



import gc
import random
import tempfile
import unittest

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

from diffusers import (
    AutoencoderKL,
    DDIMInverseScheduler,
    DDIMScheduler,
    DPMSolverMultistepInverseScheduler,
    DPMSolverMultistepScheduler,
    StableDiffusionDiffEditPipeline,
    UNetaDConditionModel,
)
from diffusers.utils import load_image, slow
from diffusers.utils.testing_utils import enable_full_determinism, floats_tensor, require_torch_gpu, torch_device

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


enable_full_determinism()


class 					_lowercase      (__lowercase					,      __lowercase					,      unittest.TestCase					):
				'''simple docstring'''







				lowercase__        =  StableDiffusionDiffEditPipeline
				lowercase__        =  TEXT_GUIDED_IMAGE_INPAINTING_PARAMS - {"""height""", """width""", """image"""} | {"""image_latents"""}
				lowercase__        =  TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS - {"""image"""} | {"""image_latents"""}
				lowercase__        =  frozenset(
				    []					)  # TO-DO: update image_params once pipeline is refactored with VaeImageProcessor.preprocess
				lowercase__        =  frozenset([]					)

				def 					_lowerCamelCase    (       self			):

									'''simple docstring'''


									torch.manual_seed(0			)
									UpperCamelCase_						     =      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   ,       attention_head_dim=(2, 4)   ,       use_linear_projection=snake_case__   ,       )
									UpperCamelCase_						     =      DDIMScheduler(
									    beta_start=0.00_085   ,       beta_end=0.012   ,       beta_schedule="scaled_linear"   ,       clip_sample=snake_case__   ,       set_alpha_to_one=snake_case__   ,       )
									UpperCamelCase_						     =      DDIMInverseScheduler(
									    beta_start=0.00_085   ,       beta_end=0.012   ,       beta_schedule="scaled_linear"   ,       clip_sample=snake_case__   ,       set_alpha_to_zero=snake_case__   ,       )
									torch.manual_seed(0			)
									UpperCamelCase_						     =      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			)
									UpperCamelCase_						     =      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   ,       hidden_act="gelu"   ,       projection_dim=512   ,       )
									UpperCamelCase_						     =      CLIPTextModel(snake_case__			)
									UpperCamelCase_						     =      CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip"			)

									UpperCamelCase_						     =      {
									    "unet": unet,
									    "scheduler": scheduler,
									    "inverse_scheduler": inverse_scheduler,
									    "vae": vae,
									    "text_encoder": text_encoder,
									    "tokenizer": tokenizer,
									    "safety_checker": None,
									    "feature_extractor": None,
									}

									return components

				def 					_lowerCamelCase    (       self   ,       snake_case__   ,       snake_case__=0			):

									'''simple docstring'''


									UpperCamelCase_						     =      floats_tensor((1, 16, 16)   ,       rng=random.Random(snake_case__			)			).to(snake_case__			)
									UpperCamelCase_						     =      floats_tensor((1, 2, 4, 16, 16)   ,       rng=random.Random(snake_case__			)			).to(snake_case__			)
									if str(snake_case__			).startswith("mps"			):
														UpperCamelCase_						     =      torch.manual_seed(snake_case__			)
									else:
														UpperCamelCase_						     =      torch.Generator(device=snake_case__			).manual_seed(snake_case__			)
									UpperCamelCase_						     =      {
									    "prompt": "a dog and a newt",
									    "mask_image": mask,
									    "image_latents": latents,
									    "generator": generator,
									    "num_inference_steps": 2,
									    "inpaint_strength": 1.0,
									    "guidance_scale": 6.0,
									    "output_type": "numpy",
									}

									return inputs

				def 					_lowerCamelCase    (       self   ,       snake_case__   ,       snake_case__=0			):

									'''simple docstring'''


									UpperCamelCase_						     =      floats_tensor((1, 3, 32, 32)   ,       rng=random.Random(snake_case__			)			).to(snake_case__			)
									UpperCamelCase_						     =      image.cpu().permute(0   ,       2   ,       3   ,       1			)[0]
									UpperCamelCase_						     =      Image.fromarray(np.uinta(snake_case__			)			).convert("RGB"			)
									if str(snake_case__			).startswith("mps"			):
														UpperCamelCase_						     =      torch.manual_seed(snake_case__			)
									else:
														UpperCamelCase_						     =      torch.Generator(device=snake_case__			).manual_seed(snake_case__			)
									UpperCamelCase_						     =      {
									    "image": image,
									    "source_prompt": "a cat and a frog",
									    "target_prompt": "a dog and a newt",
									    "generator": generator,
									    "num_inference_steps": 2,
									    "num_maps_per_mask": 2,
									    "mask_encode_strength": 1.0,
									    "guidance_scale": 6.0,
									    "output_type": "numpy",
									}

									return inputs

				def 					_lowerCamelCase    (       self   ,       snake_case__   ,       snake_case__=0			):

