





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, PNDMScheduler, StableDiffusionInpaintPipeline, UNetaDConditionModel
from diffusers.utils import floats_tensor, load_image, load_numpy, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu, slow

from ..pipeline_params import TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS, TEXT_GUIDED_IMAGE_INPAINTING_PARAMS
from ..test_pipelines_common import PipelineKarrasSchedulerTesterMixin, PipelineLatentTesterMixin, PipelineTesterMixin


enable_full_determinism()



class        _SCREAMING_SNAKE_CASE						(     __SCREAMING_SNAKE_CASE   ,							__SCREAMING_SNAKE_CASE   ,							__SCREAMING_SNAKE_CASE   ,							unittest.TestCase   ):







 '''simple docstring'''
 lowercase_		=      StableDiffusionInpaintPipeline
 lowercase_		=      TEXT_GUIDED_IMAGE_INPAINTING_PARAMS
 lowercase_		=      TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS
 lowercase_		=      frozenset(
     []   )  # TO-DO: update image_params once pipeline is refactored with VaeImageProcessor.preprocess
 lowercase_		=      frozenset([]   )

 def 							SCREAMING_SNAKE_CASE_ (self    :       Tuple)	->Optional[int]:



   '''simple docstring'''



   torch.manual_seed(0)
   lowerCamelCase__:				int        =UNetaDConditionModel(
       block_out_channels=(32, 64)	,			layers_per_block=2	,			sample_size=32	,			in_channels=9	,			out_channels=4	,			down_block_types=("DownBlock2D", "CrossAttnDownBlock2D")	,			up_block_types=("CrossAttnUpBlock2D", "UpBlock2D")	,			cross_attention_dim=32	,			attention_head_dim=(2, 4)	,			use_linear_projection=UpperCAmelCase_	,			)
   lowerCamelCase__:				Any        =PNDMScheduler(skip_prk_steps=UpperCAmelCase_)
   torch.manual_seed(0)
   lowerCamelCase__:				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	,			sample_size=128	,			)
   torch.manual_seed(0)
   lowerCamelCase__:				Union[str, Any]        =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=1_000	,			hidden_act="gelu"	,			projection_dim=512	,			)
   lowerCamelCase__:				Union[str, Any]        =CLIPTextModel(UpperCAmelCase_)
   lowerCamelCase__:				Dict        =CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip")

   lowerCamelCase__:				List[str]        ={
       "unet": unet,
       "scheduler": scheduler,
       "vae": vae,
       "text_encoder": text_encoder,
       "tokenizer": tokenizer,
       "safety_checker": None,
       "feature_extractor": None,
   }
   return components

 def 							SCREAMING_SNAKE_CASE_ (self    :       str	,			UpperCAmelCase_    :       Any	,			UpperCAmelCase_    :       Any=0)	->Optional[int]:



   '''simple docstring'''



   lowerCamelCase__:				List[Any]        =floats_tensor((1, 3, 32, 32)	,			rng=random.Random(UpperCAmelCase_)).to(UpperCAmelCase_)
   lowerCamelCase__:				List[str]        =image.cpu().permute(0	,			2	,			3	,			1)[0]
   lowerCamelCase__:				Optional[Any]        =Image.fromarray(np.uinta(UpperCAmelCase_)).convert("RGB").resize((64, 64))
   lowerCamelCase__:				Tuple        =Image.fromarray(np.uinta(image + 4)).convert("RGB").resize((64, 64))
   if str(UpperCAmelCase_).startswith("mps"):
     lowerCamelCase__:				Any        =torch.manual_seed(UpperCAmelCase_)
   else:
     lowerCamelCase__:				Union[str, Any]        =torch.Generator(device=UpperCAmelCase_).manual_seed(UpperCAmelCase_)
   lowerCamelCase__:				List[Any]        ={
       "prompt": "A painting of a squirrel eating a burger",
       "image": init_image,
       "mask_image": mask_image,
       "generator": generator,
       "num_inference_steps": 2,
       "guidance_scale": 6.0,
       "output_type": "numpy",
   }
   return inputs

 def 							SCREAMING_SNAKE_CASE_ (self    :       Optional[int])	->List[Any]:



   '''simple docstring'''



   lowerCamelCase__:				Optional[int]        ="cpu"  # ensure determinism for the device-dependent torch.Generator
   lowerCamelCase__:				Dict        =self.get_dummy_components()
   lowerCamelCase__:				Union[str, Any]        =StableDiffusionInpaintPipeline(**UpperCAmelCase_)
   lowerCamelCase__:				Optional[Any]        =sd_pipe.to(UpperCAmelCase_)
   sd_pipe.set_progress_bar_config(disable=UpperCAmelCase_)

