

'''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     SCREAMING_SNAKE_CASE(							A__							,		A__							,		A__							,		unittest.TestCase      ):







	"""simple docstring"""






	lowerCamelCase__      			=	StableDiffusionModelEditingPipeline
	lowerCamelCase__      			=	TEXT_TO_IMAGE_PARAMS
	lowerCamelCase__      			=	TEXT_TO_IMAGE_BATCH_PARAMS
	lowerCamelCase__      			=	TEXT_TO_IMAGE_IMAGE_PARAMS
	lowerCamelCase__      			=	TEXT_TO_IMAGE_IMAGE_PARAMS







	def 					A     (						self      :					Union[str, Any]		)      ->		List[str]:
								torch.manual_seed(0		)
								UpperCAmelCase			:	Dict										=		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		,      )
								UpperCAmelCase			:	List[str]										=		DDIMScheduler()
								torch.manual_seed(0		)
								UpperCAmelCase			:	Optional[Any]										=		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		)
								UpperCAmelCase			:	Union[str, Any]										=		CLIPTextConfig(
								    bos_token_id=0		,      eos_token_id=2		,      hidden_size=32		,      intermediate_size=37		,      layer_norm_eps=1E-05		,      num_attention_heads=4		,      num_hidden_layers=5		,      pad_token_id=1		,      vocab_size=1000		,      )
								UpperCAmelCase			:	List[str]										=		CLIPTextModel(__snake_case		)
								UpperCAmelCase			:	List[str]										=		CLIPTokenizer.from_pretrained('''hf-internal-testing/tiny-random-clip'''		)

								UpperCAmelCase			:	List[Any]										=		{
								    '''unet''': unet,
								    '''scheduler''': scheduler,
								    '''vae''': vae,
								    '''text_encoder''': text_encoder,
								    '''tokenizer''': tokenizer,
								    '''safety_checker''': None,
								    '''feature_extractor''': None,
								}
								return components







	def 					A     (						self      :					Optional[Any]		,      __snake_case      :					Any		,      __snake_case      :					Tuple=0		)      ->		int:
								UpperCAmelCase			:	int										=		torch.manual_seed(__snake_case		)
								UpperCAmelCase			:	Dict										=		{
								    '''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 					A     (						self      :					List[Any]		)      ->		int:
								UpperCAmelCase			:	List[Any]										=		'''cpu'''  # ensure determinism for the device-dependent torch.Generator
								UpperCAmelCase			:	int										=		self.get_dummy_components()
								UpperCAmelCase			:	Optional[Any]										=		StableDiffusionModelEditingPipeline(**__snake_case		)
								UpperCAmelCase			:	Any										=		sd_pipe.to(__snake_case		)
								sd_pipe.set_progress_bar_config(disable=__snake_case		)

								UpperCAmelCase			:	int										=		self.get_dummy_inputs(__snake_case		)
								UpperCAmelCase			:	Optional[Any]										=		sd_pipe(**__snake_case		).images
								UpperCAmelCase			:	Tuple										=		image[0, -3:, -3:, -1]
								assert image.shape == (1, 64, 64, 3)

								UpperCAmelCase			:	Optional[int]										=		np.array([0.47_55, 0.51_32, 0.49_76, 0.39_04, 0.35_54, 0.47_65, 0.51_39, 0.51_58, 0.48_89]		)

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







	def 					A     (						self      :					List[Any]		)      ->		str:
								UpperCAmelCase			:	Optional[int]										=		'''cpu'''  # ensure determinism for the device-dependent torch.Generator
								UpperCAmelCase			:	Optional[Any]										=		self.get_dummy_components()
								UpperCAmelCase			:	str										=		StableDiffusionModelEditingPipeline(**__snake_case		)
								UpperCAmelCase			:	List[str]										=		sd_pipe.to(__snake_case		)
								sd_pipe.set_progress_bar_config(disable=__snake_case		)

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

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

								UpperCAmelCase			:	Optional[Any]										=		np.array([0.49_92, 0.51_01, 0.50_04, 0.39_49, 0.36_04, 0.47_35, 0.52_16, 0.52_04, 0.49_13]		)