									'''simple docstring'''


									UpperCamelCase_						     =      floats_tensor((1, 3, 32, 32)   ,       rng=random.Random(snake_case__			)			).to(snake_case__			)
									UpperCamelCase_						     =      image.cpu().permute(0   ,       2   ,       3   ,       1			)[0]
									UpperCamelCase_						     =      Image.fromarray(np.uinta(snake_case__			)			).convert("RGB"			)
									if str(snake_case__			).startswith("mps"			):
														UpperCamelCase_						     =      torch.manual_seed(snake_case__			)
									else:
														UpperCamelCase_						     =      torch.Generator(device=snake_case__			).manual_seed(snake_case__			)
									UpperCamelCase_						     =      {
									    "image": image,
									    "prompt": "a cat and a frog",
									    "generator": generator,
									    "num_inference_steps": 2,
									    "inpaint_strength": 1.0,
									    "guidance_scale": 6.0,
									    "decode_latents": True,
									    "output_type": "numpy",
									}
									return inputs

				def 					_lowerCamelCase    (       self			):

									'''simple docstring'''


									if not hasattr(self.pipeline_class   ,       "_optional_components"			):
														return

									UpperCamelCase_						     =      self.get_dummy_components()
									UpperCamelCase_						     =      self.pipeline_class(**snake_case__			)
									pipe.to(snake_case__			)
									pipe.set_progress_bar_config(disable=snake_case__			)

									# set all optional components to None and update pipeline config accordingly
									for optional_component in pipe._optional_components:
														setattr(snake_case__   ,       snake_case__   ,       snake_case__			)
									pipe.register_modules(**{optional_component: None for optional_component in pipe._optional_components}			)

									UpperCamelCase_						     =      self.get_dummy_inputs(snake_case__			)
									UpperCamelCase_						     =      pipe(**snake_case__			)[0]

									with tempfile.TemporaryDirectory() as tmpdir:
														pipe.save_pretrained(snake_case__			)
														UpperCamelCase_						     =      self.pipeline_class.from_pretrained(snake_case__			)
														pipe_loaded.to(snake_case__			)
														pipe_loaded.set_progress_bar_config(disable=snake_case__			)

									for optional_component in pipe._optional_components:
														self.assertTrue(
														    getattr(snake_case__   ,       snake_case__			) is None   ,       F"""`{optional_component}` did not stay set to None after loading."""   ,       )

									UpperCamelCase_						     =      self.get_dummy_inputs(snake_case__			)
									UpperCamelCase_						     =      pipe_loaded(**snake_case__			)[0]

									UpperCamelCase_						     =      np.abs(output - output_loaded			).max()
									self.assertLess(snake_case__   ,       1e-4			)

				def 					_lowerCamelCase    (       self			):

									'''simple docstring'''


									UpperCamelCase_						     =      "cpu"

									UpperCamelCase_						     =      self.get_dummy_components()
									UpperCamelCase_						     =      self.pipeline_class(**snake_case__			)
									pipe.to(snake_case__			)
									pipe.set_progress_bar_config(disable=snake_case__			)

									UpperCamelCase_						     =      self.get_dummy_mask_inputs(snake_case__			)
									UpperCamelCase_						     =      pipe.generate_mask(**snake_case__			)
									UpperCamelCase_						     =      mask[0, -3:, -3:]

									self.assertEqual(mask.shape   ,       (1, 16, 16)			)
									UpperCamelCase_						     =      np.array([0] * 9			)
									UpperCamelCase_						     =      np.abs(mask_slice.flatten() - expected_slice			).max()
									self.assertLessEqual(snake_case__   ,       1e-3			)
									self.assertEqual(mask[0, -3, -4]   ,       0			)

				def 					_lowerCamelCase    (       self			):

									'''simple docstring'''


									UpperCamelCase_						     =      "cpu"

