




'''simple docstring'''

import gc
import random
import unittest

import numpy as np
import torch
from PIL import Image
from transformers import CLIPImageProcessor, CLIPVisionConfig, CLIPVisionModelWithProjection

from diffusers import (
    AutoencoderKL,
    DPMSolverMultistepScheduler,
    PNDMScheduler,
    StableDiffusionImageVariationPipeline,
    UNetaDConditionModel,
)
from diffusers.utils import floats_tensor, load_image, load_numpy, nightly, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu

from ..pipeline_params import IMAGE_VARIATION_BATCH_PARAMS, IMAGE_VARIATION_PARAMS
from ..test_pipelines_common import PipelineKarrasSchedulerTesterMixin, PipelineLatentTesterMixin, PipelineTesterMixin


enable_full_determinism()





class __magic_name__(						lowerCamelCase_    ,		lowerCamelCase_    ,		lowerCamelCase_    ,		unittest.TestCase      ):
						UpperCAmelCase_		:					Any    =  StableDiffusionImageVariationPipeline
						UpperCAmelCase_		:					int    =  IMAGE_VARIATION_PARAMS
						UpperCAmelCase_		:					Dict    =  IMAGE_VARIATION_BATCH_PARAMS
						UpperCAmelCase_		:					Tuple    =  frozenset([]      )
						# TO-DO: update image_params once pipeline is refactored with VaeImageProcessor.preprocess
						UpperCAmelCase_		:					Dict    =  frozenset([]      )






						def       __lowerCAmelCase(   self						:						List[Any]       ):



									'''simple docstring'''






									torch.manual_seed(0       )
									snake_case__          =						UNetaDConditionModel(
									    block_out_channels=(3_2, 6_4)					,   layers_per_block=2					,   sample_size=3_2					,   in_channels=4					,   out_channels=4					,   down_block_types=("""DownBlock2D""", """CrossAttnDownBlock2D""")					,   up_block_types=("""CrossAttnUpBlock2D""", """UpBlock2D""")					,   cross_attention_dim=3_2					,   )
									snake_case__          =						PNDMScheduler(skip_prk_steps=snake_case_       )
									torch.manual_seed(0       )
									snake_case__          =						AutoencoderKL(
									    block_out_channels=[3_2, 6_4]					,   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__          =						CLIPVisionConfig(
									    hidden_size=3_2					,   projection_dim=3_2					,   intermediate_size=3_7					,   layer_norm_eps=1E-05					,   num_attention_heads=4					,   num_hidden_layers=5					,   image_size=3_2					,   patch_size=4					,   )
									snake_case__          =						CLIPVisionModelWithProjection(snake_case_       )
									snake_case__          =						CLIPImageProcessor(crop_size=3_2					,   size=3_2       )

									snake_case__          =						{
									    'unet': unet,
									    'scheduler': scheduler,
									    'vae': vae,
									    'image_encoder': image_encoder,
									    'feature_extractor': feature_extractor,
									    'safety_checker': None,
									}
									return components






						def       __lowerCAmelCase(   self						:						Union[str, Any]					,   __UpperCamelCase						:						Any					,   __UpperCamelCase						:						int=0       ):



									'''simple docstring'''






									snake_case__          =						floats_tensor((1, 3, 3_2, 3_2)					,   rng=random.Random(snake_case_       )       )
									snake_case__          =						image.cpu().permute(0					,   2					,   3					,   1       )[0]
									snake_case__          =						Image.fromarray(np.uinta(snake_case_       )       ).convert("""RGB"""       ).resize((3_2, 3_2)       )
									if str(snake_case_       ).startswith("""mps"""       ):
												snake_case__          =						torch.manual_seed(snake_case_       )
									else:
												snake_case__          =						torch.Generator(device=snake_case_       ).manual_seed(snake_case_       )
									snake_case__          =						{
									    'image': image,
									    'generator': generator,
									    'num_inference_steps': 2,
									    'guidance_scale': 6.0,
									    'output_type': 'numpy',
									}
									return inputs






						def       __lowerCAmelCase(   self						:						str       ):



									'''simple docstring'''






									snake_case__          =						'cpu'  # ensure determinism for the device-dependent torch.Generator
									snake_case__          =						self.get_dummy_components()
									snake_case__          =						StableDiffusionImageVariationPipeline(**snake_case_       )
									snake_case__          =						sd_pipe.to(snake_case_       )
									sd_pipe.set_progress_bar_config(disable=snake_case_       )

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

									assert image.shape == (1, 6_4, 6_4, 3)
									snake_case__          =						np.array([0.52_39, 0.57_23, 0.47_96, 0.50_49, 0.55_50, 0.46_85, 0.53_29, 0.48_91, 0.49_21]       )

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






						def       __lowerCAmelCase(   self						:						List[str]       ):