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







	def 					A     (						self      :					List[Any]		)      ->		List[Any]:
								UpperCAmelCase			:	Any										=		'''cpu'''  # ensure determinism for the device-dependent torch.Generator
								UpperCAmelCase			:	Any										=		self.get_dummy_components()
								UpperCAmelCase			:	Any										=		EulerAncestralDiscreteScheduler(
								    beta_start=0.0_00_85		,      beta_end=0.0_12		,      beta_schedule='''scaled_linear'''		)
								UpperCAmelCase			:	Any										=		StableDiffusionModelEditingPipeline(**__snake_case		)
								UpperCAmelCase			:	Union[str, Any]										=		sd_pipe.to(__snake_case		)
								sd_pipe.set_progress_bar_config(disable=__snake_case		)

								UpperCAmelCase			:	int										=		self.get_dummy_inputs(__snake_case		)
								UpperCAmelCase			:	List[Any]										=		sd_pipe(**__snake_case		).images
								UpperCAmelCase			:	int										=		image[0, -3:, -3:, -1]

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

								UpperCAmelCase			:	List[str]										=		np.array([0.47_47, 0.53_72, 0.47_79, 0.49_82, 0.55_43, 0.48_16, 0.52_38, 0.49_04, 0.50_27]		)

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







	def 					A     (						self      :					str		)      ->		List[Any]:
								UpperCAmelCase			:	Optional[int]										=		'''cpu'''  # ensure determinism for the device-dependent torch.Generator
								UpperCAmelCase			:	Tuple										=		self.get_dummy_components()
								UpperCAmelCase			:	int										=		PNDMScheduler()
								UpperCAmelCase			:	Any										=		StableDiffusionModelEditingPipeline(**__snake_case		)
								UpperCAmelCase			:	Union[str, Any]										=		sd_pipe.to(__snake_case		)
								sd_pipe.set_progress_bar_config(disable=__snake_case		)

								UpperCAmelCase			:	Union[str, Any]										=		self.get_dummy_inputs(__snake_case		)
								# the pipeline does not expect pndm so test if it raises error.
								with self.assertRaises(__snake_case		):
															UpperCAmelCase			:	List[Any]										=		sd_pipe(**__snake_case		).images







	def 					A     (						self      :					int		)      ->		Optional[int]:
								super().test_inference_batch_single_identical(expected_max_diff=5E-3		)







	def 					A     (						self      :					List[Any]		)      ->		Union[str, Any]:
								super().test_attention_slicing_forward_pass(expected_max_diff=5E-3		)

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







	"""simple docstring"""






	def 					A     (						self      :					Optional[int]		)      ->		int:
								super().tearDown()
								gc.collect()
								torch.cuda.empty_cache()







	def 					A     (						self      :					int		,      __snake_case      :					Optional[int]=0		)      ->		List[Any]:
								UpperCAmelCase			:	Any										=		torch.manual_seed(__snake_case		)
								UpperCAmelCase			:	int										=		{
								    '''prompt''': '''A field of roses''',
								    '''generator''': generator,
								    '''num_inference_steps''': 3,
								    '''guidance_scale''': 7.5,
								    '''output_type''': '''numpy''',
								}
								return inputs







	def 					A     (						self      :					List[str]		)      ->		List[str]:
								UpperCAmelCase			:	Optional[Any]										=		'''CompVis/stable-diffusion-v1-4'''
								UpperCAmelCase			:	Dict										=		StableDiffusionModelEditingPipeline.from_pretrained(__snake_case		,      safety_checker=__snake_case		)
								pipe.to(__snake_case		)
								pipe.set_progress_bar_config(disable=__snake_case		)
								pipe.enable_attention_slicing()

								UpperCAmelCase			:	Dict										=		self.get_inputs()
								UpperCAmelCase			:	Optional[Any]										=		pipe(**__snake_case		).images
								UpperCAmelCase			:	str										=		image[0, -3:, -3:, -1].flatten()