									UpperCamelCase_						     =      self.get_dummy_components()
									UpperCamelCase_						     =      self.pipeline_class(**snake_case__			)
									pipe.to(snake_case__			)
									pipe.set_progress_bar_config(disable=snake_case__			)

									UpperCamelCase_						     =      self.get_dummy_inversion_inputs(snake_case__			)
									UpperCamelCase_						     =      pipe.invert(**snake_case__			).images
									UpperCamelCase_						     =      image[0, -1, -3:, -3:]

									self.assertEqual(image.shape   ,       (2, 32, 32, 3)			)
									UpperCamelCase_						     =      np.array(
									    [0.5_150, 0.5_134, 0.5_043, 0.5_376, 0.4_694, 0.51_050, 0.5_015, 0.4_407, 0.4_799]   ,       )
									UpperCamelCase_						     =      np.abs(image_slice.flatten() - expected_slice			).max()
									self.assertLessEqual(snake_case__   ,       1e-3			)

				def 					_lowerCamelCase    (       self			):

									'''simple docstring'''


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

				def 					_lowerCamelCase    (       self			):

									'''simple docstring'''


									UpperCamelCase_						     =      "cpu"

									UpperCamelCase_						     =      self.get_dummy_components()

									UpperCamelCase_						     =      {"beta_start": 0.00_085, "beta_end": 0.012, "beta_schedule": "scaled_linear"}
									UpperCamelCase_						     =      DPMSolverMultistepScheduler(**snake_case__			)
									UpperCamelCase_						     =      DPMSolverMultistepInverseScheduler(**snake_case__			)

									UpperCamelCase_						     =      self.pipeline_class(**snake_case__			)
									pipe.to(snake_case__			)
									pipe.set_progress_bar_config(disable=snake_case__			)

									UpperCamelCase_						     =      self.get_dummy_inversion_inputs(snake_case__			)
									UpperCamelCase_						     =      pipe.invert(**snake_case__			).images
									UpperCamelCase_						     =      image[0, -1, -3:, -3:]

									self.assertEqual(image.shape   ,       (2, 32, 32, 3)			)
									UpperCamelCase_						     =      np.array(
									    [0.5_150, 0.5_134, 0.5_043, 0.5_376, 0.4_694, 0.51_050, 0.5_015, 0.4_407, 0.4_799]   ,       )
									UpperCamelCase_						     =      np.abs(image_slice.flatten() - expected_slice			).max()
									self.assertLessEqual(snake_case__   ,       1e-3			)




@require_torch_gpu
@slow
class 					_lowercase      (unittest.TestCase					):
				'''simple docstring'''







				def 					_lowerCamelCase    (       self			):

									'''simple docstring'''


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

				@classmethod
				def 					_lowerCamelCase    (       cls			):

									'''simple docstring'''


									UpperCamelCase_						     =      load_image(
									    "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/diffedit/fruit.png"			)

									UpperCamelCase_						     =      raw_image.convert("RGB"			).resize((768, 768)			)

									UpperCamelCase_						     =      raw_image

				def 					_lowerCamelCase    (       self			):

									'''simple docstring'''


									UpperCamelCase_						     =      torch.manual_seed(0			)

									UpperCamelCase_						     =      StableDiffusionDiffEditPipeline.from_pretrained(
									    "stabilityai/stable-diffusion-2-1"   ,       safety_checker=snake_case__   ,       torch_dtype=torch.floataa			)
									UpperCamelCase_						     =      DDIMScheduler.from_config(pipe.scheduler.config			)
									UpperCamelCase_						     =      DDIMInverseScheduler.from_config(pipe.scheduler.config			)
									pipe.enable_model_cpu_offload()
									pipe.set_progress_bar_config(disable=snake_case__			)

									UpperCamelCase_						     =      "a bowl of fruit"
									UpperCamelCase_						     =      "a bowl of pears"

									UpperCamelCase_						     =      pipe.generate_mask(
									    image=self.raw_image   ,       source_prompt=snake_case__   ,       target_prompt=snake_case__   ,       generator=snake_case__   ,       )

									UpperCamelCase_						     =      pipe.invert(
									    prompt=snake_case__   ,       image=self.raw_image   ,       inpaint_strength=0.7   ,       generator=snake_case__			).latents