									'''simple docstring'''






									snake_case__          =						'cpu'  # ensure determinism for the device-dependent torch.Generator
									snake_case__          =						self.get_dummy_components()
									snake_case__          =						StableDiffusionImageVariationPipeline(**snake_case_       )
									snake_case__          =						sd_pipe.to(snake_case_       )
									sd_pipe.set_progress_bar_config(disable=snake_case_       )

									snake_case__          =						self.get_dummy_inputs(snake_case_       )
									snake_case__          =						2 * [inputs['image']]
									snake_case__          =						sd_pipe(**snake_case_       )

									snake_case__          =						output.images

									snake_case__          =						image[-1, -3:, -3:, -1]

									assert image.shape == (2, 6_4, 6_4, 3)
									snake_case__          =						np.array([0.68_92, 0.56_37, 0.58_36, 0.57_71, 0.62_54, 0.64_09, 0.55_80, 0.55_69, 0.52_89]       )

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







						def       __lowerCAmelCase(   self						:						Union[str, Any]       ):



									'''simple docstring'''






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







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






						def       __lowerCAmelCase(   self						:						str       ):



									'''simple docstring'''






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






						def       __lowerCAmelCase(   self						:						str					,   __UpperCamelCase						:						Union[str, Any]					,   __UpperCamelCase						:						Tuple="cpu"					,   __UpperCamelCase						:						Tuple=torch.floataa					,   __UpperCamelCase						:						Union[str, Any]=0       ):



									'''simple docstring'''






									snake_case__          =						torch.Generator(device=snake_case_       ).manual_seed(snake_case_       )
									snake_case__          =						load_image(
									    """https://huggingface.co/datasets/diffusers/test-arrays/resolve/main"""
									    """/stable_diffusion_imgvar/input_image_vermeer.png"""       )
									snake_case__          =						np.random.RandomState(snake_case_       ).standard_normal((1, 4, 6_4, 6_4)       )
									snake_case__          =						torch.from_numpy(snake_case_       ).to(device=snake_case_					,   dtype=snake_case_       )
									snake_case__          =						{
									    'image': init_image,
									    'latents': latents,
									    'generator': generator,
									    'num_inference_steps': 3,
									    'guidance_scale': 7.5,
									    'output_type': 'numpy',
									}
									return inputs






						def       __lowerCAmelCase(   self						:						Optional[Any]       ):



									'''simple docstring'''






									snake_case__          =						StableDiffusionImageVariationPipeline.from_pretrained(
									    """lambdalabs/sd-image-variations-diffusers"""					,   safety_checker=snake_case_       )
									snake_case__          =						sd_pipe.to(snake_case_       )
									sd_pipe.set_progress_bar_config(disable=snake_case_       )

									snake_case__          =						self.get_inputs(snake_case_       )
									snake_case__          =						sd_pipe(**snake_case_       ).images
									snake_case__          =						image[0, -3:, -3:, -1].flatten()

									assert image.shape == (1, 5_1_2, 5_1_2, 3)
									snake_case__          =						np.array([0.8_44_91, 0.9_07_89, 0.7_57_08, 0.7_87_34, 0.8_34_85, 0.7_00_99, 0.6_69_38, 0.6_87_27, 0.6_13_79]       )
									assert np.abs(image_slice - expected_slice       ).max() < 6E-3






						def       __lowerCAmelCase(   self						:						Union[str, Any]       ):



									'''simple docstring'''






									snake_case__          =						0

									def callback_fn(__UpperCamelCase						:						Union[str, Any]					,   __UpperCamelCase						:						str					,   __UpperCamelCase						:						List[str]       ) -> 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, 6_4, 6_4)
															snake_case__          =						latents[0, -3:, -3:, -1]
															snake_case__          =						np.array(
															    [-0.16_21, 0.28_37, -0.79_79, -0.12_21, -1.30_57, 0.76_81, -2.11_91, 0.04_64, 1.63_09]       )

															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, 6_4, 6_4)
															snake_case__          =						latents[0, -3:, -3:, -1]
															snake_case__          =						np.array([0.62_99, 1.75_00, 1.19_92, -2.15_82, -1.89_94, 0.73_34, -0.70_90, 1.01_37, 1.52_73]       )

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

									snake_case__          =						False

									snake_case__          =						StableDiffusionImageVariationPipeline.from_pretrained(
									    """fusing/sd-image-variations-diffusers"""					,   safety_checker=snake_case_					,   torch_dtype=torch.floataa					,   )
									pipe.to(snake_case_       )
									pipe.set_progress_bar_config(disable=snake_case_       )
									pipe.enable_attention_slicing()

									snake_case__          =						self.get_inputs(snake_case_					,   dtype=torch.floataa       )
									pipe(**snake_case_					,   callback=snake_case_					,   callback_steps=1       )
									assert callback_fn.has_been_called
									assert number_of_steps == inputs["num_inference_steps"]







						def       __lowerCAmelCase(   self						:						List[Any]       ):