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

   assert image.shape == (1, 64, 64, 3)
   lowerCamelCase__:				List[str]        =np.array([0.4727, 0.5735, 0.3941, 0.5446, 0.5926, 0.4394, 0.5062, 0.4654, 0.4476])

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







 def 							SCREAMING_SNAKE_CASE_ (self    :       int)	->int:



   '''simple docstring'''



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



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







 '''simple docstring'''

 def 							SCREAMING_SNAKE_CASE_ (self    :       Union[str, Any])	->Optional[int]:



   '''simple docstring'''



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

 def 							SCREAMING_SNAKE_CASE_ (self    :       str)	->List[Any]:



   '''simple docstring'''



   lowerCamelCase__:				Tuple        =load_image(
       "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
       "/sd2-inpaint/init_image.png")
   lowerCamelCase__:				int        =load_image(
       "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd2-inpaint/mask.png")
   lowerCamelCase__:				Optional[Any]        =load_numpy(
       "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd2-inpaint"
       "/yellow_cat_sitting_on_a_park_bench.npy")

   lowerCamelCase__:				int        ="stabilityai/stable-diffusion-2-inpainting"
   lowerCamelCase__:				Union[str, Any]        =StableDiffusionInpaintPipeline.from_pretrained(UpperCAmelCase_	,			safety_checker=UpperCAmelCase_)
   pipe.to(UpperCAmelCase_)
   pipe.set_progress_bar_config(disable=UpperCAmelCase_)
   pipe.enable_attention_slicing()

   lowerCamelCase__:				Optional[Any]        ="Face of a yellow cat, high resolution, sitting on a park bench"

   lowerCamelCase__:				Dict        =torch.manual_seed(0)
   lowerCamelCase__:				List[str]        =pipe(
       prompt=UpperCAmelCase_	,			image=UpperCAmelCase_	,			mask_image=UpperCAmelCase_	,			generator=UpperCAmelCase_	,			output_type="np"	,			)
   lowerCamelCase__:				Tuple        =output.images[0]

   assert image.shape == (512, 512, 3)
   assert np.abs(expected_image - image).max() < 9E-3

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



   '''simple docstring'''



   lowerCamelCase__:				Dict        =load_image(
       "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
       "/sd2-inpaint/init_image.png")
   lowerCamelCase__:				Dict        =load_image(
       "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd2-inpaint/mask.png")
   lowerCamelCase__:				Optional[Any]        =load_numpy(
       "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd2-inpaint"
       "/yellow_cat_sitting_on_a_park_bench_fp16.npy")

   lowerCamelCase__:				Dict        ="stabilityai/stable-diffusion-2-inpainting"
   lowerCamelCase__:				int        =StableDiffusionInpaintPipeline.from_pretrained(
       UpperCAmelCase_	,			torch_dtype=torch.floataa	,			safety_checker=UpperCAmelCase_	,			)
   pipe.to(UpperCAmelCase_)
   pipe.set_progress_bar_config(disable=UpperCAmelCase_)
   pipe.enable_attention_slicing()

   lowerCamelCase__:				List[str]        ="Face of a yellow cat, high resolution, sitting on a park bench"

   lowerCamelCase__:				str        =torch.manual_seed(0)
   lowerCamelCase__:				List[str]        =pipe(
       prompt=UpperCAmelCase_	,			image=UpperCAmelCase_	,			mask_image=UpperCAmelCase_	,			generator=UpperCAmelCase_	,			output_type="np"	,			)
   lowerCamelCase__:				int        =output.images[0]

   assert image.shape == (512, 512, 3)
   assert np.abs(expected_image - image).max() < 5E-1







 def 							SCREAMING_SNAKE_CASE_ (self    :       int)	->List[str]:



   '''simple docstring'''



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

   lowerCamelCase__:				List[str]        =load_image(
       "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
       "/sd2-inpaint/init_image.png")
   lowerCamelCase__:				Tuple        =load_image(
       "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd2-inpaint/mask.png")

   lowerCamelCase__:				Union[str, Any]        ="stabilityai/stable-diffusion-2-inpainting"
   lowerCamelCase__:				Dict        =PNDMScheduler.from_pretrained(UpperCAmelCase_	,			subfolder="scheduler")
   lowerCamelCase__:				str        =StableDiffusionInpaintPipeline.from_pretrained(
       UpperCAmelCase_	,			safety_checker=UpperCAmelCase_	,			scheduler=UpperCAmelCase_	,			torch_dtype=torch.floataa	,			)
   pipe.to(UpperCAmelCase_)
   pipe.set_progress_bar_config(disable=UpperCAmelCase_)
   pipe.enable_attention_slicing(1)
   pipe.enable_sequential_cpu_offload()

