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      UpperCamelCase    (						_UpperCAmelCase       ,    _UpperCAmelCase       ,    unittest.TestCase     ):
	lowerCAmelCase	:   int				=	StableDiffusionDiffEditPipeline
	lowerCAmelCase	:   Tuple				=	TEXT_GUIDED_IMAGE_INPAINTING_PARAMS - {"""height""", """width""", """image"""} | {"""image_latents"""}
	lowerCAmelCase	:   Any				=	TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS - {"""image"""} | {"""image_latents"""}
	lowerCAmelCase	:   Optional[int]				=	frozenset(
	    []     )  # TO-DO: update image_params once pipeline is refactored with VaeImageProcessor.preprocess
	lowerCAmelCase	:   List[Any]				=	frozenset([]     )



	def 			__A							(       self		):
							torch.manual_seed(0		)
							A__							=       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=UpperCAmelCase__			,	)
							A__							=       DDIMScheduler(
							    beta_start=0.00_085			,	beta_end=0.012			,	beta_schedule="scaled_linear"			,	clip_sample=UpperCAmelCase__			,	set_alpha_to_one=UpperCAmelCase__			,	)
							A__							=       DDIMInverseScheduler(
							    beta_start=0.00_085			,	beta_end=0.012			,	beta_schedule="scaled_linear"			,	clip_sample=UpperCAmelCase__			,	set_alpha_to_zero=UpperCAmelCase__			,	)
							torch.manual_seed(0		)
							A__							=       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		)
							A__							=       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			,	)
							A__							=       CLIPTextModel(UpperCAmelCase__		)
							A__							=       CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip"		)

							A__							=       {
							    "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 			__A							(       self			,	UpperCAmelCase__			,	UpperCAmelCase__=0		):
							A__							=       floats_tensor((1, 16, 16)			,	rng=random.Random(UpperCAmelCase__		)		).to(UpperCAmelCase__		)
							A__							=       floats_tensor((1, 2, 4, 16, 16)			,	rng=random.Random(UpperCAmelCase__		)		).to(UpperCAmelCase__		)
							if str(UpperCAmelCase__		).startswith("mps"		):
													A__							=       torch.manual_seed(UpperCAmelCase__		)
							else:
													A__							=       torch.Generator(device=UpperCAmelCase__		).manual_seed(UpperCAmelCase__		)
							A__							=       {
							    "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 			__A							(       self			,	UpperCAmelCase__			,	UpperCAmelCase__=0		):
							A__							=       floats_tensor((1, 3, 32, 32)			,	rng=random.Random(UpperCAmelCase__		)		).to(UpperCAmelCase__		)
							A__							=       image.cpu().permute(0			,	2			,	3			,	1		)[0]
							A__							=       Image.fromarray(np.uinta(UpperCAmelCase__		)		).convert("RGB"		)
							if str(UpperCAmelCase__		).startswith("mps"		):
													A__							=       torch.manual_seed(UpperCAmelCase__		)
							else:
													A__							=       torch.Generator(device=UpperCAmelCase__		).manual_seed(UpperCAmelCase__		)
							A__							=       {
							    "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 			__A							(       self			,	UpperCAmelCase__			,	UpperCAmelCase__=0		):
							A__							=       floats_tensor((1, 3, 32, 32)			,	rng=random.Random(UpperCAmelCase__		)		).to(UpperCAmelCase__		)
							A__							=       image.cpu().permute(0			,	2			,	3			,	1		)[0]
							A__							=       Image.fromarray(np.uinta(UpperCAmelCase__		)		).convert("RGB"		)
							if str(UpperCAmelCase__		).startswith("mps"		):
													A__							=       torch.manual_seed(UpperCAmelCase__		)
							else:
													A__							=       torch.Generator(device=UpperCAmelCase__		).manual_seed(UpperCAmelCase__		)
							A__							=       {
							    "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 			__A							(       self		):
							if not hasattr(self.pipeline_class			,	"_optional_components"		):
													return

							A__							=       self.get_dummy_components()
							A__							=       self.pipeline_class(**UpperCAmelCase__		)
							pipe.to(UpperCAmelCase__		)
							pipe.set_progress_bar_config(disable=UpperCAmelCase__		)

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

							A__							=       self.get_dummy_inputs(UpperCAmelCase__		)
							A__							=       pipe(**UpperCAmelCase__		)[0]

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

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

							A__							=       self.get_dummy_inputs(UpperCAmelCase__		)
							A__							=       pipe_loaded(**UpperCAmelCase__		)[0]

