
'''simple docstring'''






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     __snake_case		(   a__    ,   a__    ,   unittest.TestCase):
					_lowerCAmelCase			     =	StableDiffusionDiffEditPipeline
					_lowerCAmelCase			     =	TEXT_GUIDED_IMAGE_INPAINTING_PARAMS - {'''height''', '''width''', '''image'''} | {'''image_latents'''}
					_lowerCAmelCase			     =	TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS - {'''image'''} | {'''image_latents'''}
					_lowerCAmelCase			     =	frozenset(
					    [])  # TO-DO: update image_params once pipeline is refactored with VaeImageProcessor.preprocess
					_lowerCAmelCase			     =	frozenset([])
					def     UpperCAmelCase_							(     self      ):

									"""simple docstring"""


									torch.manual_seed(0      )
									lowerCamelCase					:			int									= 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=A,    )
									lowerCamelCase					:			Optional[Any]									= DDIMScheduler(
									    beta_start=0.0_0085,    beta_end=0.012,    beta_schedule='scaled_linear',    clip_sample=A,    set_alpha_to_one=A,    )
									lowerCamelCase					:			List[Any]									= DDIMInverseScheduler(
									    beta_start=0.0_0085,    beta_end=0.012,    beta_schedule='scaled_linear',    clip_sample=A,    set_alpha_to_zero=A,    )
									torch.manual_seed(0      )
									lowerCamelCase					:			Dict									= 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					:			Optional[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,    hidden_act='gelu',    projection_dim=512,    )
									lowerCamelCase					:			List[Any]									= CLIPTextModel(A      )
									lowerCamelCase					:			Optional[int]									= CLIPTokenizer.from_pretrained('hf-internal-testing/tiny-random-clip'      )

									lowerCamelCase					:			str									= {
									    '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     UpperCAmelCase_							(     self,    A,    A=0      ):

									"""simple docstring"""


									lowerCamelCase					:			List[Any]									= floats_tensor((1, 16, 16),    rng=random.Random(A      )      ).to(A      )
									lowerCamelCase					:			int									= floats_tensor((1, 2, 4, 16, 16),    rng=random.Random(A      )      ).to(A      )
									if str(A      ).startswith('mps'      ):
													lowerCamelCase					:			int									= torch.manual_seed(A      )
									else:
													lowerCamelCase					:			Dict									= torch.Generator(device=A      ).manual_seed(A      )
									lowerCamelCase					:			Optional[Any]									= {
									    '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     UpperCAmelCase_							(     self,    A,    A=0      ):

									"""simple docstring"""


									lowerCamelCase					:			str									= floats_tensor((1, 3, 32, 32),    rng=random.Random(A      )      ).to(A      )
									lowerCamelCase					:			List[Any]									= image.cpu().permute(0,    2,    3,    1      )[0]
									lowerCamelCase					:			int									= Image.fromarray(np.uinta(A      )      ).convert('RGB'      )
									if str(A      ).startswith('mps'      ):
													lowerCamelCase					:			Union[str, Any]									= torch.manual_seed(A      )
									else:
													lowerCamelCase					:			Dict									= torch.Generator(device=A      ).manual_seed(A      )
									lowerCamelCase					:			int									= {
									    '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     UpperCAmelCase_							(     self,    A,    A=0      ):

									"""simple docstring"""


									lowerCamelCase					:			int									= floats_tensor((1, 3, 32, 32),    rng=random.Random(A      )      ).to(A      )
									lowerCamelCase					:			Union[str, Any]									= image.cpu().permute(0,    2,    3,    1      )[0]
									lowerCamelCase					:			Dict									= Image.fromarray(np.uinta(A      )      ).convert('RGB'      )
									if str(A      ).startswith('mps'      ):
													lowerCamelCase					:			Optional[int]									= torch.manual_seed(A      )
									else:
													lowerCamelCase					:			Optional[int]									= torch.Generator(device=A      ).manual_seed(A      )
									lowerCamelCase					:			List[Any]									= {
									    '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     UpperCAmelCase_							(     self      ):