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

								UpperCAmelCase			:	int										=		np.array(
								    [0.6_74_94_96, 0.6_38_64_53, 0.51_44_32_67, 0.66_09_49_05, 0.61_92_12_15, 0.5_49_13_32, 0.5_74_44_17, 0.58_07_51_06, 0.5_17_46_58]		)

								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'''		)

								UpperCAmelCase			:	Dict										=		pipe(**__snake_case		).images
								UpperCAmelCase			:	Union[str, Any]										=		image[0, -3:, -3:, -1].flatten()

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

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







	def 					A     (						self      :					str		)      ->		Optional[int]:
								torch.cuda.empty_cache()
								torch.cuda.reset_max_memory_allocated()
								torch.cuda.reset_peak_memory_stats()

								UpperCAmelCase			:	Any										=		'''CompVis/stable-diffusion-v1-4'''
								UpperCAmelCase			:	int										=		DDIMScheduler.from_pretrained(__snake_case		,      subfolder='''scheduler'''		)
								UpperCAmelCase			:	int										=		StableDiffusionModelEditingPipeline.from_pretrained(
								    __snake_case		,      scheduler=__snake_case		,      safety_checker=__snake_case		)
								UpperCAmelCase			:	Tuple										=		pipe.to(__snake_case		)
								pipe.set_progress_bar_config(disable=__snake_case		)
								pipe.enable_attention_slicing(1		)
								pipe.enable_sequential_cpu_offload()

								UpperCAmelCase			:	List[str]										=		self.get_inputs()
								UpperCAmelCase			:	int										=		pipe(**__snake_case		)

								UpperCAmelCase			:	Union[str, Any]										=		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     SCREAMING_SNAKE_CASE:







	"""simple docstring"""






	def __init__(						self      :					List[Any]		,      __snake_case      :					Dict		)      ->		str:
								UpperCAmelCase			:	str										=		val
								# Number of nodes in left subtree
								UpperCAmelCase			:	Optional[int]										=		0
								UpperCAmelCase			:	str										=		None
								UpperCAmelCase			:	Optional[Any]										=		None
								UpperCAmelCase			:	Dict										=		None







	def 					A     (						self      :					Tuple		,      __snake_case      :					Dict		)      ->		Dict:
								assert self.left_tree_size == other.left_tree_size, "Unequal Sizes of Blocks"

								if self.val < other.val:
															UpperCAmelCase			:	Optional[int]										=		self.right
															UpperCAmelCase			:	Optional[Any]										=		None
															if self.right:
																						UpperCAmelCase			:	Tuple										=		other
															UpperCAmelCase			:	int										=		other
															UpperCAmelCase			:	Dict										=		self.left_tree_size * 2 + 1
															return self
								else:
															UpperCAmelCase			:	int										=		other.right
															UpperCAmelCase			:	List[str]										=		None
															if other.right:
																						UpperCAmelCase			:	Dict										=		self
															UpperCAmelCase			:	Optional[Any]										=		self
															UpperCAmelCase			:	Optional[int]										=		other.left_tree_size * 2 + 1
															return other

class     SCREAMING_SNAKE_CASE:







	"""simple docstring"""






	def __init__(						self      :					Union[str, Any]		,      __snake_case      :					str=None		,      __snake_case      :					Any=None		,      __snake_case      :					Any=0		)      ->		Dict:
								UpperCAmelCase			:	str										=		heap_size
								UpperCAmelCase			:	Tuple										=		bottom_root
								UpperCAmelCase			:	str										=		min_node







	def 					A     (						self      :					Tuple		,      __snake_case      :					Union[str, Any]		)      ->		Optional[Any]:

								# Empty heaps corner cases
								if other.size == 0:
															return None
								if self.size == 0:
															UpperCAmelCase			:	List[str]										=		other.size
															UpperCAmelCase			:	Union[str, Any]										=		other.bottom_root
															UpperCAmelCase			:	Optional[int]										=		other.min_node
															return None
								# Update size
								UpperCAmelCase			:	Tuple										=		self.size + other.size