									UpperCamelCase_						     =      pipe(
									    prompt=snake_case__   ,       mask_image=snake_case__   ,       image_latents=snake_case__   ,       generator=snake_case__   ,       negative_prompt=snake_case__   ,       inpaint_strength=0.7   ,       output_type="numpy"   ,       ).images[0]

									UpperCamelCase_						     =      (
									    np.array(
									        load_image(
									            "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
									            "/diffedit/pears.png"			).resize((768, 768)			)			)
									    / 255
									)
									assert np.abs((expected_image - image).max()			) < 5e-1

				def 					_lowerCamelCase    (       self			):

									'''simple docstring'''


									UpperCamelCase_						     =      torch.manual_seed(0			)

									UpperCamelCase_						     =      StableDiffusionDiffEditPipeline.from_pretrained(
									    "stabilityai/stable-diffusion-2-1"   ,       safety_checker=snake_case__   ,       torch_dtype=torch.floataa			)
									UpperCamelCase_						     =      DPMSolverMultistepScheduler.from_config(pipe.scheduler.config			)
									UpperCamelCase_						     =      DPMSolverMultistepInverseScheduler.from_config(pipe.scheduler.config			)
									pipe.enable_model_cpu_offload()
									pipe.set_progress_bar_config(disable=snake_case__			)

									UpperCamelCase_						     =      "a bowl of fruit"
									UpperCamelCase_						     =      "a bowl of pears"

									UpperCamelCase_						     =      pipe.generate_mask(
									    image=self.raw_image   ,       source_prompt=snake_case__   ,       target_prompt=snake_case__   ,       generator=snake_case__   ,       )

									UpperCamelCase_						     =      pipe.invert(
									    prompt=snake_case__   ,       image=self.raw_image   ,       inpaint_strength=0.7   ,       generator=snake_case__   ,       num_inference_steps=25   ,       ).latents

									UpperCamelCase_						     =      pipe(
									    prompt=snake_case__   ,       mask_image=snake_case__   ,       image_latents=snake_case__   ,       generator=snake_case__   ,       negative_prompt=snake_case__   ,       inpaint_strength=0.7   ,       num_inference_steps=25   ,       output_type="numpy"   ,       ).images[0]

									UpperCamelCase_						     =      (
									    np.array(
									        load_image(
									            "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
									            "/diffedit/pears.png"			).resize((768, 768)			)			)
									    / 255
									)
									assert np.abs((expected_image - image).max()			) < 5e-1


class  lowerCamelCase__							:




	'''simple docstring'''




	def __init__(					self		:int						)		-> Dict:
								__UpperCamelCase	:   Union[str, Any]			   =					{}







	def    _lowerCamelCase				(					self		:str						)		-> None:
								print(self.vertex						)
								for i in self.vertex:
															print(a     ,   " -> "     ,   " -> ".join([str(a						) for j in self.vertex[i]]						)						)







	def    _lowerCamelCase				(					self		:List[Any]     ,   a		:int     ,   a		:int						)		-> None:
								# check if vertex is already present,
								if from_vertex in self.vertex:
															self.vertex[from_vertex].append(a						)
								else:
															# else make a new vertex
															__UpperCamelCase	:   Optional[Any]			   =					[to_vertex]







	def    _lowerCamelCase				(					self		:Tuple						)		-> None:
								# visited array for storing already visited nodes
								__UpperCamelCase	:   Dict			   =					[False] * len(self.vertex						)

								# call the recursive helper function
								for i in range(len(self.vertex						)						):
															if not visited[i]:
																						self.dfs_recursive(a     ,   a						)







	def    _lowerCamelCase				(					self		:Any     ,   a		:int     ,   a		:list						)		-> None:
								# mark start vertex as visited
								__UpperCamelCase	:   int			   =					True

								print(a     ,   end=" "						)

								# Recur for all the vertices that are adjacent to this node
								for i in self.vertex:
															if not visited[i]:
																						self.dfs_recursive(a     ,   a						)


if __name__ == "__main__":
		lowercase  :					Dict													=       Graph()
		g.add_edge(0, 1)
		g.add_edge(0, 2)
		g.add_edge(1, 2)
		g.add_edge(2, 0)
		g.add_edge(2, 3)
		g.add_edge(3, 3)

		g.print_graph()
		print('DFS:')
		g.dfs()

		# OUTPUT:
		# 0  ->  1 -> 2
		# 1  ->  2
		# 2  ->  0 -> 3
		# 3  ->  3
		# DFS:
		#  0 1 2 3