )
				__lowercase   =							sd_pipe.to(_lowerCamelCase       )
				sd_pipe.set_progress_bar_config(disable=_lowerCamelCase       )

				__lowercase   =							self.get_dummy_inputs(_lowerCamelCase       )
				# the pipeline does not expect pndm so test if it raises error.
				with self.assertRaises(_lowerCamelCase       ):
							__lowercase   =							sd_pipe(**_lowerCamelCase       ).images


	def   _UpperCAmelCase   (self       )							->							int:







				'''simple docstring'''





				super().test_inference_batch_single_identical(expected_max_diff=5E-3       )


	def   _UpperCAmelCase   (self       )							->							str:







				'''simple docstring'''





				super().test_attention_slicing_forward_pass(expected_max_diff=5E-3       )






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







	'''simple docstring'''

	def   _UpperCAmelCase   (self       )							->							List[str]:







				'''simple docstring'''





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


	def   _UpperCAmelCase   (self   ,_lowerCamelCase=0       )							->							Any:







				'''simple docstring'''





				__lowercase   =							torch.manual_seed(_lowerCamelCase       )
				__lowercase   =							{
				    '''prompt''': '''A field of roses''',
				    '''generator''': generator,
				    '''num_inference_steps''': 3,
				    '''guidance_scale''': 7.5,
				    '''output_type''': '''numpy''',
				}
				return inputs


	def   _UpperCAmelCase   (self       )							->							str:







				'''simple docstring'''





				__lowercase   =							'''CompVis/stable-diffusion-v1-4'''
				__lowercase   =							StableDiffusionModelEditingPipeline.from_pretrained(_lowerCamelCase   ,safety_checker=_lowerCamelCase       )
				pipe.to(_lowerCamelCase       )
				pipe.set_progress_bar_config(disable=_lowerCamelCase       )
				pipe.enable_attention_slicing()

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

				assert image.shape == (1, 512, 512, 3)

				__lowercase   =							np.array(
				    [0.6_7_4_9_4_9_6, 0.6_3_8_6_4_5_3, 0.5_1_4_4_3_2_6_7, 0.6_6_0_9_4_9_0_5, 0.6_1_9_2_1_2_1_5, 0.5_4_9_1_3_3_2, 0.5_7_4_4_4_1_7, 0.5_8_0_7_5_1_0_6, 0.5_1_7_4_6_5_8]       )

				assert np.abs(expected_slice - image_slice       ).max() < 1E-2

				# make sure image changes after editing
				pipe.edit_model('''A pack of roses'''   ,'''A pack of blue roses'''       )

				__lowercase   =							pipe(**_lowerCamelCase       ).images
				__lowercase   =							image[0, -3:, -3:, -1].flatten()

				assert image.shape == (1, 512, 512, 3)

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


	def   _UpperCAmelCase   (self       )							->							List[str]:







				'''simple docstring'''





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

				__lowercase   =							'''CompVis/stable-diffusion-v1-4'''
				__lowercase   =							DDIMScheduler.from_pretrained(_lowerCamelCase   ,subfolder='''scheduler'''       )
				__lowercase   =							StableDiffusionModelEditingPipeline.from_pretrained(
				    _lowerCamelCase   ,scheduler=_lowerCamelCase   ,safety_checker=_lowerCamelCase       )
				__lowercase   =							pipe.to(_lowerCamelCase       )
				pipe.set_progress_bar_config(disable=_lowerCamelCase       )
				pipe.enable_attention_slicing(1       )
				pipe.enable_sequential_cpu_offload()

				__lowercase   =							self.get_inputs()
				__lowercase   =							pipe(**_lowerCamelCase       )

				__lowercase   =							torch.cuda.max_memory_allocated()
				# make sure that less than 4.4 GB is allocated
				assert mem_bytes < 4.4 * 10**9












'''simple docstring'''



class 							__lowercase				:







	'''simple docstring'''

	def __init__(self   ,_lowerCamelCase       )							->							Optional[Any]:







				'''simple docstring'''





				__lowercase   =							val
				# Number of nodes in left subtree
				__lowercase   =							0
				__lowercase   =							None
				__lowercase   =							None
				__lowercase   =							None


	def   _UpperCAmelCase   (self   ,_lowerCamelCase       )							->							Tuple:







				'''simple docstring'''





				assert self.left_tree_size == other.left_tree_size, "Unequal Sizes of Blocks"

				if self.val < other.val:
							__lowercase   =							self.right
							__lowercase   =							None
							if self.right:
										__lowercase   =							other
							__lowercase   =							other
							__lowercase   =							self.left_tree_size * 2 + 1
							return self
				else:
							__lowercase   =							other.right
							__lowercase   =							None
							if other.right:
										__lowercase   =							self
							__lowercase   =							self
							__lowercase   =							other.left_tree_size * 2 + 1
							return other






class 							__lowercase				:







	'''simple docstring'''

	def __init__(self   ,_lowerCamelCase=None   ,_lowerCamelCase=None   ,_lowerCamelCase=0       )							->							int:







				'''simple docstring'''





				__lowercase   =							heap_size
				__lowercase   =							bottom_root
				__lowercase   =							min_node


	def   _UpperCAmelCase   (self   ,_lowerCamelCase       )							->							Tuple:







				'''simple docstring'''





				if other.size == 0:
							return None
				if self.size == 0:
							__lowercase   =							other.size
							__lowercase   =							other.bottom_root
							__lowercase   =							other.min_node
							return None
				# Update size
				__lowercase   =							self.size + other.size

				# Update min.node
				if self.min_node.val > other.min_node.val:
							__lowercase   =							other.min_node
				# Merge

				# Order roots by left_subtree_size
				__lowercase   =							[]
				__lowercase  ,						__lowercase   =							self.bottom_root, other.bottom_root
				while i or j:
							if i and ((not j) or i.left_tree_size < j.left_tree_size):
										combined_roots_list.append((i, True)       )
										__lowercase   =							i.parent
							else:
										combined_roots_list.append((j, False)       )
										__lowercase   =							j.parent
        # Insert links between them
				for i in range(len(_lowerCamelCase       ) - 1       ):
							if combined_roots_list[i][1] != combined_roots_list[i + 1][1]:
										__lowercase   =							combined_roots_list[i + 1][0]
										__lowercase   =							combined_roots_list[i][0]
        # Consecutively merge roots with same left_tree_size
				__lowercase   =							combined_roots_list[0][0]
				while i.parent:
							if (
							    (i.left_tree_size == i.parent.left_tree_size) and (not i.parent.parent)
							) or (
							    i.left_tree_size == i.parent.left_tree_size
							    and i.left_tree_size != i.parent.parent.left_tree_size
							):
										# Neighbouring Nodes
										__lowercase   =							i.left
										__lowercase   =							i.parent.parent

										# Merging trees
										__lowercase   =							i.merge_trees(i.parent       )

										# Updating links
										__lowercase   =							previous_node
										__lowercase   =							next_node
										if previous_node:
													__lowercase   =							i
										if next_node:
													__lowercase   =							i
							else:
										__lowercase   =							i.parent
        # Updating self.bottom_root
				while i.left:
							__lowercase   =							i.left
				__lowercase   =							i

				# Update other
				__lowercase   =							self.size
				__lowercase   =							self.bottom_root
				__lowercase   =							self.min_node

				# Return the merged heap
				return self


	def   _UpperCAmelCase   (self   ,_lowerCamelCase       )							->							Dict:







				'''simple docstring'''





				if self.size == 0:
							__lowercase   =							Node(_lowerCamelCase       )
							__lowercase   =							1
							__lowercase   =							self.bottom_root
				else:
							# Create new node
							__lowercase   =							Node(_lowerCamelCase       )

							# Update size
							self.size += 1

							# update min_node
							if val < self.min_node.val:
										__lowercase   =							new_node
							# Put new_node as a bottom_root in heap
							__lowercase   =							new_node
							__lowercase   =							self.bottom_root
							__lowercase   =							new_node

							# Consecutively merge roots with same left_tree_size
							while (
							    self.bottom_root.parent
							    and self.bottom_root.left_tree_size
							    == self.bottom_root.parent.left_tree_size
							):
										# Next node
										__lowercase   =							self.bottom_root.parent.parent

										# Merge
										__lowercase   =							self.bottom_root.merge_trees(self.bottom_root.parent       )

										# Update Links
										__lowercase   =							next_node
										__lowercase   =							None
										if next_node:
													__lowercase   =							self.bottom_root


	def   _UpperCAmelCase   (self       )							->							Union[str, Any]:







				'''simple docstring'''





				return self.min_node.val


	def   _UpperCAmelCase   (self       )							->							List[Any]:







				'''simple docstring'''





				return self.size == 0


	def   _UpperCAmelCase   (self       )							->							Union[str, Any]:







				'''simple docstring'''





				__lowercase   =							self.min_node.val

				# Last element in heap corner case
				if self.size == 1:
							# Update size
							__lowercase   =							0

							# Update bottom root
							__lowercase   =							None

							# Update min_node
							__lowercase   =							None

							return min_value
				# No right subtree corner case
				# The structure of the tree implies that this should be the bottom root
				# and there is at least one other root
				if self.min_node.right is None:
							# Update size
							self.size -= 1

							# Update bottom root
							__lowercase   =							self.bottom_root.parent
							__lowercase   =							None

							# Update min_node
							__lowercase   =							self.bottom_root
							__lowercase   =							self.bottom_root.parent
							while i:
										if i.val < self.min_node.val:
													__lowercase   =							i
										__lowercase   =							i.parent
							return min_value
				# General case
				# Find the BinomialHeap of the right subtree of min_node
				__lowercase   =							self.min_node.right
				__lowercase   =							None
				__lowercase   =							bottom_of_new
				__lowercase   =							1

				# Size, min_node and bottom_root
				while bottom_of_new.left:
							__lowercase   =							size_of_new * 2 + 1
							__lowercase   =							bottom_of_new.left
							if bottom_of_new.val < min_of_new.val:
										__lowercase   =							bottom_of_new
        # Corner case of single root on top left path
				if (not self.min_node.left) and (not self.min_node.parent):
							__lowercase   =							size_of_new
							__lowercase   =							bottom_of_new
							__lowercase   =							min_of_new
							# print("Single root, multiple nodes case")
							return min_value
				# Remaining cases
				# Construct heap of right subtree
				__lowercase   =							BinomialHeap(
				    bottom_root=_lowerCamelCase   ,min_node=_lowerCamelCase   ,heap_size=_lowerCamelCase       )

				# Update size
				__lowercase   =							self.size - 1 - size_of_new

				# Neighbour nodes
				__lowercase   =							self.min_node.left
				__lowercase   =							self.min_node.parent

				# Initialize new bottom_root and min_node
				__lowercase   =							previous_node or next_node
				__lowercase   =							next_node

				# Update links of previous_node and search below for new min_node and
				# bottom_root
				if previous_node:
							__lowercase   =							next_node

							# Update bottom_root and search for min_node below
							__lowercase   =							previous_node
							__lowercase   =							previous_node
							while self.bottom_root.left:
										__lowercase   =							self.bottom_root.left
										if self.bottom_root.val < self.min_node.val:
													__lowercase   =							self.bottom_root
				if next_node:
							__lowercase   =							previous_node

							# Search for new min_node above min_node
							__lowercase   =							next_node
							while i:
										if i.val < self.min_node.val:
													__lowercase   =							i
										__lowercase   =							i.parent
        # Merge heaps
				self.merge_heaps(_lowerCamelCase       )

				return min_value


	def   _UpperCAmelCase   (self       )							->							str:







				'''simple docstring'''





				__lowercase   =							self.bottom_root
				while top_root.parent:
							__lowercase   =							top_root.parent
				# preorder
				__lowercase   =							[]
				self.__traversal(_lowerCamelCase   ,_lowerCamelCase       )
				return heap_pre_order


	def   _UpperCAmelCase   (self   ,_lowerCamelCase   ,_lowerCamelCase   ,_lowerCamelCase=0       )							->							str:







				'''simple docstring'''





				if curr_node:
							preorder.append((curr_node.val, level)       )
							self.__traversal(curr_node.left   ,_lowerCamelCase   ,level + 1       )
							self.__traversal(curr_node.right   ,_lowerCamelCase   ,level + 1       )
				else:
							preorder.append(('''#''', level)       )


	def __str__(self       )							->							List[str]:







				'''simple docstring'''





				if self.is_empty():
							return ""
				__lowercase   =							self.pre_order()

				return "\n".join(('''-''' * level + str(_lowerCamelCase       )) for value, level in preorder_heap       )


# Unit Tests
if __name__ == "__main__":
	import doctest

	doctest.testmod()