									"""simple docstring"""


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

									lowerCamelCase					:			Tuple									= self.get_dummy_components()
									lowerCamelCase					:			List[str]									= self.pipeline_class(**A      )
									pipe.to(A      )
									pipe.set_progress_bar_config(disable=A      )

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

									lowerCamelCase					:			Tuple									= self.get_dummy_inputs(A      )
									lowerCamelCase					:			List[str]									= pipe(**A      )[0]

									with tempfile.TemporaryDirectory() as tmpdir:
													pipe.save_pretrained(A      )
													lowerCamelCase					:			Tuple									= self.pipeline_class.from_pretrained(A      )
													pipe_loaded.to(A      )
													pipe_loaded.set_progress_bar_config(disable=A      )

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

									lowerCamelCase					:			Optional[Any]									= self.get_dummy_inputs(A      )
									lowerCamelCase					:			str									= pipe_loaded(**A      )[0]

									lowerCamelCase					:			int									= np.abs(output - output_loaded      ).max()
									self.assertLess(A,    1e-4      )
					def     UpperCAmelCase_							(     self      ):

									"""simple docstring"""


									lowerCamelCase					:			Union[str, Any]									= 'cpu'

									lowerCamelCase					:			List[str]									= self.get_dummy_components()
									lowerCamelCase					:			Any									= self.pipeline_class(**A      )
									pipe.to(A      )
									pipe.set_progress_bar_config(disable=A      )

									lowerCamelCase					:			Tuple									= self.get_dummy_mask_inputs(A      )
									lowerCamelCase					:			List[str]									= pipe.generate_mask(**A      )
									lowerCamelCase					:			int									= mask[0, -3:, -3:]

									self.assertEqual(mask.shape,    (1, 16, 16)      )
									lowerCamelCase					:			Any									= np.array([0] * 9      )
									lowerCamelCase					:			str									= np.abs(mask_slice.flatten() - expected_slice      ).max()
									self.assertLessEqual(A,    1e-3      )
									self.assertEqual(mask[0, -3, -4],    0      )
					def     UpperCAmelCase_							(     self      ):

									"""simple docstring"""


									lowerCamelCase					:			Optional[int]									= 'cpu'

									lowerCamelCase					:			Any									= self.get_dummy_components()
									lowerCamelCase					:			Optional[int]									= self.pipeline_class(**A      )
									pipe.to(A      )
									pipe.set_progress_bar_config(disable=A      )

									lowerCamelCase					:			List[str]									= self.get_dummy_inversion_inputs(A      )
									lowerCamelCase					:			Any									= pipe.invert(**A      ).images
									lowerCamelCase					:			List[Any]									= image[0, -1, -3:, -3:]

									self.assertEqual(image.shape,    (2, 32, 32, 3)      )
									lowerCamelCase					:			Dict									= np.array(
									    [0.5150, 0.5134, 0.5043, 0.5376, 0.4694, 0.5_1050, 0.5015, 0.4407, 0.4799],    )
									lowerCamelCase					:			List[str]									= np.abs(image_slice.flatten() - expected_slice      ).max()
									self.assertLessEqual(A,    1e-3      )
					def     UpperCAmelCase_							(     self      ):

									"""simple docstring"""


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




					def     UpperCAmelCase_							(     self      ):

									"""simple docstring"""


									lowerCamelCase					:			str									= 'cpu'

									lowerCamelCase					:			Union[str, Any]									= self.get_dummy_components()

									lowerCamelCase					:			Dict									= {'beta_start': 0.0_0085, 'beta_end': 0.012, 'beta_schedule': 'scaled_linear'}
									lowerCamelCase					:			int									= DPMSolverMultistepScheduler(**A      )
									lowerCamelCase					:			Optional[int]									= DPMSolverMultistepInverseScheduler(**A      )

									lowerCamelCase					:			Any									= self.pipeline_class(**A      )
									pipe.to(A      )
									pipe.set_progress_bar_config(disable=A      )