									'''simple docstring'''






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

									snake_case__          =						'fusing/sd-image-variations-diffusers'
									snake_case__          =						StableDiffusionImageVariationPipeline.from_pretrained(
									    snake_case_					,   safety_checker=snake_case_					,   torch_dtype=torch.floataa       )
									snake_case__          =						pipe.to(snake_case_       )
									pipe.set_progress_bar_config(disable=snake_case_       )
									pipe.enable_attention_slicing(1       )
									pipe.enable_sequential_cpu_offload()

									snake_case__          =						self.get_inputs(snake_case_					,   dtype=torch.floataa       )
									snake_case__          =						pipe(**snake_case_       )

									snake_case__          =						torch.cuda.max_memory_allocated()
									# make sure that less than 2.6 GB is allocated
									assert mem_bytes < 2.6 * 1_0**9







@nightly
@require_torch_gpu
class __magic_name__(						unittest.TestCase      ):






						def       __lowerCAmelCase(   self						:						Tuple       ):



									'''simple docstring'''






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






						def       __lowerCAmelCase(   self						:						str					,   __UpperCamelCase						:						Any					,   __UpperCamelCase						:						List[str]="cpu"					,   __UpperCamelCase						:						int=torch.floataa					,   __UpperCamelCase						:						List[Any]=0       ):



									'''simple docstring'''






									snake_case__          =						torch.Generator(device=snake_case_       ).manual_seed(snake_case_       )
									snake_case__          =						load_image(
									    """https://huggingface.co/datasets/diffusers/test-arrays/resolve/main"""
									    """/stable_diffusion_imgvar/input_image_vermeer.png"""       )
									snake_case__          =						np.random.RandomState(snake_case_       ).standard_normal((1, 4, 6_4, 6_4)       )
									snake_case__          =						torch.from_numpy(snake_case_       ).to(device=snake_case_					,   dtype=snake_case_       )
									snake_case__          =						{
									    'image': init_image,
									    'latents': latents,
									    'generator': generator,
									    'num_inference_steps': 5_0,
									    'guidance_scale': 7.5,
									    'output_type': 'numpy',
									}
									return inputs






						def       __lowerCAmelCase(   self						:						str       ):



									'''simple docstring'''






									snake_case__          =						StableDiffusionImageVariationPipeline.from_pretrained("""fusing/sd-image-variations-diffusers"""       )
									sd_pipe.to(snake_case_       )
									sd_pipe.set_progress_bar_config(disable=snake_case_       )

									snake_case__          =						self.get_inputs(snake_case_       )
									snake_case__          =						sd_pipe(**snake_case_       ).images[0]

									snake_case__          =						load_numpy(
									    """https://huggingface.co/datasets/diffusers/test-arrays/resolve/main"""
									    """/stable_diffusion_imgvar/lambdalabs_variations_pndm.npy"""       )
									snake_case__          =						np.abs(expected_image - image       ).max()
									assert max_diff < 1E-3







						def       __lowerCAmelCase(   self						:						Dict       ):



									'''simple docstring'''






									snake_case__          =						StableDiffusionImageVariationPipeline.from_pretrained("""fusing/sd-image-variations-diffusers"""       )
									snake_case__          =						DPMSolverMultistepScheduler.from_config(sd_pipe.scheduler.config       )
									sd_pipe.to(snake_case_       )
									sd_pipe.set_progress_bar_config(disable=snake_case_       )

									snake_case__          =						self.get_inputs(snake_case_       )
									snake_case__          =						2_5
									snake_case__          =						sd_pipe(**snake_case_       ).images[0]

									snake_case__          =						load_numpy(
									    """https://huggingface.co/datasets/diffusers/test-arrays/resolve/main"""
									    """/stable_diffusion_imgvar/lambdalabs_variations_dpm_multi.npy"""       )
									snake_case__          =						np.abs(expected_image - image       ).max()
									assert max_diff < 1E-3
'''simple docstring'''


# This Python program implements an optimal binary search tree (abbreviated BST)
# building dynamic programming algorithm that delivers O(n^2) performance.
#
# The goal of the optimal BST problem is to build a low-cost BST for a
# given set of nodes, each with its own key and frequency. The frequency
# of the node is defined as how many time the node is being searched.
# The search cost of binary search tree is given by this formula:
#
# cost(1, n) = sum{i = 1 to n}((depth(node_i) + 1) * node_i_freq)
#
# where n is number of nodes in the BST. The characteristic of low-cost
# BSTs is having a faster overall search time than other implementations.
# The reason for their fast search time is that the nodes with high
# frequencies will be placed near the root of the tree while the nodes
# with low frequencies will be placed near the leaves of the tree thus
# reducing search time in the most frequent instances.
import sys
from random import randint






class 	__SCREAMING_SNAKE_CASE		:







  '''simple docstring'''






  def __init__(					self ,						snake_case_ ,						snake_case_		):




       '''simple docstring'''

       UpperCAmelCase_				:  Union[str, Any]  = key
       UpperCAmelCase_				:  Tuple  = freq






  def __str__(					self		):




       '''simple docstring'''

       return F'''Node(key={self.key}, freq={self.freq})'''







def    _lowerCamelCase       ( lowerCamelCase_   :				str      ,	lowerCamelCase_   :				List[str]      ,	lowerCamelCase_   :				Dict      ,	lowerCamelCase_   :				List[Any]      ,	lowerCamelCase_   :				int      ,	lowerCamelCase_   :				Optional[int]  ):



     """simple docstring"""





     if i > j or i < 0 or j > len(lowerCamelCase_  ) - 1:
          return

     UpperCAmelCase_				:  Optional[int]  = root[i][j]
     if parent == -1:  # root does not have a parent
          print(F'''{key[node]} is the root of the binary search tree.'''  )
     elif is_left:
          print(F'''{key[node]} is the left child of key {parent}.'''  )
     else:
          print(F'''{key[node]} is the right child of key {parent}.'''  )

     print_binary_search_tree(lowerCamelCase_      ,	lowerCamelCase_      ,	lowerCamelCase_      ,	node - 1      ,	key[node]      ,	lowerCamelCase_  )
     print_binary_search_tree(lowerCamelCase_      ,	lowerCamelCase_      ,	node + 1      ,	lowerCamelCase_      ,	key[node]      ,	lowerCamelCase_  )


def    _lowerCamelCase       ( lowerCamelCase_   :				Optional[int]  ):



     """simple docstring"""





     nodes.sort(key=lambda lowerCamelCase_  : node.key  )

     UpperCAmelCase_				:  int  = len(lowerCamelCase_  )

     UpperCAmelCase_				:  Dict  = [nodes[i].key for i in range(lowerCamelCase_  )]
     UpperCAmelCase_				:  Any  = [nodes[i].freq for i in range(lowerCamelCase_  )]

     # This 2D array stores the overall tree cost (which's as minimized as possible);
     # for a single key, cost is equal to frequency of the key.
     UpperCAmelCase_				:  Optional[Any]  = [[freqs[i] if i == j else 0 for j in range(lowerCamelCase_  )] for i in range(lowerCamelCase_  )]
     # sum[i][j] stores the sum of key frequencies between i and j inclusive in nodes
     # array
     UpperCAmelCase_				:  Any  = [[freqs[i] if i == j else 0 for j in range(lowerCamelCase_  )] for i in range(lowerCamelCase_  )]
     # stores tree roots that will be used later for constructing binary search tree
     UpperCAmelCase_				:  Optional[int]  = [[i if i == j else 0 for j in range(lowerCamelCase_  )] for i in range(lowerCamelCase_  )]

     for interval_length in range(2      ,	n + 1  ):
          for i in range(n - interval_length + 1  ):
               UpperCAmelCase_				:  Optional[Any]  = i + interval_length - 1

               UpperCAmelCase_				:  Tuple  = sys.maxsize  # set the value to "infinity"
               UpperCAmelCase_				:  Any  = total[i][j - 1] + freqs[j]

               # Apply Knuth's optimization
               # Loop without optimization: for r in range(i, j + 1):
               for r in range(root[i][j - 1]      ,	root[i + 1][j] + 1  ):  # r is a temporal root
                    UpperCAmelCase_				:  str  = dp[i][r - 1] if r != i else 0  # optimal cost for left subtree
                    UpperCAmelCase_				:  Optional[int]  = dp[r + 1][j] if r != j else 0  # optimal cost for right subtree
                    UpperCAmelCase_				:  Optional[int]  = left + total[i][j] + right

                    if dp[i][j] > cost:
                         UpperCAmelCase_				:  Dict  = cost
                         UpperCAmelCase_				:  Tuple  = r

     print('Binary search tree nodes:'  )
     for node in nodes:
          print(lowerCamelCase_  )

     print(F'''\nThe cost of optimal BST for given tree nodes is {dp[0][n - 1]}.'''  )
     print_binary_search_tree(lowerCamelCase_      ,	lowerCamelCase_      ,	0      ,	n - 1      ,	-1      ,	lowerCamelCase_  )


def    _lowerCamelCase       ( ):



     """simple docstring"""





     UpperCAmelCase_				:  Any  = [Node(lowerCamelCase_      ,	randint(1      ,	50  )  ) for i in range(10      ,	0      ,	-1  )]
     find_optimal_binary_search_tree(lowerCamelCase_  )


if __name__ == "__main__":
 main()