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



	def 			__A							(       self		):
							A__							=       "cpu"

							A__							=       self.get_dummy_components()
							A__							=       self.pipeline_class(**UpperCAmelCase__		)
							pipe.to(UpperCAmelCase__		)
							pipe.set_progress_bar_config(disable=UpperCAmelCase__		)

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

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



	def 			__A							(       self		):
							A__							=       "cpu"

							A__							=       self.get_dummy_components()
							A__							=       self.pipeline_class(**UpperCAmelCase__		)
							pipe.to(UpperCAmelCase__		)
							pipe.set_progress_bar_config(disable=UpperCAmelCase__		)

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

							self.assertEqual(image.shape			,	(2, 32, 32, 3)		)
							A__							=       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]			,	)
							A__							=       np.abs(image_slice.flatten() - expected_slice		).max()
							self.assertLessEqual(UpperCAmelCase__			,	1e-3		)



	def 			__A							(       self		):
							super().test_inference_batch_single_identical(expected_max_diff=5e-3		)



	def 			__A							(       self		):
							A__							=       "cpu"

							A__							=       self.get_dummy_components()

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

							A__							=       self.pipeline_class(**UpperCAmelCase__		)
							pipe.to(UpperCAmelCase__		)
							pipe.set_progress_bar_config(disable=UpperCAmelCase__		)

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

							self.assertEqual(image.shape			,	(2, 32, 32, 3)		)
							A__							=       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]			,	)
							A__							=       np.abs(image_slice.flatten() - expected_slice		).max()
							self.assertLessEqual(UpperCAmelCase__			,	1e-3		)






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



	def 			__A							(       self		):
							super().tearDown()
							gc.collect()
							torch.cuda.empty_cache()



	@classmethod
	def 			__A							(       cls		):
							A__							=       load_image(
							    "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/diffedit/fruit.png"		)

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

							A__							=       raw_image



	def 			__A							(       self		):
							A__							=       torch.manual_seed(0		)

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

							A__							=       "a bowl of fruit"
							A__							=       "a bowl of pears"

							A__							=       pipe.generate_mask(
							    image=self.raw_image			,	source_prompt=UpperCAmelCase__			,	target_prompt=UpperCAmelCase__			,	generator=UpperCAmelCase__			,	)

							A__							=       pipe.invert(
							    prompt=UpperCAmelCase__			,	image=self.raw_image			,	inpaint_strength=0.7			,	generator=UpperCAmelCase__		).latents

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

							A__							=       (
							    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 			__A							(       self		):
							A__							=       torch.manual_seed(0		)

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

							A__							=       "a bowl of fruit"
							A__							=       "a bowl of pears"

							A__							=       pipe.generate_mask(
							    image=self.raw_image			,	source_prompt=UpperCAmelCase__			,	target_prompt=UpperCAmelCase__			,	generator=UpperCAmelCase__			,	)

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

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

							A__							=       (
							    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










from manim import *






class      UpperCamelCase    (						_UpperCAmelCase     ):



	def 			__A							(       self		):
							A__							=       Rectangle(height=0.5			,	width=0.5		)
							A__							=       Rectangle(height=0.46			,	width=0.46		).set_stroke(width=0		)

							A__							=       [mem.copy() for i in range(6		)]
							A__							=       [mem.copy() for i in range(6		)]
							A__							=       VGroup(*UpperCAmelCase__		).arrange(UpperCAmelCase__			,	buff=0		)
							A__							=       VGroup(*UpperCAmelCase__		).arrange(UpperCAmelCase__			,	buff=0		)
							A__							=       VGroup(UpperCAmelCase__			,	UpperCAmelCase__		).arrange(UpperCAmelCase__			,	buff=0		)
							A__							=       Text("CPU"			,	font_size=24		)
							A__							=       Group(UpperCAmelCase__			,	UpperCAmelCase__		).arrange(UpperCAmelCase__			,	buff=0.5			,	aligned_edge=UpperCAmelCase__		)
							cpu.move_to([-2.5, -0.5, 0]		)
							self.add(UpperCAmelCase__		)

							A__							=       [mem.copy() for i in range(4		)]
							A__							=       VGroup(*UpperCAmelCase__		).arrange(UpperCAmelCase__			,	buff=0		)
							A__							=       Text("GPU"			,	font_size=24		)
							A__							=       Group(UpperCAmelCase__			,	UpperCAmelCase__		).arrange(UpperCAmelCase__			,	buff=0.5			,	aligned_edge=UpperCAmelCase__		)
							gpu.move_to([-1, -1, 0]		)
							self.add(UpperCAmelCase__		)