									lowerCamelCase					:			List[Any]									= self.get_dummy_inversion_inputs(A      )
									lowerCamelCase					:			str									= pipe.invert(**A      ).images
									lowerCamelCase					:			str									= image[0, -1, -3:, -3:]

									self.assertEqual(image.shape,    (2, 32, 32, 3)      )
									lowerCamelCase					:			Any									= np.array(
									    [0.5150, 0.5134, 0.5043, 0.5376, 0.4694, 0.5_1050, 0.5015, 0.4407, 0.4799],    )
									lowerCamelCase					:			Optional[int]									= np.abs(image_slice.flatten() - expected_slice      ).max()
									self.assertLessEqual(A,    1e-3      )





@require_torch_gpu
@slow
class     __snake_case		(   unittest.TestCase):
					def     UpperCAmelCase_							(     self      ):

									"""simple docstring"""


									super().tearDown()
									gc.collect()
									torch.cuda.empty_cache()
					@classmethod
					def     UpperCAmelCase_							(     cls      ):

									"""simple docstring"""


									lowerCamelCase					:			Optional[Any]									= load_image(
									    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/diffedit/fruit.png'      )

									lowerCamelCase					:			List[str]									= raw_image.convert('RGB'      ).resize((768, 768)      )

									lowerCamelCase					:			Optional[Any]									= raw_image
					def     UpperCAmelCase_							(     self      ):

									"""simple docstring"""


									lowerCamelCase					:			Any									= torch.manual_seed(0      )

									lowerCamelCase					:			List[Any]									= StableDiffusionDiffEditPipeline.from_pretrained(
									    'stabilityai/stable-diffusion-2-1',    safety_checker=A,    torch_dtype=torch.floataa      )
									lowerCamelCase					:			int									= DDIMScheduler.from_config(pipe.scheduler.config      )
									lowerCamelCase					:			Dict									= DDIMInverseScheduler.from_config(pipe.scheduler.config      )
									pipe.enable_model_cpu_offload()
									pipe.set_progress_bar_config(disable=A      )

									lowerCamelCase					:			Tuple									= 'a bowl of fruit'
									lowerCamelCase					:			int									= 'a bowl of pears'

									lowerCamelCase					:			Optional[int]									= pipe.generate_mask(
									    image=self.raw_image,    source_prompt=A,    target_prompt=A,    generator=A,    )

									lowerCamelCase					:			Any									= pipe.invert(
									    prompt=A,    image=self.raw_image,    inpaint_strength=0.7,    generator=A      ).latents

									lowerCamelCase					:			Optional[Any]									= pipe(
									    prompt=A,    mask_image=A,    image_latents=A,    generator=A,    negative_prompt=A,    inpaint_strength=0.7,    output_type='numpy',    ).images[0]

									lowerCamelCase					:			Optional[Any]									= (
									    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     UpperCAmelCase_							(     self      ):

									"""simple docstring"""


									lowerCamelCase					:			Any									= torch.manual_seed(0      )

									lowerCamelCase					:			Optional[Any]									= StableDiffusionDiffEditPipeline.from_pretrained(
									    'stabilityai/stable-diffusion-2-1',    safety_checker=A,    torch_dtype=torch.floataa      )
									lowerCamelCase					:			List[str]									= DPMSolverMultistepScheduler.from_config(pipe.scheduler.config      )
									lowerCamelCase					:			Union[str, Any]									= DPMSolverMultistepInverseScheduler.from_config(pipe.scheduler.config      )
									pipe.enable_model_cpu_offload()
									pipe.set_progress_bar_config(disable=A      )

									lowerCamelCase					:			Optional[Any]									= 'a bowl of fruit'
									lowerCamelCase					:			int									= 'a bowl of pears'

									lowerCamelCase					:			Optional[int]									= pipe.generate_mask(
									    image=self.raw_image,    source_prompt=A,    target_prompt=A,    generator=A,    )

									lowerCamelCase					:			int									= pipe.invert(
									    prompt=A,    image=self.raw_image,    inpaint_strength=0.7,    generator=A,    num_inference_steps=25,    ).latents

									lowerCamelCase					:			Tuple									= pipe(
									    prompt=A,    mask_image=A,    image_latents=A,    generator=A,    negative_prompt=A,    inpaint_strength=0.7,    num_inference_steps=25,    output_type='numpy',    ).images[0]

									lowerCamelCase					:			str									= (
									    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