								# Update min.node
								if self.min_node.val > other.min_node.val:
															UpperCAmelCase			:	Union[str, Any]										=		other.min_node
								# Merge

								# Order roots by left_subtree_size
								UpperCAmelCase			:	List[str]										=		[]
								UpperCAmelCase							,		UpperCAmelCase			:	Optional[int]										=		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)		)
																						UpperCAmelCase			:	Union[str, Any]										=		i.parent
															else:
																						combined_roots_list.append((j, False)		)
																						UpperCAmelCase			:	List[str]										=		j.parent
        # Insert links between them
								for i in range(len(__snake_case		) - 1		):
															if combined_roots_list[i][1] != combined_roots_list[i + 1][1]:
																						UpperCAmelCase			:	Union[str, Any]										=		combined_roots_list[i + 1][0]
																						UpperCAmelCase			:	Optional[Any]										=		combined_roots_list[i][0]
        # Consecutively merge roots with same left_tree_size
								UpperCAmelCase			:	Optional[int]										=		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
																						UpperCAmelCase			:	Optional[int]										=		i.left
																						UpperCAmelCase			:	List[Any]										=		i.parent.parent

																						# Merging trees
																						UpperCAmelCase			:	Optional[int]										=		i.merge_trees(i.parent		)

																						# Updating links
																						UpperCAmelCase			:	Any										=		previous_node
																						UpperCAmelCase			:	str										=		next_node
																						if previous_node:
																													UpperCAmelCase			:	Union[str, Any]										=		i
																						if next_node:
																													UpperCAmelCase			:	Optional[Any]										=		i
															else:
																						UpperCAmelCase			:	Union[str, Any]										=		i.parent
        # Updating self.bottom_root
								while i.left:
															UpperCAmelCase			:	str										=		i.left
								UpperCAmelCase			:	Optional[int]										=		i

								# Update other
								UpperCAmelCase			:	str										=		self.size
								UpperCAmelCase			:	Union[str, Any]										=		self.bottom_root
								UpperCAmelCase			:	Union[str, Any]										=		self.min_node

								# Return the merged heap
								return self







	def 					A     (						self      :					List[Any]		,      __snake_case      :					List[str]		)      ->		List[str]:
								if self.size == 0:
															UpperCAmelCase			:	List[Any]										=		Node(__snake_case		)
															UpperCAmelCase			:	List[str]										=		1
															UpperCAmelCase			:	str										=		self.bottom_root
								else:
															# Create new node
															UpperCAmelCase			:	int										=		Node(__snake_case		)

															# Update size
															self.size += 1

															# update min_node
															if val < self.min_node.val:
																						UpperCAmelCase			:	Optional[int]										=		new_node
															# Put new_node as a bottom_root in heap
															UpperCAmelCase			:	Optional[Any]										=		new_node
															UpperCAmelCase			:	Optional[Any]										=		self.bottom_root
															UpperCAmelCase			:	Union[str, Any]										=		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
																						UpperCAmelCase			:	List[str]										=		self.bottom_root.parent.parent

																						# Merge
																						UpperCAmelCase			:	int										=		self.bottom_root.merge_trees(self.bottom_root.parent		)

																						# Update Links
																						UpperCAmelCase			:	str										=		next_node
																						UpperCAmelCase			:	int										=		None
																						if next_node:
																													UpperCAmelCase			:	Dict										=		self.bottom_root







	def 					A     (						self      :					Tuple		)      ->		List[str]:
								return self.min_node.val







	def 					A     (						self      :					Optional[Any]		)      ->		int:
								return self.size == 0







	def 					A     (						self      :					List[str]		)      ->		int:
								UpperCAmelCase			:	Tuple										=		self.min_node.val

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

															# Update bottom root
															UpperCAmelCase			:	Optional[int]										=		None

															# Update min_node
															UpperCAmelCase			:	int										=		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
															UpperCAmelCase			:	Optional[Any]										=		self.bottom_root.parent
															UpperCAmelCase			:	List[Any]										=		None