   lowerCamelCase__:				Optional[int]        ="Face of a yellow cat, high resolution, sitting on a park bench"

   lowerCamelCase__:				int        =torch.manual_seed(0)
   lowerCamelCase__:				Any        =pipe(
       prompt=UpperCAmelCase_	,			image=UpperCAmelCase_	,			mask_image=UpperCAmelCase_	,			generator=UpperCAmelCase_	,			num_inference_steps=2	,			output_type="np"	,			)

   lowerCamelCase__:				Optional[Any]        =torch.cuda.max_memory_allocated()
   # make sure that less than 2.65 GB is allocated
   assert mem_bytes < 2.65 * 10**9








from __future__ import annotations

from typing import Any



class        _SCREAMING_SNAKE_CASE						:







 '''simple docstring'''

 def __init__(self    :       Any	,			UpperCAmelCase_    :       int)	->None:



   '''simple docstring'''



   lowerCamelCase__:				int        =num_of_nodes
   lowerCamelCase__:				list[list[int]]        =[]
   lowerCamelCase__:				dict[int, int]        ={}

 def 							SCREAMING_SNAKE_CASE_ (self    :       Tuple	,			UpperCAmelCase_    :       int	,			UpperCAmelCase_    :       int	,			UpperCAmelCase_    :       int)	->None:



   '''simple docstring'''



   self.m_edges.append([u_node, v_node, weight])

 def 							SCREAMING_SNAKE_CASE_ (self    :       List[str]	,			UpperCAmelCase_    :       int)	->int:



   '''simple docstring'''



   if self.m_component[u_node] == u_node:
     return u_node
   return self.find_component(self.m_component[u_node])

 def 							SCREAMING_SNAKE_CASE_ (self    :       Union[str, Any]	,			UpperCAmelCase_    :       int)	->None:



   '''simple docstring'''



   if self.m_component[u_node] != u_node:
     for k in self.m_component:
       lowerCamelCase__:				Dict        =self.find_component(UpperCAmelCase_)

 def 							SCREAMING_SNAKE_CASE_ (self    :       Union[str, Any]	,			UpperCAmelCase_    :       list[int]	,			UpperCAmelCase_    :       int	,			UpperCAmelCase_    :       int)	->None:



   '''simple docstring'''



   if component_size[u_node] <= component_size[v_node]:
     lowerCamelCase__:				Optional[int]        =v_node
     component_size[v_node] += component_size[u_node]
     self.set_component(UpperCAmelCase_)

   elif component_size[u_node] >= component_size[v_node]:
     lowerCamelCase__:				Tuple        =self.find_component(UpperCAmelCase_)
     component_size[u_node] += component_size[v_node]
     self.set_component(UpperCAmelCase_)







 def 							SCREAMING_SNAKE_CASE_ (self    :       List[str])	->None:



   '''simple docstring'''



   lowerCamelCase__:				List[Any]        =[]
   lowerCamelCase__:				List[str]        =0

   lowerCamelCase__:				list[Any]        =[-1] * self.m_num_of_nodes

   # A list of components (initialized to all of the nodes)
   for node in range(self.m_num_of_nodes):
     self.m_component.update({node: node})
     component_size.append(1)

   lowerCamelCase__:				List[str]        =self.m_num_of_nodes

   while num_of_components > 1:
     for edge in self.m_edges:
       lowerCamelCase__  , lowerCamelCase__  , lowerCamelCase__:				Tuple        =edge

       lowerCamelCase__:				List[Any]        =self.m_component[u]
       lowerCamelCase__:				str        =self.m_component[v]

       if u_component != v_component:

         for component in (u_component, v_component):
           if (
               minimum_weight_edge[component] == -1
               or minimum_weight_edge[component][2] > w
           ):
             lowerCamelCase__:				Union[str, Any]        =[u, v, w]

     for edge in minimum_weight_edge:
       if isinstance(UpperCAmelCase_	,			UpperCAmelCase_):
         lowerCamelCase__  , lowerCamelCase__  , lowerCamelCase__:				Tuple        =edge

         lowerCamelCase__:				str        =self.m_component[u]
         lowerCamelCase__:				Any        =self.m_component[v]

         if u_component != v_component:
           mst_weight += w
           self.union(UpperCAmelCase_	,			UpperCAmelCase_	,			UpperCAmelCase_)
           print(F"""Added edge [{u} - {v}]\nAdded weight: {w}\n""")
           num_of_components -= 1

     lowerCamelCase__:				Tuple        =[-1] * self.m_num_of_nodes
   print(F"""The total weight of the minimal spanning tree is: {mst_weight}""")





def 					lowerCAmelCase_			(	)					->		None:







  """simple docstring"""







if __name__ == "__main__":
     import doctest

     doctest.testmod()