'''simple docstring'''






from manim import *




class     __snake_case		(   a__):
					def     UpperCAmelCase_							(     self      ):

									"""simple docstring"""


									lowerCamelCase					:			Optional[int]									= Rectangle(height=0.5,    width=0.5      )
									lowerCamelCase					:			List[Any]									= Rectangle(height=0.46,    width=0.46      ).set_stroke(width=0      )

									lowerCamelCase					:			List[str]									= [mem.copy() for i in range(6      )]
									lowerCamelCase					:			List[Any]									= [mem.copy() for i in range(6      )]
									lowerCamelCase					:			str									= VGroup(*A      ).arrange(A,    buff=0      )
									lowerCamelCase					:			Any									= VGroup(*A      ).arrange(A,    buff=0      )
									lowerCamelCase					:			Dict									= VGroup(A,    A      ).arrange(A,    buff=0      )
									lowerCamelCase					:			str									= Text('CPU',    font_size=24      )
									lowerCamelCase					:			int									= Group(A,    A      ).arrange(A,    buff=0.5,    aligned_edge=A      )
									cpu.move_to([-2.5, -0.5, 0]      )
									self.add(A      )

									lowerCamelCase					:			Optional[int]									= [mem.copy() for i in range(1      )]
									lowerCamelCase					:			Union[str, Any]									= VGroup(*A      ).arrange(A,    buff=0      )
									lowerCamelCase					:			Optional[Any]									= Text('GPU',    font_size=24      )
									lowerCamelCase					:			Tuple									= Group(A,    A      ).arrange(A,    buff=0.5,    aligned_edge=A      )
									gpu.align_to(A,    A      )
									gpu.set_x(gpu.get_x() - 1      )

									self.add(A      )

									lowerCamelCase					:			Optional[int]									= [mem.copy() for i in range(6      )]
									lowerCamelCase					:			Optional[Any]									= VGroup(*A      ).arrange(A,    buff=0      )

									lowerCamelCase					:			Any									= Text('Model',    font_size=24      )
									lowerCamelCase					:			Tuple									= Group(A,    A      ).arrange(A,    buff=0.5,    aligned_edge=A      )
									model.move_to([3, -1.0, 0]      )

									self.play(
									    Create(A,    run_time=1      ),    Create(A,    run_time=1      ),    Create(A,    run_time=1      ),    )

									lowerCamelCase					:			str									= MarkupText(
									    F'''First, an empty model skeleton is loaded\ninto <span fgcolor=\'{YELLOW}\'>memory</span> without using much RAM.''',    font_size=24,    )

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

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

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

									step_a.move_to([2, 2, 0]      )
									self.play(Write(A,    run_time=2.5      ),    Write(A      ),    Write(A      )      )

									self.add(A      )

									lowerCamelCase					:			str									= []
									lowerCamelCase					:			Optional[int]									= []
									lowerCamelCase					:			Optional[Any]									= []
									for i, rect in enumerate(A      ):
													lowerCamelCase					:			List[str]									= Rectangle(height=0.46,    width=0.46      ).set_stroke(width=0.0      ).set_fill(A,    opacity=0.7      )
													cpu_target.move_to(A      )
													cpu_target.generate_target()
													lowerCamelCase					:			int									= 0.46 / 4
													lowerCamelCase					:			Optional[int]									= 0.46 / 3

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

													first_animations.append(rect.animate(run_time=0.5      ).set_stroke(A      )      )
													second_animations.append(MoveToTarget(A,    run_time=1.5      )      )

									self.play(*A      )
									self.play(*A      )

									self.wait()