															# Update min_node
															UpperCAmelCase			:	int										=		self.bottom_root
															UpperCAmelCase			:	Any										=		self.bottom_root.parent
															while i:
																						if i.val < self.min_node.val:
																													UpperCAmelCase			:	Dict										=		i
																						UpperCAmelCase			:	Union[str, Any]										=		i.parent
															return min_value
								# General case
								# Find the BinomialHeap of the right subtree of min_node
								UpperCAmelCase			:	List[str]										=		self.min_node.right
								UpperCAmelCase			:	Tuple										=		None
								UpperCAmelCase			:	Any										=		bottom_of_new
								UpperCAmelCase			:	str										=		1

								# Size, min_node and bottom_root
								while bottom_of_new.left:
															UpperCAmelCase			:	Tuple										=		size_of_new * 2 + 1
															UpperCAmelCase			:	Union[str, Any]										=		bottom_of_new.left
															if bottom_of_new.val < min_of_new.val:
																						UpperCAmelCase			:	Optional[int]										=		bottom_of_new
        # Corner case of single root on top left path
								if (not self.min_node.left) and (not self.min_node.parent):
															UpperCAmelCase			:	str										=		size_of_new
															UpperCAmelCase			:	str										=		bottom_of_new
															UpperCAmelCase			:	List[str]										=		min_of_new
															# print("Single root, multiple nodes case")
															return min_value
								# Remaining cases
								# Construct heap of right subtree
								UpperCAmelCase			:	int										=		BinomialHeap(
								    bottom_root=__snake_case		,      min_node=__snake_case		,      heap_size=__snake_case		)

								# Update size
								UpperCAmelCase			:	Union[str, Any]										=		self.size - 1 - size_of_new

								# Neighbour nodes
								UpperCAmelCase			:	Any										=		self.min_node.left
								UpperCAmelCase			:	Tuple										=		self.min_node.parent

								# Initialize new bottom_root and min_node
								UpperCAmelCase			:	Optional[int]										=		previous_node or next_node
								UpperCAmelCase			:	int										=		next_node

								# Update links of previous_node and search below for new min_node and
								# bottom_root
								if previous_node:
															UpperCAmelCase			:	List[Any]										=		next_node

															# Update bottom_root and search for min_node below
															UpperCAmelCase			:	Optional[Any]										=		previous_node
															UpperCAmelCase			:	Optional[Any]										=		previous_node
															while self.bottom_root.left:
																						UpperCAmelCase			:	Any										=		self.bottom_root.left
																						if self.bottom_root.val < self.min_node.val:
																													UpperCAmelCase			:	Tuple										=		self.bottom_root
								if next_node:
															UpperCAmelCase			:	Tuple										=		previous_node

															# Search for new min_node above min_node
															UpperCAmelCase			:	Tuple										=		next_node
															while i:
																						if i.val < self.min_node.val:
																													UpperCAmelCase			:	List[str]										=		i
																						UpperCAmelCase			:	Optional[int]										=		i.parent
        # Merge heaps
								self.merge_heaps(__snake_case		)

								return min_value







	def 					A     (						self      :					Union[str, Any]		)      ->		Union[str, Any]:
								UpperCAmelCase			:	Any										=		self.bottom_root
								while top_root.parent:
															UpperCAmelCase			:	Optional[Any]										=		top_root.parent
								# preorder
								UpperCAmelCase			:	List[Any]										=		[]
								self.__traversal(__snake_case		,      __snake_case		)
								return heap_pre_order







	def 					A     (						self      :					List[str]		,      __snake_case      :					Tuple		,      __snake_case      :					int		,      __snake_case      :					Union[str, Any]=0		)      ->		Optional[int]:
								if curr_node:
															preorder.append((curr_node.val, level)		)
															self.__traversal(curr_node.left		,      __snake_case		,      level + 1		)
															self.__traversal(curr_node.right		,      __snake_case		,      level + 1		)
								else:
															preorder.append(('''#''', level)		)







	def __str__(						self      :					Tuple		)      ->		Union[str, Any]:
								if self.is_empty():
															return ""
								UpperCAmelCase			:	Dict										=		self.pre_order()

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


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

						doctest.testmod()