							A__							=       [mem.copy() for i in range(6		)]
							A__							=       VGroup(*UpperCAmelCase__		).arrange(UpperCAmelCase__			,	buff=0		)

							A__							=       Text("Model"			,	font_size=24		)
							A__							=       Group(UpperCAmelCase__			,	UpperCAmelCase__		).arrange(UpperCAmelCase__			,	buff=0.5			,	aligned_edge=UpperCAmelCase__		)
							model.move_to([3, -1.0, 0]		)
							self.add(UpperCAmelCase__		)

							A__							=       []
							for i, rect in enumerate(UpperCAmelCase__		):
													rect.set_stroke(UpperCAmelCase__		)
													# target = fill.copy().set_fill(YELLOW, opacity=0.7)
													# target.move_to(rect)
													# self.add(target)

													A__							=       Rectangle(height=0.46 / 4			,	width=0.46 / 3		).set_stroke(width=0.0		).set_fill(UpperCAmelCase__			,	opacity=0.7		)

													if i == 0:
																			cpu_target.next_to(cpu_left_col_base[0].get_corner(DOWN + LEFT		)			,	buff=0.02			,	direction=UpperCAmelCase__		)
																			cpu_target.set_x(cpu_target.get_x() + 0.1		)
													elif i == 3:
																			cpu_target.next_to(cpu_targs[0]			,	direction=UpperCAmelCase__			,	buff=0.0		)
													else:
																			cpu_target.next_to(cpu_targs[i - 1]			,	direction=UpperCAmelCase__			,	buff=0.0		)
													self.add(UpperCAmelCase__		)
													cpu_targs.append(UpperCAmelCase__		)

							A__							=       [mem.copy() for i in range(6		)]
							A__							=       VGroup(*UpperCAmelCase__		).arrange(UpperCAmelCase__			,	buff=0		)

							A__							=       Text("Loaded Checkpoint"			,	font_size=24		)
							A__							=       Group(UpperCAmelCase__			,	UpperCAmelCase__		).arrange(UpperCAmelCase__			,	aligned_edge=UpperCAmelCase__			,	buff=0.4		)
							checkpoint.move_to([3, 0.5, 0]		)

							A__							=       Square(side_length=2.2		)
							key.move_to([-5, 2, 0]		)

							A__							=       MarkupText(
							    F"""<b>Key:</b>\n\n<span fgcolor='{YELLOW}'>●</span> Empty Model"""			,	font_size=18			,	)

							key_text.move_to([-5, 2.4, 0]		)

							self.add(UpperCAmelCase__			,	UpperCAmelCase__		)

							A__							=       MarkupText(
							    F"""<span fgcolor='{BLUE}'>●</span> Checkpoint"""			,	font_size=18			,	)

							blue_text.next_to(UpperCAmelCase__			,	DOWN * 2.4			,	aligned_edge=key_text.get_left()		)

							A__							=       MarkupText(
							    F"""Next, a <i><span fgcolor=\"{BLUE}\">second</span></i> model is loaded into memory,\nwith the weights of a <span fgcolor=\"{BLUE}\">single shard</span>."""			,	font_size=24			,	)
							step_a.move_to([2, 2, 0]		)
							self.play(Write(UpperCAmelCase__		)			,	Write(UpperCAmelCase__		)		)

							self.play(Write(UpperCAmelCase__			,	run_time=1		)			,	Create(UpperCAmelCase__			,	run_time=1		)		)

							A__							=       []
							A__							=       []
							for i, rect in enumerate(UpperCAmelCase__		):
													A__							=       fill.copy().set_fill(UpperCAmelCase__			,	opacity=0.7		)
													target.move_to(UpperCAmelCase__		)
													first_animations.append(GrowFromCenter(UpperCAmelCase__			,	run_time=1		)		)

													A__							=       target.copy()
													cpu_target.generate_target()
													if i < 5:
																			cpu_target.target.move_to(cpu_left_col_base[i + 1]		)
													else:
																			cpu_target.target.move_to(cpu_right_col_base[i - 5]		)
													second_animations.append(MoveToTarget(UpperCAmelCase__			,	run_time=1.5		)		)

							self.play(*UpperCAmelCase__		)
							self.play(*UpperCAmelCase__		)
							self.wait()





